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Original Article
- Complications
- Optimizing Early Detection of Diabetic Kidney Disease through Synergistic Biomarkers and Serum Metabolites in Human
- 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
- Received March 8, 2025 Accepted September 16, 2025 Published online January 29, 2026
- DOI: https://doi.org/10.4093/dmj.2025.0193 [Epub ahead of print]
- 732 View
- 58 Download
-
Abstract
PDF
Supplementary Material
PubReader 
ePub - Background
Diabetic kidney disease (DKD) progresses to end-stage renal disease more rapidly than chronic kidney disease due to persistent hyperglycemia and early activation of multiple pathways. Early detection of DKD is crucial to identify subtle kidney damage before clinical symptoms appear.
Methods
This study combined human serum proteomics and 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 type 2 diabetes mellitus (T2DM) patients, 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 DKD early 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 DKD patients’ serum. In db/db mice, all three biomarkers increased in multiple organs by 18 weeks of age. In DKD patient sera, 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 about 13% improvements in accuracy when serum metabolites were included to distinguish the progression from early to advanced stages after DKD.
Conclusion
Integrating synergistic biomarkers with serum metabolomics enhances early detection of DKD, potentially improving outcomes by slowing disease progression in T2DM patients.
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
- 7,662 View
- 229 Download
- 8 Web of Science
- 8 Crossref
-
Abstract
PDFPubReader ePub
- 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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Citations
Citations to this article as recorded by
- Postbiotics in Functional Foods: Microbial Derivatives Shaping Health, Immunity and Next‐Generation Nutrition
Alice Njolke Mafe, Javad Sharifi‐Rad, Daniela Calina, Ayobami Joshua Ogunyemi, Abiola O. Tubi
Food Frontiers.2026;[Epub] CrossRef - Decoding the Endocrine Code of Skeletal Muscle: Myokines, Exerkines, and Inter-Organ Crosstalk in Metabolic Health and Disease
Young-Sool Hah, Jeongyun Hwang, Seung-Jun Lee, Seung-Jin Kwag
Cells.2026; 15(4): 318. CrossRef - Extracellular Vesicles: Biology, Intercellular Communication and Therapeutic Potential in Diabetes
Swayam Prakash Srivastava, Lydia Herrmann, Eden Ozkan, Abhiram Kunamneni, Vinamra Swaroop, Geetika Nehra, Rohit Srivastava, Pratima Tripathi, Ken Inoki, Julie E. Goodwin
Advanced Therapeutics.2026;[Epub] CrossRef - Association between dyslipidemia and elevated liver enzymes: A cross-sectional study from the PERSIAN Guilan cohort study
Milad Shahdkar, Mahdi Orang Goorabzarmakhi, Mahdi Shafizadeh, Farahnaz Joukar, Saman Maroufizadeh, Niloofar Faraji, Tahereh Zeinali, Fariborz Mansour-Ghanaei
Endocrine and Metabolic Science.2025; 19: 100272. CrossRef - Molecular Signatures of Obesity-Associated Infertility in Polycystic Ovary Syndrome: The Emerging Role of Exosomal microRNAs and Non-Coding RNAs
Charalampos Voros, Georgios Papadimas, Despoina Mavrogianni, Aristotelis-Marios Koulakmanidis, Diamantis Athanasiou, Kyriakos Bananis, Antonia Athanasiou, Aikaterini Athanasiou, Ioannis Papapanagiotou, Dimitrios Vaitsis, Charalampos Tsimpoukelis, Maria An
Genes.2025; 16(9): 1101. CrossRef - Molecular mechanisms linking adipose tissue-derived small extracellular vesicles/exosomes to the development or amelioration of obesity, insulin resistance, and diabetes-related complications
Linfeng Chen, Fatemeh Amraee, Sahar Sadegh-Nejadi, Mostafa Saberian, Seyed Arsalan Ghahari, Xiaolei Miao, Giuseppe Lisco, Reza Afrisham
European Journal of Medical Research.2025;[Epub] CrossRef - Mitochondria-Enriched Extracellular Vesicles (EVs) for Cardiac Bioenergetics Restoration: A Scoping Review of Preclinical Mechanisms and Source-Specific Strategies
Dhienda C. Shahannaz, Tadahisa Sugiura, Taizo Yoshida
International Journal of Molecular Sciences.2025; 26(22): 11052. CrossRef - A Comprehensive Insight Into the Roles of Exosomal circRNAs in Metabolic Syndrome
Azadeh Taherpour, Safieh Ebrahimi, Farshad Mirzavi
BioFactors.2025;[Epub] CrossRef
- Postbiotics in Functional Foods: Microbial Derivatives Shaping Health, Immunity and Next‐Generation Nutrition
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
- 6,328 View
- 349 Download
- 5 Web of Science
- 9 Crossref
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Abstract
PDFSupplementary MaterialPubReader ePub
- 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. -
Citations
Citations to this article as recorded by- Exploring potential therapeutic targets for myopia: Causal analysis and biological annotation with gut microbiota
Zixun Wang, Yimeng Sun, Xiaoling Zhang, Luqiang Wang, Desheng Song, Jingtao Yu, Xiaoxue Hu, Weiping Lin, Ruihua Wei
Computational Biology and Chemistry.2026; 120: 108634. CrossRef - Research Status of Diabetic Retinopathy Prediction Models: From Traditional Risk Factors to Artificial Intelligence
银娟 李
Journal of Clinical Personalized Medicine.2026; 05(01): 332. CrossRef - Integrative Proteogenomic Analysis Identifies Genetically Supported Plasma Proteins, Metabolites, and Pathways in Glaucoma
Jiajia Yuan, Xuehao Cui, Patrick Yu-Wai-Man, Xuan Xiao
Investigative Ophthalmology & Visual Science.2026; 67(2): 21. CrossRef - Antioxidant vitamin index and risk of age-related macular degeneration: multicenter validation and clinical translation
Xuehao Cui, Jingwen Hui, Zheya Han, Quanhong Han
npj Aging.2026;[Epub] CrossRef - Association between weight-adjusted-waist index and retinopathy among American adults: a cross-sectional study and mediation analysis
Junmeng Li, Qianshuo Yin, Jianchen Hao, Ruilin Zhu, Jing Zhang, Yadi Zhang, Xiaopeng Gu, Zihui Wu, Liu Yang
Frontiers in Nutrition.2025;[Epub] CrossRef - Exploring the impact of diet, sleep, and metabolomic pathways on Glaucoma subtypes: insights from Mendelian randomization and cross-sectional analyses
Zhang Shengnan, Wang Tao, Zhang Yanan, Sun Chao
Nutrition & Metabolism.2025;[Epub] CrossRef - Association between endothelial activation and stress index and diabetic retinopathy in patients with diabetic kidney disease: a cross-sectional study based on NHANES database
Jinping Liu, Di’en Yan, Xiaohui Wang, Yinhua Yao, Ling Wang
BMC Endocrine Disorders.2025;[Epub] CrossRef - Hypertriglyceridemic waist phenotype in relation to diabetes mellitus and cardiovascular diseases in the Indonesian and Korean populations: evidence from two national surveys
