A double-blinded, randomized, parallel intervention to evaluate biomarker-based nutrition plans for weight loss: The PREVENTOMICS study

AI4Food, a feasibility study for the implementation of automated devices in the nutritional advice and follow up within a weight loss intervention

BACKGROUND & AIMS

The widespread prevalence of NCDs calls for an improvement in their prevention and treatment. Wearable technologies can be an important asset in the development of precision nutrition strategies, for both health professionals and patients. However, their clinical use is hindered by a lack of validation against current methodologies or appropriate tools to deliver nutritional strategies based on their data. Our study includes manual and automatic data capture methods within a weight loss intervention with the aim to create an essential asset for the implementation, validation, and benchmarking of AI-based tools in nutritional clinical practice.

METHODS

This is a feasibility prospective and crossover controlled trial for weight loss in overweight and obese participants, randomized into two groups: Group 1 used manual data collection methods based on validated questionnaires for the first two weeks; while Group 2 started with automatic data collection methods consisting of wearable sensors. After two weeks, the two groups switched data collection methods. Lifestyle data, anthropometric measurements and biological samples were collected from all participants.

RESULTS

A total of 93 participants completed the nutritional intervention designed for weight loss, achieving a mean reduction of 2 kg (V1: 84.99 SD ± 13.69, V3: 82.72 SD ± 13.32, p < 0.001). Significant reductions were observed in body mass index, visceral fat, waist circumference, total cholesterol, and HbA1c levels. The use of electronic devices proved satisfactory among the participants (System Usability Scale score 78.27 ± 12.86). We also report the presence of distinct patient groups based on continuous glucose measurements.

CONCLUSION

This study has yielded a large amount of data and has showcased how automatic data collection devices can be employed to gather data in the context of a nutritional intervention. This will enable the implementation of AI-based tools in nutritional clinical practice.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, NCT05807243.

Precision nutrition: Is tailor‑made dietary intervention a reality yet? (Review)

Precision nutrition (PN) is an emerging field of science recognizing the variability in how individuals respond to different nutrients, driven by their unique biological makeup. The central aim of PN is to tailor dietary interventions to improve individual health, prevent disease and manage existing health conditions based on specific biological characteristics. The present review aimed to provide an overview of available multi-omics platforms that can be applied to extracting data from biological materials in the context of PN. Additionally, it proposed an updated pipeline for handling and integrating these data. A comprehensive search of academic publications was conducted across multiple databases, focusing on recent advances in PN, including its challenges and opportunities. Following this preparatory step, a data handling protocol for different omics layers was compiled to develop an up-to-date multi-omics pipeline applicable to PN. The successful implementation of PN requires a systems-level understanding of human physiological networks, their plasticity, variations in response to dietary exposures and the ability to classify population subgroups based on their nutritional needs. The realization of PN may currently seem distant due to several limitations such as the lack of ongoing large-scale epidemiological studies, challenges in database curation, the high cost of omics analysis and ethical concerns. A key advancement to this field would be the development of next-generation biomarkers connecting nutrition to chronic diseases. These biomarkers would help classify individuals at risk of diet-related conditions and quantify the dose-response relationships between individuals or groups of interacting nutrients and the onset and progression of diseases.

Metabolomics: Unveiling biological matrices in precision nutrition and health

Precision nutrition, an expanding field at the intersection of nutrition science and personalized medicine, is rapidly evolving with metabolomics integration. Metabolomics, facilitated by advanced technologies like mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, facilitates comprehensive profiling of metabolites across diverse biological samples. From the perspective of health care systems, precision nutrition gains relevance due to the substantial impact of prevalent non-communicable diseases (NCDs) on societal well-being, which is directly linked with dietary habits and eating behavior. Furthermore, biomarker products derived from metabolomics have been utilized in Europe, the USA, and Brazil to understand metabolic dysregulations and tailor diets accordingly. Despite its burgeoning status, metabolomics holds great potential in revolutionizing nutritional science, particularly with the integration of artificial intelligence and machine learning, offering novel insights into personalized dietary interventions and disease prediction. This narrative review emphasizes the transformative impact of metabolomics in precision and delineates avenues for future research and application, paving the way for a more tailored and practical approach to nutrition management.

