Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes

Leslie, Richard David; Ma, Ronald Ching Wan; Franks, Paul W; Nadeau, Kristen J; Pearson, Ewan R; Redondo, Maria Jose

doi:10.1016/s2213-8587(23)00159-6

Cited by 21 publications

(9 citation statements)

References 101 publications

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“…The study also found more significant effects in a younger age group (12)(13)(14)(15)(16)(17)(18)(19)(20)(21) than older (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35), indicating that patients diagnosed older (T1DE2-5) are less likely to see beneficial effects from this therapy. 44 Future studies which include younger patients (T1DE1) could yield more favourable results, given the intense T and B cell mediated immune attack associated with T1DE1.…”

Section: Potential Therapies For T1de1mentioning

confidence: 81%

Type 1 diabetes: A new vision of the disease based on endotypes

Weston,

Boehm,

Pozzilli

2024

Diabetes Metabolism Res

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Diagnosis and management of type 1 diabetes (T1D) have remained largely unchanged for the last several years. The management of the disease remains primarily focused on its phenotypical presentation and less on endotypes, namely the specific biological mechanisms behind the development of the disease. Furthermore, the treatment of T1D is essentially universal and indiscriminate—with patients administering insulin at varying dosages and frequencies to maintain adequate glycaemic control. However, it is now well understood that T1D is a heterogeneous disease with many different biological mechanisms (i.e. endotypes) behind its complex pathophysiology. A range of factors, including age of onset, immune system regulation, rate of β‐cell destruction, autoantibodies, body weight, genetics and the exposome are recognised to play a role in the development of the condition. Patients can be classified into distinct diabetic subtypes based on these factors, which can be used to categorise patients into specific endotypes. The classification of patients into endotypes allows for a greater understanding of the natural progression of the disease, giving rise to more accurate and patient‐centred therapies and follow‐up monitoring, specifically for other autoimmune diseases. This review proposes 6 unique endotypes of T1D based on the current literature. The recognition of these endotypes could then be used to direct therapeutic modalities based on patients' individual pathophysiology.

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Section: Potential Therapies For T1de1mentioning

confidence: 81%

Type 1 diabetes: A new vision of the disease based on endotypes

Weston,

Boehm,

Pozzilli

2024

Diabetes Metabolism Res

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Section: Discussionmentioning

confidence: 99%

“…Personalized Treatment Strategies: T2DM is a complex, multifactorial disease composed of genetic, environmental, and lifestyle factors [ 254 ]. This heterogeneity is manifested in diverse clinical outcomes and responses to treatment, necessitating a shift toward personalized treatment strategies [ 254 ]. Recognizing the various pathophysiological pathways leading to hyperglycemia, including dysfunction of β-cells and altered incretin responses, underscores the opportunity to tailor treatments more effectively based on the underlying pathophysiology.…”

Section: Discussionmentioning

confidence: 99%

Advances in secondary prevention mechanisms of macrovascular complications in type 2 diabetes mellitus patients: a comprehensive review

Guan,

Tian,

Wang

et al. 2024

Eur J Med Res

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Type 2 diabetes mellitus (T2DM) poses a significant global health burden. This is particularly due to its macrovascular complications, such as coronary artery disease, peripheral vascular disease, and cerebrovascular disease, which have emerged as leading contributors to morbidity and mortality. This review comprehensively explores the pathophysiological mechanisms underlying these complications, protective strategies, and both existing and emerging secondary preventive measures. Furthermore, we delve into the applications of experimental models and methodologies in foundational research while also highlighting current research limitations and future directions. Specifically, we focus on the literature published post-2020 concerning the secondary prevention of macrovascular complications in patients with T2DM by conducting a targeted review of studies supported by robust evidence to offer a holistic perspective.

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“…The same systematic review speculated ‘whether subclassification approaches at diagnosis alone are enough’ and therefore, our longitudinal follow-up of individuals and subtype classification at the end of the study period (‘prevalent’) is an advance in methodology. However, prior to clinical implementation, external validation of the subtypes observed in this study in other datasets, 20 as well as genetic validation, 26 is necessary for cluster replication, and to better understand the overlaps and differences, compared with other proposed T2D subtypes. As Misra and colleagues note, clinical utility of T2D subtypes depends on the ability to use easily accessible data.…”

Section: Discussionmentioning

confidence: 99%

Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals

Mizani,

Dashtban,

Pasea

et al. 2024

BMJ Open Diab Res Care

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IntroductionNone of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data.Research design and methodsIn population-based electronic health records (2006–2020; Clinical Practice Research Datalink) in individuals ≥18 years with incident T2D (n=420 448), we included factors (n=3787), including demography, history, examination, biomarkers and medications. Using a published framework, we identified subtypes through nine unsupervised ML methods (K-means, K-means++, K-mode, K-prototype, mini-batch, agglomerative hierarchical clustering, Birch, Gaussian mixture models, and consensus clustering). We characterized clusters using intracluster distributions and explainable artificial intelligence (AI) techniques. We evaluated subtypes for (1) internal validity (within dataset; across methods); (2) prognostic validity (prediction for 5-year all-cause mortality, hospitalization and new chronic diseases); and (3) medication burden.ResultsDevelopment: We identified four T2D subtypes: metabolic, early onset, late onset and cardiometabolic.Internal validity: Subtypes were predicted with high accuracy (F1 score >0.98).Prognostic validity: 5-year all-cause mortality, hospitalization, new chronic disease incidence and medication burden differed across T2D subtypes. Compared with the metabolic subtype, 5-year risks of mortality and hospitalization in incident T2D were highest in late-onset subtype (HR 1.95, 1.85–2.05 and 1.66, 1.58–1.75) and lowest in early-onset subtype (1.18, 1.11–1.27 and 0.85, 0.80–0.90). Incidence of chronic diseases was highest in late-onset subtype and lowest in early-onset subtype.Medications: Compared with the metabolic subtype, after adjusting for age, sex, and pre-T2D medications, late-onset subtype (1.31, 1.28–1.35) and early-onset subtype (0.83, 0.81–0.85) were most and least likely, respectively, to be prescribed medications within 5 years following T2D onset.ConclusionsIn the largest study using ML to date in incident T2D, we identified four distinct subtypes, with potential future implications for etiology, therapeutics, and risk prediction.

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Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes

Cited by 21 publications

References 101 publications

Type 1 diabetes: A new vision of the disease based on endotypes

Type 1 diabetes: A new vision of the disease based on endotypes

Advances in secondary prevention mechanisms of macrovascular complications in type 2 diabetes mellitus patients: a comprehensive review

Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals

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