SD Of Blood Glucose Research Articles

279-OR: Self-Efficacy, Diabetes Distress, Self-Care, and Blood Glucose: Within-Person Pathways in Adults with T1D

Introduction: Research links self-efficacy and diabetes distress to suboptimal diabetes self-care and blood glucose (BG) regulation. However, most studies rely on between-person comparisons that cannot rule out confounding by unmeasured variables or capture the dynamic interplay among these variables in real time. Pairing continuous glucose monitoring (CGM) with ecological momentary assessment, we sought to illuminate pathways of influence among these variables in a diverse sample of adults with T1D. Methods: Participants (N=127, 41±15 yrs, 53% women, 39% Latino, 35% White, 10% Black) wore blinded CGM for 10-14 days and completed smartphone surveys 5-6 times per day. Using dynamic structural equation modeling, we evaluated within-person pathways shown in Figure 1. Results: Hypothesized pathways were significant, as were indirect effects of:1) morning self-efficacy on that day’s mean BG via diabetes distress and self-care and2) diabetes distress on mean BG via self-care. Similar results were observed for time in range and SD of BG. Conclusion: Our findings provide novel evidence for a sequential pathway of influence of psychological, emotional, and behavioral variables on BG regulation, observed over time and within individuals. These results provide a roadmap for interventions that target self-efficacy, diabetes distress, and self-care to improve BG regulation among adults with T1D. Disclosure J.S.Gonzalez: Consultant; Virta Health Corp. H.Jin: None. L.T.Pham: None. A.Ali: None. P.Lee: None. V.F.Ruelas: n/a. R.Basile: None. E.Pyatak: Research Support; Abbott Diabetes. D.Spruijt-metz: None. S.Schneider: None. A.L.Peters: Advisory Panel; Abbott Diabetes, AstraZeneca, Eli Lilly and Company, Novo Nordisk, Shouti, Vertex Pharmaceuticals Incorporated, Zealand Pharma A/S, Other Relationship; Omada Health, Inc., Research Support; Abbott Diabetes, Dexcom, Inc., Insulet Corporation, Leona M. and Harry B. Helmsley Charitable Trust, Stock/Shareholder; Teladoc Health. J.P.Crandall: Research Support; Abbott Diabetes. G.Crespo-ramos: None. H.Mendez-rodriguez: None. C.J.Hoogendoorn: None. S.Agarwal: Advisory Panel; Medtronic, Consultant; Beta Bionics, Inc. Funding National Institutes of Health (1R01DK121298-01; P30 DK111022)

The relationship between glycemic variability and blood pressure variability in normoglycemic normotensive individuals.

Glycemic fluctuations around a mean glucose level, referred as glycemic variability and blood pressure variability (BPV) are considered as independent risk factors for cardiovascular diseases, all-cause mortality, and cardiovascular disease-mortality. With this background in mind, we aimed to investigate the association between glycemic variability and BPV and their association in normoglycemic and normotensive individuals. Twenty-seven normotensive normoglycemic individuals were recruited. Twenty-four hour Holter devices were utilized to measure ambulatory blood pressure (BP) while continuous glucose monitoring (CGM) devices were applied to measure glycemic variability simultaneously to the subjects. These devices were kept on for 48 h. For BP recordings, daytime, nighttime, and 24-h BP determinations, their mean and SD were calculated. From CGM measurements, mean blood glucose (MBG), SD of blood glucose, the mean amplitude of glycemic excursions (MAGE), the mean of daily differences (MODD), coefficient of variation (correction of variability for the MBG), and daytime and nighttime blood glucose were determined. The mean age of the subjects was 23.8 ± 2.7 years and 66% were women (18/27). In the correlation analysis between glycemic variability parameters and BPV parameters, SD of 24-h SBP was correlated with the SD of MBG (r = 0.52, P = 0.006), MAGE (r = 0.49, P = 0.009), and MODD (r = 0.46, P = 0.015). SD of daytime SBP was correlated with, MAGE (r = 0.42, P = 0.03) and MODD (r = 0.43, P = 0.02). We report correlation between glycemic variability and BPV variables in normoglycemic and normotensive healthy individuals.

