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Adult survivors of childhood cancer are at risk for cardiovascular events. In this study, we sought to determine the risk for mortality after a major cardiovascular event among childhood cancer survivors compared with noncancer populations. All-cause and cardiovascular cause-specific mortality risks after heart failure (HF), coronary artery disease (CAD), or stroke were compared among survivors and siblings in the Childhood Cancer Survivor Study (CCSS) and participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Cox proportional hazard regression models were used to estimate HRs and 95% CIs between groups, adjusted for demographic and clinical factors. Among 25,658 childhood cancer survivors (median age at diagnosis 7 years, median age at follow-up or death 38 years) and 5,051 siblings, 1,780 survivors and 91 siblings had a cardiovascular event. After HF, CAD, and stroke, 10-year all-cause mortalities were 30% (95% CI: 26%-33%), 36% (95% CI: 31%-40%), and 29% (95% CI: 24%-33%), respectively, among survivors vs 14% (95% CI: 0%-25%), 14% (95% CI: 2%-25%), and 4% (95% CI: 0%-11%) among siblings. All-cause mortality risks among childhood cancer survivors were increased after HF (HR: 7.32; 95% CI: 2.56-20.89), CAD (HR: 5.54; 95% CI: 2.37-12.93), and stroke (HR: 3.57; 95% CI: 1.12-11.37). CAD-specific mortality risk was increased (HR: 3.70; 95% CI: 1.05-13.02). Among 5,114 CARDIA participants, 345 had a major event. Although CARDIA participants were on average decades older at events (median age 57 years vs 31 years), mortality risks were similar, except that all-cause mortality after CAD was significantly increased among childhood cancer survivors (HR: 1.85; 95% CI: 1.16-2.95). Survivors of childhood cancer represent a population at high risk for mortality after major cardiovascular events. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: What are the major determinants of women's breast cancer risk? Rare mutations such as those in the BRCA1/2 genes, polygenic scores of common alleles identified by genome-wide association studies, or nongenetic factors? Methods: The population-based Nordic Twin Study of Cancer, with 3,933 breast cancer cases among 21,054 monozygotic (MZ) and 30,939 dizygotic (DZ) female twin pairs, provides three key clues to this question: (i) the average lifetime risk, approximately 8%, does not differ by twin zygosity; (ii) the mean time interval between diagnoses when both twins develop disease (i.e., disease concordance) also does not differ by zygosity; but, (iii) conditioning on one twin having developed disease, the incidence rate in the co-twin is approximately 1% per year if the pair is MZ and 0.5% per year if DZ. Results: Assuming that nongenetic risk factors are shared similarly between twins regardless of zygosity, we can draw two conclusions from (i) to (iii). Conclusions: First, (i) and (iii) imply that the chief determinant of risk is in the germline DNA, because the conditional incidence rate is several-fold higher than the average risk (8% lifetime) in MZ twins but only half as much in DZ twins. Second, the seeming inconsistency between the two-fold conditional incidence rate (iii) and the equality of the mean inter-twin disease intervals in disease concordance (ii) can be resolved if the risk factors in the germline DNA are rare variants, not common variants. Impact: This paper details simple deductive reasoning for these conclusions and draws a critical inference regarding breast cancer etiology. [ABSTRACT FROM AUTHOR]

