Smoking-attributable Mortality in Korea, 2020: A Meta-analysis of Four Databases.

Objectives: Estimating the number of deaths caused by smoking is crucial for developing and evaluating tobacco control and smoking cessation policies. This study aimed to determine smoking-attributable mortality (SAM) in Korea in 2020. Methods: Four large-scale cohorts from Korea were analyzed. A Cox proportional-hazards model was used to determine the hazard ratios (HRs) of smoking-related death. By conducting a meta-analysis of these HRs, the pooled HRs of smoking-related death for 41 diseases were estimated. Population-attributable fractions (PAFs) were calculated based on the smoking prevalence for 1995 in conjunction with the pooled HRs. Subsequently, SAM was derived using the PAF and the number of deaths recorded for each disease in 2020. Results: The pooled HR for all-cause mortality attributable to smoking was 1.73 for current male smokers (95% CI, 1.532-1.954) and 1.631 for current female smokers (95% CI, 1.371-1.94). Smoking accounted for 33.2% of all-cause deaths in men and 4.6% in women. Additionally, it was a factor in 71.8% of male lung cancer deaths and 11.9% of female lung cancer deaths. In 2020, smoking was responsible for 53,930 male deaths and 6,283 female deaths, totaling 60,213 deaths. Conclusions: Cigarette smoking was responsible for a significant number of deaths in Korea in 2020. Monitoring the impact and societal burden of smoking is essential for effective tobacco control and harm prevention policies.

The Combined Effects of Alcohol Consumption and Smoking on Cancer Risk by Exposure Level: A Systematic Review and Meta-Analysis.

BACKGROUND: Alcohol consumption is a major risk factor for cancer, and when combined with smoking, the risk increases. Nevertheless, few studies have comprehensively evaluated the combined effects of alcohol consumption and smoking on the risk of various cancer types. Therefore, to assess these effects, we conducted a systematic review and meta-analysis. METHODS: We performed a systematic search of five literature databases, focusing on cohort and case-control studies. Considering exposure levels, we quantified the combined effects of alcohol consumption and smoking on cancer risk and assessed multiplicative interaction effects. RESULTS: Of 4,452 studies identified, 24 (4 cohort studies and 20 case-control studies) were included in the meta-analysis. We detected interaction effect of light alcohol and moderate smoking on head and neck cancer risk (relative risk [RR], 4.26; 95% confidence interval [CI], 2.50-7.26; I² = 65%). A synergistic interaction was observed in heavy alcohol and heavy smoking group (RR, 35.24; 95% CI, 23.17-53.58; I² = 69%). In more detailed cancer types, the interaction effect of heavy alcohol and heavy smoking was noticeable on oral (RR, 36.42; 95% CI, 24.62-53.87; I² = 46%) and laryngeal (RR, 38.75; 95% CI, 19.25-78.01; I² = 69%) cancer risk. CONCLUSION: Our study provided a comprehensive summary of the combined effects of alcohol consumption and smoking on cancers. As their consumption increased, the synergy effect became more pronounced, and the synergy effect was evident especially for head and neck cancer. These findings provide additional evidence for the combined effect of alcohol and smoking in alcohol guidelines for cancer prevention.

Factors Influencing Compliance With Social Distancing as a Nonpharmaceutical Intervention Before Vaccine Availability During the COVID-19 Pandemic: A Cross-Sectional Study in South Korea.

This study aimed to identify factors influencing compliance with social distancing, a key nonpharmaceutical intervention during the early stages of the coronavirus disease (COVID-19) pandemic. The study population comprised 182 758 Koreans who participated in the 2020 Community Health Survey. Personal characteristics were classified into sociodemographic, health behavioral, and psychosocial factors, and factors associated with social distancing compliance were identified. Health behaviors and psychosocial factors were highly related to compliance with social distancing. Approximately 13% of smokers were less likely to practice physical distancing and 50% of high-risk drinkers were less likely to limit going out or attending gatherings and events. Higher concern about COVID-19 and a more positive perception of the government's response policy were associated with a higher compliance with social distancing. Strategic public health policies considering the characteristics of the public are needed to enhance compliance with nonpharmaceutical interventions during disease outbreaks lacking effective treatments and vaccines.

Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes.

Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.

