scholarly journals The Liability Threshold Model for Predicting the Risk of Cardiovascular Disease in Patients with Type 2 Diabetes: A Multi-Cohort Study of Korean Adults

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Eun Pyo Hong ◽  
Seong Gu Heo ◽  
Ji Wan Park
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Risk Prediction ◽  
Linear Mixed Model ◽  
Threshold Model ◽  
Snp Markers ◽  
Polygenic Risk Score ◽  
Phenotypic Variance ◽  
Prediction Approach

Personalized risk prediction for diabetic cardiovascular disease (DCVD) is at the core of precision medicine in type 2 diabetes (T2D). We first identified three marker sets consisting of 15, 47, and 231 tagging single nucleotide polymorphisms (tSNPs) associated with DCVD using a linear mixed model in 2378 T2D patients obtained from four population-based Korean cohorts. Using the genetic variants with even modest effects on phenotypic variance, we observed improved risk stratification accuracy beyond traditional risk factors (AUC, 0.63 to 0.97). With a cutoff point of 0.21, the discrete genetic liability threshold model consisting of 231 SNPs (GLT231) correctly classified 87.7% of 2378 T2D patients as high or low risk of DCVD. For the same set of SNP markers, the GLT and polygenic risk score (PRS) models showed similar predictive performance, and we observed consistency between the GLT and PRS models in that the model based on a larger number of SNP markers showed much-improved predictability. In silico gene expression analysis, additional information was provided on the functional role of the genes identified in this study. In particular, HDAC4, CDKN2B, CELSR2, and MRAS appear to be major hubs in the functional gene network for DCVD. The proposed risk prediction approach based on the liability threshold model may help identify T2D patients at high CVD risk in East Asian populations with further external validations.

scholarly journals Risk Prediction of Cardiovascular Disease in Type 2 Diabetes: A risk equation from the Swedish National Diabetes Register

Diabetes Care ◽  
2008 ◽  
Vol 31 (10) ◽  
pp. 2038-2043 ◽  
Author(s):  
J. Cederholm ◽  
K. Eeg-Olofsson ◽  
B. Eliasson ◽  
B. Zethelius ◽  
P. M. Nilsson ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Risk Prediction ◽  
Risk Equation ◽  
Diabetes Register

scholarly journals Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data

JAMIA Open ◽  
2020 ◽  
Author(s):  
Jackie Szymonifka ◽  
Sarah Conderino ◽  
Christine Cigolle ◽  
Jinkyung Ha ◽  
Mohammed Kabeto ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Risk Prediction ◽  
Incidence Rates ◽  
Risk Scores ◽  
Cvd Risk ◽  
Clinical Risk ◽  
Demographic Covariates

Abstract Objective Electronic health records (EHRs) have become a common data source for clinical risk prediction, offering large sample sizes and frequently sampled metrics. There may be notable differences between hospital-based EHR and traditional cohort samples: EHR data often are not population-representative random samples, even for particular diseases, as they tend to be sicker with higher healthcare utilization, while cohort studies often sample healthier subjects who typically are more likely to participate. We investigate heterogeneities between EHR- and cohort-based inferences including incidence rates, risk factor identifications/quantifications, and absolute risks. Materials and methods This is a retrospective cohort study of older patients with type 2 diabetes using EHR from New York University Langone Health ambulatory care (NYULH-EHR, years 2009–2017) and from the Health and Retirement Survey (HRS, 1995–2014) to study subsequent cardiovascular disease (CVD) risks. We used the same eligibility criteria, outcome definitions, and demographic covariates/biomarkers in both datasets. We compared subsequent CVD incidence rates, hazard ratios (HRs) of risk factors, and discrimination/calibration performances of CVD risk scores. Results The estimated subsequent total CVD incidence rate was 37.5 and 90.6 per 1000 person-years since T2DM onset in HRS and NYULH-EHR respectively. HR estimates were comparable between the datasets for most demographic covariates/biomarkers. Common CVD risk scores underestimated observed total CVD risks in NYULH-EHR. Discussion and conclusion EHR-estimated HRs of demographic and major clinical risk factors for CVD were mostly consistent with the estimates from a national cohort, despite high incidences and absolute risks of total CVD outcome in the EHR samples.

scholarly journals Type 2 Diabetes Risk Prediction Incorporating Family History Revealing a Substantial Fraction of Missing Heritability

