Journal of Diabetes & Metabolism

ISSN - 2155-6156

+441518081309

Review Article - (2013) Volume 0, Issue 0

The Use of Proteomics to Dissect the Molecular Specificities of T Cells in Type 1 Diabetes

Nadine L. Dudek*, Kailin Giam and Anthony W. Purcell
Department of Biochemistry and Molecular Biology, Australia
*Corresponding Author: Nadine L. Dudek, Department of Biochemistry and Molecular Biology, Clayton, Victoria 3800, Australia, Tel: +61399051557 Email:

Abstract

Presentation of peptides derived from beta cell proteins to autoreactive lymphocytes is critical for the development and progression of type 1 diabetes. How tolerance to beta cell antigens is broken is yet to be fully elucidated. The high metabolic demand on beta cells, the high concentration of granule proteins and the susceptibility of islets to cellular stress may all contribute to the presentation of abnormal ligands in the pancreas. Evidence for nonconventional presentation of peptide ligands and post-translational modification of peptides to T cells has emerged in both human studies and animal models of diabetes. Challenges in identifying targets of autoimmunity are being increasingly met by advances in modern mass spectrometry. Here we review recent advances in mass spectrometry and their application to studies of peptides involved in immune recognition in diabetes.

Keywords: Insulin secretory granule; Antigen processing; T cell epitope; SWATH-MS

Abbreviations

T1D: Type 1 Diabetes; HbA1c: Glycosylated Hemoglobin; NS: Not Statistically Significant

Introduction

Adolescence is a period of fast physical, cognitive, emotional and social development in which previously children, now adolescents, need to find a way to form various aspects of their identity, separate from their parents, choose a path toward a future career, and a more or less stable adult life. To accomplish some of these tasks a considerable proportion of youth engages in risk behaviors. These include for example cigarette smoking, use of alcohol and illicit drugs, unhealthy eating and dieting behaviors, high-risk sporting activities, neglecting physical health (not engaging in healthy physical activities, not following the doctors’ recommendations), school truancy, early or unprotected sexual activities, running away from home, and delinquency. In one way, these behaviors can be formative for one’s development, as in helping to gain independence from parents or find a place in a peer group, but on the other hand, they may pose a risk for the individual’s health and jeopardize the accomplishment of normal developmental tasks. The consequences of adolescents’ risk behavior can be mild and transient (trouble with parents or school authorities) or severe and long-lasting (alcohol and drug addiction, teen pregnancy, conflicts with the authority, dropping out of school, poor health, and sometimes death) [1].

The psychosocial effects of a chronic disease and its concomitant management on patients with type 1 diabetes (T1D) have long been under consideration, however the data published on risk behaviors in these adolescent patients are still scarce and rarely comparable [2]. There are studies reporting that risk behaviors among these patients are less frequent [3-8], or as frequent as in the general population [9-12]. A study on Slovene patients showed, that adolescent males with T1D report lower prevalence of suicidal and self-injurious behavior than their healthy peers. However, this was not true for females [13]. It is well documented, that engaging in risk behavior is gender-specific, females for example more frequently engage in unhealthy dieting or non-suicidal self-harm, while males more frequently engage in binge drinking or peer violence [13,14].

Patients with T1D, who engage in various risk behaviors, have poorer metabolic control and are more vulnerable for developing acute and chronic complications [4,7,12,15,16]. Adolescents, who experiment with risk behaviors earlier, are more likely to engage in multiple risk behaviors and to maintain them into adulthood [1]. Therefore, it is important to screen for and prevent these behaviors in the population of adolescents with T1D.

The aims of the present study were to determine the prevalence of risk behaviors in adolescent females and males with T1D in comparison to healthy control subjects, and to assess whether engagement in risk behaviors influences metabolic control of adolescents with T1D.

