Abstract
Continuous lipid profiling could reveal dynamic lipid fluctuations underlying cardiometabolic health, yet existing enzymatic sensing strategies cannot sustain the multi-enzyme reactions required for continuous lipid detection, particularly when non-regenerable cofactors are consumed. Here we introduce a wearable epidermal lipid profiler for non-invasive, continuous monitoring of cholesterol and triglycerides using dual-step and cascading enzymatic reactions supported by the continuous refreshment of adenosine triphosphate (ATP). The system integrates a polymeric cofactor-releasing module composed of polypyrrole–ATP nanostructures that provide controlled, sweat-triggered ATP replenishment for long-term biosensing. We demonstrate real-time lipid tracking during dietary challenges and habitual activities, capturing distinct postprandial responses to different macronutrient compositions. In a preclinical study, simultaneous sweat and blood analyses coupled with causal machine learning revealed key physiological confounders and established individualized mappings between sweat and blood lipids. This cofactor-refreshing platform extends continuous monitoring to complex enzymatic systems, establishing a foundation for non-invasive, longitudinal cardiometabolic health assessment.
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Subjects
Data availability
The data that support the findings of this study are available from the corresponding authors on reasonable request.
Code availability
Custom code used for the causal ML analysis is publicly availablend is cited in the reference list
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Acknowledgements
We thank all study participants for their contributions to this work.
Funding
This work was supported by the National Science Foundation (grant nos. 2145802 and 2444815), the National Institutes of Health (grant no. R01HL155815), Samsung Research America and the Heritage Medical Research Institute (all to W.G.).
Authors and Affiliations
Contributions
W.G., J.A.L.-R. and J.R.S. conceived of the project. J.A.L.-R., J.R.S., C.W. and S.J. led the main study and collected the overall data. C.W. and D.M. contributed to data processing. R.M.M. contributed to sensor development. S.S., K.K., G.K., H.H., R.M.M. and Y.S. contributed to sensor characterization and testing. S.S., B.A. and J.Y. contributed to the human studies. T.K.H., Z.L., T.L., J.R.S. and W.G. supervised the studies. W.G., J.A.L.-R., J.R.S., C.W. and S.J. cowrote the paper. All authors contributed to the data analysis and provided feedback on the paper.
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The authors declare no competing interests.
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Nature Sensors thanks Dmitry Kireev, Yuxin Liu, Hnin Nyein and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Supplementary Notes 1–3, Figs. 1–41 and Tables 1–4.
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Cite this article
Lasalde-Ramírez, J.A., Won, C., Ji, S. et al. Non-invasive continuous lipid profiling machine learning.
Nat. Sens. (2026). https://doi.org/10.1038/s44460-026-00117-0
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Version of record:29 July 2026
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DOI
:https://doi.org/10.1038/s44460-026-00117-0
