Data-driven analysis and prediction of dynamic postprandial metabolic response to multiple dietary challenges using dynamic mode decomposition

Motivation

In the field of precision nutrition, predicting metabolic response to diet and identifying groups of differential responders are two highly desirable steps toward developing tailored dietary strategies. However, data analysis tools are currently lacking, especially for complex settings such as crossover studies with repeated measures.Current methods of analysis often rely on matrix or tensor decompositions, which are well suited for identifying differential responders but lacking in predictive power, or on dynamical systems modeling, which may be used for prediction but typically requires detailed mechanistic knowledge of the system under study. To remedy these shortcomings, we explored dynamic mode decomposition (DMD), which is a recent, data-driven method for deriving low-rank linear dynamical systems from high dimensional data.Combining the two recent developments "parametric DMD" (pDMD) and "DMD with control" (DMDc) enabled us to (i) integrate multiple dietary challenges, (ii) predict the dynamic response in all measured metabolites to new diets from only the metabolite baseline and dietary input, and (iii) identify inter-individual metabolic differences, i.e., metabotypes. To our knowledge, this is the first time DMD has been applied to analyze time-resolved metabolomics data.

Results

We demonstrate the potential of pDMDc in a crossover study setting. We could predict the metabolite response to unseen dietary exposures on both measured (R2 = 0.40) and simulated data of increasing size (R max 2 = 0.65), as well as recover clusters of dynamic metabolite responses. We conclude that this method has potential for applications in personalized nutrition and could be useful in guiding metabolite response to target levels.

Availability and implementation

The measured data analyzed in this study can be provided upon reasonable request. The simulated data along with a MATLAB implementation of pDMDc is available at https://github.com/FraunhoferChalmersCentre/pDMDc.

Bridging the Gap from Enterotypes to Personalized Dietary Recommendations: A Metabolomics Perspective on Microbiome Research

Advances in high-throughput DNA sequencing have propelled research into the human microbiome and its link to metabolic health. We explore microbiome analysis methods, specifically emphasizing metabolomics, how dietary choices impact the production of microbial metabolites, providing an overview of studies examining the connection between enterotypes and diet, and thus, improvement of personalized dietary recommendations. Acetate, propionate, and butyrate constitute more than 95% of the collective pool of short-chain fatty acids. Conflicting data on acetate's effects may result from its dynamic signaling, which can vary depending on physiological conditions and metabolic phenotypes. Human studies suggest that propionate has overall anti-obesity effects due to its well-documented chemistry, cellular signaling mechanisms, and various clinical benefits. Butyrate, similar to propionate, has the ability to reduce obesity by stimulating the release of appetite-suppressing hormones and promoting the synthesis of leptin. Tryptophan affects systemic hormone secretion, with indole stimulating the release of GLP-1, which impacts insulin secretion, appetite suppression, and gastric emptying. Bile acids, synthesized from cholesterol in the liver and subsequently modified by gut bacteria, play an essential role in the digestion and absorption of dietary fats and fat-soluble vitamins, but they also interact directly with intestinal microbiota and their metabolites. One study using statistical methods identified primarily two groupings of enterotypes Bacteroides and Ruminococcus. The Prevotella-dominated enterotype, P-type, in humans correlates with vegetarians, high-fiber and carbohydrate-rich diets, and traditional diets. Conversely, individuals who consume diets rich in animal fats and proteins, typical in Western-style diets, often exhibit the Bacteroides-dominated, B-type, enterotype. The P-type showcases efficient hydrolytic enzymes for plant fiber degradation but has limited lipid and protein fermentation capacity. Conversely, the B-type features specialized enzymes tailored for the degradation of animal-derived carbohydrates and proteins, showcasing an enhanced saccharolytic and proteolytic potential. Generally, models excel at predictions but often struggle to fully elucidate why certain substances yield varied responses. These studies provide valuable insights into the potential for personalized dietary recommendations based on enterotypes.

Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data

Lifestyle interventions have been shown to prevent or delay the onset of diabetes; however, inter-individual variability in responses to such interventions makes lifestyle recommendations challenging. We analyzed the Japan Diabetes Outcome Intervention Trial-1 (J-DOIT1) study data using a previously published mechanistic simulation model of type 2 diabetes onset and progression to understand the causes of inter-individual variability and to optimize dietary intervention strategies at an individual level. J-DOIT1, a large-scale lifestyle intervention study, involved 2607 subjects with a 4.2-year median follow-up period. We selected 112 individuals from the J-DOIT1 study and calibrated the mechanistic model to each participant's body weight and HbA1c time courses. We evaluated the relationship of physiological (e.g., insulin sensitivity) and lifestyle (e.g., dietary intake) parameters with variability in outcome. Finally, we used simulation analyses to predict individually optimized diets for weight reduction. The model predicted individual body weight and HbA1c time courses with a mean (±SD) prediction error of 1.0 kg (±1.2) and 0.14% (±0.18), respectively. Individuals with the most and least improved biomarkers showed no significant differences in model-estimated energy balance. A wide range of weight changes was observed for similar model-estimated caloric changes, indicating that caloric balance alone may not be a good predictor of body weight. The model suggests that a set of optimal diets exists to achieve a defined weight reduction, and this set of diets is unique to each individual. Our diabetes model can simulate changes in body weight and glycemic control as a result of lifestyle interventions. Moreover, this model could help dieticians and physicians to optimize personalized nutritional strategies according to their patients' goals.