2016 ◽  
Author(s):  
Jungsoo Gim ◽  
Wonji Kim ◽  
Soo Heon Kwak ◽  
Kyong Soo Park ◽  
Sungho Won
Keyword(s):  
Type 2 Diabetes ◽  
Family History ◽  
Risk Prediction ◽  
Genetic Variants ◽  
Large Scale ◽  
Threshold Model ◽  
Penalized Regression ◽  
Missing Heritability ◽  
History Of

ABSTRACTDespite many successes of genome-wide association (GWA) studies, known susceptibility variants identified by GWAS have the modest effect sizes and we met noticeable skepticism about the risk prediction model building with large-scale genetic data. However, in contrast with genetic variants, family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction though; complicated structures of family history of diseases have limited their application to clinical use. Here, we develop a new method which enables the incorporation of general family history of diseases with the liability threshold model and a new analysis strategy for risk prediction with penalized regression incorporating large-scale genetic variants and clinical risk factors. An application of our model to type 2 diabetes (T2D) patients in Korean population (1846 cases out of 3692 subjects) demonstrates that SNPs accounts for 28.6% of T2D’s variability and incorporation of family history leads to additional improvement of 5.9%. Our result illustrates that family history of diseases can have an invaluable information for disease prediction and may bridge the gap originated from missing heritability.

scholarly journals Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care setting

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
K D Dziopa ◽  
F W A Asselbergs ◽  
J G Gratton ◽  
N C Chaturvedi ◽  
A F S Schmidt
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
General Population ◽  
Risk Prediction ◽  
Risk Scores ◽  
Cvd Risk ◽  
C Statistic ◽  
Cardiovascular Risk Prediction

Abstract   People with type 2 diabetes (T2DM) remain at high risk for cardiovascular disease (CVD) CVD treatment initiation and intensification are guided by risk prediction algorithms. The majority of CVD risk prediction tools have not been validated in T2DM. We compared the performance of general and diabetes specific cardiovascular risk prediction scores for cardiovascular disease (CVD ie coronary heart disease and stroke), CVD+ (including atrial fibrillation and heart failure), and their individual components, in type 2 diabetes patients (T2DM). Scores were identified through a systematic review and included irrespective of the type of predicted CVD, or inclusion of T2DM patients. Performance was assessed in a contemporary sample of 203,172 UK T2DM. We identified 22 scores: 11 derived in the general population, 9 in T2DM patients, and 2 excluded T2DM patients. Over 10 years follow-up, 63,000 events occurred. The RECODE score, derived in people with T2DM, performed best for both CVD (c-statistic 0.731 (0.728,0.734), and CVD+ (0.732 (0.729,0.735)). Calibration slopes (1 indicates perfect calibration) ranged from 0.38 (95% CI 0.37; 0.39) to 1.05 (95% CI 1.03; 1.07). A simple, population specific recalibration process considerably improved performance, now ranging between 0.98 and 1.03. Risk scores performed badly in people with pre-existing CVD (c-statistic ∼0.55). CVD risk prediction scores performed worse in T2DM than in the general population, irrespective of derivation population, and of original predicted outcome. Scores performed especially poorly in patients with established CVD. A simple population specific recalibration markedly improved score performance and is recommended for future use. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): NPIF programme

scholarly journals The genetic architecture of appendicular lean mass characterized by association analysis in the UK Biobank study

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Yu-Fang Pei ◽  
Yao-Zhong Liu ◽  
Xiao-Lin Yang ◽  
Hong Zhang ◽  
Gui-Juan Feng ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Lean Mass ◽  
Genetic Architecture ◽  
Polygenic Risk Score ◽  
Phenotypic Variance ◽  
Uk Biobank ◽  
Appendicular Lean Mass ◽  
Genome Wide ◽  
A Genome

Abstract Appendicular lean mass (ALM) is a heritable trait associated with loss of lean muscle mass and strength, or sarcopenia, but its genetic determinants are largely unknown. Here we conducted a genome-wide association study (GWAS) with 450,243 UK Biobank participants to uncover its genetic architecture. A total of 1059 conditionally independent variants from 799 loci were identified at the genome-wide significance level (p < 5 × 10−9), all of which were also significant at p < 5 × 10–5 in both sexes. These variants explained ~15.5% of the phenotypic variance, accounting for more than one quarter of the total ~50% GWAS-attributable heritability. There was no difference in genetic effect between sexes or among different age strata. Heritability was enriched in certain functional categories, such as conserved and coding regions, and in tissues related to the musculoskeletal system. Polygenic risk score prediction well distinguished participants with high and low ALM. The findings are important not only for lean mass but also for other complex diseases, such as type 2 diabetes, as ALM is shown to be a protective factor for type 2 diabetes.

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