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BMJ Open. 2019 Sep 26;9(9):e031586. doi: 10.1136/bmjopen-2019-031586.

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

Author information

1
The Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK.
2
Department of Clinical Biochemistry, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.
3
Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.
4
Molecular and Clinical Medicine, University of Dundee, Dundee, UK.
5
Macleod Diabetes and Endocrine Centre, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.
6
Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, UK.
7
Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Foundation Trust, Oxford, UK.
8
The Institute of Biomedical & Clinical Science, University of Exeter Medical School, Exeter, UK Angus.Jones@exeter.ac.uk.

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.

KEYWORDS:

C-peptide; Classification; GADA; IA-2A; Type 1 Diabetes Genetic Risk Score; Type 1 diabetes; Type 2 diabetes

PMID:
31558459
DOI:
10.1136/bmjopen-2019-031586
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Conflict of interest statement

Competing interests: None declared.

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