Foodomics-Based Approaches Shed Light on the Potential Protective Effects of Polyphenols in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic and progressive inflammatory disorder affecting the gastrointestinal tract (GT) caused by a wide range of genetic, microbial, and environmental factors. IBD is characterized by chronic inflammation and decreased gut microbial diversity, dysbiosis, with a lower number of beneficial bacteria and a concomitant increase in pathogenic species. It is well known that dysbiosis is closely related to the induction of inflammation and oxidative stress, the latter caused by an imbalance between reactive oxygen species (ROS) production and cellular antioxidant capacity, leading to cellular ROS accumulation. ROS are responsible for intestinal epithelium oxidative damage and the increased intestinal permeability found in IBD patients, and their reduction could represent a potential therapeutic strategy to limit IBD progression and alleviate its symptoms. Recent evidence has highlighted that dietary polyphenols, the natural antioxidants, can maintain redox equilibrium in the GT, preventing gut dysbiosis, intestinal epithelium damage, and radical inflammatory responses. Here, we suggest that the relatively new foodomics approaches, together with new technologies for promoting the antioxidative properties of dietary polyphenols, including novel delivery systems, chemical modifications, and combination strategies, may provide critical insights to determine the clinical value of polyphenols for IBD therapy and a comprehensive perspective for implementing natural antioxidants as potential IBD candidate treatment.

Perspective: Challenges and Future Directions in Clinical Research with Nuts and Berries

Consumption of nuts and berries are considered part of a healthy eating pattern. Nuts and berries contain a complex nutrient profile consisting of essential vitamins and minerals, fiber, polyunsaturated fatty acids, and phenolics in quantities that improve physiological outcomes. The spectrum of health outcomes that may be impacted by the consumptions of nuts and berries includes cardiovascular, gut microbiome, and cognitive, among others. Recently, new insights regarding the bioactive compounds found in both nuts and berries have reinforced their role for use in precision nutrition efforts. However, challenges exist that can affect the generalizability of outcomes from clinical studies, including inconsistency in study designs, homogeneity of test populations, variability in test products and control foods, and assessing realistic portion sizes. Future research centered on precision nutrition and multi-omics technologies will yield new insights. These and other topics such as funding streams and perceived risk-of-bias were explored at an international nutrition conference focused on the role of nuts and berries in clinical nutrition. Successes, challenges, and future directions with these foods are presented here.

Does heterogeneity underlie differences in treatment effects estimated from SuperLearner versus logistic regression? An application in nutritional epidemiology

PURPOSE

A strength of SuperLearner is that it may accommodate key interactions between model variables without a priori specification. In prior research, protective associations between fruit intake and preeclampsia were stronger when estimated using SuperLearner with targeted maximum likelihood estimation (TMLE) compared with multivariable logistic regression without any interaction terms. We explored whether heterogeneity (i.e., differences in the effect estimate due to interactions between fruit intake and covariates) may partly explain differences in estimates from these two models.

METHODS

Using a U.S. prospective pregnancy cohort (2010-2013, n = 7781), we estimated preeclampsia risk differences (RDs) for higher versus lower fruit density using multivariable logistic regression and included two-way statistical interactions between fruit density and each of the 25 model covariates. We compared the RDs with those from SuperLearner with TMLE (gold standard) and logistic regression with no interaction.

RESULTS

From the logistic regression models with two-way statistical interactions, 48% of the preeclampsia RDs were ≤-0.02 (closer to SuperLearner with TMLE estimate); 40% equaled -0.01 (same as logistic regression with no interaction estimate); the minority of RDs were at or crossed the null.

CONCLUSIONS

Our exploratory analysis provided preliminary evidence that heterogeneity may partly explain differences in estimates from logistic regression versus SuperLearner with TMLE.

Inflammation and Nutrition: Friend or Foe?

The importance of the interplay between inflammation and nutrition has generated much interest in recent times. Inflammation has been identified as a key driver for disease-related malnutrition, leading to anorexia, reduced food intake, muscle catabolism, and insulin resistance, which are stimulating a catabolic state. Interesting recent data suggest that inflammation also modulates the response to nutritional treatment. Studies have demonstrated that patients with high inflammation show no response to nutritional interventions, while patients with lower levels of inflammation do. This may explain the contradictory results of nutritional trials to date. Several studies of heterogeneous patient populations, or in the critically ill or advanced cancer patients, have not found significant benefits on clinical outcome. Vice versa, several dietary patterns and nutrients with pro- or anti-inflammatory properties have been identified, demonstrating that nutrition influences inflammation. Within this review, we summarize and discuss recent advances in both the role of inflammation in malnutrition and the effect of nutrition on inflammation.

Mediterranean diet related metabolite profiles and cognitive performance

BACKGROUND & AIMS

Evidence suggests that adherence to the Mediterranean diet (MedDiet) affects human metabolism and may contribute to better cognitive performance. However, the underlying mechanisms are not clear.

OBJECTIVE

We generated a metabolite profile for adherence to MedDiet and evaluated its cross-sectional association with aspects of cognitive performance.

METHODS

A total of 1250 healthy Greek middle-aged adults from the Epirus Health Study cohort were included in the analysis. Adherence to the MedDiet was assessed using the 14-point Mediterranean Diet Adherence Screener (MEDAS); cognition was measured using the Trail Making Test, the Verbal Fluency test and the Logical Memory test. A targeted metabolite profiling (n = 250 metabolites) approach was applied, using a high-throughput nuclear magnetic resonance platform. We used elastic net regularized regressions, with a 10-fold cross-validation procedure, to identify a metabolite profile for MEDAS. We evaluated the associations of the identified metabolite profile and MEDAS with cognitive tests, using multivariable linear regression models.

RESULTS

We identified a metabolite profile composed of 42 metabolites, mainly lipoprotein subclasses and fatty acids, significantly correlated with MedDiet adherence (Pearson r = 0.35, P-value = 5.5 × 10^-37). After adjusting for known risk factors and accounting for multiple testing, the metabolite profile and MEDAS were not associated with the cognitive tests.

CONCLUSIONS

A plasma metabolite profile related to better adherence to the MedDiet was not associated with the tested aspects of cognitive performance, in a middle-aged Mediterranean population.

