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

Energy intake is associated with dietary macronutrient densities: inversely with protein and monounsaturated fat and positively with polyunsaturated fat and carbohydrate among postmenopausal females

BACKGROUND

Associations of the macronutrient composition of the diet with total energy intake (EI) are uncertain, as are associations of macronutrient composition with self-reported energy underreporting.

OBJECTIVES

We aimed to estimate the associations of biomarker-assessed EI with both biomarker-assessed and self-reported macronutrient component densities in a Women's Health Initiative (WHI) subcohort of postmenopausal females in the United States. Secondarily, we examined energy underreporting using food records, recalls, and frequencies, for association with macronutrient densities.

METHODS

We used a previously proposed EI biomarker equation based on doubly labeled water (DLW) and updated biomarker equations for several macronutrient component densities, to estimate EI and macronutrient component densities in a WHI nutritional biomarkers subcohort (n = 436; 2007-2009). We used linear regression of EI biomarker values on biomarker and self-reported macronutrient component densities, and of EI underreporting values on biomarker densities, to examine targeted associations.

RESULTS

Using biomarker assessments, the geometric mean (95% CI) for EI corresponding to a 20% increment in carbohydrate density was 2.0% (0.1%, 3.9%) higher, and for a 20% protein density increment was 2.1% (0.5%, 3.7%) lower. The former was attributable to added sugars. Similarly, EI values for 20% increments in polyunsaturated (PUFA), and monounsaturated (MUFA) fatty acid densities were 1.4% (0.3%, 2.6%) higher and 1.5% (0.1%, 2.9%) lower, respectively. Pertinent associations were either not detected or were substantially attenuated if instead self-reported macronutrient densities were used. Also, EI underreporting was strongly related to self-reported macronutrient densities using food records, recalls, or frequencies.

CONCLUSIONS

Among postmenopausal females in the United States lower EI was associated with diets relatively high in protein or MUFA, and higher EI was associated with diets relatively high in PUFA or added sugars. These associations are of public health importance but are mostly missed using self-reported dietary density assessments. Self-reported energy underestimation is substantially associated with self-reported macronutrient densities.

CLINICAL TRIAL REGISTRY

This study is registered with clinicaltrials.gov identifier: NCT00000611.

Approaching Two Decades: Biomolecular Coronas and Bio-Nano Interactions

It has been nearly two decades since the term "protein corona" was coined. This term has since evolved to "biomolecular corona" or "biocorona" to capture the diverse biomolecules that spontaneously form on the surface of nanoparticles upon exposure to biological fluids and drive nanoparticle interactions with biological systems. In this Perspective, we highlight the significant progress in this field, including studies on nonprotein corona components, lipid nanoparticles, and the role of the corona in endogenous organ targeting. We also discuss research opportunities in this field, particularly the need for improved characterization and standardization of analysis and how recent advances in artificial intelligence and ex vivo models can improve our understanding of the biomolecular corona in guiding nanomedicine design.

Metabolite Predictors of Breast and Colorectal Cancer Risk in the Women's Health Initiative

Metabolomics has been used extensively to capture the exposome. We investigated whether prospectively measured metabolites provided predictive power beyond well-established risk factors among 758 women with adjudicated cancers [n = 577 breast (BC) and n = 181 colorectal (CRC)] and n = 758 controls with available specimens (collected mean 7.2 years prior to diagnosis) in the Women's Health Initiative Bone Mineral Density subcohort. Fasting samples were analyzed by LC-MS/MS and lipidomics in serum, plus GC-MS and NMR in 24 h urine. For feature selection, we applied LASSO regression and Super Learner algorithms. Prediction models were subsequently derived using logistic regression and Super Learner procedures, with performance assessed using cross-validation (CV). For BC, metabolites did not increase predictive performance over established risk factors (CV-AUCs~0.57). For CRC, prediction increased with the addition of metabolites (median CV-AUC across platforms increased from ~0.54 to ~0.60). Metabolites related to energy metabolism: adenosine, 2-hydroxyglutarate, N-acetyl-glycine, taurine, threonine, LPC (FA20:3), acetate, and glycerate; protein metabolism: histidine, leucic acid, isoleucine, N-acetyl-glutamate, allantoin, N-acetyl-neuraminate, hydroxyproline, and uracil; and dietary/microbial metabolites: myo-inositol, trimethylamine-N-oxide, and 7-methylguanine, consistently contributed to CRC prediction. Energy metabolism may play a key role in the development of CRC and may be evident prior to disease development.

