Diet Quality and Risk of Bladder Cancer in the Multiethnic Cohort Study.
Minji KangLynne R WilkensMichael D WirthNitin ShivappaJames R HébertChristopher A HaimanLoic Le MarchandSong-Yi ParkPublished in: Nutrients (2024)
This study analyzed the overall quality of the diet using predefined indices, including the Healthy Eating Index-2015 (HEI-2015), the Alternative Healthy Eating Index-2010 (AHEI-2010), the alternate Mediterranean Diet (aMED) score, the Dietary Approaches to Stop Hypertension (DASH) score, and the Dietary Inflammatory Index (DII ® ), to explore their association with the risk of bladder cancer in the Multiethnic Cohort Study. Data were taken from 186,979 African American, Japanese American, Latino, Native Hawaiian, and non-Hispanic White participants aged 45-75 years, with 1152 incident cases of invasive bladder cancer during a mean follow-up period of 19.2 ± 6.6 years. Cox models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) with comprehensive adjustment for smoking. Comparing the highest vs. lowest diet quality score quintile, HRs (95% CIs) in men was 1.08 (0.86-1.36) for HEI-2015, 1.05 (0.84-1.30) for AHEI-2010, 1.01 (0.80-1.27) for aMED, 1.13 (0.90-1.41) for DASH, and 0.96 (0.76-1.21) for DII ® , whereas the corresponding HRs for women were 0.75 (0.53-1.07), 0.64 (0.45-0.92), 0.60 (0.40-0.88), 0.66 (0.46-0.95), and 0.63 (0.43-0.90) with all p values for trend <0.05. The inverse association found in women did not vary by smoking status or race and ethnicity. Our findings suggest that adopting high-quality diets may reduce the risk of invasive bladder cancer among women in a multiethnic population.
Keyphrases
- african american
- weight loss
- physical activity
- polycystic ovary syndrome
- pregnancy outcomes
- blood pressure
- smoking cessation
- quality improvement
- oxidative stress
- cardiovascular disease
- metabolic syndrome
- type diabetes
- insulin resistance
- big data
- electronic health record
- muscle invasive bladder cancer
- skeletal muscle
- adipose tissue
- artificial intelligence
- deep learning
- data analysis