StartRight: Getting the Right Classification and Treatment From Diagnosis in Adults With Diabetes
Conditions: Diabetes Mellitus
Sponsor: Royal Devon and Exeter NHS Foundation Trust

 
 
StartRight Study Protocol 
 
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StartRight: Getting the right classification and treatment from 
diagnosis in adults with diabetes. 
 
 
SHORT STUDY TITLE : StartRight 
 
 
 
PROTOCOL VERSION NUMBER AND DATE 
Version  4.0  28 AUGUST 2018 
 
 
 
RESEARCH REFERENCE NUMBERS 
 
IRAS Number: 
203567 
 
Sponsors Number: 
1703396 
 
Funder Number:  
CS-2015-15-018 (NIHR) 
 
 
 
17/0005624 (DUK) 
 
 
 
 
 
 
 
 
 
 
 
 
This protocol has regard for the HRA guidance. 
 

 
 
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GENERAL INFORMATION 
Page No. 
RESEARCH REFERENCE NUMBERS 
 
1 
SIGNATURE PAGE 
3 
KEY STUDY CONTACTS 
 
4 
STUDY SUMMARY  
5 
FUNDING 
 
6 
ROLES & RESPONSIBILITIES 
6 
STUDY SUMMARY FLOW CHART  
7 
STUDY DETAILS 
1. LAY SUMMARY 
 
8 
2. BACKGROUND & RATIONALE 
 
8 
3. OBJECTIVES AND OUTCOME MEASURES/ENDPOINTS  
11 
4. STUDY DESIGN 
12 
5. STUDY PROCEDURES 
14 
6. WITHDRAWAL & END OF STUDY 
17 
7. SAMPLE PROCESSING 
18 
8. DATA COLLECTION & RECORDING 
19 
9. STATISTICS AND DATA ANALYSIS 
20 
10. STUDY MANAGEMENT 
22 
11. MONITORING & INDEMNITY 
22 
12. IMPORTANCE AND POTENTIAL BENEFIT 
22 
13. PROJECT DEVELOPMENT AND USER INVOLVEMENT 
23 
14. DISSEMINATION/IMPLEMENTATION OF RESEARCH 
23 
15. REFERENCES 
24 
16. SCHEDULE OF AMENDMENTS 
26 
 
 

 
 
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SIGNATURE PAGE 
The undersigned confirm that the following protocol has been agreed and accepted and that the Chief 
Investigator agrees to conduct the study in compliance with the approved protocol and will adhere to 
the principles of GCP, the Sponsor’s SOPs, and other regulatory requirements as amended. 
I agree to ensure that the confidential information contained in this document will not be used for any 
other purpose other than the evaluation or conduct of the clinical investigation without the prior written 
consent of the Sponsor 
I also confirm that I will make the findings of the study publically available through publication or other 
dissemination tools without any unnecessary delay and that an honest accurate and transparent 
account of the study will be given; and that any discrepancies from the study as planned in this 
protocol will be explained. 
 
For and on behalf of the Study Sponsor: 
Signature:  
...................................................................................................... 
 
Date: 
....../....../...... 
Name (please print): 
...................................................................................................... 
 
 
Position: 
...................................................................................................... 
 
 
 
Chief Investigator: 
Signature: 
...................................................................................................... 
 
Date: 
....../....../...... 
Name: (please print): 
...................................................................................................... 
 
 
 
Statistician: 
 
 
Signature: 
...................................................................................................... 
 
Date: 
....../....../...... 
Name: (please print): 
...................................................................................................... 
 
 
 
Position: 
...................................................................................................... 
 
 
 
 
 

 
 
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KEY STUDY CONTACTS 
Chief Investigator 
Dr. Angus Jones, NIHR Clinician Scientist and Honorary 
Consultant Physician, University of Exeter Medical 
School/Royal Devon and Exeter NHS Foundation Trust, NIHR 
Exeter Clinical Research Facility, RILD, Barrack Rd, Exeter 
EX2 5DW 
Tel: 01392 408538 
E-mail:  angus.jones@exeter.ac.uk 
Co-Investigators 
Prof Andrew Hattersley, Professor of Molecular Medicine and 
Honorary Consultant Physician, University of Exeter Medical 
School & Royal Devon and Exeter Hospital.  
 
Dr Tim McDonald, Principal Biochemist and Honorary Senior 
Lecturer, Royal Devon and Exeter Hospital and University of 
Exeter Medical School 
 
Dr Bridget Knight, Research Midwife, Royal Devon and Exeter 
Hospital and University of Exeter Medical School. 
Study Co-ordinator 
Anita Hill 
NIHR Exeter Clinical Research Facility, Level 2 RILD, Barrack 
Road, Exeter, EX2 5DW. 
Tel: 01392 408184 
Email: Anita.Hill2@nhs.net 
Sponsor 
Royal Devon & Exeter NHS Foundation Trust 
Barrack Road, Exeter, EX2 5DW 
Sponsor Representative: Mr Chris Gardner, Research & 
Development Directorate Manager. 
Tel: 01392 403017 
Email: christopher.gardner@nhs.net 
Funder 
National Institute for Health Research  (NIHR).  
Diabetes UK (DUK) 
Other Support  
NIHR Exeter Clinical Research Facility Tel: 01392 408181. 
Email:crf@ex.ac.uk  
Statistician 
Dr. Beverley Shields, Senior Lecturer in Medical Statistics, 
University of Exeter Medical School. 
Steering Committee 
Prof Polly Bingley, University of Bristol, Tel: 01174148069 
Email: Polly.Bingley@bristol.ac.uk 
Prof Colin Dayan, Unviersity of Cardiff, Tel: 02920742182 
Email: DayanCM@Cardiff.ac.uk 
Dr. Phil Evans, University of Exeter, Tel: 01392722747  Email: 
P.H.Evans@exeter.ac.uk 
Prof William Henley, University of Exeter, Tel: 01392 726088  
Email: W.E.Henley@exeter.ac.uk 
 
Patient respresentative(s) -  to be confirmed 

 
 
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STUDY SUMMARY 
 
StudyTitle 
STARTRIGHT: Getting the right classification and treatment 
from  diagnosis in adults with diabetes 
Internal ref. no. (or short title) 
CRF243 (StartRight Study) 
Study Design 
Prospective observational 
Study Participants 
Adults who have been diagnosed with diabetes  (excluding 
gestational or secondary diabetes) for < 1 year at the time of 
study recruitment.  Aged > 18 at the time of diabetes 
diagnosis. 
Planned Sample Size 
Main cohort: 1000 
Additional late onset diabetes cohort: 800 
Follow up duration 
3 years 
Planned Study Period 
6 years 
Study Summary 
This study aims to achieve more accurate early classification 
of diabetes and identification of which patients will rapidly 
require insulin treatment. We will recruit 1200 participants 
who have been diagnosed with diabetes in the last year and 
were aged between 18 and 50 years at the time of diagnosis. 
We will recruit an additional cohort of 800 participants 
diagnosed after age 50. We will record clinical features and 
biomarkers that may help us to determine diabetes type at 
diagnosis and follow participants for 3 years to assess the 
development of severe insulin deficiency (measured using C-
peptide) and insulin requirement. We will assess utility of 
clinical features and additional biomarkers in identifying 
patients with rapid progression to insulin requirement. 
Findings will be integrated into a freely available clinical 
prediction model. 
 