Effect of basal insulin supplement therapy on diabetic retinopathy in short‐duration type 2 diabetes: A one‐year randomized parallel‐group trial

BackgroundIn this study, we compared the effect on diabetic retinopathy (DR) between oral antidiabetic drugs (OADs) alone and in combination with basal insulin‐supported OADs therapy (BOT). [Correction added on 11 November 2019, after first online publication: In under Background section, “DR” has been corrected into “diabetic retinopathy (DR)”.]MethodsBetween January 2015 and January 2018, this study enrolled 290 patients (age 18‐65 years) with diabetes duration between 0 and 5 years. Patients were randomly assigned to receive OADs or BOT after 14 days intensive insulin treatment. Examinations were performed at the beginning and end of the study.ResultsFewer patients developed DR in the BOT than OADs group (8 [6.06%] vs 12 [8.3%], respectively), and all cases of DR were non‐proliferative. Blood glucose concentrations were higher in the BOT than OADs group at the 3rd month, but lower in the former at the 6th and 12th month. The rate of reaching target HbA1c ≤7% was lower in the BOT than OADs group at the 3rd month (63.6% vs 72.2%, respectively), similar between the two groups at the 6th month (60.6% vs 66.6%, respectively) and higher in the BOT group at the 12th month (75.0% vs 61.1%, respectively). The SD of fasting blood glucose (FBG), coefficient of variation of FBG, SD of blood glucose (SDBG), and mean amplitude of glycemic excursions were lower in the BOT than OADs group. Changes in the levels of three cytokines (interleukin [IL]‐1β, IL‐6, and IL‐17α) were significantly less in the BOT than OADs group.ConclusionsTwelve months of BOT decreased the incidence of DR in short‐duration type 2 diabetes by reducing glycemia more effectively, stably, and completely than OADs alone.

A study on role of plasma super oxide dismutase (sod) and serum Na+ levels in diabetic cataract patient: Comparative study

Background: Cataract is the most common cause of blindness which is treatable. Though there are multiple risk factors involved, exact pathogenesis of cataract is yet to be established. Diabetics are known to be associated with electrolyte disturbances, like hypo/hypernatremia, hyperkalemia. We hypothesize that serum electrolytes and oxidative marker enzyme super oxide dismutase (SOD) levels may be altered in diabetic cataract patients. The aim of the study was to compare serum electrolytes (Na), blood glucose and SOD in healthy controls and diabetic cataract patients as well as to assess the correlation of duration of diabetes with electrolytes as well as risk of cataractogenesis in diabetics. Methods: SOD was measured as indicator of oxidative stress and serum sodium levels estimated in diabetic cataract patients and healthy controls.Results: The present study involved 60 subjects, out of which 20 were Diabetic cataract cases who attended outpatient and inpatient department of Tertiary care Eye Hospital and outpatient department of RIMS & General Hospital, KADAPA, who fulfilled the inclusion criteria and other 40 were controls without diabetes and cataract. The present study was undertaken to evaluate the Blood Glucose levels, Plasma SOD levels, Serum Na+ levels in control and diabetic cataract patients. In the present study, Comparison of plasma SOD and blood Glucose levels between cases and controls. All the controls were having normal blood Glucose levels (non-diabetics) and normal SOD levels except for 7 controls with decreased SOD levels (12%). Among cases all are diabetic and majority 20 (50%) were with decreased SOD levels. In the present study, Mean ± SD for plasma Glucose levels for diabetic cataract cases is 217.62±55.85 and for controls is 105.86±61.75. Mean ± SD of Blood Glucose is higher in cases than in controls and the mean difference is statistically significant (p

Abstract WP17: Association of Blood Glucose and Clinical Outcome After Mechanical Thrombectomy for Acute Ischemic Stroke

Background: Elevated blood glucose (BG) levels at admission following acute ischemic stroke (AIS) have been associated with adverse clinical outcomes in thrombolytic and non-thrombolytic treated patients. This association has been inconsistent and has not been studied after mechanical thrombectomy (MT). The current study looks at multiple BG parameters and their association with outcome following MT in AIS. Methods: AIS patients undergoing MT with a retrievable stent between 2012 and 2016 at two stroke centers were studied. Favorable clinical outcome was defined as having a modified Rankin Scale score (mRS) of 0-2 at three months. Admission BG, maximum BG during the hospital stay, and serial measurements every 4-6 hours in the first 24 hours were recorded. The following demographic and clinical data was also collected: age, sex, baseline NIHSS score, onset-to-reperfusion times, HgA1c, and stroke mechanism. Results: Seventy-nine patients were identified (mean age 69, 49% male, SEM= 1.56); thirty-three patients had a favorable clinical outcome at three months and 46 had unfavorable outcomes. In univariate analysis, BG variability (standard deviation of BG, SD) in the first 24 hours following admission and maximum BG during hospital stay was associated with unfavorable outcomes (Table 1). The association between admission BG and favorable outcome was not statistically significant. In multivariate logistic regression analysis the SD of BG (OR= 1.08, 95% CI= 1.02-1.13, P= .005) had the highest predictive value for favorable outcome. Conclusions: Greater BG variability is associated with worse clinical outcome in patients with AIS undergoing MT. Using SD and/or serial measurements of BG appear better than single BG measurements at predicting clinical outcome in this population.