A qualitative exploration of exercise motivation among colorectal cancer survivors: an application of the theory of planned behavior.

PURPOSE: The purpose of this qualitative study was to use semi-structured interviews and thematic analysis to elicit key influencing factors (i.e., behavioral, normative, and control beliefs) related to physical activity and exercise in colorectal cancer survivors. METHODS: Colorectal cancer survivors (N = 17) were recruited from exercise programs designed for colorectal cancer survivors at the Yonsei Cancer Center, Seoul, South Korea. A purposive sampling method was used. Interview questions were informed by the theory of planned behavior (TPB). Semi-structured face-to-face interviews were conducted, and open-ended questions addressed the research question. Interviews were transcribed verbatim and analyzed using thematic analysis. RESULTS: Participants were on average 2.2 years post-treatment. The mean age of the sample was 55.9 years. Key behavioral, normative, and control beliefs emerged in the data. For behavioral beliefs, colorectal cancer survivors believed that exercise would result in physical and psychological improvements, and improve their bowel problems. For normative beliefs, most colorectal cancer survivors wanted their oncologists' approval for participation of exercise. Family members, more specifically the spouse, were also influencing factors for colorectal cancer survivors adopting physical activity. The most frequently mentioned control belief was that supervised exercise with an exercise specialist made exercise participation easier. CONCLUSIONS AND IMPLICATIONS: Beliefs identified in this study can inform TPB-based physical activity interventions tailored for colorectal cancer survivors. While information alone may not lead to behavior change, integrating these beliefs with other influential factors can potentially enhance intervention efficacy and promote physical activity in this population.

Smoking-attributable mortality among Korean adults in 2019.

OBJECTIVES: Tobacco use ranks among the leading preventable causes of death worldwide. This study was conducted to calculate the mortality rate attributable to smoking in Korea for 2019 and to highlight the importance of tracking and monitoring smoking-related deaths for public health purposes. METHODS: Population attributable risk (PAR) was used to estimate the number of deaths related to smoking in 2019. PAR percentages were applied to the estimated mortality figures for various diseases, with PAR determined based on relative risk (RR). Levin's formula was used to calculate PAR, and RR was adjusted for age and alcohol consumption using Cox proportional hazards regression model to derive disease-specific regression coefficients. The analysis incorporated previously determined smoking rates from 1985, and use rates of novel tobacco products were not considered. RESULTS: The findings revealed a total of 67,982 smoking-attributable deaths in Korea in 2019, 56,993 of which occurred in men and 11,049 in women. The PAR of smoking for various causes of death in adult men was highest for lung cancer at 74.9%, followed by pneumonia (29.4%), ischemic heart disease (42.3%), and stroke (30.2%). For women, the PAR for smoking-related death was highest for lung cancer (19.9%), followed by stroke (7.6%), pneumonia (5.7%), and ischemic heart disease (9.1%). CONCLUSIONS: In countries experiencing rapid fluctuations in smoking rates, including Korea, regular studies on smoking-related mortality is imperative. Furthermore, it is necessary to investigate smoking-related deaths, including the prevalence of novel tobacco product use, to accurately gauge the risks associated with emerging tobacco products.

A large-scale microRNA transcriptome-wide association study identifies two susceptibility microRNAs, miR-1307-5p and miR-192-3p, for colorectal cancer risk.

Transcriptome-wide association studies (TWAS) have identified many putative susceptibility genes for colorectal cancer (CRC) risk. However, susceptibility miRNAs, critical dysregulators of gene expression, remain unexplored. We genotyped DNA samples from 313 CRC East Asian patients and performed small RNA sequencing in their normal colon tissues distant from tumors to build genetic models for predicting miRNA expression. We applied these models and data from genome-wide association studies (GWAS) including 23 942 cases and 217 267 controls of East Asian ancestry to investigate associations of predicted miRNA expression with CRC risk. Perturbation experiments separately by promoting and inhibiting miRNAs expressions and further in vitro assays in both SW480 and HCT116 cells were conducted. At a Bonferroni-corrected threshold of P < 4.5 × 10-4, we identified two putative susceptibility miRNAs, miR-1307-5p and miR-192-3p, located in regions more than 500 kb away from any GWAS-identified risk variants in CRC. We observed that a high predicted expression of miR-1307-5p was associated with increased CRC risk, while a low predicted expression of miR-192-3p was associated with increased CRC risk. Our experimental results further provide strong evidence of their susceptible roles by showing that miR-1307-5p and miR-192-3p play a regulatory role, respectively, in promoting and inhibiting CRC cell proliferation, migration, and invasion, which was consistently observed in both SW480 and HCT116 cells. Our study provides additional insights into the biological mechanisms underlying CRC development.

Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort.

BACKGROUND: Pancreatic cancer is a lethal disease with a high mortality rate. The difficulty of early diagnosis is one of its primary causes. Therefore, we aimed to discover non-invasive biomarkers that facilitate the early diagnosis of pancreatic cancer risk. METHODS: The study subjects were randomly selected from the Korean Cancer Prevention Study-II and matched by age, sex, and blood collection point [pancreatic cancer incidence (n = 128) vs. control (n = 256)]. The baseline serum samples were analyzed by non-targeted metabolomics, and XGBoost was used to select significant metabolites related to pancreatic cancer incidence. Genomewide association study for the selected metabolites discovered valuable single nucleotide polymorphisms (SNPs). Moderation and mediation analysis were conducted to explore the variables related to pancreatic cancer risk. RESULTS: Eleven discriminant metabolites were selected by applying a cut-off of 4.0 in XGBoost. Five SNP presented significance in metabolite-GWAS (p ≤ 5 × 10-6) and logistic regression analysis. Among them, the pair metabolite of rs2370981, rs55870181, and rs72805402 displayed a different network pattern with clinical/biochemical indicators on comparison with allelic carrier and non-carrier. In addition, we demonstrated the indirect effect of rs59519100 on pancreatic cancer risk mediated by γ-glutamyl tyrosine, which affects the smoking status. The predictive ability for pancreatic cancer on the model using five SNPs and four pair metabolites with the conventional risk factors was the highest (AUC: 0.738 [0.661-0.815]). CONCLUSIONS: Signatures involving metabolites and SNPs discovered in the present research may be closely associated with the pathogenesis of pancreatic cancer and for use as predictive biomarkers allowing early pancreatic cancer diagnosis and therapy.

Metabolic changes preceding bladder cancer occurrence among Korean men: a nested case-control study from the KCPS-II cohort.

BACKGROUND: Bladder cancer (BLCA) research in Koreans is still lacking, especially in focusing on the prediction of BLCA. The current study aimed to discover metabolic signatures related to BLCA onset and confirm its potential as a biomarker. METHODS: We designed two nested case-control studies using Korean Cancer Prevention Study (KCPS)-II. Only males aged 35-69 were randomly selected and divided into two sets by recruitment organizations [set 1, BLCA (n = 35) vs. control (n = 35); set 2, BLCA (n = 31) vs. control (n = 31)]. Baseline serum samples were analyzed by non-targeted metabolomics profiling, and OPLS-DA and network analysis were performed. Calculated genetic risk score (GRS) for BLCA from all KCPS participants was utilized for interpreting metabolomics data. RESULTS: Critical metabolic signatures shown in the BLCA group were dysregulation of lysine metabolism and tryptophan-indole metabolism. Furthermore, the prediction model consisting of metabolites (lysine, tryptophan, indole, indoleacrylic acid, and indoleacetaldehyde) reflecting these metabolic signatures showed mighty BLCA predictive power (AUC: 0.959 [0.929-0.989]). The results of metabolic differences between GRS-high and GRS-low groups in BLCA indicated that the pathogenesis of BLCA is associated with a genetic predisposition. Besides, the predictive ability for BLCA on the model using GRS and five significant metabolites was powerful (AUC: 0.990 [0.980-1.000]). CONCLUSION: Metabolic signatures shown in the present research may be closely associated with BLCA pathogenesis. Metabolites involved in these could be predictive biomarkers for BLCA. It could be utilized for early diagnosis, prognostic diagnosis, and therapeutic targets for BLCA.

A Study on Survival Analysis Methods Using Neural Network to Prevent Cancers.

