Diabetes & Metabolism Journal

Original Article

Complications
Optimizing Early Detection of Diabetic Kidney Disease through Synergistic Biomarkers and Serum Metabolites in Humans: Xianke Zhou, Yuan Gui, Jia-Jun Liu, Shijia Liu, Dongning Liang, Yuanyuan Wang, Henry Wells Shaffer, Samantha Mae Mallari, Cameron Jones, Priya Gupta, Dier Li, Ke Zhang, Ying Yu, Jianling Tao, Yanlin Wang, Silvia Liu, Dong Zhou, Haiyan Fu; Diabetes Metab J. 2026;50(4):752-769. Published online January 29, 2026; DOI: https://doi.org/10.4093/dmj.2025.0193

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Background
Diabetic kidney disease (DKD) often progresses to end-stage renal disease more rapidly than nondiabetic kidney disease because of persistent hyperglycemia and early activation of multiple pathogenic pathways. Early detection of DKD is crucial for identifying subtle kidney damage before clinical symptoms appear.
Methods
This study combined human serum proteomics with public single-cell RNA sequencing and spatial transcriptomics data from diabetic kidneys to identify key biomarkers for DKD diagnosis. These biomarkers were validated in multiple organs of db/db mice at early and advanced stages. In a discovery cohort, sera from 173 healthy adults and 444 patients with type 2 diabetes mellitus (T2DM), with or without kidney disease, were analyzed using metabolomics and enzyme-linked immunosorbent assay (ELISA). Multiple machine learning algorithms were developed to integrate synergistic biomarkers and serum metabolites for early DKD detection, with results validated in 435 participants from four independent clinical cohorts.
Results
Metalloproteinase-7 (MMP-7) and tenascin C (TNC) were elevated in human diabetic kidneys at the single-cell and spatial levels. Proteomics indicated upregulation of serum amyloid A1 (SAA1) and TNC in the serum of patients with DKD. In db/db mice, all three biomarkers increased in multiple organs by 18 weeks of age. In sera from patients with DKD, MMP-7 and TNC levels were consistently elevated across cohorts. The new algorithms combining MMP-7, SAA1, and TNC enhanced early-stage DKD detection, with approximately 13% improvements in accuracy when serum metabolites were included to distinguish progression from early to advanced DKD stages.
Conclusion
Integrating synergistic biomarkers with serum metabolomics enhances the early detection of DKD, potentially improving outcomes by slowing disease progression in patients with T2DM.

Citations

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Recent advances in point-of-care colorimetric biosensors for detecting small molecule metabolites
Zhiqiang Zhu, Zhun Gu, Xinxing Cao, Shanshan Zhang, Shao Su
Chemical Communications.2026; 62(48): 12000. CrossRef
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Qiu Li, Mei Yang, Yingyu Luo, Nannan Zhang
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A hypoxia-derived seven-gene signature reflects immune remodeling in diabetic kidney disease: integrated transcriptomic analysis and experimental validation
Yue Ji, Jiakun Zhang, Xi Guo, Yunming Xiao, Tieshan Wang, Yifei Zhang, Zhixun Guo, Yuqi Wu, Liangzhen You, Wei Jing Liu
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Review

Basic and Translational Research
Extracellular Vesicle-Mediated Network in the Pathogenesis of Obesity, Diabetes, Steatotic Liver Disease, and Cardiovascular Disease: Joonyub Lee, Won Gun Choi, Marie Rhee, Seung-Hwan Lee; Diabetes Metab J. 2025;49(3):348-367. Published online May 1, 2025; DOI: https://doi.org/10.4093/dmj.2025.0184

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Extracellular vesicles (EVs) are lipid bilayer-enclosed particles carrying bioactive cargo, including nucleic acids, proteins, and lipids, facilitating intercellular and interorgan communication. In addition to traditional mediators such as hormones, metabolites, and cytokines, increasing evidence suggests that EVs are key modulators in various physiological and pathological processes, particularly influencing metabolic homeostasis and contributing to the progression of cardiometabolic diseases. This review provides an overview of the most recent insights into EV-mediated mechanisms involved in the pathogenesis of obesity, insulin resistance, diabetes mellitus, steatotic liver disease, atherosclerosis, and cardiovascular disease. EVs play a critical role in modulating insulin sensitivity, glucose homeostasis, systemic inflammation, and vascular health by transferring functional molecules to target cells. Understanding the EV-mediated network offers potential for identifying novel biomarkers and therapeutic targets, providing opportunities for EV-based interventions in cardiometabolic disease management. Although many challenges remain, this evolving field highlights the need for further research into EV biology and its translational applications in cardiovascular and metabolic health.

