Association between hemoglobin A1c variability and subclinical coronary atherosclerosis in subjects with type 2 diabetes Hae Kyung Yang, Borami Kang, Seung-Hwan Lee, Kun-Ho Yoon, Byung-Hee Hwang, Kiyuk Chang, Kyungdo Han, Gunseog Kang, Jae Hyoung Cho PII: DOI: Reference:
S1056-8727(15)00165-8 doi: 10.1016/j.jdiacomp.2015.04.008 JDC 6437
To appear in:
Journal of Diabetes and Its Complications
Received date: Revised date: Accepted date:
22 January 2015 4 April 2015 14 April 2015
Please cite this article as: Yang, H.K., Kang, B., Lee, S.-H., Yoon, K.-H., Hwang, B.-H., Chang, K., Han, K., Kang, G. & Cho, J.H., Association between hemoglobin A1c variability and subclinical coronary atherosclerosis in subjects with type 2 diabetes, Journal of Diabetes and Its Complications (2015), doi: 10.1016/j.jdiacomp.2015.04.008
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Association between hemoglobin A1c variability and subclinical coronary atherosclerosis in subjects with type 2 diabetes
RI P
T
Hae Kyung Yanga, Borami Kanga, Seung-Hwan Leea, Kun-Ho Yoona, Byung-Hee Hwangb,
Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St.
MA NU
a
SC
Kiyuk Changb, Kyungdo Hanc, Gunseog Kangd, Jae Hyoung Choa, *
Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea. b
Division of Cardiology, Department of Internal Medicine, Seoul St. Mary’s Hospital,
c
ED
College of Medicine, The Catholic University of Korea, Seoul, Korea
Department of Medical Statistics, College of Medicine, The Catholic University of Korea,
PT
Seoul, Korea
Department of Statistics and Actuarial Science, Soongsil University
*
Corresponding Author: Jae Hyoung Cho, MD, PhD.
AC
CE
d
Division of Endocrinology and Metabolism, Department of Internal Medicine Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, # 222 Banpo-daero, Seocho-gu, Seoul 137-701, Korea Tel: 82-2-2258-6332, Fax: 82-2-595-2534, E-mail:
[email protected] 1
ACCEPTED MANUSCRIPT ABSTRACT Aims We examined the association between hemoglobin A1c (HbA1c) variability and
RI P
T
subclinical coronary atherosclerosis in subjects with type 2 diabetes.
Methods We used the multidetector coronary computed tomography data collected from
SC
subjects with type 2 diabetes who did not have a history of cardiovascular disease or angina symptoms. HbA1c measurements preceding the date of cardiac imaging were retrospectively
MA NU
collected, and intraindividual SD (HbA1c-SD), CV and adjusted SD of HbA1c measurements were calculated. Subclinical coronary atherosclerosis was defined as calcium score > 400 without any cardiac symptoms.
ED
Results A total of 595 subjects were categorized according to the median value of each HbA1c variability indicators. The prevalence of subclinical coronary atherosclerosis was
PT
higher in higher HbA1c variability group compared with lower HbA1c variability group.
CE
Multivariable logistic regression analysis showed that higher HbA1c-SD and -CV were associated with the presence of subclinical coronary atherosclerosis, independent of mean
AC
HbA1c level in subjects with diabetes duration ≤ 10 years (OR [95% CI]; HbA1c-SD, 2.894 [1.105 – 7.584]; HbA1c-CV, 2.540 [1.022 – 6.316]). Conclusions Long-term stabilization of blood glucose level might be important in preventing subclinical coronary atherosclerosis in subjects with earlier period of type 2 diabetes. Keywords; Coronary artery disease; Hemoglobin A1c; Hemoglobin A1c variability; Multidetector computed tomography; type 2 diabetes mellitus
2
ACCEPTED MANUSCRIPT 1. Introduction Glycemic variability comprises “glucose variability” and “hemoglobin A1c (HbA1c)
RI P
T
variability”. Glucose variability relates to the within-day fluctuation of glucose levels, and may eventually reflect an increase in HbA1c level. Long-term glycemic variability, which is
SC
assessed by HbA1c fluctuation, reflects changes in glycemic control over longer periods of
MA NU
time [1].
