Precision subclassification of type 2 diabetes: a systematic review

ADA/EASD PMDI

doi:10.1038/s43856-023-00360-3

Precision subclassification of type 2 diabetes: a systematic review

ADA/EASD PMDI

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

Abstract

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.

Original language	English (US)
Article number	138
Journal	Communications Medicine
Volume	3
Issue number	1
DOIs	https://doi.org/10.1038/s43856-023-00360-3
State	Published - Dec 2023

Funding

No specific funding was received to undertake this body of work. The authors acknowledge individual and institutional funding as follows: S.M. has a personal award from Wellcome Trust Career Development scheme (223024/Z/21/Z) and holds Institutional funds from the NIHR Biomedical Research Centre Funding Scheme; B.O. is supported by American Heart Association grant (20SFRN35120152); M.S.G. is supported by the American Diabetes Association (9-22-PDFPM-04) and NIH (5UM1DK078616-14); R.J.K. is supported by NIGMS T32GM774844 and Pediatric Endocrine Society Rising Star Award; SJC is supported by a Junior Faculty Development Award from the American Diabetes Association (7-21-JDFM-005); D.D. is supported by NIH grant K23DK133690; A.C.B.T., M.A. and T.H. acknowledge that The Novo Nordisk Foundation Center for Basic Metabolic Research is supported by and unrestricted grant from the Novo Nordisk Foundation (NNF18CC0034900); A.W. is supported by NIH/NHLBI grant T32HL007024; A.L. is supported by grant 2020096 from the Doris Duke Foundation and the American Diabetes Association Grant 7-22-ICTSPM-23; A.J.D. is supported by NIH/NIDDK grant T32DK007028; W.H.H.S. obtained funding from MOST, Taiwan (MOST 107-2314-B-075A-001 -MY3 and by MOST 109-2321-B-075A-001). M.G. is supported by the Eris M. Field Chair in Diabetes Research and NIH grant P30-DK063491; D.R. is supported by NIH/NIDDK grant R21DK125888, and other grants from the NIH; E.S. is supported by NIH/NHLBI grant K24 HL152440 and other grants from the NIH; J.C.F. is supported by NIH K24 HL157960; J.B.M. reports funding from NIH U01 DK078616, R01 HL151855; M.U. is supported by an NIH K23DK114551. The ADA/EASD Precision Diabetes Medicine Initiative, within which this work was conducted, has received the following support: The Covidence licence was funded by Lund University (Sweden) for which technical support was provided by Maria Bj\u00F6rklund and Krister Aronsson (Faculty of Medicine Library, Lund University, Sweden). Administrative support was provided by Lund University (Malm\u00F6, Sweden), University of Chicago (IL, USA), and the American Diabetes Association (Washington D.C., USA). The Novo Nordisk Foundation (Hellerup, Denmark) provided grant support for in-person writing group meetings (PI: L Phillipson, University of Chicago, IL).

ASJC Scopus subject areas

Public Health, Environmental and Occupational Health
Internal Medicine
Epidemiology
Medicine (miscellaneous)
Assessment and Diagnosis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s43856-023-00360-3

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title = "Precision subclassification of type 2 diabetes: a systematic review",

abstract = "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 {\textquoteleft}simple subclassification{\textquoteright} approaches using simple categorisation of clinical characteristics, or {\textquoteleft}complex subclassification{\textquoteright} approaches which used machine learning or {\textquoteleft}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.",

author = "{ADA/EASD PMDI} and Shivani Misra and Robert Wagner and Bige Ozkan and Martin Sch{\"o}n and Magdalena Sevilla-Gonzalez and Katsiaryna Prystupa and Wang, {Caroline C.} and Kreienkamp, {Raymond J.} and Cromer, {Sara J.} and Rooney, {Mary R.} and Daisy Duan and Thuesen, {Anne Cathrine Baun} and Wallace, {Amelia S.} and Aaron Leong and Deutsch, {Aaron J.} and Andersen, {Mette K.} and Billings, {Liana K.} and Eckel, {Robert H.} and Sheu, {Wayne Huey Herng} and Torben Hansen and Norbert Stefan and Goodarzi, {Mark O.} and Debashree Ray and Elizabeth Selvin and Florez, {Jose C.} and Franks, {Paul W.} and Rich, {Stephen S.} and Tina Vilsb{\o}ll and Vesco, {Kimberly K.} and Udler, {Miriam S.} and Tiinamaija Tuomi and Arianne Sweeting and Sims, {Emily K.} and Sherr, {Jennifer L.} and Semple, {Robert K.} and Reynolds, {Rebecca M.} and Redondo, {Maria J.} and Redman, {Leanne M.} and Pratley, {Richard E.} and Rodica Pop-Busui and Pollin, {Toni I.} and Wei Perng and Pearson, {Ewan R.} and Ozanne, {Susan E.} and Owen, {Katharine R.} and Richard Oram and Josefson, {Jami L.} and Scholtens, {Denise M.} and Lowe, {William L.} and Katherine Young",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2023.",

year = "2023",

month = dec,

doi = "10.1038/s43856-023-00360-3",

language = "English (US)",

volume = "3",

journal = "Communications Medicine",

issn = "2730-664X",

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T1 - Precision subclassification of type 2 diabetes

T2 - a systematic review

AU - ADA/EASD PMDI

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 - Franks, Paul W.

AU - Rich, Stephen S.

AU - Vilsbøll, Tina

AU - Vesco, Kimberly K.

AU - Udler, Miriam S.

AU - Tuomi, Tiinamaija

AU - Sweeting, Arianne

AU - Sims, Emily K.

AU - Sherr, Jennifer L.

AU - Semple, Robert K.

AU - Reynolds, Rebecca M.

AU - Redondo, Maria J.

AU - Redman, Leanne M.

AU - Pratley, Richard E.

AU - Pop-Busui, Rodica

AU - Pollin, Toni I.

AU - Perng, Wei

AU - Pearson, Ewan R.

AU - Ozanne, Susan E.

AU - Owen, Katharine R.

AU - Oram, Richard

AU - Josefson, Jami L.

AU - Scholtens, Denise M.

AU - Lowe, William L.

AU - Young, Katherine

PY - 2023/12

Y1 - 2023/12

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.

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DO - 10.1038/s43856-023-00360-3

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Precision subclassification of type 2 diabetes: a systematic review

Abstract

Funding

ASJC Scopus subject areas

UN SDGs

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