血糖波动（diabetes care）

Does Glucose Variability Influence the Relationship BetweenMean Plasma Glucose and HbA1c Levels in Type 1 and Type 2 Diabetic Patients? JUDITH C. KUENEN, MD1 RIKKE BORG, MD2 DIRK J. KUIK, MSC3 HUI ZHENG, PHD4 DAVID SCHOENFELD, PHD4 MICHAELA DIAMANT, MD1 DAVID M. NATHAN, MD5 ROBERT J. HEINE, MD6 ON BEHALF OF THE ADAG STUDY GROUP* OBJECTIVE—The A1C-Derived Average Glucose (ADAG) study demonstrated a linear re- lationship between HbA1c and mean plasma glucose (MPG). As glucose variability (GV) may contribute to glycation, we examined the association of several glucose variability indices and the MPG-HbA1c relationship. RESEARCH DESIGN AND METHODS—Analyses included 268 patients with type 1 diabetes and 159 with type 2 diabetes. MPG during 3 months was calculated from 7-point self- monitored plasma glucose and continuous glucose monitoring. We calculated three different measures of GV and used a multiple-step regression model to determine the contribution of the respective GV measures to the MPG-HbA1c relationship. RESULTS—GV, as reflected by SD and continuous overlapping net glycemic action, had a significant effect on the MPG-HbA1c relationship in type 1 diabetic patients so that high GV led to a higher HbA1c level for the same MPG. In type 1 diabetes, the impact of confounding and effect modification of a low versus high SD at an MPG level of 160 mg/dL on the HbA1c level is 7.02 vs. 7.43 and 6.96 vs. 7.41. All GV measures showed the same tendency. CONCLUSIONS—In only type 1 diabetic patients, GV shows a significant interaction with MPG in the association with HbA1c. This effect is more pronounced at higher HbA1c levels. However, the impact of GV on the HbA1c level in type 1 diabetes is modest, particularly when HbA1c is close to the treatment target of 7%. Diabetes Care 34:1843–1847, 2011 S ince the Diabetes Control and Com-plications Trial (DCCT) and theUK Prospective Diabetes Study (UKPDS) (1,2) established the relation- ship betweenHbA1c and the development of long-term diabetes complications, HbA1c has become the key monitoring tool in diabetes management. During the lifetime of the erythrocyte, hemoglobin (Hb) is gradually glycated. The proportion of the glycated sites, HbA1c, within the erythrocyte increases through- out its life span and reflect the exposure to mean blood glucose (MBG) levels during the preceding 2–3 months (3). This non- enzymatic posttranslational modification is relatively slow. In vivo and in vitro studies have shown that HbA1c levels are directly proportional to the time-averaged con- centration of glucose during the erythro- cyte’s life span (3–6). Given the kinetics of glycation, brief periods of hyperglyce- mia should not have a major impact on HbA1c levels (7–9). However, increased glycated protein levels are documented in some nondia- betic pathological states. So, hyperglyce- mia is not the complete answer to the etiology of increased early glycated prod- ucts in nondiabetic conditions. A common denominator is oxidative stress. It has been hypothesized that oxidative stress either via increasing reactive oxygen spe- cies or by depleting the antioxidants may modulate the genesis of early glycated proteins in vivo (10,11). Hyperglycemia stimulates oxidative stress (12) and glu- cose variability; in particular, postprandial glucose excursions have been regarded as potentially deleterious as a result of, among other factors, their association with the increase of oxidative stress (13). Therefore, glucose variability (GV) could influence the glycation of HbA1c. Previous studies have examined whether the relationship between mean plasma glucose (MPG) levels and HbA1c is influenced by glucose variability and found no or minimal influence (10,14,15). However, these studies used limited self- monitoring of blood glucose (SMBG) data to assess mean glucose levels and variabil- ity in relatively small numbers of measure- ments. Thesemethods could underestimate glycemic excursions. Continuous glucose monitoring (CGM) provides a more com- plete view of glycemic excursions, includ- ing the duration and frequency of the excursions, and allows calculation of features of GV. Our aim was to examine the influence of GV on the MPG-HbA1c relationship in the A1C-Derived Average Glucose (ADAG) study. RESEARCH DESIGN AND METHODS—The ADAG study was conducted at 10 centers in the U.S., c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c From the 1Diabetes Center, VU University Medical Center, Amsterdam, the Netherlands; the 2Steno Diabetes Center, Copenhagen, Denmark; the 3Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, the Netherlands; the 4Biostatistics Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; the 5Diabetes Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; and 6Lilly, Minneapolis, Minnesota. Corresponding author: Judith C. Kuenen, jc.kuenen@me.com. Received 2 December 2010 and accepted 12 May 2011. DOI: 10.2337/dc10-2217 This article contains Supplementary Data online at http://care.diabetesjournals.org/lookup/suppl/doi:10. 