Precision Nutrition: Recent Advances in Obesity

"Precision nutrition" is an emerging area of nutrition research that focuses on understanding metabolic variability within and between individuals and helps develop customized dietary plans and interventions to maintain optimal individual health. It encompasses nutritional genomic (gene-nutrient interactions), epigenetic, microbiome, and environmental factors. Obesity is a complex disease that is affected by genetic and environmental factors and thus a relevant target of precision nutrition-based approaches. Recent studies have shown significant associations between obesity phenotypes (body weight, body mass index, waist circumference, and central and regional adiposity) and genetic variants, epigenetic factors (DNA methylation and noncoding RNA), microbial species, and environment (sociodemographics and physical activity). Additionally, studies have also shown that the interactions between genetic variants, microbial metabolites, and epigenetic factors affect energy balance and adiposity. These include variants in FTO, MC4R, PPAR, APOA, and FADS genes, DNA methylation in CpG island regions, and specific miRNAs and microbial species such as Firmicutes, Bacteriodes, Clostridiales, etc. Similarly, studies have shown that microbial metabolites, folate, B-vitamins, and short-chain fatty acids interact with miRNAs to influence obesity phenotypes. With the advent of next-generation sequencing and analytical approaches, the advances in precision nutrition have the potential to lead to new paradigms, which can further lead to interventions or customized treatments specific to individuals or susceptible groups of individuals. This review highlights the recent advances in precision nutrition as applied to obesity and projects the importance of precision nutrition in obesity and weight management.

Implementation of Nutrigenetics and Nutrigenomics Research and Training Activities for Developing Precision Nutrition Strategies in Malaysia

Nutritional epidemiological studies show a triple burden of malnutrition with disparate prevalence across the coexisting ethnicities in Malaysia. To tackle malnutrition and related conditions in Malaysia, research in the new and evolving field of nutrigenetics and nutrigenomics is essential. As part of the Gene-Nutrient Interactions (GeNuIne) Collaboration, the Nutrigenetics and Nutrigenomics Research and Training Unit (N2RTU) aims to solve the malnutrition paradox. This review discusses and presents a conceptual framework that shows the pathway to implementing and strengthening precision nutrition strategies in Malaysia. The framework is divided into: (1) Research and (2) Training and Resource Development. The first arm collects data from genetics, genomics, transcriptomics, metabolomics, gut microbiome, and phenotypic and lifestyle factors to conduct nutrigenetic, nutrigenomic, and nutri-epigenetic studies. The second arm is focused on training and resource development to improve the capacity of the stakeholders (academia, healthcare professionals, policymakers, and the food industry) to utilise the findings generated by research in their respective fields. Finally, the N2RTU framework foresees its applications in artificial intelligence and the implementation of precision nutrition through the action of stakeholders.

Dysfunctional intercellular communication and metabolic signaling pathways in thin endometrium

Background: The endometrial thickness is a key factor for successful implantation. Thin endometrium is associated with lower implantation rate and pregnancy rate. Lacking of a better understanding for the cellular and molecular mechanisms of thin endometrium, managing patients with thin endometrium still represents a major challenge for clinicians. Methods: In this study, we combined four single-cell RNA sequencing (scRNA-seq) and one bulk sequencing (bulk-seq) data for thin endometrium to perform an integrated analysis for endometrial cells in proliferating phase. Cell proportion and differentially expressed genes (DEGs) were analyzed to determine the disease-specific cell type and signaling pathways. The cell-cell communication among cell types were inferred by "CellChat" to illustrate the differential intercellular communication under normal and thin endometrium conditions. GSEA and GSVA were applied to identify dysfunctional signals and metabolic pathways before and after thin endometrium. Results: Integration of scRNA-seq identified eight cell types. The proportion of stromal cells showed a significant difference between normal and thin endometrial tissue. The DEGs in diverse cell types revealed enriched pathways in a cell-specific manner. Aberrant cell-cell signaling transduction was found in almost all cell types, especially in immune cells and epithelial cells. Furthermore, dysfunctional metabolic signaling pathways were induced in a cell-type dependent way. The down-regulation of carbohydrate metabolism and nucleotide metabolism was observed and the energy metabolism switch was indicated. Conclusion: Conclusively, we discover dysfunctional signals and metabolic pathways in thin endometrium, providing insight into mechanisms and therapeutic strategies for the atrophic endometrium.

Efficacy of a Digital Personalized Elimination Diet for the Self-Management of Irritable Bowel Syndrome and Comorbid Irritable Bowel Syndrome and Inflammatory Bowel Disease

INTRODUCTION

Most patients with irritable bowel syndrome (IBS) and dual-diagnosis IBS and inflammatory bowel disease (IBD) report that symptoms originate from or are exacerbated by trigger foods. Despite patient interest and need, there is no consensus on what diet is optimal. Popular diets have notable limitations including cost, length, implementation complexity, and lack of personalization.

METHODS

This pilot study evaluated the feasibility, desirability, and effect on gastrointestinal symptoms of a digitally delivered personalized elimination diet for patients with IBS and comorbid IBS/IBD, powered by machine learning. Participants were recruited online and were provided access to a digital personalized nutrition tool for 9 weeks (N = 37; IBS only = 16, Crohn's disease and IBS = 9, and ulcerative colitis and IBS = 12).

RESULTS

Significant symptom improvement was seen for 81% of participants at study midpoint and persisted for 70% at end point, measured by the relevant symptom severity score (IBS symptom severity score, Patient Simple Clinical Colitis Activity Index, or Mobile Health Index for Crohn's disease). Clinically significant symptom improvement was observed in 78% of participants at midpoint and 62% at end point. Twenty-five participants (67.6%) achieved total symptomatic resolution by the end of study. Patient-reported quality of life improved for 89% of participants. Ninety-five percentage daily engagement, 95% retention, 89% adherence and 92% satisfaction with the program were reported.

