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Genet Med. 2013 Aug;15(8):612-7. doi: 10.1038/gim.2013.9. Epub 2013 Mar 14.

Recommendations from the EGAPP Working Group: does genomic profiling to assess type 2 diabetes risk improve health outcomes?

Abstract

SUMMARY OF RECOMMENDATIONS:

The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group (EWG) found insufficient evidence to recommend testing for predictive variants in 28 variants (listed in Table 1) to assess risk for type 2 diabetes in the general population, on the basis of studies in populations of northern European descent. The EWG found that the magnitude of net health benefit from the use of any of these tests alone or in combination is close to zero. The EWG discourages clinical use unless further evidence supports improved clinical outcomes.The EWG found insufficient evidence to recommend testing for the TCF7L2 gene to assess risk for type 2 diabetes in high-risk individuals. The EWG found that the magnitude of net health benefit from the use of this test is close to zero. The EWG discourages clinical use unless further evidence supports improved clinical outcomes.On the basis of the available evidence for both the scenarios, the overall certainty of net health benefit is deemed "low."

RATIONALE:

It has been suggested that genomic profiling in the general population or in high-risk populations for type 2 diabetes might lead to management changes (e.g., earlier initiation or higher rates of medical interventions, or targeted recommendations for behavioral change) that improve type 2 diabetes outcomes or prevent type 2 diabetes. The EWG found no direct evidence to support this possibility; therefore, this review sought indirect evidence aimed at documenting the extent to which genomic profiling alters type 2 diabetes risk estimation, alone and in combination with traditional risk factors, and the extent to which risk classification improves health outcomes.

ANALYTIC VALIDITY:

Assay-related evidence on available genomic profiling tests was deemed inadequate. However, on the basis of existing technologies that have been or may be used, the analytic sensitivity and specificity of tests for individual gene variants might be at least satisfactory.

CLINICAL VALIDITY:

Twenty-eight candidate markers were evaluated in the general population. Evidence on clinical validity was rated inadequate for 24 of these associations (86%) and adequate for 4 (14%). Inadequate grades were based on limited evidence, poor replication, existence of possible biases, or combinations of these factors. Type 2 diabetes genomic profiling provided areas under the receiver operator characteristics curve of 55%-57%, with 4, 8, and 28 genes. Only TCF7L2 had convincing evidence of an association with type 2 diabetes with an odds ratio of 1.39 (95% confidence interval: 1.33-1.46).TCF7L2 was evaluated for high-risk populations, and the overall odds ratio was 1.66 (95% confidence interval: 1.22-2.27) for association with progression to type 2 diabetes.

CLINICAL UTILITY:

No studies were available to provide direct evidence on the balance of benefits and harms for genetic profiling for type 2 diabetes alone or in addition to traditional risk factors in the general population.Evidence for high-risk populations and TCF7L2 was inadequate on the basis of two identified studies. These studies found close to zero additional benefit with the addition of genomic markers to traditional risk factors (diet, body mass index, and glucose tolerance).

CONTEXTUAL ISSUES:

Prevention of type 2 diabetes is a public health priority. Improvements in the outcomes associated with genomic profiling could have important impacts. Traditional risk factors (e.g., body mass index, weight, fat mass, and exercise) have an advantage in clinical screening and risk assessment strategies because they measure the actual targets for therapy (e.g., fasting plasma glucose and medical interventions). To be useful in predicting disease risk, genomic testing should improve the predictive value of these traditional risk factors. Some issues important for clinical utility remain unknown, such as the level of risk that changes intervention, whether long-term disease outcomes will improve, how individuals being tested will understand/respond to test results and interact with the health-care system, and whether testing will motivate behavior change or amplify potential harms.

PMID:
23492873
[PubMed - indexed for MEDLINE]
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