Diabetes & Metabolism Journal

Soo Wan Kim

2 Articles

Complications
Dyslipidemia in Patients with Chronic Kidney Disease: An Updated Overview: Sang Heon Suh, Soo Wan Kim; Diabetes Metab J. 2023;47(5):612-629. Published online July 24, 2023; DOI: https://doi.org/10.4093/dmj.2023.0067

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Dyslipidemia is a potentially modifiable cardiovascular risk factor. Whereas the recommendations for the treatment target of dyslipidemia in the general population are being more and more rigorous, the 2013 Kidney Disease: Improving Global Outcomes clinical practice guideline for lipid management in chronic kidney disease (CKD) presented a relatively conservative approach with respect to the indication of lipid lowering therapy and therapeutic monitoring among the patients with CKD. This may be largely attributed to the lack of high-quality evidence derived from CKD population, among whom the overall feature of dyslipidemia is considerably distinctive to that of general population. In this review article, we cover the characteristic features of dyslipidemia and impact of dyslipidemia on cardiovascular outcomes in patients with CKD. We also review the current evidence on lipid lowering therapy to modify the risk of cardiovascular events in this population. We finally discuss the association between dyslipidemia and CKD progression and the potential strategy to delay the progression of CKD in relation to lipid lowering therapy.

Citations

Citations to this article as recorded by

Statin Therapy and Lipid Indices in Chronic Kidney Disease: A Systematic Review and Meta-analysis of Randomized Control Trials
Jafar Karami, Bahman Razi, Danyal Imani, Saeed Aslani, Mahdi Pakjoo, Mahdieh Fasihi, Keyhan Mohammadi, Amirhossein Sahebkar
Current Pharmaceutical Design.2024; 30(5): 362. CrossRef
Lipoprotein glomerulopathy with markedly increased arterial stiffness successfully treated with a combination of fenofibrate and losartan: a case report
Junichiro Kato, Hideo Okonogi, Go Kanzaki, Haruki Katsumata, Yasuyuki Nakada, Makoto Sagasaki, Kazumasa Komine, Kenji Ito, Takao Saito, Akira Matsunaga, Koh Tokutou, Kazuho Honda, Nobuo Tsuboi, Takashi Yokoo
BMC Nephrology.2024;[Epub] CrossRef
Atherogenic index of plasma: a new indicator for assessing the short-term mortality of patients with acute decompensated heart failure
Meng Yu, Hongyi Yang, Maobin Kuang, Jiajun Qiu, Changhui Yu, Guobo Xie, Guotai Sheng, Yang Zou
Frontiers in Endocrinology.2024;[Epub] CrossRef
Association between lipid accumulation product index and chronic kidney disease: A systematic review and meta-analysis
Feixiang Wu, Chenmin Cui, Junping Wu, Yunqing Wang
Experimental and Therapeutic Medicine.2024;[Epub] CrossRef
Potential impact of sodium glucose co-transporter (SGLT2) inhibitors on cholesterol fractions in stage 3 chronic kidney disease
Rabab Mahmoud Ahmed, Nehal Kamal Rakha, Ahmed Yousry, Amin Roshdy Soliman
The Egyptian Journal of Internal Medicine.2024;[Epub] CrossRef
Pulmonary Hypertension in Hemodialysis Patients and Its Determinants: A Hospital Based Cross-Sectional Study
Qingfei Yu, Qin Zhang
International Journal of General Medicine.2024; Volume 17: 3919. CrossRef

Cardiovascular Risk/Epidemiology
Glycemic Control and Adverse Clinical Outcomes in Patients with Chronic Kidney Disease and Type 2 Diabetes Mellitus: Results from KNOW-CKD: Ga Young Heo, Hee Byung Koh, Hyung Woo Kim, Jung Tak Park, Tae-Hyun Yoo, Shin-Wook Kang, Jayoun Kim, Soo Wan Kim, Yeong Hoon Kim, Su Ah Sung, Kook-Hwan Oh, Seung Hyeok Han; Diabetes Metab J. 2023;47(4):535-546. Published online April 25, 2023; DOI: https://doi.org/10.4093/dmj.2022.0112

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Abstract PDF Supplementary Material PubReader ePub

Background
The optimal level of glycosylated hemoglobin (HbA1c) to prevent adverse clinical outcomes is unknown in patients with chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM).
Methods
We analyzed 707 patients with CKD G1-G5 without kidney replacement therapy and T2DM from the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease (KNOW-CKD), a nationwide prospective cohort study. The main predictor was time-varying HbA1c level at each visit. The primary outcome was a composite of development of major adverse cardiovascular events (MACEs) or all-cause mortality. Secondary outcomes included the individual endpoint of MACEs, all-cause mortality, and CKD progression. CKD progression was defined as a ≥50% decline in the estimated glomerular filtration rate from baseline or the onset of end-stage kidney disease.
Results
During a median follow-up of 4.8 years, the primary outcome occurred in 129 (18.2%) patients. In time-varying Cox model, the adjusted hazard ratios (aHRs) for the primary outcome were 1.59 (95% confidence interval [CI], 1.01 to 2.49) and 1.99 (95% CI, 1.24 to 3.19) for HbA1c levels of 7.0%–7.9% and ≥8.0%, respectively, compared with <7.0%. Additional analysis of baseline HbA1c levels yielded a similar graded association. In secondary outcome analyses, the aHRs for the corresponding HbA1c categories were 2.17 (95% CI, 1.20 to 3.95) and 2.26 (95% CI, 1.17 to 4.37) for MACE, and 1.36 (95% CI, 0.68 to 2.72) and 2.08 (95% CI, 1.06 to 4.05) for all-cause mortality. However, the risk of CKD progression did not differ between the three groups.
Conclusion
This study showed that higher HbA1c levels were associated with an increased risk of MACE and mortality in patients with CKD and T2DM.

Citations

Citations to this article as recorded by

The Beneficial Effect of Glycemic Control against Adverse Outcomes in Patients with Type 2 Diabetes Mellitus and Chronic Kidney Disease
Dong-Hwa Lee
Diabetes & Metabolism Journal.2023; 47(4): 484. CrossRef
Prevalence and predictors of chronic kidney disease among type 2 diabetic patients worldwide, systematic review and meta-analysis
Eneyew Talie Fenta, Habitu Birhan Eshetu, Natnael Kebede, Eyob Ketema Bogale, Amare Zewdie, Tadele Derbew Kassie, Tadele Fentabil Anagaw, Elyas Melaku Mazengia, Sintayehu Shiferaw Gelaw
Diabetology & Metabolic Syndrome.2023;[Epub] CrossRef
Efficacy and safety of teneligliptin in patients with type 2 diabetes mellitus: a Bayesian network meta-analysis
Miao Zhu, Ruifang Guan, Guo Ma
Frontiers in Endocrinology.2023;[Epub] CrossRef

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