JOURNAL OF DIABETES, ENDOCRINOLOGY AND METABOLIC DISEASES

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Vol. 43 No. 4 (pp. 87 - 114) 2014 / Zagreb, April 2016

43 no.

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p.p. 87-114 2014.

Journal of Diabetes, Endocrinology and Metabolic Diseases VUK VRHOVAC UNIVERSITY CLINIC, ZAGREB, DAMA - DIABETOLOGY ALUMNI MEDICAL ASSOCIATION

CONTENTS ORIGINAL RESEARCH ARTICLES DIABETES SCREENING: A CROSS SECTIONAL STUDY IN RURAL POPULATION OF NAYA RAIPUR, CHHATTISGARH, INDIA V. Tushar Kumarr, A. Pereira, V. Prasad Kolla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 ADIPONECTIN AND GLUCAGON-LIKE PEPTIDE 1 IN CPEPTIDE NEGATIVE TYPE 1 DIABETIC PATIENTS WITH RETINOPATHY: A CROSS-SECTIONAL STUDY K. Zibar, K. Blaslov, T. Bulum, M. Tomić, J. Knežević Ćuća, L. Smirčić-Duvnjak . . . 95 REVIEW

UDC 616.379-008.67.43

ISSN 0351-0042

MONOGENIC FORMS OF DIABETES – IS THE TERM MODY OBSOLENT? A. Novak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

MEDICAL SCIENTIFIC JOURNAL OF THE VUK VRHOVAC INSTITUTE UNIVERSITY CLINIC FOR DIABETES, ENDOCRINOLOGY AND METABOLIC DISEASES SCHOOL OF MEDICINE, UNIVERSITY OF ZAGREB CROATIAN MEDICAL ASSOCIATION, CROATIAN SOCIETY FOR ENDOCRINOLOGY AND DIABETOLOGY DIABETOLOGY ALUMNI MEDICAL ASSOCIATION VUK VRHOVAC UNIVERSITY CLINIC, ZAGREB

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VOLUME 43, NUMBER 4, 2014

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Original Research Article

Public Health professional, Raipur, Chhattisgarh, India Research and Development Cell, ITM University, Naya Raipur, Chhattisgarh, India Faculty of Life & Allied Sciences, ITM University, Naya Raipur, Chhattisgarh, India

DIABETES SCREENING: A CROSS SECTIONAL STUDY IN RURAL POPULATION OF NAYA RAIPUR, CHHATTISGARH, INDIA V. Tushar Kumarr1, A. Pereira2, V. Prasad Kolla3

Key words: diabetes mellitus type 2, screening, blood glucose level

SUMMARY Diabetes mellitus type 2 is a growing burden on public health. Most of the rural Indian populations are unaware that they have high blood glucose levels. The risk of diabetes is greatly increased when associated with lifestyle factors, high blood pressure, overweight or obesity, insufficient physical activity and poor diet. The aim was to estimate the burden of diabetes mellitus type 2 in the rural populations of a village named Uparwara in Naya Raipur, Chhattisgarh, India. A total of 102 subjects within no specified age group but above 18 years of age were considered for screening. Screening was performed using the Thyrocare Sugar Scan glucometer strips. Blood samples of the subjects with a random blood sugar level above 200 mg/dL or >11.1 mmol/L were considered diabetes positive and further confirmed by HbA1c testing at Thyrocare. Data were analyzed and expressed using basic statistical tools in MS excel Corresponding author: Prof. Vara Prasad Kolla, MD, PhD, Faculty of Life & Allied Sciences, ITM University, Naya Raipur, India E-mail: [email protected]

Diabetologia Croatica 43-4, 2014

2007 and R. In the screened population, 10.8% tested positive for diabetes mellitus type 2 with blood glucose level >200 mg/dL or >11.1 mmol/L. The mean glucose level of subjects diagnosed positive with diabetes mellitus type 2 was 242.9 mg/dL or 13.4 mmol/L. The mean age of all positive/diabetic subjects was 49.9 years. Positive cases were confirmed with the HbA1c test, where the average HbA1c level of patients was estimated to 10%. The patients diagnosed with high glucose levels were previously undiagnosed or untreated for diabetes. Our findings suggest that 10.8% of the rural population of Uparwara, mean age 49.9 years, have undiagnosed diabetes. Being aware of the risk factors and complications associated with diabetes, these data suggest urgent need to create health awareness, knowledge of diabetes and its associated risk factors, proper health management, regular health checkups, rational planning and allocation of resources, especially to the illiterate and underprivileged rural population.

INTRODUCTION The growing public health burden of diabetes across the world is significantly high. According to the International Diabetes Federation, in 2013 approximately 50% of all people with diabetes reside

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V. Tushar Kumarr, A. Pereira, V. Prasad Kolla / DIABETES SCREENING: A CROSS SECTIONAL STUDY IN RURAL POPULATION OF NAYA RAIPUR, CHHATTISGARH, INDIA

in just three countries: China (98.4 million), India (65.1 million) and the USA (24.4 million) (1). The number of people with diabetes is increasing due to population growth, aging, urbanization, increasing prevalence of obesity and physical inactivity, especially in developing countries. The global prevalence of diabetes in the year 2000 (as used in the World Health Organization [WHO] Global Burden of Disease Study) was 2.8% and projection for 2030 is 4.4%. Globally, as of 2010, an estimated 285 million people had diabetes, with type 2 making up about 90% of the cases (2). India, the second most populous country of the world, has been severely affected by the global diabetes epidemic. India ranks among the top 10 countries estimated to have the highest numbers of 31.7 million people with diabetes in 2000 and 71.4 million in 2030 (3) and among the top 5 countries with 66,847 million diabetic people in the 20-79 age group in 2014 (1). The International Diabetes Federation revealed that 4.4 million Indians in their most productive years, aged 20 to 79, are not aware that they have diabetes, which resulted in death of 1 million Indians in the year 2011 (1). Nearly 52% of Indians are not aware that they are suffering from high blood sugar. India is presently home to 63 million diabetics. Diabetes burden was 51 million in 2010, i.e. 4.2% of India total population. Continuing the trend, 7.32% of those suspected of having diabetes are expected to rise to 80 million by 2030. The National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) reports on 54 million persons screened for diabetes and hypertension till December 2013. According to the latest Annual Health Survey 20122013 report, over 4000 people per 100,000 population in Chhattisgarh have symptoms of these chronic illnesses, with the urban leading over their rural counterparts. Recent camps held in certain districts of Chhattisgarh, which includes some rural and tribal areas, indicated that the prevalence could be anything between 4 to 5 percent in these regions. This is, however, considerably lower than the national prevalence of 9.2% assessed by the International Diabetes Federation. In Chhattisgarh, nearly 5420

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people per 100,000 urban population have symptoms of these lifestyle related diseases. However, in rural areas the figure stands at 3842 per 100,000 population. The human and economic costs of this epidemic are enormous, making its awareness and treatment unaffordable especially to rural populations of developing countries. In association with the increasing diabetes prevalence, there will be an inevitable increase in the proportion of deaths from cardiovascular diseases in these regions, as well as an increased prevalence and associated consequences of other complications of diabetes. When it comes to the global prevalence of 3%, Chhattisgarh is ahead in the race. It is an alarming concern that the present prevalence of diabetes in the state is even higher than the WHO projected global rate of 4.4% in 2030. Diabetes mellitus type 2 is a multifactorial disease resulting from an interaction between genetic and environmental factors (4). Diabetes mellitus type 2 is a metabolic disorder characterized by hyperglycemia (high blood sugar), in the context of insulin resistance or relative lack of insulin. Sedentary lifestyles, lower physical activity, and lower educational levels were found to be associated with diabetes mellitus type 2 among the Berber group of the Djerba Island (5). The common causes of diabetes mellitus type 2 include lifestyle (obesity, poor diet, lack of physical activity, stress and urbanization), genetics and other medical conditions. Hence, estimating the prevalence among populations and awareness of proper management of diabetes along with health education could benefit public health and reduce diabetic burden upon the society. Despite the worldwide importance of diabetes mellitus, relatively little is known about its actual prevalence and its associations in India, particularly in rural India. In rural areas, as a consequence of illiteracy, financial crisis and lack of health care, chronic conditions frequently go untreated or are poorly controlled until more serious and acute complications arise. Even when chronic conditions are recognized, there is often a large gap between evidence-based treatment guidelines and current practice. Chronic illness confronts patients with a spectrum of needs that require them to alter their behavior and engage in activities that promote


physical and psychological well-being, to interact with healthcare providers and adhere to treatment regimens, to monitor their health status and make associated care decisions, and to manage the impact of the illness on physical, psychological and social functioning (6). The main objective of this study was to estimate the burden of diabetes mellitus type 2 in the rural population of Naya Raipur, Chhattisgarh. We aimed to assess the gender-wise and age-wise distribution of diabetes mellitus type 2 among patients, estimate the average blood glucose levels among patients and calculate their HbA1c percentage.

