**1.1 T1D epidemiology in adults**

European researchers have proved that the epidemiological characteristics of T1D in children significantly differ from that in young adults (Kyvik et al., 2004). Therefore, studying the peculiarities of T1D in adults is a major concern. Furthermore, data on the number of diabetic patients usually found in the reports of the healthcare system are unstructured according to the history of the disease, and cannot be a source of epidemiological information on patients suffering from T1D. Owing to the development of the Diabetes Register in Ukraine, it has become possible to conduct analytical comparisons and further studies on almost all the T1D adult populations.

#### **1.2 Diabetes Register**

The Diabetes Register contains individual, structured information on the disease history, and has already been used in some epidemiological studies (Khalangot et al., 2009a;

Prevalence of Type 1 Diabetes Correlates with Daily Insulin Dose, Adverse

**1.3.1 Register-based T2D epidemiology studies** 

**1.3.2 Register-based T1D epidemiology studies** 

its prevalence. This chapter is mainly a review of these studies.

**2. Methods** 

Outcomes and with Autoimmune Process Against Glutamic Acid Decarboxylase in Adults 63

Some results obtanied by means of studying electronic registers may at first seem unusual or paradoxic. In particular our studies of T2D patients (Khalangot et al., 2009b) indicate that Hazard Ratios (HRs) of cardiovascular disease (CVD) mortality among extremely obese patients [body mass index (BMI) ≥ 35 kg/m2] adjusted for age, smoking and alcohol consumption were higher than for overweight patients [BMI 25-29 kg/m2]: HR=1.54 (95% CI 1.16-2.05) and 1.35 (95% CI 1.15-1.59) among men an women respectively, p<0.01. Furthermore, the graph that shows risks of general and CVD mortality for T2D patients depending on BMI has the shape of an asymmetric parabola: HRs associated with low and normal BMI were significantly higher comparing to those, related to overweight or moderate obesity. The above phenomenon partially corresponds to "obesity paradox" that has been recently discovered among patients suffering from CVD (Gruberg et al., 2002; Curtis et al., 2005) , however in our study this effect concerns T2D patients. An observational study that included 25 361 T2D patients showed that glibenclamide treatment is associated with much higher risk of general and CVD mortality, comparing to treatment with another derivative of sulfonylurea – gliclazide. HRs for total and CVD mortality within the glibenclamide patient cohort were 2.57 (95% CI 1.73–3.82) and 2,93 (95%CI 1.83–4.71) respectively; (p < 0,001). These data correspond to changes of OGLD distribution and trends of life duration among DM patients that we have revealed as well (Khalangot et al., 2009 e). Previously, there had been only one study where similar results concerning total mortality associated with the use of glibenclamide or gliclazide in a cohort of 568 T2D patients were obtained (Monami et al., 2007). Our study broadens this tendency onto CVD mortality.

T1D incidence among Ukrainian adults from 1994 till 2004, that we have evaluated retrospectively according to the register data, had a decreasing tendency (Khalangot, 2009). Our assessment of T1D incidence dynamics among adults does not confirm the information about steady increase of global T1D incidence (Green et al., 2001; Gale, 2002), however these studies only concerned child incidence. As Ukraine is also experiencing a rise of DM incidence among children, our data can be easily explained by earlier DM manifestation among people, who carry the genotype predisposing to T1D. Researchers of Danish DM register have recently reported a decrease of DM incidence among young adults. National diabetic register of Denmark has collected data on 359 000 DM cases between 1995 and 2006, and it includes the total population, diagnosed with DM. This register has recorded a clear tendency towards reduction of mortality among DM patients, which has been observed since 2003 (Carstensen et al., 2008). We have recently conducted a series of studies as well that focused on factors influencing mortality and territorial heterogeneity of T1D in Ukraine (Khalangot, 2008; Khalangot et al., 2009 c; 2010). The purpose of these studies (Khalangot et al., 2009 d) was also to determine whether the insulin requirement can change systematically in T1D patients, and whether this requirement depends on the same factors that determine

A database with 282,988 records of diabetic patients was developed on the basis of epidemiological analysis conducted during the 2005–2006 register verification (01.12.2006). To evaluate the completeness of the register data, we compared it to the official 2005