Glycemic variability evaluated by continuous glucose monitoring system is associated with the 10-y cardiovascular risk of diabetic patients with well-controlled HbA1c

BackgroundThe present study aimed to identify the relationship between glycemic variability (GV) and the 10-y risk of cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM) patients with good glycemic control. MethodsTwo-hundred forty consecutive T2DM patients (HbA1c≤7.0%) without CVD were included to calculate the 10-y CVD risk by Framingham risk score (FRS), and divided into 3 groups: low-risk group (FRS≤10%), intermediate-risk group (>10%, ≤20%) and high-risk group (>20%). Inter-group differences of GV were determined by comparing the SD of blood glucose (SDBG), mean amplitudes of glycemic excursion (MAGE), and mean of daily differences (MODD) gathered from 72-h continuous glucose monitoring system. ResultsThe levels of SDBG and MAGE significantly increased along with the raises of 10-y CVD risk of T2DM patients (p<0.01). FRS was positively correlated with age, systolic blood pressure, SDBG and MAGE (r=0.717, 0.525, 0.509 and 0.485 respectively, p<0.01), while negatively correlated with the level of HDL-C (r=−0.348, p<0.01). Furthermore, multivariate logistic regression analysis confirmed that increased MAGE [OR: 1.623(1.198–2.316), p<0.001] and patients with high urine albumin excretion rates [OR: 1.743(1.247–2.793), p<0.001] were independent predictors for high 10-y CVD risk. ConclusionGV predicts independently the 10-y CVD risk of T2DM patients with well-controlled HbA1c.

Evaluation of the Average Daily Risk Range as a Measure of Glycemic Variability in Youths with Type 1 Diabetes.

The Average Daily Risk Range (ADRR) is a measure of glycemic variability (GV) developed for adults with diabetes. Although the ADRR is increasingly being reported in pediatric diabetes research and may also be used in clinical management, it has never been examined for its sensitivity to predicting hyper- and hypoglycemia in youths or compared for its predictive ability with other measures of GV in youths. Thus, we present predictive validity data for the ADRR in youths with type 1 diabetes. Glucometer data for 436 youths (mean age, 11.8±3.8 years) were collected from a clinical database. Using these data, we computed the ADRR, SD of blood glucose, coefficient of variation of blood glucose, Low Blood Glucose Index, High Blood Glucose Index, the percentage of glucose values ≥70 and ≤180 mg/dL, the percentage of high glucose values >180 mg/dL and >400 mg/dL, and the percentage of low glucose values <70 mg/dL and <40 mg/dL in Month 1. We then compared these with episodes of hypo- and hyperglycemia in Month 2. The ADRR showed good concurrent validity with other measures of GV in youths experiencing hyperglycemic events but limited predictive validity in general and specifically with future hypoglycemic events. The percentages of current hyper- and hypoglycemic episodes appeared to be stronger predictors of future hyper- and hypoglycemic episodes, respectively. In a large pediatric sample, the ADRR was not the strongest predictor of future glycemic excursion. The percentages of current hyper- and hypoglycemic episodes appear to be stronger predictors.

Continuous glucose monitoring effects on maternal glycemic control and pregnancy outcomes in patients with gestational diabetes mellitus: a prospective cohort study.

