Evidence mapPaperPMID 39852113Full record

SynthesisBiosensors2025

Is Breath Best? A Systematic Review on the Accuracy and Utility of Nanotechnology Based Breath Analysis of Ketones in Type 1 Diabetes.

Kamal Marfatia, Jing Ni, Veronica Preda, Noushin Nasiri

Abstract readSystematic Review
In one paragraph

Synthesis in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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.

Kamal MarfatiaFaculty of Medicine, Health and Health Sciences, Macquarie University, Level 3, 75 Talevera Road, Macquarie Park, NSW 2113, Australia.
Jing NiFaculty of Medicine, Health and Health Sciences, Macquarie University, Level 3, 75 Talevera Road, Macquarie Park, NSW 2113, Australia.ORCID 0000-0002-7109-9680
Veronica PredaFaculty of Medicine, Health and Health Sciences, Macquarie University, Level 3, 75 Talevera Road, Macquarie Park, NSW 2113, Australia.ORCID 0000-0001-5963-5658
Noushin NasiriNanoTech Laboratory, School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia.ORCID 0000-0003-4738-098X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Timely ketone detection in patients with type 1 diabetes mellitus (T1DM) is critical for the effective management of diabetic ketoacidosis (DKA). This systematic review evaluates the current literature on breath-based analysis for ketone detection in T1DM, highlighting nanotechnology as a potential for a non-invasive alternative to blood-based ketone measurements. A comprehensive search across 5 databases identified 11 studies meeting inclusion criteria, showcasing various breath analysis techniques, such as semiconducting gas sensors, colorimetry, and nanoparticle-based chemo-resistive sensors. These studies report high sensitivity and correlation between breath acetone (BrAce) levels and blood ketones, with some demonstrating accuracies up to 94.7% and correlations reaching R

Indexed as

Diabetes Mellitus, Type 1KetonesNanotechnologyBiosensing TechniquesBreath TestsDiabetic KetoacidosisHumansKetonesacetonebreath analysisDKAketonesnanotechnologytype 1 diabetes

Identifiers

PMID39852113
PMCPMC11763468

What Socratic holds

Textmetadata
LicenceCC BY
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.