A weight-loss model based on baseline microbiota and genetic scores for selection of dietary treatments in overweight and obese population

A menu for microbes: unraveling appetite regulation and weight dynamics through the microbiota-brain connection across the lifespan

Appetite, as the internal drive for food intake, is often dysregulated in a broad spectrum of conditions associated with over- and under-nutrition across the lifespan. Appetite regulation is a complex, integrative process comprising psychological and behavioral events, peripheral and metabolic inputs, and central neurotransmitter and metabolic interactions. The microbiota-gut-brain axis has emerged as a critical mediator of multiple physiological processes, including energy metabolism, brain function, and behavior. Therefore, the role of the microbiota-gut-brain axis in appetite and obesity is receiving increased attention. Omics approaches such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics in appetite and weight regulation offer new opportunities for featuring obesity phenotypes. Furthermore, gut-microbiota-targeted approaches such as pre-, pro-, post-, and synbiotic, personalized nutrition, and fecal microbiota transplantation are novel avenues for precision treatments. The aim of this narrative review is 1) to provide an overview of the role of the microbiota-gut-brain axis in appetite regulation across the lifespan and 2) to discuss the potential of omics and gut microbiota-targeted approaches to deepen understanding of appetite regulation and obesity.

The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations

Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges. Given its overlap with leading comorbidities such as heart disease, innovative solutions are necessary to improve risk prediction and management strategies. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools in healthcare, offering novel approaches to chronic disease prevention. This narrative review explores the role of AI/ML in obesity risk prediction and management, with a special focus on childhood obesity. We begin by examining the multifactorial nature of obesity, including genetic, behavioral, and environmental factors, and the limitations of traditional approaches to predict and treat morbidity associated obesity. Next, we analyze AI/ML techniques commonly used to predict obesity risk, particularly in minimizing childhood obesity risk. We shift to the application of AI/ML in obesity management, comparing perspectives from healthcare providers versus patients. From the provider's perspective, AI/ML tools offer real-time data from electronic medical records, wearables, and health apps to stratify patient risk, customize treatment plans, and enhance clinical decision making. From the patient's perspective, AI/ML-driven interventions offer personalized coaching and improve long-term engagement in health management. Finally, we address key limitations and challenges, such as the role of social determinants of health, in embracing the role of AI/ML in obesity management, while offering our recommendations based on our literature review.

Effect of a Multifactorial Weight Loss Intervention on HDL Cholesterol Efflux Capacity and Immunosenescence: A Randomized Controlled Trial

OBJECTIVE

Life expectancy and obesity prevalence are increasing worldwide, leading to an increase in the prevalence of cardiovascular disease. High-density lipoprotein (HDL) functionality and immunosenescence play key roles in cardiovascular disease, longevity, and quality of aging. Both molecular hallmarks of aging are impacted by obesity and metabolic syndrome and can be modulated by lifestyle. We aimed to evaluate the effect of a lifestyle intervention focused on an energy-reduced Mediterranean diet (erMedDiet), physical activity (PA), and behavioral support on HDL cholesterol efflux capacity (CEC) and immunosenescence.

METHOD

CEC and immunosenescent T cells were determined in 60 participants from the control group (CG) and 56 from the intervention group (IG) of the PREDIMED-Plus trial at baseline and after 1 and 3 years of follow-up. PREDIMED-Plus is a randomized, controlled, parallel-group trial with an IG of erMedDiet, PA promotion, and behavioral support for weight loss and a CG of usual primary care advice. The sample included 116 volunteers from the PREDIMED-Plus-IMDEA subsample of the PREDIMED-Plus trial. Men aged 55 to 75 years and women aged 60 to 75 years with a body mass index between 27 and 40 kg/m2 and metabolic syndrome were included.

RESULTS

Participants within the IG had significantly improved CEC (2.42% and 10.69% after 1 and 3 years of follow-up) and a decreased in senescent T cell profile (-3.32% ± 12.54% and -6.74% ± 11.2%, p < 0.001, after 1 and 3 years of follow-up). Baseline obesity status impacted the response to the intervention.

CONCLUSIONS

A weight loss intervention program with erMedDiet and PA ameliorated senescence markers.

