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.

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.

Exploration of differential responses to FODMAPs and gluten in people with irritable bowel syndrome- a double-blind randomized cross-over challenge study.

INTRODUCTION: There is large variation in response to diet in irritable bowel syndrome (IBS) and determinants for differential response are poorly understood. OBJECTIVES: Our aim was to investigate differential clinical and molecular responses to provocation with fermentable oligo-, di-, monosaccharides, and polyols (FODMAPs) and gluten in individuals with IBS. METHODS: Data were used from a crossover study with week-long interventions with either FODMAPs, gluten or placebo. The study also included a rapid provocation test. Molecular data consisted of fecal microbiota, short chain fatty acids, and untargeted plasma metabolomics. IBS symptoms were evaluated with the IBS severity scoring system. IBS symptoms were modelled against molecular and baseline questionnaire data, using Random Forest (RF; regression and clustering), Parallel Factor Analysis (PARAFAC), and univariate methods. RESULTS: Regression and classification RF models were in general of low predictive power (Q2 ≤ 0.22, classification rate < 0.73). Out of 864 clustering models, only 2 had significant associations to clusters (0.69 < CR < 0.73, p < 0.05), but with no associations to baseline clinical measures. Similarly, PARAFAC revealed no clear association between metabolome data and IBS symptoms. CONCLUSION: Differential IBS responses to FODMAPs or gluten exposures could not be explained from clinical and molecular data despite extensive exploration with different data analytical approaches. The trial is registered at www. CLINICALTRIALS: gov as NCT03653689 31/08/2018.

Triangulating nutrigenomics, metabolomics and microbiomics toward personalized nutrition and healthy living.

The unique physiological and genetic characteristics of individuals influence their reactions to different dietary constituents and nutrients. This notion is the foundation of personalized nutrition. The field of nutrigenetics has witnessed significant progress in understanding the impact of genetic variants on macronutrient and micronutrient levels and the individual's responsiveness to dietary intake. These variants hold significant value in facilitating the development of personalized nutritional interventions, thereby enabling the effective translation from conventional dietary guidelines to genome-guided nutrition. Nevertheless, certain obstacles could impede the extensive implementation of individualized nutrition, which is still in its infancy, such as the polygenic nature of nutrition-related pathologies. Consequently, many disorders are susceptible to the collective influence of multiple genes and environmental interplay, wherein each gene exerts a moderate to modest effect. Furthermore, it is widely accepted that diseases emerge because of the intricate interplay between genetic predisposition and external environmental influences. In the context of this specific paradigm, the utilization of advanced "omic" technologies, including epigenomics, transcriptomics, proteomics, metabolomics, and microbiome analysis, in conjunction with comprehensive phenotyping, has the potential to unveil hitherto undisclosed hereditary elements and interactions between genes and the environment. This review aims to provide up-to-date information regarding the fundamentals of personalized nutrition, specifically emphasizing the complex triangulation interplay among microbiota, dietary metabolites, and genes. Furthermore, it highlights the intestinal microbiota's unique makeup, its influence on nutrigenomics, and the tailoring of dietary suggestions. Finally, this article provides an overview of genotyping versus microbiomics, focusing on investigating the potential applications of this knowledge in the context of tailored dietary plans that aim to improve human well-being and overall health.

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.

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.

Soilless cultivation systems to produce tailored microgreens for specific nutritional needs.

BACKGROUND: The awareness of the importance of following dietary recommendations that meet specific biological requirements related to an individual's health status has significantly increased interest in personalized nutrition. The aim of this research was to test agronomic protocols based on soilless cultivation for providing consumers with new dietary sources of iodine (I), as well as alternative vegetable products to limit dietary potassium (K) intake; proposed cultivation techniques were evaluated according to their suitability to obtain such products without compromising agronomic performance. RESULTS: Two independent experiments, focused on I and K respectively, were conducted in a commercial greenhouse specializing in soilless production. Four different species were cultivated using three distinct concentrations of I (0, 1.5 and 3 mg L-1 ) and K (0, 60 and 120 mg L-1 ). Microgreens grown in I-rich nutrient solution accumulate more I, and the increase is dose-dependent. Compared to unbiofortified microgreens, the treatments with 1.5 and 3 mg L-1 of I resulted in 4.5 and 14 times higher I levels, respectively. Swiss chard has the highest levels of K (14 096 mg kg-1 of FW), followed by rocket, pea and radish. In radish, rocket and Swiss chard, a total reduction of K content in the nutrient solution (0 mg L-1 ) resulted in an average reduction of 45% in K content. CONCLUSION: It is possible to produce I-biofortified microgreens to address I deficiency, and K-reduced microgreens for chronic kidney disease-affected people. Species selection is crucial to customize nutritional profiles according to specific dietary requirements due to substantial mineral content variations across different species. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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.

