Personalized Nutrition by Prediction of Glycemic Responses

Global research trends in regulating gut microbiome to improve type 2 diabetes mellitus: bibliometrics and visual analysis

Background

Gut microbiome (GM) and type 2 diabetes mellitus (T2DM) have two-way effects. Improving T2DM by modulating GM in various ways, such as diet, exercise, and medication, is gradually becoming popular, and related studies have yielded positive results. However, there is still a lack of high-quality bibliometric analyses of research in this area. This study aims to systematize and comprehensively summarize the knowledge structure, research tropics, and research trends of GM and T2DM through bibliometric analysis.

Methods

Publications related to GM and T2DM before January 9, 2024, in the Web of Science Core Collection (WOSCC) were searched in this study. Microsoft Excel 2019 was used to analyze publishing trends and CiteSpace (v.6.1.R6 Advanced) was used to analyze institutions, cited journals, references, and keywords.SCImago Graphica (v.1.0.39) was used to analyze countries/regions, institutions' collaborations, cited authors, and published journals.

Results

We finally included 1004 articles published from 2008 to 2023. The number of published articles showed an upward trend and reached its peak in 2022. China is the country with the largest number of articles, Univ Copenhagen is the institution with the largest number of articles, Fukui, Michiaki, Hamaguchi, Masahide are the scholars with the largest number of articles, and Cani and Patrice D. are the scholars with the largest number of citations. NUTRIENTS(Q1/5.9) published the most publications, while Nature (Q1/64.8; Cited 804 times) is the most frequently cited journal. Gut microbiota, Obesity, and insulin resistance are the most frequently used keywords. This study found that current researches focus on the effects of diet, exercise, and pharmacological modification of GM to improve T2DM and explores specific mechanisms. Future researches will focus on three areas: complications of T2DM and specific physiological processes, methods and measures to regulate GM, and new experimental techniques and assays.

Conclusion

The current researches confirmed the effects and specific mechanisms of modulating GM to improve T2DM. Further exploration of the effects of modulating GM on T2DM complications and specific physiologic processes is a future trend of research. Exploring specific methods for regulating GM and developing new experimental techniques and assays are important for future research.

Willingness of people with type 2 diabetes to engage in healthy eating, physical activity and medication taking

AIM

To assess the willingness of people with type 2 diabetes (T2D) to engage in healthy eating, physical activity and medication taking, and explore associated patient factors.

METHODS

Online survey among recently diagnosed T2D patients recruited in the Netherlands and the United Kingdom (UK). Patient factors included general factors and behaviour-specific beliefs. Logistic regression analyses and explorative comparisons were conducted.

RESULTS

Overall, 48% of 67 patients were willing to engage in all three management options, whereas 6% were not willing to follow any of them. 73% were willing to manage T2D with healthy eating, 73% with physical activity, and 72% with medication. Country of recruitment was significantly associated with willingness for healthy eating, with higher willingness among Dutch participants. Beliefs surrounding capability, opportunity, and motivation were significantly associated with willingness to engage in physical activity and medication taking. Many beliefs were similar regardless of willingness but those willing to engage in physical activity perceived less barriers and those willing to take medication had more positive and less negative outcome beliefs than those not willing.

CONCLUSIONS

Willingness to engage in all management options was limited among recently diagnosed patients, and partly associated with behaviour-specific patient beliefs.

Genetic Evidence of Causal Relation Between Intestinal Glucose Absorption and Early Postprandial Glucose Response: A Mendelian Randomization Study

The postprandial glucose response is an independent risk factor for type 2 diabetes. Observationally, early glucose response after an oral glucose challenge has been linked to intestinal glucose absorption, largely influenced by the expression of sodium-glucose cotransporter 1 (SGLT1). This study uses Mendelian randomization (MR) to estimate the causal effect of intestinal SGLT1 expression on early glucose response. Involving 1,547 subjects with class II/III obesity from the Atlas Biologique de l'Obésité Sévère cohort, the study uses SGLT1 genotyping, oral glucose tolerance tests, and jejunal biopsies to measure SGLT1 expression. A loss-of-function SGLT1 haplotype serves as the instrumental variable, with intestinal SGLT1 expression as the exposure and the change in 30-min postload glycemia from fasting glycemia (Δ30 glucose) as the outcome. Results show that 12.8% of the 1,342 genotyped patients carried the SGLT1 loss-of-function haplotype, associated with a mean Δ30 glucose reduction of -0.41 mmol/L and a significant decrease in intestinal SGLT1 expression. The observational study links a 1-SD decrease in SGLT1 expression to a Δ30 glucose reduction of -0.097 mmol/L. MR analysis parallels these findings, associating a statistically significant reduction in genetically instrumented intestinal SGLT1 expression with a Δ30 glucose decrease of -0.353. In conclusion, the MR analysis provides genetic evidence that reducing intestinal SGLT1 expression causally lowers early postload glucose response. This finding has a potential translational impact on managing early glucose response to prevent or treat type 2 diabetes.

ARTICLE HIGHLIGHTS

How Diet and Lifestyle Can Fine-Tune Gut Microbiomes for Healthy Aging

Many physical, social, and psychological changes occur during aging that raise the risk of developing chronic diseases, frailty, and dependency. These changes adversely affect the gut microbiota, a phenomenon known as microbe-aging. Those microbiota alterations are, in turn, associated with the development of age-related diseases. The gut microbiota is highly responsive to lifestyle and dietary changes, displaying a flexibility that also provides anactionable tool by which healthy aging can be promoted. This review covers, firstly, the main lifestyle and socioeconomic factors that modify the gut microbiota composition and function during healthy or unhealthy aging and, secondly, the advances being made in defining and promoting healthy aging, including microbiome-informed artificial intelligence tools, personalized dietary patterns, and food probiotic systems.

Association Between the Postprandial Response to an Oral Glucose Tolerance Test and Anthropometric Changes After an 8-Week Low-Calorie Formula Diet - Results From the Lifestyle Intervention (LION) Study

SCOPE

Interindividual variations in postprandial metabolism and weight loss outcomes have been reported. The literature suggests links between postprandial metabolism and weight regulation. Therefore, the study aims to evaluate if postprandial glucose metabolism after a glucose load predicts anthropometric outcomes of a weight loss intervention.

METHODS AND RESULTS

Anthropometric data from adults with obesity (18-65 years, body mass index [BMI] 30.0-39.9 kg m-2) are collected pre- and post an 8-week formula-based weight loss intervention. An oral glucose tolerance test (OGTT) is performed at baseline, from which postprandial parameters are derived from glucose and insulin concentrations. Linear regression models explored associations between these parameters and anthropometric changes (∆) postintervention. A random forest model is applied to identify predictive parameters for anthropometric outcomes after intervention. Postprandial parameters after an OGTT of 158 participants (63.3% women, age 45 ± 12, BMI 34.9 ± 2.9 kg m-2) reveal nonsignificant associations with changes in anthropometric parameters after weight loss (p > 0.05). Baseline fat-free mass (FFM) and sex are primary predictors for ∆ FFM [kg].

