The relationship between dietary fat intake and risk of colorectal cancer: evidence from the combined analysis of 13 case-control studies

The objective of this study was to examine the effects of the intakeof dietary fat upon colorectal cancer risk in a combined analysis of datafrom 13 case-control studies previously conducted in populations withdiffering colorectal cancer rates and dietary practices. Original datarecords for 5,287 cases of colorectal cancer and 10,470 controls werecombined. Logistic regression analysis was used to estimate odds ratios (OR)for intakes of total energy, total fat and its components, and cholesterol.Positive associations with energy intake were observed for 11 of the 13studies. However, there was little, if any, evidence of anyenergy-independent effect of either total fat with ORs of 1.00, 0.95, 1.01,1.02, and 0.92 for quintiles of residuals of total fat intake (P trend =0.67) or for saturated fat with ORs of 1.00, 1.08, 1.06, 1.21, and 1.06 (Ptrend = 0.39). The analysis suggests that, among these case-control studies,there is no energy-independent association between dietary fat intake andrisk of colorectal cancer. It also suggests that simple substitution of fatby other sources of calories is unlikely to reduce meaningfully the risk ofcolorectal cancer.Facultad de Ciencias Médica

Anonymisation of geographical distance matrices via Lipschitz embedding

BACKGROUND: Anonymisation of spatially referenced data has received increasing attention in recent years. Whereas the research focus has been on the anonymisation of point locations, the disclosure risk arising from the publishing of inter-point distances and corresponding anonymisation methods have not been studied systematically. METHODS: We propose a new anonymisation method for the release of geographical distances between records of a microdata file-for example patients in a medical database. We discuss a data release scheme in which microdata without coordinates and an additional distance matrix between the corresponding rows of the microdata set are released. In contrast to most other approaches this method preserves small distances better than larger distances. The distances are modified by a variant of Lipschitz embedding. RESULTS: The effects of the embedding parameters on the risk of data disclosure are evaluated by linkage experiments using simulated data. The results indicate small disclosure risks for appropriate embedding parameters. CONCLUSION: The proposed method is useful if published distance information might be misused for the re-identification of records. The method can be used for publishing scientific-use-files and as an additional tool for record-linkage studies

Beyond the Symptom: The Biology of Fatigue

A workshop titled Beyond the Symptom: The Biology of Fatigue was held virtually September 27-28, 2021. It was jointly organized by the Sleep Research Society and the Neurobiology of Fatigue Working Group of the NIH Blueprint Neuroscience Research Program. For access to the presentations and video recordings, see: https://neuroscienceblueprint.nih.gov/about/event/beyond-symptom-biology-fatigue. The goals of this workshop were to bring together clinicians and scientists who use a variety of research approaches to understand fatigue in multiple conditions and to identify key gaps in our understanding of the biology of fatigue. This workshop summary distills key issues discussed in this workshop and provides a list of promising directions for future research on this topic. We do not attempt to provide a comprehensive review of the state of our understanding of fatigue, nor to provide a comprehensive reprise of the many excellent presentations. Rather, our goal is to highlight key advances and to focus on questions and future approaches to answering them

Genome-wide linkage analyses of non-Hispanic white families identify novel loci for familial late-onset Alzheimer's disease

INTRODUCTION: Few high penetrance variants that explain risk in late-onset Alzheimer's disease (LOAD) families have been found. METHODS: We performed genome-wide linkage and identity-by-descent (IBD) analyses on 41 non-Hispanic white families exhibiting likely dominant inheritance of LOAD, and having no mutations at known familial Alzheimer's disease (AD) loci, and a low burden of APOE ε4 alleles. RESULTS: Two-point parametric linkage analysis identified 14 significantly linked regions, including three novel linkage regions for LOAD (5q32, 11q12.2-11q14.1, and 14q13.3), one of which replicates a genome-wide association LOAD locus, the MS4A6A-MS4A4E gene cluster at 11q12.2. Five of the 14 regions (3q25.31, 4q34.1, 8q22.3, 11q12.2-14.1, and 19q13.41) are supported by strong multipoint results (logarithm of odds [LOD*] ≥1.5). Nonparametric multipoint analyses produced an additional significant locus at 14q32.2 (LOD* = 4.18). The 1-LOD confidence interval for this region contains one gene, C14orf177, and the microRNA Mir_320, whereas IBD analyses implicates an additional gene BCL11B, a regulator of brain-derived neurotrophic signaling, a pathway associated with pathogenesis of several neurodegenerative diseases. DISCUSSION: Examination of these regions after whole-genome sequencing may identify highly penetrant variants for familial LOAD

Modeling Insertional Mutagenesis Using Gene Length and Expression in Murine Embryonic Stem Cells

Background. High-throughput mutagenesis of the mammalian genome is a powerful means to facilitate analysis of gene function. Gene trapping in embryonic stem cells (ESCs) is the most widely used form of insertional mutagenesis in mammals. However, the rules governing its efficiency are not fully understood, and the effects of vector design on the likelihood of genetrapping events have not been tested on a genome-wide scale. Methodology/Principal Findings. In this study, we used public gene-trap data to model gene-trap likelihood. Using the association of gene length and gene expression with gene-trap likelihood, we constructed spline-based regression models that characterize which genes are susceptible and which genes are resistant to gene-trapping techniques. We report results for three classes of gene-trap vectors, showing that both length and expression are significant determinants of trap likelihood for all vectors. Using our models, we also quantitatively identifie