A genomics perspective of personalized prevention and management of obesity.

This review discusses the landscape of personalized prevention and management of obesity from a nutrigenetics perspective. Focusing on macronutrient tailoring, we discuss the impact of genetic variation on responses to carbohydrate, lipid, protein, and fiber consumption. Our bioinformatic analysis of genomic variants guiding macronutrient intake revealed enrichment of pathways associated with circadian rhythm, melatonin metabolism, cholesterol and lipoprotein remodeling and PPAR signaling as potential targets of macronutrients for the management of obesity in relevant genetic backgrounds. Notably, our data-based in silico predictions suggest the potential of repurposing the SYK inhibitor fostamatinib for obesity treatment in relevant genetic profiles. In addition to dietary considerations, we address genetic variations guiding lifestyle changes in weight management, including exercise and chrononutrition. Finally, we emphasize the need for a refined understanding and expanded research into the complex genetic landscape underlying obesity and its management.

The Role of Artificial Intelligence in Deciphering Diet-Disease Relationships: Case Studies.

Modernization of society from a rural, hunter-gatherer setting into an urban and industrial habitat, with the associated dietary changes, has led to an increased prevalence of cardiometabolic and additional noncommunicable diseases, such as cancer, inflammatory bowel disease, and neurodegenerative and autoimmune disorders. However, while dietary sciences have been rapidly evolving to meet these challenges, validation and translation of experimental results into clinical practice remain limited for multiple reasons, including inherent ethnic, gender, and cultural interindividual variability, among other methodological, dietary reporting-related, and analytical issues. Recently, large clinical cohorts with artificial intelligence analytics have introduced new precision and personalized nutrition concepts that enable one to successfully bridge these gaps in a real-life setting. In this review, we highlight selected examples of case studies at the intersection between diet-disease research and artificial intelligence. We discuss their potential and challenges and offer an outlook toward the transformation of dietary sciences into individualized clinical translation.

Precision Nutrition: The Hype Is Exceeding the Science and Evidentiary Standards Needed to Inform Public Health Recommendations for Prevention of Chronic Disease.

As dietary guidance for populations shifts from preventing deficiency disorders to chronic disease risk reduction, the biology supporting such guidance becomes more complex due to the multifactorial risk profile of disease and inherent population heterogeneity in the diet-disease relationship. Diet is a primary driver of chronic disease risk, and population-based guidance should account for individual responses. Cascading effects on evidentiary standards for population-based guidance are not straightforward. Precision remains a consideration for dietary guidance to prevent deficiency through the identification of population subgroups with unique nutritional needs. Reducing chronic disease through diet requires greater precision in (a) establishing essential nutrient needs throughout the life cycle in both health and disease; (b) considering effects of nutrients and other food substances on metabolic, immunological, inflammatory, and other physiological responses supporting healthy aging; and (c) considering healthy eating behaviors. Herein we provide a template for guiding population-based eating recommendations for reducing chronic diseases in heterogenous populations.

The Evolution of Science and Regulation of Dietary Supplements: Past, Present, and Future.

Dietary supplement use in the United States is widespread and increasing, especially among certain population groups, such as older Americans. The science surrounding dietary supplements has evolved substantially over the last few decades since their formal regulation in 1994. Much has been learned about the mechanisms of action of many dietary supplement ingredients, but the evidence on their health effects is still building. As is true of much nutrition research, there are many studies that point to health effects, but not all are at the level of scientific evidence (e.g., randomized controlled interventions), rigor, or quality needed for definitive statements of efficacy regarding clinical end points. New technologies and approaches are being applied to the science of dietary supplements, including nutrigenomics and microbiome analysis, data science, artificial intelligence (AI), and machine learning-all of which can elevate the science behind dietary supplements. Products can contain an array of bioactive compounds derived from foods as well as from medicinal plants, which creates enormous challenges in data collection and management. Clinical applications, particularly those aimed at providing personalized nutrition options for patients, have become more sophisticated as dietary supplements are incorporated increasingly into clinical practice and self-care. The goals of this article are to provide historical context for the regulation and science of dietary supplements, identify research resources, and suggest some future directions for science in this field.

Precision nutrition in pediatric IBD: A position paper from the ESPGHAN special interest group for basic science and translational research, the IBD Porto group, and allied health professionals.

