DESIGN: Individual participant meta-analysis using data from 25 cohorts participating in the CHANCES consortium. Data were harmonised, analysed separately employing Cox proportional hazard regression models, and combined by meta-analysis.

RESULTS: Overall, 503,905 participants aged 60 and older were included in this study, of whom 37,952 died from cardiovascular disease. Random effects meta-analysis of the association of smoking status with cardiovascular mortality yielded a summary hazard ratio of 2.07 (95% CI 1.82 to 2.36) for current smokers and 1.37 (1.25 to 1.49) for former smokers compared with never smokers. Corresponding summary estimates for risk advancement periods were 5.50 years (4.25 to 6.75) for current smokers and 2.16 years (1.38 to 2.39) for former smokers. The excess risk in smokers increased with cigarette consumption in a dose-response manner, and decreased continuously with time since smoking cessation in former smokers. Relative risk estimates for acute coronary events and for stroke events were somewhat lower than for cardiovascular mortality, but patterns were similar.

PATIENTS AND METHODS: For this individual patient data meta-analysis, sociodemographic and smoking behavior information of 12 414 incident CRC patients (median age at diagnosis: 64.3 years), recruited within 14 prospective cohort studies among previously cancer-free adults, was collected at baseline and harmonized across studies. Vital status and causes of death were collected for a mean follow-up time of 5.1 years following cancer diagnosis. Associations of smoking behavior with overall and CRC-specific survival were evaluated using Cox regression and standard meta-analysis methodology.

RESULTS: A total of 5229 participants died, 3194 from CRC. Cox regression revealed significant associations between former [hazard ratio (HR) = 1.12; 95 % confidence interval (CI) = 1.04-1.20] and current smoking (HR = 1.29; 95% CI = 1.04-1.60) and poorer overall survival compared with never smoking. Compared with current smoking, smoking cessation was associated with improved overall (HR<10 years = 0.78; 95% CI = 0.69-0.88; HR≥10 years = 0.78; 95% CI = 0.63-0.97) and CRC-specific survival (HR≥10 years = 0.76; 95% CI = 0.67-0.85).

METHODS: Relative mortality and mortality rate advancement periods (RAPs) were estimated by Cox proportional hazards models for the population-based prospective cohort studies from Europe and the U.S. (CHANCES [Consortium on Health and Ageing: Network of Cohorts in Europe and the U.S.]), and subsequently pooled by individual participant meta-analysis. Statistical analyses were performed from June 2013 to March 2014.

RESULTS: A total of 489,056 participants aged ≥60 years at baseline from 22 population-based cohort studies were included. Overall, 99,298 deaths were recorded. Current smokers had 2-fold and former smokers had 1.3-fold increased mortality compared with never smokers. These increases in mortality translated to RAPs of 6.4 (95% CI=4.8, 7.9) and 2.4 (95% CI=1.5, 3.4) years, respectively. A clear positive dose-response relationship was observed between number of currently smoked cigarettes and mortality. For former smokers, excess mortality and RAPs decreased with time since cessation, with RAPs of 3.9 (95% CI=3.0, 4.7), 2.7 (95% CI=1.8, 3.6), and 0.7 (95% CI=0.2, 1.1) for those who had quit <10, 10 to 19, and ≥20 years ago, respectively.

METHODS: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.

RESULTS: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.

METHODS: The case-control portion of the study was conducted in nine UK centers with men ages 50-69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.

RESULTS: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal.

CONCLUSIONS: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk.

MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.

RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.

OBJECTIVE: We performed an analysis of genetic variants associated with leukocyte telomere length to assess the relationship between telomere length and RCC risk using Mendelian randomization, an approach unaffected by biases from temporal variability and reverse causation that might have affected earlier investigations.

DESIGN, SETTING, AND PARTICIPANTS: Genotypes from nine telomere length-associated variants for 10 784 cases and 20 406 cancer-free controls from six genome-wide association studies (GWAS) of RCC were aggregated into a weighted genetic risk score (GRS) predictive of leukocyte telomere length.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Odds ratios (ORs) relating the GRS and RCC risk were computed in individual GWAS datasets and combined by meta-analysis.

RESULTS AND LIMITATIONS: Longer genetically inferred telomere length was associated with an increased risk of RCC (OR=2.07 per predicted kilobase increase, 95% confidence interval [CI]:=1.70-2.53, p<0.0001). As a sensitivity analysis, we excluded two telomere length variants in linkage disequilibrium (R2>0.5) with GWAS-identified RCC risk variants (rs10936599 and rs9420907) from the telomere length GRS; despite this exclusion, a statistically significant association between the GRS and RCC risk persisted (OR=1.73, 95% CI=1.36-2.21, p<0.0001). Exploratory analyses for individual histologic subtypes suggested comparable associations with the telomere length GRS for clear cell (N=5573, OR=1.93, 95% CI=1.50-2.49, p<0.0001), papillary (N=573, OR=1.96, 95% CI=1.01-3.81, p=0.046), and chromophobe RCC (N=203, OR=2.37, 95% CI=0.78-7.17, p=0.13).

CONCLUSIONS: Our investigation adds to the growing body of evidence indicating some aspect of longer telomere length is important for RCC risk.