Sex-specific survival bias and interaction modeling in coronary artery disease risk prediction

Surakka, Ida; Wolford, Brooke N.; Ritchie, Scott C.; We, Hornsby; Nr, Sutton; Me, Gabrielsen; Ah, Skogholt; Thomas, Laurent F.; Inouye, Michael; Hveem, Kristian; Cj, Willer

doi:10.1101/2021.06.23.21259247

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…PRS-guided, lipid-lowering treatment, particularly for those at intermediate risk, has shown promise in decreasing cardiovascular disease events 2,39,40 . With a safe, effective and inexpensive preventative therapeutic, screening strategies for cardiovascular disease that consider PRS and conventional risk factors jointly (for example in a primary care population of at least 40 years of age 39 ) or that take a 2-stage approach (screening first with PRS then with conventional risk factors, or vice versa 40,41 ) appear to robustly provide clinical benefit; however, further refinement regarding whom and when to treat is still necessary.…”

Section: Slowing Disease Progression and Recurrencementioning

confidence: 99%

Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps

Adeyemo¹,

Balaconis²,

Darnes³

et al. 2021

Nat Med

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296

161

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Section: Slowing Disease Progression and Recurrencementioning

confidence: 99%

Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps

Adeyemo¹,

Balaconis²,

Darnes³

et al. 2021

Nat Med

Self Cite

296

161

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“…Time-stamped data allow studies of disease development and progression, such as risk prediction of coronary artery disease. 12 Some selected disease endpoints are presented in Table 2.…”

Section: Phenotypesmentioning

confidence: 99%

“…To overcome these limitations, we contribute to genetic studies worldwide through participation in consortia focused on a variety of diseases including cardiovascular disease, 43,44 lipids, 45,46 type 2 diabetes, 47 osteoporosis, 48 decline in kidney function, 49 Alzheimer's disease, 50 bipolar disease, 51 intracranial aneurysms, 52 insomnia, 53 respiratory health, 54 and sleepiness. 55 We also contributed HUNT data to studies of anthropometric traits, 56 alcohol and nicotine use, 57,58 COVID-19, 59 phenomewide discovery, 60 and genetic risk prediction, 12 among others. These contributions highlight efforts from researchers in equal parts from the K.G.…”

Section: Causal Inference and Family Effectsmentioning

confidence: 99%

The HUNT study: A population-based cohort for genetic research

Brumpton

Graham²,

Surakka³

et al. 2022

Cell Genomics

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“…The copyright holder for this preprint this version posted July 22, 2022. ; https://doi.org/10.1101/2022.07. 19.22277830 doi: medRxiv preprint Supplementary Table 1. Significantly higher AUC was continuously found in the PRS+ClinRS model even at ten years prior to disease diagnosis with an AUC of 0.79 (95% CI: 0.77-0.82), compared to baseline model (AUC: 0.72 [0.69-0.75]).…”

Section: Integrating Prs and Clinrs Enhances Heart Failure Predictionmentioning

confidence: 99%

“…Multiple studies have shown that using a PRSa weighted sum of genetic effects on certain diseases or traits across the human genomecan enhance disease prediction and further improve early prevention 6,18 . Multiple efforts have been made to summarize genetic and clinical information to identify high risk patients, but integrating high-dimensional genome-wide association study (GWAS) and electronic health record (EHR) in heart failure prediction models has not been previously evaluated [19][20][21] .…”

Section: Introductionmentioning

confidence: 99%

Integrating large scale genetic and clinical information to predict cases of heart failure

Douville

et al. 2022

Preprint

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[Background] Heart failure is a major cause of death globally and earlier initiation of treatment could mitigate disease progression. Multiple efforts have been made using genome-wide association studies (GWAS) or electronic health records (EHR) to identify individuals at high risk of heart failure (HF). However, integrating both sources using novel natural language processing (NLP) techniques and large scale global genetic predictors into heart failure prediction models has not been evaluated. [Objectives] The study aimed to improve the accuracy of HF prediction by integrating GWAS- and EHR-derived risk scores. [Methods] We previously performed the largest HF GWAS to date within the Global Biobank Meta-analysis Initiative, which includes 974,174 samples (51,274 cases; 5%) from 9 biobanks across the world, to create a polygenic risk score (PRS). Next, to extract information from the Michigan Medicine high-dimensional EHR (N=61,849 subjects), we treated diagnosis codes as words and applied NLP on the data. NLP was used to learn code co-occurrence patterns and extract 350 latent phenotypes (low-dimensional features) representing 29,346 EHR codes. Next, we regressed HF on the latent phenotypes in an independent cohort and the coefficients were used as the weights to calculate a clinical risk score (ClinRS). Model performances were compared between baseline (age and sex) model and three models with risk scores added: 1) PRS, 2) ClinRS, and 3) PRS+ClinRS, using 10-fold cross validated Area Under the Receiver Operating Characteristic Curve (AUC). [Results] Our results show that PRS and ClinRS are each, separately, able to predict HF outcomes significantly better than the baseline model, up to eight years prior to HF diagnosis. Higher AUC (95% CI) were observed in the PRS model (0.76 [0.74-0.78]) and ClinRS model (0.77 [0.74-0.79]), compared to the baseline model (0.71 [0.68-0.73]). Moreover, by including both PRS and ClinRS in the model, we achieved superior performance in predicting HF up to ten years prior to HF diagnosis (AUC: 0.79 [0.77-0.82]), 2-3 years earlier than using either single risk predictor alone. [Conclusions] We demonstrate the additive power of integrating GWAS- and EHR-derived risk scores to predict HF cases prior to diagnosis. Clinical application of this approach may allow identification of patients with higher susceptibility to HF and enable preventive therapies to be initiated at an earlier stage.

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Sex-specific survival bias and interaction modeling in coronary artery disease risk prediction

Cited by 4 publications

References 27 publications

Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps

Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps

The HUNT study: A population-based cohort for genetic research

Integrating large scale genetic and clinical information to predict cases of heart failure

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