Stratified and precision nutrition refers to disease management or prevention of disease onset, based on dietary interventions tailored to a person's characteristics, biology, gut microbiome, and environmental exposures. Such treatment models may lead to more effective management of inflammatory bowel disease (IBD) and reduce risk of disease development. This societal position paper aimed to report advances made in stratified and precision nutritional therapy in IBD. Following a structured literature search, limited to human studies, we identified four relevant themes: (a) nutritional epidemiology for risk prediction of IBD development, (b) food-based dietary interventions in IBD, (c) exclusive enteral nutrition (EEN) for Crohn's disease (CD) management, and (d) pre- and probiotics for IBD management. There is scarce literature upon which we can make recommendations for precision or stratified dietary therapy for IBD, both for risk of disease development and disease management. Certain single-nucleotide polymorphisms related to polyunsaturated fatty acid (PUFA) metabolism may modify the effect dietary PUFA have in increasing the risk of IBD development. Non-colonic CD, mild-to-moderate CD, and high microbiota richness may predict success of EEN and may be used both for prediction of treatment continuation, but also for early cessation in nonresponders. There is currently insufficient evidence to make recommendations for precision or stratified dietary therapy for patients with established IBD. Despite the great interest in stratified and precision nutrition, we currently lack data to support conclusive recommendations. Replication of early findings by independent research groups and within structured clinical interventions is required.

Higher glucose and insulin responses to a mixed meal are associated with increased risk of diabetic retinopathy in Indigenous Americans.

PURPOSE: Prior research has focused on glucose/insulin responses to meal challenges to create personalized diets to improve health, though it is unclear if these responses predict chronic diseases. We aimed to identify glucose and insulin responses to a mixed meal tolerance test (MMTT) that predict the development of diabetic retinopathy (DR) and compare the predictive abilities with the oral glucose tolerance test (OGTT). METHODS: Indigenous American adults without diabetes (n = 168) underwent a 4-h MMTT, body composition assessment, and a 3-h OGTT at baseline. During follow-up (median 13.4 years), DR was diagnosed by direct ophthalmoscopy (n = 28) after onset of type 2 diabetes. Total and incremental area under the curve (AUC and iAUC) were calculated from glucose/insulin responses after the MMTT and OGTT. RESULTS: In separate Cox proportional hazards models adjusted for age, sex, and body fat (%), MMTT glucose AUCs (180-min and 240-min) and iAUC (180-min) predicted DR (HR 1.50, 95% CI 1.06, 2.12; HR 1.50, 95% CI 1.05, 2.14; HR 1.58, 95% CI 1.01, 2.46). The predictive abilities were better than the fasting OGTT glucose (p < 0.01) but similar to the 120-min OGTT glucose (p = 0.53). MMTT insulin AUCs (180-min and 240-min) and iAUC (180-min) also predicted DR (HR 1.65, 95% CI 1.09, 2.51; HR 1.58, 95% CI 1.00, 2.35; HR 1.53 95% CI 1.06, 2.22) while insulin AUC and iAUC from the OGTT did not (p > 0.05). CONCLUSIONS: Higher MMTT glucose and insulin responses predicted DR and were comparable to the OGTT, supporting the use of a meal challenge for precision nutrition. TRIAL REGISTRATIONS: Clinical Trial Registry: ClinicalTrials.gov identifier: NCT00340132, NCT00339482.

Personalized sleep and nutritional strategies to combat adverse effects of night shift work: a controlled intervention protocol.

BACKGROUND: Working during the night interferes with the timing of normal daily activities and is associated with an increased risk of chronic diseases. Under controlled experimental conditions, interventions focusing on sleep and nutrition can mitigate the short-term adverse effects of shift work. However, it is unclear how these results translate to real-life, how they can be targeted to individual conditions, and how they relate to long-term health. Therefore, the current study aims to implement a personalized sleep and nutritional intervention among night shift workers in the field. METHODS: A non-blinded controlled intervention study is used, consisting of a run-in period, an intervention of 3 months, post-intervention measurements, and a follow-up after 12 months. Three study arms are included: sleep intervention, nutritional intervention, and control group (n = 25 each). Participants are healthy 18-60-year male night shift workers, with at least one year of experience in night shift work. Information from the run-in period will be used to personalize the interventions. The main outcomes are sleep measurements and continuous interstitial glucose levels. Furthermore, general health biomarkers and parameters will be determined to further evaluate effects on long-term health. DISCUSSION: This study aims to mitigate negative health consequences associated with night shift work by introducing two personalized preventive interventions. If proven effective, the personalized interventions may serve as practical solutions that can have a meaningful impact on the sustainable health and employability of night shift workers. This study will thereby contribute to the current need for high-quality data on preventative strategies for night shift work in a real-life context. TRIAL REGISTRATION: This trial has been registered under ClinicalTrials.gov Identifier NCT06147089. Registered 27 November 2023.