CONCLUSION

Postprandial glucose metabolism after a glucose load does not predict anthropometric outcomes after short-term weight loss via a formula-based low-calorie diet in adults with obesity.

Divergent age-associated and metabolism-associated gut microbiome signatures modulate cardiovascular disease risk

Insight into associations between the gut microbiome with metabolism and aging is crucial for tailoring interventions to promote healthy longevity. In a discovery cohort of 10,207 individuals aged 40-93 years, we used 21 metabolic parameters to classify individuals into five clusters, termed metabolic multimorbidity clusters (MCs), that represent different metabolic subphenotypes. Compared to the cluster classified as metabolically healthy (MC1), clusters classified as 'obesity-related mixed' (MC4) and 'hyperglycemia' (MC5) exhibited an increased 11.1-year cardiovascular disease (CVD) risk by 75% (multivariable-adjusted hazard ratio (HR): 1.75, 95% confidence interval (CI): 1.43-2.14) and by 117% (2.17, 1.72-2.74), respectively. These associations were replicated in a second cohort of 9,061 individuals with a 10.0-year follow-up. Based on analysis of 4,491 shotgun fecal metagenomes from the discovery cohort, we found that gut microbial composition was associated with both MCs and age. Next, using 55 age-specific microbial species to capture biological age, we developed a gut microbial age (MA) metric, which was validated in four external cohorts comprising 4,425 metagenomic samples. Among individuals aged 60 years or older, the increased CVD risk associated with MC4 or MC5, as compared to MC1, MC2 or MC3, was exacerbated in individuals with high MA but diminished in individuals with low MA, independent of age, sex and other lifestyle and dietary factors. This pattern, in which younger MA appears to counteract the CVD risk attributable to metabolic dysfunction, implies a modulating role of MA in cardiovascular health for metabolically unhealthy older people.

Participatory Development of an Integrated, eHealth-Supported, Educational Care Pathway (Diabetes Box) for People With Type 2 Diabetes: Development and Usability Study

BACKGROUND

Type 2 diabetes (T2D) tremendously affects patient health and health care globally. Changing lifestyle behaviors can help curb the burden of T2D. However, health behavior change is a complex interplay of medical, behavioral, and psychological factors. Personalized lifestyle advice and promotion of self-management can help patients change their health behavior and improve glucose regulation. Digital tools are effective in areas of self-management and have great potential to support patient self-management due to low costs, 24/7 availability, and the option of dynamic automated feedback. To develop successful eHealth solutions, it is important to include stakeholders throughout the development and use a structured approach to guide the development team in planning, coordinating, and executing the development process.

OBJECTIVE

The aim of this study is to develop an integrated, eHealth-supported, educational care pathway for patients with T2D.

METHODS

The educational care pathway was developed using the first 3 phases of the Center for eHealth and Wellbeing Research roadmap: the contextual inquiry, the value specification, and the design phase. Following this roadmap, we used a scoping review about diabetes self-management education and eHealth, past experiences of eHealth practices in our hospital, focus groups with health care professionals (HCPs), and a patient panel to develop a prototype of an educational care pathway. This care pathway is called the Diabetes Box (Leiden University Medical Center) and consists of personalized education, digital educational material, self-measurements of glucose, blood pressure, activity, and sleep, and a smartphone app to bring it all together.

RESULTS

The scoping review highlights the importance of self-management education and the potential of telemonitoring and mobile apps for blood glucose regulation in patients with T2D. Focus groups with HCPs revealed the importance of including all relevant lifestyle factors, using a tailored approach, and using digital consultations. The contextual inquiry led to a set of values that stakeholders found important to include in the educational care pathway. All values were specified in biweekly meetings with key stakeholders, and a prototype was designed. This prototype was evaluated in a patient panel that revealed an overall positive impression of the care pathway but stressed that the number of apps should be restricted to one, that there should be no delay in glucose value visualization, and that insulin use should be incorporated into the app. Both patients and HCPs stressed the importance of direct automated feedback in the Diabetes Box.

CONCLUSIONS

After developing the Diabetes Box prototype using the Center for eHealth and Wellbeing Research roadmap, all stakeholders believe that the concept of the Diabetes Box is useful and feasible and that direct automated feedback and education on stress and sleep are essential. A pilot study is planned to assess feasibility, acceptability, and usefulness in more detail.

Network impact of a single-time-point microbial sample

The human microbiome plays a crucial role in determining our well-being and can significantly influence human health. The individualized nature of the microbiome may reveal host-specific information about the health state of the subject. In particular, the microbiome is an ecosystem shaped by a tangled network of species-species and host-species interactions. Thus, analysis of the ecological balance of microbial communities can provide insights into these underlying interrelations. However, traditional methods for network analysis require many samples, while in practice only a single-time-point microbial sample is available in clinical screening. Recently, a method for the analysis of a single-time-point sample, which evaluates its 'network impact' with respect to a reference cohort, has been applied to analyze microbial samples from women with Gestational Diabetes Mellitus. Here, we introduce different variations of the network impact approach and systematically study their performance using simulated 'samples' fabricated via the Generalized Lotka-Volttera model of ecological dynamics. We show that the network impact of a single sample captures the effect of the interactions between the species, and thus can be applied to anomaly detection of shuffled samples, which are 'normal' in terms of species abundance but 'abnormal' in terms of species-species interrelations. In addition, we demonstrate the use of the network impact in binary and multiclass classifications, where the reference cohorts have similar abundance profiles but different species-species interactions. Individualized analysis of the human microbiome has the potential to improve diagnosis and personalized treatments.

Identification of carbohydrate gene clusters obtained from in vitro fermentations as predictive biomarkers of prebiotic responses

BACKGROUND

Prebiotic fibers are non-digestible substrates that modulate the gut microbiome by promoting expansion of microbes having the genetic and physiological potential to utilize those molecules. Although several prebiotic substrates have been consistently shown to provide health benefits in human clinical trials, responder and non-responder phenotypes are often reported. These observations had led to interest in identifying, a priori, prebiotic responders and non-responders as a basis for personalized nutrition. In this study, we conducted in vitro fecal enrichments and applied shotgun metagenomics and machine learning tools to identify microbial gene signatures from adult subjects that could be used to predict prebiotic responders and non-responders.

RESULTS

Using short chain fatty acids as a targeted response, we identified genetic features, consisting of carbohydrate active enzymes, transcription factors and sugar transporters, from metagenomic sequencing of in vitro fermentations for three prebiotic substrates: xylooligosacharides, fructooligosacharides, and inulin. A machine learning approach was then used to select substrate-specific gene signatures as predictive features. These features were found to be predictive for XOS responders with respect to SCFA production in an in vivo trial.

