Literature DB >> 28744894

External national validation of the Leicester Self-Assessment score for Type 2 diabetes using data from the English Longitudinal Study of Ageing.

S R Barber1,2, N N Dhalwani3, M J Davies2,3, K Khunti2,3, L J Gray1.   

Abstract

AIMS: To validate the Leicester Self-Assessment score using a representative English dataset for detecting prevalent non-diabetic hyperglycaemia or undiagnosed Type 2 diabetes (defined as HbA1c ≥6.0%) and for identifying those who may go on to develop Type 2 diabetes within 10 years.
METHODS: Data were taken from the English Longitudinal Study of Ageing, a nationally representative dataset of people aged ≥50 years. The area under the receiver-operator curve and performance metrics for the score at the recommended score threshold (≥16), were calculated for the outcomes of HbA1c ≥42 mmol/mol (6.0%) at baseline and self-reported Type 2 diabetes within 10 years in those aged 50-75 years at baseline.
RESULTS: A total of 3203 individuals had a baseline HbA1c measurement, of whom 247 (7.7%) had an HbA1c concentration ≥42 mmol/mol (6.0%). The area under the receiver-operator curve was 69.4% (95% CI 66.0-72.9) for baseline HbA1c ≥42 mmol/mol. A total of 3550 individuals had diabetes status recorded at 10 years, of whom 324 (9.1%) were diagnosed with Type 2 diabetes within this time; the area under the receiver-operator curve for this outcome was 74.9% (95% CI 72.4-77.5). The score threshold of ≥16 had a sensitivity of 89.2% (95% CI 85.3-92.4) and a specificity of 42.3% (95% CI 40.5-44.0) for Type 2 diabetes within 10 years.
CONCLUSIONS: The Leicester Self-Assessment score is validated for use across England to identify people with non-diabetic hyperglycaemia or undiagnosed Type 2 diabetes. Those with a high score are at high risk of developing diabetes in the future.
© 2017 Diabetes UK.

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Year:  2017        PMID: 28744894     DOI: 10.1111/dme.13433

Source DB:  PubMed          Journal:  Diabet Med        ISSN: 0742-3071            Impact factor:   4.359


  2 in total

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Authors:  Yingmin Zhang; Xinhua Qiao; Lihui Liu; Wensheng Han; Qinghua Liu; Yuanyuan Wang; Ting Xie; Yiheng Tang; Tiepeng Wang; Jiao Meng; Aojun Ye; Shunmin He; Runsheng Chen; Chang Chen
Journal:  Redox Biol       Date:  2022-06-30       Impact factor: 10.787

2.  Using Wearable Activity Trackers to Predict Type 2 Diabetes: Machine Learning-Based Cross-sectional Study of the UK Biobank Accelerometer Cohort.

Authors:  Benjamin Lam; Michael Catt; Sophie Cassidy; Jaume Bacardit; Philip Darke; Sam Butterfield; Ossama Alshabrawy; Michael Trenell; Paolo Missier
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  2 in total

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