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1.
Int J Cancer ; 2020 Nov 16.
Artigo em Inglês | MEDLINE | ID: mdl-33197272

RESUMO

Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1197 matched controls; and 354 younger-diagnosis cases and 944 controls frequency-matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We estimated measure-specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen-detected and younger-diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41-2.31]) and Cirrus (1.72 [1.38-2.14]); Cirrus (1.49 [1.32-1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27-1.68]), respectively. The AUCs were: 0.73 [0.68-0.77], 0.63 [0.60-0.66], and 0.72 [0.69-0.75], respectively. Combined, our new mammogram-based measures have twice the risk gradient for screen-detected and younger-diagnosis breast cancer (P ≤ 10-12 ), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk-based personalised breast screening.

2.
Clin Epigenetics ; 12(1): 158, 2020 Oct 22.
Artigo em Inglês | MEDLINE | ID: mdl-33092643

RESUMO

BACKGROUND: DNA methylation-based biological age (DNAm age) is an important biomarker for adult health. Studies in specific age ranges have found widely varying results about its genetic and environmental causes of variation. However, these studies are not able to provide a comprehensive view of the causes of variation over the lifespan. RESULTS: In order to investigate the genetic and environmental causes of DNAm age variation across the lifespan, we pooled genome-wide DNA methylation data for 4217 people aged 0-92 years from 1871 families. DNAm age was calculated using the Horvath epigenetic clock. We estimated familial correlations in DNAm age for monozygotic (MZ) twin, dizygotic (DZ) twin, sibling, parent-offspring, and spouse pairs by cohabitation status. Genetic and environmental variance components models were fitted and compared. We found that twin pair correlations were - 0.12 to 0.18 around birth, not different from zero (all P > 0.29). For all pairs of relatives, their correlations increased with time spent living together (all P < 0.02) at different rates (MZ > DZ and siblings > parent-offspring; P < 0.001) and decreased with time spent living apart (P = 0.02) at similar rates. These correlation patterns were best explained by cohabitation-dependent shared environmental factors, the effects of which were 1.41 (95% confidence interval [CI] 1.16 to 1.66) times greater for MZ pairs than for DZ and sibling pairs, and the latter were 2.03 (95% CI 1.13 to 9.47) times greater than for parent-offspring pairs. Genetic factors explained 13% (95% CI - 10 to 35%) of variation (P = 0.27). Similar results were found for another two epigenetic clocks, suggesting that our observations are robust to how DNAm age is measured. In addition, results for the other clocks were consistent with there also being a role for prenatal environmental factors in determining their variation. CONCLUSIONS: Variation in DNAm age is mostly caused by environmental factors, including those shared to different extents by relatives while living together and whose effects persist into old age. The equal environment assumption of the classic twin study might not hold for epigenetic aging.

3.
J Clin Med ; 9(3)2020 Feb 26.
Artigo em Inglês | MEDLINE | ID: mdl-32110975

RESUMO

This commentary is about predicting a woman's breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.

4.
Int J Cancer ; 147(2): 375-382, 2020 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-31609476

RESUMO

Interval breast cancers (those diagnosed between recommended mammography screens) generally have poorer outcomes and are more common among women with dense breasts. We aimed to develop a risk model for interval breast cancer. We conducted a nested case-control study within the Melbourne Collaborative Cohort Study involving 168 interval breast cancer patients and 498 matched control subjects. We measured breast density using the CUMULUS software. We recorded first-degree family history by questionnaire, measured body mass index (BMI) and calculated age-adjusted breast tissue aging, a novel measure of exposure to estrogen and progesterone based on the Pike model. We fitted conditional logistic regression to estimate odds ratio (OR) or odds ratio per adjusted standard deviation (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). The stronger risk associations were for unadjusted percent breast density (OPERA = 1.99; AUC = 0.66), more so after adjusting for age and BMI (OPERA = 2.26; AUC = 0.70), and for family history (OR = 2.70; AUC = 0.56). When the latter two factors and their multiplicative interactions with age-adjusted breast tissue aging (p = 0.01 and 0.02, respectively) were fitted, the AUC was 0.73 (95% CI 0.69-0.77), equivalent to a ninefold interquartile risk ratio. In summary, compared with using dense breasts alone, risk discrimination for interval breast cancers could be doubled by instead using breast density, BMI, family history and hormonal exposure. This would also give women with dense breasts, and their physicians, more information about the major consequence of having dense breasts-an increased risk of developing an interval breast cancer.