Intake Biomarkers for Nutrition and Health: Review and Discussion of Methodology Issues

Metabolomics profiles from blood, urine, or other body fluids have the potential to assess intakes of foods and nutrients objectively, thereby strengthening nutritional epidemiology research. Metabolomics platforms may include targeted components that estimate the relative concentrations for individual metabolites in a predetermined set, or global components, typically involving mass spectrometry, that estimate relative concentrations more broadly. While a specific metabolite concentration usually correlates with the intake of a single food or food group, multiple metabolites may be correlated with the intake of certain foods or with specific nutrient intakes, each of which may be expressed in absolute terms or relative to total energy intake. Here, I briefly review the progress over the past 20 years on the development and application intake biomarkers for foods/food groups, nutrients, and dietary patterns, primarily by drawing from several recent reviews. In doing so, I emphasize the criteria and study designs for candidate biomarker identification, biomarker validation, and intake biomarker application. The use of intake biomarkers for diet and chronic disease association studies is still infrequent in nutritional epidemiology research. My comments here will derive primarily from our research group's recent contributions to the Women's Health Initiative cohorts. I will complete the contribution by describing some opportunities to build on the collective 20 years of effort, including opportunities related to the metabolomics profiling of blood and urine specimens from human feeding studies that approximate habitual diets.

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.

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.

Utilising the precision nutrition toolkit in the path towards precision medicine

The overall aim of precision nutrition is to replace the 'one size fits all' approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more 'precision' dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene-diet or microbiota-diet interactions. Third, precision nutrition can be used for 'personalised nutrition' advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet-disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.

Anomalous Dynamics of Labile Metabolites in Cold Human Blood Detected Using 1H NMR Spectroscopy

Recent efforts in our laboratory have enabled access to an unprecedented number (∼90) of quantifiable metabolites in human blood by a simple nuclear magnetic resonance (NMR) spectroscopy method, which includes energy coenzymes, redox coenzymes, and antioxidants that are fundamental to cellular functions [ J. Magn. Reson. Open 2022, 12-13, 100082]. The coenzymes and antioxidants, however, are notoriously labile and are extremely sensitive to specimen harvesting, extraction, and measurement conditions. This problem is largely underappreciated and carries the risk of grossly inaccurate measurements and incorrect study outcomes. As a part of addressing this challenge, in this study, human blood specimens were comprehensively and quantitatively investigated using 1H NMR spectroscopy. Freshly drawn human blood specimens were treated or not treated with methanol, ethanol, or a mixture of methanol and chloroform, and stored on ice or on bench, at room temperature for different time periods from 0 to 24 h, prior to storing at -80 °C. Interestingly, the labile metabolite levels were stable in blood treated with an organic solvent. However, their levels in blood in untreated samples increased or decreased by factors of up to 5 or more within 3 h. Further, surprisingly, and contrary to the current knowledge about metabolite stability, the variation of coenzyme levels was more dramatic in blood stored on ice than on bench, at room temperature. In addition, unlike the generally observed phenomenon of oxidation of redox coenzymes, reduction was observed in untreated blood. Such preanalytical dynamics of the labile metabolites potentially arises from the active cellular metabolism. From the metabolomics perspective, the massive variation of the labile metabolite levels even in blood stored on ice is alarming and stresses the critical need to immediately quench the cellular metabolism for reliable analyses. Overall, the results provide compelling evidence that warrants a paradigm shift in the sample collection protocol for blood metabolomics involving labile metabolites.

