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Can HbA1c variability contribute to the development of microvascular complications in Type 1 diabetes? Bragd J(1), Adamson u(1), lins PE(1) and Oskarsson P(3) (1) Department of Clinical Sciences, Danderyd Hospital, Division of Internal Medicine, (3) Department of Diabetology and Endocrinology, Karolinska Huddinge, Karolinska Institutet, Stockholm, Sweden
I
t is well accepted that chronic glycemic exposure, i.e. the degree and duration of hyperglycemia, is the most important modifiable risk factor for the development of complications of diabetes (1-3). In the Diabetes Control and Complications Trial (DCCT), the development of microvascular complications was strongly related to the level of mean blood glucose, measured as HbA1c, in patients with Type 1 diabetes (5). In a multivariate analysis, HbA1c, duration of diabetes and age at the onset of diabetes appeared to be the main clinical risk factors for diabetic complications (4). Notably, and as a sign of a so-called “metabolic memory”, the risk reduction of microvascular complications achieved with intensive therapy persisted for at least another four years, despite increasing HbA1c values in the post-study period, as reported in the EDIC study (6). One subgroup analysis of the DCCT cohort found lower rates of complications in the intensively treated group compared with the control group at similar HbA1c levels (7). The question therefore arose of whether reduced glycemic variability, anticipated to exist, among the intensively treated patients, was of importance. Additional support for the idea that glucose variability affects the risk of microvascular complications came from an observational study in which the incidence of retinopathy in a group of adolescents with Type 1 diabetes appeared to fall substantially between 1990 and 2002, despite little change in HbA1c levels. These authors attributed the beneficial effect to the move over time to multiple injection regimens, anticipating that a reduction in glycemic fluctuations, despite stability in mean glucose concentrations, was a likely mechanism (8).
Vol.12 No.2 2013 INfuSySTEmS INTErNATIONAl 2492 Walnut Avenue, Suite 130 Tustin, Ca., 92780, USA Email:
[email protected] EDITOrIAl BOArD Editor in Chief: J-L. Selam (USA) Associate Editor: D. Selam (USA) Board members U. Adamson (Danderyd, Sweden) G. Bolli (Perrugia, Italy) D. Bruttomesso (Padova, Italy) M. Carvalheiro (Coimbra, Portugal) H. Hanaire-Broutin (Toulouse, France) R. Hanas (Uddevalla, Sweden) D. Kerr (Bournemouth, United Kingdom) T. Kunt (Abu Dhabi, UAE) H. Leblanc (Paris, France) V. Lassmann-Vague (Marseille, France) A. Liebl (Munchen, Germany) P-E. Lins (Danderyd, Sweden) M. Massi-Benedetti (Perrugia, Italy) C. Mathieu (Leuven, Belgium) D. Owens (Penarth, United Kingdom) T. Pieber (Graz, Austria) M. Pinget (Strasbourg, France) G. Rayman (Ipswich, United Kingdom) R. Radermecker (Liège, Belgium) E. Renard (Montpellier, France) R. Renner (Munchen, Germany) Z. Rusavy (Czech Republic) A. Scheen (Liège, Belgium) A. Scott (Derby, United Kingdom) A. Spijker (Den Haag, The Netherlands) A. Tiengo (Padova, Italy) PuBlISHEr Publiscripts 2492 Walnut Avenue, Suite 130 Tustin, Ca., 92780, USA Tel: (949) 910 0991 - Fax: (949) 429 2160
It is worth noting that, in a study performed by our research team on a cohort of 100 Type www.publiscripts.com 1 diabetes patients, we found that glucose variability measured as SDBG was related to the long-term risk of developing peripheral neuropathy (P = 0.03), hazard ratio 2.34 (1.06-5.20), as well as being a predictor of the incidence of peripheral neuropathy at borderline significance (P = 0.07), hazard ratio 1.73 (0.94-3.19) (9). In contrast, Kilpatrick and coworkers, who conducted several statistical analyses of the large DCCT database, as these data were placed in a public domain to allow other investigators to pursue additional analyses, reported that glucose variability was not associated with an increased risk of developing microangiopathy (10). The question of whether short- and/or long-term glucose variability constitutes a significant additional risk of microvascular complications is . Can HbA1c variability con tribute to the development of therefore still the subject of debate (11). In another analysis of the DCCT database, it was found that the variability of HbA1c was microvascular complications in correlated to an increase in microvascular complications (12) and, in another recent study, Type 1 diabetes? a Finnish group presented data on the variability of HbA1c and complications (13). They ............................................................. 9 performed an observational multicenter study of 2107 patients and found, in a Cox regresContinuous Subcutaneous sion model, that the SD of HbA1c was independently associated with the progression of . Insulin Infusion in Type 1 renal disease and of CVD events among patients with Type 1 diabetes mellitus. (13) Their Diabetes Patients During a study was an observational study and thus probably reflected the normal clinical setting Summer Camp: an in-field study better in terms of the variability of HbA1c compared with what might be expected in an ........................................................... 14
