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

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.

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 .

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.

A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiota

A prolonged and elevated postprandial glucose response (PPGR) is now considered a main factor contributing for the development of metabolic syndrome and type 2 diabetes, which could be prevented by dietary interventions. However, dietary recommendations to prevent alterations in PPGR have not always been successful. New evidence has supported that PPGR is not only dependent of dietary factors like the content of carbohydrates, or the glycemic index of the foods, but is also dependent on genetics, body composition, gut microbiota, among others. In recent years, continuous glucose monitoring has made it possible to establish predictions on the effect of different dietary foods on PPGRs through machine learning methods, which use algorithms that integrate genetic, biochemical, physiological and gut microbiota variables for identifying associations between them and clinical variables with aim of personalize dietary recommendations. This has allowed to improve the concept of personalized nutrition, since it is now possible to recommend through these predictions specific dietary foods to prevent elevated PPGRs that are highly variable among individuals. Additional components that can enrich the predictive algorithms are findings of nutrigenomics, nutrigenetics and metabolomics. Thus, this review aims to summarize the evidence of the components that integrate personalized nutrition focused on the prevention of PPGRs, and to show the future of personalized nutrition by laying the groundwork for the development of individualized dietary management and its impact on the improvement of metabolic diseases.

Metabotyping: a tool for identifying subgroups for tailored nutrition advice

Diet-related diseases are the leading cause of death globally and strategies to tailor effective nutrition advice are required. Personalised nutrition advice is increasingly recognised as more effective than population-level advice to improve dietary intake and health outcomes. A potential tool to deliver personalised nutrition advice is metabotyping which groups individuals into homogeneous subgroups (metabotypes) using metabolic profiles. In summary, metabotyping has been successfully employed in human nutrition research to identify subgroups of individuals with differential responses to dietary challenges and interventions and diet–disease associations. The suitability of metabotyping to identify clinically relevant subgroups is corroborated by other fields such as diabetes research where metabolic profiling has been intensely used to identify subgroups of patients that display patterns of disease progression and complications. However, there is a paucity of studies examining the efficacy of the approach to improve dietary intake and health parameters. While the application of metabotypes to tailor and deliver nutrition advice is very promising, further evidence from randomised controlled trials is necessary for further development and acceptance of the approach.

Gut Microbiome Analysis for Personalized Nutrition: The State of Science

Whereas most concepts of personalized nutrition (PN) in the past, included genotyping, recent years have brought new approaches that include microbiome analysis to optimize recommendations for diet and lifestyle changes. The new approach, offered by companies, that microbiome analysis provides a real benefit to either more concise recommendations or for increased compliance to PN, is largely lacking scientific validation. Although the microbiome field shows enormous proliferation, it has some major flaws that make its use in the public health domain currently critical. Starting with the quality and representative character of the stool samples, its processing and analysis as well as assembly of metagenome data and the interpretation. Moreover, there is still no consensus of what constitutes a "normal/healthy" microbiome, nor what features characterize a dysbiotic microbiome. And, based on hundreds of individual parameters and environmental factors, the intestinal microbiome shows a huge variability and consequently changing one factor-such as food intake-is likely to have a limited impact in achieving optimized health. The present review intends to summarize the state of consolidated knowledge on human gut microbiome in the context of diet and disease, its key features, and its influencing factors as well as its "add-on" quality for PN offers.

Distinct factors associated with short-term and long-term weight loss induced by low-fat or low-carbohydrate diet intervention

To understand what determines the success of short- and long-term weight loss, we conduct a secondary analysis of dietary, metabolic, and molecular data collected from 609 participants before, during, and after a 1-year weight-loss intervention with either a healthy low-carbohydrate (HLC) or a healthy low-fat (HLF) diet. Through systematic analysis of multidomain datasets, we find that dietary adherence and diet quality, not just caloric restriction, are important for short-term weight loss in both diets. Interestingly, we observe minimal dietary differences between those who succeeded in long-term weight loss and those who did not. Instead, proteomic and gut microbiota signatures significantly differ between these two groups at baseline. Moreover, the baseline respiratory quotient may suggest a specific diet for better weight-loss outcomes. Overall, the identification of these dietary, molecular, and metabolic factors, common or unique to the HLC and HLF diets, provides a roadmap for developing individualized weight-loss strategies.

