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Introduction Diet has a major impact on modulating the risk and severity of a number of chronic diseases including obesity and obesity-related metabolic disorders, cardiovascular diseases and cancer. Among macronutrients, [...], Objectives: This paper describes the dietary intake of total fat, saturated (SFA), monounsaturated (MUFA) and polyunsaturated fatty acids (PUFA) and cholesterol of participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) in 27 centres across 10 countries. Methods: Between 1995 and 2000, a stratified random sample of 36 034 participants (age range 35-74 years) completed a standardized 24-h dietary recall, assessed by means of the computer software EPIC-SOFT. Lipid intake data were calculated using a standardized nutrient database. Results: On average, the contribution of fat to total energy intake was ≥ 34% of energy intake (%en) in women and ≥ 36%en in men for most EPIC centres, except for the British, Dutch and most Italian cohorts. Total fat (440%en) and MUFA intakes (21%en, mainly from olive oil) were highest in Greece. Except for the Greek, Spanish and Italian centres, the average MUFA intake ranged between 10 and 13%en, with a high proportion derived from animal sources. SFA intake in women and men was lowest in the Greek, Spanish, Italian and UK cohorts with an average of ≤ 13%en (down to 9%en), and highest in the Swedish centres (16%en). The mean PUFA intake was in the range of 4-8%en, being highest in the UK health-conscious cohort. The average cholesterol intake across EPIC varied from 140 to 384 mg/d in women and 215-583 mg/d in men. Conclusions: The presented data show differences and similarities in lipid intake across the European EPIC cohorts and also show differences in food sources of dietary lipids. doi: 10.1038/ejcn.2009.75 Keywords: EPIC; 24-h diet recalls; dietary intake; lipids; EPIC-Soft; ENDB

Introduction Dietary protein is an important macronutrient, contributing to around 15-20% of the total dietary energy intake in Western countries (CDC, 2004; Elmadfa and Weichselbaum, 2005). Protein, which contributes essential [...], Objective: To describe dietary protein intakes and their food sources among 27 redefined centres in 10 countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC). Methods: Between 1995 and 2000, 36 034 persons, aged between 35 and 74 years, were administered a standardized 24-h dietary recall (24-HDR) using a computerized interview software programme (EPIC-SOFT). Intakes (g/day) of total, animal and plant proteins were estimated using the standardized EPIC Nutrient Database (ENDB). Mean intakes were adjusted for age, and weighted by season and day of recall. Results: Mean total and animal protein intakes were highest in the Spanish centres among men, and in the Spanish and French centres among women; the lowest mean intakes were observed in the UK health-conscious group, in Greek men and women, and in women in Potsdam. Intake of plant protein was highest among the UK health-conscious group, followed by some of the Italian centres and Murcia, whereas Sweden and Potsdam had the lowest intake. Cereals contributed to the highest proportion of plant protein in all centres. The combined intake of legumes, vegetables and fruit contributed to a greater proportion of plant protein in the southern than in the northern centres. Total meat intake (with some heterogeneity across subtypes of meat) was, with few exceptions, the most important contributor to animal protein in all centres, followed by dairy and fish products. Conclusions: This study shows that intake of protein, especially of animal origin, differs across the 10 European countries, and also shows some differences in food sources of protein across Europe. doi: 10.1038/ejcn.2009.73 Keywords: total proteins; animal proteins; plant proteins; EPIC; 24-h dietary recall; ENDB

Stroke, of both ischemic and hemorrhagic origin, is one of the leading causes of mortality and disability worldwide, with severe consequences at the personal and socio-economic levels. This is a review of the main risk factors for stroke, which can be divided in modifiable and non-modifiable, with a summary of the scientific evidence accumulated from relevant studies on each of these risk factors. The non-modifiable risk factors are age, gender, family history, race and previous stroke or myocardial infarction. These factors cannot be changed, but their presence helps in the identification of people at high risk and intensification of efforts to modify those risk factors that can be changed. Modifiable risk factors include high blood pressure, use of tobacco, obesity, diabetes mellitus, hypercholesterolemia, alcohol consumption, dietary factors, physical inactivity and others. This review also focuses on diet as a risk factor for stroke; on the separate food groups as well as dietary patterns, as recent increased research activity in this area has produced notable findings. Various risk factors increase the risk of stroke independently, but also interact with each other, increasing the risk even further. The detection and more efficient management of these risk factors in combination with improvements in acute medical care and advances in diagnostic imaging and treatment can play an important role in the decrease of stroke incidence and mortality. [ABSTRACT FROM AUTHOR]

