Plasma metabolomics profiles in Black and White participants of the Adventist Health Study-2 cohort.

BACKGROUND: Black Americans suffer disparities in risk for cardiometabolic and other chronic diseases. Findings from the Adventist Health Study-2 (AHS-2) cohort have shown associations of plant-based dietary patterns and healthy lifestyle factors with prevention of such diseases. Hence, it is likely that racial differences in metabolic profiles correlating with disparities in chronic diseases are explained largely by diet and lifestyle, besides social determinants of health. METHODS: Untargeted plasma metabolomics screening was performed on plasma samples from 350 participants of the AHS-2, including 171 Black and 179 White participants, using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and a global platform of 892 metabolites. Differences in metabolites or biochemical subclasses by race were analyzed using linear regression, considering various models adjusted for known confounders, dietary and/or other lifestyle behaviors, social vulnerability, and psychosocial stress. The Storey permutation approach was used to adjust for false discovery at FDR < 0.05. RESULTS: Linear regression revealed differential abundance of over 40% of individual metabolites or biochemical subclasses when comparing Black with White participants after adjustment for false discovery (FDR < 0.05), with the vast majority showing lower abundance in Blacks. Associations were not appreciably altered with adjustment for dietary patterns and socioeconomic or psychosocial stress. Metabolite subclasses showing consistently lower abundance in Black participants included various lipids, such as lysophospholipids, phosphatidylethanolamines, monoacylglycerols, diacylglycerols, and long-chain monounsaturated fatty acids, among other subclasses or lipid categories. Among all biochemical subclasses, creatine metabolism exclusively showed higher abundance in Black participants, although among metabolites within this subclass, only creatine showed differential abundance after adjustment for glomerular filtration rate. Notable metabolites in higher abundance in Black participants included methyl and propyl paraben sulfates, piperine metabolites, and a considerable proportion of acetylated amino acids, including many previously found associated with glomerular filtration rate. CONCLUSIONS: Differences in metabolic profiles were evident when comparing Black and White participants of the AHS-2 cohort. These differences are likely attributed in part to dietary behaviors not adequately explained by dietary pattern covariates, besides other environmental or genetic factors. Alterations in these metabolites and associated subclasses may have implications for the prevention of chronic diseases in Black Americans.

Enhancing Capacity for Food and Nutrient Intake Assessment in Population Sciences Research.

Nutrition influences health throughout the life course. Good nutrition increases the probability of good pregnancy outcomes, proper childhood development, and healthy aging, and it lowers the probability of developing common diet-related chronic diseases, including obesity, cardiovascular disease, cancer, and type 2 diabetes. Despite the importance of diet and health, studying these exposures is among the most challenging in population sciences research. US and global food supplies are complex; eating patterns have shifted such that half of meals are eaten away from home, and there are thousands of food ingredients with myriad combinations. These complexities make dietary assessment and links to health challenging both for population sciences research and for public health policy and practice. Furthermore, most studies evaluating nutrition and health usually rely on self-report instruments prone to random and systematic measurement error. Scientific advances involve developing nutritional biomarkers and then applying these biomarkers as stand-alone nutritional exposures or for calibrating self-reports using specialized statistics.

Associations of Biomarker-Calibrated Healthy Eating Index-2010 Scores with Chronic Disease Risk and Their Dependency on Energy Intake and Body Mass Index in Postmenopausal Women.

BACKGROUND: Prior studies examined associations between the Healthy Eating Index (HEI) and chronic disease risk based on self-reported diet without measurement error correction. OBJECTIVE: Our objective was to test associations between biomarker calibration of the food-frequency questionnaire (FFQ)-derived HEI-2010 with incident cardiovascular disease (CVD), cancer, and type 2 diabetes (T2D) among Women's Health Initiative (WHI) participants. METHODS: Data were derived from WHI postmenopausal women (n = 100,374) aged 50-79 y at enrollment (1993-1998) at 40 US clinical centers, linked to nutritional biomarker substudies and outcomes over subsequent decades of follow-up. Baseline or year 1 FFQ-derived HEI-2010 scores were calibrated with nutritional biomarkers and participant characteristics (e.g., BMI) for systematic measurement error correction. Calibrated data were then used in HR models examining associations with incidence of CVD (total, subtypes, mortality), cancer (total, subtypes, mortality), and T2D in WHI participants with approximately 2 decades of follow-up. Models were multivariable-adjusted with further adjustment for BMI and doubly labeled water (DLW)-calibrated energy. RESULTS: Multivariable-adjusted HRs modeled a 20% increment in HEI-2010 score in relation to outcomes. HRs were modest using uncalibrated HEI-2010 scores (HRs = 0.91-1.09). Using biomarker-calibrated HEI-2010, 20% increments in scores yielded multivariable-adjusted HRs (95% CIs) of 0.75 (0.60, 0.93) for coronary heart disease; 0.75 (0.61, 0.91) for myocardial infarction; 0.96 (0.92, 1.01) for stroke; 0.88 (0.75, 1.02) for CVD mortality; 0.81 (0.70, 0.94) for colorectal cancer; 0.81 (0.74, 0.88) for breast cancer; 0.79 (0.73, 0.87) for cancer mortality; and 0.45 (0.36-0.55) for T2D. Except for cancer mortality and T2D incidence, results became null when adjusted for DLW-calibrated energy intake and BMI. CONCLUSIONS: Biomarker calibration of FFQ-derived HEI-2010 was associated with lower CVD and cancer incidence and mortality and lower T2D incidence in postmenopausal women. Attenuation after adjustment with BMI and DLW-calibrated energy suggests that energy intake and/or obesity are strong drivers of diet-related chronic disease risk in postmenopausal women. The Women's Health Initiative is registered at clinicaltrials.gov at NCT00000611.