Clinical evidence on the consequential effects of continuous glucose monitoring (CGM) on pregnancy outcomes in women with gestational diabetes mellitus (GDM) is scarcely available. Our objective was to evaluate the effectiveness of CGM on maternal glycemic control and pregnancy outcomes in patients with GDM . In total, 340 Chinese pregnant women with GDM were allocated to either the routine care group (n = 190) or the CGM group (n =150). This was a prospective cohort study in the Department of Obstetrics of GuangDong Women and Children Hospital in China. Recruitment started in April 2011 and stopped in August 2012. A 72-hour CGM system was used as a supplementary tool for glucose monitoring in the CGM group. PRIMARY OUTCOME MEASUREMENTS: The parameters of glycemic variability included mean blood glucose, the SD of blood glucose, mean amplitude of glycemic excursions (MAGEs), and the mean of daily differences. The maternal outcomes (preeclampsia and cesarean delivery) and composite neonatal outcomes were analyzed. The SD of blood glucose, MAGEs, and mean of daily differences values were significantly lower in the CGM group compared with those of the routine care group (P < .001). Subjects in the CGM group were at lower risk of preeclampsia and primary cesarean delivery compared with the routine care group (P < .05). The mean infant birth weight of women in the CGM group was lower than infants of women in the routine care group (P < .001). The MAGE was associated with birth weight (β = 0.196, P < .001), and it was an independent factor for preeclampsia (odds ratio, 3.66; 95% confidence interval 2.16-6.20) and composite neonatal outcome (odds ratio, 1.34; 95% confidence interval 1.01-1.77). The use of supplementary CGM combined with routine antenatal care can improve the glycemic control and pregnancy outcomes of patients with GDM.

Hypoglycemia and Blood Glucose Fluctuations in the Application of a Sensor-Augmented Insulin Pump

The purpose of this study was to understand the effect of sensor-augmented insulin pump (SAP) use on hypoglycemia and blood glucose (BG) fluctuations. Sixty patients with type 2 diabetes mellitus were randomly assigned to three groups of treatment with SAP, continuous subcutaneous insulin infusion (CSII), or multiple daily injection (MDI) therapy for 6 days. Parameters of glycemic control that were determined included mean BG concentration (MBG), SD of BG (SDBG), mean amplitude of glycemic excursions (MAGE), absolute means of daily differences (MODD), 24-h area under the curve at 10 h (AUC10), 24-h area under the curve at 3.9 h (AUC3.9), and Low Blood Glucose Index (LBGI). No significant differences were observed among the three groups in terms of MBG, SDBG, MAGE, or MODD at the beginning of treatment. The MBG, SDBG, MAGE, MODD, and total AUC10 of the SAP group improved over the 4 days of the intervention compared with the CSII and MDI groups; however, no significant differences were observed among the three groups in terms of total AUC3.9 and LBGI. Compared with CSII and MDI therapy, SAP therapy was able to rapidly lower mean BG and reduce BG level fluctuations with no increased risks of hypoglycemia.

Nateglinide and Acarbose Are Comparably Effective Reducers of Postprandial Glycemic Excursions in Chinese Antihyperglycemic Agent–Naive Subjects with Type 2 Diabetes

Recent studies have identified postprandial glycemic excursions as risk factors for diabetes complications. This study aimed to compare the effects of nateglinide and acarbose treatments on postprandial glycemic excursions in Chinese subjects with type 2 diabetes. This was a multicenter, open-label, randomized, active-controlled, parallel-group study. One hundred three antihyperglycemic agent-naive subjects with type 2 diabetes (hemoglobin A1c range, 6.5-9.0%) were prospectively recruited from four hospitals in China. The intervention was nateglinide (120 mg three times a day) or acarbose (50 mg three times a day) therapy for 2 weeks. A continuous glucose monitoring system was used to calculate the incremental area under the curve of postprandial blood glucose (AUCpp), the incremental glucose peak (IGP), mean amplitude of glycemic excursions, SD of blood glucose, the mean of daily differences, and 24-h mean blood glucose (MBG). Subjects' serum glycated albumin and the plasma insulin levels were also analyzed. Both agents caused significant reductions on AUCpp and IGP. Similarly, both treatment groups showed significant improvements in the intra- and interday glycemic excursions, as well as the 24-h MBG and serum glycated albumin compared with baseline (P<0.001). However, neither of the agents produced a significantly better effect (P>0.05). Moreover, the nateglinide-treated group had significantly increased insulin levels at 30 min and at 120 min after a standard meal compared with baseline, whereas the acarbose-treated group decreased. No serious adverse events occurred in either group. The rates of hypoglycemic episodes were comparable in the two groups, and no severe hypoglycemic episode occurred in either group. Nateglinide and acarbose were comparably effective in reducing postprandial glycemic excursions in antihyperglycemic agent-naive Chinese patients with type 2 diabetes, possibly through different pathophysiological mechanisms.

