DESCARGAR
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.