5.
Sci Rep ; 9(1): 15055, 2019 10 21.
Artigo em Inglês | MEDLINE | ID: mdl-31636290

RESUMO

DNA methylation-based biological age (DNAm age), as well as genome-wide average DNA methylation, have been reported to predict breast cancer risk. We aimed to investigate the associations between these DNA methylation-based risk factors and 18 conventional breast cancer risk factors for disease-free women. A sample of 479 individuals from the Australian Mammographic Density Twins and Sisters was used for discovery, a sample of 3354 individuals from the Melbourne Collaborative Cohort Study was used for replication, and meta-analyses pooling results from the two studies were conducted. DNAm age based on three epigenetic clocks (Hannum, Horvath and Levine) and genome-wide average DNA methylation were calculated using the HumanMethylation 450 K BeadChip assay data. The DNAm age measures were positively associated with body mass index (BMI), smoking, alcohol drinking and age at menarche (all nominal P < 0.05). Genome-wide average DNA methylation was negatively associated with smoking and number of live births, and positively associated with age at first live birth (all nominal P < 0.05). The association of DNAm age with BMI was also evident in within-twin-pair analyses that control for familial factors. This study suggests that some lifestyle and hormonal risk factors are associated with these DNA methylation-based breast cancer risk factors, and the observed associations are unlikely to be due to familial confounding but are likely causal. DNA methylation-based risk factors could interplay with conventional risk factors in modifying breast cancer risk.


Assuntos
Envelhecimento/genética , Neoplasias da Mama/genética , Metilação de DNA/genética , Genoma Humano , Adulto , Idoso , Austrália , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Mamografia , Metanálise como Assunto , Pessoa de Meia-Idade , Fatores de Risco , Irmãos , Gêmeos
6.
Int J Cancer ; 145(7): 1768-1773, 2019 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-30694562

RESUMO

Age- and body mass index (BMI)-adjusted mammographic density is one of the strongest breast cancer risk factors. DNA methylation is a molecular mechanism that could underlie inter-individual variation in mammographic density. We aimed to investigate the association between breast cancer risk-predicting mammographic density measures and blood DNA methylation. For 436 women from the Australian Mammographic Density Twins and Sisters Study and 591 women from the Melbourne Collaborative Cohort Study, mammographic density (dense area, nondense area and percentage dense area) defined by the conventional brightness threshold was measured using the CUMULUS software, and peripheral blood DNA methylation was measured using the HumanMethylation450 (HM450) BeadChip assay. Associations between DNA methylation at >400,000 sites and mammographic density measures adjusted for age and BMI were assessed within each cohort and pooled using fixed-effect meta-analysis. Associations with methylation at genetic loci known to be associated with mammographic density were also examined. We found no genome-wide significant (p < 10-7 ) association for any mammographic density measure from the meta-analysis, or from the cohort-specific analyses. None of the 299 methylation sites located at genetic loci associated with mammographic density was associated with any mammographic density measure after adjusting for multiple testing (all p > 0.05/299 = 1.7 × 10-4 ). In summary, our study did not find evidence for associations between blood DNA methylation, as measured by the HM450 assay, and conventional mammographic density measures that predict breast cancer risk.


Assuntos
Densidade da Mama/genética , Metilação de DNA , Estudo de Associação Genômica Ampla/métodos , Gêmeos/genética , Adulto , Idoso , Austrália , Células Sanguíneas/química , Índice de Massa Corporal , Estudos de Casos e Controles , Epigênese Genética , Feminino , Humanos , Mamografia , Pessoa de Meia-Idade , Irmãos
7.
BMJ Open ; 9(12): e031041, 2019 12 31.
Artigo em Inglês | MEDLINE | ID: mdl-31892647

