HbA1c variability is associated with microalbuminuria development in type 2 diabetes: a 7-year prospective cohort study
C.C. Hsu1,2; H.Y. Chang1; M.C. Huang3; S.J. Hwang4; Y.C. Yang5; Y.S. Lee1; S.J. Shin6; T.Y. Tai7
1Division of Preventive Medicine and Health Services Research, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan 2Department of Health Services Administration, China Medical University and Hospital, Taichung, Taiwan
3Department of Public Health, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
4Division of Nephrology, Department of Medicine, Kaohsiung Medical University Hospital and College of Medicine, Kaohsiung, Taiwan
5Department of Family Medicine, National Cheng Kung University Hospital, Tainan, Taiwan
6Division of Endocrinology and Metabolism, Department of Internal Medicine, Kaohsiung Medical University Hospital and College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
Corresponding author: Shyi-Jang Shin, Division of Endocrinology and Metabolism, Department of Internal Medicine, Kaohsiung Medical University Hospital, 100 Ziyou 1st Road, Kaohsiung 807, Taiwan.
Telephone: +886-7-312-1101 E-mail: [email protected]
Running title: HbA1c variability and microalbuminuria
Word count Abstract: 221 Text: 2645
Abstract
Aims/hypothesis: HbA1c variability has been shown to be an independent risk factor
of nephropathy in type 1 diabetes. We aim to explore the association between HbA1c variability and microalbuminuria development in type 2 diabetes. We also intend to test the applicability of serially measured HbA1c in 2 years for this risk assessment.
Methods: We recruited 821 middle-aged normoalbuminuric type 2 diabetes subjects
between 2003 and 2005 and followed them through the end of 2010. Average follow-up time was 6.2 years. We defined microalbuminuria as a urine albumin-to-creatinine ratio ≥ 30 mg/g (3.4 mg/mmol). HbA1c variability was calculated by the standard deviation (SD) of serially measured HbA1c. The Cox proportional hazards model was used to evaluate association between quartile of HbA1c SD and development of microalbuminuria.
Results: The incidence of microalbuminuria for overall subjects was 58.4, 58.6, 60.8,
and 91.9 per 1000 person-years for Q1–Q4 adjusted HbA1c SD, respectively (P for trend = 0.042). Compared to those in Q1, the patients in Q4 were about 37% more likely to develop microalbuminuria. The hazard ratio derived from a series of 2-year HbA1c measurements was similar to that from data collection for longer than 4 years.
Conclusions/interpretation: In addition to mean HbA1c values, HbA1c variability,
microalbuminuria in type 2 diabetes.
Key words: Microalbuminuria, A1c variability, Type 2 diabetes mellitus
Abbreviations:
DM: Diabetes mellitus
DMIDS: The project of Diabetes Management through an Integrated Delivery System HbA1c: Glycated hemoglobin A1c
HDL: High density lipoprotein ACR: Albumin to creatinine ratio BP: Blood pressure
SD: Standard deviation HR: Hazard ratio
Introduction
The Diabetes Control and Complications Trial (DCCT) in type 1 diabetes and the United Kingdom Prospective Diabetes Study (UKPDS) in type 2 diabetes both concluded that a rise of HbA1c can increase the development of microvascular complications [1-3]. Recently, glycemic variability has also been demonstrated to have effects on the risk of micro- and macro-vascular consequences in diabetes [4-7]; however, its association with diabetic complications has not been consistently
confirmed [8-12].
The data from the Finnish Diabetic Nephropathy (FinnDiane) study indicated that HbA1c variability of type 1 diabetes patients is predictive of incident
microalbuminuria and progression of renal disease [13]. In type 1 diabetes, HbA1c variability was similarly shown as an independent risk factor for microalbuminuria development, even among the young, who are highly vulnerable to vascular
complications [14]. Up to now, however, the relationship between HbA1c variability and the development of nephropathy has not been investigated in type 2 diabetes.
Currently, there are no clear consensuses as to how long HbA1c should be measured to unwaveringly reflect clinical impacts of HbA1c variability. The DCCT and the FinnDiane study undertook 9-year and 5.7-year serial HbA1c measurements, respectively, to examine HbA1c variability [13,15]. However, the follow-up study of
the UKPDS demonstrated the important role of early strict glycemic control in prevention of vascular complications [16], implying an indicator that needs to track HbA1c measurements for more than 5 years to correlate its clinical implications may be late for a prompt intervention. The primary aim of this study is to explore
relationship between HbA1c variability and microalbuminuria development in type 2 diabetes. Furthermore, in order to emphasize the importance of early stabilization in glycemic control, we also intend to determine whether HbA1c variability derived from 2-year measurements is an early indicator independently associated with diabetic nephropathy in type 2 diabetes.
