Characterising the impact of shift work on diet and glucose variability in healthcare employees living with type 2 diabetes: The Shift-Diabetes study.

Aim. To identify the clinical and metabolic factors associated with serum concentration of high sensitivity C-reactive protein (hsCRP) and α1-acid glycoprotein (α1-AGP) in patients with type 2 diabetes.Material and methods. The study involved 210 patients with type 2 diabetes. Levels of hsCRP and α1-AGP were measured using ELISA and compared with those of the control (30 healthy normal individuals). Levels of acute-phase proteins, fat mass and glucose variability (GV) were compared among demographic, anthropometric, biochemical and haematological parameters. The fat mass was determined with Dual-energy X-ray absorptiometry (DEXA). GV parameters including mean amplitude of glycaemic excursions, continuous overlapping net glycaemic action (CONGA), J-index, M-value and mean absolute glucose change (MAG) were derived from continuous glucose monitoring.Results. Levels of hsCRP and α1-AGP significantly increased (p 0.0001) in patients with diabetes compared with controls. hsCRP level positively correlated with total, truncal and android fat (r = 0.34, r = 0.28 and r = 0.31; respectively, p 0.00004). α1-AGP level showed no relationship with fat mass but positively correlated with mean glucose, CONGA, M-value and MAG (r = 0.38, r = 0.36, r = 0.43 and r = 0.4; respectively, p 0.0001). Patients with the highest hsCRP levels (75 percentile) had a greater body mass index (p = 0.00009) as well as truncal and android fat mass (p = 0.04 and p = 0.03, respectively) than those with the lowest levels (25 percentile). High level of α1-AGP (75 percentile) was associated with urinary albumin/creatinine ratio (p = 0.01) and GV indices (M-value: p = 0.02, MAG: p = 0.04).Conclusions. Levels of acute-phase proteins (hsCRP and α1-AGP) increased in patients with type 2 diabetes. Levels of hsCRP were associated with fat mass; meanwhile, α1-AGP levels were associated with short-time GV in these patients. The results lend support to the notion that both obesity and enhanced GV are involved in the development of chronic low-grade inflammation associated with type 2 diabetes.

305-OR: Glycemic Variability and Progression of CKD: PERL Substudy

To the Editor: While it is suggested that, in addition to hyperglycaemia, glucose variability can contribute to the severity and development of diabetic neuropathy [1], it is not related to the development of retinopathy and nephropathy in type 1 diabetes To determine any additional effect of glucose variability-above that assessed by HbA 1c and mean glucose-on peripheral and autonomic diabetic neuropathy, we used the datasets collected during the DCCT (available at www.gcrc.med.umn.edu/gcrc/downloads/dcct.html, accessed 23-27 January 2009)

932-P: Impacts of Glucose Variability on the Correlation between Estimated Hemoglobin A1c and Measured Glycated Hemoglobin A1c in Patients with Type 2 Diabetes

Mounting evidence suggests a role for glucose variability in predicting intensive care unit (ICU) mortality. We investigated the association between glucose variability and intensive care unit and in-hospital deaths across several ranges of mean glucose. Retrospective cohort study. An 18-bed medical/surgical ICU in a teaching hospital. All patients admitted to the ICU from January 2004 through December 2007. None. Two measures of variability, mean absolute glucose change per hour and sd, were calculated as measures of glucose variability from 5728 patients and were related to ICU and in-hospital death using logistic regression analysis. Mortality rates and adjusted odds ratios for ICU death per mean absolute glucose change per hour quartile across quartiles of mean glucose were calculated. Patients were treated with a computerized insulin algorithm (target glucose 72-126 mg/dL). Mean age was 65 +/- 13 yrs, 34% were female, and 6.3% of patients died in the ICU. The odds ratios for ICU death were higher for quartiles of mean absolute glucose change per hour compared with quartiles of mean glucose or sd. The highest odds ratio for ICU death was found in patients with the highest mean absolute glucose change per hour in the upper glucose quartile: odds ratio 12.4 (95% confidence interval, 3.2-47.9; p < .001). Mortality rates were lowest in the lowest mean absolute glucose change per hour quartiles. High glucose variability is firmly associated with ICU and in-hospital death. High glucose variability combined with high mean glucose values is associated with highest ICU mortality. In patients treated with strict glycemic control, low glucose variability seemed protective, even when mean glucose levels remained elevated.

