Using routine clinical features to classify adult-onset diabetes at diagnosis: the StartRight prospective observational study
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Background
It is not known which clinical features optimally differentiate type 1 and 2 diabetes at diagnosis. We aimed to determine which clinical features differentiate these conditions at diagnosis and develop classification models combining clinical features with and without islet-autoantibodies.
Methods
In this prospective cohort study, we recruited 1,800 adults (aged ≥18 years) diagnosed with diabetes within the preceding 12 months, excluding secondary or monogenic diabetes. The primary outcome was diabetes subtype defined by a combination of insulin treatment and endogenous insulin production (measured by C-peptide) at ≥3 years post-diagnosis. Models were developed in participants aged 18–50 years and validated internally, alongside validation in an older cohort (aged >50 years) and using UK primary care data (n=188,232).
Results
Eleven clinical features and routinely measured biomarkers discriminated type 1 from type 2 diabetes independently of diagnosis age and BMI. Lower age of diagnosis, BMI and waist-hip ratio, unintentional weight-loss, and higher presentation HbA1c or glucose were the most discriminative, with other features only weakly discriminative. Models integrating routine features with and without islet-autoantibodies, developed in those age 18–50 years at diabetes diagnosis, had high performance in internal validation (clinical features only: Area Under the Receiver Operating Characteristic curve (AUCROC) (95% CI) 0.94 (0.93, 0.96), clinical features and islet-autoantibodies: AUCROC 0.97 (0.96, 0.98), and maintained high discrimination in older adults (age >50 years AUCROC 0.93 (0.90, 0.96), and 0.97 (0.94, 0.99). Simplifying the models to a points-based score resulted in similar performance. In primary care data models and score were strongly predictive of outcomes associated with type 1 diabetes, including in those initially treated as type 2.
Interpretation
Lower age-at-diagnosis, BMI, and wait-hip ratio, unintentional weight loss and high presentation glycaemia are the most discriminative features for diagnosis of type 1 diabetes in adults. Models combining routine clinical features, with or without islet-autoantibodies, have high accuracy and could assist clinical classification and prioritisation of classification biomarker testing.
Funding
UK National Institute of Health and Care Research (NIHR) and Diabetes UK.
Research in context
Evidence before this study
Most type 1 diabetes occurs in adults, but differentiating it from type 2 diabetes, which is much more common, is challenging, and misclassification is common. Two systematic reviews, published in 2015 (Shields et al.) and 2022 (UK National Institute for Health and Care Excellence) have identified that age at diagnosis and BMI are the only clinical features available at diagnosis which robustly discriminate type 1 and 2 diabetes, based on cross sectional studies, with rapid progression to insulin after diagnosis also discriminative. Many other features included in textbooks and guidelines have little supporting evidence. Guideline bodies, including the UK National Institute for Health and Care Excellence (NICE), have therefore identified the need for evidence on what features discriminate type 1 and 2 diabetes and how they can be combined. We repeated a search of PubMed and Google Scholar for articles published since the NICE review (1 st January 2021 to 3rd June 2026), alongside citation search of previous key articles, and identified no additional studies.
Added value of this study
This is the first study to prospectively assess utility of clinical features for diabetes subtype diagnosis and the first to develop classification models for adult-onset type 1 and 2 diabetes at diagnosis. The five most discriminative routine clinical features for distinguishing type 1 from type 2 diabetes at diagnosis are age-at-diagnosis, BMI, waist-hip ratio, pre-diagnosis unintentional weight-loss, and presentation glycaemia (HbA1c or glucose). Many features included in current guidelines were only very weakly discriminative of subtype, and no single clinical feature was able to adequately differentiate between type 1 and type 2 diabetes alone. A clinical prediction model combining nine routinely available clinical features, with or without islet-autoantibodies, as both a prototype calculator and a points-based score (the StartRight Score), had high accuracy in differentiating type 1 from type 2 diabetes and outperforms current clinical guidance and islet-autoantibody assessment alone.
Implications of all available evidence
Differentiating type 1 and 2 diabetes at diagnosis of adult-onset diabetes is challenging and misclassification common. Lower age-at-diagnosis, BMI, and wait-hip ratio, unintentional weight loss and high presentation glycaemia are the most discriminative features for diagnosis of adult-onset type 1 diabetes but are inadequate in isolation. Models combining clinical features with or without islet autoantibodies have potential to assist clinical classification and prioritisation of classification biomarker testing.
