Economic evaluation of using polygenic risk score to guide risk screening and interventions for the prevention of type 2 diabetes in individuals with high overall baseline risk

Martikainen, Janne; Lehtimäki, Aku-Ville; Jalkanen, Kari; Lavikainen, Piia; Paajanen, Teemu; Marjonen, Heidi; Kristiansson, Kati; Lindström, Jaana; Perola, Markus

doi:10.3389/fgene.2022.880799

Cited by 4 publications

(3 citation statements)

References 52 publications

(54 reference statements)

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“…Second, the potential for clinical utility of polygenic prediction (and thereby the BPC approach) strongly depends on the magnitude of the PGS's R 2 liability , which is currently prohibitively small for most traits. However, there are some traits, such as cardiovascular disease and type 2 diabetes, for which current PGSs may already be sufficiently powered to find clinical application [41][42][43] and be economically effective [44][45][46][47][48][49] . Moreover, as GWAS sample sizes grow, the PGS's R 2 liability is expected to approach the disorder's h 2 SNP , and therefore their clinical applicability will become more likely.…”

Section: Discussionmentioning

confidence: 99%

Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach

Uffelmann,

Price,

Posthuma

et al. 2024

Preprint

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Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait in a single value, based on SNP effect sizes derived from Genome-Wide Association Study (GWAS) results. Methods have been developed that apply Bayesian approaches to improve the prediction accuracy of PGSs through optimization of estimated effect sizes. While these methods are generally well-calibrated for continuous traits (implying the predicted values are on average equal to the true trait values), they are not well-calibrated for binary disorder traits in ascertained samples. This is a problem because well-calibrated PGSs are needed to reliably compute the absolute disorder probability for an individual to facilitate future clinical implementation. Here we introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual's predicted disorder probability using GWAS summary statistics, an existing Bayesian PGS method (e.g. PRScs, SBayesR), the individual's genotype data, and a prior disorder probability. The BPC approach transforms the PGS to its underlying liability scale, computes the variances of the PGS in cases and controls, and applies Bayes' Theorem to compute the absolute disorder probability; it is practical in its application as it does not require a tuning dataset with both genotype and phenotype data. We applied the BPC approach to extensive simulated data and empirical data of nine disorders. The BPC approach yielded well-calibrated results that were consistently better than the results of another recently published approach.

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Section: Discussionmentioning

confidence: 99%

Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach

Uffelmann,

Price,

Posthuma

et al. 2024

Preprint

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“…For example, evaluation of the costs associated with specific genetic variants that mimic drug targets may inform drug development and commercialization. Implementation of genetic screening either in the form of polygenic score or single variants, would require health-economic assessment 29 – 31 . Future large-scale genetic studies will be powered to provide a comprehensive assessment on the impact of genetics on healthcare costs and facilitate the implementation of such proposed genomic medicine approaches.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the causal impact of biological risk factors on healthcare costs

Lee,

Jukarainen,

Karvanen

et al. 2023

Nat Commun

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Understanding the causal impact that clinical risk factors have on healthcare-related costs is critical to evaluate healthcare interventions. Here, we used a genetically-informed design, Mendelian Randomization (MR), to infer the causal impact of 15 risk factors on annual total healthcare costs. We calculated healthcare costs for 373,160 participants from the FinnGen Study and replicated our results in 323,774 individuals from the United Kingdom and Netherlands. Robust causal effects were observed for waist circumference (WC), adult body mass index, and systolic blood pressure, in which a standard deviation increase corresponded to 22.78% [95% CI: 18.75-26.95], 13.64% [10.26-17.12], and 13.08% [8.84-17.48] increased healthcare costs, respectively. A lack of causal effects was observed for certain clinically relevant biomarkers, such as albumin, C-reactive protein, and vitamin D. Our results indicated that increased WC is a major contributor to annual total healthcare costs and more attention may be given to WC screening, surveillance, and mitigation.

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“…However, this scenario -i.e. one in which genomic information will become part of routine healthcare and available for screening purposes [24][25][26][27][28] -has been advocated by some leading authorities 29 . Such a programme of systematic collection of genomics data -with reasonable level of uptake in the population -would, of course, need to be in place to facilitate implementation of a PRS-stratified screening strategy as described here.…”

Section: Limitationsmentioning

confidence: 99%

Evaluating the cost-effectiveness of polygenic risk score-stratified screening for abdominal aortic aneurysm

Kelemen,

Roychowdhury,

Danesh

et al. 2023

Preprint

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As the heritability of abdominal aortic aneurysm (AAA) is high and AAA partially shares genetic architecture with other cardiovascular diseases, there is interest in whether genetic information could inform AAA screening strategies. Exploiting pleiotropy and meta-analysing summary data from large AAA studies, we constructed a polygenic risk score (PRS) for AAA. Compared with the low PRS tertile, the intermediate and high PRS tertiles had hazard ratios for AAA of 2.13 (95%CI 1.61, 2.82) and 3.70 (95%CI 2.86, 4.80) respectively, after adjusting for known clinical risk factors. Using simulation modelling, we compared PRS- and smoking-stratified screening with inviting men at age 65 and not inviting women (current UK strategy). In a futuristic scenario where genomic information is available on the population, our modelling suggests inviting male current smokers with high PRS earlier than 65 and targeting screening in female smokers with high/intermediate PRS at 65 and 70 respectively, may improve cost-effectiveness.

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Economic evaluation of using polygenic risk score to guide risk screening and interventions for the prevention of type 2 diabetes in individuals with high overall baseline risk

Abstract: Economic evaluation of using polygenic risk score to guide risk screening and interventions for the prevention of type 2 diabetes in individuals with high overall baseline risk.

Cited by 4 publications

References 52 publications

Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach

Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach

Quantifying the causal impact of biological risk factors on healthcare costs

Evaluating the cost-effectiveness of polygenic risk score-stratified screening for abdominal aortic aneurysm

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