BACKGROUND: Glucose variability (GV) is recognized as a risk factor for microvascular and macrovascular complications of diabetes and hypoglycemia. A number of indices have been proposed to assess GV, but there are no generally accepted normal reference values for these indices.AIM: To establish the reference values of 24-hour, day-time and nocturnal GV parameters derived from continuous glucose monitoring (CGM) data in young and middle-aged subjects with normal glucose tolerance.MATERIALS AND METHODS: A blind 6–7-day CGM was performed in 50 subjects, 20 men and 30 women, aged from 22 to 56 years, with normal values of the oral glucose tolerance test and glycated hemoglobin A1c. GV parameters: Standard Deviation (SD), Coefficient of Variation (CV), Mean Amplitude of Glycemic Excursions (MAGE), 2-hour Сontinuous Overlapping Net Glycemic Action (CONGA), Lability Index (LI), J-index, Mean Absolute Glucose rate of change (MAG), М-value, High Blood Glucose Index (HBGI), Low Blood Glucose Index (LBGI) were calculated for 24-hour records, day-time (6.00–23.59) and night (0.00–5.59) hours.RESULTS: 95% confidence intervals for 24-hour records were: mean glucose 5.2–6.6 mmol/L, SD 0.5–1.3 mmol/L, CV 9.1–23.2%, MAGE 1.2–3.2 mmol/L, CONGA 4.3–5.9 mmol/L, MAG 0.5–2.1 mmol×L-1×h-1, LI 0.1–1.3 (mmol/L)2×h-1, J-index 11.3–18.6 (mmol/L)2, M-value 0.4–4.4, HBGI 0.1–1.9, LBGI 0.3–3.2.The following day-time values were estimated: mean glucose 5.3–6.7 mmol/L, SD 0.5–1.4 mmol/L, CV 8.7–24.5%, MAGE 1.2–3.4 mmol/L, CONGA 4.3–5.9 mmol/L, MAG 0.6–2.5 mmol×L-1×h-1, LI 0.2–1.6 (mmol/L)2×h-1, J-index 11.2–19.6 (mmol/L)2, M-value 0.2–3.8, HBGI 0.1–1.9, LBGI 0.3–3.0. The values for nocturnal hours were: mean glucose 4.7–6.4 mmol/L, SD 0.3–0.9 mmol/L, CV 5.3–17.9%, MAGE 0.7–2.7 mmol/L, CONGA 4.1–5.8 mmol/L, MAG 0.3–1.8 mmol×L-1×h-1, LI 0.05–0.8 (mmol/L)2×h-1, J-index 8.5–17.5 (mmol/L)2, M-value 0.2–5.2, HBGI 0–0.9, LBGI 0.3–3.6.CONCLUSION: The obtained reference values of the GV indices should be taken into account in research and in clinical practice when interpreting the results of CGM in young and middle-aged people.

Blood glucose amplitude variability as predictor for mortality in surgical and medical intensive care unit patients: a multicenter cohort study

Meynaar, Iwan A.*; Eslami, Saeid†; Abu-Hanna, Ameen†; van der Voort, Peter‡; de Lange, Dylan W.§; de Keizer, Nicolette† Author Information

Mean blood glucose (BG) during acute myocardial infarction (AMI) is an important predictor of inpatient mortality but does not capture glucose variability (GV), which has been shown to be independently associated with mortality in critically ill patients. Whether GV is associated with in-hospital mortality during AMI, after accounting for mean BG, is unknown. We analyzed 18 563 consecutive patients with AMI with ≥3 BGs in the first 48 hours admitted to 61 US hospitals from 2000 to 2008. Five different GV metrics were compared for their ability to predict in-hospital mortality (range, standard deviation, mean amplitude of glycemic excursions, mean absolute glucose change, and average daily risk range) using hierarchical logistic regression models that sequentially adjusted for mean BG, hypoglycemia (<70 mg/dL), and multiple patient characteristics. In unadjusted analyses, range and average daily risk range had the highest C-indices (0.620 for range, 0.635 for average daily risk range; both P<0.0001). Greater GV was associated with higher mortality for all metrics (eg, mortality was 3.8%, 5.5%, 7.1%, and 11.3% for increasing quartiles of range, P<0.0001); however, the association between GV and mortality for each metric was no longer significant after multivariable adjustment. In contrast, mean BG remained an important predictor of survival (P<0.001, all models). Although greater GV is associated with increased risk of in-hospital mortality in patients with AMI in unadjusted analyses, GV is no longer independently predictive after controlling for multiple patient factors, including mean BG. These findings suggest that GV does not provide additional prognostic value above and beyond already recognized risk factors for mortality during AMI.

