Associations between Ambient Air Pollutants and Clonal Hematopoiesis of Indeterminate Potential.

BACKGROUND: Clonal hematopoiesis of indeterminate potential (CHIP) is an age-related somatic mutation associated with incident hematologic cancer. Environmental stressors which, like air pollution, generate oxidative stress at the cellular level, may induce somatic mutations and some mutations may provide a selection advantage for persistence and expansion of specific clones. METHODS: We used data from the Multi-Ethnic Study of Atherosclerosis (MESA) N = 4,379 and the Women's Health Initiative (WHI) N = 7,701 to estimate cross-sectional associations between annual average air pollution concentrations at participant address the year before blood draw using validated spatiotemporal models. We used covariate-adjusted logistic regression to estimate risk of CHIP per interquartile range increases in particulate matter (PM2.5; 4 µg/m3) and nitrogen dioxide (NO2; 10 ppb) as ORs (95% confidence intervals). RESULTS: Prevalence of CHIP at blood draw (variant allele fraction > 2%) was 4.4% and 8.7% in MESA and WHI, respectively. The most common CHIP driver mutation was in DNMT3A. Neither pollutant was associated with CHIP: ORMESA PM2.5 = 1.00 (0.68-1.45), ORMESA NO2 = 1.05 (0.69-1.61), ORWHI PM2.5 = 0.97 (0.86-1.09), ORWHI NO2 = 0.98 (0.88-1.10); or with DNMT3A-driven CHIP. CONCLUSIONS: We did not find evidence that air pollution contributes to CHIP prevalence in two large observational cohorts. IMPACT: This is the first study to estimate associations between air pollution and CHIP.

Fine Particulate Matter Air Pollution and Mortality among Pediatric, Adolescent, and Young Adult Cancer Patients.

BACKGROUND: Air pollution is a carcinogen and causes pulmonary and cardiac complications. We examined the association of fine particulate matter pollution (PM2.5) and mortality from cancer and all causes among pediatric, adolescent, and young adult (AYA) patients with cancer in Utah, a state with considerable variation in PM2.5. METHODS: We followed 2,444 pediatric (diagnosed ages 0-14) and 13,459 AYA (diagnosed ages 15-39) patients diagnosed in 1986-2015 from diagnosis to 5 and 10 years postdiagnosis, death, or emigration. We measured average monthly PM2.5 by ZIP code during follow-up. Separate pediatric and AYA multivariable Cox models estimated the association of PM2.5 and mortality. Among AYAs, we examined effect modification of PM2.5 and mortality by stage while controlling for cancer type. RESULTS: Increases in PM2.5 per 5 µg/m3 were associated with cancer mortality in pediatric lymphomas and central nervous system (CNS) tumors at both time points, and all cause mortality in lymphoid leukemias [HR5-year = 1.32 (1.02-1.71)]. Among AYAs, PM2.5 per 5 µg/m3 was associated with cancer mortality in CNS tumors and carcinomas at both time points, and all cause mortality for all AYA cancer types [HR5-year = 1.06 (1.01-1.13)]. PM2.5 ≥12 µg/m3 was associated with cancer mortality among breast [HR5-year = 1.50 (1.29-1.74); HR10-year = 1.30 (1.13-1.50)] and colorectal cancers [HR5-year = 1.74 (1.29-2.35); HR10-year = 1.67 (1.20-2.31)] at both time points. Effect modification by stage was significant, with local tumors at highest risk. CONCLUSIONS: PM2.5 was associated with mortality in pediatric and AYA patients with specific cancers. IMPACT: Limiting PM2.5 exposure may be important for young cancer patients with certain cancers.See all articles in this CEBP Focus section, "Environmental Carcinogenesis: Pathways to Prevention."

Spatial clusters of cancer incidence: analyzing 1940 census data linked to 1966-2017 cancer records.

