Fecal Metagenomics to Identify Biomarkers of Food Intake in Healthy Adults: Findings from Randomized, Controlled, Nutrition Trials.

BACKGROUND: Undigested components of the human diet affect the composition and function of the microorganisms present in the gastrointestinal tract. Techniques like metagenomic analyses allow researchers to study functional capacity, thus revealing the potential of using metagenomic data for developing objective biomarkers of food intake. OBJECTIVES: As a continuation of our previous work using 16S and metabolomic datasets, we aimed to utilize a computationally intensive, multivariate, machine-learning approach to identify fecal KEGG (Kyoto encyclopedia of genes and genomes) Orthology (KO) categories as biomarkers that accurately classify food intake. METHODS: Data were aggregated from 5 controlled feeding studies that studied the individual impact of almonds, avocados, broccoli, walnuts, barley, and oats on the adult gastrointestinal microbiota. Deoxyribonucleic acid from preintervention and postintervention fecal samples underwent shotgun genomic sequencing. After preprocessing, sequences were aligned and functionally annotated with Double Index AlignMent Of Next-generation sequencing Data v2.0.11.149 and MEtaGenome ANalyzer v6.12.2, respectively. After the count normalization, the log of the fold change ratio for resulting KOs between pre- and postintervention of the treatment group against its corresponding control was utilized to conduct differential abundance analysis. Differentially abundant KOs were used to train machine-learning models examining potential biomarkers in both single-food and multi-food models. RESULTS: We identified differentially abundant KOs in the almond (n = 54), broccoli (n = 2474), and walnut (n = 732) groups (q < 0.20), which demonstrated classification accuracies of 80%, 87%, and 86% for the almond, broccoli, and walnut groups using a random forest model to classify food intake into each food group's respective treatment and control arms, respectively. The mixed-food random forest achieved 81% accuracy. CONCLUSIONS: Our 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.

Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults.

BACKGROUND: The fecal metabolome is affected 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 data sets to generate usable information. One promising aspect of these advancements is the ability to determine objective biomarkers of food intake. OBJECTIVES: We aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake. METHODS: Data were aggregated from 5 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 GC-MS metabolomic analysis; 344 metabolites were detected in preintervention samples, whereas 307 metabolites were detected postintervention. After removing metabolites that were only detected in either pre- or postintervention and those undetectable in ≥80% of samples in all study groups, changes in 96 metabolites relative concentrations (treatment postintervention minus preintervention) were utilized in random forest models to 1) examine the relation between food consumption and fecal metabolome changes and 2) rank the fecal metabolites by their predictive power (i.e., feature importance score). RESULTS: Using the change in relative concentration of 96 fecal metabolites, 6 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 with one another, almond intake was differentiated from walnut intake with 91% classification accuracy. CONCLUSIONS: 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.

Measurement Error Affecting Web- and Paper-Based Dietary Assessment Instruments: Insights From the Multi-Cohort Eating and Activity Study for Understanding Reporting Error.

Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45-86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research.

Preanalytical Sample Handling Conditions and Their Effects on the Human Serum Metabolome in Epidemiologic Studies.

Many epidemiologic studies use metabolomics for discovery-based research. The degree to which sample handling may influence findings, however, is poorly understood. In 2016, serum samples from 13 volunteers from the US Department of Agriculture's Beltsville Human Nutrition Research Center were subjected to different clotting (30 minutes/120 minutes) and refrigeration (0 minutes/24 hours) conditions, as well as different numbers (0/1/4) and temperatures (ice/refrigerator/room temperature) of thaws. The median absolute percent difference (APD) between metabolite levels and correlations between levels across conditions were estimated for 628 metabolites. The potential for handling artifacts to induce false-positive associations was estimated using variable hypothetical scenarios in which 1%-100% of case samples had different handling than control samples. All handling conditions influenced metabolite levels. Across metabolites, the median APD when extending clotting time was 9.08%. When increasing the number of thaws from 0 to 4, the median APD was 10.05% for ice and 5.54% for room temperature. Metabolite levels were correlated highly across conditions (all r's ≥ 0.84), indicating that relative ranks were preserved. However, if handling varied even modestly by case status, our hypotheticals showed that results can be biased and can result in false-positive findings. Sample handling affects levels of metabolites, and special care should be taken to minimize effects. Shorter room-temperature thaws should be preferred over longer ice thaws, and handling should be meticulously matched by case status.

Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults.

BACKGROUND: Diet affects the human gastrointestinal microbiota. Blood and urine samples have been used to determine nutritional biomarkers. However, there is a dearth of knowledge on the utility of fecal biomarkers, including microbes, as biomarkers of food intake. OBJECTIVES: This study aimed to identify a compact set of fecal microbial biomarkers of food intake with high predictive accuracy. METHODS: Data were aggregated from 5 controlled feeding studies in metabolically healthy adults (n = 285; 21-75 y; BMI 19-59 kg/m2; 340 data observations) that studied the impact of specific foods (almonds, avocados, broccoli, walnuts, and whole-grain barley and whole-grain oats) on the human gastrointestinal microbiota. Fecal DNA was sequenced using 16S ribosomal RNA gene sequencing. Marginal screening was performed on all species-level taxa to examine the differences between the 6 foods and their respective controls. The top 20 species were selected and pooled together to predict study food consumption using a random forest model and out-of-bag estimation. The number of taxa was further decreased based on variable importance scores to determine the most compact, yet accurate feature set. RESULTS: Using the change in relative abundance of the 22 taxa remaining after feature selection, the overall model classification accuracy of all 6 foods was 70%. Collapsing barley and oats into 1 grains category increased the model accuracy to 77% with 23 unique taxa. Overall model accuracy was 85% using 15 unique taxa when classifying almonds (76% accurate), avocados (88% accurate), walnuts (72% accurate), and whole grains (96% accurate). Additional statistical validation was conducted to confirm that the model was predictive of specific food intake and not the studies themselves. CONCLUSIONS: Food consumption by healthy adults can be predicted using fecal bacteria as biomarkers. The fecal microbiota may provide useful fidelity measures to ascertain nutrition study compliance.

Walnut Consumption Alters the Gastrointestinal Microbiota, Microbially Derived Secondary Bile Acids, and Health Markers in Healthy Adults: A Randomized Controlled Trial.

Background: Epidemiologic data suggest that diets rich in nuts have beneficial health effects, including reducing total and cause-specific mortality from cancer and heart disease. Although there is accumulating preclinical evidence that walnuts beneficially affect the gastrointestinal microbiota and gut and metabolic health, these relations have not been investigated in humans. Objective: We aimed to assess the impact of walnut consumption on the human gastrointestinal microbiota and metabolic markers of health. Methods: A controlled-feeding, randomized crossover study was undertaken in healthy men and women [n = 18; mean age = 53.1 y; body mass index (kg/m2): 28.8]. Study participants received isocaloric diets containing 0 or 42 g walnuts/d for two 3-wk periods, with a 1-wk washout between diet periods. Fecal and blood samples were collected at baseline and at the end of each period to assess secondary outcomes of the study, including effects of walnut consumption on fecal microbiota and bile acids and metabolic markers of health. Results: Compared with after the control period, walnut consumption resulted in a 49-160% higher relative abundance of Faecalibacterium, Clostridium, Dialister, and Roseburia and 16-38% lower relative abundances of Ruminococcus, Dorea, Oscillospira, and Bifidobacterium (P < 0.05). Fecal secondary bile acids, deoxycholic acid and lithocholic acid, were 25% and 45% lower, respectively, after the walnut treatment compared with the control treatment (P < 0.05). Serum LDL cholesterol and the noncholesterol sterol campesterol concentrations were 7% and 6% lower, respectively, after walnut consumption compared with after the control treatment (P < 0.01). Conclusion: Walnut consumption affected the composition and function of the human gastrointestinal microbiota, increasing the relative abundances of Firmicutes species in butyrate-producing Clostridium clusters XIVa and IV, including Faecalibacterium and Roseburia, and reducing microbially derived, proinflammatory secondary bile acids and LDL cholesterol. These results suggest that the gastrointestinal microbiota may contribute to the underlying mechanisms of the beneficial health effects of walnut consumption. This trial was registered at www.clinicaltrials.gov as NCT01832909.

Evaluation of the 24-Hour Recall as a Reference Instrument for Calibrating Other Self-Report Instruments in Nutritional Cohort Studies: Evidence From the Validation Studies Pooling Project.

