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Association of Succinate and Adenosine Nucleotide Metabolic Pathways with Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus
Inha Jung, Seungyoon Nam, Da Young Lee, So Young Park, Ji Hee Yu, Ji A Seo, Dae Ho Lee, Nan Hee Kim
Received October 23, 2023  Accepted May 6, 2024  Published online July 1, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0377    [Epub ahead of print]
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
Although the prevalence of diabetic kidney disease (DKD) is increasing, reliable biomarkers for its early detection are scarce. This study aimed to evaluate the association of adenosine and succinate levels and their related pathways, including hyaluronic acid (HA) synthesis, with DKD.
Methods
We examined 235 participants and categorized them into three groups: healthy controls; those with diabetes but without DKD; and those with DKD, which was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. We compared the concentrations of urinary adenosine, succinate, and HA and the serum levels of cluster of differentiation 39 (CD39) and CD73, which are involved in adenosine generation, among the groups with DKD or albuminuria. In addition, we performed multiple logistic regression analysis to evaluate the independent association of DKD or albuminuria with the metabolites after adjusting for risk factors. We also showed the association of these metabolites with eGFR measured several years before enrollment. This study was registered with the Clinical Research Information Service (https://cris.nih.go.kr; Registration number: KCT0003573).
Results
Urinary succinate and serum CD39 levels were higher in the DKD group than in the control and non-DKD groups. Correlation analysis consistently linked urinary succinate and serum CD39 concentrations with eGFR, albuminuria, and ΔeGFR, which was calculated retrospectively. However, among the various metabolites studied, only urinary succinate was identified as an independent indicator of DKD and albuminuria.
Conclusion
Among several potential metabolites, only urinary succinate was independently associated with DKD. These findings hold promise for clinical application in the management of DKD.
Metabolic Risk/Epidemiology
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A Composite Blood Biomarker Including AKR1B10 and Cytokeratin 18 for Progressive Types of Nonalcoholic Fatty Liver Disease
Seung Joon Choi, Sungjin Yoon, Kyoung-Kon Kim, Doojin Kim, Hye Eun Lee, Kwang Gi Kim, Seung Kak Shin, Ie Byung Park, Seong Min Kim, Dae Ho Lee
Diabetes Metab J. 2024;48(4):740-751.   Published online February 1, 2024
DOI: https://doi.org/10.4093/dmj.2023.0189
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  • 1 Web of Science
  • 1 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
We aimed to evaluate whether composite blood biomarkers including aldo-keto reductase family 1 member B10 (AKR1B10) and cytokeratin 18 (CK-18; a nonalcoholic steatohepatitis [NASH] marker) have clinically applicable performance for the diagnosis of NASH, advanced liver fibrosis, and high-risk NASH (NASH+significant fibrosis).
Methods
A total of 116 subjects including healthy control subjects and patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD) were analyzed to assess composite blood-based and imaging-based biomarkers either singly or in combination.
Results
A composite blood biomarker comprised of AKR1B10, CK-18, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) showed excellent performance for the diagnosis of, NASH, advanced fibrosis, and high-risk NASH, with area under the receiver operating characteristic curve values of 0.934 (95% confidence interval [CI], 0.888 to 0.981), 0.902 (95% CI, 0.832 to 0.971), and 0.918 (95% CI, 0.862 to 0.974), respectively. However, the performance of this blood composite biomarker was inferior to that various magnetic resonance (MR)-based composite biomarkers, such as proton density fat fraction/MR elastography- liver stiffness measurement (MRE-LSM)/ALT/AST for NASH, MRE-LSM+fibrosis-4 index for advanced fibrosis, and the known MR imaging-AST (MAST) score for high-risk NASH.
Conclusion
Our blood composite biomarker can be useful to distinguish progressive forms of NAFLD as an initial noninvasive test when MR-based tools are not available.