PURPOSE: A life course perspective to cancer incidence is important for understanding effects of the environment during early life on later cancer risk. We assessed spatial clusters of cancer incidence based on early life location defined as 1940 US Census Enumeration District (ED). METHODS: A cohort of 260,585 individuals aged 0-40 years in 1940 was selected. Individuals were followed from 1940 to cancer diagnosis, death, or last residence in Utah. We geocoded ED centroids in Utah for the 1940 Census. Spatial scan statistics with purely spatial elliptic scanning window were used to identify spatial clusters of EDs with excess cancer rates across 26 cancer types, assuming a discrete Poisson model. RESULTS: Cancer was diagnosed in 66,904 (25.67%) individuals during follow-up across 892 EDs. Average follow-up was 50.9 years. We detected 15 clusters of excess risk for bladder, breast, cervix, colon, lung, melanoma, oral, ovary, prostate, and soft tissue cancers. An urban area had dense overlap of multiple cancer types, including two EDs at increased risk for five cancer types each. CONCLUSIONS: Early environments may contribute to cancer risk later in life. Life course perspectives applied to the study of cancer incidence can provide insights for increasing understanding of cancer etiology.

Family Study Designs Informed by Tumor Heterogeneity and Multi-Cancer Pleiotropies: The Power of the Utah Population Database.

BACKGROUND: Previously, family-based designs and high-risk pedigrees have illustrated value for the discovery of high- and intermediate-risk germline breast cancer susceptibility genes. However, genetic heterogeneity is a major obstacle hindering progress. New strategies and analytic approaches will be necessary to make further advances. One opportunity with the potential to address heterogeneity via improved characterization of disease is the growing availability of multisource databases. Specific to advances involving family-based designs are resources that include family structure, such as the Utah Population Database (UPDB). To illustrate the broad utility and potential power of multisource databases, we describe two different novel family-based approaches to reduce heterogeneity in the UPDB. METHODS: Our first approach focuses on using pedigree-informed breast tumor phenotypes in gene mapping. Our second approach focuses on the identification of families with similar pleiotropies. We use a novel network-inspired clustering technique to explore multi-cancer signatures for high-risk breast cancer families. RESULTS: Our first approach identifies a genome-wide significant breast cancer locus at 2q13 [P = 1.6 × 10-8, logarithm of the odds (LOD) equivalent 6.64]. In the region, IL1A and IL1B are of particular interest, key cytokine genes involved in inflammation. Our second approach identifies five multi-cancer risk patterns. These clusters include expected coaggregations (such as breast cancer with prostate cancer, ovarian cancer, and melanoma), and also identify novel patterns, including coaggregation with uterine, thyroid, and bladder cancers. CONCLUSIONS: Our results suggest pedigree-informed tumor phenotypes can map genes for breast cancer, and that various different cancer pleiotropies exist for high-risk breast cancer pedigrees. IMPACT: Both methods illustrate the potential for decreasing etiologic heterogeneity that large, population-based multisource databases can provide.See all articles in this CEBP Focus section, "Modernizing Population Science."

Harnessing Population Pedigree Data and Machine Learning Methods to Identify Patterns of Familial Bladder Cancer Risk.