CONTENTS
Vol.12 No.2 2013
Page 10
1990 (n =100)
1995 (n = 95)
2001 (n = 81)
56
56
55
Age (years)
44.7+13.2
49.3+13.1
54.6+12.5
Duration of DM (years)
19.7+11.0
24.5+10.8
28.6+10.4
HbA1c (%) ref. < 5.2 %
8.1+1.2
7.5+1.5
7.2+0.9
BMI
23.2+2.4
24.2+2.9
25.2+3.9
Insulin dose (U/24h)
40.8+12.3
40.7+12.2
42.0+12.5
Males (%)
CSII (%)
by pathologic thresholds as revealed by neurometry and/or a vibration test with a tuning fork and mono-filament testing. Autonomic neuropathy was defined as the clinical diagnosis of erectile disturbance, bladder dysfunction, orthostatic hypotension or gastroparesis. Unawareness of hypoglycemia was defined as a documented plasma glucose value of < 3.0 mmol/L without the ability to perceive symptoms of hypoglycemia (16). Macroangiopathy was defined as a clinical diagnosis of angina pectoris, intermittent claudication, myocardial infarction (MI) and/or cerebrovascular accident (CVA). Clinical data, including medical history, diabetic complications, blood pressure, laboratory tests and pharmaceutical therapy, were extracted from each patient’s medical file. HbA1c was measured using a liquid chromatographic assay (reference value for healthy subjects < 5.2%). Statistical methods
8
24
35
Table 1: Clinical data from 1990 to 2001 (mean + SD).
intervention study like the DCCT study. Their end points were nephropathy and CVD and other microvascular complications were not included in the analyses. Against this background, we have now re-analyzed data from our earlier observational study regarding the variability of HbA1c and the development of all microvascular complications. During a follow-up period of 17 years (6 plus 11 years), HbA1c was measured in accordance with the clinical practice at our outpatient clinic. HbA1c values were collected between 1984 and 2001. A total of 3855 HbA1c values were collected, corresponding to a mean of 2.3 values per year and patient. We hypothesized that HbA1c variability measured as SD was related to the development of vascular complications in subjects with Type 1 diabetes. Patients and methods From 442 consecutive Type 1 diabetic patients who attended the diabetes outpatient clinic at Danderyd Hospital in 1990, 142 were randomly selected by date of birth and invited to participate in a study to measure blood glucose vari-
ability as determined by frequent capillary blood glucose values obtained through stratified home monitoring. One hundred of these patients agreed to participate in the study and thus formed a cohort in which SDBG was calculated, based on 70 measurements over a period of four weeks. The results of this investigation were reported elsewhere in 1994 (14). This group of patients constituted the present study cohort, now re-analyzed after 6 plus 11 years with respect to established risk factors for the onset and progression of micro- and macroangiopathy, as well as peripheral neuropathy. During the follow-up period, the patients made visits two to four times a year to our outpatient clinic according to our established clinical protocol. Diabetic complications were defined and categorized in 1990 as follows. Nephropathy was defined as microalbuminuria, MA (albumin excretion 30-300 mg/24 h), or albuminuria (albumin excretion exceeding 300 mg/24 h). Retinopathy was defined as proliferative diabetic retinopathy as determined by an expert in our research team using a blinded procedure. Peripheral neuropathy was defined as sensory neuropathy indicated
The standard deviation in HbA1c (variability) and the mean HbA1c (overall level) was calculated for each patient, using individual measurements over time. To evaluate the effect of variability in and the overall level of HbA1c on the total number of complications, a generalized linear model was fitted. We assumed that a Poisson distribution with a log link as the outcome is a positively skewed discrete continuous variable. The total number of complications consisted of proliferative diabetic retinopathy, albuminuria, micro-albuminuria, peripheral neuropathy, gastroparesis and erectile dysfunction, with each variable categorized into not-present and present. Patients who died during follow-up were not included in the analysis, as they have missing information regarding the number of complications. As covariates, age at baseline, disease duration and mean BMI were included in the model. The results are presented as risk ratios (RR) with 95% confidence intervals and p-values. The RRs were transformed into clinically relevant units. A t-test for independent groups, assuming unequal variances, was used to compare the patients who were alive with those who died during followup in terms of mean variability and mean overall HbA1c. IBM SPSS statistics (version 18.0, (version 18.0, SPSS Inc., Chicago) were used for analyses and graphs.