SUBJECTS AND METHODS This cross sectional descriptive study was conducted on the rural population of Raipur, Chhattisgarh on December 12-14, 2014 in order to investigate the prevalence of diabetes mellitus type 2 in a typical rural background of the Uparwara village, Naya Raipur, Chhattisgarh, India. Members of the village Panchayat of Uparwara, Naya Raipur and Institutional Ethics Committee of ITM University provided permission to conduct the test. Public health experts and social workers were involved in the screening camp. Prior information about the screening camp was provided to all the villagers through different Grass route level workers of the village. Three-day free screening camp was conducted by ITM University staff members in the Uparwara village on consecutive days at three different locations of the village, i.e. periphery on day 1, centre of the village on day 2, and at the entrance of the village on day 3. The subjects were included irrespective of gender and age, however, only individuals above the age of 18 years were considered. All subjects were native of the village and living in the village for at least five or more years to avoid any selection bias. Before collecting the samples, the purpose of the study was explained in the local language. After explaining the purpose of the study, 102 of 130 subjects gave their consent for the screening test. After taking their verbal consent, the test was performed on 101 subjects with no specified age grouping. Random blood glucose levels of all the


subjects were tested using the Thyrocare Sugar Scan glucometer strips (HMD BioMedical Inc., Hsinchu, Taiwan) in accordance with the WHO guidelines. The kits used for the screening test were standard routinely employed kits. The kits were tested among the staff members before applying to the subjects. A first aid box, emergency medicines and glucose biscuits were provided during the screening camp. Data were collected, entered and tabulated in a data sheet. Data were analyzed and presented by using basic statistical tools in MS Excel 2007 and R. The development of diabetes was defined as a fasting blood glucose concentration of ≥126 mg/dL or a postload/postprandial blood glucose concentration of ≥200 mg/dL (7), based on the WHO criteria or a casual/random plasma glucose (PG) ≥11.1 mmol/L (200 mg/dL) also indicating diabetic type (8) or HbA1c (which reflects average plasma glucose over the previous eight to 12 weeks) (9) of 6.5% recommended as the cut off point for diagnosing diabetes (10).

RESULTS Out of the 130 subjects who were verbally informed about the test, 102 subjects participated and were screened during the camp. Among the screened subjects, 62.7% were male and 38.2% were female (Fig. 1). Of the screened population, 10.8% tested positive for diabetes mellitus type 2 with their random blood glucose levels above 11.1 mmol/L or 200 mg/dL. Among the 10.8% of positive cases, there were six (5.9%) males and five (4.9%) females (Fig. 2); their blood samples were collected and sent for HbA1c testing to Thyrocare Laboratory, Mumbai, for further confirmation. The mean age of the screened subjects was 39.4 years and of the diabetic positive subjects 49.9 years. The subjects who tested positive for diabetes mellitus type 2 were in the 30-65 age group (Fig. 3). The glucose level of positive cases ranged between 210280 mg/dL and 11.5-15.0 mmol/L (Fig. 4), while the mean glucose level of subjects diagnosed positive for

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diabetes type 2 was 242.9 mg/dL and 13.3 mmol/L and their average HbA1c percentage was estimated to 10%.

Figure 2. Proportion of diabetic patients in the screened population

Figure 1. Genderwise distribution of diabetes population screened in rural population of Naya Raipur

Figure 4. Estimated plasma glucose levels both in mg/dL and mmol/L among diabetic patients

Figure 3. Age distribution of screened population and diabetic cases found

Madhya Pradesh with just 2.5 % reported the least number of cases. In the case of hypertension, Assam ranks second with a 10.4% prevalence, after Sikkim.

DISCUSSION Diabetes is becoming an increasingly common chronic disorder, particularly in rapidly developing countries like India; however, most people with diabetes remain unaware that they are diabetic. Based on these studies, the highest prevalence is reported from Ernakulum in Kerala (19.5%) and the lowest from Kashmir valley (6.1%). Most other areas have prevalence above 10%. A study in India has projected a likely national estimate of 62.4 million patients with diabetes and 77.2 million with prediabetes. The prevalence of diabetes in different areas ranged between 5.3% and 13.6% (11). The NPCDCS screening for diabetes has reported on Karnataka (9.3%), Punjab (9.3%), Gujarat (9.1%) and Andhra Pradesh (7.42%), with a high incidence after Sikkim.

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The Prevalence of Diabetes in India study reported a prevalence of 5.9% and 2.7% in small towns and rural areas, respectively (12). Ramchandran et al. and Shrestha UK et al. found a higher prevalence of diabetes in males than females. Our study also found a similar gender-wise prevalence, marginally high in males (13, 14). Rural Mysore showed a prevalence of 8.21%. Data from the ICMR INDIAB study (11) show the prevalence of diabetes in rural areas to range from 3.0% to 8.3%. Our results showed a prevalence of 10.8%, which is higher compared to the previous study done on the rural population of Agra, where it is reported to be 7% (15). This is alarming and needs serious interventions to control the increasing risk and its complications in rural population. There are variations according to regions in the prevalence of


diabetes in rural population. In studies from Rajasthan, Vellore and Mysore, the prevalence in rural areas has been reported to be as low as 1.8%, 2.1% and 3.8%, respectively. A trend of increasing prevalence, ranging from 9.2% to 13.3% in rural areas, was noticeable in the past decade (1). The rural areas of economically backward states have a lower prevalence as reported in the ICMR-INDIAB study. The prevalence in rural areas of the economically better regions of Chandigarh, Tamil Nadu and Maharashtra was 8.3%, 7.8% and 6.5%, respectively (11). Also, in Pune, the prevalence of diabetes in 2007 was 8.5% as reported by the Indian Industrial Population Study Group (16). Our study showed a higher prevalence of diabetes mellitus type 2 as compared to other studies from rural India. With growing urbanization and sedentary lifestyle, tribal populations are also residing in and around villages and have adopted a lifestyle similar to them, and for that reason they have similar prevalence as rural population, as shown in other studies. A multi-country study in Asia estimated the mean age at diagnosis among Indians to be 43.6 years, where 50% had poor control as indicated by their HbA1c and 54% had late severe complications (17). In the ICMRINDIAB study, approximately 30% of subjects had HbA1c levels below 7%. All patients were referred for confirmatory testing. Rayappa et al. showed that there was a ten-year difference in the age at diagnosis between working and non working respondents, a seven-year gap between the highest and least educated, and a four-year gap between the highest and lowest socioeconomic groups. Those with older age at diagnosis had multiple complications, resulting from delayed diagnosis of diabetes (18). Considering the high incidence of diabetes in rural regions, their socioeconomic background, ignorance about regular health checkups and the fact that they are unaware of their high blood glucose levels and the risk factors associated with diabetes, our results suggest urgent need to improve diabetes awareness, its maintenance and control, among this population to lessen diabetic burden upon the society and to benefit public health.


A multi center, structured screening program for diabetes in the rural belt will give better perspective of the prevalence and cause of this disorder in rural population.

CONCLUSION Our study found a higher prevalence of diabetes mellitus type 2 as compared with earlier studies in rural India. So, 10.8% of the population positive for diabetes were unaware that they harbored diabetes. Fast urbanization and sedentary lifestyles have influenced the tribal populations residing in and around villages to adopt a similar way of living, resulting in the increase in the prevalence of diabetes. Considering the risk factors and complications associated with diabetes, along with illiteracy among rural population points to urgent need of health awareness and proper health management along with complete multicenter screening for the disease in rural regions of Chhattisgarh.

Acknowledgments The authors are grateful to the village Panchayat, Uparwara and all the staff members of ITM University who directly or indirectly helped us during screening camp. Special thanks to all subjects who gave us their consent and participated in this screening. Authors would also like to acknowledge Thyrocare Laboratory, Mumbai for providing Sugar Scan Glucometer kits used in diabetes screening.

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REFERENCES 1. Diabetes Atlas, 6th ed, International Diabetes Federation, 2013. 2. William’s Textbook of Endocrinology, 12th ed. Philadelphia: Elsevier/Saunders. pp. 1371-1435. 3. Wild S, Roglic G, Green A, Sicree R, & King H . Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 2004;27(5):1047-1053. 4. McCarthy MI, Froguel P. Genetic approaches to the molecular understanding of type 2 diabetes. Am J Physiol Endocrinol Metab 2002;283 (2):E217-225. 5. Ouederni TB, Fadiel A, Stambouli N, Scalize TJ, Ben Maiz H, Abid HK, et al. Influence of socioeconomic lifestyle factors and genetic polymorphism on type 2 diabetes occurrences among Tunisian Arab and Berber groups of Djerba Island. Pharmacogenom Personal Med 2009;2:4957.