Methods
Participants
The study subjects were type 2 diabetes patients who were enrolled for diabetes management through an integrated delivery system (DMIDS) project (NCT00288678 ClinicalTrial.gov) [17]. The detailed inclusion and exclusion criteria for the DMIDS project are described elsewhere [18]. Briefly, 1209 type 2 diabetes subjects were recruited from 2003 to 2005 and followed through the end of 2010. Of these enrollees, 143 with less than 3 eligible urine albumin-to-creatinine ratio (ACR) tests and 245 with microalbuminuria at baseline (ACR ≥ 3.4 mg/mmol in 2 consecutive urine tests) were excluded from analysis. The remaining 821 subjects were selected for further
investigation. Written informed consent was obtained from all enrollees. The institutional review board at the National Health Research Institutes reviewed and approved this study.
Laboratory tests
Fasting (overnight for at least 8 h) venous blood and morning spot urine specimens were collected every 6 months. Glycated hemoglobin (HbA1c) was measured by high-performance liquid chromatography (Variant II; Bio-Rad Laboratories, Hercules, CA). Triglyceride (TG) and high-density lipoprotein (HDL) cholesterol were
measured by an automatic analyzer (Hitachi 7060; Hitachi High Technologies Co, Tokyo). Urinary albumin was measured by the immunoturbidimetric method (Hitachi 7060; Hitachi High Technologies Co, Tokyo). All blood and urine samples were well kept at 2–8°C and measured within 8 h at a central laboratory.
Definition of outcome, HbA1c variability, and covariates
Those who had ACR ≥ 3.4 mg/mmol in 2 consecutive urine tests were defined as having developed microalbuminuria. The urine samples were excluded from analysis if microscopic urinalysis showed red blood cells > 5/high-power field (HPF), white blood cells > 5/HPF, epithelial cells > 5/HPF, or appearance of casts or bacteria.
The HbA1c variability was defined as standard deviation (SD) of serial HbA1c measurements, the coefficient of variation (CV) of HbA1c to correct for the
mean, or the adjusted HbA1c SD — in which SD was divided by the square root of k/ (k-1), where k stands for the number of HbA1c measurements — to control for the effects of variation in the number of HbA1c measurements [15]. Because of similar results derived from all three SD definitions, we used the adjusted HbA1c SD to account for HbA1c variability in multivariable survival analysis.
Waist circumference was measured at the level of the midpoint between the lowest rib and the iliac crest. Blood pressure was measured 3 times separated by 1 min; the mean of these 3 measurements was recorded. Smoking status was
categorized into 3 groups: current smokers, ex-smokers (having stopped smoking for at least 1 month), and non-smokers (having smoked < 100 cigarettes in lifetime). Those who have ever chewed betel nuts were defined as chewers. Those who had not performed any leisure-time physical activity in the past two weeks were defined as the sedentary group.
Statistical analysis
Data were expressed as mean ± SD for continuous variables, or as counts and proportions for categorical variables. Student t tests and chi-square analyses were used for continuous and categorical variables, respectively, to compare characteristics between non-progressors and progressors (with microalbuminuria development). The incidence rate of microalbuminuria was estimated by the number of observed new
microalbuminuria cases per 1000 person-years. The person-years were calculated as the time elapsed from the date of recruitment until the date of death, loss to follow-up, microalbuminuria development, or the end of follow-up, whichever came first. The calculation of a 95% confidence interval (95% CI) for the incidence rate was based on the assumption that the observed incident cases followed a Poisson distribution. We estimated the incidence rate of microalbuminuria in different quartiles of adjusted HbA1c SD for overall subjects and also for different subgroups according to their numbers of HbA1c measurement and baseline HbA1c. In order to test predictability of HbA1c variability for microalbuminuria development in different subgroups, we calculated mean HbA1c and adjusted HbA1c SD for 3-4 measurements (all HbA1c from recruitment to the end of the 2nd year), and for those with baseline HbA1c ≤ 8% and those > 8% (64 mmol/mol), respectively, for subgroup analysis.