 
 
 
 

 
 
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FUNDING AND SUPPORT IN KIND 
FUNDER(S) 
National Institute for Health Research (NIHR 
Clinician 
Scientist 
award 
CS-2015-15-018): 
Integrating clinical features and biomarkers to 
improve diabetes classification and treatment in 
young adults. (< age 50 cohort) 
Diabetes UK (17/0005624) Combining Clinical 
Features and biomarkers to identify patients with 
Type 1 diabetes in later life  (> age 50 cohort)  
 
FINANCIAL AND NON FINANCIAL SUPPORT 
GIVEN 
Financial: NIHR £866,655, Diabetes UK £366,766 
NIHR Exeter Clinical Research Facility and NIHR 
Clinical Research Network 
Support Infrastructure 
Research & Development Directorate, Royal 
Devon & Exeter NHS Foundation Trust 
Study Sponsorship and Research Governance 
Support 
 
 
ROLES & RESPONSIBILITIES: 
 
SPONSOR: 
The sponsor has no role in the study design or data analysis, interpretation or manuscript writing. 
The sponsor will review and approve the study protocol and supporting documents. 
The sponsor takes overall responsibility for the study site monitoring that is carried out by the CI team. 
 
 
STEERING GROUP: 
An independent steering committee will be appointed, made up of researchers, physicians and 
patients, to provide independent guidance and ensure that study progress and the conduct of the 
study is appropriate.  
The steering committee will meet 6 monthly in the first year of the study, and annually thereafter, with 
the CI and members of the study team.  
 
STUDY MANAGEMENT: 
Day to day management of the study will be undertaken by the CI and study coordinator with the 
support of the NIHR Exeter Clinical Research Facility. 
 
 
 
 
 
 

 
 
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STUDY PROCEDURE SUMMARY FLOW CHART 
 
 
 
 
 
 
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FINAL VISIT - 3 YEARS POST RECRUITMENT 
Repeat blood & urine samples, clinical characteristics, 
treatment details & questionnaire data collected at Visit 1. 
BASELINE RESEARCH VISIT & CONSENT (approx 30-45mins) 
 
Blood & urine samples, clinical characteristics, 
treatment details, demographics & questionnaire 
 
Notes review by recruiting nurse to collect/confirm 
diagnosis details.  
 
Samples analysed for c-peptide & islet autoantibodies 
(GAD, IA2 & ZNT8), DNA, serum and plasma bio 
banked 
 
Clinician & participant informed of autoantibody 
results 
NEWLY DIAGNOSED DIABETES PATIENTS IDENTIFIED 
IN PRIMARY OR SECONDARY CARE & SCREENED FOR 
ELIGIBILITY 
 
Diagnosed with Diabetes in past 12 months 
 
Aged 18 or over at the time of diabetes diagnosis  
 
Follow Up 1 Year Post Recruitment  
 
Contact (by phone,email, post or in person) to collect 
details of treatment change & hypoglycaemia 
 
Patient sends urine or blood sample by post to Exeter 
for UCPCR or serum C-peptide.. 
 
Research team collects HbA1c results from GP 
practice or hospital records. 
Follow Up 2 Year Post Recruitment  
 
Contact (by phone.email, post or in person) to collect 
details of treatment change & hypoglycaemia. 
 
Patient sends urine or blood sample by post to Exeter 
for UCPCR or serum C-peptide 
 
Research team collects HbA1c results from GP 
practice or hospital records. 

 
 
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STUDY PROTOCOL 
StartRight:  
Getting the right classification and treatment from diagnosis in adults 
with diabetes. 
 
1 
Lay summary 
The treatment of Type 1 and Type 2 diabetes is very different. People with Type 1 diabetes rapidly 
stop making their own insulin, so need insulin injections from diagnosis. People with Type 2 diabetes 
can keep making their own insulin but it may not work as well as it should, so they can be treated with 
diet or tablets. While they may eventually need insulin treatment it is usually not until many years after 
diagnosis. 
It is often difficult for doctors to tell which kind of diabetes a person has. Because of this, sometimes 
people are given the wrong diagnosis. This can have a huge impact as it means they could receive the 
wrong treatment. A person incorrectly diagnosed with Type 1 diabetes will be prescribed unnecessary 
insulin injections and miss out on other helpful therapies. A person incorrectly diagnosed with Type 2 
diabetes may develop severely high glucose and become unwell with a condition called Diabetic 
Ketoacidosis if they do not receive insulin treatment.  
This study aims to improve this situation by helping doctors more accurately tell the type of diabetes a 
person has when they are first diagnosed. We will recruit 1000 participants who have recently been 
diagnosed with diabetes between the ages of 18 and 50. We will recruit an additional 800 recently 
diagnosed participants who developed diabetes after age 50, half of whom will be receiving insulin 
therapy. We will record clinical features and measure blood tests that may help us determine diabetes 
type at diagnosis and follow participants for 3 years to see whether they stop producing their own 
insulin and need insulin treatment, which confirms Type 1 diabetes. We will assess whether clinical 
features and blood tests can help us tell if a patient needs rapid insulin treatment and should be 
initially treated as Type 1 or Type 2 diabetes. 
We will combine results from this study and existing previous studies to produce a calculator, called a 
clinical probability model that will allow doctors and patients to combine information from clinical 
features and (where necessary) blood tests to accurately diagnose what type of diabetes a person has 
and therefore give the correct treatment. This will be freely available to doctors and patients as a 
website calculator and smartphone app. 
 
