Glucose Monitoring for Diabetes Using Non-Invasive Raman Spectroscopy
Researchers at the Institute of Photonics and Photon-Technology, Northwest University, China, have described a non-invasive method for monitoring blood glucose using Raman spectroscopy. Their study, published in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, explores the technique’s effectiveness in both animal models and human subjects, showing promise for future clinical applications.
Traditional glucose level testing ©juanrvelasco - stock.adobe.com
Managing diabetes requires frequent monitoring of blood glucose levels, traditionally done via painful finger-stick tests (1). A new study led by Jing Liu, Jiahui Chu, Jie Xu, Zhanqin Zhang, and Shuang Wang from Northwest University, Xi’an, China, has demonstrated the potential of Raman spectroscopy as a non-invasive alternative. Their research, published in Spectrochimica Acta Part A, highlights the use of a 785 nm excitation laser-based Raman system to measure transcutaneous glucose concentrations with reported high accuracy (2).
Diabetes is increasing in the global population and is one of the major health concerns in modern times. The global population affected by diabetes grew significantly, from 200 million in 1990 to 830 million in 2022. The rise in prevalence has been particularly notable in low- and middle-income nations, outpacing the increase in high-income countries. Diabetes is a long-term condition that arises when the pancreas either fails to produce sufficient insulin or the body becomes less efficient at utilizing the insulin it produces. Insulin is a hormone essential for controlling blood glucose levels (1).
A Technological Leap in Diabetes Monitoring
The study presents an integrated Raman spectral system that detects glucose-specific spectral features without requiring blood samples. The researchers validated their approach through experiments on both animal models and human subjects. The technique showed a strong correlation between Raman spectral data and blood glucose levels, particularly in spectral intensity ratios at 1125 cm−1 and 1445 cm−1, which reflected glucose concentration-dependent changes (2).
Animal Model Validates Spectral Analysis
To establish the feasibility of Raman spectroscopy for glucose monitoring, the researchers conducted in vivo tests on 4-week-old specific pathogen-free (SPF) Kunming-grade mice. These mice received controlled glucose injections, and their glucose metabolism was tracked via Raman spectra collected from their tails. The results demonstrated a strong correlation between the Raman spectral data and blood glucose levels measured through conventional methods. The estimated limit of detection (LoD) for the animal model was approximately 1.977 mmol/L, reinforcing the sensitivity of this method (2).
Human Trials Confirm Predictive Accuracy
To further validate the approach, the team conducted human oral glucose tolerance tests (OGTT) on 33 volunteers. Raman spectra were recorded from the nailfolds of participants’ middle fingers at regular intervals after glucose ingestion. The study found that the intensity ratio between 1125 cm−1 and 1445 cm−1 exhibited the highest correlation with actual glucose concentrations, achieving an R2 value of 0.8496. The estimated LoD for human subjects ranged between 2.186 mmol/L and 4.28 mmol/L, demonstrating the method’s potential reliability despite individual variations in skin composition and metabolic response (2).
Advanced Machine Learning (ML) Enhances Predictive Power
The research team incorporated a particle swarm optimization-backpropagation artificial neural network (PSO-BP-ANN) model to refine glucose concentration predictions. This model improved the accuracy of Raman-based glucose monitoring by accounting for intersubject variations. After training and validation, the model was tested on additional human subjects, showing significant predictive accuracy that suggests its potential for real-world application (2).
Future Implications and Clinical Applications
The study’s findings provide compelling evidence for the potential of Raman spectroscopy in diabetes management. Unlike traditional methods, this technique minimizes patient discomfort and enables real-time monitoring without requiring invasive procedures. Future developments will focus on enhancing spectral detection sensitivity and integrating Raman-based glucose monitors into portable, user-friendly devices for clinical and home use (2).
The research represents a step-wise advancement in non-invasive glucose monitoring. By leveraging the specificity of Raman spectroscopy and the power of AI-driven predictive models, this method has the potential to revolutionize diabetes management. Further studies and clinical trials will be necessary to evaluate this technology for widespread medical use, but the current findings mark an important step toward a pain-free, real-time glucose monitoring solution for millions of diabetes patients worldwide.
References
(1) World Health Organization Diabetes Home Page.https://www.who.int/news-room/fact-sheets/detail/diabetes (accessed 2025-01-28)
(2) Liu, J.; Chu, J.; Xu, J.; Zhang, Z.; Wang, S. In Vivo Raman Spectroscopy for Non-Invasive Transcutaneous Glucose Monitoring in Animal Models and Human Subjects. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2025, 329, 125584. DOI: 10.1016/j.saa.2024.125584
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