BACKGROUND: Relatives of patients with bladder cancer have been shown to be at increased risk for kidney, lung, thyroid, and cervical cancer after correcting for smoking-related behaviors that may concentrate in some families. We demonstrate a novel approach to simultaneously assess risks for multiple cancers to identify distinct multicancer configurations (multiple different cancer types that cluster in relatives) surrounding patients with familial bladder cancer. METHODS: This study takes advantage of a unique population-level data resource, the Utah Population Database (UPDB), containing vast genealogy and statewide cancer data. Familial risk is measured using standardized incidence risk (SIR) ratios that account for sex, age, birth cohort, and person-years of the pedigree members. RESULTS: We identify 1,023 families with a significantly higher bladder cancer rate than population controls (familial bladder cancer). Familial SIRs are then calculated across 25 cancer types, and a weighted Gower distance with K-medoids clustering is used to identify familial multicancer configurations (FMC). We found five FMCs, each exhibiting a different pattern of cancer aggregation. Of the 25 cancer types studied, kidney and prostate cancers were most commonly enriched in the familial bladder cancer clusters. Laryngeal, lung, stomach, acute lymphocytic leukemia, Hodgkin disease, soft-tissue carcinoma, esophageal, breast, lung, uterine, thyroid, and melanoma cancers were the other cancer types with increased incidence in familial bladder cancer families. CONCLUSIONS: This study identified five familial bladder cancer FMCs showing unique risk patterns for cancers of other organs, suggesting phenotypic heterogeneity familial bladder cancer. IMPACT: FMC configurations could permit better definitions of cancer phenotypes (subtypes or multicancer) for gene discovery and environmental risk factor studies.

Combining Drive Time and Urologist Density to Understand Access to Urologic Care.

OBJECTIVE: To improve our understanding of timely access to urologic care, we leveraged driving time combined with a measure of urologist density. MATERIALS AND METHODS: We identified all urologists who billed Medicare using National Provider Identifier in 2015 and geocoded their practice location. We developed drive-time based service areas for each provider using Esri's street network dataset stratified into 30, 60, 90, and 120-minute areas. Population characteristics were aggregated and block groups were assigned to a Hospital Referral Region. RESULTS: We identified 10,170 urologists that billed Medicare in 2015 in the United States. Compared to the northeast, vast expanses of land across the western United States have drive times to urology care >60 minutes. However, less than 13% of the US population is unable to obtain urologic care within 30 minutes. Likely reflecting rural populations, White and American Indian populations are represented in greater proportion among those requiring a longer drive time to urologic care. Disparities were noted between areas with timely access to a high versus low density of urologists; low density areas have a greater proportion of Black and Asian populations and greater income inequality. CONCLUSIONS: Drive time to urologists combined with urologist density is a novel approach to investigating urologic care access and a tool for health disparities research. While almost all of the US population lives within 1-hour drive time to a urologist there remains important differences in the population severed by high compared to low provider density.

Charting the life course: Emerging opportunities to advance scientific approaches using life course research.

Life course research embraces the complexity of health and disease development, tackling the extensive interactions between genetics and environment. This interdisciplinary blueprint, or theoretical framework, offers a structure for research ideas and specifies relationships between related factors. Traditionally, methodological approaches attempt to reduce the complexity of these dynamic interactions and decompose health into component parts, ignoring the complex reciprocal interaction of factors that shape health over time. New methods that match the epistemological foundation of the life course framework are needed to fully explore adaptive, multilevel, and reciprocal interactions between individuals and their environment. The focus of this article is to (1) delineate the differences between lifespan and life course research, (2) articulate the importance of complex systems science as a methodological framework in the life course research toolbox to guide our research questions, (3) raise key questions that can be asked within the clinical and translational science domain utilizing this framework, and (4) provide recommendations for life course research implementation, charting the way forward. Recent advances in computational analytics, computer science, and data collection could be used to approximate, measure, and analyze the intertwining and dynamic nature of genetic and environmental factors involved in health development.

Evaluation of the Sex-and-Age-Specific Effects of PM2.5 on Hospital Readmission in the Presence of the Competing Risk of Mortality in the Medicare Population of Utah 1999-2009.