Citations

Citations to this article as recorded by  
  • Aldo-keto reductase (AKR) superfamily website and database: An update
    Andrea Andress Huacachino, Jaehyun Joo, Nisha Narayanan, Anisha Tehim, Blanca E. Himes, Trevor M. Penning
    Chemico-Biological Interactions.2024; 398: 111111.     CrossRef
Complications
Fatty Acid-Binding Protein 4 in Patients with and without Diabetic Retinopathy
Ping Huang, Xiaoqin Zhao, Yi Sun, Xinlei Wang, Rong Ouyang, Yanqiu Jiang, Xiaoquan Zhang, Renyue Hu, Zhuqi Tang, Yunjuan Gu
Diabetes Metab J. 2022;46(4):640-649.   Published online April 28, 2022
DOI: https://doi.org/10.4093/dmj.2021.0195
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  • 2 Web of Science
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AbstractAbstract PDFPubReader   ePub   
Background
Fatty acid-binding protein 4 (FABP4) has been demonstrated to be a predictor of early diabetic nephropathy. However, little is known about the relationship between FABP4 and diabetic retinopathy (DR). This study explored the value of FABP4 as a biomarker of DR in patients with type 2 diabetes mellitus (T2DM).
Methods
A total of 238 subjects were enrolled, including 20 healthy controls and 218 T2DM patients. Serum FABP4 levels were measured using a sandwich enzyme-linked immunosorbent assay. The grade of DR was determined using fundus fluorescence angiography. Based on the international classification of DR, all T2DM patients were classified into the following three subgroups: non-DR group, non-proliferative diabetic retinopathy (NPDR) group, and proliferative diabetic retinopathy (PDR) group. Multivariate logistic regression analyses were employed to assess the correlation between FABP4 levels and DR severity.
Results
FABP4 correlated positively with DR severity (r=0.225, P=0.001). Receiver operating characteristic curve analysis was used to assess the diagnostic potential of FABP4 in identifying DR, with an area under the curve of 0.624 (37% sensitivity, 83.6% specificity) and an optimum cut-off value of 76.4 μg/L. Multivariate logistic regression model including FABP4 as a categorized binary variable using the cut-off value of 76.4 μg/L showed that the concentration of FABP4 above the cut-off value increased the risk of NPDR (odds ratio [OR], 3.231; 95% confidence interval [CI], 1.574 to 6.632; P=0.001) and PDR (OR, 3.689; 95% CI, 1.306 to 10.424; P=0.014).
Conclusion
FABP4 may be used as a serum biomarker for the diagnosis of DR.

Citations

Citations to this article as recorded by  
  • Circulating AFABP, FGF21, and PEDF Levels as Prognostic Biomarkers of Sight-threatening Diabetic Retinopathy
    Chi-Ho Lee, David Tak-Wai Lui, Chloe Yu-Yan Cheung, Carol Ho-Yi Fong, Michele Mae-Ann Yuen, Yu-Cho Woo, Wing-Sun Chow, Ian Yat-Hin Wong, Aimin Xu, Karen Siu-Ling Lam
    The Journal of Clinical Endocrinology & Metabolism.2023; 108(9): e799.     CrossRef
  • A Prediction Model for Sight-Threatening Diabetic Retinopathy Based on Plasma Adipokines among Patients with Mild Diabetic Retinopathy
    Yaxin An, Bin Cao, Kun Li, Yongsong Xu, Wenying Zhao, Dong Zhao, Jing Ke, Takayuki Masaki
    Journal of Diabetes Research.2023; 2023: 1.     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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  • 61 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.

