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

Anita Lynam, Timothy McDonald, Anita Hill, John Dennis, Richard Oram, Ewan Pearson, Michael Weedon, Andrew Hattersley, Katharine Owen, Beverley Shields, Angus Jones

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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.

Original languageEnglish
Article numbere031586
Pages (from-to)1-10
Number of pages10
JournalBMJ Open
Volume9
Issue number9
DOIs
Publication statusPublished - 26 Sep 2019

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Type 1 Diabetes Mellitus
Insulin
Area Under Curve
Therapeutics
ROC Curve
Biomarkers
Secondary Care
C-Peptide
Autoantibodies
Type 2 Diabetes Mellitus
Primary Health Care
Body Mass Index
Logistic Models
Regression Analysis
Research Personnel
Outcome Assessment (Health Care)
Antigens

Keywords

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

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Lynam, Anita ; McDonald, Timothy ; Hill, Anita ; Dennis, John ; Oram, Richard ; Pearson, Ewan ; Weedon, Michael ; Hattersley, Andrew ; Owen, Katharine ; Shields, Beverley ; Jones, Angus. / Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years. In: BMJ Open. 2019 ; Vol. 9, No. 9. pp. 1-10.
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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.",
keywords = "C-peptide, Classification, GADA, IA-2A, Type 1 Diabetes Genetic Risk Score, Type 1 diabetes, Type 2 diabetes",
author = "Anita Lynam and Timothy McDonald and Anita Hill and John Dennis and Richard Oram and Ewan Pearson and Michael Weedon and Andrew Hattersley and Katharine Owen and Beverley Shields and Angus Jones",
note = "Funding - NIHR Oxford Biomedical Research Centre. The Diabetes Alliance for Research in England (DARE) study was funded by the Wellcome Trust and supported by the Exeter NIHR Clinical Research Facility. The MASTERMIND study was funded by the UK Medical Research Council (MR/N00633X/) and supported by the NIHR Exeter Clinical Research Facility. The PRIBA study was funded by the National Institute for Health Research (NIHR) (UK) (DRF-2010-03-72) and supported by the NIHR Exeter Clinical Research Facility. TJM is a National Institute for Health Research Senior Clinical Senior Lecturer. RAO is supported by a Diabetes UK Harry Keen Fellowship (16/0005529). ERP is a Wellcome Trust New Investigator (102820/Z/13/Z). ATH and BMS are supported by the NIHR Exeter Clinical Research Facility. ATH is a Wellcome Trust Senior Investigator and NIHR Senior Investigator. AGJ is supported by an NIHR Clinician Scientist award (CS-2015-15-018). This publication presents independent research funded by the National Institute for Health Research (NIHR). The views expressed are those of the author and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The study sponsor was not involved in the design of the study; the collection, analysis and interpretation of data; writing the report; or the decision to submit the report for publication. Competing interests N{\circledC} Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ.",
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Lynam, A, McDonald, T, Hill, A, Dennis, J, Oram, R, Pearson, E, Weedon, M, Hattersley, A, Owen, K, Shields, B & Jones, A 2019, 'Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years', BMJ Open, vol. 9, no. 9, e031586, pp. 1-10. https://doi.org/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. / Lynam, Anita; McDonald, Timothy; Hill, Anita; Dennis, John; Oram, Richard; Pearson, Ewan; Weedon, Michael; Hattersley, Andrew; Owen, Katharine; Shields, Beverley; Jones, Angus.

In: BMJ Open, Vol. 9, No. 9, e031586, 26.09.2019, p. 1-10.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years

AU - Lynam, Anita

AU - McDonald, Timothy

AU - Hill, Anita

AU - Dennis, John

AU - Oram, Richard

AU - Pearson, Ewan

AU - Weedon, Michael

AU - Hattersley, Andrew

AU - Owen, Katharine

AU - Shields, Beverley

AU - Jones, Angus

N1 - Funding - NIHR Oxford Biomedical Research Centre. The Diabetes Alliance for Research in England (DARE) study was funded by the Wellcome Trust and supported by the Exeter NIHR Clinical Research Facility. The MASTERMIND study was funded by the UK Medical Research Council (MR/N00633X/) and supported by the NIHR Exeter Clinical Research Facility. The PRIBA study was funded by the National Institute for Health Research (NIHR) (UK) (DRF-2010-03-72) and supported by the NIHR Exeter Clinical Research Facility. TJM is a National Institute for Health Research Senior Clinical Senior Lecturer. RAO is supported by a Diabetes UK Harry Keen Fellowship (16/0005529). ERP is a Wellcome Trust New Investigator (102820/Z/13/Z). ATH and BMS are supported by the NIHR Exeter Clinical Research Facility. ATH is a Wellcome Trust Senior Investigator and NIHR Senior Investigator. AGJ is supported by an NIHR Clinician Scientist award (CS-2015-15-018). This publication presents independent research funded by the National Institute for Health Research (NIHR). The views expressed are those of the author and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The study sponsor was not involved in the design of the study; the collection, analysis and interpretation of data; writing the report; or the decision to submit the report for publication. Competing interests N© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ.

PY - 2019/9/26

Y1 - 2019/9/26

N2 - 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.

AB - 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.

KW - C-peptide

KW - Classification

KW - GADA

KW - IA-2A

KW - Type 1 Diabetes Genetic Risk Score

KW - Type 1 diabetes

KW - Type 2 diabetes

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U2 - 10.1136/bmjopen-2019-031586

DO - 10.1136/bmjopen-2019-031586

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JO - BMJ Open

JF - BMJ Open

SN - 2044-6055

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