Background

A broad, comprehensive review of studies exploring associations between lifestyle factors and non-muscle invasive bladder cancer (NMIBC) outcomes is warranted to consolidate recommendations and identify gaps in research.

Objective

To summarize the literature on associations between lifestyle factors and clinical outcomes among patients with NMIBC.

Methods

PubMed was systematically queried for articles published through March 2019 regarding lifestyle factors and recurrence, progression, cancer-specific mortality, and all-cause mortality among patients with NMIBC.

Results

Notwithstanding many ambiguities, there is good-quality evidence suggesting a benefit of smoking avoidance/cessation, healthy body mass index (BMI), and type II diabetes mellitus prevention and treatment. Lactobacillus casei probiotic supplementation may reduce recurrence. There have been individual studies suggesting a benefit for uncooked broccoli and supplemental vitamin E as well as avoidance of supplemental vitamin B9, areca nut chewing, and a "Western diet" pattern high in fried foods and red meat. Additional studies do not suggest associations between NMIBC outcomes and use of fibrin clot inhibitors; insulin and other oral hypoglycemics; statins; supplemental selenium, vitamin A, vitamin C, and vitamin B6; fluid intake and intake of specific beverages (e.g., alcohol, coffee, green tea, cola); various dietary patterns (e.g., Tex-Mex, high fruit and vegetable, low-fat); and occupational and chemical exposures.

Conclusions

Despite a myriad of publications on lifestyle factors and NMIBC, a need remains for research on unexplored associations (e.g., physical activity) and further studies that can elucidate causal effects. This would inform future implementation strategies for healthy lifestyle change in NMIBC patients.

Exposure to ambient air pollution has significant adverse health effects; however, whether air pollution is associated with urological cancer is largely unknown. We conduct a systematic review and meta-analysis with epidemiological studies, showing that a 5 μg/m3 increase in PM2.5 exposure is associated with a 6%, 7%, and 9%, increased risk of overall urological, bladder, and kidney cancer, respectively; and a 10 μg/m3 increase in NO2 is linked to a 3%, 4%, and 4% higher risk of overall urological, bladder, and prostate cancer, respectively. Were these associations to reflect causal relationships, lowering PM2.5 levels to 5.8 μg/m3 could reduce the age-standardized rate of urological cancer by 1.5 ~ 27/100,000 across the 15 countries with the highest PM2.5 level from the top 30 countries with the highest urological cancer burden. Implementing global health policies that can improve air quality could potentially reduce the risk of urologic cancer and alleviate its burden.

BACKGROUND: The objective of this study was to evaluate associations of diabetes overall, type 1 diabetes (T1D), and type 2 diabetes (T2D) with breast cancer (BCa) risk. METHODS: We included 250,312 women aged 40-69 years between 2006 and 2010 from the UK Biobank cohort. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were calculated for associations of diabetes and its two major types with the time from enrollment to incident BCa. RESULTS: We identified 8182 BCa cases during a median follow-up of 11.1 years. We found no overall association between diabetes and BCa risk (aHR = 1.02, 95% CI = 0.92-1.14). When accounting for diabetes subtype, women with T1D had a higher risk of BCa than women without diabetes (aHR = 1.52, 95% CI = 1.03-2.23). T2D was not associated with BCa risk overall (aHR = 1.00, 95% CI = 0.90-1.12). However, there was a significantly increased risk of BCa in the short time window after T2D diagnosis. CONCLUSIONS: Though we did not find an association between diabetes and BCa risk overall, an increased risk of BCa was observed shortly after T2D diagnosis. In addition, our data suggest that women with T1D may have an increased risk of BCa.