DISCUSSION

Dietary elimination can improve symptoms and quality of life in patients with IBS and comorbid IBS/IBD. Digital technology can personalize dietary interventions and improve adherence. Randomized controlled trials are warranted.

Evaluation of the metabotype concept after intervention with oral glucose tolerance test and dietary fiber-enriched food: An enable study

BACKGROUND AND AIMS

Evidence suggests that people react differently to the same diet due to inter-individual differences. However, few studies have investigated variation in response to dietary interventions based on individuals' baseline metabolic characteristics. This study aims to examine the differential reaction of metabotype subgroups to an OGTT and a dietary fiber intervention.

METHODS AND RESULTS

We assigned 356 healthy participants of an OGTT sub-study and a 12-week dietary fiber intervention sub-study within the enable cluster to three metabotype subgroups previously identified in the KORA F4 study population. To explore the association between plasma glucose level and metabotype subgroups, we used linear mixed models adjusted for age, sex, and physical activity. Individuals in different metabotype subgroups showed differential responses to OGTT. Compared to the healthy metabotype (metabotype 1), participants in intermediate metabotype (metabotype 2) and unfavorable metabotype (metabotype 3) had significantly higher plasma glucose concentrations at 120 min after glucose bolus (β = 7.881, p = 0.005; β = 32.79, p < 0.001, respectively). Additionally, the linear regression model showed that the Area under the curve (AUC) of plasma glucose concentrations was significantly different across the metabotype subgroups. The associations between metabotype subgroups and metabolic parameters among fiber intervention participants remained insignificant in the multivariate-adjusted linear model. However, the metabotype 3 had the highest mean reduction in insulin, cholesterol parameters (TC, LDLc, and non-HDLc), and systolic and diastolic blood pressure at the end of the intervention period.

CONCLUSION

This study supports the use of the metabotype concept to identify metabolically similar subgroups and to develop targeted dietary interventions at the metabotype subgroup level for the primary prevention of diet-related diseases.

Valuing the Diversity of Research Methods to Advance Nutrition Science

The ASN Board of Directors appointed the Nutrition Research Task Force to develop a report on scientific methods used in nutrition science to advance discovery, interpretation, and application of knowledge in the field. The genesis of this report was growing concern about the tone of discourse among nutrition professionals and the implications of acrimony on the productive study and translation of nutrition science. Too often, honest differences of opinion are cast as conflicts instead of areas of needed collaboration. Recognition of the value (and limitations) of contributions from well-executed nutrition science derived from the various approaches used in the discipline, as well as appreciation of how their layering will yield the strongest evidence base, will provide a basis for greater productivity and impact. Greater collaborative efforts within the field of nutrition science will require an understanding that each method or approach has a place and function that should be valued and used together to create the nutrition evidence base. Precision nutrition was identified as an important emerging nutrition topic by the preponderance of task force members, and this theme was adopted for the report because it lent itself to integration of many approaches in nutrition science. Although the primary audience for this report is nutrition researchers and other nutrition professionals, a secondary aim is to develop a document useful for the various audiences that translate nutrition research, including journalists, clinicians, and policymakers. The intent is to promote accurate, transparent, verifiable evidence-based communication about nutrition science. This will facilitate reasoned interpretation and application of emerging findings and, thereby, improve understanding and trust in nutrition science and appropriate characterization, development, and adoption of recommendations.

Nutrition state of science and dementia prevention: recommendations of the Nutrition for Dementia Prevention Working Group

Observational studies suggest that nutritional factors have a potential cognitive benefit. However, systematic reviews of randomised trials of dietary and nutritional supplements have reported largely null effects on cognitive outcomes and have highlighted study inconsistencies and other limitations. In this Personal View, the Nutrition for Dementia Prevention Working Group presents what we consider to be limitations in the existing nutrition clinical trials for dementia prevention. On the basis of this evidence, we propose recommendations for incorporating dietary patterns and the use of genetic, and nutrition assessment tools, biomarkers, and novel clinical trial designs to guide future trial developments. Nutrition-based research has unique challenges that could require testing both more personalised interventions in targeted risk subgroups, identified by nutritional and other biomarkers, and large-scale and pragmatic study designs for more generalisable public health interventions across diverse populations.

Personalized Nutrition Using Microbial Metabolite Phenotype to Stratify Participants and Non-Invasive Host Exfoliomics Reveal the Effects of Flaxseed Lignan Supplementation in a Placebo-Controlled Crossover Trial

High-fiber plant foods contain lignans that are converted to bioactive enterolignans, enterolactone (ENL) and enterodiol (END) by gut bacteria. Previously, we conducted an intervention study to gain mechanistic insight into the potential chemoprotective effects of flaxseed lignan supplementation (secoisolariciresinol diglucoside; SDG) compared to a placebo in 42 men and women. Here, we expand on these analyses to further probe the impact of the microbial metabolite phenotype on host gene expression in response to lignan exposure. We defined metabolic phenotypes as high- or low-ENL excretion based on the microbial metabolism of SDG. RNA-seq was used to assess host gene expression in fecal exfoliated cells. Stratified by microbial ENL excretion, differentially expressed (DE) genes in high- and low-ENL excreter groups were compared. Linear discriminant analysis using the ENL phenotypes identified putative biomarker combinations of genes capable of discriminating the lignan treatment from the placebo. Following lignan intervention, a total of 165 DE genes in high-ENL excreters and 1450 DE genes in low-ENL excreters were detected. Functional analysis identified four common upstream regulators (master genes): CD3, IFNG, IGF1 and TNFRSF1A. Our findings suggest that the enhanced conversion of flaxseed lignan to ENL is associated with a suppressed inflammatory status.