 
2 
BACKGROUND AND RATIONALE  
Correct diagnosis is needed for appropriate management of Type 1 and Type 2 diabetes 
Evidence based guidelines for the treatment of Type 1 and Type 2 diabetes are very different (1-4). 
These differences predominantly relate to the rapid development of severe insulin deficiency in Type 1 
diabetes (3, 5). This means patients with type 1 diabetes need early insulin treatment, are at risk of life 
threatening ketoacidosis without insulin treatment and have poor response to non-insulin therapy(3). 
They require accurate insulin delivery (e.g. multiple injections, carbohydrate counting, pumps) due to 
the very high glycaemic variability associated with absolute insulin deficiency (5, 6). In contrast 
patients with Type 2 diabetes continue to produce substantial endogenous insulin even decades after 

 
 
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diagnosis (7). Glycaemia may therefore be initially managed with lifestyle change and non-insulin 
therapies, should insulin be required simple insulin regimens are combined with other agents (4, 8). 
Misdiagnosis will mean a patient receives inappropriate treatment, leading to unnecessary insulin 
therapy or, where Type 1 diabetes is misdiagnosed as Type 2, poor glycaemic control, potential 
ketoacidosis and earlier development of diabetes complications (9, 10).  
 
Misclassification of diabetes is common and increasingly difficult in young adults 
BMI and age of diagnosis are usually used to differentiate diabetes subtypes however with increasing 
obesity Type 1 patients may be obese and Type 2 is seen in children and very young adults (11-13). 
Diagnosis on clinical features is therefore often difficult and initial misclassification is frequent (7-25%), 
particularly in young adults, an age group where incident Type 1 and Type 2 diabetes are both 
common (9, 14-16). In a recent study using similar definitions to this project,  26% of those treated 
with insulin from diagnosis and  23% of Type 2 diabetes diagnosed before age 35 were misclassified   
(17).  . Initial diagnosis is rarely revisited therefore misdiagnosis will have long term implications.  A 
joint report by the Royal College of General Practitioners and NHS diabetes in 2011 highlighted the 
problems of misclassification of diabetes including a lack of clinical data and its importance from a 
clinician and patient perspective (9).  
Half of older patients receiving insulin from diagnosis have been misdiagnosed 
Most patients developing diabetes after age 50 will have type 2 diabetes, therefore most patients 
(97%) of those treated as type 2 diabetes will be correctly classified. However we have recently shown 
that 49.5% of those treated as Type 1 diabetes (with insulin from diagnosis) after age 50 have been 
wrongly classified, as they continue to produce large amounts of their own insulin many years after 
diagnosis, confirming Type 2 diabetes (Jones et al unpublished, with misclassification 56% in a 
separate multicentre cohort (17)).   This means that >6000 new patients annually in the UK in this age 
group are misclassified as having T1D, and consequently unnecessarily treated with insulin (18). 
Robust evidence and guidelines for clinical classification of diabetes are needed 
We have systematically reviewed evidence on diabetes classification and found only 11 papers 
assessed clinical features against robustly defined diabetes subtypes with only 2 published in the last 
decade (19). No papers assessed young adult onset diabetes and only one assessed the utility of 
combining clinical features (20). While developments in assay technology have meant biomarkers 
such as C-peptide and autoantibodies are more robust and inexpensive there is a lack of evidence to 
support or refute their use, with the evidence quality for islet autoantibody testing to aid diabetes 
classification rated as low on expert committee review (21). Large prospective studies of mixed 
populations are needed to support or refute the use of these tests in clinical care.  
 
Novel genetic biomarkers may improve diabetes classification 
The Exeter group have recently developed an inexpensive genetic risk score, based on 34 variants 
predisposing to Type 1 diabetes, which can discriminate Type 1 and 2 diabetes with AUC ROC 0.88 in 
cross-sectional datasets (22, 23).  The score is independent and additive to autoantibody status and 
clinical features. 

 
 
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A validated clinical probability model for diabetes classification has the potential to improve 
clinical care 
In many areas of clinical care decision making has been improved by using probability models 
(‘calculators’) that give appropriate weight to a multitude of patients features in a way that an individual 
clinician cannot (24). This approach has improved decision making in a wide range of conditions, for 
example physicians following NICE guidance for cardiovascular disease or osteoporosis will be 
familiar with the QRISK, GRACE and FRAX risk models. In response to widespread misdiagnosis of 
monogenic diabetes (25) the Exeter group has developed a probability model for common types of 
monogenic diabetes (‘MODY’) that outperforms standard clinical criteria (C statistic >0.94) (26). The 
resulting online calculator (http://www.diabetesgenes.org/content/mody-probability-calculator ) has 
received 11900 visits in less than 2 years and is used prior to the majority of referrals for diagnostic 
genetic testing at the UK MODY referrals laboratory.  It has recently been incorporated into an iphone 
app which aims to bring together current evidence and expert opinion to aid differential diagnosis of 
diabetes subtypes (Diabetes Diagnostics app). We are developing a model integrating clinical features 
to diagnosis diabetes subtype (Jones Shields unpublished) using cross sectional datasets which has 
not been prospectively tested. Simultaneous with the early stages of this study this model will be 
further developed using cross sectional cohorts in Exeter and Oxford. 
 
 

 
 
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3.  OBJECTIVES AND OUTCOME MEASURES/ENDPOINTS 
Primary objective 
To establish diagnostic performance of biomarkers including islet autoantibodies, C-peptide and a 
genetic risk score in identifying patients with rapid requirement for  insulin, alone  and in combination 
with clinical features  
 
Secondary objectives 
To prospectively validate a clinical prediction model developed from cross sectional datasets in 
predicting rapid insulin requirement in young onset diabetes. 
To integrate discriminative and additive biomarkers into the clinical prediction model. 
To establish a bio resource for future biomarkers discovery and assessment. 
 
Primary endpoint/outcome 
Diabetes type defined by insulin requirement at 3 years: 
Type 1 diabetes = Progression to insulin treatment and severe insulin deficiency (post meal plasma C-
peptide <0.6nmol/L) at 3 years 
Type 2 diabetes = Lack of requirement for insulin at 3 years (HbA1c <90mmol/mol without insulin 
treatment or post meal C-peptide ≥ 0.6nmol/L if insulin treated) 
 
Rationale for primary outcome 
A stimulated C-peptide less than 0.6nmol/L reflects severe insulin deficiency and is consistent with 
Type 1 diabetes outside the ‘honeymoon’ period and requirement for insulin treatment. A stimulated C-
peptide over 0.6nmol/L  represents substantial endogenous insulin secretion and is consistent with 
type 2 diabetes, non-insulin requirement and response to oral therapy, as reviewed in Jones 2013(27). 
Equivalent thresholds for fasting C-peptide and post meal urine C-peptide creatinine ratio have been 
derived from a dataset of 120 participants with insulin treated diabetes as previously reported (26). We 
have included early insulin treatment alongside measured insulin secretion as should an individual 
develop severe insulin deficiency at a late stage but have achieved glycaemic control without insulin 
for >3 years after diagnosis we would consider their initial treatment as Type 2 diabetes appropriate.  
Where urine and blood C-peptide are discordant, classification will be based on the highest value. A 
cut off of 0.6nmol/mol will be used for classification based on urine C-peptide creatinine ratio (26). 
 
