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1.
Moving beyond inclusion: Methodological considerations for the menstrual cycle and menopause in research evaluating effects of dietary nitrate on vascular function.
Baranauskas, MN, Freemas, JA, Tan, R, Carter, SJ
Nitric oxide : biology and chemistry. 2022;:39-48
Abstract
Recent reports have acknowledged the underrepresentation of women in the field of dietary nitrate (NO3-) research. Undoubtedly, greater participation from women is warranted to clarify potential sex differences in the responses to dietary NO3- interventions. However, careful consideration for the effects of sex hormones - principally 17β-estradiol - on endogenous nitric oxide (NO) synthesis and dietary NO3- reductase capacity is necessary for improved interpretation and reproducibility of such investigations. From available literature, we present a narrative review describing how hormonal variations across the menstrual cycle, as well as with menopause, may impact NO biosynthesis catalyzed by NO synthase enzymes and NO3- reduction via the enterosalivary pathway. In doing so, we address methodological considerations related to the menstrual cycle and hormonal contraceptive use relevant for the inclusion of premenopausal women along with factors to consider when testing postmenopausal women. Adherence to such methodological practices may explicate the utility of dietary NO3- supplementation as a means to improve vascular function among women across the lifespan.
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Use of Facebook, Instagram, and Twitter for recruiting healthy participants in nutrition-, physical activity-, or obesity-related studies: a systematic review.
Ellington, M, Connelly, J, Clayton, P, Lorenzo, CY, Collazo-Velazquez, C, Trak-Fellermeier, MA, Palacios, C
The American journal of clinical nutrition. 2022;(2):514-533
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Abstract
BACKGROUND There are limited systematic reviews exploring the use of social media for recruiting participants specifically for nutrition-, physical activity-, and obesity-related studies. OBJECTIVES The aim was to conduct a systematic review on the effectiveness of using social media (Facebook, Instagram, and Twitter) for recruiting healthy participants in nutrition-, physical activity-, or obesity-related studies. METHODS Studies were identified from 5 databases and included if they reported the number of participants recruited by social media (Facebook, Instagram, or Twitter) vs. traditional (print, e-mail, etc.). The effectiveness of recruitment was compared between methods by study procedures (in-person vs. online procedures). The cost-effectiveness of methods was also explored. The protocol was published in the Prospero database (ID# CRD42020204414). RESULTS Twenty-six studies were included. Among studies with both types of recruitment methods, 49% of the sample was reached through traditional methods, 40% through social media, and the rest by other methods. For in-person study procedures, the median number of participants recruited using social media was 19 (range: 3-278) and for online study procedures, it was 298 (range: 3-17,069). Median recruitment cost using social media (n = 14 studies) was $11.90 (range: $0-517) per participant, while this varied considerably for traditional methods depending on how it was calculated ($214, $18.9-$777). The ratio of participants reached vs. recruited was 0.12%; the overall ratio of participants interactions vs. recruited was 21.2%. CONCLUSIONS For in-person study procedures, traditional recruitment methods were more effective than social media, but for online study procedures, about half reported that social media was more effective. While more potential participants were reached through social media, only 21.2% of those who interacted with ads were enrolled. With the increased use of social media, their use for recruitment may be more frequent; therefore, future reviews may show different results.
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Global Characteristics and Trends in Research on Ferroptosis: A Data-Driven Bibliometric Study.
Dong, X, Tan, Y, Zhuang, D, Hu, T, Zhao, M
Oxidative medicine and cellular longevity. 2022;:8661864
Abstract
Ferroptosis, an iron-dependent form of regulated cell death, has drawn an increasing amount of attention since it was first mentioned in 2012 and is found to play a significant role in the treatment of certain diseases. Our study is aimed at analysing the scientific output of ferroptosis research and at driving future research into novel publications. Publications focused on ferroptosis were retrieved from the SCI-EXPANDED database of the Web of Science Core Collection and were screened according to inclusion criteria. CiteSpace V and Microsoft Excel 2016 were used to evaluate and visualize the results, including generating network maps and analysing annual publications, country, category, references and cocited references, and keywords. As of October 1, 2021, a total of 1690 original articles related to ferroptosis were included, and the overall trend of the number of publications rapidly increased. Among the common categories in the field of ferroptosis, the most common category was biochemistry and molecular biology. Worldwide, China and the United States were the leading countries for research production. The retrieved 1690 publications received 44,650 citations, with an average of 26.42 citations per paper (October 1, 2021). By citation analysis, Scott J Dixon's article in 2012 was the most symbolic reference and the earliest publication in the field of ferroptosis, with the highest citation rate (2709 times). Among the most common keywords, most were related to the mechanisms and regulatory networks of ferroptosis. Furthermore, with accumulating evidence demonstrating the role of ferroptosis in cancers and other diseases, inducing ferroptosis in clinical treatment is becoming a new research focus that should be closely monitored.