RESUMO

INTRODUCTION: For women of the same age and body mass index, increased mammographic density is one of the strongest predictors of breast cancer risk. There are multiple methods of measuring mammographic density and other features in a mammogram that could potentially be used in a screening setting to identify and target women at high risk of developing breast cancer. However, it is unclear which measurement method provides the strongest predictor of breast cancer risk. METHODS AND ANALYSIS: The measurement challenge has been established as an international resource to offer a common set of anonymised mammogram images for measurement and analysis. To date, full field digital mammogram images and core data from 1650 cases and 1929 controls from five countries have been collated. The measurement challenge is an ongoing collaboration and we are continuing to expand the resource to include additional image sets across different populations (from contributors) and to compare additional measurement methods (by challengers). The intended use of the measurement challenge resource is for refinement and validation of new and existing mammographic measurement methods. The measurement challenge resource provides a standardised dataset of mammographic images and core data that enables investigators to directly compare methods of measuring mammographic density or other mammographic features in case/control sets of both raw and processed images, for the purposes of the comparing their predictions of breast cancer risk. ETHICS AND DISSEMINATION: Challengers and contributors are required to enter a Research Collaboration Agreement with the University of Melbourne prior to participation in the measurement challenge. The Challenge database of collated data and images are stored in a secure data repository at the University of Melbourne. Ethics approval for the measurement challenge is held at University of Melbourne (HREC ID 0931343.3).

8.
Int J Obes (Lond) ; 43(2): 243-252, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-29777239

RESUMO

BACKGROUND: Several studies have reported DNA methylation in blood to be associated with body mass index (BMI), but few have investigated causal aspects of the association. We used a twin family design to assess this association at two life points and applied a novel analytical approach to appraise the evidence for causality. METHODS: The methylation profile of DNA from peripheral blood was measured for 479 Australian women from 130 twin families. Linear regression was used to estimate the associations of DNA methylation at ~410,000 cytosine-guanine dinucleotides (CpGs), and of the average DNA methylation at ~20,000 genes, with current BMI, BMI at age 18-21 years, and the change between the two (BMI change). A novel regression-based methodology for twins, Inference about Causation through Examination of Familial Confounding (ICE FALCON), was used to assess causation. RESULTS: At a 5% false discovery rate, nine, six and 12 CpGs at 24 loci were associated with current BMI, BMI at age 18-21 years and BMI change, respectively. The average DNA methylation of the BHLHE40 and SOCS3 loci was associated with current BMI, and of the PHGDH locus with BMI change. From the ICE FALCON analyses with BMI as the predictor and DNA methylation as the outcome, a woman's DNA methylation level was associated with her co-twin's BMI, and the association disappeared after conditioning on her own BMI, consistent with BMI causing DNA methylation. To the contrary, using DNA methylation as the predictor and BMI as the outcome, a woman's BMI was not associated with her co-twin's DNA methylation level, consistent with DNA methylation not causing BMI. CONCLUSION: For middle-aged women, peripheral blood DNA methylation at several genomic locations is associated with current BMI, BMI at age 18-21 years and BMI change. Our study suggests that BMI has a causal effect on peripheral blood DNA methylation.


Assuntos
Índice de Massa Corporal , Metilação de DNA/genética , Gêmeos Dizigóticos , Gêmeos Monozigóticos , Adolescente , Adulto , Austrália , Estudos Transversais , Epigenômica , Estudo de Associação Genômica Ampla , Humanos , Pessoa de Meia-Idade , Gêmeos Dizigóticos/genética , Gêmeos Dizigóticos/estatística & dados numéricos , Gêmeos Monozigóticos/genética , Gêmeos Monozigóticos/estatística & dados numéricos , Adulto Jovem
9.
Breast Cancer Res ; 20(1): 152, 2018 12 13.
Artigo em Inglês | MEDLINE | ID: mdl-30545395

RESUMO

BACKGROUND: Case-control studies show that mammographic density is a better risk factor when defined at higher than conventional pixel-brightness thresholds. We asked if this applied to interval and/or screen-detected cancers. METHOD: We conducted a nested case-control study within the prospective Melbourne Collaborative Cohort Study including 168 women with interval and 422 with screen-detected breast cancers, and 498 and 1197 matched controls, respectively. We measured absolute and percent mammographic density using the Cumulus software at the conventional threshold (Cumulus) and two increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were transformed and adjusted for age and body mass index (BMI). Using conditional logistic regression and adjusting for BMI by age at mammogram, we estimated risk discrimination by the odds ratio per adjusted standard deviation (OPERA), calculated the area under the receiver operating characteristic curve (AUC) and compared nested models using the likelihood ratio criterion and models with the same number of parameters using the difference in Bayesian information criterion (ΔBIC). RESULTS: For interval cancer, there was very strong evidence that the association was best predicted by Cumulus as a percentage (OPERA = 2.33 (95% confidence interval (CI) 1.85-2.92); all ΔBIC > 14), and the association with BMI was independent of age at mammogram. After adjusting for percent Cumulus, no other measure was associated with risk (all P > 0.1). For screen-detected cancer, however, the associations were strongest for the absolute and percent Cirrocumulus measures (all ΔBIC > 6), and after adjusting for Cirrocumulus, no other measure was associated with risk (all P > 0.07). CONCLUSION: The amount of brighter areas is the best mammogram-based measure of screen-detected breast cancer risk, while the percentage of the breast covered by white or bright areas is the best mammogram-based measure of interval breast cancer risk, irrespective of BMI. Therefore, there are different features of mammographic images that give clinically important information about different outcomes.


