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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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  • 45 Web of Science
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AbstractAbstract PDFPubReader   ePub   
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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Original Article
Cardiovascular Risk/Epidemiology
Article image
Mean and Variability of Lipid Measurements and Risk for Development of Subclinical Left Ventricular Diastolic Dysfunction
Jiyun Park, Mira Kang, Jiyeon Ahn, Min Young Kim, Min Sun Choi, You-Bin Lee, Gyuri Kim, Kyu Yeon Hur, Jae Hyeon Kim, Jeong Hoon Yang, Sang-Man Jin
Diabetes Metab J. 2022;46(2):286-296.   Published online November 22, 2021
DOI: https://doi.org/10.4093/dmj.2021.0080
  • 6,032 View
  • 202 Download
  • 2 Web of Science
  • 4 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Subclinical left ventricular diastolic dysfunction (LVDD) is an emerging consequence of increased insulin resistance, and dyslipidemia is one of the few correctable risk factors of LVDD. This study evaluated the role of mean and visit-to-visit variability of lipid measurements in risk of LVDD in a healthy population.
Methods
This was a 3.7-year (interquartile range, 2.1 to 4.9) longitudinal cohort study including 2,817 adults (median age 55 years) with left ventricular ejection fraction >50% who underwent an annual or biannual health screening between January 2008 and July 2016. The mean, standard deviation (SD), coefficient of variation (CV), variability independent of the mean (VIM), and average real variability of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein B (apoB), non-HDL-C, and triglycerides were obtained from three to six measurements during the 5 years preceding the first echocardiogram.
Results
Among the 2,817 patients, 560 (19.9%) developed LVDD. The mean of no component of lipid measurements was associated with risk of LVDD. CV (hazard ratio [HR], 1.35; 95% confidence interval [CI], 1.10 to 1.67), SD (HR, 1.27; 95% CI, 1.03 to 1.57), and VIM (HR, 1.26; 95% CI, 1.03 to 1.55) of LDL-C and all the variability parameters of apoB were significantly associated with development of LVDD. The association between CV-LDL and risk of LVDD did not have significant interaction with sex, increasing/decreasing trend at baseline, or use of stain and/or lipid-modifying agents.
Conclusion
The variability of LDL-C and apoB, rather than their mean, was associated with risk for LVDD.

Citations

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Review
Complications
Article image
Lost in Translation? Measuring Diabetic Neuropathy in Humans and Animals
Heung Yong Jin, Seong-Su Moon, Nigel A. Calcutt
Diabetes Metab J. 2021;45(1):27-42.   Published online December 15, 2020
DOI: https://doi.org/10.4093/dmj.2020.0216
  • 8,858 View
  • 230 Download
  • 13 Web of Science
  • 12 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFPubReader   ePub   
The worldwide diabetes epidemic is estimated to currently afflict almost 500 million persons. Long-term diabetes damages multiple organ systems with the blood vessels, eyes, kidneys and nervous systems being particularly vulnerable. These complications of diabetes reduce lifespan, impede quality of life and impose a huge social and economic burden on both the individual and society. Peripheral neuropathy is a debilitating complication that will impact over half of all persons with diabetes. There is no treatment for diabetic neuropathy and a disturbingly long history of therapeutic approaches showing promise in preclinical studies but failing to translate to the clinic. These failures have prompted re-examination of both the animal models and clinical trial design. This review focuses on the functional and structural parameters used as indices of peripheral neuropathy in preclinical and clinical studies and the extent to which they share a common pathogenesis and presentation. Nerve conduction studies in large myelinated fibers have long been the mainstay of preclinical efficacy screening programs and clinical trials, supplemented by quantitative sensory tests. However, a more refined approach is emerging that incorporates measures of small fiber density in the skin and cornea alongside these traditional assays at both preclinical and clinical phases.

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