ArticleDiabetologia2012
Detection of impaired glucose regulation and/or type 2 diabetes mellitus, using primary care electronic data, in a multiethnic UK community setting.
Article in Diabetologia, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Examining the Use of Glucose and Physical Activity Self-Monitoring Technologies in Individuals at Moderate to High Risk of Developing Type 2 Diabetes: Randomized Trial.JMIR mHealth and uHealth · 2019Trial
- Associations of Physical Behaviours and Behavioural Reallocations with Markers of Metabolic Health: A Compositional Data Analysis.International journal of environmental research and public health · 2018Trial
- Trial
- Trial
- Engagement, Retention, and Progression to Type 2 Diabetes: A Retrospective Analysis of the Cluster-Randomised "Let's Prevent Diabetes" Trial.PLoS medicine · 2016Trial
- Trial
- Trial
- Associations of objectively measured sedentary behaviour and physical activity with markers of cardiometabolic health.Diabetologia · 2013Trial
- Fruit and vegetable intake and the association with glucose parameters: a cross-sectional analysis of the Let's Prevent Diabetes Study.European journal of clinical nutrition · 2013Trial
- Walking away from type 2 diabetes: trial protocol of a cluster randomised controlled trial evaluating a structured education programme in those at high risk of developing type 2 diabetes.BMC family practice · 2012Trial
- Let's prevent diabetes: study protocol for a cluster randomised controlled trial of an educational intervention in a multi-ethnic UK population with screen detected impaired glucose regulation.Cardiovascular diabetology · 2012Trial
- Screening Tools for Early Identification of Adults at High Risk of Type 2 Diabetes: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Developing a Simple Non-Laboratory-Based Machine Learning Tool for Prediabetes Screening in a Target Population: A Proof-of-Concept Study.Journal of diabetes science and technology · 2025Article
- Multiplicative versus additive modelling of causal effects using instrumental variables for survival outcomes - a comparison.Statistical methods in medical research · 2025Article
- Long-term ambient air pollution exposure and prospective change in sedentary behaviour and physical activity in individuals at risk of type 2 diabetes in the UK.Journal of public health (Oxford, England) · 2024Article
- Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Development and Validation of a Risk Score for Diabetes Screening in Oman.Oman medical journal · 2022Article
- Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire.Computational and mathematical methods in medicine · 2022Article
- Modelling the Reallocation of Time Spent Sitting into Physical Activity: Isotemporal Substitution vs. Compositional Isotemporal Substitution.International journal of environmental research and public health · 2021Article
- Performance of Risk Assessment Models for Prevalent or Undiagnosed Type 2 Diabetes Mellitus in a Multi-Ethnic Population-The Helius Study.Global heart · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
aims/hypothesisThe aim of this study was to develop and validate a score for detecting the glycaemic categories of impaired glucose regulation (IGR) and type 2 diabetes using the WHO 2011 diagnostic criteria.
methodsWe used data from 6,390 individuals aged 40-75 years from a multiethnic population based screening study. We developed a logistic regression model for predicting IGR and type 2 diabetes (diagnosed using OGTT or HbA(1c) ≥ 6.5% [48 mmol/mol]) from data which are routinely stored in primary care. We developed the score by summing the β coefficients. We externally validated the score using data from 3,225 participants aged 40-75 years screened as part of another study.
resultsThe score includes age, ethnicity, sex, family history of diabetes, antihypertensive therapy and BMI. Fifty per cent of a population would need to be invited for testing to detect type 2 diabetes mellitus on OGTT with 80% sensitivity; this is slightly raised to 54% that need to be invited if using HbA(1c). Inviting the top 10% for testing, 9% of these would have type 2 diabetes mellitus using an OGTT (positive predictive value [PPV] 8.9% [95% CI 5.8%,12.8%]), 26% would have IGR (PPV 25.9% [95% CI 20.9%, 31.4%]). Using HbA(1c) increases the PPV to 19% for type 2 diabetes mellitus (PPV 18.6% [95% CI 14.2%, 23.7%]) and 28% for an HbA(1c) between 6.0% and 6.4% (PPV 28.3% [95% CI 23.1%, 34.0%]).
conclusionsThe score can be used to reliably identify those with undiagnosed IGR and type 2 diabetes in multiethnic populations. This is the first score developed taking into account HbA(1c) in the diagnosis of type 2 diabetes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.