Identification of novel population clusters with different susceptibilities to type 2 diabetes and their impact on the prediction of diabetes.
Ontology highlight
ABSTRACT:
SUBMITTER: Cho SB
PROVIDER: S-EPMC6399283 | biostudies-literature | 2019 Mar
REPOSITORIES: biostudies-literature
Publications
Identification of novel population clusters with different susceptibilities to type 2 diabetes and their impact on the prediction of diabetes.
Cho Seong Beom SB Kim Sang Cheol SC Chung Myung Guen MG
Scientific reports 20190304 1
Type 2 diabetes is one of the subtypes of diabetes. However, previous studies have revealed its heterogeneous features. Here, we hypothesized that there would be heterogeneity in its development, resulting in higher susceptibility in some populations. We performed risk-factor based clustering (RFC), which is a hierarchical clustering of the population with profiles of five known risk factors for type 2 diabetes (age, gender, body mass index, hypertension, and family history of diabetes). The RFC ...[more]
Similar Datasets
Project description:AIMS/HYPOTHESIS:In a recent study using a standard additive genetic model, we identified a TBC1D4 loss-of-function variant with a large recessive impact on risk of type 2 diabetes in Greenlanders. The aim of the current study was to identify additional genetic variation underlying type 2 diabetes using a recessive genetic model, thereby increasing the power to detect variants with recessive effects. METHODS:We investigated three cohorts of Greenlanders (B99, n?=?1401; IHIT, n?=?3115; and BBH, n?=?547), which were genotyped using Illumina MetaboChip. Of the 4674 genotyped individuals passing quality control, 4648 had phenotype data available, and type 2 diabetes association analyses were performed for 317 individuals with type 2 diabetes and 2631 participants with normal glucose tolerance. Statistical association analyses were performed using a linear mixed model. RESULTS:Using a recessive genetic model, we identified two novel loci associated with type 2 diabetes in Greenlanders, namely rs870992 in ITGA1 on chromosome 5 (OR 2.79, p?=?1.8?×?10-8), and rs16993330 upstream of LARGE1 on chromosome 22 (OR 3.52, p?=?1.3?×?10-7). The LARGE1 variant did not reach the conventional threshold for genome-wide significance (p?<?5?×?10-8) but did withstand a study-wide Bonferroni-corrected significance threshold. Both variants were common in Greenlanders, with minor allele frequencies of 23% and 16%, respectively, and were estimated to have large recessive effects on risk of type 2 diabetes in Greenlanders, compared with additively inherited variants previously observed in European populations. CONCLUSIONS/INTERPRETATION:We demonstrate the value of using a recessive genetic model in a historically small and isolated population to identify genetic risk variants. Our findings give new insights into the genetic architecture of type 2 diabetes, and further support the existence of high-effect genetic risk factors of potential clinical relevance, particularly in isolated populations. DATA AVAILABILITY:The Greenlandic MetaboChip-genotype data are available at European Genome-Phenome Archive (EGA; https://ega-archive.org/ ) under the accession EGAS00001002641.
| S-EPMC6096637 | biostudies-literature
Project description:Although over 60 single nucleotide polymorphisms (SNPs) have been identified by meta-analysis of genome-wide association studies for type-2 diabetes (T2D) among individuals of European descent, much of the genetic variation remains unexplained. There are likely many more SNPs that contribute to variation in T2D risk, some of which may lie in the regions surrounding established SNPs--a phenomenon often referred to as allelic heterogeneity. Here, we use the summary statistics from the DIAGRAM consortium meta-analysis of T2D genome-wide association studies along with linkage disequilibrium patterns inferred from a large reference sample to identify novel SNPs associated with T2D surrounding each of the previously established risk loci. We then examine the extent to which the use of these additional SNPs improves prediction of T2D risk in an independent validation dataset. Our results suggest that multiple SNPs at each of 3 loci contribute to T2D susceptibility (TCF7L2, CDKN2A/B, and KCNQ1; p<5×10(-8)). Using a less stringent threshold (p<5×10(-4)), we identify 34 additional loci with multiple associated SNPs. The addition of these SNPs slightly improves T2D prediction compared to the use of only the respective lead SNPs, when assessed using an independent validation cohort. Our findings suggest that some currently established T2D risk loci likely harbor multiple polymorphisms which contribute independently and collectively to T2D risk. This opens a promising avenue for improving prediction of T2D, and for a better understanding of the genetic architecture of T2D.
| S-EPMC4231111 | biostudies-literature
Project description:Autoantibodies are currently the most robust biomarkers of type 1 diabetes and are frequently used to establish entry criteria for the participation of genetically at-risk individuals in secondary prevention/intervention clinical trials. Since their original description almost 40 years ago, considerable efforts have been devoted toward identifying the precise molecular targets that are recognized. Such information can have significant benefit for developing improved metrics for identifying/stratifying of at-risk subjects, developing potential therapeutic targets, and advancing understanding of the pathophysiology of the disease. Currently, four major molecular targets ([pro]insulin, GAD65, IA-2, and ZnT8) have been confirmed, with approximately 94% of all subjects with a clinical diagnosis of type 1 diabetes expressing autoantibodies to at least one of these molecules at clinical onset. In this review, we summarize some of the salient properties of these targets that might contribute to their autoantigenicity and methods that have been used in attempts to identify new components of the humoral autoresponse.
