Pub Date : 2025-07-28DOI: 10.1016/j.advnut.2025.100486
Keeva NM Loughlin , Pol Grootswagers , Guido Camps , Lisette CPGM de Groot
Predictive algorithm-based biomarkers of aging (BoA), such as aging clocks, are increasingly applied within human nutrition research. Despite great promise of these BoA, validation efforts and guidelines for implementation are lagging behind the vast and growing number of available biomarkers, complicating their use and introducing variance across studies. Therefore, in the current perspective paper, we provide practical insights and an initial set of recommendations for consistent future implementation of BoA within nutrition research based on current knowledge, both on a general level and within different research scenarios. We critically reflect on existing observational and experimental nutrition research, and outline the potential application of BoA in identifying at-risk groups, exploring heterogeneity underlying aging and nutritional effects, and personalized approaches. This work aims to support nutritional researchers in making informed decisions on contextually appropriate biomarkers and provides directions for future nutritional research involving BoA, because, despite much needed advancements, we consider BoA exciting and promising tools in nutrition research.
{"title":"Perspective: Biomarkers of Aging in Human Nutrition Research—A Focus on Applications, Challenges, and Opportunities","authors":"Keeva NM Loughlin , Pol Grootswagers , Guido Camps , Lisette CPGM de Groot","doi":"10.1016/j.advnut.2025.100486","DOIUrl":"10.1016/j.advnut.2025.100486","url":null,"abstract":"<div><div>Predictive algorithm-based biomarkers of aging (BoA), such as aging clocks, are increasingly applied within human nutrition research. Despite great promise of these BoA, validation efforts and guidelines for implementation are lagging behind the vast and growing number of available biomarkers, complicating their use and introducing variance across studies. Therefore, in the current perspective paper, we provide practical insights and an initial set of recommendations for consistent future implementation of BoA within nutrition research based on current knowledge, both on a general level and within different research scenarios. We critically reflect on existing observational and experimental nutrition research, and outline the potential application of BoA in identifying at-risk groups, exploring heterogeneity underlying aging and nutritional effects, and personalized approaches. This work aims to support nutritional researchers in making informed decisions on contextually appropriate biomarkers and provides directions for future nutritional research involving BoA, because, despite much needed advancements, we consider BoA exciting and promising tools in nutrition research.</div></div>","PeriodicalId":7349,"journal":{"name":"Advances in Nutrition","volume":"16 9","pages":"Article 100486"},"PeriodicalIF":9.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-19DOI: 10.1016/j.advnut.2025.100483
Armando Peña , Zoe Barnsfather , Alison M Miller , Ashley Alvarado , Deanna Reinoso , Melissa Klitzman , Ann Marie Neeley , Ana Maria Linares , Katherine Harkov , Tess Phillips , Amanda Santiago , Christine Spencer , Fernanda Betti , Julie A Patterson , Ines Casanova , Karla Baquerizo , Kiran Snow , Angelica Maria Mays , Shannon Lopez , Courtnie Leeper , Richard J Holden
Increasing exclusive breastfeeding among Latino populations has the potential to reduce health disparities. There is a need for a multilevel and multidomain framework of exclusive breastfeeding determinants. This study aimed to co-create an exclusive breastfeeding determinants framework among Latino populations and map this framework using the current literature. Our community coalition convened in working groups to adapt a multilevel and multidomain determinants framework with 20 cells (4 levels × 5 domains) for exclusive breastfeeding among Latino populations. We documented all referenced determinants in working groups, and 2 independent raters deductively and inductively analyzed these specific determinants into themes by cell (level domain). An integrated scoping review mapped the determinants addressed in the literature of exclusive breastfeeding interventions among Latinos in the United States onto the framework cells. Two independent raters transcribed intervention descriptions verbatim and deductively analyzed the text using our list of determinants as the codebook. Inductive analysis allowed for emerging determinants. We mapped determinants that were addressed by theme. A total of 111 specific determinants were referenced in working groups that were categorized into 53 determinant themes. Most studies addressed Individual-level determinants at each domain (n = 11–16 studies) except for Built Environment (n = 3). At the Interpersonal level, Behavior (n = 11) and Health Care System (n = 16) domains were predominantly addressed. At the Community level, Built Environment (n = 14) and Health Care System (n = 15) domains were addressed. Most studies at the Societal level addressed the Health Care System domain but none addressed Biological, Behavior, or Built Environment domains. Extension of care, culturally relevant care, knowledge and skills, mother–infant bonding, and practitioner–dyad relationship were referenced the most of all 56 themes (n ≥ 13 each). Increasing exclusive breastfeeding among Latinos is a multifaceted challenge. Innovative areas for future work include Biological and Sociocultural domains beyond the Individual level as well as most domains at the Societal level.