Understanding interactions among diet, host and gut microbiota for personalized nutrition

Human responses to the same diets may vary to a large extent, depending on the complex diet-host-microbiota interactions. Recent scientific advance has indicated that this diet-host-microbiota interaction could be quantified to develop strategies for improving individual health (personalized nutrition). Compared to the host related factors (which are difficult to manipulate), the gut microbiome is more readily modulated by dietary exposures and has important roles in affecting human health via the synthesis of various bioactive compounds and participating in the digestion and absorption process of macro- and micronutrients. Therefore, gut microbiota alterations induced by diets could possibly be utilized to improve human health in a targeted manner. However, limitations in the processing and analysis of 'big-data' concerning human microbiome still restrict the translational capacity of diet-host-microbiota interactions into tools to improve personalized human health. In the current review, recent advances in terms of understanding the specific diet-host-microbiota interactions were summarized, aiming to help the development of strategies for personalized nutrition.

BREAst Cancer Personalised NuTrition (BREACPNT): dietary intervention in breast cancer survivors treated with endocrine therapy - a protocol for a randomised clinical trial

INTRODUCTION

Breast cancer survivors treated with adjuvant endocrine therapy commonly experience weight gain, which has been associated with low adherence to therapy and worse breast cancer prognosis. We aim to assess whether a personalised postprandial glucose targeting diet will be beneficial for weight management as compared with the recommended Mediterranean diet in this patient population METHODS AND ANALYSIS: The BREAst Cancer Personalised NuTrition study is a phase-2 randomised trial in hormone receptor positive patients with breast cancer, treated with adjuvant endocrine therapy. The study objective is to assess whether dietary intervention intended to improve postprandial glycaemic response to meals results in better weight and glycaemic control in this population as compared with the standard recommended Mediterranean diet. Consenting participants will be assigned in a single blinded fashion to either of two dietary arms (Mediterranean diet or an algorithm-based personalised diet). They will be asked to provide a stool sample for microbiome analysis and will undergo continuous glucose monitoring for 2 weeks, at the initiation and termination of the intervention period. Microbiome composition data will be used to tailor personal dietary recommendations. After randomisation and provision of dietary recommendations, participants will be asked to continuously log their diet and lifestyle activities on a designated smartphone application during the 6-month intervention period, during which they will be monthly monitored by a certified dietitian. Participants' clinical records will be followed twice yearly for 5 years for treatment adherence, disease-free survival and recurrence.

ETHICS AND DISSEMINATION

The study has been approved by the ethics committee in the Sheba medical centre (file 5725-18-SMC, Ramat Gan, Israel) and the Weizmann Institutional Review Board (file 693-2, Rehovot, Israel). The findings of this study will be published in a peer reviewed publication.

TRIAL REGISTRATION NUMBER

NCT04079270.

Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model

Despite the pivotal role played by elevated circulating triglyceride levels in the pathophysiology of cardio-metabolic diseases many of the indices used to quantify metabolic health focus on deviations in glucose and insulin alone. We present the Mixed Meal Model, a computational model describing the systemic interplay between triglycerides, free fatty acids, glucose, and insulin. We show that the Mixed Meal Model can capture deviations in the post-meal excursions of plasma glucose, insulin, and triglyceride that are indicative of features of metabolic resilience; quantifying insulin resistance and liver fat; validated by comparison to gold-standard measures. We also demonstrate that the Mixed Meal Model is generalizable, applying it to meals with diverse macro-nutrient compositions. In this way, by coupling triglycerides to the glucose-insulin system the Mixed Meal Model provides a more holistic assessment of metabolic resilience from meal response data, quantifying pre-clinical metabolic deteriorations that drive disease development in overweight and obesity.

Diabetes precision medicine: plenty of potential, pitfalls and perils but not yet ready for prime time

Rapid advances in technology and data science have the potential to improve the precision of preventive and therapeutic interventions, and enable the right treatment to be recommended, at the right time, to the right person. There are well-described examples of successful precision medicine approaches for monogenic conditions such as specific diets for phenylketonuria, and sulfonylurea treatments for certain types of MODY. However, the majority of chronic diseases are polygenic, and it is unlikely that the research strategies used for monogenic diseases will deliver similar changes to practice for polygenic traits. Type 2 diabetes, for example, is a multifactorial, heterogeneous, polygenic palette of metabolic disorders. In this non-systematic review I highlight limitations of the evidence, and the challenges that need to be overcome prior to implementation of precision medicine in the prevention and management of type 2 diabetes. Most precision medicine approaches are spuriously precise, overly complex and too narrowly focused on predicting blood glucose levels with a limited set of characteristics of individuals rather than the whole person and their context. Overall, the evidence to date is insufficient to justify widespread implementation of precision medicine approaches into routine clinical practice for type 2 diabetes. We need to retain a degree of humility and healthy scepticism when evaluating novel strategies, and to demand that existing evidence thresholds are exceeded prior to implementation.

