123 research outputs found

    Serum metabolites associated with wholegrain consumption using nontargeted metabolic profiling: a discovery and reproducibility study

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    Purpose: To identify fasting serum metabolites associated with WG intake in a free-living population adjusted for potential confounders. Methods: We selected fasting serum samples at baseline from a subset (n = 364) of the prospective population-based Kuopio Ischaemic Heart Disease Risk Factor Study (KIHD) cohort. The samples were analyzed using nontargeted metabolomics with liquid chromatography coupled with mass spectrometry (LC–MS). Association with WG intake was investigated using both random forest followed by linear regression adjusted for age, BMI, smoking, physical activity, energy and alcohol consumption, and partial Spearman correlation adjusted for the same covariates. Features selected by any of these models were shortlisted for annotation. We then checked if we could replicate the findings in an independent subset from the same cohort (n = 200). Results: Direct associations were observed between WG intake and pipecolic acid betaine, tetradecanedioic acid, four glucuronidated alkylresorcinols (ARs), and an unknown metabolite both in discovery and replication cohorts. The associations remained significant (FDR<0.05) even after adjustment for the confounders in both cohorts. Sinapyl alcohol was positively correlated with WG intake in both cohorts after adjustment for the confounders but not in linear models in the replication cohort. Some microbial metabolites, such as indolepropionic acid, were positively correlated with WG intake in the discovery cohort, but the correlations were not replicated in the replication cohort. Conclusions: The identified associations between WG intake and the seven metabolites after adjusting for confounders in both discovery and replication cohorts suggest the potential of these metabolites as robust biomarkers of WG consumption

    Decreased progesterone receptor isoform expression in luteal phase fallopian tube epithelium and high-grade serous carcinoma

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    We previously reported that BRCA1/2-mutated fallopian tube epithelium (FTE) collected during the luteal phase exhibits gene expression profiles more closely resembling that of high-grade serous carcinoma (HGSC) specimens than FTE collected during the follicular phase or from control patients. Since the luteal phase is characterised by high levels of progesterone, we determined whether the expression of progesterone receptor (PR) and PR-responsive genes was altered in FTE obtained from BRCA mutation carriers during the luteal phase of the menstrual cycle. RT-qPCR confirmed a decreased expression of PR mRNA in FTE during the luteal phase relative to follicular phase, in both BRCA1/2 mutation carriers and control patients. Immunohistochemistry using isoform-specific antibodies confirmed a low level of both PR-A and PR-B in HGSC and a lower level of staining in FTE samples obtained during the luteal phase compared with the follicular phase. No significant difference in PR-A or PR-B staining was found based on patient BRCA mutation status. Analysis of our previously reported gene expression profiles based upon known PR-A- and PR-B-specific target genes did not partition samples by BRCA mutation status, indicating that overall FTE PR response is not altered in BRCA mutation carriers. HGSC samples grouped separately from other samples, consistent with the observed loss of PR expression. These findings indicate no overall difference in PR signalling in FTE as a function of BRCA mutation status. Thus, the molecular similarity of BRCA1/2-mutated luteal phase FTE and HGSC likely results from an altered response to luteal phase factors other than progesterone

    Serum metabolites associated with wholegrain consumption using nontargeted metabolic profiling: a discovery and reproducibility study

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    Purpose To identify fasting serum metabolites associated with WG intake in a free-living population adjusted for potential confounders. Methods We selected fasting serum samples at baseline from a subset (n = 364) of the prospective population-based Kuopio Ischaemic Heart Disease Risk Factor Study (KIHD) cohort. The samples were analyzed using nontargeted metabolomics with liquid chromatography coupled with mass spectrometry (LC-MS). Association with WG intake was investigated using both random forest followed by linear regression adjusted for age, BMI, smoking, physical activity, energy and alcohol consumption, and partial Spearman correlation adjusted for the same covariates. Features selected by any of these models were shortlisted for annotation. We then checked if we could replicate the findings in an independent subset from the same cohort (n = 200). Results Direct associations were observed between WG intake and pipecolic acid betaine, tetradecanedioic acid, four glucuronidated alkylresorcinols (ARs), and an unknown metabolite both in discovery and replication cohorts. The associations remained significant (FDRConclusions The identified associations between WG intake and the seven metabolites after adjusting for confounders in both discovery and replication cohorts suggest the potential of these metabolites as robust biomarkers of WG consumption.</p

    Microarray-based DNA methylation profiling: technology and applications

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    This work is dedicated to the development of a technology for unbiased, high-throughput DNA methylation profiling of large genomic regions. In this method, unmethylated and methylated DNA fractions are enriched using a series of treatments with methylation sensitive restriction enzymes, and interrogated on microarrays. We have investigated various aspects of the technology including its replicability, informativeness, sensitivity and optimal PCR conditions using microarrays containing oligonucleotides representing 100 kb of genomic DNA derived from the chromosome 22 COMT region in addition to 12 192 element CpG island microarrays. Several new aspects of methylation profiling are provided, including the parallel identification of confounding effects of DNA sequence variation, the description of the principles of microarray design for epigenomic studies and the optimal choice of methylation sensitive restriction enzymes. We also demonstrate the advantages of using the unmethylated DNA fraction versus the methylated one, which substantially improve the chances of detecting DNA methylation differences. We applied this methodology for fine-mapping of methylation patterns of chromosomes 21 and 22 in eight individuals using tiling microarrays consisting of over 340 000 oligonucleotide probe pairs. The principles developed in this work will help to make epigenetic profiling of the entire human genome a routine procedure