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Young-Sool Hah, Jeongyun Hwang, Seung-Jun Lee, Seung-Jin Kwag
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Swayam Prakash Srivastava, Lydia Herrmann, Eden Ozkan, Abhiram Kunamneni, Vinamra Swaroop, Geetika Nehra, Rohit Srivastava, Pratima Tripathi, Ken Inoki, Julie E. Goodwin
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Nikola Pavlović, Petar Todorović, Mirko Maglica, Andrea Kopilaš, Roko Šantić, Marko Kumrić, Marino Lukenda, Joško Božić
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Original Articles

Complications
The Causal Relationship and Association between Biomarkers, Dietary Intake, and Diabetic Retinopathy: Insights from Mendelian Randomization and Cross-Sectional Study: Xuehao Cui, Dejia Wen, Jishan Xiao, Xiaorong Li; Diabetes Metab J. 2025;49(5):1087-1105. Published online March 31, 2025; DOI: https://doi.org/10.4093/dmj.2024.0731

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Background
Diabetic retinopathy (DR) is a major cause of vision loss, linked to hyperglycemia, oxidative stress, and inflammation. Despite advancements in DR treatments, approximately 40% of patients do not respond effectively, underscoring the need for novel, noninvasive biomarkers to predict DR risk and progression. This study investigates causal relationships between specific biomarkers, dietary factors, and DR development using Mendelian randomization (MR) and cross-sectional data.
Methods
We conducted a two-phase analysis combining MR and cross-sectional methods. First, MR analysis examined causal associations between 35 biomarkers, 226 dietary factors, and DR progression using data from the UK Biobank and Genome-Wide Association Study (GWAS) datasets. Second, a cross-sectional study with National Health and Nutrition Examination Survey (NHANES) and a clinical cohort from Tianjin Medical University Eye Hospital validated findings and explored biomarkers’ predictive capabilities through a nomogram-based prediction model.
Results
MR analysis identified eight biomarkers (e.g., glycosylated hemoglobin [HbA1c], high-density lipoprotein cholesterol [HDL-C]) with significant causal links to DR. Inflammatory markers and metabolic factors, such as high glucose and HDL-C levels, were strongly associated with DR risk and progression. Specific dietary factors, like cheese intake, exhibited protective roles, while alcohol intake increased DR risk. Validation within NHANES and Tianjin cohorts supported these causal associations.
Conclusion
This study elucidates causal relationships between biomarkers, dietary habits, and DR progression, emphasizing the potential for personalized dietary interventions to prevent or manage DR. Findings support the use of HDL-C, HbA1c, and dietary factors as biomarkers or therapeutics in DR, though further studies are needed for broader applicability.

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Metabolic Risk/Epidemiology
Validating Multicenter Cohort Circular RNA Model for Early Screening and Diagnosis of Gestational Diabetes Mellitus: Shuo Ma, Yaya Chen, Zhexi Gu, Jiwei Wang, Fengfeng Zhao, Yuming Yao, Gulinaizhaer Abudushalamu, Shijie Cai, Xiaobo Fan, Miao Miao, Xun Gao, Chen Zhang, Guoqiu Wu; Diabetes Metab J. 2025;49(3):462-474. Published online February 21, 2025; DOI: https://doi.org/10.4093/dmj.2024.0205

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Background
Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed.
Methods
High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression.
Results
Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts.
Conclusion
Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Citations

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Advancing Early Prediction of Gestational Diabetes Mellitus with Circular RNA Biomarkers
Joon Ho Moon, Sung Hee Choi
Diabetes & Metabolism Journal.2025; 49(3): 403. CrossRef
Enhancing early gestational diabetes mellitus prediction with imputation-based machine learning framework: A comparative study on real-world clinical records
Leyao Ma, Lin Yang, Yaxin Wang, Jie Hao, Yini Li, Liangkun Ma, Ziyang Wang, Ye Li, Suhan Zhang, Mingyue Hu, Jiao Li, Yin Sun
DIGITAL HEALTH.2025;[Epub] CrossRef
Social Services for Women with Gestational Diabetes Mellitus in Korea
Yu Jeong Park
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Metabolic Risk/Epidemiology
Comparison of SPISE and METS-IR and Other Markers to Predict Insulin Resistance and Elevated Liver Transaminases in Children and Adolescents: Kyungchul Song, Eunju Lee, Hye Sun Lee, Hana Lee, Ji-Won Lee, Hyun Wook Chae, Yu-Jin Kwon; Diabetes Metab J. 2025;49(2):264-274. Published online October 29, 2024; DOI: https://doi.org/10.4093/dmj.2024.0302