| S-EPMC3887556 | biostudies-literature
Project description:BACKGROUND:Type 2 diabetes (T2D) susceptibility is influenced by genetic and lifestyle factors. To date, the majority of genetic studies of T2D have been in populations of European and Asian descent. The focus of this study is on genetic variations underlying T2D in Qataris, a population with one of the highest incidences of T2D worldwide. RESULTS:Illumina HiSeq exome sequencing was performed on 864 Qatari subjects (574 T2D cases, 290 controls). Sequence kernel association test (SKAT) gene-based analysis identified an association for low frequency potentially deleterious variants in 6 genes. However, these findings were not replicated by SKAT analysis in an independent cohort of 12,699 exomes, primarly due to the absence of low frequency potentially deleterious variants in 5 of the 6 genes. Interestingly one of the genes identified, catenin beta 1 (CTNNB1, ?-catenin), is the key effector of the Wnt pathway and interacts with the nuclear receptor transcription factor 7-like 2 (TCF7L2), variants which are the most strongly associated with risk of developing T2D worldwide. Single variant analysis did not identify any associated variants, suggesting the SKAT association signal was not driven by individual variants. None of the 6 associated genes were among 634 previously described T2D genes. CONCLUSIONS:The observation that genes not previously linked to T2D in prior studies of European and Asian populations are associated with T2D in Qatar provides new insights into the complexity of T2D pathogenesis and emphasizes the importance of understudied populations when assessing genetic variation in the pathogenesis of common disorders.
| S-EPMC6136697 | biostudies-literature
Project description:Metabolome-genome wide association studies (mGWASs) are useful for understanding the genetic regulation of metabolites in complex diseases, including type 2 diabetes (T2D). Numerous genetic variants associated with T2D-related metabolites have been identified in previous mGWASs; however, these analyses seem to have difficulty in detecting the genetic variants with functional effects. An exome array focussed on potentially functional variants is an alternative platform to obtain insight into the genetics of biochemical conversion processes. In the present study, we performed an mGWAS using 27,140 non-synonymous variants included in the Illumina HumanExome BeadChip and nine T2D-related metabolites identified by a targetted metabolomics approach to evaluate 2,338 Korean individuals from the Korea Association REsource (KARE) cohort. A linear regression analysis controlling for age, sex, BMI, and T2D status as covariates was performed to identify novel non-synonymous variants associated with T2D-related metabolites. We found significant associations between glycine and CPS1 (rs1047883) and PC ae C36:0 and CYP4F2 (rs2108622) variants (P<2.05 × 10-7, after the Bonferroni correction for multiple testing). One of the two significantly associated variants, rs1047883 was newly identified whereas rs2108622 had been previously reported to be associated with T2D-related traits. These findings expand our understanding of the genetic determinants of T2D-related metabolites and provide a basis for further functional validation.
| S-EPMC6822494 | biostudies-literature
Project description:BACKGROUND:Emerging studies have investigated the association between different anthropometric indices with diabetes risk but the results were inconsistent. The aims of the study were to examine the associations of different anthropometric indices with incident diabetes risk and whether novel anthropometric indices improve diabetes prediction beyond traditional indices among elderly Chinese. METHODS:Nine thousand nine hundred sixty-two elderly individuals (age???60 years old) derived from the prospective Dongfeng-Tongji cohort were included. Hazard ratio (HR) and corresponding 95% confidence interval (CI) were evaluated by Cox proportional hazard model to examine the associations between traditional anthropometric indices (body mass index [BMI], waist circumference [WC], waist-to-height ratio [WHtR]), novel anthropometric indices (visceral adiposity index [VAI], a body shape index [ABSI], body roundness index [BRI]) and diabetes risk. Receiver operating characteristic (ROC) curve and area under curve (AUC) were applied to compare the novel anthropometric indices with the traditional indices in diabetes prediction. RESULTS:During mean 4.6 years of follow-up, 614 incident cases of type 2 diabetes (T2D) were identified. Significant positive associations were detected between BMI, WC, WHtR, VAI and BRI and incident T2D risk. For ABSI, no significant association was observed in either men or women. BMI was the strongest predictor in diabetes in men (AUC?=?0.655) comparable with the other anthropometric indices (P?<?0.05). Similar as men, BMI was the strongest predictor (AUC?=?0.635) in women. Except for WC, the AUC of BMI was larger than WHtR, VAI, and BRI. In contrast, ABSI was not a good predictor in either men (AUC?=?0.507) or women (AUC?=?0.503). CONCLUSIONS:In elderly Chinese, BMI, WC, WHtR, VAI and BRI were positively associated with incident T2D risk. Among them, BMI was the strongest predictor in both men and women.