Secondary endpoints/outcomes 
Stimulated C-peptide <0.2nmol/L at 3 years (‘absolute insulin deficiency’) (27, 28) 
C-peptide rate of change (UCPCR and plasma) 
HbA1c (mean and at 3 years) 
Weight change (baseline to 3 years) 
Self-reported hypoglycaemia & hypoglycaemic awareness (29, 30) 
Wellbeing and resilience (SF12 and CD-RISC questionnaire) (30, 31) 
Ketoacidosis (self-reported and confirmed from medical notes) 
 

 
 
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This research will produce a robustly validated clinical prediction model that will for the first time 
integrate validated clinical features and biomarkers to allow clinicians to accurately assess 
classification and need for insulin treatment at diabetes diagnosis. The model will be freely available to 
clinicians and patients through a website and smartphone App. 
 
4.  STUDY DESIGN 
Prospective observational study. We will recruit a prospective cohort of 1200 adults diagnosed with 
diabetes within the previous 1 year and aged between 18 and 50 at the time of diagnosis.  
 
We will also recruit an additional cohort of 800 participants diagnosed with diabetes in the last year 
aged >50 at diabetes diagnosis, with recruitment stratified by insulin treatment (400 insulin treated, 
400 non insulin treated). 
 
At baseline (visit 1) blood & urine samples will be collected for biomarker assessment and biobanking, 
and clinical features recorded. We will follow participants for 3 years with annual remote contact 
(phone, email or letter) and postal urine or capillary blood sample for C-peptide.  A further face to face 
visit with blood & urine sampling will be performed at 3 years.  
 
We will assess the utility of adding biomarkers to clinical features in diagnosing diabetes subtype 
(defined by insulin requirement at 3 years), and where utility is shown, integrate these into a 
probability model. 
 
Please also refer to the study summary flow chart on page 7 for a summary of study procedures. 
 
 
4.1 
STUDY SETTING 
NHS primary care, secondary care, or community setting. 
This is a multicentre study and participants will be recruited through up to 60 recruiting sites in the UK.   
 
Recruiting sites will need to confirm the following capabilities in order to deliver the study: 
 
Ability to recruit (possibly with the assistance of PICs) at least 30 participants before  
 
31 December 2019 
 
Sufficient research nurse time to undertake the recruitment and all follow-up procedures as 
detailed in the study design for the full duration of the study. 
 
 
Research nurse(s) must be trained and competent in phlebotomy. 
 
 
Ability to spin blood samples (in accordance with SOPs provided by the CI) and despatch on 
day of collection to the lead site in Exeter , UK.    
Note: Samples transferred to the Royal Devon and Exeter Hospital blood sciences laboratory 
for analysis will be labelled with participant identifiable data (Name, date of birth & NHS 
number) in accordance with the requirements for clinical sample analysis.   Once initial 
analysis is complete, the remaining samples will be anonymised and assigned an individual 
study code prior to storage. 
 
 
Undertake to transfer scanned (PDF) copies of data collection forms, consent forms & 
biochemistry results (via secure nhs.net email) to the CI site within 5 working days of the study 
visit. 

 
 
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 
Full contact details for both patients and clinicians (GP or secondary care) will have to be 
included in the information transferred to the lead site, to facilitate feedback of antibody results 
after visit 1 and C-Peptide result at 3 years. 
 
 
Undertake to transfer scanned (PDF) copies of recruitment logs and site specific delegation 
logs to the CI site on a monthly basis to facilitate remote site monitoring requirements. 
 
 
4.2 
ASSESSMENT AND MANAGEMENT OF RISK 
Concurrent medication and diabetes management: 
This is an observational study therefore all decisions on patient management will continue to be taken 
by participant’s usual clinicians. All diabetes medications will be unaltered.  
Blood sampling:  
Blood samples will be taken which may result in slight pain and bruising. Samples will be taken by 
staff trained and experienced in venepuncture procedures.  
Patient burden: 
Participation involves 2 blood samples (3 years apart) and 2 additional remote contacts (phone, email 
or letter according to participant preference) over the 3 year period. Participants will also collect a 
urine sample or capillary blood sample at home on 4 occasions. 
Concomitant studies: 
Recruitment to this study does not preclude recruitment to concurrent studies related or unrelated to 
diabetes   
Reporting adverse events 
This study is categorised as:  
  
• Type A = No higher than the risk of standard medical care 
This is a low risk study and it is not anticipated that participants involved in this project will be subject 
to  adverse or serious adverse events (AEs/SAEs).   
AEs/SAEs that occur as a result of study participation (e.g. venepuncture during research visit) should 
be reported to the study sponsor and CI, with 24 hours of notification, in accordance with the SAE/AE 
SOP and report forms provided to recruiting sites in their study site file.  These study related 
AEs/SAEs should also be reported locally in accordance with individual Trust protocols. 
 
AEs/SAEs that are not a result of study participation should be dealt with at each recruiting site in 
accordance with their local Trust protocols. 
 
 
4.3 
ELIGIBILITY CRITERIA 
4.3.1 Inclusion criteria 
 
Adults diagnosed with diabetes within the previous 12 months. 
 
Aged ≥18 and ≤50 at the time of diabetes diagnosis or (additional late onset diabetes cohort) 
aged >50 at the time of diabetes diagnosis*.  

 
 
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 
Able and willing to provide informed consent. 
* Clinical diagnosis of diabetes mellitus. Biochemical diagnosis confirmed from healthcare records 
post recruitment.  
4.3.2 Exclusion criteria 
• 
Gestational diabetes 
• 
Known secondary diabetes (diabetes considered likely due to medication, cystic fibrosis, 
 
pancreatitis, pancreatic cancer, pancreatic surgery, hemochromatosis or Cushing’s syndrome). 
 
5 
STUDY  PROCEDURES  
 
Please refer to the flow chart on page 7 for a summary of the study procedures. 
 
The study procedures described below will be conducted in accordance with detailed SOPs 
 
provided to all sites by the CI. 
 
5.1 
Identification & Recruitment 
5.1.1 Patient Identification & Eligibility Screening 
Eligible participants will be identified in the following ways: 
 
At routine clinical care appointment (primary and secondary care): Clinicians will introduce the 
study to eligible patients and obtain verbal consent for the research team to contact the 
participant to discuss the study in more detail. 
 
Regular screening of primary and secondary care records: Clinicians will carry out periodic 
searches of their patient lists to identify eligible patients and invite those patients to take part in 
the study by letter, email, telephone or in person at the patient’s next appointment. 
 