Fathimah S. Sigit, Sinyoung Cho, Farid Kurniawan, Hye-Ryeong Jeon, Ratu Ayu Dewi Sartika, Dicky L. Tahapary, Hyuktae Kwon
Diabetology & Metabolic Syndrome.2025;[Epub] CrossRef - Non-linear association between Life’s Essential 8 and diabetic retinopathy: mediating role of depression in US adults with diabetes
Long Xie, Yu Qin Peng, Wei Qiang Wei, Xiang Shen
BMC Public Health.2025;[Epub] CrossRef
- Exploring potential therapeutic targets for myopia: Causal analysis and biological annotation with gut microbiota
- 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
- 3,538 View
- 140 Download
- 2 Web of Science
- 3 Crossref
-
Abstract
PDFSupplementary MaterialPubReader ePub
- 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
Citations to this article as recorded by- 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
The Journal of Korean Diabetes.2025; 26(4): 245. CrossRef
- Advancing Early Prediction of Gestational Diabetes Mellitus with Circular RNA Biomarkers
- 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
- 7,164 View
- 240 Download
- 13 Web of Science
- 12 Crossref
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Abstract
PDFSupplementary MaterialPubReader ePub
- 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. -
Citations
Citations to this article as recorded by- Association between the single-point insulin sensitivity estimator and cardiovascular disease incidence: A prospective nationwide cohort study involving two cohorts
Xiaotong Yao, Lina Liu, Lifen Zhao, Nianzhu Zhang
Atherosclerosis.2026; 412: 120591. CrossRef - Purpose in Life and Insulin Resistance in a Large Occupational Cohort: Cross-Sectional Associations Using TyG, SPISE-IR, and METS-IR Indices
Pilar García Pertegaz, Pedro Juan Tárraga López, Irene Coll Campayo, Carla Busquets-Cortés, Ángel Arturo López-González, José Ignacio Ramírez-Manent
Diabetology.2026; 7(1): 16. CrossRef - Utility of the MetS-IR and SPISE indices for identifying insulin resistance in Mexican children
Edmundo Gutiérrez-Rosas, Marco A. Morales-Pérez, Mayra Cristina Torres-Castañeda, Lorena Lizárraga-Paulín, Rita A. Gómez-Díaz, Adriana L. Valdez-González, Niels H. Wacher
Obesity Research & Clinical Practice.2026; 20(1): 29. CrossRef - How Emerging Digital Health Technologies Based on Dietary and Physical Activity Regulation Improve Metabolic Syndrome-Related Outcomes in Adolescents: A Systematic Review
Ruida Yu, Angkun Li, Yufei Qi, Jianhong Hu, Fei Peng, Shengrui Cao, Siyu Rong, Hao Zhang
Metabolites.2026; 16(2): 106. CrossRef - The Prognostic Significance of the Metabolic Score for Insulin Resistance and Subclinical Myocardial Injury for Cardiovascular Mortality in the General Population
Patrick Cheon, Shannon O’Connor, Saeid Mirzai, Mohamed A. Mostafa, Chuka B. Ononye, Elsayed Z. Soliman, Richard Kazibwe
Journal of Clinical Medicine.2026; 15(3): 1141. CrossRef - Is Measuring BMI and Waist Circumference as Good in Assessing Insulin Resistance as Using Bioelectrical Impedance to Measure Total Body Fat and Visceral Fat?
María Gordito Soler, Pedro Juan Tárraga López, Ángel Arturo López-González, Hernán Paublini, Emilio Martínez-Almoyna Rifá, María Teófila Vicente-Herrero, José Ignacio Ramírez-Manent
Diabetology.2025; 6(4): 32. CrossRef - Association between cardiometabolic index and postmenopausal stress urinary incontinence: a cross-sectional study from NHANES 2013 to 2018
Ting Yin, Yue He, Huifang Cong
Lipids in Health and Disease.2025;[Epub] CrossRef - Identification of pediatric MASLD using insulin resistance indices
Kyungchul Song, Eunju Lee, Hye Sun Lee, Young Hoon Youn, Su Jung Baik, Hyun Joo Shin, Hyun Wook Chae, Ji-Won Lee, Yu-Jin Kwon
JHEP Reports.2025; 7(7): 101419. CrossRef - Screening accuracy of Single-Point Insulin Sensitivity Estimator (SPISE) for metabolic syndrome: a systematic review and meta-analysis
Alireza Azarboo, Parisa Fallahtafti, Sayeh Jalali, Amirhossein Shirinezhad, Ramin Assempoor, Amirhossein Ghaseminejad-Raeini
BMC Endocrine Disorders.2025;[Epub] CrossRef - Associations of triglyceride-glucose index and metabolic score for insulin resistance with various hypertension phenotypes in children and adolescents: results from the 2017 China nutrition and health surveillance
Haiyuan Zhu, Lianlong Yu, Qiqi Wu, Runquan Zhang, Zebang Zhang, Yumei Feng, Tao Liu, Dan Liu, Jiewen Peng, Xiongfei Chen, Xiaomei Dong
Frontiers in Endocrinology.2025;[Epub] CrossRef - Associations between the METS-IR index and cognitive function in community-dwelling Chinese middle-aged and older adult individuals: a cross-sectional study
Nian Jiang, Chenlu Ma, Zhenning Feng, Yongjun Tang, Xiaolong Chen, Yingxu He, Weiyi Pang
Frontiers in Public Health.2025;[Epub] CrossRef - Comparison of single-point insulin sensitivity estimator and other markers to predict metabolic syndrome in children and adolescents
Kyungchul Song, Eunju Lee, Hye Sun Lee, Hana Lee, Hyun Wook Chae, Yu-Jin Kwon
Obesity Research & Clinical Practice.2025; 19(5): 427. CrossRef
- Association between the single-point insulin sensitivity estimator and cardiovascular disease incidence: A prospective nationwide cohort study involving two cohorts
- 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
- 6,197 View
- 165 Download
- 10 Web of Science
- 9 Crossref
-
Abstract
PDFSupplementary MaterialPubReader ePub
- Background
Although the prevalence of diabetic kidney disease (DKD) is increasing, reliable biomarkers for its early detection are scarce. This study aimed to evaluate the association of adenosine and succinate levels and their related pathways, including hyaluronic acid (HA) synthesis, with DKD.
Methods
We examined 235 participants and categorized them into three groups: healthy controls; those with diabetes but without DKD; and those with DKD, which was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. We compared the concentrations of urinary adenosine, succinate, and HA and the serum levels of cluster of differentiation 39 (CD39) and CD73, which are involved in adenosine generation, among the groups with DKD or albuminuria. In addition, we performed multiple logistic regression analysis to evaluate the independent association of DKD or albuminuria with the metabolites after adjusting for risk factors. We also showed the association of these metabolites with eGFR measured several years before enrollment. This study was registered with the Clinical Research Information Service (https://cris.nih.go.kr; Registration number: KCT0003573).
