Toward an Improved Classification of Type 2 Diabetes: Lessons From Research into the Heterogeneity of a Complex Disease

Redondo, María J.; Balasubramanyam, Ashok

doi:10.1210/clinem/dgab545

Cited by 13 publications

(17 citation statements)

References 119 publications

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“…• The five diabetes subgroups were developed using data from European cohorts [6] but diabetes phenotypes and drug responses can differ between ethnic groups [59,65,66]. Replication studies in non-White people with diabetes identified additional subgroups but were often based on different sets of clustering variables and/or diabetes durations [27,29,30,36,[38][39][40][41].…”

Section: Methodological Aspects and Open Questionsmentioning

confidence: 99%

A novel diabetes typology: towards precision diabetology from pathogenesis to treatment

Herder

Roden

2022

Diabetologia

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The current classification of diabetes, based on hyperglycaemia, islet-directed antibodies and some insufficiently defined clinical features, does not reflect differences in aetiological mechanisms and in the clinical course of people with diabetes. This review discusses evidence from recent studies addressing the complexity of diabetes by proposing novel subgroups (subtypes) of diabetes. The most widely replicated and validated approach identified, in addition to severe autoimmune diabetes, four subgroups designated severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes and mild age-related diabetes subgroups. These subgroups display distinct patterns of clinical features, disease progression and onset of comorbidities and complications, with severe insulin-resistant diabetes showing the highest risk for cardiovascular, kidney and fatty liver diseases. While it has been suggested that people in these subgroups would benefit from stratified treatments, RCTs are required to assess the clinical utility of any reclassification effort. Several methodological and practical issues also need further study: the statistical approach used to define subgroups and derive recommendations for diabetes care; the stability of subgroups over time; the optimal dataset (e.g. phenotypic vs genotypic) for reclassification; the transethnic generalisability of findings; and the applicability in clinical routine care. Despite these open questions, the concept of a new classification of diabetes has already allowed researchers to gain more insight into the colourful picture of diabetes and has stimulated progress in this field so that precision diabetology may become reality in the future. Graphical abstract

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Section: Methodological Aspects and Open Questionsmentioning

confidence: 99%

A novel diabetes typology: towards precision diabetology from pathogenesis to treatment

Herder

Roden

2022

Diabetologia

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“…The atypical group also had a significantly higher T2D gePS than the ML T1D groups, and their T1D rsPS fell between those of the individuals with ML T1D and T2D ( Table 3 , Fig 3 , S1 Fig ). These genetic results suggest that the individuals with AD identified by these algorithms may have biologic differences as compared to patients with typical forms of diabetes, building upon the idea that forms of diabetes lie on a spectrum, and some individuals with AD may reside somewhere between “typical” T1D and “typical” T2D, or may have features characteristic of multiple forms of diabetes [ 23 ]. Regarding the non-significantly higher gePS for T2D among AD cases as compared to those with ML, this finding may be related simply to chance or may signify that individuals who develop diabetes even at lower BMI may have elevated risk for diabetes due to alternate pathways.…”

Section: Discussionmentioning

confidence: 99%

Algorithmic identification of atypical diabetes in electronic health record (EHR) systems

et al. 2022

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Aims Understanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review. Methods Patients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data. “Typical” T2D cases were removed by excluding individuals with obesity, evidence of dyslipidemia, antibody-positive diabetes, or cystic fibrosis. To filter out likely type 1 diabetes (T1D) cases, we applied six additional “branch algorithms,” relying on various clinical characteristics, which resulted in six overlapping cohorts. Diabetes type was classified by manual chart review as atypical, not atypical, or indeterminate due to missing information. Results Of 114,975 biobank participants, the algorithms collectively identified 119 (0.1%) potential AD cases, of which 16 (0.014%) were confirmed after expert review. The branch algorithm that excluded T1D based on outpatient insulin use had the highest percentage yield of AD (13 of 27; 48.2% yield). Together, the 16 AD cases had significantly lower BMI and higher HDL than either unselected T1D or T2D cases identified by ML algorithms (P<0.05). Compared to the ML T1D group, the AD group had a significantly higher T2D polygenic score (P<0.01) and lower hemoglobin A1c (P<0.01). Conclusion Our EHR-based algorithms followed by manual chart review identified collectively 16 individuals with AD, representing 0.22% of biobank enrollees with T2D. With a maximum yield of 48% cases after manual chart review, our algorithms have the potential to drastically improve efficiency of AD identification. Recognizing patients with AD may inform on the heterogeneity of T2D and facilitate enrollment in studies like the Rare and Atypical Diabetes Network (RADIANT).

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“…Adding to the confusion, monogenic forms of diabetes, including neonatal and maturity-onset diabetes of the young (MODY), are relatively rare causes of pediatric-onset diabetes [4] that may resemble T1D or T2D but require genetic testing for diagnosis. Atypical forms of diabetes or atypical presentations of common forms of diabetes are more frequent in non-European individuals [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Although there is heightened interest in the challenges surrounding diagnosis of diabetes type [5,[10][11][12][13][14][15][16][17][18], the prevalence of imprecise diagnosis of pediatric diabetes types and the factors associated with imprecise diagnosis are not fully understood. Therefore, we aimed to study imprecise diagnosis of diabetes type in racially and ethnically diverse youth.…”

Section: Introductionmentioning

confidence: 99%

Imprecise Diagnosis of Diabetes Type in Youth: Prevalence, Characteristics, and Implications

Tosur

Huang

Inglis

et al. 2023

Preprint

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Classifying diabetes at diagnosis is crucial for disease management but increasingly difficult due to overlaps in characteristics between the commonly encountered diabetes types. We evaluated the prevalence and characteristics of youth with diabetes type that was unknown at diagnosis or was revised over time. We studied 2073 youth with new-onset diabetes (median age [IQR]=11.4 [6.2] years; 50% male; 75% White, 21% Black, 4% other race; overall, 37% Hispanic) and compared youth with unknown versus known diabetes type, per pediatric endocrinologist diagnosis. In a longitudinal subcohort of patients with data for ≥3 years post-diabetes diagnosis (n=1019), we compared youth with unchanged versus changed diabetes classification. In the entire cohort, after adjustment for confounders, diabetes type was unknown in 62 youth (3%), associated with older age, negative IA-2 autoantibody, lower C-peptide, and no diabetic ketoacidosis (all, p<0.05). In the longitudinal subcohort, diabetes classification changed in 35 youth (3.4%); this was not statistically associated with any single characteristic. Having unknown or revised diabetes type was associated with less continuous glucose monitor use on follow-up (both, p<0.004). In sum, among racially/ethnically diverse youth with diabetes, 6.5% had imprecise diabetes classification at diagnosis. Further research is warranted to improve accurate diagnosis of pediatric diabetes type.

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Toward an Improved Classification of Type 2 Diabetes: Lessons From Research into the Heterogeneity of a Complex Disease

Cited by 13 publications

References 119 publications

A novel diabetes typology: towards precision diabetology from pathogenesis to treatment

A novel diabetes typology: towards precision diabetology from pathogenesis to treatment

Algorithmic identification of atypical diabetes in electronic health record (EHR) systems

Imprecise Diagnosis of Diabetes Type in Youth: Prevalence, Characteristics, and Implications

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