Evidence map›Paper›PMID 40807718›Full record

ArticleSensors (Basel, Switzerland)2025

Non-Invasive Blood Cortisol Estimation from Sweat Analysis by Kinetic Modeling of Cortisol Transport Dynamics.

Xiaoyu Yin, Sophie Adelaars, Elisabetta Peri, Eduard Pelssers, Jaap den Toonder, Arthur Bouwman, Daan van de Kerkhof, Massimo Mischi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xiaoyu YinDepartment of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.ORCID 0009-0005-2713-8450
Sophie AdelaarsDepartment of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Elisabetta PeriDepartment of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.ORCID 0000-0002-1231-9372
Eduard PelssersDepartment of Mechanical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Jaap den ToonderDepartment of Mechanical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.ORCID 0000-0002-5923-4456
Arthur BouwmanDepartment of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Daan van de KerkhofCatharina Hospital, 5623 EJ Eindhoven, The Netherlands.ORCID 0000-0002-0126-4695
Massimo MischiDepartment of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.ORCID 0000-0002-1179-5385

Funding

Dutch Research Council SEDAS18271
6 · The paper itself

Abstract

We present a novel method to estimate blood cortisol concentration from sweat cortisol measurements, incorporating a kinetic model to simulate cortisol transport dynamics. Cortisol dysregulation is observed in conditions like Cushing's syndrome, characterized by excessive cortisol production, and stress-related disorders, which can lead to metabolic disturbances, anxiety, and impaired overall health. Sweat-sensing technology offers a non-invasive and continuous alternative to blood sampling. However, the limited research exploring the sweat-blood cortisol relationship in patients shows a moderate correlation (R<0.6), hindering its clinical application for long-term monitoring. In this paper, we propose a novel kinetic model describing cortisol transport from blood to sweat. The model was validated using data from 44 patients before and after cardiac surgery. A high Pearson correlation coefficient of 0.95 (95% CI: 0.92-0.97) was observed between our model's estimated and experimental blood cortisol concentrations. Moreover, the method enables personalized estimation of physiological parameters, accurately reflecting patients' status under varying clinical conditions. The method paves the way for the clinical application of long-term, non-invasive monitoring of cortisol using sweat-sensing technology. Enabling the personalized estimation of physiological parameters could potentially support clinical decision-making, helping doctors diagnose and monitor patients with health conditions involving cortisol dysregulation.

Indexed as

HydrocortisoneSweatFemaleHumansKineticsMaleMiddle AgedHydrocortisonecortisolinverse modelingkinetic modelingpatient monitoring

Identifiers

PMID40807718
PMCPMC12349575

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.