Comparative assessment of phenotypic markers in patients with chronic inflammation: Differences on Bifidobacterium concerning liver status

BACKGROUND

The relationship between systemic lupus erythematosus (SLE) and low-grade metabolic inflammation (MI) with the microbiota is crucial for understanding the pathogenesis of these diseases and developing effective therapeutic interventions. In this context, it has been observed that the gut microbiota plays a key role in the immune regulation and inflammation contributing to the exacerbation through inflammatory mediators. This research aimed to describe similarities/differences in anthropometric, biochemical, inflammatory, and hepatic markers as well as to examine the putative role of gut microbiota concerning two inflammatory conditions: SLE and MI.

METHODS

Data were obtained from a cohort comprising adults with SLE and MI. Faecal samples were determined by 16S technique. Statistical analyses compared anthropometric and clinical variables, and LEfSe and MetagenomeSeq were used for metagenomic data. An interaction analysis was fitted to investigate associations of microbiota with fatty liver index (FLI) depending on the inflammatory condition.

RESULTS

Participants with low-grade MI showed worse values in anthropometry and biochemicals compared with patients with SLE. The liver profile of patients with MI was unhealthier, while no relevant differences were found in most of the inflammatory markers between groups. LEfSe analysis revealed an overrepresentation of Bifidobacteriaceae family in SLE group. An interactive association between gut Bifidobacterium abundance and type of disease was identified for FLI values, suggesting an effect modification of the gut microbiota concerning liver markers depending on the inflammatory condition.

CONCLUSION

This study found phenotypical and microbial similarities and disparities between these two inflammatory conditions, evidenced in clinical and hepatic markers, and showed the interactive interplay between gut Bifidobacterium and liver health (measured by FLI) that occur in a different manner depending on the type of inflammatory disease. These results underscore the importance of personalized approaches and individual microbiota in the screening of different inflammatory situations, considering unique hepatic and microbiota profiles.

Beneficial Effects of a Moderately High-Protein Diet on Telomere Length in Subjects with Overweight or Obesity

BACKGROUND AND AIM

Telomere length (TL) is a key biomarker of cellular aging, with shorter telomeres associated with age-related diseases. Lifestyle interventions mitigating telomere shortening are essential for preventing such conditions. This study aimed to examine the effects of two weight loss dietary strategies, based on a moderately high-protein (MHP) diet and a low-fat (LF) diet on TL in individuals with overweight or obesity.

METHODS AND RESULTS

A total of 164 participants, aged 18-65 years from the OBEKIT trial received the MHP (n = 83) or the LF diet (n = 81) for 4 months and had TL data for analyses. TL was measured at baseline and after 4 months of the intervention using monochrome multiplex quantitative polymerase chain reaction (MMqPCR). Both groups experienced significant improvements in anthropometric and biochemical parameters after the dietary intervention (p < 0.001). The MHP group showed an increase in TL (+0.16 ± 0.13) compared to the LF group (-0.05 ± 0.13) in multiple-adjusted models (p = 0.016). An interaction was observed between the sex and dietary group, where women in the MHP group had increased TL (+0.23 ± 0.16) after 4 months compared to women in the LF group (-0.13 ± 0.15; p = 0.001); no differences between dietary groups were found in men. This increase in TL for women was associated with an increase in protein intake (p = 0.006), measured through dietary questionnaires.

CONCLUSION

This study shows that a MHP diet may have a protective effect on TL during weight loss, particularly in women, potentially contributing to healthier aging. These results highlight the importance of considering macronutrient composition in dietary interventions aimed at preserving TL.

Body mass index and fat influences the role of Bifidobacterium genus in lupus patients concerning fibrinogen levels

Introduction

Metabolic disorders and autoimmune diseases elicit distinct yet interconnected manifestations of inflammation, which may be boosted by an excess of body adiposity. The purpose of this investigation was to analyze anthropometric, biochemical, and inflammatory/coagulation variables concerning patients diagnosed with systemic lupus erythematosus (SLE) exploiting low-grade metabolic inflammation (MI), as reference.

