Trans fatty acids (TFAs) have been hypothesised to influence breast cancer risk. However, relatively few prospective studies have examined this relationship, and well-powered analyses according to ...hormone receptor-defined molecular subtypes, menopausal status, and body size have rarely been conducted.
In the European Prospective Investigation into Cancer and Nutrition (EPIC), we investigated the associations between dietary intakes of TFAs (industrial trans fatty acids ITFAs and ruminant trans fatty acids RTFAs) and breast cancer risk among 318,607 women. Multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models, adjusted for other breast cancer risk factors.
After a median follow-up of 8.1 years, 13,241 breast cancer cases occurred. In the multivariable-adjusted model, higher total ITFA intake was associated with elevated breast cancer risk (HR for highest vs lowest quintile, 1.14, 95% CI 1.06-1.23; P trend = 0.001). A similar positive association was found between intake of elaidic acid, the predominant ITFA, and breast cancer risk (HR for highest vs lowest quintile, 1.14, 95% CI 1.06-1.23; P trend = 0.001). Intake of total RTFAs was also associated with higher breast cancer risk (HR for highest vs lowest quintile, 1.09, 95% CI 1.01-1.17; P trend = 0.015). For individual RTFAs, we found positive associations with breast cancer risk for dietary intakes of two strongly correlated fatty acids (Spearman correlation r = 0.77), conjugated linoleic acid (HR for highest vs lowest quintile, 1.11, 95% CI 1.03-1.20; P trend = 0.001) and palmitelaidic acid (HR for highest vs lowest quintile, 1.08, 95% CI 1.01-1.16; P trend = 0.028). Similar associations were found for total ITFAs and RTFAs with breast cancer risk according to menopausal status, body mass index, and breast cancer subtypes.
These results support the hypothesis that higher dietary intakes of ITFAs, in particular elaidic acid, are associated with elevated breast cancer risk. Due to the high correlation between conjugated linoleic acid and palmitelaidic acid, we were unable to disentangle the positive associations found for these fatty acids with breast cancer risk. Further mechanistic studies are needed to identify biological pathways that may underlie these associations.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Machine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML ...models, because it may bias the learning process and the model generalization ability. In this paper, we consider oversampling methods for generating synthetic categorical clinical data aiming to improve the predictive performance in ML models, and the identification of risk factors for cardiovascular diseases (CVDs). We performed a comparative study of several categorical synthetic data generation methods, including Synthetic Minority Oversampling Technique Nominal (SMOTEN), Tabular Variational Autoencoder (TVAE) and Conditional Tabular Generative Adversarial Networks (CTGANs). Then, we assessed the impact of combining oversampling strategies and linear and nonlinear supervised ML methods. Lastly, we conducted a post-hoc model interpretability based on the importance of the risk factors. Experimental results show the potential of GAN-based models for generating high-quality categorical synthetic data, yielding probability mass functions that are very close to those provided by real data, maintaining relevant insights, and contributing to increasing the predictive performance. The GAN-based model and a linear classifier outperform other oversampling techniques, improving the area under the curve by 2%. These results demonstrate the capability of synthetic data to help with both determining risk factors and building models for CVD prediction.
Except for a documented increase in osteoprotegerin (OPG) concentrations with older age, data on determinants of soluble Receptor Activator of Nuclear Factor κB (sRANKL) and OPG concentrations in ...women are limited. We evaluated reproductive and lifestyle factors as potential sources of variation in circulating sRANKL and OPG concentrations in pre- and postmenopausal women.
This study includes 2,016 controls
= 1,552 (76%) postmenopausal,
= 757 (38%) using postmenopausal hormone therapy (PMH) from a breast cancer case-control study nested in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Serum sRANKL was measured using an ELISA and serum OPG using an electrochemiluminescent assay. Generalized linear models were used to evaluate associations between these analytes and reproductive and lifestyle factors.
Older age at blood collection was associated with lower sRANKL concentrations in postmenopausal women (
≤ 0.03) and higher OPG concentrations in all women (
≤ 0.01). Longer duration of oral contraceptive use among premenopausal women and postmenopausal PMH users was associated with higher OPG (
≤ 0.04). In postmenopausal non-PMH users, sRANKL concentrations were lower with longer duration of oral contraceptive use and current (vs. never) smoking (
≤ 0.01). sRANKL concentrations were higher among women with higher BMI (
≤ 0.01). The evaluated factors accounted for 12% of the variation in sRANKL concentrations and 21% of the variation in OPG concentrations.
Circulating sRANKL and OPG concentrations are minimally impacted by hormone-related factors in pre- and postmenopausal women.
This study suggests circulating concentrations of sRANKL and OPG are unlikely to be strongly modified by hormone-related reproductive and lifestyle factors.
Cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes are the 4 main noncommunicable diseases. These noncommunicable diseases share 4 modifiable risk factors (tobacco use, ...harmful use of alcohol, physical inactivity, and unhealthy diet). Short smartphone surveys have the potential to identify modifiable risk factors for individuals to monitor trends.
We aimed to pilot a smartphone-based information communication technology solution to collect nationally representative data, annually, on 4 modifiable risk factors.
