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Peer-reviewedCC BY-NC-NDNature communications

Adaptive personalization for wearable non-invasive continuous glucose sensors.

  • Chuanjie Yao1
  • Lukang Gao1
  • Xinze Wang1
  • Haolin Wang1
  • Shuang Huang1,2
  • Guanbin Li3
  • Xiongqi Han4
  • Hanqi Yang1
  • Zhengjie Liu1
  • Shanshan Liang5
  • Peng Yun6
  • Yunuo Wang7
8 more authors
  • Yeyun Cai8
  • Peikai Zhang4
  • Yuanqing Wu9
  • Mingqiang Li10
  • Ji Wang7
  • Hui-Jiuan Chen1
  • Xinshuo Huang11
  • Xi Xie12,13,14
14 affiliations
  1. 1State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China.
  2. 2Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
  3. 3Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
  4. 4Shenzhen SiSensing Co., Ltd, Shenzhen, China.
  5. 5National Innovation Center for Advanced Medical Devices, Shenzhen, China.
  6. 6Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
  7. 7The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China.
  8. 8National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing, China.
  9. 9School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen, China.
  10. 10Laboratory of Biomaterials and Translational Medicine, Center for Nanomedicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  11. 11State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China. [email protected].
  12. 12State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China. [email protected].
  13. 13Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China. [email protected].
  14. 14The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou, China. [email protected].
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Abstract

Accurate continuous glucose monitoring (CGM) is essential for diabetes management, yet non-invasive CGM remains challenging. Universal "one-sensor-fits-all" approaches often fail, because correlations between non-invasive physiological readouts and blood glucose (BG) are highly individualized. Here, we propose a personalized "Adaptive Non-invasive CGM" methodology, which integrates a short-term minimally invasive CGM (MI-CGM) module for measuring interstitial fluid glucose, and a long-term non-invasive CGM (NI-CGM) module based on metabolic heat conformation. During a ~2-days training, the NI-CGM learns accurate sensing from the MI-CGM via GRU-based AI model, forming patient-specific sensor. The MI-CGM is then removed, leaving only the NI-CGM for long-term monitoring. In a 5-day CGM test, the system achieved MARD ~15.6%. This method also demonstrated good reproducibility and maintained reasonable accuracy (MARD ~23.9%) during long-term interval use (>2 months). This personalized approach offers a pathway from conventional "one-sensor-fits-all" strategies toward individualized sensor development, potentially mitigating inter-patient variability and non-invasive monitoring inaccuracies.

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Cite

BibTeXRIS
BibTeX
@article{yao2026adaptive,
  title = {{Adaptive personalization for wearable non-invasive continuous glucose sensors.}},
  author = {Chuanjie Yao and Lukang Gao and Xinze Wang and Haolin Wang and Shuang Huang and Guanbin Li and Xiongqi Han and Hanqi Yang and Zhengjie Liu and Shanshan Liang and Peng Yun and Yunuo Wang and Yeyun Cai and Peikai Zhang and Yuanqing Wu and Mingqiang Li and Ji Wang and Hui-Jiuan Chen and Xinshuo Huang and Xi Xie},
  journal = {Nature communications},
  year = {2026},
  month = sep,
  doi = {10.1038/s41467-026-74531-w},
  url = {https://europepmc.org/article/MED/42957678},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Adaptive personalization for wearable non-invasive continuous glucose sensors.
AU  - Chuanjie Yao
AU  - Lukang Gao
AU  - Xinze Wang
AU  - Haolin Wang
AU  - Shuang Huang
AU  - Guanbin Li
AU  - Xiongqi Han
AU  - Hanqi Yang
AU  - Zhengjie Liu
AU  - Shanshan Liang
AU  - Peng Yun
AU  - Yunuo Wang
AU  - Yeyun Cai
AU  - Peikai Zhang
AU  - Yuanqing Wu
AU  - Mingqiang Li
AU  - Ji Wang
AU  - Hui-Jiuan Chen
AU  - Xinshuo Huang
AU  - Xi Xie
PY  - 2026
DA  - 2026/09/28
JO  - Nature communications
DO  - 10.1038/s41467-026-74531-w
UR  - https://europepmc.org/article/MED/42957678
AB  - Accurate continuous glucose monitoring (CGM) is essential for diabetes management, yet non-invasive CGM remains challenging. Universal "one-sensor-fits-all" approaches often fail, because correlations between non-invasive physiological readouts and blood glucose (BG) are highly individualized. Here, we propose a personalized "Adaptive Non-invasive CGM" methodology, which integrates a short-term minimally invasive CGM (MI-CGM) module for measuring interstitial fluid glucose, and a long-term non-invasive CGM (NI-CGM) module based on metabolic heat conformation. During a ~2-days training, the NI-CGM learns accurate sensing from the MI-CGM via GRU-based AI model, forming patient-specific sensor. The MI-CGM is then removed, leaving only the NI-CGM for long-term monitoring. In a 5-day CGM test, the system achieved MARD ~15.6%. This method also demonstrated good reproducibility and maintained reasonable accuracy (MARD ~23.9%) during long-term interval use (>2 months). This personalized approach offers a pathway from conventional "one-sensor-fits-all" strategies toward individualized sensor development, potentially mitigating inter-patient variability and non-invasive monitoring inaccuracies.
SN  - 2041-1723
ER  -