11. Anjana RM, Pradeepa R, Deepa M, Datta M, Sudha V, Unnikrishnan R, et al. Prevalence of diabetes and prediabetes (impaired fasting glucose and/or impaired glucose tolerance) in urban and rural India: phase I results of the Indian Council of Medical Research-INdia Diabetes (ICMRINDIAB) study. Diabetologia 2011;54(12):30223027. 12. Sadikot SM, Nigam A, Das S, et al. The burden of diabetes and impaired glucose tolerance in India using the WHO 1999 criteria: Prevalence of Diabetes in India Study (PODIS). Diabetes Res Clin Pract 2004;66:301-307. 13. Ramchandran A, Simon M, Annasami Y, Narayanasamy M, Chamukuttan S. High prevalence of diabetes and cardiovascular risk factors associated with urbanization in India. Diabetes Care 2008;31(5):893-898.

6. Clark NM. Management of chronic disease by patients. Ann Rev Public Health 2003;24:289-313.

14. Shrestha UK, Singh DL, Bhattarai MD. The prevalence of hypertension and diabetes defined by fasting and 2-h plasma glucose criteria in Urban Nepal. Diabet Med 2006;23(10):1130-1135

7. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998;15(7):539-553.

15. Agrawal V, Misra SK, Agrawal R, Bhargava U. Article title. Effect of Sociodemographic factors on prevalence of Daibetes Mellitus in Rural Agra: A Community based study. Indian J Appl Res 2015;5(5):19-20

8. Kuzuya T, Nakagawa S, Satoh J, Kanazawa Y, Iwamoto Y, Kobayashi M, et al. Report of the Committee on the Classification and Diagnostic Criteria of Diabetes Mellitus. Diabetes Res Clin Pract 2002;55(1):65-85.

16. Gupta R. Diabetes in India: current status. Express Health Care, August 2008.

9. Nathan DM, Turgeon H, Regan S. Relationship between glycated haemoglobin levels and mean glucose levels over time. Diabetologia 2007;50 (11):2239-2244. 10. International Expert Committee Report on the role of the A1c assaying the diagnosis of diabetes. Diabetes Care 2009;32:1327-1334.

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17. Raheja BS, Kapur A, Bhoraskar A, Sathe SR, Jorgensen LA, Moorthi SR, et al. Diabcare AsiaIndia Study; Diabetes care in India – current status. J Assoc Phys India 2001;49: 717-722. 18. Rayappa PH, Raju KN, Kapur A, Bjork S, Sylvest C, Kumar KM. The impact of socioeconomic factors on diabetes care. Int J Diab Dev Count 1999;19:8-16.

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Original Research Article

Department of Endocrinology and Metabolic Diseases, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Zagreb, Croatia Department of Ophthalmology, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Zagreb, Croatia Department of Clinical Chemistry and Laboratory Medicine, Merkur University Hospital, Zagreb, Croatia School of Medicine, University of Zagreb, Zagreb, Croatia

ADIPONECTIN AND GLUCAGON-LIKE PEPTIDE 1 IN CPEPTIDE NEGATIVE TYPE 1 DIABETIC PATIENTS WITH RETINOPATHY: A CROSS-SECTIONAL STUDY K. Zibar1, K. Blaslov1, T. Bulum1, M. Tomić2, J. Knežević Ćuća3, L. Smirčić-Duvnjak1,4

Key words: diabetic retinopathy, adiponectin, glucagon-like peptide 1, C-peptide negative, type 1 diabetes mellitus

SUMMARY The aim of the study was to investigate the association between adiponectin (ADPN) and glucagon-like peptide 1 (GLP-1) with diabetic retinopathy (DR) and correlation of ADPN with GLP1 in C-peptide negative type 1 diabetic mellitus (T1DM) patients. ADPN and GLP-1 concentrations were measured by ELISA in a cross-sectional study that included 76 patients, age 45 (19-65) and disease duration 22 (1-47) years. The severity of DR was evaluated according to the EURODIAB classification. Data were statistically analyzed by SPSS with a significance level P<0.05. Patients with proliferative DR had higher ADPN (24.4±6.8 μg/mL) in comparison to nonproliferative DR patients (16.8±7.2 μg/mL) and without DR (12.3±4.7 μg/mL) adjusted for age, disease duration and renal function (P for trend Corresponding author: Karin Zibar, MD, PhD, Department of Endocrinology and Metabolic Diseases, Vuk Vrhovac University, Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Dugi dol 4a, HR-10000 Zagreb, Croatia E-mail: [email protected]


0.035). Binary logistic regression showed that ADPN remained independently associated with DR prevalence (OR 1.129, 95%CI 1.001-1.273). GLP-1 was not associated with DR or ADPN. ADPN correlated with the severity of DR in T1DM patients, whereas such relationship was not observed for GLP-1.

INTRODUCTION Adiponectin (ADPN) is an important hormone that regulates a number of metabolic processes. It has been suggested in mice model that ADPN has an important role in the development of retinopathy in patients with type 1 diabetes mellitus (T1DM) (1). Total ADPN showed positive relationship with markers of endothelial cell activation in T1DM (2) and was found to be independently increased in T1DM patients without microvascular complications (3). ADPN concentration was reported to be high and to increase further with diabetes duration in T1DM patients with microvascular complications (4, 5). The cause of increased ADPN is still controversial. It has been proposed that it might be related to diabetes duration or to the loss of β-cell function (6). Some studies suggest that an increase in ADPN concentration

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K. Zibar, K. Blaslov, T. Bulum, M. Tomić, J. Knežević Ćuća, L. Smirčić-Duvnjak / ADIPONECTIN AND GLUCAGON-LIKE PEPTIDE 1 IN C-PEPTIDE NEGATIVE TYPE 1 DIABETIC PATIENTS WITH RETINOPATHY: A CROSS-SECTIONAL STUDY

probably represents a compensatory vasoprotective effect in patients with developed microvascular complications (7). Glucagon-like peptide 1 (GLP-1) is an intestinal hormone that has an important role in glucose metabolism regulation by increasing insulin release and reducing glucagon secretion (8). Its actions are mediated by a specific receptor (GLP-1R), which is expressed in pancreatic cells and in various other tissues (9). GLP-1 receptor analogues have many pleiotropic effects and have been widely used as a glucose lowering drug (10). In type 2 diabetes mellitus (T2DM) patients, GLP-1 analogues showed promising beneficial effect on microvascular complication development (11). Diabetic retinopathy (DR) is one of the T1DM related microvascular complications and among the major causes of blindness in the Western world (12). Patients with T1DM and DR have 3.65 times greater risk of death (all-cause mortality) compared to those without DR (13). The results of experimental studies suggest a potential clinical significance of GLP-1 analogues in retinopathy treatment and development in T1DM (14, 15). Endogenous GLP-1 concentration in relation to retinopathy in T1DM patients has not been investigated yet. As T1DM represents a state of chronic low-grade inflammation, it has been hypothesized that increased ADPN concentration serves to protect patients at a high risk of inflammatory states, as in DR (4). GLP-1 analogues directly induce ADPN expression through protein kinase A pathway and prevent inflammatory adipokine expression (16) that could be a protective mechanism in DR development. Therefore, the aim of the present study was to investigate the association between plasma ADPN and GLP-1 concentrations with DR prevalence and severity and to examine the correlation of ADPN with GLP-1 in C-peptide negative T1DM patients.

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PATIENTS AND METHODS Study design and participants The cross-sectional study included 76 C-peptide negative patients with at least 1-year duration of T1DM. Patients were selected from annual review at Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Zagreb, Croatia. The diagnosis of T1DM was defined according to the following criteria: less than 40 years at the time of disease development, episode of diabetic ketoacidosis, positive autoimmune markers and continuous need for insulin therapy within 1 year of diagnosis. Sample size was not calculated before the beginning of the study, but the power of the study was examined by post hoc analysis in accordance with the G-power 3.1.3. calculation for a difference between 2 independent groups (two tailed t-test; effect size d=1.04, α=0.05, sample size of group 1, n=28, group 2, n=48, 1-β=0.99). The study included patients aged 18-65 years without a history of cardiovascular, severe liver or chronic kidney disease, with estimated glomerular filtration rate (eGFR) >45 mL/min-1/1.73-1m2. Patients received intensive insulin therapy (longacting insulin in one or two doses and short-acting insulin three times daily) and took no other medication that could affect glucose or lipid metabolism. The Hospital Ethics Committee approved the study protocol. Written informed consent was obtained from each patient and the study was performed in accordance with the Declaration of Helsinki. Detailed medical history, including age at diabetes diagnosis, type of insulin therapy and other medications, was obtained from the attending physician. All investigations were performed in the morning, following an overnight fast. Fasting venous blood samples were drawn at 8:00 am and postprandial 30 minutes after a standard diabetic breakfast. The caloric amount of the meal depended on the patient’s weight and consisted of 70% carbohydrates, 25% proteins and 5% fat. The 24-hour urine was collected for albuminuria measurement. Venous blood samples were collected for biochemistry panel measurement, glycated hemoglobin (HbA1c), fasting and


postprandial total GLP-1 (tGLP-1), active GLP-1 (aGLP-1), fasting ADPN concentration, and fasting and postprandial C-peptide level.