Nutrigenomics-guided lifestyle intervention programmes: A critical scoping review with directions for future research

Genetic testing is increasingly used in clinical practice to provide personalized information and recommendations about health risks and lifestyle habits at a relatively low cost. Research on the effectiveness of nutrigenomics-guided lifestyle interventions is growing. A scoping review approach was adopted to identify pertinent published studies on nutrigenomics-guided intervention programmes from 2007 to 2023. The review shows that despite the growing interest in nutrigenomics-guided lifestyle interventions, there are still few empirically supported studies, primarily based on developed countries. Furthermore, the findings on the impact of personalised genetic advice are mixed, leaving the field unclear. Existing studies have some empirical strength, contributing to further understanding of the relationship between food and gene expression. However, some limitations that affect the robustness of findings exist, such as a small sample size, insufficient monitoring of the data collection process, and a short follow-up period. Future research needs to address reliability concerns and provide more robust practical evidence.

Do Personalized Nutrition Interventions Improve Dietary Intake and Risk Factors in Adults With Elevated Cardiovascular Disease Risk Factors? A Systematic Review and Meta-analysis of Randomized Controlled Trials

CONTEXT

Dietary modifications can improve cardiovascular disease (CVD) risk factors. Personalized nutrition (PN) refers to individualized nutrition care based on genetic, phenotypic, medical, behavioral, and/or lifestyle characteristics. PN may be beneficial in improving CVD risk factors, including diet. However, this has not been reviewed previously.

OBJECTIVE

The aim was to evaluate the effectiveness of PN interventions on CVD risk factors and diet in adults at elevated CVD risk.

DATA SOURCES

Six databases were searched for randomized controlled trials published between 2000 and 2023 that tested the impact of PN interventions on CVD risk factors in people at elevated risk.

DATA EXTRACTION

Risk of bias was assessed using the Academy of Nutrition and Dietetics Quality Criteria checklist. Data synthesis of eligible articles included participant characteristics, intervention details, and change in primary CVD risk factor outcomes, including blood pressure (BP), plasma lipids, and CVD risk score, and secondary risk factors, including anthropometric outcomes and diet quality. Random-effects meta-analyses were conducted to explore weighted mean differences (WMDs) in change or final mean values for studies with comparable data (studies with dietary counseling interventions) for outcomes including BP, blood lipids, and anthropometric measurements.

DATA ANALYSIS

Of 7676 identified articles, 16 articles representing 15 studies met the inclusion criteria. Studies included between 40 and 563 participants and reported outcomes for CVD risk factors, including hyperlipidemia (n = 5), elevated BP (n = 3), overweight/obesity (n = 1), and multiple risk factors (n = 6). Risk of bias was low. Results suggested potential benefit of PN on systolic BP (WMD: -1.91; 95% CI: -3.51, -0.31 mmHg) and diastolic BP (WMD: -1.49; 95% CI: -2.39, -0.58 mmHg) and dietary intake in individuals at high CVD risk. Results were inconsistent for plasma lipid and anthropometric outcomes.

CONCLUSION

Results were promising for PN interventions that used dietary counseling on CVD risk factors in at-risk individuals. However, further evidence for other personalization methods is required, including improving methodological quality and longer study duration in future PN interventions.

SYSTEMATIC REVIEW REGISTRATION

OpenScience Framework (https://doi.org/10.17605/OSF.IO/SHVWP).

