Metabolomics approach reveals effects of antihypertensives and lipid-lowering drugs on the human metabolism

Integrated metabolomics analysis reveals mechanistic insights into variability in blood pressure response to thiazide diuretics and beta blockers

Hypertensive patients with a higher proportion of genetic West African ancestry (%GWAA) have better blood pressure (BP) response to thiazide diuretics (TDs) and worse response to β-blockers (BBs) than those with lower %GWAA, associated with their lower plasma renin activity (PRA). TDs and BBs are suggested to reduce BP in the long term through vasodilation via incompletely understood mechanisms. This study aimed at identifying pathways underlying ancestral differences in PRA, which might reflect pathways underlying BP-lowering mechanisms of TDs and BBs. Among hypertensive participants enrolled in the Pharmacogenomics Evaluation of Antihypertensive Responses (PEAR) and PEAR-2 trials, we previously identified 8 metabolites associated with baseline PRA and 4 metabolic clusters (including 39 metabolites) that are different between those with GWAA <45% versus ≥45%. In the current study, using Ingenuity Pathway Analysis (IPA), we integrated these signals. Three overlapping metabolic signals within three significantly enriched pathways were identified as associated with both PRA and %GWAA: ceramide signaling, sphingosine 1- phosphate signaling, and endothelial nitric oxide synthase signaling. Literature indicates that the identified pathways are involved in the regulation of the Rho kinase cascade, production of the vasoactive agents nitric oxide, prostacyclin, thromboxane A2, and endothelin 1; the pathways proposed to underlie TD- and BB-induced vasodilatation. These findings may improve our understanding of the BP-lowering mechanisms of TDs and BBs. This might provide a possible step forward in personalizing antihypertensive therapy by identifying patients expected to have robust BP-lowering effects from these drugs.

Cross-sectional analyses of metabolites across biological samples mediating dietary acid load and chronic kidney disease

Chronic kidney disease (CKD) is a major public health burden, with dietary acid load (DAL) and gut microbiota playing crucial roles. As DAL can affect the host metabolome, potentially via the gut microbiota, we cross-sectionally investigated the interplay between DAL, host metabolome, gut microbiota, and early-stage CKD (TwinsUK, n = 1,453). DAL was positively associated with CKD stage G1-G2 (Beta (95% confidence interval) = 0.34 (0.007; 0.7), p = 0.046). After adjusting for covariates and multiple testing, we identified 15 serum, 14 urine, 8 stool, and 7 saliva metabolites, primarily lipids and amino acids, associated with both DAL and CKD progression. Of these, 8 serum, 2 urine, and one stool metabolites were found to mediate the DAL-CKD association. Furthermore, the stool metabolite 5-methylhexanoate (i7:0) correlated with 26 gut microbial species. Our findings emphasize the gut microbiota's therapeutic potential in countering DAL's impact on CKD through the host metabolome. Interventional and longitudinal studies are needed to establish causality.

The determinants of fasting and post-load non-esterified fatty acids in older adults: The cardiovascular health study

Aim

Non-esterified fatty acids (NEFA) are potential targets for prevention of key cardiometabolic diseases of aging, but their population-level correlates remain uncertain. We sought to identify modifiable factors associated with fasting and post-load NEFA levels in older adults.

Methods

We used linear regression to determine the cross-sectional associations of demographic, anthropometric, and lifestyle characteristics and medication use with serum fasting and post-load NEFA concentrations amongst community-dwelling older adults enrolled in the Cardiovascular Health Study (n = 1924).

Results

Fasting NEFA levels generally demonstrated a broader set of determinants, while post-load NEFA were more consistently associated with metabolic factors. Waist circumference and weight were associated with higher fasting and post-load NEFA. Cigarette smoking and caffeine intake were associated with lower levels of both species, and moderate alcohol intake was associated with higher fasting levels whereas greater consumption was associated with lower post-load levels. Unique factors associated with higher fasting NEFA included female sex, higher age, loop and thiazide diuretic use and calcium intake, while factors associated with lower fasting levels included higher educational attainment, beta-blocker use, and protein intake. Hours spent sleeping during the daytime were associated with higher post-load NEFA, while DASH score was associated with lower levels.

Conclusion

Fasting and post-load NEFA have both common and unique modifiable risk factors, including sociodemographics, anthropometric, medications, and diet. Post-load NEFA were particularly sensitive to metabolic factors, while a broader range of determinants were associated with fasting levels. These factors warrant study as targets for lowering levels of NEFA in older adults.

Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome

BACKGROUND

B vitamins such as folate (B9), B6, and B12 are key in one carbon metabolism, which generates methyl donors for DNA methylation. Several studies have linked differential methylation to self-reported intakes of folate and B12, but these estimates can be imprecise, while metabolomic biomarkers can offer an objective assessment of dietary intakes. We explored blood metabolomic biomarkers of folate and vitamins B6 and B12, to carry out epigenome-wide analyses across up to three European cohorts. Associations between self-reported habitual daily B vitamin intakes and 756 metabolites (Metabolon Inc.) were assessed in serum samples from 1064 UK participants from the TwinsUK cohort. The identified B vitamin metabolomic biomarkers were then used in epigenome-wide association tests with fasting blood DNA methylation levels at 430,768 sites from the Infinium HumanMethylation450 BeadChip in blood samples from 2182 European participants from the TwinsUK and KORA cohorts. Candidate signals were explored for metabolite associations with gene expression levels in a subset of the TwinsUK sample (n = 297). Metabolomic biomarker epigenetic associations were also compared with epigenetic associations of self-reported habitual B vitamin intakes in samples from 2294 European participants.

RESULTS

Eighteen metabolites were associated with B vitamin intakes after correction for multiple testing (Bonferroni-adj. p < 0.05), of which 7 metabolites were available in both cohorts and tested for epigenome-wide association. Three metabolites - pipecolate (metabolomic biomarker of B6 and folate intakes), pyridoxate (marker of B6 and folate) and docosahexaenoate (DHA, marker of B6) - were associated with 10, 3 and 1 differentially methylated positions (DMPs), respectively. The strongest association was observed between DHA and DMP cg03440556 in the SCD gene (effect = 0.093 ± 0.016, p = 4.07E-09). Pyridoxate, a catabolic product of vitamin B6, was inversely associated with CpG methylation near the SLC1A5 gene promoter region (cg02711608 and cg22304262) and with SLC7A11 (cg06690548), but not with corresponding changes in gene expression levels. The self-reported intake of folate and vitamin B6 had consistent but non-significant associations with the epigenetic signals.

CONCLUSION

Metabolomic biomarkers are a valuable approach to investigate the effects of dietary B vitamin intake on the human epigenome.