CONCLUSIONS

Our results confirm the bifidogenic effect of commonly used prebiotic substrates along with inter-individual microbial responses towards these substrates. We successfully trained classifiers for the prediction of prebiotic responders towards XOS and inulin with robust accuracy (≥ AUC 0.9) and demonstrated its utility in a human feeding trial. Overall, the findings from this study highlight the practical implementation of pre-intervention targeted profiling of individual microbiomes to stratify responders and non-responders.

Metabolic Responses to an Acute Glucose Challenge: The Differential Effects of Eight Weeks of Almond vs. Cracker Consumption in Young Adults

This study investigated the dynamic responses to an acute glucose challenge following chronic almond versus cracker consumption for 8 weeks (clinicaltrials.gov ID: NCT03084003). Seventy-three young adults (age: 18-19 years, BMI: 18-41 kg/m2) participated in an 8-week randomized, controlled, parallel-arm intervention and were randomly assigned to consume either almonds (2 oz/d, n=38) or an isocaloric control snack of graham crackers (325 kcal/d, n=35) daily for 8 weeks. Twenty participants from each group underwent a 2-hour oral glucose tolerance test (oGTT) at the end of the 8-week intervention. Metabolite abundances in the oGTT serum samples were quantified using untargeted metabolomics, and targeted analyses for free PUFAs, total fatty acids, oxylipins, and endocannabinoids. Multivariate, univariate, and chemical enrichment analyses were conducted to identify significant metabolic shifts. Findings exhibit a biphasic lipid response distinguished by higher levels of unsaturated triglycerides in the earlier periods of the oGTT followed by lower levels in the latter period in the almond versus cracker group (p-value<0.05, chemical enrichment analyses). Almond (vs. cracker) consumption was also associated with higher AUC120 min of aminomalonate, and oxylipins (p-value<0.05), but lower AUC120 min of L-cystine, N-acetylmannosamine, and isoheptadecanoic acid (p-value<0.05). Additionally, the Matsuda Index in the almond group correlated with AUC120 min of CE 22:6 (r=-0.46; p-value<0.05) and 12,13 DiHOME (r=0.45; p-value<0.05). Almond consumption for 8 weeks leads to dynamic, differential shifts in response to an acute glucose challenge, marked by alterations in lipid and amino acid mediators involved in metabolic and physiological pathways.

Precision synbiotics increase gut microbiome diversity and improve gastrointestinal symptoms in a pilot open-label study for autism spectrum disorder

The efficacy of prebiotics and probiotics (synbiotics when combined) to improve symptoms associated with autism spectrum disorder (ASD) has shown considerable inter-study variation, likely due to the complex, heterogeneous nature of the disorder and its associated behavioral, developmental, and gastrointestinal symptoms. Here, we present a precision synbiotic supplementation study in 296 children and adults diagnosed with ASD versus 123 age-matched neurotypical controls. One hundred seventy ASD participants completed the study. Baseline and post-synbiotic assessment of ASD and gastrointestinal (GI) symptoms and deep metagenomic sequencing were performed. Within the ASD cohort, there were significant differences in microbes between subpopulations based on the social responsiveness scale (SRS2) survey (Prevotella spp., Bacteroides, Fusicatenibacter, and others) and gluten and dairy-free diets (Bifidobacterium spp., Lactococcus, Streptococcus spp., and others). At the baseline, the ASD cohort maintained a lower taxonomic alpha diversity and significant differences in taxonomic composition, metabolic pathways, and gene families, with a greater proportion of potential pathogens, including Shigella, Klebsiella, and Clostridium, and lower proportions of beneficial microbes, including Faecalibacterium compared to controls. Following the 3-month synbiotic supplementation, the ASD cohort showed increased taxonomic alpha diversity, shifts in taxonomy and metabolic pathway potential, and improvements in some ASD-related symptoms, including a significant reduction in GI discomfort and overall improved language, comprehension, cognition, thinking, and speech. However, the open-label study design may include some placebo effects. In summary, we found that precision synbiotics modulated the gut microbiome and could be used as supplementation to improve gastrointestinal and ASD-related symptoms.

IMPORTANCE

Autism spectrum disorder (ASD) is prevalent in 1 out of 36 children in the United States and contributes to health, financial, and psychological burdens. Attempts to identify a gut microbiome signature of ASD have produced varied results. The limited pre-clinical and clinical population sizes have hampered the success of these trials. To understand the microbiome associated with ASD, we employed whole metagenomic shotgun sequencing to classify microbial composition and genetic functional potential. Despite being one of the most extensive ASD post-synbiotic assessment studies, the results highlight the complexity of performing such a case-control supplementation study in this population and the potential for a future therapeutic approach in ASD.

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.

Characterizing human postprandial metabolic response using multiway data analysis

INTRODUCTION

Analysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities and differences in postprandial responses of individuals. Traditional data analysis methods often rely on data summaries or univariate approaches focusing on one metabolite at a time.

OBJECTIVES

Our goal is to provide a comprehensive picture in terms of the changes in the human metabolism in response to a meal challenge test, by revealing static and dynamic markers of phenotypes, i.e., subject stratifications, related clusters of metabolites, and their temporal profiles.

METHODS

We analyze Nuclear Magnetic Resonance (NMR) spectroscopy measurements of plasma samples collected during a meal challenge test from 299 individuals from the COPSAC2000 cohort using a Nightingale NMR panel at the fasting and postprandial states (15, 30, 60, 90, 120, 150, 240 min). We investigate the postprandial dynamics of the metabolism as reflected in the dynamic behaviour of the measured metabolites. The data is arranged as a three-way array: subjects by metabolites by time. We analyze the fasting state data to reveal static patterns of subject group differences using principal component analysis (PCA), and fasting state-corrected postprandial data using the CANDECOMP/PARAFAC (CP) tensor factorization to reveal dynamic markers of group differences.

RESULTS

Our analysis reveals dynamic markers consisting of certain metabolite groups and their temporal profiles showing differences among males according to their body mass index (BMI) in response to the meal challenge. We also show that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state. Furthermore, while similar dynamic patterns are observed in males and females, the BMI-related group difference is observed only in males in the dynamic state.

CONCLUSION

The CP model is an effective approach to analyze time-resolved postprandial metabolomics data, and provides a compact but a comprehensive summary of the postprandial data revealing replicable and interpretable dynamic markers crucial to advance our understanding of changes in the metabolism in response to a meal challenge.

Competitive fungal commensalism mitigates candidiasis pathology

The mycobiota are a critical part of the gut microbiome, but host-fungal interactions and specific functional contributions of commensal fungi to host fitness remain incompletely understood. Here, we report the identification of a new fungal commensal, Kazachstania heterogenica var. weizmannii, isolated from murine intestines. K. weizmannii exposure prevented Candida albicans colonization and significantly reduced the commensal C. albicans burden in colonized animals. Following immunosuppression of C. albicans colonized mice, competitive fungal commensalism thereby mitigated fatal candidiasis. Metagenome analysis revealed K. heterogenica or K. weizmannii presence among human commensals. Our results reveal competitive fungal commensalism within the intestinal microbiota, independent of bacteria and immune responses, that could bear potential therapeutic value for the management of C. albicans-mediated diseases.