Precision or Personalized Nutrition: A Bibliometric Analysis

Food systems face the challenge of maintaining adequate nutrition for all populations. Inter-individual responses to the same diet have made precision or personalized nutrition (PN) an emerging and relevant topic. The aim of this study is to analyze the evolution of the PN field, identifying the principal actors and topics, and providing a comprehensive overview. Therefore, a bibliometric analysis of the scientific research available through the Web of Science (WOS) database was performed, revealing 2148 relevant papers up to June 2024. VOSviewer and the WOS platform were employed for the processing and analysis, and included an evaluation of diverse data such as country, author or most frequent keywords, among others. The analysis revealed a period of exponential growth from 2015 to 2023, with the USA, Spain, and England as the top contributors. The field of "Nutrition and Dietetics" is particularly significant, comprising nearly 33% of the total publications. The most highly cited institutions are the universities of Tufts, College Dublin, and Navarra. The relationship between nutrition, genetics, and omics sciences, along with dietary intervention studies, has been a defining factor in the evolution of PN. In conclusion, PN represents a promising field of research with significant potential for further advancement and growth.

The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center

PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.

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

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

Effects of Weight Loss on Key Obesity-Related Biomarkers Linked to the Risk of Endometrial Cancer: A Systematic Review and Meta-Analysis

Endometrial cancer (EC) includes various histologic types, with estrogen-dependent endometrioid carcinoma being the most common. Obesity significantly increases the risk of developing this type, especially in postmenopausal women, due to elevated estrogen production by adipocytes. This review examines the impact of weight loss from different interventions on reducing obesity-related risk factors for endometrioid EC. A systematic review and meta-analysis were conducted on three weight loss interventions: bariatric surgery, pharmacotherapy, and lifestyle changes. The effects of these interventions on inflammatory biomarkers (CRP, TNF-α, IL-6) and hormones (leptin, estrogen) were analyzed. Data from controlled studies were pooled to assess the significance of weight loss in reducing these biomarkers. Despite heterogeneity, bariatric surgery resulted in an overall 25.8% weight reduction, outperforming lifestyle and pharmacotherapy interventions. Weight loss reduced CRP levels by 33.5% and IL-6 levels by 41.9%. TNF-α levels decreased by 13% with percent weight loss over 7%. Leptin levels also decreased significantly, although the exact weight loss percentage was not statistically significant. Weight loss effectively reduces proinflammatory markers and hormones associated with increased risk of endometrioid EC. The strengths of this review include a comprehensive examination of different weight-loss interventions and a large pool of participants. However, limitations include high heterogeneity among studies and only 43% of the participants being postmenopausal. Limited data on sex hormones and racial disparities underscore the need for further research.

A health technology assessment of personalized nutrition interventions using the EUnetHTA HTA Core Model

OBJECTIVES

Poor nutrition links to chronic diseases, emphasizing the need for optimized diets. The EU-funded project PREVENTOMICS, introduced personalized nutrition to address this. This study aims to perform a health technology assessment (HTA) comparing personalized nutrition interventions developed through this project, with non-personalized nutrition interventions (control) for people with normal weight, overweight, or obesity. The goal is to support decisions about further development and implementation of personalized nutrition.

METHODS

The PREVENTOMICS interventions were evaluated using the European Network for HTA Core Model, which includes a methodological framework that encompasses different domains for value assessment. Information was gathered via [1] different statistical analyses and modeling studies, [2] questions asked of project partners and, [3] other (un)published materials.

RESULTS

Clinical trials of PREVENTOMICS interventions demonstrated different body mass index changes compared to control; differences ranged from -0.80 to 0.20 kg/m2. Long-term outcome predictions showed generally improved health outcomes for the interventions; some appeared cost-effective (e.g., interventions in UK). Ethical concerns around health inequality and the lack of specific legal regulations for personalized nutrition interventions were identified. Choice modeling studies indicated openness to personalized nutrition interventions; decisions were primarily affected by intervention's price.

CONCLUSIONS

PREVENTOMICS clinical trials have shown promising effectiveness with no major safety concerns, although uncertainties about effectiveness exist due to small samples (n=60-264) and short follow-ups (10-16 weeks). Larger, longer trials are needed for robust evidence before implementation could be considered. Among other considerations, developers should explore financing options and collaborate with policymakers to prevent exclusion of specific groups due to information shortages.