Human Milk Metabolomics Are Related to Maternal Adiposity, Infant Growth Rate and Allergies: The Chinese Human Milk Project

The metabolomic profiles of Chinese human milk have been poorly documented. The objective of the study was to explore associations between human milk metabotypes, maternal adiposity, infant growth patterns, and risk of allergies. Two hundred mother−infant dyads from seven cities were randomly selected from the Chinese Human Milk Project (CHMP). Untargeted human milk metabolomic profiles were determined using HPLC-MS/MS. Two human milk metabotypes were identified using principal component analysis. Principal component (PC) 1 was characterized by high linoleic acid metabolites with low purine nucleosides and metabolites of glutamate and glutathione metabolism. PC 2 was characterized by high glycerophospholipids and sphingomyelins content. Higher PC1 scores were associated with slower infant growth rate and higher ambient temperature (p < 0.05). Higher PC 2 scores were related to higher maternal BMI and increased risk of infant allergies (p < 0.05). Future work is needed to understand the biologic mechanisms of these human milk metabotypes.

Metabotyping for Precision Nutrition and Weight Management: Hype or Hope?

PURPOSE OF REVIEW

Precision nutrition requires a solid understanding of the factors that determine individual responses to dietary treatment. We review the current state of knowledge in identifying human metabotypes - based on circulating biomarkers - that can predict weight loss or other relevant physiological outcomes in response to diet treatment.

RECENT FINDINGS

Not many studies have been conducted in this area and the ones identified here are heterogeneous in design and methodology, and therefore difficult to synthesize and draw conclusions. The basis of the creation of metabotypes varies widely, from using thresholds for a single metabolite to using complex algorithms to generate multi-component constructs that include metabolite and genetic information. Furthermore, available studies are a mix of hypothesis-driven and hypothesis-generating studies, and most of them lack experimental testing in human trials. Although this field of research is still in its infancy, precision-based dietary intervention strategies focusing on the metabotype group level hold promise for designing more effective dietary treatments for obesity.

Reproducible changes in the anorexia nervosa gut microbiota following inpatient therapy remain distinct from non-eating disorder controls

The composition of the gut microbiota in patients with anorexia nervosa (AN), and the ability of this microbial community to influence the host, remains uncertain. To achieve a broader understanding of the role of the intestinal microbiota in patients with AN, we collected fecal samples before and following clinical treatment at two geographically distinct eating disorder units (Center of Excellence for Eating Disorders [UNC-CH] and ACUTE Center for Eating Disorders [Denver Health]). Gut microbiotas were characterized in patients with AN, before and after inpatient treatment, and in non-eating disorder (non-ED) controls using shotgun metagenomic sequencing. The impact of inpatient treatment on the AN gut microbiota was remarkably consistent between eating disorder units. Although weight in patients with AN showed improvements, AN microbiotas post-treatment remained distinct from non-ED controls. Additionally, AN gut microbiotas prior to treatment exhibited more fermentation pathways and a lower ability to degrade carbohydrates than non-ED controls. As the intestinal microbiota can influence nutrient metabolism, our data highlight the complex microbial communities in patients with AN as an element needing further attention post inpatient treatment. Additionally, this study defines the effects of renourishment on the AN gut microbiota and serves as a platform to develop precision nutrition approaches to potentially mitigate impediments to recovery.

Multiomics Approach to Precision Sports Nutrition: Limits, Challenges, and Possibilities

Most sports nutrition guidelines are based on group average responses and professional opinion. Precision nutrition for athletes aims to improve the individualization of nutrition practices to optimize long-term performance and health. This is a 2-step process that first involves the acquisition of individual-specific, science-based information using a variety of sources including lifestyle and medical histories, dietary assessment, physiological assessments from the performance lab and wearable sensors, and multiomics data from blood, urine, saliva, and stool samples. The second step consists of the delivery of science-based nutrition advice, behavior change support, and the monitoring of health and performance efficacy and benefits relative to cost. Individuals vary widely in the way they respond to exercise and nutritional interventions, and understanding why this metabolic heterogeneity exists is critical for further advances in precision nutrition. Another major challenge is the development of evidence-based individualized nutrition recommendations that are embraced and efficacious for athletes seeking the most effective enhancement of performance, metabolic recovery, and health. At this time precision sports nutrition is an emerging discipline that will require continued technological and scientific advances before this approach becomes accurate and practical for athletes and fitness enthusiasts at the small group or individual level. The costs and scientific challenges appear formidable, but what is already being achieved today in precision nutrition through multiomics and sensor technology seemed impossible just two decades ago.

Utilizing preclinical models of genetic diversity to improve translation of phytochemical activities from rodents to humans and inform personalized nutrition

Mouse models are an essential tool in different areas of research, including nutrition and phytochemical research. Traditional inbred mouse models have allowed the discovery of therapeutical targets and mechanisms of action and expanded our knowledge of health and disease. However, these models lack the genetic variability typically found in human populations, which hinders the translatability of the results found in mice to humans. The development of genetically diverse mouse models, such as the collaborative cross (CC) or the diversity outbred (DO) models, has been a useful tool to overcome this obstacle in many fields, such as cancer, immunology and toxicology. However, these tools have not yet been widely adopted in the field of phytochemical research. As demonstrated in other disciplines, use of CC and DO models has the potential to provide invaluable insights for translation of phytochemicals from rodents to humans, which are desperately needed given the challenges and numerous failed clinical trials in this field. These models may prove informative for personalized use of phytochemicals in humans, including: predicting interindividual variability in phytochemical bioavailability and efficacy, identifying genetic loci or genes governing response to phytochemicals, identifying phytochemical mechanisms of action and therapeutic targets, and understanding the impact of genetic variability on individual response to phytochemicals. Such insights would prove invaluable for personalized implementation of phytochemicals in humans. This review will focus on the current work performed with genetically diverse mouse populations, and the research opportunities and advantages that these models can offer to phytochemical research.