Effects of feeding sulfate trace minerals above recommendations on nutrient digestibility, rumen fermentation, lactational performance, and trace mineral excretion in dairy cows.

Most trace minerals (TM) are fed above dairy cow requirements in commercial herds but their fate and effects on dairy cows have not been well documented. In this study, we evaluated the effects of feeding short-term sulfate TM above recommendations on apparent total-tract digestibility of nutrients, rumen fermentation characteristics, serum concentrations, and milk yield and composition, as well as milk, fecal, and urinary TM excretion in midlactation dairy cows. Eight multiparous Holstein cows with an average body weight (± SD) of 684 ± 29 kg at 82 ± 10 DIM in a quadruple 2 × 2 crossover design were fed a basal diet, differing in sulfate TM supplement concentrations, to provide either 0.11, 17, and 63 (control; CON) or 0.95, 114, and 123 (high trace minerals; HTM) mg of dietary Co, Mn, and Zn per kilogram of DM, respectively. Each experimental period had a 21-d adaptation to the diet, followed by a 10-d sample collection period. Feed ingredients and total feces and urine were collected during 4 consecutive d and rumen fluid was collected 0, 1, 2, 4, and 6 h relative to feeding. Milk yield was recorded daily, and milk samples were collected on 4 consecutive milkings. Ingestion of Co, Mn, and Zn was higher for the HTM group compared with the CON group by 216%, 233%, and 93%, respectively. Dry matter intake averaged 25.0 (SE = 0.6) kg/d, and apparent total-tract digestibility of major nutrients was similar between treatments. High trace minerals had no measurable effect on ruminal pH, major volatile fatty acids, and protozoa counts. Isovalerate molar proportion was 9.4% greater for the HTM group compared with the CON group. Neither milk yield (43.5 kg/d; SE = 0.8) nor milk fat and protein concentrations differed between treatments. Milk urea nitrogen concentration was significantly higher for HTM (11.7 mg/dL) compared with CON (9.7 mg/dL; SE = 0.7). Fecal excretion of Co, Mn, and Zn increased by 223%, 198%, and 75%, respectively, for the HTM group compared with the CON group. Urinary excretions of TM were marginal compared with feces, and only urinary Co and Mn were significantly higher for HTM cows than CON cows, as was similarly obtained for serum Co and Mn concentrations. Milk TM yields were not modified by treatments. In summary, short-term dietary sulfate TM supply over the recommendation did not improve cow performance but significantly increased fecal TM excretion, which could affect TM accumulation in soils where manure is applied and could potentially result in leaching into nearby watersheds. Further studies are needed to evaluate the impact of high fecal TM excretion on the environment using the One Health approach. Moreover, the effects of TM oversupply on milk production and cow health should be evaluated by long-term experiments.

Carbohydrate Content Classification Using Postprandial Heart Rate Responses from Non-Invasive Wearables.

The rising incidence of type 2 diabetes underscores the need for technological innovations aimed at enhancing diabetes management by aiding individuals in monitoring their dietary intake. This has resulted in the development of technologies capable of tracking the timing and content of an individual's meals. However, the ability to use non-invasive wearables to estimate or classify the carbohydrate content of the food an individual has just consumed remains a relatively unexplored area. This study investigates carbohydrate content classification using postprandial heart rate responses from non-invasive wearables. We designed and developed timeStampr, an iOS application for collecting timestamps essential for data labeling and establishing ground truth. We then conducted a pilot study in controlled, yet naturalistic settings. Data were collected from 23 participants using an Empatica E4 device worn on the upper arm, while each participant consumed either low-carbohydrate or carbohydrate-rich foods. Due to sensor irregularities with dark skin tones and non-compliance with the study's health criteria, we excluded data from three participants. Finally, we configured and trained a Light Gradient Boosting Machine (LGBM) model for carbohydrate content classification. Our classifiers demonstrated robust performance, with the carbohydrate content classification model consistently achieving at least 84% in accuracy, precision, recall, and AUCROC within a 60 s window. The results of this study demonstrate the potential of postprandial heart rate responses from non-invasive wearables in carbohydrate content classification.