Mendelian Randomization Study of Lipid Metabolites Reveals Causal Associations with Heel Bone Mineral Density

BACKGROUND

Osteoporosis, which is a bone disease, is characterized by low bone mineral density and an increased risk of fractures. The heel bone mineral density is often used as a representative measure of overall bone mineral density. Lipid metabolism, which includes processes such as fatty acid metabolism, glycerol metabolism, inositol metabolism, bile acid metabolism, carnitine metabolism, ketone body metabolism, sterol and steroid metabolism, etc., may have an impact on changes in bone mineral density. While some studies have reported correlations between lipid metabolism and heel bone mineral density, the overall causal relationship between metabolites and heel bone mineral density remains unclear.

OBJECTIVE

to investigate the causal relationship between lipid metabolites and heel bone mineral density using two-sample Mendelian randomization analysis.

METHODS

Summary-level data from large-scale genome-wide association studies were extracted to identify genetic variants linked to lipid metabolite levels. These genetic variants were subsequently employed as instrumental variables in Mendelian randomization analysis to estimate the causal effects of each lipid metabolite on heel bone mineral density. Furthermore, metabolites that could potentially be influenced by causal relationships with bone mineral density were extracted from the KEGG and WikiPathways databases. The causal associations between these downstream metabolites and heel bone mineral density were then examined. Lastly, a sensitivity analysis was conducted to evaluate the robustness of the results and address potential sources of bias.

RESULTS

A total of 130 lipid metabolites were analyzed, and it was found that acetylcarnitine, propionylcarnitine, hexadecanedioate, tetradecanedioate, myo-inositol, 1-arachidonoylglycerophosphorine, 1-linoleoylglycerophoethanolamine, and epiandrosterone sulfate had a causal relationship with heel bone mineral density (p < 0.05). Furthermore, our findings also indicate an absence of causal association between the downstream metabolites associated with the aforementioned metabolites identified in the KEGG and WikiPathways databases and heel bone mineral density.

CONCLUSION

This work supports the hypothesis that lipid metabolites have an impact on bone health through demonstrating a causal relationship between specific lipid metabolites and heel bone mineral density. This study has significant implications for the development of new strategies to osteoporosis prevention and treatment.

Machine learning identifying peripheral circulating metabolites associated with intraocular pressure alterations

AIMS

To identify blood metabolite markers associated with intraocular pressure (IOP) in a population-based cross-sectional study.

METHODS

This study was conducted in a multiethnic Asian population (Chinese, n=2805; Indians, n=3045; Malays, n=3041 aged 40-80 years) in Singapore. All subjects underwent standardised systemic and ocular examinations, and biosamples were collected. Selected metabolites (n=228) in either serum or plasma were analysed and quantified using nuclear magnetic resonance spectroscopy. Least absolute shrinkage and selection operator regression was used for metabolites selection. Multivariable linear regression was used to evaluate the relationship between metabolites and IOP in each of the three ethnic groups, followed by a meta-analysis combining the three cohorts.

RESULTS

Six metabolites, including albumin, glucose, lactate, glutamine, ratio of saturated fatty acids to total fatty acids (SFAFA) and cholesterol esters in very large high-density lipoprotein (HDL), were significantly associated with IOP in all three cohorts. Higher levels of albumin (per SD, beta=0.24, p=0.002), lactate (per SD, beta=0.27, p=0.008), glucose (per SD, beta=0.11, p=0.010) and cholesterol esters in very large HDL (per SD, beta=0.47, p=0.006), along with lower levels of glutamine (per SD, beta=0.17, p<0.001) and SFAFA (per SD, beta=0.21, p=0.008) were associated with higher IOP levels.

CONCLUSION

We identify several novel blood metabolites associated with IOP. These findings may provide insight into the physiological and pathological processes underlying IOP control.

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.

The Promises of Proteomics and Metabolomics for Unravelling the Mechanism and Side Effect Landscape of Beta-Adrenoceptor Antagonists in Cardiovascular Therapeutics

Cardiovascular medicine witnessed notable advances for the past decade. Multiomics research offers a new lens for precision/personalized medicine for existing and emerging drugs used in the cardiovascular clinic. Beta-blockers are vital in treating hypertension and chronic heart failure. However, clinical use of beta-blockers is also associated with side effects and person-to-person variations in their pharmacokinetics and pharmacodynamics. A comprehensive understanding of the mechanisms that underpin the side effect landscape of beta-blockers is imperative to optimize their therapeutic value. In addition, current research emphasizes the circadian clock's vital roles in regulating pharmacological parameters. Administration of the beta-blockers at specific dosing times could potentially improve their effectiveness and reduce their toxic effects. The rapid development of mass spectrometry technologies with chemical proteomics and thermal proteome profiling methods has also substantially advanced our understanding of underlying side effects mechanisms by unbiased deconvolution of drug targets and off-targets. Metabolomics is steadily demonstrating its utility for conducting mechanistic and toxicological analyses of pharmacological compounds. This article discusses the promises of cutting-edge proteomics and metabolomics approaches to investigate the molecular targets, mechanism of action, adverse effects, and dosing time dependency of beta-blockers.

Mendelian Randomization Study on Causal Association of Pyroglutamine with COVID-19

Background

Glutamine family amino acids such as glutamate, pyroglutamate, and glutamine have been shown to play important roles in COVID-19. However, it is still unclear about the role of pyroglutamate in COVID-19. Thus, we use a two-sample Mendelian randomization (MR) study to identify the genetic causal link between blood pyroglutamine levels and COVID-19 risk.

Methods

Pyroglutamine genetic instrumental variables (IVs) were chosen from the largest pyroglutamine-associated genome-wide association studies (GWAS). The largest COVID-19 GWAS dataset was employed to evaluate the causal link between blood pyroglutamine levels and COVID-19 risk using two-sample MR analysis.

Results

We found no significant pleiotropy or heterogeneity of pyroglutamine-associated genetic IVs in COVID-19 GWAS. Interestingly, we found that as pyroglutamine genetically increased, the risk of COVID-19 decreased using inverse variance weighted (IVW) (Beta = − 0.644, p = 0.003; OR = 0.525, 95% CI [0.346–0.798]) and weighted median (Beta = − 0.609, p = 0.013; OR = 0.544, 95% CI [0.337–0.878]).

Conclusion

Our analysis suggests a causal link between genetically increased pyroglutamine and reduced risk of COVID-19. Thus, pyroglutamine may be a protective factor for patients with COVID-19.