{"title":"Co-creating and Mapping an Exclusive Breastfeeding Framework among Latino Populations in the United States: An Integrated Framework Adaptation Process and Scoping Review","authors":"Armando Peña , Zoe Barnsfather , Alison M Miller , Ashley Alvarado , Deanna Reinoso , Melissa Klitzman , Ann Marie Neeley , Ana Maria Linares , Katherine Harkov , Tess Phillips , Amanda Santiago , Christine Spencer , Fernanda Betti , Julie A Patterson , Ines Casanova , Karla Baquerizo , Kiran Snow , Angelica Maria Mays , Shannon Lopez , Courtnie Leeper , Richard J Holden","doi":"10.1016/j.advnut.2025.100483","DOIUrl":"10.1016/j.advnut.2025.100483","url":null,"abstract":"<div><div>Increasing exclusive breastfeeding among Latino populations has the potential to reduce health disparities. There is a need for a multilevel and multidomain framework of exclusive breastfeeding determinants. This study aimed to co-create an exclusive breastfeeding determinants framework among Latino populations and map this framework using the current literature. Our community coalition convened in working groups to adapt a multilevel and multidomain determinants framework with 20 cells (4 levels × 5 domains) for exclusive breastfeeding among Latino populations. We documented all referenced determinants in working groups, and 2 independent raters deductively and inductively analyzed these specific determinants into themes by cell (level domain). An integrated scoping review mapped the determinants addressed in the literature of exclusive breastfeeding interventions among Latinos in the United States onto the framework cells. Two independent raters transcribed intervention descriptions verbatim and deductively analyzed the text using our list of determinants as the codebook. Inductive analysis allowed for emerging determinants. We mapped determinants that were addressed by theme. A total of 111 specific determinants were referenced in working groups that were categorized into 53 determinant themes. Most studies addressed Individual-level determinants at each domain (<em>n</em> = 11–16 studies) except for Built Environment (<em>n</em> = 3). At the Interpersonal level, Behavior (<em>n</em> = 11) and Health Care System (<em>n =</em> 16) domains were predominantly addressed. At the Community level, Built Environment (<em>n =</em> 14) and Health Care System (<em>n =</em> 15) domains were addressed. Most studies at the Societal level addressed the Health Care System domain but none addressed Biological, Behavior, or Built Environment domains. Extension of care, culturally relevant care, knowledge and skills, mother–infant bonding, and practitioner–dyad relationship were referenced the most of all 56 themes (<em>n ≥</em> 13 each). Increasing exclusive breastfeeding among Latinos is a multifaceted challenge. Innovative areas for future work include Biological and Sociocultural domains beyond the Individual level as well as most domains at the Societal level.</div></div>","PeriodicalId":7349,"journal":{"name":"Advances in Nutrition","volume":"16 9","pages":"Article 100483"},"PeriodicalIF":9.2,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1016/j.advnut.2025.100481
Connie Weaver , Seth Armah , Richard S Bruno , Andrew Fletcher , Raymond Glahn , Isabelle Herter-Aeberli , Tasija Karosas , Cornelia U Loechl , Veronica Lopez-Teros , Michael I McBurney , Alida Melse-Boonstra , Rachel Novotny , Manju B Reddy , Jessica Rigutto-Farebrother , Sherry Tanumihardjo , Emorn Udomkesmalee , Ellen Van Den Heuvel , Taylor Wallace , Pattanee Winichagoon
Current nutrient intake recommendations, nutritional assessments, and food labeling rely on estimated total nutrient content in foods and dietary supplements. However, the adequacy of nutrient intake depends not only on the total amount consumed but also on the fraction absorbed and utilized by the body. Accurate assessments of nutrient bioavailability require predictive equations or algorithms. This paper outlines a 4-step framework designed to guide researchers in developing such equations. The framework includes: 1) identifying key factors that influence nutrient or bioactive compound bioavailability; 2) conducting a comprehensive literature review of high-quality human studies to inform the development of predictive equations; 3) constructing predictive equations based on these insights; and 4) validate the equation, when feasible, to potentiate translation. This structured approach aims to enhance the accuracy and precision of nutrient bioavailability estimates, address data limitations, and highlight evidence gaps to inform future research and policy on nutrients and bioactive compounds.