Metabolomics-Based Biomarker for Dietary Fat and Associations with Chronic Disease Risk in Postmenopausal Women.

BACKGROUND: The Women's Health Initiative (WHI) randomized, controlled Dietary Modification (DM) trial of a low-fat dietary pattern suggested intervention benefits related to breast cancer, coronary heart disease (CHD), and diabetes. Here, we use WHI observational data for further insight into the chronic disease implications of adopting this type of low-fat dietary pattern. OBJECTIVES: We aimed to use our earlier work on metabolomics-based biomarkers of carbohydrate and protein to develop a fat intake biomarker by subtraction, to use the resulting biomarker to develop calibration equations that adjusts self-reported fat intake for measurement error, and to study associations of biomarker-calibrated fat intake with chronic disease risk in WHI cohorts. Corresponding studies for specific fatty acids will follow separately. METHODS: Prospective disease association results are presented using WHI cohorts of postmenopausal women, aged 50-79 y when enrolled at 40 United States clinical centers. Biomarker equations were developed using an embedded human feeding study (n = 153). Calibration equations were developed using a WHI nutritional biomarker study (n = 436). Calibrated intakes were associated with cancer, cardiovascular diseases, and diabetes incidence in WHI cohorts (n = 81,954) over an approximate 20-y follow-up period. RESULTS: A biomarker for fat density was developed by subtracting protein, carbohydrate, and alcohol densities from one. A calibration equation was developed for fat density. Hazard ratios (95% confidence intervals) for 20% higher fat density were 1.16 (1.06, 1.27) for breast cancer, 1.13 (1.02, 1.26) for CHD, and 1.19 (1.13, 1.26) for diabetes, in substantial agreement with findings from the DM trial. With control for additional dietary variables, especially fiber, fat density was no longer associated with CHD, with hazard ratio (95% confidence interval) of 1.00 (0.88, 1.13), whereas that for breast cancer was 1.11 (1.00, 1.24). CONCLUSIONS: WHI observational data support prior DM trial findings of low-fat dietary pattern benefits in this population of postmenopausal United States women. TRIAL REGISTRATION NUMBER: This study is registered with clinicaltrials.gov identifier: NCT00000611.

Metabolomics Biomarkers for Fatty Acid Intake and Biomarker-Calibrated Fatty Acid Associations with Chronic Disease Risk in Postmenopausal Women.

BACKGROUND: A substantial observational literature relating specific fatty acid classes to chronic disease risk may be limited by its reliance on self-reported dietary data. OBJECTIVES: We aimed to develop biomarkers for saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acid densities, and to study their associations with cardiovascular disease (CVD), cancer, and type 2 diabetes (T2D) in Women's Health Initiative (WHI) cohorts. METHODS: Biomarker equations were based primarily on serum and urine metabolomics profiles from an embedded WHI human feeding study (n = 153). Calibration equations were based on biomarker values in a WHI nutritional biomarker study (n = 436). Calibrated intakes were assessed in relation to disease incidence in larger WHI cohorts (n = 81,894). Participants were postmenopausal women, aged 50-79 when enrolled at 40 United States Clinical Centers (1993-1998), with a follow-up period of ∼20 y. RESULTS: Biomarker equations meeting criteria were developed for SFA, MUFA, and PUFA densities. That for SFA density depended somewhat weakly on metabolite profiles. On the basis of our metabolomics platforms, biomarkers were insensitive to trans fatty acid intake. Calibration equations meeting criteria were developed for SFA and PUFA density, but not for MUFA density. With or without biomarker calibration, SFA density was associated positively with risk of CVD, cancer, and T2D, but with small hazard ratios, and CVD associations were not statistically significant after controlling for other dietary variables, including trans fatty acid and fiber intake. Following this same control, PUFA density was not significantly associated with CVD risk, but there were positive associations for some cancers and T2D, with or without biomarker calibration. CONCLUSIONS: Higher SFA and PUFA diets were associated with null or somewhat higher risk for clinical outcomes considered in this population of postmenopausal United States women. Further research is needed to develop even stronger biomarkers for these fatty acid densities and their major components. This study is registered with clinicaltrials.gov identifier: NCT00000611.