| S-EPMC6142386 | biostudies-literature
Project description:ObjectiveWe aimed to identify a novel panel of biomarkers predicting renal function decline in type 2 diabetes, using biomarkers representing different disease pathways speculated to contribute to the progression of diabetic nephropathy.Research design and methodsA systematic data integration approach was used to select biomarkers representing different disease pathways. Twenty-eight biomarkers were measured in 82 patients seen at an outpatient diabetes center in The Netherlands. Median follow-up was 4.0 years. We compared the cross-validated explained variation (R2) of two models to predict eGFR decline, one including only established risk markers, the other adding a novel panel of biomarkers. Least absolute shrinkage and selection operator (LASSO) was used for model estimation. The C-index was calculated to assess improvement in prediction of accelerated eGFR decline defined as <-3.0 mL/min/1.73m2/year.ResultsPatients' average age was 63.5 years and baseline eGFR was 77.9 mL/min/1.73m2. The average rate of eGFR decline was -2.0 ± 4.7 mL/min/1.73m2/year. When modeled on top of established risk markers, the biomarker panel including matrix metallopeptidases, tyrosine kinase, podocin, CTGF, TNF-receptor-1, sclerostin, CCL2, YKL-40, and NT-proCNP improved the explained variability of eGFR decline (R2 increase from 37.7% to 54.6%; p=0.018) and improved prediction of accelerated eGFR decline (C-index increase from 0.835 to 0.896; p=0.008).ConclusionsA novel panel of biomarkers representing different pathways of renal disease progression including inflammation, fibrosis, angiogenesis, and endothelial function improved prediction of eGFR decline on top of established risk markers in type 2 diabetes. These results need to be confirmed in a large prospective cohort.
| S-EPMC4431870 | biostudies-literature
Project description:Understanding the risk for type 2 diabetes (T2D) early in the life course is important for prevention. Whether genetic information improves prediction models for diabetes from adolescence into adulthood is unknown.With the use of data from 1030 participants in the Bogalusa Heart Study aged 12 to 18 followed into middle adulthood, we built Cox models for incident T2D with risk factors assessed in adolescence (demographics, family history, physical examination, and routine biomarkers). Models with and without a 38 single-nucleotide polymorphism diabetes genotype score were compared by C statistics and continuous net reclassification improvement indices.Participant mean (± SD) age at baseline was 14.4 ± 1.6 years, and 32% were black. Ninety (8.7%) participants developed T2D over a mean 26.9 ± 5.0 years of follow-up. Genotype score significantly predicted T2D in all models. Hazard ratios ranged from 1.09 per risk allele (95% confidence interval 1.03-1.15) in the basic demographic model to 1.06 (95% confidence interval 1.00-1.13) in the full model. The addition of genotype score did not improve the discrimination of the full clinical model (C statistic 0.756 without and 0.760 with genotype score). In the full model, genotype score had weak improvement in reclassification (net reclassification improvement index 0.261).Although a genotype score assessed among white and black adolescents is significantly associated with T2D in adulthood, it does not improve prediction over clinical risk factors. Genetic screening for T2D in its current state is not a useful addition to adolescents' clinical care.
| S-EPMC3483893 | biostudies-literature
Project description:An empirical approach is presented for predicting the genomic susceptibility of an individual to the most likely one among nine traits, consisting of eight major cancer classes plus a healthy trait. We use four prediction methods by applying two supervised learning algorithms to two different descriptors of common genomic variations (the profiles of genotypes of SNPs and SNP syntaxes with low P values or low frequencies) of each individual genome from normal cells. All four methods made correct predictions substantially better than random predictions for most cancer classes, but not for some others. A combination of the four results using Bayesian inference better predicted overall than any individual method. The multiclass accuracy of the combined prediction ranges from 33% to 56% depending on cancer classes of testing sets, compared with 11% for a random prediction among nine traits. Despite limited SNP data available and the absence of rare SNPs in public databases, at present, the results suggest that the framework of this approach or its improvement can predict cancer susceptibility with probability estimates useful for making health decisions for individuals or for a population.
| S-EPMC3918817 | biostudies-other
Project description:We investigated the correlation between type 2 diabetes (T2D)-related genes and the clinical characteristics of T2D in the Chinese Han population. Our study included 319 patients and 387 controls. Age, gender, clinical features, medications intake and biochemical blood profiles were analyzed. Genotyping was performed on a total of 18 single nucleotide polymorphisms previously reported to be associated with T2D. Our analyses revealed that the CT genotype of ARHGAP22 rs4838605 is associated with T2D risk. Upon analyzing the subjects' clinical characteristics, we found that for rs2811893, the TT genotype correlated with high creatinine levels, while the AA genotype of rs17045754 and the TT genotype of rs4838605 correlated with elevated triglyceride levels. In addition, the AA genotype of rs17376456 and the TT genotype of rs6214 (p = 0.006) correlated with elevated hemoglobin A1c levels. Lastly, those carrying the TT genotype of rs7772697 and the CA genotype of rs3918227 exhibited higher mean body mass index and Cystatin C than controls. Our results showing that the ARHGAP22 gene is associated with an increased risk of T2D, and that seven SNPs in MYSM1, PLXDC2, ARHGAP22 and HS6ST3 promote T2D progression and could help predict the clinical course of T2D in patients at risk.
| S-EPMC5356749 | biostudies-literature