The objective of this study was to assess the feasibility and effectiveness of a novel WeChat applet-based personalized dietary intervention aimed at promoting healthier dietary intakes. A two-arm parallel, randomized, controlled trial was conducted in a real-world scenario and involved a total of 153 participants (the intervention group, n = 76; the control group, n = 77), lasting for 4 months in Shanghai, China. The intervention group had access to visualized nutrition evaluations through the applet during workday lunch time, while the control group received no interventions. A total of 3413 lunch dietary intake records were captured through the applet. Linear mixed models were utilized to assess the intervention effects over time. At baseline, the participants\' lunchtime dietary intakes were characterized by insufficient consumption of plant foods (86.9% of the participants) and excessive intake of animal foods (79.7% of the participants). Following the commencement of the intervention, the intervention group showed a significant decrease in the animal/plant food ratio (β = -0.03/week, p = 0.024) and the consumption of livestock and poultry meat (β = -1.80 g/week, p = 0.035), as well as a borderline significant increase in the consumption of vegetables and fruits (β = 3.22 g/week, p = 0.055) and plant foods (β = 3.26 g/week, p = 0.057) over time at lunch compared to the control group. The applet-based personalized dietary intervention was feasible and effective in improving dietary intakes and, consequently, possibly may manage body weight issues in real-world scenarios.

Nutrition service needs are huge in China. Previous studies indicated that personalized nutrition (PN) interventions were effective. The aim of the present study is to identify the effectiveness and feasibility of a novel PN approach supported by artificial intelligence (AI).
This study is a two-arm parallel, randomized, controlled trial in real world scenario. The participants will be enrolled among who consume lunch at a staff canteen. In Phase I, a total of 170 eligible participants will be assigned to either intervention or control group on 1:1 ratio. The intervention group will be instructed to use the smartphone applet to record their lunches and reach the real-time AI-based information of dish nutrition evaluation and PN evaluation after meal consumption for 3 months. The control group will receive no nutrition information but be asked to record their lunches though the applet. Dietary pattern, body weight or blood pressure optimizing is expected after the intervention. In phase II, the applet will be free to all the diners (about 800) at the study canteen for another one year. Who use the applet at least 2 days per week will be regarded as the intervention group while the others will be the control group. Body metabolism normalization is expected after this period. Generalized linear mixed models will be used to identify the dietary, anthropometric and metabolic changes.
This novel approach will provide real-time AI-based dish nutrition evaluation and PN evaluation after meal consumption in order to assist users with nutrition information to make wise food choice. This study is designed under a real-life scenario which facilitates translating the trial intervention into real-world practice.
This trial has been registered with the Chinese Clinical Trial Registry (ChiCTR2100051771; date registered: 03/10/2021).

Starch-based materials have viscoelasticity, viscous film-forming, dough pseudoplasticity, and rheological properties, which possess the structural characteristics (crystal structure, double helix structure, and layered structure) suitable for three-dimensional (3D) food printing inks. 3D food printing technology has significant advantages in customizing personalized and precise nutrition, expanding the range of ingredients, designing unique food appearances, and simplifying the food supply chain. Precision nutrition aims to consider individual nutritional needs and individual differences, which include special food product design and personalized precise nutrition, thus expanding future food resources, then simplifying the food supply chain, and attracting extensive attention in food industry. Different types of starch-based materials with different structures and rheological properties meet different 3D food printing technology requirements. Starch-based materials suitable for 3D food printing technology can accurately deliver and release active substances or drugs. These active substances or drugs have certain regulatory effects on the gut microbiome and diabetes, so as to maintain personalized and accurate nutrition.

Diet is a pivotal determinant in shaping the structure and function of resident microorganisms in the gut through different food components, nutritive proportion, and calories. The effects of diet on host metabolism and physiology can be mediated through the gut microbiota. Gut microbiota-derived metabolites have been shown to regulate glucose and lipid metabolism, energy consumption, and the immune system. On the other hand, emerging evidence indicates that baseline gut microbiota could predict the efficacy of diet intervention, highlighting gut microbiota can be harnessed as a biomarker in personalized nutrition. In this review, the alterations of gut microbiota in different dietary components and dietary patterns, and the potential mechanisms in the diet-microbiota crosstalk are summarized to understand the interactions of diet and gut microbiota on the impact of metabolic homeostasis.

暂无摘要。

Human responses to the same diets may vary to a large extent, depending on the complex diet-host-microbiota interactions. Recent scientific advance has indicated that this diet-host-microbiota interaction could be quantified to develop strategies for improving individual health (personalized nutrition). Compared to the host related factors (which are difficult to manipulate), the gut microbiome is more readily modulated by dietary exposures and has important roles in affecting human health via the synthesis of various bioactive compounds and participating in the digestion and absorption process of macro- and micronutrients. Therefore, gut microbiota alterations induced by diets could possibly be utilized to improve human health in a targeted manner. However, limitations in the processing and analysis of \'big-data\' concerning human microbiome still restrict the translational capacity of diet-host-microbiota interactions into tools to improve personalized human health. In the current review, recent advances in terms of understanding the specific diet-host-microbiota interactions were summarized, aiming to help the development of strategies for personalized nutrition.

