Metabolomics Profiles Associated with Diabetic Retinopathy
By LabMedica International staff writers Posted on 16 Nov 2020 |

Image: The AbsoluteIDQ p180 kit provides scientists with highly reproducible metabolomics data to confidently obtain detailed knowledge about the metabolic phenotypes in their studies (Photo courtesy of BIOCRATES Life Sciences).
Diabetic retinopathy (DR), like diabetic neuropathy and nephropathy, is a common complication of diabetes. It is the leading cause of loss of vision in diabetic patients. Long-standing disease, along with hyperglycemia, hyperlipidemia, hypertension, and genetic factors, is a major risk factor of diabetes retinopathy.
Metabolomics profiling is a rapidly evolving method used to identify the metabolites in biological fluids and investigate disease progression. Quantitative analyses of small-molecule metabolites in biological specimens such as blood and urine can be performed due to the rapid advances in metabolomics.
Medical Scientists at the Chungbuk National University College of Medicine (Cheongju, Republic of Korea) included in a study 317 type 2 diabetes (T2D) patients of which 143 non-DR (NDR) patients, 123 non- proliferative DR (NPDR) patients, and 51 proliferative-DR (PDR) patients. Gender, age, height, weight, body mass index (BMI), and HbA1c, glucose, and creatinine levels of all patients were recorded.
The serum samples of the T2D patients were analyzed using a targeted metabolomics approach. To quantify the metabolites, liquid chromatography (LC) and flow-injection analysis (FIA)–mass spectrometry (MS) were performed using the AbsoluteIDQ p180 Kit (BIOCRATES Life Sciences AG, Innsbruck, Austria). The serum samples were analyzed using the API 4000 QTRAP LC/MS/MS system (Applied Biosystems, Foster City, CA, USA) and the Agilent 1200 HPLC system (Agilent Technologies, Santa Clara, CA, USA).
The investigators reported that the concentrations of 62 metabolites of the NDR versus DR group, 53 metabolites of the NDR versus NPDR group, and 30 metabolites of the NDR versus PDR group were found to be significantly different. Sixteen metabolites were selected as specific metabolites common to NPDR and PDR. Among them, three metabolites including total dimethylarginine, tryptophan, and kynurenine were potential makers of DR progression in T2D patients. Additionally, several metabolites such as carnitines, several amino acids, and phosphatidylcholines also showed a biomarker potential.
The authors concluded that they had revealed via comprehensive metabolomics profiling using a high-throughput platform, several metabolites associated with DR. These new DR-related metabolites should be considered in the study of the mechanism behind the initiation and progression of DR in T2D patients. The study was published on October 29, 2020 in the journal PLOS ONE.
Related Links:
Chungbuk National University College of Medicine
BIOCRATES Life Sciences
Applied Biosystems
Agilent Technologies
Metabolomics profiling is a rapidly evolving method used to identify the metabolites in biological fluids and investigate disease progression. Quantitative analyses of small-molecule metabolites in biological specimens such as blood and urine can be performed due to the rapid advances in metabolomics.
Medical Scientists at the Chungbuk National University College of Medicine (Cheongju, Republic of Korea) included in a study 317 type 2 diabetes (T2D) patients of which 143 non-DR (NDR) patients, 123 non- proliferative DR (NPDR) patients, and 51 proliferative-DR (PDR) patients. Gender, age, height, weight, body mass index (BMI), and HbA1c, glucose, and creatinine levels of all patients were recorded.
The serum samples of the T2D patients were analyzed using a targeted metabolomics approach. To quantify the metabolites, liquid chromatography (LC) and flow-injection analysis (FIA)–mass spectrometry (MS) were performed using the AbsoluteIDQ p180 Kit (BIOCRATES Life Sciences AG, Innsbruck, Austria). The serum samples were analyzed using the API 4000 QTRAP LC/MS/MS system (Applied Biosystems, Foster City, CA, USA) and the Agilent 1200 HPLC system (Agilent Technologies, Santa Clara, CA, USA).