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Background
Studies on predictive markers of insulin resistance (IR) and elevated liver transaminases in children and adolescents are limited. We evaluated the predictive capabilities of the single-point insulin sensitivity estimator (SPISE) index, metabolic score for insulin resistance (METS-IR), homeostasis model assessment of insulin resistance (HOMA-IR), the triglyceride (TG)/ high-density lipoprotein cholesterol (HDL-C) ratio, and the triglyceride-glucose index (TyG) for IR and alanine aminotransferase (ALT) elevation in this population.
Methods
Data from 1,593 participants aged 10 to 18 years were analyzed using a nationwide survey. Logistic regression analysis was performed with IR and ALT elevation as dependent variables. Receiver operating characteristic (ROC) curves were generated to assess predictive capability. Proportions of IR and ALT elevation were compared after dividing participants based on parameter cutoff points.
Results
All parameters were significantly associated with IR and ALT elevation, even after adjusting for age and sex, and predicted IR and ALT elevation in ROC curves (all P<0.001). The areas under the ROC curve of SPISE and METS-IR were higher than those of TyG and TG/HDL-C for predicting IR and were higher than those of HOMA-IR, TyG, and TG/HDL-C for predicting ALT elevation. The proportions of individuals with IR and ALT elevation were higher among those with METS-IR, TyG, and TG/ HDL-C values higher than the cutoff points, whereas they were lower among those with SPISE higher than the cutoff point.
Conclusion
SPISE and METS-IR are superior to TG/HDL-C and TyG in predicting IR and ALT elevation. Thus, this study identified valuable predictive markers for young individuals.

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Complications
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; Diabetes Metab J. 2024;48(6):1126-1134. Published online July 1, 2024; DOI: https://doi.org/10.4093/dmj.2023.0377

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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.

Citations

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Diabetic kidney disease: integrating multi-omics insights, artificial intelligence, and novel therapeutics for precision medicine
Tao Li, Kaili Chen, Yiting Sun, Linqi Zhang
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Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review)
Hanfei Li, Yuxi Li, Bo Zhang, Wenhao Cheng, Guowei Ma, Jin Rong, Shiru Duan, Di Feng, Tingting Zhao
International Journal of Molecular Medicine.2026; 57(4): 1. CrossRef
Next-Generation Redox Mediators: Itaconate, Nitro-Fatty Acids, Reactive Sulfur Species and Succinate as Emerging Switches in Predictive Redox Medicine
Luca Gammeri, Alessandro Allegra, Fabio Stagno, Sebastiano Gangemi
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Metabolic Reprogramming and Immunometabolic Dysregulation in Diabetic Kidney Disease: From Pathogenesis to Precision Multi-target Therapies
Ziyue Zhang, Yilun Qu, Xiaochen Wang, Yuwei Ji, Jin Yao, Weizhu Deng, Guannan Sun, Hua Xu, Xiangmei Chen, Quan Hong
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AI-assisted metabolomic profiling identifies candidate metabolites associated with diabetic kidney disease staging in a Korean cohort
Inha Jung, Sungjin Park, Moongi Ji, So Young Park, Da Young Lee, Ji Hee Yu, Ji A. Seo, Man-Jeong Paik, Hyeong Kyu Park, Soon Hyo Kwon, Dae Ho Lee, Nan Hee Kim
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Omar Emiliano Aparicio-Trejo, Estefani Yaquelin Hernández-Cruz, Laura María Reyes-Fermín, Zeltzin Alejandra Ceja-Galicia, José Pedraza-Chaverri
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Chronic succinate exposure does not cause liver injury
Joseph Balnis, Emily L. Jackson, Lisa A. Drake, Catherine E. Vincent, Hwajeong Lee, Harold A. Singer, Ariel Jaitovich
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Succinate Facilitates CD4+ T Cell Infiltration and CCL1 Production to Promote Myofibroblast Activation and Renal Fibrosis in UUO Mice
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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; 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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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.

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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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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.

