1645-P: Polygenic Risk Score for Prediction of Complications in Men and Women with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1645-P
Author(s):  
JOHANNE TREMBLAY ◽  
REDHA ATTAOUA ◽  
MOUNSIF HALOUI ◽  
RAMZAN TAHIR ◽  
CAROLE LONG ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Men And Women ◽  
Polygenic Risk ◽  
Prediction Of Complications

304-OR: Polygenic Risk Score Predicts Type 2 Diabetes Susceptibility in a Diverse Consumer Genetic Database

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 304-OR
Author(s):  
MICHAEL L. MULTHAUP ◽  
RYOSUKE KITA ◽  
NICHOLAS ERIKSSON ◽  
STELLA ASLIBEKYAN ◽  
JANIE SHELTON ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Diabetes Susceptibility ◽  
Genetic Database ◽  
Polygenic Risk

1134-P: Polygenic Risk Score of Type 2 Diabetes as a Predictive Factor for Macrovascular Complications: A Prospective Population-Based UK Biobank Study

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 1134-P
Author(s):  
SANGHYUK JUNG ◽  
DOKYOON KIM ◽  
MANU SHIVAKUMAR ◽  
HONG-HEE WON ◽  
JAE-SEUNG YUN
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Predictive Factor ◽  
Population Based ◽  
Macrovascular Complications ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Polygenic Risk

scholarly journals Effects of polygenic risk score of type 2 diabetes on the hippocampal topological property and episodic memory

2021 ◽  
Author(s):  
Yang Zhang ◽  
Xin Du ◽  
Yumeng Fu ◽  
Qiuyue Zhao ◽  
Zirui Wang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Episodic Memory ◽  
Risk Score ◽  
Genetic Risk ◽  
Polygenic Risk Score ◽  
Topological Properties ◽  
Polygenic Risk ◽  
The Right ◽  
Nodal Properties

Abstract Purpose Type 2 diabetes is associated with a higher risk of dementia. The pathogenesis is complex, partly influenced by genetic factors. The hippocampus is the most vulnerable brain region in individuals with type 2 diabetes. However, whether the genetic risk of type 2 diabetes is associated with the hippocampus and episodic memory remains unclear. This study explored the influence of polygenic risk score (PRS) of type 2 diabetes on the white matter topological properties of the hippocampus among individuals with and without type 2 diabetes and its associations with episodic memory. Methods This study included 103 individuals with type 2 diabetes and 114 well-matched individuals without type 2 diabetes. All the participants were genotyped, and a diffusion tensor imaging-based structural network was constructed. PRS was calculated based on a genome-wide association study of type 2 diabetes. The PRS-by-disease interactions on the bilateral hippocampal topological network properties were evaluated by analysis of variance (ANOVA). Results There were significant PRS-by-disease interaction effects on the nodal topological properties of the right hippocampus node. In the individuals with type 2 diabetes, the PRS was correlated with the right hippocampal nodal properties, and the nodal properties were correlated with the episodic memory. In addition, the right hippocampal nodal properties mediated the effect of PRS on the episodic memory in individuals with type 2 diabetes. Conclusion Our results suggested a gene-brain-cognition biological pathway, which might help understand the neural mechanism of the genetic risk of type 2 diabetes affects episodic memory in type 2 diabetes.

Genome-wide polygenic risk score for retinopathy of type 2 diabetes

2021 ◽  
Author(s):  
Iain S Forrest ◽  
Kumardeep Chaudhary ◽  
Ishan Paranjpe ◽  
Ha My T Vy ◽  
Carla Marquez-Luna ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Large Scale ◽  
European Ancestry ◽  
Polygenic Risk Score ◽  
Standard Deviation Increase ◽  
Polygenic Risk ◽  
Genome Wide ◽  
A Genome