Kaplan-Meier analyses and univariate Cox proportional hazard models were used to explore the association between quartiles of adjusted HbA1c SD and
microalbuminuria development. The covariates used in Cox proportional hazard models include baseline demographic and metabolic profiles (age at DM onset, gender, education, DM duration, smoking status, waist circumference, TG and HDL-cholesterol, mean HbA1c, and blood pressure). Multivariable Cox proportional hazards modeling was used to determine the independent effects of HbA1c variability
on microalbuminuria development. Study entry was defined as the date of enrollment. Observations were censored at the end of the study, or the date that patients died or dropped out of the study, whichever occurred first. Results were expressed as hazard ratio (HR) compared with the group in the lowest quartile of adjusted HbA1c SD.
The proportional hazard assumption, the constant HR over time, was evaluated by comparing estimated log–log survival curves for all covariates. All assessed log– log survival plots graphically showed two parallel lines, indicating no violation of the assumption. A test for trend was conducted by treating quartiles of adjusted HbA1c SD as a continuous variable.
Analyses were performed with SAS software, version 9.1 (SAS Institute, Cary, NC). A two-sided P value < 0.05 was considered statistically significant.
Results
Table 1 shows the progressors were more likely to have lower education, longer DM duration, and poorer metabolic profiles, including higher baseline urine ACR and poorer control of blood pressure and glucose. Compared to non-progressors, those who developed microalbuminuria also had higher HbA1c variability during the follow-up period. In regard to characteristics in different quartiles of adjusted HbA1c SD (Table 2), those who had higher HbA1c variability tended to have earlier DM onset, use more anti-diabetic drugs, and bear poorer glycemic control at the baseline
and in the follow-up as well. The patients in the highest quartile (Q4) of HbA1c SD were also more likely to be smokers (32.4% vs. 23.6% for Q1-Q3 combined, P<0.001), betel nuts chewers (15.1% vs. 10.9%, P=0.031), and physically inactive (35.7% vs. 27.5%, P=0.011). As shown in Table 3, both mean and adjusted SD of HbA1c were significantly related to microalbuminuria development in univariate analysis as well as in separate multivariable regressions (model 1 [HR = 1.10, P < 0.05 for mean of HbA1c] and model 2 [P for trend = 0.001 for adjusted SD of HbA1c]); however, the effect of mean of HbA1c was attenuated (HR = 1.04, non-significant) when these two variables were put together in the same model (model 3). Compared to those in the lowest quartile of adjusted HbA1c SD as shown in Table 3, the patients in the 4th quartile were 48% more likely to develop microalbuminuria (P < 0.05 for Q4 and P for trend = 0.043 in model 3). In regard to other covariates, the impact of lower education was persistent in univariate and multivariable models; furthermore, DM duration, high blood pressure and the subsequent use of ACEi/ARB were also revealed to have marginal effects on development of microalbuminuria (Table 3) after controlling for other covariates. As shown in Table 4, the incidences of microalbuminuria for overall subjects were 58.4, 58.6, 60.8, and 91.9 per 1000 person-years for Q1–Q4 adjusted HbA1c SD, respectively (P for trend = 0.001). The graded association (P for trend) between the quartile of adjusted HbA1c SD and risk
of microalbuminuria is consistent and little affected by the HbA1c follow-up time (2 years vs. up to 7 years) and the baseline HbA1c (≤ 64 mmol/mol vs. > 64 mmol/mol ) (Figure 1 and Table 4). On the contrary, the established effects of mean HbA1c were not significant in those with baseline HbA1c ≤ 64 mmol/mol and for a 2-year of follow-up (Table 4). We also used gender-specific cutoff point [19] to define microalbuminuria and conducted sensitivity analysis, the results were similar (data not shown).
