Non-SCFA microbial metabolites associated with fiber fermentation and host health.

Dietary fiber is degraded by commensal gut microbes to yield host-beneficial short-chain fatty acids (SCFAs), but personalized responses to fiber supplementation highlight a role for other microbial metabolites in shaping host health. In this review we summarize recent findings from dietary fiber intervention studies describing health impacts attributed to microbial metabolites other than SCFAs, particularly secondary bile acids (2°BAs), aromatic amino acid derivatives, neurotransmitters, and B vitamins. We also discuss shifts in microbial metabolism occurring through altered maternal dietary fiber intake and agricultural practices, which warrant further investigation. To optimize the health benefits of dietary fibers, it is essential to survey a range of metabolites and adapt recommendations on a personalized basis, according to the different functional aspects of the microbiome.

Microbiota based personalized nutrition improves hyperglycaemia and hypertension parameters and reduces inflammation: a prospective, open label, controlled, randomized, comparative, proof of concept study.

Background: Recent studies suggest that gut microbiota composition, abundance and diversity can influence many chronic diseases such as type 2 diabetes. Modulating gut microbiota through targeted nutrition can provide beneficial effects leading to the concept of personalized nutrition for health improvement. In this prospective clinical trial, we evaluated the impact of a microbiome-based targeted personalized diet on hyperglycaemic and hyperlipidaemic individuals. Specifically, BugSpeaks®-a microbiome profile test that profiles microbiota using next generation sequencing and provides personalized nutritional recommendation based on the individual microbiota profile was evaluated. Methods: A total of 30 participants with type 2 diabetes and hyperlipidaemia were recruited for this study. The microbiome profile of the 15 participants (test arm) was evaluated using whole genome shotgun metagenomics and personalized nutritional recommendations based on their microbiota profile were provided. The remaining 15 participants (control arm) were provided with diabetic nutritional guidance for 3 months. Clinical and anthropometric parameters such as HbA1c, systolic/diastolic pressure, c-reactive protein levels and microbiota composition were measured and compared during the study. Results: The test arm (microbiome-based nutrition) showed a statistically significant decrease in HbA1c level from 8.30 (95% confidence interval (CI), [7.74-8.85]) to 6.67 (95% CI [6.2-7.05]), p < 0.001 after 90 days. The test arm also showed a 5% decline in the systolic pressure whereas the control arm showed a 7% increase. Incidentally, a sub-cohort of the test arm of patients with >130 mm Hg systolic pressure showed a statistically significant decrease of systolic pressure by 14%. Interestingly, CRP level was also found to drop by 19.5%. Alpha diversity measures showed a significant increase in Shannon diversity measure (p < 0.05), after the microbiome-based personalized dietary intervention. The intervention led to a minimum two-fold (Log2 fold change increase in species like Phascolarctobacterium succinatutens, Bifidobacterium angulatum, and Levilactobacillus brevis which might have a beneficial role in the current context and a similar decrease in species like Alistipes finegoldii, and Sutterella faecalis which have been earlier shown to have some negative effects in the host. Overall, the study indicated a net positive impact of the microbiota based personalized dietary regime on the gut microbiome and correlated clinical parameters.

Artificial Intelligence in Malnutrition: A Systematic Literature Review.

Malnutrition among the population of the world is a frequent yet underdiagnosed problem in both children and adults. Development of malnutrition screening and diagnostic tools for early detection of malnutrition is necessary to prevent long-term complications to patients' health and well-being. Most of these tools are based on predefined questionnaires and consensus guidelines. The use of artificial intelligence (AI) allows for automated tools to detect malnutrition in an earlier stage to prevent long-term consequences. In this study, a systematic literature review was carried out with the goal of providing detailed information on what patient groups, screening tools, machine learning algorithms, data types, and variables are being used, as well as the current limitations and implementation stage of these AI-based tools. The results showed that a staggering majority exceeding 90% of all AI models go unused in day-to-day clinical practice. Furthermore, supervised learning models seemed to be the most popular type of learning. Alongside this, disease-related malnutrition was the most common category of malnutrition found in the analysis of all primary studies. This research provides a resource for researchers to identify directions for their research on the use of AI in malnutrition.

Fasting and postprandial plasma metabolite responses to a 12-wk dietary intervention in tissue-specific insulin resistance: a secondary analysis of the PERSonalized glucose Optimization through Nutritional intervention (PERSON) randomized trial.