Results
Urinary succinate and serum CD39 levels were higher in the DKD group than in the control and non-DKD groups. Correlation analysis consistently linked urinary succinate and serum CD39 concentrations with eGFR, albuminuria, and ΔeGFR, which was calculated retrospectively. However, among the various metabolites studied, only urinary succinate was identified as an independent indicator of DKD and albuminuria.
Conclusion
Among several potential metabolites, only urinary succinate was independently associated with DKD. These findings hold promise for clinical application in the management of DKD. -
Citations
Citations to this article as recorded by- Diabetic kidney disease: integrating multi-omics insights, artificial intelligence, and novel therapeutics for precision medicine
Tao Li, Kaili Chen, Yiting Sun, Linqi Zhang
Frontiers in Genetics.2026;[Epub] CrossRef - 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 - Trifolirhizin: A Phytochemical with Multiple Pharmacological Properties
Varun Jaiswal, Hae-Jeung Lee
Molecules.2025; 30(2): 383. CrossRef - The role of redox signaling in mitochondria and endoplasmic reticulum regulation in kidney diseases
Omar Emiliano Aparicio-Trejo, Estefani Yaquelin Hernández-Cruz, Laura María Reyes-Fermín, Zeltzin Alejandra Ceja-Galicia, José Pedraza-Chaverri
Archives of Toxicology.2025; 99(5): 1865. CrossRef - 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
American Journal of Physiology-Endocrinology and Metabolism.2025; 329(1): E39. CrossRef - Succinate Facilitates CD4+ T Cell Infiltration and CCL1 Production to Promote Myofibroblast Activation and Renal Fibrosis in UUO Mice
Yuandong Tao, Wei Zhang, Dehong Liu, Hualin Cao, Xiaoyu Yi, Xiangling Deng, Pin Li, Xiaoli Shen, Huixia Zhou
Journal of Inflammation Research.2025; Volume 18: 7827. CrossRef - From inflammation to healing: the crucial role of GPR91 activation and SDH inhibition in chronic diabetic wound recovery
Hengdeng Liu, Shixin Zhao, Hanwen Wang, Xuefeng He, Suyue Gao, Minmin Su, Miao Zhen, Shuying Chen, Lei Chen, Julin Xie
Stem Cell Research & Therapy.2025;[Epub] CrossRef - Network pharmacology and untargeted metabolomics reveal the mechanisms of Bushen Kaixuan Tongluo formula in diabetic kidney disease
You Wang, Baosheng Zhao, Zhuqing Yang, Lingling Qin, Haiyan Wang, Cuiyan Lv, Tonghua Liu, Guangrui Huang
Journal of Chromatography B.2025; 1265: 124752. CrossRef - Oxidative Stress, Metabolic Impairment and Neuroinflammation are Associated With Target Organ Damage in SHRSP
S Hojná, L Mráziková, A Shánělová, H Pelantová, A Montezano, R Touyz, L Maletínská, J Kuneš
Physiological Research.2025; : 779. CrossRef
- Diabetic kidney disease: integrating multi-omics insights, artificial intelligence, and novel therapeutics for precision medicine
- 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
- 7,879 View
- 269 Download
- 4 Web of Science
- 5 Crossref
-
Abstract
PDFSupplementary MaterialPubReader ePub
- Background
We aimed to evaluate whether composite blood biomarkers including aldo-keto reductase family 1 member B10 (AKR1B10) and cytokeratin 18 (CK-18; a nonalcoholic steatohepatitis [NASH] marker) have clinically applicable performance for the diagnosis of NASH, advanced liver fibrosis, and high-risk NASH (NASH+significant fibrosis).
Methods
A total of 116 subjects including healthy control subjects and patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD) were analyzed to assess composite blood-based and imaging-based biomarkers either singly or in combination.
Results
A composite blood biomarker comprised of AKR1B10, CK-18, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) showed excellent performance for the diagnosis of, NASH, advanced fibrosis, and high-risk NASH, with area under the receiver operating characteristic curve values of 0.934 (95% confidence interval [CI], 0.888 to 0.981), 0.902 (95% CI, 0.832 to 0.971), and 0.918 (95% CI, 0.862 to 0.974), respectively. However, the performance of this blood composite biomarker was inferior to that various magnetic resonance (MR)-based composite biomarkers, such as proton density fat fraction/MR elastography- liver stiffness measurement (MRE-LSM)/ALT/AST for NASH, MRE-LSM+fibrosis-4 index for advanced fibrosis, and the known MR imaging-AST (MAST) score for high-risk NASH.
Conclusion
Our blood composite biomarker can be useful to distinguish progressive forms of NAFLD as an initial noninvasive test when MR-based tools are not available. -
Citations
Citations to this article as recorded by- Dynamic Lipidomic Remodeling and Clinical Correlations after Sleeve Gastrectomy in Obese Subjects
Gakyung Lee, Yeong Chan Lee, Minkuk Park, Seong Min Kim, Ji-Hyeon Park, Dae Ho Lee
Diabetes & Metabolism Journal.2026; 50(2): 396. CrossRef - Cytokeratin 18 fragment in liver inflammation and fibrosis: Systematic review and meta-analysis
Junzhao Ye, Jiaming Lai, Ling Luo, Ting Zhou, Yanhong Sun, Bihui Zhong
Clinica Chimica Acta.2025; 569: 120147. CrossRef - A Comprehensive Review on Graptopetalum paraguayense’s Phytochemical Profiles, Pharmacological Activities, and Development as a Functional Food
Varun Jaiswal, Hae-Jeung Lee
Plants.2025; 14(3): 349. CrossRef - Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning
Haoran Xie, Junjun Wang, Qiuyan Zhao
Scientific Reports.2025;[Epub] CrossRef - Aldo-keto reductase (AKR) superfamily website and database: An update
Andrea Andress Huacachino, Jaehyun Joo, Nisha Narayanan, Anisha Tehim, Blanca E. Himes, Trevor M. Penning
Chemico-Biological Interactions.2024; 398: 111111. CrossRef
- Dynamic Lipidomic Remodeling and Clinical Correlations after Sleeve Gastrectomy in Obese Subjects
- 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
- 7,773 View
- 227 Download
- 6 Web of Science
- 6 Crossref
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Abstract
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
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Review
- Complications
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- 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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Abstract
PDFPubReader ePub
- Although diabetic kidney disease (DKD) remains the leading cause of end-stage kidney disease eventually requiring chronic kidney replacement therapy, the prevalence of DKD has failed to decline over the past 30 years. In order to reduce disease prevalence, extensive research has been ongoing to improve prediction of DKD onset and progression. Although the most commonly used markers of DKD are albuminuria and estimated glomerular filtration rate, their limitations have encouraged researchers to search for novel biomarkers that could improve risk stratification. Considering that DKD is a complex disease process that involves several pathophysiologic mechanisms such as hyperglycemia induced inflammation, oxidative stress, tubular damage, eventually leading to kidney damage and fibrosis, many novel biomarkers that capture one specific mechanism of the disease have been developed. Moreover, the increasing use of high-throughput omic approaches to analyze biological samples that include proteomics, metabolomics, and transcriptomics has emerged as a strong tool in biomarker discovery. This review will first describe recent advances in the understanding of the pathophysiology of DKD, and second, describe the current clinical biomarkers for DKD, as well as the current status of multiple potential novel biomarkers with respect to protein biomarkers, proteomics, metabolomics, and transcriptomics.