Retinal Screening: Invitation letter or copy of the study poster (advertisement) sent to new 
retinal screening participants. 
 
Self-referral: Study posters (advertisements) will be displayed in primary care, secondary care 
or community settings. The study may also be promoted on research websites & appropriate 
social media (e.g. diabetes or research specific Facebook pages) using wording from ethically 
approved study documents (ie patient information sheet or study poster) 
 
Identification from existing research databases where participants have given formal written 
consent for contact about further research. 
This is a multicentre study and identification methods will vary by centre, for example it is likely some 
sites will recruit from only secondary or primary care 
 
5.1.2 Targeted recruitment of participants aged over 50 by insulin treatment status 
 To ensure sufficient prevalence of the primary study outcome of Type 1 diabetes in those over age 50 
recruitment of this age group will target equal numbers (400 each) of participants receiving insulin 
treatment at the time of recruitment (high prevalence Type 1), and those not receiving insulin 
treatment (low prevalence Type 1). This will be managed using separate recruitment targets for these 
subgroups, based on prior recruitment rate, with recruitment to the non insulin treated cohort limited to 
a subset of study sites.  
 

 
 
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5.1.3 Screening 
A member of the research team will contact referred/identified patients directly to discuss the project in 
more detail and confirm eligibility.  A copy of the project specific participant information sheet (PIS) will be 
provided. In all cases, potential participants will be given written information and the opportunity to 
discuss the project with one of the research team prior to recruitment. 
 
5.1.4 Consent Visit (Visit 1) – Duration <1 hour 
To facilitate recruitment participants will be offered home and/or evening visits where this can be 
supported by the recruiting site. 
Following written informed consent demographic and clinical features will be recorded, including 
weight, height, waist/hip ratio & blood pressure.  Data will be collected relating to the patient’s 
diabetes, diagnosis and other relevant medical condition and will include a hypoglycaemia 
questionnaire.  Non-fasting (within 1-5 hours of a meal) blood sample will be collected at all sites for  
 
Serum C-peptide and other routine biochemistry  
 
Islet autoantibodies (GAD, IA2, ZnT8) 
 
DNA extraction (T1D genetic risk score typing, biobanking) 
 
Plasma and serum samples for storage for future analysis 
At selected sites, blood sample will also be collected for additional tests including immune function 
analysis 
Total blood sample volume to be collected at visit 1 will be < 90ml (volume in sites not undertaking 
immune function studies <40ml). 
Samples will be transferred to Exeter Clinical Laboratory (Royal Devon and Exeter NHS Foundation 
Trust) directly by post or frozen for subsequent batched transfer. 
Participants will be given sterile collection kits and prepaid packaging for home post meal urine or 
capillary blood collection. The sample will be posted directly to Exeter Clinical laboratory and will be 
analysed for Urinary C-Peptide Creatinine Ratio (UCPCR) or serum C-peptide 
Samples will be collected and processed in accordance with detailed SOPs.  
Islet autoantibody results (with clinical interpretation) will be reported to clinicians and participants 
within 4 weeks of visit 1. 
With specific consent additional data will be collected from clinical and laboratory records including 
diagnosis weight, HbA1c, FBC, glucose and lipid profile, and confirmation of ketoacidosis. 
Participants will be asked to complete an optional 25 question questionnaire assessing psychological 
wellbeing and resilience (31, 32). 
At some sites, participants will be offered the option to gift samples taken during their routine diabetes 
care to be used to aid the on-going collection of research data (e.g. biochemical analysis for indicators 
of long-term insulin production). 
 
5.1.5  Follow-Up - Visits 2 & 3 

 
 
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Follow up by telephone,email, post or in person at 1 year (10-16 months) and 2 years (22-28 
months) 
Duration: <15 minutes 
 
At approximately 1 year and 2 years post recruitment, participants will be contacted to record 
concurrent treatment, hypoglycaemia and health service utilisation.  
Participants will be asked to collect a home post meal urine or capillary blood sample and post to the 
Exeter Clinical Laboratory for Urinary C-Peptide /Creatinine Ratio (UCPCR) or serum C-peptide 
HbA1c results will be obtained from participants GP practice or laboratory records. 
 
5.1.6 Follow Up - Visit 4 
Follow up visit at 3 years (34-40 months) 
Approximate duration: 30 minutes 
Participants will be invited to attend a further face to face non-fasting visit.  
Weight, blood pressure, concurrent treatment, hypoglycaemia and health service utilisation will be 
recorded. 
Participants will be asked to complete a further, optional, wellbeing/resilience questionnaire. 
A non-fasting (ideally within 1-5 hours of a meal) blood sample will be collected at all sites for  
• 
Serum C-peptide and other routine biochemistry  
• 
Plasma and serum samples for storage for future analysis 
 
At selected sites, blood sample will also be collected for additional tests including immune function 
analysis. 
 
Total blood sample volume to be collected at visit 4 will be < 85ml (sites without immune function tests 
<35ml) 
 
5.1.7  Clinical Reporting  
With specific consent from the participant, results of assays of clinical relevance (including routine 
clinical bloods analysed at local site, baseline autoantibodies and C-peptide assessment at 3 years) 
will be copied to the participant’s GP or secondary care clinician with information on test interpretation.  
Baseline islet autoantibody results and 3 year C-peptide will be sent to participants, with patient 
appropriate information on interpretation, from the central research team at the lead site.   
 
 

 
 
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6. 
WITHDRAWAL & END OF STUDY 
6.1 Withdrawal criteria  
Subjects will be informed that they are free to withdraw from the study at any time up until the samples 
and data are coded but not anonymised. When samples are fully anonymised, the participants will still 
be able to withdraw but their samples and associated data will be retained for use in analysis. If a 
patient permanently withdraws from the study, or is lost to follow-up, the reason will be recorded.  
Criteria for discontinuing participation in the study are:  
• 
Participant withdrawal of consent.  
• 
Investigator’s discretion that it is in the best interest of the participant to withdraw.  
• 
Termination of the study by the sponsor or funding body. 
 
 
6.2 
End of Study 
We will recruit participants over 30-36 months, with 36 months participant follow up (total duration of 
study =   66-72 months.  Individual participant involvement = 36 months).  The study will end when the 
last participant has completed the study. The study may be discontinued at the sponsor’s or CI’s 
discretion. 
 
