Dermal application of personal care products (PCPs) is considered an important human exposure route for siloxanes. Their presence as minor or major constituents in many personal care products (PCPs) ...and cosmetics is of concern for human exposure.
The aim of this study was to quantify cyclic volatile methylsiloxanes (cVMS) in blood plasma of pregnant and postmenopausal women, and to investigate possible links to self-reported use of PCPs for the latter group. Participants were recruited from two studies, namely the Norwegian Women and Cancer Study (NOWAC) and the North Norwegian Mother-and-child Study (MISA). For the NOWAC cohort, 94 plasma samples from postmenopausal women were analyzed (blood drawn in 2005) and information about PCP use and breast implants was derived from a self-administered questionnaire. In the MISA study, the collection of the plasma samples (blood drawn in 2009) constituted a re-sampling because the original serum vacutainers used were contaminated with cVMS. D4 (octamethylcyclotetrasiloxane) was the dominant compound in plasma for both cohorts. For the NOWAC samples, more than 85% of the women had D4 concentrations above the LOQ (2.74ng/mL), while the detection frequency was only 18% for the MISA participants. The highest cVMS plasma concentrations were observed for D4: 12.7ng/mL (NOWAC) and 2.69ng/mL (MISA). For the other cVMS, decamethylcyclopentasiloxane (D5) and dodecamethylcyclohexasiloxane (D6) concentrations were below the detection limit in most samples.
There was no significant correlation between the concentrations of D4 and the reported total body cream use. Sampling time (2005 versus 2009) and age of the donors could explain the differences between the two cohorts.
► First data on cVMS in sub-populations from the general female population showing low concentrations with D4 dominating. ► Percentage detected for D4 was below 20% for pregnant women, and 85% for postmenopausal women. ► There was no association between self-reported use of personal care products and measured cVMS concentrations in plasma. ► Age and sampling time could explain the observed differences between the cohorts.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Background: Increasing evidence suggests that general obesity measured by body mass index (BMI) is positively associated
with risk of esophageal adenocarcinoma (EAC). In contrast, previous studies ...have shown inverse relations with esophageal squamous
cell carcinoma (ESCC). However, it is still unclear whether body fat distribution, particularly abdominal obesity, is associated
with each type of esophageal cancer.
Methods: We applied multivariable adjusted Cox proportional hazards regression to investigate the association between anthropometric
measures and risk of EAC and ESCC among 346,554 men and women participating in the European Prospective Investigation into
Cancer and Nutrition. All statistical tests were two sided.
Results: During 8.9 years of follow-up, we documented 88 incident cases of EAC and 110 cases of ESCC. BMI, waist circumference,
and waist-to-hip ratio (WHR) were positively associated with EAC risk highest versus lowest quintile; relative risk (RR),
2.60; 95% confidence interval (95% CI), 1.23-5.51; P trend < 0.01; RR, 3.07; 95% CI, 1.35-6.98; P trend < 0.003; and RR, 2.12; 95% CI, 0.98-4.57; P trend < 0.004. In contrast, BMI and waist circumference were inversely related to ESCC risk, whereas WHR showed no association
with ESCC. In stratified analyses, BMI and waist circumference were significantly inversely related to ESCC only among smokers
but not among nonsmokers. However, when controlled for BMI, we found positive associations for waist circumference and WHR
with ESCC, and these associations were observed among smokers and nonsmokers.
Conclusion: General and abdominal obesity were associated with higher EAC risk. Further, our study suggests that particularly
an abdominal body fat distribution might also be a risk factor for ESCC. (Cancer Epidemiol Biomarkers Prev 2009;18(7):2079–89)
Cancer survival has been observed to be poorer in low socioeconomic groups, but the knowledge about the underlying causal factors is limited. The purpose of this study was to examine how cancer ...survival varies by socioeconomic status (SES) among women in Norway, and to identify factors that explain this variation. SES was measured by years of education and gross household income, respectively.
We used data from The Norwegian Women and Cancer Study, a prospective cohort study including 91,814 women who responded to an extensive questionnaire between 1996 and 1998. A total of 3,899 incident cancer cases were diagnosed during follow-up, of whom 1,089 women died, 919 of them from cancer. Cox Proportional Hazards Model was used to calculate relative risks (RR) of mortality and 95% confidence intervals.
