Personalized Nutrition by Prediction of Glycemic Responses

GARNN: An interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series

Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with type 1 or 2 diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been achieved by leveraging advanced deep learning methods to model multimodal data, i.e., sensor data and self-reported event data, organized as multi-variate time series (MTS). However, these methods are mostly regarded as "black boxes" and not entirely trusted by clinicians and patients. In this paper, we propose interpretable graph attentive recurrent neural networks (GARNNs) to model MTS, explaining variable contributions via summarizing variable importance and generating feature maps by graph attention mechanisms instead of post-hoc analysis. We evaluate GARNNs on four datasets, representing diverse clinical scenarios. Upon comparison with fifteen well-established baseline methods, GARNNs not only achieve the best prediction accuracy but also provide high-quality temporal interpretability, in particular for postprandial glucose levels as a result of corresponding meal intake and insulin injection. These findings underline the potential of GARNN as a robust tool for improving diabetes care, bridging the gap between deep learning technology and real-world healthcare solutions.

Predicting Postprandial Glycemic Responses With Limited Data in Type 1 and Type 2 Diabetes

BACKGROUND

A core challenge in managing diabetes is predicting glycemic responses to meals. Prior work identified significant interindividual variation in responses and developed personalized forecasts. However, intraindividual variation is still not well understood, and the most accurate approaches require invasive microbiome data. We aimed to investigate (1) whether postprandial glycemic responses (PPGRs) can be predicted with limited data and (2) sources of intraindividual variation.

METHODS

We used data collected from 397 people with Type 1 Diabetes (T1DEXI) and 100 people with Type 2 Diabetes (ShanghaiT2DM) who wore continuous glucose monitors (CGMs) and logged meals. Using dietary, demographic, and temporal features, we predicted 2 hours PPGR, and peak 2 hours postprandial glucose rise (Glumax). We evaluated the contribution of food features (eg, macronutrients, food category) and use of personal training data and investigated intraindividual variability in responses.

RESULTS

We achieved comparable accuracy to prior work for PPGR (T1DEXI R = 0.61, ShanghaiT2DM R = 0.72) and Glumax (T1DEXI R = 0.64, ShanghaiT2DM R = 0.73), without using invasive data like microbiome. Including food category features led to higher accuracy than macronutrients alone. Analysis of glycemic responses to duplicate meals identified time of day (PPGR: T1DEXI P < .05 for lunch, ShanghaiT2DM P < .001 for lunch and dinner) and menstrual cycle (Glumax: P < .05 for perimenstrual) as sources of variability.

CONCLUSIONS

We demonstrate that in individuals with T1D and T2D, glycemic responses to meals can be predicted without personalized training data or invasive physiological data.

Adherence to global diet quality score in relation to gastroesophageal reflux disease and flatulence in Iranian adults

INTRODUCTION

Gastroesophageal reflux disease (GERD) and flatulence are both prevalent afflictions and negatively impact the quality of life. This study aims to determine the relationship between the Global Diet Quality Score (GDQS), a novel metric based on the Prime Diet Quality Score with GERD and flatulence in Iranian adults.

METHODS

The cross-sectional study was conducted among 6202 adults in the context of the Shahedieh cohort study accomplished. Dietary intakes of participants were collected by food frequency questionnaires (FFQs). To calculate GDQS, 25 food groups were comprised (16 healthy and 7 unhealthy food groups and two food groups categorized as unhealthy when consumed excessively). GERD and flatulence were assessed by a self-reported questionnaire. To examine the association between GDQS with GERD and flatulence, logistic regression was performed in crude and adjusted models (Model I: adjustments for age and energy intake; Model II: gender, physical activity, marital status, occupation, educational levels, WSI, and BMI; and Model III: smoking status, depression, diabetes, hypertension, and cardio events.) RESULTS: Participants in the highest quintile of GDQS had 20% higher odds of having GERD than individuals in the lowest one (OR: 1.20; 95% CI: 0.88-1.65, P trend = 0.508). Compared to the lowest quintile, the participants in the highest quintile had no significant reduction in probability of having flatulence in the crude model (OR: 0.94; 95% CI: 0.81-1.11, P trend = 0.578). These associations remained non-significant after adjustments for confounding variables.

CONCLUSION

No significant associations were observed between higher adherence to GDQS with odds of GERD and flatulence in Iranian adults. To better understand these findings, longitudinal studies especially randomized clinical trials are needed.

Recent updates of probiotic dairy-based beverages

There is a rapid paradigm shift in the food consumption habits of consumers globally. The interest in healthier, safer, minimally processed and nature-identical foods is the driving force of this paradigm shift. Although the roots of this consumer trend go back further, especially the Covid-19 pandemic has contributed to the acceleration of this process. The effects of probiotics on human health have been known for many years. The commercial success of some probiotic microorganism strains, supported by clinical studies, is also evident. Probiotic microorganisms can be found in commercial products in a wide range of forms including powder, tablets or incorporated into liquid or solid food matrices. Milk and dairy products are suitable vehicles for the delivery of probiotics into the human body. Apart from well-established dairy-based probiotic foods including yogurt and yogurt-type beverages, in recent years some dairy products supplemented or enhanced with postbiotics and paraprobiotics are gaining popularity. The incorporation of next-generation probiotics in probiotic beverage formulations has also attracted the attention of researchers. The current state-of-the art for the utilization of next-generation probiotics, postbiotics and paraprobiotics in dairy-based probiotic beverages is the main focus of this review. Conventional milk-, whey- and buttermilk-based probiotic beverages are also covered.

Recent advances in precision nutrition and cardiometabolic diseases

A growing body of research on nutrition omics has led to recent advances in cardiovascular disease epidemiology and prevention. Within the PREDIMED trial, significant associations between diet-related metabolites and cardiovascular disease were identified, which were subsequently replicated in independent cohorts. Some notable metabolites identified include plasma levels of ceramides, acyl-carnitines, branched-chain amino acids, tryptophan, urea cycle pathways, and the lipidome. These metabolites and their related pathways have been associated with incidence of both cardiovascular disease and type 2 diabetes. Future directions in precision nutrition research include: a) developing more robust multimetabolomic scores to predict long-term risk of cardiovascular disease and mortality; b) incorporating more diverse populations and a broader range of dietary patterns; and c) conducting more translational research to bridge the gap between precision nutrition studies and clinical applications.

Application of personalized nutrition counseling according to glycemic response in obese adults: A randomized dietary intervention study

This randomized clinical trial was conducted to investigate the effects of personalized nutrition counseling according to blood glucose response and dietary intake, which can be measured using a flash continuous glucose monitoring (CGM) device, on weight changes and eating habits in obese adults. The participants of this study comprised obese adults over 30 years of age, which were randomly assigned to either the (1) personalized nutrition group (PN) or (2) control group (CON) with a study period of up to 12 weeks. Body weight, body mass index, body fat mass, body fat percentage, and waist-to-hip ratio significantly decreased in the PN group when compared with the CON group (P < 0.05; P < 0.01). Based on the findings of the flash CGM, the PN group showed a significant decrease in both blood glucose levels, hemoglobin A1c (HbA1c), and time above range (P < 0.05). The levels of blood HbA1c and insulin were significantly decreased in both groups, but the PN group showed a greater decrease (HbA1c, P = 0.000; insulin, P = 0.000) than the CON group did (HbA1c, P = 0.001; insulin, P = 0.001). The blood triglyceride levels were significantly lowered only in the PN group (P = 0.026). It was confirmed that personalized nutrition counseling using a flash CGM device was effective in reducing body weight, abdominal fat, and blood HbA1c, insulin, and triglyceride levels and improving meal quantity and eating habits in obese adults.

Personalising dietary advice for disease prevention: concepts and experiences

Personalised nutrition (PN) as a new endeavour emerged in the background of the human genome project with the ease to analyse genetic heterogeneity. First commercial offers with recommendations for diet and lifestyle changes, usually based on a few polymorphisms, entered markets soon after the presentation of the human genome blueprint. Although PN has seen many attempts, meanwhile, with the inclusion of other biomedical measures such as microbiome and/or continuous glucose monitoring, scientific assessments of such approaches in various settings revealed limited success. Although personalisation improved general compliance over generic advice, particular benefits in referring to biomedical measures and individual risks did, in most cases, not provide any significant advantage. Moreover, scholars criticised such approaches as of limited impact from a public health perspective by attracting mainly technology-open individuals of high social status and proper financial capabilities. Based on these experiences, new avenues for personalising dietary advice are developed, and those are going beyond pure biomedical data by assessing the entire food environment of the individual with its capabilities and constraints in the given life setting. Embedded into digital environments for data collection but also for bidirectional communication, new possibilities emerge. Artificial intelligence methods allow for the multitude of input data and highly complex decision trees to be derived to customize advice. And that can be delivered on the spot and in time in any language whenever decisions are made on what to buy or what to eat. But systems can also be employed to increase physical activity levels and for the adoption of a more healthy lifestyle in general.

