Single-nucleotide polymorphisms based genetic risk score in the prediction of pancreatic cancer risk

BACKGROUND Disease-related single nucleotide polymorphisms(SNPs)based genetic risk score(GRS)has been proven to provide independent inherited risk other than family history in multiple cancer types.AIM To evaluate the potential of GRS in the prediction of pancreatic cancer risk.METHODS In this case-control study(254 cases and 1200 controls),we aimed to evaluate the association between GRS and pancreatic ductal adenocarcinoma(PDAC)risk in the Chinese population.The GRS was calculated based on the genotype information of 18 PDAC-related SNPs for each study subject(personal genotyping information of the SNPs)and was weighted by external odd ratios(ORs).RESULTS GRS was significantly different in cases and controls(1.96±3.84 in PDACs vs 1.09±0.94 in controls,P<0.0001).Logistic regression revealed GRS to be associated with PDAC risk[OR=1.23,95%confidence interval(CI):1.13-1.34,P<0.0001].GRS remained significantly associated with PDAC(OR=1.36,95%CI:1.06-1.74,P=0.015)after adjusting for age and sex.Further analysis revealed an association of increased risk for PDAC with higher GRS.Compared with low GRS(<1.0),subjects with high GRS(2.0)were 99%more likely to have PDAC(OR:1.99,95%CI:1.30-3.04,P=0.002).Participants with intermediate GRS(1.0-1.9)were 39%more likely to have PDAC(OR:1.39,95%CI:1.03-1.84,P=0.031).A positive trend was observed(P trend=0.0006).CONCLUSION GRS based on PDAC-associated SNPs could provide independent information on PDAC risk and may be used to predict a high risk PDAC population.

Stair climbing,genetic predisposition,and the risk of incident type 2 diabetes:A large population-based prospective cohort study

Background:Cross-sectional evidence and small-scale trials suggest positive effects of stair climbing on cardiometabolic disease and glucose regulation.However,few studies have examined the long-term association between stair climbing and the incidence of type 2 diabetes(T2D).We aimed to prospectively evaluate the association of stair climbing with T2D and assess modifications by genetic predisposition to T2D.Methods:We included 451,699 adults(mean age=56.3±8.1 years,mean±SD;55.2%females)without T2D at baseline in the UK Biobank and followed up to March 31,2021.Stair climbing information was collected through the touchscreen questionnaire.Genetic risk score for T2D consisted of 424 single nucleotide polymorphisms.Results:During a median follow up of 12.1 years,14,896 T2D cases were documented.Compared with participants who reported no stair climbing,those who climbed stairs regularly had a lower risk of incident T2D(10-50 steps/day:hazard ratio(HR)=0.95,95%confidence interval(95%CI):0.89-1.00;60-100 steps/day:HR=0.92,95%CI:0.87-0.98;110-150 steps/day:HR=0.86,95%CI:0.80-0.91;>150 steps/day:HR=0.93,95%CI:0.87-0.99,p for trend=0.0007).We observed a significant interaction between stair climbing and genetic risk score on the subsequent T2D risk(p for interaction=0.0004),where the risk of T2D showed a downward trend in subjects with low genetic risk and those who reported stair climbing activity of 110-150 steps/day appeared to have the lowest overall T2D risk among those with intermediate to high genetic risk.Conclusion:A higher number of stairs climbed at home was associated with lower T2D incidence risk,especially among individuals with a low genetic predisposition to T2D.These findings highlight that stair climbing,as incidental physical activity,offers a simple and low-cost complement to public health interventions for T2D prevention.