Assuntos
Densidade da Mama , Neoplasias da Mama/diagnóstico por imagem , Detecção Precoce de Câncer/métodos , Processamento de Imagem Assistida por Computador/métodos , Mamografia/métodos , Idoso , Mama/diagnóstico por imagem , Mama/patologia , Neoplasias da Mama/patologia , Estudos de Casos e Controles , Feminino , Humanos , Pessoa de Meia-Idade , Prognóstico , Estudos Prospectivos , Medição de Risco/métodos , Fatores de Risco , Software
10.
Int J Epidemiol ; 47(3): 908-916, 2018 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-29518222

RESUMO

BACKGROUND: Investigating the genetic and environmental causes of variation in genome-wide average DNA methylation (GWAM), a global methylation measure from the HumanMethylation450 array, might give a better understanding of genetic and environmental influences on methylation. METHODS: We measured GWAM for 2299 individuals aged 0 to 90 years from seven twin and/or family studies. We estimated familial correlations, modelled correlations with cohabitation history and fitted variance components models for GWAM. RESULTS: The correlation in GWAM for twin pairs was ∼0.8 at birth, decreased with age during adolescence and was constant at ∼0.4 throughout adulthood, with no evidence that twin pair correlations differed by zygosity. Non-twin first-degree relatives were correlated, from 0.17 [95% confidence interval (CI): 0.05-0.30] to 0.28 (95% CI: 0.08-0.48), except for middle-aged siblings (0.01, 95% CI: -0.10-0.12), and the correlation increased with time living together and decreased with time living apart. Spouse pairs were correlated in all studies, from 0.23 (95% CI: 0.3-0.43) to 0.31 (95% CI: 0.05-0.52), and the correlation increased with time living together. The variance explained by environmental factors shared by twins alone was 90% (95% CI: 74-95%) at birth, decreased in early life and plateaued at 28% (95% CI: 17-39%) in middle age and beyond. There was a cohabitation-related environmental component of variance. CONCLUSIONS: GWAM is determined in utero by prenatal environmental factors, the effects of which persist throughout life. The variation of GWAM is also influenced by environmental factors shared by family members, as well as by individual-specific environmental factors.

11.
Clin Epigenetics ; 10: 18, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29456763

RESUMO

Background: Smoking has been reported to be associated with peripheral blood DNA methylation, but the causal aspects of the association have rarely been investigated. We aimed to investigate the association and underlying causation between smoking and blood methylation. Methods: The methylation profile of DNA from the peripheral blood, collected as dried blood spots stored on Guthrie cards, was measured for 479 Australian women including 66 monozygotic twin pairs, 66 dizygotic twin pairs, and 215 sisters of twins from 130 twin families using the Infinium HumanMethylation450K BeadChip array. Linear regression was used to estimate associations between methylation at ~ 410,000 cytosine-guanine dinucleotides (CpGs) and smoking status. A regression-based methodology for twins, Inference about Causation through Examination of Familial Confounding (ICE FALCON), was used to assess putative causation. Results: At a 5% false discovery rate, 39 CpGs located at 27 loci, including previously reported AHRR, F2RL3, 2q37.1 and 6p21.33, were found to be differentially methylated across never, former and current smokers. For all 39 CpG sites, current smokers had the lowest methylation level. Our study provides the first replication for two previously reported CpG sites, cg06226150 (SLC2A4RG) and cg21733098 (12q24.32). From the ICE FALCON analysis with smoking status as the predictor and methylation score as the outcome, a woman's methylation score was associated with her co-twin's smoking status, and the association attenuated towards the null conditioning on her own smoking status, consistent with smoking status causing changes in methylation. To the contrary, using methylation score as the predictor and smoking status as the outcome, a woman's smoking status was not associated with her co-twin's methylation score, consistent with changes in methylation not causing smoking status. Conclusions: For middle-aged women, peripheral blood DNA methylation at several genomic locations is associated with smoking. Our study suggests that smoking has a causal effect on peripheral blood DNA methylation, but not vice versa.


