Pieralice Silvia1, Zampetti Simona2, Maddaloni Ernesto3, Buzzetti Raffaella2. 1. Department of Medicine, Unit of Endocrinology and Diabetes, Campus Bio-Medico University of Rome, Via Alvaro del Portillo 21, 00128, Rome, Italy. 2. Department of Experimental Medicine, Sapienza University, Viale Regina Elena 324, 00161, Rome, Italy. 3. Department of Experimental Medicine, Sapienza University, Viale Regina Elena 324, 00161, Rome, Italy. ernesto.maddaloni@uniroma1.it.
Abstract
PURPOSE OF REVIEW: Genetic, socioeconomic and clinical features vary considerably among individuals with type 2 diabetes (T2D) influencing disease development, progression and response to therapy. Although a patient-centred approach to pharmacologic therapy of T2D is widely recommended, patients are often treated similarly, irrespective of the differences that may affect therapeutic response. Addressing the heterogeneity of T2D is a major task of diabetes research to lower the high rate of treatment failure as well as to reduce the risk of long-term complications. RECENT FINDINGS: A pathophysiology-based clustering system seems the most promising to help in the stratification of diabetes in terms of complication risk and response to treatment. This urges for clinical studies looking at novel biomarkers related to the different metabolic pathways of T2D and able to inform about the therapeutic cluster of each patient. Here, we review the main settings of diabetes heterogeneity, to what extent it has been already addressed and the current gaps in knowledge towards a personalized therapeutic approach that considers the distinctive features of each patient.
PURPOSE OF REVIEW: Genetic, socioeconomic and clinical features vary considerably among individuals with type 2 diabetes (T2D) influencing disease development, progression and response to therapy. Although a patient-centred approach to pharmacologic therapy of T2D is widely recommended, patients are often treated similarly, irrespective of the differences that may affect therapeutic response. Addressing the heterogeneity of T2D is a major task of diabetes research to lower the high rate of treatment failure as well as to reduce the risk of long-term complications. RECENT FINDINGS: A pathophysiology-based clustering system seems the most promising to help in the stratification of diabetes in terms of complication risk and response to treatment. This urges for clinical studies looking at novel biomarkers related to the different metabolic pathways of T2D and able to inform about the therapeutic cluster of each patient. Here, we review the main settings of diabetes heterogeneity, to what extent it has been already addressed and the current gaps in knowledge towards a personalized therapeutic approach that considers the distinctive features of each patient.
Entities:
Keywords:
Diabetes; Heterogeneity; Hypoglycemic drugs; Therapeutic algorithm; Type 2 diabetes
Authors: Struan F A Grant; Gudmar Thorleifsson; Inga Reynisdottir; Rafn Benediktsson; Andrei Manolescu; Jesus Sainz; Agnar Helgason; Hreinn Stefansson; Valur Emilsson; Anna Helgadottir; Unnur Styrkarsdottir; Kristinn P Magnusson; G Bragi Walters; Ebba Palsdottir; Thorbjorg Jonsdottir; Thorunn Gudmundsdottir; Arnaldur Gylfason; Jona Saemundsdottir; Robert L Wilensky; Muredach P Reilly; Daniel J Rader; Yu Bagger; Claus Christiansen; Vilmundur Gudnason; Gunnar Sigurdsson; Unnur Thorsteinsdottir; Jeffrey R Gulcher; Augustine Kong; Kari Stefansson Journal: Nat Genet Date: 2006-01-15 Impact factor: 38.330
Authors: Raffaella Buzzetti; Antonio Petrone; Assunta M Caiazzo; Irene Alemanno; Sara Zavarella; Marco Capizzi; Charles A Mein; John A Osborn; Andrea Vania; Umberto di Mario Journal: Pediatr Res Date: 2004-11-05 Impact factor: 3.756
Authors: Bo Wang; Michal Herman-Edelstein; Philip Koh; Wendy Burns; Karin Jandeleit-Dahm; Anna Watson; Moin Saleem; Gregory J Goodall; Stephen M Twigg; Mark E Cooper; Phillip Kantharidis Journal: Diabetes Date: 2010-04-14 Impact factor: 9.461
Authors: Matthijs L Becker; Loes E Visser; Ron H N van Schaik; Albert Hofman; André G Uitterlinden; Bruno H Ch Stricker Journal: Diabetes Date: 2009-02-19 Impact factor: 9.461