Metabolomics-identified metabolites associated with body mass index and prospective weight gain among Mexican American women

Sitting Interruption Modalities during Prolonged Sitting Acutely Improve Postprandial Metabolome in a Crossover Pilot Trial among Postmenopausal Women

Older adults sit during most hours of the day; more than 30% are considered physically inactive. The accumulation of prolonged sitting time is an exercise-independent risk factor for aging-related conditions such as cardiometabolic disease and cancer. Archival plasma samples from a randomized controlled, four-condition crossover study conducted in 10 postmenopausal women with overweight or obesity were analyzed. During 5-hour conditions completed on separate days, the trial tested three interruption modalities: two-minute stands each 20 min (STS), hourly ten-minute standing breaks (Stand), hourly two-minute walks (Walk), and a controlled sit. Fasting baseline and 5-hour end point (2 h postprandial) samples were used for targeted metabolomic profiling. Condition-associated metabolome changes were compared using paired t-tests. STS eliminated the postprandial elevation of amino acid metabolites that was observed in the control. A norvaline derivative shown to have anti-hypertensive and -hyperglycemic effects was significantly increased during Stand and STS. Post-hoc testing identified 19 significantly different metabolites across the interventions. Tight metabolite clustering by condition was driven by amino acid, vasoactive, and sugar metabolites, as demonstrated by partial least squares-discriminant analyses. This exploratory study suggests that brief, low-intensity modalities of interrupting prolonged sitting can acutely elucidate beneficial cardiometabolic changes in postmenopausal women with cardiometabolic risk.

Branched-Chain and Aromatic Amino Acids, Type 2 Diabetes, and Cardiometabolic Risk Factors among Puerto Rican Adults

(1) Background: Branched-chain and aromatic amino acids (BCAAs/AAAs) have been considered as markers of type 2 diabetes (T2D); however, studies on associations between these metabolites and T2D and cardiometabolic traits in Hispanic populations are limited. The aim of this study was to examine the associations between baseline BCAAs (isoleucine, leucine, valine)/AAAs (phenylalanine, tyrosine) and prevalent and incident T2D, as well as baseline and longitudinal (2 year) changes in cardiometabolic traits (measures of glycemia, dyslipidemia, inflammation, and obesity) in two large cohorts of adults of Puerto Rican descent. (2) Methods: We included participants of the Boston Puerto Rican Health Study (BPRHS, n = 670) and San Juan Overweight Adult Longitudinal study (SOALS, n = 999) with available baseline metabolite and covariate data. T2D diagnosis was defined based on American Diabetes Association criteria. Multivariable logistic (for baseline T2D), Poisson (for incident T2D), and linear (for cardiometabolic traits) regression models were used; cohort-specific results were combined in the meta-analysis and adjusted for multiple comparisons. (3) Results: Higher baseline BCAAs were associated with higher odds of prevalent T2D (OR1SD BCAA score = 1.46, 95% CI: 1.34-1.59, p < 0.0001) and higher risk of incident T2D (IRR1SD BCAA score = 1.24, 95% CI: 1.13-1.37, p < 0.0001). In multivariable longitudinal analysis, higher leucine and valine concentrations were associated with 2-year increase in insulin (beta 1SD leucine = 0.37 mcU/mL, 95% CI: 0.11-0.63, p < 0.05; beta 1SD valine = 0.43 mcU/mL, 95% CI: 0.17-0.68, p < 0.01). Tyrosine was a significant predictor of incident T2D (IRR = 1.31, 95% CI: 1.09-1.58, p < 0.05), as well as 2 year increases in HOMA-IR (beta 1SD tyrosine = 0.13, 95% CI: 0.04-0.22, p < 0.05) and insulin concentrations (beta 1SD tyrosine = 0.37 mcU/mL, 95% CI: 0.12-0.61, p < 0.05). (4) Conclusions: Our results confirmed the associations between BCAAs and prevalent and incident T2D, as well as concurrent measures of glycemia, dyslipidemia, and obesity, previously reported in predominantly White and Asian populations. Baseline leucine, valine, and tyrosine were predictors of 2 year increases in insulin, whereas tyrosine was a significant predictor of deteriorating insulin resistance over time. Our study suggests that BCAAs and tyrosine could serve as early markers of future glycemic changes in Puerto Ricans.

Metabolic remission precedes possible weight regain after gastric bypass surgery

OBJECTIVE

Some patients regain weight to a variable extent from 1 year after Roux-en-Y gastric bypass surgery (RYGB), though rarely reaching preoperative values. The aim of the present study was to investigate whether, when, and to what extent metabolic remission occurs.

METHODS

Fasting metabolite and lipid profiles were determined in blood plasma collected from a nonrandomized intervention study involving 148 patients before RYGB and at 2, 12, and 60 months post RYGB. Both short-term and long-term alterations in metabolism were assessed. Anthropometric and clinical variables were assessed at all study visits.

RESULTS

This study found that the vast majority of changes in metabolite levels occurred during the first 2 months post RYGB. Notably, thereafter the metabolome started to return toward the presurgical state. Consequently, a close-to-presurgical metabolome was observed at the time when patients reached their lowest weight and glucose level. Lipids with longer acyl chains and a higher degree of unsaturation were altered more dramatically compared with shorter and more saturated lipids, suggesting a systematic and reversible lipid remodeling.

CONCLUSIONS

Remission of the metabolic state was observed prior to notable weight regain. Further and more long-term studies are required to assess whether the extent of metabolic remission predicts future weight regain and glycemic deterioration.

