ArticleThe Journal of clinical endocrinology and metabolism2026
Classification of glucose profile in Japanese patients with type 1 diabetes and its association with complications.
Article in The Journal of clinical endocrinology and metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Classification of glucose profile in Japanese patients with type 1 diabetes and its association with complications.The Journal of clinical endocrinology and metabolism · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
contextThe importance of utilizing continuous glucose monitoring (CGM) to optimize glycemic profiles and thereby prevent the onset of diabetic complications in patients with type 1 diabetes has been increasingly recognized. Nevertheless, studies evaluating the risk of diabetic complications in patients with type 1 diabetes through machine learning approaches based on CGM data remain limited.
objectiveTo classify the glycemic profiles of Japanese patients with type 1 diabetes using a data-driven cluster analysis based on CGM and clarify the association between these clusters and diabetic complications.
methodsIn this cross-sectional study, a cluster analysis using glycemic metrics from CGM of 153 Japanese patients with type 1 diabetes was performed. Logistic regression analysis adjusted for age, sex, and duration of diabetes was performed to compare the risk of diabetic complications by cluster.
resultsThe cluster analysis identified four clusters. Cluster 1 (n = 53) exhibited an optimal glycemic profile. Cluster 2 (n = 46) demonstrated an extended duration of hyperglycemia and a higher risk of elevated brachial-ankle pulse wave velocity than Cluster 1. Cluster 3 (n = 39) demonstrated an extended duration of hypoglycemia and a higher risk of severe hypoglycemia than Cluster 1. Cluster 4 (n = 15) demonstrated large glycemic variability associated with hyperglycemia and hypoglycemia. Cluster 4 had higher risks of polyneuropathy, elevated brachial-ankle pulse wave velocity, and higher cardiovascular disease risk scores than Cluster 1.
conclusionHigh-risk diabetic complications were identified for each cluster classified by glycemic profile.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.