Associations between Metabolomic Biomarkers of Avocado Intake and Glycemia in the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Avocado consumption is linked to better glucose homeostasis, but small associations suggest potential population heterogeneity. Metabolomic data capture the effects of food intake after digestion and metabolism, thus accounting for individual differences in these processes. OBJECTIVES: To identify metabolomic biomarkers of avocado intake and to examine their associations with glycemia. METHODS: Baseline data from 6224 multi-ethnic older adults (62% female) included self-reported avocado intake, fasting glucose and insulin, and untargeted plasma proton nuclear magnetic resonance metabolomic features (metabolomic data were available for a randomly selected subset; N = 3438). Subsequently, incident type 2 diabetes (T2D) was assessed over an ∼18 y follow-up period. A metabolome-wide association study of avocado consumption status (consumer compared with nonconsumer) was conducted, and the relationship of these features with glycemia via cross-sectional associations with fasting insulin and glucose and longitudinal associations with incident T2D was examined. RESULTS: Three highly-correlated spectral features were associated with avocado intake at metabolome-wide significance levels (P < 5.3 ∗ 10-7) and combined into a single biomarker. We did not find evidence that these features were additionally associated with overall dietary quality, nor with any of 47 other food groups (all P > 0.001), supporting their suitability as a biomarker of avocado intake. Avocado intake showed a modest association only with lower fasting insulin (ß = -0.07 +/- 0.03, P = 0.03), an association that was attenuated to nonsignificance when additionally controlling for body mass index (kg/m2). However, our biomarker of avocado intake was strongly associated with lower fasting glucose (ß = -0.22 +/- 0.02, P < 2.0 ∗ 10-16), lower fasting insulin (ß = -0.17 +/- 0.02, P < 2.0 ∗ 10-16), and a lower incidence of T2D (hazard ratio: 0.68; 0.63-074, P < 2.0 ∗ 10-16), even when adjusting for BMI. CONCLUSIONS: Highly significant associations between glycemia and avocado-related metabolomic features, which serve as biomarkers of the physiological impact of dietary intake after digestion and absorption, compared to modest relationships between glycemia and avocado consumption, highlights the importance of considering individual differences in metabolism when considering diet-health relationships.

Bitter- and Umami-Related Genes are Differentially Associated with Food Group Intakes: the Framingham Heart Study.

BACKGROUND: As suboptimal diet quality remains the leading modifiable contributor to chronic disease risk, it is important to better understand the individual-level drivers of food choices. Recently, a genetic component of food choices was proposed based on variants (SNPs) in genes related to taste perception (taste-related SNPs). OBJECTIVES: This study aimed to determine the cumulative contribution of taste-related SNPs for basic tastes (bitter, sweet, umami, salt, and sour), summarized as "polygenic taste scores," to food group intakes among adults. METHODS: Cross-sectional analyses were performed on 6230 Framingham Heart Study participants (mean age ± SD: 50 ± 14 y; 54% female). Polygenic taste scores were derived for tastes with ≥2 related SNPs identified in prior genome-wide association studies, and food group intakes (servings per week [sev/wk]) were tabulated from food frequency questionnaires. Associations were determined via linear mixed-effects models, using false discovery rates and bootstrap resampling to determine statistical significance. RESULTS: Thirty-three taste-related SNPs (9 bitter, 19 sweet, 2 umami, 2 sour, 1 salt) were identified and used to derive polygenic taste scores for bitter, sweet, umami, and sour. Per additional allele for higher bitter perception, whole grain intakes were lower by 0.17 (95% CI: -0.28, -0.06) sev/wk, and for higher umami perception, total and red/orange vegetable intakes were lower by 0.73 (95% CI: -1.12, -0.34) and 0.25 (95% CI: -0.40, -0.10) sev/wk, respectively. Subsequent analyses at the SNP level identified four novel SNP-diet associations-two bitter-related SNPs with whole grains (rs10960174 and rs6782149) and one umami-related SNP with total and red/orange vegetables (rs7691456)-which may have been driving the identified associations. CONCLUSIONS: Taste-related genes for bitter and umami were differentially associated with food choices that may impact diet quality. Hence, a benefit could be derived from leveraging knowledge of taste-related genes when developing personalized risk reduction dietary guidance.

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.

Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies.

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.

Nutritional management of type 2 diabetes in subjects with obesity: an international guideline for clinical practice.