Calibrating dietary self-report instruments is recommended as a way to adjust for measurement error when estimating diet-disease associations. Because biomarkers available for calibration are limited, most investigators use self-reports (e.g., 24-hour recalls (24HRs)) as the reference instrument. We evaluated the performance of 24HRs as reference instruments for calibrating food frequency questionnaires (FFQs), using data from the Validation Studies Pooling Project, comprising 5 large validation studies using recovery biomarkers. Using 24HRs as reference instruments, we estimated attenuation factors, correlations with truth, and calibration equations for FFQ-reported intakes of energy and for protein, potassium, and sodium and their densities, and we compared them with values derived using biomarkers. Based on 24HRs, FFQ attenuation factors were substantially overestimated for energy and sodium intakes, less for protein and potassium, and minimally for nutrient densities. FFQ correlations with truth, based on 24HRs, were substantially overestimated for all dietary components. Calibration equations did not capture dependencies on body mass index. We also compared predicted bias in estimated relative risks adjusted using 24HRs as reference instruments with bias when making no adjustment. In disease models with energy and 1 or more nutrient intakes, predicted bias in estimated nutrient relative risks was reduced on average, but bias in the energy risk coefficient was unchanged.

Walnuts Consumed by Healthy Adults Provide Less Available Energy than Predicted by the Atwater Factors.

BACKGROUND: Previous studies have shown that the metabolizable energy (ME) content (energy available to the body) of certain nuts is less than predicted by the Atwater factors. However, very few nuts have been investigated to date, and no information is available regarding the ME of walnuts. OBJECTIVE: A study was conducted to determine the ME of walnuts when consumed as part of a typical American diet. METHODS: Healthy adults (n = 18; mean age = 53.1 y; body mass index = 28.8 kg/m(2)) participated in a randomized crossover study with 2 treatment periods (3 wk each). The study was a fully controlled dietary feeding intervention in which the same base diet was consumed during each treatment period; the base diet was unsupplemented during one feeding period and supplemented with 42 g walnuts/d during the other feeding period. Base diet foods were reduced in equal proportions during the walnut period to achieve isocaloric food intake during the 2 periods. After a 9 d diet acclimation period, subjects collected all urine and feces for ∼1 wk (as marked by a Brilliant Blue fecal collection marker) for analysis of energy content. Administered diets, walnuts, and fecal and urine samples were subjected to bomb calorimetry, and the resulting data were used to calculate the ME of the walnuts. RESULTS: One 28-g serving of walnuts contained 146 kcal (5.22 kcal/g), 39 kcal/serving less than the calculated value of 185 kcal/serving (6.61 kcal/g). The ME of the walnuts was 21% less than that predicted by the Atwater factors (P < 0.0001). CONCLUSION: Consistent with other tree nuts, Atwater factors overestimate the metabolizable energy value of walnuts. These results could help explain the observations that consumers of nuts do not gain excessive weight and could improve the accuracy of food labeling. This trial was registered at clinicaltrials.gov as NCT01832909.

Application of a New Statistical Model for Measurement Error to the Evaluation of Dietary Self-report Instruments.

Most statistical methods that adjust analyses for dietary measurement error treat an individual's usual intake as a fixed quantity. However, usual intake, if defined as average intake over a few months, varies over time. We describe a model that accounts for such variation and for the proximity of biomarker measurements to self-reports within the framework of a meta-analysis, and apply it to the analysis of data on energy, protein, potassium, and sodium from a set of five large validation studies of dietary self-report instruments using recovery biomarkers as reference instruments. We show that this time-varying usual intake model fits the data better than the fixed usual intake assumption. Using this model, we estimated attenuation factors and correlations with true longer-term usual intake for single and multiple 24-hour dietary recalls (24HRs) and food frequency questionnaires (FFQs) and compared them with those obtained under the "fixed" method. Compared with the fixed method, the estimates using the time-varying model showed slightly larger values of the attenuation factor and correlation coefficient for FFQs and smaller values for 24HRs. In some cases, the difference between the fixed method estimate and the new estimate for multiple 24HRs was substantial. With the new method, while four 24HRs had higher estimated correlations with truth than a single FFQ for absolute intakes of protein, potassium, and sodium, for densities the correlations were approximately equal. Accounting for the time element in dietary validation is potentially important, and points toward the need for longer-term validation studies.

Cranberry juice consumption lowers markers of cardiometabolic risk, including blood pressure and circulating C-reactive protein, triglyceride, and glucose concentrations in adults.