 
 

 
 
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7. 
SAMPLE PROCESSING  
The sample processing procedures described below will be conducted in accordance with detailed 
SOPs provided to all sites by the CI. 
7.1 Sample Transfer & Initial Analysis 
Following sample collection, initial analysis may be undertaken locally or centrally, depending on the 
best logistical method that optimises sample analysis accuracy (this may differ for different 
biomarkers, some being more stable than others).  
At each study visit blood and/or urine samples will be transferred to the Royal Devon & Exeter NHS 
Foundation Trust Blood Sciences Laboratory for central analysis and biobanking according to detailed 
sample handling SOPs.  
Samples transferred to the Exeter blood sciences laboratory for analysis will be labelled with 
participant identifiable data (Name, date of birth & NHS number) in accordance with the requirements 
for clinical sample analysis.   Once initial analysis is complete, the remaining samples will be 
anonymised and assigned an individual study code prior to storage. 
 
DNA will be extracted by the Royal Devon and Exeter Hospital Clinical Genetics Laboratory who will 
store extracted DNA pending analysis.  Blood samples for DNA extraction will be labelled with the 
study name and unique study code but not patient identifiable data. 
 
7.2  Sample Storage Procedures  
All saved serum, plasma and DNA samples will be stored under a unique ID code, with the file linking 
the code to personal identifiable information held securely by the CI and appropriate local PI and 
accessible only to personnel with training in data protection who require this information to perform 
their duties.  
 
7.3 Long Term Storage & Future Analysis 
Enduring consent will be sought to store the samples (serum, plasma and DNA) in the Peninsula 
Research Bank (PRB) following completion of analysis for this study. All future analysis of samples 
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8.  DATA COLLECTION AND RECORDING  
8.1 Data Collection Form  
Data will be obtained and recorded on a paper data collection form (DCF) with all forms returned to 
the NIHR Exeter Clinical Research Facility and entered onto a specially designed database. Data 
collected on the DCFs and subsequently entered onto the database will be reviewed for 
discrepancies, missing data and queries.  
8.2 Source Data  
CVs, delegations logs, DCFs and questionnaires will be deemed source data.   
8.3 Data Monitoring  
The coordinating research team will undertake regular audits of raw data for both accuracy and 
completeness and research data will be cleaned before analysis is undertaken. The CI reserves the 
right to close recruiting sites who fail to meet data and sample quality standards on a regular basis.  
8.4 Data Confidentiality  
All participant data will be held in a link-anonymised format, with personal identifiable data only 
accessible to personnel with training in data protection who require this information to perform their 
duties. Participants’ research and sample data will be identified by unique study ID numbers and all 
data will be held on password-protected computers. All paper copies of study data will be stored under 
ID number and kept in locked offices within the research facilities. Researchers involved in data and 
sample analysis will not have access to personal identifiable data.  
8.5 Data Storage and Archiving  
All consent and data collection forms will be held in the relevant local research centre, with access to 
personal identifiable data restricted to personnel with training in data protection who require this 
information to perform their duties. Where consent and data collection forms are completed at 
primary/secondary care or home locations, these forms will be sent to the research centre for 
monitoring and data entry. The coordinating research team will undertake regular audits of raw data 
for both accuracy and completeness and research data will be cleaned before analysis is undertaken.  
At the end of the study, archiving at each recruitment site will be carried out in accordance with local 
policy. 
At the end of the study, following analysis, anonymised data (together with samples) will be 
transferred to the Peninsula Research Bank for long term storage and management. 
 
 
 

 
 
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9 
STATISTICS AND DATA ANALYSIS 
9.1 Sample Size 
Diagnosis <= age 50 cohort 
Assuming conservative estimates that 25% of participants will have Type 1 diabetes (18) and 80% of 
participants will have the primary outcome measure a sample size of 1000 (<50 cohort) will give 
confidence intervals of 85-94% and 87-92% around a biomarker sensitivity/specificity of 90% for Type 
1 diabetes (33).  
Diagnosis >age 50 cohort  
We expect 26% of participants diagnosed  after age 50 will have Type 1 diabetes using the study 
definition, based on analysis of study primary outcome in population based cohort (DARE study – 
prevalence T1D as defined by primary outcome where insulin treated within 12 months of diagnosis 
48%, prevalence where non-insulin treated at diagnosis 3.9%). Assuming the primary outcome is 
available in 90% of participants (higher retention expected in older participants) this will give 
confidence intervals of 85-94% and 87-92% around a biomarker sensitivity/specificity of 90% for Type 
1 diabetes.   
 
9.2 Feasibility  
With >200 new cases of diabetes per 100,000 population annually in the <=age 50 age group this 
sample size represents <6% of expected incidence at each centre for the recruitment period. In the 
>age 50 cohort incidence of diabetes treated with insulin from diagnosis is 149/100000, and the 
recruitment target is 5% of expected incidence at recruiting sites. For non insulin treated participants 
diagnosed with diabetes after age 50 the recruitment target represents <1% of expected incidence at 
recruiting sites.  A 18 month window for recruitment means we should be able to invite a large 
proportion of eligible participants through regular screening of practice records or hospital records or 
writing to participants identified through the retinal screening service (all newly diagnosed patients are 
referred for retinal screening). A pilot study for this project is currently running at the Exeter site and 27 
participants have been recruited by identification through routine care and secondary care records in a 
period of 4 months at this one recruiting site.  
9.3 Statistical analysis 
Analysis will be pre-registered with clinicaltrials.gov. Both analysis and reporting will conform to 
STARD and TRIPOD (34, 35). We will assess the discriminative ability of the clinical probability model 
for early insulin requirement and perform updating if necessary. We will identify discriminatory 
biomarkers (alone and in combination with clinical features) using logistic regression and generate 
updated models integrating both clinical features and discriminatory biomarkers (26). Where 
appropriate covariates will be log transformed, or if necessary recoded into categories. Independent 
discriminators will be combined using backward stepwise approaches to enter explanatory variables 
(using predefined candidate co-variates as below), with the combination of variables assessed by the 
Deviance statistic. Performance of the resulting models will be assessed using Receiver Operating 
Characteristic (ROC) analysis. Incremental benefit of additional characteristics in nested models will 

 
 
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be assessed using Net Reclassification Improvement and internal validation using bootstrapping and 
assess goodness of fit. Analysis will be performed separately for participants diagnosed before and 
after age 50. Model predictions for the >age 50 cohort will be adjusted to reflect population incidence  
of Type 1 diabetes in this age group using data from Clinical Practice Research Datalink.  
 