{"title":"Perspective: Framework for Developing Prediction Equations for Estimating the Absorption and Bioavailability of Nutrients from Foods","authors":"Connie Weaver , Seth Armah , Richard S Bruno , Andrew Fletcher , Raymond Glahn , Isabelle Herter-Aeberli , Tasija Karosas , Cornelia U Loechl , Veronica Lopez-Teros , Michael I McBurney , Alida Melse-Boonstra , Rachel Novotny , Manju B Reddy , Jessica Rigutto-Farebrother , Sherry Tanumihardjo , Emorn Udomkesmalee , Ellen Van Den Heuvel , Taylor Wallace , Pattanee Winichagoon","doi":"10.1016/j.advnut.2025.100481","DOIUrl":"10.1016/j.advnut.2025.100481","url":null,"abstract":"<div><div>Current nutrient intake recommendations, nutritional assessments, and food labeling rely on estimated total nutrient content in foods and dietary supplements. However, the adequacy of nutrient intake depends not only on the total amount consumed but also on the fraction absorbed and utilized by the body. Accurate assessments of nutrient bioavailability require predictive equations or algorithms. This paper outlines a 4-step framework designed to guide researchers in developing such equations. The framework includes: <em>1</em>) identifying key factors that influence nutrient or bioactive compound bioavailability; <em>2</em>) conducting a comprehensive literature review of high-quality human studies to inform the development of predictive equations; <em>3</em>) constructing predictive equations based on these insights; and <em>4</em>) validate the equation, when feasible, to potentiate translation. This structured approach aims to enhance the accuracy and precision of nutrient bioavailability estimates, address data limitations, and highlight evidence gaps to inform future research and policy on nutrients and bioactive compounds.</div></div>","PeriodicalId":7349,"journal":{"name":"Advances in Nutrition","volume":"16 9","pages":"Article 100481"},"PeriodicalIF":9.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1016/j.advnut.2025.100482
Dana Lee Olstad, Lynn McIntyre
Inequities in diet quality are evident worldwide and reflect structural disadvantages. There is increasing evidence that dietary inequities may be most meaningful in relation to educational attainment, a finding that contradicts the common belief that dietary inequities are primarily attributable to material disadvantage (i.e. inadequate incomes). Moreover, diet quality declines with each step down the educational ladder, and therefore, these educational inequities affect all of society. The purpose of this perspective is to posit that educational attainment is a key structural stratifier of diet quality and dietary inequities—what we term a super determinant—and that greater research attention should be given to interrogating pathways through which educational attainment shapes diet quality. To inform our perspective, we conducted extensive keyword searches in PubMed and Google Scholar to identify concepts, theories, and empirical data pertaining to educational inequities in diet quality, health, and mortality, followed by a conceptual synthesis of findings. On the basis of these findings, we first describe pathways through which educational attainment shapes diet quality. We then demonstrate that educational inequities in diet quality are often much larger than they are for income. For instance, absolute gaps and gradients in Healthy Eating Index-2015 scores between the most and least educated adults were 7–11 points in Canada, whereas they were just 2–5 points in relation to household income. We provide converging evidence related to large and growing educational inequities in diet quality, health, and mortality internationally. We subsequently consider an important counterfactual—that the affordability of a healthy diet is the key determinant of dietary inequities—and empirically demonstrate that economic factors are not primary drivers of socioeconomic inequities in diet quality. We conclude that attributing dietary inequities primarily to the higher costs of healthy foods is overly simplistic and ignores the critical role of educational attainment as a structural stratifier of dietary inequities.
