Personalized Postprandial Glucose Response-Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes

Gut microbiota development across the lifespan: Disease links and health-promoting interventions

The gut microbiota plays a pivotal role in human life and undergoes dynamic changes throughout the human lifespan, from infancy to old age. During our life, the gut microbiota influences health and disease across life stages. This review summarizes the discussions and presentations from the symposium "Gut microbiota development from infancy to old age" held in collaboration with the Journal of Internal Medicine. In early infancy, microbial colonization is shaped by factors such as mode of delivery, antibiotic exposure, and milk-feeding practices, laying the foundation for subsequent increased microbial diversity and maturation. Throughout childhood and adolescence, microbial maturation continues, influencing immune development and metabolic health. In adulthood, the gut microbiota reaches a relatively stable state, influenced by genetics, diet, and lifestyle. Notably, disruptions in gut microbiota composition have been implicated in various inflammatory diseases-including inflammatory bowel disease, Type 1 diabetes, and allergies. Furthermore, emerging evidence suggests a connection between gut dysbiosis and neurodegenerative disorders such as Alzheimer's disease. Understanding the role of the gut microbiota in disease pathogenesis across life stages provides insights into potential therapeutic interventions. Probiotics, prebiotics, and dietary modifications, as well as fecal microbiota transplantation, are being explored as promising strategies to promote a healthy gut microbiota and mitigate disease risks. This review focuses on the gut microbiota's role in infancy, adulthood, and aging, addressing its development, stability, and alterations linked to health and disease across these critical life stages. It outlines future research directions aimed at optimizing the gut microbiota composition to improve health.

Continuous Glucose Monitoring for Prediabetes - Roles, Evidence, and Gaps

Continuous glucose monitoring (CGM) has transformed the care of patients with diabetes, and there is great potential to extend these benefits to prediabetes. The recent FDA approval of over the counter CGMs has increased interest for use in individuals with prediabetes. It is of particular interest to use CGM to guide early individualized lifestyle interventions to prevent the progression of prediabetes to diabetes and support reversion to normoglycemia. In this review, we discuss published evidence regarding CGM metrics in normoglycemia, briefly review the use of CGM to diagnose prediabetes, and review available evidence for CGM use during lifestyle interventions in individuals with prediabetes. Future studies are needed to validate CGM metrics for prediabetes and evaluate effects of early intervention with CGM in this population.

Use of technology in prediabetes and precision prevention

Controlling the epidemic of diabetes is an urgent global healthcare challenge. The low uptake of diabetes prevention programs highlights difficulties in scalability, partly due to the need for intensive face-to-face contact and its impact on healthcare resource utilization. In this narrative review, we will summarize the latest evidence in technology-assisted lifestyle interventions. We will appraise evidence of digital diabetes prevention programs that use internet platforms or text messaging tools to support information delivery, lifestyle coaching, or peer support. We will also discuss the use of wearables, including physical activity trackers and continuous glucose monitoring (CGM) as part of lifestyle intervention. Experience from diabetes highlights the potential for CGM as a motivational tool to promote lifestyle change. The integration of digital data may facilitate earlier detection of prediabetes, sub-phenotyping, and personalized nutritional predictions. We will highlight major gaps in research and the need for rigorous clinical trials to evaluate the acceptability and cost-effectiveness of integrating technologies as part of a multicomponent strategy in diabetes prevention.

Differential effect of nonpharmacological interventions according to prediabetes phenotype: Systematic review and meta-analysis of randomized clinical trials

BACKGROUND AND AIMS

Impaired glucose intolerance (IGT) and impaired fasting glucose (IFG) are totally different. Lifestyle modification is effective in moving from prediabetes to normoglycaemia. There is a lack of information showing the effect of lifestyle modification according to each prediabetes and assessing its effect on the degree of reversibility to normoglycaemia and on cardiometabolic markers.

METHODS AND RESULTS

We searched for randomized controlled trials (RCT) that enrolled individuals with IGT or IFG. Meta-analysis was performed to compare the proportion of subjects progressing to type 2 diabetes mellitus (T2DM); proportion reversing to normoglycaemia and mean differences in glucose level and cardiometabolic parameters. Thirty-six RCTs were included. The proportion of subjects progressing from impaired glycaemia to T2DM was higher among those with IGT (16.3% vs. 10.9%), whereas reversion to normoglycaemia was higher in subjects with IFG (27.2% vs. 24.8%). The effect of lifestyle modification on glucose level was significant on those with IFG (mean difference [MD] = -1.56 mg/dL, 95% CI: -2.71, -0.40), but not on those with IGT of (MD = 1.47 mg/dL, 95% CI: -1.33, 4.28).

CONCLUSION

Diverse lifestyle modification interventions improved glucose levels in people with IFG, but not in those with IGT. Our findings imply that different non-pharmacological interventions are warranted for IGT and IFG.

Precision nutrition for cardiometabolic diseases

Precision nutrition is a vibrant and rapidly evolving field of scientific research and innovation with the potential to deliver health, societal and economic benefits by improving healthcare delivery and policies. Advances in deep phenotyping technologies, digital tools and artificial intelligence have made possible early proof-of-concept research that expands the understanding of within- and between-person variability in responses to diet. These studies illustrate the promise of precision nutrition to complement the traditional 'one size fits all' dietary guidelines, which, while considering broad life-stage and disease-specific nutritional requirements, often lack the granularity to account fully for individual variations in nutritional needs and dietary responses. Despite these developments, however, considerable challenges remain before precision nutrition can be implemented on a broader scale. This Review examines the current state of precision nutrition research, with a focus on its application to reducing the incidence and burden of cardiometabolic diseases. We critically examine the evidence base, explore the potential benefits and discuss the challenges and opportunities ahead.

Diet-microbiome interactions in cancer

Diet impacts cancer in diverse manners. Multiple nutritional effects on tumors are mediated by dietary modulation of commensals, residing in mucosal surfaces and possibly also within the tumor microenvironment. Mechanistically understanding such diet-microbiome-host interactions may enable to develop precision nutritional interventions impacting cancer development, dissemination, and treatment responses. However, data-driven nutritional strategies integrating diet-microbiome interactions are infrequently incorporated into cancer prevention and treatment schemes. Herein, we discuss how dietary composition affects cancer-related processes through alterations exerted by specific nutrients and complex foods on the microbiome. We highlight how dietary timing, including time-restricted feeding, impacts microbial function in modulating cancer and its therapy. We review existing and experimental nutritional approaches aimed at enhancing microbiome-mediated cancer treatment responsiveness while minimizing adverse effects, and address challenges and prospects in integrating diet-microbiome interactions into precision oncology. Collectively, mechanistically understanding diet-microbiome-host interactomes may enable to achieve a personalized and microbiome-informed optimization of nutritional cancer interventions.