Background: Cancer is one of the main global health threats. Early personalized prediction of cancer incidence is crucial for the population at risk. This study introduces a novel cancer prediction model based on modern recurrent survival deep learning algorithms. Methods: The study includes 160,407 participants from the blood-based cohort of the Korea Cancer Prevention Research-II Biobank, which has been ongoing since 2004. Data linkages were designed to ensure anonymity, and data collection was carried out through nationwide medical examinations. Predictive performance on ten cancer sites, evaluated using the concordance index (c-index), was compared among nDeep and its multitask variation, Cox proportional hazard (PH) regression, DeepSurv, and DeepHit. Results: Our models consistently achieved a c-index of over 0.8 for all ten cancers, with a peak of 0.8922 for lung cancer. They outperformed Cox PH regression and other survival deep neural networks. Conclusion: This study presents a survival deep learning model that demonstrates the highest predictive performance on censored health dataset, to the best of our knowledge. In the future, we plan to investigate the causal relationship between explanatory variables and cancer to reduce cancer incidence and mortality.

Cancer risk based on alcohol consumption levels: a comprehensive systematic review and meta-analysis.

OBJECTIVES: Alcohol consumption is a well-established risk factor for cancer. Despite extensive research into the relationship between alcohol consumption and cancer risk, the effect of light alcohol consumption on cancer risk remains a topic of debate. To contribute to this discourse, we conducted a comprehensive systematic review and meta-analysis. METHODS: Our systematic review aimed to investigate the associations between different levels of alcohol consumption and the risk of several cancer types. We focused on analyzing prospective associations using data from 139 cohort studies. Among them, 106 studies were included in the meta-analysis after a quantitative synthesis. RESULTS: Our analysis did not find a significant association between light alcohol consumption and all-cause cancer risk (relative risk, 1.02; 95% confidence interval, 0.99 to 1.04), but we observed a dose-response relationship. Light alcohol consumption was significantly associated with higher risks of esophageal, colorectal, and breast cancers. Light to moderate drinking was associated with elevated risks of esophageal, colorectal, laryngeal, and breast cancers. Heavy drinking was also found to contribute to the risk of stomach, liver, pancreas, and prostate cancers, thereby increasing the risk of almost all types of cancer. Additionally, females generally had lower cancer risks compared to males. CONCLUSIONS: Our findings highlight that cancer risks extend beyond heavy alcohol consumption to include light alcohol consumption as well. These findings suggest that there is no safe level of alcohol consumption associated with cancer risk. Our results underscore the importance of public health interventions addressing alcohol consumption to mitigate cancer risks.

Comparisons of the prediction models for undiagnosed diabetes between machine learning versus traditional statistical methods.

We compared the prediction performance of machine learning-based undiagnosed diabetes prediction models with that of traditional statistics-based prediction models. We used the 2014-2020 Korean National Health and Nutrition Examination Survey (KNHANES) (N = 32,827). The KNHANES 2014-2018 data were used as training and internal validation sets and the 2019-2020 data as external validation sets. The receiver operating characteristic curve area under the curve (AUC) was used to compare the prediction performance of the machine learning-based and the traditional statistics-based prediction models. Using sex, age, resting heart rate, and waist circumference as features, the machine learning-based model showed a higher AUC (0.788 vs. 0.740) than that of the traditional statistical-based prediction model. Using sex, age, waist circumference, family history of diabetes, hypertension, alcohol consumption, and smoking status as features, the machine learning-based prediction model showed a higher AUC (0.802 vs. 0.759) than the traditional statistical-based prediction model. The machine learning-based prediction model using features for maximum prediction performance showed a higher AUC (0.819 vs. 0.765) than the traditional statistical-based prediction model. Machine learning-based prediction models using anthropometric and lifestyle measurements may outperform the traditional statistics-based prediction models in predicting undiagnosed diabetes.

Genetically determined alcohol consumption and cancer risk in Korea.