Influence of Genetic West African Ancestry on Metabolomics among Hypertensive Patients

Patients with higher genetic West African ancestry (GWAA) have hypertension (HTN) that is more difficult to treat and have higher rates of cardiovascular diseases (CVD) and differential responses to antihypertensive drugs than those with lower GWAA. The mechanisms underlying these disparities are poorly understood. Using data from 84 ancestry-informative markers in US participants from the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) and PEAR-2 trials, the GWAA proportion was estimated. Using multivariable linear regression, the baseline levels of 886 metabolites were compared between PEAR participants with GWAA < 45% and those with GWAA ≥ 45% to identify differential metabolites and metabolic clusters. Metabolites with a false discovery rate (FDR) < 0.2 were used to create metabolic clusters, and a cluster analysis was conducted. Differential clusters were then tested for replication in PEAR-2 participants. We identified 353 differential metabolites (FDR < 0.2) between PEAR participants with GWAA < 45% (n = 383) and those with GWAA ≥ 45% (n = 250), which were used to create 24 metabolic clusters. Of those, 13 were significantly different between groups (Bonferroni p < 0.002). Four clusters, plasmalogen and lysoplasmalogen, sphingolipid metabolism and ceramide, cofactors and vitamins, and the urea cycle, were replicated in PEAR-2 (Bonferroni p < 0.0038) and have been previously linked to HTN and CVD. Our findings may give insights into the mechanisms underlying HTN racial disparities.

Diet- and microbiota-related metabolite, 5-aminovaleric acid betaine (5-AVAB), in health and disease

5-Aminovaleric acid betaine (5-AVAB) is a trimethylated compound associated with the gut microbiota, potentially produced endogenously, and related to the dietary intake of certain foods such as whole grains. 5-AVAB accumulates within the metabolically active tissues and has been typically found in higher concentrations in the heart, muscle, and brown adipose tissue. Furthermore, 5-AVAB has been associated with positive health effects such as fetal brain development, insulin secretion, and reduced cancer risk. However, it also has been linked with some negative health outcomes such as cardiovascular disease and fatty liver disease. At the cellular level, 5-AVAB can influence cellular energy metabolism by reducing β-oxidation of fatty acids. This review will focus on the metabolic role of 5-AVAB with respect to both physiology and pathology. Moreover, the analytics and origin of 5-AVAB and related compounds will be reviewed.

Impact of medication on blood transcriptome reveals off-target regulations of beta-blockers

Background

For many drugs, mechanisms of action with regard to desired effects and/or unwanted side effects are only incompletely understood. To investigate possible pleiotropic effects and respective molecular mechanisms, we describe here a catalogue of commonly used drugs and their impact on the blood transcriptome.

Methods and results

From a population-based cohort in Germany (LIFE-Adult), we collected genome-wide gene-expression data in whole blood using in Illumina HT12v4 micro-arrays (n = 3,378; 19,974 gene expression probes per individual). Expression profiles were correlated with the intake of active substances as assessed by participants’ medication. This resulted in a catalogue of fourteen substances that were identified as associated with differential gene expression for a total of 534 genes. As an independent replication cohort, an observational study of patients with suspected or confirmed stable coronary artery disease (CAD) or myocardial infarction (LIFE-Heart, n = 3,008, 19,966 gene expression probes per individual) was employed. Notably, we were able to replicate differential gene expression for three active substances affecting 80 genes in peripheral blood mononuclear cells (carvedilol: 25; prednisolone: 17; timolol: 38). Additionally, using gene ontology enrichment analysis, we demonstrated for timolol a significant enrichment in 23 pathways, 19 of them including either GPER1 or PDE4B. In the case of carvedilol, we showed that, beside genes with well-established association with hypertension (GPER1, PDE4B and TNFAIP3), the drug also affects genes that are only indirectly linked to hypertension due to their effects on artery walls or their role in lipid biosynthesis.

Conclusions

Our developed catalogue of blood gene expressions profiles affected by medication can be used to support both, drug repurposing and the identification of possible off-target effects.

Urine Metabolites as a Predictor of Warfarin Response Based on INR in Atrial Fibrillation

Background:

Warfarin is an anticoagulant with wide inter-individual variations in drug responses monitored based on the International Normalized Ratio (INR). It is commonly prescribed for atrial fibrillation (AF) and stroke. Oral anticoagulants (e.g., warfarin) reduce the risk of getting a stroke but increase the risk of hemorrhage. The proton nuclear magnetic resonance (1H-NMR) pharmacometabonomics technique is useful for determining drug responses. Furthermore, pharmacometabonomics analysis can help identify novel biomarkers of warfarin outcome/INR stability in urine.

Objectives:

The focus of this research was to determine if urine metabolites could predict the warfarin response based on INR in patients who were already taking warfarin (identification; phase I) and to determine if urine metabolites could distinguish between unstable and stable INR in patients who had just started taking warfarin (validation; phase II).

Methods:

A cross-sectional study was conducted. Ninety urine samples were collected for phase 1, with 49 having unstable INR and 41 having stable INR. In phase II, 21 urine samples were obtained, with 13 having an unstable INR and eight having a stable INR. The metabolites associated with unstable INR and stable INR could be determined using univariate and multivariate logistic regression analysis.

Results:

Multivariate logistic regression (MVLR) analysis showed that unstable INR was linked with seven regions.

Discussion:

The urine pharmacometabonomics technique utilized could differentiate between the urine metabolite profiles of the patients on warfarin for INR stability.

Conclusion:

1H-NMR-based pharmacometabonomics can help lead to a more individualized, controlled side effect for warfarin, thus minimizing undesirable effects in the future.

Serum Metabolomic and Lipidomic Profiling Reveals Novel Biomarkers of Efficacy for Benfotiamine in Alzheimer's Disease

Serum metabolomics and lipidomics are powerful approaches for discovering unique biomarkers in various diseases and associated therapeutics and for revealing metabolic mechanisms of both. Treatment with Benfotiamine (BFT), a thiamine prodrug, for one year produced encouraging results for patients with mild cognitive impairment and mild Alzheimer’s disease (AD). In this study, a parallel metabolomics and lipidomics approach was applied for the first exploratory investigation on the serum metabolome and lipidome of patients treated with BFT. A total of 315 unique metabolites and 417 lipids species were confidently identified and relatively quantified. Rigorous statistical analyses revealed significant differences between the placebo and BFT treatment groups in 25 metabolites, including thiamine, tyrosine, tryptophan, lysine, and 22 lipid species, mostly belonging to phosphatidylcholines. Additionally, 10 of 11 metabolites and 14 of 15 lipid species reported in previous literature to follow AD progression changed in the opposite direction to those reported to reflect AD progression. Enrichment and pathway analyses show that significantly altered metabolites by BFT are involved in glucose metabolism and biosynthesis of aromatic amino acids. Our study discovered that multiple novel biomarkers and multiple mechanisms that may underlie the benefit of BFT are potential therapeutic targets in AD and should be validated in studies with larger sample sizes.

