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Dietary fiber intake may reduce the inflammation associated with obesity and metabolic disease. Our cross-sectional analysis revealed that dietary fiber intake and fecal short-chain fatty acids are inversely associated with lipopolysaccharide-binding protein, a marker of systemic inflammation. In addition, plasma interleukin-6 and C-reactive protein were positively related to markers of adiposity.

Most previous work has focused on a single-nutrient, bottom-up approach when examining the influence of diet on neurocognition. Thus, the impact of dietary patterns on cognitive health is under-investigated.We aimed to investigate the relationship between different diet indices (i.e., Mediterranean, Dietary Approaches to Stop Hypertension [DASH], Healthy Eating Index-2015 [HEI-2015], and Mediterranean-DASH Intervention for Neurodegenerative Delay [MIND]) and attentional inhibition and neuroelectric function.Cross-sectional analysis of adults aged 34.1 ± 6.0 years (N = 207, 116 female) with a body mass index (BMI) of 18.5 to 40 kg/m2 that completed the Dietary History Questionnaire II (DHQII) food frequency questionnaire (Past Month and Year with Serving Sizes) to assess adherence to different diet quality indices. Attentional inhibition was assessed using a modified Eriksen Flanker task during which event-related potentials (ERPs) were recorded. The amplitude and latency of the P3/P300 ERP were used to index attentional resource allocation and information processing speed, respectively.P3 peak latency was inversely associated with greater adherence to the MIND dietary pattern during incongruent Flanker trials (∆R2 = 0.02, β = -0.14, p = 0.043) but not during congruent trials. Adherence to the Mediterranean, DASH, and HEI-2015 patterns was not associated with P3 latency (p 0.05). No associations were observed between the diet indices and attentional inhibition at the behavioral level (i.e., accuracy or reaction time) or P3 amplitude (all p's 0.05).Greater adherence to the MIND diet was selectively associated with faster information processing speed among middle-aged adults with healthy to obese BMI. Further, the influence of the MIND diet for faster information processing speed may be particularly beneficial when cognitive control demands are increased. Future intervention trials testing the effects of consuming a MIND diet on cognitive function are warranted to help inform dietary recommendations for healthy cognitive aging.

BackgroundDietary intake provides nutrients for humans and their gastrointestinal microorganisms, as some dietary constituents bypass human digestion. These undigested components affect the composition and function of the microorganisms present. Metagenomic analyses allow researchers to study functional capacity. As dietary components affect the composition and function of the gastrointestinal microbiome, there is potential for developing objective biomarkers of food intake using metagenomic data.ObjectiveWe aimed to utilize a computationally intensive, multivariate, machine learning approach to identify fecal Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO) categories as biomarkers that accurately predict food intake.DesignData were aggregated from five controlled feeding studies in adults that studied the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. DNA from pre- and post-intervention fecal samples underwent shotgun genomic sequencing. After pre-processing, sequences were aligned (DIAMONDv2.0.11.149) and functionally annotated (MEGANv6.12.2). After count normalization, the log of the fold change ratio for resulting features between pre- and post-intervention of the treatment group against its corresponding control was utilized to conduct differential KO abundance analysis. Differentially abundant KOs were used to train machine learning models examining potential biomarkers in both single-food and multi-food models.ResultsWe identified differentially abundant KOs for almond (n = 54), broccoli (n = 2,474), and walnut (n = 732) (q < 0.20). Using the differentially abundant KOs, prediction accuracies were 80%, 87%, and 86% prediction accuracies for the almond, broccoli, and walnut groups, respectively using a random forest model to classify food intake. The mixed-food random forest achieved 81% prediction accuracy.ConclusionsOur findings reveal promise in utilizing fecal metagenomics to objectively complement self-reported measures of food intake. Future research on various foods and dietary patterns will expand these exploratory analyses for eventual use in feeding study compliance and clinical settings.

Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolic responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in our gastrointestinal tract, is highly personalized and plays a key role in our metabolic responses to foods and nutrients. Accurately predicting metabolic responses to dietary interventions based on individuals’ gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a new method McMLP (Metabolic response predictor usingcoupledMultilayerPerceptrons) to fill in this gap. We provide clear evidence that McMLP outperforms existing methods on both synthetic data generated by the microbial consumer-resource model and real data obtained from six dietary intervention studies. Furthermore, we perform sensitivity analysis of McMLP to infer the tripartite food-microbe-metabolite interactions, which are then validated using the ground-truth (or literature evidence) for synthetic (or real) data, respectively. The presented tool has the potential to inform the design of microbiota-based personalized dietary strategies to achieve precision nutrition.

Human intestinal enzymes do not hydrolyze nondigestible carbohydrates (NDCs), and thus, they are not digested and absorbed in the small intestine. Instead, NDCs are partially to completely fermented by the intestinal microbiota. Select NDCs are associated with health benefits such as laxation and lowering of blood cholesterol and glucose. NDCs provide functional attributes to processed foods, including sugar or fat replacers, thickening agents, and bulking agents. Additionally, NDCs are incorporated into processed foods to increase their fiber content. Although consumption of NDCs can benefit health and contribute functional characteristics to foods, they can cause gastrointestinal symptoms, such as flatulence and bloating. As gastrointestinal symptoms negatively affect consumer well-being and their acceptance of foods containing NDC ingredients, it is crucial to consider tolerance when designing food products and testing their physiological health benefits in clinical trials. This perspective provides recommendations for the approach to assess gastrointestinal tolerance to NDCs, with a focus on study design, population criteria, intervention, comparator, and outcome. Special issues related to studies in children and implications for stakeholders are also discussed. It is recommended that the evaluation of gastrointestinal tolerance to NDCs be conducted in randomized, blinded, controlled crossover studies using standard gastrointestinal questionnaires, with attention to study participant background diets, health status, lifestyle, and medications. ispartof: ADVANCES IN NUTRITION vol:13 issue:6 pages:2084-2097 ispartof: location:United States status: published

The fecal metabolome is impacted by diet and includes metabolites generated by human and microbial metabolism. Advances in -omics technologies and analytic approaches have allowed researchers to identify metabolites and better utilize large datasets to generate usable information. One promising aspect of these advancements is the ability to determine objective biomarkers of food intake.We aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake.Data were aggregated from five controlled feeding studies in adults that tested the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. Fecal samples underwent gas chromatography-mass spectrometry (GC-MS) metabolomic analysis; 307 metabolites were detected. Changes in metabolite relative concentration (treatment post-intervention minus pre-intervention) were utilized in random forest models to 1) examine the relationship between food consumption and fecal metabolome changes and 2) rank the fecal metabolites by their predictive power (i.e., feature importance score).Using the change in relative concentration of fecal metabolites, six single-food random forest models for almond, avocado, broccoli, walnuts, whole-grain barley, and whole-grain oats revealed prediction accuracies between 47% and 89%. When comparing foods to one another, almond intake was differentiated from walnut intake with 91% classification accuracy.Our findings reveal promise in utilizing fecal metabolites as objective complements to certain self-reported food intake estimates. Future research on other foods at different doses and dietary patterns is needed to identify biomarkers that can be applied in feeding study compliance and clinical settings.