ArticlePloS one2013
Use of net reclassification improvement (NRI) method confirms the utility of combined genetic risk score to predict type 2 diabetes.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Quantitative Relationship Between Cumulative Risk Alleles Based on Genome-Wide Association Studies and Type 2 Diabetes Mellitus: A Systematic Review and Meta-analysis.Journal of epidemiology · 2018Pooled it
- Association of SLC30A8 gene polymorphism with type 2 diabetes, evidence from 46 studies: a meta-analysis.Endocrine · 2016Pooled it
- Association, characterisation and meta-analysis of SNPs linked to general reading ability in a German dyslexia case-control cohort.Scientific reports · 2016Pooled it
- Risk prediction for sporadic Alzheimer's disease using genetic risk score in the Han Chinese population.Oncotarget · 2015Trial
- The Association of Four-Limb Blood Pressure with History of Stroke in Chinese Adults: A Cross-Sectional Study.PloS one · 2015Trial
- High-grade serous ovarian carcinoma, the "Achiles' hill" for clinicians and molecular biologists: a molecular insight.Molecular biology reports · 2023Review
- Effect of KCNQ1 rs2237892 polymorphism on the predisposition to type 2 diabetes mellitus: An updated meta-analysis.Diabetology & metabolic syndrome · 2021Article
- A Genetic Risk Score Improves the Prediction of Type 2 Diabetes Mellitus in Mexican Youths but Has Lower Predictive Utility Compared With Non-Genetic Factors.Frontiers in endocrinology · 2021Article
- BMI, Waist Circumference and All-Cause Mortality in a Middle-Aged and Elderly Chinese Population.The journal of nutrition, health & aging · 2018Article
- Type 2 Diabetes Genetic Risk Scores Are Associated With Increased Type 2 Diabetes Risk Among African Americans by Cardiometabolic Status.Clinical medicine insights. Endocrinology and diabetes · 2018Article
- Association Between SLC30A8 rs13266634 Polymorphism and Risk of T2DM and IGR in Chinese Population: A Systematic Review and Meta-Analysis.Frontiers in endocrinology · 2018Article
- Functional annotation of sixty-five type-2 diabetes risk SNPs and its application in risk prediction.Scientific reports · 2017Article
- Genetic-risk assessment of GWAS-derived susceptibility loci for type 2 diabetes in a 10 year follow-up of a population-based cohort study.Journal of human genetics · 2016Article
- Islet biology, the CDKN2A/B locus and type 2 diabetes risk.Diabetologia · 2016Review
- Article
- Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives.Human genetics · 2016Review
- Association of JAZF1 and TSPAN8/LGR5 variants in relation to type 2 diabetes mellitus in a Saudi population.Diabetology & metabolic syndrome · 2015Article
- Article
- The cumulative effects of known susceptibility variants to predict primary biliary cirrhosis risk.Genes and immunityArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundRecent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population. METHODOLOGY: We selected 14 single nucleotide polymorphisms (SNPs) in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 healthy controls. A combined genetic score (CGS) was calculated by summing up the number of risk alleles or weighted by the effect size for each SNP under an additive genetic model. We tested for associations by either logistic or linear regression analysis for T2D and quantitative traits, respectively. The contribution of the CGS for predicting T2D risk was evaluated by receiver operating characteristic (ROC) analysis and net reclassification improvement (NRI).
resultsWe observed consistent and significant associations of IGF2BP2, WFS1, CDKAL1, SLC30A8, CDKN2A/B, HHEX, TCF7L2 and KCNQ1 (8.5×10(-18)<P<8.5×10(-3)), as well as nominal associations of NOTCH2, JAZF1, KCNJ11 and HNF1B (0.05<P<0.1) with T2D risk, which yielded odds ratios ranging from 1.07 to 2.09. The 8 significant SNPs exhibited joint effect on increasing T2D risk, fasting plasma glucose and use of insulin therapy as well as reducing HOMA-β, BMI, waist circumference and younger age of diagnosis of T2D. The addition of CGS marginally increased AUC (2%) but significantly improved the predictive ability on T2D risk by 11.2% and 11.3% for unweighted and weighted CGS, respectively using the NRI approach (P<0.001).
conclusionIn a Chinese population, the use of a CGS of 8 SNPs modestly but significantly improved its discriminative ability to predict T2D above and beyond that attributed to clinical risk factors (sex, age and BMI).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.