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 baseline to two-year GV and subsequent vascular and mortality outcomes were analysed using landmark logistic and Cox proportional hazards regression. Results Baseline factors associated with higher on-study GV included younger age, male gender, longer diabetes duration and higher pharmacological therapies usage. Both HbA1c and fasting glucose CV were associated with increased risk of microvascular complications (HR 1.02 (95% CI 1.01–1.03) p<0.01; HR 1.01 (95% CI 1.00–1.01) p<0.001, respectively). HbA1c and fasting glucose CV were associated with increased cardiovascular disease (HR 1.02 (95% CI 1.00–1.04); HR 1.01 (95% CI 1.00–1.02), both p<0.05). HbA1c CV associated with increased stroke (HR 1.03 (95% CI 1.01–1.06) p<0.01). Glucose CV associated with increased coronary events (HR 1.01 (95% CI 1.00–1.02) p<0.05). Both HbA1c and glucose CV associated with increased total mortality (HR 1.04 (95% CI 1.02–1.06); HR 1.01 (95% CI 1.01–1.02), both p<0.001) and non-cardiovascular mortality (HR 1.05 (95% CI (1.03–1.07); HR 1.02 (95% CI 1.01–1.03), both p<0.001). HbA1c CV associated with coronary mortality (HR 1.04 (95% CI 1.01–1.07) p<0.05). Conclusions Long-term GV was associated with increased risk of vascular outcomes in Type 2 diabetes.

Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong

“… 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.…”

“… 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.…”

Comprehensive elaboration of glycemic variability in diabetic macrovascular and microvascular complications

“…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].…”

Glycemic variability and cardiovascular disease in patients with type 2 diabetes

“…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.…”