A multi-omics microbiome signature is associated with the benefits of gastric bypass surgery and is differentiated from diet induced weight loss through 2 years of follow-up

Roux-en-Y gastric bypass (GBP) surgery is an effective treatment for reducing body weight and correcting metabolic dysfunction in individuals with severe obesity. Herein, we characterize the differences between very low energy diet (VLED) and GBP induced weight loss by multi-omic analyses of microbiome and host features in a non-randomized, controlled, single-center study. Eighty-eight participants with severe obesity were recruited into two arms - GBP versus VLED with matching weight loss for 6 weeks and 2-years of follow-up. A dramatic shift in the distribution of gut microbial taxa and their functional capacity was seen in the GBP group at Week 2 after surgery and was sustained through 2 years. Multi-omic analyses were performed after 6 weeks of matching weight loss between the GBP and VLED groups, which pointed to microbiome derived metabolites such as indoxyl sulphate as characterizing the GBP group. We also identified an inverse association between Streptococcus parasanguinis (an oral commensal) and plasma levels of tryptophan and tyrosine. These data have important implications, as they reveal a significant robust restructuring of the microbiome away from a baseline dysbiotic state in the GBP group. Furthermore, multi-omics modelling points to potentially novel mechanistic insights at the intersection of the microbiome and host.

A Scoping Review of Artificial Intelligence for Precision Nutrition

With the role of artificial intelligence (AI) in precision nutrition rapidly expanding, a scoping review on recent studies and potential future directions is needed. This scoping review examines: 1) the current landscape, including publication venues, targeted diseases, AI applications, methods, evaluation metrics, and considerations of minority and cultural factors; 2) common patterns in AI-driven precision nutrition studies; and 3) gaps, challenges, and future research directions. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) process, we extracted 198 articles from major databases using search keywords in 3 categories: precision nutrition, AI, and natural language processing. The extracted literature reveals a surge in AI-driven precision nutrition research, with ∼75% (n = 148) published since 2020. It also showcases a diverse publication landscape, with the majority of studies focusing on diet-related diseases, such as diabetes and cardiovascular conditions, while emphasizing health optimization, disease prevention, and management. We highlight diverse datasets used in the literature and summarize methodologies and evaluation metrics to guide future studies. We also emphasize the importance of minority and cultural perspectives in promoting equity for precision nutrition using AI. Future research should further integrate these factors to fully harness AI's potential in precision nutrition.

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.

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.

A Narrative Review of the Interplay Between Carbohydrate Intake and Diabetes Medications: Unexplored Connections and Clinical Implications

This narrative review examines the dynamic interplay between carbohydrate intake and diabetes medications, highlighting their combined molecular and clinical effects on glycemic control. Carbohydrates, a primary energy source, significantly influence postprandial glucose regulation and necessitate careful coordination with pharmacological therapies, including insulin, metformin, glucagon-like peptide (GLP-1) receptor agonists, and sodium-glucose cotransporter-2 (SGLT2) inhibitors. Low-glycemic-index (GI) foods enhance insulin sensitivity, stabilize glycemic variability, and optimize medication efficacy, while high-GI foods exacerbate glycemic excursions and insulin resistance. Continuous glucose monitoring (CGM) offers real-time insights to tailor dietary and pharmacological interventions, improving glycemic outcomes and reducing complications. Despite advancements, gaps persist in understanding nutrient-drug interactions, particularly with emerging antidiabetic agents. This review underscores the need for integrating carbohydrate-focused dietary strategies with pharmacotherapy to enhance diabetes management. Future research should prioritize clinical trials leveraging CGM technology to explore how glycemic index, glycemic load, and carbohydrate quality interact with newer diabetes medications. Such studies can refine evidence-based recommendations, support individualized care plans, and improve long-term outcomes. Addressing systemic barriers, such as limited access to dietitians and CGM technology in underserved regions, is critical for equitable care. Expanding the roles of community health workers and training healthcare providers in basic nutrition counseling can bridge gaps, promoting sustainable and inclusive diabetes management strategies. These efforts are essential for advancing personalized, effective, and equitable care for individuals with diabetes.

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.

Lifestyle interventions for cardiometabolic health

Unhealthy lifestyle behaviors such as poor diets and physical inactivity account for most of the cardiometabolic disease (CMD) burden, including type 2 diabetes and cardiovascular diseases. Much of this burden is mediated by the effects of unhealthy lifestyle behaviors on overweight and obesity, and disproportionally impacts certain population groups-including those from disadvantaged socioeconomic backgrounds. Combined lifestyle interventions (CLIs), which target multiple behaviors, have the potential to prevent CMD, but their implementation, reach and effectiveness in routine practice are often limited. Considering the increasing availability of effective but expensive pharmaceutical options for weight loss, we review the short-term and long-term benefits and cost-effectiveness of CLIs on overweight, obesity and associated CMDs, in controlled studies and in routine care. Against the backdrop of changing living environments, we discuss the effective components of CLIs and the many challenges associated with implementing them. Finally, we outline future directions for research and implications for policy and practice to improve lifestyle behaviors and cardiometabolic health at the population level.

Enhancing the Impact of Individualized Nutrition Therapy with Real-Time Continuous Glucose Monitoring Feedback in Overweight and Obese Individuals with Prediabetes

BACKGROUND/OBJECTIVES

prediabetes is a significant risk factor for the development of type 2 diabetes, cardiovascular diseases, chronic kidney disease, and other complications. Early diagnosis of prediabetes, coupled with education on lifestyle changes that support blood glucose management, are crucial for the prevention or delay of type 2 diabetes and related complications. This study aimed to evaluate the impact of incorporating real-time feedback from continuous glucose monitoring (CGM) into individualized nutrition therapy (INT) on blood glucose control in individuals with prediabetes who are overweight or obese.

METHODS

participants (mean age ± SD: 55 ± 6 years; BMI: 31.1 ± 4.1 kg/m²) were randomly assigned to either the treatment group (n = 15) or the control group (n = 15). Both groups received INT and CGM, but the control group was blinded to the CGM data until the end of this study. Participants were followed for 30 days and visited the lab every 10 days for CGM replacement, study measurements, and dietary consultations.

RESULTS

the treatment group showed a significant increase in the percentage of time spent in the target blood glucose range (p = 0.02) and a significant decrease in the mean blood glucose concentration (p < 0.05), glucose management indicator (p = 0.02), percent coefficient of variation for blood glucose (p = 0.01), and percent time spent in the high or very high blood glucose ranges (p = 0.04). These changes were not statistically significant for the control group.

CONCLUSIONS

adding CGM feedback to INT resulted in better management of blood glucose levels in overweight or obese individuals with prediabetes.