Methods

A population stratification by body mass index (BMI), allowed to assess the impact of adiposity on the putative role of gut microbiota composition on coagulation markers. A total of 127 participants with MI and SLE were categorized into two main groups based on their BMI, following WHO criteria: a low BMI group (<30 kg/m2) and a high BMI group (≥30 kg/m2). Each group included recorded data on demographics, comorbidities, and key clinical markers. Anthropometric and body composition variables, clinical features, and inflammatory/coagulation markers were measured while fecal 16S rRNA sequencing was examined at the genus Bifidobacterium. Regression models were fitted to evaluate the relationship between gut microbiota, inflammatory/coagulation markers, and body weight in these types of diseases.

Results

The study revealed worse clinical outcomes in anthropometric, body composition, and clinical markers in low-grade MI conditions as compared to SLE. However, inflammatory and coagulation markers such as C-reactive protein (CRP) and fibrinogen were significantly more elevated in patients with SLE, which was exacerbated by high BMI/ body fat as compared to the other screened groups. An interaction analysis revealed that fibrinogen levels showed different trends when Bifidobacterium was increased depending on BMI/adiposity, which evidenced an effect modification by this microorganism in patients with SLE.

Discussion

These findings underline that gut microbiota composition, particularly the presence of Bifidobacterium, may play a crucial role in modulating inflammation and coagulation processes in patients with SLE and high fat. These insights highlight the potential of targeting gut microbiota as a therapeutic strategy to mitigate inflammation and improve clinical outcomes in SLE patients.

Revealing the Potential Impacts of Nutraceuticals Formulated with Freeze-Dried Jabuticaba Peel and Limosilactobacillus fermentum Strains Candidates for Probiotic Use on Human Intestinal Microbiota

This study evaluated the impacts of novel nutraceuticals formulated with freeze-dried jabuticaba peel (FJP) and three potentially probiotic Limosilactobacillus fermentum strains on the abundance of bacterial groups forming the human intestinal microbiota, metabolite production, and antioxidant capacity during in vitro colonic fermentation. The nutraceuticals had high viable counts of L. fermentum after freeze-drying (≥ 9.57 ± 0.09 log CFU/g). The nutraceuticals increased the abundance of Lactobacillus ssp./Enterococcus spp. (2.46-3.94%), Bifidobacterium spp. (2.28-3.02%), and Ruminococcus albus/R. flavefaciens (0.63-4.03%), while decreasing the abundance of Bacteroides spp./Prevotella spp. (3.91-2.02%), Clostridium histolyticum (1.69-0.40%), and Eubacterium rectale/C. coccoides (3.32-1.08%), which were linked to positive prebiotic indices (> 1.75). The nutraceuticals reduced the pH and increased the sugar consumption, short-chain fatty acid production, phenolic acid content, and antioxidant capacity, besides altering the metabolic profile during colonic fermentation. The combination of FJP and probiotic L. fermentum is a promising strategy to produce nutraceuticals targeting intestinal microbiota.

Potential Applications of Blautia wexlerae in the Regulation of Host Metabolism

Blautia wexlerae (B. wexlerae) is a strong candidate with the potential to become a next-generation probiotics (NGPs) and has recently been shown for the first time to exhibit potential in modulating host metabolic levels and alleviating metabolic diseases. However, the factors affecting the change in abundance of B. wexlerae and the pattern of its abundance change in the associated indications remain to be further investigated. Here, we summarize information from published studies related to B. wexlerae; analyze the effects of food source factors such as prebiotics, probiotics, low protein foods, polyphenols, vitamins, and other factors on the abundance of B. wexlerae; and explore the patterns of changes in the abundance of B. wexlerae in metabolic diseases, neurological diseases, and other diseases. At the same time, the development potential of B. wexlerae was evaluated in the direction of functional foods and special medical foods.

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.