Citations

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  • GLP-1RA Combined with SGLT2 Inhibitors for the Treatment of Diabetic Kidney Disease: A Meta Analysis
    莹 郭
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  • Potential application of Klotho as a prognostic biomarker for patients with diabetic kidney disease: a meta-analysis of clinical studies
    Li Xia Yu, Min Yue Sha, Yue Chen, Fang Tan, Xi Liu, Shasha Li, Qi-Feng Liu
    Therapeutic Advances in Chronic Disease.2023;[Epub]     CrossRef
  • Research progress of natural active compounds on improving podocyte function to reduce proteinuria in diabetic kidney disease
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    Renal Failure.2023;[Epub]     CrossRef
  • Identification of potential crosstalk genes and mechanisms between periodontitis and diabetic nephropathy through bioinformatic analysis
    Huijuan Lu, Jia Sun, Jieqiong Sun
    Medicine.2023; 102(52): e36802.     CrossRef
  • Mitochondrial RNAs as Potential Biomarkers of Functional Impairment in Diabetic Kidney Disease
    Stefania Di Mauro, Alessandra Scamporrino, Agnese Filippello, Maurizio Di Marco, Maria Teresa Di Martino, Francesca Scionti, Antonino Di Pino, Roberto Scicali, Roberta Malaguarnera, Francesco Purrello, Salvatore Piro
    International Journal of Molecular Sciences.2022; 23(15): 8198.     CrossRef
  • Renoprotective Mechanism of Sodium-Glucose Cotransporter 2 Inhibitors: Focusing on Renal Hemodynamics
    Nam Hoon Kim, Nan Hee Kim
    Diabetes & Metabolism Journal.2022; 46(4): 543.     CrossRef
  • Partial Synthetic PPARƳ Derivative Ameliorates Aorta Injury in Experimental Diabetic Rats Mediated by Activation of miR-126-5p Pi3k/AKT/PDK 1/mTOR Expression
    Yasmin M. Ahmed, Raha Orfali, Nada S. Abdelwahab, Hossam M. Hassan, Mostafa E. Rateb, Asmaa M. AboulMagd
    Pharmaceuticals.2022; 15(10): 1175.     CrossRef
  • Polydatin attenuates tubulointerstitial fibrosis in diabetic kidney disease by inhibiting YAP expression and nuclear translocation
    Manlin He, Lan Feng, Yang Chen, Bin Gao, Yiwei Du, Lu Zhou, Fei Li, Hongbao Liu
    Frontiers in Physiology.2022;[Epub]     CrossRef
  • Prevalence of diabetic nephropathy in the diabetes mellitus population: A protocol for systematic review and meta-analysis
    Sicheng Li, Huidi Xie, Yang Shi, Hongfang Liu
    Medicine.2022; 101(42): e31232.     CrossRef
  • Stratification of diabetic kidney diseases via data-independent acquisition proteomics–based analysis of human kidney tissue specimens
    Qinghua Huang, Xianming Fei, Zhaoxian Zhong, Jieru Zhou, Jianguang Gong, Yuan Chen, Yiwen Li, Xiaohong Wu
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
  • Novel biomarkers and therapeutic approaches for diabetic retinopathy and nephropathy: Recent progress and future perspectives
    Ziyan Xie, Xinhua Xiao
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
  • Diabetic Kidney Disease
    Susanne B. Nicholas, Amy K. Mottl
    Nephrology Self-Assessment Program.2022; 21(5): 394.     CrossRef
Original Articles
Type 1 Diabetes
Article image
Differential Profile of Plasma Circular RNAs in Type 1 Diabetes Mellitus
Yangyang Li, Ying Zhou, Minghui Zhao, Jing Zou, Yuxiao Zhu, Xuewen Yuan, Qianqi Liu, Hanqing Cai, Cong-Qiu Chu, Yu Liu
Diabetes Metab J. 2020;44(6):854-865.   Published online July 13, 2020
DOI: https://doi.org/10.4093/dmj.2019.0151
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Background

No currently available biomarkers or treatment regimens fully meet therapeutic needs of type 1 diabetes mellitus (T1DM). Circular RNA (circRNA) is a recently identified class of stable noncoding RNA that have been documented as potential biomarkers for various diseases. Our objective was to identify and analyze plasma circRNAs altered in T1DM.

Methods

We used microarray to screen differentially expressed plasma circRNAs in patients with new onset T1DM (n=3) and age-/gender-matched healthy controls (n=3). Then, we selected six candidates with highest fold-change and validated them by quantitative real-time polymerase chain reaction in independent human cohort samples (n=12). Bioinformatic tools were adopted to predict putative microRNAs (miRNAs) sponged by these validated circRNAs and their downstream messenger RNAs (mRNAs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to gain further insights into T1DM pathogenesis.

Results

We identified 68 differentially expressed circRNAs, with 61 and seven being up- and downregulated respectively. Four of the six selected candidates were successfully validated. Curations of their predicted interacting miRNAs revealed critical roles in inflammation and pathogenesis of autoimmune disorders. Functional relations were visualized by a circRNA-miRNA-mRNA network. GO and KEGG analyses identified multiple inflammation-related processes that could be potentially associated with T1DM pathogenesis, including cytokine-cytokine receptor interaction, inflammatory mediator regulation of transient receptor potential channels and leukocyte activation involved in immune response.

Conclusion

Our study report, for the first time, a profile of differentially expressed plasma circRNAs in new onset T1DM. Further in silico annotations and bioinformatics analyses supported future application of circRNAs as novel biomarkers of T1DM.