Predictive models of post-prandial glucose response in persons with prediabetes and early onset type 2 diabetes: A pilot study

OBJECTIVE

Post-prandial glucose response (PPGR) is a risk factor for cardiovascular disease. Meal carbohydrate content is an important predictor of PPGR, but dietary interventions to mitigate PPGR are not always successful. A personalized approach, considering behaviour and habitual pattern of glucose excursions assessed by continuous glucose monitor (CGM), may be more effective.

RESEARCH DESIGN AND METHODS

Data were collected under free-living conditions, over 2 weeks, in older adults (age 60 ± 7, BMI 33.0 ± 6.6 kg/m2), with prediabetes (n = 35) or early onset type 2 diabetes (n = 3), together with sleep and physical activity by actigraphy. We assessed the predictive value of habitual CGM glucose excursions and fasting glucose on PPGR after a research meal (hereafter MEAL-PPGR) and during an oral glucose tolerance test (hereafter OGTT-PPGR).

RESULTS

Mean amplitude of glucose excursions (MAGE) and fasting glucose were highly predictive of all measures of OGTT-PPGR (AUC, peak, delta, mean glucose and glucose at 120 min; R2 between 0.616 and 0.786). Measures of insulin sensitivity and β-cell function (Matsuda index, HOMA-B and HOMA-IR) strengthened the prediction of fasting glucose and MAGE (R2 range 0.651 to 0.832). Similarly, MAGE and premeal glucose were also strong predictors of MEAL-PPGR (R2 range 0.546 to 0.722). Meal carbohydrates strengthened the prediction of 3 h AUC (R2 increase from 0.723 to 0.761). Neither anthropometrics, age nor habitual sleep and physical activity added to the prediction models significantly.

CONCLUSION

These data support a CGM-guided personalized nutrition and medicine approach to control PPGR in older individuals with prediabetes and diet and/or metformin-treated type 2 diabetes.

A menu for microbes: unraveling appetite regulation and weight dynamics through the microbiota-brain connection across the lifespan

Appetite, as the internal drive for food intake, is often dysregulated in a broad spectrum of conditions associated with over- and under-nutrition across the lifespan. Appetite regulation is a complex, integrative process comprising psychological and behavioral events, peripheral and metabolic inputs, and central neurotransmitter and metabolic interactions. The microbiota-gut-brain axis has emerged as a critical mediator of multiple physiological processes, including energy metabolism, brain function, and behavior. Therefore, the role of the microbiota-gut-brain axis in appetite and obesity is receiving increased attention. Omics approaches such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics in appetite and weight regulation offer new opportunities for featuring obesity phenotypes. Furthermore, gut-microbiota-targeted approaches such as pre-, pro-, post-, and synbiotic, personalized nutrition, and fecal microbiota transplantation are novel avenues for precision treatments. The aim of this narrative review is 1) to provide an overview of the role of the microbiota-gut-brain axis in appetite regulation across the lifespan and 2) to discuss the potential of omics and gut microbiota-targeted approaches to deepen understanding of appetite regulation and obesity.

Metagenome-informed metaproteomics of the human gut microbiome, host, and dietary exposome uncovers signatures of health and inflammatory bowel disease

Host-microbiome-dietary interactions play crucial roles in regulating human health, yet their direct functional assessment remains challenging. We adopted metagenome-informed metaproteomics (MIM), in mice and humans, to non-invasively explore species-level microbiome-host interactions during commensal and pathogen colonization, nutritional modification, and antibiotic-induced perturbation. Simultaneously, fecal MIM accurately characterized the nutritional exposure landscape in multiple clinical and dietary contexts. Implementation of MIM in murine auto-inflammation and in human inflammatory bowel disease (IBD) characterized a "compositional dysbiosis" and a concomitant species-specific "functional dysbiosis" driven by suppressed commensal responses to inflammatory host signals. Microbiome transfers unraveled early-onset kinetics of these host-commensal cross-responsive patterns, while predictive analyses identified candidate fecal host-microbiome IBD biomarker protein pairs outperforming S100A8/S100A9 (calprotectin). Importantly, a simultaneous fecal nutritional MIM assessment enabled the determination of IBD-related consumption patterns, dietary treatment compliance, and small intestinal digestive aberrations. Collectively, a parallelized dietary-bacterial-host MIM assessment functionally uncovers trans-kingdom interactomes shaping gastrointestinal ecology while offering personalized diagnostic and therapeutic insights into microbiome-associated disease.

The gut microbiota in spondyloarthritis: an update

PURPOSE OF REVIEW

This review provides an updated overview of the gut microbiota's involvement in spondyloarthritis (SpA) from a clinical perspective. It explores mechanisms by which the gut microbiota may influence SpA pathogenesis and considers the therapeutic implications of targeting the microbiome in SpA treatment.

RECENT FINDINGS

The pathogenesis of SpA is multifactorial, involving genetic predisposition, external factors and dysregulation of the immune system. Recent studies have identified alterations in the gut microbiome of patients with SpA, including changes in microbial diversity and specific taxa linked to disease activity. HLA-B27 status seems to influence gut microbiota composition, potentially impacting disease progression. In HLA-B27 transgenic rats, the association between gut microbiota and SpA development has been confirmed, supporting findings from human studies. A compromised gut barrier, influenced by proteins like zonulin, may allow microbial antigens to translocate, triggering immune responses associated with SpA.

SUMMARY

These findings highlight the potential for microbiota-modulating therapies, such as probiotics, prebiotics, diet and exercise, in managing SpA. However, methodological variability in human studies exposes the need for more rigorous research to better understand these associations. This may offer the opportunity to refine treatment strategies, offering a personalized approach to managing the disease.

The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations

Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges. Given its overlap with leading comorbidities such as heart disease, innovative solutions are necessary to improve risk prediction and management strategies. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools in healthcare, offering novel approaches to chronic disease prevention. This narrative review explores the role of AI/ML in obesity risk prediction and management, with a special focus on childhood obesity. We begin by examining the multifactorial nature of obesity, including genetic, behavioral, and environmental factors, and the limitations of traditional approaches to predict and treat morbidity associated obesity. Next, we analyze AI/ML techniques commonly used to predict obesity risk, particularly in minimizing childhood obesity risk. We shift to the application of AI/ML in obesity management, comparing perspectives from healthcare providers versus patients. From the provider's perspective, AI/ML tools offer real-time data from electronic medical records, wearables, and health apps to stratify patient risk, customize treatment plans, and enhance clinical decision making. From the patient's perspective, AI/ML-driven interventions offer personalized coaching and improve long-term engagement in health management. Finally, we address key limitations and challenges, such as the role of social determinants of health, in embracing the role of AI/ML in obesity management, while offering our recommendations based on our literature review.

The Power of Environment: A Comprehensive Review of the Exposome's Role in Healthy Aging, Longevity, and Preventive Medicine-Lessons from Blue Zones and Cilento

Aging and longevity are shaped by the exposome, a dynamic network of environmental, social, and biological factors. Understanding how these exposures interact with biological mechanisms can inform strategies for healthier aging. Background/Objectives: This review explores the exposome as a dynamic system encompassing both protective and risk factors, with a specific focus on how beneficial environmental exposures, microbiome diversity, lifestyle behaviors, and resilience mechanisms contribute to successful aging. By analyzing high-longevity populations, such as the Blue Zones and Cilento, it aims to identify common determinants of successful aging. Methods: A mixed-method study was conducted, combining a systematic review of the English literature (2003-2024) with a comparative analysis of longevity regions. A structured search was performed in PubMed, Scopus, and Google Scholar using keywords such as "longevity", "Blue Zones", "Cilento", "microbiome", "environmental factors", and related terms. Additionally, qualitative and quantitative analysis were applied to assess key protective factors across different aging models. Results: This study identified key factors contributing to successful aging in longevity hotspots, including sustained exposure to biodiverse natural environments, adherence to Mediterranean or plant-based diet rich in polyphenols and probiotics, regular physical activity, strong social networks, and psychological resilience. A novel aspect of this review is the role of the gut microbiome as a mediator between environmental exposures and immune-metabolic health, influencing inflammation modulation and cellular aging. Despite geographic and cultural differences, case studies reveal a shared pattern of protective factors that collectively enhance lifespan and healthspan. Conclusions: The exposome is a critical determinant of aging trajectories, acting through complex interactions between environmental and biological mechanisms. By integrating insights from high-longevity populations, this mixed-method study proposes a comprehensive framework for optimizing microbiome health, enhancing resilience, and promoting protective environmental exposures. These findings provide a translational perspective to guide future interventions in aging research and global health initiatives.