Acute ambient air pollution exposure increases risk of cardiac events. We evaluated sex-and-age-specific effects of PM2.5 on hospital readmission and death among 19,602 Medicare beneficiaries (Nevents = 30,510) who survived cardiovascular events including myocardial infarction (MI), heart failure (HF), ischemic heart disease (IHD), and cardiac arrhythmias in Utah from 1999-2009. Fine and Gray regression jointly modeled the effect of PM2.5 on readmission hazard rates while allowing for the competing risk of death. Models were stratified by age and sex and adjusted for Charlson Comorbidity Index, dual enrollment in Medicare Parts A and B, temperature, and household income. There were 2032 cardiac readmissions and 1420 deaths after discharge. Among males age 65-74 years admitted for HF, a 10 µm/m3 increase in PM2.5 was associated with a 30% increase in risk of readmission (p = 0.01). Among females age 75-84 admitted for HF, PM2.5 was associated with a 22% increase in risk of readmission (p = 0.01). Among females age 75-84 years admitted for IHD, PM2.5 was associated with a 25% decrease in readmission (p = 0.01), however this was explained by a 36% increase in risk of death (p = 0.01). Exposure to PM2.5 was associated with increased risk of readmission and death. Associations were dependent upon age, sex, and index condition.

The Relative Importance of Race Compared to Health Care and Social Factors in Predicting Prostate Cancer Mortality: A Random Forest Approach.

PURPOSE: There is a differential in prostate cancer mortality between black and white men. Advances in precision medicine have shifted the research focus toward underlying genetic differences. However, nonbiological factors may have a large role in these observed disparities. Therefore, we sought to measure the relative importance of race compared to health care and social factors on prostate cancer specific mortality. MATERIALS AND METHODS: Using the SEER (Surveillance, Epidemiology, and End Results) database we identified 514,878 men diagnosed with prostate cancer at age 40 years or greater between 2004 and 2012. We also selected a subset of black and white men matched by age, stage and birth year. We stratified patients by age 40 to 54, 55 to 69 and 70 years or older and disease stage, resulting in 18 groups. By applying random forest methods with variable importance measures we analyzed 15 variables and interactions across 4 categories of factors (tumor characteristics, race, and health care and social factors) and the relative importance for prostate cancer specific mortality. RESULTS: Tumor characteristics at diagnosis were the most important factors for prostate cancer mortality. Across all groups race was less than 5% as important as tumor characteristics and only more important than health care and social factors in 2 of the 18 groups. Although race had a significant impact, health care and social factors known to be associated with racial disparities had greater or similarly important effects across all ages and stages. CONCLUSIONS: Eradicating disparities in prostate cancer survival will require a multipronged approach, including advances in precision medicine. Disparities will persist unless health care access and social equality are achieved among all populations.

Fine Particulate Matter and Respiratory Healthcare Encounters among Survivors of Childhood Cancers.

Some chemotherapies that treat childhood cancers have pulmonary-toxic properties that increase risk for adverse respiratory-health outcomes. PM2.5 causes similar outcomes but its effect among pulmonary compromised cancer survivors is unknown. This case-crossover study identified the PM2.5-associated odds for primary-respiratory hospitalizations and emergency department visits among childhood cancer survivors in Utah. We compared risk among chemotherapy-treated survivors to a cancer-free sample. We calculated 3-day-average PM2.5 by ZIP code and county for event and control days. Conditional logistic regression estimated odds ratios. Models were stratified by cause of admission (infection, respiratory disease, asthma), previous chemotherapy, National Ambient Air Quality Standard (NAAQS), and other variables. Results are presented per 10 µg/m³ of PM2.5. 90% of events occurred at 3-day PM2.5 averages <35.4 µg/m³, the NAAQS 24-h standard. For survivors, PM2.5 was associated with respiratory hospitalizations (OR = 1.84, 95% CI = 1.13⁻3.00) and hospitalizations from respiratory infection (OR = 2.09, 95% CI = 1.06⁻4.14). Among chemotherapy-treated survivors, the PM2.5-associated odds of respiratory hospitalization (OR = 2.03, 95% CI = 1.14⁻3.61) were significantly higher than the cancer-free sample (OR = 0.84, 95% CI = 0.57⁻1.25). This is the first study to report significant associations between PM2.5 and respiratory healthcare encounters in childhood cancer survivors. Chemotherapy-treated survivors displayed the highest odds of hospitalization due to PM2.5 exposure and their risk is significantly higher than a cancer-free sample.