Economic Evaluation of a Personalized Nutrition Plan Based on Omic Sciences Versus a General Nutrition Plan in Adults with Overweight and Obesity: A Modeling Study Based on Trial Data in Denmark

BACKGROUND

Since there is no diet that is perfect for everyone, personalized nutrition approaches are gaining popularity to achieve goals such as the prevention of obesity-related diseases. However, appropriate choices about funding and encouraging personalized nutrition approaches should be based on sufficient evidence of their effectiveness and cost-effectiveness. In this study, we assessed whether a newly developed personalized plan (PP) could be cost-effective relative to a non-personalized plan in Denmark.

METHODS

Results of a 10-week randomized controlled trial were combined with a validated obesity economic model to estimate lifetime cost-effectiveness. In the trial, the intervention group (PP) received personalized home-delivered meals based on metabolic biomarkers and personalized behavioral change messages. In the control group these meals and messages were not personalized. Effects were measured in body mass index (BMI) and quality of life (EQ-5D-5L). Costs [euros (€), 2020] were considered from a societal perspective. Lifetime cost-effectiveness was assessed using a multi-state Markov model. Univariate, probabilistic sensitivity, and scenario analyses were performed.

RESULTS

In the trial, no significant differences were found in the effectiveness of PP compared with control, but wide confidence intervals (CIs) were seen [e.g., BMI (-0.07, 95% CI -0.51, 0.38)]. Lifetime estimates showed that PP increased costs (€520,102 versus €518,366, difference: €1736) and quality-adjusted life years (QALYs) (15.117 versus 15.106, difference: 0.011); the incremental cost-utility ratio (ICUR) was therefore high (€158,798 to gain one QALY). However, a 20% decrease in intervention costs would reduce the ICUR (€23,668 per QALY gained) below an unofficial gross domestic product (GDP)-based willingness-to-pay threshold (€47,817 per QALY gained).

CONCLUSION

On the basis of the willingness-to-pay threshold and the non-significant differences in short-term effectiveness, PP may not be cost-effective. However, scaling up the intervention would reduce the intervention costs. Future studies should be larger and/or longer to reduce uncertainty about short-term effectiveness.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov registry (NCT04590989).

Nutritional metabolomics: Recent developments and future needs

Metabolomics has rapidly been adopted as one of the key methods in nutrition research. This review focuses on the recent developments and updates in the field, including the analytical methodologies that encompass improved instrument sensitivity, sampling techniques and data integration (multiomics). Metabolomics has advanced the discovery and validation of dietary biomarkers and their implementation in health research. Metabolomics has come to play an important role in the understanding of the role of small molecules resulting from the diet-microbiota interactions when gut microbiota research has shifted towards improving the understanding of the activity and functionality of gut microbiota rather than composition alone. Currently, metabolomics plays an emerging role in precision nutrition and the recent developments therein are discussed.

Precision Nutrition to Improve Risk Factors of Obesity and Type 2 Diabetes

PURPOSE OF REVIEW

Existing dietary and lifestyle interventions and recommendations, to improve the risk factors of obesity and type 2 diabetes with the target to mitigate this double global epidemic, have produced inconsistent results due to interpersonal variabilities in response to these conventional approaches, and inaccuracies in dietary assessment methods. Precision nutrition, an emerging strategy, tailors an individual's key characteristics such as diet, phenotype, genotype, metabolic biomarkers, and gut microbiome for personalized dietary recommendations to optimize dietary response and health. Precision nutrition is suggested to be an alternative and potentially more effective strategy to improve dietary intake and prevention of obesity and chronic diseases. The purpose of this narrative review is to synthesize the current research and examine the state of the science regarding the effect of precision nutrition in improving the risk factors of obesity and type 2 diabetes.