Type 2 diabetes mellitus (T2DM) and obesity represent a global public health problem. Current nutritional recommendations focused on weight loss and overall dietary quality. However, there is no consensus on the optimal macronutrient composition of the diet, particularly for the long-term management of T2DM in subjects with obesity. An international panel of experts reviewed and critically appraised the updated literature published on the topic. This review primarily examines the evidence for areas of consensus and uncertainty about nutritional therapy in patients with T2DM and obesity. The aim of this article is to provide nutritional advice to manage these patients in clinical practice.

Protocol of a parallel, randomized controlled trial on the effects of a novel personalized nutrition approach by artificial intelligence in real world scenario.

BACKGROUND: 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). METHODS: 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. DISCUSSION: 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. TRIAL REGISTRATION: This trial has been registered with the Chinese Clinical Trial Registry (ChiCTR2100051771; date registered: 03/10/2021).

Design Issues in Personalized Nutrition Advice Systems.

The current health status of the general public can substantially benefit from a healthy diet. Using a personalized approach to initiate healthy dietary behavior seems to be a promising strategy, as individuals differ in terms of health status, subsequent dietary needs, and their desired behavior change support. However, providing personalized advice to a wide audience over a long period is very labor-intensive. This bottleneck can possibly be overcome by digitalizing the process of creating and providing personalized advice. An increasing number of personalized advice systems for different purposes is becoming available in the market, ranging from systems providing advice about just a single parameter to very complex systems that include many variables characterizing each individual situation. Scientific background is often lacking in these systems. In designing a personalized nutrition advice system, many design questions need to be answered, ranging from the required input parameters and accurate measurement methods (sense), type of modeling techniques to be used (reason), and modality in which the personalized advice is provided (act). We have addressed these topics in this viewpoint paper, and we have demonstrated the feasibility of setting up an infrastructure for providing personalized dietary advice based on the experience of 2 practical applications in a real-life setting.

The microbiome's fiber degradation profile and its relationship with the host diet.

BACKGROUND: The relationship between the gut microbiome and diet has been the focus of numerous recent studies. Such studies aim to characterize the impact of diet on the composition of the microbiome, as well as the microbiome's ability to utilize various compounds in the diet and produce metabolites that may be beneficial for the host. Consumption of dietary fibers (DFs)-polysaccharides that cannot be broken down by the host's endogenous enzymes and are degraded primarily by members of the microbiome-is known to have a profound effect on the microbiome. Yet, a comprehensive characterization of microbiome compositional and functional shifts in response to the consumption of specific DFs is still lacking. RESULTS: Here, we introduce a computational framework, coupling metagenomic sequencing with careful annotation of polysaccharide degrading enzymes and DF structures, for inferring the metabolic ability of a given microbiome sample to utilize a broad catalog of DFs. We demonstrate that the inferred fiber degradation profile (IFDP) generated by our framework accurately reflects the dietary habits of various hosts across four independent datasets. We further demonstrate that IFDPs are more tightly linked to the host diet than commonly used taxonomic and functional microbiome-based profiles. Finally, applying our framework to a set of ~700 metagenomes that represents large human population cohorts from 9 different countries, we highlight intriguing global patterns linking DF consumption habits with microbiome capacities. CONCLUSIONS: Combined, our findings serve as a proof-of-concept for the use of DF-specific analysis for providing important complementary information for better understanding the relationship between dietary habits and the gut microbiome.

Chrono-Nutrition: Circadian Rhythm and Personalized Nutrition.

The human circadian system has a period of approximately 24 h and studies on the consequences of "chornodisruption" have greatly expanded. Lifestyle and environmental factors of modern societies (i.e., artificial lighting, jetlag, shift work, and around-the-clock access to energy-dense food) can induce disruptions of the circadian system and thereby adversely affect individual health. Growing evidence demonstrates a complex reciprocal relationship between metabolism and the circadian system, in which perturbations in one system affect the other one. From a nutritional genomics perspective, genetic variants in clock genes can both influence metabolic health and modify the individual response to diet. Moreover, an interplay between the circadian rhythm, gut microbiome, and epigenome has been demonstrated, with the diet in turn able to modulate this complex link suggesting a remarkable plasticity of the underlying mechanisms. In this view, the study of the impact of the timing of eating by matching elements from nutritional research with chrono-biology, that is, chrono-nutrition, could have significant implications for personalized nutrition in terms of reducing the prevalence and burden of chronic diseases. This review provides an overview of the current evidence on the interactions between the circadian system and nutrition, highlighting how this link could in turn influence the epigenome and microbiome. In addition, possible nutritional strategies to manage circadian-aligned feeding are suggested.

