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Importance: Understanding the mechanisms by which physical activity is associated with a lower risk of psychiatric symptoms may stimulate the identification of cost-efficient strategies for preventing and treating mental illness at early life stages. Objective: To examine neurobiological, psychosocial, and behavioral mechanisms that mediate associations of physical activity with psychiatric symptoms in youth by testing an integrated model. Design, setting, and participants: Generation R is an ongoing prospective population-based cohort study collecting data from fetal life until young adulthood in a multiethnic urban population in the Netherlands. Pregnant women living in Rotterdam with an expected delivery date between April 2002 and January 2006 were eligible for participation along with their children born during this time. Data were collected at a single research center in the Erasmus Medical Center Sophia Children’s Hospital. For the current study, data were analyzed from 4216 children with complete data on both exposure and outcome at ages 6, 10, and 13 years. Data were analyzed from January 2021 to November 2022. Exposures: Physical activity was ascertained at age 6 years (visit 1) via parent report and included weekly frequency and duration of walking or cycling to or from school, physical education at school, outdoor play, swimming, and sports participation. Main Outcomes and Measures: Psychiatric symptoms (internalizing and externalizing symptoms) were assessed at age 6 years (visit 1) and at age 13 years (visit 3) using the Child Behavior Checklist. Several mechanisms were explored as mediators, measured at age 10 years (visit 2). Neurobiological mechanisms included total brain volume, white matter microstructure, and resting-state connectivity assessed using a 3-T magnetic resonance imaging scanner. Psychosocial mechanisms included self-esteem, body image, and friendship. Behavioral mechanisms included sleep quality, diet quality, and recreational screen time. Pearson correlations between physical activity measures and psychiatric symptoms were calculated, with false discovery rate correction applied to account for the number of tests performed. Mediation analyses were performed when a correlation (defined as false discovery rate P < .05) between exposure and outcome was observed and were adjusted for confounders. Results: Among the 4216 children included in this study, the mean (SD) age was 6.0 (0.4) years at visit 1, and 2115 participants (50.2%) were girls. More sports participation was associated with fewer internalizing symptoms (β for direct effect, −0.025; SE, 0.078; P = .03) but not externalizing symptoms. Self-esteem mediated the association between sports participation and internalizing symptoms (β for indirect effect, −0.009; SE, 0.018; P = .002). No evidence was found for associations between any other neurobiological, psychosocial, or behavioral variables. No association was found between other types of physical activity and psychiatric symptoms at these ages. Conclusions and Relevance: The integrated model presented in this cohort study evaluated potential mechanisms mediating associations between physical activity and psychiatric symptoms in youth. Self-esteem mediated an association between sports participation in childhood and internalizing symptoms in adolescence; other significant mediations were not observed. Further studies might explore whether larger effects are present in certain subgroups (eg, children at high risk of developing psychiatric symptoms), different ages, or structured sport-based physical activity interventions.
Abstract Background: There is increasing evidence that elevated body mass index (BMI) is associated with reduced survival for women with breast cancer. However, the underlying reasons remain unclear. We conducted a Mendelian randomization analysis to investigate a possible causal role of BMI in survival from breast cancer. Methods: We used individual-level data from six large breast cancer case-cohorts including a total of 36 210 individuals (2475 events) of European ancestry. We created a BMI genetic risk score (GRS) based on genotypes at 94 known BMI-associated genetic variants. Association between the BMI genetic score and breast cancer survival was analysed by Cox regression for each study separately. Study-specific hazard ratios were pooled using fixed-effect meta-analysis. Results: BMI genetic score was found to be associated with reduced breast cancer-specific survival for estrogen receptor (ER)-positive cases [hazard ratio (HR) = 1.11, per one-unit increment of GRS, 95% confidence interval (CI) 1.01–1.22, P = 0.03). We observed no association for ER-negative cases (HR = 1.00, per one-unit increment of GRS, 95% CI 0.89–1.13, P = 0.95). Conclusions: Our findings suggest a causal effect of increased BMI on reduced breast cancer survival for ER-positive breast cancer. There is no evidence of a causal effect of higher BMI on survival for ER-negative breast cancer cases.
Abstract The breast cancer risk variants identified in genome-wide association studies explain only a small fraction of the familial relative risk, and the genes responsible for these associations remain largely unknown. To identify novel risk loci and likely causal genes, we performed a transcriptome-wide association study evaluating associations of genetically predicted gene expression with breast cancer risk in 122,977 cases and 105,974 controls of European ancestry. We used data from the Genotype-Tissue Expression Project to establish genetic models to predict gene expression in breast tissue and evaluated model performance using data from The Cancer Genome Atlas. Of the 8,597 genes evaluated, significant associations were identified for 48 at a Bonferroni-corrected threshold of P < 5.82 × 10−6, including 14 genes at loci not yet reported for breast cancer. We silenced 13 genes and showed an effect for 11 on cell proliferation and/or colony-forming efficiency. Our study provides new insights into breast cancer genetics and biology.
Abstract Background: We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry. Methods: Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP). Results: We did not find any variant associated with breast cancer-specific mortality at P < 5 × 10−8. For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10−7, hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84–0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10−7, HR = 1.27, 95% CI = 1.16–1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster. Conclusions: We uncovered germline variants on chromosome 7 at BFDP < 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients.
Abstract Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57–1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628–0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.
Abstract Background: The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study. Methods: We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T>G and c.3113G>A, CHEK2 c.349A>G, c.538C>T, c.715G>A, c.1036C>T, c.1312G>T, and c.1343T>G and ATM c.7271T>G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant. Results: For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p = 7.1 × 10−5), PALB2 c.3113G>A OR 4.21 (95% CI 1.84 to 9.60, p = 6.9 × 10−8) and ATM c.7271T>G OR 11.0 (95% CI 1.42 to 85.7, p = 0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A>G OR 2.26 (95% CI 1.29 to 3.95), c.1036C>T OR 5.06 (95% CI 1.09 to 23.5) and c.538C>T OR 1.33 (95% CI 1.05 to 1.67) (p ≤ 0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T>G OR 3.03 (95% CI 1.53 to 6.03, p = 0.0006) for African men and CHEK2 c.1312G>T OR 2.21 (95% CI 1.06 to 4.63, p = 0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants. Conclusions: This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.