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Carla J. Greenbaum, Benaroya Research Institute at Virginia Mason, Seattle, WA 98101 USA & Leonard C. Harrison, Walter and Eliza Hall Institute of Medical Research, Royal Melbourne Hospital PO, Parkville 3050 Victoria, Australia

On behalf of the Immunology of Diabetes Society.The following individuals provided comment on these Guidelines: Desmond A. Schatz, George S. Eisenbarth, Jerry P. Palmer, Kevan C. Herold, Paolo Pozzilli, Edwin A. Gale, Hubert Kolb, Olov Rolandsson, Didac Mauricio, Peter G. Colman and Spiros Fourlanos

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Type 1 or insulin-dependent diabetes is an autoimmune disease that culminates in destruction of insulin-producing b cells in the islets of the pancreas. Studies in the non-obese diabetic (NOD) mouse model of spontaneous type 1 diabetes provide 'proof-of-concept' that the disease is preventable (1). People with type 1 diabetes and their relatives, researchers, government and industry are eager to move forward and trial candidate intervention/prevention therapies in humans. Such therapies may entail risks, including accelerated loss of b-cell function, malignancy and infection. Scientifically and ethically, investigators are obliged to maximize the information gained from intervention trials and minimize risks. One way of achieving this is by standardizing trial protocols. Standardization of islet autoantibody assays (2-13) and of the intravenous glucose tolerance test for measuring first phase insulin response (FPIR) (14-18) have been major advances, allowing stratification for disease risk among relatives. The literature on intervention trials in newly-diagnosed type 1 patients (19-44) reveals that entry criteria, trial design and duration, and outcome measures differ considerably. Adoption of standardized protocols would permit comparative and pooled data analysis, and facilitate evaluation of potential therapies.

Our purpose here is to highlight issues pertaining to trial variables and suggest ways of standardizing protocols for phase I and II intervention trials in newly-diagnosed patients. These issues will be discussed under three major headings: trial subjects, trial design and trial outcome measures.



1. What is the nature of the disease at clinical presentation?
2. When should trials be initiated in relation to time since diagnosis?
3. Should age be an exclusion/inclusion criterion?
4. Should subjects who have participated in pre-clinical trials be excluded?
5. What is the effect of concurrent treatment on trial outcomes?

1. What is the nature of the disease at clinical presentation?


Type 1 diabetes can have different clinical presentations that presumably reflect the nature of the underlying disease pathology, to which we have no direct access. Some patients present acutely with dehydration and ketoacidosis, whereas others have minimal or no symptoms. Natural history studies have indicated that these differences may correlate with the rate of loss of b-cell function and residual b-cell function, determined by genetic (45-47) and other (48-64) factors that modify disease pathology. However, the relationship between the nature of the clinical presentation and the effectiveness of intervention therapy is not known.


  • Define onset of diabetes from time of diagnosis by a physician, based on recognized, e.g. American Diabetes Association, criteria.
  • Document the following at clinical presentation: age, gender, pubertal status, family history of diabetes, blood glucose, bicarbonate, presence or absence of ketoacidosis, weight loss, polyuria, polydypsia, HbA1c, islet autoantibodies, insulin requirement and HLA typing. Other measures could be considered, e.g. serum pancreatic enzymes, viral markers.
  • Subjects should have at least one of three islet autoantibodies: to glutamic acid decarboxylase (GAD 65), insulin (if on insulin treatment <2 weeks) or ICA 512/IA2.

2. When should trial treatment be initiated in relation to time since diagnosis?


In general, time from diagnosis is inversely related to C-peptide secretion. However, data from the DCCT and other studies indicate that some subjects with type 1 diabetes continue to have residual C-peptide even 5 years after diagnosis (48,51,65-68). Time from diagnosis is therefore not necessarily an accurate index of residual b-cell function. Additionally, measurement of C-peptide secretion when diabetes is poorly controlled is unreliable (see below).

Two models of the disease have been proposed: in one, clinical onset occurs on a continuum of the immune assault, b-cell function finally being inadequate to maintain normoglycemia; in the other, the process of b-cell injury becomes abruptly destructive heralding clinical diagnosis (69,63,64). In the latter, initiation of treatment within a short time-window would be essential.


Document peak C-peptide of > 0.2 nM after liquid mixed meal stimulation (Sustacal/Boost).
Studies defined as Early onset trials should only include subjects less than 3 months from diagnosis. Otherwise, no specific time from diagnosis parameter is recommended.