Analyses of serum and urinary metabolites in individuals with peripheral artery disease (PAD) consuming a bean-rich diet: Relationships with drug metabolites

Peripheral artery disease (PAD) has high morbidity and mortality rates. A metabolomics approach was employed to determine whether consumption of bean-rich diets for 8 weeks would impact the metabolomic profile of PAD individuals. Serum and urine, collected from 54 participants with clinical PAD at baseline and after 8 weeks on 0.3 cups beans/d (n=19), 0.6 cups beans/d (n= 20), or control (n=23) diet, and the beans were extracted and analyzed using LC-QTOF-MS. As a result, PGE2 p-acetamidophenyl ester, PGF2α diethyl amide and 5-L-glutamyl-L-alanine were significantly changed in the serum or urine of bean groups compared to control. Significant changes (P<0.05) in the profile and/or levels of 22 flavonoids present in bean extracts showed the potential importance of the mixture of beans used in this study. In a subset of participants taking metoprolol, after 8 weeks the bean-rich diets significantly elevated metoprolol in the serum while reducing it in urine compared to baseline. In addition, the diets significantly enhanced the urinary excretion of metformin. In conclusion, several biochemical pathways including prostaglandins and glutathione were affected by bean consumption. Significant changes in the metabolism of metoprolol and metformin with bean consumption suggested the presence of diet-drug interactions that may require adjustment of the prescribed dose. ClinicalTrials.gov Identifier: NCT01382056 Novelty: • Bean consumption by people with PAD alters the levels of certain metabolites in serum and urine • Different bean types (black, red kidney, pinto, navy) have unique flavonoid profiles • Metabolomics revealed potential diet-dug interactions as serum and/or urinary levels of metoprolol and metformin are modified by bean consumption.

Self-Reported Medication Use and Urinary Drug Metabolites in the German Chronic Kidney Disease (GCKD) Study

BACKGROUND

Polypharmacy is common among patients with CKD, but little is known about the urinary excretion of many drugs and their metabolites among patients with CKD.

METHODS

To evaluate self-reported medication use in relation to urine drug metabolite levels in a large cohort of patients with CKD, the German Chronic Kidney Disease study, we ascertained self-reported use of 158 substances and 41 medication groups, and coded active ingredients according to the Anatomical Therapeutic Chemical Classification System. We used a nontargeted mass spectrometry-based approach to quantify metabolites in urine; calculated specificity, sensitivity, and accuracy of medication use and corresponding metabolite measurements; and used multivariable regression models to evaluate associations and prescription patterns.

RESULTS

Among 4885 participants, there were 108 medication-drug metabolite pairs on the basis of reported medication use and 78 drug metabolites. Accuracy was excellent for measurements of 36 individual substances in which the unchanged drug was measured in urine (median, 98.5%; range, 61.1%-100%). For 66 pairs of substances and their related drug metabolites, median measurement-based specificity and sensitivity were 99.2% (range, 84.0%-100%) and 71.7% (range, 1.2%-100%), respectively. Commonly prescribed medications for hypertension and cardiovascular risk reduction-including angiotensin II receptor blockers, calcium channel blockers, and metoprolol-showed high sensitivity and specificity. Although self-reported use of prescribed analgesics (acetaminophen, ibuprofen) was <3% each, drug metabolite levels indicated higher usage (acetaminophen, 10%-26%; ibuprofen, 10%-18%).

CONCLUSIONS

This comprehensive screen of associations between urine drug metabolite levels and self-reported medication use supports the use of pharmacometabolomics to assess medication adherence and prescription patterns in persons with CKD, and indicates under-reported use of medications available over the counter, such as analgesics.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

Metabolomic and transcriptomic analysis reveals endogenous substrates and metabolic adaptation in rats lacking Abcg2 and Abcb1a transporters

Abcg2/Bcrp and Abcb1a/Pgp are xenobiotic efflux transporters limiting substrate permeability in the gastrointestinal system and brain, and increasing renal and hepatic drug clearance. The systemic impact of Bcrp and Pgp ablation on metabolic homeostasis of endogenous substrates is incompletely understood. We performed untargeted metabolomics of cerebrospinal fluid (CSF) and plasma, transcriptomics of brain, liver and kidney from male Sprague Dawley rats (WT) and Bcrp/Pgp double knock-out (dKO) rats, and integrated metabolomic/transcriptomic analysis to identify putative substrates and perturbations in canonical metabolic pathways. A predictive Bayesian machine learning model was used to predict in silico those metabolites with greater substrate-like features for either transporters. The CSF and plasma levels of 169 metabolites, nutrients, signaling molecules, antioxidants and lipids were significantly altered in dKO rats, compared to WT rats. These metabolite changes suggested alterations in histidine, branched chain amino acid, purine and pyrimidine metabolism in the dKO rats. Levels of methylated and sulfated metabolites and some primary bile acids were increased in dKO CSF or plasma. Elevated uric acid levels appeared to be a primary driver of changes in purine and pyrimidine biosynthesis. Alterations in Bcrp/Pgp dKO CSF levels of antioxidants, precursors of neurotransmitters, and uric acid suggests the transporters may contribute to the regulation of a healthy central nervous system in rats. Microbiome-generated metabolites were found to be elevated in dKO rat plasma and CSF. The altered dKO metabolome appeared to cause compensatory transcriptional change in urate biosynthesis and response to lipopolysaccharide in brain, oxidation-reduction processes and response to oxidative stress and porphyrin biosynthesis in kidney, and circadian rhythm genes in liver. These findings present insight into endogenous functions of Bcrp and Pgp, the impact that transporter substrates, inhibitors or polymorphisms may have on metabolism, how transporter inhibition could rewire drug sensitivity indirectly through metabolic changes, and identify functional Bcrp biomarkers.

