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Original Articles
Metabolic Risk/Epidemiology
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
Received June 18, 2023  Accepted August 16, 2023  Published online February 1, 2024  
DOI: https://doi.org/10.4093/dmj.2023.0189    [Epub ahead of print]
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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.
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
  • 3,319 View
  • 192 Download
  • 2 Web of Science
  • 2 Crossref
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
  • 11,290 View
  • 774 Download
  • 41 Web of Science
  • 44 Crossref
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

Citations to this article as recorded by  
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    Yubing Chen, Lijuan Liao, Baoju Wang, Zhan Wu
    Frontiers in Immunology.2024;[Epub]     CrossRef
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    Aadhira Pillai, Darshna Fulmali
    Cureus.2023;[Epub]     CrossRef
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    Yaqian Cheng, Siqi Wan, Linna Yao, Ding Lin, Tong Wu, Yongjian Chen, Ailian Zhang, Chenfei Lu
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    Lan Wei, Yuanyuan Han, Chao Tu
    Diabetes, Metabolic Syndrome and Obesity.2023; Volume 16: 117.     CrossRef
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    Nadja Grobe, Josef Scheiber, Hanjie Zhang, Christian Garbe, Xiaoling Wang
    Advances in Kidney Disease and Health.2023; 30(1): 47.     CrossRef
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    Soumik Das, Ramanathan Gnanasambandan
    Life Sciences.2023; 316: 121414.     CrossRef
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    Francesca Lanzaro, Annalisa Barlabà, Angelica De Nigris, Federica Di Domenico, Valentina Verde, Emanuele Miraglia del Giudice, Anna Di Sessa
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  • Novel Biomarkers of Diabetic Kidney Disease
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    Frontiers in Endocrinology.2023;[Epub]     CrossRef
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    Alessandra Scamporrino, Stefania Di Mauro, Agnese Filippello, Grazia Di Marco, Antonino Di Pino, Roberto Scicali, Maurizio Di Marco, Emanuele Martorana, Roberta Malaguarnera, Francesco Purrello, Salvatore Piro
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    Asian Case Reports in Emergency Medicine.2023; 11(02): 53.     CrossRef
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    Xiangri Kong, Yunyun Zhao, Xingye Wang, Yongjiang Yu, Ying Meng, Guanchi Yan, Miao Yu, Lihong Jiang, Wu Song, Bingmei Wang, Xiuge Wang
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    Diabetes, Metabolic Syndrome and Obesity.2023; Volume 16: 3707.     CrossRef
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Original Articles
Type 1 Diabetes
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
  • 6,040 View
  • 131 Download
  • 19 Web of Science
  • 19 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
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

Citations to this article as recorded by  
  • Non-coding RNAs and exosomal non-coding RNAs in diabetic retinopathy: A narrative review
    Yuhong Zhong, Juan Xia, Li Liao, Mohammad Reza Momeni
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    Yuka Ikeda, Sae Morikawa, Moeka Nakashima, Sayuri Yoshikawa, Kurumi Taniguchi, Haruka Sawamura, Naoko Suga, Ai Tsuji, Satoru Matsuda
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    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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    Ziwei Zhang, Shuoming Luo, Zilin Xiao, Wenfeng Yin, Xiajie Shi, Hongzhi Chen, Zhiguo Xie, Zhenqi Liu, Xia Li, Zhiguang Zhou
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    Lei Ren
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    Wenqi Fan, Haipeng Pang, Zhiguo Xie, Gan Huang, Zhiguang Zhou
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
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    Jianni Chen, Guanfei Jia, Xue Lv, Shufa Li, Christos K. Kontos
    BioMed Research International.2022; 2022: 1.     CrossRef
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    Monisha Prasad, Selvaraj Jayaraman, Vishnu Priya Veeraraghavan
    Hypertension Research.2022; 45(11): 1843.     CrossRef
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    Miao Liu, Junli Zhao
    Aging and disease.2022; 13(5): 1365.     CrossRef
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    Zeyu Liu, Yanhong Zhou, Jian Xia
    Biomedicine & Pharmacotherapy.2022; 156: 113845.     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
    Non-Coding RNA.2022; 8(5): 69.     CrossRef
  • 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
    Journal of Immunology Research.2022; 2022: 1.     CrossRef
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    Wenfeng Yin, Ziwei Zhang, Zilin Xiao, Xia Li, Shuoming Luo, Zhiguang Zhou
    Frontiers in Genetics.2022;[Epub]     CrossRef
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    Yurong Huang, Qiuyun Xue, Chenglong Cheng, Yuting Wang, Xiao Wang, Jun Chang, Chenggui Miao
    Journal of Pharmacy and Pharmacology.2022;[Epub]     CrossRef
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    Xin Wang, Rui Ma, Weimin Shi, Zhouwei Wu, Yuling Shi
    Molecular Therapy - Nucleic Acids.2021; 24: 212.     CrossRef
  • Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches
    Xuanzi Yi, Xu Cheng
    Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 3865.     CrossRef
Metabolic Risk/Epidemiology
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
  • 5,849 View
  • 83 Download
  • 3 Web of Science
  • 3 Crossref
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.