Personalised prevention of type 2 diabetes

It is well established from clinical trials that behavioural interventions can halve the risk of progression from prediabetes to type 2 diabetes but translating this evidence of efficacy into effective real-world interventions at scale is an ongoing challenge. A common suggestion is that future preventive interventions need to be more personalised in order to enhance effectiveness. This review evaluates the degree to which existing interventions are already personalised and outlines how greater personalisation could be achieved through better identification of those at high risk, division of type 2 diabetes into specific subgroups and, above all, more individualisation of the behavioural targets for preventive action. Approaches using more dynamic real-time data are in their scientific infancy. Although these approaches are promising they need longer-term evaluation against clinical outcomes. Whatever personalised preventive approaches for type 2 diabetes are developed in the future, they will need to be complementary to existing individual-level interventions that are being rolled out and that are demonstrably effective. They will also need to ideally synergise with, and at the very least not detract attention from, efforts to develop and implement strategies that impact on type 2 diabetes risk at the societal level.

Precision nutrition in diabetes: when population-based dietary advice gets personal

Diet plays a fundamental role in maintaining long-term health, with healthful diets being endorsed by current dietary guidelines for the prevention and management of type 2 diabetes. However, the response to dietary interventions varies widely, highlighting the need for refinement and personalisation beyond population-based 'one size fits all'. This article reviews the clinical evidence supporting precision nutrition as a fundamental approach for dietary advice in diabetes. Further, it proposes a framework for the eventual implementation of precision nutrition and discusses key challenges for the application of this approach in the prevention of diabetes. One implication of this approach is that precision nutrition would not exclude the parallel goal of population-based healthy dietary advice. Nevertheless, the shift in prioritising precision nutrition is needed to reflect the dynamic nature of responses to dietary interventions that vary among individuals and change over the life course.

Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies

Digital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the potential of wearables and smartphones to (1) detect eating moments and (2) predict and explain individual glucose levels in healthy individuals, ultimately supporting health self-management. Twenty-four individuals collected continuous data from interstitial glucose monitoring, food logging, activity, and sleep tracking over 14 days. We demonstrated the use of continuous glucose monitoring and activity tracking in detecting eating moments with a prediction model showing an accuracy of 92.3% (87.2-96%) and 76.8% (74.3-81.2%) in the training and test datasets, respectively. Additionally, we showed the prediction of glucose peaks from food logging, activity tracking, and sleep monitoring with an overall mean absolute error of 0.32 (+/-0.04) mmol/L for the training data and 0.62 (+/-0.15) mmol/L for the test data. With Shapley additive explanations, the personal lifestyle elements important for predicting individual glucose peaks were identified, providing a basis for personalized lifestyle advice. Pending further validation of these digital biomarkers, they show promise in supporting the prevention and management of type 2 diabetes through personalized lifestyle recommendations.

Effect of a Personalized Diet to Reduce Postprandial Glycemic Response vs a Low-fat Diet on Weight Loss in Adults With Abnormal Glucose Metabolism and Obesity: A Randomized Clinical Trial

IMPORTANCE

Interindividual variability in postprandial glycemic response (PPGR) to the same foods may explain why low glycemic index or load and low-carbohydrate diet interventions have mixed weight loss outcomes. A precision nutrition approach that estimates personalized PPGR to specific foods may be more efficacious for weight loss.

OBJECTIVE

To compare a standardized low-fat vs a personalized diet regarding percentage of weight loss in adults with abnormal glucose metabolism and obesity.

DESIGN, SETTING, AND PARTICIPANTS

The Personal Diet Study was a single-center, population-based, 6-month randomized clinical trial with measurements at baseline (0 months) and 3 and 6 months conducted from February 12, 2018, to October 28, 2021. A total of 269 adults aged 18 to 80 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) ranging from 27 to 50 and a hemoglobin A1c level ranging from 5.7% to 8.0% were recruited. Individuals were excluded if receiving medications other than metformin or with evidence of kidney disease, assessed as an estimated glomerular filtration rate of less than 60 mL/min/1.73 m2 using the Chronic Kidney Disease Epidemiology Collaboration equation, to avoid recruiting patients with advanced type 2 diabetes.

INTERVENTIONS

Participants were randomized to either a low-fat diet (<25% of energy intake; standardized group) or a personalized diet that estimates PPGR to foods using a machine learning algorithm (personalized group). Participants in both groups received a total of 14 behavioral counseling sessions and self-monitored dietary intake. In addition, the participants in the personalized group received color-coded meal scores on estimated PPGR delivered via a mobile app.