Khalangot et al., 2009b; Vaiserman et al., 2009; Khalangot et al., 2009d; Vaiserman & Khalangot, 2008). Until recently there was no evidence on age and gender structure of patients diagnosed with diabetes mellitus in Ukraine. Neither is there any information on risk factors that may influence main aetiological diabetes mellitus type's incidence, as well as development of diabetic complications in Ukraine. Diabetes register is recognized as an important tool of diabetes research: it is a fully functioning diabetes register created in Ukraine. It includes over 620 000 diabetes patients (2010) and gives a unique possibility to analyze the structure of aetiological types, gender and age features, prevalence, trends of incidence, risk factors of non-fatal events and mortality among Ukrainian diabetes patients. Observational cross-sectional (distribution of diabetes types and treatment, trends of life span) and cohort (assessment of mortality risks) epidemiological studies using national patient register based on data provided by primary care doctors became possible. The register included most of Ukraine's insulin treated patients, as well as significant part of patients receiving oral glucose lowering drugs (OGLD). The insulin-treated patient data covers 24 out of 25 Ukrainian regions, meaning that at least nearly all of Ukraine's T1D population is included in the register. According to the Health Ministry data, the total amount of patients with known diabetes is 1 048375 (2006), which means that nearly half of type 2 diabetes (T2D) patients have yet to be included in the register, which consists of 509 933 patients, including 37 406 death cases.

#### **1.3 Register-based diabetes epidemiology studies**

Systematic epidemiological study of main diabetes mellitus (DM) types through analysis of electronic population registers has been lasting for over 10 years. Usage of DM population registers has become quite advanced in the UK, in particular in Scotland, where by the end of 2004, 161 946 diabetics have been included into local diabetic registers which is equal to 3.2% of the general population. In Scotland, 14 out of 15 healthcare institutions are involved in controlling treatment of DM patients. An important aspect is that Scottish DM registers include all DM patients, unlike others, that only include patients receiving certain kind of treatment. It seems that at the moment, the most advanced and successful Scottish local register is Tayside (Boyle et al., 2001; Leese et al., 2006; Morris et al., 1997). It should be noted that this relatively small, but constantly functioning register became a source of not just "traditional" epidemiologic information concerning prevalence and annual incidence of T1D and T2D, dynamics of DM complications frequency, and quality of treatment, which is a generalization of routine data from active GPs working in the region, and can be accessed through the register's website (http://www.diabetes-healthnet.ac.uk/), but also of purely scientific fundamental data (Doney et al., 2003, 2005; Evans et al., 2005, 2006; Schofield et al., 2006). A few of these papers have entirely clinically-epidemiological nature, comparing mortality among patients with limb amputations depending on presence of DM (Schofield et al., 2006 ), or mortality risks depending on certain type of treatment (Evans et al., 2005, 2006), while others use the register to study genetic characteristics among different categories of DM patients (Doney et al., 2003, 2005). One of the researchers of Belgian Diabetic Register (BDR) Prof. Frans K. Gorus (Diabetes Research Center, Vrije Universiteit Brussel) indicated the possibility of such scientific use of diabetic registers (Gorus, 1996). Important epidemiologic, immunologic, and genetic studies of T1D in children and adolescents were carried out using BDR (Gorus, 1996; Vandewalle et al., 1997; Weets et al., 2001, 2002).