3. Should age be an exclusion/inclusion criterion?


The natural history of pre- and post-clinical type 1 diabetes varies with age. The rate of b-cell destruction appears to be related inversely to age (48-51,56), directly to the presence of higher risk HLA class II (e.g. DR3,4;DQ2,8) and class I (e.g. A24) alleles (45-47,70) and directly to the number of islet autoantibodies (71-73). Therefore, it could be argued that the requirement for effective intervention treatment is likely to be more demanding in younger subjects, but may be less likely to accelerate the disease. On the other hand, a slower rate of b-cell destruction in older subjects may indicate a wider window of opportunity for intervention, although if the process was regulated it would be important that intervention treatment did not jeopardize this.

Though an upper age limit may delineate classic type 1 diabetes from slowly progressive type 1 diabetes or latent autoimmune diabetes of adults (LADA) (74-76), the combination of clinical type 2 diabetes and autoantibodies may still occur in children and younger adults (77).

Age is also an issue with respect to consent and recruitment.


Clearly define age and other parameters at diagnosis (see above).
Match subjects in treatment and control groups as closely as possible for age.
In Phase I trials, enroll only subjects aged 18 and over.
Set an upper age limit of 40.

4. Should subjects who have participated in pre-clinical trials be excluded?


People who have participated as subjects in control arms of pre-clinical intervention trials could in theory participate in subsequent trials involving newly-diagnosed subjects, but two objections have been raised. First, they may have been diagnosed when asymptomatic (78,79), which could influence both natural history and treatment effects. Second, further information about b-cell function post-diagnosis may be needed in these subjects. However, most people with newly-diagnosed diabetes will not have participated in prevention trials and this is not likely to be a major issue. If such a guideline was adopted, enrollees in prevention trials should be informed that their participation would exclude them from future trials.


Subjects who have participated in pre-clinical intervention trials may be included unless there are specific reasons for exclusion relating to entry criteria or therapy used. However, the number of these subjects participating should be reported and results should include subgroup analysis of this subset.

5. What is the effect of concurrent treatment on trial outcomes?


Chronic ingestion of certain drugs, e.g. glucocorticoids, may affect glucose metabolism.


Exclude subjects taking chronic glucocorticoid treatment, but not necessarily periodic oral, inhaled or topical glucocorticoid treatment.

Avoid testing subjects taking glucocorticoids or any treatment likely to affect glucose or insulin metabolism.



1. What variables may influence outcome measures?
2. Can trials stratify subjects to control for important variables?
3. What is the appropriate trial period?

1. What factors may influence outcome measures?


  • Diabetes treatment (24,36,68,80,81), physical activity, diet, time of testing and other variables influence diabetes control and outcome measures.
  • The standard of care for people with diabetes is tight control, i.e. HbA1c < 7% (82). In some intervention trials subjects have been taken off insulin when euglycemia was achieved (83-85). It remains unknown whether continuing insulin therapy even during the honeymoon phase is beneficial, but indirect evidence suggests it is (4,6,68,80,81).


  • Aim to standardize variables that could influence diabetes control and/or outcome measures.
  • Randomize subjects in phase II and III trials.
  • Aim to placebo control and double mask.
  • Mask blood samples before analysis.
  • Review safety and other data by external committee (e. g. data safety monitoring board), with code broken to investigators and subjects if necessary for reasons of safety. Otherwise, dont break codes for data analysis until termination of the trial.
  • Aim for 'tight' control, e.g. as close to normal HbA1c as possible without causing hypoglycemia.
  • Continue insulin treatment whenever possible, avoiding hypoglycemia.

2. Can trials stratify subjects to control for important variables?


Phase I and II studies are often not large enough to stratify subjects according to important variables


Collect standardized raw data on all subjects, for later combined analysis. Document age, gender, pubertal status, family history of diabetes, time from diagnosis, nature of clinical presentation (see above), HLA, baseline immune marker and C-peptide status.

3. What is the appropriate trial period?


It is assumed that mixed meal- or glucagon-stimulated C-peptide falls after diagnosis and power calculations may be predicated on intervention reducing the rate of fall. However, data from control arms of trials in recently-diagnosed adults indicate there may be little or no fall in C-peptide over the first year (22,42). Therefore, evaluation out to one year after either diagnosis or treatment initiation may fail to accurately reflect outcome, particularly in adults in whom there may only be a minimal fall in C-peptide over this period (22,42). Evaluation at this time may, however, provide short-term safety data.


Evaluate treatment for at least two years, particularly in adults; one year may be appropriate for safety.