The Association of Metabolomic Profiles of a Healthy Lifestyle with Heart Failure Risk in a Prospective Study

Lifestyle has been linked to the incidence of heart failure, but the underlying biological mechanisms remain unclear. Using the metabolomic, lifestyle, and heart failure data of the UK Biobank, we identified and validated healthy lifestyle-related metabolites in a matched case-control and cohort study, respectively. We then evaluated the association of healthy lifestyle-related metabolites with heart failure (HF) risk and the added predictivity of these healthy lifestyle-associated metabolites for HF. Of 161 metabolites, 8 were identified to be significantly related to healthy lifestyle. Notably, omega-3 fatty acids and docosahexaenoic acid (DHA) positively associated with a healthy lifestyle score (HLS) and exhibited a negative association with heart failure risk. Conversely, creatinine negatively associated with a HLS, but was positively correlated with the risk of HF. Adding these three metabolites to the classical risk factor prediction model, the prediction accuracy of heart failure incidence can be improved as assessed by the C-statistic (increasing from 0.806 [95% CI, 0.796-0.816] to 0.844 [95% CI, 0.834-0.854], p-value < 0.001). A healthy lifestyle is associated with significant metabolic alterations, among which metabolites related to healthy lifestyle may be critical for the relationship between healthy lifestyle and HF. Healthy lifestyle-related metabolites might enhance HF prediction, but additional validation studies are necessary.

Obesity, Gut Microbiota, and Metabolome: From Pathophysiology to Nutritional Interventions

Obesity is a disorder identified by an inappropriate increase in weight in relation to height and is considered by many international health institutions to be a major pandemic of the 21st century. The gut microbial ecosystem impacts obesity in multiple ways that yield downstream metabolic consequences, such as affecting systemic inflammation, immune response, and energy harvest, but also the gut-host interface. Metabolomics, a systematized study of low-molecular-weight molecules that take part in metabolic pathways, represents a serviceable method for elucidation of the crosstalk between hosts' metabolism and gut microbiota. In the present review, we confer about clinical and preclinical studies exploring the association of obesity and related metabolic disorders with various gut microbiome profiles, and the effects of several dietary interventions on gut microbiome composition and the metabolome. It is well established that various nutritional interventions may serve as an efficient therapeutic approach to support weight loss in obese individuals, yet no agreement exists in regard to the most effective dietary protocol, both in the short and long term. However, metabolite profiling and the gut microbiota composition might represent an opportunity to methodically establish predictors for obesity control that are relatively simple to measure in comparison to traditional approaches, and it may also present a tool to determine the optimal nutritional intervention to ameliorate obesity in an individual. Nevertheless, a lack of adequately powered randomized trials impedes the application of observations to clinical practice.

Systematic metabolomic studies identified adult adiposity biomarkers with acetylglycine associated with fat loss in vivo

Obesity is associated with various adverse health outcomes. Body fat (BF) distribution is recognized as an important factor of negative health consequences of obesity. Although metabolomics studies, mainly focused on body mass index (BMI) and waist circumference, have explored the biological mechanisms involved in the development of obesity, these proxy composite measures are not accurate and cannot reflect BF distribution, and thus may hinder accurate assessment of metabolic alterations and differential risk of metabolic disorders among individuals presenting adiposity differently throughout the body. Thus, the exact relations between metabolites and BF remain to be elucidated. Here, we aim to examine the associations of metabolites and metabolic pathways with BF traits which reflect BF distribution. We performed systematic untargeted serum metabolite profiling and dual-energy X-ray absorptiometry (DXA) whole body fat scan for 517 Chinese women. We jointly analyzed DXA-derived four BF phenotypes to detect cross-phenotype metabolite associations and to prioritize important metabolomic factors. Topology-based pathway analysis was used to identify important BF-related biological processes. Finally, we explored the relationships of the identified BF-related candidate metabolites with BF traits in different sex and ethnicity through two independent cohorts. Acetylglycine, the top distinguished finding, was validated for its obesity resistance effect through in vivo studies of various diet-induced obese (DIO) mice. Eighteen metabolites and fourteen pathways were discovered to be associated with BF phenotypes. Six of the metabolites were validated in varying sex and ethnicity. The obesity-resistant effects of acetylglycine were observed to be highly robust and generalizable in both human and DIO mice. These findings demonstrate the importance of metabolites associated with BF distribution patterns and several biological pathways that may contribute to obesity and obesity-related disease etiology, prevention, and intervention. Acetylglycine is highlighted as a potential therapeutic candidate for preventing excessive adiposity in future studies.

Gut bacteria-derived 3-phenylpropionylglycine mitigates adipocyte differentiation of 3T3-L1 cells by inhibiting adiponectin-PPAR pathway

BACKGROUND

Gut microbiota provide numerous types of metabolites that humans cannot produce and have a huge influence on the host metabolism. Accordingly, gut bacteria-derived metabolites can be employed as a resource to develop anti-obesity and metabolism-modulating drugs.

OBJECTIVE

This study aimed to examine the anti-adipogenic effect of 3-phenylpropionylglycine (PPG), which is a glycine conjugate of bacteria-derived 3-phenylpropionic acid (PPA).

METHODS

The effect of PPG on preadipocyte-to-adipocyte differentiation was evaluated in 3T3-L1 differentiation models and the degree of the differentiation was estimated by Oil red O staining. The molecular mechanisms of the PPG effect were investigated with transcriptome analyses using RNA-sequencing and quantitative real-time PCR.