Subjects and Methods

Every Slovene child or adolescent with suspected T1D is referred to University Children’s Hospital in Ljubljana and included in the Slovene childhood-onset Type 1 diabetes register [17]. The cross-sectional study included all the adolescents from the register who were 14-19 years old at the beginning of the study. Patients with intellectual disability were excluded. The control group consisted of healthy pupils from randomly chosen secondary schools from the entire country. Participation was voluntary and confidential. The protocol was approved by the National Medical Ethics Committee (No. 84/02/05). The detailed protocol and part of the acquired data were published previously [13].

Subjects were administered a specially designed questionnaire used previously on Slovene adolescent population [14]. The questions covered, among others, the general and demographic characteristics, family factors, cigarette smoking, the use of alcohol and illegal drugs, sexual activity, cutting class, running away from home, eating and dieting behavior, and engagement in sports. The patients’ questionnaires were modified by adding questions on diabetes management, duration and the most recent glycosylated hemoglobin (HbA1c) value. As described elsewhere [13], the patients received the questionnaires at regular outpatients’ visits and returned them by mail, when completed. The patients and their parents/guardians were presented the study by their diabetologist. If they agreed to participate, they signed informed assents/consents. The control group were administered the questionnaires during a school class.

Metabolic control was assessed by the patients’ average one year’s HbA1c levels at the time they were presented the study.

Statistical analysis

The comparisons between groups were made separately for each gender using the statistical package SPSS 13.0 for Windows. Pearson chi-square test was used to compare categorical variables and independent samples T-test was used to compare continuous variables. Analysis of variance (ANOVA) or Mann Whitney U-test, depending on the data distribution, were used to compare average HbA1c values of the patients who reported ever engaging in specific risk behaviors to average HbA1c values of the patients who denied such engagement. The differences were considered statistically significant at the p value of or below 0.05.

Results

The questionnaires were returned by 126 patients, aged 16.9 years (SD=1.7), 75 were females and 51 males, participation rate was 61.8%. There were no differences between the patients who returned the questionnaires and the non-responders in age, gender, and previous years’ average HbA1c values [13]. In the control group the questionnaires were filled out by 499 subjects, aged 16.9 years (SD=1.2), they were 307 females and 192 males. Their participation rate was 90.1%.

There were no statistically significant differences between groups in general characteristics, demographic characteristics, and family characteristics as described elsewhere [13].

Females

Females with T1D reported significantly more often that they never smoked (p<0.05), never drank liquors (p<0.001), had not been drunk in the previous month (p<0.01), physically exercised at least twice a week (p<0.001). They reported significantly higher prevalence of frequently binge eating (twice as often; p<0.01) and purging using laxatives/vomiting/insulin manipulation (10 times as often; p<0.001). The results are shown in Table 1.

  Females Males
  Patients N=75 Controls N=307 χ2 p value Patients N=51 Controls N=192 χ2 p value
I never smoke cigarettes 52 (69.3) 174 (56.7) 3.996 0.046 44 (86.3) 118 (61.5) 11.167 0.001
I never drink                
                         beer 44 (58.7) 146 (49.7)   NS 26 (51.0) 45 (24.6) 13.142 <0.001
                         wine 43 (58.1) 133 (51.4)   NS 30 (58.8) 68 (42.0) 4.433 0.035
                         liquors 52 (70.3) 112 (43.4) 17.305 <0.001 38 (74.5) 67 (40.9) 17.636 <0.001
I was not drunk in the previous month 61 (81.3) 194 (64.0) 8.203 0.004 41 (80.4) 103 (53.6) 11.940 0.001
I have never used                
                        soft drugsa 64 (85.3) 242 (78.8)   NS 48 (94.1) 140 (72.9) 10.344 0.001
                        hard drugsb 71 (97.3) 297 (98.0)   NS 51 (100.0) 178 (94.2)   NS
I have not decided to have sex yet 38 (52.1) 176 (60.7)   NS 43 (87.8) 116 (65.9) 8.825 0.003
I never cut classes 31 (41.3) 103 (33.6)   NS 24 (47.1) 56 (29.2) 5.842 0.016
I never ran away from home 72 (96.0) 279 (91.2)   NS 51 (100.0) 173 (90.1) 5.475 0.019
I have frequent periods of binge eating 16 (21.3) 28 (9.1) 10.849 0.004 6 (11.8) 18 (9.6)   NS
I have used laxatives/vomiting/manipulated insulin in order to lose weight 8 (10.7) 3 (1.0) 29.260 <0.001 1 (2.0) 0 (0.0)   NS
I exercise at least twice a week 57 (76.0) 146 (47.6) 19.589 <0.001 42 (82.4) 159 (83.7)   NS
Data are n (%). NS: Not Statistically Significant. acannabis. bheroin, cocaine, synthetic drugs

Table 1: Risk behaviors in patients and controls.