The Ambiguous Correlation of Blautia with Obesity: A Systematic Review

Obesity is a complex and multifactorial disease with global epidemic proportions, posing significant health and economic challenges. Whilst diet and lifestyle are well-established contributors to the pathogenesis, the gut microbiota's role in obesity development is increasingly recognized. Blautia, as one of the major intestinal bacteria of the Firmicutes phylum, is reported with both potential probiotic properties and causal factors for obesity in different studies, making its role controversial. To summarize the current understanding of the Blautia-obesity correlation and to evaluate the evidence from animal and clinical studies, we used "Blautia" AND "obesity" as keywords searching through PubMed and SpringerLink databases for research articles. After removing duplicates and inadequate articles using the exclusion criteria, we observed different results between studies supporting and opposing the beneficial role of Blautia in obesity at the genus level. Additionally, several studies showed probiotic effectiveness at the species level for Blautia coccoides, B. wexlerae, B. hansenii, B. producta, and B. luti. Therefore, the current evidence does not demonstrate Blautia's direct involvement as a pathogenic microbe in obesity development or progression, which informs future research and therapeutic strategies targeting the gut Blautia in obesity management.

The past, present, and future of tumour deposits in colorectal cancer: Advancing staging for improved prognosis and treatment decision-making

Tumour deposits (TDs) significantly impact colorectal cancer (CRC) prognosis. Integrating TDs into the TNM staging system can enhance individualized disease management. Keeping abreast of evolving TDs research is pivotal for clinical advancement. We comprehensively reviewed both recent and popular literature to grasp the field's essence. Subsequently, a data retrieval sourced articles on TDs in CRC for bibliometric analysis, spanning from 1 January 1935 to 30 April 2023. Bibliometrix software facilitated paper analysis and visualization. Bibliometric indicators, the trends and hotspots were determined. A total of 2147 articles were successfully retrieved. Brown G emerged as the most productive author, and the USA as the most prolific country. Central South University and Memorial Sloan Kettering Cancer Center led productivity. Bradford's law categorized 48 journals into zone 1. Keywords co-occurrence analysis identified three main clusters: the application of TDs in TNM staging, the pathogenesis of TDs, and the assessment of TDs. The trend topic analysis highlighted research focused on refining TDs incorporation into tumour staging. TDs wield enduring medical significance, shaping ongoing research. Much literature focused on confirming TD's prognostic value and optimizing TNM integration. Additionally, it is worth highlighting that TD's enigmatic pathogenesis demands research priority, as it holds the potential to unveil concealed knowledge regarding their development.

Fermenting Acerola (Malpighia emarginata D.C.) and Guava (Psidium guayaba L.) Fruit Processing Co-Products with Probiotic Lactobacilli to Produce Novel Potentially Synbiotic Circular Ingredients

This study evaluated the effects of acerola and guava fruit processing co-products fermented with probiotic Lactobacillus acidophilus LA-05 and Lacticaseibacillus paracasei L-10 on the abundance of different intestinal bacterial groups and microbial metabolic activity during 48 h of in vitro fecal fermentation. Digested fermented fruit co-products increased the relative abundance of beneficial bacterial groups while overall decreasing or maintaining the relative abundance of non-beneficial bacterial groups, suggesting selective stimulatory effects on beneficial bacterial intestinal populations. The fermented co-products stimulated microbial metabolic activity due to decreased pH, sugar consumption, short-chain fatty acid production, phenolic compound and metabolic profile alteration, and high antioxidant capacity during fecal fermentation. Acerola and guava co-products have high nutritional value and bioactive compounds whose fermentation with probiotics improves their potential functionalities. The results show that fermented fruit co-products could induce beneficial changes in the relative abundance of several bacterial groups as well as in the metabolic activity of the human intestinal microbiota. These results highlight their potential as novel and circular candidates for use as synbiotic ingredients.

Characterizing Factors Associated with Excess Body Weight: A Descriptive Study Using Principal Component Analysis in a Population with Overweight and Obesity

Obesity is a worldwide epidemic, making it crucial to understand how it can be effectively prevented/treated. Considering that obesity is a multifactorial condition, this article carried out a baseline cross-sectional study of the variables involved in the disorder. Eighty-four subjects with overweight/obesity were recruited. Dietary baseline information was obtained by analysing three 24 h recalls. Resting metabolic rate was measured using indirect calorimetry, physical activity was measured through accelerometry, cardiometabolic parameters were determined in blood samples and body composition via anthropometry and bioimpedance. A univariant and multivariate exploratory approach was carried out using principal component analysis (PCA). Large inter-individual variability was observed in dietetic, biochemical, and physical activity measurements (coefficient of variation ≥ 30%), but body composition was more uniform. Volunteers had an unbalanced diet and low levels of physical activity. PCA reduced the 26 analysed variables to 4 factors, accounting for 65.4% of the total data variance. The main factor was the "dietetic factor", responsible for 24.0% of the total variance and mainly related to energy intake, lipids, and saturated fatty acids. The second was the "cardiometabolic factor" (explaining 16.8% of the variability), the third was the "adiposity factor" (15.2%), and the last was the "serum cholesterol factor" (9.4%).