RESULTS

PPG suppressed lipid droplet accumulation during the adipogenic differentiation of 3T3-L1 cells, which is attributed to down-regulation of lipogenic genes such as acetyl CoA carboxylase 1 (Acc1) and fatty acid synthase (Fasn). However, other chemicals with chemical structures similar to PPG, including cinnamoylglycine and hippuric acid, had little effect on the lipid accumulation of 3T3-L1 cells. In transcriptomic analysis, PPG suppressed the expression of adipogenesis and metabolism-related gene sets, which is highly associated with downregulation of the peroxisome proliferator-activated receptor (PPAR) signaling pathway. Protein-protein association network analysis suggested adiponectin as a hub gene in the network of genes that were differentially expressed genes in response to PPG treatment.

CONCLUSION

PPG inhibits preadipocyte-to-adipocyte differentiation by suppressing the adiponectin-PPAR pathway. These data provide a potential candidate from bacteria-derived metabolites with anti-adipogenic effects.

A Metabolomic Analysis of the Sex-Dependent Hispanic Paradox

In Mexican Americans, metabolic conditions, such as obesity and type 2 diabetes (T2DM), are not necessarily associated with an increase in mortality; this is the so-called Hispanic paradox. In this cross-sectional analysis, we used a metabolomic analysis to look at the mechanisms behind the Hispanic paradox. To do this, we examined dietary intake and body mass index (BMI; kg/m²) in men and women and their effects on serum metabolomic fingerprints in 70 Mexican Americans (26 men, 44 women). Although having different BMI values, the participants had many similar anthropometric and biochemical parameters, such as systolic and diastolic blood pressure, total cholesterol, and LDL cholesterol, which supported the paradox in these subjects. Plasma metabolomic phenotypes were measured using liquid chromatography tandem mass spectrometry (LC-MS/MS). A two-way ANOVA assessing sex, BMI, and the metabolome revealed 23 significant metabolites, such as 2-pyrrolidinone (p = 0.007), TMAO (p = 0.014), 2-aminoadipic acid (p = 0.019), and kynurenine (p = 0.032). Pathway and enrichment analyses discovered several significant metabolic pathways between men and women, including lysine degradation, tyrosine metabolism, and branch-chained amino acid (BCAA) degradation and biosynthesis. A log-transformed OPLS-DA model was employed and demonstrated a difference due to BMI in the metabolomes of both sexes. When stratified for caloric intake (<2200 kcal/d vs. >2200 kcal/d), a separate OPLS-DA model showed clear separation in men, while females remained relatively unchanged. After accounting for caloric intake and BMI status, the female metabolome showed substantial resistance to alteration. Therefore, we provide a better understanding of the Mexican-American metabolome, which may help demonstrate how this population—particularly women—possesses a longer life expectancy despite several comorbidities, and reveal the underlying mechanisms of the Hispanic paradox.

The long-term genetic stability and individual specificity of the human gut microbiome

By following up the gut microbiome, 51 human phenotypes and plasma levels of 1,183 metabolites in 338 individuals after 4 years, we characterize microbial stability and variation in relation to host physiology. Using these individual-specific and temporally stable microbial profiles, including bacterial SNPs and structural variations, we develop a microbial fingerprinting method that shows up to 85% accuracy in classifying metagenomic samples taken 4 years apart. Application of our fingerprinting method to the independent HMP cohort results in 95% accuracy for samples taken 1 year apart. We further observe temporal changes in the abundance of multiple bacterial species, metabolic pathways, and structural variation, as well as strain replacement. We report 190 longitudinal microbial associations with host phenotypes and 519 associations with plasma metabolites. These associations are enriched for cardiometabolic traits, vitamin B, and uremic toxins. Finally, mediation analysis suggests that the gut microbiome may influence cardiometabolic health through its metabolites.

Mouse Age Matters: How Age Affects the Murine Plasma Metabolome

A large part of metabolomics research relies on experiments involving mouse models, which are usually 6 to 20 weeks of age. However, in this age range mice undergo dramatic developmental changes. Even small age differences may lead to different metabolomes, which in turn could increase inter-sample variability and impair the reproducibility and comparability of metabolomics results. In order to learn more about the variability of the murine plasma metabolome, we analyzed male and female C57BL/6J, C57BL/6NTac, 129S1/SvImJ, and C3HeB/FeJ mice at 6, 10, 14, and 20 weeks of age, using targeted metabolomics (BIOCRATES AbsoluteIDQ™ p150 Kit). Our analysis revealed high variability of the murine plasma metabolome during adolescence and early adulthood. A general age range with minimal variability, and thus a stable metabolome, could not be identified. Age-related metabolomic changes as well as the metabolite profiles at specific ages differed markedly between mouse strains. This observation illustrates the fact that the developmental timing in mice is strain specific. We therefore stress the importance of deliberate strain choice, as well as consistency and precise documentation of animal age, in metabolomics studies.

HIF3A DNA methylation, obesity and weight gain, and breast cancer risk among Mexican American women

OBJECTIVE

In previous epigenome-wide association studies, Hypoxia inducible Factor 3 Alpha Subunit (HIF3A) DNA methylation has been reported to be associated with body mass index (BMI) and weight change. However, none of these studies have included Mexican Americans.

METHODS

In the current study, we assessed levels of HIF3A methylation in 927 Mexican American women identified from Mano-A-Mano, the Mexican American Cohort study.