{"title":"Educational Attainment as a Super Determinant of Diet Quality and Dietary Inequities☆","authors":"Dana Lee Olstad, Lynn McIntyre","doi":"10.1016/j.advnut.2025.100482","DOIUrl":"10.1016/j.advnut.2025.100482","url":null,"abstract":"<div><div>Inequities in diet quality are evident worldwide and reflect structural disadvantages. There is increasing evidence that dietary inequities may be most meaningful in relation to educational attainment, a finding that contradicts the common belief that dietary inequities are primarily attributable to material disadvantage (i.e. inadequate incomes). Moreover, diet quality declines with each step down the educational ladder, and therefore, these educational inequities affect all of society. The purpose of this perspective is to posit that educational attainment is a key structural stratifier of diet quality and dietary inequities—what we term a super determinant—and that greater research attention should be given to interrogating pathways through which educational attainment shapes diet quality. To inform our perspective, we conducted extensive keyword searches in PubMed and Google Scholar to identify concepts, theories, and empirical data pertaining to educational inequities in diet quality, health, and mortality, followed by a conceptual synthesis of findings. On the basis of these findings, we first describe pathways through which educational attainment shapes diet quality. We then demonstrate that educational inequities in diet quality are often much larger than they are for income. For instance, absolute gaps and gradients in Healthy Eating Index-2015 scores between the most and least educated adults were 7–11 points in Canada, whereas they were just 2–5 points in relation to household income. We provide converging evidence related to large and growing educational inequities in diet quality, health, and mortality internationally. We subsequently consider an important counterfactual—that the affordability of a healthy diet is the key determinant of dietary inequities—and empirically demonstrate that economic factors are not primary drivers of socioeconomic inequities in diet quality. We conclude that attributing dietary inequities primarily to the higher costs of healthy foods is overly simplistic and ignores the critical role of educational attainment as a structural stratifier of dietary inequities.</div></div>","PeriodicalId":7349,"journal":{"name":"Advances in Nutrition","volume":"16 9","pages":"Article 100482"},"PeriodicalIF":9.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.advnut.2025.100438
Marco Sguanci , Sara Morales Palomares , Giovanni Cangelosi , Fabio Petrelli , Elena Sandri , Gaetano Ferrara , Stefano Mancin
Malnutrition is a critical complication among cancer patients, affecting ≤80% of individuals depending on cancer type, stage, and treatment. Artificial intelligence (AI) has emerged as a promising tool in healthcare, with potential applications in nutritional management to improve early detection, risk stratification, and personalized interventions. This systematic review evaluated the role of AI in identifying and managing malnutrition in cancer patients, focusing on its effectiveness in nutritional status assessment, prediction, clinical outcomes, and body composition monitoring. A systematic search was conducted across PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica Database from June to July 2024, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Quantitative primary studies investigating AI-based interventions for malnutrition detection, body composition analysis, and nutritional optimization in oncology were included. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools, and evidence certainty was evaluated with the Oxford Centre for Evidence-Based Medicine framework. Eleven studies (n = 52,228 patients) met the inclusion criteria and were categorized into 3 overarching domains: nutritional status assessment and prediction, clinical and functional outcomes, and body composition and cachexia monitoring. AI-based models demonstrated high predictive accuracy in malnutrition detection (area under the curve >0.80). Machine learning algorithms, including decision trees, random forests, and support vector machines, outperformed conventional screening tools. Deep learning models applied to medical imaging achieved high segmentation accuracy (Dice similarity coefficient: 0.92–0.94), enabling early cachexia detection. AI-driven virtual dietitian systems improved dietary adherence (84%) and reduced unplanned hospitalizations. AI-enhanced workflows streamlined dietitian referrals, reducing referral times by 2.4 d. AI demonstrates significant potential in optimizing malnutrition screening, body composition monitoring, and personalized nutritional interventions for cancer patients. Its integration into oncology nutrition care could enhance patient outcomes and optimize healthcare resource allocation. Further research is necessary to standardize AI models and ensure clinical applicability. This systematic review followed a protocol registered prospectively on Open Science Framework (https://doi.org/10.17605/OSF.IO/A259M).