Children With Celiac Disease Consume Specific Food Additives More Frequently Compared to Children Without Celiac Disease.

BACKGROUND: Gluten-free foods often contain food additives to improve palatability, but the long-term effects on the human gastrointestinal tract are not well known. AIMS: This study aimed to quantify frequency of food additive exposure in children with and without celiac disease (CD). METHODS: Children with and without CD were enrolled and demographic data and three-day diet records were obtained. Foods were classified as gluten-free products (GFP) and "processed food", and were evaluated for presence of select food additives: polysorbate 80, carboxymethylcellulose, xanthan gum, guar gum, soy lecithin, titanium dioxide, carrageenan, maltodextrin, and aluminosilicates. The frequency of exposure was described. RESULTS: Twenty-eight participants were included in final analysis. Children with CD had a higher number of daily exposures to xanthan gum (5.3 ± 3.1 vs 2.3 ± 2.4; p = 0.009), but similar exposures to the other additives. GFP contributed 29% of total calories in the GF diet. Both groups had similar intake of processed foods. Comparing GFP and gluten-containing processed foods, 68% vs. 25% contained at least one food additive of interest (p < 0.0001); in the celiac group, those with higher consumption of GFP tended to have a higher frequency of exposure to food additives (p = 0.09). CONCLUSION: A gluten-free diet and consumption of GFP may contribute to differences in food additive intake; quantifying food additive exposures and their effect on humans requires further study.

Four-Day Food Record Macronutrient Intake, With and Without Biomarker Calibration, and Chronic Disease Risk in Postmenopausal Women.

We recently evaluated associations of biomarker-calibrated protein intake, protein density, carbohydrate intake, and carbohydrate density with the incidence of cardiovascular disease, cancer, and diabetes among postmenopausal women in the Women's Health Initiative (1993-present, 40 US clinical centers). The biomarkers relied on serum and urine metabolomics profiles, and biomarker calibration used regression of biomarkers on food frequency questionnaires. Here we develop corresponding calibration equations using food records and dietary recalls. In addition, we use calibrated intakes based on food records in disease association estimation in a cohort subset (n = 29,294) having food records. In this analysis, more biomarker variation was explained by food records than by FFQs for absolute macronutrient intake, with 24-hour recalls being intermediate. However, the percentage of biomarker variation explained was similar for each assessment approach for macronutrient densities. Invasive breast cancer risk was related inversely to carbohydrate and protein densities using food records, in analyses that included (calibrated) total energy intake and body mass index. Corresponding analyses for absolute intakes did not differ from the null, nor did absolute or relative intakes associate significantly with colorectal cancer or coronary heart disease. These analyses do not suggest major advantages for food records or dietary recalls in comparison with less costly and logistically simpler food frequency questionnaires for these nutritional variables.

Biomarkers for Components of Dietary Protein and Carbohydrate with Application to Chronic Disease Risk in Postmenopausal Women.

BACKGROUND: We recently developed protein and carbohydrate intake biomarkers using metabolomics profiles in serum and urine, and used them to correct self-reported dietary data for measurement error. Biomarker-calibrated carbohydrate density was inversely associated with chronic disease risk, whereas protein density associations were mixed. OBJECTIVES: To elucidate and extend this earlier work through biomarker development for protein and carbohydrate components, including animal protein and fiber. METHODS: Prospective disease association analyses were undertaken in Women's Health Initiative (WHI) cohorts of postmenopausal US women, aged 50-79 y when enrolled at 40 US clinical centers. Biomarkers were developed using an embedded human feeding study (n = 153). Calibration equations for protein and carbohydrate components were developed using a WHI nutritional biomarker study (n = 436). Calibrated intakes were associated with chronic disease incidence in WHI cohorts (n = 81,954) over a 20-y (median) follow-up period, using HR regression methods. RESULTS: Previously reported elevations in cardiovascular disease (CVD) with higher-protein diets tended to be explained by animal protein density. For example, for coronary heart disease a 20% increment in animal protein density had an HR of 1.20 (95% CI: 1.02, 1.42) relative to the HR for total protein density. In comparison, cancer and diabetes risk showed little association with animal protein density beyond that attributable to total protein density. Inverse carbohydrate density associations with total CVD were mostly attributable to fiber density, with a 20% increment HR factor of 0.89 (95% CI: 0.83, 0.94). Cancer risk showed little association with fiber density, whereas diabetes risk had a 20% increment HR of 0.93 (95% CI: 0.88, 0.98) relative to the HRs for total carbohydrate density. CONCLUSIONS: In a population of postmenopausal US women, CVD risk was associated with high-animal-protein and low-fiber diets, cancer risk was associated with low-carbohydrate diets, and diabetes risk was associated with low-fiber/low-carbohydrate diets.