RESULTS

Significantly higher methylation levels at three CpG sites (position 46801557, 46801642, and 46801699) were observed in obese women compared to non-obese women (P < 0.05). Furthermore, we found that elevated methylation levels at those three CpG sites were associated with significant weight gain (P < 0.05), defined as an increase in BMI by at least one category between the baseline and the follow-up, with a median follow-up time of 39 months. Then, using pre-diagnostic blood DNA samples, we found increased DNA methylation at CpG 46801642 to be associated with a 1.35-fold increased risk of breast cancer (Hazard Ratio (HR) = 1.35, 95% Confidence Interval (CI): 1.02, 3.01), with a median follow-up time of 127 months. Using the Cancer Genome Atlas (TCGA) data, we further found that levels of HIF3A were significantly higher-methylated and down-regulated in breast tumor than in normal tissues (P < 1 × 10¹² for both).

CONCLUSION

Thus, our results provide evidence to support the role of HIF3A in obesity, weight gain, and the development of breast cancer.

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

The metabolic syndrome is a multifactorial disease developed due to accumulation and chronification of several risk factors associated with disrupted metabolism. The early detection of the biomarkers by NMR spectroscopy could be helpful to prevent multifactorial diseases. The exposure of each risk factor can be detected by traditional molecular markers but the current biomarkers have not been enough precise to detect the primary stages of disease. Thus, there is a need to obtain novel molecular markers of pre-disease stages. A promising source of new molecular markers are metabolomics standing out the research of biomarkers in NMR approaches. An increasing number of nutritionists integrate metabolomics into their study design, making nutrimetabolomics one of the most promising avenues for improving personalized nutrition. This review highlight the major five risk factors associated with metabolic syndrome and related diseases including carbohydrate dysfunction, dyslipidemia, oxidative stress, inflammation, and gut microbiota dysbiosis. Together, it is proposed a profile of metabolites of each risk factor obtained from NMR approaches to target them using personalized nutrition, which will improve the quality of life for these patients.

Prediction of disease-related metabolites using bi-random walks

Metabolites play a significant role in various complex human disease. The exploration of the relationship between metabolites and diseases can help us to better understand the underlying pathogenesis. Several network-based methods have been used to predict the association between metabolite and disease. However, some methods ignored hierarchical differences in disease network and failed to work in the absence of known metabolite-disease associations. This paper presents a bi-random walks based method for disease-related metabolites prediction, called MDBIRW. First of all, we reconstruct the disease similarity network and metabolite functional similarity network by integrating Gaussian Interaction Profile (GIP) kernel similarity of diseases and GIP kernel similarity of metabolites, respectively. Then, the bi-random walks algorithm is executed on the reconstructed disease similarity network and metabolite functional similarity network to predict potential disease-metabolite associations. At last, MDBIRW achieves reliable performance in leave-one-out cross validation (AUC of 0.910) and 5-fold cross validation (AUC of 0.924). The experimental results show that our method outperforms other existing methods for predicting disease-related metabolites.

Linking chemical exposure to lipid homeostasis: A municipal waste water treatment plant influent is obesogenic for zebrafish larvae

Obesity, a risk factor for the development of type-2 diabetes, hypertension, cardiovascular disease, hepatic steatosis and some cancers, has been ranked in the top 10 health risk in the world by the World Health Organization. Despite the growing body of literature evidencing an association between the obesity epidemic and specific chemical exposure across a wide range of animal taxa, very few studies assessed the effects of chemical mixtures and environmental samples on lipid homeostasis. Additionally, the mode of action of several chemicals reported to alter lipid homeostasis is still poorly understood. Aiming to fill some of these gaps, we combined an in vivo assay with the model species zebrafish (Danio rerio) to screen lipid accumulation and evaluate expression changes of key genes involved in lipid homeostasis, alongside with an in vitro transactivation assay using human and zebrafish nuclear receptors, retinoid X receptor α and peroxisome proliferator-activated receptor γ. Zebrafish larvae were exposed from 4 th day post-fertilization until the end of the experiment (day 18), to six different treatments: experimental control, solvent control, tributyltin at 100 ng/L Sn and 200 ng/L Sn (positive control), and wastewater treatment plant influent at 1.25% and 2.5%. Exposure to tributyltin and to 2.5% influent led to a significant accumulation of lipids, with white adipose tissue deposits concentrating in the perivisceral area. The highest in vitro tested influent concentration (10%) was able to significantly transactivate the human heterodimer PPARγ/RXRα, thus suggesting the presence in the influent of HsPPARγ/RXRα agonists. Our results demonstrate, for the first time, the ability of complex environmental samples from a municipal waste water treatment plant influent to induce lipid accumulation in zebrafish larvae.

Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain

Background

Excess weight gain throughout adulthood can lead to adverse clinical outcomes and are influenced by complex factors that are difficult to measure in free-living individuals. Metabolite profiling offers an opportunity to systematically discover new predictors for weight gain that are relatively easy to measure compared to traditional approaches.

Methods and results

Using baseline metabolite profiling data of middle-aged individuals from the Framingham Heart Study (FHS; n = 1,508), we identified 42 metabolites associated (p < 0.05) with longitudinal change in body mass index (BMI). We performed stepwise linear regression to select 8 of these metabolites to build a metabolite risk score (MRS) for predicting future weight gain. We replicated the MRS using data from the Mexico City Diabetes Study (MCDS; n = 768), in which one standard deviation increase in the MRS corresponded to ~0.03 increase in BMI (kg/m²) per year (i.e. ~0.09 kg/year for a 1.7 m adult). We observed that none of the available anthropometric, lifestyle, and glycemic variables fully account for the MRS prediction of weight gain. Surprisingly, we found the MRS to be strongly correlated with baseline insulin sensitivity in both cohorts and to be negatively predictive of T2D in MCDS. Genome-wide association study of the MRS identified 2 genome-wide (p < 5 × 10⁻⁸) and 5 suggestively (p < 1 × 10⁻⁶) significant loci, several of which have been previously linked to obesity-related phenotypes.

