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Objective: To establish an accurate and robust calculation model for predicting hemoglobin A1c (HbA1c) for people with type 2 diabetes (T2D) by using the fewest discrete blood glucose values according to an irregular data set and propose an appropriate cost-effective and scientific scheme for routine blood glucose monitoring. Methods: By using two data sets obtained from 2017 to 2022, which involved 2432 people with T2D, ∼420,000 irregular blood glucose values, and 10,000 HbA1c values, multiple blood glucose monitoring schemes were designed and compared to find the optimal one. The data were structured and then fitted using a regularized extreme learning machine, and the results were evaluated on the basis of indicators such as mean absolute error (MAE), root mean square error, and the relevance analysis (R) value; the optimal scheme for routine blood glucose monitoring was determined by combining the accuracy and the cost and was compared with previous studies in terms of accuracy and stability. Results: Data fitting results for the chosen scheme: R = 0.8029 (P < 0.001), MAE = 0.3181% (95% confidence interval, 0.2666-0.3695%). Within the last 4 weeks before the prediction of HbA1c, a minimum of only seven fasting and seven postprandial blood glucose values are needed, of which are one fasting and one postprandial blood glucose values per 4 days. Compared with previous studies, the prediction model shows better accuracy and stability (P < 0.05), especially under the great glucose fluctuation group. Conclusion: A minimized calculation model for accurately and robustly predicting HbA1c using discrete self-monitoring of blood glucose data within 4 weeks for people with T2D has been established and provides a new reference for the design of a scheme for blood glucose monitoring. The diabetes care clinic of Peking University First Hospital (Registration Number: ChiCTR2300068139).

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INTRODUCTION: The aim of this study was to evaluate the stability and accuracy of glucose measurements determined using the metabolic heat conformation (MHC)-based non-invasive glucometer in a multicentre, self-controlled clinical trial. This device is the first to obtain a medical device registration certificate awarded by the National Medical Products Administration of China (NMPA). METHODS: The multicentre clinical study was conducted at three sites and enrolled 200 subjects whose glucose was measured with a non-invasive glucometer (the Contour Plus blood glucose monitoring system) and by venous plasma glucose (VPG) measurements, in a fasted state and at 2 and 4 h after meals. RESULTS: Based on both the non-invasive and VPG measurements, 93.9% (95% confidence interval 91.7-95.6%) of the blood glucose (BG) values fell within consensus error grid (CEG) zones A + B. The measurements obtained in a fasted state and at 2 h after meals were more accurate, with 99.0% and 97.0% of the BG values, respectively, falling within zones A + B. Compared to those subjects who received insulin, the proportion of values in zones A + B and the correlation coefficients were 3.1% and 0.0596 higher, respectively. The accuracy of the non-invasive glucometer was influenced by the level of insulin resistance calculated by the homeostatic model assessment method, which had a correlation coefficient with the mean absolute relative difference of - 0.1588 (P = 0.0001). CONCLUSION: The MHC-based non-invasive glucometer assessed in the present study demonstrates generally high stability and accuracy in the glucose monitoring of people with diabetes. The calculation model needs to be further explored and optimised for patients with different diabetes subtypes, levels of insulin resistance and insulin secretion capacity. CLINICAL TRIAL REGISTRY NUMBER: ChiCTR1900020523.

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Daily continuous glucose monitoring is very helpful in the control of glucose levels for people with diabetes and impaired glucose tolerance. In this study, a multisensor-based, noninvasive continuous glucometer was developed, which can continuously estimate glucose levels via monitoring of physiological parameter changes such as impedance spectroscopy at low and high frequency, optical properties, temperature and humidity. Thirty-three experiments were conducted for six healthy volunteers and three volunteers with diabetes. Results showed that the average correlation coefficient between the estimated glucose profiles and reference glucose profiles reached 0.8314, with a normalized root mean squared error (NRMSE) of 14.6064. The peak time of postprandial glucose was extracted from the glucose profile, and its estimated value had a correlation coefficient of 0.9449 with the reference value, wherein the root mean square error (RMSE) was 6.8958 min. Using Clarke error grid (CEG) analysis, 100% of the estimated glucose values fell in the clinically acceptable zones A and B, and 92.86% fell in zone A. The application of a multisensor-based, noninvasive continuous glucometer and time series analysis can endure the time delay between human physiological parameters and glucose level changes, so as to potentially accomplish noninvasive daily continuous glucose monitoring.

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