The sports performance improving effects of konjac glucomannan with varying molecular weights in overtrained mice

Overtraining affects individuals engaged in high-volume training, potentially hindering athletic performance and revealing shortcomings in suggested solutions. This study evaluated the impact of konjac glucomannan (KGM) with varying molecular weights on the gut microbiome, endurance, and strength in mice subjected to excessive training. The native KGM (1.82 × 107 Da) was enzymatically degraded using endo-1,4-β-mannanase to generate moderate molecular weight KGM (KGM-EM, 1.89 × 105 Da) and low molecular weight KGM (KGM-EL, 1.34 × 104 Da). These fractions were characterized and compared with the native KGM regarding their effects on mice undergoing excessive training. The results demonstrated a positive correlation between KGM's molecular weight and its capacity to mitigate the adverse impacts of excessive training on strength or/and endurance (a significant increase of 55.57 % and 55.70 % by the native KGM compared with the excessive training group). In addition, the native KGM exhibited superior preservation of microbial diversity and composition in fecal samples against excessive training-induced shifts, along with increased production of individual and total short-chain fatty acids in plasma compared with the two degraded products. Overall, these results highlight the potential benefits of high molecular weight KGM for preventing overtraining syndrome and enhancing athletic performance in animal models.

Impact of baseline dietary quality on the efficacy of a dietitian-guided weight reduction program

AIM

This pre-post intervention study aimed to assess the relationship between baseline dietary quality and the efficacy of a dietitian-guided weight reduction program, which has not been thoroughly documented to date.

METHODS

Ninety-two consecutive obese or overweight patients visiting a tertiary center clinic for weight reduction were enrolled in this study. Participants received a dietitian-guided weight reduction education program aimed at reducing daily caloric intake by 500 kcal and improving adherence to the Mediterranean diet for 3 months. Baseline dietary quality was assessed using the 14-item Taiwanese Mediterranean Diet Adherence Screener (T-MEDAS), where a higher T-MEDAS score reflects greater adherence to the Mediterranean diet. Additional covariates, including dietary behaviors, lifestyle factors, and comorbidities were also recorded. The primary outcome was the percentage of weight reduction at 3 months, analyzed using restricted cubic spline models and generalized estimating equations (GEE) to account for the correlation between weight change and the baseline T-MEDAS scores.

RESULTS

Thirty-nine participants were excluded due to major illnesses, use of anti-obesity medications, or loss to follow-up. Among the remaining 53 participants (mean age 41.2 ± 12.8 years, 56.6% female), the average weight reduction was 3.9 ± 3.3% from a baseline weight of 98.5 ± 12.8 kg. Participants who did not achieve a weight reduction of more than 5% had higher baseline T-MEDAS scores compared to those who did (5.4 ± 1.7 vs. 4.1 ± 1.8, p = 0.026). A restricted cubic spline model, adjusted for covariates including age, gender, diabetes mellitus (DM), dyslipidemia, and smoking, revealed a significant inverse relationship between higher baseline T-MEDAS scores and weight loss. After controlling for various confounders, GEE analysis demonstrated that higher baseline T-MEDAS scores were significantly associated with less weight loss (beta: -4.1, 95% CI: -5.6 to -2.6, p < 0.001).

CONCLUSIONS

Higher baseline dietary quality was associated with reduced effectiveness of a dietitian-guided weight reduction program. This suggests that additional strategies may be required to improve the success of weight loss interventions in individuals with higher baseline dietary quality.

Approach to the Treatment of Children and Adolescents with Obesity

Pediatric obesity continues to be an omnipresent disease; 1 in 5 children and adolescents have obesity in the United States. The comorbidities associated with youth-onset obesity tend to have a more severe disease progression in youth compared to their adult counterparts with the same obesity-related condition. A comorbidity of focus in this study is metabolism-associated steatotic liver disease (MASLD), which has rapidly evolved into the most common liver disease seen in the pediatric population. A direct association exists between the treatment of MASLD and the treatment of pediatric obesity. The current evidence supports that obesity treatment is safe and effective.

Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes

BACKGROUND

Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into food choices' impact on dysglycemia. However, current approaches to analyze PPGR using a CGM require manual meal logging, limiting the scalability of CGM-driven applications like personalized nutrition and at-home diabetes risk assessment.

OBJECTIVE

We propose a machine learning (ML) framework to automatically identify and characterize breakfast-related PPGRs from CGM profiles in adults at risk of or living with noninsulin-treated type 2 diabetes (T2D).

METHODS

Our PPGR estimation framework uses a random forest ML algorithm trained on 15 adults without diabetes who wore a CGM for up to four weeks. The algorithm performance was evaluated on a held-out subset of the participants' CGM data as well as on an external validation data set of 36 individuals at risk for or with noninsulin-treated T2D.

RESULTS

Our algorithm's estimations of breakfast PPGRs displayed no statistically significant differences to annotated PPGRs, in terms of incremental area under the curve and glucose rise (P > .05 for both data sets), while a small difference in prebreakfast glucose was found in the nondiabetes data set (P = .005) but not in the validation T2D data set (P = .18).

CONCLUSIONS

We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.

Personalized nutrition: aligning science, regulation, and marketing

Interest in personalized nutrition among researchers and industry has grown rapidly in recent years and shows no signs of abating. In this paper, we discuss the growth of the personalized nutrition market, the evidence for the approach, and the regulatory landscape for personalized nutrition products. We found that regulatory gaps have led to market growth of products with unknown efficacy that are making bold, and possibly unsubstantiated, claims. As personalized nutrition products and related treatments continue to enter the market without regulation, unreliable products may cause consumers financial, psychological, and physical harm. Stronger regulation will help engender trust in these products among consumers and ensure their safety and effectiveness.

Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes

BACKGROUND

The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE).

METHODS

Eligible participants are between 21 and 80 years of age diagnosed with moderately controlled T2D (HbA1c: 6.0 to 8.0%) and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: (1) Personalized, (2) Standardized, or (3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrient targets to meet Mediterranean diet guidelines, in addition to 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same educational content as those in the UCC arm, following the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, in addition to real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app.

DISCUSSION

The DiaTeleMed Study aims to address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05046886. Registered on September 16, 2021.

Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial

Large variability exists in people's responses to foods. However, the efficacy of personalized dietary advice for health remains understudied. We compared a personalized dietary program (PDP) versus general advice (control) on cardiometabolic health using a randomized clinical trial. The PDP used food characteristics, individual postprandial glucose and triglyceride (TG) responses to foods, microbiomes and health history, to produce personalized food scores in an 18-week app-based program. The control group received standard care dietary advice (US Department of Agriculture Guidelines for Americans, 2020-2025) using online resources, check-ins, video lessons and a leaflet. Primary outcomes were serum low-density lipoprotein cholesterol and TG concentrations at baseline and at 18 weeks. Participants (n = 347), aged 41-70 years and generally representative of the average US population, were randomized to the PDP (n = 177) or control (n = 170). Intention-to-treat analysis (n = 347) between groups showed significant reduction in TGs (mean difference = -0.13 mmol l-1; log-transformed 95% confidence interval = -0.07 to -0.01, P = 0.016). Changes in low-density lipoprotein cholesterol were not significant. There were improvements in secondary outcomes, including body weight, waist circumference, HbA1c, diet quality and microbiome (beta-diversity) (P < 0.05), particularly in highly adherent PDP participants. However, blood pressure, insulin, glucose, C-peptide, apolipoprotein A1 and B, and postprandial TGs did not differ between groups. No serious intervention-related adverse events were reported. Following a personalized diet led to some improvements in cardiometabolic health compared to standard dietary advice. ClinicalTrials.gov registration: NCT05273268 .

