2020
DOI: 10.1210/clinem/dgaa361
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Long-Term Glycemic Variability and Vascular Complications in Type 2 Diabetes: Post Hoc Analysis of the FIELD Study

Abstract: Aims To investigate whether long-term glycaemic variability (GV) is associated with vascular complication development in Type 2 diabetes Methods In a post-hoc FIELD trial analysis, GV was calculated as the standard deviation and coefficient of variation (CV) of HbA1c and fasting plasma glucose. Baseline variables were compared across quartiles of on-study variability by Chi square and ANOVA. Prospective associations between b… Show more

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Cited by 49 publications
(67 citation statements)
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“… 51–55 There are several methods that can be used to calculate variability, such as SD, CV and score based on the frequency exceeding a fixed percentage change in the absolute values. Prior studies have demonstrated the importance of such measures of variability in the prediction of adverse outcomes, 16 17 but a systematic and direct comparison of different methodologies has not been made with regard to their predictive performance. In our study, eight different measures of variability for HbA1c and FBG were compared, all of which showed significant predictive values.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“… 51–55 There are several methods that can be used to calculate variability, such as SD, CV and score based on the frequency exceeding a fixed percentage change in the absolute values. Prior studies have demonstrated the importance of such measures of variability in the prediction of adverse outcomes, 16 17 but a systematic and direct comparison of different methodologies has not been made with regard to their predictive performance. In our study, eight different measures of variability for HbA1c and FBG were compared, all of which showed significant predictive values.…”
Section: Discussionmentioning
confidence: 99%
“… 10 11 While Asian population-specific models are available, 12–15 these have generally not incorporated temporal measures of variability for longitudinal data or machine learning approaches, both of which can enhance risk prediction. 16 17 Indeed, with the rapid development of big data analytics, it has become easier to improve discrimination by analyzing complex interactions among variables. Previously, a machine learning-driven approach has demonstrated superior performance for predicting diabetes onset in a Chinese cohort.…”
Section: Introductionmentioning
confidence: 99%
“…There is growing evidence supporting that GV has drawn a great attention for its role in diabetic macrovascular and microvascular complications [15,[37][38][39][40][41]. Among type 2 diabetes mellitus (T2DM) patients from the Hoorn Diabetes Care System cohort, the individuals with a higher visit-to-visit GV had an unfavorable metabolic profile and had an increased risk of macrovascular and macrovascular complications as well as mortality [42].…”
Section: The Role Of Gv In Diabetic Macrovascular and Microvascular Cmentioning
confidence: 99%
“…13 High GV has been associated with increased risk of hypoglycemia, [13][14][15][16] reduced patient psychological well-being and quality of life, 17 and increased risk of cardiovascular disease [18][19][20] and mortality in patients with T2D. [21][22][23] Although the underlying mechanisms are not completely understood, increasing evidence indicates that daily fluctuations in blood glucose are associated with endothelial dysfunction, [23][24][25] inflammation and oxidative stress, 22 24 factors associated with the pathogenesis of vascular damage and atherosclerosis.…”
Section: Introductionmentioning
confidence: 99%