Lab test for dm
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Laboratory Tests for Diabetes Mellitus (DM)
Blood Glucose Tests for DM Diagnosis
Fasting Plasma Glucose (FPG) and HbA1c Tests
Fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c) are commonly used laboratory tests for diagnosing diabetes mellitus (DM). FPG measures blood glucose levels after an overnight fast, while HbA1c reflects average blood glucose levels over the past two to three months. Despite their widespread use, FPG has shown low sensitivity in detecting DM, with a sensitivity of only 29% in some studies1. In contrast, HbA1c is often preferred due to its ability to provide a longer-term view of blood glucose levels and its higher sensitivity and specificity4 7.
Oral Glucose Tolerance Test (OGTT)
The OGTT is another critical test for diagnosing DM. It involves measuring blood glucose levels before and after consuming a glucose-rich drink. This test is particularly useful for identifying impaired glucose tolerance (IGT), a precursor to diabetes1 4.
Non-Invasive and Rapid Diagnostic Technologies
EZSCAN Technology
A novel non-invasive device, EZSCAN, based on electrochemical principles using iontophoresis, has shown promise in detecting DM and IGT. EZSCAN demonstrated a sensitivity of 75% for DM and 70% for IGT, making it a powerful tool for early detection1.
ATR-FTIR Spectroscopy
Attenuated Total Reflection Fourier-Transform Infrared (ATR-FTIR) spectroscopy combined with multivariate analysis has been developed as a rapid and inexpensive diagnostic method. This technique achieved an accuracy of 97%, with high sensitivity and specificity for both DM and pre-diabetes, indicating its potential for effective diagnosis5.
Nanotechnology-Based Point-of-Care (PoC) Devices
A nanotechnology-based PoC device has been validated for early detection of DM and diabetic kidney disease (DKD). This device measures multiple biomarkers, including HbA1c, serum albumin, and urine albumin-to-creatinine ratio, providing results in less than a minute. It showed excellent correlation with laboratory gold standard tests, making it particularly useful in resource-poor settings6.
Machine Learning Approaches
Predicting HbA1c from Routine Blood Tests
Machine learning models have been employed to predict HbA1c levels from routine blood count tests. These models, including K-nearest neighbors (KNN) and artificial neural networks (ANN), have shown promising results in identifying undiagnosed cases of diabetes. The KNN model, for instance, achieved a sensitivity of 53.6% and an accuracy of 90.1% in predicting diabetes7.
Conclusion
The landscape of laboratory tests for diagnosing diabetes mellitus is evolving with the advent of new technologies and methodologies. Traditional tests like FPG and HbA1c remain foundational, but novel non-invasive devices, rapid diagnostic technologies, and machine learning approaches are enhancing early detection and diagnosis. These advancements hold significant promise for improving diabetes management and patient outcomes, especially in underserved areas.
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Most relevant research papers on this topic
A new non-invasive technology to screen for dysglycaemia including diabetes.
The EZSCAN device, using iontophoresis, effectively detects impaired glucose tolerance, type 2 diabetes, and metabolic syndrome, offering a powerful non-invasive method for early detection.
Observational Study
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ATR-FTIR spectroscopy combined with multivariate analysis effectively diagnoses diabetes mellitus and pre-diabetes, offering a simple, rapid, and inexpensive method for detection.
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A nanotechnology-based point-of-care device effectively detects early diabetic kidney disease, potentially benefiting remote communities in underserved areas.
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