Rothenbühler Martina, Lizoain Aritz, Rebeaud Fabien, Perotte Adler, Stoffel Marc, DeVries J Hans
Diabetes Center Berne, Bern, Switzerland.
Liom Health AG, Pfaeffikon, Switzerland.
J Diabetes Sci Technol. 2025 Jan 29:19322968251313811. doi: 10.1177/19322968251313811.
Glucose is an essential molecule in energy metabolism. Dysregulated glucose metabolism, the defining feature of diabetes, requires active monitoring and treatment to prevent significant morbidity and mortality. Current technologies for intermittent and continuous glucose measurement are invasive. Noninvasive glucose measurement would eliminate this barrier toward making glucose monitoring more accessible, extending the benefits from people living with diabetes to prediabetes and the healthy.
A novel spectroscopy-based system for measuring glucose noninvasively was used in an exploratory, prospective, single-center clinical study (NCT06272136) to develop and test a machine learning-based computational model for continuous glucose monitoring without per-subject calibration. The study design blinded the development investigators to the validation analyses.
Twenty subjects were enrolled. Fifteen were used for the development set, and five in the validation set. All study participants were adults with insulin-treated diabetes and median glycated hemoglobin (HbA) of 7.3% (interquartile range [IQR] = 6.7-7.7). The computational model resulted in a mean absolute relative difference (MARD) of 14.5% and 96.5% of the paired glucose data points in the A plus B zones of the Diabetes Technology Society (DTS) error grid. The correlation between the average model sensitivity by wavelength and the spectrum of glucose was 0.45 ( < .001).
Our findings suggest that Raman spectroscopy coupled with advanced computational methods can enable continuous, noninvasive glucose measurement without per-subject invasive calibration.
葡萄糖是能量代谢中的一种重要分子。葡萄糖代谢失调是糖尿病的主要特征，需要积极监测和治疗以预防严重的发病率和死亡率。当前用于间歇性和连续性血糖测量的技术具有侵入性。非侵入性血糖测量将消除这一障碍，使血糖监测更容易实现，将糖尿病患者的益处扩展到糖尿病前期患者和健康人群。
一种基于光谱学原理的新型非侵入性血糖测量系统被用于一项探索性、前瞻性、单中心临床研究（NCT06272136），以开发和测试一种基于机器学习的计算模型，用于无需个体校准的连续血糖监测。研究设计使开发研究人员对验证分析不知情。
招募了20名受试者。15名用于开发集，5名用于验证集。所有研究参与者均为接受胰岛素治疗的成年糖尿病患者，糖化血红蛋白（HbA）中位数为7.3%（四分位间距[IQR]=6.7 - 7.7）。该计算模型得出的平均绝对相对差异（MARD）为14.5%，糖尿病技术协会（DTS）误差网格A加B区中配对血糖数据点的比例为96.5%。按波长计算的平均模型灵敏度与葡萄糖光谱之间的相关性为0.45（P<0.001）。
我们的研究结果表明，拉曼光谱结合先进的计算方法能够实现连续的非侵入性血糖测量，而无需对个体进行侵入性校准。