Conclusions

We have constructed and validated a generalizable MRS for future weight gain that is an independent predictor distinct from several other known risk factors. The MRS captures a composite biological picture of weight gain, perhaps hinting at the anabolic effects of preserved insulin sensitivity. Future investigation is required to assess the relationships between MRS-predicted weight gain and other obesity-related diseases.

Breast cancer risk in relation to plasma metabolites among Hispanic and African American women

PURPOSE

The metabolic etiology of breast cancer has been explored in the past several years using metabolomics. However, most of these studies only included non-Hispanic White individuals.

METHODS

To fill this gap, we performed a two-step (discovery and validation) metabolomics profiling in plasma samples from 358 breast cancer patients and 138 healthy controls. All study subjects were either Hispanics or non-Hispanic African Americans.

RESULTS

A panel of 14 identified metabolites significantly differed between breast cancer cases and healthy controls in both the discovery and validation sets. Most of these identified metabolites were lipids. In the pathway analysis, citrate cycle (TCA cycle), arginine and proline metabolism, and linoleic acid metabolism pathways were observed, and they significantly differed between breast cancer cases and healthy controls in both sets. From those 14 metabolites, we selected 9 non-correlated metabolites to generate a metabolic risk score. Increased metabolites risk score was associated with a 1.87- and 1.63-fold increased risk of breast cancer in the discovery and validation sets, respectively (Odds ratio (OR) 1.87, 95% Confidence interval (CI) 1.50, 2.32; OR 1.63, 95% CI 1.36, 1.95).

CONCLUSIONS

In summary, our study identified metabolic profiles and pathways that significantly differed between breast cancer cases and healthy controls in Hispanic or non-Hispanic African American women. The results from our study might provide new insights on the metabolic etiology of breast cancer.

Impact of dietary fiber supplementation on modulating microbiota-host-metabolic axes in obesity

Low dietary fiber intake is associated with higher rates of microbiota-associated chronic diseases such as obesity. Low-fiber diets alter not only microbial composition but also the availability of metabolic end products derived from fermentation of fiber. Our objective was to examine the effects of dietary fiber supplementation on gut microbiota and associated fecal and serum metabolites in relation to metabolic markers of obesity. We conducted a 12-week, single-center, double-blind, placebo-controlled trial with 53 adults with overweight or obesity. They were randomly assigned to a pea fiber (PF, 15 g/d in wafer form; n=29) or control (CO, isocaloric amount of wafers; n=24) group. Blood and fecal samples were collected at baseline and 12 weeks. Serum metabolomics, gut microbiota and fecal short-chain fatty acids (SCFAs) and bile acids (BAs) were examined. Within-group but not between-group analysis showed a significant effect of treatment on serum metabolites at 12 weeks compared to baseline. Fiber significantly altered fecal SCFAs and BAs with higher acetate and reduced isovalerate, cholate, deoxycholate and total BAs content in the PF group compared to baseline. Microbiota was differentially modulated in the two groups, including an increase in the SCFA producer Lachnospira in the PF group and decrease in the CO group. The change in body weight of participants showed a negative correlation with their change in Lachnospira (r=-0.463, P=.006) abundance. The current study provides insight into the actions of pea fiber and its impact on modulating microbiota-host-metabolic axes in obesity.

The Metabolome and Osteoarthritis: Possible Contributions to Symptoms and Pathology

Osteoarthritis (OA) is a progressive, deteriorative disease of articular joints. Although traditionally viewed as a local pathology, biomarker exploration has shown that systemic changes can be observed. These include changes to cytokines, microRNAs, and more recently, metabolites. The metabolome is the set of metabolites within a biological sample and includes circulating amino acids, lipids, and sugar moieties. Recent studies suggest that metabolites in the synovial fluid and blood could be used as biomarkers for OA incidence, prognosis, and response to therapy. However, based on clinical, demographic, and anthropometric factors, the local synovial joint and circulating metabolomes may be patient specific, with select subsets of metabolites contributing to OA disease. This review explores the contribution of the local and systemic metabolite changes to OA, and their potential impact on OA symptoms and disease pathogenesis.

Familial resemblances in human plasma metabolites are attributable to both genetic and common environmental effects