We observed an overall negative socioeconomic gradient in cancer survival, which was most evident in the site specific analyses for survival of ovarian cancer by years of education. For colorectal cancer, mortality increased with years of education, but not with income. After adjustment for household size, marital status, disease stage, and smoking status the SES variation in cancer survival became non-significant. We found that the unequal socioeconomic distribution of smoking status prior to diagnosis contributed considerably to the poorer survival in low SES groups.
We found an overall negative socioeconomic gradient in cancer survival when SES is measured as years of education or gross household income. Smoking status prior to diagnosis was an important predictive factor for socioeconomic variation in survival.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background
Normal breast tissue is utilized in tissue‐based studies of breast carcinogenesis. While gene expression in breast tumor tissue is well explored, our knowledge of transcriptomic signatures ...in normal breast tissue is still incomplete. The aim of this study was to investigate variability of gene expression in a large sample of normal breast tissue biopsies, according to breast cancer related exposures (obesity, smoking, alcohol, hormone therapy, and parity).
Methods
We analyzed gene expression profiles from 311 normal breast tissue biopsies from cancer‐free, post‐menopausal women, using Illumina bead chip arrays. Principal component analysis and K‐means clustering was used for initial analysis of the dataset. The association of exposures and covariates with gene expression was determined using linear models for microarrays.
Results
Heterogeneity of the breast tissue and cell composition had the strongest influence on gene expression profiles. After adjusting for cell composition, obesity, smoking, and alcohol showed the highest numbers of associated genes and pathways, whereas hormone therapy and parity were associated with negligible gene expression differences.
Conclusion
Our results provide insight into associations between major exposures and gene expression profiles and provide an informative baseline for improved understanding of exposure‐related molecular events in normal breast tissue of cancer‐free, post‐menopausal women.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
There is growing evidence that gene expression profiling of peripheral blood cells is a valuable tool for assessing gene signatures related to exposure, drug-response, or disease. However, the true ...promise of this approach can not be estimated until the scientific community has robust baseline data describing variation in gene expression patterns in normal individuals. Using a large representative sample set of postmenopausal women (N = 286) in the Norwegian Women and Cancer (NOWAC) postgenome study, we investigated variability of whole blood gene expression in the general population. In particular, we examined changes in blood gene expression caused by technical variability, normal inter-individual differences, and exposure variables at proportions and levels relevant to real-life situations. We observe that the overall changes in gene expression are subtle, implying the need for careful analytic approaches of the data. In particular, technical variability may not be ignored and subsequent adjustments must be considered in any analysis. Many new candidate genes were identified that are differentially expressed according to inter-individual (i.e. fasting, BMI) and exposure (i.e. smoking) factors, thus establishing that these effects are mirrored in blood. By focusing on the biological implications instead of directly comparing gene lists from several related studies in the literature, our analytic approach was able to identify significant similarities and effects consistent across these reports. This establishes the feasibility of blood gene expression profiling, if they are predicated upon careful experimental design and analysis in order to minimize confounding signals, artifacts of sample preparation and processing, and inter-individual differences.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The understanding of changes in temporal processes related to human carcinogenesis is limited. One approach for prospective functional genomic studies is to compile trajectories of differential ...expression of genes, based on measurements from many case-control pairs. We propose a new statistical method that does not assume any parametric shape for the gene trajectories.
The trajectory of a gene is defined as the curve representing the changes in gene expression levels in the blood as a function of time to cancer diagnosis. In a nested case-control design it consists of differences in gene expression levels between cases and controls. Genes can be grouped into curve groups, each curve group corresponding to genes with a similar development over time. The proposed new statistical approach is based on a set of hypothesis testing that can determine whether or not there is development in gene expression levels over time, and whether this development varies among different strata. Curve group analysis may reveal significant differences in gene expression levels over time among the different strata considered. This new method was applied as a "proof of concept" to breast cancer in the Norwegian Women and Cancer (NOWAC) postgenome cohort, using blood samples collected prospectively that were specifically preserved for transcriptomic analyses (PAX tube). Cohort members diagnosed with invasive breast cancer through 2009 were identified through linkage to the Cancer Registry of Norway, and for each case a random control from the postgenome cohort was also selected, matched by birth year and time of blood sampling, to create a case-control pair. After exclusions, 441 case-control pairs were available for analyses, in which we considered strata of lymph node status at time of diagnosis and time of diagnosis with respect to breast cancer screening visits.