Childhood cancer survivors treated with radiation therapy (RT) and osteotoxic chemotherapies are at increased risk for fractures. However, understanding of how genetic and clinical susceptibility factors jointly contribute to fracture risk among survivors is limited. To address this gap, we conducted genome‐wide association studies of fracture risk after cancer diagnosis in 2453 participants of European ancestry from the Childhood Cancer Survivor Study (CCSS) with 930 incident fractures using Cox regression models (ie, time‐to‐event analysis) and prioritized sex‐ and treatment‐stratified genetic associations. We performed replication analyses in 1417 survivors of European ancestry with 652 incident fractures from the St. Jude Lifetime Cohort Study (SJLIFE). In discovery, we identified a genome‐wide significant (p < 5 × 10−8) fracture risk locus, 16p13.3 (HAGHL), among female CCSS survivors (n = 1289) with strong evidence of sex‐specific effects (psex‐heterogeneity < 7 × 10−6). Combining discovery and replication data, rs1406815 showed the strongest association (hazard ratio [HR] = 1.43, p = 8.2 × 10−9; n = 1935 women) at this locus. In treatment‐stratified analyses in the discovery cohort, the association between rs1406815 and fracture risk among female survivors with no RT exposures was weak (HR = 1.22, 95% confidence interval [CI] 0.95–1.57, p = 0.11) but increased substantially among those with greater head/neck RT doses (any RT: HR = 1.88, 95% CI 1.54–2.28, p = 2.4 × 10−10; >36 Gray only: HR = 3.79, 95% CI 1.95–7.34, p = 8.2 × 10−5). These head/neck RT‐specific HAGHL single‐nucleotide polymorphism (SNP) effects were replicated in female SJLIFE survivors. In silico bioinformatics analyses suggest these fracture risk alleles regulate HAGHL gene expression and related bone resorption pathways. Genetic risk profiles integrating this locus may help identify female survivors who would benefit from targeted interventions to reduce fracture risk. © 2020 American Society for Bone and Mineral Research (ASBMR). [ABSTRACT FROM AUTHOR]

Logic regression, an extension of generalized linear models with Boolean combinations of binary variables as predictors, is a useful tool in exploring interactions among single-nucleotide polymorphisms (SNPs) in genome-wide association studies. However, since the search space defined by all possible combinations of SNPs, their complements, and logical operators in Boolean expressions can be exceedingly large in such studies, objective function optimization is slow and likely to be trapped in many local solutions, resulting in model over-fitting. We introduce a new search algorithm, parallel repulsive logic regression (PRLR), to efficiently estimate parameters of a logic regression to find a best model within the large space of SNP interactions by incorporating: (i) relevant biological adjacency matrix between SNPs to define similarity of estimation paths or trees, which are derived from physical SNP positions on chromosomes and/or memberships in biological gene pathways; and (ii) two repulsive forces to counter the similarity between and within estimation paths considered in parallel, which are introduced as penalty terms in the objective function. We compare our method's performance for identifying biologically-meaningful SNP interactions through simulations and with real genetic-epidemiological data. PRLR's detection-accuracy measures outperform existing approaches, especially in terms of positive predictive value and sensitivity for detecting SNP-SNP interactions. [ABSTRACT FROM AUTHOR]

Background Emergency department ( ED) visit volumes can be especially high during the Christmas-New Year holidays, a period occurring during the influenza season in Canada. Methods Using daily data, we examined the relationship between ED visits for the chief complaint 'cough' (for Edmonton, Alberta residents) and laboratory detections for influenza A and respiratory syncytial virus ( RSV) (for Edmonton and surrounding areas), lagged 0-5 days ahead, for non-pandemic years (2004-2008 and 2010-2014) using multivariable linear regression adjusting for temporal variables. We defined these cough-related visits as influenza-like illness ( ILI)-related ED visits and, for 2004-2014, compared Christmas-New Year holiday (December 24-January 3) and non-holiday volumes during the influenza season (October-April). Results Adjusting for temporal variables, ILI-related ED visits were significantly associated with laboratory detections for influenza A and RSV. During non-pandemic years, the highest peak in ILI-related visit volumes always occurred during the holidays. The median number of holiday ILI-related visits/day (42.5) was almost twice the non-holiday median (24) and was even higher in 2012-2013 (80) and 2013-2014 (86). Holiday ILI-related ED visit volumes/100 000 population ranged from 56.0 (2010-2011) to 117.4 (2012-2013). In contrast, lower visit volumes occurred during the holidays of pandemic-affected years (2008-2010). Conclusions During non-pandemic years, ILI-related ED visit volumes were associated with variations in detections for influenza A and RSV and always peaked during the Christmas-New Year holidays. This predictability should be used to prepare for, and possibly prevent, this increase in healthcare use; however, interventions beyond disease prevention strategies are likely needed. [ABSTRACT FROM AUTHOR]