Metabolites are of great importance for understanding the pathogenesis of several diseases. Understanding the genetic contribution to metabolite concentrations may provide insights into mechanisms of complex diseases. Several studies have investigated heritability of metabolites but none investigated potential influences of genetic and environmental factors on the relationship between metabolites and cardiometabolic (CM) risk factors. Thus, we tested the hypothesis that both genetic and common environmental effects contribute to the variance of plasma metabolite concentrations and that shared genetic and environmental effects explain their phenotypic correlations with CM risk factors. To test this hypothesis, variance component method and bivariate genetic analysis were performed in a family-based sample of 48 French Canadians from 16 families. Familial resemblances were computed for all 147 detected metabolites and 9 (acetylornithine, acylcarnitine C9, arginine, phosphatidylcholine acyl-alkyl C36:4, serotonin, lysophosphatidylcholine acyl C20:4, citrulline, asymmetric dimethylarginine, phosphatidylcholine acyl-alkyl C36:5) showed a significant familial effect (55.7%, 18.7%, and 37.0% for maximal heritability, genetic heritability, and common environmental effect, respectively). Citrulline, phosphatidylcholine acyl-alkyl C36:4, phosphatidylcholine acyl-alkyl C36:5, and serotonin had significant phenotypic correlations with CM risk factors. Citrulline had a positive genetic correlation with apolipoprotein B100, while phosphatidylcholine acyl-alkyl C36:5 had a positive environmental correlation with total cholesterol. In conclusion, familial resemblances in metabolite concentrations were mainly attributable to common environmental effect when considering metabolites with a significant familial effect. Common genetic and environmental factors may also influence the relationship between metabolites and CM risk factors.

Serum Metabolomic Profiling of All-Cause Mortality: A Prospective Analysis in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study Cohort

Tobacco use, hypertension, hyperglycemia, overweight, and inactivity are leading causes of overall and cardiovascular disease (CVD) mortality worldwide, yet the relevant metabolic alterations responsible are largely unknown. We conducted a serum metabolomic analysis of 620 men in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (1985-2013). During 28 years of follow-up, there were 435 deaths (197 CVD and 107 cancer). The analysis included 406 known metabolites measured with ultra-high-performance liquid chromatography/mass spectrometry-gas chromatography/mass spectrometry. We used Cox regression to estimate mortality hazard ratios for a 1-standard-deviation difference in metabolite signals. The strongest associations with overall mortality were N-acetylvaline (hazard ratio (HR) = 1.28; P < 4.1 × 10-5, below Bonferroni statistical threshold) and dimethylglycine, 7-methylguanine, C-glycosyltryptophan, taurocholate, and N-acetyltryptophan (1.23 ≤ HR ≤ 1.32; 5 × 10-5 ≤ P ≤ 1 × 10-4). C-Glycosyltryptophan, 7-methylguanine, and 4-androsten-3β,17β-diol disulfate were statistically significantly associated with CVD mortality (1.49 ≤ HR ≤ 1.62, P < 4.1 × 10-5). No metabolite was associated with cancer mortality, at a false discovery rate of <0.1. Individuals with a 1-standard-deviation higher metabolite risk score had increased all-cause and CVD mortality in the test set (HR = 1.4, P = 0.05; HR = 1.8, P = 0.003, respectively). The several serum metabolites and their composite risk score independently associated with all-cause and CVD mortality may provide potential leads regarding the molecular basis of mortality.

Pre-clinical Pharmacokinetic and Metabolomic Analyses of Isorhapontigenin, a Dietary Resveratrol Derivative

Background: Isorhapontigenin (trans-3,5,4'-trihydroxy-3'-methoxystilbene, ISO), a dietary resveratrol (trans-3,5,4'-trihydroxystilbene) derivative, possesses various health-promoting activities. To further evaluate its medicinal potentials, the pharmacokinetic and metabolomic profiles of ISO were examined in Sprague-Dawley rats. Methods: The plasma pharmacokinetics and metabolomics were monitored by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-tandem mass spectrometry (GC-MS/MS), respectively. Results: Upon intravenous injection (90 μmol/kg), ISO exhibited a fairly rapid clearance (CL) and short mean residence time (MRT). After a single oral administration (100 μmol/kg), ISO was rapidly absorbed and showed a long residence in the systemic circulation. Dose escalation to 200 μmol/kg resulted in higher dose-normalized maximal plasma concentrations (C_max/Dose), dose-normalized plasma exposures (AUC/Dose), and oral bioavailability (F). One-week repeated daily dosing of ISO did not alter its major oral pharmacokinetic parameters. Pharmacokinetic comparisons clearly indicated that ISO displayed pharmacokinetic profiles superior to resveratrol as its C_max/Dose, AUC/Dose, and F were approximately two to three folds greater than resveratrol. Metabolomic investigation revealed that 1-week ISO administration significantly reduced plasma concentrations of arachidonic acid, cholesterol, fructose, allantoin, and cadaverine but increased tryptamine levels, indicating its impact on metabolic pathways related to health-promoting effects. Conclusion: ISO displayed favorable pharmacokinetic profiles and may be a promising nutraceutical in view of its health-promoting properties.

Arsenic Exposure from Drinking Water and Urinary Metabolomics: Associations and Long-Term Reproducibility in Bangladesh Adults

BACKGROUND

Chronic exposure to inorganic arsenic from drinking water has been associated with a host of cancer and noncancer diseases. The application of metabolomics in epidemiologic studies may allow researchers to identify biomarkers associated with arsenic exposure and its health effects.

OBJECTIVE

Our goal was to evaluate the long-term reproducibility of urinary metabolites and associations between reproducible metabolites and arsenic exposure.

METHODS

We studied samples and data from 112 nonsmoking participants (58 men and 54 women) who were free of any major chronic diseases and who were enrolled in the Health Effects of Arsenic Longitudinal Study (HEALS), a large prospective cohort study in Bangladesh. Using a global gas chromatography-mass spectrometry platform, we measured metabolites in their urine samples, which were collected at baseline and again 2 y apart, and estimated intraclass correlation coefficients (ICCs). Linear regression was used to assess the association between arsenic exposure at baseline and metabolite levels in baseline urine samples.