Data from the Diabetes Control and Complications Trials (DCCT) and Finnish Diabetic Nephropathy (FinnDiane) study have suggested that HbA1c variability is an independent risk factor for the development of diabetic retinopathy and nephropathy in subjects with type 1
ED
diabetes [2, 3]. The Oxford Regional Prospective Study has shown that HbA1c variability is an independent variable that adds to the effect of HbA1c on the risk for microalbuminuria in
PT
young patients with type 1 diabetes [4]. The association between HbA1c variability and
CE
cardiovascular disease was confirmed in several studies with type 1 diabetes. In the FinnDiane Study, HbA1c variability was predictive of incident cardiovascular events [3]. The
AC
Pittsburgh Epidemiology of Diabetes Complications Study reported that changes in HbA1c over time were more strongly associated with coronary artery disease than was the baseline HbA1c value in subjects with type 1 diabetes [5]. Recently, a large clinical cohort of Caucasian subjects with type 2 diabetes from the Renal Insufficiency and Cardiovascular Events (RIACE) study reported that HbA1c variability is an independent correlate of albuminuria and the albuminuric phenotypes of chronic kidney disease (CKD), but not of non-abuminuric CKD or diabetic retinopathy [6]. Two prospective cohort studies from Japan and Taiwan, the Tsukuba Kawai Diabetes Registry 2 [7] and the 3
ACCEPTED MANUSCRIPT Diabetes Management through an Integrated Delivery System project [8], have shown that
T
HbA1c level is associated with microalbuminuria in patients with type 2 diabetes.
RI P
However, studies on the impact of HbA1c variability on cardiovascular disease in subjects with type 2 diabetes are inconclusive [9-13]. Previous reports were based on detecting overt
SC
cardiovascular events, and the association between subclinical coronary atherosclerosis and
MA NU
HbA1c variability has not been studied.
Diabetes is known as a coronary heart disease equivalent, and images by computed tomography (CT) have shown extensive calcification of vascular beds in subjects with diabetes, reported as coronary artery calcium score [14]. Coronary artery calcium score
ED
(CACS) is a useful method to identify subclinical atherosclerosis [15], and reflect the longterm impact of elevated cardiovascular risk factors and independently predict future risk of
PT
cardiovascular morbidity and mortality [16, 17].
CE
The aim of this study was to explore the relationship between HbA1c variability and subclinical coronary atherosclerosis determined with multi-detector coronary CT (MDCT) in
AC
patients with type 2 diabetes. To best of our knowledge, the association between HbA1c variability and MDCT finding has not been studied in subjects with or without type 2 diabetes.
2. Material and Methods 2.1 Study design We used the MDCT data collected for the CRONOS-ADM Registry (Coronary CT 4
ACCEPTED MANUSCRIPT angiography evaluation for clinical outcomes in asymptomatic patients with type 2 diabetes mellitus, registered with ClinicalTrials.gov NCT02070926), an observational cohort study on
T
the clinical outcomes in asymptomatic patients with type 2 diabetes. This study enrolled
RI P
asymptomatic patients with type 2 diabetes who underwent 64-slice, dual source MDCT at two hospitals affiliated with the Catholic University of Korea between January 2006 and
SC
December 2010. Patients were eligible for this study if they were >18 years of age and did
MA NU
not have past history of coronary artery disease, or angina or angina-equivalent symptoms. Asymptomatic status was confirmed using the Rose questionnaire for angina [18]. Those with type 1 diabetes, with known or suspected coronary artery disease; with a history of prior myocardial infarction, coronary revascularization, or cardiac transplantation; receiving
ED
treatment with antianginal medication; with ventricular or supraventricular arrhythmias; or with contraindications for the use of iodinated contrast were excluded. All the HbA1c
PT
measurements performed preceding the date of MDCT imaging were retrospectively
CE
collected. Among 935 subjects enrolled at the registry, 595 subjects who had 4 HbA1c recordings were analyzed in this study. The duration of diabetes and baseline HbA1c level of
AC
subjects who had < 4 HbA1c recordings (n=340) was 9.63 ± 9.04 years and 8.69 ± 2.30 %, respectively.
