Cardiac Arrhythmias During Hypoglycemia in T2DM with CV Risk
Twenty-five individuals with type 2 diabetes on insulin treatment for at least 4 years were recruited from Sheffield Teaching Hospitals diabetes outpatient clinics. All had a history of cardiovascular disease (CVD) (ischemic heart disease or peripheral vascular or cerebrovascular disease) and/or two additional cardiovascular risk factors: hypertension, dyslipidemia (both defined as requiring medication), current smoking, and obesity. Those on QT prolonging drugs were excluded. All patients with permanent atrial fibrillation or bundle branch block on baseline electrocardiogram (ECG) were excluded from the study. Written informed consent was obtained from all participants. The study received local ethics approval.
Cardiovascular autonomic reflex tests were performed as previously described in accordance with the latest consensus on diagnosis of CAN. Patients were instructed to avoid vigorous exercise, caffeine, and smoking 12 h prior to morning testing. All patients had a capillary glucose test of >4 mmol/L at the time of autonomic function testing. Patient status was classified as definite CAN if two or more cardioreflex tests were below the age-adjusted reference range. Glycated hemoglobin A1c (HbA1c) was measured using ion-exchange high-performance liquid chromatography.
All patients underwent 5 days of simultaneous 12-lead Holter and continuous interstitial glucose (IG) monitoring (CGM). Patients carried on with their usual daily activities and diabetes treatments. Twelve-lead ambulatory ECGs (Lifecard 12; Spacelabs Healthcare, Hertford, U.K.) were recorded at a sampling rate of 128 Hz with electrodes in a Mason-Likar configuration. Patients also had a time-synchronized CGM attached (FreeStyle Navigator Continuous Glucose Monitoring System; Abbott Diabetes Care, Maidenhead, U.K.). Calibrations were performed at least four times during the study week according to the manufacturer's instructions. Mindful of the limitations of CGM, we selected a system that has been reported to follow the descent in blood glucose to the hypoglycemic nadir, with the lowest detection limit of 1.1 mmol/L (20 mg/dL). In published data, the mean absolute difference between this CGM system and blood glucose was 0.7 mmol/L (12.7 mg/dL) when CGM glucose was <3.9 mmol/L (70 mg/dL) and the rate of change was between −1 and 1 mg/dL/min. The rate of change of 93% of our hypoglycemic data fell within this limit. Predictive alarms were switched off, and participants were instructed not to view CGM glucose values except during calibrations. Patients were also asked to keep a record of any symptomatic hypoglycemia. An episode of low IG (<3.5 mmol/L) on CGM without simultaneous self-report of symptoms was regarded as asymptomatic.
The IG was measured every minute by the CGM, and 10-min averages were reported (CoPilot Health Management; Abbott Diabetes Care). Hypoglycemia was defined as IG ≤3.5 mmol/L in accordance with previously published studies, and hyperglycemia was defined as IG ≥15 mmol/L. A valid hypoglycemic episode consists of IG below the threshold for ≥20 min. The first reading of IG ≤3.5 mmol/L marked the start of the hypoglycemia, and the first reading of IG ≥3.5 mmol/L signified the end of the episode. The lowest IG within the hypoglycemic episode was designated the glucose nadir. Each episode was matched with a euglycemic period, equivalent in duration, at the same time of day (within 20 min) on a different day. Similarly, valid hyperglycemic episodes were identified at IG above the 15 mmol/L threshold for >20 min. The highest IG within the episode was designated as the maxima, and matched euglycemic periods on a different day were identified.
The 12-lead ambulatory ECG data were analyzed with the Pathfinder Ambulatory ECG analysis system (version 8.701; Del Mar Reynolds Medical Ltd., Hertford, U.K.). Leads I, II, and V5 were used for analysis, as they represented orthogonal leads. Normal and aberrant beats were labeled by the Pathfinder system with preset sensitivity to optimize the trade-off between preserving useful information versus eliminating artifacts. The ECG was manually screened for gross arrhythmias. The software automatically detected arrhythmic events according to predetermined event definitions Supplementary Data. These included atrial ectopic beats, bradycardia (defined as ≥4 consecutive beats at <45 bpm), VPBs, and complex VPB (bigeminy, trigeminy, couplet, Salvos, and ventricular tachycardia). All identified arrhythmic events were manually verified for accuracy. Investigators were blinded to glucose values during arrhythmia analysis. Hourly counts for each type of arrhythmia were paired against hourly mean IG, which was categorized into hypoglycemia (IG ≤3.5 mmol/L), hyperglycemia (IG ≥15 mmol/L), and euglycemia (5 mmol/L < IG < 10 mmol/L). Analyses were separated into day and night (2300–0700 h) to take into account diurnal variation.