Population-standardized genetic risk score： the SNP-based method of choice for inherited risk assessment of prostate cancer

Several different approaches are available to clinicians for determining prostate cancer （PCa） risk. The clinical validity of various PCa risk assessment methods utilizing single nucleotide polymorphisms （SNPs） has been established; however, these SNP-based methods have not been compared. The objective of this study was to compare the three most commonly used SNP-based methods for PCa risk assessment. Participants were men （n = 1654） enrolled in a prospective study of PCa development. Genotypes of 59 PCa risk-associated SNPs were available in this cohort. Three methods of calculating SNP-based genetic risk scores （GRSs） were used for the evaluation of individual disease risk such as risk allele count （GRS-RAC）, weighted risk allele count （GRS-wRAC）, and population-standardized genetic risk score （GRS-PS）. Mean GRSs were calculated, and performances were compared using area under the receiver operating characteristic curve （AUC） and positive predictive value （PPV）. All SNP-based methods were found to be independently associated with PCa （all P 〈 0.05; hence their clinical validity）. The mean GRSs in men with or without PCa using GRS-RAC were 55.15 and 53.46, respectively, using GRS-wRAC were 7.42 and 6.97, respectively, and using GRS-PS were 1.12 and 0.84, respectively （all P 〈 0.05 for differences between patients with or without PCa）. All three SNP-based methods performed similarly in discriminating PCa from non-PCa based on AUC and in predicting PCa risk based on PPV （all P 〉 0.05 for comparisons between the three methods）, and all three SNP-based methods had a significantly higher AUC than family history （all P 〈 0.05）. Results from this study suggest that while the three most commonly used SNP-based methods performed similarly in discriminating PCa from non-PCa at the population level, GRS-PS is the method of choice for risk assessment at the individual level because its value （where 1.0 represents average population risk） can be easily interpreted regardless of the number of risk-associated SNPs used in the calculation.

Race-specific genetic risk score is more accurate than nonrace-specific genetic risk score for predicting prostate cancer and high-grade diseases

Genetic risk score （GRS） based on disease risk-associated single nucleotide polymorphisms （SNPs） is an informative tool that can be used to provide inherited information for specific diseases in addition to family history, However, it is still unknown whether only SNPs that are implicated in a specific racial group should be used when calculating GRSs. The objective of this study is to compare the performance of race-specific GRS and nonrace-specitic GRS for predicting prostate cancer （PCa） among 1338 patients underwent prostate biopsy in Shanghai, China. A race-specific GRS was calculated with seven PCa risk-associated SNPs implicated in East Asians （GRS7）, and a nonrace-specific GRS was calculated based on 76 PCa risk-associated SNPs implicated in at least one racial group （GRS76）. The means of GRS7 and GRS76 were 1.19 and 1.85, respectively, in the study population. Higher GRS7 and GRS76 were independent predictors for PCa and high-grade PCa in univariate and multivariate analyses. GRS7 had a better area under the receiver-operating curve （AUC） than GRS76 for discriminating PCa （0.602 vs 0.573） and high-grade PCa （0.603 vs 0.575） but did not reach statistical significance. GRS7 had a better （up to 13% at different cutoffs） positive predictive value （PPV） than GRS76. In conclusion, a race-specific GRS is more robust and has a better performance when predicting PCa in East Asian men than a GRS calculated using SNPs that are not shown to be associated with East Asians.

A comparison of genetic risk score with family history for estimating prostate cancer risk

Prostate cancer （PCa） testing is recommended by most authoritative groups for high-risk men including those with a family history of the disease. However, family history information is often limited by patient knowledge and clinician intake, and thus, many men are incorrectly assigned to different risk groups. Alternate methods to assess PCa risk are required. In this review, we discuss how genetic variants, referred to as PCa-risk single-nucleotide polymorphisms, can be used to calculate a genetic risk score （GRS）. GRS assigns a relatively unique value to all men based on the number of PCa-risk SNPs that an individual carries. This GRS value can provide a more precise estimate of a man＇s PCa risk. This is particularly relevant in situations when an individual is unaware of his family history. In addition, GRS has utility and can provide a more precise estimate of risk even among men with a positive family history. It can even distinguish risk among relatives with the same degree of family relationships. Taken together, this review serves to provide support for the clinical utility of GRS as an independent test to provide supplemental information to family history. As such, GRS can serve as a platform to help guide-shared decision-making processes regarding the timing and frequency of PCa testing and biopsies.