Efficacy of microbiome-targeted interventions in obesity management- A comprehensive systematic review

BACKGROUND

Obesity is a global health crisis linked to numerous chronic diseases. The gut microbiome plays a crucial role in human metabolism, and emerging evidence suggests that modulating the microbiome may offer novel therapeutic avenues for obesity management.

OBJECTIVE

This systematic review aimed to assess the efficacy and safety of microbiome-targeted interventions, including probiotics, prebiotics, synbiotics, and fecal microbiota transplantation, in improving body composition, metabolic parameters, and inflammatory markers in overweight and obese adults.

METHODS

A comprehensive search of PubMed, Scopus, and ScienceDirect was conducted to identify relevant studies published between 2005 and 2023. Included studies were assessed for methodological quality and risk of bias using the Cochrane Collaboration tool.

RESULTS

Body composition: Most studies demonstrated significant reductions in body weight, Body mass index, and body fat percentage.

METABOLIC PARAMETERS

Improvements were observed in lipid profiles (reduced cholesterol, triglycerides) and glucose metabolism (improved insulin sensitivity).

INFLAMMATORY MARKERS

Significant reductions were observed in inflammatory markers such as Interleukins (IL-6, IL-8) and C-reactive protein.

MICROBIAL COMPOSITION

Interventions generally led to shifts in microbial composition, with increases in beneficial bacteria such as Bifidobacterium and Lactobacillus.

ADVERSE EVENTS

Adverse events were generally minimal and limited.

CONCLUSION

This review provides strong evidence that microbiome-targeted interventions can effectively improve body composition, metabolic parameters, and inflammatory markers in individuals with obesity. Further research is needed to optimize intervention strategies, identify specific microbial targets, and translate these findings into effective clinical applications.

Harnessing precision nutrition to individualize weight restoration in anorexia nervosa

Anorexia nervosa (AN) is a severe psychiatric disorder for which effective treatment and sustained recovery are contingent upon successful weight restoration, yet the efficacy of existing treatments is suboptimal. This narrative review considers the potential of precision nutrition for tailoring dietary interventions to individual characteristics to enhance acute and longer-term weight outcomes in AN. We review key factors that drive variation in nutritional requirements, including energy expenditure, fecal energy loss, the gut microbiota, genetic factors, and psychiatric comorbidities. Although scientific evidence supporting precision nutrition in AN is limited, preliminary findings suggest that individualized nutrition therapies, particularly those considering duration of illness and the gut microbiota, may augment weight gain. Some patients may benefit from microbiota-directed dietary plans that focus on restoring microbial diversity, keystone taxa, or functions that promote energy absorption, which could enhance weight restoration-although stronger evidence is needed to support this approach. Furthermore, accounting for psychiatric comorbidities such as depression and anxiety as well as genetic factors influencing metabolism may help refine nutrition prescriptions improving upon existing energy estimation equations, which were not developed for patients with AN. Given the reliance on large sample sizes, costly data collection, and the need for computationally intensive artificial intelligence algorithms to assimilate deep phenotypes into personalized interventions, we highlight practical considerations related to the implementation of precision nutrition approaches in clinical practice. More research is needed to identify which factors, including metabolic profiles, genetic markers, demographics, and habitual lifestyle behaviors, are most critical to target for individualizing weight restoration, and whether personalized recommendations can be practicably applied to improve and sustain patient recovery from this debilitating disorder with high relapse and mortality rates.

Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota

We developed a prediction model for postprandial glycemic response (PPGR) in pregnant women, including those with diet-treated gestational diabetes mellitus (GDM) and healthy women, and explored the role of gut microbiota in improving prediction accuracy. The study involved 105 pregnant women (77 with GDM, 28 healthy), who underwent continuous glucose monitoring (CGM) for 7 days, provided food diaries, and gave stool samples for microbiome analysis. Machine learning models were created using CGM data, meal content, lifestyle factors, biochemical parameters, and microbiota data (16S rRNA gene sequence analysis). Adding microbiome data increased the explained variance in peak glycemic levels (GLUmax) from 34 to 42% and in incremental area under the glycemic curve (iAUC120) from 50 to 52%. The final model showed better correlation with measured PPGRs than one based only on carbohydrate count (r = 0.72 vs. r = 0.51 for iAUC120). Although microbiome features were important, their contribution to model performance was modest.

Associations Between TCF7L2, PPARγ, and KCNJ11 Genotypes and Insulin Response to an Oral Glucose Tolerance Test: A Systematic Review

SCOPE

Insulin responses to standardized meals differ between individuals. This variability may in part be explained by genotype. This systematic review evaluates associations between genotype and insulin response to an oral glucose tolerance test (OGTT) in terms of insulin area under the curve (AUC).

METHODS AND RESULTS

Three electronic databases (Web of Science, Embase, PubMed) were searched for studies investigating associations between insulin AUC after an OGTT and single nucleotide polymorphisms (SNPs) belonging to the transcription factor 7 like 2 (TCF7L2) gene, the peroxisome proliferator-activated receptor gamma (PPARγ) gene, or the potassium inwardly rectifying channel subfamily J member 11 (KCNJ11) gene in persons without diabetes. A total of 5199 articles were identified, of which 38 were included. Among them were family-based studies (9), twin studies (2), and studies with unrelated participants (27). Seventeen articles investigated TCF7L2 (7 SNPs), 14 investigated PPARγ (1 SNP), and 8 investigated KCNJ11 (5 SNPs). For all investigated SNPs, at least half of the reports indicated no statistically significant association with postprandial insulin AUC.

CONCLUSION

No evidence was found for associations between TCF7L2, PPARγ, and KCNJ11 genotypes and insulin AUC after an OGTT. Future studies should investigate the effect of genetic risk scores on postprandial insulin.

Association of the glucose patterns after a single nonstandardized meal with the habitual diet composition and features of the daily glucose profile in individuals without diabetes

BACKGROUND

The postprandial glucose response (PPGR), contributing to the glycemic variability (GV), is positively associated with cardiovascular disease risk in people without diabetes, and can thus represent a target for cardiometabolic prevention strategies.

OBJECTIVES

The study aimed to distinguish patterns of PPGR after a single nonstandardized meal and to evaluate their relationship with the habitual diet and the daily glucose profile (DGP) in individuals at high-cardiometabolic risk.

METHODS

Baseline 4-d continuous glucose monitoring was performed in 159 adults recruited in the MEDGI-Carb trial. After a nonstandardized breakfast, parameters of the PPGR were estimated by a mechanistic model: baseline glucose; amplitude-the magnitude of postmeal glucose concentrations; frequency-the velocity of postmeal glucose oscillations; damping-the rate of postmeal glucose decay. PPGR patterns were identified by cluster analysis. Differences between clusters and the relationship between PPGR parameters and individual features were explored by one-way analysis of variance and correlation analysis, respectively.

RESULTS

Two patterns of PPGR emerged. Pattern A had a higher baseline, amplitude, frequency, and damping than B. Individuals in cluster A compared with B had higher energy (2002 ± 526 compared with 1766 ± 455 kcal, P = 0.025), protein (82 ± 22 compared with 72 ± 21 g, P = 0.028), and fat (87 ± 30 compared with 75 ± 22 g, P = 0.041), but not carbohydrate habitual intake. Pattern A compared to B associated with a higher average daily glucose (6.12 ± 0.50 compared with 5.88 ± 0.62 mmol/L, P = 0.019) and lower GV (11.67 ± 3.52 compared with 13.43 ± 3.78%, P = 0.010). Mean daily glucose correlated directly with baseline (rs = 0.419, P < 0.001) and amplitude (rs = 0.189, P = 0.022) of the PPGR, whereas DGP variability correlated directly with amplitude (rs = 0.218, P = 0.008), and inversely with frequency (rs = -0.179, P = 0.031) and damping (rs = -0.309, P < 0.001).

