Integrated Risk Model Research Articles

Colorectal cancer risk stratification using a polygenic risk score in symptomatic primary care patients-a UK Biobank retrospective cohort study.

Colorectal cancer (CRC) is a leading cause of cancer mortality worldwide. Accurate cancer risk assessment approaches could increase rates of early CRC diagnosis, improve health outcomes for patients and reduce pressure on diagnostic services. The faecal immunochemical test (FIT) for blood in stool is widely used in primary care to identify symptomatic patients with likely CRC. However, there is a 6-16% noncompliance rate with FIT in clinic and ~90% of patients over the symptomatic 10 µg/g test threshold do not have CRC. A polygenic risk score (PRS) quantifies an individual's genetic risk of a condition based on many common variants. Existing PRS for CRC have so far been used to stratify asymptomatic populations. We conducted a retrospective cohort study of 50,387 UK Biobank participants with a CRC symptom in their primary care record at age 40+. A PRS based on 201 variants, 5 genetic principal components and 22 other risk factors and markers for CRC were assessed for association with CRC diagnosis within 2 years of first symptom presentation using logistic regression. Associated variables were included in an integrated risk model and trained in 80% of the cohort to predict CRC diagnosis within 2 years. An integrated risk model combining PRS, age, sex, and patient-reported symptoms was predictive of CRC development in a testing cohort (receiver operating characteristic area under the curve, ROCAUC: 0.76, 95% confidence interval: 0.71-0.81). This model has the potential to improve early diagnosis of CRC, particularly in cases of patient noncompliance with FIT.

Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model.

The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is, a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time-dependent hazard and survival (defined as disease-free time) of an individual for the onset of a disease. This provides a practitioner with a much more differentiated view of absolute survival as a function of time. Second, to compute the time-dependent risk of an individual, we use published methodology to fit a Cox's proportional hazard model to data from a genetic SNP study of time to Alzheimer's disease (AD) onset, using the lasso to incorporate further epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status, 10 leading principal components, and selected genomic loci. We apply the lasso for Cox's proportional hazards to a data set of 6792 AD patients (composed of 4102 cases and 2690 controls) and 87 covariates. We demonstrate that fitting a lasso model for Cox's proportional hazards allows one to obtain more accurate survival curves than with state-of-the-art (likelihood-based) methods. Moreover, the methodology allows one to obtain personalized survival curves for a patient, thus giving a much more differentiated view of the expected progression of a disease than the view offered by integrated risk models. The runtime to compute personalized survival curves is under a minute for the entire data set of AD patients, thus enabling it to handle datasets with 60,000-100,000 subjects in less than 1 h.

Polygenic inheritance and its interplay with smoking history in predicting lung cancer diagnosis: a French-Canadian case-control cohort

The most near-term clinical application of genome-wide association studies in lung cancer is a polygenic risk score (PRS). A case-control dataset was generated consisting of 4002 lung cancer cases from the LORD project and 20,010 ethnically matched controls from CARTaGENE. A genome-wide PRS including >1.1 million genetic variants was derived and validated in UK Biobank (n=5419 lung cancer cases). The predictive ability and diagnostic discrimination performance of the PRS was tested in LORD/CARTaGENE and benchmarked against previous PRSs from the literature. Stratified analyses were performed by smoking status and genetic risk groups defined as low (<20th percentile), intermediate (20-80th percentile) and high (>80th percentile) PRS. The phenotypic variance explained and the effect size of the genome-wide PRS numerically outperformed previous PRSs. Individuals with high genetic risk had a 2-fold odds of lung cancer compared to low genetic risk. The PRS was an independent predictor of lung cancer beyond conventional clinical risk factors, but its diagnostic discrimination performance was incremental in an integrated risk model. Smoking increased the odds of lung cancer by 7.7-fold in low genetic risk and by 11.3-fold in high genetic risk. Smoking with high genetic risk was associated with a 17-fold increase in the odds of lung cancer compared to individuals who never smoked and with low genetic risk. Individuals at low genetic risk are not protected against the smoking-related risk of lung cancer. The joint multiplicative effect of PRS and smoking increases the odds of lung cancer by nearly 20-fold. This work was supported by the CQDM and the IUCPQ Foundation owing to a generous donation from Mr. Normand Lord.

