Literature DB >> 27049155

How well do glucose variability measures predict patient glycaemic outcomes during treatment intensification in type 2 diabetes?

Silvio E Inzucchi, Guillermo Umpierrez, Andres DiGenio, Rong Zhou, Boris Kovatchev.   

Abstract

AIM: Despite links to clinical outcomes in patients with type 2 diabetes mellitus (T2DM), the clinical utility of glycaemic variability (GV) measures is unknown. We evaluated the correlation between baseline GV, and glycated haemoglobin (HbA1c) attainment and hypoglycaemic events during treatment intensification in a large group of patients.
METHODS: Patient-level data from six 24-week clinical trials of T2DM patients undergoing treatment intensification with basal insulin or comparators (N = 1699) were pooled. Baseline GV measures included standard deviation (SD), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG), coefficient of variation (CV), high blood glucose index (HBGI), and low blood glucose index (LBGI) and were correlated with HbA1c change and hypoglycaemic events.
RESULTS: All mean GV measures, excluding CV which worsened, improved significantly from baseline to Week 24, with the largest proportional reduction obtained for HBGI (-65.5%). When assessed as mean individual percentage changes, only HBGI improved significantly. Baseline GV correlated positively with Week 24 HbA1c for SD, MAGE, and HBGI. Baseline HBGI and CV correlated negatively and positively, respectively, with Week 24 HbA1c change. Correlations also existed between most baseline GV measures and age, body mass index, Week 24 fasting plasma glucose, Week 24 postprandial plasma glucose, and hypoglycaemic events; statistical significance depended on the specific measure.
CONCLUSIONS: Pre-treatment GV is associated with glycaemic outcomes in T2DM patients undergoing treatment intensification over 24 weeks. HBGI might be the most robust predictor, warranting validation in dedicated prospective studies or randomized trials to assess the predictive value of measuring GV.

Entities:  

Mesh:

Substances:

Year:  2015        PMID: 27049155     DOI: 10.1016/j.diabres.2015.09.002

Source DB:  PubMed          Journal:  Diabetes Res Clin Pract        ISSN: 0168-8227            Impact factor:   5.602


  5 in total

Review 1.  Glycemic Variability: Risk Factors, Assessment, and Control.

Authors:  Boris Kovatchev
Journal:  J Diabetes Sci Technol       Date:  2019-01-29

Review 2.  Metrics for glycaemic control - from HbA1c to continuous glucose monitoring.

Authors:  Boris P Kovatchev
Journal:  Nat Rev Endocrinol       Date:  2017-03-17       Impact factor: 43.330

3.  Insulin glargine/lixisenatide fixed-ratio combination improves glycaemic variability and control without increasing hypoglycaemia.

Authors:  Ronnie Aronson; Guillermo Umpierrez; William Stager; Boris Kovatchev
Journal:  Diabetes Obes Metab       Date:  2018-12-10       Impact factor: 6.577

4.  Glucose variability during the early course of acute pancreatitis predicts two-year probability of new-onset diabetes: A prospective longitudinal cohort study.

Authors:  Sakina H Bharmal; Jaelim Cho; Juyeon Ko; Maxim S Petrov
Journal:  United European Gastroenterol J       Date:  2022-02-20       Impact factor: 4.623

5.  Retrospective study of glycemic variability, BMI, and blood pressure in diabetes patients in the Digital Twin Precision Treatment Program.

Authors:  Paramesh Shamanna; Mala Dharmalingam; Rakesh Sahay; Jahangir Mohammed; Maluk Mohamed; Terrence Poon; Nathan Kleinman; Mohamed Thajudeen
Journal:  Sci Rep       Date:  2021-07-21       Impact factor: 4.379

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.