Discussion
Intrapersonal HbA1c variability, as expressed by standard deviation (SD) of serially measured HbA1c, is a reliable and stable indicator to predict microalbuminuria development in type 2 diabetes patients. Our findings not only enrich previous
knowledge about the impact of HbA1c variability on type 1 diabetes [13-15], but also provide the first empirical evidence for a possible association of HbA1c variability with development of microalbuminuria in type 2 diabetes. Furthermore, the current study also demonstrates a 2-year estimate of HbA1c variability is able to be used as a short-term monitoring indicator for progression of diabetic nephropathy. This
prospective cohort study may provide useful guidance for clinical applications. In addition to mean value of serially measured HbA1c, HbA1c variability is frequently shown to be associated with diabetic complications in type 1 diabetes. The
adult patients with higher HbA1c variability were more likely to develop
cardiovascular events and albuminuria as shown in the FinnDiane Study [13]. The similar association was also observed in young type 1 diabetes patients. The Oxford Regional Prospective Study [14] and the Pittsburgh Epidemiology of Diabetes Complications (EDC) Study [20] demonstrated that higher SD of HbA1c could predict microalbuminuria and coronary artery disease in type 1 diabetes patients younger than 17 years of age. Although the DCCT data [21,22] could not associate microvascular complications with acute glucose variability derived from the intra-day 7-point blood glucose profile, they revealed a significant linkage between long-term glycemic stability and the development of retinopathy and nephropathy by using the 9-year adjusted SD of HbA1c as an indicator [15].
To the best of our knowledge, HbA1c variability has never been used to predict clinical outcomes in type 2 diabetes. Instability of fasting glucose level has been reported as a risk factor for development of complications in type 2 diabetes; but the results have been inconsistent. Intra-day glucose variability was shown to be associated with coronary artery disease in type 2 diabetes in a cross-sectional study [7]; however, it couldn’t predict recurrent cardiovascular outcomes in the prospective HEART2D study [23]. The predictability of all-cause and cardiovascular mortality from 3-year fasting glucose variability in type 2 diabetes patients was shown in the
Verona Diabetes Study [24,25]; but it is still controversial in regard to association between glucose variability and microvascular outcomes. A small-scale study (n = 130) in Spain [26] found that fasting glucose variability was an independent risk factor for retinopathy in type 2 diabetes in a 5.2-year follow-up; however, another Italian study (n = 746) couldn’t confirm this association [4]. The inconsistency in the results of the aforementioned studies may be attributable to the influence of food intakes on the serial glucose measurements. The acute glucose profile measured sporadically may also not be able to reflect a long-term dynamic pattern of glycemic variability. Moreover, the standard measurement of acute glucose fluctuation using continuous glucose monitoring or intra-day 7-point glucose profile to calculate SD or MAGE (the mean amplitude of glycemic excursion) [27] is not clinically applicable for most noninsulin-using type 2 diabetes patients.
In this study, we used HbA1c, an indicator reflecting glycemic control over 2-3 months [28], to detect microalbuminuria development. The mean and SD derived from 3 or 4 HbA1c measurements in 2 years are shown adequate to predict
microalbuminuria, while most previous studies used HbA1c variability from long-term observations, varying from 5 to 16 years [13,15,20], to delineate its impacts on diabetic complications in type 1 diabetes. A series of HbA1c measurements is able to reveal a general pattern of glycemic control during a certain period for risk
assessment; but it is difficult to apply in clinical practice if the required data collection period for a reliable indicator is too long because clinicians usually need to be aware of their patients’ risks at the earliest possible time to make prompt clinical decisions. Apart from the fact that the long-term follow-up of serial HbA1c level is essential to better diabetes care, our findings indicate the use of 2-year variability and mean of HbA1c to correlate microalbuminuria development is a clinically responsive indicator, which emphasizes the importance of optimizing an un-fluctuated HbA1c early to prevent diabetic nephropathy.
High variability of HbA1c implies that poor glycemic control does exist, at least temporarily, although the average HbA1c may be desirable in our patients. According to “metabolic memory” theory [29], poor glycemic control, even if it lasts only a short time, can be “memorized” and still cause detrimental effects later. Glucose fluctuations have been demonstrated to cause oxidant overproduction and endothelial dysfunction, and this effect is even stronger in stable higher glucose status in type 2 diabetic patients [30,31]. The overproduction of reactive oxygen species is the common mediator of several hyperglycemia-activated pathways in the
pathogenesis of diabetic nephropathy. Patients with high HbA1c variability often live with unhealthier lifestyles and, as shown in this study, may also intensify their
epigenetic changes could be induced by transient hyperglycemia [32], although other mechanisms of nephropathy caused by higher HbA1c variability are still unknown.