Informing Evidence-based Practice in Nutritional Genomics: An Educational Needs Assessment of Nutrition Care Providers in Canada.

Purpose: To investigate why Canadian nutrition care providers choose, or not, to integrate nutritional genomics into practice, and to evaluate the nutritional genomics training/education experiences and needs of nutrition providers in Canada, while comparing those of dietitians to non-dietitians.Methods: A cross-sectional online survey was distributed across Canada from June 2021 to April 2022.Results: In total, 457 healthcare providers (HCPs) [n = 371 dietitians (81.2%)] met the inclusion criteria. The majority (n = 372; 82.1%) reported having no experience offering nutritional genomics to clients (n = 4 did not respond). Of the 81 respondents with experience (17.9%), the most common reason to integrate nutrigenetic testing into practice was the perception that clients would be more motivated to change their eating habits (70.4%), while the most common reason for not integrating such tests was the perception that the nutrigenetic testing process is too complicated (n = 313; 84.1%). Dietitians were more likely than non-dietitians to view existing scientific evidence as an important educational topic (p = 0.002). The most selected useful educational resource by all HCPs was clinical practice guidelines (n = 364; 85.4%).Conclusions: Both dietitians and non-dietitians express a desire for greater nutritional genomics training/education; specific educational needs differ by type of HCP. Low implementation of nutrigenetic testing may be partly attributed to other identified barriers.

Nutritional and Lifestyle Features in a Mediterranean Cohort: An Epidemiological Instrument for Categorizing Metabotypes Based on a Computational Algorithm.

Background and Objectives: Modern classification and categorization of individuals' health requires personalized variables such as nutrition, physical activity, lifestyle, and medical data through advanced analysis and clustering methods involving machine learning tools. The objective of this project was to categorize Mediterranean dwellers' health factors and design metabotypes to provide personalized well-being in order to develop professional implementation tools in addition to characterizing nutritional and lifestyle features in such populations. Materials and Methods: A two-phase observational study was conducted by the Pharmacists Council to identify Spanish nutritional and lifestyle characteristics. Adults over 18 years of age completed questionnaires on general lifestyle habits, dietary patterns (FFQ, MEDAS-17 p), physical activity (IPAQ), quality of life (SF-12), and validated well-being indices (LS7, MEDLIFE, HHS, MHL). Subsequently, exploratory factor, clustering, and random forest analysis methods were conducted to objectively define the metabotypes considering population determinants. Results: A total of 46.4% of the sample (n = 5496) had moderate-to-high adherence to the Mediterranean diet (>8 points), while 71% of the participants declared that they had moderate physical activity. Almost half of the volunteers had a good self-perception of health (49.9%). Regarding lifestyle index, population LS7 showed a fair cardiovascular health status (7.9 ± 1.7), as well as moderate quality of life by MEDLIFE (9.3 ± 2.6) and MHL scores (2.4 ± 0.8). In addition, five metabotype models were developed based on 26 variables: Westernized Millennial (28.6%), healthy (25.1%), active Mediterranean (16.5%), dysmetabolic/pre-morbid (11.5%), and metabolically vulnerable/pro-morbid (18.3%). Conclusions: The support of tools related to precision nutrition and lifestyle integrates well-being characteristics and contributes to reducing the impact of unhealthy lifestyle habits with practical implications for primary care. Combining lifestyle, metabolic, and quality of life traits will facilitate personalized precision interventions and the implementation of targeted public health policies.

[Application strategy of artificial intelligence technology in feed-based development of medicinal and edible homologous traditional Chinese medicine(TCM)resources in era of the "antibiotic ban"].

In the context of the "antibiotic ban" era, the feed conversion of medicinal and edible traditional Chinese medicine(TCM) resources is a research hotspot in the field of antibiotic alternatives development. How to develop feed products that are beneficial to agriculture and livestock while ensuring nutrient balance and precision using medicinal and edible TCM resources as raw materials has become a challenge. Artificial intelligence(AI) technology has unique advantages in feed production and improving the efficiency of intelligent breeding. If AI technology is applied to the feed development of medicinal and edible TCM resources, it is possible to realize feeding and antibiotic-replacement value while ensuring precise nutrition. In order to better apply AI technology in the field of feed development of medicinal and edible TCM resources, this article used CiteSpace software to carry out literature visualization analysis and found that AI technology had a good application in the field of feed formulation optimization in recent years. However, there is still a gap in the research on the intelligent utilization of medicinal and edible TCM resources. Nonetheless, it is feasible for AI technology to be applied to the feed conversion of medicinal and edible TCM resources. Therefore, this article proposed for the first time an intelligent formulation system framework for feed materials derived from medicinal and edible TCM resources to provide new ideas for research in the field of feed development of medicinal and edible TCM resources and the research on the development of antibiotic alternatives. At the same time, it can pave the way for a new green industry chain for contemporary animal husbandry and the TCM industry.