Biomarker-Calibrated Red and Combined Red and Processed Meat Intakes with Chronic Disease Risk in a Cohort of Postmenopausal Women.

BACKGROUND: The associations of red and processed meat with chronic disease risk remain to be clarified, in part because of measurement error in self-reported diet. OBJECTIVES: We sought to develop metabolomics-based biomarkers for red and processed meat, and to evaluate associations of biomarker-calibrated meat intake with chronic disease risk among postmenopausal women. METHODS: Study participants were women who were members of the Women's Health Initiative (WHI) study cohorts. These participants were postmenopausal women aged 50-79 y when enrolled during 1993-1998 at 40 US clinical centers with embedded human feeding and nutrition biomarker studies. Literature reports of metabolomics correlates of meat consumption were used to develop meat intake biomarkers from serum and 24-h urine metabolites in a 153-participant feeding study (2010-2014). Resulting biomarkers were used in a 450-participant biomarker study (2007-2009) to develop linear regression calibration equations that adjust FFQ intakes for random and systematic measurement error. Biomarker-calibrated meat intakes were associated with cardiovascular disease, cancer, and diabetes incidence among 81,954 WHI participants (1993-2020). RESULTS: Biomarkers and calibration equations meeting prespecified criteria were developed for consumption of red meat and red plus processed meat combined, but not for processed meat consumption. Following control for nondietary confounding factors, hazard ratios were calculated for a 40% increment above the red meat median intake for coronary artery disease (HR: 1.10; 95% CI: 1.07, 1.14), heart failure (HR: 1.26; 95% CI: 1.20, 1.33), breast cancer (HR: 1.10; 95% CI: 1.07, 1.13) for, total invasive cancer (HR: 1.07; 95% CI: 1.05, 1.09), and diabetes (HR: 1.37; 95% CI: 1.34, 1.39). HRs for red plus processed meat intake were similar. HRs were close to the null, and mostly nonsignificant following additional control for dietary potential confounding factors, including calibrated total energy consumption. CONCLUSIONS: A relatively high-meat dietary pattern is associated with somewhat higher chronic disease risks. These elevations appear to be largely attributable to the dietary pattern, rather than to consumption of red or processed meat per se.

Mortality Associated with Healthy Eating Index Components and an Empirical-Scores Healthy Eating Index in a Cohort of Postmenopausal Women.

BACKGROUND: Studies of diet and chronic disease include a recent important focus on dietary patterns. Patterns are typically defined by listing dietary variables and by totaling scores that reflect whether consumption is encouraged or discouraged for listed variables. However, precision may be improved by including total energy consumption among the dietary variables and by scoring dietary variables empirically. OBJECTIVES: To relate Healthy Eating Index (HEI)-2010 components and total energy intake to all-cause and cause-specific mortality in Women's Health Initiative (WHI) cohorts and to define and evaluate an associated Empirical-Scores Healthy Eating Index (E-HEI). METHODS: Analyses are conducted in WHI cohorts (n = 67,247) of healthy postmenopausal women, aged 50-79 y, when enrolled during 1993-1998 at 40 US clinical centers, with embedded nutrition biomarker studies. Replicate food-frequency assessments for HEI-2010 ratio variables and doubly labeled water total energy assessments, separated by ∼6 mo, are used as response variables to jointly calibrate baseline dietary data to reduce measurement error influences, using 2 nutrition biomarker studies (n = 199). Calibrated dietary variables are associated with mortality risk, and an E-HEI is defined, using cross-validated HR regression estimation. RESULTS: Of 15 dietary variables considered, all but empty calories calibrated well. Ten variables related significantly (P < 0.05) to total mortality, with favorable fruit, vegetable, whole grain, refined grain, and unsaturated fat associations and unfavorable sodium, saturated fat, and total energy associations. The E-HEI had cross-validated total mortality HRs (95% CIs) of 0.87 (0.82, 0.93), 0.80 (0.76, 0.86), 0.77 (0.72, 0.82), and 0.74 (0.69, 0.79) respectively, for quintiles 2 through 5 compared with quintile 1. These depart more strongly from the null than do HRs for HEI-2010 quintiles, primarily because of total energy. CONCLUSIONS: Mortality among US postmenopausal women depends strongly on diet, as evidenced by a new E-HEI that differs substantially from earlier dietary pattern score specifications.