#### **1.3.1 Register-based T2D epidemiology studies**

62 Type 1 Diabetes – Complications, Pathogenesis, and Alternative Treatments

Khalangot et al., 2009b; Vaiserman et al., 2009; Khalangot et al., 2009d; Vaiserman & Khalangot, 2008). Until recently there was no evidence on age and gender structure of patients diagnosed with diabetes mellitus in Ukraine. Neither is there any information on risk factors that may influence main aetiological diabetes mellitus type's incidence, as well as development of diabetic complications in Ukraine. Diabetes register is recognized as an important tool of diabetes research: it is a fully functioning diabetes register created in Ukraine. It includes over 620 000 diabetes patients (2010) and gives a unique possibility to analyze the structure of aetiological types, gender and age features, prevalence, trends of incidence, risk factors of non-fatal events and mortality among Ukrainian diabetes patients. Observational cross-sectional (distribution of diabetes types and treatment, trends of life span) and cohort (assessment of mortality risks) epidemiological studies using national patient register based on data provided by primary care doctors became possible. The register included most of Ukraine's insulin treated patients, as well as significant part of patients receiving oral glucose lowering drugs (OGLD). The insulin-treated patient data covers 24 out of 25 Ukrainian regions, meaning that at least nearly all of Ukraine's T1D population is included in the register. According to the Health Ministry data, the total amount of patients with known diabetes is 1 048375 (2006), which means that nearly half of type 2 diabetes (T2D) patients have yet to be included in the register, which consists of 509

Systematic epidemiological study of main diabetes mellitus (DM) types through analysis of electronic population registers has been lasting for over 10 years. Usage of DM population registers has become quite advanced in the UK, in particular in Scotland, where by the end of 2004, 161 946 diabetics have been included into local diabetic registers which is equal to 3.2% of the general population. In Scotland, 14 out of 15 healthcare institutions are involved in controlling treatment of DM patients. An important aspect is that Scottish DM registers include all DM patients, unlike others, that only include patients receiving certain kind of treatment. It seems that at the moment, the most advanced and successful Scottish local register is Tayside (Boyle et al., 2001; Leese et al., 2006; Morris et al., 1997). It should be noted that this relatively small, but constantly functioning register became a source of not just "traditional" epidemiologic information concerning prevalence and annual incidence of T1D and T2D, dynamics of DM complications frequency, and quality of treatment, which is a generalization of routine data from active GPs working in the region, and can be accessed through the register's website (http://www.diabetes-healthnet.ac.uk/), but also of purely scientific fundamental data (Doney et al., 2003, 2005; Evans et al., 2005, 2006; Schofield et al., 2006). A few of these papers have entirely clinically-epidemiological nature, comparing mortality among patients with limb amputations depending on presence of DM (Schofield et al., 2006 ), or mortality risks depending on certain type of treatment (Evans et al., 2005, 2006), while others use the register to study genetic characteristics among different categories of DM patients (Doney et al., 2003, 2005). One of the researchers of Belgian Diabetic Register (BDR) Prof. Frans K. Gorus (Diabetes Research Center, Vrije Universiteit Brussel) indicated the possibility of such scientific use of diabetic registers (Gorus, 1996). Important epidemiologic, immunologic, and genetic studies of T1D in children and adolescents were carried out using BDR (Gorus, 1996; Vandewalle et al., 1997; Weets et al.,

933 patients, including 37 406 death cases.

2001, 2002).

**1.3 Register-based diabetes epidemiology studies** 

Some results obtanied by means of studying electronic registers may at first seem unusual or paradoxic. In particular our studies of T2D patients (Khalangot et al., 2009b) indicate that Hazard Ratios (HRs) of cardiovascular disease (CVD) mortality among extremely obese patients [body mass index (BMI) ≥ 35 kg/m2] adjusted for age, smoking and alcohol consumption were higher than for overweight patients [BMI 25-29 kg/m2]: HR=1.54 (95% CI 1.16-2.05) and 1.35 (95% CI 1.15-1.59) among men an women respectively, p<0.01. Furthermore, the graph that shows risks of general and CVD mortality for T2D patients depending on BMI has the shape of an asymmetric parabola: HRs associated with low and normal BMI were significantly higher comparing to those, related to overweight or moderate obesity. The above phenomenon partially corresponds to "obesity paradox" that has been recently discovered among patients suffering from CVD (Gruberg et al., 2002; Curtis et al., 2005) , however in our study this effect concerns T2D patients. An observational study that included 25 361 T2D patients showed that glibenclamide treatment is associated with much higher risk of general and CVD mortality, comparing to treatment with another derivative of sulfonylurea – gliclazide. HRs for total and CVD mortality within the glibenclamide patient cohort were 2.57 (95% CI 1.73–3.82) and 2,93 (95%CI 1.83–4.71) respectively; (p < 0,001). These data correspond to changes of OGLD distribution and trends of life duration among DM patients that we have revealed as well (Khalangot et al., 2009 e). Previously, there had been only one study where similar results concerning total mortality associated with the use of glibenclamide or gliclazide in a cohort of 568 T2D patients were obtained (Monami et al., 2007). Our study broadens this tendency onto CVD mortality.

