Precision subclassification of type 2 diabetes: a systematic review

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 resul...

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Published inCommunications medicine Vol. 3; no. 1; pp. 138 - 19
Main Authors Misra, Shivani, Wagner, Robert, Ozkan, Bige, Schön, Martin, Sevilla-Gonzalez, Magdalena, Prystupa, Katsiaryna, Wang, Caroline C, Kreienkamp, Raymond J, Cromer, Sara J, Rooney, Mary R, Duan, Daisy, Thuesen, Anne Cathrine Baun, Wallace, Amelia S, Leong, Aaron, Deutsch, Aaron J, Andersen, Mette K, Billings, Liana K, Eckel, Robert H, Sheu, Wayne Huey-Herng, Hansen, Torben, Stefan, Norbert, Goodarzi, Mark O, Ray, Debashree, Selvin, Elizabeth, Florez, Jose C, Meigs, James B, Udler, Miriam S
Format Journal Article
LanguageEnglish
Published England Springer Nature B.V 05.10.2023
Nature Publishing Group UK
Nature Portfolio
Subjects
Biomarkers
Body mass index
Diabetes
Glucose
Machine learning
Pathophysiology
Precision medicine
Systematic review
Urine
Online AccessGet full text

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Abstract 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. 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. 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. 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.
AbstractList 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. 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. 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. 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.
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.BACKGROUNDHeterogeneity 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.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.METHODSWe 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.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.RESULTSHere 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.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.CONCLUSIONCritical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.
In people with type 2 diabetes there may be differences in the way people present, including for example, their symptoms, body weight or how much insulin they make. We looked at recent publications describing research in this area to see whether it is possible to separate people with type 2 diabetes into different subgroups and, if so, whether these groupings were useful for patients. We found that it is possible to group people with type 2 diabetes into different subgroups and being in one subgroup can be more strongly linked to the likelihood of developing complications over others. This might mean that in the future we can treat people in different subgroups differently in ways that improves their treatment and their health but it requires further study. Misra, Wagner et al. systematically review if strategies to subclassify type 2 diabetes (T2D) are associated with high quality evidence and patient outcomes. Cluster-based stratification yields T2D subtypes that associate with outcomes, suggesting subclassification could have future clinical use.
BackgroundHeterogeneity 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.MethodsWe 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.ResultsHere 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.ConclusionCritical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.Plain language summaryIn people with type 2 diabetes there may be differences in the way people present, including for example, their symptoms, body weight or how much insulin they make. We looked at recent publications describing research in this area to see whether it is possible to separate people with type 2 diabetes into different subgroups and, if so, whether these groupings were useful for patients. We found that it is possible to group people with type 2 diabetes into different subgroups and being in one subgroup can be more strongly linked to the likelihood of developing complications over others. This might mean that in the future we can treat people in different subgroups differently in ways that improves their treatment and their health but it requires further study.
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.
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.
ArticleNumber 138
Author Ozkan, Bige
Schön, Martin
Thuesen, Anne Cathrine Baun
Eckel, Robert H
Misra, Shivani
Deutsch, Aaron J
Kreienkamp, Raymond J
Udler, Miriam S
Duan, Daisy
Goodarzi, Mark O
Billings, Liana K
Wallace, Amelia S
Andersen, Mette K
Sheu, Wayne Huey-Herng
Meigs, James B
Selvin, Elizabeth
Ray, Debashree
Prystupa, Katsiaryna
Stefan, Norbert
Sevilla-Gonzalez, Magdalena
Leong, Aaron
Hansen, Torben
Rooney, Mary R
Wang, Caroline C
Florez, Jose C
Wagner, Robert
Cromer, Sara J
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/37798471$$D View this record in MEDLINE/PubMed
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Chivers, Sian C
Naylor, Rochelle N
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Kettunen, Jarno L T
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Lowe, Jr, William L
Beltrand, Jacques
Leung, Gloria K W
Udler, Miriam S
Goodarzi, Mark O
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Snippet Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a...
Abstract Background Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical...
BackgroundHeterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We...
In people with type 2 diabetes there may be differences in the way people present, including for example, their symptoms, body weight or how much insulin they...
Abstract Background Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical...
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StartPage 138
SubjectTerms Biomarkers
Body mass index
Diabetes
Glucose
Machine learning
Pathophysiology
Precision medicine
Systematic review
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Title Precision subclassification of type 2 diabetes: a systematic review
URI https://www.ncbi.nlm.nih.gov/pubmed/37798471
https://www.proquest.com/docview/2873114476/abstract/
https://www.proquest.com/docview/2874259993/abstract/
https://pubmed.ncbi.nlm.nih.gov/PMC10556101
https://doaj.org/article/46232ccfcc854e40a1a6c4c420987338
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