2.2 Imaging protocol and image reconstruction All patients were in normal sinus rhythm and were able to hold their breath as required for MDCT. Patients with a heart rate >70 beats per minute (bpm) were administered intravenous esmolol at a dose of 3 mg at 5-minute intervals, up to a total dose of 15 mg. If the patient’s 5
ACCEPTED MANUSCRIPT heart rate did not decrease to 1.5 mm were included in the analysis. Coronary plaques were identified as structures >1 mm2 within or adjacent to the coronary artery lumen that could be distinguished 6
ACCEPTED MANUSCRIPT clearly from the vessel lumen and the surrounding pericardial tissue.
T
The severity and extent of coronary artery disease was measured by several coronary CT
RI P
angiography scores, including the CACS, segment involvement score (SIS), and segment stenosis score (SSS). The CACS was assessed with dedicated software (Siemens Calcium
SC
Score; Siemens). Coronary artery calcium was identified as a dense area in the coronary artery that exceeded the threshold of 130 HU. Subjects with CACS > 400 and without cardiac
MA NU
symptom were considered as having subclinical coronary atherosclerosis in this study. The SIS was calculated as the total number of coronary artery segments exhibiting plaque, irrespective of the degree of luminal stenosis within each segment (minimum = 0; maximum = 16). The SSS was used as a measure of overall coronary artery plaque extent. Each
ED
coronary segment was graded as having absent to severe plaque (scores from 0 to 3), based
PT
on the extent of obstruction of the coronary luminal diameter. The extent scores of each of the 16 segments were summed to yield a total score that ranged from 0 to 48. The plaque
CE
characteristics were described as calcified plaque (>130 HU), soft plaque (7%. The discrepancy between previous reports might be explained by differences in the study design (prospective study vs cross-sectional study) and different degree of HbA1c variability, baseline HbA1c level or ethnicity. Regarding the association between glycemic variability and the presence of subclinical atherosclerosis, one study
ED
demonstrated that mean amplitude of glycemic excursion calculated from continuous glucose monitoring was associated with subclinical atherosclerosis measured by carotid intima-media
PT
thickness in 216 Chinese subjects with type 2 diabetes [20]. Another study reported positive
CE
association between glucose variability and coronary artery calcium score in men with type 1 diabetes [21]. However, none of the previous studies reported the association between HbA1c
imaging.
AC
variability and the presence of subclinical atherosclerosis measured by coronary MDCT
Coronary MDCT is considered an excellent noninvasive tool for measuring the coronary artery calcification burden in subjects at high risk of cardiovascular disease. Moreover, the ability of coronary MDCT to characterize the composition of lesions according to the attenuation of plaque enables the identification of calcified, noncalcified, or mixed plaque lesions [22]. Several studies have shown that acute coronary syndrome and sudden cardiac death originate from nonstenotic, lipid-rich necrotic plaques rather than from more advanced, 13
ACCEPTED MANUSCRIPT stable, calcified lesions [23, 24]. In our analysis, HbA1c-CV and adj-HbA1c-SD were closely related to the presence of soft or mixed plaque but not to the presence of calcified plaque.
T
Factors that determine the composition of coronary artery plaques are not fully understood.
RI P
Previous studies have shown that increased LDL-cholesterol level [25] and visceral adipose tissue [26], and decreased HDL-cholesterol level [27] were associated with more noncalcified
SC
and less calcified plaque on coronary CT. Others have reported that apolipoprotein B and
MA NU
small HDL particles are associated with a larger plaque burden and more noncalcified plaque, whereas larger HDL and pre-2-HDL particles are associated with lower plaque burden and less non-calcified plaque [28]. In subjects with diabetes, coronary artery plaque composition and volume were associated with glycemic control status [29]. However, the exact
ED
mechanisms underlying these relationships are unclear.