2337/dc10-2217/-/DC1. *A complete list of the members of the ADAG Study Group can be found in the Supplementary Data. © 2011 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and thework is not altered. See http://creativecommons.org/ licenses/by-nc-nd/3.0/ for details. care.diabetesjournals.org DIABETES CARE, VOLUME 34, AUGUST 2011 1843 P a t h o p h y s i o l o g y / C o m p l i c a t i o n s O R I G I N A L A R T I C L E Europe, and Africa from 2006 to 2008 to define the relationship between HbA1c and average glucose levels. Because a full description of this observational study has been published (14), we de- scribe it here only briefly. A total of 268 individuals with type 1 diabetes and 159 individuals with type 2 diabetes (age 18–70 years) completed the study. Participants were selected based on sta- ble glycemic control as evidenced by two HbA1c values within one percentage point of each other in the 6 months prior to recruitment. Individuals with a wide range of HbA1c levels were included. Participants with conditions leading to major changes in glycemia (infectious disease, steroid therapy, and pregnancy) or conditions thatmight interfere with the measurement of HbA1c or the relation- ship between HbA1c and MPG (hemoglo- binopathies [16], anemia, increased erythrocyte turnover, blood loss and/or transfusions, or chronic renal or liver dis- ease) were excluded (14). The study was approved by the human studies commit- tees at the participating institutions, and informed consent was obtained from all participants. Measurements of glycemia During the study period, CGM (Med- tronic Minimed, Northridge, CA) was performed at home four times with 4-week intervals during the 16-week study pe- riod.Monitoring period lasted at least 48h, during which time glucose levels were assessed every 5 min. CGM data were accepted for analysis if there were no gaps longer than 120 min and if the mean absolute difference with the Hemocue calibration results was ,18%, as recom- mended by the manufacturer. For calibra- tion purposes, participants performed SMBGwith the Hemocuemeter (Hemocue Glucose 201 plus; Hemocue, Ängelholm, Sweden) during the days of CGM. For adequate calculation of MPG, subjects additionally performed a seven- point SMBG (OneTouch Ultra; Lifescan, Milipitas, CA) for at least 3 days per week during the weeks when CGM was not performed. All blood glucose values stated are plasma equivalents. HbA1c samples were analyzed with four highly intercorrelated DCCT-aligned assays: a high-performance liquid chroma- tography assay, two immunoassays, and an affinity assay (all approved by the Na- tional Glycohemoglobin Study Program). The mean HbA1c value at the end of the 12 week study period was used (14). Calculating glucose variability Three indices of intraday glucose variabil- ity were calculated based on CGM: the SD of mean glucose concentrations, the mean amplitude of glycemic excursions (MAGE), and the continuous overlapping net glycemic action (CONGA). High SD, MAGE, and CONGA values indicate high intraday glucose variability. MAGE is the mean of the differences between consec- utive peaks and nadirs, only including changes of.1 SD of glycemic values and thus capturing only major fluctuations (17). For the calculation of CONGAn, the difference of the current value com- pared with the value n hours previously was calculated for each observation after the first n hours. The CONGAn is the SD of these differences (18). In the analyses, we used CONGA at 4 h (CONGA4). Cal- culations based on CGM data were calcu- lated after exclusion of the initial 2 h of monitoring, which is considered to be an unstable calibration period. Statistical analysis First, we explored the correlations be- tween MPG and HbA1c and measures of glycemic variability as SD, MAGE, and/or CONGA4 for the total diabetic population and the two diabetes types. Multiple lin- ear regression was used to investigate confounding and effect-modifying influ- ence of clinical parameters (glycemic var- iability) on the relation between the determinant (MPG) and outcome (HbA1c) of interest. We then assessed which of the variability measures (SD, MAGE, or CONGA4) had the strongest impact on the MPG-HbA1c relationship by con- founding or effect modification. Effect modification was concluded when the slope of the interaction term of glycemic variability and determinant was significant. If no effect modificationmight be concluded, a parameter DB was com- puted as the relative difference of the slope of the determinant in the model without and with the clinical parameter. Confounding was concluded when the absolute value of DB exceeded the gener- ally accepted threshold of 10%. Multivariate confounding was inves- tigated with a variant of stepwise regres- sion, in