R-R intervals were extracted from annotated normal beats (NN intervals) using the Pathfinder Ambulatory ECG analysis system. A 5-min segment of successive NN intervals was selected around each reported IG value, and spectral analysis was performed on each segment using the Fourier transform. Spectral analysis was performed in accordance with recommendations of the Taskforce on Heart Rate Variability. The low-frequency (LF) band was defined as 0.04–0.15 Hz and high-frequency (HF) band as 0.15–0.4 Hz. The ratio between the LF power and total power (LF + HF power) was calculated (LFnorm), which was previously suggested to indicate the level of sympathetic modulation in heart rate variability (HRV).
Analysis of QT intervals was performed using custom-built, semiautomatic software based on a selective beat averaging approach. Annotated normal ECG beats were identified using the Pathfinder system. On each lead, a 40-Hz high pass filter was implemented to reduce noise. Cubic spline interpolation was then applied to remove LF baseline wander without affecting the higher-frequency ECG components. A composite wave was generated from the orthogonal leads I, II, and V5 to represent global repolarization. Analysis of the composite wave was performed on a 5-min window centered on each IG value. Within this window, beats with stable preceding heart rate were selected to respect restitution properties of ventricular repolarization. Namely, beats with a preceding R-R interval within ±15 ms and a second preceding R-R interval within ±50 ms of the prevailing mean R-R across the 5-min segment were averaged. The composite wave was then calculated from averaged beats derived from leads I, II, and V5. On the composite wave, the onset of the Q wave was marked as the first positive deflection from the isoelectric line >10 microvolts. The end of the T wave was determined using the tangent method, where the tangent to the steepest downslope of the T wave crosses the isoelectric line. All median beats were manually reviewed and fiducial points adjusted if necessary by two independent observers blinded to glucose values. Further, all T waves were manually classified as normal, notched, or fusion according to predetermined criteria. QT intervals were corrected for heart rate (QTc) using subject-specific regression formulae generated from QT/R-R values during euglycemia.
This was an observational study, and thus no power calculations were performed. The numbers chosen were based upon an assessment of the number of patients it was possible to examine given the constraints on recruitment and projected hypoglycemia rates. Data were inspected for normality. Data that followed an approximate normal distribution were summarized using mean ± SD, while skewed data were summarized using the median (IQR). We compared demographic data between patients who experienced at least one hypoglycemic episode and those who experienced none by the independent t test, Mann-Whitney U test, or Fisher exact test for effect of insulin regimen and insulin type on hypoglycemia. We used the generalized estimated equations approach to investigate the effect of glycemic status on arrhythmia counts while taking into account correlated measurements from individuals who experienced more than one episode of hypoglycemia or hyperglycemia. The Poisson model, which is usually used in analyzing count data, is not optimal in the current case, as there were many individuals who did not experience arrhythmic events. For this reason, data were fitted with a negative binomial model that takes into account the exposure time and individuals experiencing no arrhythmic events. A first-order autoregressive correlation structure was applied to adjust for within-individual correlation. Exponentiated regression coefficients represent incident rate ratios (IRRs). The IRRs of arrhythmias during hypoglycemia and hyperglycemia compared with euglycemia were calculated. HRV parameters and corrected QT intervals (QTc) were compared at the glucose nadir of the hypoglycemic or glucose maxima of the hyperglycemic episode against an equivalent euglycemic time point on a different day. Where there was more than one matching hypoglycemic-euglycemic episode in an individual participant over the course of the recording period, the mean from all daytime and nocturnal episodes from that individual was taken. Data were analyzed using a paired t test. Statistical analysis was performed with SPSS (version 19.0; IBM, Chicago, IL). A P value ≤0.05 was deemed statistically significant.
