Evidence mapPaperPMID 41820567Full record

ReviewNature biotechnology2026

Biomolecular profiling for noninvasive health monitoring.

Moon-Ju Kim, José A Lasalde-Ramírez, Wenzheng Heng, Wei Gao

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature biotechnology, 2026. 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

4 authors.

Moon-Ju KimAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.ORCID http://orcid.org/0000-0002-8026-5409
José A Lasalde-RamírezAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.
Wenzheng HengAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.
Wei GaoAndrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA. weigao@caltech.edu.ORCID http://orcid.org/0000-0002-8503-4562

Funding

Cutaneous uric acid and metabolite monitoring to improve individual response to pharmaceutical and dietary treatment in patients with goutR33DK132666 · CALIFORNIA INSTITUTE OF TECHNOLOGY · 2025 to 2025
$809k
National Science Foundation (NSF) 2145802United States Department of Defense | United States Army | Army Medical Command | Congressionally Directed Medical Research Programs (CDMRP) HT9425-24-1-0249United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (ARO) W911NF-23-1-0041United States Department of Defense | United States Navy | Office of Naval Research (ONR) N00014-25-1-2258U.S. Department of Health & Human Services | National Institutes of Health (NIH) R33DK132666
6 · The paper itself

Abstract

Biomolecular profiling offers a powerful lens into human physiology, yet current diagnostics often rely on invasive sampling and delayed, centralized analysis. Advances in mass spectrometry (MS), particularly untargeted metabolomics and proteomics, have expanded molecular access to noninvasive biofluids such as sweat, saliva, tears and interstitial fluid, revealing dynamic biomarkers linked to both chronic and acute conditions. In parallel, wearable biosensors enable real-time, on-body chemical sensing, but remain limited to a narrow panel of predefined analytes. This Review highlights how MS-based molecular discovery and wearable sensing serve as complementary approaches-MS enabling high-dimensional untargeted profiling and wearables delivering longitudinal real-time data-and also discusses how their bidirectional integration and co-evolution open new possibilities for personalized noninvasive health monitoring. We discuss advances in sampling strategies, sensing modalities and system integration, and outline criteria for identifying biomarkers amenable to sensor translation. By uniting untargeted discovery with real-world deployment, this convergence shifts personalized noninvasive healthcare from episodic diagnostics to continuous, context-aware monitoring.

Indexed as

MetabolomicsProteomicsBiomarkersBiosensing TechniquesDigital HealthHumansMass SpectrometryMonitoring, PhysiologicWearable Electronic DevicesBiomarkers

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.