Diabetes & Metabolism Journal

Original Article

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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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
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Miao Zhu, Ruifang Guan, Guo Ma
Frontiers in Endocrinology.2023;[Epub] CrossRef

Review

Complications
Pathophysiologic Mechanisms and Potential Biomarkers in Diabetic Kidney Disease: Chan-Young Jung, Tae-Hyun Yoo; Diabetes Metab J. 2022;46(2):181-197. Published online March 24, 2022; DOI: https://doi.org/10.4093/dmj.2021.0329

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Although diabetic kidney disease (DKD) remains the leading cause of end-stage kidney disease eventually requiring chronic kidney replacement therapy, the prevalence of DKD has failed to decline over the past 30 years. In order to reduce disease prevalence, extensive research has been ongoing to improve prediction of DKD onset and progression. Although the most commonly used markers of DKD are albuminuria and estimated glomerular filtration rate, their limitations have encouraged researchers to search for novel biomarkers that could improve risk stratification. Considering that DKD is a complex disease process that involves several pathophysiologic mechanisms such as hyperglycemia induced inflammation, oxidative stress, tubular damage, eventually leading to kidney damage and fibrosis, many novel biomarkers that capture one specific mechanism of the disease have been developed. Moreover, the increasing use of high-throughput omic approaches to analyze biological samples that include proteomics, metabolomics, and transcriptomics has emerged as a strong tool in biomarker discovery. This review will first describe recent advances in the understanding of the pathophysiology of DKD, and second, describe the current clinical biomarkers for DKD, as well as the current status of multiple potential novel biomarkers with respect to protein biomarkers, proteomics, metabolomics, and transcriptomics.

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