Vol.12 No.2 2013
Prevalence 1990 (%)
Incidence 1990-2001 (n)
Incidence rate per 100 patient-years
MI/angina
6
9
1,2
Claudication
8
8
1,1
CVA
1
8
1,0
Nephropathy
9
11
1,4
Neuropathy Peripheral
29
21
3,7
Autonomic – Orthostatic hypotension
1
1
0,1
– Erectile dysfunction
4
13
3,0
– GI dysfunction
5
16
1,9
Retinopathy Proliferative diabetic retinopathy
0
10
1,2
Hypoglycemic unawareness
9
15
2,0
results Baseline data are presented in Table 1. There was a slight predominance of males (56%) in this group in 1990. The mean age of the patients in 1990 was 45 years, with a range of 19-78 years, and their mean duration of diabetes was 20 years, with a range of 2-62 years. In the 1990 cohort, insulin pumps were used by 8%, with only 4% of patients not using multiple injections or pumps. In the follow-up analysis, we found that 17 subjects had died. They were older compared with the study cohort, with a mean age of 57 years. Their mean duration of diabetes at inclusion was 25 years. They had higher mean HbA1c and also increased HbA1c variability. Death certificates listed the cause of death as MI in five patients, CVA in four patients, heart failure in four patients, hypoglycemia in three patients and Huntington’s chorea in one patient. In 1998, two patients moved abroad and were thus lost to follow-up. In the study cohort, the use of continuous subcutaneous insulin infusion (CSII) increased by 27% and BMI increased slightly over time. As expected, micro- and macrovascular complications became more prevalent during the follow-up period. Most prominently, peripheral neuropathy increased from 29% to 47% and erectile dysfunction increased from 7% to 33%. Hypertension or ongoing medication for hypertension also increased markedly from 15% to 44%. Only 10% progressed to proliferative diabetic retinopathy and the prevalence of nephropathy in the survivors remained low (Table 2). The statistical analysis revealed that HbA1c variability measured as the SD was independently related to an increase in the total number of complications (p=0.017) (Table 4) and also the total number of microvascular complications (p=0.019) (Table 3). We were unable to find any relationship with macrovascular complications. According to the Poisson regression analysis, an increase in HbA1c variability of 0.3% increases the number of microvascular complications by 35%, RR 1.35 (1.05-12.11). Discussion In this study, we found that long-term glucose variability measured as the SD of HbA1c was related to an increased number of microvascular complications. Our
Page 11
Macroangiopathy
Table 2: Prevalence and incidence data of diabetic complications.
finding supports the study presented from Waden and coworkers (13), in which they used a Cox regression model and showed that the SD of HbA1c was independently associated with the progression of renal disease and of CVD events among patients with Type 1 diabetes mellitus. Compared with our study, they analyzed nephropathy among the microvascular complications. In our study, all types of microvascular event were included and were of importance for the statistical association. Our study consisted of only 100 patients at the start, but we had a follow-up period of no less than 11 years and the analysis revealed a clear statistical significance, which leads us to believe that it is of clinical relevance. The HbA1c values were collected over 17 years, 6 years before 1990 and during the followup period 1990-2001.