Abstract PO3-08-01: Decision curve analysis to compare breast cancer risk predictions for a polygenic integrated clinical risk model with those of a gold standard

Abstract Risk-based guidelines have been developed to provide advice on options that are available to women who are at increased risk of breast cancer. We and others are focused on improving risk prediction models for general population use. For breast cancer, polygenic risk has been identified as important for risk prediction, especially when combined with clinical risk factors such as family history, anthropometric measures, breast imaging measures, and hormonal and reproductive risk factors. When comparing the performance of risk prediction models, the AUC is most often used but it focuses on performance in terms of distinguishing between affected and unaffected individuals and does not take into account differences in the specificity (true negative rate) and sensitivity (true positive rate) of the models being compared. In contrast, decision curve analysis takes sensitivity and specificity into account and enables the comparison of the net benefit of risk prediction tools at the clinical risk thresholds that are used to guide clinical management decisions. Herein, we use decision curve analysis to compare breast cancer risk prediction guidelines for BRISK and the Breast Cancer Risk Assessment Tool (BCRAT) in the UK Biobank, and for BRISK and IBIS version 7 in the Nurses’ Health Study. We evaluated the net benefit at the 5-year risk thresholds (1.67% and 3%) that are used to guide clinical management decisions around risk-reducing medication and at the remaining lifetime risk of 20% that is used to guide supplemental MRI surveillance. In the UK Biobank, BRISK showed a net benefit over BCRAT at the 5-year risk thresholds of 1.67% and 3.0%. The AUCs were 0.649 (95% CI = 0.640, 0.695) for BRISK and 0.567 (95% CI = 0.556, 0.577) for BCRAT and showed that, overall, BRISK discriminated between affected and unaffected women better than BCRAT (P &amp;lt; 0.001). In the Nurses’ Health Study, BRISK showed a net benefit over IBIS at the remaining lifetime risk threshold of 20%. The AUCs were 0.647; 95% CI = 0.627, 0.668 for BRISK and 0.571; 95% CI = 0.546, 0.595 for IBIS, a statistically significant improvement for BRISK over IBIS (P &amp;lt; 0.0001). We also looked at sensitivity, specificity, positive predictive value and negative predictive value even though these risk models are one-step removed from breast cancer diagnosis by screening/risk-reduction options. The positive predictive values are higher for the BRISK (27.1%) model compared to BCRAT (5.5%) in the UK Biobank dataset and higher for BRISK (15.4%) compared to IBISv7 (14.0%) in the Nurses’ dataset. We have shown the application of a statistical tool that can be used to directly compare risk models at clinically relevant thresholds. We have shown that BRISK, which comprises polygenic risk and clinical risk factors, shows higher net benefit at both 1.67% and 3% thresholds compared to BCRAT and at 20% remaining lifetime risk for IBIS version 7. In breast cancer risk prediction, a tool with high specificity (and therefore a necessary trade-off for low sensitivity) is preferred because we do not want to incorrectly classify women as not being at high risk and deny them the opportunity for intervention in the form of more frequent screening, alternative modes of screening or risk-reducing medication. This improvement in general population risk stratification that BRISK can provide has the potential to have a clinically significant effect on women’s health. Citation Format: Erika Spaeth, Bernard A Rosner, Gill Dite. Decision curve analysis to compare breast cancer risk predictions for a polygenic integrated clinical risk model with those of a gold standard [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-08-01.

Treatment escalation and de-escalation of de-novo metastatic castration-sensitive prostate cancer.

Androgen receptor signaling inhibitors combined with androgen deprivation therapy have become the standard of care for metastatic castration-sensitive prostate cancer (mCSPC), regardless of tumor volume or risk. However, survival of approximately one-third of these patients has not improved, necessitating further treatment escalation. On the other hand, for patients with oligometastatic mCSPC, there is an emerging role for local radiation therapy. Although data remain scarce, it is expected that treatment of both primary tumor as well as metastasis-directed therapy may improve survival outcomes. In these patients, systemic therapy may be de-escalated to intermittent therapy. However, precise risk stratification is necessary for risk-based treatment escalation or de-escalation. In addition to risk stratification based on clinical parameters, research has been conducted to incorporate genomic and/or transcriptomic data into risk stratification. In future, an integrated risk model is expected to precisely stratify patients and guide treatment strategies. Here, we first review the transition of the standard treatment for mCSPC over the last decade and further discuss the newest concept of escalating or de-escalating treatment using a multi-modal approach based on the currently available literature.