which the stepping criterion was not a P value but the DB as long as it ex- ceeded the threshold. For significance, a threshold of a = 0.05 was used. Analyses were done for the total pop- ulation and stratified for the type of diabetes. Finally, we illustrated the magnitude of the effect caused by the variability indices, by confounding or effect modification, on the MPG-HbA1c relationship. RESULTS—Of the 507 patients en- rolled, 427 completed the study and had adequate glucose monitoring and HbA1c samples for inclusion in the analyses. Two hundred and sixty-eight participants had type 1 diabetes, and 159 had type 2 diabe- tes. The CGM and the SMBG data during the 3-month period included approxi- mately 2,400 and 300 measurements per subject, respectively. The relationship be- tween the HbA1c level at the end of the 3-month study period and MPG calcu- lated over the preceding 3 months was expressed as the simple linear regressions. The formula for the total diabetic popula- tion was as follows: HbA1c (%) = 0.028 3 MPG (mg/dL) + 2.66 (R2 = 0.80). The for- mula for type 1 diabetes was as follows: HbA1c (%) = 0.028 3 MPG (mg/dL) + 2.77 (R2 = 0.77). The formula for type 2 diabetes was as follows: HbA1c (%) = 0.0283MPG (mg/dL) + 2.62 (R2 = 0.82). The clinical and glycemic character- istics are shown in Table 1. Mean HbA1c (SD) for type 1 diabetic patients was 7.3% (1.1) and for type 2 diabetic patients was 6.8% (1.1). All GV measures had significant in- fluence on the MPG-HbA1c relationship for the total population. The variability index SD showed the strongest influence on the MPG-HbA1c relationship. None of theGVmeasures showed confounding for all diabetic patients pooled or for the type 1 or type 2 diabetic patients separately (Table 2). In the type 1 diabetic patients, the effect modification of SD and CONGA4 was significant (P , 0.01 and P = 0.02), and for the MAGE it was just not signifi- cant (P = 0.06) (Table 2). TheMPG/HbA1c linear regression formula with confound- ing for type 2 diabetes was as follows: HbA1c (%) = 2.64 + 2.63 3 MPG/100 + 0.58 3 SD/100. The MPG-HbA1c linear regression formula with effect modifica- tion for type 1 diabetes was as follows: HbA1c (%) = 3.91 + 1.79 3 MPG/100 2 1.37 3 SD/100 + 1.25 3 MPG/100 3 SD/100. The impact of effect modification of low GV (SD = 30 mg/dL) versus high GV (SD = 100 mg/dL) for an MPG level of 160 mg/dL in type 1 diabetes on the HbA1c level was 6.96 vs. 7.41%, respec- tively, as shown in Table 3. At an MPG level of 220 mg/dL (HbA1c following the regression formula of 8.89%), a decline in the SD parameter from 100 to 30 mg/dL reduced HbA1c from 9.23 to 8.26%. 1844 DIABETES CARE, VOLUME 34, AUGUST 2011 care.diabetesjournals.org Glucose variability, plasma glucose, and HbA1c For all patients pooled, there was no effect modification of the respective GV measures on the MPG-HbA1c relation- ship. For type 2 diabetic patients, the im- pact of effect modification from the respective GV measures was far from sig- nificant (Table 2). The number of patients with a predefined SD is shown in Table 1 for all patients pooled and for the type 1 and type 2 diabetic patients separately. CONCLUSIONS—This study demon- strated a significant effect of GV, as reflected by SD, on the MPG-HbA1c rela- tionship. High GV (SD) is associated with higher HbA1c levels for a given MPG, and this effect wasmore pronounced at higher HbA1c and MPG values. However, the magnitude of this effect of GV was small and only demonstrable in type 1 diabetic patients. Possibly, the type 2 diabetic pa- tient group was too small (n = 159) and the variability in this group too low to find this interaction. The ADAG study showed a tight correlation between HbA1c and MPG, al- lowing the translation of HbA1c into esti- mated average glucose (14,19). It has been suggested that GV could affect the MPG-HbA1c relationship, but this has not previously been demonstrated (20–22). To our knowledge, the current study is the largest study reporting an influence of GV—as expressed by SD, MAGE, and CONGA4 calculated from CGM—on the MPG-HbA1c relationship. The discrepan- cies in the MPG-HbA1c relationship are less likely caused by technical errors be- cause this study included accurate and centralized measurements of HbA1c val- ues and intensively measured plasma glu- cose concentrations (;2,700 values) in a large and diverse population. Also, indi- viduals with conditions or treatment that might result in major changes in glycemia or interference with the HbA1c assay, or the MPG-HbA1c relationship, were ex- cluded. These precautions allowed us to search for factors other than MPG that may contribute to HbA1c. In general, GV is higher in patients with poor glycemic control and in type 1 diabetic patients compared with type 2 diabetic patients, which can be attributed to insulin therapy and higher insulin sensitivity. High GV may affect glycation because of