Citations

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    Yuhong Zhong, Juan Xia, Li Liao, Mohammad Reza Momeni
    International Journal of Biological Macromolecules.2024; 259: 128182.     CrossRef
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    Ren-Jie Zhao, Wan-Ying Zhang, Xing-Xing Fan
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    Ziwei Zhang, Shuoming Luo, Zilin Xiao, Wenfeng Yin, Xiajie Shi, Hongzhi Chen, Zhiguo Xie, Zhenqi Liu, Xia Li, Zhiguang Zhou
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    Pengqiang Zhong, Lu Bai, Mengzhi Hong, Juan Ouyang, Ruizhi Wang, Xiaoli Zhang, Peisong Chen
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  • Circulating non-coding RNA in type 1 diabetes mellitus as a source of potential biomarkers – An emerging role of sex difference
    Lucyna Stachowiak, Weronika Kraczkowska, Aleksandra Świercz, Paweł Piotr Jagodziński
    Biochemical and Biophysical Research Communications.2024; 736: 150482.     CrossRef
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    Manuela Cabiati, Giovanni Federico, Silvia Del Ry
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  • Circular RNA PIP5K1A Promotes Glucose and Lipid Metabolism Disorders and Inflammation in Type 2 Diabetes Mellitus
    Ge Song, YiQian Zhang, YiHua Jiang, Huan Zhang, Wen Gu, Xiu Xu, Jing Yao, ZhengFang Chen
    Molecular Biotechnology.2023;[Epub]     CrossRef
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    Simranjeet Kaur, Caroline Frørup, Aashiq H. Mirza, Tina Fløyel, Reza Yarani, Maikel L. Colli, Jesper Johannesen, Joachim Størling, Decio L. Eizirik, Flemming Pociot
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  • Differential Expression and Bioinformatics Analysis of Plasma-Derived Exosomal circRNA in Type 1 Diabetes Mellitus
    Haipeng Pang, Wenqi Fan, Xiajie Shi, Shuoming Luo, Yimeng Wang, Jian Lin, Yang Xiao, Xia Li, Gan Huang, Zhiguo Xie, Zhiguang Zhou, Jinhui Liu
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Metabolic Risk/Epidemiology
Article image
Sex-, Age-, and Metabolic Disorder-Dependent Distributions of Selected Inflammatory Biomarkers among Community-Dwelling Adults
So Mi Jemma Cho, Hokyou Lee, Jee-Seon Shim, Hyeon Chang Kim
Diabetes Metab J. 2020;44(5):711-725.   Published online April 16, 2020
DOI: https://doi.org/10.4093/dmj.2019.0119
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AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background

Inflammatory cytokines are increasingly utilized to detect high-risk individuals for cardiometabolic diseases. However, with large population and assay methodological heterogeneity, no clear reference currently exists.

Methods

Among participants of the Cardiovascular and Metabolic Diseases Etiology Research Center cohort, of community-dwelling adults aged 30 to 64 without overt cardiovascular diseases, we presented distributions of tumor necrosis factor (TNF)-α and -β, interleukin (IL)-1α, -1β, and 6, monocyte chemoattractant protein (MCP)-1 and -3 and high sensitivity C-reactive protein (hsCRP) with and without non-detectable (ND) measurements using multiplex enzyme-linked immunosorbent assay. Then, we compared each markers by sex, age, and prevalence of type 2 diabetes mellitus, hypertension, and dyslipidemia, using the Wilcoxon Rank-Sum Test.

Results

In general, there were inconsistencies in direction and magnitude of differences in distributions by sex, age, and prevalence of cardiometabolic disorders. Overall, the median and the 99th percentiles were higher in men than in women. Older participants had higher TNF-α, high sensitivity IL-6 (hsIL-6), MCP-1, hsCRP, TNF-β, and MCP-3 median, after excluding the NDs. Participants with type 2 diabetes mellitus had higher median for all assayed biomarkers, except for TNF-β, IL-1α, and MCP-3, in which the medians for both groups were 0.00 due to predominant NDs. Compared to normotensive group, participants with hypertension had higher TNF-α, hsIL-6, MCP-1, and hsCRP median. When stratifying by dyslipidemia prevalence, the comparison varied significantly depending on the treatment of NDs.

Conclusion

Our findings provide sex-, age-, and disease-specific reference values to improve risk prediction and diagnostic performance for inflammatory diseases in both population- and clinic-based settings.