Citations

Citations to this article as recorded by  
  • 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
    ImmunoTargets and Therapy.2024; Volume 13: 111.     CrossRef
  • Association between physical activity and inflammatory markers in community-dwelling, middle-aged adults
    So Mi Jemma Cho, Hokyou Lee, Jee-Seon Shim, Justin Y. Jeon, Hyeon Chang Kim
    Applied Physiology, Nutrition, and Metabolism.2021; 46(7): 828.     CrossRef
  • The monocyte-to-lymphocyte ratio: Sex-specific differences in the tuberculosis disease spectrum, diagnostic indices and defining normal ranges
    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
    PLOS ONE.2021; 16(8): e0247745.     CrossRef
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.

Citations

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  • The association between retinol-binding protein 4 and risk of type 2 diabetes: A systematic review and meta-analysis
    Xiaomeng Tan, Han Zhang, Limin Liu, Zengli Yu, Xinxin Liu, Lingling Cui, Yao Chen, Huanhuan Zhang, Zhan Gao, Zijian Zhao
    International Journal of Environmental Health Research.2024; 34(2): 1053.     CrossRef
  • Remnant Cholesterol Is an Independent Predictor of Type 2 Diabetes: A Nationwide Population-Based Cohort Study
    Ji Hye Huh, Eun Roh, Seong Jin Lee, Sung-Hee Ihm, Kyung-Do Han, Jun Goo Kang
    Diabetes Care.2023; 46(2): 305.     CrossRef
  • A FRAMEWORK FOR THE ANALYSIS OF COMORBID CONDITIONS USING INTELLIGENT EXTRACTION OF MULTIPLE FLUID BIOMARKERS
    PRIYANKA JADHAV, VINOTHINI SELVARAJU, SARITH P SATHIAN, RAMAKRISHNAN SWAMINATHAN
    Journal of Mechanics in Medicine and Biology.2023;[Epub]     CrossRef
  • Strikes and Gutters: Biomarkers and anthropometric measures for predicting diagnosed diabetes mellitus in adults in low- and middle-income countries
    Sally Sonia Simmons
    Heliyon.2023; 9(9): e19494.     CrossRef
  • Association of IL-16 rs11556218 T/G polymorphism with the risk of developing type 2 diabetes mellitus
    Dalia Ghareeb Mohammad, Hamdy Omar, Taghrid B. El-Abaseri, Wafaa Omar, Shaymaa Abdelraheem
    Journal of Diabetes & Metabolic Disorders.2021; 20(1): 649.     CrossRef
  • Biomarker Score in Risk Prediction: Beyond Scientific Evidence and Statistical Performance
    Heejung Bang
    Diabetes & Metabolism Journal.2020; 44(2): 245.     CrossRef
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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  • The Multifunctionality of CD36 in Diabetes Mellitus and Its Complications—Update in Pathogenesis, Treatment and Monitoring
    Kamila Puchałowicz, Monika Ewa Rać
    Cells.2020; 9(8): 1877.     CrossRef
  • 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
    Diabetes & Metabolism Journal.2020; 44(2): 222.     CrossRef
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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  • Angiopoietin-Like Proteins: Cardiovascular Biology and Therapeutic Targeting for the Prevention of Cardiovascular Diseases
    Eric Thorin, Pauline Labbé, Mélanie Lambert, Pauline Mury, Olina Dagher, Géraldine Miquel, Nathalie Thorin-Trescases
    Canadian Journal of Cardiology.2023; 39(12): 1736.     CrossRef
  • Hyperlipidemia and hypothyroidism
    Xin Su, Hua Peng, Xiang Chen, Xijie Wu, Bin Wang
    Clinica Chimica Acta.2022; 527: 61.     CrossRef
  • Multidimensional Biomarker Analysis Including Mitochondrial Stress Indicators for Nonalcoholic Fatty Liver Disease
