Novel subgroups of type 2 diabetes and their association with microvascular outcomes in an Asian Indian population: a data-driven cluster analysis: the INSPIRED study

Anjana, Ranjit Mohan; Baskar, Viswanathan; Nair, Anand Thakarakkattil Narayanan; Jebarani, Saravanan; Siddiqui, Moneeza K; Pradeepa, Rajendra; Unnikrishnan, Ranjit; Palmer, Colin N.A.; Pearson, Ewan R.; Mohan, Viswanathan

doi:10.1136/bmjdrc-2020-001506

Cited by 127 publications

(145 citation statements)

References 17 publications

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“…Other studies have investigated the heterogeneity of type 2 diabetes in Indians: The INSPIRED study from a chain of private diabetes clinics in India reported four subclasses, two of which were similar to the Swedish study (SIDD and MARD) [5], and two were new (insulin resistant obese diabetes -IROD and combined insulin resistance and deficient diabetes -CIRDD) [14].…”

Section: Discussionmentioning

confidence: 99%

“…Lean type 2 diabetes is prevalent in India, especially in undernourished regions [13]. Recent studies have shown that subgroups of type 2 diabetes in Indians showed partial concordance with those in Europeans [14,15]. Type 2 diabetes is diagnosed at a younger age in India and its pathophysiology and heterogeneity warrants further investigation.…”

Section: Introductionmentioning

confidence: 99%

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Subgroups of young type 2 diabetes in India reveal insulin deficiency as a major driver

Prasad

Asplund

Shukla

et al. 2021

Preprint

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Aim/Hypothesis: Five subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian young-onset type 2 diabetes patients. Methods: We applied the European derived centroids to the Indian type 2 diabetes patients diagnosed before 45 years of age from the WellGen (n = 1612) cohort. We also applied denovo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts ANDIS (n = 962) and DIREVA (n = 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205). Results: Both Indian and European young type 2 diabetes patients were predominantly classified into severely insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severely insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to figures in Europeans (Swedish: 26% vs 68%, Finnish: 24% vs 71% respectively). A higher proportion of SIDD compared to MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterized by insulin deficiency and hyperglycemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared to MOD while the latter had higher prevalence of neuropathy. Conclusions/Interpretation: Our data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European patients in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in young Indian type 2 diabetes, and suggest a need to review management strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subgroups of young type 2 diabetes in India reveal insulin deficiency as a major driver

Prasad

Asplund

Shukla

et al. 2021

Preprint

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“… Note: Data from Anjana et al, 2020. 9 Abbreviations: BMI, body mass index; HOMA, homeostasis model assessment; HDL, high-density lipoprotein. …”

Section: Challenges In Application Of Personalised Medicine In Diabetmentioning

confidence: 99%

Challenges Involved in Incorporating Personalised Treatment Plan as Routine Care of Patients with Diabetes

Unnikrishnan¹,

Radha²,

Mohan³

2021

PGPM

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Diabetes is a heterogenous disorder, and patients with this disorder vary considerably in their clinical presentation, response to therapy and risk of complications. Expanding knowledge about the pathophysiology of various forms of diabetes has raised the possibility that diagnostic and therapeutic modalities can be tailored to the individual patient in a personalized manner. The recent publication of a Consensus Statement on precision diabetes care underlines the major strides made in this field in the recent past. However, while personalized diabetes care has the potential to significantly improve outcomes in patients with diabetes in a safe and cost-effective manner, its wider application presents several challenges, especially in resource-strained settings. These challenges pertain equally to precision diagnostics, precision therapeutics and precision monitoring. This article discusses some of the important challenges that care providers are likely to face in applying the personalized approach in caring for their patients with diabetes, in the context of diagnosis and management of type 1 diabetes, type 2 diabetes and monogenic forms of diabetes. Suggestions are also presented for overcoming some of these challenges.

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“…[2-9] In addition, the original and subsequent papers have shown that people in different clusters have different risks for a number of diabetes related outcomes. [1-4] The autoimmunity and insulin deficient clusters were defined by high HbA1c at diagnosis and had higher risk on ketoacidosis and retinopathy[2, 7] and progressed more rapidly onto insulin relative to the other clusters. [1] Moreover, a recent study comprising multiple cohorts enriched for cardiovascular risk assigned people to the clusters identified by Ahlqvist et al[1] based on the distance to the respective cluster centres.…”

Section: Introductionmentioning

confidence: 99%

Replication and cross-validation of T2D subtypes based on clinical variables: an IMI-RHAPSODY study

Slieker

Donnelly

Fitipaldi

et al. 2020

Preprint

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Aims/hypothesisFive clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes (T2D). In the current study we replicate and cross-validate these T2D clusters in three large cohorts using readily measured variables in the clinic.MethodsIn this cross-sectional study, 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide and HDL in three independent cohorts. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort’s cluster centres.ResultsFive distinct T2D clusters were identified and mapped back to the original four ANDIS clusters. Using C-peptide and HDL instead of HOMA-B and HOMA-S three of the clusters mapped with high sensitivity (80.6 – 90.7%) to the previously identified Severe Insulin Deficient (SIDD), Severe insulin resistant (SIRD) and Obese (MOD) clusters. The previously described ANDIS MARD cluster could be mapped to the two milder groups in our study – one characterised by high HDL, and the other having not any extreme characteristic (MDH cluster). When these two milder groups were combined they mapped well to the previously labelled MARD cluster (sensitivity 79.4%). In the cross-validation between cohorts, particularly the SIDD and MDH cluster cross-validated well with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity ranging from 36.1% to 92.3% where individuals shifted from SIRD to MD and vice versa.Conclusions/interpretationClusters based on C-peptide instead of HOMA measures result in clusters that resemble those based on HOMA measures, especially for SIDD, SIRD and MOD. By adding HDL, the MARD cluster based upon HOMA measures resulted in the current clustering in two clusters with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts.

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Novel subgroups of type 2 diabetes and their association with microvascular outcomes in an Asian Indian population: a data-driven cluster analysis: the INSPIRED study

Cited by 127 publications

References 17 publications

Subgroups of young type 2 diabetes in India reveal insulin deficiency as a major driver

Subgroups of young type 2 diabetes in India reveal insulin deficiency as a major driver

Challenges Involved in Incorporating Personalised Treatment Plan as Routine Care of Patients with Diabetes

Replication and cross-validation of T2D subtypes based on clinical variables: an IMI-RHAPSODY study

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