OBJECTIVES: The purpose of this study was to determine the causal relationship between the genetically determined amount of alcohol consumption and the occurrence of major cancers. METHODS: The data used in this study were from 129,324 people selected from the Korean Cancer Prevention Study-II, the participants of which visited 18 health examination centers between 2004 and 2013. Cancer incidence was confirmed as of 2020 using data from the National Cancer Center. A genome-wide association study (GWAS) on alcohol consumption was performed using PLINK 2.0, and sex, age, chip type, and principal components were adjusted. RESULTS: From the GWAS, a genetic risk score for alcohol consumption was calculated and genetically determined alcohol consumption (GDAC) was estimated. GDAC was divided into quintile groups and showed significant causal relationships with rectal cancer and liver cancer, but not with other cancers. For liver cancer, an association was shown in the hepatitis B surface antigen (HBsAg)-negative group, and a particularly strong association was found in the over-60-year-old HBsAg-negative group, in which, compared to the GDAC Q1 group, the Q4 group had a 2.35 times higher risk (95% confidence interval [CI], 1.05 to 5.23), and the Q5 group had a 2.40 times higher risk (95% CI, 1.09 to 5.30). CONCLUSIONS: The results of this study provided evidence that the amount of alcohol consumed is causally related to the occurrence of rectal cancer and liver cancer in HBsAg-negative individuals. Additional studies should be continued for other cancer types through long-term follow-up.

Prognostic value of resting heart rate in predicting undiagnosed diabetes in adults: Korean National Health and Nutrition Examination Survey 2008-2018.

BACKGROUND AND AIM: Although resting heart rate (RHR) is associated with prevalence and incidence of diabetes, whether it is associated with undiagnosed diabetes is still unclear. We aimed to investigate whether the RHR is associated with the prevalence of undiagnosed diabetes in a large Korean national dataset. METHODS AND RESULTS: The Korean National Health and Nutrition Examination Survey data from 2008 to 2018 were used. After screening, 51,637 participants were included in this study. The odds ratios and 95% confidence intervals (CIs) for undiagnosed diabetes were calculated using multivariable-adjusted logistic regression analyses. Analyses showed that participants with a RHR of ≥90 bpm showed a 4.00- (95% CI: 2.77-5.77) and 3.21-times (95% CI: 2.01-5.14) higher prevalence of undiagnosed diabetes for men and women, respectively, than those with a RHR of <60 bpm. The linear dose-response analyses showed that each 10-bpm increment in RHR was associated with a 1.39- (95% CI: 1.32-1.48) and 1.28-times (95% CI: 1.19-1.37) higher prevalence of undiagnosed diabetes for men and women, respectively. In the stratified analyses, the positive association between RHR and the prevalence of undiagnosed diabetes was tended to be stronger among those who were younger (age: <40 years) and lean (BMI: <23 kg/m2). CONCLUSIONS: Elevated RHR was significantly associated with a higher prevalence of undiagnosed diabetes in Korean men and women, independent of demographic, lifestyle, and medical factors. Accordingly, the value of RHR as a clinical indicator and health marker, especially in reducing the prevalence of undiagnosed diabetes, is suggestible.

Do Weight Changes Affect the Association between Smoking Cessation and the Risk of Stroke Subtypes in Korean Males?

(1) Background: We investigated whether weight changes affect the association between smoking cessation and stroke risk; (2) Methods: Overall, 719,040 males were categorized into eight groups according to smoking status (sustained smokers, non-smokers, long-term quitters (quit > 4 years), and recent quitters (quit < 4 years)) and post-cessation weight change (-5 kg, -5.0 to 0.1 kg, maintainers, 0.1-5.0 kg, and >5.0 kg). The hazard ratios (HR) and 95% confidence intervals (CI) for incident total, ischemic, and hemorrhagic strokes, including subarachnoid and intracerebral hemorrhage, were calculated using Cox proportional hazard models; (3) Results: We detected 38,730 strokes (median follow-up, 25.7 years), including 30,609 ischemic and 9055 hemorrhagic strokes. For recent quitters with a >5.0 kg or 0.1-5.0 kg weight increase, maintainers, or those who lost 0.1-5 kg, the multivariable HR for total stroke was 0.73 (95% CI, 0.67-0.79), 0.78 (95% CI, 0.74-0.82), 0.77 (95% CI, 0.69-0.85), 0.84 (95% CI, 0.77-0.90), and 1.06 (95% CI, 0.92-1.23), respectively, compared with that of sustained smokers; (4) Conclusions: Comparable patterns were obtained for stroke subtypes. Thus, we strongly recommend quitting smoking, as weight gain after quitting smoking does not alter the stroke-related benefits.

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries.