    Eunha Chang, Jae Seung Chang, In Deok Kong, Soon Koo Baik, Moon Young Kim, Kyu-Sang Park
    Gut and Liver.2022; 16(2): 171.     CrossRef
  • Triglyceride and Triglyceride-Rich Lipoproteins in Atherosclerosis
    Bai-Hui Zhang, Fan Yin, Ya-Nan Qiao, Shou-Dong Guo
    Frontiers in Molecular Biosciences.2022;[Epub]     CrossRef
  • Relationship of ANGPTL6 With Neonatal Glucose Homeostasis and Fat Mass Is Disrupted in Gestational Diabetic Pregnancies
    Abel Valencia-Martínez, Ute Schaefer-Graf, Encarnación Amusquivar, Emilio Herrera, Henar Ortega-Senovilla
    The Journal of Clinical Endocrinology & Metabolism.2022; 107(10): e4078.     CrossRef
  • Update on dyslipidemia in hypothyroidism: the mechanism of dyslipidemia in hypothyroidism
    Huixing Liu, Daoquan Peng
    Endocrine Connections.2022;[Epub]     CrossRef
  • RETRACTED ARTICLE: Relationship between the development of hyperlipidemia in hypothyroidism patients
    Xin Su, Xiang Chen, Bin Wang
    Molecular Biology Reports.2022; 49(11): 11025.     CrossRef
  • Effects of Exercise Intervention on Mitochondrial Stress Biomarkers in Metabolic Syndrome Patients: A Randomized Controlled Trial
    Jae Seung Chang, Jun Namkung
    International Journal of Environmental Research and Public Health.2021; 18(5): 2242.     CrossRef
  • Angiopoietin-like proteins in atherosclerosis
    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
  • Serum levels of angiopoietin-related growth factor in diabetes mellitus and chronic hemodialysis
    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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  • Identification of potential serum biomarkers associated with HbA1c levels in Indian type 2 diabetic subjects using NMR-based metabolomics
    Saleem Yousf, Hitender S. Batra, Rakesh M. Jha, Devika M. Sardesai, Kalyani Ananthamohan, Jeetender Chugh, Shilpy Sharma
    Clinica Chimica Acta.2024; 557: 117857.     CrossRef
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    Wenqiang Sun, Mengze Li, Hanjun Ren, Yang Chen, Wei Zeng, Xiong Tan, Xianbo Jia, Shiyi Chen, Jie Wang, Songjia Lai
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    Diana Calderón-DuPont, Ivan Torre-Villalvazo, Andrea Díaz-Villaseñor
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    Yifei Gu, Qianmei Jin, Jinquan Hu, Xinwei Wang, Wenchao Yu, Zhanchao Wang, Chen Wang, Yang Liu, Yu Chen, Wen Yuan
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    Tianlong Zhang, Yina Cao, Jianqiang Zhao, Jiali Yao, Gang Liu
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    Eun Ji Kim, Radha Ramachandran, Anthony S. Wierzbicki
    Current Opinion in Endocrinology, Diabetes & Obesity.2022; 29(2): 124.     CrossRef
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    Margarita Ortiz-Martínez, Mirna González-González, Alexandro J. Martagón, Victoria Hlavinka, Richard C. Willson, Marco Rito-Palomares
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  • Discrete Correlation Summation Clustering Reveals Differential Regulation of Liver Metabolism by Thrombospondin-1 in Low-Fat and High-Fat Diet-Fed Mice
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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

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  • 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
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    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
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Diabetes Metab J : Diabetes & Metabolism Journal