Laboratory methods Biochemistry panel, HbA1c, serum creatinine concentration and C-peptide level were assayed using routine laboratory methods. eGFR was calculated by Chronic Kidney Disease Epidemiology Collaboration (CKD EPI) formula (17). Plasma tGLP-1 and aGLP-1 were measured using DRG Diagnostic (Germany) Human ELISA (sandwich) commercial kit, while fasting total plasma ADPN was measured using BioVendor’s (Germany) Human ELISA (sandwich) commercial kit for research use only. The detection limit of the GLP-1 assays was 0.1 pmol/L (for tGLP-1 and aGLP-1). The number of the samples that resulted in value below the detection limit was as follows: fasting tGLP-1, 10 patients; postprandial tGLP-1, 2 patients; fasting aGLP-1, 24 patients; and postprandial GLP-1, 6 patients. All patients had C-peptide (both fasting and postprandial) concentrations below the lower reference range, and were considered C-peptide negative. Blood pressure was measured in the sitting position with a mercury sphygmomanometer after a resting period of 10 minutes and expressed in mmHg. Arterial hypertension was diagnosed based on systolic blood pressure >130 mmHg or diastolic blood pressure >85 mmHg. Diabetic nephropathy was diagnosed by albuminuria (>30 mg/24 hour) presence or/and eGFR <60 mL/lmin-1/1.73-1m2. Diabetic retinopathy and neuropathy were tested by ophthalmologist-retinal specialist and neurologist, respectively, using standardized protocols. Retinopathy was diagnosed by binocular indirect slit lamp funduscopy and fundus photography after mydriasis with eye drops containing 0.5% tropicamide and 5% phenylephrine. Color fundus photographs of two fields (macular field, disc/nasal field; macular field: positioned in such a way that the exact center of the optic disc laid at the nasal end of the horizontal meridian of the field view; disc/nasal field: such that the optic disc was positioned one disc-diameter from the temporal edge of the field, on the horizontal meridian) of both eyes were taken with a suitable 45° fundus camera (VISUCAM, Zeiss)


according to the EURODIAB retinal photography methodology (18). EURODIAB classification scheme was used because it uses two-field 45° fundus photography and standard photographs to grade retinal lesions. In each patient, the ‘worse’ eye was graded for retinopathy using fundus photographs. Patients were classified into 3 groups: absence of DR (n=28), nonproliferative DR (n=37) and proliferative DR (n=11).

Statistical analysis Statistical analysis was done using Statistical Package for the Social Sciences (SPSS) ver. 17.0 for Windows. Normality of distribution for continuous variables was tested using Kolmogorov-Smirnov test. Variables with normal distribution were described by mean and standard deviation (SD), while variables that were not normally distributed by median and minimum-maximum range. The normally distributed variables were age, disease duration, HbA1c, shortacting insulin requirement, ADPN and eGFR. The variables that were not normally distributed were body mass index (BMI), long-acting insulin requirement, fasting and postprandial total and active GLP-1. Nominal variables were presented as frequencies and/or percentages. The difference between two independent numerical variables was tested using parametric Student’s t-test or non-parametric MannWhitney test (for more than 2 independent variables ANOVA parametric test with post hoc Scheffe test or Kruskal-Wallis non-parametric test with post hoc Mann-Whitney U test). Differences between 2 or more nominal variables were tested using χ2-test. Correlations of ADPN with GLP-1 and other parameters were tested with non-parametric Spearman’s correlation test. ANCOVA analysis was done to examine the correlation between ADPN and the severity of DR, adjusted for age, disease duration and renal function. Linear regression analysis was done to evaluate the association between ADPN and disease duration. Multiple linear regression analysis was used to determine the strongest predictors of ADPN concentration. Predictor variables were analyzed for DR prevalence by binary logistic

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regression analysis (Hosmer-Leveshow goodness-offit test), using OR with 95% CI. The level of significance level was set at P<0.05.

RESULTS More than half of the participants were male (62%), age range 19 to 65 (45±12) years, mean diabetes duration 22±12 (1-47) years, median body mass index (BMI) 25 (18-36) kg/m2 and mean HbA1c level 7.4±1.3%. Table 1 shows characteristics of T1DM patients classified into 3 groups according to DR status (absence of DR, nonproliferative DR and proliferative DR). Disease duration increased progressively with the severity of DR (P for trend <0.001) and age (P for trend 0.012). Patients with proliferative DR had higher ADPN concentration adjusted for age, disease duration and eGFR (24.4±6.8 μg/mL) in comparison to patients with nonproliferative DR (16.8±7.2 μg/mL) and without DR (12.3±4.7 μg/mL) (P for trend 0.035).

Figure 1 illustrates greater magnitude of ADPN concentration with rising DR severity. Patients with proliferative DR had lower eGFR (P for trend 0.004). There were no differences in fasting and postprandial tGLP-1 and aGLP-1 concentrations between the 3 groups of DR. Binary logistic regression analysis (Table 2) showed that ADPN independently increased the risk of DR (1=yes, 0=no) in the crude model, and the relation remained significant after adjustment for diabetes duration, age, sex, BMI and eGFR (OR 1.129; 95% CI 1.001-1.273). Bivariate correlation analysis showed no correlation of ADPN with fasting or postprandial tGLP-1 or with aGLP-1 concentrations (Table 3). ADPN correlated significantly positively with diabetes duration (ρ=0.36, P=0.002) and significantly negatively with BMI (ρ=-0.43, P<0.001). There was also significant negative correlation of long-acting insulin requirement (ρ=-0.26, P=0.025) and eGFR (ρ=-0.24, P=0.041) with ADPN. Fasting aGLP-1 concentration showed

Table 1. Characteristics of type 1 diabetes mellitus (T1DM) patients (N=76) stratified into three groups according to diabetic retinopathy (DR) status Variable

Absence of DR

Nonproliferative DR

Proliferative DR

28

37

11

40±13||

47±10

51±7||

0.012†

19/9

22/15

6/5

0.682‡

14±9**

25±9††

36±9**††

<0.001†

BMI (kg/m2)

26 (18-33)

25 (21-36)

24 (21-26)

0.134§

HbA1c (%)

7.4±1.2

7.6±1.5

7±0.5

0.315†

Long-acting insulin requirement (IU/kg/day)

0.3 (0.1-0.8)

0.3 (0.1-0.6)

0.2 (0.1-0.5)

0.121§

Short-acting insulin requirement (IU/kg/day)

0.1±0.06

0.1±0.06

0.1±0.04

0.259†

22/6

28/9

10/1

0.552‡

6.4 (2.8-195)

7.3 (1.7-809)

27 (4.2-723.2)

0.066

103±17‡‡§§

90±19‡‡

85±19§§

0.004†

Nephropathy (yes/no)

0/28

8/29

5/6

0.002‡

Neuropathy (yes/no)

19/9

34/3

11/0

0.009‡

1.1 (0-9.7)

0.9 (0-4.2)

1.3 (0-6.3)

0.27§

Postprandial tGLP-1 (pmol/L)

2.7 (0.2-8.1)

1.6 (0-10)

2.9 (0.6-7.5)

0.272§

Fasting aGLP-1 (pmol/L)

0.3 (0-12.3)

0.1 (0-2.2)

0.2 (0-2.6)

0.151§

Postprandial aGLP-1 (pmol/L)

0.7 (0-10.4)

0.4 (0-5.1)

0.5 (0.1-1.9)

0.217§

12.3±4.7

16.8±7.2

24.4±6.8

0.035

Number of patients (n) Age (years) Gender (M/F) Diabetes duration (years)

Arterial hypertension (yes/no) Albuminuria (mg/24h) eGFR

(mlmin-11.73-1m2)

Fasting tGLP-1 (pmol/L)

Adiponectin (μg/mL) |||

P for trend

BMI = body mass index; HbA1c = glycated hemoglobin; eGFR = estimated glomerular filtration rate; tGLP-1 = total glucagon-like peptide 1; aGLP-1 = active glucagon-like peptide 1; †ANOVA test; ‡χ2-test; §Kruskal-Wallis test; ||P=0.036; ¶P<0.001; **P<0.001; ††P=0.002; ‡‡P=0.015; §§P=0.024 (post hoc Scheffe or post hoc Mann-Whitney U test); ||||adiponectin concentration adjusted for age, disease duration and eGFR (ANCOVA test)

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Table 2. Association of adiponectin with the presence of retinopathy (1=yes, 0=no) in type 1 diabetes mellitus patients (N=76): binary logistic regression Independent variable

Model A OR (95% CI)

Model B OR (95% CI)

Model C OR (95% CI)

1.161 (1.061-1.270)

1.124 (1.014-1.245)

1.129 (1.001-1.273)

Adiponectin

Model A: crude (P=0.001); Model B: adjusted for diabetes duration (P=0.026); Model C: adjusted for diabetes duration, age, gender, body mass index and estimated glomerular filtration rate (P=0.047)

statistically significant correlation with HbA1c (ρ=0.26, P=0.025) but not with other relevant parameters (data not shown).