Statin Intake Is Associated With Decreased Insulin Sensitivity During Cardiac Surgery

OBJECTIVESurgical trauma impairs intraoperative insulin sensitivity and is associated with postoperative adverse events. Recently, preprocedural statin therapy is recommended for patients with coronary artery disease. However, statin therapy is reported to increase insulin resistance and the risk of new-onset diabetes. Thus, we investigated the association between preoperative statin therapy and intraoperative insulin sensitivity in nondiabetic, dyslipidemic patients undergoing coronary artery bypass grafting.RESEARCH DESIGN AND METHODSIn this prospective, nonrandomized trial, patients taking lipophilic statins were assigned to the statin group and hypercholesterolemic patients not receiving any statins were allocated to the control group. Insulin sensitivity was assessed by the hyperinsulinemic-normoglycemic clamp technique during surgery. The mean, SD of blood glucose, and the coefficient of variation (CV) after surgery were calculated for each patient. The association between statin use and intraoperative insulin sensitivity was tested by multiple regression analysis.RESULTSWe studied 120 patients. In both groups, insulin sensitivity gradually decreased during surgery with values being on average ∼20% lower in the statin than in the control group. In the statin group, the mean blood glucose in the intensive care unit was higher than in the control group (153 ± 20 vs. 140 ± 20 mg/dL; P < 0.001). The oscillation of blood glucose was larger in the statin group (SD, P < 0.001; CV, P = 0.001). Multiple regression analysis showed that statin use was independently associated with intraoperative insulin sensitivity (β = −0.16; P = 0.03).CONCLUSIONSPreoperative use of lipophilic statins is associated with increased insulin resistance during cardiac surgery in nondiabetic, dyslipidemic patients.

Perioperative Glucose and Insulin Administration While Maintaining Normoglycemia (GIN Therapy) in Patients Undergoing Major Liver Resection

Although hyperglycemia is a well-recognized risk factor in the context of cardiac surgery, the relevance of perioperative glycemic control for patients undergoing major noncardiac operations has received little attention. We designed this study to assess the hyperglycemic response to liver resection, and to test the hypothesis that perioperative glucose and insulin administration while maintaining normoglycemia (GIN therapy) provides glycemic control superior to that achieved by the conventional use of insulin. Patients were randomly assigned to GIN therapy or standard therapy (control group). In the GIN therapy group, insulin was administered at 2 mU . kg(-1) . min(-1) during surgery. At the end of surgery, the insulin infusion was decreased to 1 mU . kg(-1) . min(-1) and continued for 24 hours. Dextrose 20% was infused at a rate adjusted to maintain blood glucose within the target range of 3.5 to 6.1 mmol . L(-1) (63-110 mg . dL(-1)). Patients in the standard therapy group received a conventional insulin sliding scale during and after surgery. The mean and SD of blood glucose as well as the percentage of blood glucose values within the target range were calculated. To evaluate intrasubject variability, the coefficient of variability (CV) of blood glucose was calculated for each patient. Episodes of severe hypoglycemia, i.e., blood glucose <2.2 mmol . L(-1) (40 mg . dL(-1)), were recorded. The primary outcome was the proportion of normoglycemic measurements. We studied 52 patients. The mean blood glucose value in patients receiving GIN therapy always remained within the target range. The blood glucose levels were lower in the GIN therapy group than in the standard therapy group (during surgery, P < 0.01; after surgery, P < 0.001). In nondiabetic patients receiving GIN therapy (n = 19), target glycemia was achieved in 90.1% of the blood glucose measurements during surgery and in 77.8% of the measurements after surgery. In diabetic patients receiving GIN therapy (n = 7), target glycemia was achieved in 81.2% of the blood glucose measurements during surgery and in 70.5% of the measurements after surgery. In nondiabetic patients receiving standard therapy (n = 19), target glycemia was achieved in 37.4% of the blood glucose measurements during surgery and in 18.3% of the measurements after surgery. In diabetic patients receiving standard therapy (n = 7), target glycemia was achieved in 4.3% of the blood glucose measurements during surgery and in 2.9% of the measurements after surgery. The SD and CV of blood glucose were smaller in the GIN therapy group than in the standard therapy group, especially in nondiabetic patients after surgery (SD, P < 0.001; CV, P = 0.027). No patients receiving GIN therapy experienced severe hypoglycemia during surgery. One patient receiving GIN therapy experienced hypoglycemia in the intensive care unit after surgery without neurological sequelae. GIN therapy effectively provides normoglycemia in patients undergoing liver resection (clinicaltrials.gov, NCT00774098).