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  • Characterizing CD8+ TEMRA Cells in CP/CPPS Patients: Insights from Targeted Single-Cell Transcriptomic and Functional Investigations
    Fei Zhang, Qintao Ge, Jialin Meng, Jia Chen, Chaozhao Liang, Meng Zhang
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    Thomas S. Buttle, Claire Y. Hummerstone, Thippeswamy Billahalli, Richard J. B. Ward, Korina E. Barnes, Natalie J. Marshall, Viktoria C. Spong, Graham H. Bothamley, Selvakumar Subbian
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Metabolic Risk/Epidemiology
Multiple Biomarkers Improved Prediction for the Risk of Type 2 Diabetes Mellitus in Singapore Chinese Men and Women
Yeli Wang, Woon-Puay Koh, Xueling Sim, Jian-Min Yuan, An Pan
Diabetes Metab J. 2020;44(2):295-306.   Published online November 22, 2019
DOI: https://doi.org/10.4093/dmj.2019.0020
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AbstractAbstract PDFSupplementary MaterialPubReader   
Background

Multiple biomarkers have performed well in predicting type 2 diabetes mellitus (T2DM) risk in Western populations. However, evidence is scarce among Asian populations.

Methods

Plasma triglyceride-to-high density lipoprotein (TG-to-HDL) ratio, alanine transaminase (ALT), high-sensitivity C-reactive protein (hs-CRP), ferritin, adiponectin, fetuin-A, and retinol-binding protein 4 were measured in 485 T2DM cases and 485 age-and-sex matched controls nested within the prospective Singapore Chinese Health Study cohort. Participants were free of T2DM at blood collection (1999 to 2004), and T2DM cases were identified at the subsequent follow-up interviews (2006 to 2010). A weighted biomarker score was created based on the strengths of associations between these biomarkers and T2DM risks. The predictive utility of the biomarker score was assessed by the area under receiver operating characteristics curve (AUC).

Results

The biomarker score that comprised of four biomarkers (TG-to-HDL ratio, ALT, ferritin, and adiponectin) was positively associated with T2DM risk (P trend <0.001). Compared to the lowest quartile of the score, the odds ratio was 12.0 (95% confidence interval [CI], 5.43 to 26.6) for those in the highest quartile. Adding the biomarker score to a base model that included smoking, history of hypertension, body mass index, and levels of random glucose and insulin improved AUC significantly from 0.81 (95% CI, 0.78 to 0.83) to 0.83 (95% CI, 0.81 to 0.86; P=0.002). When substituting the random glucose levels with glycosylated hemoglobin in the base model, adding the biomarker score improved AUC from 0.85 (95% CI, 0.83 to 0.88) to 0.86 (95% CI, 0.84 to 0.89; P=0.032).

Conclusion

A composite score of blood biomarkers improved T2DM risk prediction among Chinese.

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  • Baseline glycated albumin level and risk of type 2 diabetes mellitus in Healthy individuals: a retrospective longitudinal observation in Korea
    Kang-Su Shin, Min-Seung Park, Mi Yeon Lee, Eun Hye Cho, Hee-Yeon Woo, Hyosoon Park, Min-Jung Kwon
    Scandinavian Journal of Clinical and Laboratory Investigation.2024; 84(3): 168.     CrossRef
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    Ji Hye Huh, Eun Roh, Seong Jin Lee, Sung-Hee Ihm, Kyung-Do Han, Jun Goo Kang
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    Xiaomeng Tan, Han Zhang, Limin Liu, Zengli Yu, Xinxin Liu, Lingling Cui, Yao Chen, Huanhuan Zhang, Zhan Gao, Zijian Zhao
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    PRIYANKA JADHAV, VINOTHINI SELVARAJU, SARITH P SATHIAN, RAMAKRISHNAN SWAMINATHAN
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    Sally Sonia Simmons
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Metabolic Risk/Epidemiology
Plasma CD36 and Incident Diabetes: A Case-Cohort Study in Danish Men and Women
Yeli Wang, Jingwen Zhu, Sarah Aroner, Kim Overvad, Tianxi Cai, Ming Yang, Anne Tjønneland, Aase Handberg, Majken K. Jensen
Diabetes Metab J. 2020;44(1):134-142.   Published online October 18, 2019
DOI: https://doi.org/10.4093/dmj.2018.0273
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AbstractAbstract PDFSupplementary MaterialPubReader   
Background

Membrane CD36 is a fatty acid transporter implicated in the pathogenesis of metabolic disease. We aimed to evaluate the association between plasma CD36 levels and diabetes risk and to examine if the association was independent of adiposity among Danish population.

Methods

We conducted a case-cohort study nested within the Danish Diet, Cancer and Health study among participants free of cardiovascular disease, diabetes and cancer and with blood samples and anthropometric measurements (height, weight, waist circumference, and body fat percentage) at baseline (1993 to 1997). CD36 levels were measured in 647 incident diabetes cases that occurred before December 2011 and a total of 3,515 case-cohort participants (236 cases overlap).

