Evidence mapPaperPMID 39720301Full record

ArticleBiomedical reports2025

Glucose control and variability assessed by continuous glucose monitoring in patients with type 1 diabetes and diabetic kidney disease.

Aleksejs Fedulovs, Jana Janevica, Lelde Kruzmane, Jelizaveta Sokolovska

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Article in Biomedical reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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  5. [Nan fang yi ke da xue xue bao = Journal of Southern Medical University
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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Aleksejs FedulovsFaculty of Medicine and Life Sciences, University of Latvia, Riga LV-1004, Latvia.
Jana JanevicaFaculty of Medicine and Life Sciences, University of Latvia, Riga LV-1004, Latvia.
Lelde KruzmaneFaculty of Medicine and Life Sciences, University of Latvia, Riga LV-1004, Latvia.
Jelizaveta SokolovskaFaculty of Medicine and Life Sciences, University of Latvia, Riga LV-1004, Latvia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Continuous glucose monitoring (CGM) has emerged as a superior method to glycated hemoglobin (HbA1c) monitoring for glycemic control assessment in type 1 diabetes (T1D). The association between CGM parameters and diabetic kidney disease (DKD) has not been extensively researched. The aim of the present study was to compare CGM metrics between patients with stable and progressive DKD and T1D. A cross-sectional study was performed with 75 patients with T1D, of which 28 had progressive DKD, defined as an estimated glomerular filtration rate decrease of ≥3 ml/min/year or an increased albuminuria stage over the median follow-up time of 7.46 (6.50-8.16) years. FreeStyle Libre ProiQ Sensors were used for CGM. Insulin sensitivity was calculated according to the estimated glucose disposal rate (eGDR) formula. The results revealed that as compared with subjects with stable DKD, individuals with progressive DKD exhibited a higher average glucose level (P=0.03), spent more time above the target range (P=0.05), less time in time in range (TIR; P=0.03), had a higher median estimated HbA1c (P=0.02) and glucose management indicator (P=0.03), as well as a longer duration of hypoglycemic events (P=0.03). There were no differences in compliance levels and recognition of hypoglycemia between the DKD study groups. Differences in correlation patterns between CGM parameters in patients with stable and progressive DKD were observed. For example, glucose variability was significantly positively correlated with TIR in subjects with DKD (Ρ=0.390; P=0.04) but not in individuals without DKD. The progression of DKD was statistically significantly associated with several CGM parameters in multivariate logistic regression models. Collectively, associations between CGM metrics and DKD status were demonstrated in patients with T1D. The findings of the present study indicate the necessity for regular CGM in patients with progressive DKD for improvement of their glycemic control and DKD outcomes but also call for the development of a personalized approach to CGM data interpretation and establishing therapeutic targets in these subjects.

Indexed as

continuous glucose monitoringdiabetic kidney diseaseglucose variabilitytype 1 diabetes

Identifiers

PMID39720301
PMCPMC11668136

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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 Socratic graph.