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

Associations of biomarkers for exposure to tobacco smoke with lung cancer risk in Korea.

BACKGROUND: Nicotine metabolite ratio (NMR) can be used to predict total nicotine clearance. However, it is unknown whether NMR could be used as a marker of lung cancer risk. OBJECTIVE: To evaluate the blood metabolites of nicotine relating to the risk of developing lung cancer and investigate the combined effects of NMR and cigarette per day on the risk of lung cancer. METHODS: Among the 1,054 eligible subjects from the Korean Cancer Prevention Study-II biobank cohort, those with cotinine values below 0 ng/ml were excluded. Slow and fast metabolizer groups were defined using the median value of the NMR, calculated with the control group data, as the cut-point. RESULTS: The multivariable Cox proportional hazard models demonstrated that, the fast metabolizer group had a significantly higher risk of lung cancer than the slow metabolizer group (Adjusted HR: 2.02, 95% CI: 1.32-3.10). Fast metabolizers who smoked more than 15 cigarettes per day had an even higher risk of lung cancer (Adjusted HR: 3.51, 95% CI: 1.96-6.29) than the slow metabolizers who smoked less than 15 cigarettes per day. CONCLUSIONS: In summary, the NMR may be an effective marker for estimating tobacco-related disease risks such as lung cancer.

The Korea Cohort Consortium: The Future of Pooling Cohort Studies.

OBJECTIVES: We introduced the cohort studies included in the Korea Cohort Consortium (KCC), focusing on large-scale cohort studies established in Korea with a prolonged follow-up period. Moreover, we also provided projections of the follow-up and estimates of the sample size that would be necessary for big-data analyses based on pooling established cohort studies, including population-based genomic studies. METHODS: We mainly focused on the characteristics of individual cohort studies from the KCC. We developed "PROFAN", a Shiny application for projecting the follow-up period to achieve a certain number of cases when pooling established cohort studies. As examples, we projected the follow-up periods for 5000 cases of gastric cancer, 2500 cases of prostate and breast cancer, and 500 cases of non-Hodgkin lymphoma. The sample sizes for sequencing-based analyses based on a 1:1 case-control study were also calculated. RESULTS: The KCC consisted of 8 individual cohort studies, of which 3 were community-based and 5 were health screening-based cohorts. The population-based cohort studies were mainly organized by Korean government agencies and research institutes. The projected follow-up period was at least 10 years to achieve 5000 cases based on a cohort of 0.5 million participants. The mean of the minimum to maximum sample sizes for performing sequencing analyses was 5917-72 102. CONCLUSIONS: We propose an approach to establish a large-scale consortium based on the standardization and harmonization of existing cohort studies to obtain adequate statistical power with a sufficient sample size to analyze high-risk groups or rare cancer subtypes.

Prediction of cancer survivors' mortality risk in Korea: a 25-year nationwide prospective cohort study.

OBJECTIVES: This study aimed to investigate the factors affecting cancer survival and develop a mortality prediction model for Korean cancer survivors. Our study identified lifestyle and mortality risk factors and attempted to determine whether health-promoting lifestyles affect mortality. METHODS: Among the 1,637,287 participants in the Korean Cancer Prevention Study (KCPS) cohort, 200,834 cancer survivors who were alive after cancer diagnosis were analyzed. Discrimination and calibration for predicting the 10-year mortality risk were evaluated. A prediction model was derived using the Cox model coefficients, mean risk factor values, and mean mortality from the cancer survivors in the KCPS cohort. RESULTS: During the 21.6-year follow-up, the all-cause mortality rates of cancer survivors were 57.2% and 39.4% in men and women, respectively. Men, older age, current smoking, and a history of diabetes were high-risk factors for mortality, while exercise habits and a family history of cancer were associated with reduced risk. The prediction model discrimination in the validation dataset for both KCPS all-cause mortality and KCPS cancer mortality was shown by C-statistics of 0.69 and 0.68, respectively. Based on the constructed prediction models, when we modified exercise status and smoking status, as modifiable factors, the cancer survivors' risk of mortality decreased linearly. CONCLUSIONS: A mortality prediction model for cancer survivors was developed that may be helpful in supporting a healthy life. Lifestyle modifications in cancer survivors may affect their risk of mortality in the future.