Acute inhalation of tungsten particles results in early signs of cardiac injury

Epidemiological studies have established that exposure to tungsten increases the risk of developing cardiovascular diseases. However, no studies have investigated how tungsten affects cardiac function or the development of cardiovascular disease. Inhalation of tungsten particulates is relevant in occupational settings, and inhalation of particulate matter has a known causative role in driving cardiovascular disease. This study examined if acute inhalation to tungsten particulates affects cardiac function and leads to heart tissue alterations. Female BALB/c mice were exposed to Filtered Air or 1.5 ± 0.23 mg/m3 tungsten particles, using a whole-body inhalation chamber, 4 times over the course of two weeks. Inhalation exposure resulted in mild pulmonary inflammation characterized by an increased percentage and number of macrophages and metabolomic changes in the lungs. Cardiac output was significantly decreased in the tungsten-exposed group. Additionally, A', an indicator of the amount of work required by the atria to fill the heart was elevated. Cardiac gene expression analysis revealed, tungsten exposure increased expression of pro-inflammatory cytokines, markers of remodeling and fibrosis, and oxidative stress genes. These data strongly suggest exposure to tungsten results in cardiac injury characterized by early signs of diastolic dysfunction. Functional findings are in parallel, demonstrating cardiac oxidative stress, inflammation, and early fibrotic changes. Tungsten accumulation data would suggest these cardiac changes are driven by systemic consequences of pulmonary damage.

Cerebrospinal Fluid Metabolomics: Pilot Study of Using Metabolomics to Assess Diet and Metabolic Interventions in Alzheimer's Disease and Mild Cognitive Impairment

Brain glucose hypometabolism is an early sign of Alzheimer's disease (AD), and interventions which offset this deficit, such as ketogenic diets, show promise as AD therapeutics. Conversely, high-fat feeding may exacerbate AD risk. We analyzed the metabolomic profile of cerebrospinal fluid (CSF) in a pilot study of older adults who underwent saline and triglyceride (TG) infusions. Older adults (12 cognitively normal (CN), age 65.3 ± 8.1, and 9 with cognitive impairment (CI), age 70.9 ± 8.6) underwent a 5 h TG or saline infusion on different days using a random crossover design; CSF was collected at the end of infusion. Aqueous metabolites were measured using a targeted mass spectroscopy (MS) platform focusing on 215 metabolites from over 35 different metabolic pathways. Data were analyzed using MetaboAnalyst 4.0 and SAS. Of the 215 targeted metabolites, 99 were detectable in CSF. Only one metabolite significantly differed by treatment: the ketone body 3-hydroxybutyrate (HBA). Post hoc analyses showed that HBA levels were associated with age and markers of metabolic syndrome and demonstrated different correlation patterns for the two treatments. When analyzed by cognitive diagnosis group, TG-induced increases in HBA were over 3 times higher for those with cognitive impairment (change score CN +9.8 uM ± 8.3, CI +32.4 ± 7.4, p = 0.0191). Interestingly, individuals with cognitive impairment had higher HBA levels after TG infusion than those with normal cognition. These results suggest that interventions that increase plasma ketones may lead to higher brain ketones in groups at risk for AD and should be confirmed in larger intervention studies.

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.

Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.

NMR foodomics in the assessment of diet and effects beyond nutrients

PURPOSE OF REVIEW

This review provides an overview of most recent research studies employing nuclear magnetic resonance (NMR)-based metabolomics in the assessment of effects of diet and food ingestion.

RECENT FINDINGS

NMR metabolomics is a useful tool in the elucidation of specific diets, for example, the Mediterranean diet, the New Nordic diet types, and also for comparing vegan, vegetarian and omnivore diets where specific diet-linked metabolite perturbations have been identified. Another core area where NMR metabolomics is employed involves research focused on examining specific food components or ingredients, including dietary fibers and other functional components. In several cases, NMR metabolomics has aided to document how specific food components exert effects on the metabolic activity of the gut microbiota. Research has also demonstrated the potential use of NMR metabolomics in assessing diet quality and interactions between specific food components such as meat and diet quality. The implications of these findings are important as they address that background diet can be decisive for if food items turn out to exert either harmful or health-promoting effects.

SUMMARY

NMR metabolomics can provide important mechanistic insight and aid to biomarker discovery with implications for compliance and food registration purposes.