BACKGROUND: We previously showed that dietary intervention effects on cardiometabolic health were driven by tissue-specific insulin resistance (IR) phenotype: individuals with predominant muscle IR (MIR) benefited more from a low-fat, high-protein, and high-fiber (LFHP) diet, whereas individuals with predominant liver insulin resistance (LIR) benefited more from a high-monounsaturated fatty acid (HMUFA) diet. OBJECTIVES: To further characterize the effects of LFHP and HMUFA diets and their interaction with tissue-specific IR, we investigated dietary intervention effects on fasting and postprandial plasma metabolite profile. METHODS: Adults with MIR or LIR (40-75 y, BMI 25-40 kg/m2) were randomly assigned to a 12-wk HMUFA or LFHP diet (n = 242). After the exclusion of statin use, 214 participants were included in this prespecified secondary analysis. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, and 240 min) a high-fat mixed meal for quantification of 247 metabolite measures using nuclear magnetic resonance spectroscopy. RESULTS: A larger reduction in fasting VLDL-triacylglycerol (TAG) and VLDL particle size was observed in individuals with MIR following the LFHP diet and those with LIR following the HMUFA diet, although no longer statistically significant after false discovery rate (FDR) adjustment. No IR phenotype-by-diet interactions were found for postprandial plasma metabolites assessed as total area under the curve (tAUC). Irrespective of IR phenotype, the LFHP diet induced greater reductions in postprandial plasma tAUC of the larger VLDL particles and small HDL particles, and TAG content in most VLDL subclasses and the smaller LDL and HDL subclasses (for example, VLDL-TAG tAUC standardized mean change [95% CI] LFHP = -0.29 [-0.43, -0.16] compared with HMUFA = -0.04 [-0.16, 0.09]; FDR-adjusted P for diet × time = 0.041). CONCLUSIONS: Diet effects on plasma metabolite profiles were more pronounced than phenotype-by-diet interactions. An LFHP diet may be more effective than an HMUFA diet for reducing cardiometabolic risk in individuals with tissue-specific IR, irrespective of IR phenotype. Am J Clin Nutr 20xx;x:xx. This trial was registered at the clinicaltrials.gov registration (https://clinicaltrials.gov/study/NCT03708419?term=NCT03708419&rank=1) as NCT03708419 and CCMO registration (https://www.toetsingonline.nl/to/ccmo_search.nsf/fABRpop?readform&unids=3969AABCD9BA27FEC12587F1001BCC65) as NL63768.068.17.

How Diet and Lifestyle Can Fine-Tune Gut Microbiomes for Healthy Aging.

Many physical, social, and psychological changes occur during aging that raise the risk of developing chronic diseases, frailty, and dependency. These changes adversely affect the gut microbiota, a phenomenon known as microbe-aging. Those microbiota alterations are, in turn, associated with the development of age-related diseases. The gut microbiota is highly responsive to lifestyle and dietary changes, displaying a flexibility that also provides anactionable tool by which healthy aging can be promoted. This review covers, firstly, the main lifestyle and socioeconomic factors that modify the gut microbiota composition and function during healthy or unhealthy aging and, secondly, the advances being made in defining and promoting healthy aging, including microbiome-informed artificial intelligence tools, personalized dietary patterns, and food probiotic systems.

Relationship between energy balance and reward system gene polymorphisms and appetitive traits in young Mexican subjects.