RECENT FINDINGS

The results of the research review indicate to a large extent significant evidence supporting the effectiveness of precision nutrition in improving the risk factors of obesity and type 2 diabetes. Deeper insights and further rigorous research into the diet-phenotype-genotype and interactions of other components of precision nutrition may enable this innovative approach to be adapted in health care and public health to the special needs of individuals. Precision nutrition provides the strategy to make individualized dietary recommendations by integrating genetic, phenotypic, nutritional, lifestyle, medical, social, and other pertinent characteristics about individuals, as a means to address the challenges of generalized dietary recommendations. The evidence presented in this review shows that precision nutrition markedly improves risk factors of obesity and type 2 diabetes, particularly behavior change.

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

Background

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

Methods

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

Results

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

Conclusion

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

Clinical trial registration

isrctn.com, identifier ISRCTN15305840.

Precision gestational diabetes treatment: a systematic review and meta-analyses

BACKGROUND

Gestational Diabetes Mellitus (GDM) affects approximately 1 in 7 pregnancies globally. It is associated with short- and long-term risks for both mother and baby. Therefore, optimizing treatment to effectively treat the condition has wide-ranging beneficial effects. However, despite the known heterogeneity in GDM, treatment guidelines and approaches are generally standardized. We hypothesized that a precision medicine approach could be a tool for risk-stratification of women to streamline successful GDM management. With the relatively short timeframe available to treat GDM, commencing effective therapy earlier, with more rapid normalization of hyperglycaemia, could have benefits for both mother and fetus.

METHODS

We conducted two systematic reviews, to identify precision markers that may predict effective lifestyle and pharmacological interventions.

RESULTS

There was a paucity of studies examining precision lifestyle-based interventions for GDM highlighting the pressing need for further research in this area. We found a number of precision markers identified from routine clinical measures that may enable earlier identification of those requiring escalation of pharmacological therapy (to metformin, sulphonylureas or insulin). This included previous history of GDM, Body Mass Index and blood glucose concentrations at diagnosis.

CONCLUSIONS

Clinical measurements at diagnosis could potentially be used as precision markers in the treatment of GDM. Whether there are other sensitive markers that could be identified using more complex individual-level data, such as omics, and if these can feasibly be implemented in clinical practice remains unknown. These will be important to consider in future studies.

A phenotypic approach to obesity treatment

Obesity is a chronic disease that increases morbidity and mortality and adversely affects quality of life. The rapid rise of obesity has outpaced the development and deployment of effective therapeutic interventions, thereby creating a global health crisis. The presentation, complications, and response to obesity treatments vary, yet lifestyle modification, which is the foundational therapeutic intervention for obesity, is often "one size fits all." The concept of personalized medicine uses genetic and phenotypic information as a guide for disease prevention, diagnosis, and treatment and has been successfully applied in diseases such as cancer, but not in obesity. As we gain insight into the pathophysiologic mechanisms of obesity and its phenotypic expression, specific pathways can be targeted to yield a greater, more sustained therapeutic impact in an individual patient with obesity. A phenotype-based pharmacologic treatment approach utilizing objective measures to classify patients into predominant obesity mechanism groups resulted in greater weight loss (compared with a non-phenotype-based approach) in a recent study by Acosta and colleagues. In this review, we discuss the application of lifestyle modifications, behavior therapy and pharmacotherapy using the obesity phenotype-based approach as a framework.

Role of metabolomics in the delivery of precision nutrition

Precision nutrition aims to deliver personalised dietary advice to individuals based on their personal genetics, metabolism and dietary/environmental exposures. Recent advances have demonstrated promise for the use of omic technologies for furthering the field of precision nutrition. Metabolomics in particular is highly attractive as measurement of metabolites can capture information on food intake, levels of bioactive compounds and the impact of diets on endogenous metabolism. These aspects contain useful information for precision nutrition. Furthermore using metabolomic profiles to identify subgroups or metabotypes is attractive for the delivery of personalised dietary advice. Combining metabolomic derived metabolites with other parameters in prediction models is also an exciting avenue for understanding and predicting response to dietary interventions. Examples include but not limited to role of one carbon metabolism and associated co-factors in blood pressure response. Overall, while evidence exists for potential in this field there are also many unanswered questions. Addressing these and clearly demonstrating that precision nutrition approaches enable adherence to healthier diets and improvements in health will be key in the near future.