PT
Several mechanisms may be involved in the association between short-term glycemic variability and outcomes. Glycemic excursions measured by continuous glucose
CE
measurement correlated with oxidative stress in subjects with type 2 diabetes [30], a well-
AC
known pathogenic factor in diabetes complications [31]. Glycemic fluctuation has been shown to cause an increase in inflammatory cytokines, and monocyte and macrophage adhesion to endothelial cells in animals and humans [32-34]. Additionally, the exposure to intermittent high glucose level leads to apoptosis of pancreatic -cells [35]. It remains unclear whether long-term glycemic variability has similar pathophysiological consequences on the micro- or macrovascular complications of diabetes. Several studies have suggested that a higher HbA1c variability may reflect overall glycemic control status [36], use of glucocorticoids or anti-psychotics [37], complex underlying disorders, poor quality of life, low socioeconomic status [38], or the lack of physiological and/or psychological support 14
ACCEPTED MANUSCRIPT systems [39].
T
In our study, strong association was confirmed between HbA1c variability and the presence
RI P
of subclinical coronary atherosclerosis among subjects with diabetes duration < 10 years, but not in those with longer duration of diabetes. Our finding is in agreement with the hypothesis
SC
of “metabolic memory” or “legacy effect”, the idea that early glycemic environment is remembered in the target organ and that early metabolic control has beneficial effects in
MA NU
preventing vascular complications of diabetes [40]. As reviewed by Chilelli et al. [41], although hyperglycemia has a key, early role in the development of diabetes complications, oxidative and glycoxidative stress mainly perpetuates tissue damage and the progression of complications in the longer term. They also demonstrated that macrovascular damage could
ED
be related to a later phase of diabetes and hyperglycemia itself plays a minor role with respect
PT
to other pathophysiological mechanisms (i.e. chronic interaction between oxidative stress and and glycation at tissue or vessel level). Although detailed underlying mechanism is unclear
CE
until know, our finding might suggest the importance of long-term stabilization of blood
AC
glucose level especially in those with earlier period of diabetes to prevent the development of subclinical coronary atherosclerosis. There is no gold standard definition on HbA1c variability cutoff, and we have categorized the HbA1c variability indices into 2 groups according to the median values of each index. Various previous reports regarding the association between HbA1c variability and micro- or macrovascular complications of diabetes, also categorized HbA1c variability measurements into 2 to 4 groups [3, 6, 8, 9]. The HbA1c-MEAN of our study population was 7.47 %, which shows an overall good glycemic control status of the enrolled subjects. While the HbA1cMEAN, HbA1c-SD and adj-HbA1c-SD of our patients were similar to that from the RIACE 15
ACCEPTED MANUSCRIPT study [6, 9] (7.57, 0.40 and 0.46 % respectively), the values were lower compared to the data from the Oxford Regional Prospective Study [4] (9.50, 1.05 and 0.91 %, respectively) or the
T
Diabetes Management through an Integrated Delivery System project [8] (7.90, 1.12 and
RI P
1.03 % respectively). Since HbA1c-SD and adj-HbA1c-SD can be affected by the HbA1cMEAN, we used HbA1c-CV to adjust for HbA1c-MEAN value and demonstrated that the
SC
association between HbA1c variability and subclinical coronary atherosclerosis was still
MA NU
intact. We tried to perform subgroup analysis according to HbA1c-MEAN of 8.0%, but due to small number of patients in each group, further analysis was not available. This study is limited in that further survey on overt cardiac event and mortality was not performed. Second, all patients were Korean, and it is uncertain whether our findings can be
ED
generalized to other ethnic groups. Third, our study was a retrospective study and HbA1c
PT
values were a part of a routine clinical follow-up plan. There were various intervals between HbA1c measurements for each patient and the number of measurements of HbA1c during
CE
observation period also differed. In order to minimize this limitation, we used SD of serially-
AC
measured HbA1c levels adjusted by the number of measurements (adj-HbA1c-SD) and HbA1c-CV to account for the mean HbA1c level. While HbA1c-CV was a significant predictor for subclinical coronary atherosclerosis in subjects with shorter duration of diabetes, this association was not confirmed with adj-HbA1c-SD. Since there is no gold standard for HbA1c variability index, we could only suggest a possibility that HbA1c variability (measured as HbA1c-SD or HbA1c-CV) might be an independent predictor for subclinical coronary atherosclerosis in subjects with diabetes duration ≤ 10 yrs. Other variables such as fasting or postprandial glucose level, inflammatory parameters, frequencies and degree of hypoglycemia and intrapersonal variation in body weight, blood pressure or lipid profiles 16
ACCEPTED MANUSCRIPT were not fully evaluated. Finally, our study was a cross-sectional study and the beneficial role of minimizing HbA1c variability by medication adjustment, education, encouragements or
T
increasing compliance cannot be evaluated. Further prospective investigations are required to
RI P
confirm the role of stabilizing HbA1c variability. The strengths of this study include the use of noninvasive MDCT to detect subclinical atherosclerosis, exclusion of previously known
SC
cardiovascular events and the adjustment for possible confounding factors.