 

 
 
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Candidate clinical features and biomarkers which will be assessed and where appropriate integrated 
into prediction models include: 
Model development on cross sectional 
data 
Additional features assessed in 
prospective study only 
Age at diagnosis 
BMI 
Waist hip ratio 
Ethnicity 
Gender 
Family history diabetes (1st degree relative) 
Diagnosis HbA1c, glucose, lipid profile 
 
Symptoms at diagnosis (thirst, polyuria, 
weight loss (patient reported)) 
Ketosis (hospital diagnosis DKA, 
hospital/GP ketone level (urine dip/ 
capillary) where tested) 
Acanthosis 
Personal and family history of autoimmune 
disease (thyroid, IBD, coeliac, pernicious 
anaemia,  vitiligo, Addison’s, rheumatoid 
arthritis) 
Hypertension 
C-peptide & islet autoantibodies (GAD, 
IA2, ZnT8), Type 1 diabetes genetic risk 
score (T1DGRS), CRP 
 
 
 
 
 
 

 
 
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10. Study Management: 
The study will be managed by the NIHR Exeter Clinical Research Facility providing infrastructure 
support and facilitate recruitment and sample collection. Study management will be overseen by a 
steering group which will include external academic and lay members. 
11. Monitoring & Indemnity 
Monitoring of this study will ensure compliance with Good Clinical Practice. The Investigator(s) will 
permit monitoring, audits, REC review, and regulatory inspections by providing the Sponsor(s), 
Regulators and REC direct access to source data and other documents (eg patients’ data collection 
forms, consent forms etc. Periodic remote monitoring will be carried out by the CI study team and the 
sponsor. Consent will be taken to permit access to data by external staff responsible for monitoring 
and auditing the project as a requirement of participating in the project. 
Remote monitoring of recruitment activities at all sites will be carried out periodically by the study 
management team (on behalf of the CI & Sponsor) by exploring data, consent forms, delegation logs 
& recruitment logs copied to the CI in accordance with the study SOPs. 
To facilitate this remote monitoring, and ensure timely and efficient transfer of data to the central study 
database, recruiting sites will be required to  
 
Transfer scanned (PDF) copies of completed data collection forms, consent forms & 
biochemistry results (via secure nhs.net email) to the CI site within 5 working days of the study 
visit. 
 
Transfer scanned (PDF) copies of recruitment logs & site specific delegation logs to the CI site 
on a monthly basis. 
Monitoring reports will be stored in the study site file and copied to the Sponsor.  
 
NHS Indemnity will apply. 
 
12. Importance and potential benefit 
Diabetes affects 6% of the adult UK population, accounts for 10% of NHS expenditure and is rapidly 
increasing in prevalence. Misclassification and subsequent suboptimal management are a widespread 
problem as reported by the Royal College of General Practitioners and NHS Diabetes (9), with an 
incorrect classification at diagnosis rarely revisited. This research therefore addresses a fundamental 
problem for one of the most common (and costly) conditions managed by the NHS, and has the 
potential to improve care for large numbers of patients. This project aims to directly improve patient 
care by for the first time integrating clinical features and biomarkers to allow clinicians to accurately 
assess classification and need for early insulin treatment at diabetes diagnosis. This will improve 
patient experience, outcomes and NHS costs by reducing inappropriate insulin treatment (where Type 
2 diabetes is misdiagnosed as Type 1), and improving early glycaemic control and healthcare 
utilisation (where Type 1 diabetes is misdiagnosed as Type 2).  
 

 
 
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13  
Public and Patient Involvement 
The study team has access to the user representative group of the NIHR Exeter Clinical Research 
Facility (ECRF).  In keeping with the NHS Patient Carer and Public Involvement (PCPI) strategy the 
ECRF invites user representatives to contribute to the development of various projects within its 
portfolio. These individuals have agreed to maintain contact and regular meetings have been 
established at which researchers discuss the development of current projects within the ECRF.  
The ECRF user group have been actively involved in the study design and wording of the patient-
facing documents for this study and the feasibility (pilot) study currently recruiting in Exeter.   
Study progress will be overseen by a steering group which will include external lay and patient 
members.  
 
14. Dissemination/implementation of research 
Results will be written up and submitted for publication in a peer-reviewed journal. Abstracts will be 
submitted to national and international conferences. Results will be presented to clinical colleagues at 
regular in-house meetings. Written information in the form of a letter outlining the key findings of the 
study will be sent to all participants.  
The output of this research will be immediately accessible post publication to clinicians and patients 
through a website calculator (www.diabetesgenes.org) and smartphone app (Diabetes Diagnostics). 
 
 
15. References 
 
1. 
NICE. CG15 Type 1 diabetes in adults: full guideline 2008. 
2. 
NICE. Type 2 diabetes: The management of type 2 diabetes. 2009. 
3. 
American Diabetes A. Standards of medical care in diabetes--2013. Diabetes care. 2013;36 
Suppl 1:S11-66. 
4. 
Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M, et al. Management 
of Hyperglycemia in Type 2 Diabetes: A Patient-Centered Approach: Position Statement of the 
American Diabetes Association (ADA) and the European Association for the Study of Diabetes 
(EASD). Diabetes care. 2012. 
5. 
DeWitt DE, Hirsch IB. Outpatient insulin therapy in type 1 and type 2 diabetes mellitus: 
scientific review. JAMA. 2003;289(17):2254-64. 
6. 
Effect of intensive therapy on residual beta-cell function in patients with type 1 diabetes in the 
diabetes control and complications trial. A randomized, controlled trial. The Diabetes Control and 
Complications Trial Research Group. Annals of internal medicine. 1998;128(7):517-23. 
7. 
Hope SV, Jones AG, Goodchild E, Shepherd M, Besser RE, Shields B, et al. Urinary C-peptide 
creatinine ratio detects absolute insulin deficiency in Type 2 diabetes. Diabetic medicine : a journal of 
the British Diabetic Association. 2013;30(11):1342-8. 
8. 
Yki-Jarvinen H. Combination therapies with insulin in type 2 diabetes. Diabetes care. 
2001;24(4):758-67. 

 
 
