Evidence map›Paper›PMID 41723302›Full record

ArticleDiabetologia2026

Subtypes of newly diagnosed type 2 diabetes and risk of complications: analysis of electronic health records in the USA.

Zhongyu Li, Star Liu, Joyce C Ho, K M Venkat Narayan, Mohammed K Ali, Jithin Sam Varghese

Abstract read
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Article in Diabetologia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Zhongyu LiNutrition and Health Sciences Doctoral Program, Laney Graduate School, Emory University, Atlanta, GA, USA.
Star LiuBiomedical Informatics and Data Science Doctoral Program, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Joyce C HoDepartment of Computer Science, College of Arts and Sciences, Emory University, Atlanta, GA, USA.
K M Venkat NarayanEmory Global Diabetes Research Center of the Woodruff Health Sciences Center and Emory University, Atlanta, GA, USA.
Mohammed K AliEmory Global Diabetes Research Center of the Woodruff Health Sciences Center and Emory University, Atlanta, GA, USA.
Jithin Sam VargheseEmory Global Diabetes Research Center of the Woodruff Health Sciences Center and Emory University, Atlanta, GA, USA. jvargh7@emory.edu.

Funding

Johns Hopkins Training Program in Biomedical Informatics and Data ScienceT15LM013979 · NLM · JOHNS HOPKINS UNIVERSITY · PI CHRISTOPHER G CHUTE, Hadi Kharrazi · 2022 to 2026
$2.2M
NLM NIH HHS T15 LM013979
6 · The paper itself

Abstract

aims/hypothesisData-driven subtyping of type 2 diabetes has not been translated into clinical practice due to the lack of routine fasting glucose and insulin measurements. We aimed to identify type 2 diabetes subtypes in clinical settings using electronic health records and study their epidemiology.

methodsWe identified 727,076 adults (≥18 years) with newly diagnosed type 2 diabetes from Epic Cosmos research platform data across all 50 states and the District of Columbia between 2012 and 2023. Classification models developed in cohort studies were applied to study the sociodemographic distribution of subtypes. Cox proportional hazards regression models, adjusted for age and sex, were used to assess the rates of microvascular complications (retinopathy, neuropathy and nephropathy) and macrovascular complications (severe atherosclerotic cardiovascular disease [ASCVD], other ASCVD and heart failure).

resultsAmong newly diagnosed individuals (mean age 64.4 years [SD 13.3], 52% female), 21.6% were classified as having severe insulin-deficient diabetes (SIDD), 23.8% were classified as having mild obesity-related diabetes (MOD), 40.9% were classified as having mild age-related diabetes and 13.7% were classified as having the mixed subtype. Compared with those classified as having MOD, individuals classified as having SIDD had higher HRs for retinopathy (HR 2.83; 95% CI 2.73, 2.93), neuropathy (HR 1.57; 95% CI 1.54, 1.60), nephropathy (HR 1.34; 95% CI 1.32, 1.37), severe ASCVD (HR 1.49; 95% CI 1.46, 1.53), other ASCVD (HR 1.23; 95% CI 1.21, 1.25) and heart failure (HR 1.17; 95% CI 1.15, 1.20). SIDD and MOD were more prevalent among Hispanics (28.4% and 30.1%, respectively) and non-Hispanic Black people (25.5% and 30.0%, respectively) compared with non-Hispanic White people (20.1% and 21.6%, respectively), and were also more prevalent in the District of Columbia and Utah, respectively, compared with the rest of the country. CONCLUSIONS/

interpretationIndividuals with different type 2 diabetes subtypes, identified through electronic health records, differ in terms of their risk of vascular complications. These findings support leveraging routine electronic health record data to improve the characterisation of patient heterogeneity at the time of diabetes diagnosis.

Indexed as

Diabetes ComplicationsDiabetes Mellitus, Type 2Electronic Health RecordsAdultAgedDiabetic RetinopathyFemaleHumansMaleMiddle AgedRisk FactorsUnited StatesClinical translationClustersElectronic health recordsHealth disparitiesMachine learningMacrovascular complicationsMicrovascular complicationsReal-world evidenceSubphenotypes

Identifiers

PMID41723302
PMCPMC13221843

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.