Improving predictive accuracy in primary biliary cholangitis: Anew genetic risk score.

Genetic variants influence primary biliary cholangitis (PBC) risk. We established and tested an accurate polygenic risk score (PRS) using these variants. Data from two Italian cohorts (OldIT 444 cases, 901 controls; NewIT 255 cases, 579 controls) were analysed. The latest international genome-wide meta-analysis provided effect size estimates. The PRS, together with human leukocyte antigen (HLA) status and sex, was included in an integrated risk model. Starting from 46 non-HLA genes, 22 variants were selected. PBC patients in the OldIT cohort showed a higher risk score than controls: -.014 (interquartile range, IQR, -.023, .005) versus -.022 (IQR -.030, -.013) (p < 2.2 × 10-16). For genetic-based prediction, the area under the curve (AUC) was .72; adding sex increased the AUC to .82. Validation in the NewIT cohort confirmed the model's accuracy (.71 without sex, .81 with sex). Individuals in the top group, representing the highest 25%, had a PBC risk approximately 14 times higher than that of the reference group (lowest 25%; p < 10-6). The combination of sex and a novel PRS accurately discriminated between PBC cases and controls. The model identified a subset of individuals at increased risk of PBC who might benefit from tailored monitoring.

Integration of polygenic and gut metagenomic risk prediction for common diseases

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Integrative analysis and risk model construction for super‑enhancer‑related immune genes in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer associated with poor prognosis, and accounts for the majority of RCC-related deaths. The lack of comprehensive diagnostic and prognostic biomarkers has limited further understanding of the pathophysiology of ccRCC. Super-enhancers (SEs) are congregated enhancer clusters that have a key role in tumor processes such as epithelial-mesenchymal transition, metabolic reprogramming, immune escape and resistance to apoptosis. RCC may also be immunogenic and sensitive to immunotherapy. In the present study, an Arraystar human SE-long non-coding RNA (lncRNA) microarray was first employed to profile the differentially expressed SE-lncRNAs and mRNAs in 5 paired ccRCC and peritumoral tissues and to identify SE-related genes. The overlap of these genes with immune genes was then determined to identify SE-related immune genes. A model for predicting clinical prognosis and response to immunotherapy was built following the comprehensive analysis of a ccRCC gene expression dataset from The Cancer Genome Atlas (TCGA) database. The patients from TCGA were divided into high- and low-risk groups based on the median score derived from the risk model, and the Kaplan-Meier survival analysis showed that the low-risk group had a higher survival probability. In addition, according to the receiver operating characteristic curve analysis, the risk model had more advantages than other clinical factors in predicting the overall survival (OS) rate of patients with ccRCC. Using this model, it was demonstrated that the high-risk group had a more robust immune response. Furthermore, 61 potential drugs with half-maximal inhibitory concentration values that differed significantly between the two patient groups were screened to investigate potential drug treatment of ccRCC. In summary, the present study provided a novel index for predicting the survival probability of patients with ccRCC and may provide some insights into the mechanisms through which SE-related immune genes influence the diagnosis, prognosis and potential treatment drugs of ccRCC.

Supporting Multilingualism in Immigrant Children: An Integrative Approach

Immigrant children are a growing and demographically important segment of the world's population. One key aspect of immigrant children's experience is navigating multiple languages, creating both opportunities, and challenges. However, the literature on bilingualism rarely centers the experiences of immigrant children. Focusing on immigrant children in the United States, this article brings together cognitive science research on bilingualism with the integrative risk and resilience model of adaptation in immigrant-origin children to elucidate how common contexts that immigrant children encounter can support or discourage multilingualism. Policy must consider immigrant children's intersecting identities—both as immigrants and as learners of minoritized, and often racialized, languages. A proposed framework can guide policies to support multilingualism in immigrant children, with downstream consequences for their health and development.

