Risk models and scores for type 2 diabetes: systematic review
Open accessAbstract
OBJECTIVE: To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. DESIGN: Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. DATA SOURCES: Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. DATA EXTRACTION: Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. RESULTS: 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as "simple" or "easily implemented," although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. CONCLUSION: Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk "hotspots" for targeted public health interventions.
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Fasting glucose is a core component of most type 2 diabetes risk assessment models.
“Fasting glucose is a core component of most T2DM risk assessment models (5)”
From The effectiveness of digital health intervention on glycemic control and physical activity in patients with type 2 diabetes: a systematic review and meta-analysis (2025) · cites this paper as [5]
The Results > Quantitative findings table lists fasting plasma glucose as a predictor in several diabetes risk models. But the Results > Qualitative findings section describes FINDRISC as having no laboratory data, and the cited passages do not establish that fasting glucose is a component of most models. The claim’s “most” qualifier is therefore unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measuredHbA1c is included in the American Diabetes Association diagnostic criteria for diabetes.
“HbA1c is included in the American Diabetes Association diagnostic criteria for diabetes (5, 6)”
From The effectiveness of digital health intervention on glycemic control and physical activity in patients with type 2 diabetes: a systematic review and meta-analysis (2025) · cites this paper as [5]
The Results section lists haemoglobin A1c among measures used to diagnose diabetes in some studies, and the Quantitative findings section lists it in risk-prediction models (page not provided). Neither passage says it is included in the American Diabetes Association's diagnostic criteria. Searches for ADA diagnostic criteria and an HbA1c diagnostic threshold found no passage establishing that connection. (LLM, conf=0.72)
Experimental — accuracy not yet measuredThere is no single universally accepted diabetes prediction model, and recalibration may be necessary when prediction algorithms are applied to different populations because ethnicity is strongly related to diabetes risk.
“There is no single universally accepted diabetes prediction model and given that ethnicity is strongly related to diabetes risk, recalibration of prediction algorithms may be necessary when they are applied to different populations.30”
From Prediabetes: a high-risk state for diabetes development (2012) · cites this paper as [30]
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- Authors
- Noble, D., Mathur, R., Dent, T., Meads, C., & Greenhalgh, T.
- Affiliations
- Queen Mary University of London (GB)
- PHG Foundation (GB)
- Corresponding author
- Noble, D.
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2011-11-28
- Volume
- 343
- Issue
- nov28 1
- Pages
- d7163-d7163
- ISSN
- 0007-1447 (ISSN-L)
- 0959-8138
- 1468-5833
- PMC
- PMC3225074
- OpenAlex
- W2008035769
- Cited by
- 551
- References
- 91
- Citations by year
- 2026: 20 · 2025: 22 · 2024: 17 · 2023: 30 · 2022: 32 · 2021: 49 · 2020: 48 · 2019: 41 · 2018: 37 · 2017: 40 · 2016: 34 · 2015: 54 · 2014: 54 · 2013: 45 · 2012: 26
- FWCI
- 5.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- BMJ (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by-nc)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by-nc)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Keywords
- type 2 diabetes, diabetes risk prediction, risk score, risk prediction model, external validation, discrimination and calibration, genetic markers, targeted public health interventions, risk hotspots
- MeSH terms
- Adult, Aged, Aged, 80 and over, Diabetes Mellitus, Type 2, Humans, Middle Aged, Prognosis, Risk Factors, Reproducibility of Results, Models, Statistical, Risk Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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