Continuous glucose monitoring and intrapersonal variability in fasting glucose

Plasma fasting glucose (FG) levels play a pivotal role in the diagnosis of prediabetes and diabetes worldwide. Here we investigated FG values using continuous glucose monitoring (CGM) devices in nondiabetic adults aged 40-70 years. FG was measured during 59,565 morning windows of 8,315 individuals (7.16 ± 3.17 days per participant). Mean FG was 96.2 ± 12.87 mg dl-1, rising by 0.234 mg dl-1 per year with age. Intraperson, day-to-day variability expressed as FG standard deviation was 7.52 ± 4.31 mg dl-1. As there are currently no CGM-based criteria for diabetes diagnosis, we analyzed the potential implications of this variability on the classification of glycemic status based on current plasma FG-based diagnostic guidelines. Among 5,328 individuals who would have been considered to have normal FG based on the first FG measurement, 40% and 3% would have been reclassified as having glucose in the prediabetes and diabetes ranges, respectively, based on sequential measurements throughout the study. Finally, we revealed associations between mean FG and various clinical measures. Our findings suggest that careful consideration is necessary when interpreting FG as substantial intraperson variability exists and highlight the potential impact of using CGM data to refine glycemic status assessment.

The Impact of a Nutritional Intervention on Glycemic Control and Cardiovascular Risk Markers in Type 2 Diabetes

INTRODUCTION

Nutritional management plays a crucial role in treating patients with type 2 diabetes (T2D), working to prevent and control the progression of chronic non-communicable diseases.

OBJECTIVES

To evaluate the effects of individualized nutritional interventions on weight, body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), fasting blood glucose (FBG), hemoglobin A1c (HbA1c), total cholesterol (TC), LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), triglycerides (TGs), systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate (HR)} over 12 months and subsequently at follow-up (15 months).

METHODS

This longitudinal experimental study (without randomization and blinding) enrolled 84 sedentary participants with T2D (both sexes, aged 18-80 years). They were divided into a control group of 40 participants who received only medical consultations, and an intervention group of 44 participants who received the same medical care along with a nutritional assessment. Consultations occurred quarterly from August 2020 to November 2022 (first-twelfth month), with six to nine patients per session. Subsequently, a follow-up was conducted from December 2022 to November 2023, during which the intervention group had only medical care (during the 12th-15th months). Personalized dietary planning was inspired by the Mediterranean/DASH diets adapted to Brazilian foods and socioeconomic cultures.

STATISTICAL ANALYSIS

Normal variables were compared between groups for each time point and also within each group across different time points using a two-way ANOVA (repeated measures for intragroup) followed by the Šídák post hoc test. Non-normal variables were compared between groups for each time point using Kruskal-Wallis followed by the Dunn post hoc test, and within each group across different time points using Friedman followed by the Dunn post hoc test. Data with a Gaussian distribution were presented as mean ± standard deviation (SD), and data with a non-Gaussian distribution were presented as median ± interquartile range (IQR). For all cases, α < 0.05 and p < 0.05 were adopted.

RESULTS

In the intervention group, significant reductions were observed between the first and twelfth month for all parameters (p < 0.05), (except for TC), along with an increase in HDL-C (p = 0.0105). Conversely, in the control group, there was a significant increase in HbA1c, weight, BMI, FBG, and WHR (p < 0.05) between the first and twelfth months. Regarding the comparison between groups, there was a significant difference for all analyzed parameters (p < 0.05) from the first to the twelfth month. In the follow-up, differences were also observed (p < 0.05), except for BMI (p > 0.05).

CONCLUSION

The individualized nutritional intervention improved eating habits, anthropometric, biochemical, and cardiovascular markers in T2D over 12 months, with sustained results during follow-up. The dietary plan inspired by the Mediterranean and DASH diets demonstrated good adaptation to the Brazilian food culture and the patients' socioeconomic contexts. Consistent monitoring and personalized nutritional management are essential for optimizing long-term outcomes. However, more clinical trials are necessary in order to optimize the level of evidence for longitudinal interventions.

Transforming Big Data into AI-ready data for nutrition and obesity research

OBJECTIVE

Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions.

METHODS

We reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI- and ML-ready products were detailed.

RESULTS

Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented.

CONCLUSIONS

Big Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI- and ML-ready data need to be transparent to investigators and clinicians relying on the conclusions.

Nutrient-epigenome interactions: Implications for personalized nutrition against aging-associated diseases

Aging is a multifaceted process involving genetic and environmental interactions often resulting in epigenetic changes, potentially leading to aging-related diseases. Various strategies, like dietary interventions and calorie restrictions, have been employed to modify these epigenetic landscapes. A burgeoning field of interest focuses on the role of microbiota in human health, emphasizing system biology and computational approaches. These methods help decipher the intricate interplay between diet and gut microbiota, facilitating the creation of personalized nutrition strategies. In this review, we analysed the mechanisms related to nutritional interventions while highlighting the influence of dietary strategies, like calorie restriction and intermittent fasting, on microbial composition and function. We explore how gut microbiota affects the efficacy of interventions using tools like multi-omics data integration, network analysis, and machine learning. These tools enable us to pinpoint critical regulatory elements and generate individualized models for dietary responses. Lastly, we emphasize the need for a deeper comprehension of nutrient-epigenome interactions and the potential of personalized nutrition informed by individual genetic and epigenetic profiles. As knowledge and technology advance, dietary epigenetics stands on the cusp of reshaping our strategy against aging and related diseases.

Ageing, microbes and health

The human gut microbiome is a modifier of the risk for many non-communicable diseases throughout the lifespan. In ageing, the effect of the microbiome appears to be more pronounced because of the lower physiological reserve. Microbial metabolites and other bioactive products act upon some of the key physiological processes involved in the Hallmarks of Ageing. Dietary interventions that delay age-related change in the microbiome have also led to delayed onset of ageing-related health loss, and improved levels of cognitive function, inflammatory status and frailty. Cross-sectional analysis of thousands of gut microbiome datasets from around the world has identified key taxa that are depleted during accelerated health loss, and other taxa that become more abundant, but these signatures differ in some geographical regions. The key challenges for research in this area are to experimentally prove that particular species or strains directly contribute to health-related ageing outcomes, and to develop practical ways of retaining or re-administering them on a population basis. The promotion of a health-associated gut microbiome in ageing mirrors the challenge of maintaining planetary microbial ecosystems in the face of anthropogenic effects and climate change. Lessons learned from acting at the individual level can inform microbiome-targeting strategies for achieving Sustainable Development Goals at a global level.

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.