HMG-CoA reductase inhibitors ('statins') represent the most effective and widely prescribed drugs currently available for the reduction of low-density lipoprotein cholesterol, a critical therapeutic target for primary and secondary prevention of cardiovascular atherosclerotic disease. In the face of the established lipid lowering and the emerging pleiotropic properties of statins, the patient population suitable for long-term statin treatment is expected to further expand. An overall positive safety and tolerability profile of statins has been established, although adverse events have been reported. Skeletal muscle-related events are the most common adverse events of statin treatment. Statin-induced myopathy can (rarely) manifest with severe and potentially fatal cases of rhabdomyolysis, thus rendering the identification of the underlying predisposing factors critical. The purpose of this review is to summarize the factors that increase the risk of statin-related myopathy. Data from published clinical trials, meta-analyses, postmarketing studies, spontaneous report systems and case reports for rare effects were reviewed. Briefly, the epidemiology, clinical spectrum and molecular mechanisms of statin-associated myopathy are discussed. We further analyse in detail the risk factors that precipitate or increase the likelihood of statin-related myopathy. Individual demographic features, genetic factors and co-morbidities that may account for the significant interindividual variability in the myopathic risk are presented. Physicochemical properties of statins have been implicated in the differential risk of currently marketed statins. Pharmacokinetic interactions with concomitant medications that interfere with statin metabolism and alter their systemic bioavailability are reviewed. Of particular clinical interest in cases of resistant dyslipidaemia is the interaction of statins with other classes of lipid-lowering agents; current data on the relative safety of available combinations are summarized. Finally, we provide an update of current guidelines for the prevention and management of statin myopathy. The identification of patients with an increased proclivity to statin-induced myopathy could allow more cost-effective approaches of monitoring and screening, facilitate targeted prevention of potential complications, and further improve the already overwhelmingly positive benefit-risk ratio of statins. [ABSTRACT FROM AUTHOR]

BACKGROUND: Some evidence indicates that a low selenium intake may be associated with an increased risk of prostate cancer. OBJECTIVE: The aim of this study was to investigate the association of plasma selenium concentration with subsequent prostate cancer risk and to examine this association by stage and grade of disease and other factors. DESIGN: A nested case-control study was performed among men in the European Prospective Investigation into Cancer and Nutrition (EPIC). The association between plasma selenium concentration and prostate cancer risk was assessed in 959 men with incident prostate cancer and 1059 matched controls. RESULTS: Overall, plasma selenium concentration was not associated with prostate cancer risk; the multivariate relative risk for men in the highest fifth of selenium concentration compared with the lowest fifth was 0.96 (95% CI: 0.70, 1.31; P for trend = 0.25). There were no significant differences in the association of plasma selenium with risk when analyzed by stage or grade of disease. Similarly, the association of selenium with risk did not differ by smoking status or by plasma alpha- or gamma-tocopherol concentration. CONCLUSION: Plasma selenium concentration was not associated with prostate cancer risk in this large cohort of European men. Copyright © 2008 American Society for Nutrition [ABSTRACT FROM AUTHOR]

The use of microorganisms for the production of industrially important compounds and enzymes is becoming increasingly important. Eukaryotes have been less widely used than prokaryotes in biotechnology, because of the complexity of their genomic structure and biology. The Yeast2.0 project is an international effort to engineer the yeast Saccharomyces cerevisiae to make it easy to manipulate, and to generate random variants using a system called SCRaMbLE. SCRaMbLE relies on artificial evolution in vitro to identify useful variants, an approach which is time consuming and expensive. We developed an in silico simulator for the SCRaMbLE system, using an evolutionary computing approach, which can be used to investigate and optimize the fitness landscape of the system. We applied the system to the investigation of the fitness landscape of one of the S. saccharomyces chromosomes, and found that our results fitted well with those previously published. We then simulated directed evolution with or without manipulation of SCRaMbLE, and revealed that controlling the SCRaMbLE process could effectively impact directed evolution. Our simulator can be applied to the analysis of the fitness landscapes of any organism for which SCRaMbLE has been implemented., (Copyright © 2022 Elsevier B.V. All rights reserved.)

People who engineer biological organisms often find it useful to draw diagrams in order to communicate both the structure of the nucleic acid sequences that they are engineering and the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. SBOL Visual aims to organize and systematize such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 3.0 of SBOL Visual, a new major revision of the standard. The major difference between SBOL Visual 3 and SBOL Visual 2 is that diagrams and glyphs are defined with respect to the SBOL 3 data model rather than the SBOL 2 data model. A byproduct of this change is that the use of dashed undirected lines for subsystem mappings has been removed, pending future determination on how to represent general SBOL 3 constraints; in the interim, this annotation can still be used as an annotation. Finally, deprecated material has been removed from collection of glyphs: the deprecated "insulator" glyph and "macromolecule" alternative glyphs have been removed, as have the deprecated BioPAX alternatives to SBO terms., (© 2021 Hasan Baig et al., published by De Gruyter, Berlin/Boston.)

A standardized approach to annotating computational biomedical models and their associated files can facilitate model reuse and reproducibility among research groups, enhance search and retrieval of models and data, and enable semantic comparisons between models. Motivated by these potential benefits and guided by consensus across the COmputational Modeling in BIology NEtwork (COMBINE) community, we have developed a specification for encoding annotations in Open Modeling and EXchange (OMEX)-formatted archives. This document details version 1.2 of the specification, which builds on version 1.0 published last year in this journal. In particular, this version includes a set of initial model-level annotations (whereas v 1.0 described exclusively annotations at a smaller scale). Additionally, this version uses best practices for namespaces, and introduces omex-library.org as a common root for all annotations. Distributing modeling projects within an OMEX archive is a best practice established by COMBINE, and the OMEX metadata specification presented here provides a harmonized, community-driven approach for annotating a variety of standardized model representations. This specification acts as a technical guideline for developing software tools that can support this standard, and thereby encourages broad advances in model reuse, discovery, and semantic analyses., (© 2021 John H. Gennari et al., published by De Gruyter, Berlin/Boston.)