Novel Application of Nutritional Biomarkers From a Controlled Feeding Study and an Observational Study to Characterization of Dietary Patterns in Postmenopausal Women.

Dietary guidance emphasizes healthy dietary patterns, but supporting evidence comes from self-reported dietary data, which are prone to measurement error. We explored whether nutritional biomarkers from the Women's Health Initiative Nutrition and Physical Activity Assessment Study Feeding Study (NPAAS-FS) (n = 153; 2010-2014) and the Women's Health Initiative Nutrition and Physical Activity Assessment Study Observational Study (NPAAS-OS) (n = 450; 2006-2009) could identify biomarker signatures of dietary patterns for development of corresponding regression calibration equations to help mitigate measurement error. Fasting blood samples were assayed for a specific panel of vitamins, carotenoids, and phospholipid fatty acids; 24-hour urine samples were assayed for nitrogen, sodium, and potassium levels. Intake records from the NPAAS-FS were used to calculate Healthy Eating Index 2010 (HEI-2010), Alternative Healthy Eating Index 2010 (AHEI-2010), alternative Mediterranean diet (aMED), and Dietary Approaches to Stop Hypertension (DASH) scores. Scores were regressed on blood and urine nutritional measures for discovery of dietary pattern biomarkers using a cross-validated model R2 ≥ 36% criterion (stage 1). Next, stepwise models (P ≤ 0.10 for entry/removal) using NPAAS-OS data were used to regress stage 1 dietary pattern biomarkers on NPAAS-OS self-reported dietary pattern scores using a food frequency questionnaire, a 4-day food record, and a 24-hour recall (stage 2). HEI-2010 and aMED analyses met the cross-validated R2 ≥ 36% criterion in stage 1, while AHEI-2010 and DASH analyses did not. The R2 values for HEI-2010 stage 2 calibration equations were as follows: food frequency questionnaire, 63.5%; 4-day food record, 83.1%; and 24-hour recall, 77.8%. Stage 2 aMED R2 values were 34.9%-46.8%. Dietary pattern biomarkers have potential for calibrating self-reports to enhance studies of diet-disease associations.

Diet and Liver Adiposity in Older Adults: The Multiethnic Cohort Adiposity Phenotype Study.

BACKGROUND: Diet plays a key role in the pathogenesis of nonalcoholic fatty liver disease. Limited data exist regarding specific nutrients and food groups and liver fat continuously, particularly among different ethnicities. OBJECTIVES: We aimed to determine the relationship between usual dietary intake and accurately measured liver fat content in a multiethnic population. METHODS: Participants from the Multiethnic Cohort were recruited into the cross-sectional Adiposity Phenotype Study including women and men aged 60-77 y and 5 race/ethnic groups (African American, Japanese American, Latino, Native Hawaiian, and white). They filled out a detailed FFQ and underwent abdominal MRI for liver fat quantification and whole-body DXA for total adiposity. Intake of a priori-selected dietary factors (total and macronutrient energy, specific micronutrients, and food groups) was analyzed in relation to liver fat by estimating the mean percentage liver fat for quartiles of each dietary factor in a general linear model that adjusted for age, sex, race/ethnicity, percentage body fat, and daily energy intake (kcal/d). RESULTS: In total, 1682 participants (mean age: 69.2 y; 51% female) were included. Mean ± SD liver fat percentage was 5.7 ± 4.6. A significant positive association with liver fat was found across quartiles of percentage energy from fat, saturated fat, cholesterol, total red meat, red meat excluding processed red meat, and coffee (Bonferroni-adjusted P-trend < 0.05). A significant inverse association was observed for dietary fiber, vitamin C, and vitamin E (Bonferroni-adjusted P-trend < 0.05). CONCLUSIONS: This study of ethnically diverse older adults shows that certain dietary factors, in particular red meat and saturated fat from red meat, were strongly associated with liver fat, whereas dietary fiber was inversely associated with liver fat, replicating some of the previous studies conducted mostly in whites.

Biomarker-Calibrated Macronutrient Intake and Chronic Disease Risk among Postmenopausal Women.

