scholarly journals Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

Diabetes Care ◽  
2021 ◽  
pp. dc202049
Author(s):  
Yixuan He ◽  
Chirag M. Lakhani ◽  
Danielle Rasooly ◽  
Arjun K. Manrai ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Prediction ◽  
Risk Scores ◽  
Clinical Risk

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 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.

scholarly journals Use of Biomarkers in Predicting the Onset, Monitoring the Progression, and Risk Stratification for Patients with Type 2 Diabetes Mellitus

2017 ◽  
Vol 63 (1) ◽  
pp. 186-195 ◽  
Author(s):  
Benjamin M Scirica
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Risk Stratification ◽  
Cardiac Biomarkers ◽  
Risk Scores ◽  
Oral Glucose ◽  
Clinical Risk ◽  
Monitoring Therapy

Abstract BACKGROUND As the worldwide prevalence of type 2 diabetes mellitus (T2DM) increases, it is even more important to develop cost-effective methods to predict and diagnose the onset of diabetes, monitor progression, and risk stratify patients in terms of subsequent cardiovascular and diabetes complications. CONTENT Nonlaboratory clinical risk scores based on risk factors and anthropomorphic data can help identify patients at greatest risk of developing diabetes, but glycemic indices (hemoglobin A1c, fasting plasma glucose, and oral glucose tolerance tests) are the cornerstones for diagnosis, and the basis for monitoring therapy. Although family history is a strong predictor of T2DM, only small populations of patients carry clearly identifiable genetic mutations. Better modalities for detection of insulin resistance would improve earlier identification of dysglycemia and guide effective therapy based on therapeutic mechanisms of action, but improved standardization of insulin assays will be required. Although clinical risk models can stratify patients for subsequent cardiovascular risk, the addition of cardiac biomarkers, in particular, high-sensitivity troponin and natriuretic peptide provide, significantly improves model performance and risk stratification. CONCLUSIONS Much more research, prospectively planned and with clear treatment implications, is needed to define novel biomarkers that better identify the underlying pathogenic etiologies of dysglycemia. When compared with traditional risk features, biomarkers provide greater discrimination of future risk, and the integration of cardiac biomarkers should be considered part of standard risk stratification in patients with T2DM.

scholarly journals Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores

2016 ◽  
Vol 19 (3) ◽  
pp. 322-329 ◽  
Author(s):  
Kristi Läll ◽  
Reedik Mägi ◽  
Andrew Morris ◽  
Andres Metspalu ◽  
Krista Fischer
Keyword(s):  
Type 2 Diabetes ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Risk Scores ◽  
Genetic Risk Scores ◽  
Personalized Risk

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

2020 ◽  
Author(s):  
K Dziopa ◽  
F W Asselbergs ◽  
J Gratton ◽  
N Chaturvedi ◽  
A F Schmidt
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Risk Scores ◽  
Routine Practice ◽  
Cvd Risk ◽  
C Statistic ◽  
Cardiovascular Risk Prediction ◽  
Multiple Variables

AbstractObjectiveTo compare performance of general and diabetes specific cardiovascular risk prediction scores in type 2 diabetes patients (T2DM).DesignCohort study.SettingScores were identified through a systematic review and included irrespective of predicted outcome, or inclusion of T2DM patients. Performance was assessed using data from routine practice.ParticipantsA contemporary representative sample of 203,172 UK T2DM patients (age ≥ 18 years).Main outcome measuresCardiovascular disease (CVD i.e., coronary heart disease and stroke) and CVD+ (including atrial fibrillation and heart failure).ResultsWe identified 22 scores: 11 derived in the general population, 9 in only T2DM patients, and 2 that 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)). Overall, neither derivation population, nor original predicted outcome influenced performance. 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, ranging between 0.98 and 1.03. Risk scores performed badly in people with pre-existing CVD (c-statistic ∼0.55). Scores with more predictors did not perform scores better: for CVD+ QRISK3 (19 variables) c-statistic 0.69 (95%CI 0.68;0.69), compared to CHD Basic (8 variables) 0.71 (95%CI 0.70; 0.71).ConclusionsCVD risk prediction scores performed well in T2DM, irrespective of derivation population and of original predicted outcome. Scores performed poorly in patients with established CVD. Complex scores with multiple variables did not outperform simple scores. A simple population specific recalibration markedly improved score performance and is recommended for future use.

scholarly journals Multi-ethnic polygenic risk scores improve risk prediction in diverse populations

2016 ◽  
Author(s):  
Carla Márquez-Luna ◽  
Po-Ru Loh ◽  
Alkes L. Price ◽  
◽  
Keyword(s):  
Type 2 Diabetes ◽  
Sample Size ◽  
Risk Prediction ◽  
Prediction Accuracy ◽  
Target Population ◽  
Training Data ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Relative Improvement

