The local environment and germline genetic variation predict cancer risk in the UK Biobank prospective cohort.

BACKGROUND: There is a growing body of evidence on the effect of the local environment exposure on cancer susceptibility. Nonetheless, several of the associations remain controversial. Moreover, our understanding of the possible interaction between the local environment and the genetic variability is still very limited. OBJECTIVE: The aim of this study was to clarify the role of the local environment and its possible interplay with genetics on common cancers development. METHODS: Using the UK Biobank (UKBB) prospective cohort, we selected 12 local environment exposures: nitrogen oxides, nitrogen dioxides, particulate matter (10 and 2.5 µm), noise pollution, urban traffic, living distance from the coast, percentage of greenspace, natural environment, water, and domestic garden within 1000 m from the residential coordinates of each participant. All these exposures were tested for association with 17 different types of cancer for a total of 53,270 cases and 302,645 controls. Additionally, a polygenic score (PGS) was computed for each cancer, to test possible gene-environment interactions. Finally, mediation analyses were carried out. RESULTS: Thirty-six statistically significant associations considering multiple testing (p < 2.19 × 10-4) were observed. Among the novel associations we observed that individuals living farther from the coast had a higher risk of developing prostate cancer (OR = 1.13, CI95% = 1.06-1.20, P = 1.98 × 10-4). This association was partially mediated by physical activity (indirect effect (IE) = -8.48 × 10-7) and the time spent outdoor (IE = 9.07 × 10-6). All PGSs showed statistically significant associations. Finally, genome-environment interaction analysis showed that local environment and genetic variability affect cancer risk independently. DISCUSSION: Living close to the coast and air pollution were associated with a decreased risk of prostate cancer and skin melanoma, respectively. These findings from the UKBB support the role of the local environment on cancer development, which is independent from genetics and may be mediated by several lifestyle factors.

Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals.

The biomedical field is characterized by an ever-increasing production of sequential data, which often come in the form of biosignals capturing the time-evolution of physiological processes, such as blood pressure and brain activity. This has motivated a large body of research dealing with the development of machine learning techniques for the predictive analysis of such biosignals. Unfortunately, in high-stakes decision making, such as clinical diagnosis, the opacity of machine learning models becomes a crucial aspect to be addressed in order to increase the trust and adoption of AI technology. In this paper, we propose a model agnostic explanation method, based on occlusion, that enables the learning of the input's influence on the model predictions. We specifically target problems involving the predictive analysis of time-series data and the models that are typically used to deal with data of such nature, i.e., recurrent neural networks. Our approach is able to provide two different kinds of explanations: one suitable for technical experts, who need to verify the quality and correctness of machine learning models, and one suited to physicians, who need to understand the rationale underlying the prediction to make aware decisions. A wide experimentation on different physiological data demonstrates the effectiveness of our approach both in classification and regression tasks.

Give more data, awareness and control to individual citizens, and they will help COVID-19 containment.

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.

Gastroesophageal reflux symptoms among Italian university students: epidemiology and dietary correlates using automatically recorded transactions.

BACKGROUND: Gastroesophageal reflux disease (GERD) is one of the most common gastrointestinal disorders worldwide, with relevant impact on the quality of life and health care costs.The aim of our study is to assess the prevalence of GERD based on self-reported symptoms among university students in central Italy. The secondary aim is to evaluate lifestyle correlates, particularly eating habits, in GERD students using automatically recorded transactions through cashiers at university canteen. METHODS: A web-survey was created and launched through an app, ad-hoc developed for an interactive exchange of information with students, including anthropometric data and lifestyle habits. Moreover, the web-survey allowed users a self-diagnosis of GERD through a simple questionnaire. As regard eating habits, detailed collection of meals consumed, including number and type of dishes, were automatically recorded through cashiers at the university canteen equipped with an automatic registration system. RESULTS: We collected 3012 questionnaires. A total of 792 students (26.2% of the respondents) reported typical GERD symptoms occurring at least weekly. Female sex was more prevalent than male sex. In the set of students with GERD, the percentage of smokers was higher, and our results showed that when BMI tends to higher values the percentage of students with GERD tends to increase. When evaluating correlates with diet, we found, among all users, a lower frequency of legumes choice in GERD students and, among frequent users, a lower frequency of choice of pasta and rice in GERD students. DISCUSSION: The results of our study are in line with the values reported in the literature. Nowadays, GERD is a common problem in our communities, and can potentially lead to serious medical complications; the economic burden involved in the diagnostic and therapeutic management of the disease has a relevant impact on healthcare costs. CONCLUSIONS: To our knowledge, this is the first study evaluating the prevalence of typical GERD-related symptoms in a young population of University students in Italy. Considering the young age of enrolled subjects, our prevalence rate, relatively high compared to the usual estimates, could represent a further negative factor for the future economic sustainability of the healthcare system.

Analysis of exposome and genetic variability suggests stress as a major contributor for development of pancreatic ductal adenocarcinoma.

BACKGROUND: The current knowledge on pancreatic ductal adenocarcinoma (PDAC) risk factors is limited and no study has comprehensively tested the exposome in combination with the genetic variability in relation to the disease susceptibility. AIM: The aim of this study was to analyze the exposome and its interaction with known genetic susceptibility loci, in relation to PDAC risk. METHODS: A case-control study nested in UK Biobank cohort was conducted on 816 PDAC cases and 302,645 controls. A total of 347 exposure variables, and a polygenic risk score (PRS) were analyzed through logistic regression. Gene-environment interaction analyses were conducted. RESULTS: A total of 52 associations under the Bonferroni corrected threshold of p < 1.46 × 10-4 were observed. Known risk factors such as smoking, pancreatitis, diabetes, PRS, heavy alcohol drinking and overweight were replicated in this study. As for novel associations, a clear indication for length and intensity of mobile phone use and the stress-related factors and stressful events with increase of PDAC risk was observed. Although the PRS was associated with PDAC risk (P = 2.09 × 10-9), statistically significant gene-exposome interactions were not identified. CONCLUSION: In conclusion, our results suggest that a stressful lifestyle and sedentary behaviors may play a major role in PDAC susceptibility independently from the genetic background.