TY - JOUR

T1 - Precision subclassification of type 2 diabetes

T2 - a systematic review

AU - Misra, Shivani

AU - Wagner, Robert

AU - Ozkan, Bige

AU - Schön, Martin

AU - Sevilla-Gonzalez, Magdalena

AU - Prystupa, Katsiaryna

AU - Wang, Caroline C

AU - Kreienkamp, Raymond J

AU - Cromer, Sara J

AU - Rooney, Mary R

AU - Duan, Daisy

AU - Thuesen, Anne Cathrine Baun

AU - Wallace, Amelia S

AU - Leong, Aaron

AU - Deutsch, Aaron J

AU - Andersen, Mette K

AU - Billings, Liana K

AU - Eckel, Robert H

AU - Sheu, Wayne Huey-Herng

AU - Hansen, Torben

AU - Stefan, Norbert

AU - Goodarzi, Mark O

AU - Ray, Debashree

AU - Selvin, Elizabeth

AU - Florez, Jose C

AU - Meigs, James B

AU - Udler, Miriam S

AU - ADA/EASD PMDI

N1 - © 2023. Springer Nature Limited.

PY - 2023

Y1 - 2023

N2 - BACKGROUND: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.METHODS: We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.RESULTS: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.CONCLUSION: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

AB - BACKGROUND: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.METHODS: We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.RESULTS: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.CONCLUSION: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

U2 - 10.1038/s43856-023-00360-3

DO - 10.1038/s43856-023-00360-3

M3 - Journal article

C2 - 37798471

VL - 3

JO - Communications Medicine

JF - Communications Medicine

SN - 2730-664X

M1 - 138

ER -