Evidence map›Paper›PMID 42402557›Full record

ArticleBMC primary care2026

Clinical phenotypes of type 2 diabetes and their association with microvascular complications in primary care: a cluster analysis.

Alcibíades Segundo Díaz Vera, José Abellán Alemán, Miladi Del Socorro Golac Rabanal, Cédric Marco Detchart, Isabel Arrechea Irigoyen, Manuela García Sánchez, Daime Pérez Feíto, Virginia Íñiguez García, Roberto Dair García de la Rosa, Rosa Eva Perez-Siguas and 11 more

Abstract read
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Article in BMC primary care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

The trial behind it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

21 authors.

Alcibíades Segundo Díaz VeraInstitute for Health Research of Navarra (IdiSNA), Pamplona, Spain. alcibiadessegundo.diaz@unavarra.es.ORCID http://orcid.org/0000-0002-3715-1713
José Abellán AlemánChair of Cardiovascular Risk, Catholic University of Murcia (UCAM), Murcia, Spain.ORCID http://orcid.org/0000-0002-1957-7032
Miladi Del Socorro Golac RabanalInstitute for Health Research of Navarra (IdiSNA), Pamplona, Spain.
Cédric Marco DetchartInstitute for Health Research of Navarra (IdiSNA), Pamplona, Spain.ORCID http://orcid.org/0000-0002-4310-9060
Isabel Arrechea IrigoyenInstitute for Health Research of Navarra (IdiSNA), Pamplona, Spain.
Manuela García SánchezInstitute for Health Research of Navarra (IdiSNA), Pamplona, Spain.
Daime Pérez FeítoInstitute for Health Research of Navarra (IdiSNA), Pamplona, Spain.
Virginia Íñiguez GarcíaInstitute for Health Research of Navarra (IdiSNA), Pamplona, Spain.
Roberto Dair García de la RosaInstitute for Health Research of Navarra (IdiSNA), Pamplona, Spain.
Rosa Eva Perez-SiguasEscuela de Posgrado Newman S.A.C. Unidad de Posgrado Ciencias Médicas de la Salud, Lima, Peru.ORCID http://orcid.org/0000-0003-1195-0426
Hernán Hugo Matta-SolísInstituto Peruano de Salud Familiar, Lima, Peru.ORCID http://orcid.org/0000-0001-7403-6717
Eduardo Percy Matta-SolísEscuela de Postgrado, Universidad María Auxiliadora, Lima, Peru.ORCID http://orcid.org/0000-0001-9422-7932
María Lourdes Azcona CortésDepartment of Health Sciences, Public University of Navarra (UPNA), Pamplona, Spain.
Julieth Paola Sulaiman RojasInstitute for Health Research of Navarra (IdiSNA), Pamplona, Spain.
Yohanna Elizabeth Barría JujiharaDepartment of Health Sciences, Public University of Navarra (UPNA), Pamplona, Spain.
Julienne Ntumba MumbaDepartment of Health Sciences, Public University of Navarra (UPNA), Pamplona, Spain.
Martín Andrés Astudillo AstudilloHospital Universitario San Pedro, Logroño, Spain.
Carlos José Zhuzhingo VásquezInstitute for Health Research of Navarra (IdiSNA), Pamplona, Spain.
Javier Nieto IglesiasHospital General Universitario de Ciudad Real, Servicio de Salud de Castilla-La Mancha (SESCAM), Ciudad Real, Spain.
Luis Alberto Díaz VeraHospital Nacional Edgardo Rebagliati Martins, Lima, Peru.
Gonzalo Luis Alonso SalinasInstitute for Health Research of Navarra (IdiSNA), Pamplona, Spain.ORCID http://orcid.org/0000-0001-5914-8526

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundType 2 diabetes mellitus is a heterogeneous condition associated with a substantial burden of microvascular complications. Conventional assessment based on isolated clinical variables may not fully capture this heterogeneity. Cluster analysis offers a potential strategy to identify clinically relevant diabetes phenotypes using routinely collected primary care variables.

objectiveTo identify clinical phenotypes of type 2 diabetes in a primary care population using cluster analysis and to examine their association with prevalent diabetic retinopathy and nephropathy.

methodsA cross-sectional study was conducted using routinely collected data from primary care centres in the Cantabrian Health Service. Centres were randomly selected using probability proportional to size sampling. People with type 2 diabetes mellitus were identified from clinical records, and those with missing key variables were excluded. K-means clustering was applied using routinely collected clinical variables, including body mass index, systolic and diastolic blood pressure, HbA1c, age, and years since diagnosis. Associations between clinical phenotypes and prevalent microvascular complications were assessed using logistic regression models adjusted for age, sex, and years since diagnosis. Model discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow test, Nagelkerke R², and Brier score.

resultsOf 742 initially identified individuals, 680 were included in the final analytical sample. Phenotype assignment was possible for 674 individuals, who were classified into three clinically interpretable diabetes phenotypes: controlled (n = 492), metabolic (n = 146), and hypertensive (n = 36). The prevalence of diabetic retinopathy was 20.5%, 58.2%, and 27.8%, respectively, while the prevalence of diabetic nephropathy was 25.9%, 51.1%, and 27.8%. After adjustment, the metabolic phenotype was associated with higher odds of prevalent retinopathy (OR 4.69, 95% CI 2.99-7.36; p < 0.001) and prevalent nephropathy (OR 2.52, 95% CI 1.67-3.79; p < 0.001) compared with the controlled phenotype. The hypertensive phenotype was associated with prevalent retinopathy (OR 2.43, 95% CI 1.03-5.69; p = 0.042), but not with prevalent nephropathy (OR 1.23, 95% CI 0.56-2.68; p = 0.611). The retinopathy model showed good discrimination (AUC 0.82, 95% CI 0.78-0.86), whereas the nephropathy model showed lower discrimination (AUC 0.67, 95% CI 0.63-0.72). Sensitivity analyses yielded consistent results.

conclusionsThree clinically interpretable diabetes phenotypes were identified in a real-world primary care population and were associated with different patterns of prevalent microvascular complications. The metabolic phenotype showed the highest prevalence and higher adjusted odds of both retinopathy and nephropathy, while the hypertensive phenotype showed a more selective association with retinopathy. These findings suggest that routinely collected primary care data can be used to describe clinically meaningful patterns of co-occurrence between type 2 diabetes phenotypes and microvascular complications. Given the cross-sectional design, these findings should not be interpreted as predictive or causal, and longitudinal studies and external validation are required before these phenotypes can be considered for clinical implementation.

Indexed as

Diabetes Mellitus, Type 2Diabetic NephropathiesDiabetic RetinopathyPrimary Health CareAgedCluster AnalysisClustering AlgorithmsCross-Sectional StudiesFemaleGlycated HemoglobinHumansMaleMiddle AgedPhenotypePrevalenceGlycated HemoglobinClinical phenotypesCluster analysisCross-sectional studyDiabetic nephropathyDiabetic retinopathyMicrovascular complicationsPrimary careType 2 diabetes mellitus

Identifiers

PMID42402557
PMCPMC13617723

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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.