Lifestyle Risk Prediction Model for Prostate Cancer in a Korean Population.
Ontology highlight
ABSTRACT:
SUBMITTER: Kim SH
PROVIDER: S-EPMC6192929 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:PurposeWe evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk.MethodsUsing 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort.ResultsThe major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (p = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls (p<0.001 and <0.001, respectively). The observed incidence of breast cancer in the two cohorts was similar to the expected incidence from the KoBCRAT (KMCC, p = 0.880; NCC, p = 0.878). The AUC using the KoBCRAT was 0.61 for the KMCC and 0.89 for the NCC cohort.ConclusionsOur findings suggest that the KoBCRAT is a better tool for predicting the risk of breast cancer in Korean women, especially urban women.
| S-EPMC3808381 | biostudies-literature
Project description:BackgroundPredicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.MethodBased on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.ResultsDuring a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women).ConclusionsIn this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.
| S-EPMC4506054 | biostudies-literature
Project description:PurposeLung cancer is the leading cause of cancer deaths in Korea. The objective of the present study was to develop an individualized risk prediction model for lung cancer in Korean men using population-based cohort data.MethodsFrom a population-based cohort study of 1,324,804 Korean men free of cancer at baseline, the individualized absolute risk of developing lung cancer was estimated using the Cox proportional hazards model. We checked the validity of the model using C statistics and the Hosmer-Lemeshow chi-square test on an external validation dataset.ResultsThe risk prediction model for lung cancer in Korean men included smoking exposure, age at smoking initiation, body mass index, physical activity, and fasting glucose levels. The model showed excellent performance (C statistic?=?0.871, 95% CI?=?0.867-0.876). Smoking was significantly associated with the risk of lung cancer in Korean men, with a four-fold increased risk in current smokers consuming more than one pack a day relative to non-smokers. Age at smoking initiation was also a significant predictor for developing lung cancer; a younger age at initiation was associated with a higher risk of developing lung cancer.ConclusionThis is the first study to provide an individualized risk prediction model for lung cancer in an Asian population with very good model performance. In addition to current smoking status, earlier exposure to smoking was a very important factor for developing lung cancer. Since most of the risk factors are modifiable, this model can be used to identify those who are at a higher risk and who can subsequently modify their lifestyle choices to lower their risk of lung cancer.
| S-EPMC3567090 | biostudies-literature
Project description:AimsThe purpose of this study is to evaluate an aggregate influence of prostate cancer (PCa) susceptibility variants on the development of PCa in Korean men by using the polygenic risk score (PRS) approach.MethodsAn analysis of 1,001 cases of PCa and 2,641 controls was performed to: (i) identify potential PCa-related risk loci in Koreans and (ii) validate the cumulative association between these loci and PCa using the PRS. Subgroup analyses based on risk stratification were conducted to better characterize the potential correlation to key PCa-related clinical outcomes (e.g., Gleason score, prostate-specific antigen levels). The results were replicated using 514 cases of PCa and 548 controls from an independent cohort.ResultsGenome-wide association analysis from our discovery cohort revealed 11 candidate single-nucleotide polymorphisms (SNPs) associated with PCa showing statistical significance of p < 5.0 × 10-5. Seven variants were located at 8q24.21 (rs1016343, rs16901979, and rs13252298 in PRNCR1; rs4242384, rs7837688, and rs1447295 in CASC8; and rs1512268 in NKX3). Two variants located within HNF1B (rs7501939 and rs4430796) had a significant negative association with PCa risk [odds ratio (OR) = 0.717 and 0.747, p = 6.42 × 10-7 and 3.67 × 10-6, respectively]. Of the six independent SNPs that remained after linkage disequilibrium (LD) pruning, the top four SNPs best predicted PCa risk with an area under the receiver operating characteristic curve (AUC) of 0.637 (95% CI: 0.582-0.692). Those with top 25% polygenic risk had a 4.2-fold increased risk of developing PCa compared with those with low risk.ConclusionEleven PCa risk variants in Korean men were identified; PRSs of a subset of these variants could help predict PCa susceptibility.