Assuntos
Metilação de DNA , Estudo de Associação Genômica Ampla/métodos , Fumar/genética , Gêmeos Dizigóticos/genética , Gêmeos Monozigóticos/genética , Austrália , Ilhas de CpG , DNA/sangue , Epigênese Genética , Feminino , Estudos de Associação Genética , Humanos , Modelos Lineares , Pessoa de Meia-Idade , Linhagem , Fumar/sangue
12.
JNCI Cancer Spectr ; 2(4): pky057, 2018 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31360877

RESUMO

Background: We applied machine learning to find a novel breast cancer predictor based on information in a mammogram. Methods: Using image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case-control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index. Results: Combining studies, almost all textural features were associated with case-control status. The ORs for Cirrus measures trained on one study and tested on another study ranged from 1.56 to 1.78 (all P < 10-6). For the Cirrus measure derived from combining studies, the OR was 1.90 (95% confidence interval [CI] = 1.73 to 2.09), equivalent to a fourfold interquartile risk ratio, and was little attenuated after adjusting for conventional measures. In contrast, the OR for the conventional measure was 1.34 (95% CI = 1.25 to 1.43), and after adjusting for Cirrus it became 1.16 (95% CI = 1.08 to 1.24; P = 4 × 10-5). Conclusions: A fully automated personal risk measure created from combining textural image features performs better at predicting breast cancer risk than conventional mammographic density risk measures, capturing half the risk-predicting ability of the latter measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer.

13.
Radiology ; 286(2): 433-442, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-29040039

RESUMO

Purpose To compare three mammographic density measures defined by different pixel intensity thresholds as predictors of breast cancer risk for two different digital mammographic systems. Materials and Methods The Korean Breast Cancer Study included 398 women with invasive breast cancer and 737 control participants matched for age at mammography (±1 year), examination date, mammographic system, and menopausal status. Mammographic density was measured by using the automated Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software and the semiautomated Cumulus software at the conventional threshold (Cumulus) and at increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were Box-Cox-transformed and adjusted for age, body mass index, and menopausal status. Conditional logistic regression was used to estimate risk associations. For calculation of measures of predictive value, the change in odds per standard deviation (OPERA) and the area under the receiver operating characteristic curve (AUC) were used. Results For dense area, with use of the direct conversion system the OPERAs were 1.72 (95% confidence interval [CI]: 1.38, 2.15) for LIBRA, 1.58 (95% CI: 1.27, 1.97) for Cumulus, 2.04 (95% CI: 1.60, 2.59) for Altocumulus, and 3.48 (95% CI: 2.45, 4.47) for Cirrocumulus (P < .001). The corresponding AUCs were 0.70, 0.69, 0.76, and 0.89, respectively. With use of the indirect conversion system, the corresponding OPERAs were 1.50 (95% CI: 1.28, 1.76), 1.36 (95% CI: 1.16, 1.59), 1.40 (95% CI: 1.19, 1.64), and 1.47 (95% CI: 1.25, 1.73) (P < .001) and the AUCs were 0.64, 0.60, 0.61, and 0.63, respectively. Conclusion It is possible that mammographic density defined by higher pixel thresholds could capture more risk-predicting information with use of a direct conversion mammographic system; the mammographically bright, rather than white, regions are etiologically important. © RSNA, 2017.