### **1.3.2 Register-based T1D epidemiology studies**

T1D incidence among Ukrainian adults from 1994 till 2004, that we have evaluated retrospectively according to the register data, had a decreasing tendency (Khalangot, 2009). Our assessment of T1D incidence dynamics among adults does not confirm the information about steady increase of global T1D incidence (Green et al., 2001; Gale, 2002), however these studies only concerned child incidence. As Ukraine is also experiencing a rise of DM incidence among children, our data can be easily explained by earlier DM manifestation among people, who carry the genotype predisposing to T1D. Researchers of Danish DM register have recently reported a decrease of DM incidence among young adults. National diabetic register of Denmark has collected data on 359 000 DM cases between 1995 and 2006, and it includes the total population, diagnosed with DM. This register has recorded a clear tendency towards reduction of mortality among DM patients, which has been observed since 2003 (Carstensen et al., 2008). We have recently conducted a series of studies as well that focused on factors influencing mortality and territorial heterogeneity of T1D in Ukraine (Khalangot, 2008; Khalangot et al., 2009 c; 2010). The purpose of these studies (Khalangot et al., 2009 d) was also to determine whether the insulin requirement can change systematically in T1D patients, and whether this requirement depends on the same factors that determine its prevalence. This chapter is mainly a review of these studies.

#### **2. Methods**

A database with 282,988 records of diabetic patients was developed on the basis of epidemiological analysis conducted during the 2005–2006 register verification (01.12.2006). To evaluate the completeness of the register data, we compared it to the official 2005

Prevalence of Type 1 Diabetes Correlates with Daily Insulin Dose, Adverse

**2.4 Diabetes-associated antibodies and c-peptide measurements** 

**3. Register analysis results and discussion** 

**3.1 T1D territorial dissimilarity and clusterization** 

Chernivetska, and Luganska regions.

= 214.4; *p*< 0.001), as shown in figure 1.

disease (Khalangot et al., 2009 c; Khalangot et al., 2009 d; 2010).

dissimilarity: chi-square = 648.30, degree of freedom, *k* =23 (*p*< 0.001).

regional clusters were distinguished according to the T1D prevalence:

Maximal prevalence cluster = Zaporizka, Khmelnytska, and Chernigivska

Ternopilska, Poltavska, Khersonska, and Cherkaska regions.

regression to compare PR and AH.

pmol/l.

regions.

Outcomes and with Autoimmune Process Against Glutamic Acid Decarboxylase in Adults 65

published elsewhere (Khalangot et al., 2009c ; 2010). In brief, mortality was assessed using the Cox regression model, determining hazard ratios (HRs) and corresponding 95% confidence intervals (95% CI). We calculated odds ratios (ORs), and used a logistic

A total of 86 T1D patients (42 males and 44 females), with a mean age of 27.5 years (0.86) and mean diabetes duration of 10.3 (0.72) years (SE), were randomly selected from four regional diabetes-mellitus registers: Chernihivska, Zaporizka; Ivano-Frankivska, and Chernivetska. The glutamic acid decarboxylase 65 antibody (GADA), insulin antibody (IA), and plasma c-peptide levels were determined using radioimmunoassay (RIA) kits (IMMUNOTECH™) after obtaining the patients' informed consent. The model of the logistic regression was used for the multifactor data analysis of GADA, IA, c-peptide persistence, OR, and the 95% CI that were determined. The plasma was considered GADA- or IApositive, if GADA >1 U/ml or IA >0.4 U/ml, and low c-peptide, if its level was <32.6

The analysis of the register of diabetic patients has allowed for the first time to assess the adult prevalence of T1D in Ukraine in comparison with important clinical (daily insulin dose, mortality, and complications) and some paraclinical (GADA) characteristics of the

The data on adult T1D prevalence in 24 Ukrainian regions (Table 1) indicated territorial