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A framework for microbiome science in public health.
Wilkinson, JE, Franzosa, EA, Everett, C, Li, C, , , , , Hu, FB, Wirth, DF, Song, M, Chan, AT, et al
Nature medicine. 2021;(5):766-774
Abstract
Human microbiome science has advanced rapidly and reached a scale at which basic biology, clinical translation and population health are increasingly integrated. It is thus now possible for public health researchers, practitioners and policymakers to take specific action leveraging current and future microbiome-based opportunities and best practices. Here we provide an outline of considerations for research, education, interpretation and scientific communication concerning the human microbiome and public health. This includes guidelines for population-scale microbiome study design; necessary physical platforms and analysis methods; integration into public health areas such as epidemiology, nutrition, chronic disease, and global and environmental health; entrepreneurship and technology transfer; and educational curricula. Particularly in the near future, there are both opportunities for the incorporation of microbiome-based technologies into public health practice, and a growing need for policymaking and regulation around related areas such as prebiotic and probiotic supplements, novel live-cell therapies and fecal microbiota transplants.
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The Immune System in Human Milk: A Historic Perspective.
Goldman, AS, Chheda, S
Annals of nutrition & metabolism. 2021;(4):189-196
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BACKGROUND Human milk contains a remarkable array of immunological agents that evolved over millions of years to protect the recipient human infant. Furthermore, much of the protection persists long after weaning. However, the scientists who first discovered some components of this immune system have rarely been acknowledged. SUMMARY The scientists who made many fundamental immunological discoveries concerning the immune system in human milk include Alfred François Donné, Paul Ehrlich, Lars Å. Hanson, and Jules Bordet. Based upon their discoveries, a wealth of antimicrobial, anti-inflammatory, and immunomodulating agents, and living, activated leukocytes in human milk were later revealed during the last half of the 20th and the first part of the 21st century. Moreover, it was found that human milk enhances the colonization of commensal bacteria that aid to protect the human infant. Key Message: Their discoveries helped to revitalize breastfeeding in industrialized countries during the past several decades.
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Global mapping of overviews of systematic reviews in healthcare published between 2000 and 2020: a bibliometric analysis.
Bougioukas, KI, Vounzoulaki, E, Mantsiou, CD, Papanastasiou, GD, Savvides, ED, Ntzani, EE, Haidich, AB
Journal of clinical epidemiology. 2021;:58-72
Abstract
OBJECTIVE To conduct a bibliometric analysis using a large sample of overviews of systematic reviews (OoSRs) and reveal research trends and areas of interest about these studies. STUDY DESIGN AND SETTING We searched MEDLINE, Scopus and Cochrane Database of Systematic Reviews from 1/1/2000 to 15/10/2020. We used Scopus meta-data and two authors recorded supplementary information independently. We summarized the data using frequencies with percentages. RESULTS A total of 1558 studies were considered eligible for analysis. We found that the publications have been increasing yearly and their nomenclature was not uniform (the most frequent label in the title was "overview of systematic reviews"). The largest number of papers and the most cited ones were published by corresponding authors from the UK. The publications were distributed across 737 scholarly journals and many of them were published in the field of complementary/alternative medicine, psychiatry/psychology, nutrition/dietetics, and pediatrics. The co-authorship analysis revealed collaborations among countries. The most common clinical conditions were depression, diabetes, cancer, dementia, pain, cardiovascular disease, stroke, obesity, and schizophrenia. CONCLUSION OoSRs have recently become a popular approach of evidence synthesis. International collaborations between overview authors from countries with increased research productivity and countries with less research activity should be encouraged.
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Examining variation in the measurement of multimorbidity in research: a systematic review of 566 studies.
