Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT04521634 |
| Other study ID # |
20CX5808 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
September 30, 2020 |
| Est. completion date |
December 31, 2022 |
Study information
| Verified date |
October 2023 |
| Source |
Imperial College London |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Increased glycaemic variability is associated with worse outcome in patients with diabetes
after acute stroke.
Description:
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).