Research Design and Methods
Twenty-five individuals with type 2 diabetes on insulin treatment for at least 4 years were recruited from Sheffield Teaching Hospitals diabetes outpatient clinics. All had a history of cardiovascular disease (CVD) (ischemic heart disease or peripheral vascular or cerebrovascular disease) and/or two additional cardiovascular risk factors: hypertension, dyslipidemia (both defined as requiring medication), current smoking, and obesity. Those on QT prolonging drugs were excluded. All patients with permanent atrial fibrillation or bundle branch block on baseline electrocardiogram (ECG) were excluded from the study. Written informed consent was obtained from all participants. The study received local ethics approval.
Baseline Assessment
Cardiovascular autonomic reflex tests were performed as previously described in accordance with the latest consensus on diagnosis of CAN. Patients were instructed to avoid vigorous exercise, caffeine, and smoking 12 h prior to morning testing. All patients had a capillary glucose test of >4 mmol/L at the time of autonomic function testing. Patient status was classified as definite CAN if two or more cardioreflex tests were below the age-adjusted reference range. Glycated hemoglobin A1c (HbA1c) was measured using ion-exchange high-performance liquid chromatography.
Monitoring
All patients underwent 5 days of simultaneous 12-lead Holter and continuous interstitial glucose (IG) monitoring (CGM). Patients carried on with their usual daily activities and diabetes treatments. Twelve-lead ambulatory ECGs (Lifecard 12; Spacelabs Healthcare, Hertford, U.K.) were recorded at a sampling rate of 128 Hz with electrodes in a Mason-Likar configuration. Patients also had a time-synchronized CGM attached (FreeStyle Navigator Continuous Glucose Monitoring System; Abbott Diabetes Care, Maidenhead, U.K.). Calibrations were performed at least four times during the study week according to the manufacturer's instructions. Mindful of the limitations of CGM, we selected a system that has been reported to follow the descent in blood glucose to the hypoglycemic nadir, with the lowest detection limit of 1.1 mmol/L (20 mg/dL). In published data, the mean absolute difference between this CGM system and blood glucose was 0.7 mmol/L (12.7 mg/dL) when CGM glucose was <3.9 mmol/L (70 mg/dL) and the rate of change was between −1 and 1 mg/dL/min. The rate of change of 93% of our hypoglycemic data fell within this limit. Predictive alarms were switched off, and participants were instructed not to view CGM glucose values except during calibrations. Patients were also asked to keep a record of any symptomatic hypoglycemia. An episode of low IG (<3.5 mmol/L) on CGM without simultaneous self-report of symptoms was regarded as asymptomatic.
CGM Analysis
The IG was measured every minute by the CGM, and 10-min averages were reported (CoPilot Health Management; Abbott Diabetes Care). Hypoglycemia was defined as IG ≤3.5 mmol/L in accordance with previously published studies, and hyperglycemia was defined as IG ≥15 mmol/L. A valid hypoglycemic episode consists of IG below the threshold for ≥20 min. The first reading of IG ≤3.5 mmol/L marked the start of the hypoglycemia, and the first reading of IG ≥3.5 mmol/L signified the end of the episode. The lowest IG within the hypoglycemic episode was designated the glucose nadir. Each episode was matched with a euglycemic period, equivalent in duration, at the same time of day (within 20 min) on a different day. Similarly, valid hyperglycemic episodes were identified at IG above the 15 mmol/L threshold for >20 min. The highest IG within the episode was designated as the maxima, and matched euglycemic periods on a different day were identified.
Arrhythmia Analysis
The 12-lead ambulatory ECG data were analyzed with the Pathfinder Ambulatory ECG analysis system (version 8.701; Del Mar Reynolds Medical Ltd., Hertford, U.K.). Leads I, II, and V5 were used for analysis, as they represented orthogonal leads. Normal and aberrant beats were labeled by the Pathfinder system with preset sensitivity to optimize the trade-off between preserving useful information versus eliminating artifacts. The ECG was manually screened for gross arrhythmias. The software automatically detected arrhythmic events according to predetermined event definitions Supplementary Data. These included atrial ectopic beats, bradycardia (defined as ≥4 consecutive beats at <45 bpm), VPBs, and complex VPB (bigeminy, trigeminy, couplet, Salvos, and ventricular tachycardia). All identified arrhythmic events were manually verified for accuracy. Investigators were blinded to glucose values during arrhythmia analysis. Hourly counts for each type of arrhythmia were paired against hourly mean IG, which was categorized into hypoglycemia (IG ≤3.5 mmol/L), hyperglycemia (IG ≥15 mmol/L), and euglycemia (5 mmol/L < IG < 10 mmol/L). Analyses were separated into day and night (2300–0700 h) to take into account diurnal variation.