Personalized nutrition for dementia prevention

The role of nutrition has been investigated for decades under the assumption of one-size-fits-all. Yet there is heterogeneity in metabolic and neurobiological responses to diet. Thus a more personalized approach may better fit biological reality and have increased efficacy to prevent dementia. Personalized nutrition builds on the food exposome, defined as the history of diet-related exposures over the lifetime, and on its interactions with the genome and other biological characteristics (eg, metabolism, the microbiome) to shape health. We review current advances of personalized nutrition in dementia research. We discuss key questions, success milestones, and future roadmap from observational epidemiology to clinical studies through basic science. A personalized nutrition approach based on the best prescription for the most appropriate target population in the most relevant time-window has the potential to strengthen dementia-prevention efforts.

NAD Modulates DNA Methylation and Cell Differentiation

Nutritional intake impacts the human epigenome by directing epigenetic pathways in normal cell development via as yet unknown molecular mechanisms. Consequently, imbalance in the nutritional intake is able to dysregulate the epigenetic profile and drive cells towards malignant transformation. Here we present a novel epigenetic effect of the essential nutrient, NAD. We demonstrate that impairment of DNMT1 enzymatic activity by NAD-promoted ADP-ribosylation leads to demethylation and transcriptional activation of the CEBPA gene, suggesting the existence of an unknown NAD-controlled region within the locus. In addition to the molecular events, NAD- treated cells exhibit significant morphological and phenotypical changes that correspond to myeloid differentiation. Collectively, these results delineate a novel role for NAD in cell differentiation, and indicate novel nutri-epigenetic strategies to regulate and control gene expression in human cells.

Reframing anorexia nervosa as a metabo-psychiatric disorder

Anorexia nervosa (AN) is a serious and often fatal illness. Despite decades of research, investigators have failed to adequately advance our understanding of the biological aspects of AN that could inform the development of effective interventions. Genome-wide association studies are revealing the important role of metabolic factors in AN, and studies of the gastrointestinal tract are shedding light on disruptions in enteric microbial communities and anomalies in gut morphology. In this opinion piece, we review the state of the science through the lens of the clinical presentation of illness. We project how the integration of rigorous science in genomics and microbiology, in collaboration with experienced clinicians, has the potential to markedly enhance treatment outcome via precision interventions.

GeNuIne (gene-nutrient interactions) Collaboration: towards implementing multi-ethnic population-based nutrigenetic studies of vitamin B12 and D deficiencies and metabolic diseases

Gene-nutrient interactions (GeNuIne) collaboration, a large-scale collaborative project, has been initiated to investigate the impact of gene-nutrient interactions on cardiometabolic diseases using population-based studies from ethnically diverse populations. In this project, the relationship between deficiencies of vitamins B12 and D, and metabolic diseases was explored using a nutrigenetic approach. A genetic risk score (GRS) analysis was used to examine the combined effect of several genetic variations that have been shown to be associated with metabolic diseases and vitamin B12 and D deficiencies, respectively. In Sri Lankan, Indonesian and Brazilian populations, those carrying a high B12-GRS had an increased risk of metabolic diseases under the influence of dietary protein, fibre and carbohydrate intakes, respectively; however, in Asian Indians, genetically instrumented metabolic disease risk showed a significant association with low vitamin B12 status. With regards to nutrigenetic studies on vitamin D status, although high metabolic-GRS showed an interaction with dietary carbohydrate intake on vitamin D status, the study in Indonesian women demonstrated a vitamin D GRS-carbohydrate interaction on body fat percentage. In summary, these nutrigenetic studies from multiple ethnic groups have provided evidence for the influence of the dietary factors on the relationship between vitamin B12/D deficiency and metabolic outcomes. Furthermore, these studies highlight the existence of genetic heterogeneity in gene-diet interactions across ethnically diverse populations, which further implicates the significance of personalised dietary approaches for the prevention of these micronutrient deficiencies and metabolic diseases.

Causes and Consequences of Interindividual Response Variability: A Call to Apply a More Rigorous Research Design in Acute Exercise-Cognition Studies

The different responses of humans to an apparently equivalent stimulus are called interindividual response variability. This phenomenon has gained more and more attention in research in recent years. The research field of exercise-cognition has also taken up this topic, as shown by a growing number of studies published in the past decade. In this perspective article, we aim to prompt the progress of this research field by (i) discussing the causes and consequences of interindividual variability, (ii) critically examining published studies that have investigated interindividual variability of neurocognitive outcome parameters in response to acute physical exercises, and (iii) providing recommendations for future studies, based on our critical examination. The provided recommendations, which advocate for a more rigorous study design, are intended to help researchers in the field to design studies allowing them to draw robust conclusions. This, in turn, is very likely to foster the development of this research field and the practical application of the findings.

Precision nutrition based on phenotypical traits and the (epi)genotype: nutrigenetic and nutrigenomic approaches for obesity care

PURPOSE OF REVIEW

The purpose of this article is to rationally review and critically appraise the current knowledge in the most relevant nongenetic and genetic factors influencing obesity predisposition. This information may be translated into the implementation of personalized nutrition approaches involving precision nutrigenetic and nutrigenomic strategies for obesity monitoring and weight management.

RECENT FINDINGS

The importance and influence of several nongenetic contributors to obesity onset and individual responses to weight-loss interventions have been highlighted including the role of age, sex or perinatal feeding and others related to an individual's lifestyle and modifiable. Nutrigenetic studies have analysed potential interactions between polymorphisms influencing energy homeostasis/body composition and dietary factors in relation to adiposity phenotypes and therapy responsiveness. A second approach comprises the Nutrigenomic analysis of gene expression modifications in response to the consumption of specific nutrients or dietary bioactive compounds, which may involve epigenetic mechanisms including deoxyribonucleic acid methylation and micro-ribonucleic acid expression profiles.