UNASSIGNED: Interindividual differences in response to personalized nutrition (PN) intervention were affected by multiple factors, including genetic backgrounds and gut microbiota. The fat mass and obesity associated (FTO) gene is an important factor related to hyperlipidemia and occurrence of cardiovascular diseases. However, few studies have explored the differences in response to intervention among subjects with different genotypes of FTO, and the associations between gut microbiota and individual responses.
UNASSIGNED: To explore the differential lipid metabolism outcomes associated with FTO gene polymorphisms in response to PN intervention, the altered taxonomic features of gut microbiota caused by the intervention, and the associations between gut microbiota and lipid metabolism outcomes.
UNASSIGNED: A total of 400 overweight or obese adults were recruited in the study and randomly divided into the PN group and control group, of whom 318 completed the 12-week intervention. The single nucleotide polymorphism (SNP) of rs1121980 in FTO was genotyped. Gut microbiota and blood lipids were determined at baseline and week 12. Functional property of microbiota was predicted using Tax4Fun functional prediction analysis.
UNASSIGNED: Subjects with the risk genotype of FTO had significantly higher weight and waist circumference (WC) at baseline. Generalized linear regression models showed that the reduction in weight, body mass index (BMI), WC, body fat percentage, total cholesterol (TCHO), and low-density lipoprotein (LDL) was greater in subjects with the risk genotype of FTO and in the PN group. Significant interaction effects between genotype and intervention on weight, BMI, WC, TCHO, and LDL were found after stratifying for specific genotype of FTO. All subjects showed significant increasement in α diversity of gut microbiota after intervention except for those with the non-risk genotype in the control group. Gut microbiota, including Blautia and Firmicutes, might be involved in lipid metabolism in response to interventions. The predicted functions of the microbiota in subjects with different genotypes were related to lipid metabolism-related pathways, including fatty acid biosynthesis and degradation.
UNASSIGNED: Subjects with the risk genotype of FTO had better response to nutrition intervention, and PN intervention showed better amelioration in anthropometric parameters and blood lipids than the control. Gut microbiota might be involved in modulating differential lipid metabolism responses to intervention in subjects with different genotypes.
UNASSIGNED: [Chictr.org.cn], identifier [ChiCTR1900026226].

Nutritional disorders have become a major public health issue, requiring increased targeted approaches. Personalized nutrition adapted to individual needs has garnered dramatic attention as an effective way to improve nutritional balance and maintain health. With the rapidly evolving fields of genomics and nutrigenetics, accumulation of genetic variants has been indicated to alter the effects of nutritional supplementation, suggesting its indispensable role in the genotype-based personalized nutrition. Additionally, the metabolism of nutrients, such as lipids, especially omega-3 polyunsaturated fatty acids, glucose, vitamin A, folic acid, vitamin D, iron, and calcium could be effectively improved with related genetic variants. This review focuses on existing literatures linking critical genetic variants to the nutrient and the ways in which these variants influence the outcomes of certain nutritional supplementations. Although further studies are required in this direction, such evidence provides valuable insights for the guidance of appropriate interventions using genetic information, thus paving the way for the smooth transition of conventional generic approach to genotype-based personalized nutrition.

Dietary restriction (DR), including caloric restriction (CR), intermittent fasting (IF), and restriction of specific food compositions, can delay aging, and the main mechanisms include regulation of nutrient-sensing pathways and gut microbiota. However, the effects of DR regimens on longevity remain controversial, as some studies have demonstrated that IF, rather than CR or diet composition, influences longevity, while other studies have shown that the restricted-carbohydrate or -protein diets, rather than CR, determine health and longevity. Many factors, including DR-related factors (carbohydrate or protein composition, degree and duration of DR), and individual differences (health status, sex, genotype, and age of starting DR), would be used to explain the controversial anti-aging effects of DR, thus highlighting the necessity of precise DR intervention for anti-aging. Personalized DR intervention in humans is challenging because of the lack of accurate aging molecular biomarkers and vast individual variability. Using machine learning to build a predictive model based on the data set of clinical features, gut microbiome and metabolome, may be a good method to achieve precise DR intervention. Therefore, this review analyzed the anti-aging effects of various DR regimens, summarized their mechanisms and influencing factors, and proposed a future research direction for achieving personalized DR regimens for slowing aging.

UNASSIGNED: Overweight and obesity increase the risk of noncommunicable diseases (NCDs). Personalized nutrition (PN) approaches may provide tailored nutritional advice/service by focusing on individual\'s unique characteristics to prevent against NCDs.
UNASSIGNED: We aimed to compare the effect of PN intervention with the traditional \"one size fits all\" intervention on health status in overweight/obese Chinese adults.
UNASSIGNED: In this 12-week randomized controlled trial, 400 adults with BMI ≥24 kg/m2 were randomized to control group (CG, n = 200) and PN group (PNG, n = 200). The CG received conventional health guidance according to the Dietary Guidelines for Chinese Residents and Chinese DRIs Handbook, whereas the PNG experienced PN intervention that was developed by using decision trees based on the subjects\' anthropometric measurements, blood samples (phenotype), buccal cells (genotype), and dietary and physical activity (PA) assessments (baseline and updated).
UNASSIGNED: Compared with the conventional intervention, PN intervention significantly improved clinical outcomes of anthropometric (e.g., body mass index (BMI), body fat percentage, waist circumference) and blood biomarkers (e.g., blood lipids, uric acid, homocysteine). The improvement in clinical outcomes was achieved through behavior change in diet and PA. The subjects in the PNG had higher China dietary guidelines index values and PA levels. Personalized recommendations of \"lose weight,\" \"increase fiber\" and \"take multivitamin/mineral supplements\" were the major contributors to the decrease of BMI and improvement of lipid profile.
UNASSIGNED: We provided the first evidence that PN intervention was more beneficial than conventional nutrition intervention to improve health status in overweight/obese Chinese adults. This study provides a model of framework for developing personalized advice in Chinese population.Chictr.org.cn (ChiCTR1900026226).