scholarly journals Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Li-Na Liao ◽  
Tsai-Chung Li ◽  
Chia-Ing Li ◽  
Chiu-Shong Liu ◽  
Wen-Yuan Lin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Risk Score ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Conventional Risk Factors ◽  
Diabetes Patients

AbstractWe 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.

Clinical risk factors for depressive syndrome in Type 2 diabetes: the Fremantle Diabetes Study

2018 ◽  
Vol 35 (7) ◽  
pp. 903-910 ◽  
Author(s):  
D. G. Bruce ◽  
W. A. Davis ◽  
S. E. Starkstein ◽  
T. M. E. Davis
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Depressive Syndrome ◽  
Clinical Risk Factors ◽  
Clinical Risk

Clinical risk factors and the role of VDR gene polymorphisms in diabetic retinopathy in Polish type 2 diabetes patients

2006 ◽  
Vol 43 (4) ◽  
pp. 114-119 ◽  
Author(s):  
K. Cyganek ◽  
B. Mirkiewicz-Sieradzka ◽  
M. T. Malecki ◽  
P. Wolkow ◽  
J. Skupien ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Gene Polymorphisms ◽  
Clinical Risk Factors ◽  
Vdr Gene ◽  
Clinical Risk ◽  
Vdr Gene Polymorphisms

scholarly journals The association of ADIPOQ gene polymorphisms and clinical risk factors with nephropathy progression in type 2 diabetes

2012 ◽  
Vol 26 (S1) ◽  
Author(s):  
Hsin-Fang Chung ◽  
Pao-Shan Chen ◽  
Kurt Long ◽  
Chih-Cheng Hsu ◽  
Meng-Chuan Huang
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Gene Polymorphisms ◽  
Clinical Risk Factors ◽  
Adipoq Gene ◽  
Clinical Risk

Common Type 2 Diabetes Genetic Risk Variants Improve the Prediction of Gestational Diabetes

2019 ◽  
Vol 51 (10) ◽  
pp. 655-660 ◽  
Author(s):  
Violetta Dziedziejko ◽  
Krzysztof Safranow ◽  
Maciej Tarnowski ◽  
Andrzej Pawlik
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Gestational Diabetes ◽  
Glucose Tolerance ◽  
Pregnant Women ◽  
Genetic Polymorphisms ◽  
Roc Curves ◽  
Clinical Risk Factors ◽  
Clinical Risk

AbstractGestational diabetes mellitus (GDM) is a carbohydrate intolerance that occurs in women during pregnancy. The aims of this study were to develop a model to predict the risk of GDM development using common clinical parameters and selected genetic polymorphisms and to analyse the performance of the model using receiver operator characteristic (ROC) curves. ROC analysis was used to examine whether the evaluation of genetic polymorphisms may enhance the accuracy of GDM prediction in comparison to using common clinical risk factors only. This study included 204 pregnant women with GDM and 207 pregnant women with normal glucose tolerance. The diagnosis of GDM was based on a 75 g oral glucose tolerance test at 24–28 weeks gestation. The difference between the AUC of ROC curves for the model 1 including only age and BMI and the model 2 also including 8 genetic polymorphisms was highly significant (p=0.0001) in favour of model 2 (0.090±0.023). Moreover, the additional use of 8 genetic polymorphisms may increase both the sensitivity and specificity of GDM prediction by 10%. The results of this study indicate that the use of 8 genetic polymorphisms associated with carbohydrate and lipid metabolism and type 2 diabetes [PTGS2 (COX2) rs6681231, FADS1 rs174550, HNF1B rs4430796, ADIPOQ rs266729, IL18 rs187238, CCL2 rs1024611, HHEX rs5015480 and CDKN2A/2B rs10811661] together with clinical risk factors (BMI and age) may significantly improve the prediction of GDM.