BACKGROUND: Cardiometabolic risk is the risk of cardiovascular disease (CVD), diabetes, or stroke, which are leading causes of mortality and morbidity worldwide. OBJECTIVE: The objective of this study was to determine the potential of low-calorie cranberry juice (LCCJ) to lower cardiometabolic risk. METHODS: A double-blind, placebo-controlled, parallel-arm study was conducted with controlled diets. Thirty women and 26 men (mean baseline characteristics: 50 y; weight, 79 kg; body mass index, 28 kg/m(2)) completed an 8-wk intervention with LCCJ or a flavor/color/energy-matched placebo beverage. Twice daily volunteers consumed 240 mL of LCCJ or the placebo beverage, containing 173 or 62 mg of phenolic compounds and 6.5 or 7.5 g of total sugar per 240-mL serving, respectively. RESULTS: Fasting serum triglycerides (TGs) were lower after consuming LCCJ and demonstrated a treatment × baseline interaction such that the participants with higher baseline TG concentrations were more likely to experience a larger treatment effect (1.15 ± 0.04 mmol/L vs. 1.25 ± 0.04 mmol/L, respectively; P = 0.027). Serum C-reactive protein (CRP) was lower for individuals consuming LCCJ than for individuals consuming the placebo beverage [ln transformed values of 0.522 ± 0.115 ln(mg/L) vs. 0.997 ± 0.120 ln(mg/L), P = 0.0054, respectively, and equivalent to 1.69 mg/L vs. 2.71 mg/L back-transformed]. LCCJ lowered diastolic blood pressure (BP) compared with the placebo beverage (69.2 ± 0.8 mm Hg for LCCJ vs. 71.6 ± 0.8 mm Hg for placebo; P = 0.048). Fasting plasma glucose was lower (P = 0.03) in the LCCJ group (5.32 ± 0.03 mmol/L) than in the placebo group (5.42 ± 0.03 mmol/L), and LCCJ had a beneficial effect on homeostasis model assessment of insulin resistance for participants with high baseline values (P = 0.035). CONCLUSION: LCCJ can improve several risk factors of CVD in adults, including circulating TGs, CRP, and glucose, insulin resistance, and diastolic BP. This trial was registered at clinicaltrials.gov as NCT01295684.

Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake.

We pooled data from 5 large validation studies of dietary self-report instruments that used recovery biomarkers as references to clarify the measurement properties of food frequency questionnaires (FFQs) and 24-hour recalls. The studies were conducted in widely differing US adult populations from 1999 to 2009. We report on total energy, protein, and protein density intakes. Results were similar across sexes, but there was heterogeneity across studies. Using a FFQ, the average correlation coefficients for reported versus true intakes for energy, protein, and protein density were 0.21, 0.29, and 0.41, respectively. Using a single 24-hour recall, the coefficients were 0.26, 0.40, and 0.36, respectively, for the same nutrients and rose to 0.31, 0.49, and 0.46 when three 24-hour recalls were averaged. The average rate of under-reporting of energy intake was 28% with a FFQ and 15% with a single 24-hour recall, but the percentages were lower for protein. Personal characteristics related to under-reporting were body mass index, educational level, and age. Calibration equations for true intake that included personal characteristics provided improved prediction. This project establishes that FFQs have stronger correlations with truth for protein density than for absolute protein intake, that the use of multiple 24-hour recalls substantially increases the correlations when compared with a single 24-hour recall, and that body mass index strongly predicts under-reporting of energy and protein intakes.

Dietary intake of advanced glycation end products did not affect endothelial function and inflammation in healthy adults in a randomized controlled trial.