Abstract Diabetic retinopathy (DR) is a common consequence in type 2 diabetes (T2D) and a leading cause of blindness in working-age adults. Yet, its genetic predisposition is largely unknown. Here, we examined the polygenic architecture underlying DR by deriving and assessing a genome-wide polygenic risk score (PRS) for DR. We evaluated the PRS in 6079 individuals with T2D of European, Hispanic, African and other ancestries from a large-scale multi-ethnic biobank. Main outcomes were PRS association with DR diagnosis, symptoms and complications, and time to diagnosis, and transferability to non-European ancestries. We observed that PRS was significantly associated with DR. A standard deviation increase in PRS was accompanied by an adjusted odds ratio (OR) of 1.12 [95% confidence interval (CI) 1.04–1.20; P = 0.001] for DR diagnosis. When stratified by ancestry, PRS was associated with the highest OR in European ancestry (OR = 1.22, 95% CI 1.02–1.41; P = 0.049), followed by African (OR = 1.15, 95% CI 1.03–1.28; P = 0.028) and Hispanic ancestries (OR = 1.10, 95% CI 1.00–1.10; P = 0.050). Individuals in the top PRS decile had a 1.8-fold elevated risk for DR versus the bottom decile (P = 0.002). Among individuals without DR diagnosis, the top PRS decile had more DR symptoms than the bottom decile (P = 0.008). The PRS was associated with retinal hemorrhage (OR = 1.44, 95% CI 1.03–2.02; P = 0.03) and earlier DR presentation (10% probability of DR by 4 years in the top PRS decile versus 8 years in the bottom decile). These results establish the significant polygenic underpinnings of DR and indicate the need for more diverse ancestries in biobanks to develop multi-ancestral PRS.

Economic Evaluation of a New Polygenic Risk Score to Predict Nephropathy in Adult Patients With Type 2 Diabetes

2020 ◽  
Author(s):  
Kimberly Guinan ◽  
Catherine Beauchemin ◽  
Johanne Tremblay ◽  
John Chalmers ◽  
Mark Woodward ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Economic Evaluation ◽  
Risk Score ◽  
Adult Patients ◽  
Polygenic Risk Score ◽  
Polygenic Risk

scholarly journals Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis

BMJ ◽  
2019 ◽  
pp. l4292 ◽  
Author(s):  
Jordi Merino ◽  
Marta Guasch-Ferré ◽  
Christina Ellervik ◽  
Hassan S Dashti ◽  
Stephen J Sharp ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cohort Studies ◽  
Risk Score ◽  
Dietary Fat ◽  
Genetic Risk ◽  
Polygenic Risk Score ◽  
Polyunsaturated Fat ◽  
Polygenic Risk

Abstract Objective To investigate whether the genetic burden of type 2 diabetes modifies the association between the quality of dietary fat and the incidence of type 2 diabetes. Design Individual participant data meta-analysis. Data sources Eligible prospective cohort studies were systematically sourced from studies published between January 1970 and February 2017 through electronic searches in major medical databases (Medline, Embase, and Scopus) and discussion with investigators. Review methods Data from cohort studies or multicohort consortia with available genome-wide genetic data and information about the quality of dietary fat and the incidence of type 2 diabetes in participants of European descent was sought. Prospective cohorts that had accrued five or more years of follow-up were included. The type 2 diabetes genetic risk profile was characterized by a 68-variant polygenic risk score weighted by published effect sizes. Diet was recorded by using validated cohort-specific dietary assessment tools. Outcome measures were summary adjusted hazard ratios of incident type 2 diabetes for polygenic risk score, isocaloric replacement of carbohydrate (refined starch and sugars) with types of fat, and the interaction of types of fat with polygenic risk score. Results Of 102 305 participants from 15 prospective cohort studies, 20 015 type 2 diabetes cases were documented after a median follow-up of 12 years (interquartile range 9.4-14.2). The hazard ratio of type 2 diabetes per increment of 10 risk alleles in the polygenic risk score was 1.64 (95% confidence interval 1.54 to 1.75, I 2 =7.1%, τ 2 =0.003). The increase of polyunsaturated fat and total omega 6 polyunsaturated fat intake in place of carbohydrate was associated with a lower risk of type 2 diabetes, with hazard ratios of 0.90 (0.82 to 0.98, I 2 =18.0%, τ 2 =0.006; per 5% of energy) and 0.99 (0.97 to 1.00, I 2 =58.8%, τ 2 =0.001; per increment of 1 g/d), respectively. Increasing monounsaturated fat in place of carbohydrate was associated with a higher risk of type 2 diabetes (hazard ratio 1.10, 95% confidence interval 1.01 to 1.19, I 2 =25.9%, τ 2 =0.006; per 5% of energy). Evidence of small study effects was detected for the overall association of polyunsaturated fat with the risk of type 2 diabetes, but not for the omega 6 polyunsaturated fat and monounsaturated fat associations. Significant interactions between dietary fat and polygenic risk score on the risk of type 2 diabetes (P>0.05 for interaction) were not observed. Conclusions These data indicate that genetic burden and the quality of dietary fat are each associated with the incidence of type 2 diabetes. The findings do not support tailoring recommendations on the quality of dietary fat to individual type 2 diabetes genetic risk profiles for the primary prevention of type 2 diabetes, and suggest that dietary fat is associated with the risk of type 2 diabetes across the spectrum of type 2 diabetes genetic risk.