We developed an information communication technology solution with functionalities for capturing sensitive data from smartphones, receiving, and handling data in accordance with general data protection regulations. The main survey comprised 26 questions: 8 on socioeconomic factors, 17 on the 4 risk factors, and 1 about current or previous noncommunicable diseases. For answers to the continuous questions, a keyboard was displayed for entering numbers; there were preset upper and lower limits for acceptable response values. For categorical questions, pull-down menus with response options were displayed. The second survey comprised 9 yes-or-no questions. For both surveys, we used SMS text messaging. For the main survey, we invited 11,000 individuals, aged 16 to 69 years, selected randomly from the Norwegian National Population Registry (1000 from each of the 11 counties). For the second survey, we invited a random sample of 100 individuals from each county who had not responded to the main survey. All data, except county of residence, were self-reported. We calculated the distribution for socioeconomic background, tobacco use, diet, physical activity, and health condition factors overall and by sex.
The response rate was 21.9% (2303/11,000; women: 1397/2263; 61.7%, men: 866/2263, 38.3%; missing: 40/2303, 1.7%). The median age for men was 52 years (IQR 40-61); the median age for women was 48 years (IQR 35-58). The main reported reason for nonparticipation in the main survey was that the sender of the initial SMS was unknown.
We successfully developed and piloted a smartphone-based information communication technology solution for collecting data on the 4 modifiable risk factors for the 4 main noncommunicable diseases. Approximately 1 in 5 invitees responded; thus, these data may not be nationally representative. The smartphone-based information communication technology solution should be further developed with the long-term goal to reduce premature mortality from the 4 main noncommunicable diseases.
Abbreviations AGEs advanced glycation endproducts BMI body mass index CEL N-epsilon-(1-carboxyethyl)-lysine CI confidence intervals CML N-epsilon-(carboxymethyl)-lysine DQ dietary questionnaires EPIC ...European Prospective Investigation into Cancer and Nutrition HR hazard ratios MG-H1 N-delta-5-hydro-5-methyl-4-imidazolon-2-yl-ornithine RAGE receptor of AGEs SD standard deviation sRAGE soluble receptor of AGEs UPLC-MS/MS ultra-performance liquid chromatography-tandem mass spectrometry Dear Editor, In the European region, which shares 22.8% of the global cancer burden for 10% of the global population, there were around 4.4 million new cancer cases and 1.9 million deaths from cancer in 2020 1. Previous clinical and experimental studies suggested that AGEs are proinflammatory, increase oxidative stress and activate pro-carcinogenic transcription factors such as NFκB and STAT3 as well as cell signaling pathways like mitogen-activated protein kinase (MAPK) by binding on the receptor for AGEs (RAGE) 7. ...there always remains the possibility of unmeasured confounding. ...in this large multinational prospective cohort study across 20 anatomical cancer sites, a higher intake of dietary AGEs was not associated with an increased risk of overall cancer and most cancer types studied.
A higher selenium (Se) status has been shown to be associated with lower risk for colorectal cancer (CRC), but the importance of Se in survival after CRC diagnosis is not well studied. The ...associations of prediagnostic circulating Se status (as indicated by serum Se and selenoprotein P (SELENOP) measurements) with overall and CRC-specific mortality were estimated using multivariable Cox proportional hazards regression among 995 CRC cases (515 deaths, 396 from CRC) in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Se and SELENOP serum concentrations were measured on average 46 months before CRC diagnosis. Median follow-up time was 113 months. Participants with Se concentrations in the highest quintile (≥100 µg/L) had a multivariable-adjusted hazard ratio (HR) of 0.73 (95% CI: 0.52-1.02; P
= 0.06) for CRC-specific mortality and 0.77 (95% CI: 0.57-1.03; P
= 0.04) for overall mortality, compared with the lowest quintile (≤67.5 µg/L). Similarly, participants with SELENOP concentrations in the highest (≥5.07 mg/L) compared with the lowest quintile (≤3.53 mg/L) had HRs of 0.89 (95% CI: 0.64-1.24; P
= 0.39) for CRC-specific mortality and 0.83 (95% CI: 0.62-1.11; P
= 0.17) for overall mortality. Higher prediagnostic exposure to Se within an optimal concentration (100-150 µg/L) might be associated with improved survival among CRC patients, although our results were not statistically significant and additional studies are needed to confirm this potential association. Our findings may stimulate further research on selenium's role in survival among CRC patients especially among those residing in geographic regions with suboptimal Se availability.
Anthropometry and the Risk of Lung Cancer in EPIC Dewi, Nikmah Utami; Boshuizen, Hendriek C; Johansson, Mattias ...
American journal of epidemiology,
07/2016, Letnik:
184, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The associations of body mass index (BMI) and other anthropometric measurements with lung cancer were examined in 348,108 participants in the European Investigation Into Cancer and Nutrition (EPIC) ...between 1992 and 2010. The study population included 2,400 case patients with incident lung cancer, and the average length of follow-up was 11 years. Hazard ratios were calculated using Cox proportional hazard models in which we modeled smoking variables with cubic splines. Overall, there was a significant inverse association between BMI (weight (kg)/height (m)(2)) and the risk of lung cancer after adjustment for smoking and other confounders (for BMI of 30.0-34.9 versus 18.5-25.0, hazard ratio = 0.72, 95% confidence interval: 0.62, 0.84). The strength of the association declined with increasing follow-up time. Conversely, after adjustment for BMI, waist circumference and waist-to-height ratio were significantly positively associated with lung cancer risk (for the highest category of waist circumference vs. the lowest, hazard ratio = 1.25, 95% confidence interval: 1.05, 1.50). Given the decline of the inverse association between BMI and lung cancer over time, the association is likely at least partly due to weight loss resulting from preclinical lung cancer that was present at baseline. Residual confounding by smoking could also have influenced our findings.
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