{"title":"Artificial Intelligence in the Management of Malnutrition in Cancer Patients: A Systematic Review","authors":"Marco Sguanci , Sara Morales Palomares , Giovanni Cangelosi , Fabio Petrelli , Elena Sandri , Gaetano Ferrara , Stefano Mancin","doi":"10.1016/j.advnut.2025.100438","DOIUrl":"10.1016/j.advnut.2025.100438","url":null,"abstract":"<div><div>Malnutrition is a critical complication among cancer patients, affecting ≤80% of individuals depending on cancer type, stage, and treatment. Artificial intelligence (AI) has emerged as a promising tool in healthcare, with potential applications in nutritional management to improve early detection, risk stratification, and personalized interventions. This systematic review evaluated the role of AI in identifying and managing malnutrition in cancer patients, focusing on its effectiveness in nutritional status assessment, prediction, clinical outcomes, and body composition monitoring. A systematic search was conducted across PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica Database from June to July 2024, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Quantitative primary studies investigating AI-based interventions for malnutrition detection, body composition analysis, and nutritional optimization in oncology were included. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools, and evidence certainty was evaluated with the Oxford Centre for Evidence-Based Medicine framework. Eleven studies (<em>n</em> = 52,228 patients) met the inclusion criteria and were categorized into 3 overarching domains: nutritional status assessment and prediction, clinical and functional outcomes, and body composition and cachexia monitoring. AI-based models demonstrated high predictive accuracy in malnutrition detection (area under the curve >0.80). Machine learning algorithms, including decision trees, random forests, and support vector machines, outperformed conventional screening tools. Deep learning models applied to medical imaging achieved high segmentation accuracy (Dice similarity coefficient: 0.92–0.94), enabling early cachexia detection. AI-driven virtual dietitian systems improved dietary adherence (84%) and reduced unplanned hospitalizations. AI-enhanced workflows streamlined dietitian referrals, reducing referral times by 2.4 d. AI demonstrates significant potential in optimizing malnutrition screening, body composition monitoring, and personalized nutritional interventions for cancer patients. Its integration into oncology nutrition care could enhance patient outcomes and optimize healthcare resource allocation. Further research is necessary to standardize AI models and ensure clinical applicability. This systematic review followed a protocol registered prospectively on Open Science Framework (<span><span>https://doi.org/10.17605/OSF.IO/A259M</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":7349,"journal":{"name":"Advances in Nutrition","volume":"16 7","pages":"Article 100438"},"PeriodicalIF":8.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.advnut.2025.100450
Ronilson Corrêa , Ana Elisa Toscano , Paula Brielle Pontes , Raul Manhães de Castro
{"title":"The Urgent Need for Clinical Nutrition Education in Medical Training: Integrating Developmental Origin of Health and Disease and Perinatal Nutrition into Programs and Credentialing","authors":"Ronilson Corrêa , Ana Elisa Toscano , Paula Brielle Pontes , Raul Manhães de Castro","doi":"10.1016/j.advnut.2025.100450","DOIUrl":"10.1016/j.advnut.2025.100450","url":null,"abstract":"","PeriodicalId":7349,"journal":{"name":"Advances in Nutrition","volume":"16 7","pages":"Article 100450"},"PeriodicalIF":8.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.advnut.2025.100465
Matthew Snelson , Jessica R Biesiekierski , Susanna Chen , Nessmah Sultan , Barbara R Cardoso
The reduced risk of chronic diseases such as cardiovascular disease and type 2 diabetes associated with nut consumption may occur via modulation of the gut microbiota, although this has not been comprehensively assessed. This systematic review of clinical trials aimed to assess the effects of nuts on gut microbiota composition and metabolites, as well astheir effects on gut function and symptoms in adults. The systematic review was conducted following PRISMA guidelines and registered in PROSPERO (CRD42023451282). Outcomes included microbiota diversity, specific bacterial abundances, gastrointestinal symptoms, intestinal permeability, fecal pH, fecal moisture, and short-chain fatty acid (SCFA) concentrations. We performed meta-analyses to assess the overall effect of nuts on fecal moisture, pH, intestinal permeability, and SCFA concentrations. Among the 28 intervention trials included in this review, almonds were the most commonly studied (12 trials), whereas other nuts, such as walnuts, peanuts, pistachios, and Brazil nuts, were also examined. Nineteen articles reported the effects of almond, walnut, peanut, or mixed nuts on the microbiota composition. Additionally, 6 trials used interventions involving a mixture of different nuts. A total of 19 trials assessed the community structure of the gut microbiota by evaluating α-diversity and β-diversity metrics, with most finding no significant differences following the nut intervention. Regarding taxonomic changes, the majority of studies reported no significant changes across nut interventions. However, several studies noted increases in Clostridium and Roseburia species, with mixed results for Bifidobacterium species abundance following almond or walnut intervention. Five studies assessed fecal SCFA concentrations, with positive effects of nut interventions on propionate. There were no effects of nut interventions on fecal pH and intestinal permeability, with an unfavorable effect on fecal moisture. In summary, the available evidence indicates that nuts have modest effect on gut health, but the substantial heterogeneity between studies may hinder further conclusions.