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PLOS One.2025; 20(9): e0331900. CrossRef - 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 - Protective effects of Shenkang injection against diabetic kidney disease via p38 MAPK/NFκB/MCP-1/CCR2 pathway inhibition
Xin Zhou, Sitong Wang, Ge Jin, Kaidong Zhou, Yanmo Cai, Zongjiang Zhao
Frontiers in Endocrinology.2025;[Epub] CrossRef - Prevalence of diabetes and prediabetes among working-age adults and influencing factors of new-onset diabetes: a five-year cohort study (2018–2023)
Jing Tan, Mingzhu Chen, Yang Lei, Xiaofen Shi, Cuiping Cao, Naili Du, Yuyou Yao, Xiaojuan Yao
Frontiers in Endocrinology.2025;[Epub] CrossRef - 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
Yue-Wen Tang, Jia-Wei Cao, Meng-Dan Lu, Ru-Chun Yang, Feng Wan
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
Juan Li, Lulu Han, Ting Wang, Lin Yang, Hong Zhou, Pawel Grzmil
International Journal of Endocrinology.2025;[Epub] CrossRef - 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 - Research on Glycemic Variability Characteristics in Diabetic Patients Undergoing Hemodialysis
俊杰 苏
Journal of Clinical Personalized Medicine.2025; 04(05): 100. CrossRef - Short-Term: Cellular Metabolism and Gene Expression During the Onset of Diabetic Kidney Disease: A Diabetes Mellitus Experimental Model
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
Lili Huang, Wenjing Wu, Shenhui Lv, Danfang Deng, Xiaoqin Wang
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
Journal of Extracellular Biology.2025;[Epub] CrossRef - Metabolic reprogramming in diabetes and other endocrine and metabolic disorders: exploring the Warburg effect, ketones, and SGLT2 inhibitors
Pedro Carrera-Bastos, Marcel H. A. Muskiet, Fernando Mata-Ordoñez, Leo Pruimboom, Alejandro Lucia, Raul M. Luque, Frits A. J. Muskiet
Reviews in Endocrine and Metabolic Disorders.2025;[Epub] CrossRef - Multi-omics and machine learning identify FN1 and ALDH2 as diagnostic biomarkers and therapeutic targets in early and late diabetic kidney disease
Jingwei Lin, Yingying Zheng, Diman Mai, Fuxiang Fang, Zhuokun Wei, Mengyu Liu, Trung Hieu Pham, Ming Li, Jiawen Zhao
Renal Failure.2025;[Epub] CrossRef - 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
Chinese Medicine.2025;[Epub] CrossRef - Qing-Re-Xiao-Zheng-yi-qi formula protects renal function by stabilizing lysosomal membranes and inhibiting NLRP3-inflammasome-mediated pyroptosis in diabetic kidney disease
Lei Tian, Lin Wang, Yutong Wang, Weijing Liu, Wenjing Zhao, Yaoxian Wang
Annals of Medicine.2025;[Epub] CrossRef - Andrographolide alleviates type 2 diabetic nephropathy through suppressing PI3K/AKT1/RRM2-triggered oxeiptosis
Mingzheng Han, Jingchun Wang, Shuaihao Guo, Heyun Zhu, Lijiao Zhao, Ziqi Wu, Yijin Wu, Jianying Guo, Jianzhao Liao, Zhaoxin Tang
Journal of Advanced Research.2025;[Epub] CrossRef - The Inflammatory Cell Death in Diabetic Kidney Disease: Integrating Multifactorial Mechanisms into Novel Therapeutics
Bin Fang, Wei Huang, Sijia Du, Yu Hao, Fangfang He, Chun Zhang
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
Wei Hu, Dan Xiong
BIOCELL.2025; 49(9): 1711. CrossRef - 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
Pan Yu, Nan Hong, Qiong Wu, Zhipeng Zhao
Endocrine, Metabolic & Immune Disorders - Drug Targets.2025;[Epub] CrossRef - The Clinical Significance of Serum Sirtuin 2 in Diabetic Nephropathy: Evidence for a Potential Biomarker of Renal Injury
Ozgur Yilmaz, Osman Erinc, Murvet Algemi, Recep Demirci, Sengul Aydin Yoldemir, Murat Akarsu
Journal of Clinical Medicine.2025; 14(22): 8095. CrossRef - The “Picasso faces” of diabetic kidney disease – how the art of phenotyping and molecular biomarkers is transforming clinical nephrology: an observational study in patients with type 2 diabetes
Małgorzata Rodzoń-Norwicz, Natalia Potocka, Marzena Skrzypa, Izabela Zawlik, Katarzyna Milian–Ciesielska, Patryk Kogut, Krzysztof Gargasz, Michael Maes, Magdalena Sowa-Kućma, Agnieszka Gala-Błądzińska
BMC Nephrology.2025;[Epub] CrossRef - The Association of Lipoic Acid Synthase (LIAS) Gene Methylation With Diabetic Kidney Disease
Ziyi Feng, Ting Liu, Peng Gao, Yongwei Jiang, Meimei Zhao, Yi Liu, Haoyan Zhu, Mo Li, Nan Li, Xiaomu Kong, Liang Ma, Zhipeng Tao
Journal of Diabetes Research.2025;[Epub] CrossRef - Association between triglyceride–glucose index and its derivatives and lung adenocarcinoma risk: a case–control study in Chinese adults
Pengcheng Qiu, Jinshi Xie, Yayuan He, Rizheng Cong, Keyue Huang, Bo Huang
Frontiers in Endocrinology.2025;[Epub] CrossRef - Klotho in diabetes mellitus: research progress and clinical implications
Tingyuan Zhu, Songfeng Zhao, Ning Lu
Frontiers in Endocrinology.2025;[Epub] CrossRef - The mediating role of inflammation in the association between coagulation parameters and diabetic kidney diseases in patients with type 2 diabetes mellitus: a muti-center cross-sectional retrospective study in Southwest China from 2013 to 2022
Bei-Hui Ren, Ling-Zhi Xing, Bai-Yu-Lu Chen, Guo-Jia Liao, Li-Yuan Chen, Ying-Dan Zhang, Jie Fan, Ling Chen
BMC Nephrology.2025;[Epub] CrossRef - Syndecan-1 as a Prognostic Biomarker for Early Diabetic Kidney Disease Progression: A Cohort Study
Yiqun Peng, Dongguang Wang, Kaifa Guo, Liang Peng
Journal of Medicine and Physical Education.2025; 2(5): 40. CrossRef - Role of polyphenols in the management of diabetic complications
Jeevika Raina, Atika Firdous, Gurvinder Singh, Rajesh Kumar, Charanjit Kaur
Phytomedicine.2024; 122: 155155. CrossRef - Single-Cell RNA Sequencing Reveals RAC1 Involvement in Macrophages Efferocytosis in Diabetic Kidney Disease
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Inflammation.2024; 47(2): 753. CrossRef - Role of MCP-1 as an inflammatory biomarker in nephropathy
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Frontiers in Immunology.2024;[Epub] CrossRef - Urinary podocyte stress marker as a prognostic indicator for diabetic kidney disease
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BMC Nephrology.2024;[Epub] CrossRef - Identification and validation of immune and cuproptosis - related genes for diabetic nephropathy by WGCNA and machine learning