CONCLUSIONS

Two PPGR patterns after a single nonstandardized breakfast were identified in high-cardiometabolic risk individuals. The habitual diet was associated with the patterns and their dynamic parameters, which, in turn, could predict the individuals' DGP. Our findings could support the implementation of dietary strategies targeting the PPGR to ameliorate the cardiometabolic risk profile.

TRIAL REGISTRATION NUMBER

This study was registered at clinicaltrials.gov as NCT03410719.

The effect of breakfast skipping and sleep disorders on glycemic control, cardiovascular risk, and weight loss in type 2 diabetes

BACKGROUND & AIMS

Meal timing is an emerging branch of science that investigates the influence of eating patterns on the circadian rhythm and overall health. There are still discrepancies in the literature as to whether late distribution of food intake and sleep disorders could impact biochemical, anthropometric, and cardiovascular markers. The objectives of this study were firstly observe skipping breakfast and sleep disorders over 12 months. Secondarily, analyze the individual influence of these findings on changes biochemical, anthropometric, and cardiovascular markers during the same period.

METHODS

This descriptive study is part of a tertiary analysis in a recently published study. This research recruited 84 participants with Type 2 Diabetes (T2D) who were divided: Control-40 participants received only medical care; Intervention-44 participants received the same medical care along with nutritional assessment. Consultations occurred quarterly over 12th months, and a follow-up was conducted after 3 months. For influence analysis, non-normal variables were compared using Mann-Whitney, while normal variables were compared using unpaired t-tests. In all instances, α = 0.05 and P < 0.05 were adopted.

RESULTS

Analysis revealed a high percentage of patients in both groups who skipped breakfast, slept less than 6 h, and experienced nighttime awakenings during the 1st visit. By the 12th month, there was deterioration in all data in the control group and significant improvement in the intervention group. Those with sleep disturbances also had lower HDL-cholesterol (HDL-C) values (p = 0.0054). For the other analyzes no significant differences were found.

CONCLUSION

Participants who skipped breakfast and had more nocturnal awakenings possibly had worse glycemic and weight control, but this difference was not statistically significant and only trends were observed. Sleep disorders could affect HDL-C levels. However, the influence analysis does not establish a causal relationship and more clinical trials are needed to analyze this topic on T2D.

The impact of gut microbiome on immune and metabolic homeostasis in type 1 diabetes: Clinical insights for prevention and treatment strategies

Type 1 diabetes (T1D) is a complex disease triggered by a combination of genetic and environmental factors, where abnormal autoimmune responses lead to progressive damage of the pancreatic β cells and severe glucose metabolism disorder. Recent studies have increasingly highlighted the close link between gut microbiota dysbiosis and the development of T1D. This review delves into existing population studies to explore the intricate interactions between the gut microbiota and the immune and metabolic homeostasis in T1D. It summarizes how changes in the structure and function of the gut microbiota are closely associated with the onset and progression of T1D across its natural course and clinical stages. More importantly, based on evidence accumulated from clinical observations and trials, we pioneer the discussion on gut microbiota-based T1D prevention and treatment strategies, this not only enriches our understanding of the complex pathological mechanisms of T1D but also provides potential directions for developing novel prevention and treatment strategies.

Peeling off the layers from microbial dark matter (MDM): recent advances, future challenges, and opportunities

Microbes represent the most common organisms on Earth; however, less than 2% of microbial species in the environment can undergo cultivation for study under laboratory conditions, and the rest of the enigmatic, microbial world remains mysterious, constituting a kind of "microbial dark matter" (MDM). In the last two decades, remarkable progress has been made in culture-dependent and culture-independent techniques. More recently, studies of MDM have relied on culture-independent techniques to recover genetic material through either unicellular genomics or shotgun metagenomics to construct single-amplified genomes (SAGs) and metagenome-assembled genomes (MAGs), respectively, which provide information about evolution and metabolism. Despite the remarkable progress made in the past decades, the functional diversity of MDM still remains uncharacterized. This review comprehensively summarizes the recently developed culture-dependent and culture-independent techniques for characterizing MDM, discussing major challenges, opportunities, and potential applications. These activities contribute to expanding our knowledge of the microbial world and have implications for various fields including Biotechnology, Bioprospecting, Functional genomics, Medicine, Evolutionary and Planetary biology. Overall, this review aims to peel off the layers from MDM, shed light on recent advancements, identify future challenges, and illuminate the exciting opportunities that lie ahead in unraveling the secrets of this intriguing microbial realm.

Testing a Personalized Behavioral Weight Loss Approach Using Multifactor Prescriptions and Self-Experimentation: 12-Week mHealth Pilot Randomized Controlled Trial Results

Background

Behavioral weight loss (WL) interventions typically follow standard diet and activity prescriptions for intervention duration to produce an energy deficit. Though average weight losses in these programs are clinically meaningful, there is heterogeneity in weight outcomes. Personalized diet and activity prescriptions may help increase the potency of WL programs by reducing this heterogeneity.

Methods

This 12-week pilot study randomized participants (n = 35; BMI 34.6 ± 4.9 kg/m2, 34% with HbA1c 5.7%-6.4%) in a 3:1 ratio to a Personalized Behavioral Weight Loss (PBWL) or standard BWL and compared the feasibility and efficacy of these approaches. Both groups received a study mobile app, smart scale, activity tracker, and weekly telephone coaching sessions; PBWL participants received a continuous glucose monitoring device. PBWL participants had goals for 1) macronutrient composition (low fat or carbohydrate), 2) meal frequency (3 meals or meals and snacks), and 3) activity focus (daily or weekly goal); they experimented with different 3-part prescriptions, in random order and combination, for the first 4 weeks then picked their 3 goals to follow for weeks 5-12.

Results

Study retention (100%) and satisfaction were high. Mean 3-month weight loss (kg) was greater in PBWL (-7.08 (0.74)) than BWL (-3.79 (0.84), P = 0.03); 74% of PBWL and 63% of BWL participants were "optimizers" who achieved a 5% weight loss at 3 months. PBWL optimizers lost more weight (-8.66 (0.66)) than BWL optimizers (-4.76 (0.43), p < 0.001).

Conclusions

Experimentally-derived personalized prescriptions supported greater 12-week weight loss than standard recommendations. Trial Registration: ClinicalTrials.gov NCT04639076.

Impact of Dietary Intake and Cardiorespiratory Fitness on Glycemic Variability in Adolescents: An Observational Study

Background

Cardiorespiratory fitness (CRF), estimated by maximum oxygen consumption (VO2 max) during exercise, is worsening among adolescents and associated with a decline in metabolic health into adulthood. Glycemic patterns may provide a mechanism between CRF and health.

Objectives

This study assessed the feasibility of measuring glycemic patterns using continuous glucose monitoring (CGM) in adolescents, aged 14-22 y, to estimate the relationship between VO2 max and glucose patterns.

Methods

Healthy adolescents (n = 30) were recruited for a treadmill VO2 max test and to complete the following activities for 7-10 d: 1) wear a Dexcom G6 CGM, 2) complete ≥3 24-h dietary recalls, and 3) complete 1 at-home oral glucose tolerance test (OGTT, 75 g glucose). Glycemic patterns were extracted as mean glucose, the coefficient of variance, the mean amplitude of glycemic excursions, and the mean of daily differences. The 2-h glucose responses to the OGTT and individual meals were extracted. Statistical analyses evaluated the relationship between VO2 max and 1) overall glycemic patterns and 2) the maximum glucose level and AUC response to OGTT and meals, stratified by sex.

Results

Participant feasibility demonstrated that 90% completed CGM data (n = 27), 87% ≥7 d of CGM data (n = 26), 97% attempted OGTT (n = 29), and 93% completed ≥3 dietary recalls (n = 28). Most participants had normal BMI (70%) with an even distribution of sex (44% male). Males exhibited an inverse relationship between VO2 max and overall mean glucose (ß= -7.7, P = 0.04). Males demonstrated an inverse relationship between VO2 max and 1) maximum glucose (ß = -29, P = 0.006) and AUC (ß = -2702, P = 0.001) in response to the OGTT and 2) AUC (ß = -1293, P = 0.03) in response to meals. No association was observed between VO2 max and glucose patterns in females.