BACKGROUND: Knowledge about macronutrient intake and chronic disease risk has been limited by the absence of objective macronutrient measures. Recently, we proposed novel biomarkers for protein, protein density, carbohydrate, and carbohydrate density, using established biomarkers and serum and urine metabolomics profiles in a human feeding study. OBJECTIVES: We aimed to use these biomarkers to develop calibration equations for macronutrient variables using dietary self-reports and personal characteristics and to study the association between biomarker-calibrated intake estimates and cardiovascular disease, cancer, and diabetes risk in Women's Health Initiative (WHI) cohorts. METHODS: Prospective disease association analyses are based on WHI cohorts of postmenopausal US women aged 50-79 y when enrolled at 40 US clinical centers (n = 81,954). We used biomarker intake values in a WHI nutritional biomarker study (n = 436) to develop calibration equations for each macronutrient variable, leading to calibrated macronutrient intake estimates throughout WHI cohorts. We then examined the association of these intakes with chronic disease incidence over a 20-y (median) follow-up period using HR regression methods. RESULTS: In analyses that included doubly labeled water-calibrated total energy, HRs for cardiovascular diseases and cancers were mostly unrelated to calibrated protein density. However, many were inversely related to carbohydrate density, with HRs (95% CIs) for a 20% increment in carbohydrate density of 0.81 (0.69, 0.95) and 0.83 (0.74, 0.93), respectively, for primary outcomes of coronary heart disease and breast cancer, as well as 0.74 (0.60, 0.91) and 0.87 (0.81, 0.93) for secondary outcomes of heart failure and total invasive cancer. Corresponding HRs (95% CIs) for type 2 diabetes incidence in relation to protein density and carbohydrate density were 1.17 (1.09, 1.75) and 0.73 (0.66, 0.80), respectively. CONCLUSIONS: At specific energy intake, a diet high in carbohydrate density is associated with substantially reduced risk of major chronic diseases in a population of US postmenopausal women. This trial was registered at clinicaltrials.gov as NCT00000611.

Long-term association between diet quality and characteristics of the gut microbiome in the multiethnic cohort study.

As past usual diet quality may affect gut microbiome (GM) composition, we examined the association of the Healthy Eating Index (HEI)-2015 assessed 21 and 9 years before stool collection with measures of fecal microbial composition in a subset of the Multiethnic Cohort. A total of 5936 participants completed a validated quantitative FFQ (QFFQ) at cohort entry (Q1, 1993-1996), 5280 at follow-up (Q3, 2003-2008) and 1685 also at a second follow-up (Adiposity Phenotype Study (APS), 2013-2016). All participants provided a stool sample in 2013-2016. Fecal microbial composition was obtained from 16S rRNA gene sequencing (V1-V3 regions). HEI-2015 scores were computed based on each QFFQ. Using linear regression adjusted for relevant covariates, we calculated associations of HEI-2015 scores with gut microbial diversity and 152 individual genera. The mean HEI-2015 scores increased from Q1 (67 (sd 10)) to Q3 (71 (sd 11)) and APS (72 (sd 10)). Alpha diversity assessed by the Shannon Index was significantly higher with increasing tertiles of HEI-2015. Of the 152 bacterial genera tested, seven (Anaerostipes, Coprococcus_2, Eubacterium eligens, Lachnospira, Lachnospiraceae_ND3007, Ruminococcaceae_UCG-013 and Ruminococcus_1) were positively and five (Collinsella, Parabacteroides, Ruminiclostridium_5, Ruminococcus gnavus and Tyzzerella) were inversely associated with HEI-2015 assessed in Q1, Q3 and APS. The estimates of change per unit of the HEI-2015 score associated with the abundance of these twelve genera were consistent across the three questionnaires. The quality of past diet, assessed as far as ∼20 years before stool collection, is equally predictive of GM composition as concurrently assessed diet, indicative of the long-term consistency of this relation.

Evaluation of potential metabolomic-based biomarkers of protein, carbohydrate and fat intakes using a controlled feeding study.

PURPOSE: Objective biomarkers of dietary exposure are needed to establish reliable diet-disease associations. Unfortunately, robust biomarkers of macronutrient intakes are scarce. We aimed to assess the utility of serum, 24-h urine and spot urine high-dimensional metabolites for the development of biomarkers of daily intake of total energy, protein, carbohydrate and fat, and the percent of energy from these macronutrients (%E). METHODS: A 2-week controlled feeding study mimicking the participants' habitual diets was conducted among 153 postmenopausal women from the Women's Health Initiative (WHI). Fasting serum metabolomic profiles were analyzed using a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for aqueous metabolites and a direct-injection-based quantitative lipidomics platform. Urinary metabolites were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy at 800 MHz and by untargeted gas chromatography-mass spectrometry (GC-MS). Variable selection was performed to build prediction models for each dietary variable. RESULTS: The highest cross-validated multiple correlation coefficients (CV-R2) for protein intake (%E) and carbohydrate intake (%E) using metabolites only were 36.3 and 37.1%, respectively. With the addition of established dietary biomarkers (doubly labeled water for energy and urinary nitrogen for protein), the CV-R2 reached 55.5% for energy (kcal/d), 52.0 and 45.0% for protein (g/d, %E), 55.9 and 37.0% for carbohydrate (g/d, %E). CONCLUSION: Selected panels of serum and urine metabolites, without the inclusion of doubly labeled water and urinary nitrogen biomarkers, give a reliable and robust prediction of daily intake of energy from protein and carbohydrate.