Although not statistically significant (NS), the prevalence of ever drinking beer and wine tended to be lower for T1D females, the difference being smallest for wine. Of the females who answered positively to ever drinking alcohol, more controls than patients admitted to drinking six or more glasses of alcoholic beverage (beer, wine, and liquor) the previous month (NS).

Males

Males with T1D reported significantly lower prevalence of ever smoking (p<0.01), ever drinking beer (p<0.001), wine (p<0.05), and liquors (p<0,001), being drunk the previous month (p=0.001), and ever using soft drugs (p=0.001). They reported significantly more often that they never cut class (p<0.05) and they have not had sex yet (p<0.01). The results are presented in Table 1.

Of the males with T1D who ever drank wine one (4.8%) reported drinking six or more glasses the previous month as opposed to 41 (43.6%) male controls (p=0.001). Eight patients (32.0%) and 71 (51.4%) controls reported drinking six or more glasses of beer the previous month (NS), and three (23.1%) patients compared to 37 (38.1%) controls reported drinking six or more glasses of liquor the previous month (NS).

Metabolic control

The average HbA1c values were higher for the patients who engaged in some of the risk behaviors as compared to the ones who never have. The difference between groups was ≥ 0.5 % HbA1c for smoking cigarettes (NS), using hard drugs (p=0.005), using insulin omission/laxatives/vomiting for purging (p=0.04) and running away from home (NS). Average HbA1c values for the patients who ever drank wine were significantly lower (p=0.03) as compared to those who never have (Table 2).

  Yes No Statistical analysis
  n HbA1c (%)a n HbA1c (%)a Test statisticsb p value
Ever smoked cigarettes 28 8.6 ± 1.4 86 8.1 ± 1.0 1041.5e NS
Ever drank:            
                 beer 52 8.3 ± 1.2 62 8.2 ± 1.1 0.355 NS
                 wine 47 8.0 ± 1.1 66 8.4 ± 1.1 4.811 0.03
                 liquors 32 8.3 ± 1.3 81 8.2 ± 1.0 0.096 NS
Drunk in the previous month 22 8.3 ± 1.1 92 7.9 ± 1.1 2.381 NS
Ever used:            
                 soft drugsc 14 8.3 ± 0.9 100 8.2 ± 1.1 0.165 NS
                 hard drugsd 2 10.4 ± 1.7 111 8.2 ± 1.1 8.272 0.005
Used insulin omission/laxatives/vomiting to control body weight 9 9.0 ± 1.1 104 8.2 ± 1.1 4.189 0.04
Ever cut class 65 8.2 ± 1.2 49 8.3 ± 1.0 0.070 NS
Ever ran away from home 3 8.9 ± 2.3 111 8.2 ± 1.1 153.5e NS
NS: Difference not Statistically Significant. aaverage one year’s HbA1c values ± standard deviation, bANOVA except where stated otherwise, ccannabis, hashish, dheroin, cocaine, synthetic drugs, eMann Whitney U-test

Table 2: Metabolic control of patients with T1D according to lifetime engagement in risk behaviors.

Discussion

To the authors’ knowledge the present is the first study that directly compared risk behaviors of an entire country’s adolescent population with T1D to a representative cohort of healthy peers.

Males with T1D reported significantly lower frequencies of risk behaviors than their healthy peers. Females with T1D also less frequently engaged in most of the risk behaviors, however the differences were less marked than in males. Importantly, females with T1D reported significantly higher prevalence of frequently bingeing (twice as often) and maladaptive purging behavior (10 times as often) then their healthy peers.