Comparative Study of Metabolite Changes After Antihypertensive Therapy With Calcium Channel Blockers or Angiotensin Type 1 Receptor Blockers

ABSTRACT

The high prevalence of hypertension contributes to an increased global burden of cardiovascular diseases. Calcium channel blockers (CCBs) and angiotensin type 1 receptor blockers (ARBs) are the most widely used antihypertensive drugs, and the effects of these drugs on serum metabolites remain unknown. Untargeted metabolomics has been proved to be a powerful approach for the detection of biomarkers and new compounds. In this study, we aimed to determine the changes in metabolites after single-drug therapy with a CCB or ARB in patients newly diagnosed with mild to moderate primary hypertension. We enrolled 33 patients and used an untargeted metabolomics approach to measure 625 metabolites associated with the response to a 4-week treatment of antihypertensive drugs. After screening based on P < 0.05, fold change > 1.2 or fold change < 0.83, and variable importance in projection > 1, 63 differential metabolites were collected. Four metabolic pathways-cysteine and methionine metabolism, phenylalanine metabolism, taurine and hypotaurine metabolism, and tyrosine metabolism-were identified in participants treated with ARBs. Only taurine and hypotaurine metabolism were identified in participants treated with CCBs. Furthermore, homocitrulline and glucosamine-6-phosphate were relevant to whether the blood pressure reduction achieved the target blood pressure (P < 0.05). Our study provides some evidence that changes in certain metabolites may be a potential marker for the dynamic monitoring of the protective effects and side effects of antihypertensive drugs.

Metabolomics of Interstitial Fluid, Plasma and Urine in Patients with Arterial Hypertension: New Insights into the Underlying Mechanisms

There is growing evidence that lymphatic system plays a pivotal role in the pathogenesis of hypertension. Here, for the first time, the metabolome of interstitial fluid is analyzed in patients with arterial hypertension. Due to ethical issues to obtain human interstitial fluid samples, this study included only oncological patients after axillary lymph node dissection (ALND). These patients were matched into hypertensive (n = 29) and normotensive (n = 35) groups with similar oncological status. Simultaneous evaluation of interstitial fluid, plasma, and urine was obtained by combining high-resolution proton nuclear magnetic resonance (¹H NMR) spectroscopy with chemometric analysis. Orthogonal partial least squares discriminant analysis (OPLS-DA) provided a clear differentiation between the hypertension and normotensive group, with the discrimination visible in each biofluid. In interstitial fluid nine potential metabolomic biomarkers for hypertension could be identified (creatinine, proline, pyroglutamine, glycine, alanine, 1-methylhistidine, the lysyl group of albumin, threonine, lipids), seven distinct markers in plasma (creatinine, mannose, isobutyrate, glycine, alanine, lactate, acetate, ornithine), and seven respectively in urine (methylmalonate, citrulline, phenylacetylglycine, fumarate, citrate, 1-methylnicotinamide, trans-aconitate). Biomarkers in plasma and urine allowed for the identification of specific biochemical pathways involved in hypertension, as previously suggested. Analysis of the interstitial fluid metabolome provided additional biomarkers compared to plasma or urine. Those biomarkers reflected primarily alterations in the metabolism of lipids and amino acids, and indicated increased levels of oxidative stress/inflammation in patients with hypertension.

Metabolomics applications in coronary artery disease personalized medicine

Coronary artery disease (CAD), the most common cardiovascular disease (CVD), contributes to significant mortality worldwide. CAD is a multifactorial disease wherein various factors contribute to its pathogenesis often complicating management. Biomarker based personalized medicine may provide a more effective way to individualize therapy in multifactorial diseases in general and CAD specifically. Systems' biology "Omics" biomarkers have been investigated for this purpose. These biomarkers provide a more comprehensive understanding on pathophysiology of the disease process and can help in identifying new therapeutic targets and tailoring therapy to achieve optimum outcome. Metabolomics biomarkers usually reflect genetic and non-genetic factors involved in the phenotype. Metabolomics analysis may provide better understanding of the disease pathogenesis and drug response variation. This will help in guiding therapy, particularly for multifactorial diseases such as CAD. In this chapter, advances in metabolomics analysis and its role in personalized medicine will be reviewed with comprehensive focus on CAD. Assessment of risk, diagnosis, complications, drug response and nutritional therapy will be discussed. Together, this chapter will review the current application of metabolomics in CAD management and highlight areas that warrant further investigation.

The Effect of Simvastatin on Gut Microbiota and Lipid Metabolism in Hyperlipidemic Rats Induced by a High-Fat Diet

The objective of this study was to investigate the effects of simvastatin (SIM) on lipid metabolism disorders and gut microbiota in high-fat diet-induced hyperlipidemic rats. The obtained results revealed that feeding rats with SIM (20 mg/kg/day) significantly decreased serum lipid level and inhibited hepatic lipid accumulation and steatosis. Histological analysis further indicated that SIM reduced lipid deposition in adipocytes and hepatocytes in comparison with that of the HFD group. The underlying mechanisms of SIM administration against HFD-induced hyperlipidemia were also studied by UPLC-Q-TOF/MS-based liver metabonomics coupled with pathway analysis. Metabolic pathway enrichment analysis of liver metabolites with significant difference in abundance indicated that fatty acids metabolism and amino acid metabolism were the main metabolic pathways altered by SIM administration. Meanwhile, operational taxonomic units (OTUs) analysis revealed that oral administration of SIM altered the composition of gut microbiota, including Ruminococcaceae (OTU960) and Lactobacillus (OTU152), and so on. Furthermore, SIM treatment also regulated the mRNA levels of the genes involved in lipid and cholesterol metabolism. Immunohistochemistry (IHC) analysis of the liver-related proteins (CD36, CYP7A1 and SREBP-1C) showed that oral administration of SIM could regulate the levels of the protein expression related to hepatic lipid metabolism.

Metabolomic profiling of metoprolol-induced cardioprotection in a murine model of acute myocardial ischemia

Metoprolol (Met) is widely applied in the treatment of myocardial infarction and coronary heart disease in clinic. However, the metabolic network in vivo affected by Met manipulation is still unclear and it's therapeutic molecular mechanisms were remained to be furthered elucidated except β1 adrenergic receptor. Myocardial infarction (MI) was induced by permanent CAL for 24 h in ICR mice. Myocardial infarct size, biochemical indicators such as creatine kinase (CK), lactate dehydrogenase (LDH), C-reactive Protein (CRP), tumor necrosis factor-α (TNF-α) and cardiac troponin I(cTn-I), cardiac function and myocardial pathological changes were detected to ensure the improvement of Met on MI. Subsequently, the significantly changed endogenous metabolites and the network in both serum and urine were screened and constructed through metabolomics by using HPLC-Q-TOF/MS. Finally, the potential regulatory enzymes that could be the possible new therapeutic targets of Met were selected and validated by western blotting and immunohistochemistry based on the screened differential metabolites and the enrichment analysis. Met effectively reduced the infarct size of myocardial infarction mice, improved the biochemical indicators, and ameliorated the cardiac function and pathological conditions. Our study further found that Met could regulate the pathways of glycine, serine and threonine metabolism, cysteine and methionine metabolism, purine and pyrimidine metabolism under the pathological conditions of MI. Moreover, several regulatory enzymes involved GATM, CSE and NT5E were demonstrated to be regulated by Met. This study constructed the regulatory metabolic network map of Met, elucidated the endogenous metabolic pathway regulated by Met, and validated the new potential therapeutic targets of Met in MI, which might provide a further reference for the clinical application of Met.

Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).

Purine Catabolism Shows a Dampened Circadian Rhythmicity in a High-fat Diet-Induced Mouse Model of Obesity

High-calorie diet, circadian rhythms and metabolic features are intimately linked. However, the mediator(s) between nutritional status, circadian rhythms and metabolism remain largely unknown. This article aims to clarify the key metabolic pathways bridging nutritional status and circadian rhythms based on a combination of metabolomics and molecular biological techniques. A mouse model of high-fat diet-induced obesity was established and serum samples were collected in obese and normal mice at different zeitgeber times. Gas chromatography/mass spectrometry, multivariate/univariate data analyses and metabolic pathway analysis were used to reveal changes in metabolism. Metabolites involved in the metabolism of purines, carbohydrates, fatty acids and amino acids were markedly perturbed in accordance with circadian related variations, among which purine catabolism showed a typical oscillation. What's more, the rhythmicity of purine catabolism dampened in the high-fat diet group. The expressions of clock genes and metabolic enzymes in the liver were measured. The mRNA expression of Xanthine oxidase (Xor) was highly correlated with the rhythmicity of Clock, Rev-erbα and Bmal1, as well as the metabolites involved in purine catabolism. These data showed that a high-fat diet altered the circadian rhythm of metabolic pathways, especially purine catabolism. It had an obvious circadian oscillation and a high-fat diet dampened its circadian rhythmicity. It was suggested that circadian rhythmicity of purine catabolism is related to circadian oscillations of expression of Xor, Uox and corresponding clock genes.

A randomized controlled crossover trial evaluating differential responses to antihypertensive drugs (used as mono- or dual therapy) on the basis of ethnicity: The comparIsoN oF Optimal Hypertension RegiMens; part of the Ancestry Informative Markers in HYpertension program-AIM-HY INFORM trial

Background

Ethnicity, along with a variety of genetic and environmental factors, is thought to influence the efficacy of antihypertensive therapies. Current UK guidelines use a “black versus white” approach; in doing so, they ignore the United Kingdom's largest ethnic minority: Asians from South Asia.

Study design

The primary purpose of the AIM-HY INFORM trial is to identify potential differences in response to antihypertensive drugs used as mono- or dual therapy on the basis of self-defined ethnicity. A multicenter, prospective, open-label, randomized study with 2 parallel, independent trial arms (mono- and dual therapy), AIM-HY INFORM plans to enroll a total of 1,320 patients from across the United Kingdom. Those receiving monotherapy (n = 660) will enter a 3-treatment (amlodipine 10 mg od; lisinopril 20 mg od; chlorthalidone 25 mg od), 3-period crossover, lasting 24 weeks, whereas those receiving dual therapy (n = 660) will enter a 4-treatment (amlodipine 5 mg od and lisinopril 20 mg od; amlodipine 5 mg od and chlorthalidone 25 mg od; lisinopril 20 mg od and chlorthalidone 25 mg od; amiloride 10 mg od and chlorthalidone 25 mg od), 4-period crossover, lasting 32 weeks. Equal numbers of 3 ethnic groups (white, black/black British, and Asian/Asian British) will ultimately be recruited to each of the trial arms (ie, 220 participants per ethnic group per arm). Seated, automated, unattended, office, systolic blood pressure measured 8 weeks after each treatment period begins will serve as the primary outcome measure.

Conclusion

AIM-HY INFORM is a prospective, open-label, randomized trial which aims to evaluate first- and second-line antihypertensive therapies for multiethnic populations.

Postprandial metabolite profiles associated with type 2 diabetes clearly stratify individuals with impaired fasting glucose

INTRODUCTION

Fasting metabolite profiles have been shown to distinguish type 2 diabetes (T2D) patients from normal glucose tolerance (NGT) individuals.

OBJECTIVES

We investigated whether, besides fasting metabolite profiles, postprandial metabolite profiles associated with T2D can stratify individuals with impaired fasting glucose (IFG) by their similarities to T2D.

METHODS

Three groups of individuals (age 45-65 years) without any history of IFG or T2D were selected from the Netherlands Epidemiology of Obesity study and stratified by baseline fasting glucose concentrations (NGT (n = 176), IFG (n = 186), T2D (n = 171)). 163 metabolites were measured under fasting and postprandial states (150 min after a meal challenge). Metabolite profiles specific for a high risk of T2D were identified by LASSO regression for fasting and postprandial states. The selected profiles were utilised to stratify IFG group into high (T2D probability ≥ 0.7) and low (T2D probability ≤ 0.5) risk subgroups. The stratification performances were compared with clinically relevant metabolic traits.

RESULTS

Two metabolite profiles specific for T2D (n_fasting = 12 metabolites, n_postprandial = 4 metabolites) were identified, with all four postprandial metabolites also being identified in the fasting state. Stratified by the postprandial profile, the high-risk subgroup of IFG individuals (n = 72) showed similar glucose concentrations to the low-risk subgroup (n = 57), yet a higher BMI (difference: 3.3 kg/m² (95% CI 1.7-5.0)) and postprandial insulin concentrations (21.5 mU/L (95% CI 1.8-41.2)).

CONCLUSION

Postprandial metabolites identified T2D patients as good as fasting metabolites and exhibited enhanced signals for IFG stratification, which offers a proof of concept that metabolomics research should not focus on the fasting state alone.

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling

¹H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum ¹H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

The Pharmacogenomic and Metabolomic Predictors of ACE Inhibitor and Angiotensin II Receptor Blocker Effectiveness and Safety

Hypertension (HTN) is the most common chronic disease in the USA. Hypertensive patients frequently require repeat primary care visits to find an effective drug or drug combination to control their disease. Currently, patients are prescribed drugs for HTN based on race, age, and comorbidities and although the current guidelines are reasonable starting points for prescribing, 50% of hypertensive patients still fail to achieve target blood pressures. Despite numerous strategies to improve compliance, drug effectiveness, and optimization of initial drug choice, effectiveness has remained largely unchanged over the past two decades. Therefore, it is important to pursue alternative strategies to more effectively treat patients and to decrease medical costs. Additional precision medicine work is needed to identify factors associated with effectiveness of commonly used antihypertensive medications. The objective of this manuscript is to present a comprehensive review of the pharmacogenomic and metabolomic factors associated with ACEI and ARB effectiveness and safety.

Metabolome alterations in severe critical illness and vitamin D status

BACKGROUND

Metabolic homeostasis is substantially disrupted in critical illness. Given the pleiotropic effects of vitamin D, we hypothesized that metabolic profiles differ between critically ill patients relative to their vitamin D status.