Yubing Chen, Lijuan Liao, Baoju Wang, Zhan Wu
Frontiers in Immunology.2024;[Epub] CrossRef - Specific Alternation of Gut Microbiota and the Role of Ruminococcus gnavus in the Development of Diabetic Nephropathy
Jinni Hong, Tingting Fu, Weizhen Liu, Yu Du, Junmin Bu, Guojian Wei, Miao Yu, Yanshan Lin, Cunyun Min, Datao Lin
Journal of Microbiology and Biotechnology.2024; 34(3): 547. CrossRef - The triglyceride-glucose index is superior to homeostasis model assessment of insulin resistance in predicting metabolic syndrome in an adult population in the United States
Beverley Adams-Huet, Rafael Zubirán, Alan T. Remaley, Ishwarlal Jialal
Journal of Clinical Lipidology.2024; 18(4): e518. CrossRef - Association of the dietary inflammatory index with complicated diabetic kidney disease in people with diabetes mellitus: evidence from NHANES 2009–2018
Yixin Rui, Xiumeng Zhang, Hongxiao Xie, Hu Qi, Rong Liu, Nan Zeng
Acta Diabetologica.2024; 61(11): 1375. CrossRef - Multiomics Analyses Identify AKR1A1 as a Biomarker for Diabetic Kidney Disease
DengFeng Li, Fang-Chi Hsu, Nicholette D. Palmer, Liang Liu, Young A. Choi, Mariana Murea, John S. Parks, Donald W. Bowden, Barry I. Freedman, Lijun Ma
Diabetes.2024; 73(7): 1188. CrossRef - Serum L C3-II levels in type 2 diabetic patients with impaired renal functions
Shahab Ahmed Salıh Gezh, Koksal Deveci, Hakan Sivgin, Figen Guzelgul
Cytokine.2024; 181: 156683. CrossRef - Diabetic Nephropathy as a Result of Uncontrolled Hyperglycemia
Camila Silva de Aquino, Estênio Gabriel Miranda, Francisco José Pascoal Ribeiro Júnior, Hérika Juliana de Araújo Lucena, Jéssica Pará Amaral de Oliveira, João Ferreira Lima Neto, Lênio Airam de Pinho, Miguel Valentim Rodrigues, Pedro Henrique Borges Silve
Revista de Gestão Social e Ambiental.2024; 18(1): e07716. CrossRef - Pharmacological effects of bioactive agents in earthworm extract: A comprehensive review
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Animal Models and Experimental Medicine.2024; 7(5): 653. CrossRef - Efficacy and safety of polyethylene glycol loxenatide in treating mild-to-moderate diabetic kidney disease in type 2 diabetes patients: a randomized, open-label, clinical trial
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Frontiers in Endocrinology.2024;[Epub] CrossRef - Fish oil supplementation in relation to the risk of chronic kidney disease among patients with diabetes
Yang Ao, Hao Ye, Xiaohui Liu, Yin Li, Haoyin Liu, Shu Ye, Yepeng Hu, Pan Zhuang, Yu Zhang, Chao Zheng, Jingjing Jiao
Diabetes, Obesity and Metabolism.2024; 26(11): 5283. CrossRef - CYSTATIN C—A Monitoring Perspective of Chronic Kidney Disease in Patients with Diabetes
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International Journal of Molecular Sciences.2024; 25(15): 8135. CrossRef - Role of proadrenomedullin in the progression of diabetic kidney disease in patients with type 2 diabetes mellitus
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INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine).2024; 20(4): 266. CrossRef - Sirt6 overexpression relieves ferroptosis and delays the progression of diabetic nephropathy via Nrf2/GPX4 pathway
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Renal Failure.2024;[Epub] CrossRef - Single-cell RNA sequencing in diabetic kidney disease: a literature review
Wei Tan, Jiaoyan Chen, Yunyan Wang, Kui Xiang, Xianqiong Lu, Qiuyu Han, Mingyue Hou, Jurong Yang
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Suye Zhong, Na Wang, Chun Zhang
International Journal of Molecular Sciences.2024; 25(16): 9035. CrossRef - Serum biomarkers for predicting microvascular complications of diabetes mellitus
Jiajia Wang, Xiaoyi Song, Ziqiao Xia, Shu Feng, Hangfeng Zhang, Chengjie Xu, Hui Zhang
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Yanyan Li, Xia Deng, Xunan Wu, Ligang Zhou, Guoyue Yuan
Diabetes, Metabolic Syndrome and Obesity.2024; Volume 17: 3295. CrossRef - Liraglutide ameliorates inflammation and fibrosis by downregulating the TLR4/MyD88/NF-κB pathway in diabetic kidney disease
Linjing Huang, Tingting Lin, Meizhen Shi, Peiwen Wu
American Journal of Physiology-Regulatory, Integrative and Comparative Physiology.2024; 327(4): R410. CrossRef - Molecular Therapeutics for Diabetic Kidney Disease: An Update
Man Guo, Fangfang He, Chun Zhang
International Journal of Molecular Sciences.2024; 25(18): 10051. CrossRef - Angiopoietin‐like 4 is a potential biomarker for diabetic kidney disease in type 2 diabetes patients
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Journal of Diabetes Investigation.2024; 15(12): 1763. CrossRef - Integrating network pharmacology and experimental validation to decipher the pharmacological mechanism of DXXK in treating diabetic kidney injury
Chenxu Zhang, Zhangxin Ji, Na Xu, Jingjing Yuan, Wen Zeng, Yadong Wang, Qing He, Jiaxing Dong, Xinyu Zhang, Dongmei Yang, Wei Jiang, Yibo Yan, Wencui Shang, Jun Chu, Quangen Chu
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Scientific Reports.2024;[Epub] CrossRef - Update on the Pathogenesis, Diagnosis, and Treatment of Diabetic Tubulopathy
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Integrative Medicine in Nephrology and Andrology.2024;[Epub] CrossRef - Identification of circulating microbial DNA and its association with kidney function in patients with diabetic kidney disease
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Nephrology.2024; 29(12): 909. CrossRef - Association of Succinate and Adenosine Nucleotide Metabolic Pathways with Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus
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Diabetes & Metabolism Journal.2024; 48(6): 1126. CrossRef - Role of Epigenetic Changes in the Pathophysiology of Diabetic Kidney Disease
Marita Liebisch, Gunter Wolf
Glomerular Diseases.2024; 4(1): 211. CrossRef - Characteristics and Risk of Incident Diabetic Nephropathy between Early-onset versus Late-onset Type 2 Diabetics of Nigerian Origin
Collins Amadi, Johnbosco Chidozie Okafor, Ochuko Otokunefor, Ezra Agbo
European Journal of Medical and Health Sciences.2024; 6(6): 60. CrossRef - Research progress of gut microbiome and diabetic nephropathy