Figure 1. Adiponectin concentration through diabetic retinopathy status in 76 patients with type 1 diabetes mellitus

As shown by univariate linear regression analysis, plasma ADPN concentration increased by 0.241 μg/mL with each annual increase in diabetes duration (P=0.001) (data not shown). On multiple linear regression analysis, ADPN remained significantly independently associated with diabetes duration (regression coefficient 0.179, P=0.013) and BMI (regression coefficient -0.696, P=0.001) (Table 4).

DISCUSSION We found different ADPN concentration in patients with proliferative DR as compared with patients with nonproliferative DR and without DR, showing that ADPN concentration was progressively higher, after appropriate adjustment, with DR severity in C-peptide negative T1DM patients. In the multivariate binary regression model that included diabetes duration, age, gender, BMI and estimated glomerular filtration rate, Table 3. Correlation of adiponectin with demographics and biochemical parameters in type 1 diabetes mellitus patients (N=76) Variable Age (years)

Correlation coefficient ρ

P

0.13

0.279

Disease duration (years)

0.36

0.002

BMI (kg/m2)

-0.43

<0.001

HbA1c (%)

-0.02

0.896


-0.26

0.025

Short-acting insulin requirement (IU/kg/day)

0.07

0.535

(mlmin-11.73-1m2)

-0.24

0.041

Fasting tGLP-1 (pmol/L)

0.07

0.568

Postprandial tGLP-1 (pmol/L)

0.11

0.362

Fasting aGLP-1 (pmol/L)

0.07

0.529

Postprandial aGLP-1 (pmol/L)

0.1

0.4

eGFR

BMI = body mass index; HbA1c = glycated hemoglobin; eGFR = estimated glomerular filtration rate; tGLP-1 = total glucagon-like peptide; aGLP-1 = active glucagon-like peptide 1


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Table 4. Multiple linear regression analysis of plasma adiponectin (dependent variable) concentrations in type 1 diabetes mellitus patients (N=76) Multiple R=0.549, P<0.001 Independent covariate (unit of measure)

Regression coefficient

Standard error

P

Disease duration (years)

0.179

0.07

0.013

BMI (kgm-2)

-0.696

0.203

0.001


-11.348

6.143

0.069

eGFR (mL/min-1/1.73-1m2)

-0.032

0.043

0.467

Intercept (β0)

32.83

7.687

<0.001

BMI = body mass index; eGFR = estimated glomerular filtration rate

ADPN concentration remained a significant independent risk of DR presence. In addition, ADPN remained independently associated with diabetes duration and BMI by multiple linear regression analysis. We found no difference in fasting and postprandial tGLP-1or aGLP-1 concentration among the three DR classified groups of T1DM patients. There was no correlation between GLP-1 and ADPN either. In support of previous studies (4, 19-21), we confirmed that increased ADPN concentration was associated with DR presence. Previous studies showed that total ADPN concentration and all its subforms were generally higher in patients with T1DM than in healthy controls, suggesting that high ADPN concentration was part of the disease, not only a marker of the complications. Study by Lindstrom et al. showed that C-peptide negative T1DM patients with microangiopathy had higher ADPN concentration in comparison with C-peptide positive T1DM patients, but they had almost three times longer diabetes duration as compared with C-peptide positive patients (21). By contrast, Forsblom et al. showed that ADPN concentration was independently associated with allcause mortality in T1DM patients, but did not declare C-peptide levels in those groups of patients (19). In the present study of C-peptide negative patients, we showed that ADPN concentration was significantly increased in patients with DR presence, after adjustment for diabetes duration and other confounding factors in a multivariate model. It is likely that longer diabetes duration might not be responsible for higher ADPN concentration in the DR

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group of our patients, but it is not excluded that longer disease duration is a major risk factor for DR per se. In that way, it could be speculated that ADPN mirrors the progressive and exacerbated metabolic impairment. On the other hand, all our patients were C-peptide negative, thus we could assume that β-cell dysfunction might be responsible for higher ADPN concentration in our patients. It was also suggested that higher ADPN concentration could be a marker of an increased catabolism, which was seen in poorly adjusted for T1DM patients (19). However, in our study, ADPN was not observed to correlate with glucoregulation, while statistically there was no significant difference in HbA1c level between the patients with different stages of DR and no correlation of ADPN with HbA1c. It is known that patients with chronic kidney disease exhibit higher ADPN concentrations and that ADPN inversely correlates with eGFR (22). However, in our study, eGFR was 94 mL/min/1.73 m2 and the majority of our patients had normal or mildly increased urinary albumin excretion rate, indicating that principally kidney malfunction was not the cause of higher ADPN. Still, we showed negative correlation of ADPN with eGFR and our patients with DR had a significantly higher incidence of diabetic nephropathy compared to those without DR. Therefore, the contribution of kidney function to higher ADPN concentration cannot be fully excluded, but our results showed that ADPN was higher in the DR group of patients independently of kidney function.


Adiponectin exhibits endogenous insulin-sensitizing properties (23, 24), which are explained by stimulation of AMP-activated protein kinase and peroxisome proliferator-activated receptor-γ ligand activity, fattyacid oxidation and glucose uptake (25). Exogenously administered insulin was shown to increase ADPN gene expression in 3T3-L1 adipocytes (26). Additionally, it has been speculated that insulin might exacerbate ADPN release from fat tissue. By contrast, in our study we found negative correlation between ADPN concentrations and exogenous long-acting insulin requirement. Another study also described negative correlation between ADPN and daily insulin dose (4). Few experimental studies suggested a possible protective effect of GLP-1 on retinopathy development in T1DM. The presence of GLP-1R in rat retina, protective role of GLP-1 analogues in early experimental DR (14) and neuroretinal protective effects of exenatide (27) have been reported so far. One study showed that GLP-1 analogue induced secretion of ADPN into the culture medium of 3T3-L1 adipocytes due to increased adiponectin mRNA level through GLP-1R and prevented inflammatory adipokine expression (16). Thus, we speculated that endogenous GLP-1 concentration could be related to ADPN concentration and DR prevalence and severity. To our knowledge, to date no clinical study examined the association of plasma tGLP-1 and aGLP-1 concentrations with DR prevalence and severity or with ADPN concentration in T1DM patients. We found no correlation between fasting and postprandial tGLP-1 or aGLP-1 and ADPN, or any difference in fasting or postprandial tGLP-1and aGLP-1 concentrations between patients with and without DR or DR severity. These findings could not support GLP1R activation in the protection of retinal function and potential therapeutic effects of GLP-1 analogues in the treatment of DR, as described in experimental studies. However, it should be emphasized that in our study, fasting and postprandial tGLP-1 and aGLP-1 concentrations were lower in comparison with previous reports on T1DM patients (28, 29).


We would like to address some limitations of our study. First, it was a cross-sectional study, which restricted the ability to establish causality. Second, we did not measure hormones in duplicates and there were single blood collections. Therefore, we were limited by reliance on the resulting data, especially considering GLP-1 concentration. Third, as we had no control group of T1DM patients with preserved β-cell function, the contribution of β-cell failure to ADPN concentration could have only been hypothesized. Finally, although the sample size was not large, we can say that it represented data from the investigated population. In conclusion, the major finding of the study was that ADPN, after appropriate adjustment, correlated with the prevalence and severity of DR in C-peptide negative T1DM patients and showed increasing ADPN concentration with rising DR severity in EastEuropean citizens, whereas such a relationship was not observed for different GLP-1 measurements. No significant association of GLP-1 with ADPN concentrations was found. Whether increased ADPN concentration in the DR group of patients is related to β-cell loss or it is a compensatory effect, requires further clarification. Prospective studies are needed to clarify the relationship of plasma GLP-1 concentration with ADPN and DR presence and severity in T1DM patients and to investigate the potential clinical role of GLP-1 analogues in the treatment of DR in T1DM.