We have to be cautious when interpreting the results of this study because we may not be able to fully control all confounding factors in an observational study. To clarify the possibility of reverse causation, which is often suspected in observational cohort design, a randomized clinical trial is needed to further validate effects of the proposed 2-year HbA1c variability on diabetic nephropathy. Other limitations of this study are the measurement issues. We checked HbA1c every 6 months from which the glycemic variability was derived; however, HbA1c is an indicator reflecting glycemic control over 2-3 months, therefore the HbA1c variability assessed in the current study may be underestimated owing to the inadequate monitoring period. Furthermore, for practical reasons, instead of measurement of 24-hour albumin excretion or early morning first voiding urine, ACR was measured using morning spot urine in this study, which is acceptable according to the KDOQI Clinical Practice Guideline [33] but may overestimate incidence of microalbuminuria.
In conclusion, this current study is the first prospective study showing that higher HbA1c variability is associated with development of microalbuminuria in type 2 diabetes patients. The predictability of the 2-year HbA1c SD for development of microalbuminuria conveys a clinical message that sustaining glycemic control at the
early stage is crucial for management of type 2 diabetes.
Acknowledgement: This study was supported by the National Health Research
Institutes, which had no role in the study design, data analysis, data interpretation, or writing of the manuscript.
Contribution statement: CCH designed the study and conceived the idea. CCH,
HYC, and YSL analyzed data and interpreted results. CCH and SJS drafted the article. CCH, HYC, MCH, SJH, YCY, and TYT critically revised the article for important intellectual content. All authors reviewed the manuscript and had final responsibility for the decision to submit for publication.
Duality of interest: All authors declare that there is no duality of interest associated
References
1. The Diabetes Control and Complications Trial Research Group (1993) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 329: 977-986.
2. The Diabetes Control and Complications Trial Research Group (1995) The relationship of glycemic exposure (HbA1c) to the risk of development and progression of retinopathy in the diabetes control and complications trial. Diabetes 44: 968-983.
3. Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, et al. (2000) Association of glycaemia with macrovascular and microvascular
complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 321: 405-412.
4. Zoppini G, Verlato G, Targher G, Casati S, Gusson E, et al. (2009) Is fasting glucose variability a risk factor for retinopathy in people with type 2 diabetes? Nutr Metab Cardiovasc Dis 19: 334-339.
5. Snell-Bergeon JK, Roman R, Rodbard D, Garg S, Maahs DM, et al. (2010) Glycaemic variability is associated with coronary artery calcium in men with Type 1 diabetes: the Coronary Artery Calcification in Type 1 Diabetes study. Diabet Med 27: 1436-1442.
6. Takao T, Ide T, Yanagisawa H, Kikuchi M, Kawazu S, et al. (2011) The effects of fasting plasma glucose variability and time-dependent glycemic control on the long-term risk of retinopathy in type 2 diabetic patients. Diabetes Res Clin Pract 91: 29.
variability and the presence and severity of coronary artery disease in patients with type 2 diabetes. Cardiovasc Diabetol 10: 19.
8. Zaccardi F, Pitocco D, Ghirlanda G (2009) Glycemic risk factors of diabetic vascular complications: the role of glycemic variability. Diabetes Metab Res Rev 25: 199-207.
9. Ceriello A, Ihnat MA (2010) 'Glycaemic variability': a new therapeutic challenge in diabetes and the critical care setting. Diabet Med 27: 862-867.
10. Kilpatrick ES, Rigby AS, Atkin SL (2010) For debate. Glucose variability and diabetes complication risk: we need to know the answer. Diabet Med 27: 868-871.
11. Siegelaar SE, Holleman F, Hoekstra JBL, DeVries JH (2010) Glucose variability; does it matter? Endocr Rev 31: 171-182.
12. Nalysnyk L, Hernandez-Medina M, Krishnarajah G (2010) Glycaemic variability and complications in patients with diabetes mellitus: evidence from a
systematic review of the literature. Diabetes Obes Metab 12: 288-298. 13. Waden J, Forsblom C, Thorn LM, Gordin D, Saraheimo M, et al. (2009) A1C
variability predicts incident cardiovascular events, microalbuminuria, and overt diabetic nephropathy in patients with type 1 diabetes. Diabetes 58: 2649-2655.
14. Marcovecchio ML, Dalton RN, Chiarelli F, Dunger DB (2011) A1C variability as an independent risk factor for microalbuminuria in young people with type 1 diabetes. Diabetes Care 34: 1011-1013.