Results

Higher plasma CD36 levels were associated with higher diabetes risk after adjusting for age, sex and other lifestyle factors. The hazard ratio (HR) comparing high versus low tertile of plasma CD36 levels was 1.36 (95% confidence interval [CI], 1.00 to 1.86). However, the association lost its significance after further adjustment for different adiposity indices such as body mass index (HR, 1.23; 95% CI, 0.87 to 1.73), waist circumference (HR, 1.21; 95% CI, 0.88 to 1.68) or body fat percentage (HR, 1.20; 95% CI, 0.86 to 1.66). Moreover, raised plasma CD36 levels were moderately associated with diabetes risk among lean participants, but the association was not present among overweight/obese individuals.

Conclusion

Higher plasma CD36 levels were associated with higher diabetes risk, but the association was not independent of adiposity. In this Danish population, the association of CD36 with diabetes risk could be either mediated or confounded by adiposity.

Citations

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  • Biomarkers of insulin sensitivity/resistance
    Constantine E Kosmas, Andreas Sourlas, Konstantinos Oikonomakis, Eleni-Angeliki Zoumi, Aikaterini Papadimitriou, Christina E Kostara
    Journal of International Medical Research.2024;[Epub]     CrossRef
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    Kamila Puchałowicz, Monika Ewa Rać
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  • The Role of CD36 in Type 2 Diabetes Mellitus: β-Cell Dysfunction and Beyond
    Jun Sung Moon, Udayakumar Karunakaran, Elumalai Suma, Seung Min Chung, Kyu Chang Won
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Obesity and Metabolic Syndrome
Increased Serum Angiopoietin-Like 6 Ahead of Metabolic Syndrome in a Prospective Cohort Study
Jun Namkung, Joon Hyung Sohn, Jae Seung Chang, Sang-Wook Park, Jang-Young Kim, Sang-Baek Koh, In Deok Kong, Kyu-Sang Park
Diabetes Metab J. 2019;43(4):521-529.   Published online March 29, 2019
DOI: https://doi.org/10.4093/dmj.2018.0080
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AbstractAbstract PDFPubReader   
Background

Despite being an anti-obesity hepatokine, the levels of serum angiopoietin-like 6 (ANGPTL6) are elevated in various metabolic diseases. Thus, ANGPTL6 expression may reflect metabolic burden and may have compensatory roles. This study investigated the association between serum ANGPTL6 levels and new-onset metabolic syndrome.

Methods

In total, 221 participants without metabolic syndrome were randomly selected from a rural cohort in Korea. Baseline serum ANGPTL6 levels were measured using an enzyme-linked immunosorbent assay. Anthropometric and biochemical markers were analyzed before and after follow-up examinations.

Results

During an average follow-up period of 2.75 (interquartile range, 0.76) years, 82 participants (37.1%) presented new-onset metabolic syndrome and had higher ANGPTL6 levels before onset than those without metabolic syndrome (48.03±18.84 ng/mL vs. 64.75±43.35 ng/mL, P=0.001). In the multivariable adjusted models, the odds ratio for the development of metabolic syndrome in the highest quartile of ANGPTL6 levels was 3.61 (95% confidence interval, 1.27 to 10.26). The use of ANGPTL6 levels in addition to the conventional components improved the prediction of new-onset metabolic syndrome (area under the receiver operating characteristic curve: 0.775 vs. 0.807, P=0.036).

Conclusion

Increased serum ANGPTL6 levels precede the development of metabolic syndrome and its components, including low high density lipoprotein, high triglyceride, and high glucose levels, which have an independent predictive value for metabolic syndrome.