Further multiple comparisons using the modified Marascuilo procedure (Marascuilo, 1966) allowed conducting a pairwise assessment of each region. This assessment enabled clustering of the regions according to T1D prevalence. The flagged regions that did not statistically differ from the minimal level according to prevalence were considered as a cluster. This procedure was repeated for the remaining regions as well. The following

Minimal prevalence cluster = AR Crimea, Ivano-Frankivska, Mykolaivska, Odeska,

Intermediate prevalence cluster = Vinnitska, Volynska, Dnipropetrovska, Donetska, Zhytomyrska, Zakarpatska, Kirovogradska, Lvivska, Rivnenska, Kievska, Sumska,

Cases of T1D in each regional cluster were unified and the prevalence was calculated for the actual clusters. The T1D prevalence was found to be 6 (5–6), −7 (6–7), and −9 (8–9) per 10,000 adults, for the minimal, intermediate, and maximal prevalence clusters respectively. A comparison of the differences between these groups indicated a high level of confidence (χ<sup>2</sup>

Ministry of Health statistical data (Anonymous, 2006). The integrity of the register, i.e., the data on the number of patients who have received insulin, was assessed based on the information provided by the primary care doctors (district endocrinologists) to the regional diabetic registers. Consequently, the regional endocrinologists were responsible for updating the data and endorsing it to the central level. Accordingly, by assuming that the data were encoded into the regional registers with various degrees of completeness, significant limitations were noted in the assessment of the prevalence of insulin-dependent diabetes as well as in further epidemiological evaluations. Considering the fact that Ukraine has a national, free-of-charge insulin supply to the patients who require it, the Ministry of Health data reflect the number of these patients to the fullest extent. However, the Ukrainian Ministry of Health receives only non-personalized data that are difficult to verify. A comparison of the data from the 2006 Diabetes Register with the 2005 data on the insulintreated patients from the Ministry of Health (considered 100%) revealed certain similarities: the fraction of the patients included in the register was 91.1%, based on the number of the patients according to the Ministry of Health data. However, in the Kharkiv region, only 58.6% of the Ministry of Health patients were in the register. It was assumed that the Kharkiv region data in the register could be incomplete, and hence, was not used in the analysis of T1D prevalence among adults.

#### **2.1 T1D cases selection**

Therefore, the analysis was carried out using the T1D criteria used by the epidemiologists– researchers for the European diabetes population databases (Kyvik et al., 2004; Soedamah-Muthu, 2006). The patients were selected based on the following conditions: T1D primary care diagnosis; age at the time of being included in the register ≥15 years; place of residence and gender; and data on diagnoses before the age of 30 years.

#### **2.2 T1D prevalence assessment**

The prevalence of T1D in the Ukrainian regions was determined as of the end of 2004. The T1D prevalence was calculated using the official data on the adult population of the corresponding regions (Anonymous, 2006), and 95% confidence interval (CI) was determined using arcsine transformation (Altman et al., ed-s., 2003). Multiple comparisons of T1D regional prevalence were subsequently carried out using the modified (Liakh & Gurianov, 2004) L. Marascuilo mathematical procedure (Marascuilo, 1966). The MedStat statistical package was used for the calculations (Liakh & Gurianov, 2004). Logistic regression analysis was used to determine the influence of the explanatory variables on the resulting variable (Bland, 2000). For each input variable, we evaluated the estimated logistic regression coefficient with the standard error, estimated as the odds ratio (OR) with a CI for its actual value and associated *p* value, and performed a Wald test (testing the null hypothesis on the congruency of the OR of the "disease" associated with the increase of this variable by 1). We used this information to determine whether each variable was related to the outcome of interest, and to quantify the extent of such a relationship (Bland, 2000). The Statistica 5.5 (StatSoft Inc., 1999) package was used in this set of calculations.

#### **2.3 T1D outcomes assessment**

We have evaluated the prevalence of proliferative retinopathy (PR), arterial hypertension (AH), and mortality risks in the retrospective cohort of T1D (27,896 patients); these data was published elsewhere (Khalangot et al., 2009c ; 2010). In brief, mortality was assessed using the Cox regression model, determining hazard ratios (HRs) and corresponding 95% confidence intervals (95% CI). We calculated odds ratios (ORs), and used a logistic regression to compare PR and AH.