MA NU
In conclusion, HbA1c-SD and HbA1c-CV were associated with subclinical coronary atherosclerosis in subjects with diabetes duration ≤ 10 yrs. Therefore, long-term stabilization of blood glucose level might be important in preventing subclinical coronary atherosclerosis in subjects with earlier period of type 2 diabetes. Further studies with a larger population in
AC
CE
PT
ED
other ethnic groups are required to verify the present results.
17
ACCEPTED MANUSCRIPT Acknowledgments
T
The authors acknowledge the study field workers and nurses involved in CRONOS-ADM
SC
RI P
Registry.
Conflict of interest
MA NU
All authors declare that there is no conflict of interest associated with this manuscript.
ED
Contributions to authorship
H.K.Y. designed the study, researched and analyzed data and wrote manuscript. B.K.
PT
researched data and contributed to discussion. S.H.L. and K.H.Y. contributed to discussion and reviewed and edited the manuscript. B.H.H. and K.C. researched and analyzed data. K.H
CE
and G.K analyzed data and contributed to discussion. J.H.C. designed the study, contributed
AC
to discussion, edited the manuscript and made final approval of the version to be published.
Grant support: There is no funding resource.
18
ACCEPTED MANUSCRIPT References
T
1. Kilpatrick ES. The rise and fall of HbA(1c) as a risk marker for diabetes complications.
RI P
Diabetologia. 2012;55:2089-91.
2. Kilpatrick ES, Rigby AS, Atkin SL. A1C variability and the risk of microvascular
SC
complications in type 1 diabetes: data from the Diabetes Control and Complications Trial. Diabetes Care. 2008;31:2198-202.
MA NU
3. Waden J, Forsblom C, Thorn LM, Gordin D, Saraheimo M, Groop PH et al. A1C variability predicts incident cardiovascular events, microalbuminuria, and overt diabetic nephropathy in patients with type 1 diabetes. Diabetes. 2009;58:2649-55. 4. Marcovecchio ML, Dalton RN, Chiarelli F, Dunger DB. A1C variability as an independent
ED
risk factor for microalbuminuria in young people with type 1 diabetes. Diabetes Care.
PT
2011;34:1011-3.
5. Prince CT, Becker DJ, Costacou T, Miller RG, Orchard TJ. Changes in glycaemic control
CE
and risk of coronary artery disease in type 1 diabetes mellitus: findings from the Pittsburgh
AC
Epidemiology of Diabetes Complications Study (EDC). Diabetologia. 2007;50:2280-8. 6. Penno G, Solini A, Bonora E, Fondelli C, Orsi E, Zerbini G et al. HbA1c variability as an independent correlate of nephropathy, but not retinopathy, in patients with type 2 diabetes: the Renal Insufficiency And Cardiovascular Events (RIACE) Italian multicenter study. Diabetes Care. 2013;36:2301-10. 7. Sugawara A, Kawai K, Motohashi S, Saito K, Kodama S, Yachi Y et al. HbA(1c) variability and the development of microalbuminuria in type 2 diabetes: Tsukuba Kawai Diabetes Registry 2. Diabetologia. 2012;55:2128-31. 8. Hsu CC, Chang HY, Huang MC, Hwang SJ, Yang YC, Lee YS et al. HbA1c variability is 19
ACCEPTED MANUSCRIPT associated with microalbuminuria development in type 2 diabetes: a 7-year prospective cohort study. Diabetologia. 2012;55:3163-72.
