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Combining Artificial Intelligence for Nutrition Applications: Recent Advancements

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A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutritional Epidemiology".

Deadline for manuscript submissions: 5 June 2024 | Viewed by 8476

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Special Issue Editors

Dr. Gang Wang

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Guest Editor

State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
Interests: prebiotics and probiotics; gut microbiota; nutrition; immunology; metabolic diseases; gut–brain axis; gastrointestinal diseases; endocrine disease
Special Issues, Collections and Topics in MDPI journals

Dr. Jinlin Zhu

E-Mail Website
Guest Editor

State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
Interests: big data; health; data analysis; nutrition

Special Issue Information

Dear Colleagues,

As artificial intelligence (AI) gains momentum in scientific labs and industries worldwide, the potential benefits and power of AI for enhancing our daily lives have become increasingly apparent. The use of AI to enhance our understanding of nutrition and health has been gradually embraced by the nutrition and health community over the past decade, and this trend is still on the rise. As a cutting-edge advancement, can today's AI offer more value in terms of dietary nutrition and health for a deeper understanding?

We are pleased to announce this Special Issue as a platform for a wide range of topics related to the integration of artificial intelligence for nutrition applications. The scope of this Special Issue will cover the intelligent collection of dietary nutrition and health information, intelligent analysis related to diet and nutrition, and intelligent recommendations for diet and nutrition, as well as providing intelligent solutions and tools for dietary nutrition and health management. We aim to drive the realization of personalized nutrition through this Special Issue.

We encourage authors to share their views and findings on the development and application of AI to solve the problems related to dietary nutrition and health. We seek papers that shed light on the complex role of dietary nutrition in health, particularly those with high innovation and practical value.

Dr. Gang Wang
Dr. Jinlin Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nutrients is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
dietary nutrition
smart dietary recommendation
intelligent health management

Published Papers (4 papers)

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Editorial

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4 pages, 196 KiB

Open AccessEditorial

Artificial Intelligence Technology for Food Nutrition

by Jinlin Zhu and Gang Wang

Nutrients 2023, 15(21), 4562; https://doi.org/10.3390/nu15214562 - 27 Oct 2023

Cited by 1 | Viewed by 2615

Abstract

Food nutrition is generally defined as the heat energy and nutrients obtained from food by the human body, such as protein, fat, carbohydrates and so on [...] Full article

(This article belongs to the Special Issue Combining Artificial Intelligence for Nutrition Applications: Recent Advancements)

Research

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20 pages, 3796 KiB

Open AccessArticle

Weight Loss Promotion in Individuals with Obesity through Gut Microbiota Alterations with a Multiphase Modified Ketogenic Diet

by Hongchao Wang, Xinchen Lv, Sijia Zhao, Weiwei Yuan, Qunyan Zhou, Faizan Ahmed Sadiq, Jianxin Zhao, Wenwei Lu and Wenjun Wu

Nutrients 2023, 15(19), 4163; https://doi.org/10.3390/nu15194163 - 27 Sep 2023

Cited by 1 | Viewed by 2371

Abstract

The occurrence of obesity and related metabolic disorders is rising, necessitating effective long-term weight management strategies. With growing interest in the potential role of gut microbes due to their association with responses to different weight loss diets, understanding the mechanisms underlying the interactions between diet, gut microbiota, and weight loss remains a challenge. This study aimed to investigate the potential impact of a multiphase dietary protocol, incorporating an improved ketogenic diet (MDP-i-KD), on weight loss and the gut microbiota. Using metagenomic sequencing, we comprehensively analyzed the taxonomic and functional composition of the gut microbiota in 13 participants before and after a 12-week MDP-i-KD intervention. The results revealed a significant reduction in BMI (9.2% weight loss) among obese participants following the MDP-i-KD intervention. Machine learning analysis identified seven key microbial species highly correlated with MDP-i-KD, with Parabacteroides distasonis exhibiting the highest response. Additionally, the co-occurrence network of the gut microbiota in post-weight-loss participants demonstrated a healthier state. Notably, metabolic pathways related to nucleotide biosynthesis, aromatic amino acid synthesis, and starch degradation were enriched in pre-intervention participants and positively correlated with BMI. Furthermore, species associated with obesity, such as Blautia obeum and Ruminococcus torques, played pivotal roles in regulating these metabolic activities. In conclusion, the MDP-i-KD intervention may assist in weight management by modulating the composition and metabolic functions of the gut microbiota. Parabacteroides distasonis, Blautia obeum, and Ruminococcus torques could be key targets for gut microbiota-based obesity interventions. Full article

