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A wearable electrochemical biosensor for the monitoring of metabolites and nutrients

Nature Biomedical Engineering volume 6, pages 1225–1235 (2022)Cite this article

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Abstract

Wearable non-invasive biosensors for the continuous monitoring of metabolites in sweat can detect a few analytes at sufficiently high concentrations, typically during vigorous exercise so as to generate sufficient quantity of the biofluid. Here we report the design and performance of a wearable electrochemical biosensor for the continuous analysis, in sweat during physical exercise and at rest, of trace levels of multiple metabolites and nutrients, including all essential amino acids and vitamins. The biosensor consists of graphene electrodes that can be repeatedly regenerated in situ, functionalized with metabolite-specific antibody-like molecularly imprinted polymers and redox-active reporter nanoparticles, and integrated with modules for iontophoresis-based sweat induction, microfluidic sweat sampling, signal processing and calibration, and wireless communication. In volunteers, the biosensor enabled the real-time monitoring of the intake of amino acids and their levels during physical exercise, as well as the assessment of the risk of metabolic syndrome (by correlating amino acid levels in serum and sweat). The monitoring of metabolites for the early identification of abnormal health conditions could facilitate applications in precision nutrition.

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Fig. 1: Schematics and images of the wearable biosensor ‘NutriTrek’.
Fig. 2: Schematics and characterizations of the LEG–MIP sensors.
Fig. 3: Wearable system design for autonomous sweat induction, sampling, analysis and calibration.
Fig. 4: Wearable system evaluation across activities towards prolonged physiological and nutritional monitoring.
Fig. 5: Personalized monitoring of metabolic syndrome risk factors using LEG–MIP BCAA sensors.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. Source data for Figs. 4 and 5 and for Supplementary Figs. 36 and 39–41 are provided with this paper. All raw and analysed datasets generated during the study are available from the corresponding author on request. Source data are provided with this paper.

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Acknowledgements

This project was supported by the National Institutes of Health grant R01HL155815, Office of Naval Research grants N00014-21-1-2483 and N00014-21-1-2845, the Translational Research Institute for Space Health through NASA NNX16AO69A, NASA Cooperative Agreement 80NSSC20M0167, High Impact Pilot Research Award T31IP1666 and grant R01RG3746 from the Tobacco-Related Disease Research Program, Caltech-City of Hope Biomedical Initiative Pilot Grant and the Rothenberg Innovation Initiative Program at California Institute of Technology. J.T. was supported by the National Science Scholarship (NSS) from the Agency of Science Technology and Research (A*STAR) Singapore. We gratefully acknowledge critical support and infrastructure provided for this work by the Kavli Nanoscience Institute at Caltech. This project benefitted from the use of instrumentation made available by the Caltech Environmental Analysis Center, and we gratefully acknowledge support on GC–MS from N. Dalleska. We also gratefully acknowledge Z. Wang for the contribution to mobile app development, R. M. Torrente-Rodríguez for the insulin assay optimization and S. Bao for the valuable inputs.

Author information

Author notes

  1. These authors contributed equally: Minqiang Wang, Yiran Yang, Jihong Min.

Authors and Affiliations

  1. Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA

    Minqiang Wang, Yiran Yang, Jihong Min, Yu Song, Jiaobing Tu, Cui Ye, Changhao Xu & Wei Gao

  2. Department of Applied Physics and Materials Science, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA

    Daniel Mukasa

  3. Department of Electrical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA

    Nicole Heflin

  4. Department of Hematologic Malignancy Translational Sciences, Beckman Research Institute at City of Hope, Duarte, CA, USA

    Jeannine S. McCune

  5. Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA

    Tzung K. Hsiai

  6. Division of Clinical Nutrition, David Geffen School of Medicine, University of California, Los Angeles, CA, USA

    Zhaoping Li

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Contributions

W.G., M.W., Y.Y. and J.M. initiated the concept and designed the studies; W.G. supervised the work; M.W., Y.Y. and J.M. led the experiments and collected the overall data; Y.S., J.T., D.M., C.Y. and C.X. contributed to sensor characterization, validation and sample analysis; N.H. contributed to the signal processing and app development. J.S.M., T.K.H. and Z.L. contributed to the design of the human studies. W.G., M.W., Y.Y. and J.M. co-wrote the paper. All authors contributed to the data analysis and provided feedback on the manuscript.

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Correspondence to Wei Gao.

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Supplementary information

Supplementary Information (download PDF )

Supplementary notes, figures, tables and references.

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Supplementary Video 1 (download MP4 )

On-body demonstration with the designed sensing patch and app.

Supplementary Video 2 (download MP4 )

Simulation and validation of microfluidic sweat refreshing.

Supplementary Video 3 (download MP4 )

Iontophoresis-induced microfluidic sweat sampling at rest.

Source data

Source data (download XLSX )

Source data for Figs. 4 and 5 and Supplementary Figs. 36 and 39–41.

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Wang, M., Yang, Y., Min, J. et al. A wearable electrochemical biosensor for the monitoring of metabolites and nutrients. Nat. Biomed. Eng 6, 1225–1235 (2022). https://doi.org/10.1038/s41551-022-00916-z

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