A robust and validated integrated prognostic index for defining risk groups in adult acute lymphoblastic leukemia: an EWALL collaborative study

AbstractRisk stratification is crucial to the successful treatment of acute lymphoblastic leukemia (ALL). Although numerous risk factors have been identified, an optimal prognostic model for integrating variables has not been developed. We used individual patient data from 4 contemporary academic national clinical trials, UKALL14, NILG-ALL10/07, GIMEMA-LAL1913, and PETHEMA-ALL-HR2011, to generate and validate the European Working Group for Adult ALL prognostic index (EWALL-PI), which is based on white blood cell count, genetics, and end of induction minimal residual disease (MRD). Individual patient risk scores were calculated for 778 patients aged 15 to 67 years in complete remission using the validated UKALL-PI formula, applying minor modifications to reflect differences between pediatric and adult ALL. Per-trial analysis revealed that EWALL-PI correlated with relapse and death. Regression analysis revealed that each unit increase in EWALL-PI increased the risk of relapse or death by ∼30% with no evidence of heterogeneity across trials or patient subgroups. EWALL-PI–defined risk models outperformed the stratification algorithms used by each trial. Threshold analysis revealed an EWALL-PI threshold that divided patients with B cell and T cell into standard (EWALL-PI <2.50) and high (EWALL-PI ≥2.50) risk groups, respectively. Per-trial analysis showed that patients at high risk had a significantly increased relapse rate and inferior survival compared with patients with standard risk (subdistribution hazard ratio for relapse, ranged from 1.85 to 3.28; hazard ratio for death, 1.73 to 3.03). Subgroup analysis confirmed the robustness of these risk groups by sex, age, white blood cell count, and lineage. In conclusion, we validated an integrated risk model across 4 independent adult ALL clinical trials, demonstrating its utility defining clinically relevant risk groups.

Comprehensive analysis reveals potential therapeutic targets and an integrated risk stratification model for solitary fibrous tumors

Solitary fibrous tumors (SFTs) are rare mesenchymal tumors with unpredictable evolution and with a recurrence or metastasis rate of 10-40%. Current medical treatments for relapsed SFTs remain ineffective. Here, we identify potential therapeutic targets and risk factors, including IDH1 p.R132S, high PD-L1 expression, and predominant macrophage infiltration, suggesting the potential benefits of combinational immune therapy and targeted therapy for SFTs. An integrated risk model incorporating mitotic count, density of Ki-67+ cells and CD163+ cells, MTOR mutation is developed, applying a discovery cohort of 101 primary non-CNS patients with negative tumor margins (NTM) and validated in three independent cohorts of 210 SFTs with the same criteria, and in 36 primary CNS SFTs with NTM. Compared with the existing models, our model shows significantly improved efficacy in identifying high-risk primary non-CNS and CNS SFTs with NTM for tumor progression.Our findings hold promise for advancing therapeutic strategies and refining risk prediction in SFTs.

Clinical and vessel characteristics associated with hard outcomes after PCI and their combined prognostic implications