| S-EPMC7643004 | biostudies-literature
Project description:BackgroundThe polygenic risk score (PRS) is used to predict the risk of developing common complex diseases or cancers using genetic markers. Although PRS is used in clinical practice to predict breast cancer risk, it is more accurate for Europeans than for non-Europeans because of the sample size of training genome-wide association studies (GWAS). To address this disparity, we constructed a PRS model for predicting the risk of renal cell carcinoma (RCC) in the Korean population.ResultsUsing GWAS analysis, we identified 43 Korean-specific variants and calculated the PRS. Subsequent to plotting receiver operating characteristic (ROC) curves, we selected the 31 best-performing variants to construct an optimal PRS model. The resultant PRS model with 31 variants demonstrated a prediction rate of 77.4%. The pathway analysis indicated that the identified non-coding variants are involved in regulating the expression of genes related to cancer initiation and progression. Notably, favorable lifestyle habits, such as avoiding tobacco and alcohol, mitigated the risk of RCC across PRS strata expressing genetic risk.ConclusionA Korean-specific PRS model was established to predict the risk of RCC in the underrepresented Korean population. Our findings suggest that lifestyle-associated factors influencing RCC risk are associated with acquired risk factors indirectly through epigenetic modification, even among individuals in the higher PRS category.
| S-EPMC10777500 | biostudies-literature
Project description:AbstractTo explore the influencing factors of prostate cancer occurrence, set up risk prediction model, require reference for the preliminary diagnosis of clinical doctors, this model searched database through the data of prostate cancer patients and prostate hyperplasia patients National Clinical Medical Science Data Center.With the help of Stata SE 12.0 and SPSS 25.0 software, the biases between groups were balanced by propensity score matching. Based on the matched data, the relevant factors were further screened by stepwise logistic regression analysis, the key variable and artificial neural network model are established. The prediction accuracy of the model is evaluated by combining the probability of test set with the area under receiver operating characteristic curve (ROC).After 1:2 PSM, 339 pairs were matched successfully. There are 159 cases in testing groups and 407 cases in training groups. And the regression model was P = 1 / (1 + e (0.122 ∗ age + 0.083 ∗ Apo lipoprotein C3 + 0.371 ∗ total prostate specific antigen (tPSA) -0.227 ∗ Apo lipoprotein C2-6.093 ∗ free calcium (iCa) + 0.428 ∗ Apo lipoprotein E-1.246 ∗ triglyceride-1.919 ∗ HDL cholesterol + 0.083 ∗ creatine kinase isoenzyme [CKMB])). The logistic regression model performed very well (ROC, 0.963; 95% confidence interval, 0.951 to 0.978) and artificial neural network model (ROC, 0.983; 95% confidence interval, 0.964 to 0.997). High degree of Apo lipoprotein E (Apo E) (Odds Ratio, [OR], 1.535) in blood test is a risk factor and high triglyceride (TG) (OR, 0.288) is a protective factor.It takes the biochemical examination of the case as variables to establish a risk prediction model, which can initially reflect the risk of prostate cancer and bring some references for diagnosis and treatment.
| S-EPMC8084031 | biostudies-literature
Project description:Disease risk-associated single nucleotide polymorphisms (SNPs) identified from genome-wide association studies (GWAS) have the potential to be used for disease risk prediction. An important feature of these risk-associated SNPs is their weak individual effect but stronger cumulative effect on disease risk. To date, a stable summary estimate of the joint effect of genetic variants on disease risk prediction is not available. In this study, we propose to use the graded response model (GRM), which is based on the item response theory, for estimating the individual risk that is associated with a set of SNPs. We compare the GRM with a recently proposed risk prediction model called cumulative relative risk (CRR). Thirty-three prostate cancer risk-associated SNPs were originally discovered in GWAS by December 2009. These SNPs were used to evaluate the performance of GRM and CRR for predicting prostate cancer risk in three GWAS populations, including populations from Sweden, Johns Hopkins Hospital, and the National Cancer Institute Cancer Genetic Markers of Susceptibility study. Computational results show that the risk prediction estimates of GRM, compared to CRR, are less biased and more stable.