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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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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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Diabetology & Metabolic Syndrome.2025;[Epub] CrossRef
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Urine Biomarkers for Diabetic Kidney Disease Progression in Participants of the Chronic Renal Insufficiency Cohort Study
Sarah J. Schrauben, Xiaoming Zhang, Dawei Xie, Steven Coca, Jason H. Greenberg, Joachim H. Ix, Michael G. Shlipak, Chi-yuan Hsu, Jonathan J. Taliercio, Chirag R. Parikh, Orlando Gutierrez, Mark J. Sarnak, Mirela A. Dobre, Debbie Cohen, Jeffrey R. Schellin
Clinical Journal of the American Society of Nephrology.2025; 20(7): 958. CrossRef
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Nephrology.2025;[Epub] CrossRef
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The FASEB Journal.2025;[Epub] CrossRef
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Journal of Diabetes Research.2025;[Epub] CrossRef
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Translational Research.2025; 282: 1. CrossRef
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Frontiers in Pharmacology.2025;[Epub] CrossRef
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Jiawei Zhang, Xiangge Ren, Bing Li, Zhifen Zhao, Shoudao Li, Wensheng Zhai
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Chenhui Xia, Jiale Zhang, Huixi Chen, Weimin Jiang, Shaofeng Zhou, Huijuan Zheng, Weiwei Sun
International Immunopharmacology.2025; 162: 115121. CrossRef
Polygonati Rhizoma Polysaccharides Ameliorated Diabetic Kidney Disease in db/db Mice via Inhibiting TGFβ/Smad2 Signaling Pathway
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Food Science & Nutrition.2025;[Epub] CrossRef
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Jovana Ristic, Sena Kodalak, Gonzalo Peralta-Jiménez, Maria Moura de Lima, Marijana Kovacevic, Srdjan Masic, Tatjana Nikolic
Diabetes, Metabolic Syndrome and Obesity.2025; Volume 18: 2493. CrossRef
Notch signaling in diabetic kidney disease: recent progress
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Mechanism of Rhizoma Chuanxiong for the treatment of diabetic kidney disease based on network pharmacology
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Renal Failure.2025;[Epub] CrossRef
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Amira Reda Muhammad Galal, Nahla Mohamed Teama, Karema Abu–Elfotuha, Ahmed D. Alatawi, Hanan Alshareef, Ahmed M. E. Hamdan, Ahmed Aljabri, Mohannad Alshibani, Zeinab AlKasaby Mahmoud Zalat
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Asprosin Aggravates Tubular Epithelial Cell Injury and Phenotypic Transformation via Mitochondrial Dynamics Disorder Mediated by Excessive Drp1 SUMOylation in Diabetic Nephropathy Mice
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C3AR1 may aggravate diabetic nephropathy by mediating oxidative stress via ITGB2 regulation in renal tubular epithelial cells
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Exploration of the renoprotective effect of Yi-Shen-Hua-Shi granules on db/db mice and the mechanism of podocyte apoptosis based on the GRP78/CHOP signaling pathway
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Frontiers in Pharmacology.2025;[Epub] CrossRef
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Celastrol, a bioactive compound from Tripterygium wilfordii, exerts renoprotective effects by modulating the gut-kidney axis and PPAR signaling in a mouse model of diabetic kidney disease
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Asian Pacific Journal of Tropical Biomedicine.2025; 15(9): 353. CrossRef
Expression Profile of Urinary Exosomal miRNAs in Patients With Diabetic Kidney Disease and Their Association With Kidney Damage
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Commentary on: Inhibition of LINC00707/miR‐223‐3p/FKBP5 axis has a protective effect on diabetic kidney disease
Yirui Zhao, Fengyue Zhang
Journal of Diabetes Investigation.2025; 16(12): 2251. CrossRef
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俊杰苏
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Jéssica Encinas, Glaucia Veiga, Joyce Raimundo, Matheus Perez, Giuliana Petri, Renan Cavalheiro, Pedro Reis, Laura Maifrino, Beatriz Alves, Fernando Fonseca
International Journal of Molecular Sciences.2025; 26(19): 9676. CrossRef
Construction of a diagnostic model and identification of effect genes for diabetic kidney disease with concurrent vascular calcification based on bioinformatics and multiple machine learning approaches
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Frontiers in Molecular Biosciences.2025;[Epub] CrossRef
Selected miRNAs in Urinary Extracellular Vesicles Show Promise for Early and Specific Diagnostics of Diabetic Kidney Disease
Karina Barreiro, Jenni Karttunen, Erkka Valo, Essi Viippola, Ileana Quintero, Annemari Käräjämäki, Antti Rannikko, Harry Holthöfer, Andrea Ganna, Niina Sandholm, Lena M. Thorn, Per‐Henrik Groop, Tiinamaija Tuomi, Om Prakash Dwivedi, Maija Puhka
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Jingwei Lin, Yingying Zheng, Diman Mai, Fuxiang Fang, Zhuokun Wei, Mengyu Liu, Trung Hieu Pham, Ming Li, Jiawen Zhao
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Yiqi Wenyang Formula ameliorates diabetic kidney disease via inhibiting inflammation and regulating the gut microbiota-bile acid axis in mice by FXR signaling pathway
Tongyi Yuan, Xiang Gao, Xinxin Wang, Xiaolei Tang, Zuguo Liang, Yifei Yin, Songyan Liu, Jianze Ou, Wenjie Su, Zepeng Zhang, Xiangyan Li, Qingxia Huang, Daqing Zhao, He Zhang
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Qing-Re-Xiao-Zheng-yi-qi formula protects renal function by stabilizing lysosomal membranes and inhibiting NLRP3-inflammasome-mediated pyroptosis in diabetic kidney disease
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The Inflammatory Cell Death in Diabetic Kidney Disease: Integrating Multifactorial Mechanisms into Novel Therapeutics
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International Journal of Molecular Sciences.2025; 26(22): 11033. CrossRef
Rhein Inhibits Podocyte Ferroptosis and Epithelial-Mesenchymal Transition in Diabetic Nephropathy by Activating the SIRT1/p53/SLC7A11 Pathway
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Diagnostic Roles of circXPNPEP3 as Biomarker for Diabetic Nephropathy
Miao Liu, Qiunan Zhan, Junjun Zhu, Junli Zhao, Danyan Min
Diabetes, Metabolic Syndrome and Obesity.2025; Volume 18: 4287. CrossRef
Protective Effects of Hydrogen Treatment Against High Glucose-Induced Oxidative Stress and Apoptosis via Inhibition of the AGEs/RAGE/NF-κB Signaling Pathway in Skin Cells
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Yiqun Peng, Dongguang Wang, Kaifa Guo, Liang Peng
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Role of polyphenols in the management of diabetic complications
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Single-Cell RNA Sequencing Reveals RAC1 Involvement in Macrophages Efferocytosis in Diabetic Kidney Disease
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Specific Alternation of Gut Microbiota and the Role of Ruminococcus gnavus in the Development of Diabetic Nephropathy
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CYSTATIN C—A Monitoring Perspective of Chronic Kidney Disease in Patients with Diabetes
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Sirt6 overexpression relieves ferroptosis and delays the progression of diabetic nephropathy via Nrf2/GPX4 pathway
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Association of Serum Tsukushi Levels with Urinary Albumin-Creatinine Ratio in Type 2 Diabetes Patients
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Liraglutide ameliorates inflammation and fibrosis by downregulating the TLR4/MyD88/NF-κB pathway in diabetic kidney disease
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Angiopoietin‐like 4 is a potential biomarker for diabetic kidney disease in type 2 diabetes patients
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Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics
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Omics and Artificial Intelligence in Kidney Diseases
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Novel Biomarkers of Diabetic Kidney Disease
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Analysis of Serum Pyrodeath Re-lated Proteins and Renal Injury in Patients with Type 2 DKD
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Loganin reduces diabetic kidney injury by inhibiting the activation of NLRP3 inflammasome-mediated pyroptosis
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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
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Diabetes & Metabolism Journal.2022; 46(4): 543. CrossRef
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Yasmin M. Ahmed, Raha Orfali, Nada S. Abdelwahab, Hossam M. Hassan, Mostafa E. Rateb, Asmaa M. AboulMagd
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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
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Sicheng Li, Huidi Xie, Yang Shi, Hongfang Liu
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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

