Implications of Lifestyle Factors and Polygenic Risk Score for Absolute Risk Prediction of Colorectal Neoplasm and Risk-Adapted Screening.
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SUBMITTER: Chen H
PROVIDER: S-EPMC8324207 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionWhether and to what extent the relationship between physical activity (PA) and colorectal cancer (CRC) differs according to CRC-related genetic risk remains to be determined, and no studies to date have quantified how much genetically determined risk could be compensated for with active exercise.MethodsGenetic risk was quantified by a polygenic risk score (PRS) summarizing the estimated effect of 140 CRC-associated genetic variants. Associations of PA with CRC risk were estimated by multivariable logistic regression across PRS levels. We also compared the impact of PA and specific PA types to the PRS using "genetic risk equivalent (GRE)", a novel approach to enhance effective risk communication.ResultsAmong 5058 CRC patients and 4134 controls, we observed no significant association between overall PA level in quartiles and CRC risk. However, the highest versus lowest lifetime leisure time physical activity (LTPA) was associated with a 13% lower CRC risk [odds ratio 0.87, 95% confidence interval (CI) 0.77-1.00] independent of PRS levels (adjusted p value for interaction = 0.18). This effect was equivalent to the effect of having 11 percentiles lower PRS (GRE -10.6, 95% CI -20.7 to -0.6). The GRE (95% CI) for the highest lifetime sports tertile was -23.0 (-33.9 to -12.0).ConclusionsLTPA was inversely associated with CRC risk irrespective of polygenic risk for CRC, which reinforces the importance of LTPA in CRC prevention among the general population. Adequate sports activity can compensate for a large share of polygenic risk for CRC.
| S-EPMC9972112 | biostudies-literature
Project description:Hereditary factors have a strong influence on prostate cancer (PC) risk and poorer outcomes, thus stratification by genetic factors addresses a critical need for targeted PC screening and risk-adapted follow-up. In this Finnish population-based retrospective study 2283 clinically diagnosed and 455 screen-detected patients from the Finnish Randomised Study of Screening for Prostate Cancer (FinRSPC), 2400 healthy individuals have been involved. Individual genetic risk through establishment of a polygenic risk score based on 55 PC risk SNPs identified through the Finnish subset of the Collaborative Oncological Gene-Environment Study was assessed. Men with PC had significantly higher median polygenic risk score compared to the controls (6.59 vs. 3.83, P < 0.0001). The polygenic risk score above the control median was a significant predictor of PC (OR 2.13, 95% CI 1.90-2.39). The polygenic risk score predicted the risk of PC with an AUC of 0.618 (95% CI 0.60-0.63). Men in the highest polygenic risk score quartile were 2.8-fold (95% CI 2.4-3.30) more likely to develop PC compared with men in the lowest quartile. In the FinRSPC cohort, a significantly higher percentage of men had a PSA level of ≥ 4 ng/mL in polygenic risk score quartile four compared to quartile one (18.7% vs 8.3%, P < 0.00001). Adding the PRS to a PSA-only model contributed additional information in predicting PC in the FinRSPC model. Results strongly suggest that use of the polygenic risk score would facilitate the identification of men at increased risk for PC.
| S-EPMC7553910 | biostudies-literature
Project description:Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.