This trial was registered at PROSPERO as CRD42023451282.
{"title":"Effects of Nut Intake on Gut Microbiome Composition and Gut Function in Adults: A Systematic Review and Meta-analysis","authors":"Matthew Snelson , Jessica R Biesiekierski , Susanna Chen , Nessmah Sultan , Barbara R Cardoso","doi":"10.1016/j.advnut.2025.100465","DOIUrl":"10.1016/j.advnut.2025.100465","url":null,"abstract":"<div><div>The reduced risk of chronic diseases such as cardiovascular disease and type 2 diabetes associated with nut consumption may occur via modulation of the gut microbiota, although this has not been comprehensively assessed. This systematic review of clinical trials aimed to assess the effects of nuts on gut microbiota composition and metabolites, as well astheir effects on gut function and symptoms in adults. The systematic review was conducted following PRISMA guidelines and registered in PROSPERO (CRD42023451282). Outcomes included microbiota diversity, specific bacterial abundances, gastrointestinal symptoms, intestinal permeability, fecal pH, fecal moisture, and short-chain fatty acid (SCFA) concentrations. We performed meta-analyses to assess the overall effect of nuts on fecal moisture, pH, intestinal permeability, and SCFA concentrations. Among the 28 intervention trials included in this review, almonds were the most commonly studied (12 trials), whereas other nuts, such as walnuts, peanuts, pistachios, and Brazil nuts, were also examined. Nineteen articles reported the effects of almond, walnut, peanut, or mixed nuts on the microbiota composition. Additionally, 6 trials used interventions involving a mixture of different nuts. A total of 19 trials assessed the community structure of the gut microbiota by evaluating α-diversity and β-diversity metrics, with most finding no significant differences following the nut intervention. Regarding taxonomic changes, the majority of studies reported no significant changes across nut interventions. However, several studies noted increases in <em>Clostridium</em> and <em>Roseburia</em> species, with mixed results for <em>Bifidobacterium</em> species abundance following almond or walnut intervention. Five studies assessed fecal SCFA concentrations, with positive effects of nut interventions on propionate. There were no effects of nut interventions on fecal pH and intestinal permeability, with an unfavorable effect on fecal moisture. In summary, the available evidence indicates that nuts have modest effect on gut health, but the substantial heterogeneity between studies may hinder further conclusions.</div><div>This trial was registered at PROSPERO as CRD42023451282.</div></div>","PeriodicalId":7349,"journal":{"name":"Advances in Nutrition","volume":"16 7","pages":"Article 100465"},"PeriodicalIF":8.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.advnut.2025.100453
Fatemeh Jafari, Janhavi J Damani, Kristina S Petersen
Cardiovascular concerns exist about the effect of red meat on circulating concentrations of trimethylamine N-oxide (TMAO), an emerging cardiovascular disease risk factor. The aim was to conduct a systematic review of randomized controlled trials (RCTs) to evaluate the effect of higher red meat intake, compared with lower intake, on circulating, urinary, and fecal TMAO concentrations in generally healthy adults and/or adults with stable chronic diseases. A systematic literature search was conducted using PubMed, the Cochrane Collaboration Library, and Web of Science. RCTs examining the effect of a ≥7-d dietary intervention featuring red meat on urinary, fecal, and/or circulating (plasma or serum) concentrations of TMAO in adults (≥18 y) were included. Eligible trials had a comparator group/condition that was exposed to a dietary