Literature DB >> 29697198

Pathophysiology-based phenotyping in type 2 diabetes: A clinical classification tool.

Jacob V Stidsen1, Jan E Henriksen1, Michael H Olsen2, Reimar W Thomsen3, Jens S Nielsen1, Jørgen Rungby4,5, Sinna P Ulrichsen3, Klara Berencsi3, Johnny A Kahlert3, Søren G Friborg1, Ivan Brandslund6, Aneta A Nielsen6, Jens S Christiansen7, Henrik T Sørensen3, Thomas B Olesen1, Henning Beck-Nielsen1.   

Abstract

BACKGROUND: Type 2 diabetes may be a more heterogeneous disease than previously thought. Better understanding of pathophysiological subphenotypes could lead to more individualized diabetes treatment. We examined the characteristics of different phenotypes among 5813 Danish patients with new clinically diagnosed type 2 diabetes.
METHODS: We first identified all patients with rare subtypes of diabetes, latent autoimmune diabetes of adults (LADA), secondary diabetes, or glucocorticoid-associated diabetes. We then used the homeostatic assessment model to subphenotype all remaining patients into insulinopenic (high insulin sensitivity and low beta cell function), classical (low insulin sensitivity and low beta cell function), or hyperinsulinemic (low insulin sensitivity and high beta cell function) type 2 diabetes.
RESULTS: Among 5813 patients diagnosed with incident type 2 diabetes in the community clinical setting, 0.4% had rare subtypes of diabetes, 2.8% had LADA, 0.7% had secondary diabetes, 2.4% had glucocorticoid-associated diabetes, and 93.7% had WHO-defined type 2 diabetes. In the latter group, 9.7% had insulinopenic, 63.1% had classical, and 27.2% had hyperinsulinemic type 2 diabetes. Classical patients were obese (median waist 105 cm), and 20.5% had cardiovascular disease (CVD) at diagnosis, while insulinopenic patients were fairly lean (waist 92 cm) and 17.5% had CVD (P = 0.14 vs classical diabetes). Hyperinsulinemic patients were severely obese (waist 112 cm), and 25.5% had CVD (P < 0.0001 vs classical diabetes).
CONCLUSIONS: Patients clinically diagnosed with type 2 diabetes are a heterogeneous group. In the future, targeted treatment based on pathophysiological characteristics rather than the current "one size fits all" approach may improve patient prognosis.
Copyright © 2018 John Wiley & Sons, Ltd.

Entities:  

Keywords:  clinical diabetes; individualized treatment; insulin secretion; insulin sensitivity and resistance; pathophysiology; treatment guidelines

Mesh:

Substances:

Year:  2018        PMID: 29697198     DOI: 10.1002/dmrr.3005

Source DB:  PubMed          Journal:  Diabetes Metab Res Rev        ISSN: 1520-7552            Impact factor:   4.876


  10 in total

1.  Making sense of subclinical cardiac alterations in patients with diabetes.

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2.  β-Cell Succinate Dehydrogenase Deficiency Triggers Metabolic Dysfunction and Insulinopenic Diabetes.

Authors:  Sooyeon Lee; Haixia Xu; Aidan Van Vleck; Alex M Mawla; Albert Mao Li; Jiangbin Ye; Mark O Huising; Justin P Annes
Journal:  Diabetes       Date:  2022-07-01       Impact factor: 9.337

3.  Evaluation of the value of conventional and unconventional lipid parameters for predicting the risk of diabetes in a non-diabetic population.

Authors:  Guotai Sheng; Maobin Kuang; Ruijuan Yang; Yanjia Zhong; Shuhua Zhang; Yang Zou
Journal:  J Transl Med       Date:  2022-06-11       Impact factor: 8.440

4.  A Pathophysiology of Type 2 Diabetes Unrelated to Metabolic Syndrome.

Authors:  Marietta Rottenkolber; Christina Gar; Cornelia Then; Lorena Wanger; Vanessa Sacco; Friederike Banning; Anne L Potzel; Stefanie Kern-Matschilles; Claudia Nevinny-Stickel-Hinzpeter; Harald Grallert; Nina Hesse; Jochen Seissler; Andreas Lechner
Journal:  J Clin Endocrinol Metab       Date:  2021-04-23       Impact factor: 5.958

5.  Clinical phenotyping of newly diagnosed type 2 diabetes in Yemen.

Authors:  Abdallah Ahmed Gunaid; Mohammed Mohammed Al-Kebsi; Mahfouth Abdalla Bamashmus; Saleh Ahmed Al-Akily; Ahmed Nasser Al-Radaei
Journal:  BMJ Open Diabetes Res Care       Date:  2018-12-07

6.  Fasting Glucose State Determines Metabolic Response to Supplementation with Insoluble Cereal Fibre: A Secondary Analysis of the Optimal Fibre Trial (OptiFiT).

Authors:  Stefan Kabisch; Nina M T Meyer; Caroline Honsek; Christiana Gerbracht; Ulrike Dambeck; Margrit Kemper; Martin A Osterhoff; Andreas L Birkenfeld; Ayman M Arafat; Mads F Hjorth; Martin O Weickert; Andreas F H Pfeiffer
Journal:  Nutrients       Date:  2019-10-06       Impact factor: 5.717

7.  Type 2 diabetes classification: a data-driven cluster study of the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) cohort.

Authors:  Diana Hedevang Christensen; Sia K Nicolaisen; Reimar W Thomsen; Allan Vaag; Emma Ahlqvist; Jacob V Stidsen; Jens Steen Nielsen; Kurt Hojlund; Michael H Olsen; Sonia García-Calzón; Charlotte Ling; Jørgen Rungby; Ivan Brandslund; Peter Vestergaard; Niels Jessen; Torben Hansen; Charlotte Brøns; Henning Beck-Nielsen; Henrik T Sørensen
Journal:  BMJ Open Diabetes Res Care       Date:  2022-04

8.  A Normalized Real-Life Glucose Profile After Diet-Induced Remission of Type 2 Diabetes: A Pilot Trial.

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Review 9.  Phenotypic and genetic classification of diabetes.

Authors:  Aaron J Deutsch; Emma Ahlqvist; Miriam S Udler
Journal:  Diabetologia       Date:  2022-08-12       Impact factor: 10.460

10.  Obesity Does Not Modulate the Glycometabolic Benefit of Insoluble Cereal Fibre in Subjects with Prediabetes-A Stratified Post Hoc Analysis of the Optimal Fibre Trial (OptiFiT).

Authors:  Stefan Kabisch; Nina Marie Tosca Meyer; Caroline Honsek; Christiana Gerbracht; Ulrike Dambeck; Margrit Kemper; Martin A Osterhoff; Andreas L Birkenfeld; Ayman M Arafat; Martin O Weickert; Andreas F H Pfeiffer
Journal:  Nutrients       Date:  2019-11-11       Impact factor: 5.717

  10 in total

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