ArticleThe Journal of clinical endocrinology and metabolism2023
Data Mining Framework for Discovering and Clustering Phenotypes of Atypical Diabetes.
Article in The Journal of clinical endocrinology and metabolism, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled 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.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of glutamic acid decarboxylase antibodies for the identification of adult-onset type 1 diabetes mellitus: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Identifying clinically useful COVID-19 population and emergency department phenotypes across the pre-Omicron and Omicron periods.Archives of public health = Archives belges de sante publique · 2025Article
- A Research Roadmap to Address the Heterogeneity of Diabetes and Advance Precision Medicine.The Journal of clinical endocrinology and metabolism · 2025Review
- Identification of atypical pediatric diabetes mellitus cases using electronic medical records.BMJ open diabetes research & care · 2024Article
- TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
- Atypical Diabetes: What Have We Learned and What Does the Future Hold?Diabetes care · 2024Review
- Inaccurate diagnosis of diabetes type in youth: prevalence, characteristics, and implications.Scientific reports · 2024Article
- Imprecise Diagnosis of Diabetes Type in Youth: Prevalence, Characteristics, and Implications.Research square · 2023Article
- Data Mining Framework for Discovering and Clustering Phenotypes of Atypical Diabetes.The Journal of clinical endocrinology and metabolism · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
contextSome individuals present with forms of diabetes that are "atypical" (AD), which do not conform to typical features of either type 1 diabetes (T1D) or type 2 diabetes (T2D). These forms of AD display a range of phenotypic characteristics that likely reflect different endotypes based on unique etiologies or pathogenic processes.
objectiveTo develop an analytical approach to identify and cluster phenotypes of AD.
methodsWe developed Discover Atypical Diabetes (DiscoverAD), a data mining framework, to identify and cluster phenotypes of AD. DiscoverAD was trained against characteristics of manually classified patients with AD among 278 adults with diabetes within the Cameron County Hispanic Cohort (CCHC) (Study A). We then tested DiscoverAD in a separate population of 758 multiethnic children with T1D within the Texas Children's Hospital Registry for New-Onset Type 1 Diabetes (TCHRNO-1) (Study B).
resultsWe identified an AD frequency of 11.5% in the CCHC (Study A) and 5.3% in the pediatric TCHRNO-1 (Study B). Cluster analysis identified 4 distinct groups of AD in Study A: cluster 1, positive for the 65 kDa glutamate decarboxylase autoantibody (GAD65Ab), adult-onset, long disease duration, preserved beta-cell function, no insulin treatment; cluster 2, GAD65Ab negative, diagnosed at age ≤21 years; cluster 3, GAD65Ab negative, adult-onset, poor beta-cell function, lacking central obesity; cluster 4, diabetic ketoacidosis (DKA)-prone participants lacking a typical T1D phenotype. Applying DiscoverAD to the pediatric patients with T1D in Study B revealed 2 distinct groups of AD: cluster 1, autoantibody negative, poor beta-cell function, lower body mass index (BMI); cluster 2, autoantibody positive, higher BMI, higher incidence of DKA.
conclusionDiscoverAD can be adapted to different datasets to identify and define phenotypes of participants with AD based on available clinical variables.
Indexed as
Identifiers
What Socratic 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 Socratic graph.