METHODS

We performed a metabolomics study on biorepository samples collected from a single academic medical center on 65 adults with systemic inflammatory response syndrome or sepsis treated in a 20-bed medical ICU between 2008 and 2010. To identify key metabolites and metabolic pathways related to vitamin D status in critical illness, we first generated metabolomic data using gas and liquid chromatography mass spectroscopy. We followed this by partial least squares-discriminant analysis to identify individual metabolites that were significant. We then interrogated the entire metabolomics profile using metabolite set enrichment analysis to identify groups of metabolites and pathways that were differentiates of vitamin D status. Finally we performed logistic regression to construct a network model of chemical-protein target interactions important in vitamin D status.

RESULTS

Metabolomic profiles significantly differed in critically ill patients with 25(OH)D ≤ 15 ng/ml relative to those with levels >15 ng/ml. In particular, increased 1,5-anhydroglucitol, tryptophan betaine, and 3-hydroxyoctanoate as well as decreased 2-arachidonoyl-glycerophosphocholine and N-6-trimethyllysine were strong predictors of 25(OH)D >15 ng/ml. The combination of these five metabolites led to an area under the curve for discrimination for 25(OH)D > 15 ng/ml of 0.82 (95% CI 0.71-0.93). The metabolite pathways related to glutathione metabolism and glutamate metabolism are significantly enriched with regard to vitamin D status.

CONCLUSION

Vitamin D status is associated with differential metabolic profiles during critical illness. Glutathione and glutamate pathway metabolism, which play principal roles in redox regulation and immunomodulation, respectively, were significantly altered with vitamin D status.

Circulating acylcarnitine profile in human heart failure: a surrogate of fatty acid metabolic dysregulation in mitochondria and beyond

Heart failure (HF) is associated with metabolic perturbations, particularly of fatty acids (FAs), which remain to be better understood in humans. This study aimed at testing the hypothesis that HF patients with reduced ejection fraction display systemic perturbations in levels of energy-related metabolites, especially those reflecting dysregulation of FA metabolism, namely, acylcarnitines (ACs). Circulating metabolites were assessed using mass spectrometry (MS)-based methods in two cohorts. The main cohort consisted of 72 control subjects and 68 HF patients exhibiting depressed left ventricular ejection fraction (25.9 ± 6.9%) and mostly of ischemic etiology with ≥2 comorbidities. HF patients displayed marginal changes in plasma levels of tricarboxylic acid cycle-related metabolites or indexes of mitochondrial or cytosolic redox status. They had, however, 22-79% higher circulating ACs, irrespective of chain length (P < 0.0001, adjusted for sex, age, renal function, and insulin resistance, determined by shotgun MS/MS), which reflects defective mitochondrial β-oxidation, and were significantly associated with levels of NH₂-terminal pro-B-type natriuretic peptide levels, a disease severity marker. Subsequent extended liquid chromatography-tandem MS analysis of 53 plasma ACs in a subset group from the primary cohort confirmed and further substantiated with a comprehensive lipidomic analysis in a validation cohort revealed in HF patients a more complex circulating AC profile. The latter included dicarboxylic-ACs and dihydroxy-ACs as well as very long chain (VLC) ACs or sphingolipids with VLCFAs (>20 carbons), which are proxies of dysregulated FA metabolism in peroxisomes. Our study identified alterations in circulating ACs in HF patients that are independent of biological traits and associated with disease severity markers. These alterations reflect dysfunctional FA metabolism in mitochondria but also beyond, namely, in peroxisomes, suggesting a novel mechanism contributing to global lipid perturbations in human HF.NEW & NOTEWORTHY Mass spectrometry-based profiling of circulating energy metabolites, including acylcarnitines, in two cohorts of heart failure versus control subjects revealed multiple alterations in fatty acid metabolism in peroxisomes in addition to mitochondria, thereby highlighting a novel mechanism contributing to global lipid perturbations in heart failure.Listen to this article's corresponding podcast at http://ajpheart.podbean.com/e/acylcarnitines-in-human-heart-failure/.

Connecting genetic risk to disease end points through the human blood plasma proteome

Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.

Individual genetic variation can affect the levels of protein in blood, but detailed data sets linking these two types of data are rare. Here, the authors carry out a genome-wide association study of levels of over a thousand different proteins, and describe many new SNP-protein interactions.

Key elements of metabolomics in the study of biomarkers of diabetes

Metabolomics is instrumental in the analysis of disease mechanisms and biomarkers of disease. The human metabolome is influenced by genetics and environmental interactions and reveals characteristic signatures of disease. Population studies with metabolomics require special study designs and care needs to be taken with pre-analytics. Gas chromatography coupled to mass spectrometry, liquid chromatography coupled to mass spectrometry or NMR are popular techniques used for metabolomic analyses in human cohorts. Metabolomics has been successfully used in the biomarker search for disease prediction and progression, for analyses of drug action and for the development of companion diagnostics. Several metabolites or metabolite classes identified by metabolomics have gained much attention in the field of diabetes research in the search for early disease detection, differentiation of progressor types and compliance with medication. This review summarises a presentation given at the 'New approaches beyond genetics' symposium at the 2015 annual meeting of the EASD. It is accompanied by another review from this symposium by Bernd Mayer (DOI: 10.1007/s00125-016-4032-2 ) and an overview by the Session Chair, Leif Groop (DOI: 10.1007/s00125-016-4014-4 ).

Metformin Effect on Nontargeted Metabolite Profiles in Patients With Type 2 Diabetes and in Multiple Murine Tissues

Metformin is the first-line oral medication to increase insulin sensitivity in patients with type 2 diabetes (T2D). Our aim was to investigate the pleiotropic effect of metformin using a nontargeted metabolomics approach. We analyzed 353 metabolites in fasting serum samples of the population-based human KORA (Cooperative Health Research in the Region of Augsburg) follow-up survey 4 cohort. To compare T2D patients treated with metformin (mt-T2D, n = 74) and those without antidiabetes medication (ndt-T2D, n = 115), we used multivariable linear regression models in a cross-sectional study. We applied a generalized estimating equation to confirm the initial findings in longitudinal samples of 683 KORA participants. In a translational approach, we used murine plasma, liver, skeletal muscle, and epididymal adipose tissue samples from metformin-treated db/db mice to further corroborate our findings from the human study. We identified two metabolites significantly (P < 1.42E-04) associated with metformin treatment. Citrulline showed lower relative concentrations and an unknown metabolite X-21365 showed higher relative concentrations in human serum when comparing mt-T2D with ndt-T2D. Citrulline was confirmed to be significantly (P < 2.96E-04) decreased at 7-year follow-up in patients who started metformin treatment. In mice, we validated significantly (P < 4.52E-07) lower citrulline values in plasma, skeletal muscle, and adipose tissue of metformin-treated animals but not in their liver. The lowered values of citrulline we observed by using a nontargeted approach most likely resulted from the pleiotropic effect of metformin on the interlocked urea and nitric oxide cycle. The translational data derived from multiple murine tissues corroborated and complemented the findings from the human cohort.