RESULTS

We identified 2,519 molecular features that were present in all 224 urine samples from the 112 participants, of which 301 had an ICC of ≥0.60. Of the 301 molecular features, water arsenic was significantly related to 31 molecular features and urinary arsenic was significantly related to 74 molecular features after adjusting for multiple comparisons. Six metabolites with a confirmed identity were identified from the 82 molecular features that were significantly associated with either water arsenic or urinary arsenic after adjustment for multiple comparisons.

CONCLUSIONS

Our study identified urinary metabolites with long-term reproducibility that were associated with arsenic exposure. The data established the feasibility of using metabolomics in future larger studies. https://doi.org/10.1289/EHP1992.

Peripheral Tryptophan - Kynurenine Metabolism Associated with Metabolic Syndrome is Different in Parkinson's and Alzheimer's Diseases

Insulin resistance (IR), obesity and other components of metabolic syndrome [MetS] are highly associated with Alzheimer's (AD) and Parkinson's (PD) diseases. Dysregulation of kynurenine (Kyn) pathway (KP) of tryptophan (Trp) metabolism was suggested as major contributor to pathogenesis of AD and PD and MetS. KP, the major source of NAD+ in humans, occurs in brain and peripheral organs. Considering that some, but not all, peripherally originated derivatives of Kyn penetrate blood brain barrier, dysregulation of central and peripheral KP might have different functional impact. Up-regulated Kyn formation from Trp was discovered in central nervous system of AD and PD while assessments of peripheral KP in these diseases yield controversial results. We were interested to compare peripheral kynurenines in AD and PD with emphasis on MetS-associated kynurenines, i.e., kynurenic (KYNA) and anthranilic (ANA) acids and 3-hydroxykynurenine (3-HK). Serum concentrations of KP metabolites were evaluated (HPLC-MS method). In PD patients Trp concentrations were lower, and Kyn: Trp ratio, Kyn, ANA and KYNA were higher than in controls. 3-HK concentrations of PD patients were below the sensitivity threshold of the method. In AD patients. ANA serum concentrations were approximately 3 fold lower, and KYNA concentrations were approximately 40% higher than in controls. Our data suggest different patterns of KP dysregulation in PD and AD: systemic chronic subclinical inflammation activating central and peripheral KP in PD, and central, rather than peripheral, activation of KP in AD triggered by Aβ1-42. Dysregulation of peripheral KP in PD and AD patients might underline association between neurodegenerative diseases and MetS.

Insulin resistance and glycine metabolism in humans

Plasma glycine level is low in patients with obesity or diabetes and the improvement of insulin resistance increases plasma glycine concentration. In prospective studies, hypoglycinemia at baseline predicts the risk of developing type 2 diabetes and higher serum glycine level is associated with decreased risk of incident type 2 diabetes. Consistently, plasma glycine concentration is lower in the lean offspring of parents with type 2 diabetes compared to healthy subjects. Among patients with type 2 diabetes, hypoglycinemia occurs before clinical manifestations of the disease, but the pathophysiological mechanisms underlying glycine deficit and its potential clinical repercussions are unclear. Glycine participates in several metabolic pathways, being required for relevant human physiological processes. Humans synthesize glycine from glyoxylate, glucose (via serine), betaine and likely from threonine and during the endogenous synthesis of L-carnitine. Glycine conjugates bile acids and other acyl moieties producing acyl-glycine derivatives. The glycine cleavage system catalyzes glycine degradation to carbon dioxide and ammonium while tetrahydrofolate is converted into 5,10-methylene-tetrahydrofolate. Glycine is utilized to synthesize serine, sarcosine, purines, creatine, heme group, glutathione, and collagen. Glycine is a major quantitative component of collagen. In addition, the role of glycine maintaining collagen structure is critical, as glycine residues are required to stabilize the triple helix of the collagen molecule. This quality of glycine likely contributes to explain the occurrence of medial arterial calcification and the elevated cardiovascular risk associated with diabetes and chronic kidney disease, as emerging evidence links normal collagen content with the initiation and progression of vascular calcification in humans.

Large Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis

Recent metabolomics studies of Rheumatoid Arthritis (RA) reported few metabolites that were associated with the disease, either due to small cohort sizes or limited coverage of metabolic pathways. Our objective is to identify metabolites associated with RA and its cofounders using a new untargeted metabolomics platform. Moreover, to investigate the pathomechanism of RA by identifying correlations between RA-associated metabolites. 132 RA patients and 104 controls were analyzed for 927 metabolites. Metabolites were tested for association with RA using linear regression. OPLS-DA was used to discriminate RA patients from controls. Gaussian Graphical Models (GGMs) were used to identify correlated metabolites. 32 metabolites are identified as significantly (Bonferroni) associated with RA, including the previously reported metabolites as DHEAS, cortisol and androstenedione and extending that to a larger set of metabolites in the steroid pathway. RA classification using metabolic profiles shows a sensitivity of 91% and specificity of 88%. Steroid levels show variation among the RA patients according to the corticosteroid treatment; lowest in those taking the treatment at the time of the study, higher in those who never took the treatment, and highest in those who took it in the past. Finally, the GGM reflects metabolite relations from the steroidogenesis pathway.

