TMAO is elevated in individuals with cardiometabolic diseases, but it is unknown whether the metabolite is a biomarker of concern in healthy individuals. We conducted a cross-sectional study in metabolically healthy adults aged 18–66 years with BMI 18–44 kg/m2 and assessed the relationship between TMAO and diet, the fecal microbiome, and cardiometabolic risk factors. TMAO was measured in fasted plasma samples by liquid chromatography mass spectrometry. The fecal microbiome was assessed by 16S ribosomal RNA sequencing and recent food intake was captured by multiple ASA24 dietary recalls. Endothelial function was assessed via EndoPAT. Descriptive statistics were computed by fasting plasma TMAO tertiles and evaluated by ANOVA and Tukey’s post-hoc test. Multiple linear regression was used to assess the relationship between plasma TMAO and dietary food intake and metabolic health parameters. TMAO concentrations were not associated with average intake of animal protein foods, fruits, vegetables, dairy, or grains. TMAO was related to the fecal microbiome and the genera Butyribrio, Roseburia, Coprobaciullus, and Catenibacterium were enriched in individuals in the lowest versus the highest TMAO tertile. TMAO was positively associated with α-diversity and compositional differences were identified between groups. TMAO was not associated with classic cardiovascular risk factors in the healthy cohort. Similarly, endothelial function was not related to fasting TMAO, whereas the inflammatory marker TNF-α was significantly associated. Fasting plasma TMAO may not be a metabolite of concern in generally healthy adults unmedicated for chronic disease. Prospective studies in healthy individuals are necessary.

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Background Prior studies of adults with constipation or diarrhea suggest that dietary intake, physical activity, and stress may affect stool consistency. However, the influence of these factors is unresolved and has not been investigated in healthy adults. Objectives We assessed the relations of technician-scored stool consistency in healthy adults with self-reported diet, objectively monitored physical activity, and quantifiable markers of stress. Methods Stool consistency was scored by an independent technician using the Bristol Stool Form Scale (BSFS) to analyze samples provided by healthy adults, aged 18–65 y, BMI 18–44 kg/m2, in the USDA Nutritional Phenotyping Study (n = 364). A subset of participants (n = 109) were also asked to rate their sample using the BSFS. Dietary intake was assessed with two to three 24-h recalls completed at home and energy expenditure from physical activity was monitored using an accelerometer in the 7-d period preceding the stool collection. Stress was measured using the Wheaton Chronic Stress Inventory and allostatic load (AL). Statistical and machine learning analyses were conducted to determine which dietary, physiological, lifestyle, and stress factors differed by stool form. Results Technician-scored BSFS scores were significantly further (P = 0.003) from the central score (mean ± SEM distance: 1.41 ± 0.089) than the self-reported score (1.06 ± 0.086). Hard stool was associated with higher (P = 0.005) intake of saturated fat (13.8 ± 0.40 g/1000 kcal) than was normal stool (12.5 ± 0.30 g/1000 kcal). AL scores were lower for normal stool (2.49 ± 0.15) than for hard (3.07 ± 0.18) (P = 0.009) or soft stool (2.89 ± 0.18) (P = 0.049). Machine learning analyses revealed that various dietary components, physiological characteristics, and stress hormones predicted stool consistency. Conclusions Technician-scored stool consistency differed by dietary intake and stress hormones, but not by physical activity, in healthy adults. This trial was registered at clincialtrials.gov as NCT02367287.

Background Automated dietary assessment tools such as ASA24® are useful for collecting 24-hour recall data in large-scale studies. Modifications made during manual data cleaning may affect nutrient intakes. Objectives We evaluated the effects of modifications made during manual data cleaning on nutrients intakes of interest: energy, carbohydrate, total fat, protein, and fiber. Methods Differences in mean intake before and after data cleaning modifications for all recalls and average intakes per subject were analyzed by paired t-tests. Chi-squared test was used to determine whether unsupervised recalls had more open-ended text responses that required modification than supervised recalls. We characterized food types of text response modifications. Correlations between predictive energy requirements, measured total energy expenditure (TEE), and mean energy intake from raw and modified data were examined. Results After excluding 11 recalls with invalidating technical errors, 1499 valid recalls completed by 393 subjects were included in this analysis. We found significant differences before and after modifications for energy, carbohydrate, total fat, and protein intakes for all recalls (p < 0.05). Limiting to modified recalls, there were significant differences for all nutrients of interest, including fiber (p < 0.02). There was not a significantly greater proportion of text responses requiring modification for home compared to supervised recalls (p = 0.271). Predicted energy requirements correlated highly with TEE. There was no significant difference in correlation of mean energy intake with TEE for modified compared to raw data. Mean intake for individual subjects was significantly different for energy, protein, and fat intakes following cleaning modifications (p < 0.001). Conclusions Manual modifications can change mean nutrient intakes for an entire cohort and individuals. However, modifications did not significantly affect correlation of energy intake with predictive requirements and measured expenditure. Investigators can consider their research question and nutrients of interest when deciding to make cleaning modifications.