Introduction: Appetitive traits are influenced by the interplay between genetic and environmental factors. This study aimed to explore the relationship between gene polymorphisms involved in the regulation of energy balance and food reward and appetitive traits in young Mexican subjects. Methods: This cross-sectional study involved 118 university freshman undergraduates who completed the Adult Eating Behaviour Questionnaire for Spanish speakers (AEBQ-Esp) to assess their appetitive traits. A real-time PCR system was employed to determine gene polymorphisms involved in energy balance (LEP rs7799039, MC4R rs17782313, FTO rs9939609, GHRL rs696217), and reward system (DRD2/ANKK1 Taq1A rs1800497 and COMT rs4680). Results: The mean age of participants was 20.14 ± 3.95 years, 71.2% were women and their mean BMI was 23.52 ± 4.05 kg/m2. COMT Met allele carriers presented a significantly higher "Emotional overeating" mean score than Val allele carriers (2.63 ± 0.70 vs. 2.23 ± 0.70, p = 0.028). The MC4R CC + CT genotype correlated positively with "Emotional overeating" (Phi = 0.308, p = 0.01). The COMT MetMet+MetVal genotype correlated with higher "Emotional overeating" (r = 0.257, p = 0.028; Phi = 0.249, p = 0.033). The protective genotype FTO TT correlated positively with "Emotional undereating" (Phi = 0.298, p = 0.012). Carriers of the risk genotype MC4R CC + CT presented a higher risk of "Emotional overeating" than TT carriers (OR = 2.4, 95% CI 1.3-4.8, p = 0.034). Carriers of the risk genotype COMT MetMet+MetVal (OR = 3.4, 95% CI 1.1-10.3, p = 0.033), were associated with a higher risk of "Emotional overeating" than ValVal carriers. The protective FTO genotype TT was associated with "Emotional undereating" (OR = 1.8, 95% CI 1.1-9.1, p = 0.014). Discussion: The study found a relationship between the protective genotypes of FTO TT and "Emotional undereating" and risk genotypes of COMT Met/Met+Met/Val and MC4R CC + CT with "Emotional overeating." These genetic factors may increase weight gain by enhancing hedonic food consumption and reducing satiety control. Future studies should focus on replication studies in ethnically diverse young adults and life stages to explore the relationship between polymorphisms and appetitive traits and weight. This will help tailor personalized nutrigenetic strategies to counteract disordered eating patterns leading to obesity and associated co-morbidities.

Multilevel functional data analysis modeling of human glucose response to meal intake.

Glucose meal response information collected via Continuous Glucose Monitoring (CGM) is relevant to the assessment of individual metabolic status and the support of personalized diet prescriptions. However, the complexity of the data produced by CGM monitors pushes the limits of existing analytic methods. CGM data often exhibits substantial within-person variability and has a natural multilevel structure. This research is motivated by the analysis of CGM data from individuals without diabetes in the AEGIS study. The dataset includes detailed information on meal timing and nutrition for each individual over different days. The primary focus of this study is to examine CGM glucose responses following patients' meals and explore the time-dependent associations with dietary and patient characteristics. Motivated by this problem, we propose a new analytical framework based on multilevel functional models, including a new functional mixed R-square coefficient. The use of these models illustrates 3 key points: (i) The importance of analyzing glucose responses across the entire functional domain when making diet recommendations; (ii) The differential metabolic responses between normoglycemic and prediabetic patients, particularly with regards to lipid intake; (iii) The importance of including random, person-level effects when modelling this scientific problem.

Do Precision and Personalised Nutrition Interventions Improve Risk Factors in Adults with Prediabetes or Metabolic Syndrome? A Systematic Review of Randomised Controlled Trials.

This review aimed to synthesise existing literature on the efficacy of personalised or precision nutrition (PPN) interventions, including medical nutrition therapy (MNT), in improving outcomes related to glycaemic control (HbA1c, post-prandial glucose [PPG], and fasting blood glucose), anthropometry (weight, BMI, and waist circumference [WC]), blood lipids, blood pressure (BP), and dietary intake among adults with prediabetes or metabolic syndrome (MetS). Six databases were systematically searched (Scopus, Medline, Embase, CINAHL, PsycINFO, and Cochrane) for randomised controlled trials (RCTs) published from January 2000 to 16 April 2023. The Academy of Nutrition and Dietetics Quality Criteria were used to assess the risk of bias. Seven RCTs (n = 873), comprising five PPN and two MNT interventions, lasting 3-24 months were included. Consistent and significant improvements favouring PPN and MNT interventions were reported across studies that examined outcomes like HbA1c, PPG, and waist circumference. Results for other measures, including fasting blood glucose, HOMA-IR, blood lipids, BP, and diet, were inconsistent. Longer, more frequent interventions yielded greater improvements, especially for HbA1c and WC. However, more research in studies with larger sample sizes and standardised PPN definitions is needed. Future studies should also investigate combining MNT with contemporary PPN factors, including genetic, epigenetic, metabolomic, and metagenomic data.

Individualized Supplementation of Immunoactive Micronutrients and Severity of Upper Respiratory Infection Symptoms-A Randomized Intervention Study.