High-fat diet-induced acceleration of osteoarthritis is associated with a distinct and sustained plasma metabolite signature

Metabolic changes induced by high fat diet (HFD) that contribute to osteoarthritis (OA) are poorly understood. We investigated longitudinal changes to metabolites and their contribution to OA pathogenesis in response to HFD. HFD-fed mice exhibited acceleration of spontaneous age-related and surgically-induced OA compared to lean diet (LD)-fed mice. Using metabolomics, we identified that HFD-fed mice exhibited a distinct and sustained plasma metabolite signature rich in phosphatidylcholines (PC) and lysophosphatidylcholines (lysoPCs), even after resumption of normal chow diet. Using receiver operator curve analysis and prediction modelling, we showed that the concentration of these identified metabolites could efficiently predict the type of diet and OA risk with an accuracy of 93%. Further, longitudinal evaluation of knee joints of HFD- compared to LD- fed mice showed a greater percentage of leptin-positive chondrocytes. Mechanistic data showed that leptin-treated human OA chondrocytes exhibited enhanced production of lysoPCs and expression of autotaxin and catabolic MMP-13. Leptin-induced increased MMP13 expression was reversed by autotaxin inhibition. Together, this study is the first to describe a distinct and sustained HFD-induced metabolite signature. This study suggests that in addition to increased weight, identified metabolites and local leptin-signaling may also contribute in part, towards the accelerated OA-phenotype observed in HFD mice.

Does the 1H-NMR plasma metabolome reflect the host-tumor interactions in human breast cancer?

Breast cancer (BC) is the most common diagnosed cancer and the leading cause of cancer death in women worldwide. There is an obvious need for a better understanding of BC biology. Alterations in the serum metabolome of BC patients have been identified but their clinical significance remains elusive. We evaluated by 1H-Nuclear Magnetic Resonance (1H-NMR) spectroscopy, filtered plasma metabolome of 50 early (EBC) and 15 metastatic BC (MBC) patients. Using Principal Component Analysis, Partial Least-Squares Discriminant Analysis and Hierarchical Clustering we show that plasma levels of glucose, lactate, pyruvate, alanine, leucine, isoleucine, glutamate, glutamine, valine, lysine, glycine, threonine, tyrosine, phenylalanine, acetate, acetoacetate, β-hydroxy-butyrate, urea, creatine and creatinine are modulated across patients clusters. In particular lactate levels are inversely correlated with the tumor size in the EBC cohort (Pearson correlation r = -0.309; p = 0.044). We suggest that, in BC patients, tumor cells could induce modulation of the whole patient's metabolism even at early stages. If confirmed in a lager study these observations could be of clinical importance.

Peripheral kynurenine-3-monooxygenase deficiency as a potential risk factor for metabolic syndrome in schizophrenia patients

Increased predisposition of schizophrenia patients (SP) to development of obesity and insulin resistance suggested common signaling pathway between metabolic syndrome (MetS) and schizophrenia. Deficiency of kynurenine-3-monooxygenase (KMO), enzyme catalyzing formation of 3-hydroxykynurenine (3-HK) from kynurenine (Kyn), a tryptophan (Trp) metabolite, might contribute to development of MetS as suggested by non-expression of KMO genes in human fat tissue and elevated serum concentrations of Kyn and its metabolites, kynurenic (KYNA) and anthranilic (ANA) acids, in diabetic patients and Zucker fatty rats (ZFR). Markers of KMO deficiency: decreased 3-HK and elevated Kyn, KYNA and ANA, were observed in brains and spinal fluids of SP, and in brains and serum of experimental animals with genetically- or pharmacologically-induced KMO deficiency. However, elevated concentrations of ANA and decreased 3-HK were reported in serum of SP without concurrent increase of Kyn and KYNA. Present study aimed to re-assess serum Kyn metabolites (HPLC-MS) in a sub-group of SP with elevated KYNA. We found increased Kyn concentrations (by 30%) and Kyn:Trp ratio (by 20%) in serum of SP with elevated KYNA concentrations (by 40%). Obtained results and our previous data suggest that peripheral KMO deficiency might be manifested by, at least, two different patterns: elevated ANA with decreased 3-HK; and elevated KYNA and KYN. The latter pattern was previously described in type 2 diabetes patients and might underline increased predisposition of SP to development of MetS. Assessment of peripheral KMO deficiency might identify SP predisposed to MetS. Attenuation of the consequences of peripheral KMO deficiency might be a new target for prevention/treatment of obesity and diabetes in SP.

Metabolomics-identified metabolites associated with body mass index and prospective weight gain among Mexican American women

Objective

Obesity is a metabolic disease. However, the underlying molecular mechanisms linking metabolic profiles and weight gain are largely unknown.

Methods

Here, we used semi‐targeted metabolomics to assay 156 metabolites selected from 25 key metabolic pathways in plasma samples from 300 non‐smoking healthy women identified from Mano‐A‐Mano, the Mexican American Cohort study. The study subjects were randomly divided into two cohorts: training (N = 200) and testing (N = 100) cohorts. Linear regression and Cox proportional hazard regression were used to assess the effect of body mass index (BMI) at baseline on metabolite levels and the effects of metabolites on significant weight gain during a 5‐year follow‐up.

Results

At baseline, we observed 7 metabolites significantly associated with BMI in both training and testing cohorts. They were Methyl succinate, Asparagine, Urate, Kynurenic acid, Glycine, Glutamic acid, and Serine. In further analysis, we identified 6 metabolites whose levels at baseline predicted significant weight gain during 5‐year follow‐up in both cohorts. They were Acetylcholine, Leucine, Hippuric acid, Acetylglycine, Urate, and Xanthine.

Conclusions

The findings establish the baseline metabolic profiles for BMI, and suggest new metabolic targets for researchers attempting to understand the molecular mechanisms of weight gain and obesity.