Glycaemic Variability in Acute Stroke

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People with diabetes who have a stroke have worse outcomes (Burton JK et al. 2019; Muir KW et al. 2011; Masrur S et al. 2015). Evidence for tight glycaemic control (e.g. maintaining blood glucose between 4.0 and ~7.5mmol/L) on the days immediately after stroke is lacking; studies have not shown impr...

People with diabetes who have a stroke have worse outcomes (Burton JK et al. 2019; Muir KW et al. 2011; Masrur S et al. 2015). Evidence for tight glycaemic control (e.g. maintaining blood glucose between 4.0 and ~7.5mmol/L) on the days immediately after stroke is lacking; studies have not shown improved outcomes and have noted higher rates of hypoglycaemia in intensively treated patients (Bellolio MF et al. 2014; American Heart Association. 2019). However the National Institute for Health and Care Excellence Guidance states that, 'People with acute stroke should be treated to maintain a blood glucose concentration between 4 and 11mmol/L and hyperglycaemia (determined by both admission blood glucose and HbA1c) is associated with adverse outcomes (National Institute for Health and Care Excellence. 2017). In practice, maintaining blood glucose levels below 12mmol/L in people with acute stroke can be challenging, in particular when parental feeding is required. Intermittent glucose measurement and measures of protein glycation provide limited information on the dynamic changes in glucose over time and do not take into account variability in glucose concentrations. Glycaemic variability (GV) is the consequence of multiple endogenous and exogenous factors and is a measurable variable. To measure GV a data series of glucose values is required. These may be derived from continuous glucose monitoring and may be from within one time period (such as a day) or over several periods, allowing comparisons between periods. Initial methodologies for GV calculation were defined for self-monitoring data and newer methodologies have been expressly designed for continuous monitoring data. There is no minimum length of time defined for satisfactory glycaemic variability calculation but, as with all statistical measures, the larger the dataset the more robust the metrics. Glucose concentration is not normally distributed about the mean. There is a long 'tail' to the glucose distribution extending into the hyperglycaemic range. Measures such as standard deviation do not take into account this asymmetric distribution and are thus relatively insensitive to hypoglycaemia. Hypoglycaemia is a significant barrier to improving glycaemic control and is a source of anxiety to people with diabetes. Not only that, it is unpleasant, is associated with morbidity and mortality and contributes to the global healthcare and financial burden of diabetes. In vitro data has suggested that GV is more deleterious than consistent hyperglycaemia. Human umbilical vein endothelial cells exposed to a glucose concentration alternating between 5 and 20mmol/L every 24 hours show significantly more apoptosis than cells exposed to a constant concentration of 5mmol/L or 20mmol/L over 14 days (Risso A, et al. 2001). Using the same constant and alternating glucose concentrations in human umbilical vein endothelial cells overproduction of reactive oxygen species is highest with oscillating glucose concentrations (Quagliaro L, et al. 2003). In the same sequence of studies expression of the cytokine IL-6 was highest with oscillating glucose concentrations (Piconi L, et al. 2004). In human proximal tubular cells exposed to increased glucose concentrations (25mmol/L), cell growth, collagen synthesis and cytokine production are elevated, and this is increased further by oscillating the glucose concentration between 25mmol/L and 6.1mmol/L (Jones SC, et al. 1999). In the critical care scenario, where glucose control is considered important, even in people without diabetes, variability is associated with mortality. In 7049 critically ill subjects the SD of blood glucose concentrations was a significant independent predictor of mortality in the intensive care unit and in hospital (Egi M, et al. 2006). These data have been confirmed by other authors in 3250 subjects with a five-fold mortality increase between the lowest and highest quartiles of standard deviation (Calles-Escandon J, et al. 2010) and in 5728 patients in a study which demonstrated that high variability accompanied by a high mean glucose conferred the highest mortality (Hermanides J, et al. 2010). These data have also been shown in a paediatric intensive care unit where a retrospective review of 1094 patients showed that those in the highest quintile of glycaemic variability had a longer length of stay and significantly elevated mortality (Wintergerst KA, et al. 2006). In people with stroke, GV has been investigated in people with and without diabetes using finger-prick glucose testing. Increased GV on day 1 after acute ischaemic stroke has been associated with poor functional outcome on hospital discharge but this effect was lost at 3 months follow-up (Camara-Lemarroy et al. 2016.). Early neurological deterioration in acute ischaemic stroke has also been associated with GV (Hui et al. 2018) In people without diabetes, more pronounced stress hyperglycaemic responses measured by continuous glucose monitoring over the initial 72 hours after acute stroke were associated with death or dependency at 3 months (Wada et al. 2018).

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