As already indicated above, our findings are consistent with those of Kilpatrick and coworkers, who reported that the variability in HbA1c adds to the mean glucose value in predicting microvascular complications in Type 1 diabetes (12). Moreover, in a recent report from the Oxford Regional Prospective Study and the Nephropathy Family Study, comprising a total of 1232 participants, it was concluded that HbA1c variability was an independent variable that added to the effect of HbA1c on the risk of microalbuminuria in young people with Type 1 diabetes (15). We have previously reported that shortterm glucose variability measured as SDBG was an independent predictor of the prevalence of peripheral neuropathy, as well as a predictor of the incidence of peripheral neuropathy at borderline sig-
Vol.12 No.2 2013
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Parameter estimates Parameter
B
Std. error 95% Wald confidence interval
Hypothesis test
Exp(B)
Lower
Upper
Wald chisquare
df
Sig
95% Wald confidence interval for Exp(B) Lower
Upper
(Intercept)
-4,184
1,4467
-7,019
-1,348
8,364
1
,004
,015
,001
,260
SD Hba1C
1,004
,4291
,163
1,845
5,477
1
,019
2,730
1,177
6,329
Mean HbA1C
,166
,1452
-,118
,451
1,314
1
,252
1,181
,889
1,570
Mean BMI
,053
,0402
-,026
,131
1,706
1
,191
1,054
,974
1,140
Age 1990
,012
,0104
-,008
,033
1,399
1
,237
1,012
,992
1,033
Duration
,018
,0106
-,003
,039
2,837
1
,092
1,018
,997
1,039
Table 3: Statistical analysis of total number of microvascular complications.
nificance (9). The question of whether short-term or long-term variability might add to long-term glycemia for the risk of diabetic complications has been debated for many years. In our group of patients, who were studied for a period of eleven years, it appears that long-term glucose variability, as well as short-term glucose variability, has an impact on the development of microvascular complications. This was shown to be an independent predictor (??) of mean blood glucose measured as HbA1c and there was no correlation between HbA1c variability and SDBG. Our patients had a long disease duration of approximately 20 years already at inclusion, which differs from the DCCT study and may make the patients in our study more prone to develop a larger number of complications during the follow-up period. However, this may not be the case in terms of nephropathy, as studies have shown that the incidence of nephropathy declines after a duration of diabetes of approximately 20 to 25 years (16). Surprisingly, there was no major decline in the incidence of nephropathy in our study cohort during the follow-up period. This may be due to
different definitions of nephropathy, as we include patients with microalbuminuria. Another reason could be that our cohort consists of patients who developed diabetes in the 1970s and were therefore not protected against microalbuminuria /nephropathy as effectively as more recent patient populations, due to the introduction of ACE inhibitors. The patients were followed up for a long period, which we think should compensate for the relatively small number of patients in our study. The study by Wadén et al. also showed a correlation between HbA1c variability and macrovascular complications (CVD), which we did not find. However, the number of macrovascular events in our study was small and a correlation would therefore have been difficult to find. Mechanisms for the development of diabetic complications are usually described as arising from sustained periods of hyperglycemia which lead to the intracellular overproduction of superoxide. The formation of superoxide is the key event in the activation of all the other pathways, such as the polyol/sorbitol pathway flux,
increased advanced glycated end (AGE) product formation, increased hexosamine flux, the activation of oxidative stress and so on. The effect of variable blood glucose, with periods of high glucose levels followed by periods of low levels, or vice versa, might be more deleterious than continuous hyperglycemia in this respect. There are studies that have shown that glucose variability in vitro triggers oxidative stress more than sustained periods of hyperglycemia (17). Another possible mechanism to consider is the theory of a ”metabolic memory”, where periods of high glucose levels could induce harmful effects later on, i.e. at a time point at which glucose has been normalized (18). Another concept to consider is the idea that a considerable biological variation in HbA1cmight exist and that such a variation in itself could provide a predictor of microvascular complications in patients with Type 1 diabetes, independent of estimates of mean blood glucose (19). We conclude that the variability of HbA1c may be of importance in the development of microvascular complications in subjects with Type 1 diabetes. Our findings originate from an observa-
Vol.12 No.2 2013
Page 13
Parameter estimates Parameter
B
Std. error 95% Wald confidence interval
Hypothesis test
Exp(B)
Lower
Upper
Wald chisquare
df
Sig
95% Wald confidence interval for Exp(B) Lower
Upper
(Intercept)
-4,306
1,4197
-7,089
-1,524
9,200
1
,002
,013
,001
,218
SD Hba1C
,991
,4170
,174
1,809
5,650
1
,017
2,695
1,190
6,102
Mean HbA1C
,182
,1429
-,098
,462
1,619
1
,203
1,199
,906
1,587
Mean BMI
,053
,0392
-,024
,130
1,830
1
,176
1,054
,977
1,139
Age 1990
,011
,0102
-,009
,032
1,245
1
,264
1,011
,991
1,032
,0102
,002
,042
4,695
1
,030
1,022
1,002
1,043
Duration ,022
Table 4: Statistical analysis of total number of all complications.