MedDietAgent: An AI-based Mobile App for Harmonizing Individuals' Dietary Choices with the Mediterranean Diet Pattern

Recently, there has been an increasing interest in applying technological advances to offer specific dietary recommendations in the field of nutrition and health. Dietary recommendation systems are advanced tools designed to assist individuals in making well-informed and health-conscious decisions on their food choices, taking into account their personal needs, preferences, and health targets or habits. In this study, we present an AI-based mobile app for harmonizing individuals' dietary choices with the pattern of the Mediterranean diet. A combination of computer vision, natural language processing, machine learning, and reinforcement techniques are used to record the nutritional information via images or speech and to generate dynamic recommendations tailored to the user's performance across key nutritional areas, encompassing calories, combined fats, proteins, carbohydrates, sugars, dietary fibers, sodium intake, fruits, vegetables, and dairy products. The image-based dietary assessment subsystem achieves a mean absolute percentage error of 3.73%, while the reinforcement learning subsystem achieves a 96% average reward. Then, a well-designed approach was taken to develop the MedDietAgent mobile app, using cutting-edge technologies and applying a simplistic approach. One of the key aspects of MedDietAgent is its ability to offer dynamic recommendations by monitoring the user's environment.

Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes

Background

The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately-controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE).

Methods

Eligible participants are between 21 to 80 years of age diagnosed with moderately-controlled T2D (HbA1c: 6.0-8.0%), and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: 1) Personalized, 2) Standardized, or 3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrients targets to meet Mediterranean diet guidelines plus 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same education content as UCC on the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, plus real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app.

Discussion

The DiaTeleMed study will address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level.

Trial registration

The DiaTeleMed Study is registered with ClinicalTrials.gov (Identifier: NCT05046886).

Do Precision and Personalised Nutrition Interventions Improve Risk Factors in Adults with Prediabetes or Metabolic Syndrome? A Systematic Review of Randomised Controlled Trials

This review aimed to synthesise existing literature on the efficacy of personalised or precision nutrition (PPN) interventions, including medical nutrition therapy (MNT), in improving outcomes related to glycaemic control (HbA1c, post-prandial glucose [PPG], and fasting blood glucose), anthropometry (weight, BMI, and waist circumference [WC]), blood lipids, blood pressure (BP), and dietary intake among adults with prediabetes or metabolic syndrome (MetS). Six databases were systematically searched (Scopus, Medline, Embase, CINAHL, PsycINFO, and Cochrane) for randomised controlled trials (RCTs) published from January 2000 to 16 April 2023. The Academy of Nutrition and Dietetics Quality Criteria were used to assess the risk of bias. Seven RCTs (n = 873), comprising five PPN and two MNT interventions, lasting 3-24 months were included. Consistent and significant improvements favouring PPN and MNT interventions were reported across studies that examined outcomes like HbA1c, PPG, and waist circumference. Results for other measures, including fasting blood glucose, HOMA-IR, blood lipids, BP, and diet, were inconsistent. Longer, more frequent interventions yielded greater improvements, especially for HbA1c and WC. However, more research in studies with larger sample sizes and standardised PPN definitions is needed. Future studies should also investigate combining MNT with contemporary PPN factors, including genetic, epigenetic, metabolomic, and metagenomic data.

Utilization of the microbiome in personalized medicine

Inter-individual human variability, driven by various genetic and environmental factors, complicates the ability to develop effective population-based early disease detection, treatment and prognostic assessment. The microbiome, consisting of diverse microorganism communities including viruses, bacteria, fungi and eukaryotes colonizing human body surfaces, has recently been identified as a contributor to inter-individual variation, through its person-specific signatures. As such, the microbiome may modulate disease manifestations, even among individuals with similar genetic disease susceptibility risks. Information stored within microbiomes may therefore enable early detection and prognostic assessment of disease in at-risk populations, whereas microbiome modulation may constitute an effective and safe treatment tailored to the individual. In this Review, we explore recent advances in the application of microbiome data in precision medicine across a growing number of human diseases. We also discuss the challenges, limitations and prospects of analysing microbiome data for personalized patient care.

Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models

The manifestation of metabolic deteriorations that accompany overweight and obesity can differ greatly between individuals, giving rise to a highly heterogeneous population. This inter-individual variation can impede both the provision and assessment of nutritional interventions as multiple aspects of metabolic health should be considered at once. Here, we apply the Mixed Meal Model, a physiology-based computational model, to characterize an individual's metabolic health in silico. A population of 342 personalized models were generated using data for individuals with overweight and obesity from three independent intervention studies, demonstrating a strong relationship between the model-derived metric of insulin resistance (ρ = 0.67, p < 0.05) and the gold-standard hyperinsulinemic-euglycemic clamp. The model is also shown to quantify liver fat accumulation and β-cell functionality. Moreover, we show that personalized Mixed Meal Models can be used to evaluate the impact of a dietary intervention on multiple aspects of metabolic health at the individual level.

Potential Mechanisms of Precision Nutrition-Based Interventions for Managing Obesity

Precision nutrition (PN) considers multiple individual-level and environmental characteristics or variables to better inform dietary strategies and interventions for optimizing health, including managing obesity and metabolic disorders. Here, we review the evidence on potential mechanisms-including ones to identify individuals most likely to respond-that can be leveraged in the development of PN interventions addressing obesity. We conducted a review of the literature and included laboratory, animal, and human studies evaluating biochemical and genetic data, completed and ongoing clinical trials, and public programs in this review. Our analysis describes the potential mechanisms related to 6 domains including genetic predisposition, circadian rhythms, physical activity and sedentary behavior, metabolomics, the gut microbiome, and behavioral and socioeconomic characteristics, i.e., the factors that can be leveraged to design PN-based interventions to prevent and treat obesity-related outcomes such as weight loss or metabolic health as laid out by the NIH 2030 Strategic Plan for Nutrition Research. For example, single nucleotide polymorphisms can modify responses to certain dietary interventions, and epigenetic modulation of obesity risk via physical activity patterns and macronutrient intake have also been demonstrated. Additionally, we identified limitations including questions of equitable implementation across a limited number of clinical trials. These include the limited ability of current PN interventions to address systemic influences such as supply chains and food distribution, healthcare systems, racial or cultural inequities, and economic disparities, particularly when designing and implementing PN interventions in low- and middle-income communities. PN has the potential to help manage obesity by addressing intra- and inter-individual variation as well as context, as opposed to "one-size fits all" approaches though there is limited clinical trial evidence to date.