Acknowledgment The work was supported by the Ministry of Science, Education and Sports of the Republic of Croatia, grant 045-1080230-0516.

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9. Holz GG, Chepurny OG. Glucagon-like peptide-1 synthetic analogs: new therapeutic agents for use in the treatment of diabetes mellitus. Curr Med Chem 2003;10(22):2471-2483.

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11. Puddu A, Mach F, Nencioni A, Viviani GL, Montecucco F. An emerging role of glucagon-like peptide-1 in preventing advanced-glycation-endproduct-mediated damages in diabetes. Mediators Inflamm 2013;2013:591056. 12. Williams R, Airey M, Baxter H, Forrester J, Kennedy-Martin T, Girach A. Epidemiology of diabetic retinopathy and macular oedema: a systematic review. Eye 2004;18(10):963-983. 13. Kramer CK, Rodrigues TC, Canani LH, Gross JL, Azevedo MJ. Diabetic retinopathy predicts allcause mortality and cardiovascular events in both type 1 and 2 diabetes: meta-analysis of observational studies. Diabetes Care 2011;34(5):1238-1244. 14. Zhang Y, Wang Q, Zhang J, Lei X, Xu GT, Ye W. Protection of exendin-4 analogue in early experimental diabetic retinopathy. Graefes Arch Clin Exp Ophthalmol 2009;247(5):699-706. 15. Fu Z, Kuang HY, Hao M, Gao XY, Liu Y, Shao N. Protection of exenatide for retinal ganglion cells with different glucose concentrations. Peptides 2012;37(1):25-31. 16. Kim Chung le T, Hosaka T, Yoshida M, Harada N, Sakaue H, Sakai T, et al. Exendin-4, a GLP-1 receptor agonist, directly induces adiponectin expression through protein kinase A pathway and prevents inflammatory adipokine expression. Biochem Biophys Res Commun2009;390(3):613618. 17. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150(9):604-612.


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25. Yamauchi T, Kamon J, Minokoshi Y, Ito Y, Waki H, Uchida S, et al. Adiponectin stimulates glucose utilization and fatty-acid oxidation by activating AMP-activated protein kinase. Nat Med 2002;8(11):1288-1295.

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Section of Endocrinology, Department of Internal Medicine, Split University Hospital Center, Split, Croatia

Review

MONOGENIC FORMS OF DIABETES – IS THE TERM MODY OBSOLENT? A. Novak

Key words: genetics, monogenic diabetes, maturity onset diabetes of the young

HISTORY, GENETICS AND PATHOPHYSIOLOGY OF MONOGENIC FORMS OF DIABETES

SUMMARY

Maturity-onset diabetes of the young (MODY) is a group of clinically heterogeneous disorders characterized by non-ketotic diabetes, typically found in the young (<25 years of age) as a result of a partial defect in glucose-induced insulin release with autosomal dominant transmission and a primary defect in pancreatic β-cell function. In contrast to most patients with type 2 diabetes, these patients are generally nonobese and lack associated insulin resistance. Because they are not ketosis-prone and may initially achieve good glycemic control without insulin therapy, their disease has been called MODY (1).

The most common forms of diabetes, type 1 and type 2, are polygenic, meaning the risk of developing these forms of diabetes is related to multiple genes. Environmental factors, such as obesity in the case of type 2 diabetes, also play a part in the development of polygenic forms of diabetes. Some rare forms of diabetes result from mutations in a single gene and are called monogenic. It may be dominantly or recessively inherited or may be a de novo mutation and hence a spontaneous case. Genetic testing can diagnose most forms of monogenic diabetes. Some monogenic forms of diabetes can be treated with oral antidiabetic medications, while other forms require insulin injections. A correct diagnosis that allows for proper treatment to be chosen should lead to better glucose control and improved health at long term. Corresponding author: Anela Novak, MD, Section of Endocrinology, Department of Internal Medicine, Split University Hospital Center, Spinčićeva 1, HR-21000 Split, Croatia E-mail: [email protected]


Based on prospective studies of first-degree relatives of patients with type 2 diabetes mellitus, Fajans and Conn were the first to report in 1960 that mild asymptomatic diabetes may occur in nonobese children, adolescents, and young adults. Their diabetic glucose tolerance and fasting hyperglycemia could be improved or normalized by sulfonylurea therapy, currently for up to four decades (2).

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A. Novak / MONOGENIC FORMS OF DIABETES – IS THE TERM MODY OBSOLENT?

In 1964 at the Fifth Congress of the International Diabetes Federation in Toronto, Fajans was the first to use the term “maturity onset type diabetes of childhood or of the young” for this type of diabetes and emphasized its strong familial basis (2). In 1974, Tattersall reported on a mild form of diabetes in three families from King’s College Hospital in London and recognized that diabetes in these families had an autosomal-dominant mode of inheritance. In 1975, Tattersall and Fajans differentiated inheritance of diabetes in 35 families with type 1 diabetes and 24 Michigan families (known as the “R-W pedigree”) from MODY, confirming the autosomal-dominant inheritance for the latter and using the acronym MODY for the first time (3). The confusing term MODY originates from that time when the terms juvenile-onset and maturity-onset were used to distinguish between type 1 (insulin dependent) and type 2 (non-insulin dependent) diabetes. MODY was used to describe a subgroup of autosomaldominantly inherited diabetes that despite having a young age at onset (at least one family member diagnosed before 25 years of age) was non-insulin dependent (as patients had moderate but insufficient circulating C-peptide levels 5 years after diagnosis) (4). The most common forms of diabetes, type 1 and type 2, are polygenic, meaning the risk of developing these forms of diabetes is related to multiple genes. Some rare forms of diabetes result from mutations in a single gene and are called monogenic. Monogenic forms of diabetes account for 1%-2% of all diabetes cases, yet remaining underdiagnosed (5). Almost all monogenic diabetes results from mutations in genes that regulate β-cell function to produce insulin (etiologic classification as genetic defects of β-cell function), although diabetes can rarely occur from mutations resulting in very severe insulin resistance (etiologic classification as genetic defects in insulin action). It may be dominantly or recessively inherited or may be a de novo mutation and hence a spontaneous case (6). Since the classification of diabetes was revised in 1998 to reflect the etiology (7), many authors propose that

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the term MODY is now obsolete and that the correct monogenic names of the different forms of youngonset diabetes should be used when possible (4). Monogenic forms of beta cell diabetes can result from mutations (in the heterozygous state) in at least six different genes for the majority cases of monogenic diabetes in patients of European ancestry (1). All of these genes are expressed in the insulin-producing pancreatic β-cells, and mutations in the heterozygous state lead to β-cell dysfunction and diabetes mellitus. One encodes the glycolytic enzyme glucokinase (gene symbol GCK; MODY2), and the other five encode transcription factors: hepatocyte nuclear factor (HNF)4α (HNF4A; MODY1); HNF-1α (HNF1A; MODY3); pancreatic duodenal homeobox-1 (PDX1; MODY4); HNF-1β (HNF1B; MODY5); and neurogenic differentiation 1 (NeuroD1), also known as beta-cell E box transactivator 2 (NEUROD1; MODY6) (8). Several rare variants in other genes have been implicated in autosomal dominant diabetes in a few families (see Table 1) (1). These genes are also expressed in other tissues, and altered liver function and kidney and genital abnormalities may be evident in some forms of MODY, especially HNF-1β-related MODY (MODY5) (8). Autosomal recessive genetic defects are less common, causing autosomal recessive syndromes. Many of these present with neonatal diabetes and extrapancreatic defects like Mitchell-Riley syndrome (mutations in RFX6); Wolcott-Rallison syndrome (mutations in EIF2AK3); Wolfram syndrome (mutations in WFS1); TRMA (thiamine-responsive megaloblastic anemia) syndrome (mutations in SLC19A2); MIDD (maternally inherited diabetes and deafness) (mt.3243A>G gene mutation); and RCAD renal cysts and diabetes (mutations in HNF1B) (1,4).