Abstract Background While clinical outcomes after percutaneous coronary intervention (PCI) have improved in recent decades, cardiac death or myocardial infarction (MI) still occurs in patients undergoing contemporary PCI. An integrative risk model for post-PCI hard clinical endpoints incorporating clinical risk factors, angiographic and physiological vessel characteristics has not been established. Purpose To identify adverse clinical and vessel characteristics related to hard outcomes after PCI and to investigate their individual and combined prognostic implications. Methods From an individual patient data meta-analysis of 17 cohorts of patients who underwent post-PCI fractional flow reserve (FFR) measurement after drug eluting stent (DES) implantation, 2,081 patients with available 10 clinical characteristics (age, sex, clinical diagnosis at index procedure, diabetes, hypertension, dyslipidemia, smoking history, chronic kidney disease [CKD], prior myocardial infarction, left ventricular ejection fraction [LVEF]) and 8 vessel characteristics (target vessel, reference vessel diameter, % diameter stenosis, minimum lumen diameter [MLD], post-PCI % diameter stenosis, post-PCI MLD, total stent length in the target vessel, and post-PCI FFR) were analyzed. The least absolute shrinkage and selection operator (LASSO) model was applied for feature selection. The primary outcome was cardiac death or target vessel-MI at 2-years. Results The mean age of patients was 64.2±10.2 years, and the mean angiographic % diameter stenosis was 63.9±14.3%. Among 10 clinical and 8 vessel features, 4 adverse clinical characteristics (ACCs; age ≥65 years, diabetes, chronic kidney disease, and left ventricular ejection fraction &amp;lt;50%) and 2 adverse vessel characteristics (AVCs; post-PCI FFR ≤0.80 and total stent length ≥54 mm) were selected by the LASSO model. All ACCs and AVCs were independent predictors for the primary outcome (HR 3.33 [95% CI 1.34 – 8.25], P =0.009 for age ≥65 years; HR 2.62 [1.22 – 5.65], P =0.014 for diabetes; HR 5.95 [2.52 – 14.1], P &amp;lt;0.001 for CKD; HR 2.91 [1.17 – 7.20], P =0.021 for LVEF &amp;lt;50%; HR 3.79 [1.73 – 8.28], P &amp;lt;0.001 for post-PCI FFR ≤0.80; HR 2.42 [1.08 – 5.39], P =0.031 for total stent length ≥54 mm). The number of AVCs had additive predictability for the primary endpoint to that of ACCs (AUC 0.72 vs 0.78, P =0.03), and vice versa (AUC 0.68 vs 0.78, P =0.03). The cumulative event rate increased in the order of none, either, and both of ACCs ≥2 and AVCs ≥1 (0.3%, 2.4%, and 5.3%, P-for-trend &amp;lt;0.01) (Figure). Conclusions In patients undergoing DES implantation, adverse clinical and vessel characteristics were associated with the risk of cardiac death or target vessel-MI. Since these characteristics showed independent and additive prognostic value, their integrative assessment can optimize post-PCI risk stratification.

Promoting positive development among refugee adolescents.

Of the estimated 35.3 million refugees around the world (UNHCR, Figures at a Glance, 2022), approximately 50% are children under the age of 18. Refugee adolescents represent a unique group as they navigate developmental tasks in an unstable and often threatening environment or in resettlement contexts in which they often face marginalization. In addition to physiological, social, and psychological changes that mark adolescence, refugee youth often face traumatic experiences, acculturative stress, discrimination, and a lack of basic resources. In this consensus statement, we examine research on refugee adolescents' developmental tasks, acculturative tasks, and psychological adjustment using Suárez-Orozco and colleague's integrative risk and resilience model for immigrant-origin children and youth proposed by Suárez-Orozco et al. Finally, we discuss recommendations-moving from proximal to more distal contexts.

Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score

BackgroundPolygenic risk score (PRS) analysis is used to predict disease risk. Although PRS has been shown to have great potential in improving clinical care, PRS accuracy assessment has been mainly focused on European ancestry. This study aimed to develop an accurate genetic risk score for knee osteoarthritis (OA) using a multi-population PRS and leveraging a multi-trait PRS in the Japanese population.MethodsWe calculated PRS using PRS-CS-auto, derived from genome-wide association study (GWAS) summary statistics for knee OA in the Japanese population (same ancestry) and multi-population. We further identified risk factor traits for which PRS could predict knee OA and subsequently developed an integrated PRS based on multi-trait analysis of GWAS (MTAG), including genetically correlated risk traits. PRS performance was evaluated in participants of the Nagahama cohort study who underwent radiographic evaluation of the knees (n = 3,279). PRSs were incorporated into knee OA integrated risk models along with clinical risk factors.ResultsA total of 2,852 genotyped individuals were included in the PRS analysis. The PRS based on Japanese knee OA GWAS was not associated with knee OA (p = 0.228). In contrast, PRS based on multi-population knee OA GWAS showed a significant association with knee OA (p = 6.7 × 10−5, odds ratio (OR) per standard deviation = 1.19), whereas PRS based on MTAG of multi-population knee OA, along with risk factor traits such as body mass index GWAS, displayed an even stronger association with knee OA (p = 5.4 × 10−7, OR = 1.24). Incorporating this PRS into traditional risk factors improved the predictive ability of knee OA (area under the curve, 74.4% to 74.7%; p = 0.029).ConclusionsThis study showed that multi-trait PRS based on MTAG, combined with traditional risk factors, and using large sample size multi-population GWAS, significantly improved predictive accuracy for knee OA in the Japanese population, even when the sample size of GWAS of the same ancestry was small. To the best of our knowledge, this is the first study to show a statistically significant association between the PRS and knee OA in a non-European population.Trial registrationNo. C278.

