Objectives: To investigate the factors affecting cancer survivors and develop a Korean mortality prediction model for cancer survivors. Our study identified lifestyle and mortality risk factors and attempted to determine if health-promoting lifestyles affect mortality.Methods: Among the 1,637,287 participants, 200,834 cancer survivors who were alive after cancer diagnosis were analyzed in the Korean Cancer Prevention Study (KCPS) cohort. Discrimination and calibration for predicting the 10-year mortality risk were evaluated. The prediction model was derived using the Cox model coefficients, mean risk factor values, and mean mortality from the cancer survivors in 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 history of diabetes were high-risk factors for mortality. In contrast, exercise habits and a family history of cancer showed a reduced risk. The prediction model discriminations in the validation dataset for both KCPS all-cause mortality (KAR) and KCPS cancer mortality (KCR) were C-statistics, 0.69 and 0.68, respectively. Based on the constructed prediction models, when we modified exercise status and smoking status, which are modifiable factors, the risk of mortality of cancer survivors decreased linearly (30% to 9%). Moreover, there was an equally linear reduction in the risk of cancer-related mortality, decreasing from 24% to 3%.Conclusions: A mortality prediction model for cancer survivors was developed and may be helpful in supporting a healthy life. Lifestyle modifications in cancer survivors may affect the risk of mortality in the future.

Background: The rates of smoking among women are rising. Previous studies have shown that smoking is associated with early menopause. However, the association of gynecological cancer, including breast and cervical cancer, with early menopause and smoking, remains unclear. Therefore, this study aimed to determine the association between smoking and early menopause, breast cancer, and cervical cancer. Methods: This cross-sectional study used data from the Korean National Health and Nutritional Survey Examination (KHANES) (2016–2018). Early menopause was defined as menopause before 50 years of age. Results: A total of 4,481 participants were included in the analysis. There was no association between early menopause and cervical cancer (adjusted odds ratio [aOR]: 1.435, 95% confidence interval [CI]: 0.730–2.821), but women who had experienced early menopause had a significantly higher risk of breast cancer than women who had experienced normal menopause (aOR: 1.683, 95% CI: 1.089–2.602, p=0.019). Early menopause was not associated with an increased risk of breast cancer in ever-smoker (aOR: 0.475, 95% CI: 0.039–5.748), but was associated with a significantly increased risk of breast cancer in never-smokers (aOR: 1.828, 95% CI: 1.171–2.852). Conclusions: Early menopause was associated with an increased risk of breast cancer in women who had never smoked, but not in women who had ever smoked.

(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.