Search

Background: Renal cell carcinoma (RCC) is twice as common among men compared with women, and hormonal factors have been suggested to partially explain this difference. There is currently little evidence on the roles of reproductive and hormonal risk factors in RCC aetiology. Materials & Methods: We investigated associations of age at menarche and age at menopause, pregnancy-related factors, hysterectomy and ovariectomy and exogenous hormone use with RCC risk among 298,042 women in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Results: During 15 years of follow-up, 438 RCC cases were identified. Parous women had higher rates of RCC compared with nulliparous women (HR = 1.71, 95% CI 1.18, 2.46), and women who were older at age of first pregnancy had lower rates of RCC (30 years + vs. <20 years HR = 0.53, 95% CI 0.34, 0.82). Additionally, we identified a positive association for hysterectomy (HR = 1.43 95% CI 1.09, 1.86) and bilateral ovariectomy (HR = 1.67, 95% CI 1.13, 2.47), but not unilateral ovariectomy (HR = 0.99, 95% CI 0.61, 1.62) with RCC risk. No clear associations were found for age at menarche, age at menopause or exogenous hormone use. Conclusion: Our results suggest that parity and reproductive organ surgeries may play a role in RCC aetiology.

BACKGROUND: Vitamin B6 insufficiency has been linked to increased risk of cancer and other chronic diseases. The circulating concentration of pyridoxal 5'-phosphate (PLP) is a commonly used measure of vitamin B6 status. Ratios of substrates indicating PLP coenzymatic function and metabolism may be useful complementary measures to further explore the role of vitamin B6 in health. OBJECTIVES: We explored the sensitivity of 5 outcomes, namely PLP concentration, homocysteine:cysteine (Hcy:Cys), cystathionine:cysteine (Cysta:Cys), the 3´-hydroxykynurenine ratio (HKr), and the 4-pyridoxic acid ratio (PAr) to vitamin B6 intake as well as personal and lifestyle characteristics. MEDTHODS: Dietary intake and biomarker data were collected from participants from 3 nested case-control studies within the European Prospective Investigation into Cancer and Nutrition (EPIC). Bayesian regression models assessed the associations of the 5 biomarker outcomes with vitamin B6 intake and personal and lifestyle covariates. Analogous models examined the relations of Hcy:Cys, Cysta:Cys, and HKr with PLP. RESULTS: In total, 4608 participants were included in the analyses. Vitamin B6 intake was most strongly associated with PLP, moderately associated with Hcy:Cys, Cysta:Cys, and HKr, and not associated with PAr (fold change in marker given a doubling of vitamin B6 intake: PLP 1.60 [95% credible interval (CrI): 1.50, 1.71]; Hcy:Cys 0.87 [95% CrI: 0.84, 0.90]; Cysta:Cys 0.89 [95% CrI: 0.84, 0.94]; HKr 0.88 [95% CrI: 0.85, 0.91]; PAr 1.00 [95% CrI: 0.95, 1.05]). PAr was most sensitive to age, and HKr was least sensitive to BMI and alcohol intake. Sex and menopause status were strongly associated with all 5 markers. CONCLUSIONS: We found that 5 different markers, capturing different aspects of vitamin B6-related biological processes, varied in their associations with vitamin B6 intake and personal and lifestyle predictors.

Background: Renal cell carcinoma (RCC) accounts for more than 80% of kidney cancers in adults, and obesity is a known risk factor. Regular consumption of sweetened beverages has been linked to obesity and several chronic diseases, including some types of cancer. It is uncertain whether soft drink and juice consumption is associated with risk of RCC. We investigated the associations of soft drink and juice consumption with RCC incidence and mortality in the European Prospective Investigation into Cancer and Nutrition (EPIC). Methods: A total of 389,220 EPIC participants with median age of 52 years at recruitment (1991-2000) were included. Cox regression yielded adjusted HRs and 95% confidence intervals (CI) for RCC incidence and mortality in relation to intakes of juices and total, sugar-sweetened, and artificially sweetened soft drinks. Results: A total of 888 incident RCCs and 356 RCC deaths were identified. In models including adjustment for body mass index and energy intake, there was no higher risk of incident RCC associated with consumption of juices (HR per 100 g/day increment ¼ 1.03; 95% CI, 0.97-1.09), total soft drinks (HR ¼ 1.01; 95% CI, 0.98-1.05), sugar-sweetened soft drinks (HR ¼ 0.99; 95% CI, 0.94-1.05), or artificially sweetened soft drinks (HR ¼ 1.02; 95% CI, 0.96-1.08). In these fully adjusted models, none of the beverages was associated with RCC mortality (HR, 95% CI per 100 g/day increment 1.06, 0.97-1.16; 1.03, 0.98-1.09; 0.97, 0.89-1.07; and 1.06, 0.99-1.14, respectively). Conclusions: Consumption of juices or soft drinks was not associated with RCC incidence or mortality after adjusting for obesity. Impact: Soft drink and juice intakes are unlikely to play an independent role in RCC development or mortality.

Background: Early detection of renal cell carcinoma (RCC) has the potential to improve disease outcomes. No screening program for sporadic RCC is in place. Given relatively low incidence, screening would need to focus on people at high risk of clinically meaningful disease so as to limit overdiagnosis and screen-detected false positives. Methods: Among 192,172 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (including 588 incident RCC cases), we evaluated a published RCC risk prediction model (including age, sex, BMI, and smoking status) in terms of discrimination (C-statistic) and calibration (observed probability as a function of predicted probability). We used a flexible parametric survival model to develop an expanded model including age, sex, BMI, and smoking status, with the addition of self-reported history of hypertension and measured blood pressure. Results: The previously published model yielded well-calibrated probabilities and good discrimination (C-statistic [95% CI]: 0.699 [0.679–0.721]). Our model had slightly improved discrimination (0.714 [0.694–0.735], bootstrap optimism-corrected C-statistic: 0.709). Despite this good performance, predicted risk was low for the vast majority of participants, with 70% of participants having 10-year risk less than 0.0025. Conclusions: Although the models performed well for the prediction of incident RCC, they are currently insufficiently powerful to identify individuals at substantial risk of RCC in a general population. Impact: Despite the promising performance of the EPIC RCC risk prediction model, further development of the model, possibly including biomarkers of risk, is required to enable risk stratification of RCC.

Background: Early detection of renal cell carcinoma (RCC) has the potential to improve disease outcomes. No screening program for sporadic RCC is in place. Given relatively low incidence, screening would need to focus on people at high risk of clinically meaningful disease so as to limit overdiagnosis and screen-detected false positives.Methods: Among 192,172 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (including 588 incident RCC cases), we evaluated a published RCC risk prediction model (including age, sex, BMI, and smoking status) in terms of discrimination (C-statistic) and calibration (observed probability as a function of predicted probability). We used a flexible parametric survival model to develop an expanded model including age, sex, BMI, and smoking status, with the addition of self-reported history of hypertension and measured blood pressure.Results: The previously published model yielded well-calibrated probabilities and good discrimination (C-statistic [95% CI]: 0.699 [0.679-0.721]). Our model had slightly improved discrimination (0.714 [0.694-0.735], bootstrap optimism-corrected C-statistic: 0.709). Despite this good performance, predicted risk was low for the vast majority of participants, with 70% of participants having 10-year risk less than 0.0025.Conclusions: Although the models performed well for the prediction of incident RCC, they are currently insufficiently powerful to identify individuals at substantial risk of RCC in a general population.Impact: Despite the promising performance of the EPIC RCC risk prediction model, further development of the model, possibly including biomarkers of risk, is required to enable risk stratification of RCC.