Precision nutrition for targeting pathophysiology of cardiometabolic phenotypes.

Obesity is a heterogenous disease accompanied by a broad spectrum of cardiometabolic risk profiles. Traditional paradigms for dietary weight management do not address biological heterogeneity between individuals and have catastrophically failed to combat the global pandemic of obesity-related diseases. Nutritional strategies that extend beyond basic weight management to instead target patient-specific pathophysiology are warranted. In this narrative review, we provide an overview of the tissue-level pathophysiological processes that drive patient heterogeneity to shape distinct cardiometabolic phenotypes in obesity. Specifically, we discuss how divergent physiology and postprandial phenotypes can reveal key metabolic defects within adipose, liver, or skeletal muscle, as well as the integrative involvement of the gut microbiome and the innate immune system. Finally, we highlight potential precision nutritional approaches to target these pathways and discuss recent translational evidence concerning the efficacy of such tailored dietary interventions for different obesity phenotypes, to optimise cardiometabolic benefits.

Metabolic phenotyping in people living with obesity: Implications for dietary prevention.

Given the increasing number of people living with obesity and related chronic metabolic disease, precision nutrition approaches are required to increase the effectiveness of prevention strategies. This review addresses these approaches in different metabolic phenotypes (metabotypes) in obesity. Although obesity is typically associated with an increased cardiometabolic disease risk, some people with obesity are relatively protected against the detrimental effects of excess adiposity on cardiometabolic health, also referred to as 'metabolically healthy obesity' (MHO). Underlying mechanisms, the extent to which MHO is a transient state as well as lifestyle strategies to counteract the transition from MHO to metabolically unhealthy obesity (MUO) are discussed. Based on the limited resources that are available for dietary lifestyle interventions, it may be reasonable to prioritize interventions for people with MUO, since targeting high-risk patients for specific nutritional, lifestyle or weight-loss strategies may enhance the cost-effectiveness of these interventions. Additionally, the concept of tissue insulin resistant (IR) metabotypes is discussed, representing distinct etiologies towards type 2 diabetes (T2D) as well as cardiovascular disease (CVD). Recent evidence indicates that these tissue IR metabotypes, already present in individuals with obesity with a normal glucose homeostasis, respond differentially to diet. Modulation of dietary macronutrient composition according to these metabotypes may considerably improve cardiometabolic health benefits. Thus, nutritional or lifestyle intervention may improve cardiometabolic health, even with only minor or no weight loss, which stresses the importance of focusing on a healthy lifestyle and not on weight loss only. Targeting different metabotypes towards T2D and cardiometabolic diseases may lead to more effective lifestyle prevention and treatment strategies. Age and sex-related differences in tissue metabotypes and related microbial composition and functionality (fermentation), as important drivers and/or mediators of dietary intervention response, have to be taken into account. For the implementation of these approaches, more prospective trials are required to provide the knowledge base for precision nutrition in the prevention of chronic metabolic diseases.

Precision nutrition in the context of bariatric surgery.

Bariatric surgery (BS) is the most effective long-term treatment for severe obesity. This review summarizes the main nutritional deficiencies before and after BS, as well as current dietary and supplementation recommendations to avoid them. Likewise, we have reviewed all those aspects that in recent years have been shown to be related to postoperative weight loss (WL) and its subsequent maintenance, such as hormonal changes, dietary patterns, changes in food preference, adherence to recommendations and follow-up, genetic factors and microbiota, among others. Despite all the knowledge, nutritional deficiencies and weight regain after BS are frequent. It is essential to continue studying in this field in order to establish more precise recommendations according to the individual characteristics of patients. It is also a major objective to understand more deeply the role of the factors involved in WL and its maintenance. This will allow the development of precision treatments and nutrition for patients with obesity, optimizing their benefit after BS.