The development of gene expression levels in the NOWAC postgenome cohort varied in the last years before breast cancer diagnosis, and this development differed by lymph node status and participation in the Norwegian Breast Cancer Screening Program. The differences among the investigated strata appeared larger in the year before breast cancer diagnosis compared to earlier years.
This approach shows good properties in term of statistical power and type 1 error under minimal assumptions. When applied to a real data set it was able to discriminate between groups of genes with non-linear similar patterns before diagnosis.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
MicroRNA profiling represents an important first-step in deducting individual RNA-based regulatory function in a cell, tissue, or at a specific developmental stage. Currently there are several ...different platforms to choose from in order to make the initial miRNA profiles. In this study we investigate recently developed digital microRNA high-throughput technologies. Four different platforms were compared including next generation SOLiD ligation sequencing and Illumina HiSeq sequencing, hybridization-based NanoString nCounter, and miRCURY locked nucleic acid RT-qPCR. For all four technologies, full microRNA profiles were generated from human cell lines that represent noninvasive and invasive tumorigenic breast cancer. This study reports the correlation between platforms, as well as a more extensive analysis of the accuracy and sensitivity of data generated when using different platforms and important consideration when verifying results by the use of additional technologies. We found all the platforms to be highly capable for microRNA analysis. Furthermore, the two NGS platforms and RT-qPCR all have equally high sensitivity, and the fold change accuracy is independent of individual miRNA concentration for NGS and RT-qPCR. Based on these findings we propose new guidelines and considerations when performing microRNA profiling.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Vigorous physical training
1
–
5
and even moderate exercise
6
–
9
can interrupt the menstrual cycle, perhaps by suppressing the pulsatile release of gonadotropin-releasing hormone.
10
,
11
This ...effect of physical activity may lower a woman's cumulative exposure to estrogen and progesterone, thereby inhibiting carcinogenesis in the breast.
12
–
22
Energy balance might also influence the risk of breast cancer. Caloric restriction in rodents reduces the proliferative activity of the mammary glands
23
and inhibits carcinogenesis.
24
,
25
However, the effect of energy balance, as indicated by energy intake, body-mass index (the weight in kilograms divided by the square of the height in meters), and energy . . .
Metabolomics is a potentially powerful tool for identification of biomarkers associated with lifestyle exposures and risk of various diseases. This is the rationale of the 'meeting-in-the-middle' ...concept, for which an analytical framework was developed in this study. In a nested case-control study on hepatocellular carcinoma (HCC) within the European Prospective Investigation into Cancer and nutrition (EPIC), serum (1)H nuclear magnetic resonance (NMR) spectra (800 MHz) were acquired for 114 cases and 222 matched controls. Through partial least square (PLS) analysis, 21 lifestyle variables (the 'predictors', including information on diet, anthropometry and clinical characteristics) were linked to a set of 285 metabolic variables (the 'responses'). The three resulting scores were related to HCC risk by means of conditional logistic regressions. The first PLS factor was not associated with HCC risk. The second PLS metabolomic factor was positively associated with tyrosine and glucose, and was related to a significantly increased HCC risk with OR = 1.11 (95% CI: 1.02, 1.22, P = 0.02) for a 1SD change in the responses score, and a similar association was found for the corresponding lifestyle component of the factor. The third PLS lifestyle factor was associated with lifetime alcohol consumption, hepatitis and smoking, and had negative loadings on vegetables intake. Its metabolomic counterpart displayed positive loadings on ethanol, glutamate and phenylalanine. These factors were positively and statistically significantly associated with HCC risk, with 1.37 (1.05, 1.79, P = 0.02) and 1.22 (1.04, 1.44, P = 0.01), respectively. Evidence of mediation was found in both the second and third PLS factors, where the metabolomic signals mediated the relation between the lifestyle component and HCC outcome. This study devised a way to bridge lifestyle variables to HCC risk through NMR metabolomics data. This implementation of the 'meeting-in-the-middle' approach finds natural applications in settings characterised by high-dimensional data, increasingly frequent in the omics generation.
PDF