Background: Targeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR)., Methods: In this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18-74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions., Findings: We included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65-74 years), adults in the youngest age group (18-24 years) had the highest OR (4·22 [95% CI 3·86-4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06-5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23-6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18-1·27), for men versus women was 1·12 (1·08-1·16), and for Black individuals versus White individuals was 1·13 (1·04-1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period., Interpretation: A radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18-24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care., Funding: The British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research., Competing Interests: Declaration of interests RLB reports consulting fees from Novo Nordisk, ViiV Healthcare, Pfizer, and Gila Therapeutics; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Novo Nordisk, ViiV Healthcare, International Medical Press, and Medscape; participation on a data safety monitoring board or advisory board for Novo Nordisk, Pfizer, and ViiV Healthcare; being a committee member of the British Obesity and Metabolic Surgery Society, a trustee for the Association for the Study of Obesity, a scientific chair of the International Federation for the Surgery for Obesity (IFSO) and metabolic disorders European Chapter, a chair of the Royal College of Physicians advisory Committee on Nutrition, Weight and Health, an European Society of Endocrinology clinical committee member, and a trustee of Obesity Empowerment Network UK; being a member of the IFSO scientific committee; being a member of the NICE Weight Management Guideline Development Group; and being a principal investigator on two obesity clinical trials of cagrilintide versus placebo and semaglutide versus placebo (sponsored by Novo Nordisk), and one clinical trial of liraglutide versus placebo (both drugs were provided by Novo Nordisk). AB reports grants from Astra Zeneca. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.3 of SBOL Visual, which builds on the prior SBOL Visual 2.2 in several ways. First, the specification now includes higher-level "interactions with interactions," such as an inducer molecule stimulating a repression interaction. Second, binding with a nucleic acid backbone can be shown by overlapping glyphs, as with other molecular complexes. Finally, a new "unspecified interaction" glyph is added for visualizing interactions whose nature is unknown, the "insulator" glyph is deprecated in favor of a new "inert DNA spacer" glyph, and the polypeptide region glyph is recommended for showing 2A sequences., (© 2021 Hasan Baig et al., published by De Gruyter, Berlin/Boston.)

Synthetic biology builds upon genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. When designing a synthetic system, synthetic biologists need to exchange information about multiple types of molecules, the intended behavior of the system, and actual experimental measurements. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, following an open community process involving both wet bench scientists and dry scientific modelers and software developers, across academia, industry, and other institutions. This document describes SBOL 3.0.0, which condenses and simplifies previous versions of SBOL based on experiences in deployment across a variety of scientific and industrial settings. In particular, SBOL 3.0.0, (1) separates sequence features from part/sub-part relationships, (2) renames Component Definition/Component to Component/Sub-Component, (3) merges Component and Module classes, (4) ensures consistency between data model and ontology terms, (5) extends the means to define and reference Sub-Components, (6) refines requirements on object URIs, (7) enables graph-based serialization, (8) moves Systems Biology Ontology (SBO) for Component types, (9) makes all sequence associations explicit, (10) makes interfaces explicit, (11) generalizes Sequence Constraints into a general structural Constraint class, and (12) expands the set of allowed constraints.

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.2 of SBOL Visual, which builds on the prior SBOL Visual 2.1 in several ways. First, the grounding of molecular species glyphs is changed from BioPAX to SBO, aligning with the use of SBO terms for interaction glyphs. Second, new glyphs are added for proteins, introns, and polypeptide regions (e. g., protein domains), the prior recommended macromolecule glyph is deprecated in favor of its alternative, and small polygons are introduced as alternative glyphs for simple chemicals.

Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems is to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.3.0 of SBOL, which builds upon version 2.2.0 published in last year's JIB Standards in Systems Biology special issue. In particular, SBOL 2.3.0 includes means of succinctly representing sequence modifications, such as insertion, deletion, and replacement, an extension to support organization and attachment of experimental data derived from designs, and an extension for describing numerical parameters of design elements. The new version also includes specifying types of synthetic biology activities, unambiguous locations for sequences with multiple encodings, refinement of a number of validation rules, improved figures and examples, and clarification on a number of issues related to the use of external ontology terms.

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.2.0 of SBOL that builds upon version 2.1.0 published in last year's JIB special issue. In particular, SBOL 2.2.0 includes improved description and validation rules for genetic design provenance, an extension to support combinatorial genetic designs, a new class to add non-SBOL data as attachments, a new class for genetic design implementations, and a description of a methodology to describe the entire design-build-test-learn cycle within the SBOL data model.