Presented results are in accordance with the studies stating that adolescents with T1D smoke [3-6,8,10], drink alcohol [3,5,6] and use soft drugs less frequently than their healthy peers [3,5,7]. The ages of the subjects in the present study were also comparable to the aforementioned studies. The studies with adults, however, found no differences in the prevalence of risk behaviors between the patients with T1D and the general population [9-12]. Similarly, using an objective measure of active smoking in a clinical sample of diabetic teenagers Shaw et al. concluded, that majority of their patients started smoking only after the transfer to the adult diabetes services [8]. These findings may highlight a period of increased vulnerability for the development of risk behaviors.

The patients in the present sample drank alcoholic beverages less frequently and reported less frequent binge drinking (drank smaller amounts at a single occasion) than healthy adolescents. The differences between groups were smallest for drinking wine. Presented results can be the consequence of thorough medical management of the patients with T1D and their families. From the diagnosis onwards, they are enrolled in intensive education on healthy dieting and lifestyle, which becomes a part of the families’ life, it integrates into the beliefs of the family and the child/adolescent. These results could represent the subsequently internalized beliefs on healthy dieting, transferred to the patients through education by experienced pediatric diabetic teams from the professional recommendations and relevant research stating, that the patients with diabetes, who consume moderate amounts of wine, have least diabetic complications [18].

Females with T1D reported significantly more frequent engagement in sports than healthy females. There were no differences between the groups of males. These results are in accordance with other studies on high school students reporting that various sports represent an important part of everyday living for males more than for females [14]. It is likely that intensive education on healthy living with frequent emphasis on engagement in sports reflects in adolescent females with T1D being much more physically active than their peers.

Females with T1D in the present study reported more frequent binge eating and maladaptive purging behavior, but there were no differences in males. These results support the findings of multiple studies reporting more than twice as frequent clinical bulimic spectrum eating disorders and their subtreshold variants in females with T1D as in the general population of females [19,20]. They are also in accordance with the fact that eating disorders are rarer in males and the research findings failing to show higher prevalence of eating disorders in males with T1D compared to healthy males [21,22]. Nevertheless, one of the males reporting using maladaptive purging behaviors and a slightly higher prevalence of bingeing in males with T1D, could support the finding of Svensson et al., whose adolescent male patients reported significantly higher Drive for Thinnes scores than healthy age-matched controls, from which they predicted an increased risk of future eating disorders in the male patients with T1D as well [23].

In a representative sample of high school students Tomori et al. found, that males more frequently than females drank alcohol, smoked tobacco, and used soft drugs; females on the other hand more frequently reported disordered eating behaviors [14]. A similar conclusion can be drawn from presented data on healthy adolescents. Data on patients with T1D on the contrary, showed less pronounced and even the opposite differences (females more frequently than males drank wine and liquors, used drugs, cut classes, ran away from home and were sexually active). These results could be due to the slower psychosocial maturation of boys with T1D. Hauser et al. reported that males with T1D present with important delay in personality development in comparison to healthy adolescent males or females with T1D, even though they reported no differences between healthy males and healthy females [24]. These differences in patients with T1D were reported to diminish and finally disappear by adulthood [10,25,26]. Therefore it would be interesting to follow up these behaviors in the current sample.

Risk behaviors in adolescence are frequently socially organized; they are most often performed in a group of peers [1]. A question remains, whether adolescent males with T1D spend less time with their peers and consequently share less mutual activities? Hegelson et al. investigated the role of friendship in adolescents with T1D compared to healthy peers. Their results confirmed a protective function of friendship for psychological health, however their results also showed, that boys with diabetes as compared to girls or healthy boys had the lowest levels of friend support [27]. These scarce results may indicate less intense inclusion of adolescents with T1D in their peer group, at a time when influence from peers should be more important than influence from parents (or health professionals), and all the consequences this brings for their development. Lower prevalence of all risk behaviors in males in the present study support the observation Hauser et al. made on the patients’ delayed psychosocial development [24].

