Evidence map›Paper›PMID 41215667›Full record

ReviewJournal of diabetes science and technology2026

The Impact of Interfering Substances on Continuous Glucose Monitors: Part 1: Classification of Continuous Glucose Monitoring Devices and Mechanisms of Substance Interference.

Steven John Setford

Abstract readReview
In one paragraph

Review in Journal of diabetes science and technology, 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

1 author.

Steven John SetfordLifeScan Scotland Ltd., Inverness, UK.ORCID 0000-0003-1983-0461

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Presented is a series of narrative reviews that summarize published information regarding the effect or potential effect of interfering substances on the accuracy of continuous glucose monitoring (CGM) devices. While drawing together what is currently known regarding this topic, the future direction in this field and clinical implications posed by polypharmacy on CGM performance are considered. This first in a series of four review articles classifies commercially available CGMs by glucose measurement principle before reviewing what is currently known regarding substance interference mechanisms and design approaches that may serve to reduce interfering effects. Points covered include the following: minimally invasive electrochemical CGMs, which may be classified by first-, second-, or third-generational design (these models are at risk of interference from electroactive substances, or substances that can interfere with the enzymatic biorecognition process); non-invasive fluid sampling CGMs, which draw glucose across the skin barrier but are similarly reliant on the electrochemical measurement of an enzymatic reaction product; and minimally invasive implantable CGMs, which exhibit different interfering substance behaviors to other CGM classes, using a non-enzyme-based glucose-recognition agent coupled to optical detection. An understanding of substance-interfering mechanisms allows consideration of the potential impact on clinical accuracy of substances that are routinely prescribed, can be purchased over the counter, or are new to market.

Indexed as

Blood GlucoseBlood Glucose Self-MonitoringContinuous Glucose MonitoringEquipment DesignHumansBlood Glucoseaccuracyclinical implicationscontinuous glucose monitorinterferentsmechanismspolypharmacy

Identifiers

PMID41215667
PMCPMC12614895

What OpenQuestion 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 OpenQuestion graph.