SUMMARY

Taken together, these findings encompass the importance of taking into account up-to-date advances in Nutrigenetic and Nutrigenomic hallmarks, globally analysing the risk of weight gain and related outcomes after following nutrition counselling, this contributing to improve obesity care considering phenotypical traits and the genetic make-up for precision obesity care.

What is the promise of personalised nutrition?

Personalised nutrition (PN) is an emerging field that bears great promise. Several definitions of PN have been proposed and different modelling approaches have been used to claim PN effects. We tentatively propose to group these approaches into two categories, which we term outcome-based and population reference approaches, respectively. Understanding the fundamental differences between these two types of modelling approaches may allow a more realistic appreciation of what to expect from PN interventions presently and may be helpful for designing and planning future studies investigating PN interventions.

Nutritional markers of undiagnosed type 2 diabetes in adults: Findings of a machine learning analysis with external validation and benchmarking

OBJECTIVES

Using a nationally-representative, cross-sectional cohort, we examined nutritional markers of undiagnosed type 2 diabetes in adults via machine learning.

METHODS

A total of 16429 men and non-pregnant women ≥ 20 years of age were analysed from five consecutive cycles of the National Health and Nutrition Examination Survey. Cohorts from years 2013-2016 (n = 6673) was used for external validation. Undiagnosed type 2 diabetes was determined by a negative response to the question "Have you ever been told by a doctor that you have diabetes?" and a positive glycaemic response to one or more of the three diagnostic tests (HbA1c > 6.4% or FPG >125 mg/dl or 2-hr post-OGTT glucose > 200mg/dl). Following comprehensive literature search, 114 potential nutritional markers were modelled with 13 behavioural and 12 socio-economic variables. We tested three machine learning algorithms on original and resampled training datasets built using three resampling methods. From this, the derived 12 predictive models were validated on internal- and external validation cohorts. Magnitudes of associations were gauged through odds ratios in logistic models and variable importance in others. Models were benchmarked against the ADA diabetes risk test.

RESULTS

The prevalence of undiagnosed type 2 diabetes was 5.26%. Four best-performing models (AUROC range: 74.9%-75.7%) classified 39 markers of undiagnosed type 2 diabetes; 28 via one or more of the three best-performing non-linear/ensemble models and 11 uniquely by the logistic model. They comprised 14 nutrient-based, 12 anthropometry-based, 9 socio-behavioural, and 4 diet-associated markers. AUROC of all models were on a par with ADA diabetes risk test on both internal and external validation cohorts (p>0.05).

CONCLUSIONS

Models performed comparably to the chosen benchmark. Novel behavioural markers such as the number of meals not prepared from home were revealed. This approach may be useful in nutritional epidemiology to unravel new associations with type 2 diabetes.

Multiomic Predictors of Short-Term Weight Loss and Clinical Outcomes During a Behavioral-Based Weight Loss Intervention

OBJECTIVE

Identifying predictors of weight loss and clinical outcomes may increase understanding of individual variability in weight loss response. We hypothesized that baseline multiomic features, including DNA methylation (DNAme), metabolomics, and gut microbiome, would be predictive of short-term changes in body weight and other clinical outcomes within a comprehensive weight loss intervention.

METHODS

Healthy adults with overweight or obesity (n = 62, age 18-55 years, BMI 27-45 kg/m2 , 75.8% female) participated in a 1-year behavioral weight loss intervention. To identify baseline omic predictors of changes in clinical outcomes at 3 and 6 months, whole-blood DNAme, plasma metabolites, and gut microbial genera were analyzed.

RESULTS

A network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A1c , homeostatic model assessment of insulin resistance, total cholesterol, triglycerides, C-reactive protein, leptin, and ghrelin) that changed significantly (P < 0.05). For eight of these, adjusted R2 ranged from 0.34 to 0.78. Our models identified specific DNAme sites, gut microbes, and metabolites that were predictive of variability in weight loss, waist circumference, and circulating triglycerides and that are biologically relevant to obesity and metabolic pathways.

CONCLUSIONS

These data support the feasibility of using baseline multiomic features to provide insight for precision nutrition-based weight loss interventions.

Precision nutrition: A systematic literature review

Precision Nutrition research aims to use personal information about individuals or groups of individuals to deliver nutritional advice that, theoretically, would be more suitable than generic advice. Machine learning, a subbranch of Artificial Intelligence, has promise to aid in the development of predictive models that are suitable for Precision Nutrition. As such, recent research has applied machine learning algorithms, tools, and techniques in precision nutrition for different purposes. However, a systematic overview of the state-of-the-art on the use of machine learning in Precision Nutrition is lacking. Therefore, we carried out a Systematic Literature Review (SLR) to provide an overview of where and how machine learning has been used in Precision Nutrition from various aspects, what such machine learning models use as input features, what the availability status of the data used in the literature is, and how the models are evaluated. Nine research questions were defined in this study. We retrieved 4930 papers from electronic databases and 60 primary studies were selected to respond to the research questions. All of the selected primary studies were also briefly discussed in this article. Our results show that fifteen problems spread across seven domains of nutrition and health are present. Four machine learning tasks are seen in the form of regression, classification, recommendation and clustering, with most of these utilizing a supervised approach. In total, 30 algorithms were used, with 19 appearing more than once. Models were through the use of four groups of approaches and 23 evaluation metrics. Personalized approaches are promising to reduce the burden of these current problems in nutrition research, and the current review shows Machine Learning can be incorporated into Precision Nutrition research with high performance. Precision Nutrition researchers should consider incorporating Machine Learning into their methods to facilitate the integration of many complex features, allowing for the development of high-performance Precision Nutrition approaches.