intervention for ≥ 7 d lower in red meat and featuring white meat, fish, eggs, dairy, or plant-based protein sources. In total, 375 publications were identified. Fifteen publications reporting the results of 13 RCTs (n = 553; median duration 28 d), including 15 diet comparisons, were eligible. In 6 comparisons, higher circulating or urinary TMAO concentrations were observed after higher red meat intake (∼71–420 g/d) compared with comparator conditions lower in red meat. In 7 comparisons, no differences in serum/plasma TMAO concentrations were observed with higher red meat-containing diets (∼60–156 g/d) compared with diets lower in red meat. Two comparisons showed that consuming higher red meat diets lowered TMAO concentrations after 28 d compared with lower red meat diets containing seafood. In short-term studies (median duration of 28 d), higher red meat intake had inconsistent effects on circulating and urinary TMAO concentrations. Further high-quality research on red meat-related TMAO modulation, including effect magnitude and clinical relevance, is needed. This study was registered at Prospective Register of Systematic Reviews (PROSPERO) as CRD42023396799.
背景：人们对红肉对循环中三甲胺n -氧化物（TMAO）浓度的影响存在担忧，TMAO是一种新兴的心血管危险因素。目的：本研究旨在对随机对照试验（rct）进行系统回顾，以评估与低摄入量相比，高红肉摄入量对一般健康成年人和/或患有稳定慢性疾病的成年人血液、尿液和粪便中氧化三甲胺浓度的影响。方法：使用PubMed、Cochrane协作图书馆和Web of Science进行系统文献检索。纳入了以红肉为特征的≥7天饮食干预对成人（≥18岁）尿液、粪便和/或循环（血浆或血清）氧化三甲胺浓度影响的随机对照试验。符合条件的试验有一个对照组/条件，该组/条件暴露于饮食干预≥7天，减少红肉，以白肉、鱼、蛋、乳制品或植物性蛋白质来源为主。结果：共发现375篇文献。15篇文献报道了13项随机对照试验的结果(n=553；中位持续时间28天)，包括15个饮食比较，符合条件。在六项比较中，与红肉摄入量较低的对照条件相比，红肉摄入量较高（~ 71-420 g/天）后，观察到较高的循环或尿液TMAO浓度。在七项比较中，与低红肉饮食相比，高红肉饮食（~ 60-156 g/天）的血清/血浆TMAO浓度没有差异。两项比较表明，28天后，食用较多红肉的小鼠与食用较少含海鲜的红肉小鼠相比，氧化三甲胺浓度降低。结论：在短期研究中（中位持续时间为28天），摄入更多红肉对循环和尿中氧化三甲胺浓度的影响不一致。需要对红肉相关的氧化三甲胺调节进行进一步的高质量研究，包括效果大小和临床相关性。意义说明：本系统综述总结了与低摄入量红肉相比，高摄入量红肉对一般健康成人和/或患有稳定慢性疾病的成人血液、尿液和粪便中三甲胺n -氧化物（TMAO）浓度影响的证据。较高的红肉摄入量对氧化三甲胺浓度的影响不一致，这可能部分与临床试验方法的差异、饮食相关的氧化三甲胺调节的个体差异和/或含红肉饮食的整体健康状况有关。
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Pub Date : 2025-07-01DOI: 10.1016/j.advnut.2025.100377
Jakob Linseisen , Britta Renner , Kurt Gedrich , Jan Wirsam , Christina Holzapfel , Stefan Lorkowski , Bernhard Watzl , Hannelore Daniel , Michael Leitzmann , Working Group “Personalized Nutrition” of the German Nutrition Society
Personalized nutrition (PN) represents an approach aimed at delivering tailored dietary recommendations, products, or services to support both prevention and treatment of nutrition-related conditions and to improve individual health using genetic, phenotypic, medical, nutritional, and other pertinent information. However, current approaches have yielded limited scientific success in improving diets or in mitigating diet-related conditions. In addition, PN currently caters to a specific subgroup of the population rather than having a widespread impact on diet and health at a population level. Addressing these challenges requires integrating traditional biomedical and dietary assessment methods with psycho-behavioral, and novel digital and diagnostic methods for comprehensive data collection, which holds considerable promise in alleviating present PN shortcomings. This comprehensive approach not only allows for deriving personalized goals (“what should be achieved”) but also customizing behavioral change processes (“how to bring about change”). We herein outline and discuss the concept of “Adaptive Personalized Nutrition Advice Systems,” which blends data from 3 assessment domains: 1) biomedical/health phenotyping; 2) stable and dynamic behavioral signatures; and 3) food environment data. Personalized goals and behavior change processes are envisaged to no longer be based solely on static data but will adapt dynamically in-time and in-situ based on individual-specific data. To successfully integrate biomedical, behavioral, and environmental data for personalized dietary guidance, advanced digital tools (e.g., sensors) and artificial intelligence-based methods will be essential. In conclusion, the integration of both established and novel static and dynamic assessment paradigms holds great potential for transitioning PN from its current focus on elite nutrition to a widely accessible tool that delivers meaningful health benefits to the general population.