| S-EPMC7477007 | biostudies-literature
Project description:BackgroundNutrition and lifestyle have been long established as risk factors for colorectal cancer (CRC). Modifiable lifestyle behaviours bear potential to minimize long-term CRC risk; however, translation of lifestyle information into individualized CRC risk assessment has not been implemented. Lifestyle-based risk models may aid the identification of high-risk individuals, guide referral to screening and motivate behaviour change. We therefore developed and validated a lifestyle-based CRC risk prediction algorithm in an asymptomatic European population.MethodsThe model was based on data from 255,482 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) study aged 19 to 70?years who were free of cancer at study baseline (1992-2000) and were followed up to 31 September 2010. The model was validated in a sample comprising 74,403 participants selected among five EPIC centres. Over a median follow-up time of 15?years, there were 3645 and 981 colorectal cancer cases in the derivation and validation samples, respectively. Variable selection algorithms in Cox proportional hazard regression and random survival forest (RSF) were used to identify the best predictors among plausible predictor variables. Measures of discrimination and calibration were calculated in derivation and validation samples. To facilitate model communication, a nomogram and a web-based application were developed.ResultsThe final selection model included age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary. The risk score demonstrated good discrimination overall and in sex-specific models. Harrell's C-index was 0.710 in the derivation cohort and 0.714 in the validation cohort. The model was well calibrated and showed strong agreement between predicted and observed risk. Random survival forest analysis suggested high model robustness. Beyond age, lifestyle data led to improved model performance overall (continuous net reclassification improvement?=?0.307 (95% CI 0.264-0.352)), and especially for young individuals below 45?years (continuous net reclassification improvement?=?0.364 (95% CI 0.084-0.575)).ConclusionsLiFeCRC score based on age and lifestyle data accurately identifies individuals at risk for incident colorectal cancer in European populations and could contribute to improved prevention through motivating lifestyle change at an individual level.
| S-EPMC7780676 | biostudies-literature
Project description:BackgroundAccurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction.MethodsWe examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors.ResultsA standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13-1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727-0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791-0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072.ConclusionsWe generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.
| S-EPMC7860212 | biostudies-literature
Project description:Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome-wide association studies. We conducted a nested case-control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95 % CI 1.69-3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95 % CI 0.57-0.64), and an Asian-specific PRS had AUROC 0.64 (95 % CI 0.53-0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p = 0.01). The BCSC-PRS model classified 18 % of cases as high-risk (5-year risk ?3 %), compared with 7 % using the BCSC model. The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Further consideration of the PRS's role in decision-making around screening and prevention strategies is merited.
| S-EPMC5033764 | biostudies-literature
Project description:Melanoma is one of the most commonly diagnosed cancers in the Western world: third in Australia, fifth in the USA and sixth in the European Union. Predicting an individual's personal risk of developing melanoma may aid them in undertaking effective risk reduction measures. The objective of this study was to use the UK Biobank to predict the 10-year risk of melanoma using a newly developed polygenic risk score (PRS) and an existing clinical risk model. We developed the PRS using a matched case-control training dataset ( N = 16 434) in which age and sex were controlled by design. The combined risk score was developed using a cohort development dataset ( N = 54 799) and its performance was tested using a cohort testing dataset ( N = 54 798). Our PRS comprises 68 single-nucleotide polymorphisms and had an area under the receiver operating characteristic curve of 0.639 [95% confidence interval (CI) = 0.618-0.661]. In the cohort testing data, the hazard ratio per SD of the combined risk score was 1.332 (95% CI = 1.263-1.406). Harrell's C-index was 0.685 (95% CI = 0.654-0.715). Overall, the standardized incidence ratio was 1.193 (95% CI = 1.067-1.335). By combining a PRS and a clinical risk score, we have developed a risk prediction model that performs well in terms of discrimination and calibration. At an individual level, information on the 10-year risk of melanoma can motivate people to take risk-reduction action. At the population level, risk stratification can allow more effective population-level screening strategies to be implemented.
| S-EPMC10309112 | biostudies-literature
Project description:PurposeBoth genetic and lifestyle factors contribute to the risk of colorectal cancer, but each individual factor has a limited effect. Therefore, we investigated the association between colorectal cancer and the combined effects of genetic factors or/and lifestyle risk factors.Materials and methodsIn a case-control study of 632 colorectal cancer patients and 1,295 healthy controls, we quantified the genetic risk score for colorectal cancer using 13 polymorphisms. Furthermore, we determined a combined lifestyle risk score including obesity, physical activity, smoking, alcohol consumption, and dietary inflammatory index. The associations between colorectal cancer and risk score using these factors were examined using a logistic regression model.ResultsHigher genetic risk scores were associated with an increased risk of colorectal cancer (odds ratio [OR], 2.57; 95% confidence interval [CI], 1.89 to 3.49 for the highest tertile vs. lowest tertile). Among the modifiable factors, previous body mass index, physical inactivity, heavy alcohol consumption, and a high inflammatory diet were associated with an increased risk of colorectal cancer. A higher lifestyle risk score was associated with an increased risk of colorectal cancer (OR, 5.82; 95% CI, 4.02 to 8.44 for the highest tertile vs. lowest tertile). This association was similar in each genetic risk category.ConclusionAdherence to a healthy lifestyle is associated with a substantially reduced risk of colorectal cancer regardless of individuals' genetic risk.