Difference on Glucose Profile From Continuous Glucose Monitoring in People With Prediabetes vs. Normoglycemic Individuals: A Matched-Pair Analysis

INTRODUCTION

Comprehensive characteristics of the glycemic profile for prediabetes derived by continuous glucose monitoring (CGM) are unknown. We evaluate the difference of CGM profiles between individuals with prediabetes and normoglycemic individuals, including the response to oral glucose tolerance test (OGTT).

METHODS

Individuals with prediabetes matched for age, sex, and BMI with normoglycemic individuals were instructed to use professional CGM for 1 week. OGTT was performed on the second day. The primary outcomes were percentages of glucose readings time below range (TBR): <54 or <70 mg/dL, time in range (TIR): 70 to 180 mg/dL, and time above range (TAR): >180 or >250 mg/dL. Area under the curve (AUC) was calculated following the OGTT. Glucose variability was depicted by coefficient of variation (CV), SD, and mean amplitude of glucose excursion (MAGE). Wilcoxon sign-ranked test, McNemar mid P-test and linear regression models were employed.

RESULTS

In all, 36 participants (median age 51 years; median body mass index [BMI] = 26.4 kg/m2) formed 18 matched pairs. Statistically significant differences were observed for 24-hour time in range (TIR; median 98.5% vs. 99.9%, P = .013), time above range (TAR) >180 mg/dl (0.4% vs. 0%, P = .0062), and 24-hour mean interstitial glucose (113.8 vs. 108.8 mg/dL, P = .0038) between people with prediabetes compared to normoglycemic participants. Statistically significant differences favoring the normoglycemic group were found for glycemic variability indexes (median CV 15.2% vs. 11.9%, P = .0156; median MAGE 44.3 vs. 33.3 mg/dL, P = 0.0043). Following OGTT, the AUC was significantly lower in normoglycemic compared to the prediabetes group (median 18615.3 vs. 16370.0, P = .0347 for total and 4666.5 vs. 2792.7, P = .0429 for incremental 2-hour post OGTT).

CONCLUSION

Individuals with prediabetes have different glucose profiles compared to normoglycemic individuals. CGM might be helpful in individuals with borderline glucose values for a more accurate reclassification.

A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring

In type 2 diabetes (T2D), the dawn phenomenon is an overnight glucose rise recognized to contribute to overall glycemia and is a potential target for therapeutic intervention. Existing CGM-based approaches do not account for sensor error, which can mask the true extent of the dawn phenomenon. To address this challenge, we developed a probabilistic framework that incorporates sensor error to assign a probability to the occurrence of dawn phenomenon. In contrast, the current approaches label glucose fluctuations as dawn phenomena as a binary yes/no. We compared the proposed probabilistic model with a standard binary model on CGM data from 173 participants (71% female, 87% Hispanic/Latino, 54 ± 12 years, with either a diagnosis of T2D for six months or with an elevated risk of T2D) stratified by HbA1c levels into normal but at risk for T2D, with pre-T2D, or with non-insulin-treated T2D. The probabilistic model revealed a higher dawn phenomenon frequency in T2D [49% (95% CI 37-63%)] compared to pre-T2D [36% (95% CI 31-48%), p = 0.01] and at-risk participants [34% (95% CI 27-39%), p < 0.0001]. While these trends were also found using the binary approach, the probabilistic model identified significantly greater dawn phenomenon frequency than the traditional binary model across all three HbA1c sub-groups (p < 0.0001), indicating its potential to detect the dawn phenomenon earlier across diabetes risk categories.

Imprecision nutrition? Duplicate meals result in unreliable individual glycemic responses measured by continuous glucose monitors across four dietary patterns in adults without diabetes

Background

Continuous glucose monitors (CGMs) are being used to characterize postprandial glycemic responses and thereby provide personalized dietary advice to minimize glycemic excursions. However, the efficacy of such advice depends on reliable CGM responses.

Objective

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

Methods

CGM data were collected in two controlled feeding studies (NCT03407053 and NCT03878108) in 30 participants without diabetes capturing 1056 meal responses in duplicate ~1 week 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 each 2-h post-meal period and compared within-subject iAUCs using the same CGM for the duplicate meals using linear correlations, intra-class correlation coefficients (ICC), Bland-Altman analyses, and compared individual variability of glycemic responses to duplicate meals versus different meals using standard deviations (SDs).

Results

There were weak to moderate positive linear correlations between within- subject iAUCs for duplicate meals (Abbott r=0.47, p<0.0001, Dexcom r=0.43, p<0.0001), with low within-participant reliability indicated by ICC (Abbott 0.31, Dexcom 0.14). Bland-Altman analyses indicated wide limits of agreement (Abbott -31.3 to 31.5 mg/dL, Dexcom -30.8 to 30.4 mg/dL) but no significant bias of mean iAUCs for duplicate meals (Abbott 0.1 mg/dL, Dexcom -0.2 mg/dL). Individual variability of glycemic responses to duplicate meals was similar to that of different meals evaluated each diet week for both Abbott (SDduplicate = 10.7 mg/dL , SDweek 1 =12.4 mg/dL, SDweek 2 =11.6 mg/dL, p=0.38) and Dexcom (SDduplicate = 11.1 mg/dL, SDweek 1 = 11.5 mg/dL, SDweek 2 =11.9 mg/dL, p=0.60).

Conclusions

Individual postprandial CGM responses to duplicate meals were unreliable in adults without diabetes. Personalized diet advice based on CGM measurements in adults without diabetes requires more reliable methods involving aggregated repeated measurements.

New insights in the mechanisms of weight-loss maintenance: Summary from a Pennington symposium

Obesity is a chronic disease that affects more than 650 million adults worldwide. Obesity not only is a significant health concern on its own, but predisposes to cardiometabolic comorbidities, including coronary heart disease, dyslipidemia, hypertension, type 2 diabetes, and some cancers. Lifestyle interventions effectively promote weight loss of 5% to 10%, and pharmacological and surgical interventions even more, with some novel approved drugs inducing up to an average of 25% weight loss. Yet, maintaining weight loss over the long-term remains extremely challenging, and subsequent weight gain is typical. The mechanisms underlying weight regain remain to be fully elucidated. The purpose of this Pennington Biomedical Scientific Symposium was to review and highlight the complex interplay between the physiological, behavioral, and environmental systems controlling energy intake and expenditure. Each of these contributions were further discussed in the context of weight-loss maintenance, and systems-level viewpoints were highlighted to interpret gaps in current approaches. The invited speakers built upon the science of obesity and weight loss to collectively propose future research directions that will aid in revealing the complicated mechanisms involved in the weight-reduced state.