MAIN OUTCOMES AND MEASURES

The primary outcome was the percentage of weight loss from baseline to 6 months. Secondary outcomes included changes in body composition (fat mass, fat-free mass, and percentage of body weight), resting energy expenditure, and adaptive thermogenesis. Data were collected at baseline and 3 and 6 months. Analysis was based on intention to treat using linear mixed modeling.

RESULTS

Of a total of 204 adults randomized, 199 (102 in the personalized group vs 97 in the standardized group) contributed data (mean [SD] age, 58 [11] years; 133 women [66.8%]; mean [SD] body mass index, 33.9 [4.8]). Weight change at 6 months was -4.31% (95% CI, -5.37% to -3.24%) for the standardized group and -3.26% (95% CI, -4.25% to -2.26%) for the personalized group, which was not significantly different (difference between groups, 1.05% [95% CI, -0.40% to 2.50%]; P = .16). There were no between-group differences in body composition and adaptive thermogenesis; however, the change in resting energy expenditure was significantly greater in the standardized group from 0 to 6 months (difference between groups, 92.3 [95% CI, 0.9-183.8] kcal/d; P = .05).

CONCLUSIONS AND RELEVANCE

A personalized diet targeting a reduction in PPGR did not result in greater weight loss compared with a low-fat diet at 6 months. Future studies should assess methods of increasing dietary self-monitoring adherence and intervention exposure.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03336411.

Local and systemic effects of microbiome‐derived metabolites

Commensal microbes form distinct ecosystems within their mammalian hosts, collectively termed microbiomes. These indigenous microbial communities broadly expand the genomic and functional repertoire of their host and contribute to the formation of a “meta‐organism.” Importantly, microbiomes exert numerous biochemical reactions synthesizing or modifying multiple bioactive small molecules termed metabolites, which impact their host's physiology in a variety of contexts. Identifying and understanding molecular mechanisms of metabolite–host interactions, and how their disrupted signaling can contribute to diseases, may enable their therapeutic application, a modality termed “postbiotic” therapy. In this review, we highlight key examples of effects of bioactive microbe‐associated metabolites on local, systemic, and immune environments, and discuss how these may impact mammalian physiology and associated disorders. We outline the challenges and perspectives in understanding the potential activity and function of this plethora of microbially associated small molecules as well as possibilities to harness them toward the promotion of personalized precision therapeutic interventions.

Metabolic control by the microbiome

The interaction between the metabolic activities of the intestinal microbiome and its host forms an important part of health. The basis of this interaction is in part mediated by the release of microbially-derived metabolites that enter the circulation. These products of microbial metabolism thereby interface with the immune, metabolic, or nervous systems of the host to influence physiology. Here, we review the interactions between the metabolic activities of the microbiome and the systemic metabolism of the host. The concept that the endocrine system includes more than just the eukaryotic host component enables the rational design of exogenous interventions that shape human metabolism. An improved mechanistic understanding of the metabolic microbiome-host interaction may therefore pioneer actionable microbiota-based diagnostics or therapeutics that allow the control of host systemic metabolism via the microbiome.

An expanded reference map of the human gut microbiome reveals hundreds of previously unknown species

The gut is the richest ecosystem of microbes in the human body and has great influence on our health. Despite many efforts, the set of microbes inhabiting this environment is not fully known, limiting our ability to identify microbial content and to research it. In this work, we combine new microbial metagenomic assembled genomes from 51,052 samples, with previously published genomes to produce a curated set of 241,118 genomes. Based on this set, we procure a new and improved human gut microbiome reference set of 3594 high quality species genomes, which successfully matches 83.65% validation samples’ reads. This improved reference set contains 310 novel species, including one that exists in 19% of validation samples. Overall, this study provides a gut microbial genome reference set that can serve as a valuable resource for further research.

Here, Leviatan et al. produce 241,118 genome assemblies to produce a new human gut microbiome reference set of 3,594 species genomes, of which 310 represent previously undescribed species, making the catalog a valuable resource for further research.

Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.

Association between Habitual Diet and the Postprandial Glucose Response-An Enable Study

SCOPE

It is inconclusive which factors influence inter-individual variations of postprandial glucose response (PPGR). This study investigates whether the habitual diet is associated with PPGR.

