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Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years

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Lynam, Anita, McDonald, Timothy, Hill, Anita, Dennis, John, Oram, Richard, Pearson, Ewan, Weedon, Michael, Hattersley, Andrew +3 more·2019·BMJ Open
DOI 10.1136/bmjopen-2019-031586PMC PMC6773323
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Abstract

OBJECTIVE: To develop and validate multivariable clinical diagnostic models to assist distinguishing between type 1 and type 2 diabetes in adults aged 18-50. DESIGN: Multivariable logistic regression analysis was used to develop classification models integrating five pre-specified predictor variables, including clinical features (age of diagnosis, body mass index) and clinical biomarkers (GADA and Islet Antigen 2 islet autoantibodies, Type 1 Diabetes Genetic Risk Score), to identify type 1 diabetes with rapid insulin requirement using data from existing cohorts. SETTING: UK cohorts recruited from primary and secondary care. PARTICIPANTS: 1352 (model development) and 582 (external validation) participants diagnosed with diabetes between the age of 18 and 50 years of white European origin. MAIN OUTCOME MEASURES: Type 1 diabetes was defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (C-peptide <200 pmol/L). Type 2 diabetes was defined by either a lack of rapid insulin requirement or, where insulin treated within 3 years, retained endogenous insulin secretion (C-peptide >600 pmol/L at ≥5 years diabetes duration). Model performance was assessed using area under the receiver operating characteristic curve (ROC AUC), and internal and external validation. RESULTS: Type 1 diabetes was present in 13% of participants in the development cohort. All five predictor variables were discriminative and independent predictors of type 1 diabetes (p<0.001 for all) with individual ROC AUC ranging from 0.82 to 0.85. Model performance was high: ROC AUC range 0.90 (95% CI 0.88 to 0.93) (clinical features only) to 0.97 (95% CI 0.96 to 0.98) (all predictors) with low prediction error. Results were consistent in external validation (clinical features and GADA ROC AUC 0.93 (0.90 to 0.96)). CONCLUSIONS: Clinical diagnostic models integrating clinical features with biomarkers have high accuracy for identifying type 1 diabetes with rapid insulin requirement, and could assist clinicians and researchers in accurately identifying patients with type 1 diabetes.

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  • In people with clinical features suggesting type 1 diabetes, the presence of one or more positive islet autoantibodies is highly predictive of rapid progression and severe insulin deficiency.

    “In people with clinical features suggesting type 1 diabetes, the presence of one or more positive islet autoantibodies is highly predictive of rapid progression and severe insulin deficiency and these individuals should be considered to have type 1 diabetes, even if they did not require insulin at diagnosis (23,24).”

    From The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) (2021) · cites this paper as [24]

    The Results report that positive islet autoantibodies were more common in participants meeting the study’s criteria for type 1 diabetes (71% versus 5%), supporting their diagnostic value. But the paper says these features overlap between diabetes types and describes GADA-positive participants who took more than three years to start insulin, suggesting slow-onset autoimmune diabetes. It therefore does not support treating antibody positivity alone as highly predictive of rapid progression in every person with suggestive clinical features. (LLM, conf=0.86)

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  • Positive GAD antibodies may be found at a low level in adults without autoimmune diabetes and false positive results may occur.

    “As positive GAD antibodies may be found at a low level in adults without autoimmune diabetes and false positive results may occur, GAD should only be measured in those suspected to have type 1 diabetes (24).”

    From The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) (2021) · cites this paper as [24]

    The Methods (Laboratory measurement; page unavailable) set the GADA positivity threshold at the 97.5th percentile of controls without diabetes and report assay specificity below 100%, supporting the possibility of positive results in people without diabetes and false positives. The Introduction also notes antibody positivity in some patients with a type 2 diabetes phenotype. The paper does not establish that such results occur specifically at low levels in adults without autoimmune diabetes, or recommend measuring GAD only when type 1 diabetes is suspected. (LLM, conf=0.83)

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Research works printed but not linked: 3

  1. [2]National Institute for Health and Care Excellence Type 1 diabetes in adults: diagnosis and management (NICE guideline NG17), 2015 Available: https://www.nice.org.uk/guidance/ng17 [Accessed 14 Aug 2018]. 26334079
  2. [3]National Institute for Health and Care Excellence Type 2 diabetes in adults: management (NICE guideline NG28), 2015 Available: https://www.nice.org.uk/guidance/ng28 [Accessed 14 Aug 2018]. 26741015
  3. [37]Harrell F Regression modeling strategies. Switzerland: Springer International Publishing, 2015.

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Authors
Lynam, A., McDonald, T., Hill, A., Dennis, J., Oram, R., Pearson, E., Weedon, M., Hattersley, A., Owen, K., Shields, B., & Jones, A.
Affiliations
  • University of Exeter (GB)
  • University of Dundee (GB)
  • University of Oxford (GB)
Corresponding author
Jones, Angus
ORCID
Venue
BMJ Open
Publisher
BMJ
Published
2019-09
Volume
9
Issue
9
Pages
e031586
ISSN
2044-6055 (ISSN-L)
DOI
10.1136/bmjopen-2019-031586
PMC
PMC6773323
OpenAlex
W2977126534
Cited by
72
References
38
Citations by year
2026: 7 · 2025: 8 · 2024: 12 · 2023: 14 · 2022: 13 · 2021: 8 · 2020: 7 · 2019: 3
FWCI
4.66
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • BMJ Open (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Discovery Research Portal (University of Dundee) (acceptedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • HighWire Press Open Archive (submittedVersion)
  • Open Research Exeter (University of Exeter) (submittedVersion, cc-by)
  • Royal Devon and Exeter Research Repository (Royal Devon and Exeter) (submittedVersion, cc-by)
Funders
Research Trainees Coordinating Centre (CS-2015-15-018)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
Keywords
type 1 diabetes, type 2 diabetes, diabetes classification, GADA, islet autoantibodies, C-peptide, body mass index, age at diagnosis
MeSH terms
Adolescent, Adult, Autoantibodies, C-Reactive Protein, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Female, Glutamate Decarboxylase, Humans, Insulin, Male, Middle Aged, Models, Biological, Risk Factors, ROC Curve, Reproducibility of Results, Cohort Studies, Body Mass Index, Prevalence, Area Under Curve, Genetic Predisposition to Disease, White People, Young Adult

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