| S-EPMC3734950 | biostudies-literature
Project description:Individual lifestyle risk factors have been associated with an increased risk of mortality. However, limited evidence is available on the combined association of lifestyle risk factors with mortality in non-Western populations. The analysis included 37,472 participants (aged??19 years) in the Korea National Health and Nutrition Examination Surveys (2007-2014) for whom the data were linked to death certificates/medical records through December 2016. A lifestyle risk score was created using five unhealthy behaviors: current smoking, high-risk alcohol drinking, unhealthy weight, physical inactivity, and insufficient/prolonged sleep. Cox proportional hazards models were used to estimate hazard ratio (HR) and 95% confidence interval (CI). During up to 9 years of follow-up, we documented 1,057 total deaths. Compared to individuals with zero lifestyle risk factor, those with 4-5 lifestyle risk factors had 2.01 times (HR?=?2.01, 95% CI?=?1.43-2.82) and 2.59 times (HR?=?2.59, 95% CI?=?1.24-5.40) higher risk of all-cause and cardiovascular mortality, respectively. However, higher lifestyle risk score was not significantly associated with cancer mortality (p-trend >0.05). In stratified analyses, the positive associations tended to be stronger in adults aged <65 years, unemployed, and those with lower levels of education. In conclusion, combined unhealthy lifestyle behaviors were associated with substantially increased risk of total and cardiovascular mortality in Korean adults.
| S-EPMC7314763 | biostudies-literature
Project description:BackgroundTo develop a breast cancer prediction model for Korean women using published polygenic risk scores (PRS) combined with nongenetic risk factors (NGRF).MethodsThirteen PRS models generated from single or multiple combinations of the Asian and European PRSs were evaluated among 20,434 Korean women. The AUC and increase in OR per SD were compared for each PRS. The PRSs with the highest predictive power were combined with NGRFs; then, an integrated prediction model was established using the Individualized Coherent Absolute Risk Estimation (iCARE) tool. The absolute breast cancer risk was stratified for 18,142 women with available follow-up data.ResultsPRS38_ASN+PRS190_EB, a combination of Asian and European PRSs, had the highest AUC (0.621) among PRSs, with an OR per SD increase of 1.45 (95% confidence interval: 1.31-1.61). Compared with the average risk group (35%-65%), women in the top 5% had a 2.5-fold higher risk of breast cancer. Incorporating NGRFs yielded a modest increase in the AUC of women ages >50 years. For PRS38_ASN+PRS190_EB+NGRF, the average absolute risk was 5.06%. The lifetime absolute risk at age 80 years for women in the top 5% was 9.93%, whereas that of women in the lowest 5% was 2.22%. Women at higher risks were more sensitive to NGRF incorporation.ConclusionsCombined Asian and European PRSs were predictive of breast cancer in Korean women. Our findings support the use of these models for personalized screening and prevention of breast cancer.ImpactOur study provides insights into genetic susceptibility and NGRFs for predicting breast cancer in Korean women.
| S-EPMC10472098 | biostudies-literature
Project description:Genome-wide association studies (GWASs) have identified many single nucleotide polymorphisms (SNPs) that are significantly associated with pancreatic cancer susceptibility. We sought to replicate the associations of 61 GWAS-identified SNPs at 42 loci with pancreatic cancer in Japanese and to develop a risk model for the identification of individuals at high risk for pancreatic cancer development in the general Japanese population. The model was based on data including directly determined or imputed SNP genotypes for 664 pancreatic cancer case and 664 age- and sex-matched control subjects. Stepwise logistic regression uncovered five GWAS-identified SNPs at five loci that also showed significant associations in our case-control cohort. These five SNPs were included in the risk model and also applied to calculation of the polygenic risk score (PRS). The area under the curve determined with the leave-one-out cross-validation method was 0.63 (95% confidence interval, 0.60-0.66) or 0.61 (0.58-0.64) for versions of the model that did or did not include cigarette smoking and family history of pancreatic cancer in addition to the five SNPs, respectively. Individuals in the lowest and highest quintiles for the PRS had odds ratios of 0.62 (0.42-0.91) and 1.98 (1.42-2.76), respectively, for pancreatic cancer development compared with those in the middle quintile. We have thus developed a risk model for pancreatic cancer that showed moderately good discriminatory ability with regard to differentiation of pancreatic cancer patients from control individuals. Our findings suggest the potential utility of a risk model that incorporates replicated GWAS-identified SNPs and established demographic or environmental factors for the identification of individuals at increased risk for pancreatic cancer development.
| S-EPMC6128543 | biostudies-literature