María Gómez-Martín

Background: Interest in the intake of safe live microbes (LM) present in foods has increased recently. However, evidence on how estimated live dietary microbe intake relates to gut microbiota composition in Australian adults remains limited. Objectives: The primary study aim was to estimate live dietary microbe intakes and explore associations with gut microbiota composition in fecal samples from Australian adults. The secondary aim was to evaluate whether distinct participant clusters, grouped by broad dietary patterns and selected phenotypic characteristics, were associated with differences in metabolic and/or microbiota composition profiles. Methods: Estimated intake of foods containing LM was determined using previously published classification methods, with foods categorized as of low (10⁷ CFU/g) LM content. Linear regression estimated the associations between estimated live dietary microbe intake and microbiota taxa. K-means cluster analysis was used to generate participant clusters. Results: In 34 healthy adults, biologically plausible associations between estimated Med/Hi LM food group intake and specific microbial taxa were identified, with 9/23 associations remaining significant after false discovery rate correction (P < .05). Cluster analysis showed no differences in overall gut microbial community structure. Nominal species-level differences did not remain significant after multiple comparison correction. Conclusions: This study provides preliminary evidence that estimated intake of foods containing live dietary microbes is associated with the relative abundance of specific gut microbial taxa in Australian adults. Separating the effects of live microbes from other nutrients within the same food and dietary matrix remains challenging, making it difficult to attribute health outcomes to microbes alone. These findings highlight challenges in quantifying microbial intake from foods. Development of a live microbe-specific dietary assessment tool, along with larger longitudinal studies, are needed to clarify relationships with gut microbiome and health.

Background/Objectives: Skin yellowness measured using reflectance spectroscopy can be used as a proxy for ‘skin carotenoid’ concentration and as a valid estimate of fruit and vegetable intake. However, the association between different ethnic backgrounds, age, sex and self-reported skin colour on skin yellowness is yet to be explored. Methods: This exploratory cross-sectional study used a convenience sample to assess the relationship between skin yellowness (b*), measured using skin reflectance spectroscopy, and fruit and vegetable intakes across different age groups, sex, self-reported Caucasian and Asian ethnicities and self-reported skin colour. Recruited adults were aged ≥18 years who self-reported as Caucasian or of Asian ethnicity (N = 137). Data were collected on skin L*, a*, b* colours using skin reflectance spectroscopy, height, weight, body composition, waist circumference, demographic variables (e.g., age, sex, self-reported skin colour), and dietary intake measured using the online Australian Eating Survey^® (AES^®). Multiple linear regression models were used to investigate factors associated with the dependent variable, skin yellowness (b*). Results: Participants were on average 38.6 (SD = 17.7) years of age, self-reported as female (61%), Caucasian (77%) and met the recommended fruit intake of two serves/day (mean = 2.1, SD = 1.5) but not the recommended vegetable intake of five to six serves per day (mean = 4.4, SD = 3.1). There were significant associations between skin yellowness (b*) and several independent variables, including combined fruit and vegetable intake, total carotenoid intake, body fat percentage and self-reported skin colour. Conclusions: This study adds additional support for using skin yellowness as a proxy for fruit and vegetable intake when adjusting for self-reported skin colour, age and sex.

Diet is a modifiable determinant of gut microbiome composition, yet the impact of contrasting whole-dietary patterns on microbial metabolic capacity and coordinated host metabolic signatures remains incompletely characterized. In a randomized crossover feeding trial, 34 Australian adults were provided with a Healthy Australian Diet (HAD), aligned with national dietary guidelines, and a Typical Australian Diet (TAD), reflecting average population intake for two weeks each, separated by a two-week washout. Fecal microbiome composition and function were assessed using shotgun metagenomics, plasma and urine metabolites by untargeted metabolomics, with cardiometabolic markers including blood pressure, plasma lipids, and glucose quantified. HAD was associated with reduced taxonomic and functional alpha diversity relative to baseline, with no change following TAD. Species-level responses were modest, 105 functional pathways differed between diets, with 99 increasing following HAD, predominantly related to amino acid and nucleotide biosynthesis and vitamin/cofactor metabolism. Multi-omic integration using DIABLO achieved strong discrimination of dietary responses (held-out accuracy 91.7%; permutation p = 0.005). In total, 77 individual omic feature-cardiometabolic outcome associations survived FDR correction (q < 0.05), spanning microbial gene functions, plasma metabolites, and urinary metabolites linked to cholesterol, blood pressure, and triglyceride responses. These exploratory findings suggest that integrated microbiome-metabolome profiling may capture inter-individual variation in dietary cardiometabolic responses, though replication in larger, independent, robustly designed studies is needed before translational personalized nutrition strategies can be assessed.