Supplementation with inulin-type fructans affects gut microbiota and attenuates some of the cardiometabolic benefits of a plant-based diet in individuals with overweight or obesity

Background

The gut microbiota has emerged as a potential therapeutic target to improve the management of obesity and its comorbidities.

Objective

We investigated the impact of a high fiber (∼38 g/d) plant-based diet, consumed ad libitum, with or without added inulin-type fructans (ITF), on the gut microbiota composition and cardiometabolic outcomes in subjects with obesity. We also tested if baseline Prevotella/Bacteroides (P/B) ratio predicts weight loss outcomes.

Methods

This is a secondary exploratory analysis from the PREVENTOMICS study, in which 100 subjects (82 completers) aged 18-65 years with body mass index 27-40 kg/m2 were randomized to 10 weeks of double-blinded treatment with a personalized or a generic plant-based diet. Changes from baseline to end-of-trial in gut microbiota composition (16S rRNA gene amplicon sequencing), body composition, cardiometabolic health and inflammatory markers were evaluated in the whole cohort (n = 82), and also compared in the subgroup of subjects who were supplemented with an additional 20 g/d ITF-prebiotics (n = 21) or their controls (n = 22).

Results

In response to the plant-based diet, all subjects lost weight (-3.2 [95% CI -3.9, -2.5] kg) and experienced significant improvements in body composition and cardiometabolic health indices. Addition of ITF to the plant-based diet reduced microbial diversity (Shannon index) and selectively increased Bifidobacterium and Faecalibacterium (q < 0.05). The change in the latter was significantly associated with higher values of insulin and HOMA-IR and lower HDL cholesterol. In addition, the LDL:HDL ratio and the concentrations of IL-10, MCP-1 and TNFα were significantly elevated in the ITF-subgroup. There was no relationship between baseline P/B ratio and changes in body weight (r = -0.07, p = 0.53).

Conclusion

A plant-based diet consumed ad libitum modestly decreases body weight and has multiple health benefits in individuals with obesity. Addition of ITF-prebiotics on top this naturally fiber-rich background selectively changes gut microbiota composition and attenuates some of the realized cardiometabolic benefits.

Clinical trial registration

[https://clinicaltrials.gov/ct2/show/NCT04590989], identifier [NCT04590989].

Metabotyping: a tool for identifying subgroups for tailored nutrition advice

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

Personalized Dietary Regimens for Inflammatory Bowel Disease: Current Knowledge and Future Perspectives

Inflammatory bowel diseases (IBD) are chronic and incurable conditions of the gastro-intestinal tract with an increasing incidence and prevalence worldwide. Common symptoms are abdominal pain, diarrhea, and weight loss. Despite recent advances in medical management, many patients fail to achieve clinical remission and healing of the mucosa of the bowel. The cause is thought to involve an inappropriate reaction of the immune system, the microbiome and the environment in genetically susceptible individuals, leading to chronic bowel inflammation. Evidence is emerging that diet is a key environmental factor that might influence disease onset and course, and therefore may become a therapeutic strategy to mitigate inflammation and symptoms. Since IBD is a heterogeneous disease on a clinical and a molecular level, personalizing dietary advice could be the crucial factor to achieve long-lasting changes in dietary behaviors that could not only improve nutritional status but also tackle gut inflammation and abdominal symptoms on an individual level. In this review, we first discuss different aspects of personalized nutrition, namely the level, focus, and scope of personalized dietary regimens. Then, we provide a framework for the different goals of nutritional therapy in IBD and current evidence for personalized dietary approaches. Lastly, we discuss the need for adequate trial designs, access to the right data types and the bioinformatic tools that are necessary to develop algorithms that will allow us to move from general "healthy eating" advice to truly personalized nutritional plans for the individual IBD patient.

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.