Certain micronutrients exhibit immunomodulatory effects. However, no intervention has yet investigated the effect of individualized supplementation on the severity of upper respiratory tract infections (URIs). Therefore, we investigated whether a personalized supplementation moderates the incidence and severity of URI. Selenium, zinc, and vitamin D were measured in dried blood spots from 59 healthy participants. Accordingly, a personalized supplement was provided with or without the respective micronutrients. We used WURSS-21 questionnaires to assess the disease status. The blood values converged during the intervention and micronutrients no longer differed between treated and untreated volunteers at the end of the intervention period. The incidence and severity of the illness did not significantly differ between the groups. However, when analyzing the WURSS-21 scores by the intention to treat, the initially randomized treatment arm revealed a significantly higher score than the placebo arm. Upon acute administration, individualized combinations of selenium, zinc and vitamin D do not reduce the number, or contribute to a milder course of URIs. Therefore, supplementation in acute infectious situations seems questionable. Further studies must address the habitual diet in more detail, to better understand the impact of individual micronutrient status on the prevention of URI.

Identification of carbohydrate gene clusters obtained from in vitro fermentations as predictive biomarkers of prebiotic responses.

BACKGROUND: Prebiotic fibers are non-digestible substrates that modulate the gut microbiome by promoting expansion of microbes having the genetic and physiological potential to utilize those molecules. Although several prebiotic substrates have been consistently shown to provide health benefits in human clinical trials, responder and non-responder phenotypes are often reported. These observations had led to interest in identifying, a priori, prebiotic responders and non-responders as a basis for personalized nutrition. In this study, we conducted in vitro fecal enrichments and applied shotgun metagenomics and machine learning tools to identify microbial gene signatures from adult subjects that could be used to predict prebiotic responders and non-responders. RESULTS: Using short chain fatty acids as a targeted response, we identified genetic features, consisting of carbohydrate active enzymes, transcription factors and sugar transporters, from metagenomic sequencing of in vitro fermentations for three prebiotic substrates: xylooligosacharides, fructooligosacharides, and inulin. A machine learning approach was then used to select substrate-specific gene signatures as predictive features. These features were found to be predictive for XOS responders with respect to SCFA production in an in vivo trial. CONCLUSIONS: Our results confirm the bifidogenic effect of commonly used prebiotic substrates along with inter-individual microbial responses towards these substrates. We successfully trained classifiers for the prediction of prebiotic responders towards XOS and inulin with robust accuracy (≥ AUC 0.9) and demonstrated its utility in a human feeding trial. Overall, the findings from this study highlight the practical implementation of pre-intervention targeted profiling of individual microbiomes to stratify responders and non-responders.

The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center.

PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.

Exploring the Experiences and Perspectives of Division III Athletes Regarding Personalized Nutrition Plans for Improved Performance-A Qualitative Investigation.

(1) Background: This qualitative study explores Division III college student-athletes' experiences and perceptions of personalized nutrition plans in collegiate sports settings. (2) Methods: Semi-structured interviews were conducted using a general qualitative research design. Using a grounded theory approach, a thematic analysis was utilized to analyze the interview transcripts, allowing for the identification of recurring themes and patterns. (3) Results: A total of 30 Division III college student-athletes, 16 males (53.3%) and 14 females (46.7%), representing a diverse range of sports disciplines, engaged in discussions about personalized nutrition plans. Analysis of the data revealed five main themes: (1) Nutritional Knowledge and Awareness, (2) Perceived Benefits of Personalized Nutrition Plans, (3) Challenges and Barriers to Implementation, (4) Influence of Team Culture and Environment, and (5) Suggestions for Improvement. (4) Conclusion: This study sheds light on the complexities of implementing personalized nutrition plans in collegiate sports settings and emphasizes the need for comprehensive, athlete-centered approaches to optimize performance and well-being.

Impacts of preparation technologies on biological activities of edible mushroom polysaccharides - novel insights for personalized nutrition achievement.

Edible mushroom polysaccharides (EMPs) as a natural macromolecular carbohydrate have a very complex structure and composition. EMPs are considered ideal candidates for developing healthy products and functional foods and have received significant research attention due to their unique physiological activities such as immunomodulatory, anti-inflammatory, anti-tumor/cancer, gut microbiota regulation, metabolism improvement, and nervous system protection. The structure and monosaccharide composition of edible mushroom polysaccharides have an unknown relationship with their functional activity, which has not been widely studied. Therefore, we summarized the preparation techniques of EMPs and discussed the association between functional activity, preparation methods, structure and composition of EMPs, laying a theoretical foundation for the personalized nutritional achievements of EMP. We also establish the foundation for the further investigation and application of EMPs as novel functional foods and healthy products.