Perspective: Developing a Nutrient-Based Framework for Protein Quality.

The future of precision nutrition requires treating amino acids as essential nutrients. Currently, recognition of essential amino acid requirements is embedded within a generalized measure of protein quality known as the PDCAAS (Protein Digestibility-Corrected Amino Acid Score). Calculating the PDCAAS includes the FAO/WHO/UNU amino acid score, which is based on the limiting amino acid in a food, that is, the single amino acid with the lowest concentration compared to the reference standard. That "limiting" amino acid score is then multiplied by a bioavailability factor to obtain the PDCAAS, which ranks proteins from 0.0 (poor quality) to 1.0 (high quality). However, the PDCAAS has multiple limitations: it only allows for direct protein quality comparison between 2 proteins, and it is not scalable, transparent, or additive. We therefore propose that shifting the protein quality evaluation paradigm from the current generalized perspective to a precision nutrition focus treating amino acids as unique, metabolically active nutrients will be valuable for multiple areas of science and public health. We report the development and validation of the Essential Amino Acid 9 (EAA-9) score, an innovative, nutrient-based protein quality scoring framework. EAA-9 scores can be used to ensure that dietary recommendations for each essential amino acid are met. The EAA-9 scoring framework also offers the advantages of being additive and, perhaps most importantly, allows for personalization of essential amino acid needs based on age or metabolic conditions. Comparisons of the EAA-9 score with PDCAAS demonstrated the validity of the EAA-9 framework, and practical applications demonstrated that the EAA-9 framework is a powerful tool for precision nutrition applications.

Participant Characteristics Associated with High Responsiveness to Personalized Healthy Food Incentives: a Secondary Analysis of the Randomized Controlled Crossover Smart Cart Study.

BACKGROUND: Personalized dietary behavioral interventions could be enhanced by understanding factors accounting for individual variability in dietary decisions. OBJECTIVE: This study was a secondary analysis of the Smart Cart randomized controlled trial to determine whether participant characteristics predicted high responsiveness to personalized grocery incentives for purchasing healthy food. METHODS: This secondary analysis of a 9-mo crossover study included 192 regular shoppers (86%) from a Rhode Island supermarket. To analyze whether health, behavioral, and/or sociodemographic characteristics predicted responsiveness to a personalized grocery incentive intervention, participants were divided into 3 categories [high (n = 47), moderate (n = 50), and unresponsive (n = 95)] based on percentage changes in their Grocery Purchase Quality Index scores during the intervention versus control period calculated from sales data. We tested whether participant characteristics, including individual, household, and intervention-related factors, differed across responsiveness groups using ANOVA and whether they predicted the likelihood of being high responsive versus unresponsive or moderate responsive using logistic regression. RESULTS: Participants had a mean (SD) age of 56.0 (13.8) y and were 89% female. Education, self-reported BMI, income, diet-related medical condition, food insecurity, cooking adequacy, and value consciousness differed across responsiveness categories (P < 0.1). High versus moderate and unresponsive participants increased their percentage of spending on targeted foods (P < 0.0001) and purchased fewer unique items (P = 0.01). In multinomial adjusted models, the odds of being high versus unresponsive or moderate responsive were lower for participants with a BMI (in kg/m2) <25 versus ≥25 (OR: 0.41; 95% CI: 0.19, 0.90) and higher with a diet-related medical condition present (OR: 3.75; 95% CI: 1.20, 11.8). Other characteristics were not associated with responsiveness. CONCLUSIONS: Findings demonstrated that a BMI ≥25 and having a diet-related medical condition within the household predicted high responsiveness to a personalized grocery purchasing intervention, suggesting that personalized dietary interventions may be particularly effective for households with higher health risk. This trial is registered at www.clinicaltrials.gov as NCT03748056.

Precision Nutrition and Cardiovascular Disease Risk Reduction: the Promise of High-Density Lipoproteins.