scholarly journals 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

2021 ◽  
Vol 12 ◽  
Author(s):  
América Liliana Miranda-Lora ◽  
Jenny Vilchis-Gil ◽  
Daniel B. Juárez-Comboni ◽  
Miguel Cruz ◽  
Miguel Klünder-Klünder
Keyword(s):  
Risk Factors ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Clinical Risk Factors ◽  
Predictive Utility ◽  
Single Nucleotide ◽  
Clinical Risk ◽  
History Of ◽  
Pediatric Onset

BackgroundType 2 diabetes (T2D) is a multifactorial disease caused by a complex interplay between environmental risk factors and genetic predisposition. To date, a total of 10 single nucleotide polymorphism (SNPs) have been associated with pediatric-onset T2D in Mexicans, with a small individual effect size. A genetic risk score (GRS) that combines these SNPs could serve as a predictor of the risk for pediatric-onset T2D.ObjectiveTo assess the clinical utility of a GRS that combines 10 SNPs to improve risk prediction of pediatric-onset T2D in Mexicans.MethodsThis case-control study included 97 individuals with pediatric-onset T2D and 84 controls below 18 years old without T2D. Information regarding family history of T2D, demographics, perinatal risk factors, anthropometric measurements, biochemical variables, lifestyle, and fitness scores were then obtained. Moreover, 10 single nucleotide polymorphisms (SNPs) previously associated with pediatric-onset T2D in Mexicans were genotyped. The GRS was calculated by summing the 10 risk alleles. Pediatric-onset T2D risk variance was assessed using multivariable logistic regression models and the area under the receiver operating characteristic curve (AUC).ResultsThe body mass index Z-score (Z-BMI) [odds ratio (OR) = 1.7; p = 0.009] and maternal history of T2D (OR = 7.1; p < 0.001) were found to be independently associated with pediatric-onset T2D. No association with other clinical risk factors was observed. The GRS also showed a significant association with pediatric-onset T2D (OR = 1.3 per risk allele; p = 0.006). The GRS, clinical risk factors, and GRS plus clinical risk factors had an AUC of 0.66 (95% CI 0.56–0.75), 0.72 (95% CI 0.62–0.81), and 0.78 (95% CI 0.70–0.87), respectively (p < 0.01).ConclusionThe GRS based on 10 SNPs was associated with pediatric-onset T2D in Mexicans and improved its prediction with modest significance. However, clinical factors, such the Z-BMI and family history of T2D, continue to have the highest predictive utility in this population.

Simulation of Quality-Adjusted Survival in Chronic Diseases

2011 ◽  
Vol 31 (4) ◽  
pp. 559-570 ◽  
Author(s):  
Alison J. Hayes ◽  
Philip M. Clarke ◽  
Merryn Voysey ◽  
Anthony Keech
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Health Utility ◽  
Clinical Risk Factors ◽  
Patient Level ◽  
Clinical Risk ◽  
Life Years

Background. Recent studies have demonstrated that measures of health-related quality of life can predict complications and mortality in patients with diabetes, even after adjustment for clinical risk factors. Methods. The authors developed a simulation model of disease progression in type 2 diabetes to investigate the impact of patient quality of life on lifetime outcomes and its potential response to therapy. Changes in health utility over time are captured as a result of complications and aging. All risk equations, model parameter estimates, and input data were derived from patient-level data from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial. Results. Healthier patients with type 2 diabetes enjoy more life years, quality-adjusted life years (QALYs), and more life years free of complications. A 65-year-old patient at full health (utility = 1) can expect to live approximately 2 years longer and achieve 6 more QALYs than a patient at average health (utility = 0.8), given similar clinical risk factors. For patients with higher EQ-5D utility, the additional years lived without complications contribute more to longer life expectancy than years lived with complications. Conclusions. The authors have developed a model for progression of disease in diabetes that has a number of novel features; it captures the observed relationships between measures of quality of life and future outcomes, the number of states have been minimized, and it can be parameterized with just 4 risk equations. Underlying the simple model structure is important patient-level heterogeneity in health and outcomes. The simulations suggest that differences in patients’ EQ-5D utility can account for large differences in QALYs, which could be relevant in cost-utility analyses.

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.

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