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

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

A genomics perspective of personalized prevention and management of obesity

This review discusses the landscape of personalized prevention and management of obesity from a nutrigenetics perspective. Focusing on macronutrient tailoring, we discuss the impact of genetic variation on responses to carbohydrate, lipid, protein, and fiber consumption. Our bioinformatic analysis of genomic variants guiding macronutrient intake revealed enrichment of pathways associated with circadian rhythm, melatonin metabolism, cholesterol and lipoprotein remodeling and PPAR signaling as potential targets of macronutrients for the management of obesity in relevant genetic backgrounds. Notably, our data-based in silico predictions suggest the potential of repurposing the SYK inhibitor fostamatinib for obesity treatment in relevant genetic profiles. In addition to dietary considerations, we address genetic variations guiding lifestyle changes in weight management, including exercise and chrononutrition. Finally, we emphasize the need for a refined understanding and expanded research into the complex genetic landscape underlying obesity and its management.

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.

Metabolic Disorders, the Microbiome as an Endocrine Organ, and Their Relations with Obesity: A Literature Review

The etiology of metabolic disorders, such as obesity, has been predominantly associated with the gut microbiota, which is acknowledged as an endocrine organ that plays a crucial role in modulating energy homeostasis and host immune responses. The presence of dysbiosis has the potential to impact the functioning of the intestinal barrier and the gut-associated lymphoid tissues by allowing the transit of bacterial structural components, such as lipopolysaccharides. This, in turn, may trigger inflammatory pathways and potentially lead to the onset of insulin resistance. Moreover, intestinal dysbiosis has the potential to modify the production of gastrointestinal peptides that are linked to the feeling of fullness, hence potentially leading to an increase in food consumption. In this literature review, we discuss current developments, such as the impact of the microbiota on lipid metabolism as well as the processes by which its changes led to the development of metabolic disorders. Several methods have been developed that could be used to modify the gut microbiota and undo metabolic abnormalities.

METHODS

After researching different databases, we examined the PubMed collection of articles and conducted a literature review.

RESULTS

After applying our exclusion and inclusion criteria, the initial search yielded 1345 articles. We further used various filters to narrow down our titles analysis and, to be specific to our study, selected the final ten studies, the results of which are included in the Results section.

CONCLUSIONS

Through gut barrier integrity, insulin resistance, and other influencing factors, the gut microbiota impacts the host's metabolism and obesity. Although the area of the gut microbiota and its relationship to obesity is still in its initial stages of research, it offers great promise for developing new therapeutic targets that may help prevent and cure obesity by restoring the gut microbiota to a healthy condition.

Heterogeneity in the response to n-3 polyunsaturated fatty acids

PURPOSE OF REVIEW

A central goal in the study of long chain n-3 polyunsaturated fatty acids (PUFA) is to translate findings from the basic sciences to the population level to improve human health and prevent chronic diseases. A tenet of this vision is to think in terms of precision medicine and nutrition, that is, stratification of individuals into differing groups that will have different needs across the lifespan for n-3 PUFAs. Therefore, there is a critical need to identify the sources of heterogeneity in the human population in the dietary response to n-3 PUFA intervention.

RECENT FINDINGS

We briefly review key sources of heterogeneity in the response to intake of long chain n-3 PUFAs. These include background diet, host genome, composition of the gut microbiome, and sex. We also discuss the need to integrate data from newer rodent models (e.g. population-based approaches), multi -omics, and analyses of big data using machine learning and data-driven cluster analyses.

SUMMARY

Accounting for vast heterogeneity in the human population, particularly with the use of big data integrated with preclinical evidence, will drive the next generation of precision nutrition studies and randomized clinical trials with long-chain n-3 PUFAs.