DIFFERENTIATION OF MONOGENIC FROM OTHER TYPES OF DIABETES The majority of patients with genetically proven monogenic diabetes are initially incorrectly diagnosed as type 1 or type 2 diabetes. It is important to correctly diagnose monogenic diabetes as it can predict the clinical course of the patient, guide the most


appropriate treatment, genetic counseling and prognostic information. In addition, making a diagnosis will have implications for other family members often correcting the diagnosis and treatment for other diabetic family members (6). Features that should suggest a possible diagnosis of monogenic diabetes in patients initially thought to have type 1 diabetes are two-generation or threegeneration family history of diabetes with evidence of non-insulin dependence, absence of autoantibodies against pancreatic antigens (especially if measured at diagnosis), and detection of measurable C-peptide in the presence of hyperglycemia (glucose >8 mmol/L) outside the ‘honeymoon period’ (after 3 years) (9). Monogenic forms of diabetes should be suspected in cases of young-onset diabetes, initially thought to have type 2 diabetes, when obesity and features of insulin resistance are absent. In patients with young onset diabetes, lack of obesity, absence of acanthosis nigricans or polycystic ovarian syndrome (10), elevated or normal HDL-cholesterol and reduced or normal triglyceride levels are all features that make the presence of monogenic β-cell forms of diabetes likely (11).

CLINICAL PRESENTATION OF MONOGENIC DIABETES When monogenic diabetes is diagnosed, it can be classified under four phenotypic categories: - neonatal diabetes and diabetes diagnosed before 6 months of age; - familial, mild fasting hyperglycemia; - familial, young-onset diabetes; and - diabetes with extrapancreatic features (Figure 1) (4).

Neonatal diabetes and diabetes diagnosed within the first 6 months of life Diabetes diagnosed before 6 months of age is likely to be one of the monogenic forms of neonatal diabetes (NDM) and not autoimmune type 1 diabetes. NDM may be permanent (PNDM) or transient (TNDM), in which case the diabetes may remit spontaneously


within 1-18 months (relapse to permanent diabetes later in life is common) (12). Fifty percent of PNDM cases and 20% of TNDM cases are estimated to result from activating mutations of the KATP channel genes (KCNJ11 or ABCC8) encoding the Kir6.2 and SUR1 subunits, respectively. Activating mutations in the KATP channels impair the ability of ATP to close the channel, thereby preventing β-cell depolarization and insulin secretion. Consequently, these patients present with diabetic ketoacidosis or marked hyperglycemia and low levels of circulating endogenous insulin, so they were previously assumed to require lifelong insulin treatment (13). The identification of KATP channel mutations in patients with PNDM has had a dramatic impact on their diabetes therapy. Sulfonylureas do, however, bind to the SUR1 subunits of the KATP channel and close the channel in an ATPindependent manner. Approximately 90% of patients with Kir6.2 neonatal diabetes can switch from insulin to sulfonylurea tablets and achieve improved glycemic control (14). TNDM is usually diagnosed in the first week of life (range 1-81 days). Affected children are typically born with lower birth weight (mean 2000 g). The majority of patients with TNDM have an abnormality of imprinting of the ZAC and HYMAI genes on chromosome 6q24 (6). Many other genetic etiologies of NDM have been documented, some which are associated with extrapancreatic manifestations. Insulin therapy is essential for patients with other non-KATP channel forms of NDM (12).

Familial, mild fasting hyperglycemia Mild fasting hyperglycemia (5.5-8.0 mmol/L) persistent and stable over a period of months or years, HbA1c just below or just above the upper limit of normal (5.5%-5.7%), and small increment in the oral glucose tolerance test (typically <3.5 mmol/L) suggest a diagnosis of a glucokinase (GCK) mutation: heterozygous inactivating mutation in GCK, known as MODY 2 (6). Very rarely, severe PNDM can result from homozygous mutations in GCK (15). The population prevalence of heterozygous inactivating GCK mutations is approximately 0.1%, accounting for 20%-50% of all cases of monogenic diabetes (16).

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Figure 1. Diagnostic algorithm for assessment of suspected monogenic diabetes: diabetes diagnosed at <35 years of age (4)

Most cases are detected later in life during incidental glucose screening, often mistakenly diagnosed and treated as either type 1 or type 2 diabetes. Parents may have ‘type 2 diabetes’ or may not be diabetic. On testing, one parent will have mildly raised fasting blood glucose, in the range of 5.5-8.5 mmol/L, as this is an autosomal dominant condition (17). Since microvascular complications are extremely rare in these patients, the confirmation of GCK mutation

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allows for glucose lowering therapy to be stopped without the risk of progression of diabetes. Screening for microvascular risk may be discontinued (12). There is very little, if any, response to either oral hypoglycemic agents or insulin. Exogenous insulin results in reduction of endogenous insulin secretion and so the degree of glycemia will be maintained, explaining why these patients can be treated with insulin without significant hypoglycemia (6).


Pregnancy is the one exception. Women with a GCK mutation have a 50% chance of carrying a baby without a GCK mutation, in which case there is an increased risk of macrosomia and its obstetric consequences, and insulin maternal treatment is indicated. Conversely, if the mother carries a baby with a GCK mutation, no treatment is required (18). Ultrasonographic monitoring of fetal size is currently recommended to decide whether or not to lower maternal glycemia with insulin, which is reserved for those who have accelerated fetal growth (surrogate indication of negative fetal GCK mutation) and usually required large insulin doses (at least 0.6-1 U/kg) (12). The inheritance of a GCK mutation does not protect against the concurrent development of type 2 diabetes, which occurs at a similar prevalence in those with GCK mutations as in the general population. Screening and therapy for cardiovascular risk factors should be based on traditional individual risk profiles (4).

Familial, young-onset diabetes Patients in whom diabetes is diagnosed before age 25 years and does not fit the phenotypes of either type 1 or type 2 diabetes, and who also have a strong family history of diabetes need to be evaluated for mutations in transcription factors, most commonly hepatocyte nuclear factor 1-α (HNF1A) known in the past as MODY 3 (4). Mutations in HNF1A are the most commonly encountered form of monogenic diabetes (accounting for 52% of all cases of monogenic diabetes) or 1%-2% of all patients with diabetes (16). Patients with HNF1A typically present in their teens or early adult life, although there is marked variability in clinical phenotype (age at onset of diabetes, presenting features). Penetrance increases with age such that approximately two-thirds of HNF1A mutation carriers will have diabetes by the age of 25, and >95% by the age of 40 (4). HNF1A mutation carriers often have fasting plasma glucose levels that remain normal initially. Oral glucose tolerance tests in early stages tend to show a very large glucose increment, usually >5 mmol/L. This test result occurs


because initially the insulin secretion rate in HNF1 Amutation carriers is appropriate to their insulin sensitivity at glucose values below 8 mmol/L (6). Young-onset diabetes shows characteristics of not being insulin dependent, e.g., not developing Table 1. Autosomal dominant genetic defects of pancreatic β-cell function (1) Syndrome

Mutated protein

Gene

MODY 1

Hepatocyte nuclear factor-4α

HNF4A

MODY 2

Glucokinase

GCK

MODY 3

Hepatocyte nuclear factor-1α

HNF1A

MODY 4

Pancreatic duodenal homeobox1(insulin promoter factor-1)

PDX1 (IPF1)

MODY 5

Hepatocyte nuclear factor-1β

HNF1B

MODY 6

Neurogenic differentiation 1

NEUROD1

MODY 7

Kruppel-like factor 11

KLF11

MODY 8

Carboxyl-ester lipase

CEL

MODY 9

Paired homeobox-4

PAX4

Mutant insulin or proinsulin

Preproinsulin

INS

Inward-rectifying K+ channel 6.2

KCNJ11

Sulfonylurea receptor 1

ABCC8

KATP mutations

ketoacidosis in the absence of insulin; good glycemic control on a low dose of insulin; detectable C-peptide measured when on insulin with glucose >8 mmol/L outside the normally expected honeymoon period (3 years); absence of pancreatic islet autoantibodies; absence of acanthosis nigricans; and no marked obesity (19). Glycosuria at blood glucose levels <10 mmol/L is a key feature of HNF1A mutation carriers before they develop diabetes. HNF1A directly