T
9. Penno G, Solini A, Zoppini G, Orsi E, Fondelli C, Zerbini G et al. Hemoglobin A1c
RI P
variability as an independent correlate of cardiovascular disease in patients with type 2 diabetes: a cross-sectional analysis of the renal insufficiency and cardiovascular events
SC
(RIACE) Italian multicenter study. Cardiovasc Diabetol. 2013;12:98.
MA NU
10. Luk AO, Ma RC, Lau ES, Yang X, Lau WW, Yu LW et al. Risk association of HbA1c variability with chronic kidney disease and cardiovascular disease in type 2 diabetes: prospective analysis of the Hong Kong Diabetes Registry. Diabetes Metab Res Rev. 2013;29:384-90.
ED
11. Lee EJ, Kim YJ, Kim TN, Kim TI, Lee WK, Kim MK et al. A1c variability can predict coronary artery disease in patients with type 2 diabetes with mean a1c levels greater than 7.
PT
Endocrinol Metab (Seoul). 2013;28:125-32.
CE
12. Hirakawa Y, Arima H, Zoungas S, Ninomiya T, Cooper M, Hamet P et al. Impact of Visitto-Visit Glycemic Variability on the Risks of Macrovascular and Microvascular Events and
65.
AC
All-Cause Mortality in Type 2 Diabetes: The ADVANCE Trial. Diabetes Care. 2014;37:2359-
13. Kim CS, Park SY, Yu SH, Kang JG, Ryu OH, Lee SJ et al. Is A1C Variability an Independent Predictor for the Progression of Atherosclerosis in Type 2 Diabetic Patients? Korean Diabetes J. 2010;34:174-81. 14. Hoff JA, Quinn L, Sevrukov A, Lipton RB, Daviglus M, Garside DB et al. The prevalence of coronary artery calcium among diabetic individuals without known coronary artery disease. J Am Coll Cardiol. 2003;41:1008-12. 20
ACCEPTED MANUSCRIPT 15. Budoff MJ, Achenbach S, Blumenthal RS, Carr JJ, Goldin JG, Greenland P et al. Assessment of coronary artery disease by cardiac computed tomography: a scientific
T
statement from the American Heart Association Committee on Cardiovascular Imaging and
RI P
Intervention, Council on Cardiovascular Radiology and Intervention, and Committee on Cardiac Imaging, Council on Clinical Cardiology. Circulation. 2006;114:1761-91.
SC
16. Keelan PC, Bielak LF, Ashai K, Jamjoum LS, Denktas AE, Rumberger JA et al. Long-
MA NU
term prognostic value of coronary calcification detected by electron-beam computed tomography in patients undergoing coronary angiography. Circulation. 2001;104:412-7. 17. Agarwal S, Cox AJ, Herrington DM, Jorgensen NW, Xu J, Freedman BI et al. Coronary calcium score predicts cardiovascular mortality in diabetes: diabetes heart study. Diabetes
ED
Care. 2013;36:972-7.
18. Rose G, McCartney P, Reid DD. Self-administration of a questionnaire on chest pain and
PT
intermittent claudication. Br J Prev Soc Med. 1977;31:42-8.
CE
19. Raff GL, Abidov A, Achenbach S, Berman DS, Boxt LM, Budoff MJ et al. SCCT guidelines for the interpretation and reporting of coronary computed tomographic
AC
angiography. J Cardiovasc Comput Tomogr. 2009;3:122-36. 20. Mo Y, Zhou J, Li M, Wang Y, Bao Y, Ma X et al. Glycemic variability is associated with subclinical atherosclerosis in Chinese type 2 diabetic patients. Cardiovasc Diabetol. 2013;12:15. 21. Snell-Bergeon JK, Roman R, Rodbard D, Garg S, Maahs DM, Schauer IE et al. 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. 2010;27:1436-42. 22. Schroeder S, Kopp AF, Baumbach A, Meisner C, Kuettner A, Georg C et al. Noninvasive 21
ACCEPTED MANUSCRIPT detection and evaluation of atherosclerotic coronary plaques with multislice computed tomography. J Am Coll Cardiol. 2001;37:1430-5.