A pervasive large conjugative plasmid mediates multispecies biofilm formation in the intestinal microbiota increasing resilience to perturbations

Although horizontal gene transfer is pervasive in the intestinal microbiota, we understand only superficially the roles of most exchanged genes and how the mobile repertoire affects community dynamics. Similarly, little is known about the mechanisms underlying the ability of a community to recover after a perturbation. Here, we identified and functionally characterized a large conjugative plasmid that is one of the most frequently transferred elements among Bacteroidales species and is ubiquitous in diverse human populations. This plasmid encodes both an extracellular polysaccharide and fimbriae, which promote the formation of multispecies biofilms in the mammalian gut. We use a hybridization-based approach to visualize biofilms in clarified whole colon tissue with unprecedented 3D spatial resolution. These biofilms increase bacterial survival to common stressors encountered in the gut, increasing strain resiliency, and providing a rationale for the plasmid's recent spread and high worldwide prevalence.

Oxidative Stress in Military Missions-Impact and Management Strategies: A Narrative Analysis

This narrative review comprehensively examines the impact of oxidative stress on military personnel, highlighting the crucial role of physical exercise and tailored diets, particularly the ketogenic diet, in minimizing this stress. Through a meticulous analysis of the recent literature, the study emphasizes how regular physical exercise not only enhances cardiovascular, cognitive, and musculoskeletal health but is also essential in neutralizing the effects of oxidative stress, thereby improving endurance and performance during long-term missions. Furthermore, the implementation of the ketogenic diet provides an efficient and consistent energy source through ketone bodies, tailored to the specific energy requirements of military activities, and significantly contributes to the reduction in reactive oxygen species production, thus protecting against cellular deterioration under extreme stress. The study also underlines the importance of integrating advanced technologies, such as wearable devices and smart sensors that allow for the precise and real-time monitoring of oxidative stress and physiological responses, thus facilitating the customization of training and nutritional regimes. Observations from this review emphasize significant variability among individuals in responses to oxidative stress, highlighting the need for a personalized approach in formulating intervention strategies. It is crucial to develop and implement well-monitored, personalized supplementation protocols to ensure that each member of the military personnel receives a regimen tailored to their specific needs, thereby maximizing the effectiveness of measures to combat oxidative stress. This analysis makes a valuable contribution to the specialized literature, proposing a detailed framework for addressing oxidative stress in the armed forces and opening new directions for future research with the aim of optimizing clinical practices and improving the health and performance of military personnel under stress and specific challenges of the military field.

Pragmatic Carbohydrate Quality Metrics in Relation to Glycemic Index, Glycemic Load, and Front-of-Pack Warning Labels in Grain Foods

The challenges in the characterization of the nutritional quality of grain foods comprise obstacles to public health actions toward promotion of healthier grain-based foods. The present study investigated how carbohydrate metrics related to glycemic index (GI), glycemic load (GL), and warning labels of grain foods consumed by individuals living in São Paulo, Brazil. Information on intake of grain foods at individual level was obtained using 24 h recalls within a cross-sectional population-based survey conducted in 2015. There were 244 unique grain products reported by individuals in the survey, assessed through four metrics of carbohydrate quality, considering contents per 10 g of total carbohydrate: (1) ≥1 g fiber, (2) ≥1 g fiber and <1 g free sugars, (3) ≥1 g fiber and <2 g free sugars, and (4) ≥1 g fiber, and <2 g free sugars per 1 g of fiber. Outcomes included GI, GL, and inclusion of warning labels proposed by the Brazilian National Health Surveillance Agency (ANVISA), the Chilean Ministry of Health (1st and 3rd stages), and the Pan American Health Organization (PAHO). Metrics identified products with lower mean GI (-12.8 to -9.0 [p-values < 0.001]), and GL (-12.5 to -10.3 [p-values < 0.001]). Warning systems showed a certain degree of discrimination between products according to the metrics (p-value < 0.01 each); however, >50% of products with good nutritional quality according to the carbohydrate metrics still would receive warnings. Findings suggest that carbohydrate metrics identified products with lower GI and GL, and current warning labels may not adequately capture overall nutritional quality of grain foods.

Coin-sized, fully integrated, and minimally invasive continuous glucose monitoring system based on organic electrochemical transistors

Continuous glucose monitoring systems (CGMs) are critical toward closed-loop diabetes management. The field's progress urges next-generation CGMs with enhanced antinoise ability, reliability, and wearability. Here, we propose a coin-sized, fully integrated, and wearable CGM, achieved by holistically synergizing state-of-the-art interdisciplinary technologies of biosensors, minimally invasive tools, and hydrogels. The proposed CGM consists of three major parts: (i) an emerging biochemical signal amplifier, the organic electrochemical transistor (OECT), improving the signal-to-noise ratio (SNR) beyond traditional electrochemical sensors; (ii) a microneedle array to facilitate subcutaneous glucose sampling with minimized pain; and (iii) a soft hydrogel to stabilize the skin-device interface. Compared to conventional CGMs, the OECT-CGM offers a high antinoise ability, tunable sensitivity and resolution, and comfort wearability, enabling personalized glucose sensing for future precision diabetes health care. Last, we discuss how OECT technology can help push the limit of detection of current wearable electrochemical biosensors, especially when operating in complicated conditions.

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.

The Role of Gut-derived Short-Chain Fatty Acids in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic condition affecting the central nervous system (CNS), where the interplay of genetic and environmental factors influences its pathophysiology, triggering immune responses and instigating inflammation. Contemporary research has been notably dedicated to investigating the contributions of gut microbiota and their metabolites in modulating inflammatory reactions within the CNS. Recent recognition of the gut microbiome and dietary patterns as environmental elements impacting MS development emphasizes the potential influence of small, ubiquitous molecules from microbiota, such as short-chain fatty acids (SCFAs). These molecules may serve as vital molecular signals or metabolic substances regulating host cellular metabolism in the intricate interplay between microbiota and the host. A current emphasis lies on optimizing the health-promoting attributes of colonic bacteria to mitigate urinary tract issues through dietary management. This review aims to spotlight recent investigations on the impact of SCFAs on immune cells pivotal in MS, the involvement of gut microbiota and SCFAs in MS development, and the considerable influence of probiotics on gastrointestinal disruptions in MS. Comprehending the gut-CNS connection holds promise for the development of innovative therapeutic approaches, particularly probiotic-based supplements, for managing MS.

Exercise Prescription for Postprandial Glycemic Management

The detrimental impacts of postprandial hyperglycemia on health are a critical concern, and exercise is recognized a pivotal tool in enhancing glycemic control after a meal. However, current exercise recommendations for managing postprandial glucose levels remain fairly broad and require deeper clarification. This review examines the existing literature aiming to offer a comprehensive guide for exercise prescription to optimize postprandial glycemic management. Specifically, it considers various exercise parameters (i.e., exercise timing, type, intensity, volume, pattern) for crafting exercise prescriptions. Findings predominantly indicate that moderate-intensity exercise initiated shortly after meals may substantially improve glucose response to a meal in healthy individuals and those with type 2 diabetes. Moreover, incorporating short activity breaks throughout the exercise session may provide additional benefits for reducing glucose response.