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regulates renal tubular expression of the sodiumglucose cotransporter (SGLT); HNF1A mutations consequently result in a low renal threshold for glucose (i.e. glycosuria despite normal or only slightly elevated blood glucose levels) as a result of impaired tubular glucose reabsorption. This clinical feature can be useful as a screening tool (16). Raised HDLcholesterol levels are observed in patients with HNF1A diabetes, in contrast to the reduced levels seen in patients with type 2 diabetes and the normal levels seen in patients with type 1 diabetes. However, the elevated HDL-cholesterol level does not seem to be cardioprotective (11). Progressive β-cell failure over time, however, leads to generalized hyperglycemia, and unlike GCK-related monogenic diabetes, the risk of micro- and macrovascular complications appears to be comparable to type 1 and type 2 diabetes and is directly related to glycemic control (16). Patients with HNF1A mutations have an increased all-cause and cardiovascular-specific mortality rate when compared with unaffected relatives (20). The importance of diagnosing patients who have HNF1A diabetes is that this type of diabetes is very sensitive to sulfonylurea therapy. Sulfonylurea stimulates insulin secretion by binding directly to the β-cell membrane KATP receptor, thereby bypassing the metabolic pathways rendered dysfunctional by an HNF mutation. Consequently, patients misdiagnosed as type 1 diabetes and treated with insulin can be switched to oral sulfonylurea therapy, initially in very low doses (e.g., 20-40 mg gliclazide daily) to avoid hypoglycemia (16). Thus, insulin therapy can be avoided for many years after diagnosis in most cases, which has clear practical, social, occupational and health cost benefits (12). Heterozygous inheritance of an HNF4A mutation (MODY1) is less common than HNF1A mutation and accounts for approximately 5%-10% of all cases of monogenic diabetes. This mutation results in a similar phenotype to the patients with HNF1A mutations, except that there is not a low renal threshold, not elevated high density lipoprotein (HDL) levels and the age at diagnosis may be older. HNF-4α mutations should be considered when HNF-1α sequencing is negative but clinical features are strongly suggestive

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of HNF-1α (6). Somewhat paradoxically, hyperinsulinemic hypoglycemia has been documented in neonates with heterozygous HNF4A mutations, with resultant macrosomia (average increased birth weight of approximately 800 g). Genetic testing for HNF4A should be considered in patients with positive family history of macrosomia or neonatal hypoglycemia (16). Patients are often sensitive to sulfonylureas and long-term treatment with low-dose sulfonylureas seems effective for HNF4A diabetes. Collectively, less than 5% of cases of monogenic diabetes may be caused by other mutations (see Table 1) but these are so unusual that they do not need to be tested except for research setting or when there are additional phenotypes (6).

Diabetes with extrapancreatic features Autosomal recessive genetic defects are less common, causing autosomal recessive syndromes. Two diabetes subtypes with extrapancreatic features that are frequently underdiagnosed at present are renal cysts and diabetes syndrome (RCAD) and maternally inherited diabetes and deafness (MIDD) (4).

Renal cysts and diabetes syndrome Although initially described as a subgroup of familial diabetes (MODY5), it is now clear that patients with mutations in HNF-1β rarely present with isolated diabetes (21). The predominant phenotype of patients with HNF1B mutations is developmental renal cysts (the most common phenotype), renal dysplasia, renaltract malformations and/or familial hypoplastic glomerulocystic kidney disease. Female genital tract malformations, gout and hyperuricemia can also occur. Patients with HNF-1β mutations, unlike patients with HNF-1α mutations, are not sensitive to sulfonylureas and so usually require insulin treatment. A diagnosis of HNF-1β should be considered in any child with diabetes that also has nondiabetic renal disease (6).


Maternally inherited diabetes and deafness Maternally inherited diabetes and deafness (MIDD) is the most commonly encountered mitochondrial diabetes syndrome with the incidence of about 1.5% in Japanese, which seems to be higher than that in Europeans and other ethnic groups (0.4%). The syndrome is defined by the presence of diabetes and bilateral sensorineural deafness with an inheritance pattern consistent with a mitochondrial disorder and is most commonly the result of a point mutation (m.3243A>G) (16). At the most severe end of the spectrum, this mutation can manifest as mitochondrial myopathy, encephalopathy, lactic acidosis and strokelike episodes syndrome (MELAS syndrome). The majority of patients with MIDD are initially treated with dietary modification or oral hypoglycemic agents, but insulin is usually required by 2 years after diagnosis (4). Very rare diabetes-related disorders, such as Wolfram syndrome and thiamine-responsive megaloblastic anemia, are fairly easy to recognize because of the presence of comorbidities.

CONCLUSIONS Monogenic diabetes should be considered in the differential diagnosis of diabetes, especially for atypical features among those classified as having either type 1 or type 2 diabetes, and account for approximately 1%-2% of all diabetes cases. These therapies are different from those used to treat type 1 or type 2 diabetes, so it is important that we identify individuals with monogenic diabetes. Patients with Kir6.2 or SUR1PNDM require high-dose sulfonylurea therapy, most cases of transcription factor diabetes require low-dose sulfonylurea therapy, and glucokinase diabetes requires no hypoglycemic treatment. At the present time, molecular genetic testing for monogenic diabetes is relatively expensive and phenotypic selection prior to testing is normal practice. With the development of new technologies, it is likely that these costs will decrease with time and that the analysis of genes associated with monogenic diabetes may become routine for all newly diagnosed patients.

Wolfram syndrome also known as DIDMOAD is characterized by diabetes insipidus, diabetes mellitus, optic atrophy and deafness. Patients with thiamineresponsive megaloblastic anemia (TRMA) in addition to hematologic manifestations might also have deafness, cardiac abnormalities and neurologic abnormalities (4).


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9. Lambert AP, Ellard S, Allen LI, Gallen IW, Gillespie KM, Bingley PJ, et al. Identifying hepatic nuclear factor 1 alpha mutations in children and young adults with a clinical diagnosis of type 1 diabetes. Diabetes Care. 2003;26(2):333337. doi:10.2337/diacare.26.2.333 10. Pearson ER, Pruhova S, Tack CJ, Johansen A, Castleden HAJ, Lumb PJ, et al. Molecular genetics and phenotypic characteristics of MODY caused by hepatocyte nuclear factor 4α mutations in a large European collection. Diabetologia. 2005;48: 878-885. doi:10.1007/s00125-005-1738-y 11. McDonald TJ, McEneny J, Pearson ER, Thanabalasinqham G, Szopa M, Shields BM, et al. Lipoprotein composition in HNF1A-MODY: differentiating between HNF1A-MODY and type 2 diabetes. Clin Chim Acta. 2012;18;413(910):927-932. doi:10.1016/j.cca.2012.02.005 12. Carroll RW, Murphy R. Monogenic diabetes: a diagnostic algorithm for clinicians. Genes. 2013;4:522-535. doi:10.3390/genes4040522 13. Flanagan SE, Patch A, Mackay DJG, Edghill EL, Gloyn AL, Robinson D, et al. Mutations in ATPsensitive K+ channel genes cause transient neonatal diabetes and permanent diabetes in childhood or adulthood. Diabetes. 2007;56:19301937. doi:10.2337/db07-0043 14. Pearson ER, Flechtner I, Njølstad PR, Malecki MT, Flanagan SE, Larkin B, et al. Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2mutations. N Engl J Med. 2006;355:467-477. doi:10.1056/NEJMoa061759 15 Njølstad PR, Sagen JV, BjørkhaugL, Odili S, Shehadeh N, Bakry D, et al. Permanent neonatal diabetes caused by glucokinase deficiency: inborn error of the glucose-insulin signaling pathway. Diabetes. 2003;52:2854-2860. doi:10.2337/ diabetes.52.11.2854


16. Murphy R, Carroll RW, Krebs JD. Pathogenesis of the metabolic syndrome: insights from monogenic disorders. Review Article. Mediat Inflamm. 2013;2013:920214. doi:10.1155/2013/920214 17. Stride A, Vaxillaire M, Tuomi T, Barbetti F, Njolstad PR, Hansen T, et al. The genetic abnormality in the beta cell determines the response to an oral glucose load. Diabetologia. 2002:45(3):427-435. doi:10.1007/s00125-0010770-9 18. Spyer G, Hattersley AT, Sykes JE, Sturley RH, MacLeod KM. Influence of maternal and fetal glucokinase mutations in gestational diabetes. Am J Obstet Gynecol. 2001;185:240-241. doi.org/ 10.1067/mob.2001.113127

20. Steele AM, Shields BM, Shepherd M, Ellard S, Hattersley AT, Pearson ER. Increased all-cause and cardiovascular mortality in monogenic diabetes as a result of mutations in the HNF1A gene. Diabet Med.2010;27(2):157-161. doi: 10.1111/j.14645491.2009.02913.x. 21. Bingham C, Hattersley AT. Renal cysts and diabetes syndrome resulting from mutations in hepatocyte nuclear factor-1beta. Nephrol Dial Transplant. 2004:19(11):2703-2708. doi:10.1093/ ndt/gfh348

19. Ellard S, Bellanne-Chantelot C, Hattersley AT. Best practice guidelines for the molecular genetic diagnosis of maturity-onset diabetes of the young. Diabetologia. 2008;51:546-553. doi:10.1007/ s00125-008-0942-y


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