The investigators reported that the concentrations of 62 metabolites of the NDR versus DR group, 53 metabolites of the NDR versus NPDR group, and 30 metabolites of the NDR versus PDR group were found to be significantly different. Sixteen metabolites were selected as specific metabolites common to NPDR and PDR. Among them, three metabolites including total dimethylarginine, tryptophan, and kynurenine were potential makers of DR progression in T2D patients. Additionally, several metabolites such as carnitines, several amino acids, and phosphatidylcholines also showed a biomarker potential.
The authors concluded that they had revealed via comprehensive metabolomics profiling using a high-throughput platform, several metabolites associated with DR. These new DR-related metabolites should be considered in the study of the mechanism behind the initiation and progression of DR in T2D patients. The study was published on October 29, 2020 in the journal PLOS ONE.
Related Links:
Chungbuk National University College of Medicine
BIOCRATES Life Sciences
Applied Biosystems
Agilent Technologies
Latest Pathology News
- Novel UV and Machine Learning-Aided Method Detects Microbial Contamination in Cell Cultures
- New Error-Corrected Method to Help Detect Cancer from Blood Samples Alone
- "Metal Detector" Algorithm Hunts Down Vulnerable Tumors
- Novel Technique Uses ‘Sugar’ Signatures to Identify and Classify Pancreatic Cancer Cell Subtypes
- Advanced Imaging Reveals Mechanisms Causing Autoimmune Disease
- AI Model Effectively Predicts Patient Outcomes in Common Lung Cancer Type
- AI Model Predicts Patient Response to Bladder Cancer Treatment
- New Laser-Based Method to Accelerate Cancer Diagnosis
- New AI Model Predicts Gene Variants’ Effects on Specific Diseases
- Powerful AI Tool Diagnoses Coeliac Disease from Biopsy Images with Over 97% Accuracy
- Pre-Analytical Conditions Influence Cell-Free MicroRNA Stability in Blood Plasma Samples
- 3D Cell Culture System Could Revolutionize Cancer Diagnostics
- Painless Technique Measures Glucose Concentrations in Solution and Tissue Via Sound Waves
- Skin-Based Test to Improve Diagnosis of Rare, Debilitating Neurodegenerative Disease
- Serum Uromodulin Could Indicate Acute Kidney Injury in COVID-19 Patients
- AI Model Reveals True Biological Age From Five Drops of Blood
Channels
Molecular Diagnostics
view channel
Blood Biomarker Test Could Detect Genetic Predisposition to Alzheimer’s
New medications for Alzheimer’s disease, the most common form of dementia, are now becoming available. These treatments, known as “amyloid antibodies,” work by promoting the removal of small deposits from... Read more
Novel Autoantibody Against DAGLA Discovered in Cerebellitis
Autoimmune cerebellar ataxias are strongly disabling disorders characterized by an impaired ability to coordinate muscle movement. Cerebellar autoantibodies serve as useful biomarkers to support rapid... Read more
Gene-Based Blood Test Accurately Predicts Tumor Recurrence of Advanced Skin Cancer
Melanoma, an aggressive form of skin cancer, becomes extremely difficult to treat once it spreads to other parts of the body. For patients with metastatic melanoma tumors that cannot be surgically removed... Read moreHematology
view channel
New Scoring System Predicts Risk of Developing Cancer from Common Blood Disorder
Clonal cytopenia of undetermined significance (CCUS) is a blood disorder commonly found in older adults, characterized by mutations in blood cells and a low blood count, but without any obvious cause or... Read more
Non-Invasive Prenatal Test for Fetal RhD Status Demonstrates 100% Accuracy
In the United States, approximately 15% of pregnant individuals are RhD-negative. However, in about 40% of these cases, the fetus is also RhD-negative, making the administration of RhoGAM unnecessary.... Read moreImmunology
view channel
Stem Cell Test Predicts Treatment Outcome for Patients with Platinum-Resistant Ovarian Cancer
Epithelial ovarian cancer frequently responds to chemotherapy initially, but eventually, the tumor develops resistance to the therapy, leading to regrowth. This resistance is partially due to the activation... Read more