Metabolic control of the patients, who ever consumed wine, was significantly better as compared to the ones who never did. These results are in accordance with the results of the studies stating that consumption of moderate amounts of wine in patients with diabetes results in lower frequency of complications as compared with abstainers [18,28]. It also reflects a probable mode of wine consumption. Namely, adolescents with T1D most likely drink wine in moderate amounts, which is supported by lower prevalence of ever being drunk and binge drinking in the present sample. The research also suggests that the patients who drink large amounts of alcoholic beverages have worse metabolic control and more frequent chronic complications [12,18]. Nevertheless, these results could also reflect more frequent undetected episodes of hypoglycemia. Research on adults with T1D reported more frequent episodes, as well as impaired ability to recognize symptoms of hypoglycemia, the morning after the evening alcohol consumption [29].

Even though only two patients in the present study ever used hard drugs, their metabolic control was clinically and statistically significantly worse as compared to the ones who never used hard drugs. Other research similarly suggests that, together with psychological and behavioral influence on compliance with treatment regimens and diet, hard drugs can cause suboptimal acute insulin response to hyperglycemia (opiates) as well as increased release of catecholamine from adrenals (amphetamines), which may all lead to the worsening of metabolic control, more complications and an increased mortality of patients with T1D [7,30-32].

Even though presented results trended towards poorer metabolic control in the patients who ever smoked cigarettes and ran away from home, they were not able to completely support the findings of previous research, which mostly concludes poorer metabolic control and more frequent complications in patients with T1D who smoke [16] or live in conflicting family environments [33,34]. Poorer metabolic control of the patients who engaged in maladaptive purging, however, is in line with abundant research evidence on poorer outcomes in the patients with comorbid clinical or subclinical forms of eating disorders or disordered eating behavior [21,35,36].

Limitations

There are benefits and drawbacks to the use of self-report questionnaires for the study of adolescent behavior. On one hand, the study is limited to subjective willingness of the adolescents to share with the research team a very personal set of information, which could (if conveyed to the patients’ treatment team) potentially endanger their relationship and influence their management. The confidentiality of the protocol described elsewhere [13], the possibility of filling out the questionnaires in private (as opposed to the presence of interviewer), and the observed data on higher prevalence of some of these behaviors (for example disordered eating) in the patients support the belief that the answers were honest.

On the other hand, the observed differences in response rates between the patients and controls (61.8% and 90.1%) could influence the data in a way that “problematic” patients, who engaged in more risk behaviors, would also be more likely to decline participation in the study and not return the questionnaires. These patients, according to the literature, would also be more likely to have poorer metabolic control [16]. There were no differences in the enrolled patients however, between responders and non-responders in age, gender and previous years’ average HbA1C values.

Data for the present study were obtained in years 2005-2008 [13]. According to the statistics on adolescent risk behaviors performed by the European School Survey Project on Alcohol and Other Drugs (ESPAD) the frequencies of observed behaviors in the Slovene population have not changed significantly during the years 2007-2011 [37].

Conclusions

Prevalence of adolescent risk behaviors increases with age. Even though present study reported lower prevalence of most risk behaviors in adolescents with T1D they affected metabolic control and put an already jeopardized population under even higher risk. Given that health professionals, with whom these adolescents often have good relationships, frequently see them, these are important opportunities for implementing preventive measures targeting risk behaviors.

Acknowledgements

At the time of the study M. D. Radobuljac was a recipient of a young researcher stipend (3311-03-831761) from the Slovenian Research Agency. The study was supported in part by the Slovenian Research Agency grants J3-9663 and P3-0343. The authors would like to thank all adolescents for participating in the study and Miljana Vegnuti for her help and advice with statistical analysis.