The Impact of a Nutritional Intervention on Glycemic Control and Cardiovascular Risk Markers in Type 2 Diabetes.

INTRODUCTION: Nutritional management plays a crucial role in treating patients with type 2 diabetes (T2D), working to prevent and control the progression of chronic non-communicable diseases. OBJECTIVES: To evaluate the effects of individualized nutritional interventions on weight, body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), fasting blood glucose (FBG), hemoglobin A1c (HbA1c), total cholesterol (TC), LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), triglycerides (TGs), systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate (HR)} over 12 months and subsequently at follow-up (15 months). METHODS: This longitudinal experimental study (without randomization and blinding) enrolled 84 sedentary participants with T2D (both sexes, aged 18-80 years). They were divided into a control group of 40 participants who received only medical consultations, and an intervention group of 44 participants who received the same medical care along with a nutritional assessment. Consultations occurred quarterly from August 2020 to November 2022 (first-twelfth month), with six to nine patients per session. Subsequently, a follow-up was conducted from December 2022 to November 2023, during which the intervention group had only medical care (during the 12th-15th months). Personalized dietary planning was inspired by the Mediterranean/DASH diets adapted to Brazilian foods and socioeconomic cultures. STATISTICAL ANALYSIS: Normal variables were compared between groups for each time point and also within each group across different time points using a two-way ANOVA (repeated measures for intragroup) followed by the Sídák post hoc test. Non-normal variables were compared between groups for each time point using Kruskal-Wallis followed by the Dunn post hoc test, and within each group across different time points using Friedman followed by the Dunn post hoc test. Data with a Gaussian distribution were presented as mean ± standard deviation (SD), and data with a non-Gaussian distribution were presented as median ± interquartile range (IQR). For all cases, α < 0.05 and p < 0.05 were adopted. RESULTS: In the intervention group, significant reductions were observed between the first and twelfth month for all parameters (p < 0.05), (except for TC), along with an increase in HDL-C (p = 0.0105). Conversely, in the control group, there was a significant increase in HbA1c, weight, BMI, FBG, and WHR (p < 0.05) between the first and twelfth months. Regarding the comparison between groups, there was a significant difference for all analyzed parameters (p < 0.05) from the first to the twelfth month. In the follow-up, differences were also observed (p < 0.05), except for BMI (p > 0.05). CONCLUSION: The individualized nutritional intervention improved eating habits, anthropometric, biochemical, and cardiovascular markers in T2D over 12 months, with sustained results during follow-up. The dietary plan inspired by the Mediterranean and DASH diets demonstrated good adaptation to the Brazilian food culture and the patients' socioeconomic contexts. Consistent monitoring and personalized nutritional management are essential for optimizing long-term outcomes. However, more clinical trials are necessary in order to optimize the level of evidence for longitudinal interventions.

What factors influence sustainable and healthy diet consumption? A review and synthesis of literature within the university setting and beyond.

Globally, typical dietary patterns are neither healthy nor sustainable. Recognizing the key role of dietary change in reducing noncommunicable disease risk and addressing environmental degradation, it is crucial to understand how to shift individuals toward a sustainable and healthy diet (SHD). In this literature review, we introduced the concept of a SHD and outlined the dietary behaviors necessary to transition toward SHD consumption; we reviewed the literature on factors that may influence sustainable (and unsustainable) dietary behaviors in adults; and we developed a novel scoring system to rank factors by priority for targeting in future research. Given the significant potential to promote a sustainable and healthy dietary transition on the university campus-where factors that may impact dietary behaviors can be targeted at all levels of influence (i.e., individual, interpersonal, environmental, policy)-we narrowed our focus to this setting throughout. Aided by our novel scoring system, we identified conscious habitual eating, product price, food availability/accessibility, product convenience, self-regulation skills, knowledge of animal ethics/welfare, food promotion, and eating norms as important modifiable factors that may influence university students' dietary behaviors. When scored without consideration for the university population, these factors were also ranked as highest priority, as was modified portion sizes. Our findings offer insight into factors that may warrant attention in future research aimed at promoting SHDs. In particular, the high-priority factors identified from our synthesis of the literature could help guide the development of more personalized dietary behavioral interventions within the university setting and beyond.

Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings.

Noncommunicable diseases (NCDs) are influenced by the interplay between genetics and environmental exposures, particularly diet. However, many healthcare professionals, including nutritionists and dietitians, have limited genetic background and, therefore, they may lack understanding of gene-environment interactions (GxEs) studies. Even researchers deeply involved in nutrition studies, but with a focus elsewhere, can struggle to interpret, evaluate, and conduct GxE studies. There is an urgent need to study African populations that bear a heavy burden of NCDs, demonstrate unique genetic variability, and have cultural practices resulting in distinctive environmental exposures compared with Europeans or Americans, who are studied more. Although diverse and rapidly changing environments, as well as the high genetic variability of Africans and difference in linkage disequilibrium (ie, certain gene variants are inherited together more often than expected by chance), provide unparalleled potential to investigate the omics fields, only a small percentage of studies come from Africa. Furthermore, research evidence lags behind the practices of companies offering genetic testing for personalized medicine and nutrition. We need to generate more evidence on GxEs that also considers continental African populations to be able to prevent unethical practices and enable tailored treatments. This review aims to introduce nutrition professionals to genetics terms and valid methods to investigate GxEs and their challenges, and proposes ways to improve quality and reproducibility. The review also provides insight into the potential contributions of nutrigenetics and nutrigenomics to the healthcare sphere, addresses direct-to-consumer genetic testing, and concludes by offering insights into the field's future, including advanced technologies like artificial intelligence and machine learning.

The Role of Dietary Peptides Gluten and Casein in the Development of Autism Spectrum Disorder: Biochemical Perspectives.

This paper examines the role of dietary peptides gluten and casein in modulating brain function in individuals with autism spectrum disorder (ASD) from a biochemical perspective. Neurotransmitter systems and neural networks are crucial for brain function, and alterations at the biochemical level can contribute to the characteristic symptoms and behaviors of ASD. The paper explores how dietary peptides influence neurotransmitter systems and neural networks, highlighting their potential as interventions to improve brain function in ASD. The evidence suggests that dietary peptides can impact neurotransmitter synthesis, release, and receptor interactions, disrupting the balance of neurotransmitter systems and affecting neural network function. The findings underscore the potential of dietary interventions in modulating brain function in ASD and call for further research to elucidate the underlying mechanisms and optimize clinical practice. Considering individual dietary sensitivities and preferences, personalized dietary approaches may be necessary for optimal outcomes. Dietary interventions' timing, duration, and integration with other evidence-based treatments are crucial considerations. Safety considerations and regular monitoring are important to ensure the implementation of dietary interventions safely and effectively.

A health technology assessment of personalized nutrition interventions using the EUnetHTA HTA Core Model.

OBJECTIVES: Poor nutrition links to chronic diseases, emphasizing the need for optimized diets. The EU-funded project PREVENTOMICS, introduced personalized nutrition to address this. This study aims to perform a health technology assessment (HTA) comparing personalized nutrition interventions developed through this project, with non-personalized nutrition interventions (control) for people with normal weight, overweight, or obesity. The goal is to support decisions about further development and implementation of personalized nutrition. METHODS: The PREVENTOMICS interventions were evaluated using the European Network for HTA Core Model, which includes a methodological framework that encompasses different domains for value assessment. Information was gathered via [1] different statistical analyses and modeling studies, [2] questions asked of project partners and, [3] other (un)published materials. RESULTS: Clinical trials of PREVENTOMICS interventions demonstrated different body mass index changes compared to control; differences ranged from -0.80 to 0.20 kg/m2. Long-term outcome predictions showed generally improved health outcomes for the interventions; some appeared cost-effective (e.g., interventions in UK). Ethical concerns around health inequality and the lack of specific legal regulations for personalized nutrition interventions were identified. Choice modeling studies indicated openness to personalized nutrition interventions; decisions were primarily affected by intervention's price. CONCLUSIONS: PREVENTOMICS clinical trials have shown promising effectiveness with no major safety concerns, although uncertainties about effectiveness exist due to small samples (n=60-264) and short follow-ups (10-16 weeks). Larger, longer trials are needed for robust evidence before implementation could be considered. Among other considerations, developers should explore financing options and collaborate with policymakers to prevent exclusion of specific groups due to information shortages.