BackgroundPlasma glucose variability may confer a risk for development of chronic diabetic complications. Glycosylated hemoglobin (HbA1c) reflects average glucose level but not glucose variability, which is measured by mean amplitude of glycemic excursions (MAGEs) and continuous glucose monitoring (CGM).AimTo study glucose variability using CGM/MAGE compared with sugar profiles and to assess their value as a therapy guide in patients with diabetic nephropathy on hemodialysis.Patients and methodsGroup 1 included 50 patients with type 2 diabetes mellitus (T2DM) without diabetic nephropathy. Group 2 included 50 patients with T2DM with diabetic nephropathy. Group 3 included 50 patients with T2DM with diabetic nephropathy on hemodialysis. Measurements of fasting blood glucose, postprandial blood glucose, HbA1c, and glucose variability by MAGE and CGM were done.ResultsCGM showed significant blood glucose variability (amplitude>130 mg/dl in 40 patients=80% using CGM and in 45 patients=90% using MAGE) in dialysis group (group 3) in comparison with glucose variability in nondialysis groups (group 1+group 2) (amplitude>130 mg/dl in 20 patients=20% using either CGM or MAGE). Group 1 showed significant correlations between either CGM or MAGE and all sugar profiles. Group 2 showed significant correlations between CGM and MAGE with either fasting or postprandial blood glucose but not with HbA1c, whereas group 3 showed nonsignificant correlations between either CGM or MAGE and any of sugar profiles.ConclusionCGM/MAGE have high specificity and sensitivity to measure variability of sugar levels, especially in patients with diabetic nephropathy on hemodialysis or not, in which HbA1c may not be a reliable tool.

The effect of continuous subcutaneous insulin infusion (CSII) and glucose variability on vascular health in type 1 diabetes (T1D) is not known. We aimed to determine whether initiation of CSII improves vascular function and reduces glucose variability, independent of changes in HbA1c. Twenty-two children with T1D (12.5 ± 2.9 yr) were reviewed immediately prior, 3 wk, and 12 months after initiation of CSII. Vascular function [flow-mediated dilatation (FMD), glyceryl trinitrate-mediated dilatation (GTN)], glucose variability [mean of daily differences (MODD), mean amplitude of glycaemic excursions (MAGE) and continuous overlapping net glycaemic action (CONGA)], and clinical and biochemical data were measured at each visit. Results for the first two visits were compared to a previously studied cohort of 31 children with T1D who remained on multiple daily injections (MDI). FMD, GTN, blood pressure, HbA1c, fructosamine, and glucose variability significantly improved 3 wk after CSII commencement (all p < 0.05), but there was no change in the MDI control group. At 3 wk, vascular function related to glucose variability [(FMD: MODD, r = -0.62, p = 0.002) and (GTN: MAGE, r = -0.59, p = 0.004; CONGA-4, r = -0.51, p = 0.01; MODD, r = -0.62, p = 0.002)] but not to blood pressure, HbA1c, or fructosamine. At 12 months, FMD, GTN, blood pressure, and glucose variability returned to baseline levels, while HbA1c deteriorated. Carotid intima media thickness was unchanged over 12 months. Initiation of CSII rapidly improves vascular function in association with decreased glucose variability; however, the effects are not sustained with deterioration of metabolic control and glucose variability.

BackgroundAcute reductions in postprandial glucose excursions because of movement behaviors have been demonstrated in experimental studies but less so in free-living settings.ObjectiveThe objective of this study was to explore the nature of the acute stimulus-response model between accelerometer-assessed physical activity, sedentary time, and glucose variability over 13 days in nondiabetic adults.MethodsThis study measured physical activity, sedentary time, and interstitial glucose continuously over 13 days in 29 participants (mean age in years: 44.9 [SD 9.1]; female: 59%, 17/29; white: 90%, 26/29; mean body mass index: 25.3 [SD 4.1]) as part of the Sensing Interstitial Glucose to Nudge Active Lifestyles (SIGNAL) research program. Daily minutes spent sedentary, in light activity, and moderate to vigorous physical activity were associated with daily mean glucose, SD of glucose, and mean amplitude of glycemic excursions (MAGE) using generalized estimating equations.ResultsAfter adjustment for covariates, sedentary time in minutes was positively associated with a higher daily mean glucose (mmol/L; beta=0.0007; 95% CI 0.00030-0.00103; P<.001), SD of glucose (mmol/L; beta=0.0006; 95% CI 0.00037-0.00081; P<.001), and MAGE (mmol/L; beta=0.002; 95% CI 0.00131-0.00273; P<.001) for those of a lower fitness. Additionally, light activity was inversely associated with mean glucose (mmol/L; beta=−0.0004; 95% CI −0.00078 to −0.00006; P=.02), SD of glucose (mmol/L; beta=−0.0006; 95% CI −0.00085 to −0.00039; P<.001), and MAGE (mmol/L; beta=−0.002; 95% CI −0.00285 to −0.00146; P<.001) for those of a lower fitness. Moderate to vigorous physical activity was only inversely associated with mean glucose (mmol/L; beta=−0.002; 95% CI −0.00250 to −0.00058; P=.002).ConclusionsEvidence of an acute stimulus-response model was observed between sedentary time, physical activity, and glucose variability in low fitness individuals, with sedentary time and light activity conferring the most consistent changes in glucose variability. Further work is required to investigate the coupling of movement behaviors and glucose responses in larger samples and whether providing these rich data sources as feedback could induce lifestyle behavior change.

