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 ‘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.

Etiologies underlying subtypes of long-standing type 2 diabetes

“…Ahlqvist et al [ 11 ] employed a data-driven, unsupervised hard clustering method to identify mutually exclusive patient subgroups within large, newly diagnosed T2D cohorts. Their subtyping scheme, although replicated in multiple studies [ 9 – 13 , 18 – 24 ]; including our current investigation, has been challenged by soft, unsupervised clustering techniques that revealed overlapping and alternative subgroupings [ 14 – 17 ]. Both approaches rely on continuous (non-discrete) clinical characteristics.…”

“…Previously, most T2D subtyping studies recruited newly diagnosed patients [ 9 – 17 ]. In contrast, in the present study, we tested the feasibility of clinical subtyping in T2D patients with long standing disease.…”

“…Thus, there is a need to subtype T2D into distinct, well-defined groups [8] to better understand the underlying mechanisms, treatment responses, and prognoses associated with the disease. Several studies have attempted to identify T2D subtypes in patients of white European origin using various approaches, such as statistical clustering algorithms, clinical characteristics, genetics, and biomarkers [9][10][11][12][13][14][15][16][17]. T2D clustering was also replicated in other ethnic groups [18][19][20][21][22].…”

“…T2D clustering was also replicated in other ethnic groups [18][19][20][21][22]. In most of these studies, subtyping of T2D was based on newly diagnosed patients [9][10][11][12][13][14][15][16][17] with a few exceptions where the temporal stability of clusters was tested in patients with short-term disease [13,16,23]. Few studies have attempted T2D subtyping in long-standing disease [3,[21][22][23][24] to avoid the different rates of disease progression and the impact of complications [3,[25][26][27][28][29].…”

Exploring the Anti-Diabetic Potential of Quercetagitrin through Dual Inhibition of PTPN6 and PTPN9

“…Type 2 diabetes mellitus (T2DM), which is the most common type of diabetes, has become a major metabolic disease of clinical importance and has grown into a worldwide pandemic over the last few decades [ 1 ]. According to the International Diabetes Federation, the prevalence of T2DM is expected to increase to up to 783 million cases by 2045.…”