Emerging tools and technologies for GM modulation in personalized nutrition and type 2 diabetes management.
| Tool/Technology | Description | Purpose/Application | Examples | References |
|---|---|---|---|---|
| Omics technology | Omics is characterized as the study of biological systems’ massive data sets, including transcriptomics, proteomics, and metabolomics, and offering fresh perspectives on cell control. It is essential to employ omics to assess small intraindividual variability in order to use the data to develop precision nutrition. | Utilize biological diversity to tailor diets according to individual characteristics. | Epigenomics (DNA methylation, histone modifications, long non-coding RNA, telomere length), genomics (polymorphisms and other structural genetic variants), proteomics (protein signatures), metabolomics (metabolite profiles), transcriptomics (RNA expression patterns), and metagenomics (GM makeup and enterotypes). | [97–99] |
| Wearable and mobile sensors | Real-time information about a person’s behavior and health, including heart rate, activity level, sleep habits, and more, is gathered by wearable technology with sensors built in. Customers can use the information gathered by these devices directly to get real-time health information. | Continuously monitor behavior and physiological cues to guide in-the-moment dietary modifications. | For customized dietary feedback, mobile apps are connected with electrochemical, motion, and CGM sensors. The Apple Watch, Fitbit, Garmin, Polar, Amazon Fit, and the Samsung Galaxy Watch are all well-known wearable technologies. | [100, 101] |
| Machine learning and artificial intelligence (AI) models, advanced AI-enabled monitoring systems | To create customized diet regimens, AI models, such as machine learning, statistical, and mathematical models use information like weight, age, sex, and physical activity levels. The following are the principal uses of AI in digital nutrition: Food identification, customized diet regimens, and weight prediction. | To forecast dietary reactions and produce suggestions, analyze intricate, multifaceted data. Integrate sensor data from many models to estimate intake with high accuracy. | To predict macronutrient consumption based on physiological data, ML models connecting CGM, microbiota, genetic algorithm, Forbes model, and anthropometry to glucose reactions, Meal meter uses sensors in conjunction with machine learning. | [101–103] |
| Digital applications and platforms | Apps that measure food consumption, track calorie and nutrient intake, and even provide individualized suggestions based on dietary needs and goals are digital, most commonly mobile. Diet monitoring software is the primary digital tool for nutrition, diet planning, and tracking. | Facilitate scalable individualized interventions, personalized planning, and self-monitoring. | Web tools that combine AI advice, diet, logging, and professional help, as well as applications like Nutrium, Practice Better, Yazio, MyFitnessPal, Calorie Counter, MyNetDiary, Healthie, Genopalate, nutrigenomix, and DNALife. | [101, 103] |
| Decision support systems | An electronic tool to assist professionals in providing evidence-based, individualized nutrition treatment. These systems use rules, ontologies, guideline-based logic, or AI algorithms, gather and evaluate patient-specific data, and produce customized nutrition advice or alarms. The ultimate objective is to improve nutritional evaluation, optimize care processes, and raise the standard and level of customization of dietary interventions. | Integrate professional expertise and data to support clinical nutrition decisions. | AI chatbots such as Chat Diet; ontology-based diabetes nutrition therapy systems. | [104] |
| Vision-based dietary assessment tools | Allows users to snap photos of their meals, with food identification aiding in the completion of a diet journal. These models examine the photos and extract properties like color, shape, and texture to identify the food items and estimate their nutritional content using sophisticated computer vision and deep learning techniques. | Automate food consumption measurement and identification from photos. | Food detection and nutrition and content estimation using deep learning systems from meal images; applications include Wellnavi, FoodLog, GoCARB, and the MealLogger platform. | [101, 105] |
The authors extend their sincere gratitude to the Department of Food Science & Technology at Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India, for offering the essential facilities required for the preparation of this review paper.
SJ: Conceptualization, Investigation, Data curation, Writing—original draft. DB: Conceptualization, Writing—review & editing. AD: Conceptualization, Writing—review & editing. RK: Conceptualization, Writing—review & editing. RM: Conceptualization, Writing—review & editing. SK: Writing—review & editing. All authors read and approved the submitted version.
The authors declare that they have no conflicts of interest.
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