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Risk Assessment Tools for Identifying Individuals at Risk of Developing Type 2 Diabetes
Open accessAbstract
Trials have demonstrated the preventability of type 2 diabetes through lifestyle modifications or drugs in people with impaired glucose tolerance. However, alternative ways of identifying people at risk of developing diabetes are required. Multivariate risk scores have been developed for this purpose. This article examines the evidence for performance of diabetes risk scores in adults by 1) systematically reviewing the literature on available scores and 2) their validation in external populations; and 3) exploring methodological issues surrounding the development, validation, and comparison of risk scores. Risk scores show overall good discriminatory ability in populations for whom they were developed. However, discriminatory performance is more heterogeneous and generally weaker in external populations, which suggests that risk scores may need to be validated within the population in which they are intended to be used. Whether risk scores enable accurate estimation of absolute risk remains unknown; thus, care is needed when using scores to communicate absolute diabetes risk to individuals. Several risk scores predict diabetes risk based on routine noninvasive measures or on data from questionnaires. Biochemical measures, in particular fasting plasma glucose, can improve prediction of such models. On the other hand, usefulness of genetic profiling currently appears limited.
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“In addition, risk scores for incident diabetes based on a combination of non-invasive or blood-based risk factors are under development to identify individuals at high risk of developing diabetes.6”
From Prediabetes: a high-risk state for diabetes development (2012) · cites this paper as [6]
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Flagged first, then by citations
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- Pearson, 2003, The American journal of managed care, 9, 57
- 2007, ERNAHRUNGSUMSCHAU, 54, 122
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- Alssema, 2008, Nederlands tijdschrift voor geneeskunde, 152, 2418
- 2007, ERNAHRUNGS UMSCH, 54, 698
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Metadata
- Authors
- Buijsse, B., Simmons, R. K., Griffin, S. J., & Schulze, M. B.
- Affiliations
- German Institute of Human Nutrition (DE)
- Corresponding author
- Schulze, M. B.
- Venue
- Epidemiologic Reviews
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-05-27
- Volume
- 33
- Issue
- 1
- Pages
- 46-62
- ISSN
- 0193-936X (ISSN-L)
- 1478-6729
- PMC
- PMC3132807
- OpenAlex
- W2113648435
- Cited by
- 297
- References
- 130
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 12 · 2023: 12 · 2022: 21 · 2021: 11 · 2020: 16 · 2019: 13 · 2018: 17 · 2017: 19 · 2016: 22 · 2015: 31 · 2014: 26 · 2013: 35 · 2012: 29
- FWCI
- 4.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Epidemiologic Reviews (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institute for Health and Care Research (RP-PG-0606-1259)
- European Commission (LSHM_CT_2006_037197)
- Medical Research Council
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- type 2 diabetes, diabetes risk score, risk prediction, impaired glucose tolerance, external validation, discriminatory ability, absolute risk estimation, fasting blood glucose, genetic profiling, diabetes prevention, risk communication
- MeSH terms
- Diabetes Mellitus, Type 2, Humans, Predictive Value of Tests, Surveys and Questionnaires, Risk Factors, Reproducibility of Results, Risk Assessment, Genetic Predisposition to Disease
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.
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