Cancer Biobank Aichi (CBBA) was established at Aichi Cancer Center in 2017 as a hospital-based prospective biobank embedded in first-visit outpatient care. With written informed consent, up to 13 mL of blood is collected, processed within 2 hours, fractionated into serum, plasma, and buffy coat, dispensed into 2D-barcoded aliquots, and stored at-80 degrees C under continuous monitoring with distributed freezer storage. DNA is extracted from buffy coat and stored as ready-to-use aliquots. Epidemiological information is obtained using a structured questionnaire; most lifestyle exposures (physical activity, alcohol intake, smoking, and diet) refer to the year preceding the index visit. The questionnaire includes outpatient dietary assessment and separate ascertainment of electronic/heat-not-burn tobacco product use. Clinical information is updated annually through linkage with the institutional hospital-based cancer registry based on standardized registration items recommended by the National Cancer Center Japan; for approved projects, linkage to department-level clinical data is available under governance review with data minimization and pseudonymization. Among 34,574 first-visit outpatients approached or informed about CBBA, 29,470 (85.2%) provided biospecimens and/or questionnaire data and were included, including 10,075 non-cancer participants. Biospecimens were available for 25,105 participants and questionnaire data for 26,570; 22,205 provided both components. CBBA enables multiple epidemiologic and translational study designs using biospecimens collected at first presentation, one-year-referenced lifestyle data, an internal non-cancer reference group, and registry follow-up.
Background Elevated low-density lipoprotein cholesterol (LDL-C) is a well-established risk factor for coronary heart disease, but its role in stroke remains unclear. This study examined the association between LDL-C levels and the risk of cardiovascular disease (CVD) and its subtypes in a large cohort of Japanese workers. Methods We analyzed data from 69,605 participants in the Japan Epidemiology Collaboration on Occupational Health study. Baseline LDL-C levels were categorized as <70, 70-99, 100-119, 120-139, 140-159, and >= 160 mg/dL. Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results Over a mean follow-up of 8.5 years (range, 0.1-13.4 years), 406 participants developed CVD. Compared with LDL-C levels of 100-119 mg/dL, participants with LDL-C <70 mg/dL had a higher risk of intracerebral hemorrhage (HR, 3.56; 95% CI, 1.31-9.66), whereas no association was observed between LDL-C levels and subarachnoid hemorrhage. For ischemic stroke, a U-shaped association was observed; adjusted HRs (95% CIs) versus 100-119 mg/dL were 2.26 (0.97-5.25), 1.53 (0.92-2.56), 0.95 (0.56-1.60), 1.71 (1.04-2.81), and 1.52 (0.85-2.72) for LDL-C levels of <70, 70-99, 120-139, 140-159, and >= 160 mg/dL, respectively. In contrast, the risk of myocardial infarction increased linearly with increasing LDL-C levels. Conclusions Very low LDL-C levels were associated with an increased risk of intracerebral hemorrhage, whereas a suggestive U-shaped association was observed between LDL-C levels and ischemic stroke. The risk of myocardial infarction increased with higher LDL-C levels.
Background: Smoking increases the risk of cancer and cardiovascular disease, and cessation after diagnosis improves prognosis. However, long-term associations of disease diagnoses and surgery with smoking cessation remain unclear. We evaluated associations of major disease diagnoses (myocardial infarction, stroke, and cancer) and surgery with smoking cessation at the 10-year follow-up among adults who smoked at baseline. Methods: This prospective cohort study followed 17,779 participants aged 40-69 years who current smoking at baseline in the Japan Public Health Center-based Prospective Study for 10 years. Participants had no history of major diseases or surgery at baseline. Smoking status was assessed at baseline and 10-year follow-up. Incident myocardial infarction, stroke, and cancer were ascertained from questionnaire-reported diagnosis timing and population-based registries. Surgeries were identified from self-administered questionnaires. Multivariable logistic regression with multiple imputation estimated adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the association between incident disease diagnoses or surgeries and smoking cessation. Results: During follow-up, 4,789 participants (26.9%) quit smoking. Any major disease diagnosis (N = 1,726) was associated with cessation compared with no diagnosis (OR 5.39, 95% CI 4.80-6.05), including myocardial infarction (OR 6.87, 95% CI 5.18-9.12), stroke (OR 4.03, 95% CI 3.40-4.78) and cancer (OR 2.91, 95% CI 2.53-3.34). Lung cancer (OR 7.41, 95% CI 4.44-12.51) and lung surgery (OR 16.60, 95% CI 9.08-30.36) showed strong associations. Conclusions: Major disease diagnoses and surgery were strongly associated with long-term smoking cessation over 10 years. These events may provide potential "teachable moments" for health professionals to encourage cessation.
Background: Secondary healthcare databases are increasingly used in health-related research, but ensuring data validity is essential for generating accurate real-world evidence. This study aimed to validate mortality definitions based on Japanese medical and long-term care (LTC) claims data. Methods: Using claims data between April 2015 and March 2023 from 13 Japanese municipalities, we developed eight algorithms to identify death. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each algorithm were calculated using vital statistics death records as the reference standard. These metrics were calculated for the overall cohort, and also stratified by age (0-39, 40-74, and >= 75 years) and place/cause of death. Results: We analyzed 1,894,123 participants (mean age: 61.2 years; women: 55.9%) with 68,966,581 person-months of claims. Specificity, PPV, and NPV were consistently high (>= 0.97) across age groups, but sensitivity varied by algorithm, age, and place/cause of death. Medical claims-based algorithms identified approximately 90% of deaths in medical facilities, while the addition of LTC claims captured >95% of deaths in LTC facilities. Sensitivity for home deaths was generally low at 0.52-0.56 for individuals aged 0-39 years, which may have been due to sudden deaths (suicides or accidents) before medical services could be provided. However, home deaths among those receiving ongoing home-based care (e.g., cancer) were more accurately identifiable. Conclusions: Claims-based algorithms to identify death can be effectively used in epidemiological research in Japan by strategically applying different definitions according to type of death and target age.