tional study which fails to address the question of causality. For this reason, further studies of these issues are warranted in order to scrutinize the possible clinical implications of our observations. references 1. The Expert Committee on the Diagnosis and Classification of Diabetes mellitus: Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 1997; 20: 1183–1197. 2. The Expert Committee on the Diagnosis and Classification of Diabetes mellitus: Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003; 26: 3160–3167. 3. Brownlee m: Biochemistry and molecular cell biology of diabetic complications. Nature 2001; 414: 813–820. 4. Dyck PJ, Davies Jl, Clark Vm, litchy WJ, Dyck PJB, Klein CJ et al. Modeling chronic glycemic exposure variables as correlates and predictors of microvascular complications of diabetes. Diabetes Care 2006; 29:2282-2288. 5. The Diabetes Control and Complications Trial research Team: 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 1993; 329: 977–986. 6. The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions
and Complications research Group. Retinopathy and nephropathy in patients with type 1 diabetes four years after a trial of intensive therapy. N Engl J Med 2000: 342: 381–389. 7. Bloomgarden Z. The epidemiology of complications. Diabetes Care 2002: 25: 924–932. 8. mohsin f, Craig mE, Cusumano J, Chan AKf, Hing S, lee JW et al. Discordant trends in microvascular complications in adolescents with type 1 diabetes from 1990 to 2002. Diabetes Care 2005; 28: 1974–1980 9. Can glycaemic variability, as calculated from blood glucose self-monitoring, predict the development of complications in type 1 diabetes over a decade? Bragd J, Adamson u, Bäcklund lB, lins PE, moberg E, Oskarsson P. Diabetes Metab. 2008 Dec;34(6 Pt 1):612-6. Epub 2008 Sep 27. 10. Kilpatrick ES, rigby AS, Atkin Sl. The effect of glucose variability on the risk of microvascular complications in Type 1 diabetes, Diabetes Care 2006; 29:1486–1490. 11. lachin Jm, Genuth S, Nathan Dm, Zinman B, rutledge BN. DCCT/EDIC research Group. Effect of glycemic exposure on the risk of microvascular complications in the Diabetes Control and Complications Trial-revisited. Diabetes 57:995-1001; 2008. 12. Kilpatrick ES, rigby AS, Atkin Sl, A1C variability and the risk of microvascularcomplications in type 1 diabetes: data from the Diabetes Control and Complications Trial. Diabetes Care. 2008 Nov;31(11):2198-202. Epub 2008 Jul 23. 13. Wadén J, forsblom C, Thorn lm, Gordin D, Saraheimo m, Groop PH; Finnish Diabetic
Nephropathy Study Group Diabetes. A1C variability predicts incident cardiovascular events, microalbuminuria, and overt diabetic nephropathy in patients with type 1 diabetes. Diabetes.2009 Nov;58(11):2649-55. Epub 2009 Aug 3. 14. moberg EAr, lins PES, Adamson uKC. Variability of blood glucose levels in patients with type 1 diabetes mellitus on intensified insulin regimens. Diabetes Metab 1994; 20: 546¬552. 15. marcovecchio ml, Ciarelli f, Dalton rN, Dunger DB. A1c variability as an independent risk factor for microalbuminuria in young people with typ-1 diabetes. Diabetes Care 34:1011-1013; 2011. 16. rossing P. The changing epidemiology of diabetic microangiopathy in type 1 diabetes. Diabetologia. 2005 Aug;48(8):1439-44. Epub 2005 Jun 29. 17. Piconi l, Quagliaro l, Assaloni r, Da ros r, maier A, Zuodar G, Ceriello A. Constant and intermittent high glucose enhances endothelial cell apoptosis through mitochondrial superoxide overproduction. Diabetes Metab Res Rev. 2006 May-Jun;22(3):198-203. 18. Ihnat mA, Thorpe JE, Ceriello A, Hypothesis: the 'metabolic memory', the new challenge of diabetes. Diabet Med. 2007 Jun;24(6):582-6. Epub 2007 May 8. 19. mcCarter rJ, Hempe Jm, Gomez r, Chalew SA. Biological variation in HbA1c predicts risk of retinopathy and nephropathy in type 1 diabetes. Diabetes Care 28:1259-64; 2004.