Citations

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  • Circulating Angiopoietin-like Protein 6 Levels and Clinical Features in Patients with Type 2 Diabetes
    Kohzo Takebayashi, Tatsuhiko Suzuki, Mototaka Yamauchi, Kenji Hara, Takafumi Tsuchiya, Toshihiko Inukai, Koshi Hashimoto
    Internal Medicine.2024;[Epub]     CrossRef
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    Eric Thorin, Pauline Labbé, Mélanie Lambert, Pauline Mury, Olina Dagher, Géraldine Miquel, Nathalie Thorin-Trescases
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    Xin Su, Hua Peng, Xiang Chen, Xijie Wu, Bin Wang
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    Eunha Chang, Jae Seung Chang, In Deok Kong, Soon Koo Baik, Moon Young Kim, Kyu-Sang Park
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    Bai-Hui Zhang, Fan Yin, Ya-Nan Qiao, Shou-Dong Guo
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    Abel Valencia-Martínez, Ute Schaefer-Graf, Encarnación Amusquivar, Emilio Herrera, Henar Ortega-Senovilla
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    Huixing Liu, Daoquan Peng
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    Xin Su, Xiang Chen, Bin Wang
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    Jae Seung Chang, Jun Namkung
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    Yi-Zhang Liu, Chi Zhang, Jie-Feng Jiang, Zhe-Bin Cheng, Zheng-Yang Zhou, Mu-Yao Tang, Jia-Xiang Sun, Liang Huang
    Clinica Chimica Acta.2021; 521: 19.     CrossRef
  • Effects of Bariatric Surgeries on Fetuin-A, Selenoprotein P, Angiopoietin-Like Protein 6, and Fibroblast Growth Factor 21 Concentration
    Jakub Poloczek, Wojciech Kazura, Ewa Kwaśnicka, Janusz Gumprecht, Jerzy Jochem, Dominika Stygar, Munmun Chattopadhyay
    Journal of Diabetes Research.2021; 2021: 1.     CrossRef
  • Hepatokines and Non-Alcoholic Fatty Liver Disease: Linking Liver Pathophysiology to Metabolism
    Tae Hyun Kim, Dong-Gyun Hong, Yoon Mee Yang
    Biomedicines.2021; 9(12): 1903.     CrossRef
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    Semra ÖZKAN ÖZTÜRK, Hilmi ATASEVEN
    Cumhuriyet Medical Journal.2021;[Epub]     CrossRef
  • ANGPTL6 Level in Patient with Coronary Heart Disease and Its Relationship with the Severity of Coronary Artery Lesions
    蕾 任
    Advances in Clinical Medicine.2020; 10(05): 714.     CrossRef
  • Investigating the Role of Myeloperoxidase and Angiopoietin-like Protein 6 in Obesity and Diabetes
    Mohammad G. Qaddoumi, Muath Alanbaei, Maha M. Hammad, Irina Al Khairi, Preethi Cherian, Arshad Channanath, Thangavel Alphonse Thanaraj, Fahd Al-Mulla, Mohamed Abu-Farha, Jehad Abubaker
    Scientific Reports.2020;[Epub]     CrossRef
  • Letter: Increased Serum Angiopoietin-Like 6 Ahead of Metabolic Syndrome in a Prospective Cohort Study (Diabetes Metab J 2019;43:521-9)
    Jin Hwa Kim
    Diabetes & Metabolism Journal.2019; 43(5): 727.     CrossRef
  • Response: Increased Serum Angiopoietin-Like 6 Ahead of Metabolic Syndrome in a Prospective Cohort Study (Diabetes Metab J 2019;43:521-9)
    Jun Namkung, Kyu-Sang Park
    Diabetes & Metabolism Journal.2019; 43(5): 729.     CrossRef
Pathophysiology
The Phospholipid Linoleoylglycerophosphocholine as a Biomarker of Directly Measured Insulin Resistance
Maria Camila Pérez-Matos, Martha Catalina Morales-Álvarez, Freddy Jean Karlo Toloza, Maria Laura Ricardo-Silgado, Jose Oscar Mantilla-Rivas, Jairo Arturo Pinzón-Cortes, Maritza Perez-Mayorga, Elizabeth Jiménez, Edwin Guevara, Carlos O Mendivil
Diabetes Metab J. 2017;41(6):466-473.   Published online November 27, 2017
DOI: https://doi.org/10.4093/dmj.2017.41.6.466
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AbstractAbstract PDFPubReader   
Background

Plasma concentrations of some lysophospholipids correlate with metabolic alterations in humans, but their potential as biomarkers of insulin resistance (IR) is insufficiently known. We aimed to explore the association between plasma linoleoylglycerophosphocholine (LGPC) and objective measures of IR in adults with different metabolic profiles.

Methods

We studied 62 men and women, ages 30 to 69 years, (29% normal weight, 59% overweight, 12% obese). Participants underwent a 5-point oral glucose tolerance test (5p-OGTT) from which we calculated multiple indices of IR and insulin secretion. Fifteen participants additionally underwent a hyperinsulinemic-euglycemic clamp for estimation of insulin-stimulated glucose disposal. Plasma LGPC was determined using high performance liquid chromatography/time-of-flight mass spectrometry. Plasma LGPC was compared across quartiles defined by the IR indices.