T
23. Beckman JA, Ganz J, Creager MA, Ganz P, Kinlay S. Relationship of clinical
RI P
presentation and calcification of culprit coronary artery stenoses. Arterioscler Thromb Vasc Biol. 2001;21:1618-22.
SC
24. Schuijf JD, Beck T, Burgstahler C, Jukema JW, Dirksen MS, de Roos A et al. Differences
MA NU
in plaque composition and distribution in stable coronary artery disease versus acute coronary syndromes; non-invasive evaluation with multi-slice computed tomography. Acute Car Care. 2007;9:48-53.
25. Cheng VY, Wolak A, Gutstein A, Gransar H, Wong ND, Dey D et al. Low-density
ED
lipoprotein and noncalcified coronary plaque composition in patients with newly diagnosed coronary artery disease on computed tomographic angiography. Am J Cardiol. 2010;105:761-
PT
6.
CE
26. Osawa K, Miyoshi T, Koyama Y, Sato S, Akagi N, Morimitsu Y et al. Differential association of visceral adipose tissue with coronary plaque characteristics in patients with and
AC
without diabetes mellitus. Cardiovasc Diab. 2014;13:61. 27. Shiga Y, Miura S, Mitsutake R, Kawamura A, Uehara Y, Saku K. Significance of serum high-density lipoprotein cholesterol levels for diagnosis of coronary stenosis as determined by MDCT in patients with suspected coronary artery disease. J Atheroscler Thromb. 2010;17:870-8. 28. Voros S, Joshi P, Qian Z, Rinehart S, Vazquez-Figueroa JG, Anderson H et al. Apoprotein B, small-dense LDL and impaired HDL remodeling is associated with larger plaque burden and more noncalcified plaque as assessed by coronary CT angiography and intravascular 22
ACCEPTED MANUSCRIPT ultrasound with radiofrequency backscatter: results from the ATLANTA I study. J Am Heart Assoc. 2013;2:e000344.
T
29. Yang DJ, Lee MS, Kim WH, Park HW, Kim KH, Kwon TG et al. The impact of glucose
RI P
control on coronary plaque composition in patients with diabetes mellitus. J Invasive Cardiol. 2013;25:137-41.
SC
30. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP et al. Activation of oxidative
MA NU
stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA. 2006;295:1681-7.
31. Ceriello A, Ihnat MA. 'Glycaemic variability': a new therapeutic challenge in diabetes and the critical care setting. Diabetic Med. 2010;27:862-7..
ED
32. Watada H, Azuma K, Kawamori R. Glucose fluctuation on the progression of diabetic macroangiopathy--new findings from monocyte adhesion to endothelial cells. Diabetes Res
PT
Clin Pract. 2007;77 Suppl 1:S58-61.
CE
33. Otsuka A, Azuma K, Iesaki T, Sato F, Hirose T, Shimizu T et al. Temporary hyperglycaemia provokes monocyte adhesion to endothelial cells in rat thoracic aorta.
AC
Diabetologia. 2005;48:2667-74. 34. Quagliaro L, Piconi L, Assaloni R, Da Ros R, Maier A, Zuodar G et al. Intermittent high glucose enhances ICAM-1, VCAM-1 and E-selectin expression in human umbilical vein endothelial cells in culture: the distinct role of protein kinase C and mitochondrial superoxide production. Atherosclerosis. 2005;183:259-67. 35. Del Guerra S, Grupillo M, Masini M, Lupi R, Bugliani M, Torri S et al. Gliclazide protects human islet beta-cells from apoptosis induced by intermittent high glucose. Diabetes Metab Res Rev. 2007;23:234-8. 23
ACCEPTED MANUSCRIPT 36. Asche C, LaFleur J, Conner C. A review of diabetes treatment adherence and the association with clinical and economic outcomes. Clin Ther. 2011;33:74-109.
T
37. Newcomer JW. Antipsychotic medications: metabolic and cardiovascular risk. J Clin
RI P
Psychiatry. 2007;68 Suppl 4:8-13.