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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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Nadia M. Hamdy, Sherien M. El-Daly, Ola Elazazy, Ahmed M. Kabel, Rania M. Salama, Shaimaa A. Gouhar, Al-Aliaa M. Sallam, Samar F. Darwish
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Yan Wang, Zhaoran Wang, Wenya Diao, Tong Shi, Jiahe Xu, Tiantian Deng, Chaoying Wen, Jienan Gu, Tingting Deng, Sixuan Wang, Cheng Xiao
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Ge Song, YiQian Zhang, YiHua Jiang, Huan Zhang, Wen Gu, Xiu Xu, Jing Yao, ZhengFang Chen
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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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Yuka Ikeda, Sae Morikawa, Moeka Nakashima, Sayuri Yoshikawa, Kurumi Taniguchi, Haruka Sawamura, Naoko Suga, Ai Tsuji, Satoru Matsuda
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Zi Li, Yuanyuan Ren, Ziwei Lv, Man Li, Yujia Li, Xiaobin Fan, Yuyan Xiong, Lu Qian
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Lei Ren
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Wenqi Fan, Haipeng Pang, Zhiguo Xie, Gan Huang, Zhiguang Zhou
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Type 1 Diabetes Mellitus‐Related circRNAs Regulate CD4+ T Cell Functions
Jianni Chen, Guanfei Jia, Xue Lv, Shufa Li, Christos K. Kontos
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Monisha Prasad, Selvaraj Jayaraman, Vishnu Priya Veeraraghavan
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Circular RNAs in Diabetic Nephropathy: Updates and Perspectives
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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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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
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Emerging roles of circular RNAs in systemic lupus erythematosus
Xin Wang, Rui Ma, Weimin Shi, Zhouwei Wu, Yuling Shi
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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