Does sucrose affect the glucose variability in patients with type 1 diabetes? a pilot crossover clinical study.

ABSTRACTThere are no studies evaluating the glucose variability in different periods of Ramadan fasting in patients with type 2 diabetes using continuous glucose monitoring (CGM). This study examined the effect of Ramadan fasting on interstitial glucose (IG) variability in early,- late-, and post-Ramadan compared to pre-Ramadan days in non-insulin-treated type 2 diabetes patients. Participants had a CGM system connected 2 or 3 days before Ramadan start, which was removed on the third or fourth day of Ramadan. CGM performance continued for a total of 6 days. A second CGM performance started on the 27th or 28th day of Ramadan and ended on the 4th or 5th post-Ramadan day. First, CGM recordings were divided into pre-Ramadan and early-Ramadan CGM, and second recordings into late-Ramadan and post-Ramadan. At each visit, blood pressure, body weight, and waist circumference were measured, and fasting blood samples were collected for HbA1c and plasma glucose. All patients received recommended Ramadan education before Ramadan. Thirty-three patients (mean age 55.0 ± 9.8 years, 73% males) were prospectively included. IG variability, estimated as mean amplitude of glycaemic excursions (MAGE), increased significantly in early-Ramadan compared to pre-Ramadan (P = 0.006) but not in late-Ramadan and post-Ramadan recording days. Only patients on >2 anti-diabetic drugs (n = 16, P = 0.019) and those on sulphonylureas (n = 14, P = 0.003) showed significant increase in MAGE in early-Ramadan. No significant changes were seen in coefficient of variation, time in range, time in hyperglycaemia, or time in hypoglycaemia. Except for an initial increase in glucose variability, fasting Ramadan for patients with non-insulin-treated type 2 diabetes did not cause any significant changes in glucose variability or time in hypoglycaemia during CGM recording days compared to non-fasting pre-Ramadan period.

We thank Picconi and colleagues1 for their valuable comments on our study.2 They observe a lack of standardization for measurement of glycemic variability (GV) and suggest that there might be different outcomes with different GV measures. Furthermore, the authors suggest a possible confounding role for insulin secretagogues on GV and oxidative stress parameters. The lack of standardization of GV measurement methods is a problem we had discussed.3 This lack limits comparison between trials and we would highly support development of a consensus on, ideally, one measure. We agree that mean amplitude of glycemic excursions has several limitations: not measuring excursions smaller than the standard deviation as well as not taking the frequency of excursions into account. Therefore, our group has developed a new measure that we believe overcomes these limitations and has proven robust in diabetes4 and intensive care unit populations5: mean absolute glucose change (MAG). This is a simple summation of all changes in glucose per unit of time and can be taken from self-monitored blood glucose profiles, blood glucoses, and continuous glucose monitoring traces. To investigate the influence of different measurement methods, we also measured the relation between oxidative stress and continuous net glycemic action (reflected as CONGA-1, CONGA-2, CONGA-4) as well as mean of daily differences originally in the subject study.2 As expected, all GV measures were highly correlated and, also for these additional measures, we were not able to show a correlation with oxidative stress in our population. To improve readability, we decided not to include them in the paper. Picconi and colleagues suggest that our outcomes might have been influenced by insulin secretagogues and other drugs used in our study population. It could be that the formation of oxidative stress is prohibited by these medicaments but, as mentioned, our population showed optimal glycemic control. The results of our study therefore reflect the current situation of diabetes regulation, which relies on polypharmacy. Moreover, it is plausible that mean glucose is the main driver of oxidative stress6 and not the way by which that mean glucose is reached. Noteworthy in this perspective is that the patients in the original Monnier article7 were allowed to use insulin secretagogues, which did not seem to influence the positive correlation found between GV and oxidative stress. In conclusion, we support that future studies use standardized GV measures, for which we propose the MAG, and, in case of isoprostane measurement, high-performance liquid chromatography tandem mass spectrometry to measure oxidative stress.