Assuntos
Densidade da Mama , Neoplasias da Mama/patologia , Área Sob a Curva , Neoplasias da Mama/diagnóstico por imagem , Estudos de Casos e Controles , Detecção Precoce de Câncer , Feminino , Humanos , Mamografia/métodos , Pessoa de Meia-Idade , Fatores de Risco
14.
Epigenetics ; 12(11): 973-981, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29099274

RESUMO

To address the limitations in current classic twin/family research on the genetic and/or environmental causes of human methylomic variation, we measured blood DNA methylation for 479 women (mean age 56 years) including 66 monozygotic (MZ), 66 dizygotic (DZ) twin pairs and 215 sisters of twins, and 11 random technical duplicates using the HumanMethylation450 array. For each methylation site, we estimated the correlation for pairs of duplicates, MZ twins, DZ twins, and siblings, fitted variance component models by assuming the variation is explained by genetic factors, by shared and individual environmental factors, and by independent measurement error, and assessed the best fitting model. We found that the average (standard deviation) correlations for duplicate, MZ, DZ, and sibling pairs were 0.10 (0.35), 0.07 (0.21), -0.01 (0.14) and -0.04 (0.07). At the genome-wide significance level of 10-7, 93.3% of sites had no familial correlation, and 5.6%, 0.1%, and 0.2% of sites were correlated for MZ, DZ, and sibling pairs. For 86.4%, 6.9%, and 7.1% of sites, the best fitting model included measurement error only, a genetic component, and at least one environmental component. For the 13.6% of sites influenced by genetic and/or environmental factors, the average proportion of variance explained by environmental factors was greater than that explained by genetic factors (0.41 vs. 0.37, P value <10-15). Our results are consistent with, for middle-aged woman, blood methylomic variation measured by the HumanMethylation450 array being largely explained by measurement error, and more influenced by environmental factors than by genetic factors.


Assuntos
Metilação de DNA , Interação Gene-Ambiente , Variação Genética , DNA/sangue , Teste em Amostras de Sangue Seco/normas , Feminino , Humanos , Pessoa de Meia-Idade , Análise de Sequência com Séries de Oligonucleotídeos/normas , Gêmeos/genética
15.
Sci Rep ; 7(1): 8463, 2017 08 16.
Artigo em Inglês | MEDLINE | ID: mdl-28814741

RESUMO

We asked if twin birth influences the DNA methylation of subsequent siblings. We measured whole blood methylation using the HumanMethylation450 array for siblings from two twin and family studies in Australia and Korea. We compared the means and correlations in methylation between pairs of siblings born before a twin birth (BT siblings), born on either side of a twin birth (B/AT pairs) and born after a twin birth (AT siblings). For the genome-wide average DNA methylation, the correlation for AT pairs (rAT) was larger than the correlation for BT pairs (rBT) in both studies, and from the meta-analysis, rAT = 0.46 (95% CI: 0.26, 0.63) and rBT = -0.003 (95% CI: -0.30, 0.29) (P = 0.02). B/AT pairs were not correlated (from the meta-analysis rBAT = 0.08; 95% CI: -0.31, 0.45). Similar results were found for the average methylation of several genomic regions, e.g., CpG shelf and gene body. BT and AT pairs were differentially correlated in methylation for 15 probes (all P < 10-7), and the top 152 differentially correlated probes (at P < 10-4) were enriched in cell signalling and breast cancer regulation pathways. Our observations are consistent with a twin birth changing the intrauterine environment such that siblings both born after a twin birth are correlated in DNA methylation.


Assuntos
Metilação de DNA , Gravidez de Gêmeos/genética , Irmãos , Gêmeos/genética , Adulto , Idoso , Austrália , Ordem de Nascimento , Ilhas de CpG/genética , Feminino , Genoma Humano/genética , Humanos , Masculino , Pessoa de Meia-Idade , Gravidez , República da Coreia
16.
Int J Epidemiol ; 46(2): 652-661, 2017 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-28338721

RESUMO

Background: Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age and body mass index (BMI), is a well-established risk factor for breast cancer. We asked if higher thresholds better separate women with and without breast cancer. Methods: We studied Australian women, 354 with breast cancer over-sampled for early-onset and family history, and 944 unaffected controls frequency-matched for age at mammogram. We measured mammographic dense area and percent density using the CUMULUS software at the conventional threshold, which we call Cumulus , and at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus , respectively. All measures were Box-Cox transformed and adjusted for age and BMI. We estimated the odds per adjusted standard deviation (OPERA) using logistic regression and the area under the receiver operating characteristic curve (AUC). Results: Altocumulus and Cirrocumulus were correlated with Cumulus (r ∼ 0.8 and 0.6 , respectively) . For dense area, the OPERA was 1.62, 1.74 and 1.73 for Cumulus, Altocumulus and Cirrocumulus , respectively (all P < 0.001). After adjusting for Altocumulus and Cirrocumulus , Cumulus was not significant ( P > 0.6). The OPERAs for percent density were less but gave similar findings. The mean of the standardized adjusted Altocumulus and Cirrocumulus dense area measures was the best predictor; OPERA = 1.87 [95% confidence interval (CI): 1.64-2.14] and AUC = 0.68 (0.65-0.71). Conclusions: The areas of higher mammographically dense regions are associated with almost 30% stronger breast cancer risk gradient, explain the risk association of the conventional measure and might be more aetiologically important. This has substantial implications for clinical translation and molecular, genetic and epidemiological research.