References

  1. Jessor R (1991) Risk behavior in adolescence: a psychosocial framework for understanding and action. J Adolesc Health 12: 597-605.
  2. Jaser SS, Yates H, Dumser S, Whittemore R (2011) Risky business: risk behaviors in adolescents with type 1 diabetes. Diabetes Educ 37: 756-764.
  3. Frey MA, Guthrie B, Loveland-Cherry C, Park PS, Foster CM (1997) Risky behavior and risk in adolescents with IDDM. J Adolesc Health 20: 38-45.
  4. Gay EC, Cai Y, Gale SM, Baron A, Cruickshanks KJ, et al. (1992) Smokers with IDDM experience excess morbidity. The Colorado IDDM Registry. Diabetes Care 15: 947-952.
  5. Gold MA, Gladstein J (1993) Substance use among adolescents with diabetes mellitus: preliminary findings. J Adolesc Health 14: 80-84.
  6. Hanna KM, Guthrie DW (1999) Involvement in health behaviors among youth with diabetes. Diabetes Educ 25: 211-219.
  7. Ng RS, Darko DA, Hillson RM (2004) Street drug use among young patients with Type 1 diabetes in the UK. Diabet Med 21: 295-296.
  8. Shaw NJ, McClure RJ, Kerr S, Lawton K, Smith CS (1993) Smoking in diabetic teenagers. Diabet Med 10: 275-277.
  9. Ingberg CM, Palmér M, Aman J, Larsson S (1996) Social consequences of insulin-dependent diabetes mellitus are limited: a population-based comparison of young adult patients vs healthy controls. Diabet Med 13: 729-733.
  10. Jacobson AM, Hauser ST, Willett JB, Wolfsdorf JI, Dvorak R, et al. (1997) Psychological adjustment to IDDM: 10-year follow-up of an onset cohort of child and adolescent patients. Diabetes Care 20: 811-818.
  11. Masson EA, MacFarlane IA, Priestley CJ, Wallymahmed ME, Flavell HJ (1992) Failure to prevent nicotine addition in young people with diabetes. Arch Dis Child 67: 100-102.
  12. Peveler RC, Davies BA, Mayou RA, Fairburn CG, Mann JI (1993) Self-care behaviour and blood glucose control in young adults with type 1 diabetes mellitus. Diabet Med 10: 74-80.
  13. Radobuljac MD, Bratina NU, Battelino T, Tomori M (2009) Lifetime prevalence of suicidal and self-injurious behaviors in a representative cohort of Slovenian adolescents with type 1 diabetes. Pediatr Diabetes 10: 424-431.
  14. Tomori M, Zalar B, Plesnicar BK (2000) Gender differences in psychosocial risk factors among Slovenian adolescents. Adolescence 35: 431-443.
  15. Danne T, Kordonouri O, Hovener G, Weber B (1997) Diabetic angiopathy in children. Diabet Med 14: 1012-1025.
  16. Hofer SE, Rosenbauer J, Grulich-Henn J, Naeke A, Fröhlich-Reiterer E, et al. (2009) Smoking and metabolic control in adolescents with type 1 diabetes. J Pediatr 154: 20-23.
  17. Bratina NU, Tahirovic H, Battelino T, Krzisnik C (2001) Incidence of childhood-onset Type I diabetes in Slovenia and the Tuzia region (Bosnia and Herzegovina) in the period 1990-1998. Diabetologia 44 Suppl 3: B27-31.
  18. Beulens JW, Kruidhof JS, Grobbee DE, Chaturvedi N, Fuller JH, et al. (2008) Alcohol consumption and risk of microvascular complications in type 1 diabetes patients: the EURODIAB Prospective Complications Study. Diabetologia 51: 1631-1638.
  19. Jones JM, Lawson ML, Daneman D, Olmsted MP, Rodin G (2000) Eating disorders in adolescent females with and without type 1 diabetes: cross sectional study. BMJ 320: 1563-1566.
  20. Mannucci E, Rotella F, Ricca V, Moretti S, Placidi GF, et al. (2005) Eating disorders in patients with type 1 diabetes: a meta-analysis. J Endocrinol Invest 28: 417-419.