The current state and future of cardiometabolic polygenic risk scores in European and non-European population cohorts

Abstract Funding Acknowledgements Type of funding sources: None. Background A polygenic risk score (PRS) is derived from a genome-wide association study (GWAS) and represents an aggregate of thousands of single-nucleotide polymorphisms (SNPs) that provide a baseline estimate of an individual’s genetic risk for a specific disease or trait at birth. Cardiometabolic disease represents a set of disease processes that historically have disproportionally affected underrepresented racial and minority groups. Furthermore, these groups represent a population generally not well captured by traditional risk scores compared to European cohorts. Since the first GWAS studying myocardial infarction was published, PRSs have increasingly been seen as a promising tool to improve risk stratification of non-European populations. However, how PRSs can be best used in clinical practice remains unclear. Purpose To provide an overview of the PRSs related to cardiometabolic disease, analyze the ancestral diversity of GWAS cohorts, and discuss the evidence supporting their clinical applications. Methods The Preferred Reporting Items For Systematic Reviews and Meta-analysis extension for Scoping Reviews protocol was used to conduct a scoping review of the MEDLINE, EMBASE, and CENTRAL databases. English studies that published a PRS related to atrial fibrillation (AF), cerebrovascular disease (CVD), coronary artery disease (CAD), dyslipidemia, heart failure, heritable cardiomyopathy, hypertension, and type 2 diabetes were reviewed. Results Across the 4,863 studies screened, 82 articles met the inclusion criteria. The most common PRS related to CAD, followed by hypertension and CVD. Limited ancestral diversity was observed as most studies (56) included only individuals of European ancestry. A smaller proportion of studies (16) published PRSs derived in multi-ancestry cohorts. Only ten studies published a PRS derived solely from a sample population of non-European ancestry (Chinese, East Asian, Japanese, and Korean). The predictive performance of most PRSs was similar to or superior to traditional risk factors. More than half of the included studies (42) reported an integrated risk model combining a PRS with traditional risk factors or a clinical risk tool (FRS, PCE, CHADS2). The integrated risk model consistently improved predictive accuracy, but few studies investigated the performance in a non-European population. Conclusion In conclusion, this scoping review is the first of its kind and reports strong evidence for the clinical use of PRSs in AF, CAD, CVD, and hypertension. However, most PRSs are derived in cohorts of European ancestry, which contributes to a lack of PRS transferability across different ancestral groups, likely exacerbating health inequities. Future prospective studies should focus on further establishing the clinical utility of PRSs. Additionally, diversity in future GWAS cohorts is essential to ensure that PRSs reflect the multi-ancestry society at large.

Abstract P339: The Clinical Application and Ancestral Diversity of Cardiometabolic Polygenic Risk Scores

Introduction: A polygenic risk score (PRS) is derived from a genome-wide association study (GWAS) and represents an aggregate of thousands of single-nucleotide polymorphisms (SNPs) that provide a baseline estimate of an individual’s genetic risk for a specific disease or trait at birth. Cardiometabolic disease represents a set of disease processes that historically have disproportionally affected underrepresented racial and minority groups. Furthermore, these groups represent a population generally not well captured by traditional risk scores compared to European cohorts. Since the first GWAS studying myocardial infarction was published, PRSs have increasingly been seen as a promising tool to improve risk stratification of non-European populations. However, how PRSs can be best used in clinical practice remains unclear. Hypothesis: We provide an overview of the PRSs related to cardiometabolic disease, analyze the ancestral diversity of GWAS cohorts, and discuss the evidence supporting their clinical applications. Methods: The Preferred Reporting Items For Systematic Reviews and Meta-analysis extension for Scoping Reviews protocol was used to conduct a scoping review of the MEDLINE, EMBASE, and CENTRAL databases. English studies that published a PRS related to atrial fibrillation (AF), cerebrovascular disease (CVD), coronary artery disease (CAD), dyslipidemia, heart failure, heritable cardiomyopathy, hypertension, and type 2 diabetes were reviewed. Results: Across the 4,863 studies screened, 82 articles met the inclusion criteria. The most common PRS related to CAD, followed by hypertension and CVD. Limited ancestral diversity was observed as most studies (56) included only individuals of European ancestry. A smaller proportion of studies (16) published PRSs derived in multi-ancestry cohorts. Only ten studies published a PRS derived solely from a sample population of non-European ancestry (Chinese, East Asian, Japanese, and Korean). The predictive performance of most PRSs was similar to or superior to traditional risk factors. More than half of the included studies (42) reported an integrated risk model combining a PRS with traditional risk factors or a clinical risk tool (FRS, PCE, CHADS2). The integrated risk model consistently improved predictive accuracy, but few studies investigated the performance in a non-European population. Conclusion: In conclusion, this scoping review reports strong evidence for the clinical use of PRSs in AF, CAD, CVD, and hypertension. However, most PRSs are derived in cohorts of European ancestry, which contributes to a lack of PRS transferability across different ancestral groups, likely exacerbating health inequities. Future prospective studies should focus on further establishing the clinical utility of PRSs. Additionally, diversity in future GWAS cohorts is essential to ensure that PRSs reflect the multi-ancestry society at large.