Metaproteomics and DNA metabarcoding as tools to assess dietary intake in humans

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

Human metabolic chambers reveal a coordinated metabolic-physiologic response to nutrition

The emerging field of precision nutrition is based on the notion that inter-individual responses across diets of different calorie-macronutrient content may contribute to inter-individual differences in metabolism, adiposity, and weight gain. Free-living diet studies have been traditionally challenged by difficulties in controlling adherence to prescribed calories and macronutrient content and rarely allow a period of metabolic stability prior to metabolic measures (to minimize influences of weight changes). In this context, key physiologic measures central to precision nutrition responses may be most precisely quantified via whole room indirect calorimetry over 24-h, in which precise control of activity and nutrition can be achieved. In addition, these studies represent unique "N of 1" human crossover metabolic-physiologic experiments during which specific molecular pathways central to nutrient metabolism may be discerned. Here, we quantified 263 circulating metabolites during a ≈40-day inpatient admission in which up to 94 participants underwent seven monitored 24-h nutritional interventions of differing macronutrient composition in a whole-room indirect calorimeter to capture precision metabolic responses. Broadly, we observed heterogenous responses in metabolites across dietary chambers, with the exception of carnitines which tracked with 24-h respiratory quotient. We identified excursions in shared metabolic species (e.g., carnitines, glycerophospholipids, amino acids) that mapped onto gold-standard calorimetric measures of substrate oxidation preference and lipid availability. These findings support a coordinated metabolic-physiologic response to nutrition, highlighting the relevance of these controlled settings to uncover biological pathways of energy utilization during precision nutrition studies.

Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism

Continuous glucose monitoring (CGM) is a promising, minimally invasive alternative to plasma glucose measurements for calibrating physiology-based mathematical models of insulin-regulated glucose metabolism, reducing the reliance on in-clinic measurements. However, the use of CGM glucose, particularly in combination with insulin measurements, to develop personalized models of glucose regulation remains unexplored. Here, we simultaneously measured interstitial glucose concentrations using CGM as well as plasma glucose and insulin concentrations during an oral glucose tolerance test (OGTT) in individuals with overweight or obesity to calibrate personalized models of glucose-insulin dynamics. We compared the use of interstitial glucose with plasma glucose in model calibration, and evaluated the effects on model fit, identifiability, and model parameters' association with clinically relevant metabolic indicators. Models calibrated on both plasma and interstitial glucose resulted in good model fit, and the parameter estimates associated with metabolic indicators such as insulin sensitivity measures in both cases. Moreover, practical identifiability of model parameters was improved in models estimated on CGM glucose compared to plasma glucose. Together these results suggest that CGM glucose may be considered as a minimally invasive alternative to plasma glucose measurements in model calibration to quantify the dynamics of glucose regulation.

A novel questionnaire for evaluating digital tool use (DTUQ-D) among individuals with type 2 diabetes: exploring the digital landscape

Introduction

Effective healthcare currently incorporates a patient-centric system and accessible technology for patient self-management. This study aimed to develop and validate a novel questionnaire titled the Digital Tool Use Questionnaire for Diabetes (DTUQ-D) - a screening tool identifying the type, number, and frequency of digital tools used by Type 2 Diabetes Mellitus (T2DM) patients with within HMOs, online, and via applications.

Methods

The questionnaire was administered to two ethnic groups and both genders. A mixed-methods approach was used. In the qualitative phase, the questionnaire was developed through phone surveys of 29 T2DM patients, two endocrinologists and two technology experts. In the quantitative phase, involving 367 participants, convergent validity, construct validity, and reliability were examined.

Results

Findings indicated that the DTUQ-D is valid and reliable, successfully identifying digital tools utilized by T2DM patients, notwithstanding variations in factor structures between ethnic groups. This questionnaire provides a foundation for future research, offering a standardized approach to evaluating digital tool usage.

Discussion

The study enhances understanding of the role of digital tools in healthcare, especially for T2DM self-management. It also can be easily adapted to assess digital tool use for other illnesses by adjusting instructions and the wording of certain items.

Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management

The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.

Nutritional modulation of antitumor immunity

The composition and quantity of food we eat have a drastic impact on the development and function of immune responses. In this review, we highlight defined nutritional interventions shown to enhance antitumor immunity, including ketogenic, low-protein, high-fructose, and high-fiber diets, as well as dietary restriction. We propose that incorporating such nutritional interventions into immunotherapy protocols has the potential to increase therapeutic responsiveness and long-term tumor control in patients with cancer.

The effect of preprandial versus postprandial physical activity on glycaemia: Meta-analysis of human intervention studies

This meta-analysis aims to investigate the effect of preprandial physical activity (PA) versus postprandial PA on glycaemia in human intervention studies. Medline and Embase.com were searched until February 2023 for intervention studies in adults, directly comparing preprandial PA versus postprandial PA on glycaemia. Studies were screened using ASReview (34,837) and full texts were read by two independent reviewers (42 full text, 28 included). Results were analysed using pooled mean differences in random-effects models. Studies were either acute response studies (n = 21) or Randomized Controlled Trials (RCTs) over multiple weeks (n = 7). In acute response studies, postprandial outcomes followed the expected physiological patterns, and outcomes measured over 24 h showed no significant differences. For the RCTs, glucose area under the curve during a glucose tolerance test was slightly, but not significantly lower in preprandial PA vs postprandial PA (-0.29 [95 %CI:-0.66, 0.08] mmol/L, I2 = 64.36 %). Subgroup analyses (quality, health status, etc.) did not significantly change the outcomes. In conclusion, we found no differences between preprandial PA versus postprandial PA on glycaemia both after one PA bout as well as after multiple weeks of PA. The studies were of low to moderate quality of evidence as assessed by GRADE, showed contradictive results, included no long-term studies and used various designs and populations. We therefore need better RCTs, with more similar designs, in larger populations and longer follow-up periods (≥12 weeks) to have a final answer on the questions eat first, then exercise, or the reverse?

Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis

BACKGROUND & AIMS

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes.

METHODS

Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome.

RESULTS

Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease.

CONCLUSION

GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.

Survival and cancer recurrence after short-course perioperative probiotics in a randomized trial

BACKGROUND & AIMS

The long-term impact of perioperative probiotics remains understudied while mounting evidence links microbiome and oncogenesis. Therefore, we analyzed overall survival and cancer recurrence among patients enrolled in a randomized trial of perioperative probiotics.

METHODS

6-year follow-up of surgical patients participating in a randomized trial evaluating short-course perioperative oral probiotic VSL#3 (n = 57) or placebo (n = 63).