Background: Hypertension is a major global public health issue. The mechanisms through which diet influences blood pressure (BP) remain to be fully elucidated. Nutritional metabolomics offers an objective method for examining diet-health outcomes, including the contribution of intermediate molecules and metabolic byproducts or metabolites to BP regulation. To date, no review has investigated the relationship between diet, metabolites and BP regulation. Objective: This systematic review aim was to synthesise findings of human dietary intervention studies on BP, including feeding studies providing all foods and meals, as well as those that provided supplements. Methods: Six databases were systematically searched (Scopus, Medline, Embase, CINAHL, PsycINFO, and Cochrane) for intervention studies examining the relationship between dietary metabolites and BP regulation. The Academy of Nutrition and Dietetics Quality Criteria were used to assess the risk of bias. Results: Twelve articles (11 unique studies) met the inclusion criteria, reporting 11 significant associations between metabolites and BP, while one study found no significant associations. More than 100 metabolites were associated with BP, 40 associated with SBP, 29 with DBP, 31 with both, and 2 did not differentiate between SBP or DBP. Only two metabolites, proline-betaine and N-acetylneuraminate, had significant relationships with BP measurement in more than one study. Conclusions: This review identified a shortlist of potential metabolite indicators of response to dietary interventions for BP regulation. Findings highlight nutritional metabolomics as a potential contributor to understanding diet-induced changes in BP and CVD risk reduction. However, variability in reported metabolites and limited replication across studies may affect specificity and limit generalizability. Further research is needed to better understand this relationship.

Background/Objectives: Thus far, no studies have examined the relationship between fruit and vegetable (F and V) intake, urinary metabolite quantities, and weight change. Therefore, the aim of the current study was to explore changes in urinary metabolomic profiles during and after a 10-week weight loss intervention where participants were prescribed a high F and V diet (7 servings daily). Methods: Adults with overweight and obesity (n = 34) received medical nutrition therapy counselling to increase their F and V intakes to national targets (7 servings a day). Data collection included weight, dietary intake, and urine samples at baseline at week 2 and week 10. Urinary metabolite profiles were quantified using ¹H NMR spectroscopy. Machine learning statistical approaches were employed to identify novel urine-based metabolite biomarkers associated with high F and V diet patterns at weeks 2 and 10. Metabolic changes appearing in urine in response to diet were quantified using Metabolite Set Enrichment Analysis (MSEA). Results: Energy intake was significantly lower (p = 0.02) at week 10 compared with baseline. Total F and V intake was significantly higher at week 2 and week 10 (p < 0.05). In total, 123 urinary metabolites were quantified. At week 10, 21 metabolites showed significant changes relative to baseline. Of these, 11 metabolites also significantly changed at week 2. These overlapping metabolites were acetic acid, dimethylamine, choline, fumaric acid, glutamic acid, L-tyrosine, histidine, succinic acid, uracil, histamine, and 2-hydroxyglutarate. Ridge Classifier and Linear Discriminant Analysis provided best prediction accuracy values of 0.96 when metabolite level of baseline was compared to week 10. Conclusions: Urinary metabolites quantified represent potential candidate biomarkers of high F and V intake, associated with a reduction in energy intake. Further studies are needed to validate these findings in larger population studies.