Evidence map›Paper›PMID 41334420›Full record

ReviewJugan geon-gang gwa jilbyeong2024

[The Evolution of Diabetes Treatment: Combining Innovative Pharmacological Therapies and Advanced Devices toward Remission].

Jong Han Choi, Min Kyong Moon

Abstract readEnglish AbstractReview
In one paragraph

Review in Jugan geon-gang gwa jilbyeong, 2024. 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

2 authors.

Jong Han Choi건국대학교 의과대학/의학전문대학원 내과학교실.ORCID https://orcid.org/0000-0002-2667-4332
Min Kyong Moon서울대학교 의과대학 내과학교실.ORCID https://orcid.org/0000-0002-2667-4332

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global prevalence of diabetes is rapidly increasing, with one in six adults in the Republic of Korea being affected. Traditional diabetes management focuses on glycemic control to prevent complications. However, recent advancements emphasize individualized treatment strategies. These involve using drugs with a low risk of hypoglycemia and the potential to prevent various metabolic diseases, aiming for diabetes remission, or selecting medications based on comorbidities, regardless of blood glucose levels. Sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists not only improve glycemic control but also offer cardiovascular and renal protective effects, making them direct therapeutic options for these conditions. Additionally, early combination therapy, involving medications with different mechanisms of action from those used in the early stages of diabetes, is being increasingly promoted to minimize treatment failure and reduce diabetes-related complications. Innovations in diabetes management devices, including continuous glucose monitoring systems, smart insulin pens, and automated insulin delivery systems, have improved glucose control accuracy and enhanced treatment adherence. For type 1 diabetes, novel therapies, such as once-weekly basal insulin and immunotherapies, have been introduced, with various approaches targeting autoimmune responses currently under investigation. This study explored the impact of these emerging therapeutic agents and management devices on diabetes care and presented future prospects for advancing diabetes remission, based on recent research trends.

Indexed as

Continuous glucose monitoringDiabetes mellitusGlucagon-like peptide-1 receptor agonistsInsulin infusion systemSodium-glucose transporter 2 inhibitors

Identifiers

PMID41334420
PMCPMC12480074

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.