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Vol.12 No.2 2013
Continuous Subcutaneous Insulin Infusion in Type 1 Diabetes Patients During a Summer Camp: an in-field study Ivana Rabbone(a), Davide Tinti(a), Valeria R Di Gianni(a), Elisa Giani(b), Maddalena Macedoni(b), Alessandra Gazzari(b), Matteo Ferrari(b), Sabrina Sicignano(a), Valentina Comaschi(b), Riccardo Schiaffini(c), Vincenzo Zuccotti(b), Franco Cerutti(a), Andrea Scaramuzza(b) (a) Department of Pediatrics, University of Turin, Turin, Italy; (b) Department of Pediatrics, Azienda Ospedaliera, University of Milano, “Ospedale Luigi Sacco”, Milano, Italy; (c) Endocrinology and Diabetes Unit, University Department of Pediatric Medicine, Bambino Gesù Children's Hospital, Rome, Italy
R
egular physical activity is one of the three cornerstones of type 1 diabetes mellitus (T1DM) management, together with insulin and diet therapy. Physical exercise has many potential benefits for patients with T1DM including decreased risk of diabetes-related complications and mortality (1). In addition, insulin requirement and glycemia are reduced both in adult and in pediatric population during sport. However it remains unclear whether exercise is beneficial for glycemic control in T1DM (2). A few studies tried to identify the best insulin therapy modifications needed to perform exercises safely, avoiding hypo- and hyperglycemia. However no real guidelines are at present available. In recent years, continuous subcutaneous insulin infusion (CSII) therapy has been wildly used in children and adolescents with T1DM. A low number of studies have investigated the best CSII therapy protocols during exercises. Admon et al. (3) found that the best option is to turn off the pump during exercise in order to avoid postexercise hypoglycemia. Others studies observed similar results (4, 5). The aim of the present study was to evaluate the effects of different CSII therapy protocols on glycemic profiles during an in-field exercise in ten T1DM children.
Subjects and methods During a summer camp, 10 children aged 12,2 ± 1 years, with T1DM for at least 4 ± 2 years using CSII for more than 1 year, were enrolled in the study. All patients were well-trained (weekly physical exercise: 12h/wk) and in good shape (BMI 19 ± 2 kg/m2) and in good metabolic control (mean HbA1C value: 7,46 ± 0,50%) All children underwent two 45-min bouts of moderate-intensity exercise (soccer), wearing a retrospective continuous glucose monitoring (r-CGM: iPro2, Medtronic, Northridge, CA, USA). Furthermore each child performed blood glucose monitoring before and after each exercise bout,
every 60-min for three hours after exercise, and during the night. All patients followed the same diet regimen. Exercise bouts were performed at the same time (at least 2 hours after lunch) in two consecutive days. Insulin protocol were as follows: a) pump off; b) pump on with temporary basal rate reduced by 30% for exercise duration. Being an in-field study, in case of low pre-exercise glycemia (less than 100 mg/dl or 5,6 mmol/l) we made corrections according to published guidelines (6), while in case of hyperglycemia (more than 180 mg/dl or 10 mmol/l) a correction bolus according to insulin sensitivity factor of each child was performed. In the first day
Variable
First day
Second day
P-value
Mean glycemic value (mg/dL)
157 ± 36
140 ± 62
ns
Standard deviation (mg/dL)
68 ± 26
53 ± 21
ns
Time spent in hyperglycemia (%)
50 ± 21
40 ± 31
ns
Time spent in euglycemia (%)
39 ± 19
37 ± 20
ns
Time spent in hypoglycemia (%)
9 ± 13
22 ± 27
ns
Table 1: Mean glycemic values, time spent in hyperglycemia, euglycemia and hypoglycemia in the two days of summer camp.
Vol.12 No.2 2013
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Figure 1: Glycemic values in 10 children with T1DM according to the 2 different protocols (protocol 1 pump OFF vs protocol 2 pump ON) before, at the end of exercise, and 1-h, 2-h and 3-h after.
of the camp most of the children had quite high glycemic values. For each glycemic value >250 mg/dl a blood ketone test was run (Glucomen Plus, Menarini, Italy). CGM data concerning mean blood glucose values, time spent in hyperglycemia, euglycemia and hypoglycemia were downloaded after the camp finished and analyzed using IBM SPSS statistics package version 20. results All children finished their bouts of exercise safely, without any severe adverse events (severe hypoglycemia and/or diabetic ketoacidosis) occurring. None of the patients showed a blood ketone level higher than 0,6 mmol/l. The glycemic values before and after exercise using the two different protocols showed a statistical difference before and 2-h after exercise, but not at the end or 1-h and 3-h after exercise (Figure 1). Since the pre-exercise glycemic values of the first protocol were significantly higher than those of the second protocol, we evaluated the CGM data of the two days as a whole. In contrast to what we expected, there was no statistical difference regarding mean glycemic values, time spent in hypo-, eu-, or hyperglycemic range between the two days (Table 1).
