Risk models and scores for type 2 diabetes: systematic review

Abstract

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. ConclusionMuch 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.

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

“…The ultimate aim of these interventions is the prevention or the delay of the onset of diabetes-related macroand microvascular complications that often lead to considerable morbidity and premature death, but a considerable number of individuals who could benefit from such interventions are not aware of their disease risk. Numerous prognostic models and scores for type 2 diabetes have been developed [1][2][3] based on known risk factors, including age, sex, obesity, metabolic and lifestyle factors, family history of diabetes or ethnic background. Given that the performance of these risk scores is often far from perfect, it is desirable to identify novel prognostic factors, such as biomarkers from '-omics' technologies, with the aim of achieving better model accuracy.…”

International Variation in Outcomes Among People with Cardiovascular Disease or Cardiovascular Risk Factors and Impaired Glucose Tolerance: Insights from the NAVIGATOR Trial

“…Externally validated risk‐prediction models use factors such as age, sex, ethnicity, body mass index, waist circumference, family history of diabetes mellitus, systolic blood pressure, and high‐density lipoprotein cholesterol, among other factors, to predict the risk of developing diabetes mellitus 18. Despite the prespecified inclusion and exclusion criteria, the significant variability in the baseline characteristics of patients enrolled based on geographic region combined with unmeasured and unrecognized social, physical activity participation, health care delivery, and genetic factors likely contributes to the different risks of diabetes mellitus that we described.…”

Preventing Type 2 Diabetes in Communities Across the U.S.

“…21 However, epidemiologic and intervention studies point to four natural tiers (Table 1). At the first tier, adults with fasting plasma glucose (FPG) >110 mg/dL; HbA1c levels >5.7%; 2-hour glucose >140 mg/dL; a history of gestational diabetes; or a predicted 10-year diabetes incidence of 30% or more are particularly good candidates for structured lifestyle interventions in community settings as described in the National DPP, owing to their very high risk and potential to benefit.…”

“…First, the majority of cases of type 2 diabetes over a 5-10-year period occur among those with hyperglycemia or a clustering of several major risk factors, such as obesity, hypertension, increased age, and history of gestational diabetes. 21,23 Second, the strongest and clearest evidence for diabetes prevention comes from RCTs conducted among people with impaired glucose tolerance. By contrast, no major RCTs have examined and demonstrated the impact of diabetes prevention among those with normal glucose tolerance.…”