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 A variety of modifiable and non-modifiable factors such as ethnicity, age, and diet have been shown to influence bone health. Previous studies are usually limited to analyses focused on the association of a few a priori variables or on a specific subset of the population. Objective Dietary, physiological, and lifestyle data were used to identify directly modifiable and non-modifiable variables predictive of bone mineral content (BMC) and bone mineral density (BMD) in healthy U.S. men and women using machine learning models. Methods Ridge, lasso, elastic net, and random forest models were used to predict whole-body, femoral neck, and spine BMC and BMD in healthy U.S. men and women ages 18–66 y, BMI 18–44 kg/m2 (n = 313) using non-modifiable anthropometric, physiological, and demographic variables, directly modifiable lifestyle (physical activity, tobacco use) and dietary (via food frequency questionnaire) variables, and variables approximating directly-modifiable behavior (circulating 25-hydroxycholecalciferol and stool pH). Results Machine learning models using non-modifiable variables explained more variation in BMC and BMD (highest R2 = 0.75) compared to when using only directly modifiable variables (highest R2 = 0.11). Machine learning models had better performance compared to multivariate linear regression, which had lower predictive value (highest R2 = 0.06) when using directly modifiable variables only. BMI, body fat %, height, and menstruation history were predictors of BMC and BMD. For directly modifiable features, betaine, cholesterol, hydroxyproline, menaquinone-4, dihydrophylloquinone, eggs, cheese, cured meat, refined grains, fruit juice, and alcohol consumption were predictors of BMC and BMD. Low stool pH, a proxy for fermentable fiber intake, was also predictive of higher BMC and BMD. Conclusion Modifiable factors, such as diet, explained less variation in the data compared to non-modifiable factors, such as age, sex, and ethnicity in healthy U.S. men and women. Low stool pH predicted higher BMC and BMD. Clinical trial: registered on clinicaltrials.gov (Identifier: NCT02367287)
Objectives A variety of modifiable and non-modifiable factors such as ethnicity, age, and diet have been shown to influence bone health. Previous studies are usually limited to analyses focused on the association of a few a priori variables or on a specific subset of the population. The objective of this study was to use dietary, physiological, and lifestyle data to identify directly modifiable and non-modifiable variables predictive of bone mineral content (BMC) and bone mineral density (BMD) in healthy US men and women using machine learning models. Methods Ridge, lasso, elastic net, and random forest models were used to predict whole-body, femoral neck, and spine BMC and BMD in healthy US adults (n = 313) using non-modifiable anthropometric, physiological, and demographic variables, directly modifiable lifestyle (physical activity, tobacco use) and dietary (nutrient or food groups intake via food frequency questionnaire) variables, and variables approximating directly modifiable behavior (circulating vitamin D and stool pH). Model feature importances were used to identify variables useful for predicting BMC and BMD. Results Machine learning models using non-modifiable variables explained more variation in BMC and BMD (highest R2 = 0.750) compared to when using only directly modifiable variables (highest R2 = 0.107). Machine learning models had better performance compared to multivariate linear regression, which had lower predictive value (highest R2 = 0.063) when using directly modifiable variables only. BMI, body fat %, height, and menstruation history were predictors of BMC and BMD. For the directly modifiable features, betaine, cholesterol, hydroxyproline, menaquinone-4, dihydrophylloquinone, eggs, cheese, cured meat, refined grains, fruit juice, and alcohol consumption were predictors of BMC and BMD. Low stool pH, a proxy for fermentable fiber intake, was also predictive of higher BMC and BMD. Conclusions Machine learning models can be used to identify previously unforeseen variables that may contribute to bone health. Modifiable factors explained less variation in the data compared to other features. Low stool pH, which has been shown to be associated with fermentable fiber intake, short chain fatty acid production, and enhanced calcium absorption, was associated with higher BMC and BMD in a healthy US population. Funding Sources USDA-ARS
Dairy products are a good source of essential nutrients and past reviews have shown associations of dairy consumption with decreased systemic inflammation. Links between dairy intake and gastrointestinal (GI) inflammation are under-investigated. Therefore, we examined associations between reported dairy intake and markers of GI inflammation in healthy adults in a cross-sectional observational study, hypothesizing a negative association with yogurt intake, suggesting a protective effect, and no associations with total dairy, fluid milk, and cheese intake. Participants completed 24-h dietary recalls and a food frequency questionnaire (FFQ) to assess recent and habitual intake, respectively. Those who also provided a stool sample (n = 295), and plasma sample (n = 348) were included in analysis. Inflammation markers from stool, including calprotectin, neopterin, and myeloperoxidase, were measured along with LPS-binding protein (LBP) from plasma. Regression models tested associations between dairy intake variables and inflammation markers with covariates: age, sex, and body mass index (BMI). As yogurt is episodically consumed, we examined differences in inflammation levels between consumers (>0 cup equivalents/day reported in recalls) and non-consumers. We found no significant associations between dairy intake and markers of GI inflammation. In this cohort of healthy adults, dairy intake was not associated with GI inflammation.
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