  21. Neumark-Sztainer D, Patterson J, Mellin A, Ackard DM, Utter J, et al. (2002) Weight control practices and disordered eating behaviors among adolescent females and males with type 1 diabetes: associations with sociodemographics, weight concerns, familial factors, and metabolic outcomes. Diabetes Care 25: 1289-1296.
  22. Bryden KS, Neil A, Mayou RA, Peveler RC, Fairburn CG, et al. (1999) Eating habits, body weight, and insulin misuse. A longitudinal study of teenagers and young adults with type 1 diabetes. Diabetes Care 22: 1956-1960.
  23. Svensson M, Engström I, Aman J (2003) Higher drive for thinness in adolescent males with insulin-dependent diabetes mellitus compared with healthy controls. Acta Paediatr 92: 114-117.
  24. Hauser ST, Pollets D, Turner BL, Jacobson A, Powers S, et al. (1979) Ego Development and Self-Esteem in Diabetic Adolescents. Diabetes Care 2: 465-471.
  25. Jacobson AM, Hauser ST, Cole C, Willett JB, Wolfsdorf JI, et al. (1997) Social relationships among young adults with insulin-dependent diabetes mellitus: ten-year follow-up of an onset cohort. Diabet Med 14: 73-79.
  26. Pacaud D, Crawford S, Stephure DK, Dean HJ, Couch R, et al. (2007) Effect of type 1 diabetes on psychosocial maturation in young adults. J Adolesc Health 40: 29-35.
  27. Helgeson VS, Reynolds KA, Escobar O, Siminerio L, Becker D (2007) The role of friendship in the lives of male and female adolescents: does diabetes make a difference? J Adolesc Health 40: 36-43.
  28. Koppes LL, Dekker JM, Hendriks HF, Bouter LM, Heine RJ (2006) Meta-analysis of the relationship between alcohol consumption and coronary heart disease and mortality in type 2 diabetic patients. Diabetologia 49: 648-652.
  29. Richardson T, Weiss M, Thomas P, Kerr D (2005) Day after the night before: influence of evening alcohol on risk of hypoglycemia in patients with type 1 diabetes. Diabetes Care 28: 1801-1802.
  30. Baudrie V, Chaouloff F (1992) Mechanisms involved in the hyperglycemic effect of the 5-HT1C/5-HT2 receptor agonist, DOI. Eur J Pharmacol 213: 41-46.
  31. Giugliano D (1984) Morphine, opioid peptides, and pancreatic islet function. Diabetes Care 7: 92-98.
  32. Laing SP, Jones ME, Swerdlow AJ, Burden AC, Gatling W (2005) Psychosocial and socioeconomic risk factors for premature death in young people with type 1 diabetes. Diabetes Care 28: 1618-1623.
  33. Pereira MG, Berg-Cross L, Almeida P, Machado JC (2008) Impact of family environment and support on adherence, metabolic control, and quality of life in adolescents with diabetes. Int J Behav Med 15: 187-193.
  34. Wysocki T, Harris MA, Buckloh LM, Mertlich D, Lochrie AS, et al. (2007) Randomized trial of behavioral family systems therapy for diabetes: maintenance of effects on diabetes outcomes in adolescents. Diabetes Care 30: 555-560.
  35. Peveler RC, Bryden KS, Neil HA, Fairburn CG, Mayou RA, et al. (2005) The relationship of disordered eating habits and attitudes to clinical outcomes in young adult females with type 1 diabetes. Diabetes Care 28: 84-88.
  36. Goebel-Fabbri AE, Fikkan J, Franko DL, Pearson K, Anderson BJ, et al. (2008) Insulin restriction and associated morbidity and mortality in women with type 1 diabetes. Diabetes Care 31: 415-419.
  37. Hibell B, Guttormsson U, Ahlström S, Balakireva O, Bjarnason T, et al. (2012) The 2011 ESPAD Report - Substance Use Among Students in 36 European Countries. CAN, Stockholm.
Citation: Giam K, Purcell AW (2013) The Use of Proteomics to Dissect the Molecular Specificities of T Cells in Type 1 Diabetes. J Diabetes Metab S12:006.

Copyright: © 2013 Radobuljac MD, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.