ReviewJournal of personalized medicine2026
Personalized Diabetes Therapy Part 1-Functional Phenotyping as a Conceptual Basis for Individualized Treatment.
Review in Journal of personalized medicine, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
The diagnosis of type 2 diabetes using classical clinical and laboratory biomarkers (HbA1c, glucose, lipids, BMI, and blood pressure) is a classification by symptoms and does not provide insight into the underlying pathophysiological disorders (insulin resistance, β-cell dysfunction, visceral adipose tissue hormonal secretion, and chronic systemic inflammation). A better understanding of these disorders may help in the selection of appropriate and potentially more successful personalized therapeutic interventions. Based on extensive clinical trial experience, a method for individual phenotyping and consecutive personalized diabetes therapy has been developed in our practice, which we have been using for more than 15 years and would like to share for discussion and debate. In this Part 1, the pathophysiological background and diagnostic approach to phenotyping is described. A consecutive Part 2 will present the translation of the phenotyping result into a personalized diabetes therapy, and another consecutive Part 3 will provide more comprehensive real-world patient observations when practicing this concept. This article is intended as a discussion/concept paper and does not present unpublished patient-level outcome data or formal effectiveness analyses. Prospective validation studies are needed to evaluate the clinical utility of this phenotype-based framework.
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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 Socratic graph.