Propensity for Intra-abdominal and Hepatic Adiposity Varies Among Ethnic Groups.

BACKGROUND & AIMS: We compared fat storage in the abdominal region among individuals from 5 different ethnic-racial groups to determine whether fat storage is associated with disparities observed in metabolic syndrome and other obesity-associated diseases. METHODS: We collected data from 1794 participants in the Multiethnic Cohort Study (60-77 years old; of African, European [white], Japanese, Latino, or Native Hawaiian ancestry) with body mass index values of 17.1-46.2 kg/m2. From May 2013 through April 2016, participants visited the study clinic to undergo body measurements, an interview, and a blood collection. Participants were evaluated by dual-energy x-ray absorptiometry and abdominal magnetic resonance imaging. Among ethnic groups, we compared adiposity of the trunk, intra-abdominal visceral cavity, and liver, adjusting for total fat mass; we evaluated the association of adult weight change with abdominal adiposity; and we examined the prevalence of metabolic syndrome mediated by abdominal adiposity. RESULTS: Relative amounts of trunk, visceral, and liver fat varied significantly with ethnicity-they were highest in Japanese Americans, lowest in African Americans, and intermediate in the other groups. Compared with African Americans, the mean visceral fat area was 45% and 73% greater in Japanese American men and women, respectively, and the mean measurements of liver fat were 61% and 122% greater in Japanese American men and women. The visceral and hepatic adiposity associated with weight gain since participants were 21 years old varied in a similar pattern among ethnic-racial groups. In the mediation analysis, visceral and liver fat jointly accounted for a statistically significant fraction of the difference in metabolic syndrome prevalence, compared with white persons, for African Americans, Japanese Americans, and Native Hawaiian women, independently of total fat mass. CONCLUSIONS: In an analysis of data from the participants in the Multiethnic Cohort Study, we found extensive differences among ethnic-racial groups in the propensity to store fat intra-abdominally. This observation should be considered by clinicians in the prevention and early detection of metabolic disorders.

Workshop report: Toward the development of a human whole stool reference material for metabolomic and metagenomic gut microbiome measurements.

INTRODUCTION: To date, there has been little effort to develop standards for metabolome-based gut microbiome measurements despite the significant efforts toward standard development for DNA-based microbiome measurements. OBJECTIVES: The National Institute of Standards and Technology (NIST), The BioCollective (TBC), and the North America Branch of the International Life Sciences Institute (ILSI North America) are collaborating to extend NIST's efforts to develop a Human Whole Stool Reference Material for the purpose of method harmonization and eventual quality control. METHODS: The reference material will be rationally designed for adequate quality assurance and quality control (QA/QC) for underlying measurements in the study of the impact of diet and nutrition on functional aspects of the host gut microbiome and relationships of those functions to health. To identify which metabolites deserve priority in their value assignment, NIST, TBC, and ILSI North America jointly conducted a workshop on September 12, 2019 at the NIST campus in Gaithersburg, Maryland. The objective of the workshop was to identify metabolites for which evidence indicates relevance to health and disease and to decide on the appropriate course of action to develop a fit-for-purpose reference material. RESULTS: This document represents the consensus opinions of workshop participants and co-authors of this manuscript, and provides additional supporting information. In addition to developing general criteria for metabolite selection and a preliminary list of proposed metabolites, this paper describes some of the strengths and limitations of this initiative given the current state of microbiome research. CONCLUSIONS: Given the rapidly evolving nature of gut microbiome science and the current state of knowledge, an RM (as opposed to a CRM) measured for multiple metabolites is appropriate at this stage. As the science evolves, the RM can evolve to match the needs of the research community. Ultimately, the stool RM may exist in sequential versions. Beneficial to this evolution will be a clear line of communication between NIST and the stakeholder community to ensure alignment with current scientific understanding and community needs.

Plasma lipidomic profiles after a low and high glycemic load dietary pattern in a randomized controlled crossover feeding study.