Chenling Chu, Tapas Ranjan Behera, Ying Huang, Wenhui Qiu, Jiayi Chen, Quanquan Shen
Frontiers in Medicine.2024;[Epub] CrossRef - Targeting programmed cell death in diabetic kidney disease: from molecular mechanisms to pharmacotherapy
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Molecular Medicine.2024;[Epub] CrossRef - Dynamic changes of inflammatory markers in the early stages of chronic kidney disease in patients with type 1 diabetes mellitus
MV Osikov, LA Efros, LYu Zhuravleva, AA Fedosov
Bulletin of Russian State Medical University.2024;[Epub] CrossRef - A Narrative Review of New Treatment Options for Diabetic Nephropathy
Aadhira Pillai, Darshna Fulmali
Cureus.2023;[Epub] CrossRef - Bamboo leaf: A review of traditional medicinal property, phytochemistry, pharmacology, and purification technology
Yaqian Cheng, Siqi Wan, Linna Yao, Ding Lin, Tong Wu, Yongjian Chen, Ailian Zhang, Chenfei Lu
Journal of Ethnopharmacology.2023; 306: 116166. CrossRef - Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics
Lan Wei, Yuanyuan Han, Chao Tu
Diabetes, Metabolic Syndrome and Obesity.2023; Volume 16: 117. CrossRef - Omics and Artificial Intelligence in Kidney Diseases
Nadja Grobe, Josef Scheiber, Hanjie Zhang, Christian Garbe, Xiaoling Wang
Advances in Kidney Disease and Health.2023; 30(1): 47. CrossRef - Intestinal microbiome diversity of diabetic and non-diabetic kidney disease: Current status and future perspective
Soumik Das, Ramanathan Gnanasambandan
Life Sciences.2023; 316: 121414. CrossRef - Pediatric Diabetic Nephropathy: Novel Insights from microRNAs
Francesca Lanzaro, Annalisa Barlabà, Angelica De Nigris, Federica Di Domenico, Valentina Verde, Emanuele Miraglia del Giudice, Anna Di Sessa
Journal of Clinical Medicine.2023; 12(4): 1447. CrossRef - Novel Biomarkers of Diabetic Kidney Disease
Jorge Rico-Fontalvo, Gustavo Aroca-Martínez, Rodrigo Daza-Arnedo, José Cabrales, Tomás Rodríguez-Yanez, María Cardona-Blanco, Juan Montejo-Hernández, Dairo Rodelo Barrios, Jhonny Patiño-Patiño, Elber Osorio Rodríguez
Biomolecules.2023; 13(4): 633. CrossRef - Diabetic vascular diseases: molecular mechanisms and therapeutic strategies
Yiwen Li, Yanfei Liu, Shiwei Liu, Mengqi Gao, Wenting Wang, Keji Chen, Luqi Huang, Yue Liu
Signal Transduction and Targeted Therapy.2023;[Epub] CrossRef - Metabolic phenotypes and risk of end-stage kidney disease in patients with type 2 diabetes
Lijun Zhao, Yutong Zou, Yucheng Wu, Linli Cai, Yuancheng Zhao, Yiting Wang, Xiang Xiao, Qing Yang, Jia Yang, Honghong Ren, Nanwei Tong, Fang Liu
Frontiers in Endocrinology.2023;[Epub] CrossRef - Identification of a New RNA and Protein Integrated Biomarker Panel Associated with Kidney Function Impairment in DKD: Translational Implications
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
International Journal of Molecular Sciences.2023; 24(11): 9412. CrossRef - Increased serum PCSK9 levels are associated with renal function impairment in patients with type 2 diabetes mellitus
Zhicai Feng, Xiangyu Liao, Hao Zhang, Juan Peng, Zhijun Huang, Bin Yi
Renal Failure.2023;[Epub] CrossRef - Analysis of Serum Pyrodeath Re-lated Proteins and Renal Injury in Patients with Type 2 DKD
茹洁 马
Asian Case Reports in Emergency Medicine.2023; 11(02): 53. CrossRef - Loganin reduces diabetic kidney injury by inhibiting the activation of NLRP3 inflammasome-mediated pyroptosis
Xiangri Kong, Yunyun Zhao, Xingye Wang, Yongjiang Yu, Ying Meng, Guanchi Yan, Miao Yu, Lihong Jiang, Wu Song, Bingmei Wang, Xiuge Wang
Chemico-Biological Interactions.2023; 382: 110640. CrossRef - Machine-learning algorithm-based prediction of a diagnostic model based on oxidative stress-related genes involved in immune infiltration in diabetic nephropathy patients
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Frontiers in Immunology.2023;[Epub] CrossRef - The roles of gut microbiota and its metabolites in diabetic nephropathy
Hui Zhao, Cheng-E Yang, Tian Liu, Ming-Xia Zhang, Yan Niu, Ming Wang, Jun Yu
Frontiers in Microbiology.2023;[Epub] CrossRef - High triglyceride levels increase the risk of diabetic microvascular complications: a cross-sectional study
Jiahang Li, Lei Shi, Guohong Zhao, Fei Sun, Zhenxing Nie, Zhongli Ge, Bin Gao, Yan Yang
Lipids in Health and Disease.2023;[Epub] CrossRef - Correlation of Kidney Injury Molecule-1 and Nephrin Levels in Iraqi Patients with Diabetic Nephropathy
Raghda Hisham Aljorani, Eman Saadi Saleh , Khalaf Gata Hussein Al Mohammadawi
Al-Rafidain Journal of Medical Sciences ( ISSN 2789-3219 ).2023; 5: 99. CrossRef - Diabetic Nephropathy: Significance of Determining Oxidative Stress and Opportunities for Antioxidant Therapies
Marina Darenskaya, Sergey Kolesnikov, Natalya Semenova, Lyubov Kolesnikova
International Journal of Molecular Sciences.2023; 24(15): 12378. CrossRef - Evaluation of Neutrophil/Lymphocyte Ratio, Low-Density Lipoprotein/Albumin Ratio, and Red Cell Distribution Width/Albumin Ratio in the Estimation of Proteinuria in Uncontrolled Diabetic Patients
Duygu Tutan, Murat Doğan
Cureus.2023;[Epub] CrossRef - Hedysarum polybotrys polysaccharide attenuates renal inflammatory infiltration and fibrosis in diabetic mice by inhibiting the HMGB1/RAGE/TLR4 pathway
Changqing Xu, Yanxu Cheng, Zongmei Liu, Xiaoyan Fu
Experimental and Therapeutic Medicine.2023;[Epub] CrossRef - Abdominal adipose tissue and type 2 diabetic kidney disease: adipose radiology assessment, impact, and mechanisms
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Abdominal Radiology.2023; 49(2): 560. CrossRef - Inhibition of MD2 by natural product-drived JM-9 attenuates renal inflammation and diabetic nephropathy in mice
Minxiu Wang, Qianhui Zhang, Shuaijie Lou, Leiming Jin, Gaojun Wu, Wenqi Wu, Qidong Tang, Yi Wang, Xiaohong Long, Ping Huang, Wu Luo, Guang Liang
Biomedicine & Pharmacotherapy.2023; 168: 115660. CrossRef - Multifaceted relationship between diabetes and kidney diseases: Beyond diabetes
Pasquale Esposito, Daniela Picciotto, Francesca Cappadona, Francesca Costigliolo, Elisa Russo, Lucia Macciò, Francesca Viazzi
World Journal of Diabetes.2023; 14(10): 1450. CrossRef - Mitochondrial antiviral signaling protein: a potential therapeutic target in renal disease