Ho, IS, Azcoaga-Lorenzo, A, Akbari, A, Black, C, Davies, J, Hodgins, P, Khunti, K, Kadam, U, Lyons, RA, McCowan, C, et al
The Lancet. Public health. 2021;(8):e587-e597
Abstract
BACKGROUND A systematic understanding of how multimorbidity has been constructed and measured is unavailable. This review aimed to examine the definition and measurement of multimorbidity in peer-reviewed studies internationally. METHODS We systematically reviewed studies on multimorbidity, via a search of nine bibliographic databases (Ovid [PsycINFO, Embase, Global Health, and MEDLINE], Web of Science, the Cochrane Library, CINAHL Plus, Scopus, and ProQuest Dissertations & Theses Global), from inception to Jan 21, 2020. Reference lists and tracked citations of retrieved articles were hand-searched. Eligible studies were full-text articles measuring multimorbidity for any purpose in community, primary care, care home, or hospital populations receiving a non-specialist service. Abstracts, qualitative research, and case series were excluded. Two reviewers independently reviewed the retrieved studies with conflicts resolved by discussion or a third reviewer, and a single researcher extracted data from published papers. To assess our objectives of how multimorbidity has been measured and examine variation in the chronic conditions included (in terms of number and type), we used descriptive analysis (frequencies, cross-tabulation, and negative binomial regression) to summarise the characteristics of multimorbidity studies and measures (study setting, source of morbidity data, study population, primary study purpose, and multimorbidity measure type). This systematic review is registered with PROSPERO, CRD420201724090. FINDINGS 566 studies were included in our review, of which 206 (36·4%) did not report a reference definition for multimorbidity and 73 (12·9%) did not report the conditions their measure included. The number of conditions included in measures ranged from two to 285 (median 17 [IQR 11-23). 452 (79·9%) studies reported types of condition within a single multimorbidity measure; most included at least one cardiovascular condition (441 [97·6%] of 452 studies), metabolic and endocrine condition (440 [97·3%]), respiratory condition (422 [93·4%]), musculoskeletal condition (396 [87·6%]), or mental health condition (355 [78·5%]) in their measure of multimorbidity. Chronic infections (123 [27·2%]), haematological conditions (110 [24·3%]), ear, nose, and throat conditions (107 [23·7%]), skin conditions (70 [15·5%]), oral conditions (19 [4·2%]), and congenital conditions (14 [3·1%]) were uncommonly included. Only eight individual conditions were included by more than half of studies in the multimorbidity measure used (diabetes, stroke, cancer, chronic obstructive pulmonary disease, hypertension, coronary heart disease, chronic kidney disease, and heart failure), with individual mental health conditions under-represented. Of the 566 studies, 419 were rated to be of moderate risk of bias, 107 of high risk of bias, and 40 of low risk of bias according to the Effective Public Health Practice Project quality assessment tool. INTERPRETATION Measurement of multimorbidity is poorly reported and highly variable. Consistent reporting of measure definitions should be required by journals, and consensus studies are needed to define core and study-dependent conditions to include in measures of multimorbidity. FUNDING Health Data Research UK.
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A Scoping Review of the Application of Metabolomics in Nutrition Research: The Literature Survey 2000-2019.
Shibutami, E, Takebayashi, T
Nutrients. 2021;(11)
Abstract
Nutrimetabolomics is an emerging field in nutrition research, and it is expected to play a significant role in deciphering the interaction between diet and health. Through the development of omics technology over the last two decades, the definition of food and nutrition has changed from sources of energy and major/micro-nutrients to an essential exposure factor that determines health risks. Furthermore, this new approach has enabled nutrition research to identify dietary biomarkers and to deepen the understanding of metabolic dynamics and the impacts on health risks. However, so far, candidate markers identified by metabolomics have not been clinically applied and more efforts should be made to validate those. To help nutrition researchers better understand the potential of its application, this scoping review outlined the historical transition, recent focuses, and future prospects of the new realm, based on trends in the number of human research articles from the early stage of 2000 to the present of 2019 by searching the Medical Literature Analysis and Retrieval System Online (MEDLINE). Among them, objective dietary assessment, metabolic profiling, and health risk prediction were positioned as three of the principal applications. The continued growth will enable nutrimetabolomics research to contribute to personalized nutrition in the future.
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Artificial Intelligence in Nutrients Science Research: A Review.
Sak, J, Suchodolska, M
Nutrients. 2021;(2)
Abstract
Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.
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Four decades of the Bart's Oxford study: Improved tests to predict type 1 diabetes.
Gillespie, KM, Fareed, R, Mortimer, GL
Diabetic medicine : a journal of the British Diabetic Association. 2021;(12):e14717
Abstract
Recent success in clinical trials to delay the onset of type 1 diabetes has heralded a new era of type 1 diabetes research focused on the most accurate methods to predict risk and progression rate in the general population. Risk prediction for type 1 diabetes has been ongoing since the 1970s and 1980s when human leucocyte antigen (HLA) variants and islet autoantibodies associated with type 1 diabetes were first described. Development of prediction methodologies has relied on well-characterised cohorts and samples. The Bart's Oxford (BOX) study of type 1 diabetes has been recruiting children with type 1 diabetes and their first (and second)-degree relatives since 1985. In this review, we use the timeline of the study to review the accompanying basic science developments which have facilitated improved prediction by genetic (HLA analysis through to genetic risk scores) and biochemical strategies (islet cell autoantibodies through to improved individual tests for antibodies to insulin, glutamate decarboxylase, the tyrosine phosphatase IA-2, zinc transporter 8 and tetraspanin 7). The type 1 diabetes community are poised to move forward using the best predictive markers to predict and delay the onset of type 1 diabetes.