Background: There exists compelling evidence that some genetic variants are associated with the risk of multiple cancer sites (i.e., pleiotropy). However, the biological mechanisms through which the pleiotropic variants operate are unclear.Methods: We obtained all cancer risk associations from the National Human Genome Research Institute-European Bioinformatics Institute GWAS Catalog, and correlated cancer risk variants were clustered into groups. Pleiotropic variant groups and genes were functionally annotated. Associations of pleiotropic cancer risk variants with noncancer traits were also obtained.Results: We identified 1,431 associations between variants and cancer risk, comprised of 989 unique variants associated with 27 unique cancer sites. We found 20 pleiotropic variant groups (2.1%) composed of 33 variants (3.3%), including novel pleiotropic variants rs3777204 and rs56219066 located in the ELL2 gene. Relative to single-cancer risk variants, pleiotropic variants were more likely to be in genes (89.0% vs. 65.3%, P = 2.2 × 10^-16), and to have somewhat larger risk allele frequencies (median RAF = 0.49 versus 0.39, P = 0.046). The 27 genes to which the pleiotropic variants mapped were suggestive for enrichment in response to radiation and hypoxia, alpha-linolenic acid metabolism, cell cycle, and extension of telomeres. In addition, we observed that 8 of 33 pleiotropic cancer risk variants were associated with 16 traits other than cancer.Conclusions: This study identified and functionally characterized genetic variants showing pleiotropy for cancer risk.Impact: Our findings suggest biological pathways common to different cancers and other diseases, and provide a basis for the study of genetic testing for multiple cancers and repurposing cancer treatments. Cancer Epidemiol Biomarkers Prev; 27(1); 75-85. ©2017 AACR.

Background

The National Birth Defects Prevention Study (NBDPS) contains a wealth of information on affected and unaffected family triads, and thus provides numerous opportunities to study gene-environment interactions (G×E) in the etiology of birth defect outcomes. Depending on the research objective, several analytic options exist to estimate G×E effects that use varying combinations of individuals drawn from available triads.

Methods

In this study, we discuss important considerations in the collection of genetic data and environmental exposures.

Results

We will also present several population- and family-based approaches that can be applied to data from the NBDPS including case-control, case-only, family-based trio, and maternal versus fetal effects. For each, we describe the data requirements, applicable statistical methods, advantages, and disadvantages.

Conclusion

A range of approaches can be used to evaluate potentially important G×E effects in the NBDPS. Investigators should be aware of the limitations inherent to each approach when choosing a study design and interpreting results.

Background

Inflammatory and insulin pathways have been linked to prostate cancer; postdiagnostic behaviors activating these pathways may lead to poor outcomes. The empirical dietary inflammatory pattern (EDIP), empirical dietary index for hyperinsulinemia (EDIH), and empirical dietary index for insulin resistance (EDIR), and associated lifestyle indices (ELIH, ELIR) predict biomarkers of inflammation (EDIP: IL6, TNFaR2, CRP) and insulin secretion (EDIH/ELIH: c-peptide; EDIR/ELIR: TAG:HDL) from whole foods and behaviors.

Methods

Associations of these indices with time to prostate cancer progression (primary, n = 2,056) and prostate cancer-specific mortality (PCSM; secondary, n = 2,447) were estimated among men diagnosed with nonmetastatic prostate cancer in the Cancer of the Prostate Strategic Urologic Research Endeavor cohort diet and lifestyle sub-study. Because the true (versus clinically documented) date of progression is unobserved, we used parametric (Weibull) survival models to accommodate interval-censoringand estimated adjusted HR and 95% confidence intervals (CI) for prostate cancer progression per 1-SD increase in index. Cox proportional hazards models were used to estimate PCSM associations.

Results

During a median [interquartile range (IQR)] 6.4 years (IQR, 1.3-12.7), 192 progression and 73 PCSM events were observed. Inflammatory (EDIP: HR, 1.27; CI, 1.17-1.37), hyperinsulinemic (EDIH: HR, 1.24; CI, 1.05-1.46. ELIH: HR, 1.34; CI, 1.17-1.54), and insulin-resistant (EDIR: HR, 1.22; CI, 1.00-1.48. ELIR: HR, 1.36; CI, 1.12-1.64) indices were positively associated with risk of prostate cancer progression. There was no evidence of associations between the indices and PCSM.

Conclusions

Both inflammatory and insulinemic dietary and lifestyle patterns are associated with risk of prostate cancer progression.

Impact

For men with prostate cancer, consuming dietary patterns that limit chronic systemic inflammation and insulin hypersecretion may improve survivorship, especially when coupled with active lifestyle and healthy body weight. See related commentary by Kucuk, p. 1673.