BACKGROUND: Dietary patterns low in glycemic load are associated with reduced risk of cardiometabolic diseases. Improvements in serum lipid concentrations may play a role in these observed associations. OBJECTIVE: We investigated how dietary patterns differing in glycemic load affect clinical lipid panel measures and plasma lipidomics profiles. METHODS: In a crossover, controlled feeding study, 80 healthy participants (n = 40 men, n = 40 women), 18-45 y were randomized to receive low-glycemic load (LGL) or high glycemic load (HGL) diets for 28 days each with at least a 28-day washout period between controlled diets. Fasting plasma samples were collected at baseline and end of each diet period. Lipids on a clinical panel including total-, VLDL-, LDL-, and HDL-cholesterol and triglycerides were measured using an auto-analyzer. Lipidomics analysis using mass-spectrometry provided the concentrations of 863 species. Linear mixed models and lipid ontology enrichment analysis were implemented. RESULTS: Lipids from the clinical panel were not significantly different between diets. Univariate analysis showed that 67 species on the lipidomics panel, predominantly in the triacylglycerol class, were higher after the LGL diet compared to the HGL (FDR < 0.05). Three species with FA 17:0 were lower after LGL diet with enrichment analysis (FDR < 0.05). CONCLUSION: In the context of controlled eucaloric diets with similar macronutrient distribution, these results suggest that there are relative shifts in lipid species, but the overall pool does not change. Further studies are needed to better understand in which compartment the different lipid species are transported in blood, and how these shifts are related to health outcomes. This trial was registered at clinicaltrials.gov as NCT00622661.

The Carbon Isotope Ratios of Serum Amino Acids in Combination with Participant Characteristics can be Used to Estimate Added Sugar Intake in a Controlled Feeding Study of US Postmenopausal Women.

BACKGROUND: The carbon isotope ratio (CIR) is a proposed biomarker for added sugar (AS) intake in the United States; however, because the CIR is also associated with meat intake in most populations the need for specificity remains. The CIR of amino acids (AAs) has the potential to differentiate sugars from meat intakes, because essential AAs must derive from dietary protein whereas certain nonessential AAs can be synthesized from sugars. OBJECTIVES: We tested whether serum CIR-AAs in combination with participant characteristics could meet a prespecified biomarker criterion for AS intake in the Nutrition and Physical Activity Assessment Study Feeding Study (NPAAS-FS) of the Women's Health Initiative, a population in which the whole-serum CIR was not associated with AS intake. METHODS: Postmenopausal women (n = 145) from Seattle, WA, were provided with individualized diets that approximated their habitual food intakes for 2 wk. Dietary intakes from consumed foods were characterized over the feeding period using the Nutrition Data System for Research. The CIR of 7 AAs-Ala, Gly, Val, Leu, Ile, Pro, and Phe-were measured in fasting serum collected at the end of the 2-wk feeding period, using gas chromatography-combustion isotope ratio mass spectrometry. Biomarker models were evaluated using regression R2 ≥ 0.36 as a major biomarker criterion, based on the benchmark R2 values of well-established recovery biomarkers in the NPAAS-FS. RESULTS: AS intake was associated with CIR-Ala (ρ = 0.32; P < 0.0001). A model of AS intake based on CIR-Ala, CIR-Gly, CIR-Ile, smoking, leisure physical activity, and body weight met the biomarker criterion (R2 = 0.37). Biomarker-estimated AS intake was not associated with meat or animal protein intake. CONCLUSIONS: Results support serum CIR-AAs in combination with participant characteristics as potential biomarkers of AS intake in US populations, including those with low AS intake.The Women's Health Initiative is registered at clinicaltrials.gov (NCT00000611).

Diet and Gut Microbes Act Coordinately to Enhance Programmed Cell Death and Reduce Colorectal Cancer Risk.

Diet is an important risk factor for colorectal cancer (CRC), and several dietary constituents implicated in CRC are modified by gut microbial metabolism. Microbial fermentation of dietary fiber produces short-chain fatty acids, e.g., acetate, propionate, and butyrate. Dietary fiber has been shown to reduce colon tumors in animal models, and, in vitro, butyrate influences cellular pathways important to cancer risk. Furthermore, work from our group suggests that the combined effects of butyrate and omega-3 polyunsaturated fatty acids (n-3 PUFA) may enhance the chemopreventive potential of these dietary constituents. We postulate that the relatively low intakes of n-3 PUFA and fiber in Western populations and the failure to address interactions between these dietary components may explain why chemoprotective effects of n-3 PUFA and fermentable fibers have not been detected consistently in prospective cohort studies. In this review, we summarize the evidence outlining the effects of n-3 long-chain PUFA and highly fermentable fiber with respect to alterations in critical pathways important to CRC prevention, particularly intrinsic mitochondrial-mediated programmed cell death resulting from the accumulation of lipid reactive oxygen species (ferroptosis), and epigenetic programming related to lipid catabolism and beta-oxidation-associated genes.