AbstractMethods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multi-ethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size and Latino training data in small sample size. We attained a >70% relative improvement in prediction accuracy (from R2=0.027 to R2=0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. Application of our approach to predict T2D in a South Asian UK Biobank cohort attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.Author SummaryThe use of genetic information to predict disease risk is of great interest because of its potential clinical application. Prediction is performed via the construction of polygenic risk scores, which separate individuals into different risk categories. Polygenic risk scores can also be applied to improve our understanding of the genetic architecture of complex diseases. The ideal training data set would be a large cohort from the same population as the target sample, but this is generally unavailable for non-European populations. Thus, we propose a summary statistics based polygenic risk score that leverages both a large European training sample and a training sample from the same population as the target population. This approach produces a substantial relative improvement in prediction accuracy compared to methods that use a single training population when applied to predict type 2 diabetes in a Latino cohort, consistent with simulation results. We observed similar relative improvements in applications to predict type 2 diabetes in a South Asian cohort and height in an African cohort.

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

Diabetologia ◽  
2022 ◽  
Author(s):  
Katarzyna Dziopa ◽  
Folkert W. Asselbergs ◽  
Jasmine Gratton ◽  
Nishi Chaturvedi ◽  
Amand F. Schmidt
Keyword(s):  
Type 2 Diabetes ◽  
General Population ◽  
Risk Prediction ◽  
Risk Scores ◽  
P Value ◽  
Cvd Risk ◽  
Discriminative Ability ◽  
C Statistic ◽  
The Uk

Abstract Aims/hypothesis We aimed to compare the performance of risk prediction scores for CVD (i.e., coronary heart disease and stroke), and a broader definition of CVD including atrial fibrillation and heart failure (CVD+), in individuals with type 2 diabetes. Methods Scores were identified through a literature review and were included irrespective of the type of predicted cardiovascular outcome or the inclusion of individuals with type 2 diabetes. Performance was assessed in a contemporary, representative sample of 168,871 UK-based individuals with type 2 diabetes (age ≥18 years without pre-existing CVD+). Missing observations were addressed using multiple imputation. Results We evaluated 22 scores: 13 derived in the general population and nine in individuals with type 2 diabetes. The Systemic Coronary Risk Evaluation (SCORE) CVD rule derived in the general population performed best for both CVD (C statistic 0.67 [95% CI 0.67, 0.67]) and CVD+ (C statistic 0.69 [95% CI 0.69, 0.70]). The C statistic of the remaining scores ranged from 0.62 to 0.67 for CVD, and from 0.64 to 0.69 for CVD+. Calibration slopes (1 indicates perfect calibration) ranged from 0.38 (95% CI 0.37, 0.39) to 0.74 (95% CI 0.72, 0.76) for CVD, and from 0.41 (95% CI 0.40, 0.42) to 0.88 (95% CI 0.86, 0.90) for CVD+. A simple recalibration process considerably improved the performance of the scores, with calibration slopes now ranging between 0.96 and 1.04 for CVD. Scores with more predictors did not outperform scores with fewer predictors: for CVD+, QRISK3 (19 variables) had a C statistic of 0.68 (95% CI 0.68, 0.69), compared with SCORE CVD (six variables) which had a C statistic of 0.69 (95% CI 0.69, 0.70). Scores specific to individuals with diabetes did not discriminate better than scores derived in the general population: the UK Prospective Diabetes Study (UKPDS) scores performed significantly worse than SCORE CVD (p value <0.001). Conclusions/interpretation CVD risk prediction scores could not accurately identify individuals with type 2 diabetes who experienced a CVD event in the 10 years of follow-up. All 22 evaluated models had a comparable and modest discriminative ability. Graphical abstract

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 Could personalised risk prediction for type 2 diabetes using polygenic risk scores direct prevention, enhance diagnostics, or improve treatment?

2020 ◽  
Vol 5 ◽  
pp. 206
Author(s):  
Mathilde Boecker ◽  
Alvina G. Lai
Keyword(s):  
Type 2 Diabetes ◽  
Risk Prediction ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Genome Wide

Over the past three decades, the number of people globally with diabetes mellitus has more than doubled. It is estimated that by 2030, 439 million people will be suffering from the disease, 90-95% of whom will have type 2 diabetes (T2D). In 2017, 5 million deaths globally were attributable to T2D, placing it in the top 10 global causes of death. Because T2D is a result of both genetic and environmental factors, identification of individuals with high genetic risk can help direct early interventions to prevent progression to more serious complications. Genome-wide association studies have identified ~400 variants associated with T2D that can be used to calculate polygenic risk scores (PRS). Although PRSs are not currently more accurate than clinical predictors and do not yet predict risk with equal accuracy across all ethnic populations, they have several potential clinical uses. Here, we discuss potential usages of PRS for predicting T2D and for informing and optimising interventions. We also touch on possible health inequality risks of PRS and the feasibility of large-scale implementation of PRS in clinical practice. Before PRSs can be used as a therapeutic tool, it is important that further polygenic risk models are derived using non-European genome-wide association studies to ensure that risk prediction is accurate for all ethnic groups. Furthermore, it is essential that the ethical, social and legal implications of PRS are considered before their implementation in any context.

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