Polyunsaturated fatty acids, specialized pro-resolving mediators, and targeting inflammation resolution in the age of precision nutrition

Chronic inflammation contributes toward the pathogenesis of numerous diseases including, but not limited to, obesity, autoimmunity, cardiovascular diseases, and cancers. The discovery of specialized pro-resolving mediators (SPMs), which are critical for resolving inflammation, has commenced investigation into targeting pathways of inflammation resolution to improve physiological outcomes. SPMs are predominately synthesized from the n-3 polyunsaturated fatty acids (PUFA) eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids. Therefore, one viable strategy to promote inflammation resolution would be to increase dietary intake of EPA/DHA, which are deficient in select populations. However, there are inconsistencies between the use of EPA/DHA as dietary or pharmacological supplements and improved inflammatory status. Herein, we review the literature on the relationship between the high n-6/n-3 PUFA ratio, downstream SPM biosynthesis, and inflammatory endpoints. We highlight key studies that have investigated how dietary intake of EPA/DHA increase tissue SPMs and their effects on inflammation. We also discuss the biochemical pathways by which EPA/DHA drive SPM biosynthesis and underscore mechanistic gaps in knowledge about these pathways which include a neglect for host genetics/ethnic differences in SPM metabolism, sexual dimorphism in SPM levels, and potential competition from select dietary n-6 PUFAs for enzymes of SPM synthesis. Altogether, establishing how dietary PUFAs control SPM biosynthesis in a genetic- and sex-dependent manner will drive new precision nutrition studies with EPA/DHA to prevent chronic inflammation in select populations.

Mechanistic Targets and Nutritionally Relevant Intervention Strategies to Break Obesity-Breast Cancer Links

The worldwide prevalence of overweight and obesity has tripled since 1975. In the United States, the percentage of adults who are obese exceeds 42.5%. Individuals with obesity often display multiple metabolic perturbations, such as insulin resistance and persistent inflammation, which can suppress the immune system. These alterations in homeostatic mechanisms underlie the clinical parameters of metabolic syndrome, an established risk factor for many cancers, including breast cancer. Within the growth-promoting, proinflammatory milieu of the obese state, crosstalk between adipocytes, immune cells and breast epithelial cells occurs via obesity-associated hormones, angiogenic factors, cytokines, and other mediators that can enhance breast cancer risk and/or progression. This review synthesizes evidence on the biological mechanisms underlying obesity-breast cancer links, with emphasis on emerging mechanism-based interventions in the context of nutrition, using modifiable elements of diet alone or paired with physical activity, to reduce the burden of obesity on breast cancer.

Differentially methylated regions (DMRs) in PON3 gene between responders and non-responders to a weight loss dietary intervention: a new tool for precision management of obesity

Differentially methylated regions (DMR) are genomic regions with different methylation status. The aim of this research was to identify DMRs in subjects with obesity that predict the response to a weight-loss dietary intervention and its association with metabolic variables. Based on the change in body mass index (BMI), 201 subjects with overweight and obesity were categorized in tertiles according to their response to a hypocaloric diet: Responders (R; n = 64) and Non-Responders (NR; n = 63). The R group lost 4.55 ± 0.91 BMI units (kg/m²) and the NR group lost 1.95 ± 0.73 kg/m² (p < 0.001). DNA methylation was analysed in buffy coat through a methylation array at baseline. DMRs were analysed using a function of ChAMP (Chip Analysis Methylation Pipeline) in R software. Baseline DNA methylation analysis between R and NR exhibited a DMR located at paraoxonase 3 gene (PON3) consisting of 13 CpG sites, eleven of them significantly hypermethylated in R. To analyse the implication of these 11 CpGs on weight loss, a z-score was performed as a measure of DMR methylation. This analysis showed a correlation between PON3 DNA methylation and BMI loss. This z-score negatively correlated with PON3 protein serum levels. Total paraoxonase activity in serum was not different between groups, but PON enzymatic activity positively correlated with oxidized LDL levels. The present study identified a DMR within PON3 gene that is related to PON3 protein levels in serum, and that could be used as a potential biomarker to predict the response to weight-loss dietary interventions.

Personalized Nutrition and -Omics

With change in global concern toward food quality over food quantity, consumer concern and choice of healthy food has become a matter of prime importance. It gave rise to concept of “personalized or precision nutrition”. The theory behind personalization of nutrition is supported by multiple factors including advances in food analytics, nutrition based diseases and public health programs, increasing use of information technology in nutrition science, concept of gene-diet interaction and growing consumer capacity or concern by better and healthy foods. The advances in “omics” tools and related analytical techniques have resulted into tremendous scope of their application in nutrition science. As a consequence, a better understanding of underlying interaction between diet and individual is expected with addressing of key challenges for successful implementation of this science. In this chapter, the above aspects are discussed to get an insight into driving factors for increasing concern in personalized nutrition.

The Effect of Inflammation and Insulin Resistance on Lipid and Lipoprotein Responsiveness to Dietary Intervention

Lipids and lipoproteins are major targets for cardiovascular disease (CVD) prevention. Findings from a limited number of clinical trials suggest diet-induced atherogenic lipoprotein lowering can be altered in the presence of chronic low-grade inflammation or insulin resistance. This review summarizes results from randomized controlled trials that have examined diet-induced changes in lipids/lipoproteins by inflammatory or insulin sensitivity status. In addition, mechanisms to explain these clinical observations are explored. Post hoc analyses of data from a limited number of randomized controlled trials suggest attenuation of diet-induced lipid/lipoprotein lowering in individuals with inflammation and/or insulin resistance. These findings are supported by experimental studies showing that inflammatory stimuli and hyperinsulinemia alter genes involved in endogenous cholesterol synthesis and cholesterol uptake, reduce cholesterol efflux, and increase fatty acid biosynthesis. Further a priori defined research is required to better characterize how chronic low-grade inflammation and insulin resistance modulate lipid and lipoprotein responsiveness to guide CVD risk reduction in individuals presenting with these phenotypes.