Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes

By James B. Meigs, P. Shrader, Lisa Sullivan, J. Mcateer, Caroline S. Fox, Josée Dupuis, A. Manning, J. Florez, Peter W. F. Wilson, R. D’Agostino and 1 more. New England Journal of Medicine, 2008

778 citations recorded by the source corpus.

BACKGROUND Multiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus. We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone. METHODS We genotyped single-nucleotide polymorphisms (SNPs) at 18 loci associated with diabetes in 2377 participants of the Framingham Offspring Study. We created a genotype score from the number of risk alleles and used logistic regression to generate C statistics indicating the extent to which the genotype score can discriminate the risk of diabetes when used alone and in addition to clinical risk factors. RESULTS There were 255 new cases of diabetes during 28 years of follow-up. The mean (±SD) genotype score was 17.7±2.7 among subjects in whom diabetes developed and 17.1±2.6 among those in whom diabetes did not develop (P<0.001). The sex-adjusted odds ratio for diabetes was 1.12 per risk allele (95% confidence interval, 1.07 to 1.17). The C statistic was 0.534 without the genotype score and 0.581 with the score (P=0.01). In a model adjusted for sex and self-reported family history of diabetes, the C statistic was 0.595 without the genotype score and 0.615 with the score (P=0.11). In a model adjusted for age, sex, family history, body-mass index, fasting glucose level, systolic blood pressure, high-density lipoprotein cholesterol level, and triglyceride level, the C statistic was 0.900 without the genotype score and 0.901 with the score (P=0.49). The genotype score resulted in the appropriate risk reclassification of, at most, 4% of the subjects. CONCLUSIONS A genotype score based on 18 risk alleles predicted new cases of diabetes in the community but provided only a slightly better prediction of risk than knowledge of common risk factors alone.

Subfields: Cardiology and Cardiovascular Medicine, Genetics, Surgery

View Paper: https://www.semanticscholar.org/paper/ab6de407ad6078d26fe8f39a6009576669cc5afc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Surgery

Citations in the corpus

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus (2002, background): “3), however, and do not significantly enhance our ability to predict the risk of type 2 diabetes (274).”
Metabolite Profiles and the Risk of Developing Diabetes (2011, neutral): “There has been interest in genetic risk prediction, but the known diabetes polymorphisms add modestly to risk assessmen”
Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all! (2012, methodology): “Initial results published for example in Meigs et al. (2008) and Paynter et al. (2010) however showed that genetic data added limited predicted values to well established risk factors of Type II diabetes and cardiovascular disease.”
Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. (2002, background): “3), however, and do not significantly enhance our ability to predict the risk of type 2 diabetes (274).”
Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (2015, background): “To date, the utility of common, complex risk allele testing for patient care(54) has been unclear and models to combine multiple markers into a cumulative risk score are often flawed and are usually no better than traditional risk factors such as family history, demographics and non-genetic clinical”
Epigenetic mechanisms that underpin metabolic and cardiovascular diseases (2009, background): “First, although genomic information has added little to clinical risk factors in the prediction of the onset of type 2 diabetes mellitus”
Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers (2010, neutral): “On the other hand, the NRI seems to have gained popularity in medical journals, most likely due to its simplicity [7, 13].”
Human Gut Microbiota Changes Reveal the Progression of Glucose Intolerance (2013, background): “However, common genetic variants that are associated with diabetes have only very slightly improved the prediction of future T2DM [4,5].”