Genetic risk profiling for prediction of type 2 diabetes.
Ontology highlight
ABSTRACT:
SUBMITTER: Mihaescu R
PROVIDER: S-EPMC3024707 | biostudies-literature | 2011 Jan
REPOSITORIES: biostudies-literature
Publications
Genetic risk profiling for prediction of type 2 diabetes.
Mihaescu Raluca R Meigs James J Sijbrands Eric E Janssens A Cecile AC
PLoS currents 20110111
Type 2 diabetes (T2D) is a common disease caused by a complex interplay between many genetic and environmental factors. Candidate gene studies and recent collaborative genome-wide association efforts revealed at least 38 common single nucleotide polymorphisms (SNPs) associated with increased risk of T2D. Genetic testing of multiple SNPs is considered a potentially useful tool for early detection of individuals at high diabetes risk leading to improved targeting of preventive interventions. ...[more]
Similar Datasets
Project description:BackgroundPrecise risk prediction of type 1 diabetes (T1D) facilitates early intervention and identification of risk factors prior to irreversible beta-islet cell destruction, and can significantly improve T1D prevention and clinical care. Sharp et al. developed a genetic risk scoring (GRS) system for T1D (T1D-GRS2) capable of predicting T1D risk in children of European ancestry. The T1D-GRS2 was developed on the basis of causal genetic variants, thus may be applicable to minor populations, while a trans-ethnic GRS for T1D may avoid the exacerbation of health disparities due to the lack of genomic information in minorities.MethodsHere, we describe a T1D-GRS2 calculator validated in two independent cohorts, including African American children and European American children. Participants were recruited by the Center for Applied Genomics at the Children's Hospital of Philadelphia.ResultsIt demonstrates that GRS2 is applicable to the T1D risk prediction in the AA cohort, while population-specific thresholds are needed for different populations.ConclusionsThe study highlights the potential to further improve T1D-GRS2 performance with the inclusion of additional genetic markers.
| S-EPMC8983464 | biostudies-literature
Project description:PurposeUsing effect estimates from genome-wide association studies (GWAS), we identified a genetic risk score (GRS) that has the strongest association with type 2 diabetes (T2D) status in a population-based cohort and investigated its potential for prospective T2D risk assessment.MethodsBy varying the number of single-nucleotide polymorphisms (SNPs) and their respective weights, alternative versions of GRS can be computed. They were tested in 1,181 T2D cases and 9,092 controls of the Estonian Biobank cohort. The best-fitting GRS was chosen for the subsequent analysis of incident T2D (386 cases).ResultsThe best fit was provided by a novel doubly weighted GRS that captures the effect of 1,000 SNPs. The hazard for incident T2D was 3.45 times (95% CI: 2.31-5.17) higher in the highest GRS quintile compared with the lowest quintile, after adjusting for body mass index and other known predictors. Adding GRS to the prediction model for 5-year T2D risk resulted in continuous net reclassification improvement of 0.324 (95% CI: 0.211-0.444). In addition, a significant effect of the GRS on all-cause and cardiovascular mortality was observed.ConclusionThe proposed GRS would improve the accuracy of T2D risk prediction when added to the currently used set of predictors.Genet Med 19 3, 322-329.
| S-EPMC5506454 | biostudies-literature
Project description:The purpose of this study was to identify attitudes and perceptions of willingness to participate in genetic testing for type 2 diabetes (T2D) risk prediction in the general population. Adults (n = 598) were surveyed on attitudes about utilizing genetic testing to predict future risk of T2D. Participants were recruited from public libraries (53%), online registry (37%) and a safety net hospital emergency department (10%). Respondents were 37 ± 11 years old, primarily White (54%), female (69%), college educated (46%), with an annual income ≥$25,000 (56%). Half of participants were interested in genetic testing for T2D (52%) and 81% agreed/strongly agreed genetic testing should be available to the public. Only 57% of individuals knew T2D is preventable. A multivariate model to predict interest in genetic testing was adjusted for age, gender, recruitment location and BMI; significant predictors were motivation (high perceived personal risk of T2D [OR = 4.38 (1.76, 10.9)]; family history [OR = 2.56 (1.46, 4.48)]; desire to know risk prior to disease onset [OR = 3.25 (1.94, 5.42)]; and knowing T2D is preventable [OR = 2.11 (1.24, 3.60)], intention (if the cost is free [OR = 10.2 (4.27, 24.6)]; and learning T2D is preventable [OR = 5.18 (1.95, 13.7)]) and trust of genetic testing results [OR = 0.03 (0.003, 0.30)]. Individuals are interested in genetic testing for T2D risk which offers unique information that is personalized. Financial accessibility, validity of the test and availability of diabetes prevention programs were identified as predictors of interest in T2D testing.
| S-EPMC4720283 | biostudies-literature
Project description:Transcriptional Profiling of Insulin Sensitive and Insulin Resistant Samples Sixty two participants at the tail ends of the distribution of insulin sensitivity adjusted for age, gender and natural logarithm of BMI for each ethnic group separately. Individuals at tail ends were well matched for age, gender, BMI, and percent fat, but were different for insulin sensitivity. Participants were of age 20 years to 55 years, body mass index (BMI) between 19 kg/m2 and 42 kg/m2, and had all biopsies obtained in the fasting state.