Distinct patterns of personalised dietary advice delivered by a metabotype framework similarly improve dietary quality and metabolic health parameters: secondary analysis of a randomised controlled trial

Background

In a 12-week randomised controlled trial, personalised nutrition delivered using a metabotype framework improved dietary intake, metabolic health parameters and the metabolomic profile compared to population-level dietary advice. The objective of the present work was to investigate the patterns of dietary advice delivered during the intervention and the alterations in dietary intake and metabolic and metabolomic profiles to obtain further insights into the effectiveness of the metabotype framework.

Methods

Forty-nine individuals were randomised into the intervention group and subsequently classified into metabotypes using four biomarkers (triacylglycerol, HDL-C, total cholesterol, glucose). These individuals received personalised dietary advice from decision tree algorithms containing metabotypes and individual characteristics. In a secondary analysis of the data, patterns of dietary advice were identified by clustering individuals according to the dietary messages received and clusters were compared for changes in dietary intake and metabolic health parameters. Correlations between changes in blood clinical chemistry and changes in metabolite levels were investigated.

Results

Two clusters of individuals with distinct patterns of dietary advice were identified. Cluster 1 had the highest percentage of messages delivered to increase the intake of beans and pulses and milk and dairy products. Cluster 2 had the highest percentage of messages delivered to limit the intake of foods high in added sugar, high-fat foods and alcohol. Following the intervention, both patterns improved dietary quality assessed by the Alternate Mediterranean Diet Score and the Alternative Healthy Eating Index, nutrient intakes, blood pressure, triacylglycerol and LDL-C (p ≤ 0.05). Several correlations were identified between changes in total cholesterol, LDL-C, triacylglycerol, insulin and HOMA-IR and changes in metabolites levels, including mostly lipids (sphingomyelins, lysophosphatidylcholines, glycerophosphocholines and fatty acid carnitines).

Conclusion

The findings indicate that the metabotype framework effectively personalises and delivers dietary advice to improve dietary quality and metabolic health.

Clinical trial registration

isrctn.com, identifier ISRCTN15305840.

Nutri-microbiome epidemiology, an emerging field to disentangle the interplay between nutrition and microbiome for human health

Diet and nutrition have a substantial impact on the human microbiome, and interact with the microbiome, especially gut microbiome, to modulate various diseases and health status. Microbiome research has also guided the nutrition field to a more integrative direction, becoming an essential component of the rising area of precision nutrition. In this review, we provide a broad insight into the interplay among diet, nutrition, microbiome, and microbial metabolites for their roles in the human health. Among the microbiome epidemiological studies regarding the associations of diet and nutrition with microbiome and its derived metabolites, we summarize those most reliable findings and highlight evidence for the relationships between diet and disease-associated microbiome and its functional readout. Then, the latest advances of the microbiome-based precision nutrition research and multidisciplinary integration are described. Finally, we discuss several outstanding challenges and opportunities in the field of nutri-microbiome epidemiology.

Differential Responders to a Mixed Meal Tolerance Test Associated with Type 2 Diabetes Risk Factors and Gut Microbiota-Data from the MEDGI-Carb Randomized Controlled Trial

The global prevalence of type 2 diabetes mellitus (T2DM) has surged in recent decades, and the identification of differential glycemic responders can aid tailored treatment for the prevention of prediabetes and T2DM. A mixed meal tolerance test (MMTT) based on regular foods offers the potential to uncover differential responders in dynamical postprandial events. We aimed to fit a simple mathematical model on dynamic postprandial glucose data from repeated MMTTs among participants with elevated T2DM risk to identify response clusters and investigate their association with T2DM risk factors and gut microbiota. Data were used from a 12-week multi-center dietary intervention trial involving high-risk T2DM adults, comparing high- versus low-glycemic index foods within a Mediterranean diet context (MEDGICarb). Model-based analysis of MMTTs from 155 participants (81 females and 74 males) revealed two distinct plasma glucose response clusters that were associated with baseline gut microbiota. Cluster A, inversely associated with HbA1c and waist circumference and directly with insulin sensitivity, exhibited a contrasting profile to cluster B. Findings imply that a standardized breakfast MMTT using regular foods could effectively distinguish non-diabetic individuals at varying risk levels for T2DM using a simple mechanistic model.

Impact of individual and environmental factors on dietary or lifestyle interventions to prevent type 2 diabetes development: a systematic review

BACKGROUND

The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular factors modify the efficacy of dietary or lifestyle interventions to prevent T2D.

METHODS

We searched MEDLINE, Embase, and Cochrane databases for studies reporting on the effect of a lifestyle, dietary pattern, or dietary supplement interventions on the incidence of T2D and reporting the results stratified by any effect modifier. We extracted relevant statistical findings and qualitatively synthesized the evidence for each modifier based on the direction of findings reported in available studies. We used the Diabetes Canada Clinical Practice Scale to assess the certainty of the evidence for a given effect modifier.

RESULTS

The 81 publications that met our criteria for inclusion are from 33 unique trials. The evidence is low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition.

CONCLUSIONS

We report evidence, albeit low certainty, that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.

Konjac Glucomannan Counteracted the Side Effects of Excessive Exercise on Gut Microbiome, Endurance, and Strength in an Overtraining Mice Model

Excessive exercise without adequate rest can lead to overtraining syndrome, which manifests a series of side effects, including fatigue, gut dysbiosis, and decremental sports performance. Konjac glucomannan (KGM) is a plant polysaccharide with numerous health-improving effects, but few studies reported its effects on the gut microbiome, endurance, and strength in an overtraining model. This study assessed the effect of KGM on gut microbiome, endurance, and strength in mice with excessive exercise. Three doses of KGM (1.25, 2.50, and 5.00 mg/mL) were administrated in drinking water to mice during 42 days of a treadmill overtraining program. The results showed that excessive exercise induced a significant microbial shift compared with the control group, while a high dose (5.00 mg/mL) of KGM maintained the microbial composition. The proportion of Sutterella in feces was significantly increased in the excessive exercise group, while the moderate dose (2.50 mg/mL) of KGM dramatically increased the relative abundance of Lactobacillus and SCFA production in feces. Additionally, the moderate dose and high dose of KGM counteracted the negative effects of excessive exercise on strength or/and endurance (43.14% and 39.94% increase through a moderate dose of KGM, Bonferroni corrected p < 0.05, compared with the excessive exercise group). Therefore, it suggests that KGM could prevent overtraining and improve sports performance in animal models.

Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines

Diabetes and associated comorbidities are a global health threat on the rise. We conducted a six-month dietary intervention in pre-diabetic individuals (NCT03222791), to mitigate the hyperglycemia and enhance metabolic health. The current work explores early diabetes markers in the 200 individuals who completed the trial. We find 166 of 2,803 measured features, including oral and gut microbial species and pathways, serum metabolites and cytokines, show significant change in response to a personalized postprandial glucose-targeting diet or the standard of care Mediterranean diet. These changes include established markers of hyperglycemia as well as novel features that can now be investigated as potential therapeutic targets. Our results indicate the microbiome mediates the effect of diet on glycemic, metabolic and immune measurements, with gut microbiome compositional change explaining 12.25% of serum metabolites variance. Although the gut microbiome displays greater compositional changes compared to the oral microbiome, the oral microbiome demonstrates more changes at the genetic level, with trends dependent on environmental richness and species prevalence in the population. In conclusion, our study shows dietary interventions can affect the microbiome, cardiometabolic profile and immune response of the host, and that these factors are well associated with each other, and can be harnessed for new therapeutic modalities.

Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: a functional data analysis

Continuous glucose monitoring systems (CGM) are a very useful tool to understand the behaviour of glucose in different situations and populations. Despite the widespread use of CGM systems in both clinical practice and research, our understanding of the reproducibility of CGM data remains limited. The present work examines the reproducibility of the results provided by a CGM system in a random sample of a free-living adult population, from a functional data analysis approach. Functional intraclass correlation coefficients (ICCs) and their 95% confidence intervals (CI) were calculated to assess the reproducibility of CGM results in 581 individuals. 62% were females 581 participants (62% women) mean age 48 years (range 18-87) were included, 12% had previously been diagnosed with diabetes. The inter-day reproducibility of the CGM results was greater for subjects with diabetes (ICC 0.46 [CI 0.39-0.55]) than for normoglycaemic subjects (ICC 0.30 [CI 0.27-0.33]); the value for prediabetic subjects was intermediate (ICC 0.37 [CI 0.31-0.42]). For normoglycaemic subjects, inter-day reproducibility was poorer among the younger (ICC 0.26 [CI 0.21-0.30]) than the older subjects (ICC 0.39 [CI 0.32-0.45]). Inter-day reproducibility was poorest among normoglycaemic subjects, especially younger normoglycaemic subjects, suggesting the need to monitor some patient groups more often than others.

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.

Individual Postprandial Glycemic Responses to Meal Types by Different Carbohydrate Levels and Their Associations with Glycemic Variability Using Continuous Glucose Monitoring

This study aimed to investigate individual postprandial glycemic responses (PPGRs) to meal types with varying carbohydrate levels and examine their associations with 14-day glycemic variability using continuous glucose monitoring (CGM) in young adults. In a two-week intervention study with 34 participants connected to CGM, four meal types and glucose 75 g were tested. PPGRs were recorded for up to 2 h with a 15 min interval after meals. Data-driven cluster analysis was used to group individual PPGRs for each meal type, and correlation analysis was performed of 14-day glycemic variability and control with related factors. Participants had a mean age of 22.5 years, with 22.8% being male. Four meal types were chosen according to carbohydrate levels. The mean glucose excursion for all meal types, except the fruit bowl, exhibited a similar curve with attenuation. Individuals classified as high responders for each meal type exhibited sustained peak glucose levels for a longer duration compared to low responders, especially in meals with carbohydrate contents above 50%. A meal with 45% carbohydrate content showed no correlation with either 14-day glycemic variability or control. Understanding the glycemic response to carbohydrate-rich meals and adopting a meal-based approach when planning diets are crucial to improving glycemic variability and control.

Gut microbiome modulates the effects of a personalised postprandial-targeting (PPT) diet on cardiometabolic markers: a diet intervention in pre-diabetes

OBJECTIVE

To explore the interplay between dietary modifications, microbiome composition and host metabolic responses in a dietary intervention setting of a personalised postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet in pre-diabetes.

DESIGN

In a 6-month dietary intervention, adults with pre-diabetes were randomly assigned to follow an MED or PPT diet (based on a machine-learning algorithm for predicting postprandial glucose responses). Data collected at baseline and 6 months from 200 participants who completed the intervention included: dietary data from self-recorded logging using a smartphone application, gut microbiome data from shotgun metagenomics sequencing of faecal samples, and clinical data from continuous glucose monitoring, blood biomarkers and anthropometrics.

RESULTS

PPT diet induced more prominent changes to the gut microbiome composition, compared with MED diet, consistent with overall greater dietary modifications observed. Particularly, microbiome alpha-diversity increased significantly in PPT (p=0.007) but not in MED arm (p=0.18). Post hoc analysis of changes in multiple dietary features, including food-categories, nutrients and PPT-adherence score across the cohort, demonstrated significant associations between specific dietary changes and species-level changes in microbiome composition. Furthermore, using causal mediation analysis we detect nine microbial species that partially mediate the association between specific dietary changes and clinical outcomes, including three species (from Bacteroidales, Lachnospiraceae, Oscillospirales orders) that mediate the association between PPT-adherence score and clinical outcomes of hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C) and triglycerides. Finally, using machine-learning models trained on dietary changes and baseline clinical data, we predict personalised metabolic responses to dietary modifications and assess features importance for clinical improvement in cardiometabolic markers of blood lipids, glycaemic control and body weight.

CONCLUSIONS

Our findings support the role of gut microbiome in modulating the effects of dietary modifications on cardiometabolic outcomes, and advance the concept of precision nutrition strategies for reducing comorbidities in pre-diabetes.

TRIAL REGISTRATION NUMBER

NCT03222791.

A randomized clinical trial comparing low-fat with precision nutrition-based diets for weight loss: impact on glycemic variability and HbA1c

BACKGROUND

Recent studies have demonstrated considerable interindividual variability in postprandial glucose response (PPGR) to the same foods, suggesting the need for more precise methods for predicting and controlling PPGR. In the Personal Nutrition Project, the investigators tested a precision nutrition algorithm for predicting an individual's PPGR.

OBJECTIVE

This study aimed to compare changes in glycemic variability (GV) and HbA1c in 2 calorie-restricted weight loss diets in adults with prediabetes or moderately controlled type 2 diabetes (T2D), which were tertiary outcomes of the Personal Diet Study.

METHODS

The Personal Diet Study was a randomized clinical trial to compare a 1-size-fits-all low-fat diet (hereafter, standardized) with a personalized diet (hereafter, personalized). Both groups received behavioral weight loss counseling and were instructed to self-monitor diets using a smartphone application. The personalized arm received personalized feedback through the application to reduce their PPGR. Continuous glucose monitoring (CGM) data were collected at baseline, 3 mo and 6 mo. Changes in mean amplitude of glycemic excursions (MAGEs) and HbA1c at 6 mo were assessed. We performed an intention-to-treat analysis using linear mixed regressions.