periodic exposure of the eryth- rocyte to high glucose levels and therefore to faster irreversible glycation. Other factors like hyperglycemia- induced oxidative stress may affect the glycation process. In recent literature, it has been speculated that oxygen free rad- icals per se or with an associated decrease in antioxidants may modulate the forma- tion of early glycated protein (10,11). Brownlee (12) demonstrated that hy- perglycemia stimulates oxidative stress. High GV and especially postprandial glu- cose excursions were also previously Table 1—Baseline clinical and glycemic characteristics All Type 1 diabetes Type 2 diabetes n 427 268 159 Age (years) 47.6 6 13.6 44.1 6 12.9 56.6 6 9.4 Sex (% female) 54 52 51 Ethnicity (% non-Hispanic whites) 83 93 73 Current smokers 11 12 9 Insulin treatment 76 100 38 Glycemic measures HbA1c (%) 6.8 6 1.3 7.3 6 1.1 6.8 6 1.1 MPG (mg/dL) 149.4 6 39.6 162 6 36 149.4 6 36 Measures of GV CGM SD (mg/dL) 48.6 6 25.2 64.8 6 16.2 39.6 6 16.2 MAGE (mg/dL) 86.4 6 43.2 115.2 6 32.4 68.4 6 27 CONGA4 (mg/dL) 66.6 6 28.8 88.2 6 23.4 52.2 6 21.6 SD (mg/dL) #30 61 (14.3) 9 (3.4) 52 (32.7) ,30–60 173 (40.5) 84 (31.3) 89 (56) ,60–90 173 (40.5) 155 (57.8) 18 (11.3) .90 20 (4.7) 20 (7.5) 0 (0) Data are means 6 SD, %, or n (%). Table 2—The P values of the influence of the respective GV measures themselves, as well as effect modification and the D of confounding, calculated from the respective slopes (B and B9) from the regression equations, on the HbA1c-MPG relationship for all patients pooled and for type 1 and type 2 diabetic patients separately Influence of the GV measure (P) Slope of MPG (B) in the main regression formula Slope of MPG (B9) in the regression formula with the GV measure DConfounding (%)* Effect modification (P) SD All ,0.01 2.818 2.624 6.9 0.06 Type 1 diabetic 0.01 2.781 2.631 5.4 ,0.01 Type 2 diabetic 0.06 2.782 2.637 5.2 0.74 MAGE All ,0.01 2.818 2.700 4.2 0.37 Type 2 diabetic 0.19 2.781 2.721 2.2 0.06 Type 2 diabetic 0.19 2.782 2.698 3.0 0.19 CONGA4 All ,0.01 2.818 2.667 5.4 0.15 Type 1 diabetic 0.06 2.781 2.687 3.4 0.02 Type 2 diabetic 0.07 2.782 2.661 4.3 0.46 *DConfounding in % = 100 3 absolute (B9 – B)/B. care.diabetesjournals.org DIABETES CARE, VOLUME 34, AUGUST 2011 1845 Kuenen and Associates associated with oxidative stress in type 2 diabetes (13). The activation of oxidative stress, estimated from urinary excretion rates of isoprostanes, was highly corre- lated with MAGE calculated from CGM (13). However, Wentholt et al. (23) could not replicate these results in type 1 diabe- tes. Recently, Ceriello et al. (15) demon- strated that high intraday GV was more damaging to endothelial function than stable hyperglycemia and that oxidative stress plays a key role. Whether oxidative stress influences glycation needs to be determined. On the other hand, it has been dem- onstrated that erythrocyte survival is shorter at chronic high glucose concen- trations levels, which might falsely lower HbA1c levels. Peterson et al. (24) showed that the life span of 51Cr-labeled eryth- rocytes increased in all seven subjects when their poorly controlled diabetes was adequately treated. Virtue et al. (25) concluded that there is a hyperglycemia- related decrease in erythrocyte survival as measured by carbon monoxide in the expired air, which results in an exponen- tial underestimation of the severity of hyperglycemia at higher HbA1c levels. Similarly, hyperglycemia-related osmotic stress may influence erythrocyte perme- ability and could cause damage to the erythrocyte and shorten its life span. These findings could lead to underesti- mation of HbA1c at higher MPG levels, concealing a glycemic control worse than indicated by HbA1c measurements. However, we found that type 1 diabetic patients with high GV display higher HbA1c levels than suspected based on MPG. This effect was more pronounced at higher HbA1c levels, indicating that fo- cus on reducing GV, especially in patients with poor glycemic control, could help reduce HbA1c levels. Limitations of our study are that CGM has a limited range of reliable measure- ments between 2.2 mmol/L and 22.2 mmol/L. Therefore, theoretically, CGM performance could be less precise in patients with high glycemic variability. Furthermore, CGM has a lag time in glucose values compared with the venous measured values (the physiological gap), and this can result in underestimation of the influence of GV on the glycation of HbA1c, and no measures of erythrocyte survival, oxidative stress, or clinical fol- low-up are available in this population. In conclusion, at higher levels of GV the relationship between HbA1c and MPG in patients with type 1 diabetes is altered, leading to a higher HbA1c level for a given MPG.However, the impact (near theHbA1c treatment target of 7%) is only modest. Acknowledgments—The ADAG study is sup- ported by research grants from the American Diabetes Association an