When food is heated to high temperatures, the characteristic "browning" generates advanced glycation end products (AGEs). AGEs are associated with an increased risk of cardiovascular disease, diabetes, and other adverse outcomes. Whether dietary AGEs are absorbed and are harmful to human health remains highly controversial. The objective of this study was to compare the effects of a diet high or low in AGEs on endothelial function, circulating AGEs, inflammatory mediators, and circulating receptors for AGEs in healthy adults. A randomized, parallel-arm, controlled dietary intervention was conducted for 6 wk with 24 healthy adults, aged 50-69 y, that compared isocaloric, food-equivalent diets that were prepared at either high or mild temperatures. Peripheral arterial tonometry, serum and urine carboxymethyl-lysine (CML), inflammatory mediators (interleukin-6, C-reactive protein, vascular adhesion molecule-1, and tumor necrosis factor-α receptors I and II), soluble receptor for AGEs, and endogenous secretory receptor for AGEs were measured at baseline and after 6 wk of dietary intervention. In the low-AGE diet group, the following changed from baseline to 6 wk (mean ± SE): serum CML from 763 ± 24 to 679 ± 29 ng/mL (P = 0.03) and urine CML from 1.37 ± 1.47 to 0.77 ± 2.01 µg/mL creatinine (P = 0.02). There were no significant changes in serum and urinary CML concentrations from baseline to follow-up in the high-AGE diet group. A high- or low-AGE diet had no significant impact on peripheral arterial tonometry or any inflammatory mediators after 6 wk of dietary intervention. In healthy middle-aged to older adults, consumption of a diet high or low in AGEs for 6 wk had no impact on endothelial function and inflammatory mediators, 2 precursors of cardiovascular disease.

The metabolizable energy of dietary resistant maltodextrin is variable and alters fecal microbiota composition in adult men.

Resistant maltodextrin (RM) is a novel soluble, nonviscous dietary fiber. Its metabolizable energy (ME) and net energy (NE) values derived from nutrient balance studies are unknown, as is the effect of RM on fecal microbiota. A randomized, placebo-controlled, double-blind crossover study was conducted (n = 14 men) to determine the ME and NE of RM and its influence on fecal excretion of macronutrients and microbiota. Participants were assigned to a sequence consisting of 3 treatment periods [24 d each: 0 g/d RM + 50 g/d maltodextrin and 2 amounts of dietary RM (25 g/d RM + 25 g of maltodextrin/d and 50 g/d RM + 0 g/d maltodextrin)] and were provided all the foods they were to consume to maintain their body weight. After an adaptation period, excreta were collected during a 7-d period. After the collection period, 24-h energy expenditure was measured. Fluorescence in situ hybridization, quantitative polymerase chain reaction, and 454 titanium technology-based 16S rRNA sequencing were used to analyze fecal microbiota composition. Fecal amounts of energy (544, 662, 737 kJ/d), nitrogen (1.5, 1.8, 2.1 g/d), RM (0.3, 0.6, 1.2 g/d), and total carbohydrate (11.1, 14.2, 16.2 g/d) increased with increasing dose (0, 25, 50 g) of RM (P < 0.0001). Fat excretion did not differ among treatments. The ME value of RM was 8.2 and 10.4 kJ/g, and the NE value of RM was -8.2 and 2.0 kJ/g for the 25 and 50 g/d RM doses, respectively. Both doses of RM increased fecal wet weight (118, 148, 161 g/d; P < 0.0001) and fecal dry weight (26.5, 32.0, 35.8 g/d; P < 0.0001) compared with the maltodextrin placebo. Total counts of fecal bacteria increased by 12% for the 25 g/d RM dose (P = 0.17) and 18% for the 50 g/d RM dose (P = 0.019). RM intake was associated with statistically significant increases (P < 0.001) in various operational taxonomic units matching closest to ruminococcus, eubacterium, lachnospiraceae, bacteroides, holdemania, and faecalibacterium, implicating RM in their growth in the gut. Our findings provide empirical data important for food labeling regulations related to the energy value of RM and suggest that RM increases fecal bulk by enhancing the excretion of nitrogen and carbohydrate and the growth of specific microbial populations.

Effects of almond and pistachio consumption on gut microbiota composition in a randomised cross-over human feeding study.

The modification of microbiota composition to a 'beneficial' one is a promising approach for improving intestinal as well as overall health. Natural fibres and phytochemicals that reach the proximal colon, such as those present in various nuts, provide substrates for the maintenance of healthy and diverse microbiota. The effects of increased consumption of specific nuts, which are rich in fibre as well as various phytonutrients, on human gut microbiota composition have not been investigated to date. The objective of the present study was to determine the effects of almond and pistachio consumption on human gut microbiota composition. We characterised microbiota in faecal samples collected from volunteers in two separate randomised, controlled, cross-over feeding studies (n 18 for the almond feeding study and n 16 for the pistachio feeding study) with 0, 1·5 or 3 servings/d of the respective nuts for 18 d. Gut microbiota composition was analysed using a 16S rRNA-based approach for bacteria and an internal transcribed spacer region sequencing approach for fungi. The 16S rRNA sequence analysis of 528 028 sequence reads, retained after removing low-quality and short-length reads, revealed various operational taxonomic units that appeared to be affected by nut consumption. The effect of pistachio consumption on gut microbiota composition was much stronger than that of almond consumption and included an increase in the number of potentially beneficial butyrate-producing bacteria. Although the numbers of bifidobacteria were not affected by the consumption of either nut, pistachio consumption appeared to decrease the number of lactic acid bacteria (P< 0·05). Increasing the consumption of almonds or pistachios appears to be an effective means of modifying gut microbiota composition.