A new limit for blood metabolite analysis using 1H NMR spectroscopy

Human blood is the most widely used biospecimen in the clinic and the metabolomics field. While both mass spectrometry and NMR spectroscopy are the two premier analytical platforms in the metabolomics field, NMR exhibits several unsurpassed characteristics for blood metabolite analysis, the most important of which are its ability to identify unknown metabolites and its quantitative nature. However, the relatively small number of metabolites accessible by NMR has restricted the scope of its applications. Enhancing the limit of identified metabolites in blood will therefore greatly impact NMR-based metabolomics. Continuing our efforts to address this major issue, our current study describes the identification of 12 metabolites, which expands the number of quantifiable blood metabolites by ~15%. These results, in combination with our earlier efforts, now provide access to nearly 90 metabolites, which is the highest to date for a simple 1D 1H NMR experiment that is widely used in the metabolomics field. Metabolites were identified based on the comprehensive investigation of human blood and plasma using 1D/2D NMR techniques. The newly identified metabolites were validated based on chemical shift databases, spectra of authentic compounds obtained under conditions identical to blood/plasma, and, finally, spiking experiments using authentic compounds. Considering the high reproducibility of NMR and the sensitivity of chemical shifts to altered sample conditions, experimental protocols and peak annotations are provided for the newly identified metabolites, which serve as a template for identification of blood metabolites for routine applications. Separately, the identified metabolites were evaluated for their sensitivity to preanalytical conditions. The results reveal that among the newly identified metabolites, inosine monophosphate (IMP) and nicotinamide are associated with labile coenzymes and their levels are sensitive to preanalytical conditions. The study demonstrates the expansion of quantifiable blood metabolites using NMR to a new height and is expected to greatly impact blood metabolomics.

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.

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.

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.

Predictive Modeling of Alzheimer's and Parkinson's Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid

In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer's disease (AD), Parkinson's disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.

Biomarker-Based Methods and Study Designs to Calibrate Dietary Intake for Assessing Diet-Disease Associations

BACKGROUND

Dietary biomarkers measured in biospecimens can play an important role in correcting for random and systematic measurement error in self-reported nutrient intake when assessing diet-disease associations. To date, high-quality biomarkers for calibrating self-reported dietary intake have only been developed for a few nutrients.

OBJECTIVES

To investigate new study designs and regression calibration approaches for calibrating self-reported nutrient intake for use in disease association analyses.

METHODS

We studied 3 regression calibration approaches: 1) an existing approach built on a calibration cohort assuming the existence of an objective biomarker (i.e., biomarker with random independent measurement error), 2) a proposed approach using a biomarker development cohort, and 3) a proposed 2-stage approach using both cohorts. We conducted simulation studies to compare the performance of different study designs/methods for estimating diet-disease associations and applied suitable methods to examine the association of sodium and potassium intake with cardiovascular disease (CVD) risk in Women's Health Initiative cohorts.

RESULTS

Simulation studies showed that the first approach can lead to biased association estimation when the objective biomarker assumption is violated; the second and third proposed approaches obviate the need for such an objective biomarker. Precision for estimating the association depends critically on sample size of the biomarker development cohort and the strength of the self-reported nutrient intake. Analyses based on the second and third approaches support previously reported significant findings using the first approach about associations of the ratio of sodium to potassium intake with CVD risk while providing efficiency gain for some outcomes.

CONCLUSIONS

Self-reported dietary intake needs to be calibrated for measurement error correction in diet-disease association analyses. When there are no existing objective biomarkers that can be used for calibration purpose, controlled feeding studies can be used to develop new biomarkers for use in calibration or can be used to calibrate self-reported dietary intake directly.

Plasma Metabolites Associated with a Protein-Rich Dietary Pattern: Results from the OmniHeart Trial

Scope

Lack of biomarkers is a challenge for the accurate assessment of protein intake and interpretation of observational study data. The study aims to identify biomarkers of a protein‐rich dietary pattern.

Methods and Results

The Optimal Macronutrient Intake Trial to Prevent Heart Disease (OmniHeart) trial is a randomized cross‐over feeding study which tested three dietary patterns with varied macronutrient content (carbohydrate‐rich; protein‐rich with about half from plant sources; and unsaturated fat‐rich). In 156 adults, differences in log‐transformed plasma metabolite levels at the end of the protein‐ and carbohydrate‐rich diet periods using paired t‐tests is examined. Partial least‐squares discriminant analysis is used to identify a set of metabolites which are influential in discriminating between the protein‐rich versus carbohydrate‐rich dietary patterns. Of 839 known metabolites, 102 metabolites differ significantly between the protein‐rich and the carbohydrate‐rich dietary patterns after Bonferroni correction, the majority of which are lipids (n = 35), amino acids (n = 27), and xenobiotics (n = 24). Metabolites which are the most influential in discriminating between the protein‐rich and the carbohydrate‐rich dietary patterns represent plant protein intake, food or beverage intake, and preparation methods.

Conclusions

The study identifies many plasma metabolites associated with the protein‐rich dietary pattern. If replicated, these metabolites may be used to assess level of adherence to a similar dietary pattern.