Heart Rate Variability Analysis
R-R intervals were extracted from annotated normal beats (NN intervals) using the Pathfinder Ambulatory ECG analysis system. A 5-min segment of successive NN intervals was selected around each reported IG value, and spectral analysis was performed on each segment using the Fourier transform. Spectral analysis was performed in accordance with recommendations of the Taskforce on Heart Rate Variability. The low-frequency (LF) band was defined as 0.04–0.15 Hz and high-frequency (HF) band as 0.15–0.4 Hz. The ratio between the LF power and total power (LF + HF power) was calculated (LFnorm), which was previously suggested to indicate the level of sympathetic modulation in heart rate variability (HRV).
Repolarization Analysis
Analysis of QT intervals was performed using custom-built, semiautomatic software based on a selective beat averaging approach. Annotated normal ECG beats were identified using the Pathfinder system. On each lead, a 40-Hz high pass filter was implemented to reduce noise. Cubic spline interpolation was then applied to remove LF baseline wander without affecting the higher-frequency ECG components. A composite wave was generated from the orthogonal leads I, II, and V5 to represent global repolarization. Analysis of the composite wave was performed on a 5-min window centered on each IG value. Within this window, beats with stable preceding heart rate were selected to respect restitution properties of ventricular repolarization. Namely, beats with a preceding R-R interval within ±15 ms and a second preceding R-R interval within ±50 ms of the prevailing mean R-R across the 5-min segment were averaged. The composite wave was then calculated from averaged beats derived from leads I, II, and V5. On the composite wave, the onset of the Q wave was marked as the first positive deflection from the isoelectric line >10 microvolts. The end of the T wave was determined using the tangent method, where the tangent to the steepest downslope of the T wave crosses the isoelectric line. All median beats were manually reviewed and fiducial points adjusted if necessary by two independent observers blinded to glucose values. Further, all T waves were manually classified as normal, notched, or fusion according to predetermined criteria. QT intervals were corrected for heart rate (QTc) using subject-specific regression formulae generated from QT/R-R values during euglycemia.
Statistical Analysis
This was an observational study, and thus no power calculations were performed. The numbers chosen were based upon an assessment of the number of patients it was possible to examine given the constraints on recruitment and projected hypoglycemia rates. Data were inspected for normality. Data that followed an approximate normal distribution were summarized using mean ± SD, while skewed data were summarized using the median (IQR). We compared demographic data between patients who experienced at least one hypoglycemic episode and those who experienced none by the independent t test, Mann-Whitney U test, or Fisher exact test for effect of insulin regimen and insulin type on hypoglycemia. We used the generalized estimated equations approach to investigate the effect of glycemic status on arrhythmia counts while taking into account correlated measurements from individuals who experienced more than one episode of hypoglycemia or hyperglycemia. The Poisson model, which is usually used in analyzing count data, is not optimal in the current case, as there were many individuals who did not experience arrhythmic events. For this reason, data were fitted with a negative binomial model that takes into account the exposure time and individuals experiencing no arrhythmic events. A first-order autoregressive correlation structure was applied to adjust for within-individual correlation. Exponentiated regression coefficients represent incident rate ratios (IRRs). The IRRs of arrhythmias during hypoglycemia and hyperglycemia compared with euglycemia were calculated. HRV parameters and corrected QT intervals (QTc) were compared at the glucose nadir of the hypoglycemic or glucose maxima of the hyperglycemic episode against an equivalent euglycemic time point on a different day. Where there was more than one matching hypoglycemic-euglycemic episode in an individual participant over the course of the recording period, the mean from all daytime and nocturnal episodes from that individual was taken. Data were analyzed using a paired t test. Statistical analysis was performed with SPSS (version 19.0; IBM, Chicago, IL). A P value ≤0.05 was deemed statistically significant.
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