    Exome-wide association study of pancreatic cancer risk

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    We conducted a case-control exome-wide association study to discover germline variants in coding regions that affect risk for pancreatic cancer, combining data from 5 studies. We analyzed exome and genome sequencing data from 437 patients with pancreatic cancer (cases) and 1922 individuals not known to have cancer (controls). In the primary analysis, BRCA2 had the strongest enrichment for rare inactivating variants (17/437 cases vs 3/1922 controls) (P=3.27x10(-6); exome-wide statistical significance threshold P&lt;2.5x10(-6)). Cases had more rare inactivating variants in DNA repair genes than controls, even after excluding 13 genes known to predispose to pancreatic cancer (adjusted odds ratio, 1.35, P=.045). At the suggestive threshold (P&lt;.001), 6 genes were enriched for rare damaging variants (UHMK1, AP1G2, DNTA, CHST6, FGFR3, and EPHA1) and 7 genes had associations with pancreatic cancer risk, based on the sequence-kernel association test. We confirmed variants in BRCA2 as the most common high-penetrant genetic factor associated with pancreatic cancer and we also identified candidate pancreatic cancer genes. Large collaborations and novel approaches are needed to overcome the genetic heterogeneity of pancreatic cancer predisposition

    The Ontario Data Safe Haven: Bringing High Performance Computing to Population-wide Data Assets

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    Introduction Canadian provincial health systems have a data advantage – longitudinal population-wide data for publicly funded health services, in many cases going back 20 years or more. With the addition of high performance computing (HPC), these data can serve as the foundation for leading-edge research using machine learning and artificial intelligence. Objectives and Approach The Institute for Clinical Evaluative Sciences (ICES) and HPC4Health are creating the Ontario Data Safe Haven (ODSH) – a secure HPC cloud located within the HPC4Health physical environment at the Hospital for Sick Children in Toronto. The ODSH will allow research teams to post, access and analyze individual datasets over which they have authority, and enable linkage to Ontario administrative and other data. To start, the ODSH is focused on creating a private cloud meeting ICES’ legislated privacy and security requirements to support HPC-intensive analyses of ICES data. The first ODSH projects are partnerships between ICES scientists and machine learning. Results As of March 2018, the technological build of the ODSH was tested and completed and the privacy and security policy framework and documentation were completed. We will present the structure of the ODSH, including the architectural choices made when designing the environment, and planned functionality in the future. We will describe the experience to-date for the very first analysis done using the ODSH: the automatic mining of clinical terminology in primary care electronic medical records using deep neural networks. We will also present the plans for a high-cost user Risk Dashboard program of research, co-designed by ICES scientists and health faculty from the Vector Institute for artificial intelligence, that will make use of the ODSH beginning May 2018. Conclusion/Implications Through a partnership of ICES, HPC4Health and the Vector Institute, a secure private cloud ODSH has been created as is starting to be used in leading edge machine learning research studies that make use of Ontario’s population-wide data assets

    Exposure to p,p′-DDE: A Risk Factor for Type 2 Diabetes

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    BACKGROUND: Persistent organic pollutants (POPs), such as PCBs, DDT and dioxins have in several cross-sectional studies shown strong associations with type 2 diabetes mellitus. Reversed causality can however not be excluded. The aim of this case-control study was to evaluate whether POPs concentration is a risk factor for type 2 diabetes. METHODOLOGY/PRINCIPAL FINDINGS: A case-control study was performed within a well-defined cohort of women, age 50-59 years, from the Southern part of Sweden. Biomarkers for POP exposure, 2,2',4,4',5,5'-hexachlorobiphenyl (CB-153) and 1,1-dichloro-2,2-bis (p-chlorophenyl)-ethylene (p,p'-DDE) were analyzed in stored serum samples, which were collected at the baseline examination when the cohort was established. For 107 out of the 371 cases, serum samples were stored at least three years before their type 2 diabetes was diagnosed. In this data set, CB-153 and p,p'-DDE were not associated with an increased risk to develop type 2 diabetes. However, when only the cases (n = 39) that were diagnosed more than six years after the baseline examination and their controls were studied, the women in the highest exposed quartile showed an increased risk to develop type 2 diabetes (OR of 1.6 [95% 0.61, 4.0] for CB-153 and 5.5 [95% CI 1.2, 25] for p,p'-DDE). CONCLUSIONS/SIGNIFICANCE: The results from the present case-control study, including a follow-up design, confirms that p,p'-DDE exposure can be a risk factor for type 2 diabetes
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