scholarly journals Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes

2021 ◽  
Vol 11 (6) ◽  
pp. 582
Author(s):  
Avigail Moldovan ◽  
Yedael Y. Waldman ◽  
Nadav Brandes ◽  
Michal Linial
Keyword(s):  
Type 2 Diabetes ◽  
Body Mass Index ◽  
Birth Weight ◽  
Body Mass ◽  
Risk Score ◽  
Early Life ◽  
Integrated Approach ◽  
Polygenic Risk Score ◽  
Polygenic Risk

One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We present here a sex-specific integrated approach that combines PRS with additional measurements and age to define a new risk score. We show that such approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n = 290,584). Likewise, integrating PRS with self-reports on birth weight (n = 172,239) and comparative body size at age ten (n = 287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.

scholarly journals Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes

2020 ◽  
Vol 21 (5) ◽  
pp. 1703 ◽  
Author(s):  
Felipe Padilla-Martínez ◽  
Francois Collin ◽  
Miroslaw Kwasniewski ◽  
Adam Kretowski
Keyword(s):  
Systematic Review ◽  
Type 2 Diabetes ◽  
Area Under The Curve ◽  
Healthy People ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Diabetes Patients

Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.

scholarly journals Harnessing the power of polygenic risk scores to predict type 2 diabetes and its subtypes in a high-risk population of British Pakistanis and Bangladeshis in a routine healthcare setting

2021 ◽  
Author(s):  
Sam Hodgson ◽  
Qin Qin Huang ◽  
Neneh Sallah ◽  
Chris J Griffiths ◽  
William Newman ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
High Risk ◽  
European Ancestry ◽  
Risk Scores ◽  
P Value ◽  
High Risk Population ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
British Pakistanis

Background: Type 2 diabetes is a heterogeneous condition highly prevalent in British Pakistanis and Bangladeshis (BPB). The Genes & Health (G&H) cohort offers means to explore genetic determinants of disease in BPBs, combining genetic and lifelong health record data. Methods: We assessed whether common genetic loci associated with type 2 diabetes in European-ancestry individuals (EUR) replicate in G&H. We constructed a type 2 diabetes polygenic risk score (PRS) and combined it with a clinical risk instrument (QDiabetes) to build a novel, integrated risk tool (IRT). We compared IRT performance using net reclassification index (NRI) versus QDiabetes alone. We assessed the ability of the PRS to predict type 2 diabetes following gestational diabetes (GDM). We compared PRS distribution between type 2 diabetes subgroups identified by clinical features at diagnosis. Findings: Accounting for power, we replicated fewer loci associated with type 2 diabetes in G&H (n = 76/338, 22%) than would be expected if all EUR-ascertained loci were transferable (n = 95, 28%) (binomial p value = 0.01). In 13,648 patients free from type 2 diabetes followed up for 10 years, NRI was 3.2% for IRT versus QDiabetes (95% confidence interval 2.0 - 4.4%). IRT performance was best in reclassification of young adults deemed low risk by QDiabetes as high risk. PRS was independently associated with progression to type 2 diabetes after GDM (p = 0.028). Mean type 2 diabetes PRS differed between phenotypically-defined type 2 diabetes subgroups (p = 0.002). Interpretation: The type 2 diabetes PRS has broad potential clinical application in BPB, improving identification of type 2 diabetes risk (especially in the young), and characterisation of type 2 diabetes subgroups at diagnosis. Funding: Wellcome Trust, MRC, NIHR, and others. Full funding disclosed within.

scholarly journals PDB54 ECONOMIC EVALUATION OF A NEW POLYGENIC RISK SCORE TO PREVENT NEPHROPATHIES IN TYPE-2 DIABETIC PATIENTS

2019 ◽  
Vol 22 ◽  
pp. S582
Author(s):  
K. Guinan ◽  
C. Beauchemin ◽  
J. Tremblay ◽  
P. Hamet ◽  
J. Chalmers ◽  
...  
Keyword(s):  
Economic Evaluation ◽  
Risk Score ◽  
Diabetic Patients ◽  
Polygenic Risk Score ◽  
Type 2 Diabetic Patients ◽  
Polygenic Risk ◽  
Type 2 Diabetic
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