Structural, rheological, and gelling characteristics of starch-based materials in context to 3D food printing applications in precision nutrition.

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.

Effects of personalized diets by prediction of glycemic responses on glycemic control and metabolic health in newly diagnosed T2DM: a randomized dietary intervention pilot trial.

BACKGROUND: Dietary modifications are crucial for managing newly diagnosed type 2 diabetes mellitus (T2DM) and preventing its health complications, but many patients fail to achieve clinical goals with diet alone. We sought to evaluate the clinical effects of a personalized postprandial-targeting (PPT) diet on glycemic control and metabolic health in individuals with newly diagnosed T2DM as compared to the commonly recommended Mediterranean-style (MED) diet. METHODS: We enrolled 23 adults with newly diagnosed T2DM (aged 53.5 ± 8.9 years, 48% males) for a randomized crossover trial of two 2-week-long dietary interventions. Participants were blinded to their assignment to one of the two sequence groups: either PPT-MED or MED-PPT diets. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses (PPGR). We further evaluated the long-term effects of PPT diet on glycemic control and metabolic health by an additional 6-month PPT intervention (n = 16). Participants were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using a smartphone application. RESULTS: In the crossover intervention, the PPT diet lead to significant lower levels of CGM-based measures as compared to the MED diet, including average PPGR (mean difference between diets, - 19.8 ± 16.3 mg/dl × h, p < 0.001), mean glucose (mean difference between diets, - 7.8 ± 5.5 mg/dl, p < 0.001), and daily time of glucose levels > 140 mg/dl (mean difference between diets, - 2.42 ± 1.7 h/day, p < 0.001). Blood fructosamine also decreased significantly more during PPT compared to MED intervention (mean change difference between diets, - 16.4 ± 37 µmol/dl, p < 0.0001). At the end of 6 months, the PPT intervention leads to significant improvements in multiple metabolic health parameters, among them HbA1c (mean ± SD, - 0.39 ± 0.48%, p < 0.001), fasting glucose (- 16.4 ± 24.2 mg/dl, p = 0.02) and triglycerides (- 49 ± 46 mg/dl, p < 0.001). Importantly, 61% of the participants exhibited diabetes remission, as measured by HbA1c < 6.5%. Finally, some clinical improvements were significantly associated with gut microbiome changes per person. CONCLUSION: In this crossover trial in subjects with newly diagnosed T2DM, a PPT diet improved CGM-based glycemic measures significantly more than a Mediterranean-style MED diet. Additional 6-month PPT intervention further improved glycemic control and metabolic health parameters, supporting the clinical efficacy of this approach. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT01892956.

Unique Metabolic Profiles Associate with Gestational Diabetes and Ethnicity in Low- and High-Risk Women Living in the UK.

BACKGROUND: Gestational diabetes mellitus (GDM) is the most common global pregnancy complication; however, prevalence varies substantially between ethnicities, with South Asians (SAs) experiencing up to 3 times the risk of the disease compared with white Europeans (WEs). Factors driving this discrepancy are unclear, although the metabolome is of great interest as GDM is known to be characterized by metabolic dysregulation. OBJECTIVES: The primary aim was to characterize and compare the metabolic profiles of GDM in SA and WE women (at <28 wk of gestation) from the Born in Bradford (BIB) prospective birth cohort in the United Kingdom. METHODS: In total, 146 fasting serum metabolites, from 2,668 pregnant WE and 2,671 pregnant SA women (average BMI 26.2 kg/m2, average age 27.3 y) were analyzed using partial least squares discriminatory analyses to characterize GDM status. Linear associations between metabolite values and post-oral glucose tolerance test measures of dysglycemia (fasting glucose and 2 h postglucose) were also examined. RESULTS: Seven metabolites associated with GDM status in both ethnicities (variable importance in projection ≥1), whereas 6 additional metabolites associated with GDM only in WE women. Unique metabolic profiles were observed in healthy-weight women who later developed GDM, with distinct metabolite patterns identified by ethnicity and BMI status. Of the metabolite values analyzed in relation to dysglycemia, lactate, histidine, apolipoprotein A1, HDL cholesterol, and HDL2 cholesterol associated with decreased glucose concentration, whereas DHA and the diameter of very low-density lipoprotein particles (nm) associated with increased glucose concertation in WE women, and in SAs, albumin alone associated with decreased glucose concentration. CONCLUSIONS: This study shows that the metabolic risk profile for GDM differs between WE and SA women enrolled in BiB in the United Kingdom. This suggests that etiology of the disease differs between ethnic groups and that ethnic-appropriate prevention strategies may be beneficial.