Acute effects of air pollutants on spontaneous pregnancy loss: a case-crossover study.

OBJECTIVE: To investigate the relationship between acute exposure to air pollutants and spontaneous pregnancy loss. DESIGN: Case-crossover study from 2007 to 2015. SETTING: An academic emergency department in the Wasatch Front area of Utah. PATIENT(S): A total of 1,398 women who experienced spontaneous pregnancy loss events. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Odds of spontaneous pregnancy loss. RESULT(S): We found that a 10-ppb increase in 7-day average levels of nitrogen dioxide was associated with a 16% increase in the odds of spontaneous pregnancy loss (odds ratio [OR] = 1.16; 95% confidence interval [CI] 1.01-1.33; P=.04). A 10-µg/m3 increase in 3-day and 7-day averages of fine particulate matter were associated with increased risk of spontaneous pregnancy loss, but the associations did not reach statistical significance (OR3-day average = 1.09; 95% CI 0.99-1.20; P=.05) (OR7-day average = 1.11; 95% CI 0.99-1.24; P=.06). We found no evidence of increased risk for any other metrics of nitrogen dioxide or fine particulate matter or any metric for ozone. CONCLUSIONS: We found that short-term exposure to elevated levels of air pollutants was associated with higher risk for spontaneous pregnancy loss.

Familial Cancer Clustering in Urothelial Cancer: A Population-Based Case-Control Study.

Background: Family history of bladder cancer confers an increased risk for concordant and discordant cancers in relatives. However, previous studies investigating this relationship lack any correction for smoking status of family members. We conducted a population-based study of cancer risks in relatives of bladder cancer patients and matched controls with exclusion of variant subtypes to improve the understanding of familial cancer clustering. Methods: Case subjects with urothelial carcinoma were identified using the Utah Cancer Registry and matched 1:5 to cancer-free controls from the Utah Population Database. Cox regression was used to determine the risk of cancer in first-degree relatives, second-degree relatives, first cousins, and spouses. A total of 229 251 relatives of case subjects and 1 197 552 relatives of matched control subjects were analyzed. To correct for smoking status, we performed a secondary analysis excluding families with elevated rates of smoking-related cancers. All statistical tests were two-sided. Results: First- and second-degree relatives of case subjects had an increased risk for any cancer diagnosis (hazard ratio [HR] = 1.06, 95% confidence interval [CI] = 1.03 to 1.09, P < .001; HR = 1.04, 95% CI = 1.02 to 1.07, P = .001) and urothelial cancer (HR = 1.73, 95% CI = 1.50 to 1.99, P < .001; HR = 1.35, 95% CI = 1.21 to 1.51, P < .001). Site-specific analysis found increased risk for bladder (HR = 1.69, 95% CI = 1.47 to 1.95, P < .001), kidney (HR = 1.30, 95% CI = 1.08 to 1.57, P = .006), cervical (HR = 1.25, 95% CI = 1.06 to 1.49, P = .01), and lung cancer (HR = 1.34, 95% CI = 1.19 to 1.51, P < .001) in first-degree relatives. Second-degree relatives had increased risk for bladder (HR = 1.35, 95% CI = 1.2 to 1.5, P < .001) and thyroid cancer (HR = 1.18, 95% CI = 1.03 to 1.35, P = .02). Spouses showed an increased risk for laryngeal (HR = 2.68, 95% CI = 1.02 to 7.05, P = .04) and cervical cancer (HR = 1.57, 95% CI = 1.13 to 2.17, P = .007). These results did not substantively change after correction for suspected smoking behaviors. Conclusion: Our results suggest familial urothelial cancer clustering independent of smoking, with increased risk in relatives for both concordant and discordant cancers, suggesting shared genetic or environmental roots. Identifying families with statistically significant risks for non-smoking-related urothelial cancer would be extremely informative for genetic linkage studies.