Conclusions

A sex-specific relationship between VO2 max and glycemic patterns was observed, suggesting a unique metabolic capacity during late adolescence by sex.This trial was registered at clinicaltrials.gov as NCT05845827.

Pitfalls in gut single-cell eukaryote research

Gut single-celled eukaryotes (GSCEs) are found in billions of people worldwide, but we still know little about their functions and relationships in human gut ecology. Lately, retrospective analysis of bacterial data obtained by next-generation sequencing (NGS) methods has been used to identify links between GSCEs, gut bacteria, host metabolism, and host phenotypical traits, suggesting possible direct or indirect associations to favorable gut microbiome features and other health parameters. Here, we highlight some of the pitfalls related to the research strategy typically used so far and propose action points that could pave the way for a more accurate understanding of GSCEs in human health and disease.

Gut microbiota of patients insusceptible to olanzapine-induced fatty liver disease relieves hepatic steatosis in rats

Olanzapine-induced fatty liver disease continues to pose vital therapeutic challenges in the treatment of psychiatric disorders. In addition, we observed that some patients were less prone to hepatic steatosis induced by olanzapine. Therefore, we aimed to investigate the role and the underlying mechanism of the intestinal flora in olanzapine-mediated hepatic side effects and explore the possible countermeasures. Our results showed that patients with different susceptibilities to olanzapine-induced fatty liver disease had different gut microbial diversity and composition. Furthermore, we performed fecal microbiota treatment (FMT), and confirmed that the gut microbiome of patients less prone to the fatty liver caused by olanzapine exhibited an alleviation against fatty liver disease in rats. In terms of mechanism, we revealed that the cross talk of leptin with the gut-short-chain fatty acid (SCFA)-liver axis play a critical role in olanzapine-related fatty degeneration in liver. These findings propose a promising strategy for overcoming the issues associated with olanzapine application and will hopefully inspire future in-depth research of fecal microbiota-based therapy in olanzapine-induced fatty liver disease.NEW & NOTEWORTHY Patients who were less inclined to have olanzapine-induced fatty liver had different gut microbiota profiles than did those in the susceptible cohort. Lachnospiraceae, Ruminococcaceae, Oscillospiraceae, Butyricicoccaceae, and Christensenellaceae were enriched in patients who were less prone to fatty liver disease caused by olanzapine. Fecal microbiota treatment (FMT) with these fecal samples promoted short-chain fatty acid (SCFA) production, which attenuated the circulating leptin and inhibited FASN and ACC1, thereby suppressing lipid synthesis in the liver, ultimately leading to alleviation of hepatic steatosis.

The Interplay of Nutrition, the Gut Microbiota and Immunity and Its Contribution to Human Disease

Nutrition, the gut microbiota and immunity are all important factors in the maintenance of health. However, there is a growing realization of the complex interplay between these elements coalescing in a nutrition-gut microbiota-immunity axis. This regulatory axis is critical for health with disruption being implicated in a broad range of diseases, including autoimmune disorders, allergies and mental health disorders. This new perspective continues to underpin a growing number of innovative therapeutic strategies targeting different elements of this axis to treat relevant diseases. This review describes the inter-relationships between nutrition, the gut microbiota and immunity. It then details several human diseases where disruption of the nutrition-gut microbiota-immunity axis has been identified and presents examples of how the various elements may be targeted therapeutically as alternate treatment strategies for these diseases.

Precision nutrition-based interventions for the management of obesity in children and adolescents up to the age of 19 years

BACKGROUND

Precision nutrition-based methods develop tailored interventions and/or recommendations accounting for determinants of intra- and inter-individual variation in response to the same diet, compared to current 'one-size-fits-all' population-level approaches. Determinants may include genetics, current dietary habits and eating patterns, circadian rhythms, health status, gut microbiome, socioeconomic and psychosocial characteristics, and physical activity. ​​​​In this systematic review, we examined the evidence base for the effect of interventions based on precision nutrition approaches on overweight and obesity in children and adolescents to help inform future research and global guidelines.

OBJECTIVES

To examine the impact of precision nutrition-based interventions for the management of obesity in children and adolescents in all their diversity.

SEARCH METHODS

We searched CENTRAL, MEDLINE, CINAHL, Web of Science Core Collection, BIOSIS Previews, Global Index Medicus (all regions), IBECS, SciELO, PAHO, PAHO IRIS, WHO IRIS, WHOLIS, Bibliomap, and TRoPHI, as well as the WHO ICTRP and ClinicalTrials.gov. We last searched the databases on 23 July 2024. We did not apply any language restrictions.

SELECTION CRITERIA

We included randomised or quasi-randomised controlled trials that evaluated precision nutrition-based interventions (accounting for 'omics' such as phenotyping, genotyping, gut microbiome; clinical data, baseline dietary intake, postprandial glucose response, etc., and/or including artificial intelligence such as machine learning methods) compared to general or one-size-fits-all interventions or no intervention in children and adolescents aged 0 to 9 years or 10 to 19 years with overweight or obesity.

DATA COLLECTION AND ANALYSIS

Two review authors independently conducted study screening, data extraction, and risk of bias and GRADE assessments. We used fixed-effect analyses. Our outcomes of interest were physical and mental well-being, physical activity, health-related quality of life, obesity-associated disability, and adverse events associated with the interventions as defined or measured by trialists, and weight change (reduction, stabilisation or maintenance).

MAIN RESULTS

Two studies (3 references, 105 participants) conducted in Ukraine and Greece met our eligibility criteria. One study reported nonprofit funding sources, whilst the other did not report funding, and the certainty of evidence ranged from very low to low across outcomes (all measured at endpoint). Only one trial (65 participants) contributed data on our primary outcomes of interest. Precision nutrition-based intervention versus one-size-fits-all intervention or standard of care In children 0 to 9 years of age, evidence is very uncertain about the effect of a precision nutrition-based intervention (a computerised Decision Support Tool (DST) that incorporates a variety of participant data and provides personalised diet recommendations based on decision-tree algorithms) on body mass index (BMI) (mean difference (MD) -1.40 kg/m2, 95% confidence interval (CI) -3.48 to 0.68; 1 study, 35 participants; very low-certainty evidence) and on weight (MD -2.60 kg, 95% CI -8.42 to 3.22; 1 study, 35 participants; very low-certainty evidence) compared with a one-size-fits-all control intervention. In children and adolescents 10 to 19 years of age, evidence is very uncertain about the effect of a precision nutrition-based intervention (computerised DST) on BMI (MD 3.00 kg/m2, 95% CI -0.26 to 6.26; 1 study, 30 participants; very low-certainty evidence) and on weight (MD 11.40 kg, 95% CI -0.47 to 23.27; 1 study, 30 participants; very low-certainty evidence) compared with a one-size-fits-all control intervention.

AUTHORS' CONCLUSIONS

Based on data from two small studies with a total of 105 participants, the evidence is very uncertain about the effect of precision nutrition-based interventions on body weight or BMI. This review was limited by the number of available randomised controlled trials in this relatively nascent field. Given these limitations, the two studies do not provide sufficient evidence to adequately inform practice. Future research should report participant outcome data, including outcomes related to mental, emotional, and functional well-being, in addition to biochemical and physical measures, stratified by World Health Organization-defined age groups (children (0 to 9 years), and children and adolescents (10 to 19 years)). Future studies should also report methods related to randomisation, blinding, and compliance, as well as include prespecified analysis plans.

Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study

BACKGROUND

Type 2 diabetes (T2D) is a leading cause of premature morbidity and mortality globally and affects more than 100 million people in the world's most populous country, India. Nutrition is a critical and evidence-based component of effective blood glucose control and most dietary advice emphasizes carbohydrate and calorie reduction. Emerging global evidence demonstrates marked interindividual differences in postprandial glucose response (PPGR) although no such data exists in India and previous studies have primarily evaluated PPGR variation in individuals without diabetes.

OBJECTIVE

This prospective cohort study seeks to characterize the PPGR variability among individuals with diabetes living in India and to identify factors associated with these differences.