RESULTS

Study groups did not differ in age, preoperative hemoglobin, ASA status, and Charlson comorbidity index. There was a significant difference in preoperative serum albumin (placebo group 4.0 ± 0.1 vs. 3.7 ± 0.1 g/dL in the probiotic group, p = 0.030). Thirty-seven deaths (30.8 %) have occurred during a median follow-up of 6.2 years. Overall survival stratified on preoperative serum albumin and surgical specialty was similar between groups (p = 0.691). Age (aHR = 1.081, p = 0.001), serum albumin (aHR = 0.162, p = 0.001), and surgical specialty (aHR = 0.304, p < 0.001) were the only predictors of overall survival in the multivariate model, while the placebo/probiotic group (aHR = 0.808, p = 0.726) was not predictive. The progression rate among cancer patients was similar in the probiotic group (30.3 %, 10/33) compared to the placebo group (21.2 %, 7/33; p = 0.398). The progression-free survival was not significantly different (unstratified p = 0.270, stratified p = 0.317).

CONCLUSIONS

Perioperative short-course use of VSL#3 probiotics does not influence overall or progression-free survival after complex surgery for visceral malignancy.

Temporal gut microbiota variability and association with dietary patterns: From the one-year observational Diet, Cancer, and Health - Next Generations MAX study

BACKGROUND

Knowledge about the variability of gut microbiota within an individual over time is important to allow meaningful investigations of the gut microbiota in relation to diet and health outcomes in observational studies. Plant-based dietary patterns have been associated with a lower risk of morbidity and mortality and may alter gut microbiota in a favorable direction.

OBJECTIVES

To assess the gut microbiota variability during one year and investigate the association between adherence to diet indexes and the gut microbiota in a Danish population.

METHODS

Four hundred forty-four participants were included in the Diet, Cancer, and Health - Next Generations MAX study (DCH-NG MAX). Stool samples collected up to three times during a year were analyzed by 16S ribosomal ribonucleic acid gene sequencing. Diet was obtained by 24-hour dietary recalls. Intraclass correlation coefficient (ICC) was calculated to assess temporal microbial variability based on 214 individuals. Diet indexes (Nordic, Mediterranean, and plant-based diets) and food groups thereof were associated with gut microbiota using linear regression analyses.

RESULTS

We found that 91 out of 234 genera had an ICC >0.5. We identified three subgroups dominated by Bacteroides, Prevotella 9, and Ruminococcaceae and adherence to diet indexes differed between subgroups. Higher adherence to diet indexes was associated with the relative abundance of 22 genera. Across diet indexes, higher intakes of fruit, vegetables, whole grains/cereals, and nuts were most frequently associated with these genera.

CONCLUSIONS

In the DCH-NG MAX study, 39% of the genera had an ICC >0.5 over one year, suggesting that these genera could be studied with health outcomes in prospective analyses with acceptable precision. Adherence to the Nordic, Mediterranean, and plant-based diets differed between bacterial subgroups and was associated with a higher abundance of genera with fiber-degrading properties. Fruits, vegetables, whole grains/cereals, and nuts were frequently associated with these genera.

Relationships between diet and gut microbiome in an Italian and Dutch cohort: does the dietary protein to fiber ratio play a role?

PURPOSE

To investigate the relationships between the habitual diet, the protein to fiber ratio (P/F), and the gut microbiome in one Italian and one Dutch cohort of healthy subjects consuming an omnivore diet.

METHODS

The Italian cohort included 19 males (M_IT, BMI 25.2 ± 0.72 kg/m2, age 25.4 ± 0.96 years) and 20 females (F_IT, BMI 23.9 ± 0.81 kg/m2, age 23.8 ± 0.54 years); the Dutch cohort included 30 females (F_NL, BMI: 23.9 ± 0.81 kg/m2, age: 23.8 ± 0.54 years). Individual diets were recorded through Food Frequency Questionnaires and analyzed to assess the nutrient composition. Gut microbiome was assessed in fecal samples.

RESULTS

M_IT consumed higher levels of proteins than F_NL and F_IT, whereas dietary fiber intake did not differ among groups. Data showed that consumption of plant protein to animal protein (PP/AP) and PP to total proteins ratio can determine a differentiation of F_NL more than the absolute amount of dietary fiber. Conversely, the protein to fiber (P/F) and AP to total proteins better characterized M_IT. M_IT harbored the highest abundance of proteolytic microorganisms and the lowest microbial gene richness. Conversely, F_NL had more fiber-degrading microorganisms like Bacteroides thetaiotaomicron, Bacteroides xylanisolvens, Roseburia sp., Coprococcus eutactus and Parabacteroides along with the highest number of genes encoding carbohydrate-active enzymes and gene richness. It was predicted that by each unit decrease in the P/F a 3% increase in gene richness occurred.

CONCLUSION

Study findings suggested that dietary P/F, rather than the absolute amount of dietary fiber, could contribute to the shaping of the microbiome towards a more proteolytic or fiber-degrading gut ecosystem.

CLINICALTRIALS

gov Identifier NCT04205045-01-10-2018, retrospectively registered. Dutch Trial Register NTR7531-05-10-2018.

Perspective: Promoting Healthy Aging through Nutrition: A Research Centers Collaborative Network Workshop Report

Within 20 y, the number of adults in the United States over the age of 65 y is expected to more than double and the number over age 85 y is expected to more than triple. The risk for most chronic diseases and disabilities increases with age, so this demographic shift carries significant implications for the individual, health care providers, and population health. Strategies that delay or prevent the onset of age-related diseases are becoming increasingly important. Although considerable progress has been made in understanding the contribution of nutrition to healthy aging, it has become increasingly apparent that much remains to be learned, especially because the aging process is highly variable. Most federal nutrition programs and nutrition research studies define all adults over age 65 y as "older" and do not account for physiological and metabolic changes that occur throughout older adulthood that influence nutritional needs. Moreover, the older adult population is becoming more racially and ethnically diverse, so cultural preferences and other social determinants of health need to be considered. The Research Centers Collaborative Network sponsored a 1.5-d multidisciplinary workshop that included sessions on dietary patterns in health and disease, timing and targeting interventions, and health disparities and the social context of diet and food choice. The agenda and presentations can be found at https://www.rccn-aging.org/nutrition-2023-rccn-workshop. Here we summarize the workshop's themes and discussions and highlight research gaps that if filled will considerably advance our understanding of the role of nutrition in healthy aging.

Personalized Versus Non-personalized Nutritional Recommendations/Interventions for Type 2 Diabetes Mellitus Remission: A Narrative Review

It is a well-evidenced fact that diet significantly impacts type 2 diabetes mellitus (T2DM) prevention and management. However, dietary responses vary among different populations, necessitating personalized recommendations. Substantial evidence supports the role of diet in T2DM remission, particularly low-energy or low-carbohydrate diets that facilitate weight loss, enhance glycemic control, and achieve remission. This review aims to comprehensively analyze and compare personalized nutritional interventions with non-personalized approaches in T2DM remission. We conducted a literature search using the Academy of Nutrition and Dietetics guidelines, focusing on clinical and observational trials published within the past decade. We present the strengths and drawbacks of incorporating personalized nutrition into practice, along with the areas for research in implementing personalized interventions, such as cost-effectiveness and accessibility. The findings reveal consistently higher diabetes remission rates in personalized nutrition studies compared to non-personalized interventions.