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9. 
RCGP. Coding, Classification and Diagnosis of Diabetes. A review of the coding, classification 
and diagnosis of diabetes in primary care in England with recommendations for improvement. NHS 
England; 2011. 
10. 
Tripathi A, Rizvi AA, Knight LM, Jerrell JM. Prevalence and impact of initial misclassification 
of pediatric type 1 diabetes mellitus. Southern medical journal. 2012;105(10):513-7. 
11. 
Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of 
overweight and obesity in the United States, 1999-2004. JAMA. 2006;295(13):1549-55. 
12. 
Writing Group for the SfDiYSG, Dabelea D, Bell RA, D'Agostino RB, Jr., Imperatore G, 
Johansen JM, et al. Incidence of diabetes in youth in the United States. JAMA. 2007;297(24):2716-24. 
13. 
Holden SH, Barnett AH, Peters JR, Jenkins-Jones S, Poole CD, Morgan CL, et al. The 
incidence of type 2 diabetes in the United Kingdom from 1991 to 2010. Diabetes, obesity & 
metabolism. 2013;15(9):844-52. 
14. 
Farmer A, Fox R. Diagnosis, classification, and treatment of diabetes. Bmj. 2011;342:d3319. 
15. 
Stone MA, Camosso-Stefinovic J, Wilkinson J, de Lusignan S, Hattersley AT, Khunti K. 
Incorrect and incomplete coding and classification of diabetes: a systematic review. Diabetic medicine : 
a journal of the British Diabetic Association. 2010;27(5):491-7. 
16. 
de Lusignan S, Khunti K, Belsey J, Hattersley A, van Vlymen J, Gallagher H, et al. A method 
of identifying and correcting miscoding, misclassification and misdiagnosis in diabetes: a pilot and 
validation study of routinely collected data. Diabetic medicine : a journal of the British Diabetic 
Association. 2010;27(2):203-9. 
17. 
Hope SV, Wienand-Barnett S, Shepherd M, King SM, Fox C, Khunti K, et al. Practical 
Classification Guidelines for Diabetes in patients treated with insulin: a cross-sectional study of the 
accuracy of diabetes diagnosis. Br J Gen Pract. 2016;66(646):e315-22. 
18. 
Scottish Diabetes Survey 2015. In: Group SDSM, editor.: NHS Scotland. 
19. 
Shields BM, Peters JL, Cooper C, Lowe J, Knight BA, Powell RJ, et al. Can clinical features be 
used to differentiate type 1 from type 2 diabetes? A systematic review of the literature. BMJ open. 
2015;5(11):e009088. 
20. 
Boyle JP, Engelgau MM, Thompson TJ, Goldschmid MG, Beckles GL, Timberlake DS, et al. 
Estimating prevalence of type 1 and type 2 diabetes in a population of African Americans with diabetes 
mellitus. Am J Epidemiol. 1999;149(1):55-63. 
21. 
Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Kirkman MS, et al. Guidelines and 
recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. 
Clinical chemistry. 2011;57(6):e1-e47. 
22. 
Weedon MN HA, McDonald TJ, Patel KA, Jones A, Hattersley AT, Oram R. A novel 
inexpensive test can discriminate between Type 1 and Type 2 diabetes. Diabetic Medicine. 2015;32:22. 
23. 
Oram RA, Patel K, Hill A, Shields B, McDonald TJ, Jones A, et al. A Type 1 Diabetes Genetic 
Risk Score Can Aid Discrimination Between Type 1 and Type 2 Diabetes in Young Adults. Diabetes 
care. 2015. 
24. 
Adams ST, Leveson SH. Clinical prediction rules. Bmj. 2012;344:d8312. 
25. 
Shields BM, Hicks S, Shepherd MH, Colclough K, Hattersley AT, Ellard S. Maturity-onset 
diabetes of the young (MODY): how many cases are we missing? Diabetologia. 2010;53(12):2504-8. 
26. 
Shields BM, McDonald TJ, Ellard S, Campbell MJ, Hyde C, Hattersley AT. The development 
and validation of a clinical prediction model to determine the probability of MODY in patients with 
young-onset diabetes. Diabetologia. 2012. 
27. 
Jones AG, Hattersley AT. The clinical utility of C-peptide measurement in the care of patients 
with diabetes. Diabetic medicine : a journal of the British Diabetic Association. 2013;30(7):803-17. 
28. 
Steffes MW, Sibley S, Jackson M, Thomas W. Beta-cell function and the development of 
diabetes-related complications in the diabetes control and complications trial. Diabetes care. 
2003;26(3):832-6. 

 
 
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29. 
Gold AE, MacLeod KM, Frier BM. Frequency of severe hypoglycemia in patients with type I 
diabetes with impaired awareness of hypoglycemia. Diabetes care. 1994;17(7):697-703. 
30. 
Clarke WL, Cox DJ, Gonder-Frederick LA, Julian D, Schlundt D, Polonsky W. Reduced 
awareness of hypoglycemia in adults with IDDM. A prospective study of hypoglycemic frequency and 
associated symptoms. Diabetes care. 1995;18(4):517-22. 
31. 
Connor KM, Davidson JR. Development of a new resilience scale: the Connor-Davidson 
Resilience Scale (CD-RISC). Depression and anxiety. 2003;18(2):76-82. 
32. 
Gandek B, Ware JE, Aaronson NK, Apolone G, Bjorner JB, Brazier JE, et al. Cross-validation 
of item selection and scoring for the SF-12 Health Survey in nine countries: results from the IQOLA 
Project. International Quality of Life Assessment. Journal of clinical epidemiology. 1998;51(11):1171-
8. 
33. 
Flahault A, Cadilhac M, Thomas G. Sample size calculation should be performed for design 
accuracy in diagnostic test studies. Journal of clinical epidemiology. 2005;58(8):859-62. 
34. 
Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable 
prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Annals of 
internal medicine. 2015;162(1):55-63. 
35. 
Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig LM, et al. The STARD 
statement for reporting studies of diagnostic accuracy: explanation and elaboration. Annals of internal 
medicine. 2003;138(1):W1-12. 
 
 
 
 
 
 
 
 
 
16.  
SCHEDULE OF AMENDMENTS 
Amendment 
No. 
Protocol 
version no. 
Date issued 
Author(s) of 
changes 
Details of changes made 
SA01 
2.0 
13jan17 
Dr Angus 
Jones 
Extend recruitment to patients 
diagnosed over the age of 50 who are 
insulin treated and recruit an additional 
400 patients to this older cohort of 
patients. 
SA02 
3.0 
19 Sept 17 
Dr Angus 
Jones 
Extend recruitment of the cohort 
diagnosed at >50 years to include 400 
non-insulin treated patients to be 
recruited at selected sites.  
Remove the word ‘young’ from the full 
study title to reflect the inclusion of the 
older cohort and avoid any 
misunderstanding of inclusion criteria. 
Extend the recruitment period to 30th 
September 2018 and increase the 
number of recruiting sites. 

 
 
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Funder details updated to include 
additional grant from Diabetes UK. 
Update the AE/SAE reporting 
procedures in line with Sponsor 
requirements and agreement. 
Update the means of contact for follow-
up visits to include by post or in person. 
Promotion of the study on appropriate 
website and social media. 
SA03 
4.0 
28 Aug 18 
Dr Angus 
Jones 
To extend the recruitment period to 31 
December 2019. 
To modify recruitment criteria for cohort 
1 to continue to recruit a further 200 
participants receiving insulin treatment 
diagnosed after age 30 (total cohort 1 
participants 1200), and cease recruiting 
other participants in this cohort. 
To include the option of capillary blood 
collection for C-peptide analysis at 
follow-up in accordance with participant 
preference.