Machine Learning-Enabled Blood Test Predicts Immunotherapy Response in Lymphoma Patients
Chimeric antigen receptor (CAR) T-cell therapy has emerged as one of the most promising recent developments in the treatment of blood cancers. However, over half of non-Hodgkin lymphoma (NHL) patients... Read moreMicrobiology
view channel
Handheld Device Delivers Low-Cost TB Results in Less Than One Hour
Tuberculosis (TB) remains the deadliest infectious disease globally, affecting an estimated 10 million people annually. In 2021, about 4.2 million TB cases went undiagnosed or unreported, mainly due to... Read more
New AI-Based Method Improves Diagnosis of Drug-Resistant Infections
Drug-resistant infections, particularly those caused by deadly bacteria like tuberculosis and staphylococcus, are rapidly emerging as a global health emergency. These infections are more difficult to treat,... Read more
Breakthrough Diagnostic Technology Identifies Bacterial Infections with Almost 100% Accuracy within Three Hours
Rapid and precise identification of pathogenic microbes in patient samples is essential for the effective treatment of acute infectious diseases, such as sepsis. The fluorescence in situ hybridization... Read morePathology
view channel
Novel UV and Machine Learning-Aided Method Detects Microbial Contamination in Cell Cultures
Cell therapy holds great potential in treating diseases such as cancers, inflammatory conditions, and chronic degenerative disorders by manipulating or replacing cells to restore function or combat disease.... Read more
New Error-Corrected Method to Help Detect Cancer from Blood Samples Alone
"Liquid biopsy" technology, which relies on blood tests for early cancer detection and monitoring cancer burden in patients, has the potential to transform cancer care. However, detecting the mutational... Read more
"Metal Detector" Algorithm Hunts Down Vulnerable Tumors
Scientists have developed an algorithm capable of functioning as a "metal detector" to identify vulnerable tumors, marking a significant advancement in personalized cancer treatment. This breakthrough... Read more
Novel Technique Uses ‘Sugar’ Signatures to Identify and Classify Pancreatic Cancer Cell Subtypes
Pancreatic cancer is often asymptomatic in its early stages, making it difficult to detect until it has progressed. Consequently, only 15% of pancreatic cancers are diagnosed early enough to allow for... Read moreTechnology
view channel
Disposable Microchip Technology Could Selectively Detect HIV in Whole Blood Samples
As of the end of 2023, approximately 40 million people globally were living with HIV, and around 630,000 individuals died from AIDS-related illnesses that same year. Despite a substantial decline in deaths... Read more
Pain-On-A-Chip Microfluidic Device Determines Types of Chronic Pain from Blood Samples
Chronic pain is a widespread condition that remains difficult to manage, and existing clinical methods for its treatment rely largely on self-reporting, which can be subjective and especially problematic... Read more
Innovative, Label-Free Ratiometric Fluorosensor Enables More Sensitive Viral RNA Detection
Viruses present a major global health risk, as demonstrated by recent pandemics, making early detection and identification essential for preventing new outbreaks. While traditional detection methods are... Read moreIndustry
view channel
Cepheid and Oxford Nanopore Technologies Partner on Advancing Automated Sequencing-Based Solutions
Cepheid (Sunnyvale, CA, USA), a leading molecular diagnostics company, and Oxford Nanopore Technologies (Oxford, UK), the company behind a new generation of sequencing-based molecular analysis technologies,... Read more
Grifols and Tecan’s IBL Collaborate on Advanced Biomarker Panels
Grifols (Barcelona, Spain), one of the world’s leading producers of plasma-derived medicines and innovative diagnostic solutions, is expanding its offer in clinical diagnostics through a strategic partnership... Read more