1. What metabolic tests can be used to determine the effects of intervention therapy?
2. What immune tests can be used to monitor the effects of intervention therapy?
3. What are the primary and secondary outcomes?

1. What metabolic tests can be used to determine the effects of intervention therapy?


Several tests can be used to evaluate b-cell function. C-peptide in healthy subjects can be stimulated by IV, IM or SC glucagon, IV sulfonylurea, IV glucagon-like peptide (GLP)-1, IV or oral amino acids, IV or oral glucose, or a mixed meal (86-90). During intervention with cyclosporin, subjects with type 1 diabetes had C-peptide responses to an oral mixed meal (Sustacal/Boost) at a time when IV glucose and glucagon responses were absent (91). Most studies have only evaluated the response over 2 hours, though it is suggested that a 4 hour test may provide additional useful information. There is considerable variability in both tests, which may be related to intestinal absorption. Use of IV glucose or arginine may reduce this variation (92), but this has not been studied. Alternatively, IV glucagon-stimulated C-peptide has been used in new-onset trials. However, there is limited information regarding the relationship between mixed meal test and glucagon test results (93,94).

There are little published data on conditions that affect C-peptide stimulation tests in patients with established type 1 diabetes. An important consideration is the control of diabetes in the peri-test period. Although one study reported no effect of exogenous insulin on mixed meal stimulated responses (95), most protocols advise withholding of insulin prior to the test. Should this only apply to short-acting insulin? What about insulin via pump? The importance of the prevailing blood glucose level on stimulated C-peptide remains controversial. Some studies suggest no effect (86), whereas others indicate that the test is only valid in the absence of hypoglycemia (93,96) or hyperglycemia (97-99).


  • Evaluate stimulated C-peptide with the liquid (Sustacal/Boost) mixed meal tolerance test (MMTT). Although the standard meal is for 2 hours, C-peptide may peak later and a 4 hour test should be performed if possible.
    Administer evening insulin as usual but withhold morning insulin of any type. If on pump, continue basal rate but withhold bolus. Conduct test only if fasting blood glucose is 4-11.1 mM (72-200 mg/dl).
  • Perform mixed meal test quarterly. Consider performing IV glucagon stimulation of C-peptide at baseline and annually to obtain comparative data.
  • Consider IV glucagon stimulation of C-peptide as an alternative to the mixed meal test. If this test is performed quarterly in lieu of the mixed meal test, a mixed meal test should be performed at baseline and annually to obtain comparative data.

2. What immune markers can be used to monitor the effects of intervention therapy?


Antibodies (titer, isotypes, IgG subclasses, epitope specificity) and T-cell responses (proliferation, activation markers, cytokine production) may change in response to intervention therapy and therefore provide important mechanistic surrogate marker information. However, the relationship between changes in these markers and therapeutic benefit remains unknown. Islet antibody levels did not correlate with benefit in the cyclosporin trials (100), although remission of autoantibody-mediated diseases, e.g. Graves' hyperthyroidism, has been associated with a decrease in autoantibody levels (101,102).

The place of markers such as IgG autoantibody subclasses (103,104), and islet antigen-reactive T-cell responses (105-107) is not yet clear. Assays for these cells are being evaluated by Immunology of Diabetes Society Workshops (108). T-cell assays require substantial improvement so that reproducible, quantitative and qualitative responses can be measured.


Measure islet autoantibodies and freeze sera/plasma for future studies. Continue to explore the feasibility of T-cell studies. Consider freezing blood mononuclear cells for future, e.g. genetic analysis.
Evaluate immune markers in regard to HLA types.

3. What are the primary and secondary outcomes?


Studies have reported changes in fasting, peak and area-under-curve (AUC) C-peptide values over time. It remains unclear which is most useful. In addition, it is not known whether C-peptide expressed as a function of the blood glucose is more reliable. There are pre-pubertal vs. post-pubertal/age differences in C-peptide which are often not taken into account.


  • Define primary outcome as a significant difference in 2 hour AUC C-peptide response between treated and control groups over time.
  • In addition, analyze incremental and peak C-peptide responses. Additional analysis such as time to peak C-peptide response or 4 hour AUC for C-peptide may be appropriate exploratory outcomes.
  • Define secondary outcomes as insulin dose/kg, glucose and HbA1c levels.


These Immunology of Diabetes Society guidelines have been developed in order to facilitate comparison of intervention therapies. Development and validation of novel assay technologies as well as new data on alternative outcome measures will undoubtedly require modifications to these recommendations in the future, but the principal that standardization of clinical intervention trials will benefit patients, families, and investigators will continue to underlie these efforts.


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