Effects of Preanalytical Sample Collection and Handling on Comprehensive Metabolite Measurements in Human Urine Biospecimens.

Epidemiology studies evaluate associations between the metabolome and disease risk. Urine is a common biospecimen used for such studies due to its wide availability and non-invasive collection. Evaluating the robustness of urinary metabolomic profiles under varying preanalytical conditions is thus of interest. Here we evaluate the impact of sample handling conditions on urine metabolome profiles relative to the gold standard condition (no preservative, no refrigeration storage, single freeze thaw). Conditions tested included the use of borate or chlorhexidine preservatives, various storage and freeze/thaw cycles. We demonstrate that sample handling conditions impact metabolite levels, with borate showing the largest impact with 125 of 1,048 altered metabolites (adjusted P < 0.05). When simulating a case-control study with expected inconsistencies in sample handling, we predicted the occurrence of false positive altered metabolites to be low (< 11). Predicted false positives increased substantially (³63) when cases were simulated to undergo alternate handling. Finally, we demonstrate that sample handling impacts on the urinary metabolome were markedly smaller than those in serum. While changes in urine metabolites incurred by sample handling are generally small, we recommend implementing consistent handling conditions and evaluating robustness of metabolite measurements for those showing significant associations with disease outcomes.

Single nucleotide polymorphisms in CETP, SLC46A1, SLC19A1, CD36, BCMO1, APOA5, and ABCA1 are significant predictors of plasma HDL in healthy adults.

BACKGROUND: In a marker-trait association study we estimated the statistical significance of 65 single nucleotide polymorphisms (SNP) in 23 candidate genes on HDL levels of two independent Caucasian populations. Each population consisted of men and women and their HDL levels were adjusted for gender and body weight. We used a linear regression model. Selected genes corresponded to folate metabolism, vitamins B-12, A, and E, and cholesterol pathways or lipid metabolism. METHODS: Extracted DNA from both the Sacramento and Beltsville populations was analyzed using an allele discrimination assay with a MALDI-TOF mass spectrometry platform. The adjusted phenotype, y, was HDL levels adjusted for gender and body weight only statistical analyses were performed using the genotype association and regression modules from the SNP Variation Suite v7. RESULTS: Statistically significant SNP (where P values were adjusted for false discovery rate) included: CETP (rs7499892 and rs5882); SLC46A1 (rs37514694; rs739439); SLC19A1 (rs3788199); CD36 (rs3211956); BCMO1 (rs6564851), APOA5 (rs662799), and ABCA1 (rs4149267). Many prior association trends of the SNP with HDL were replicated in our cross-validation study. Significantly, the association of SNP in folate transporters (SLC46A1 rs37514694 and rs739439; SLC19A1 rs3788199) with HDL was identified in our study. CONCLUSIONS: Given recent literature on the role of niacin in the biogenesis of HDL, focus on status and metabolism of B-vitamins and metabolites of eccentric cleavage of ß-carotene with lipid metabolism is exciting for future study.

Nuts, Energy Balance and Body Weight.

Over several decades, the health benefits of consuming nuts have been investigated, resulting in a large body of evidence that nuts can reduce the risk of chronic diseases. The consumption of nuts, being a higher-fat plant food, is restricted by some in order to minimize weight gain. In this review, we discuss several factors related to energy intake from nuts, including food matrix and its impact on digestibility, and the role of nuts in regulating appetite. We review the data from randomized controlled trials and observational studies conducted to examine the relationship between nut intake and body weight or body mass index. Consistently, the evidence from RCTs and observational cohorts indicates that higher nut consumption does not cause greater weight gain; rather, nuts may be beneficial for weight control and prevention of long-term weight gain. Multiple mechanisms likely contribute to these findings, including aspects of nut composition which affect nutrient and energy availability as well as satiety signaling.