RESULTS

We included 156 participants [66.5% women, 55.7% White, 24.1% Black, mean age 59.1 y (standard deviation (SD) = 10.7 y)] in these analyses (standardized = 75, personalized = 81). MAGE decreased by 0.83 mg/dL per month for standardized (95% CI: 0.21, 1.46 mg/dL; P = 0.009) and 0.79 mg/dL per month for personalized (95% CI: 0.19, 1.39 mg/dL; P = 0.010) diet, with no between-group differences (P = 0.92). Trends were similar for HbA1c values.

CONCLUSIONS

Personalized diet did not result in an increased reduction in GV or HbA1c in patients with prediabetes and moderately controlled T2D, compared with a standardized diet. Additional subgroup analyses may help to identify patients who are more likely to benefit from this personalized intervention. This trial was registered at clinicaltrials.gov as NCT03336411.

Effects of aerobic exercises in prediabetes patients: a systematic review and meta-analysis

Aims

To evaluate the effects of different durations of continuous aerobic exercise on prediabetic patients.

Materials and methods

The research encompassed randomized controlled trials that examined how various durations of aerobic exercise training affected outcomes related to Body Mass Index (BMI), Fasting blood glucose (FBG), 2-hour plasma glucose (2hPG), and glycated hemoglobin (HbA1c) in individuals diagnosed with prediabetes. PubMed, Embase, Web of Science, and the Cochrane Library were searched as of January 7, 2023. The Cochrane Risk of Bias, version 2 (ROB 2) tool was used to assess the risk of bias.

Results

A total of 10 RCTs with 815 prediabetic patients were included. The average age of the participants was 56.1 years, with a standard deviation of 5.1 years. Among the participants, 39.2% were male. The interventions consisted of aerobic dance, treadmill running, walking, and a combination of aerobic exercises. The training sessions occurred three or four times per week. In prediabetic patients, aerobic exercise demonstrated a significant reduction in BMI compared to the control group, with a weighted mean difference (WMD) of -1.44 kg/m2 (95% confidence interval [CI] -1.89, -0.98). There was a decrease in FBG levels, with WMD of -0.51 mmol/L (95% CI -0.70, -0.32). Additionally, aerobic training led to significant improvements in 2hPG levels, with a WMD of -0.76 mmol/L (95% CI -1.14, -0.38). Furthermore, prediabetic patients showed a decrease in HbA1c levels after engaging in aerobic training compared to the control group, with a WMD of -0.34% (95% CI -0.45, -0.23).

Conclusion

In summary, engaging in aerobic exercise can have a significant positive impact on glycemic levels in individuals with prediabetes. It can also lead to reductions in BMI, FBG, 2hPG, HbA1c, and other relevant indicators. The extent of these improvements may vary slightly depending on the duration of the aerobic exercise intervention.

Systematic review registration

PROSPERO https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023395515.

Trial protocol for the study of recommendation system DiaCompanion with personalized dietary recommendations for women with gestational diabetes mellitus (DiaCompanion I)

Background

Gestational diabetes mellitus (GDM) is a common complication of pregnancy associated with serious adverse outcomes for mothers and their offspring. Achieving glycaemic targets is the mainstream in the treatment of GDM in order to improve pregnancy outcomes. As GDM is usually diagnosed in the third trimester of pregnancy, the time frame for the intervention is very narrow. Women need to get new knowledge and change their diet very quickly. Usually, these patients require additional frequent visits to healthcare professionals. Recommender systems based on artificial intelligence could partially substitute healthcare professionals in the process of educating and controlling women with GDM, thus reducing the burden on the women and healthcare systems. We have developed a mobile-based personalized recommendation system DiaCompanion I with data-driven real time personal recommendations focused primarily on postprandial glycaemic response prediction. The study aims to clarify the effect of using DiaCompanion I on glycaemic levels and pregnancy outcomes in women with GDM.

Methods

Women with GDM are randomized to 2 treatment groups: utilizing and not utilizing DiaCompanion I. The app provides women in the intervention group the resulting data-driven prognosis of 1-hour postprandial glucose level every time they input their meal data. Based on the predicted glucose level, they can adjust their current meal so that the predicted glucose level falls within the recommended range below 7 mmol/L. The app also provides reminders and recommendations on diet and lifestyle to the participants of the intervention group. All the participants are required to perform 6 blood glucose measurements a day. Capillary glucose values are retrieved from the glucose meter and if not available, from the woman's diary. Additionally, data on glycaemic levels during the study and consumption of major macro- and micronutrients will be collected using the mobile app with electronic report forms in the intervention group. Women from the control group receive standard care without the mobile app. All participants are prescribed with insulin therapy if needed and modifications in their lifestyle. A total of 216 women will be recruited. The primary outcome is the percentage of postprandial capillary glucose values above target (>7.0 mmol/L). Secondary outcomes include the percentage of patients requiring insulin therapy during pregnancy, maternal and neonatal outcomes, glycaemic control using glycated hemoglobin (HbA1c), continuous glucose monitoring data and other blood glucose metrics, the number of patient visits to endocrinologists and acceptance/satisfaction of the two strategies assessed using a questionnaire.

Discussion

We believe that the approach including DiaCompanion I will be more effective in patients with GDM for improving glycaemic levels and pregnancy outcomes. We also expect that the use of the app will help reduce the number of clinic visits.

Trial registration number

ClinicalTrials.gov, Identifier NCT05179798.

Role of sociodemographic, clinical, behavioral, and molecular factors in precision prevention of type 2 diabetes: a systematic review

The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular characteristics modify the efficacy of dietary or lifestyle interventions to prevent T2D. Among the 80 publications that met our criteria for inclusion, the evidence was low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition. We found evidence, albeit low certainty, to support conclusions that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.

The maternal gut microbiome during pregnancy and its role in maternal and infant health

There is growing knowledge that the maternal gut microbiome undergoes substantial changes during pregnancy. However, despite the recognition that the maternal gut microbiome influences maternal and infant health, we still have a limited understanding of the clinical and environmental factors that can impact the maternal gut microbiome during pregnancy and the consequences of these changes. Here, we review the current body of knowledge about factors shaping the maternal gut microbiome during pregnancy and its role in the development of pregnancy complications and infant health.

Wearable and digital devices to monitor and treat metabolic diseases

Cardiometabolic diseases are a major public-health concern owing to their increasing prevalence worldwide. These diseases are characterized by a high degree of interindividual variability with regards to symptoms, severity, complications and treatment responsiveness. Recent technological advances, and the growing availability of wearable and digital devices, are now making it feasible to profile individuals in ever-increasing depth. Such technologies are able to profile multiple health-related outcomes, including molecular, clinical and lifestyle changes. Nowadays, wearable devices allowing for continuous and longitudinal health screening outside the clinic can be used to monitor health and metabolic status from healthy individuals to patients at different stages of disease. Here we present an overview of the wearable and digital devices that are most relevant for cardiometabolic-disease-related readouts, and how the information collected from such devices could help deepen our understanding of metabolic diseases, improve their diagnosis, identify early disease markers and contribute to individualization of treatment and prevention plans.