| S-EPMC6639208 | biostudies-literature
Project description:BackgroundEvidence is lacking on the impact of alcohol consumption on colorectal cancer (CRC) risk (overall and by age at diagnosis) by polygenic risk score (PRS) levels, and it is unclear how the magnitude of CRC risk associated with alcohol consumption compares to the magnitude of genetically determined risk.MethodsMultiple logistic regression was used to assess the association between alcohol consumption and colorectal cancer (CRC) across PRS levels based on 140 CRC-related loci among 5104 CRC cases and 4131 controls from a large population-based case-control study. We compared the effects for alcohol consumption and PRS on CRC risk using the "Genetic Risk Equivalent (GRE)" for effective risk communication. Specific analyses were conducted for early-onset CRC (EOCRC, <55 years) and late-onset CRC (LOCRC, ≥55 years).FindingsHigh alcohol consumption, and to a lower extent, also alcohol abstinence were associated with increased CRC risk. Compared to low alcohol consumption (0·1-<25 g/d), lifetime average alcohol consumption ≥25 g/d was more strongly associated with EOCRC [odds ratio (OR) 1·8, 95% confidence interval (CI) 1·2-2·8] than with LOCRC risk (OR 1·3, 95% CI 1·1-1·4) (P-value for interaction with age =0·011). Interactions between alcohol consumption and PRS did not reach statistical significance for either EOCRC or LOCRC risk. The estimated impact of high lifetime alcohol consumption on EOCRC was equivalent to the effect of having 47 percentiles higher PRS (GRE 47, 95% CI 12-82), stronger than the impact on LOCRC (GRE 18, 95% CI 8-29).InterpretationExcessive alcohol use was strongly associated with EOCRC risk, independent of PRS levels. Abstaining from heavy drinking could reduce risk for CRC, in particular for EOCRC to an extent that would be equivalent to having a much lower genetically determined risk.FundingThe first author (X.C.) was supported by the Guangzhou Elite Project (GEP). The DACHS study was supported by grants from the German Research Council (BR 1704/6-1, BR1704/6-3, BR 1704/6-4, BR 1704/6-6, CH 117/1-1, BR 1704/17-1, HO 5117/2-1) and the German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815, 01GL1712).
| S-EPMC9126769 | biostudies-literature
Project description:We investigated the performance of a gastric cancer (GC) risk assessment model in combination with single-nucleotide polymorphisms (SNPs) as a polygenic risk score (PRS) in consideration of Helicobacter pylori (H. pylori) infection status. Six SNPs identified from genome-wide association studies and a marginal association with GC in the study population were included in the PRS. Discrimination of the GC risk assessment model, PRS, and the combination of the two (PRS-GCS) were examined regarding incremental risk and the area under the receiver operating characteristic curve (AUC), with grouping according to H. pylori infection status. The GC risk assessment model score showed an association with GC, irrespective of H. pylori infection. Conversely, the PRS exhibited an association only for those with H. pylori infection. The PRS did not discriminate GC in those without H. pylori infection, whereas the GC risk assessment model showed a modest discrimination. Among individuals with H. pylori infection, discrimination by the GC risk assessment model and the PRS were comparable, with the PRS-GCS combination resulting in an increase in the AUC of 3%. In addition, the PRS-GCS classified more patients and fewer controls at the highest score quintile in those with H. pylori infection. Overall, the PRS-GCS improved the identification of a GC-susceptible population of people with H. pylori infection. In those without H. pylori infection, the GC risk assessment model was better at identifying the high-risk group.
| S-EPMC7923020 | biostudies-literature