METHODS

Adults with T2D and a hemoglobin A1c of ≥7 are being enrolled from 14 sites around India. Participants wear a continuous glucose monitor, eat a series of standardized meals, and record all free-living foods, activities, and medication use for a 14-day period. The study's primary outcome is PPGR, calculated as the incremental area under the curve 2 hours after each logged meal.

RESULTS

Data collection commenced in May 2022, and the results will be ready for publication by September 2025. Results from our study will generate data to facilitate the creation of machine learning models to predict individual PPGR responses and to facilitate the prescription of personalized diets for individuals with T2D.

CONCLUSIONS

This study will provide the first large scale examination variability in blood glucose responses to food in India and will be among the first to estimate PPGR variability for individuals with T2D in any jurisdiction.

TRIAL REGISTRATION

Clinical Trials Registry-India CTRI/2022/02/040619; https://tinyurl.com/mrywf6bf.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/59308.

Are standardized conditions needed for correct CGM data interpretation in subjects at early stages of glucose intolerance?

AIM

The present study comparatively evaluated glucose variability (GV) parameters derived from both continuous glucose monitoring (CGM) performed under standard conditions for a 24-h period and under usual everyday conditions for a 14-day period in a high-risk population without diabetes.

METHODS AND RESULTS

Seventy five subjects: 14 with normal glucose tolerance (NGT; mean age 43.6 ± 10.7 years; BMI 30.5 ± 6.9 kg/m2), 19 with high 1-h postload glucose > 8.6 mmol/l (1hrOGTT; mean age 45.6 ± 8.9 years; BMI 33.7 ± 6.9 kg/m2), and 42 with isolated impaired glucose tolerance (iIGT; mean age 47.6 ± 11.8 years; BMI 31.0 ± 6.5 kg/m2), were enrolled. An OGTT was performed. CGM was performed with blinded FreeStyleLibrePro for 24 h under standard conditions and for the rest of the 14-day period under usual everyday conditions. GV parameters derived from both periods were compared. There was a significant increase in GV with worsening of glucose tolerance from NGT, to 1hrOGTT and iIGT, independently of the conditions. Our findings showed moderate to strong correlations among GV indices between the studied periods in the cohort and in the 1hrOGTT and iIGT groups. However, a significant difference was found in some of the GV parameters between the analyzed periods.

CONCLUSION

The trend in GV is independent of the conditions, under which CGM is performed, in subjects at early stages of glucose intolerance. Although its measurements to some extend differ in standard and everyday conditions, there is no need of standardized conditions for correct interpretation of GV indices in this population.

Macronutrient Modulation in Metabolic Dysfunction-Associated Steatotic Liver Disease-the Molecular Role of Fatty Acids compared with Sugars in Human Metabolism and Disease Progression

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a significant public health concern, with its progression to metabolic dysfunction-associated steatohepatitis (MASH) and fibrosis leading to severe outcomes including cirrhosis, hepatocellular carcinoma, and liver failure. Whereas obesity and excess energy intake are well-established contributors to the development and progression of MASLD, the distinct role of specific macronutrients is less clear. This review examines the mechanistic pathways through which dietary fatty acids and sugars contribute to the development of hepatic inflammation and fibrosis, offering a nuanced understanding of their respective roles in MASLD progression. In terms of addressing potential therapeutic options, human intervention studies that investigate whether modifying the intake of dietary fats and carbohydrates affects MASLD progression are reviewed. By integrating this evidence, this review seeks to bridge the gap in the understanding between the mechanisms of macronutrient-driven MASLD progression and the effect of altering the intake of these nutrients in the clinical setting and presents a foundation for future research into targeted dietary strategies for the treatment of the disease.

Predicting metabolite response to dietary intervention using deep learning

Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in the gastrointestinal tract, is highly personalized and plays a key role in the metabolite responses to foods and nutrients. Accurately predicting metabolite responses to dietary interventions based on individuals' gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a method McMLP (Metabolite response predictor using coupled Multilayer Perceptrons) to fill in this gap. We provide clear evidence that McMLP outperforms existing methods on both synthetic data generated by the microbial consumer-resource model and real data obtained from six dietary intervention studies. Furthermore, we perform sensitivity analysis of McMLP to infer the tripartite food-microbe-metabolite interactions, which are then validated using the ground-truth (or literature evidence) for synthetic (or real) data, respectively. The presented tool has the potential to inform the design of microbiota-based personalized dietary strategies to achieve precision nutrition.

Noninvasive Optical Sensing of Aging and Diet Preferences Using Raman Spectroscopy

Effective dietary strategies and interventions for monitoring dietary exposures require accurate and noninvasive methods to understand how diet modulates health and risk of obesity; advances in technology are transforming the landscape and enabling more specific tailored approaches to nutritional guidance. This study explores the use of Raman spectroscopy (RS), a noninvasive and nondestructive analytical technique, to identify changes in the mice skin in response to constant dietary exposures. We found that RS is highly accurate to determine body composition as a result of habitual dietary patterns, specifically Vegan, Typical American, and Ketogenic diets, all very common in the US context. RS is based on major differences in the intensities of vibrational bands that originate from collagen. Moreover, RS could be used to predict folate deficiency and identify the sex of the animals. Finally, we found that RS could be used to track the chronological age of the mice. Considering the hand-held nature of the utilized spectrometer, one can expect that RS could be used to monitor and, consequently, personalize effects of diet on the body composition.

High interindividual variability of indoxyl sulfate production identified by an oral tryptophan challenge test

Indoxyl sulfate (IS) has been implicated in the pathogenesis of cardiovascular diseases. IS is converted from indole, a metabolite of dietary tryptophan through the action of gut microbial tryptophanase, by two hepatic enzymes: CYP2E1 and SULT1A1. We hypothesized that the effect of tryptophan intake on IS production might differ from person to person. We enrolled 72 healthy persons (33 ± 7 years; 54.2% women) to undergo an oral tryptophan challenge test (OTCT), in which 7 blood samples were collected at 0, 4, 8, 12, 24, 36, and 48 h following oral administration of L-tryptophan 2000 mg. We observed high interindividual variability of IS production in the response to an OTCT. Twenty-four subjects in the lowest tertile of the baseline-adjusted area under the curve of IS were defined as low-IS producers, whereas 24 subjects in the highest tertile were defined as high-IS producers. There was no significant difference in baseline characteristics or CYP2E1 and SULT1A1-SNP genotyping distributions between the two IS-producing phenotypes. However, distinct differences in gut microbial composition were identified. In addition, the abundance of tryptophanase was significantly higher in the high-IS producers than in the low-IS producers (P = 0.01). The OTCT may serve as personalized dietary guidance. High-IS producers are more likely to be at greater risk of cardiovascular diseases and may benefit from consuming foods low in tryptophan. Potential clinical applications of the OTCT in precision nutrition warrant further investigation.

Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations

The microbiota in individual habitats differ in both relative composition and absolute abundance. While sequencing approaches determine the relative abundances of taxa and genes, they do not provide information on their absolute abundances. Here, we developed a machine-learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applying our prediction model to a large-scale metagenomic dataset (n = 34,539), we demonstrated that microbial load is the major determinant of gut microbiome variation and is associated with numerous host factors, including age, diet, and medication. We further found that for several diseases, changes in microbial load, rather than the disease condition itself, more strongly explained alterations in patients' gut microbiome. Adjusting for this effect substantially reduced the statistical significance of the majority of disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.

