The SCRIPT Trial: study protocol for a randomised controlled trial of a polygenic risk score to tailor colorectal cancer screening in primary care (preprint)

Background: Polygenic risk scores (PRS) can predict risk of colorectal cancer (CRC) and target screening more precisely than current guidelines using age and family history alone. Primary care, as a far-reaching point of healthcare and routine provider of cancer screening and risk information, may be an ideal location for their widespread implementation. Methods: : This trial aims to determine whether the SCRIPT intervention results in more risk-appropriate CRC screening after 12 months in individuals attending general practice, compared with standard cancer risk reduction information. The SCRIPT intervention consists of a CRC PRS, tailored risk-specific screening recommendations and a risk report for participants and their GP, delivered in general practice. Patients aged between 45 and 70 inclusive, attending their GP, will be approached for participation. For those over 50, only those overdue for CRC screening will be eligible to participate. Two hundred and seventy-four participants will be randomised to the intervention or control arms, stratified by general practice, using a computer-generated allocation sequence. The primary outcome is risk-appropriate CRC screening after 12 months. For those in the intervention arm, risk-appropriate screening is defined using PRS-derived risk; for those in the control arm, it is defined using family history and national screening guidelines. Timing, type and results of previous screening are considered in both arms. Objective health service data will capture screening behaviour. Secondary outcomes include cancer-specific worry, risk perception, predictors of CRC screening behaviour, screening intentions and health service use at 1-, 6- and 12-months post intervention delivery. Discussion: This trial aims to determine whether a PRS-derived personalised CRC risk estimate delivered in primary care increases risk-appropriate CRC screening. A future population risk-stratified CRC screening program could incorporate risk assessment within primary care, while encouraging adherence to targeted screening recommendations. Trial registration: Australian and New Zealand Clinical Trial Registry, ACTRN12621000092897p. Registered on 1 February 2021, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12621000092897

Validation of a clinical and genetic model for predicting severe COVID-19 (preprint)

Using nested case-control data from the Lifelines COVID-19 cohort, we undertook a validation study of a clinical and genetic model to predict the risk of severe COVID-19 in people with confirmed COVID-19 and in people with confirmed or self-reported COVID-19. The model performed well in terms of discrimination of cases and controls for all ages (area under the receiver operating characteristic curve [AUC] = 0.680 for confirmed COVID-19 and AUC = 0.689 for confirmed and self-reported COVID-19) and in the age group in which the model was developed (50 years and older; AUC = 0.658 for confirmed COVID-19 and AUC= 0.651 for confirmed and self-reported COVID-19). There was no evidence of over- or under-dispersion of risk scores but there was evidence of overall over-estimation of risk in all analyses (all P < 0.0001). In the light of large numbers of people worldwide remaining unvaccinated and continuing uncertainty regarding vaccine efficacy over time and against variants of concern, identification of people at high risk of severe COVID-19 may encourage the uptake of vaccinations (including boosters) and the use of non-pharmaceutical inventions.

Genetic associations with severe COVID-19 (preprint)

Identification of host genetic factors that predispose individuals to severe COVID-19 is important, not only for understanding the disease and guiding the development of treatments, but also for risk prediction when combined to form a polygenic risk score (PRS). Using population controls, Pairo-Castineira et al. identified 12 SNPs (a panel of 8 SNPs and a panel of 6 SNPs, with two SNPs in both panels) associated with severe COVID-19. Using controls with asymptomatic or mild COVID-19, we were able to replicate the association with severe COVID-19 for only three of their SNPs and found marginal evidence for an association for one other. When combined as an 8-SNP PRS and a 6-SNP PRS, we found no evidence of association with severe COVID-19. The difference in our results and the results of Pairo-Castineira et al. might be the choice of controls: population controls vs controls with asymptomatic or mild COVID-19.

The health effects in the US of quarantine policies based on predicted individual risk of severe COVID-19 outcomes (preprint)

Background Social distancing, testing and public health measures are the principal protections against COVID-19 in the US. Social distancing based on an accurate assessment of the individual risk of severe outcomes could reduce harm even as infection rates accelerate. Methods An SEIR dynamic transmission model of COVID-19 was created to simulate the disease in the US after October 2020. The model comprised 8 age groups with US-specific contact rates and low- and high-risk sub-groups defined in terms of the risk of a severe outcome determined by relevant comorbidities and a genetic test. Monte Carlo analysis was used to compare quarantine measures applied to at risk persons identified with and without the genetic test. Results Under the piecemeal social distancing measures currently in place, absent a vaccine the US can expect 114 million symptomatic infections, 4.8 million hospitalisations and 262,000 COVID-19 related deaths. Social distancing based solely on comorbidities with 80% compliance reduces symptomatic infections by between 1.2 and 2.2 million, hospitalisations by between 1.2 and 1.3 million, and deaths by between 71,800 and 80,900. Refining the definition of at risk using a test of single-nucleotide polymorphisms further reduces symptomatic infections by 1.0 to 1.2 million, hospitalisations by 0.4 million and deaths by between 20,500 and 24,100. Conclusions Models are now available that can accurately predict the likelihood of severe COVID-19 outcomes based on age, sex, comorbidities and polygenetic testing. Quarantine based on risk of severe outcomes could substantially reduce pandemic harm, even when infection rates outside of quarantine are high.

Development and validation of a clinical and genetic model for predicting risk of severe COVID-19 (preprint)

Age, sex, and comorbidities are known risk factors for severe COVID-19 but are frequently considered independently and without accurate knowledge of the magnitude of their effects on risk. Single-nucleotide polymorphisms (SNPs) associated with risk of severe COVID-19 have appeared in the literature, but their application in predictive risk testing has not been validated. Reliance on age and sex alone to determine risk of severe COVID-19 will fail to accurately quantify risk. Here, we report the development and validation of a clinical and genetic model to predict risk of severe COVID-19 using confirmed SARS-CoV-2 positive participants from the UK Biobank. Our new model out-performed an age and sex model and had excellent discrimination and was well calibrated in the validation dataset. We also report validation studies of our prototype model and polygenic risk scores based on 8-SNP and 6-SNP panels identified in the literature. Accurate prediction of individual risk will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.

An integrated clinical and genetic model for predicting risk of severe COVID-19 (preprint)

BackgroundAge and gender are often the only considerations in determining risk of severe COVID-19. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. MethodsClinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). ResultsA model incorporating the SNP score and clinical risk factors (AUC=0.786) had 111% better discrimination of disease severity than a model with just age and gender (AUC=0.635). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors - not age and gender - that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one- third are at two-fold or more increased risk. ConclusionsWe have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.