Cancer risk is determined by a complex interplay of environmental and heritable factors. Polygenic risk scores (PRS) provide a personalized genetic susceptibility profile that may be leveraged for disease prediction. Using data from the UK Biobank (413,753 individuals; 22,755 incident cancer cases), we quantify the added predictive value of integrating cancer-specific PRS with family history and modifiable risk factors for 16 cancers. We show that incorporating PRS measurably improves prediction accuracy for most cancers, but the magnitude of this improvement varies substantially. We also demonstrate that stratifying on levels of PRS identifies significantly divergent 5-year risk trajectories after accounting for family history and modifiable risk factors. At the population level, the top 20% of the PRS distribution accounts for 4.0% to 30.3% of incident cancer cases, exceeding the impact of many lifestyle-related factors. In summary, this study illustrates the potential for improving cancer risk assessment by integrating genetic risk scores.

Introduction

We evaluated whether competing risk of death or selective survival could explain the reported inverse association between cancer history and dementia incidence (incidence rate ratio [IRR] ≈ 0.62-0.85).

Methods

A multistate simulation model of a cancer- and dementia-free cohort of 65-year-olds was parameterized with real-world data (cancer and dementia incidence, mortality), assuming no effect of cancer on dementia (true IRR = 1.00). To introduce competing risk of death, cancer history increased mortality. To introduce selective survival, we included a factor (prevalence ranging from 10% to 50%) that reduced cancer mortality and dementia incidence (IRRs ranged from 0.30 to 0.90). We calculated IRRs for cancer history on dementia incidence in the simulated cohorts.

Results

Competing risk of death yielded unbiased cancer-dementia IRRs. With selective survival, bias was small (IRRs = 0.89 to 0.99), even under extreme scenarios.

Discussion

The bias induced by selective survival in simulations was too small to explain the observed inverse cancer-dementia link, suggesting other mechanisms drive this association.

Background: Technology-based interventions are increasingly used to improve physical activity (PA) and diet. Methods: We conducted a systematic review of randomized controlled trials (RCTs) published up to August 2021 that tested mobile health (mHealth) PA and/or dietary interventions among cancer survivors and reported on the feasibility, satisfaction, behavioral change, and/or quality of life (QOL) outcomes. Results: In total, 61 articles were identified on PubMed, and 23 of those met the inclusion criteria. The most common cancers were breast (n = 1000), prostate (n = 713), and colorectal (n = 650). Participants were predominantly White (median: 84%, interquartile range (IQR): 20%) and college-educated (58%). The interventions varied, but the most common combination of components (six studies) was a website/mobile app with an activity tracker and coaching. In terms of duration, 70% (n = 16) of the interventions lasted 12 weeks. The median total tracker wear was 87% of the study days (IQR: 6%) and the median text-message reply rate was 73% (IQR 4%). Most participants (median: 87%; IQR: 16%) were satisfied with at least one intervention component. Eleven out of 18 studies examining behavioral change reported significant between-group differences and six out of 11 studies examining QoL reported significant improvements. Conclusions: mHealth interventions are a promising approach to improving the PA and diets of cancer survivors. Research in racially/ethnically and socioeconomically diverse populations is needed.

Epidemiological studies have identified an inverse association between cancer and dementia. Underlying methodological biases have been postulated, yet no studies have systematically investigated the potential for each source of bias within a single dataset. We used the UK Biobank to compare estimates for the cancer-dementia association using different analytical specifications designed to sequentially address multiple sources of bias, including competing risk of death, selective survival, confounding bias, and diagnostic bias. We included 140,959 UK Biobank participants aged ≥ 55 without dementia before enrollment and with linked primary care data. We used cancer registry data to identify cancer cases prevalent before UK Biobank enrollment and incident cancer diagnosed after enrollment. We used Cox models to evaluate associations of prevalent and incident cancer with all-cause dementia, Alzheimers disease (AD), and vascular dementia. We used time-varying models to evaluate diagnostic bias. Over a median follow-up of 12.3 years, 3,310 dementia cases were diagnosed. All-site incident cancer was positively associated with all-cause dementia incidence (hazard ratio [HR] = 1.14, 95% CI: 1.02-1.29), but prevalent cancer was not (HR = 1.04, 95% CI: 0.92-1.17). Results were similar for vascular dementia. AD was not associated with prevalent or incident cancer. Dementia diagnosis was substantially elevated in the first year after cancer diagnosis (HR = 1.83, 95% CI: 1.42-2.36), after which the association attenuated to null, suggesting diagnostic bias. Following a cancer diagnosis, health care utilization or cognitive consequences of diagnosis or treatment may increase chance of receiving a dementia diagnosis, creating potential diagnostic bias in electronic health records-based studies.