Clinical validity and utility of genetic risk scores in prostate cancer

Current issues related to prostate cancer （PCa） clinical care （e.g., over-screening, over-diagnosis, and over-treatment of nonaggressive PCa） call for risk assessment tools that can be combined with family history （FH） to stratify disease risk among men in the general population. Since 2007, genome-wide association studies （GWASs） have identified more than 100 SNPs associated with PCa susceptibility. In this review, we discuss （1） the validity of these PCa risk-associated SNPs, individually and collectively; （2） the various methods used for measuring the cumulative effect of multiple SNPs, including genetic risk score （GRS）; （3） the adequate number of SNPs needed for risk assessment; （4） reclassification of risk based on evolving numbers of SNPs used to calculate genetic risk, （5） risk assessment for men from various racial groups, and （6） the clinical utility of genetic risk assessment. In conclusion, data available to date support the clinical validity of PCa risk-associated SNPs and GRS in risk assessment among men with or without FH. PCa risk-associated SNPs are not intended for diagnostic use; rather, they should be used the same way as FH. Combining GRS and FH can significantly improve the performance of risk assessment. Improved risk assessment may have important clinical utility in targeted PCa testing. However, clinical trials are urgently needed to evaluate this clinical utility as well as the acceptance of GRS by patients and physicians.

Association of sleep quality with myopia based on different genetic risk levels

AIM: To analyse the association of sleep quality with myopia under different genetic risk(GR) levels.METHODS: A cross-sectional survey of students aged 9-14 y in Wenzhou, China, was conducted. Refraction without cycloplegia and ocular parameters were measured. Sleep quality was assessed with the Pittsburgh Sleep Quality Index(PSQI). Seventeen single nucleotide polymorphisms(SNPs) were replicated by association analysis and used to compute the GR score(GRS). Possible confounders were assessed by a questionnaire that collected information about the children and their parents. Generalized linear models were used to analyse the sleep quality, the GR, and their interaction effects on the risk of myopia.RESULTS: Out of 1354 children included in this study, 353(26.07%) had sleep disturbances. The GRS ranged from 4.49 to 12.89 with a mean of 7.74±1.23, and the participants were divided into a low GR group, a moderate GR group and a high GR group according to the GRS quartile. In the generalized linear model, the children with sleep disturbances and high GR had a higher risk of myopia than those without sleep disturbances and with low GR(OR=1.59, 95%CI: 1.12-2.25;OR=1.88, 95%CI: 1.23-2.88, respectively). Compared to those with low GR and SDs, children with high GR with or without SDs had a higher risk of myopia(OR=4.88, 95%CI: 2.03-11.71;OR=1.70, 95%CI: 1.06-2.72, respectively).CONCLUSION: The prevalence of sleep disturbances in elementary school students in Wenzhou was 26.07%. There is a significant interaction between sleep disturbances and a high GR of myopia, suggesting that a high GR of myopia may increase children’s sensitivity to sleep disturbances. This study indicates that children with a high GR of myopia need to achieve adequate sleep duration and excellent sleep quality.

Personalized prostate cancer care： from screening totreatment

Unprecedented progress has been made in genomic personalized medicine in the last several years, allowing for more individualized healthcare assessments and recommendations than ever before. However, most of this progress in prostate cancer （PCa） care has focused on developing and selecting therapies for late-stage disease. To address this issue of limited focus, we propose a model for incorporating genomic-based personalized medicine into all levels of PCa care, from prevention and screening to diagnosis, and ultimately to the treatment of both early-stage and late-stage cancers. We have termed this strategy the ＂Pyramid Model＂ of personalized cancer care. In this perspective paper, our objective is to demonstrate the potential application of the Pyramid Model to PCa care. This proactive and comprehensive personalized cancer care approach has the potential to achieve three important medical goals： reducing mortality, improving quality of life and decreasing both individual and societal healthcare costs.