Soluble, Diferuloylated Corn Bran Glucuronoarabinoxylans Modulate the Human Gut Microbiota In Vitro

Corn bran is exceptionally rich in substituted glucuronoarabinoxylan polysaccharides, which are monoferuloylated and cross-linked by diferulic acid moieties. Here, we assessed the potential prebiotic activity of three enzymatically solubilized corn bran glucuronoarabinoxylans: medium feruloylated (FGAX-M), laccase cross-linked FGAX-M (FGAX-H), and alkali-treated FGAX-M devoid of feruloyl substitutions (FGAX-B). We examined the influence of these soluble FGAX samples on the gut microbiome composition and functionality during in vitro simulated colon fermentations, determined by 16S rRNA gene amplicon sequencing and assessment of short-chain fatty acid (SCFAs) production. All FGAX samples induced changes in the relative composition of the microbiota and the SCFA levels after 24 h of in vitro fermentation. The changes induced by FGAX-M and FGAX-H tended to be more profound and more similar to the changes induced by inulin than changes conferred by FGAX-B. The microbiota changes induced by FGAX-M and FGAX-H correlated with an increase in the relative abundance of Anaerostipes and with increased butyric acid production, while the changes induced by the FGAX-B sample were less compelling. The results imply that solubilized, substituted diferuloylated corn bran glucuronoarabinoxylans may be potential prebiotic candidates and that both single feruloylations and diferuloyl cross-links influence the prebiotic potential of these arabinoxylan compounds.

Holistic Integration of Omics Tools for Precision Nutrition in Health and Disease

The combination of multiple omics approaches has emerged as an innovative holistic scope to provide a more comprehensive view of the molecular and physiological events underlying human diseases (including obesity, dyslipidemias, fatty liver, insulin resistance, and inflammation), as well as for elucidating unique and specific metabolic phenotypes. These omics technologies include genomics (polymorphisms and other structural genetic variants), epigenomics (DNA methylation, histone modifications, long non-coding RNA, telomere length), metagenomics (gut microbiota composition, enterotypes), transcriptomics (RNA expression patterns), proteomics (protein quantities), and metabolomics (metabolite profiles), as well as interactions with dietary/nutritional factors. Although more evidence is still necessary, it is expected that the incorporation of integrative omics could be useful not only for risk prediction and early diagnosis but also for guiding tailored dietary treatments and prognosis schemes. Some challenges include ethical and regulatory issues, the lack of robust and reproducible results due to methodological aspects, the high cost of omics methodologies, and high-dimensional data analyses and interpretation. In this review, we provide examples of system biology studies using multi-omics methodologies to unravel novel insights into the mechanisms and pathways connecting the genotype to clinically relevant traits and therapy outcomes for precision nutrition applications in health and disease.

The microbiota composition drives personalized nutrition: Gut microbes as predictive biomarkers for the success of weight loss diets

The investigation of the human gut microbiome during recent years has permitted us to understand its relevance for human health at a systemic level, making it possible to establish different functional axes (e.g., the gut-brain, gut-liver, and gut-lung axes), which support the organ-like status conferred to this microecological component of our body. The human gut microbiota is extremely variable but modifiable via diet, a fact that allows targeting of microbes through defined dietary strategies to uncover cost-effective therapies to minimize the burden of non-communicable diseases such as pandemic obesity and overweight and its metabolic comorbidities. Nevertheless, randomly controlled dietary interventions regularly exhibit low to moderate degrees of success in weight control, making their implementation difficult in clinical practice. Here, we review the predictive value of the baseline gut microbiota configurations to anticipate the success of dietary interventions aimed at weight loss, mostly based on caloric restriction regimes and oral fiber supplementation. This emergent research concept fits into precision medicine by considering different diet patterns and adopting the best one, based on the individual microbiota composition, to reach significant adiposity reduction and improve metabolic status. We review the results from this fresh perspective of investigation, taking into account studies released very recently. We also discuss some future outlooks in the field and potential pitfalls to overcome with the aim of gaining knowledge in the field and achieving breakthroughs in personalized nutrition.