Almond consumption for 8 weeks differentially modulates metabolomic responses to an acute glucose challenge compared to crackers in young adults

This study investigated the dynamic responses to an acute glucose challenge after 8 weeks of almond or cracker consumption (clinicaltrials.gov ID: NCT03084003). Young adults (n = 73, age: 18-19 years, BMI: 18-41 kg/m2) participated in an 8-week randomized, controlled, parallel-arm intervention and were assigned to consume either almonds (2 oz/d, n = 38) or an isocaloric control snack of graham crackers (325 kcal/d, n = 35) daily. Twenty participants from each group underwent a 2-hour oral glucose tolerance test (oGTT) at the end of the intervention. Metabolite abundances in the oGTT serum samples were quantified using untargeted metabolomics, and targeted analyses for free PUFAs, total fatty acids, oxylipins, and endocannabinoids. We hypothesized that 8-week almond consumption would differentially modulate the metabolomic response to a glucose challenge compared to crackers. Multivariate, univariate, and chemical enrichment analyses were conducted to identify significant metabolic shifts. Findings exhibit a biphasic lipid response with higher levels of unsaturated triglycerides earlier in the oGTT followed by lower levels later in the almond vs cracker group (p-value <.05, chemical enrichment analyses). Almond (vs cracker) consumption was also associated with higher AUC120 min of aminomalonate, and oxylipins (P-value <.05), but lower AUC120 min of l-cystine, N-acetylmannosamine, and isoheptadecanoic acid (P-value <.05). Additionally, the Matsuda Index in the almond group correlated with AUC120 min of CE 22:6 (r = -0.46; P-value <.05) and 12,13 DiHOME (r = 0.45; P-value <.05). Almond consumption for 8 weeks leads to dynamic, differential shifts in response to an acute glucose challenge, marked by alterations in lipid and amino acid mediators involved in metabolic and physiological pathways.

GutMetaNet: an integrated database for exploring horizontal gene transfer and functional redundancy in the human gut microbiome

Metagenomic studies have revealed the critical roles of complex microbial interactions, including horizontal gene transfer (HGT) and functional redundancy (FR), in shaping the gut microbiome's functional capacity and resilience. However, the lack of comprehensive data integration and systematic analysis approaches has limited the in-depth exploration of HGT and FR dynamics across large-scale gut microbiome datasets. To address this gap, we present GutMetaNet (https://gutmetanet.deepomics.org/), a first-of-its-kind database integrating extensive human gut microbiome data with comprehensive HGT and FR analyses. GutMetaNet contains 21 567 human gut metagenome samples with whole-genome shotgun sequencing data related to various health conditions. Through systematic analysis, we have characterized the taxonomic profiles and FR profiles, and identified 14 636 HGT events using a shared reference genome database across the collected samples. These HGT events have been curated into 8049 clusters, which are annotated with categorized mobile genetic elements, including transposons, prophages, integrative mobilizable elements, genomic islands, integrative conjugative elements and group II introns. Additionally, GutMetaNet incorporates automated analyses and visualizations for the HGT events and FR, serving as an efficient platform for in-depth exploration of the interactions among gut microbiome taxa and their implications for human health.

A pilot study of metaproteomics and DNA metabarcoding as tools to assess dietary intake in humans

Objective biomarkers of food intake are a sought-after goal in nutrition research. Most biomarker development to date has focused on metabolites detected in blood, urine, skin, or hair, but detection of consumed foods in stool has also been shown to be possible via DNA sequencing. An additional food macromolecule in stool that harbors sequence information is protein. However, the use of protein as an intake biomarker has only been explored to a very limited extent. Here, we evaluate and compare measurement of residual food-derived DNA and protein in stool as potential biomarkers of intake. We performed a pilot study of DNA sequencing-based metabarcoding and mass spectrometry-based metaproteomics in five individuals' stool sampled in short, longitudinal bursts accompanied by detailed diet records (n = 27 total samples). Dietary data provided by stool DNA, stool protein, and written diet record independently identified a strong within-person dietary signature, identified similar food taxa, and had significantly similar global structure in two of the three pairwise comparisons between measurement techniques (DNA-to-protein and DNA-to-diet record). Metaproteomics identified proteins including myosin, ovalbumin, and beta-lactoglobulin that differentiated food tissue types like beef from dairy and chicken from egg, distinctions that were not possible by DNA alone. Overall, our results lay the groundwork for development of targeted metaproteomic assays for dietary assessment and demonstrate that diverse molecular components of food can be leveraged to study food intake using stool samples.

Visual and Quantitative Analysis of Dietary Fiber-Microbiota Interactions via Metabolic Labeling In Vivo

Dietary fiber (DF)-based interventions are crucial in establishing a health-promoting gut microbiota. However, directly investigating DFs' in vivo interactions with intestinal bacteria remains challenging due to the lack of suitable tools. Here, we develop an in vivo metabolic labeling-based strategy, which enables not only imaging and identifying the bacteria that bind with specific DF in the intestines, but also quantifying DF's impact on their metabolic status. Four DFs, including galactan, rhamnogalacturonan and two inulins, are fluorescently derivatized and used for in vivo labeling to visually record DFs' interactions with gut bacteria. The subsequent cell-sorting, 16S rDNA sequencing, and fluorescence in situ hybridization identify the taxa that bind each DF. We then select a DF-binding species newly identified herein and verify its DF-catabolizing capability in vitro. Furthermore, we find that the indigenous metabolic status of Gram-positive bacteria, whether inulin-binders or not, is significantly enhanced by the inulin supplement. This trend is not observed in Gram-negative microbiota, even for the inulin-binders, demonstrating the ability of our methods in differentiating the primary, secondary DF-degraders from cross-feeders, a question that is difficult to answer by using other methods. Our strategy provides a novel chemical biology tool for deciphering the complex DF-bacteria interactions in the gut.

Two-dimensional Health State Map to define metabolic health using separated static and dynamic homeostasis features: a proof-of-concept study

Defining metabolic health is critical for the earlier reversing of metabolic dysfunction and disease, and fasting-based diagnosis may not adequately assess an individual's metabolic adaptivity under stress. We constructed a novel Health State Map (HSM) comprising a Health Phenotype Score (HPS) with fasting features alone and a Homeostatic Resilience Score (HRS) with five time-point features only (t = 30, 60, 90, 180, 240 min) following a standardized mixed macronutrient tolerance test (MMTT). Among 111 Chinese adults, when the same set of fasting and post-MMTT data as for the HSM was used, the mixed-score was highly correlated with the HPS. The HRS was significantly associated with metabolic syndrome prevalence, independently of the HPS (OR [95% CI]: 0.41 [0.18, 0.92]) and the mixed-score (0.34 [0.15, 0.69]). Moreover, the HRS could discriminate metabolic characteristics unseparated by the HPS and the mixed-score. Participants with higher HRSs had better metabolic traits than those with lower HRSs. Large interpersonal variations were also evidenced by evaluating postprandial homeostatic resiliencies for glucose, lipids and amino acids when participants had similar overall HRSs. Additionally, the HRS was positively associated with physical activity level and specific gut microbiome structure. Collectively, our HSM model might offer a novel approach to precisely define an individual's metabolic health and nutritional capacity.

Imprecision nutrition? Intraindividual variability of glucose responses to duplicate presented meals in adults without diabetes

BACKGROUND

Continuous glucose monitors (CGMs) are used to characterize postprandial glucose responses and provide personalized dietary advice to minimize glucose excursions. The efficacy of such advice depends on reliable glucose responses.

OBJECTIVES

To explore within-subject variability of CGM responses to duplicate presented meals in an inpatient setting.

METHODS

CGM data were collected from two inpatient feeding studies in 30 participants without diabetes, capturing 1189 responses to duplicate meals presented ∼1 wk apart from four dietary patterns. One study used two different CGMs (Abbott Freestyle Libre Pro and Dexcom G4 Platinum) whereas the other study used only Dexcom. We calculated the incremental area under the curve (iAUC) for glucose for each 2-h postmeal period and compared within-subject, within-CGM responses to duplicate presented meals using linear correlations, intra-class correlation coefficients (ICC), and Bland-Altman analyses. Individual variability of interstitial glucose responses to duplicate meals were also compared with different meals using standard deviations (SDs).

RESULTS

There were weak-to-moderate positive linear correlations between within-subject iAUCs for duplicate meals [Abbott r = 0.46, 95% confidence interval (CI): 0.38, 0.54, P < 0.0001 and Dexcom r = 0.45, 95% CI: 0.39, 0.50, P < 0.0001], with low within-participant reliability indicated by ICC (Abbott 0.28, Dexcom 0.17). Bland-Altman analyses indicated wide limits of agreement (LoA) (Abbott -29.8 to 28.4 mg/dL and Dexcom -29.4 to 32.1 mg/dL) but small bias of mean iAUCs for duplicate meals (Abbott -0.7 mg/dL and Dexcom 1.3 mg/dL). The individual variability of interstitial glucose responses to duplicate meals was similar to that of different meals evaluated each diet week for both Abbott [SDweek1 11.7 mg/dL (compared with duplicate P = 0.01), SDweek2 10.6 mg/dL (P = 0.43), and SDduplicate 10.1 mg/dL] and Dexcom [SDweek1 10.9 mg/dL (P = 0.62), SDweek2 11.0 mg/dL (P = 0.73), and SDduplicate 11.2 mg/dL].