2013-04-16 | E-GEOD-40234 | biostudies-arrayexpress
Project description:BackgroundType 2 diabetes (T2D) is a multifactorial disease in which environmental triggers interact with genetic variants in the predisposition to the disease. A number of common variants have been associated with T2D but our knowledge of their ability to predict T2D prospectively is limited.Methods and findingsBy using a Cox proportional hazard model, common variants in the PPARG (P12A), CAPN10 (SNP43 and 44), KCNJ11 (E23K), UCP2 (-866G>A), and IRS1 (G972R) genes were studied for their ability to predict T2D in 2,293 individuals participating in the Botnia study in Finland. After a median follow-up of 6 y, 132 (6%) persons developed T2D. The hazard ratio for risk of developing T2D was 1.7 (95% confidence interval [CI] 1.1-2.7) for the PPARG PP genotype, 1.5 (95% CI 1.0-2.2) for the CAPN10 SNP44 TT genotype, and 2.6 (95% CI 1.5-4.5) for the combination of PPARG and CAPN10 risk genotypes. In individuals with fasting plasma glucose > or = 5.6 mmol/l and body mass index > or = 30 kg/m(2), the hazard ratio increased to 21.2 (95% CI 8.7-51.4) for the combination of the PPARG PP and CAPN10 SNP43/44 GG/TT genotypes as compared to those with the low-risk genotypes with normal fasting plasma glucose and body mass index < 30 kg/m(2).ConclusionWe demonstrate in a large prospective study that variants in the PPARG and CAPN10 genes predict future T2D. Genetic testing might become a future approach to identify individuals at risk of developing T2D.
| S-EPMC1274281 | biostudies-literature
Project description:Transcriptional Profiling of Insulin Sensitive and Insulin Resistant Samples
2013-04-16 | GSE40234 | GEO
Project description:We evaluated whether genetic information could offer improvement on risk prediction of diabetic nephropathy (DN) while adding susceptibility variants into a risk prediction model with conventional risk factors in Han Chinese type 2 diabetes patients. A total of 995 (including 246 DN cases) and 519 (including 179 DN cases) type 2 diabetes patients were included in derivation and validation sets, respectively. A genetic risk score (GRS) was constructed with DN susceptibility variants based on findings of our previous genome-wide association study. In derivation set, areas under the receiver operating characteristics (AUROC) curve (95% CI) for model with clinical risk factors only, model with GRS only, and model with clinical risk factors and GRS were 0.75 (0.72-0.78), 0.64 (0.60-0.68), and 0.78 (0.75-0.81), respectively. In external validation sample, AUROC for model combining conventional risk factors and GRS was 0.70 (0.65-0.74). Additionally, the net reclassification improvement was 9.98% (P = 0.001) when the GRS was added to the prediction model of a set of clinical risk factors. This prediction model enabled us to confirm the importance of GRS combined with clinical factors in predicting the risk of DN and enhanced identification of high-risk individuals for appropriate management of DN for intervention.
| S-EPMC6934611 | biostudies-literature
Project description:BACKGROUND:Genetic predisposition for type 1 diabetes (T1D) is largely determined by human leukocyte antigen (HLA) genes; however, over 50 other genetic regions confer susceptibility. We evaluated a previously reported 10-factor weighted model derived from the Type 1 Diabetes Genetics Consortium to predict the development of diabetes in the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. Performance of the model, derived from individuals with first-degree relatives (FDR) with T1D, was evaluated in DAISY general population (GP) participants as well as FDR subjects. METHODS:The 10-factor weighted risk model (HLA, PTPN22 , INS , IL2RA , ERBB3 , ORMDL3 , BACH2 , IL27 , GLIS3 , RNLS ), 3-factor model (HLA, PTPN22, INS ), and HLA alone were compared for the prediction of diabetes in children with complete SNP data (n = 1941). RESULTS:Stratification by risk score significantly predicted progression to diabetes by Kaplan-Meier analysis (GP: P = .00006; FDR: P = .0022). The 10-factor model performed better in discriminating diabetes outcome than HLA alone (GP, P = .03; FDR, P = .01). In GP, the restricted 3-factor model was superior to HLA (P = .03), but not different from the 10-factor model (P = .22). In contrast, for FDR the 3-factor model did not show improvement over HLA (P = .12) and performed worse than the 10-factor model (P = .02) CONCLUSIONS: We have shown a 10-factor risk model predicts development of diabetes in both GP and FDR children. While this model was superior to a minimal model in FDR, it did not confer improvement in GP. Differences in model performance in FDR vs GP children may lead to important insights into screening strategies specific to these groups.