Assuntos
Densidade da Mama , Neoplasias da Mama/diagnóstico por imagem , Mama/diagnóstico por imagem , Mamografia , Adulto , Austrália , Índice de Massa Corporal , Estudos de Casos e Controles , Detecção Precoce de Câncer , Reações Falso-Positivas , Feminino , Humanos , Modelos Logísticos , Pessoa de Meia-Idade , Curva ROC , Sistema de Registros , Fatores de Risco , Software
17.
Breast Cancer Res Treat ; 156(1): 163-70, 2016 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-26907766

RESUMO

The aim of the present study is to determine if body mass index (BMI) during childhood is associated with the breast cancer risk factor 'adult mammographic density adjusted for age and BMI'. In 1968, the Tasmanian Longitudinal Health Study studied every Tasmanian school child born in 1961. We obtained measured heights and weights from annual school medical records across ages 7-15 years and imputed missing values. Between 2009 and 2012, we administered to 490 women a questionnaire that asked current height and weight and digitised at least one mammogram per woman. Absolute and percent mammographic densities were measured using the computer-assisted method CUMULUS. We used linear regression and adjusted for age at interview and log current BMI. The mammographic density measures were negatively associated: with log BMI at each age from 7 to 15 years (all p < 0.05); with the average of standardised log BMIs across ages 7-15 years (p < 0.0005); and more strongly with standardised log BMI measures closer to age 15 years (p < 0.03). Childhood BMI measures explained 7 and 10 % of the variance in absolute and percent mammographic densities, respectively, and 25 and 20 % of the association between current BMI and absolute and percent mammographic densities, respectively. Associations were not altered by adjustment for age at menarche. There is a negative association between BMI in late childhood and the adult mammographic density measures that predict breast cancer risk. This could explain, at least in part, why BMI in adolescence is negatively associated with breast cancer risk.


Assuntos
Densidade da Mama , Neoplasias da Mama/diagnóstico por imagem , Adolescente , Índice de Massa Corporal , Neoplasias da Mama/patologia , Criança , Detecção Precoce de Câncer , Feminino , Humanos , Modelos Lineares , Estudos Longitudinais , Mamografia , Pessoa de Meia-Idade , Fatores de Risco , Tasmânia
18.
Eur J Cancer Prev ; 24(5): 422-9, 2015 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-25370685

RESUMO

We carried out this study to evaluate the association between mammographic density adjusted for age and BMI and early-onset breast cancer in Asian women. We recruited 213 Korean patients with breast cancer (45% diagnosed before the age of 50 years) and 630 controls matched for age, menopausal status, and examination date. The percentage and absolute size of dense areas on digital mammograms were measured using a computer-assisted thresholding technique (Cumulus). We carried out an analysis using the conditional logistic regression model with adjustment for covariates. An increase by 1 SD in age and BMI-adjusted absolute dense area and percentage dense area was associated with a 1.15-fold (95% confidence interval: 1.03, 1.29) and 1.20-fold (95% confidence interval: 1.06, 1.37) increased risk of breast cancer, respectively. These associations were stronger for premenopausal disease (P=0.07 and 0.01, respectively) and for disease diagnosed before age 50 (P=0.07 and 0.02, respectively) than for postmenopausal disease (P=0.16 and 0.23, respectively) or later onset disease (P=0.10 and 0.10, respectively). There was no difference in the associations with premenopausal versus postmenopausal and early-onset versus late-onset disease. After adjusting for age and BMI, both a greater absolute dense area and a greater percentage dense area were associated with an increased risk of breast cancer, particularly at a young age.