In Figure 2, glycemic patterns before, at the end, and 1-h, 2-h, 3-h after exercise are shown according to a pre-exercise glycemia stratification: 250 mg/dL (13.9 mmol/L). Irrespective of pre-exercise glycemia, 3-h after exercise all patients reached the same intarget glycemic value. Evaluating the glycemia recorded during the night we did not observe any statistical difference between the two days regarding mean values and hypoglycemic episodes (data not shown). Discussion No difference has been observed between the two different protocols (pump on vs pump off) in terms of glycemic profiles during exercises, post-exercise glycemia and number of hypoglycemic episodes. Our findings are someway different from those observed by Admon et al (3). However our study has been run in a real-life condition and no selection has been done according to pre-exercise glycemia. For this reason we could observe similar results using the pump on or off. In our study the first day pre-exercise glycemia was significantly higher than that of the second day. This data is difficult to explain especially since
the mean glycemic values of the two days are not statistically different. We do not feel that this difference can be attributed to a different insulin sensitivity in the two days because the children were used to exercising and the exercises themselves were too short. Probably the only difference between the two days was due to an
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Figure 2: Glycemic values in 10 children with T1DM stratified according to the pre-exercise glycemia recorded during the 2 days, before, at the end of exercise, and 1-h, 2-h and 3-h after.
cise, and it is very widespread in the real life setting, at least in Italy. We demonstrated that this kind of sport can be done by every child without an increased risk of acute complications. In conclusion, insulin pump therapy in children helps to safely exercise reaching a good glycemic control during and after physical activity without an increased risk of hypoglycemia. references 1. lehmann r, Kaplan V, Bingisser r, Bloch KE, Spinas GA: Impact of physical activity on cardiovascular risk factors in IDDM. Diabetes care 1997, 20(10):1603-1611. 2. Chimen m, Kennedy A, Nirantharakumar K, Pang TT, Andrews r, Narendran P: What are the health benefits of physical activity in type 1 diabetes mellitus? A literature review. Diabetologia 2012, 55(3):542-551.
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3. Admon G, Weinstein y, falk B, Weintrob N, Benzaquen H, Ofan r, fayman G, Zigel l, Constantini N, Phillip m: Exercise with and without an insulin pump among children and adolescents with type 1 diabetes mellitus. Pediatrics 2005, 116(3):e348-355. 4. Schiffrin A, Parikh S: Accommodating planned exercise in type I diabetic patients on intensive treatment. Diabetes care 1985, 8(4):337-342. 5. Tsalikian E, Kollman C, Tamborlane WB, Beck rW, fiallo-Scharer r, fox l, Janz Kf, ruedy KJ, Wilson D, Xing D, Weinzimer SA: Prevention of hypoglycemia during exercise in children with type 1 diabetes by suspending basal insulin. Diabetes care 2006, 29(10):22002204. 6. Bantle JP, Wylie-rosett J, Albright Al, Apovian Cm, Clark NG, franz mJ, Hoogwerf BJ, lichtenstein AH, mayer-Davis E, mooradian AD, Wheeler ml: Nutrition recommendations and interventions for diabetes: a position statement of the American Diabetes Association. Diabetes care 2008, 31 Suppl 1:S61-78.
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emotional factor linked to the camp experience. A second important point highlighted by the present study is that when hyperglycemia is present before exercising, a properly correction insulin bolus allows the patient to exercise safely without any risk of diabetic ketoacidosis either during or after exercise. This is true even for very high glycemic values provided a negative ketonemia. Indeed, as is shown in Figure 2, all patients irrespective of pre-exercise glycemia reached the in-target glycemic value. Our findings support the evidence that insulin pump can be kept active even during exercise if a 30% temporary basal rate reduction has been set. We did not observe any severe hypoglycemic episodes for the whole period (including the night). Another feature of the present study is the kind of physical activity tested. Soccer is an aerobic- anaerobic exer-