(This article belongs to the Special Issue Combining Artificial Intelligence for Nutrition Applications: Recent Advancements)

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19 pages, 7330 KiB

Open AccessArticle

Uncovering Predictive Factors and Interventions for Restoring Microecological Diversity after Antibiotic Disturbance

by Jing Chen, Jinlin Zhu, Wenwei Lu, Hongchao Wang, Mingluo Pan, Peijun Tian, Jianxin Zhao, Hao Zhang and Wei Chen

Nutrients 2023, 15(18), 3925; https://doi.org/10.3390/nu15183925 - 10 Sep 2023

Cited by 2 | Viewed by 1325

Abstract

Antibiotic treatment can lead to a loss of diversity of gut microbiota and may adversely affect gut microbiota composition and host health. Previous studies have indicated that the recovery of gut microbes from antibiotic-induced disruption may be guided by specific microbial species. We expect to predict recovery or non-recovery using these crucial species or other indices after antibiotic treatment only when the gut microbiota changes. This study focused on this prediction problem using a novel ensemble learning framework to identify a set of common and reasonably predictive recovery-associated bacterial species (p-RABs), enabling us to predict the host microbiome recovery status under broad-spectrum antibiotic treatment. Our findings also propose other predictive indicators, suggesting that higher taxonomic and functional diversity may correlate with an increased likelihood of successful recovery. Furthermore, to explore the validity of p-RABs, we performed a metabolic support analysis and identified Akkermansia muciniphila and Bacteroides uniformis as potential key supporting species for reconstruction interventions. Experimental results from a C57BL/6J male mouse model demonstrated the effectiveness of p-RABs in facilitating intestinal microbial reconstitution. Thus, we proved the reliability of the new p-RABs and validated a practical intervention scheme for gut microbiota reconstruction under antibiotic disturbance. Full article

(This article belongs to the Special Issue Combining Artificial Intelligence for Nutrition Applications: Recent Advancements)

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19 pages, 3505 KiB

Open AccessArticle

Modulating the Human Gut Microbiota through Hypocaloric Balanced Diets: An Effective Approach for Managing Obesity

by Hongchao Wang, Wenyan Song, Weiwei Yuan, Qunyan Zhou, Faizan Ahmed Sadiq, Jianxin Zhao, Wenjun Wu and Wenwei Lu

Nutrients 2023, 15(14), 3101; https://doi.org/10.3390/nu15143101 - 11 Jul 2023

Cited by 3 | Viewed by 1412

Abstract

This study aimed to investigate the effects of a hypocaloric balanced diet (HBD) on anthropometric measures and gut microbiota of 43 people with obesity. Fecal samples were collected from the study subjects at weeks 0 and 12, and a detailed analysis of gut microbiota was performed using 16S rRNA gene sequencing. By comparing anthropometric measures and microbiota changes in subjects before and after the HBD intervention, we revealed the potential effects of HBD on weight loss and gut microbiota. Our results indicated that the HBD resulted in a significant decrease in body mass index (BMI), and most of the physiological indicators were decreased to a greater degree in the effective HBD group (EHBD, weight loss ≥ 5%) than in the ineffective HBD group (IHBD, weight loss < 5%). The HBD intervention also modified the gut microbiota of the subjects with obesity. Specifically, Blautia, Lachnoclostridium, Terrisporobacter, Ruminococcus (R. torques, R. gnavus), and Pseudomonas were significantly reduced. In addition, we employed machine learning models, such as XGBRF and GB models, to rank the importance of various features and identified the top 10 key bacterial genera involved. Gut microbiota co-occurrence networks showed the dominance of healthier microbiota following successful weight loss. These results suggested that the HBD intervention enhanced weight loss, which may be related to diet-induced changes in the gut microbiota. Full article

(This article belongs to the Special Issue Combining Artificial Intelligence for Nutrition Applications: Recent Advancements)

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