Assuntos
Neoplasias da Mama/diagnóstico , Neoplasias da Mama/etiologia , Mama/anormalidades , Mama/patologia , Glândulas Mamárias Humanas/anormalidades , Mamografia/métodos , Densidade da Mama , Neoplasias da Mama/epidemiologia , Estudos de Casos e Controles , Feminino , Seguimentos , Humanos , Modelos Logísticos , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Pós-Menopausa , Pré-Menopausa , Prognóstico , República da Coreia/epidemiologia , Fatores de Risco
19.
Ann Epidemiol ; 24(3): 222-7, 2014 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-24360852

RESUMO

PURPOSE: Bone mineral density (BMD) may be useful as a surrogate marker reflecting lifetime exposure to estrogen in a woman. Our study aimed to investigate an association between BMD and breast cancer risk. METHODS: A case-control study was conducted using 253 breast cancer cases and 506 age and menopausal status-matched controls from the same institution. Cases were ascertained through medical record review of the women with abnormal mammographic findings. BMD was measured at the lumbar spine and femoral neck using a dual-energy X-ray absorptiometry. The association was estimated by conditional logistic regression analysis with an adjustment for covariates. RESULTS: Although there was no difference in the association between pre- and postmenopausal disease, the association between BMD and breast cancer was evident for postmenopausal breast cancer. One standard deviation in age and menopausal status adjusted BMD at lumbar spine and femur neck was associated with 1.35-fold (standard error = 0.19, P = .04) and 1.34-fold (standard error = 0.20, P = .05) increased likelihood of breast cancer risk, respectively, for postmenopausal women. CONCLUSION: After adjusting for covariates, higher BMD at lumbar spine and femur neck are associated with increased likelihood of breast cancer risk for postmenopausal women. These findings suggest that BMD could be included in breast cancer risk prediction models for postmenopausal Korean women.


Assuntos
Grupo com Ancestrais do Continente Asiático/estatística & dados numéricos , Densidade Óssea , Neoplasias da Mama/epidemiologia , Absorciometria de Fóton , Adulto , Estudos de Casos e Controles , Feminino , Colo do Fêmur/diagnóstico por imagem , Humanos , Modelos Logísticos , Vértebras Lombares/diagnóstico por imagem , Mamografia , Menopausa , Pessoa de Meia-Idade , República da Coreia/epidemiologia , Fatores de Risco , Inquéritos e Questionários
20.
Cancer Epidemiol Biomarkers Prev ; 22(12): 2395-403, 2013 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-24130221

RESUMO

BACKGROUND: Mammographic density, the area of the mammographic image that appears white or bright, predicts breast cancer risk. We estimated the proportions of variance explained by questionnaire-measured breast cancer risk factors and by unmeasured residual familial factors. METHODS: For 544 MZ and 339 DZ twin pairs and 1,558 non-twin sisters from 1,564 families, mammographic density was measured using the computer-assisted method Cumulus. We estimated associations using multilevel mixed-effects linear regression and studied familial aspects using a multivariate normal model. RESULTS: The proportions of variance explained by age, body mass index (BMI), and other risk factors, respectively, were 4%, 1%, and 4% for dense area; 7%, 14%, and 4% for percent dense area; and 7%, 40%, and 1% for nondense area. Associations with dense area and percent dense area were in opposite directions than for nondense area. After adjusting for measured factors, the correlations of dense area with percent dense area and nondense area were 0.84 and -0.46, respectively. The MZ, DZ, and sister pair correlations were 0.59, 0.28, and 0.29 for dense area; 0.57, 0.30, and 0.28 for percent dense area; and 0.56, 0.27, and 0.28 for nondense area (SE = 0.02, 0.04, and 0.03, respectively). CONCLUSIONS: Under the classic twin model, 50% to 60% (SE = 5%) of the variance of mammographic density measures that predict breast cancer risk are due to undiscovered genetic factors, and the remainder to as yet unknown individual-specific, nongenetic factors. IMPACT: Much remains to be learnt about the genetic and environmental determinants of mammographic density.


Assuntos
Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/epidemiologia , Mamografia/métodos , Irmãos , Gêmeos Dizigóticos , Gêmeos Monozigóticos , Fatores Etários , Austrália/epidemiologia , Índice de Massa Corporal , Mama/patologia , Neoplasias da Mama/genética , Neoplasias da Mama/patologia , Estudos de Casos e Controles , Estudos de Coortes , Feminino , Predisposição Genética para Doença , Humanos , Mamografia/instrumentação , Pessoa de Meia-Idade , Valor Preditivo dos Testes
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