{"title":"Data in Personalized Nutrition: Bridging Biomedical, Psycho-behavioral, and Food Environment Approaches for Population-wide Impact","authors":"Jakob Linseisen , Britta Renner , Kurt Gedrich , Jan Wirsam , Christina Holzapfel , Stefan Lorkowski , Bernhard Watzl , Hannelore Daniel , Michael Leitzmann , Working Group “Personalized Nutrition” of the German Nutrition Society","doi":"10.1016/j.advnut.2025.100377","DOIUrl":"10.1016/j.advnut.2025.100377","url":null,"abstract":"<div><div>Personalized nutrition (PN) represents an approach aimed at delivering tailored dietary recommendations, products, or services to support both prevention and treatment of nutrition-related conditions and to improve individual health using genetic, phenotypic, medical, nutritional, and other pertinent information. However, current approaches have yielded limited scientific success in improving diets or in mitigating diet-related conditions. In addition, PN currently caters to a specific subgroup of the population rather than having a widespread impact on diet and health at a population level. Addressing these challenges requires integrating traditional biomedical and dietary assessment methods with psycho-behavioral, and novel digital and diagnostic methods for comprehensive data collection, which holds considerable promise in alleviating present PN shortcomings. This comprehensive approach not only allows for deriving personalized goals (“what should be achieved”) but also customizing behavioral change processes (“how to bring about change”). We herein outline and discuss the concept of “Adaptive Personalized Nutrition Advice Systems,” which blends data from 3 assessment domains: <em>1</em>) biomedical/health phenotyping; <em>2</em>) stable and dynamic behavioral signatures; and <em>3</em>) food environment data. Personalized goals and behavior change processes are envisaged to no longer be based solely on static data but will adapt dynamically in-time and in-situ based on individual-specific data. To successfully integrate biomedical, behavioral, and environmental data for personalized dietary guidance, advanced digital tools (e.g., sensors) and artificial intelligence-based methods will be essential. In conclusion, the integration of both established and novel static and dynamic assessment paradigms holds great potential for transitioning PN from its current focus on elite nutrition to a widely accessible tool that delivers meaningful health benefits to the general population.</div></div>","PeriodicalId":7349,"journal":{"name":"Advances in Nutrition","volume":"16 7","pages":"Article 100377"},"PeriodicalIF":8.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.advnut.2025.100447
Salvatore Carbone
{"title":"Artificial intelligence in cancer-related malnutrition and cachexia: a transformative tool in clinical nutrition","authors":"Salvatore Carbone","doi":"10.1016/j.advnut.2025.100447","DOIUrl":"10.1016/j.advnut.2025.100447","url":null,"abstract":"","PeriodicalId":7349,"journal":{"name":"Advances in Nutrition","volume":"16 7","pages":"Article 100447"},"PeriodicalIF":8.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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