Effect of Nuts on Gastrointestinal Health.

Nuts are high nutrient-dense foods containing healthy lipids, dietary fiber, and bioactive phytochemicals, including vitamins and minerals. Although the beneficial effect of nut consumption on different chronic diseases has been well documented, especially in relation to their cardiometabolic benefits, less scientific evidence is available on their possible beneficial effects on gastrointestinal health. In this narrative review, we summarize the most important findings and new research perspectives in relation to the importance of nut consumption on gastrointestinal health. The integrity of the cell wall structure, cell size and particle size after mastication are known to play a crucial role in energy, nutrient and bioactive release from nuts during digestion, therefore affecting bioaccessibility. Other mechanisms, such as cell wall composition, thickness and porosity, as well as stability of the membranes surrounding the oil bodies within the cell, are also important for energy extraction. As the undigested nutrients and phytochemicals are delivered to the colon, effects on gut microbiota composition are predicted. Although the overall effect of nut consumption on microbial alpha- and beta-diversity has been inconsistent, some scientific evidence suggests an increase in fecal butyrate after almond consumption, and a beneficial role of walnuts on the prevention of ulcerative colitis and protection against the development of gastric mucosal lesions.

Mixed Berry Juice and Cellulose Fiber Have Differential Effects on Peripheral Blood Mononuclear Cell Respiration in Overweight Adults.

Berries and other anthocyanin-rich foods have demonstrated anti-obesity effects in rodents and humans. However, the bioactive components of these foods and their mechanisms of action are unclear. We conducted an intervention study with overweight and obese adults to isolate the effects of different berry components on bioenergetics. Subjects consumed whole mixed berries (high anthocyanin, high fiber), pressed berry juice (high anthocyanin, low fiber), berry-flavored gelatin (low anthocyanin, low fiber), or fiber-enriched gelatin (low anthocyanin, high fiber) for one week prior to a meal challenge with the same treatment food as the pre-feed period. Peripheral blood mononuclear cells were collected 2 h after the meal challenge, and cellular respiration was assessed via high-resolution respirometry. The high-anthocyanin, low-fiber treatment (berry juice) and the low-anthocyanin, high-fiber treatment (fiber-enriched gelatin) had opposite effects on cellular respiration. In the fasted state, berry juice resulted in the highest oxygen-consumption rate (OCR), while fiber-enriched gelatin resulted in the highest OCR in the fed state. Differences were observed in multiple respiration states (basal, state 3, state 4, uncoupled), with the greatest differences being between the pressed berry juice and the fiber-enriched gelatin. Different components of berries, specifically anthocyanins/flavonoids and fiber, appear to have differential effects on cellular respiration.

Gender and single nucleotide polymorphisms in MTHFR, BHMT, SPTLC1, CRBP2, CETP, and SCARB1 are significant predictors of plasma homocysteine normalized by RBC folate in healthy adults.

Using linear regression models, we studied the main and 2-way interaction effects of the predictor variables gender, age, BMI, and 64 folate/vitamin B-12/homocysteine (Hcy)/lipid/cholesterol-related single nucleotide polymorphisms (SNP) on log-transformed plasma Hcy normalized by RBC folate measurements (nHcy) in 373 healthy Caucasian adults (50% women). Variable selection was conducted by stepwise Akaike information criterion or least angle regression and both methods led to the same final model. Significant predictors (where P values were adjusted for false discovery rate) included type of blood sample [whole blood (WB) vs. plasma-depleted WB; P < 0.001] used for folate analysis, gender (P < 0.001), and SNP in genes SPTLC1 (rs11790991; P = 0.040), CRBP2 (rs2118981; P < 0.001), BHMT (rs3733890; P = 0.019), and CETP (rs5882; P = 0.017). Significant 2-way interaction effects included gender × MTHFR (rs1801131; P = 0.012), gender × CRBP2 (rs2118981; P = 0.011), and gender × SCARB1 (rs83882; P = 0.003). The relation of nHcy concentrations with the significant SNP (SPTLC1, BHMT, CETP, CRBP2, MTHFR, and SCARB1) is of interest, especially because we surveyed the main and interaction effects in healthy adults, but it is an important area for future study. As discussed, understanding Hcy and genetic regulation is important, because Hcy may be related to inflammation, obesity, cardiovascular disease, and diabetes mellitus. We conclude that gender and SNP significantly affect nHcy.