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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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Yunha Suh, Kwang-eun Kim
International Journal of Molecular Sciences.2026; 27(4): 1712. CrossRef
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Anna Lena Huber, Angelika Bauer, Julius Beirer, Katharina Frede, Katharina Kirchmair, Reinhard Angermann, Matus Rehak, Claus Zehetner, Yvonne Nowosielski
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Fei Zhang, Qintao Ge, Jialin Meng, Jia Chen, Chaozhao Liang, Meng Zhang
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Alex Gough, Alice Sitch, Erica Ferris, Tom Marshall, Andreas Zirlik
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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
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The monocyte-to-lymphocyte ratio: Sex-specific differences in the tuberculosis disease spectrum, diagnostic indices and defining normal ranges
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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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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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Walid Saeed, Molham AL-Habori, Riyadh Saif-Ali
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Zahra Ansari Aval, Mohammad Mahdavi, Maryam Bahramian, Mozhgan Bahramian, Amirhossein Jalali
Research in Cardiovascular Medicine.2025; 14(3): 113. CrossRef
Hypertriglyceridemia is a dose-dependent risk factor for type 2 diabetes mellitus: a systematic review and meta-analysis
Luca Havelda, Eszter Ágnes Szalai, Mahmoud Obeidat, Dalma Dobszai, Dániel Sándor Veres, Tamás Kói, Emese Sipter, Szilárd Váncsa, Péter Jenő Hegyi, Maria Bucur, Anita Molnár, Klára Lara Vámossy, Péter Hegyi, Andrea Szentesi
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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
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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Metabolic Syndrome and Related Disorders.2024; 22(9): 679. CrossRef
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Remnant Cholesterol Is an Independent Predictor of Type 2 Diabetes: A Nationwide Population-Based Cohort Study
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PRIYANKA JADHAV, VINOTHINI SELVARAJU, SARITH P SATHIAN, RAMAKRISHNAN SWAMINATHAN
Journal of Mechanics in Medicine and Biology.2023;[Epub] CrossRef
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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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Abstract PDF Supplementary Material PubReader ePub

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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CD36 and Its Role in Obesity
Nahuel Garcia, Maiken Mellergaard, Carlos Salomon, Pilar Sepulveda, Peter Kristensen, Aase Handberg
Obesity Reviews.2026;[Epub] CrossRef
Molecular Signature of Prediabetes With High‐Risk of Diabetes Revealed by Deep Plasma Proteome
Yuanyuan Yin, Xiaojing Gao, Qingrun Li, Ling Lu, Haoqiang Zhang, Qingqing Wu, Jincheng Shi, Conghui Liu, Ling Yue, Shifu Xiao, Jiarui Wu, Xu Lin, Rong Zeng
Diabetes, Obesity and Metabolism.2026; 28(8): 7483. CrossRef
Human CD36: Gene Regulation, Protein Function, and Its Role in Atherosclerosis Pathogenesis
Monika Rac
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Constantine E Kosmas, Andreas Sourlas, Konstantinos Oikonomakis, Eleni-Angeliki Zoumi, Aikaterini Papadimitriou, Christina E Kostara
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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
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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
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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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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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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Abstract PDF PubReader ePub

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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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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Abstract 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.

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Lack of Association between Serum Cystatin C Levels and Coronary Artery Disease in Diabetic Patients
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Metabolism.2010; 59(2): 241. CrossRef

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