Novel strategies for glycaemic control and preventing diabetic complications applying the clustering-based classification of adult-onset diabetes mellitus: A perspective. (October 2021)

• Record Type: Journal Article
• Authors: Tanabe, Hayato; Masuzaki, Hiroaki; Shimabukuro, Michio
• Is Part Of: Diabetes research and clinical practice. Volume 180(2021)
• Publication Date: 2021-10
• Subjects: Atherosclerotic cardiovascular disease -- Diabetic kidney disease -- Ectopic fat -- Insulin resistance -- Insulin secretion -- Neuropathy -- Obesity -- Retinopathy -- Visceral fat; A1c haemoglobin A1c -- ASCVD atherosclerotic cardiovascular disease -- BMI body mass index -- DKD diabetic kidney disease -- DPP-4 dipeptidyl peptidase 4 -- GADA glutamate decarboxylase antibody -- GLP1RA GLP-1 receptor agonists -- HLA human leukocyte antigen -- HOMA2-B homeostasis model assessment estimates 2 of beta-cell function -- HOMA2-IR homeostasis model assessment estimates 2 of insulin resistance -- LADA latent autoimmune diabetes in adults -- MARD mild age-related diabetes -- MHO metabolically healthy obesity -- MOD mild obesity-related diabetes -- MUO metabolically unhealthy obesity -- NAFLD non-alcoholic fatty liver disease -- NASH non-alcoholic steatohepatitis -- SAID severe autoimmune diabetes -- SGLT2 sodium-glucose cotransporter 2 -- SIDD severe insulin-deficient diabetes -- SIRD severe insulin-resistant diabetes -- SPIDDM slowly progressive type 1 insulin-dependent diabetes mellitus -- T1DM type 1 diabetes mellitus -- T2DM type 2 diabetes mellitus; Diabetes -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462
• DOI: 10.1016/j.diabres.2021.109067
• Languages: English
• ISSNs: 0168-8227

Abstract

Abstract: Diabetes mellitus results from an interplay between insulin resistance and β-cell dysfunction. Since their relative contributions to its pathogenesis are difficult to quantify, therapeutic strategies for glycaemic control are determined primarily based on two limited metrics: plasma glucose and haemoglobin A1c. Recent attempts have been made to subclassify diabetes mellitus to better predict its associated pathology and plan appropriate therapeutic strategies. These classifications are based on data-driven cluster analysis using autoimmunity, age, obesity (metabolically unhealthy and healthy phenotypes), insulin secretory capacity and resistance, and ethnicity. This review addresses potential therapeutic strategies for the cluster-based classifications of adult-onset diabetes mellitus to achieve better glycaemic control and prevent or at least delay the concomitant complications.