Applying a genetic risk score for prostate cancer to men with lower urinary tract symptoms in primary care to predict prostate cancer diagnosis: a cohort study in the UK Biobank

BackgroundProstate cancer is highly heritable, with >250 common variants associated in genome-wide association studies. It commonly presents with non-specific lower urinary tract symptoms that are frequently associated with benign conditions.MethodsCohort study using UK Biobank data linked to primary care records. Participants were men with a record showing a general practice consultation for a lower urinary tract symptom. The outcome measure was prostate cancer diagnosis within 2 years of consultation. The predictor was a genetic risk score of 269 genetic variants for prostate cancer.ResultsA genetic risk score (GRS) is associated with prostate cancer in symptomatic men (OR per SD increase = 2.12 [1.86–2.41] P = 3.5e-30). An integrated risk model including age and GRS applied to symptomatic men predicted prostate cancer (AUC 0.768 [0.739–0.796]). Prostate cancer incidence was 8.1% (6.7–9.7) in the highest risk quintile. In the lowest quintile, prostate cancer incidence was <1%.ConclusionsThis study is the first to apply GRS in primary care to improve the triage of symptomatic patients. Men with the lowest genetic risk of developing prostate cancer could safely avoid invasive investigation, whilst those identified with the greatest risk could be fast-tracked for further investigation. These results show that a GRS has potential application to improve the diagnostic pathway of symptomatic patients in primary care.

An integrated risk model stratifying seizure risk following brain tumor resection among seizure-naive patients without antiepileptic prophylaxis.

The natural history of seizure risk after brain tumor resection is not well understood. Identifying seizure-naive patients at highest risk for postoperative seizure events remains a clinical need. In this study, the authors sought to develop a predictive modeling strategy for anticipating postcraniotomy seizures after brain tumor resection. The IBM Watson Health MarketScan Claims Database was canvassed for antiepileptic drug (AED)- and seizure-naive patients who underwent brain tumor resection (2007-2016). The primary event of interest was short-term seizure risk (within 90 days postdischarge). The secondary event of interest was long-term seizure risk during the follow-up period. To model early-onset and long-term postdischarge seizure risk, a penalized logistic regression classifier and multivariable Cox regression model, respectively, were built, which integrated patient-, tumor-, and hospitalization-specific features. To compare empirical seizure rates, equally sized cohort tertiles were created and labeled as low risk, medium risk, and high risk. Of 5470 patients, 983 (18.0%) had a postdischarge-coded seizure event. The integrated binary classification approach for predicting early-onset seizures outperformed models using feature subsets (area under the curve [AUC] = 0.751, hospitalization features only AUC = 0.667, patient features only AUC = 0.603, and tumor features only AUC = 0.694). Held-out validation patient cases that were predicted by the integrated model to have elevated short-term risk more frequently developed seizures within 90 days of discharge (24.1% high risk vs 3.8% low risk, p < 0.001). Compared with those in the low-risk tertile by the long-term seizure risk model, patients in the medium-risk and high-risk tertiles had 2.13 (95% CI 1.45-3.11) and 6.24 (95% CI 4.40-8.84) times higher long-term risk for postdischarge seizures. Only patients predicted as high risk developed status epilepticus within 90 days of discharge (1.7% high risk vs 0% low risk, p = 0.003). The authors have presented a risk-stratified model that accurately predicted short- and long-term seizure risk in patients who underwent brain tumor resection, which may be used to stratify future study of postoperative AED prophylaxis in highest-risk patient subpopulations.

Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors.

ObjectiveIntradural spinal tumors are uncommon and while associations between clinical characteristics and surgical outcomes have been explored, there remains a paucity of literature unifying diverse predictors into an integrated risk model. To predict postresection outcomes for patients with spinal tumors. MethodsIBM MarketScan Claims Database was queried for adult patients receiving surgery for intradural tumors between 2007 and 2016. Primary outcomes-of-interest were nonhome discharge and 90-day postdischarge readmissions. Secondary outcomes included hospitalization duration and postoperative complications. Risk modeling was developed using a regularized logistic regression framework (LASSO, least absolute shrinkage and selection operator) and validated in a withheld subset. ResultsA total of 5,060 adult patients were included. Most surgeries utilized a posterior approach (n=5,023, 99.3%) and tumors were most commonly found in the thoracic region (n=1,941, 38.4%), followed by the lumbar (n=1,781, 35.2%) and cervical (n=1,294, 25.6%) regions. Compared to models using only tumor-specific or patient-specific features, our integrated models demonstrated better discrimination (area under the curve [AUC] [nonhome discharge] = 0.786; AUC [90-day readmissions] = 0.693) and accuracy (Brier score [nonhome discharge] = 0.155; Brier score [90-day readmissions] = 0.093). Compared to those predicted to be lowest risk, patients predicted to be highest-risk for nonhome discharge required continued care 16.3 times more frequently (64.5% vs. 3.9%). Similarly, patients predicted to be at highest risk for postdischarge readmissions were readmitted 7.3 times as often as those predicted to be at lowest risk (32.6% vs. 4.4%). ConclusionUsing a diverse set of clinical characteristics spanning tumor-, patient-, and hospitalization-derived data, we developed and validated risk models integrating diverse clinical data for predicting nonhome discharge and postdischarge readmissions.

Prediction of High-Altitude Cardiorespiratory Fitness Impairment Using a Combination of Physiological Parameters During Exercise at Sea Level and Genetic Information in an Integrated Risk Model.

Insufficient cardiorespiratory compensation is closely associated with acute hypoxic symptoms and high-altitude (HA) cardiovascular events. To avoid such adverse events, predicting HA cardiorespiratory fitness impairment (HA-CRFi) is clinically important. However, to date, there is insufficient information regarding the prediction of HA-CRFi. In this study, we aimed to formulate a protocol to predict individuals at risk of HA-CRFi. We recruited 246 volunteers who were transported to Lhasa (HA, 3,700 m) from Chengdu (the sea level [SL], <500 m) through an airplane. Physiological parameters at rest and during post-submaximal exercise, as well as cardiorespiratory fitness at HA and SL, were measured. Logistic regression and receiver operating characteristic (ROC) curve analyses were employed to predict HA-CRFi. We analyzed 66 pulmonary vascular function and hypoxia-inducible factor- (HIF-) related polymorphisms associated with HA-CRFi. To increase the prediction accuracy, we used a combination model including physiological parameters and genetic information to predict HA-CRFi. The oxygen saturation (SpO2) of post-submaximal exercise at SL and EPAS1 rs13419896-A and EGLN1 rs508618-G variants were associated with HA-CRFi (SpO2, area under the curve (AUC) = 0.736, cutoff = 95.5%, p < 0.001; EPAS1 A and EGLN1 G, odds ratio [OR] = 12.02, 95% CI = 4.84–29.85, p < 0.001). A combination model including the two risk factors—post-submaximal exercise SpO2 at SL of <95.5% and the presence of EPAS1 rs13419896-A and EGLN1 rs508618-G variants—was significantly more effective and accurate in predicting HA-CRFi (OR = 19.62, 95% CI = 6.42–59.94, p < 0.001). Our study employed a combination of genetic information and the physiological parameters of post-submaximal exercise at SL to predict HA-CRFi. Based on the optimized prediction model, our findings could identify individuals at a high risk of HA-CRFi in an early stage and reduce cardiovascular events.