Influence of Glycemic Variables on Hemoglobin ALC

To assess the influences of a wide variety of glucose variables on hemoglobin A1c (A1C). The Diabetes Control and Complications Trial database, restricted to volunteers whose 7-point daily capillary glucose profiles were complete in >or=80% of quarterly collections and who were in the study for >or=4 years, was used for analysis. Regression analyses were done to develop an equation for estimating A1C based on concurrent and prior mean blood glucose (MBG) values. The multivariate coefficient of determination (R2) was calculated for MBG, mean postprandial blood glucose, mean preprandial blood glucose, digestive glycemia, interdigestive glycemia, individual time points of the 7-point glucose profile, range of blood glucose, SD of blood glucose, M-value, and mean amplitude of glycemic excursions in relationship to A1C. By using regression analysis, the correlation between A1C and MBG within each study subject was determined. The most accurate prediction of A1C was obtained from the concurrent MBG. With use of univariate analysis, all glucose variables correlated significantly with concurrent A1C, the strongest correlation occurring with MBG. In multivariate analysis, the primary predictor of A1C was MBG; all other glucose variables added nothing to the models. Within-subject correlations between MBG and A1C showed considerable variation. A1C correlates best with MBG derived from 7-point-daily capillary glucose profiles. The influences of glucose measured at specific time points during the day or various measures of glucose variability on A1C are less than that of MBG. Within the limitations of the intermittent glucose determinations, wide variations in the relationship of MBG to A1C among and within patients with type 1 diabetes remain unexplained.

Mean Blood Glucose and Biological Variation Have Greater Influence on HbA1c Levels Than Glucose Instability

Mean blood glucose (MBG) over 2-3 months is a strong predictor of HbA(1c) (A1C) levels. Glucose instability, the variability of blood glucose levels comprising the MBG, and biological variation in A1C (BV) have also been suggested as predictors of A1C independent of MBG. To assess the relative importance of MBG, BV, and glucose instability on A1C, we analyzed patient data from the Diabetes Control and Complications Trial (DCCT). A glucose profile set and sample for A1C were collected quarterly over the course of the DCCT from each participant (n = 1,441). The glucose profile set consisted of seven samples, one each drawn before and 90 min after breakfast, lunch, and dinner and one before bedtime. MBG and glucose instability (SD of blood glucose [SDBG]) were calculated as the arithmetic mean and SD of glucose profile set samples for each visit, respectively. A statistical model was developed to predict A1C from MBG, SDBG, and BV, adjusted for diabetes duration, sex, treatment group, stratum, and race. Data from 32,977 visits were available. The overall model was highly statistically significant (log likelihood = -41,818.75, likelihood ratio chi2[7] = 7,218.71, P > chi2 = 0.0000). MBG and BV had large influences on A1C based on their standardized coefficients. SDBG had only 1/14 of the impact of MBG and 1/10 of the impact of BV. MBG and BV have a large influence on A1C, whereas SDBG is relatively unimportant. Consideration of BV as well as MBG in the interpretation of A1C may enhance our ability to monitor diabetes management and predict complications.

Is HbA(1c) affected by glycemic instability?

HbA(1c) is a standard clinical assessment of glycemia and the basis of most data relating glycemic control to complications. It remains unclear, however, whether HbA(1c) is affected by glycemic variation and mean glycemia. To test this question, we analyzed the statistical relationship between HbA(1c) levels and glycemic variability as measured by self-monitoring of blood glucose (SMBG). The records of 256 subjects were studied. SMBG data for the preceding 3 months were downloaded, and HbA(1c) was measured by ion-exchange high-performance liquid chromatography. Simple- and random-effects linear regression models were used to assess the independent contributions of mean blood glucose (BG) and SD of BG to HbA(1c), after adjusting for the mean BG. Mean +/- SD for HbA(1c) was 7.66 +/- 1.11% and for BG was 8.5 +/- 1.9 mmol/l (153.3 +/- 34.9 mg/dl); SD of BG for individual subjects was 3.5 mmol/l (63.3 mg/dl), varying from 0.4 mmol/l (8.1 mg/dl; very stable glycemia) to 8.4 mmol/l (152.5 mg/dl; very unstable glycemia). A close correlation between mean BG and HbA(1c) was demonstrated (r = 0.62). Also, within-subject SD of BG correlated with HbA(1c) (r = 0.375), indicating that people with poorer glycemic control had higher BG variance. After adjusting for mean BG in a linear regression model, however, the effect of the within-subject SD of BG on the HbA(1c) was insignificant. Several further analyses confirmed the strength of the observation. HbA(1c) reflects mean glycemia and is not meaningfully affected by glycemic instability after adjusting for mean BG.