15. Kilpatrick ES, Rigby AS, Atkin SL (2008) A1C variability and the risk of microvascular complications in type 1 diabetes: data from the Diabetes Control and Complications Trial. Diabetes Care 31: 2198-2202.
follow-up of intensive glucose control in type 2 diabetes. N Engl J Med 359:1577-89.
17. Huang MC, Hsu CC, Wang HS, Shin SJ (2010) Prospective randomized controlled trial to evaluate effectiveness of registered dietitian-led diabetes management on glycemic and diet control in a primary care setting in Taiwan. Diabetes Care 33: 233-239.
18. Hsu CC, Chang HY, Huang MC, Hwang SJ, Yang YC, et al. (2011) Association between insulin resistance and development of microalbuminuria in type 2 diabetes: a prospective cohort study. Diabetes Care 34: 982-987.
19. Haller H, Ito S, Izzo JL Jr, Januszewicz A, Katayama S, et al (2011)
Olmesartan for the delay or prevention of microalbuminuria in type 2 diabetes. N Engl J Med 364:907-17.
20. Prince CT, Becker DJ, Costacou T, Miller RG, Orchard TJ (2007) Changes in glycaemic control and risk of coronary artery disease in type 1 diabetes mellitus: findings from the Pittsburgh Epidemiology of Diabetes Complications Study (EDC). Diabetologia 50: 2280-2288.
21. Siegelaar SE, Kilpatrick ES, Rigby AS, Atkin SL, Hoekstra JBL, et al. (2009) Glucose variability does not contribute to the development of peripheral and autonomic neuropathy in type 1 diabetes: data from the DCCT. Diabetologia 52: 2229-2232.
22. Kilpatrick ES, Rigby AS, Atkin SL (2006) The effect of glucose variability on the risk of microvascular complications in type 1 diabetes. Diabetes Care 29: 1486-1490.
23. Siegelaar SE, Kerr L, Jacober SJ, Devries JH (2011) A decrease in glucose variability does not reduce cardiovascular event rates in type 2 diabetic patients after acute myocardial infarction: a reanalysis of the HEART2D
study. Diabetes Care 34: 855-857.
24. Muggeo M, Verlato G, Bonora E, Zoppini G, Corbellini M, et al. (1997) Long-term instability of fasting plasma glucose, a novel predictor of cardiovascular mortality in elderly patients with non-insulin-dependent diabetes mellitus: the Verona Diabetes Study. Circulation 96: 1750-1754.
25. Muggeo M, Zoppini G, Bonora E, Brun E, Bonadonna RC, et al. (2000) Fasting plasma glucose variability predicts 10-year survival of type 2 diabetic patients: the Verona Diabetes Study. Diabetes Care 23: 45-50.
26. Gimeno-Orna JA, Castro-Alonso FJ, Boned-Juliani B, Lou-Arnal LM (2003) Fasting plasma glucose variability as a risk factor of retinopathy in Type 2 diabetic patients. J Diabetes Complications 17: 78-81.
27. Monnier L, Colette C, Owens DR (2008) Glycemic variability: the third
component of the dysglycemia in diabetes. Is it important? How to measure it? J Diabetes Sci Technol 2: 1094-1100.
28. Nathan DM, Turgeon H, Regan S (2007) Relationship between glycated haemoglobin levels and mean glucose levels over time. Diabetologia 50: 2239-2244.
29. Nathan DM, Cleary PA, Backlund J-YC, Genuth SM, Lachin JM, et al. (2005) Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med 353: 2643-2653.
30. Monnier L, Mas E, Ginet C, Michel F, Villon L, et al. (2006) Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA 295: 1681-1687.
31. Ceriello A, Esposito K, Piconi L, Ihnat MA, Thorpe JE, et al. (2008) Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients. Diabetes 57: 1349-1354.
32. El-Osta A, Brasacchio D, Yao D, Pocai A, Jones PL, et al. (2008) Transient high glucose causes persistent epigenetic changes and altered gene expression during subsequent normoglycemia. J Exp Med. 205: 2409-17.
33. National Kidney Foundation KDOQI Clinical Practice Guidelines. Available from
http://www.kidney.org/professionals/kdoqi/guidelines_ckd/p5_lab_g5.htm. Accessed 5 May 2012.