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Frontiers in Immunology.2023;[Epub] CrossRef - Research progress on multiple cell death pathways of podocytes in diabetic kidney disease
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Molecular Medicine.2023;[Epub] CrossRef - Quantitative profiling of carboxylic compounds by gas chromatography-mass spectrometry for revealing biomarkers of diabetic kidney disease
Rongrong Zhu, Yan Yuan, Rourou Qi, Jianying Liang, Yan Shi, Hongbo Weng
Journal of Chromatography B.2023; 1231: 123930. CrossRef - Jiangtang Decoction Ameliorates Diabetic Kidney Disease Through the Modulation of the Gut Microbiota
Jinni Hong, Tingting Fu, Weizhen Liu, Yu Du, Junmin Bu, Guojian Wei, Miao Yu, Yanshan Lin, Cunyun Min, Datao Lin
Diabetes, Metabolic Syndrome and Obesity.2023; Volume 16: 3707. CrossRef - GLP-1RA Combined with SGLT2 Inhibitors for the Treatment of Diabetic Kidney Disease: A Meta Analysis
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Advances in Clinical Medicine.2023; 13(11): 18117. CrossRef - Potential application of Klotho as a prognostic biomarker for patients with diabetic kidney disease: a meta-analysis of clinical studies
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Therapeutic Advances in Chronic Disease.2023;[Epub] CrossRef - Research progress of natural active compounds on improving podocyte function to reduce proteinuria in diabetic kidney disease
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International Journal of Molecular Sciences.2022; 23(15): 8198. CrossRef - Renoprotective Mechanism of Sodium-Glucose Cotransporter 2 Inhibitors: Focusing on Renal Hemodynamics
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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
- 10,055 View
- 160 Download
- 27 Web of Science
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Abstract
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
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Zi Li, Yuanyuan Ren, Ziwei Lv, Man Li, Yujia Li, Xiaobin Fan, Yuyan Xiong, Lu Qian
Biomedicine & Pharmacotherapy.2023; 168: 115744. CrossRef - Circular RNA PIP5K1A act as microRNA-552-3p sponge to regulates inflammation, oxidative damage in glucolipotoxicity-induced pancreatic INS-1 β-cells via Janus kinase 1
Lei Ren
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Wenqi Fan, Haipeng Pang, Zhiguo Xie, Gan Huang, Zhiguang Zhou
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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
- Emerging role and clinical applications of circular RNAs in human diseases
- 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
- 10,883 View
- 92 Download
- 7 Web of Science
- 7 Crossref
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Abstract
PDFSupplementary MaterialPubReader ePub
Background Inflammatory cytokines are increasingly utilized to detect high-risk individuals for cardiometabolic diseases. However, with large population and assay methodological heterogeneity, no clear reference currently exists.
Methods Among participants of the Cardiovascular and Metabolic Diseases Etiology Research Center cohort, of community-dwelling adults aged 30 to 64 without overt cardiovascular diseases, we presented distributions of tumor necrosis factor (TNF)-α and -β, interleukin (IL)-1α, -1β, and 6, monocyte chemoattractant protein (MCP)-1 and -3 and high sensitivity C-reactive protein (hsCRP) with and without non-detectable (ND) measurements using multiplex enzyme-linked immunosorbent assay. Then, we compared each markers by sex, age, and prevalence of type 2 diabetes mellitus, hypertension, and dyslipidemia, using the Wilcoxon Rank-Sum Test.
Results In general, there were inconsistencies in direction and magnitude of differences in distributions by sex, age, and prevalence of cardiometabolic disorders. Overall, the median and the 99th percentiles were higher in men than in women. Older participants had higher TNF-α, high sensitivity IL-6 (hsIL-6), MCP-1, hsCRP, TNF-β, and MCP-3 median, after excluding the NDs. Participants with type 2 diabetes mellitus had higher median for all assayed biomarkers, except for TNF-β, IL-1α, and MCP-3, in which the medians for both groups were 0.00 due to predominant NDs. Compared to normotensive group, participants with hypertension had higher TNF-α, hsIL-6, MCP-1, and hsCRP median. When stratifying by dyslipidemia prevalence, the comparison varied significantly depending on the treatment of NDs.
Conclusion Our findings provide sex-, age-, and disease-specific reference values to improve risk prediction and diagnostic performance for inflammatory diseases in both population- and clinic-based settings.
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Yunha Suh, Kwang-eun Kim
International Journal of Molecular Sciences.2026; 27(4): 1712. CrossRef - Systemic counterregulatory response of angiopoietin-2 after intravitreal injections with faricimab for nAMD
Anna Lena Huber, Angelika Bauer, Julius Beirer, Katharina Frede, Katharina Kirchmair, Reinhard Angermann, Matus Rehak, Claus Zehetner, Yvonne Nowosielski
Graefe's Archive for Clinical and Experimental Ophthalmology.2025; 263(5): 1259. CrossRef - Immune regulation mechanism of polysaccharide from Dendrobium officinale Kimura et Migo on intestinal lamina propria cells
Jia Li, Xiaoli Yang, Ziying Zheng, Shuang Song, Yang Tian, Zhongbin Bai
Food Bioscience.2025; 71: 107410. CrossRef - 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 - Within-subject variation of C-reactive protein and high-sensitivity C-reactive protein: A systematic review and meta-analysis
Alex Gough, Alice Sitch, Erica Ferris, Tom Marshall, Andreas Zirlik
PLOS ONE.2024; 19(11): e0304961. 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
- Impact of Sex on Plasma Biomarkers in ob/ob Mice
- 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
- 9,757 View
- 128 Download
- 11 Web of Science
- 12 Crossref
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Abstract
PDFSupplementary MaterialPubReader ePub
Background Multiple biomarkers have performed well in predicting type 2 diabetes mellitus (T2DM) risk in Western populations. However, evidence is scarce among Asian populations.