CONCLUSIONS

Individual postprandial CGM responses to duplicate meals were highly variable in adults without diabetes. Personalized diet advice on the basis of CGM measurements requires more reliable methods involving aggregated repeated measurements. This trial was registered at clinicaltrials.gov as NCT03407053 and NCT03878108.

Exploring the effect of different diet types on ageing and age-related diseases

In recent times, there has been growing interest in understanding the factors contributing to prolonged and healthy lifespans observed in specific populations, tribes, or countries. Factors such as environmental and dietary play significant roles in shaping the ageing process and are often the focus of inquiries seeking to unravel the secrets behind longevity. Among these factors, diet emerges as a primary determinant, capable of either promoting or mitigating the onset of age-related diseases that impact the ageing trajectory. This review examines the impact of various diet types on ageing and age-related conditions, including cardiovascular disease, cancer, neurodegenerative disorders, and metabolic syndrome. Different dietary patterns, such as the Mediterranean diet, the Japanese diet, vegetarian and vegan diets, as well as low-carbohydrate and ketogenic diets, are evaluated for their potential effects on longevity and health span. Each diet type is characterized by distinct nutritional profiles, emphasizing specific food groups, macronutrient compositions, and bioactive components, which may exert diverse effects on ageing processes and disease risk. Additionally, dietary factors such as calorie restriction, intermittent fasting, and dietary supplementation are explored for their potential anti-ageing and disease-modifying effects. Understanding the influence of various diet types on ageing and age-related diseases can inform personalized dietary recommendations and lifestyle interventions aimed at promoting healthy aging and mitigating age-associated morbidities.

Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development

The food flavor science, traditionally reliant on experimental methods, is now entering a promising era with the help of artificial intelligence (AI). By integrating existing technologies with AI, researchers can explore and develop new flavor substances in a digital environment, saving time and resources. More and more research will use AI and big data to enhance product flavor, improve product quality, meet consumer needs, and drive the industry toward a smarter and more sustainable future. In this review, we elaborate on the mechanisms of flavor recognition and their potential impact on nutritional regulation. With the increase of data accumulation and the development of internet information technology, food flavor databases and food ingredient databases have made great progress. These databases provide detailed information on the nutritional content, flavor molecules, and chemical properties of various food compounds, providing valuable data support for the rapid evaluation of flavor components and the construction of screening technology. With the popularization of AI in various fields, the field of food flavor has also ushered in new development opportunities. This review explores the mechanisms of flavor recognition and the role of AI in enhancing food flavor analysis through high-throughput omics data and screening technologies. AI algorithms offer a pathway to scientifically improve product formulations, thereby enhancing flavor and customized meals. Furthermore, it discusses the safety challenges of integrating AI into the food flavor industry.

Healthy and Unhealthy Aging and the Human Microbiome

An altered gut microbiome is a feature of many multifactorial diseases, and microbiome effects on host metabolism, immune function, and possibly neurological function are implicated. Increased biological age is accompanied by a change in the gut microbiome. However, age-related health loss does not occur uniformly across all subjects but rather depends on differential loss of gut commensals and gain of pathobionts. In this article, we summarize the known and possible effects of the gut microbiome on the hallmarks of aging and describe the most plausible mechanisms. Understanding and targeting these factors could lead to prolonging health span by rationally maintaining the gut microbiome.

Human gut microbiome, diet, and mental disorders

Diet is one of the most important external factor shaping the composition and metabolic activities of the gut microbiome. The gut microbiome plays a crucial role in host health, including immune system development, nutrients metabolism, and the synthesis of bioactive molecules. In addition, the gut microbiome has been described as critical for the development of several mental disorders. Nutritional psychiatry is an emerging field of research that may provide a link between diet, microbial function, and brain health. In this study, we have reviewed the influence of different diet types, such as Western, Mediterranean, vegetarian, and ketogenic, on the gut microbiota composition and function, and their implication in various neuropsychiatric and psychological disorders.

Diet-gut microbiome interaction and its impact on host blood glucose homeostasis: a series of nutritional n-of-1 trials

BACKGROUND

The interplay between diet and gut microbiome substantially influences host metabolism, but uncertainties remain regarding their relationships tailored for each subject given the huge inter-individual variability. Here we aim to investigate diet-gut microbiome interaction at single-subject resolution and explore its effects on blood glucose homeostasis.

METHODS

We conducted a series of nutritional n-of-1 trials (NCT04125602), in which 30 participants were assigned high-carbohydrate (HC) and low-carbohydrate (LC) diets in a randomized sequence across 3 pair of cross-over periods lasting 72 days. We used shotgun metagenomic sequencing and continuous glucose monitoring systems to profile the gut microbiome and blood glucose, respectively. An independent cohort of 1219 participants with available metagenomics data are included as a validation cohort.

FINDINGS

We demonstrated that the gut microbiome exhibited both intra-individually dynamic and inter-individually personalized signatures during the interventions. At the single-subject resolution, we observed person-specific response patterns of gut microbiota to interventional diets. Furthermore, we discovered a personal gut microbial signature represented by a carb-sensitivity score, which was closely correlated with glycemic phenotypes during the HC intervention, but not LC intervention. We validate the role of this score in the validation cohort and find that it reflects host glycemic sensitivity to the personal gut microbiota profile when sensing the dietary carbohydrate inputs.

INTERPRETATION

Our finding suggests that the HC diet modulates gut microbiota in a person-specific manner and facilitates the connection between gut microbiota and glycemic sensitivity. This study represents a new paradigm for investigating the diet-microbiome interaction in the context of precision nutrition.

FUNDING

This work was supported by the National Key R&D Program of China, National Natural Science Foundation of China and Zhejiang Provincial Natural Science Foundation of China.

Exploring Opportunities to Better Characterize the Effects of Dietary Protein on Health across the Lifespan

Remarkable advances have been made over the last 30 y in understanding the role of dietary protein in optimizing muscle health across the lifespan. That is, acute (<24 h) stable isotope-derived measures of muscle protein synthesis have led to established recommendations for protein quantity, quality, source, and timing of protein ingestion to support muscle health at rest, post exercise, and to overcome age-related anabolic resistance in older adults. Although muscle health is undoubtedly important, moving from muscle to other associated or disease-specific outcomes is a critical next step for the field, given the mounting evidence documenting the effects of dietary protein on measures of chronic disease and age-related decline (for example, cardiovascular disease, type 2 diabetes mellitus, obesity, frailty, and osteoporosis). In this narrative review, we posit that future studies evaluating the potential role of dietary protein build off of the existing knowledge base generated from decades of past research and focus their efforts on closing unanswered knowledge gaps pertaining to dietary protein and health across the lifespan. Throughout this review, we highlight potential methodologies and novel outcome measures that researchers may consider as starting points to facilitate the next 30 y of advances in the field of dietary protein and health.

Narrative review of comprehensive management strategies for diabetic retinopathy: interdisciplinary approaches and future perspectives

This review examines the epidemiological trends, pathophysiologic mechanisms, and current and future therapeutic strategies for diabetic retinopathy (DR), focusing on innovative management countermeasures in the face of this global public health challenge. As the number of patients with diabetes continues to increase, DR, as one of its major complications, poses a significant threat to global visual health. This review not only summarises the latest advances in personalised treatment and emerging therapeutic modalities (such as anti-vascular endothelial growth factor therapy, laser treatment, surgical procedures and cutting-edge gene and stem cell therapies) but also emphasises the revolutionary potential of telemedicine technologies and digital health platforms to improve DR screening and adherence among people with diabetes. We show how these technological innovations, especially in resource-limited settings, can achieve early diagnosis and effective treatment, thereby significantly reducing the public health burden of DR. In addition, this article highlights the critical role of interdisciplinary teamwork in optimising the comprehensive management of DR, involving close collaboration among physicians, researchers, patient education specialists and policy-makers, as well as the importance of implementing these innovative solutions through societal engagement and policy support. By highlighting these innovative strategies and their specific impact on improving public health practices, this review offers new perspectives and strategies for the future management of DR, with the goal of promoting the prevention, diagnosis and treatment of DR worldwide, improving patient prognosis and enhancing quality of life.