Variations in the effects of extrusion treatments and ferulic acid addition on starch digestibility with different botanical backgrounds

In the current study, the effects of extrusion using a haake rheometer with a twin-roll mixer, with and without FA addition, on the structures and in vitro digestibility of starches from different sources were investigated. After extruding for 15 min at 90 °C with a moisture content of 40 %, no matter FA was added or not, lager Ap molecules were preferentially debranched, while Am with longer CL were depolymerized simultaneously, resulting to reduced averaged molecular size of Ap and shortened Am chains. Of all starches, regardless of their botanical backgrounds, although synergic effects were found between extrusion and FA addition on reducing their relative crystallinity and the ordered structures, distinctly different effects on the in vitro digestibility of these starches have also been observed especially regarding the digestion of starch branches with DP > 10 Particularly, the Am chains with DP 10-1000 was remaining undigested when FA was added. This study provides important information concerning how to adjust starch digestibility into a healthy range through altering the starch structures using extrusion technique with the addition of phytochemicals or not.

The transition from genomics to phenomics in personalized population health

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

A Proactive Intervention Study in Metabolic Syndrome High-Risk Populations Using Phenome-Based Actionable P4 Medicine Strategy

The integration of predictive, preventive, personalized, and participatory (P4) healthcare advocates proactive intervention, including dietary supplements and lifestyle interventions for chronic disease. Personal profiles include deep phenotypic data and genetic information, which are associated with chronic diseases, can guide proactive intervention. However, little is known about how to design an appropriate intervention mode to precisely intervene with personalized phenome-based data. Here, we report the results of a 3-month study on 350 individuals with metabolic syndrome high-risk that we named the Pioneer 350 Wellness project (P350). We examined: (1) longitudinal (two times) phenotypes covering blood lipids, blood glucose, homocysteine (HCY), and vitamin D3 (VD3), and (2) polymorphism of genes related to folic acid metabolism. Based on personalized data and questionnaires including demographics, diet and exercise habits information, coaches identified 'actionable possibilities', which combined exercise, diet, and dietary supplements. After a 3-month proactive intervention, two-thirds of the phenotypic markers were significantly improved in the P350 cohort. Specifically, we found that dietary supplements and lifestyle interventions have different effects on phenotypic improvement. For example, dietary supplements can result in a rapid recovery of abnormal HCY and VD3 levels, while lifestyle interventions are more suitable for those with high body mass index (BMI), but almost do not help the recovery of HCY. Furthermore, although people who implemented only one of the exercise or diet interventions also benefited, the effect was not as good as the combined exercise and diet interventions. In a subgroup of 226 people, we examined the association between the polymorphism of genes related to folic acid metabolism and the benefits of folate supplementation to restore a normal HCY level. We found people with folic acid metabolism deficiency genes are more likely to benefit from folate supplementation to restore a normal HCY level. Overall, these results suggest: (1) phenome-based data can guide the formulation of more precise and comprehensive interventions, and (2) genetic polymorphism impacts clinical responses to interventions. Notably, we provide a proactive intervention example that is operable in daily life, allowing people with different phenome-based data to design the appropriate intervention protocol including dietary supplements and lifestyle interventions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00115-z.

Resistant starch and the gut microbiome: Exploring beneficial interactions and dietary impacts

The intricate relationship between resistant starch (RS) and the gut microbiome presents a dynamic frontier in nutrition science. This review synthesizes current understandings of how RS, an indigestible form of starch found naturally in certain foods and also enhanced through various modification methods, interacts with the gut microbiome. We particularly focus on how RS fermentation in the colon contributes to the production of beneficial volatile fatty acids (VFAs) such as butyrate, acetate, and propionate. These VFAs have been recognized for their vital roles in maintaining gut barrier integrity, modulating inflammation, and potentially influencing systemic health. Additionally, we discuss the dietary implications of consuming foods rich in RS, both in terms of gut health and broader metabolic outcomes. By consolidating these insights, we emphasize the significance of RS in the context of dietary strategies aimed at harnessing the gut microbiome's potential to impact human health.

Cluster-specific associations between the gut microbiota and behavioral outcomes in preschool-aged children

BACKGROUND

The gut microbiota is recognized as a regulator of brain development and behavioral outcomes during childhood. Nonetheless, associations between the gut microbiota and behavior are often inconsistent among studies in humans, perhaps because many host-microbe relationships vary widely between individuals. This study aims to stratify children based on their gut microbiota composition (i.e., clusters) and to identify novel gut microbiome cluster-specific associations between the stool metabolomic pathways and child behavioral outcomes.

METHODS

Stool samples were collected from a community sample of 248 typically developing children (3-5 years). The gut microbiota was analyzed using 16S sequencing while LC-MS/MS was used for untargeted metabolomics. Parent-reported behavioral outcomes (i.e., Adaptive Skills, Internalizing, Externalizing, Behavioral Symptoms, Developmental Social Disorders) were assessed using the Behavior Assessment System for Children (BASC-2). Children were grouped based on their gut microbiota composition using the Dirichlet multinomial method, after which differences in the metabolome and behavioral outcomes were investigated.

RESULTS

Four different gut microbiota clusters were identified, where the cluster enriched in both Bacteroides and Bifidobacterium (Ba2) had the most distinct stool metabolome. The cluster characterized by high Bifidobacterium abundance (Bif), as well as cluster Ba2, were associated with lower Adaptive Skill scores and its subcomponent Social Skills. Cluster Ba2 also had significantly lower stool histidine to urocanate turnover, which in turn was associated with lower Social Skill scores in a cluster-dependent manner. Finally, cluster Ba2 had increased levels of compounds involved in Galactose metabolism (i.e., stachyose, raffinose, alpha-D-glucose), where alpha-D-glucose was associated with the Adaptive Skill subcomponent Daily Living scores (i.e., ability to perform basic everyday tasks) in a cluster-dependent manner.

CONCLUSIONS

These data show novel associations between the gut microbiota, its metabolites, and behavioral outcomes in typically developing preschool-aged children. Our results support the concept that cluster-based groupings could be used to develop more personalized interventions to support child behavioral outcomes. Video Abstract.

Response to "Plant-based diets and postprandial hyperkalemia"

Diet therapy for hyperkalemia in people with chronic kidney disease (CKD) has shifted considerably in recent years with the observations that reported potassium intake is weakly, or not at all, associated with plasma potassium levels in this population. One of the lingering debates is whether dietary potassium presents a risk of hyperkalemia in the postprandial state. Although there is general agreement about the need for additional research, the commentary by Varshney et al contends that the available research sufficiently demonstrates that high-potassium plant foods do not pose a risk of postprandial hyperkalemia. Others argue that this remains unsettled science. Although the traditional approach of providing people with CKD lists of high-potassium foods to limit or avoid may be unnecessary, those at high risk of hyperkalemia should be encouraged to consume balanced meals and control portions, at least until some of the key research gaps in this area are resolved. This editorial critiques the analyses offered by Varshney et al and explains the rationale for a more cautious approach to care.