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

T2DM/CKD genetic risk scores and the progression of diabetic kidney disease in T2DM subjects

This study aimed at understanding the predictive potential of genetic risk scores (GRS) for diabetic kidney disease (DKD) progression in patients with type 2 diabetes mellitus (T2DM) and Major Cardiovascular Events (MCVE) and All-Cause Mortality (ACM) as secondary outcomes. We evaluated 30 T2DM and CKD GWAS-derived single nucleotide polymorphisms (SNPs) and their association with clinical outcomes in a central European cohort (n = 400 patients). Our univariate Cox analysis revealed significant associations of age, duration of diabetes, diastolic blood pressure, total cholesterol and eGFR with progression of DKD (all P < 0.05). However, no single SNP was conclusively associated with progression to DKD, with only CERS2 and SHROOM3 approaching statistical significance. While a single SNP was associated with MCVE - WSF1 (P = 0.029), several variants were associated with ACM - specifically CANCAS1, CERS2 and C9 (all P < 0.02). Our GRS did not outperform classical clinical factors in predicting progression to DKD, MCVE or ACM. More precisely, we observed an increase only in the area under the curve (AUC) in the model combining genetic and clinical factors compared to the clinical model alone, with values of 0.582 (95 % CI 0.487-0.676) and 0.645 (95 % CI 0.556-0.735), respectively. However, this difference did not reach statistical significance (P = 0.06). This study highlights the complexity of genetic predictors and their interplay with clinical factors in DKD progression. Despite the promise of personalised medicine through genetic markers, our findings suggest that current clinical factors remain paramount in the prediction of DKD. In conclusion, our results indicate that GWAS-derived GRSs for T2DM and CKD do not offer improved predictive ability over traditional clinical factors in the studied Czech T2DM population.

Risk prediction of nephropathy by integrating clinical and genetic information among adult patients with type 2 diabetes

AIMS

Diabetic nephropathy (DN) is a major healthcare challenge. We developed and internally and externally validated a risk prediction model of DN by integrating clinical factors and SNPs from genes of multiple CKD-related pathways in the Han Chinese population.

MATERIALS AND METHODS

A total of 1526 patients with type 2 diabetes were randomly allocated into derivation (n = 1019) or validation (n = 507) sets. External validation was performed with 3899 participants from the Taiwan Biobank. We selected 66 SNPs identified from literature review for building our weighted genetic risk score (wGRS). The steps for prediction model development integrating clinical and genetic information were based on the Framingham Heart Study.

RESULTS

The AUROC (95% CI) for this DN prediction model with combined clinical factors and wGRS was 0.81 (0.78, 0.84) in the derivation set. Furthermore, by directly using the information of these 66 SNPs, our final prediction model had AUROC values of 0.85 (0.82, 0.87), 0.89 (0.86, 0.91), and 0.77 (0.74, 0.80) in the derivation, internal validation, and external validation sets, respectively. Under the combined model, the results with a cutoff point of 30% showed 70.91% sensitivity, 67.84% specificity, 51.54% positive predictive value, and 82.86% negative predictive value.

CONCLUSIONS

We developed and internally and externally validated a model with clinical factors and SNPs from genes of multiple CKD-related pathways to predict DN in Taiwan. This model can be used in clinical risk management practice as a screening tool to identify persons who are genetically predisposed to DN for early intervention and prevention.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

Polygenic risk for type 2 diabetes, lifestyle, metabolic health, and cardiovascular disease: a prospective UK Biobank study

BACKGROUND

Few studies have examined associations between genetic risk for type 2 diabetes (T2D), lifestyle, clinical risk factors, and cardiovascular disease (CVD). We aimed to investigate the association of and potential interactions among genetic risk for T2D, lifestyle behavior, and metabolic risk factors with CVD.

METHODS

A total of 345,217 unrelated participants of white British descent were included in analyses. Genetic risk for T2D was estimated as a genome-wide polygenic risk score constructed from > 6 million genetic variants. A favorable lifestyle was defined in terms of four modifiable lifestyle components, and metabolic health status was determined according to the presence of metabolic syndrome components.

RESULTS

During a median follow-up of 8.9 years, 21,865 CVD cases (6.3%) were identified. Compared with the low genetic risk group, participants at high genetic risk for T2D had higher rates of overall CVD events, CVD subtypes (coronary artery disease, peripheral artery disease, heart failure, and atrial fibrillation/flutter), and CVD mortality. Individuals at very high genetic risk for T2D had a 35% higher risk of CVD than those with low genetic risk (HR 1.35 [95% CI 1.19 to 1.53]). A significant gradient of increased CVD risk was observed across genetic risk, lifestyle, and metabolic health status (P for trend > 0.001). Those with favorable lifestyle and metabolically healthy status had significantly reduced risk of CVD events regardless of T2D genetic risk. This risk reduction was more apparent in young participants (≤ 50 years).