Methods Plasma triglyceride-to-high density lipoprotein (TG-to-HDL) ratio, alanine transaminase (ALT), high-sensitivity C-reactive protein (hs-CRP), ferritin, adiponectin, fetuin-A, and retinol-binding protein 4 were measured in 485 T2DM cases and 485 age-and-sex matched controls nested within the prospective Singapore Chinese Health Study cohort. Participants were free of T2DM at blood collection (1999 to 2004), and T2DM cases were identified at the subsequent follow-up interviews (2006 to 2010). A weighted biomarker score was created based on the strengths of associations between these biomarkers and T2DM risks. The predictive utility of the biomarker score was assessed by the area under receiver operating characteristics curve (AUC).
Results The biomarker score that comprised of four biomarkers (TG-to-HDL ratio, ALT, ferritin, and adiponectin) was positively associated with T2DM risk (
P trend <0.001). Compared to the lowest quartile of the score, the odds ratio was 12.0 (95% confidence interval [CI], 5.43 to 26.6) for those in the highest quartile. Adding the biomarker score to a base model that included smoking, history of hypertension, body mass index, and levels of random glucose and insulin improved AUC significantly from 0.81 (95% CI, 0.78 to 0.83) to 0.83 (95% CI, 0.81 to 0.86;P =0.002). When substituting the random glucose levels with glycosylated hemoglobin in the base model, adding the biomarker score improved AUC from 0.85 (95% CI, 0.83 to 0.88) to 0.86 (95% CI, 0.84 to 0.89;P =0.032).Conclusion A composite score of blood biomarkers improved T2DM risk prediction among Chinese.
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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
Frontiers in Endocrinology.2025;[Epub] CrossRef - 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 - Are Oxidative Stress Biomarkers Reliable Part of Multimarker Panel in Female Patients with Type 2 Diabetes Mellitus?
Aleksandra Klisic, Paschalis Karakasis, Dimitrios Patoulias, Amirmohammad Khalaji, Ana Ninić
Metabolic Syndrome and Related Disorders.2024; 22(9): 679. CrossRef - Development and evaluation of a chronic kidney disease risk prediction model using random forest
Krish Mendapara
Frontiers in Genetics.2024;[Epub] 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
- The predictive value of combined insulin resistance and β-cell secretion in Yemeni school-aged children for type 2 diabetes mellitus
- 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
- 7,832 View
- 88 Download
- 5 Web of Science
- 5 Crossref
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Abstract
PDFSupplementary MaterialPubReader 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.
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Nahuel Garcia, Maiken Mellergaard, Carlos Salomon, Pilar Sepulveda, Peter Kristensen, Aase Handberg
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Monika Rac
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Constantine E Kosmas, Andreas Sourlas, Konstantinos Oikonomakis, Eleni-Angeliki Zoumi, Aikaterini Papadimitriou, Christina E Kostara
Journal of International Medical Research.2024;[Epub] CrossRef - 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
- CD36 and Its Role in Obesity
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- 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
- 8,487 View
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- 17 Web of Science
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Abstract
PDFPubReader ePub
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.
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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
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蕾 任
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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
- 7,443 View
- 50 Download
- 13 Web of Science
- 14 Crossref
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Abstract
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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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Citations
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Andra Melissa Entuc, Maria Bogdan, Ianis Kevyn Stefan Boboc, Liliana Mititelu Tartau, Delia Reurean Pintilei, Liliana Lacramioara Pavel, Ana-Maria Pelin, Aurelia Spinei, Liliana Georgeta Foia
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- Epidemiology
- Serum Betatrophin Concentrations and the Risk of Incident Diabetes: A Nested Case-Control Study from Chungju Metabolic Disease Cohort
- Seung-Hwan Lee, Marie Rhee, Hyuk-Sang Kwon, Yong-Moon Park, Kun-Ho Yoon
- Diabetes Metab J. 2018;42(1):53-62. Published online November 3, 2017
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Abstract
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Background Betatrophin is a newly identified hormone derived from the liver and adipose tissue, which has been suggested to regulate glucose and lipid metabolism. Circulating levels of betatrophin are altered in various metabolic diseases, although the results are inconsistent. We aimed to examine whether betatrophin is a useful biomarker in predicting the development of diabetes.
Methods A nested case-control study was performed using a prospective Chungju Metabolic disease Cohort Study. During a 4-year follow-up period, we analyzed 167 individuals who converted to diabetes and 167 non-converters, who were matched by age, sex, and body mass index. Serum betatrophin levels were measured by an ELISA (enzyme-linked immunosorbent assay).
Results Baseline serum betatrophin levels were significantly higher in the converter group compared to the non-converter group (1,315±598 pg/mL vs. 1,072±446 pg/mL,
P <0.001). After adjusting for age, sex, body mass index, fasting plasma glucose, systolic blood pressure, total cholesterol, and family history of diabetes, the risk of developing diabetes showed a stepwise increase across the betatrophin quartile groups. Subjects in the highest baseline quartile of betatrophin levels had more than a threefold higher risk of incident diabetes than the subjects in the lowest quartile (relative risk, 3.275; 95% confidence interval, 1.574 to 6.814;P =0.010). However, no significant relationships were observed between serum betatrophin levels and indices of insulin resistance or β-cell function.Conclusion Circulating levels of betatrophin could be a potential biomarker for predicting new-onset diabetes. Further studies are needed to understand the underlying mechanism of this association.
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Citations
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Review
- Functional and Mechanistic Integration of Infection and the Metabolic Syndrome
- Peter Sommer, Gary Sweeney
- Korean Diabetes J. 2010;34(2):71-76. Published online April 30, 2010
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Abstract
PDFPubReader ePub
The metabolic syndrome refers to a well defined group of risk factors, including central obesity and inflammation, for the development of diabetes and cardiovascular disease. Interestingly, many studies have recently led to the emergence of somewhat unexpected relationships between several infectious diseases and various aspects of the metabolic syndrome. Our understanding of the mechanisms underlying these interactions is also rapidly developing and some of these are summarized in this article. We will focus first on bacterial infection, and most notably the role of gut microbiota in regulaton of both obesity and inflammation. In particular, we focus on the role of inflammasomes and propose that understanding the role of Toll-like receptors and Nod-like receptors in the pathogenesis of inflammatory disorders with or without infection may provide novel targets for prevention and/or treatment of associated diseases. Secondly, chronic bacterial or viral infection and emerging links with metabolism will be reviewed. Finally, consideratons of biomarkers for metabolic syndrome, in particular lipocalin-2, and their link with infection will be discussed.
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Original Article
- 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
- 3,894 View
- 34 Download
- 2 Crossref
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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. -
Citations
Citations to this article as recorded by- Lack of Association between Serum Cystatin C Levels and Coronary Artery Disease in Diabetic Patients
Eun Hee Kim, Ji Hee Yu, Sang Ah Lee, Eui Young Kim, Won Gu Kim, Seung Hun Lee, Eun Hee Cho, Eun Hee Koh, Woo Je Lee, Min-Seon Kim, Joong-Yeol Park, Ki-Up Lee
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