METHODS: This is a meta-analysis of seven prospective cohort studies participating in the CHANCES consortium including 18 668 men and 24 751 women with a mean age of 62 and 63 years, respectively. Harmonised individual participant data from all seven cohorts were analysed separately and alternatively for each anthropometric indicator using multivariable Cox proportional hazards models.
RESULTS: After a median follow-up period of 12 years, 1656 first-incident obesity-related cancers (defined as postmenopausal female breast, colorectum, lower oesophagus, cardia stomach, liver, gallbladder, pancreas, endometrium, ovary, and kidney) had occurred in men and women. In the meta-analysis of all studies, associations between indicators of adiposity, per s.d. increment, and risk for all obesity-related cancers combined yielded the following summary hazard ratios: 1.11 (95% CI 1.02-1.21) for BMI, 1.13 (95% CI 1.04-1.23) for WC, 1.09 (95% CI 0.98-1.21) for HC, and 1.15 (95% CI 1.00-1.32) for WHR. Increases in risk for colorectal cancer were 16%, 21%, 15%, and 20%, respectively per s.d. of BMI, WC, HC, and WHR. Effect modification by hormone therapy (HT) use was observed for postmenopausal breast cancer (Pinteraction<0.001), where never HT users showed an ∼20% increased risk per s.d. of BMI, WC, and HC compared to ever users.
CONCLUSIONS: BMI, WC, HC, and WHR show comparable positive associations with obesity-related cancers combined and with colorectal cancer in older adults. For postmenopausal breast cancer we report evidence for effect modification by HT use.
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.
CONCLUSIONS: Our study corroborates and expands evidence from previous studies in showing that smoking is a strong independent risk factor of cardiovascular events and mortality even at older age, advancing cardiovascular mortality by more than five years, and demonstrating that smoking cessation in these age groups is still beneficial in reducing the excess risk.
STUDY DESIGN: Individual data on SRH and important covariates were obtained for 424,791 European and United States residents, ≥60 years at recruitment (1982-2008), in eight prospective studies in the Consortium on Health and Ageing: Network of Cohorts in Europe and the United States (CHANCES). In each study, adjusted mortality ratios (hazard ratios, HRs) in relation to SRH were calculated and subsequently combined with random-effect meta-analyses.
MAIN OUTCOME MEASURES: All-cause, cardiovascular and cancer mortality.
RESULTS: Within the median 12.5 years of follow-up, 93,014 (22%) deaths occurred. SRH "fair" or "poor" vs. "at-least-good" was associated with increased mortality: HRs 1.46 (95% CI 1·23-1.74) and 2.31 (1.79-2.99), respectively. These associations were evident: for cardiovascular and, to a lesser extent, cancer mortality, and within-study, within-subgroup analyses. Accounting for lifestyle, sociodemographic, somatometric factors and, subsequently, for medical history explained only a modest amount of the unadjusted associations. Factors favourably associated with SRH were: sex (males), age (younger-old), education (high), marital status (married/cohabiting), physical activity (active), body mass index (non-obese), alcohol consumption (low to moderate) and previous morbidity (absence).
CONCLUSION: SRH provides a quick and simple tool for assessing health and identifying groups of elders at risk of early mortality that may be useful also in clinical settings. Modifying determinants of favourably rating health, e.g. by increasing physical activity and/or by eliminating obesity, may be important for older adults to "feel healthy" and "be healthy".
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).
CONCLUSION: In this large meta-analysis including primary data of incident CRC patients from 14 prospective cohort studies on the association between smoking and CRC prognosis, former and current smoking were associated with poorer CRC prognosis compared with never smoking. Smoking cessation was associated with improved survival when compared with current smokers. Future studies should further quantify the benefits of nonsmoking, both for cancer prevention and for improving survival among CRC patients, in particular also in terms of treatment response.
METHODS: Individual participant data meta-analysis included 362,114 participants (43% women), from seven prospective cohort studies, free from cancer at enrollment. The WCRF/AICR diet score was based on: (i) energy-dense foods and sugary drinks, (ii) plant foods, (iii) red and processed meat, and (iv) alcoholic drinks. Cox proportional hazards regression was used to examine the association between the diet score and cancer risks. Adjusted, cohort-specific HRs were pooled using random-effects meta-analysis. Risk advancement periods (RAP) were calculated to quantify the time period by which the risk of cancer was postponed among those adhering to the recommendations.
RESULTS: After a median follow-up of 11 to 15 years across cohorts, 70,877 cancer cases were identified. Each one-point increase in the WCRF/AICR diet score [range, 0 (no) to 4 (complete adherence)] was significantly associated with a lower risk of total cancer [HR, 0.94; 95% confidence interval (CI), 0.92-0.97], cancers of the colorectum (HR, 0.84; 95% CI, 0.80-0.89) and prostate (HR, 0.94; 95% CI, 0.92-0.97), but not breast or lung. Adherence to an additional component of the WCRF/AICR diet score significantly postponed the incidence of cancer at any site by 1.6 years (RAP, -1.6; 95% CI, -4.09 to -2.16).
CONCLUSIONS: Adherence to WCRF/AICR dietary recommendations is associated with lower risk of cancer among older adults.
IMPACT: Dietary recommendations for cancer prevention are applicable to the elderly. Cancer Epidemiol Biomarkers Prev; 26(1); 136-44. ©2016 AACR.
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.
CONCLUSIONS: Smoking remains as a strong risk factor for premature mortality in older individuals and cessation remains beneficial even at advanced ages. Efforts to support smoking abstinence at all ages should be a public health priority.
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.
CONCLUSIONS: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.
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.
IMPACT: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.
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.
CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.
METHODS: We used pooled data on tumor markers (estrogen and progesterone receptor, human epidermal growth factor receptor-2 (HER2)) and reproductive risk factors (parity, age at first full-time pregnancy (FFTP) and age at menarche) from 28,095 patients with invasive BC from 34 studies participating in the Breast Cancer Association Consortium (BCAC). In a case-only analysis, we used logistic regression to assess associations between reproductive factors and BC subtype compared to luminal A tumors as a reference. The interaction between age and parity in BC subtype risk was also tested, across all ages and, because age was modeled non-linearly, specifically at ages 35, 55 and 75 years.
RESULTS: Parous women were more likely to be diagnosed with triple negative BC (TNBC) than with luminal A BC, irrespective of age (OR for parity = 1.38, 95% CI 1.16-1.65, p = 0.0004; p for interaction with age = 0.076). Parous women were also more likely to be diagnosed with luminal and non-luminal HER2-like BCs and this effect was slightly more pronounced at an early age (p for interaction with age = 0.037 and 0.030, respectively). For instance, women diagnosed at age 35 were 1.48 (CI 1.01-2.16) more likely to have luminal HER2-like BC than luminal A BC, while this association was not significant at age 75 (OR = 0.72, CI 0.45-1.14). While age at menarche was not significantly associated with BC subtype, increasing age at FFTP was non-linearly associated with TNBC relative to luminal A BC. An age at FFTP of 25 versus 20 years lowered the risk for TNBC (OR = 0.78, CI 0.70-0.88, p