| S-EPMC5764829 | biostudies-literature
Project description:ObjectivesTo assess the performance of a panel of common single nucleotide polymorphisms (genotypes) associated with type 2 diabetes in distinguishing incident cases of future type 2 diabetes (discrimination), and to examine the effect of adding genetic information to previously validated non-genetic (phenotype based) models developed to estimate the absolute risk of type 2 diabetes.DesignWorkplace based prospective cohort study with three 5 yearly medical screenings.Participants5535 initially healthy people (mean age 49 years; 33% women), of whom 302 developed new onset type 2 diabetes over 10 years.Outcome measuresNon-genetic variables included in two established risk models-the Cambridge type 2 diabetes risk score (age, sex, drug treatment, family history of type 2 diabetes, body mass index, smoking status) and the Framingham offspring study type 2 diabetes risk score (age, sex, parental history of type 2 diabetes, body mass index, high density lipoprotein cholesterol, triglycerides, fasting glucose)-and 20 single nucleotide polymorphisms associated with susceptibility to type 2 diabetes. Cases of incident type 2 diabetes were defined on the basis of a standard oral glucose tolerance test, self report of a doctor's diagnosis, or the use of anti-diabetic drugs.ResultsA genetic score based on the number of risk alleles carried (range 0-40; area under receiver operating characteristics curve 0.54, 95% confidence interval 0.50 to 0.58) and a genetic risk function in which carriage of risk alleles was weighted according to the summary odds ratios of their effect from meta-analyses of genetic studies (area under receiver operating characteristics curve 0.55, 0.51 to 0.59) did not effectively discriminate cases of diabetes. The Cambridge risk score (area under curve 0.72, 0.69 to 0.76) and the Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82) led to better discrimination of cases than did genotype based tests. Adding genetic information to phenotype based risk models did not improve discrimination and provided only a small improvement in model calibration and a modest net reclassification improvement of about 5% when added to the Cambridge risk score but not when added to the Framingham offspring risk score.ConclusionThe phenotype based risk models provided greater discrimination for type 2 diabetes than did models based on 20 common independently inherited diabetes risk alleles. The addition of genotypes to phenotype based risk models produced only minimal improvement in accuracy of risk estimation assessed by recalibration and, at best, a minor net reclassification improvement. The major translational application of the currently known common, small effect genetic variants influencing susceptibility to type 2 diabetes is likely to come from the insight they provide on causes of disease and potential therapeutic targets.
| S-EPMC2806945 | biostudies-literature
Project description:ObjectiveA genetic risk score (GRS) comprised of single nucleotide polymorphisms (SNPs) and metabolite biomarkers have each been shown, separately, to predict incident type 2 diabetes. We tested whether genetic and metabolite markers provide complementary information for type 2 diabetes prediction and, together, improve the accuracy of prediction models containing clinical traits.Research design and methodsDiabetes risk was modeled with a 62-SNP GRS, nine metabolites, and clinical traits. We fit age- and sex-adjusted logistic regression models to test the association of these sources of information, separately and jointly, with incident type 2 diabetes among 1,622 initially nondiabetic participants from the Framingham Offspring Study. The predictive capacity of each model was assessed by area under the curve (AUC).ResultsTwo hundred and six new diabetes cases were observed during 13.5 years of follow-up. The AUC was greater for the model containing the GRS and metabolite measurements together versus GRS or metabolites alone (0.820 vs. 0.641, P < 0.0001, or 0.820 vs. 0.803, P = 0.01, respectively). Odds ratios for association of GRS or metabolites with type 2 diabetes were not attenuated in the combined model. The AUC was greater for the model containing the GRS, metabolites, and clinical traits versus clinical traits only (0.880 vs. 0.856, P = 0.002).ConclusionsMetabolite and genetic traits provide complementary information to each other for the prediction of future type 2 diabetes. These novel markers of diabetes risk modestly improve the predictive accuracy of incident type 2 diabetes based only on traditional clinical risk factors.
| S-EPMC4140156 | biostudies-literature