CONCLUSIONS

Genetic risk for T2D was associated with increased risks of overall CVD, various CVD subtypes, and fatal CVD. Engaging in a healthy lifestyle and maintaining metabolic health may reduce subsequent risk of CVD regardless of genetic risk for T2D.

Modeling Chronic Kidney Disease in Type 2 Diabetes Mellitus: A Systematic Literature Review of Models, Data Sources, and Derivation Cohorts

INTRODUCTION

As novel therapies for chronic kidney disease (CKD) in type 2 diabetes mellitus (T2DM) become available, their long-term benefits should be evaluated using CKD progression models. Existing models offer different modeling approaches that could be reused, but it may be challenging for modelers to assess commonalities and differences between the many available models. Additionally, the data and underlying population characteristics informing model parameters may not always be evident. Therefore, this study reviewed and summarized existing modeling approaches and data sources for CKD in T2DM, as a reference for future model development.

METHODS

This systematic literature review included computer simulation models of CKD in T2DM populations. Searches were implemented in PubMed (including MEDLINE), Embase, and the Cochrane Library, up to October 2021. Models were classified as cohort state-transition models (cSTM) or individual patient simulation (IPS) models. Information was extracted on modeled kidney disease states, risk equations for CKD, data sources, and baseline characteristics of derivation cohorts in primary data sources.

RESULTS

The review identified 49 models (21 IPS, 28 cSTM). A five-state structure was standard among state-transition models, comprising one kidney disease-free state, three kidney disease states [frequently including albuminuria and end-stage kidney disease (ESKD)], and one death state. Five models captured CKD regression and three included cardiovascular disease (CVD). Risk equations most commonly predicted albuminuria and ESKD incidence, while the most predicted CKD sequelae were mortality and CVD. Most data sources were well-established registries, cohort studies, and clinical trials often initiated decades ago in predominantly White populations in high-income countries. Some recent models were developed from country-specific data, particularly for Asian countries, or from clinical outcomes trials.

CONCLUSION

Modeling CKD in T2DM is an active research area, with a trend towards IPS models developed from non-Western data and single data sources, primarily recent outcomes trials of novel renoprotective treatments.

A systematic review and analysis of the use of polygenic scores in pharmacogenomics

Polygenic scores (PGS) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities to improve the prediction of treatment outcomes. To gain insight into this area of research, we conducted a systematic review and accompanying analysis. This review uncovered 51 papers examining the use of PGS for drug-related outcomes, with the majority of these papers focusing on the treatment of psychiatric disorders (n=30). Due to difficulties in collecting large cohorts of uniformly treated patients, the majority of pharmacogenomic PGS were derived from large-scale genome-wide association studies of disease phenotypes that were related to the pharmacogenomic phenotypes under investigation (e.g. schizophrenia-derived PGS for antipsychotic response prediction). Examination of the research participants included in these studies revealed that the majority of cohort participants were of European descent (78.4%). These biases were also reflected in research affiliations, which were heavily weighted towards institutions located in Europe and North America, with no first or last authors originating from institutions in Africa or South Asia. There was also substantial variability in the methods used to develop PGS, with between 3 and 6.6 million variants included in the PGS. Finally, we observed significant inconsistencies in the reporting of PGS analyses and results, particularly in terms of risk model development and application, coupled with a lack of data transparency and availability, with only three pharmacogenomics PGS deposited on the PGS Catalog. These findings highlight current gaps and key areas for future pharmacogenomic PGS research.

Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control

AIMS/HYPOTHESIS

Type 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk.

METHODS

We developed a multi-polygenic risk score (multiPRS) that combines ten weighted PRSs (10 wPRS) composed of 598 SNPs associated with main risk factors and outcomes of type 2 diabetes, derived from summary statistics data of genome-wide association studies. The 10 wPRS, first principal component of ethnicity, sex, age at onset and diabetes duration were included into one logistic regression model to predict micro- and macrovascular outcomes in 4098 participants in the ADVANCE study and 17,604 individuals with type 2 diabetes in the UK Biobank study.

RESULTS

The model showed a similar predictive performance for cardiovascular and renal complications in different cohorts. It identified the top 30% of ADVANCE participants with a mean of 3.1-fold increased risk of major micro- and macrovascular events (p = 6.3 × 10^-21 and p = 9.6 × 10^-31, respectively) and a 4.4-fold (p = 6.8 × 10^-33) higher risk of cardiovascular death. While in ADVANCE overall, combined intensive blood pressure and glucose control decreased cardiovascular death by 24%, the model identified a high-risk group in whom it decreased the mortality rate by 47%, and a low-risk group in whom it had no discernible effect. High-risk individuals had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years.

CONCLUSIONS/INTERPRETATION

This novel multiPRS model stratified individuals with type 2 diabetes according to risk of complications and helped to target earlier those who would receive greater benefit from intensive therapy.