Evidence map›Paper›PMID 38907936›Full record

ArticleDiabetes therapy : research, treatment and education of diabetes and related disorders2024

Perspective of Continuous Glucose Monitoring-Based Interventions at the Various Stages of Type 2 Diabetes.

R A Ajjan, S Seidu, J P Riveline

Abstract read
In one paragraph

Article in Diabetes therapy : research, treatment and education of diabetes and related disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
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

3 authors.

R A AjjanLeeds Institute for Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, LS2 9JT, UK. R.Ajjan@leeds.ac.uk.ORCID http://orcid.org/0000-0002-1636-3725
S SeiduDiabetes Research Centre, Applied Research Collaboration East Midlands, National Institute for Health Research, University of Leicester, Leicester, UK.
J P RivelineLariboisière Hospital, 2 Rue Ambroise Paré, 75010, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Continuous glucose monitoring (CGM) is now advocated for the clinical management of individuals with type 1 diabetes (T1D). However, this glucose monitoring strategy is not routinely used in type 2 diabetes (T2D), given the large population, significant cost implications and relatively limited supporting evidence. T2D is a more heterogenous condition compared with T1D with various glucose lowering therapies that do not necessarily require CGM to ensure within target glucose levels. While all individuals with T2D may benefit from CGM at certain time points, the whole T2D population does not necessarily require this technology continuously, which should be prioritized based on patient benefit and cost effectiveness. In this pragmatic opinion piece, we describe the rationale and evidence for CGM use in different subgroups of individuals with T2d, divided according to the stage of the condition, glycemic therapies, presence of diabetes complications, or associated co-morbidities. We discuss a total of 16 T2D subgroups and provide a clinical view on CGM use in each, based on current evidence while also highlighting areas of knowledge gaps. This work provides health care professionals with a simple guide to CGM use in different T2D groups and gives suggestion for future studies to justify expansion of this technology.

Indexed as

Continuous glucose monitoring (CGM)Glycemic variabilityHyperglycemiaHypoglycemiaTechnologyType 2 diabetes

Identifiers

PMID38907936
PMCPMC11263446

What OpenQuestion holds

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Registered trials

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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.