Results

Mean LGPC was 15.4±7.6 ng/mL in women and 14.1±7.3 ng/mL in men. LGPC did not correlate with body mass in-dex, percent body fat, waist circumference, blood pressure, glycosylated hemoglobin, log-triglycerides, or high density lipoprotein cholesterol. Plasma LGPC concentrations was not systematically associated with any of the studied 5p-OGTT-derived IR indices. However, LGPC exhibited a significant negative correlation with glucose disposal in the clamp (Spearman r=−0.56, P=0.029). Despite not being diabetic, participants with higher plasma LGPC exhibited significantly higher post-challenge plasma glucose excursions in the 5p-OGTT (P trend=0.021 for the increase in glucose area under the curve across quartiles of plasma LGPC).

Conclusion

In our sample of Latino adults without known diabetes, LGPC showed potential as a biomarker of IR and impaired glucose metabolism.

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    Saleem Yousf, Hitender S. Batra, Rakesh M. Jha, Devika M. Sardesai, Kalyani Ananthamohan, Jeetender Chugh, Shilpy Sharma
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Cystatin C is a Valuable Marker for Predicting Future Cardiovascular Diseases in Type 2 Diabetic Patients.
Seung Hwan Lee, Kang Woo Lee, Eun Sook Kim, Ye Ree Park, Hun Sung Kim, Shin Ae Park, Mi Ja Kang, Yu Bai Ahn, Kun Ho Yoon, Bong Yun Cha, Ho Young Son, Hyuk Sang Kwon
Korean Diabetes J. 2008;32(6):488-497.   Published online December 1, 2008
DOI: https://doi.org/10.4093/kdj.2008.32.6.488
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AbstractAbstract PDF
BACKGROUND
Recent studies suggest that serum Cystatin C is both a sensitive marker for renal dysfunction and a predictive marker for cardiovascular diseases. We aimed to evaluate the association between Cystatin C and various biomarkers and to find out its utility in estimating risk for cardiovascular diseases in type 2 diabetic patients. METHODS: From June 2006 to March 2008, anthropometric measurements and biochemical studies including biomarkers for risk factors of cardiovascular diseases were done in 520 type 2 diabetic patients. A 10-year risk for coronary heart diseases and stroke was estimated using Framingham risk score and UKPDS risk engine. RESULTS: The independent variables showing statistically significant associations with Cystatin C were age (beta = 0.009, P < 0.0001), hemoglobin (beta = -0.038, P = 0.0006), serum creatinine (beta = 0.719, beta < 0.0001), uric acid (beta = 0.048, P = 0.0004), log hsCRP (beta = 0.035, P = 0.0021) and homocysteine (beta = 0.005, P = 0.0228). The levels of microalbuminuria, carotid intima-media thickness, fibrinogen and lipoprotein (a) also correlated with Cystatin C, although the significance was lost after multivariate adjustment. Calculated risk for coronary heart diseases increased in proportion to Cystatin C quartiles: 3.3 +/- 0.4, 6.2 +/- 0.6, 7.6 +/- 0.7, 8.4 +/- 0.7% from Framingham risk score (P < 0.0001); 13.1 +/- 0.9, 21.2 +/- 1.6, 26.1 +/- 1.7, 35.4 +/- 2.0% from UKPDS risk engine (P < 0.0001) (means +/- SE). CONCLUSIONS: Cystatin C is significantly correlated with various emerging biomarkers for cardiovascular diseases. It was also in accordance with the calculated risk for cardiovascular diseases. These findings verify Cystatin C as a valuable and useful marker for predicting future cardiovascular diseases in type 2 diabetic patients.

Citations

Citations to this article as recorded by  
  • Lack of Association between Serum Cystatin C Levels and Coronary Artery Disease in Diabetic Patients
    Eun Hee Kim, Ji Hee Yu, Sang Ah Lee, Eui Young Kim, Won Gu Kim, Seung Hun Lee, Eun Hee Cho, Eun Hee Koh, Woo Je Lee, Min-Seon Kim, Joong-Yeol Park, Ki-Up Lee
    Korean Diabetes Journal.2010; 34(2): 95.     CrossRef
  • Insulin resistance and inflammation may have an additional role in the link between cystatin C and cardiovascular disease in type 2 diabetes mellitus patients
    Seung-Hwan Lee, Shin-Ae Park, Seung-Hyun Ko, Hyeon-Woo Yim, Yu-Bae Ahn, Kun-Ho Yoon, Bong-Yun Cha, Ho-Young Son, Hyuk-Sang Kwon
    Metabolism.2010; 59(2): 241.     CrossRef

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