METHODS AND RESULTS

Data from healthy adults (young adults with 18-25 years, middle-aged adults with 40-65 years, and older adults with 75-85 years) is collected at baseline and during an oral glucose tolerance test (OGTT) collected. Habitual diet is assessed by a food frequency questionnaire and two 24-h food lists. Associations between habitual diet and glucose incremental area under the curve (iAUC_min ) are examined by regression models. The intake of cereals and cereal products is negatively associated with glucose iAUC_min (p = 0.002) in the total cohort (N = 459, 50% women, 55 ± 21 years, BMI 26 ± 5 kg m^{- 2} ). Up to 9% of the variance in the glycemic response is explained by the respective dietary parameters identified in the models of the specific age groups.

CONCLUSION

There are age-specific diet-related effects on PPGR. The usual intake of cereals and cereal products seems to play a greater role in PPGR in more than one age group. Further research is needed, to establish how diet can be optimized based on age and PPGR.

Personal diet-microbiota interactions and weight loss

The aim of this review is to provide an overview of how person-specific interactions between diet and the gut microbiota could play a role in affecting diet-induced weight loss responses. The highly person-specific gut microbiota, which is shaped by our diet, secretes digestive enzymes and molecules that affect digestion in the colon. Therefore, weight loss responses could in part depend on personal colonic fermentation responses, which affect energy extraction of food and production of microbial metabolites, such as short-chain fatty acids (SCFAs), which exert various effects on host metabolism. Colonic fermentation is the net result of the complex interplay between availability of dietary substrates, the functional capacity of the gut microbiome and environmental (abiotic) factors in the gut such as pH and transit time. While animal studies have demonstrated that the gut microbiota can causally affect obesity, causal and mechanistic evidence from human studies is still largely lacking. However, recent human studies have proposed that the baseline gut microbiota composition may predict diet-induced weight loss-responses. In particular, individuals characterised by high relative abundance of Prevotella have been found to lose more weight on diets rich in dietary fibre compared to individuals with low Prevotella abundance. Although harnessing of personal diet-microbiota interactions holds promise for more personalised nutrition and obesity management strategies to improve human health, there is currently insufficient evidence to unequivocally link the gut microbiota and weight loss in human subjects. To move the field forward, a greater understanding of the mechanistic underpinnings of personal diet-microbiota interactions is needed.

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

BACKGROUND

Dietary modifications are crucial for managing newly diagnosed type 2 diabetes mellitus (T2DM) and preventing its health complications, but many patients fail to achieve clinical goals with diet alone. We sought to evaluate the clinical effects of a personalized postprandial-targeting (PPT) diet on glycemic control and metabolic health in individuals with newly diagnosed T2DM as compared to the commonly recommended Mediterranean-style (MED) diet.

METHODS

We enrolled 23 adults with newly diagnosed T2DM (aged 53.5 ± 8.9 years, 48% males) for a randomized crossover trial of two 2-week-long dietary interventions. Participants were blinded to their assignment to one of the two sequence groups: either PPT-MED or MED-PPT diets. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses (PPGR). We further evaluated the long-term effects of PPT diet on glycemic control and metabolic health by an additional 6-month PPT intervention (n = 16). Participants were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using a smartphone application.

RESULTS

In the crossover intervention, the PPT diet lead to significant lower levels of CGM-based measures as compared to the MED diet, including average PPGR (mean difference between diets, - 19.8 ± 16.3 mg/dl × h, p < 0.001), mean glucose (mean difference between diets, - 7.8 ± 5.5 mg/dl, p < 0.001), and daily time of glucose levels > 140 mg/dl (mean difference between diets, - 2.42 ± 1.7 h/day, p < 0.001). Blood fructosamine also decreased significantly more during PPT compared to MED intervention (mean change difference between diets, - 16.4 ± 37 μmol/dl, p < 0.0001). At the end of 6 months, the PPT intervention leads to significant improvements in multiple metabolic health parameters, among them HbA1c (mean ± SD, - 0.39 ± 0.48%, p < 0.001), fasting glucose (- 16.4 ± 24.2 mg/dl, p = 0.02) and triglycerides (- 49 ± 46 mg/dl, p < 0.001). Importantly, 61% of the participants exhibited diabetes remission, as measured by HbA1c < 6.5%. Finally, some clinical improvements were significantly associated with gut microbiome changes per person.

CONCLUSION

In this crossover trial in subjects with newly diagnosed T2DM, a PPT diet improved CGM-based glycemic measures significantly more than a Mediterranean-style MED diet. Additional 6-month PPT intervention further improved glycemic control and metabolic health parameters, supporting the clinical efficacy of this approach.

TRIAL REGISTRATION

ClinicalTrials.gov number, NCT01892956.