PURPOSE OF REVIEW: Emerging evidence supports the promise of precision nutritional approaches for cardiovascular disease (CVD) prevention. Here, we discuss current findings from precision nutrition trials and studies reporting substantial inter-individual variability in responses to diets and dietary components relevant to CVD outcomes. We highlight examples where early precision nutrition research already points to actionable intervention targets tailored to an individual's biology and lifestyle. Finally, we make the case for high-density lipoproteins (HDL) as a compelling next generation target for precision nutrition aimed at CVD prevention. HDL possesses complex structural features including diverse protein components, lipids, size distribution, extensive glycosylation, and interacts with the gut microbiome, all of which influence HDL's anti-inflammatory, antioxidant, and cholesterol efflux properties. Elucidating the nuances of HDL structure and function at an individual level may unlock personalized dietary and lifestyle strategies to optimize HDL-mediated atheroprotection and reduce CVD risk. RECENT FINDINGS: Recent human studies have demonstrated that HDL particles are key players in the reduction of CVD risk. Our review highlights the role of HDL and the importance of personalized therapeutic approaches to improve their potential for reducing CVD risk. Factors such as diet, genetics, glycosylation, and gut microbiome interactions can modulate HDL structure and function at the individual level. We emphasize that fractionating HDL into size-based subclasses and measuring particle concentration are necessary to understand HDL biology and for developing the next generation of diagnostics and biomarkers. These discoveries underscore the need to move beyond a one-size-fits-all approach to HDL management. Precision nutrition strategies that account for personalized metabolic, genetic, and lifestyle data hold promise for optimizing HDL therapies and function to mitigate CVD risk more potently. While human studies show HDL play a key role in reducing CVD risk, recent findings indicate that factors such as diet, genetics, glycosylation, and gut microbes modulate HDL function at the individual level, underscoring the need for precision nutrition strategies that account for personalized variability to optimize HDL's potential for mitigating CVD risk.

Food and drug design for gut microbiota-directed regulation: Current experimental landscape and future innovation.

Most diets and medications enhance host health via microbiota-dependent ways, but it is in the present situation of untargeted regulation. Non-targeted regulation may lead to the ineffectiveness of dietary supplements or drug treatment. Microbiota-directed food, aiming to improve diseases by targeting specific microbes without affecting other bacteria, have been proposed to deal with this problem. However, there is currently no universally applicable method to explore such foods or drugs. In this review, thirty studies on recent efforts in microbiota directed diets and medications are summarized from various databases. The methods used to find new foods and medications are primarily divided into four groups depending on the experimental models: in vivo and in vitro, as well as predictions based on bioinformatics. We also discuss their implementation, interpretation, and respective limitations, and describe the present situation. We further put forward a framework for microbiota-directed foods and medicine according to above methods and other microbiome manipulation, which will spur precision medicine.

Characterizing Precision Nutrition Discourse on Twitter: Quantitative Content Analysis.

BACKGROUND: It is possible that tailoring dietary approaches to an individual's genomic profile could provide optimal dietary inputs for biological functioning and support adherence to dietary management protocols. The science required for such nutrigenetic and nutrigenomic profiling is not yet considered ready for broad application by the scientific and medical communities; however, many personalized nutrition products are available in the marketplace, creating the potential for hype and misleading information on social media. Twitter provides a unique big data source that provides real-time information. Therefore, it has the potential to disseminate evidence-based health information, as well as misinformation. OBJECTIVE: We sought to characterize the landscape of precision nutrition content on Twitter, with a specific focus on nutrigenetics and nutrigenomics. We focused on tweet authors, types of content, and presence of misinformation. METHODS: Twitter Archiver was used to capture tweets from September 1, 2020, to December 1, 2020, using keywords related to nutrition and genetics. A random sample of tweets was coded using quantitative content analysis by 4 trained coders. Codebook-driven, quantified information about tweet authors, content details, information quality, and engagement metrics were compiled and analyzed. RESULTS: The most common categories of tweets were precision nutrition products and nutrigenomic concepts. About a quarter (132/504, 26.2%) of tweet authors presented themselves as science experts, medicine experts, or both. Nutrigenetics concepts most frequently came from authors with science and medicine expertise, and tweets about the influence of genes on weight were more likely to come from authors with neither type of expertise. A total of 14.9% (75/504) of the tweets were noted to contain untrue information; these were most likely to occur in the nutrigenomics concepts topic category. CONCLUSIONS: By evaluating social media discourse on precision nutrition on Twitter, we made several observations about the content available in the information environment through which individuals can learn about related concepts and products. Tweet content was consistent with the indicators of medical hype, and the inclusion of potentially misleading and untrue information was common. We identified a contingent of users with scientific and medical expertise who were active in discussing nutrigenomics concepts and products and who may be encouraged to share credible expert advice on precision nutrition and tackle false information as this technology develops.