Introduction: A Swedish data-driven cluster study identified four distinct type 2 diabetes (T2D) clusters, based on age at diagnosis, body mass index (BMI), hemoglobin A1c (HbA1c) level, and homeostatic model assessment 2 (HOMA2) estimates of insulin resistance and beta-cell function. A Danish study proposed three T2D phenotypes (insulinopenic, hyperinsulinemic, and classical) based on HOMA2 measures only. We examined these two new T2D classifications using the Danish Centre for Strategic Research in Type 2 Diabetes cohort.
Research Design And Methods: In 3529 individuals, we first performed a k-means cluster analysis with a forced k-value of four to replicate the Swedish clusters: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild age-related (MARD), and mild obesity-related (MOD) diabetes. Next, we did an analysis open to alternative k-values (ie, data determined the optimal number of clusters). Finally, we compared the data-driven clusters with the three Danish phenotypes.
Results: Compared with the Swedish findings, the replicated Danish SIDD cluster included patients with lower mean HbA1c (86 mmol/mol vs 101 mmol/mol), and the Danish MOD cluster patients were less obese (mean BMI 32 kg/m vs 36 kg/m). Our data-driven alternative k-value analysis suggested the optimal number of T2D clusters in our data to be three, rather than four. When comparing the four replicated Swedish clusters with the three proposed Danish phenotypes, 81%, 79%, and 69% of the SIDD, MOD, and MARD patients, respectively, fitted the classical T2D phenotype, whereas 70% of SIRD patients fitted the hyperinsulinemic phenotype. Among the three alternative data-driven clusters, 60% of patients in the most insulin-resistant cluster constituted 76% of patients with a hyperinsulinemic phenotype.
Conclusion: Different HOMA2-based approaches did not classify patients with T2D in a consistent manner. The T2D classes characterized by high insulin resistance/hyperinsulinemia appeared most distinct.
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http://dx.doi.org/10.1136/bmjdrc-2021-002731 | DOI Listing |
JMIR Diabetes
January 2025
Center for Evaluation and Survey Research, HealthPartners Institute, Bloomington, MN, United States.
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View Article and Find Full Text PDFMedicine (Baltimore)
January 2025
Department of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University, Guangzhou, China.
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Medicine (Baltimore)
January 2025
The Department of Clinical Laboratory, Zhejiang Hospital, Hangzhou, China.
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View Article and Find Full Text PDFSci Adv
January 2025
Department of Physiology, University of Toronto, Toronto, Ontario, Canada.
Gestational diabetes mellitus (GDM), a transient form of diabetes that resolves postpartum, is a major risk factor for type 2 diabetes (T2D) in women. While the progression from GDM to T2D is not fully understood, it involves both genetic and environmental components. By integrating clinical, metabolomic, and genome-wide association study (GWAS) data, we identified associations between decreased sphingolipid biosynthesis and future T2D, in part through the allele of the gene in Hispanic women shortly after a GDM pregnancy.
View Article and Find Full Text PDFComput Inform Nurs
January 2025
Author Affiliations: Duke University School of Nursing (Drs Lee, Silva, Yang, Hatch, and Shaw and Mss Pennington, Matters, and Urlichich); and Duke University School of Medicine (Dr Crowley), Durham, NC.
Digital health literacy is emerging as an important element in chronic illness management, yet its relationship with clinical outcomes remains unclear. Utilizing data from the ongoing EXpanding Technology-Enabled, Nurse-Delivered Chronic Disease Care trial, this cross-sectional, correlational study explored the association between digital health literacy, health literacy, and patient outcomes, specifically blood pressure and hemoglobin A1c levels in 76 patients managing comorbid type 2 diabetes and hypertension. Results indicate patients had moderate digital health literacy, which was not significantly correlated with health literacy (r = 0.
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