38. Welch HG, Sharp SM, Gottlieb DJ, Skinner JS, Wennberg JE. Geographic variation in
SC
diagnosis frequency and risk of death among Medicare beneficiaries. JAMA. 2011;305:1113-
MA NU
8.
39. Hamer M, Stamatakis E, Kivimaki M, Pascal Kengne A, Batty GD. Psychological distress, glycated hemoglobin, and mortality in adults with and without diabetes. Psychosom Med. 2010;72:882-6.
ED
40. Ceriello A, Ihnat MA, Thorpe JE. The "metabolic memory": is more than just tight
PT
glucose control necessary to prevent diabetic complication? J Clin Endocrinol Metab.
CE
2009;94:410-5.
41. Chilelli NC, Burlina S, Lapolla A. AGEs, rather than hyperglycemia, are responsible for
AC
microvascular complications in diabetes: a "glycoxidation-centric" point of view. Nutr Metab Cardiovasc Dis. 2013;23:913-9.
24
ACCEPTED MANUSCRIPT Figure legend
T
Figure 1. Prevalence of subclinical coronary atherosclerosis in lower and higher
RI P
HbA1c variability group.
The numbers in the bar represent the prevalence of subclinical coronary atherosclerosis in
AC
CE
PT
ED
MA NU
SC
lower and higher HbA1c variability groups. *, P-value < 0.05
25
ACCEPTED MANUSCRIPT Table 1. Baseline clinical characteristics.
AC
CE
T
RI P
62.50% 24.02 ± 2.98 126.48 ± 15.69 72.55 ± 9.02 18.34 ± 9.41
57.20% 24.37 ± 3.19 124.81 ± 12.77 74.42 ± 9.2 12.26 ± 8.51
81.30% 72.70% 27.00% 973.43 ± 454 7.65 ± 1.29
58.20% 60.60% 28.90% 973.38 ± 434.95 7.42 ± 1.23
0.71 (0.61,0.81) 8.87 (7.82, 9.92) 0.65 (0.57, 0.74) 8.12 ± 2.62 85.32 ± 18.2 86.35 ± 19.83
0.65 (0.60, 0.69) 8.32 (7.80, 8.85) 0.60 (0.56, 0.61) 7.87 ± 2.49 80.40 ± 19.35 95.40 ± 24.53
4.01 ± 0.75 1.30 (1.18, 1.42) 1.23 ± 0.32 2.18 ± 0.66 47.70% 62.50% 33.40% 4.70% 70.30% 50.20%
4.25 ± 0.82 1.44 (1.35, 1.53) 1.27 ± 0.31 2.36 ± 0.70 42.00% 66.60% 22.90% 7.90% 62.10% 57.90%
MA NU
PT
Males (%) Body mass index (kg/m2) Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Duration of diabetes (year) Diabetes duration over 10 yrs (%) Hypertension (%) Current or ex-smoker (%) Follow up period (day) HbA1c-MEAN (%) HbA1c-SD (%) HbA1c-CV Adj-HbA1c-SD (%) Fasting plasma glucose (mmol/L) Baseline serum creatinine (μmol/L) eGFR (MDRD) Total cholesterol (mmol/L) Triglyceride (mmol/L) HDL-cholesterol (mmol/L) LDL-cholesterol (mmol/L) Use of sulfonylurea Use of metformin Use of insulin Use of DPP4-inhibitor Use of statin (%) Use of aspirin (%)
595 64.88 ± 8.90 58.32% 24.29 ± 3.15 125.17 ± 13.46 74.02 ± 9.19 13.57 ± 9.05 54.04% 59.43% 28.45% 973 ± 439 7.47 ± 1.25 0.51 (0.49, 0.54) 6.98 (6.64, 7.33) 0.48 (0.45, 0.51) 7.93 ± 2.52 81.33 ± 19.45 93.4±23.85 4.20 ± 0.81 1.21 (1.16, 1.26) 1.26 ± 0.31 2.32 ± 0.70 43.19% 65.71% 25.38% 7.23% 63.87% 58.32%
ED
Number Age (year)
Subclinical coronary atherosclerosis Presence Absence 128 467 69.77 ± 7.42 63.54 ± 8.81
SC
Total
P