Metabolomic profiling of doxycycline treatment in chronic obstructive pulmonary disease

Serum metabolic profiling can identify the metabolites responsible for discrimination between doxycycline treated and untreated chronic obstructive pulmonary disease (COPD) and explain the possible effect of doxycycline in improving the disease conditions. ¹H nuclear magnetic resonance (NMR)-based metabolomics was used to obtain serum metabolic profiles of 60 add-on doxycycline treated COPD patients and 40 patients receiving standard therapy. The acquired data were analyzed using multivariate principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). A clear metabolic differentiation was apparent between the pre and post doxycycline treated group. The distinguishing metabolites lactate and fatty acids were significantly down-regulated and formate, citrate, imidazole and l-arginine upregulated. Lactate and folate are further validated biochemically. Metabolic changes, such as decreased lactate level, inhibited arginase activity and lowered fatty acid level observed in COPD patients in response to add-on doxycycline treatment, reflect the anti-inflammatory action of the drug. Doxycycline as a possible therapeutic option for COPD seems promising.

Metabolites of milk intake: a metabolomic approach in UK twins with findings replicated in two European cohorts

Purpose

Milk provides a significant source of calcium, protein, vitamins and other minerals to Western populations throughout life. Due to its widespread use, the metabolic and health impact of milk consumption warrants further investigation and biomarkers would aid epidemiological studies.

Methods

Milk intake assessed by a validated food frequency questionnaire was analyzed against fasting blood metabolomic profiles from two metabolomic platforms in females from the TwinsUK cohort (n = 3559). The top metabolites were then replicated in two independent populations (EGCUT, n = 1109 and KORA, n = 1593), and the results from all cohorts were meta-analyzed.

Results

Four metabolites were significantly associated with milk intake in the TwinsUK cohort after adjustment for multiple testing (P < 8.08 × 10⁻⁵) and covariates (BMI, age, batch effects, family relatedness and dietary covariates) and replicated in the independent cohorts. Among the metabolites identified, the carnitine metabolite trimethyl-N-aminovalerate (β = 0.012, SE = 0.002, P = 2.98 × 10⁻¹²) and the nucleotide uridine (β = 0.004, SE = 0.001, P = 9.86 × 10⁻⁶) were the strongest novel predictive biomarkers from the non-targeted platform. Notably, the association between trimethyl-N-aminovalerate and milk intake was significant in a group of MZ twins discordant for milk intake (β = 0.050, SE = 0.015, P = 7.53 × 10⁻⁴) and validated in the urine of 236 UK twins (β = 0.091, SE = 0.032, P = 0.004). Two metabolites from the targeted platform, hydroxysphingomyelin C14:1 (β = 0.034, SE = 0.005, P = 9.75 × 10⁻¹⁴) and diacylphosphatidylcholine C28:1 (β = 0.034, SE = 0.004, P = 4.53 × 10⁻¹⁶), were also replicated.

Conclusions

We identified and replicated in independent populations four novel biomarkers of milk intake: trimethyl-N-aminovalerate, uridine, hydroxysphingomyelin C14:1 and diacylphosphatidylcholine C28:1. Together, these metabolites have potential to objectively examine and refine milk-disease associations.

Electronic supplementary material

The online version of this article (doi:10.1007/s00394-016-1278-x) contains supplementary material, which is available to authorized users.

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine

"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics, and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow, and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status, and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

Metabolomic profiling to dissect the role of visceral fat in cardiometabolic health

OBJECTIVE

Abdominal obesity is associated with increased risk of type 2 diabetes (T2D) and cardiovascular disease. The aim of this study was to assess whether metabolomic markers of T2D and blood pressure (BP) act on these traits via visceral fat (VF) mass.

METHODS

Metabolomic profiling of 280 fasting plasma metabolites was conducted on 2,401 women from TwinsUK. The overlap was assessed between published metabolites associated with T2D, insulin resistance, or BP and those that were identified to be associated with VF (after adjustment for covariates) measured by dual-energy X-ray absorptiometry.

RESULTS

In addition to glucose, six metabolites were strongly associated with both VF mass and T2D: lactate and branched-chain amino acids, all of them related to metabolism and the tricarboxylic acid cycle; on average, 38.5% of their association with insulin resistance was mediated by their association with VF mass. Five metabolites were associated with BP and VF mass including the inflammation-associated peptide HWESASXX, the steroid hormone androstenedione, lactate, and palmitate. On average, 29% of their effect on BP was mediated by their association with VF mass.

CONCLUSIONS

Little overlap was found between the metabolites associated with BP and those associated with insulin resistance via VF mass.

The Pharmacogenetic Footprint of ACE Inhibition: A Population-Based Metabolomics Study

Angiotensin-I-converting enzyme (ACE) inhibitors are an important class of antihypertensives whose action on the human organism is still not fully understood. Although it is known that ACE especially cleaves COOH-terminal dipeptides from active polypeptides, the whole range of substrates and products is still unknown. When analyzing the action of ACE inhibitors, effects of genetic variation on metabolism need to be considered since genetic variance in the ACE gene locus was found to be associated with ACE-concentration in blood as well as with changes in the metabolic profiles of a general population. To investigate the interactions between genetic variance at the ACE-locus and the influence of ACE-therapy on the metabolic status we analyzed 517 metabolites in 1,361 participants from the KORA F4 study. We replicated our results in 1,964 individuals from TwinsUK. We observed differences in the concentration of five dipeptides and three ratios of di- and oligopeptides between ACE inhibitor users and non-users that were genotype dependent. Such changes in the concentration affected major homozygotes, and to a lesser extent heterozygotes, while minor homozygotes showed no or only small changes in the metabolite status. Two of these resulting dipeptides, namely aspartylphenylalanine and phenylalanylserine, showed significant associations with blood pressure which qualifies them—and perhaps also the other dipeptides—as readouts of ACE-activity. Since so far ACE activity measurement is substrate specific due to the usage of only one oligopeptide, taking several dipeptides as potential products of ACE into account may provide a broader picture of the ACE activity.

The acute impact of polyphenols from Hibiscus sabdariffa in metabolic homeostasis: an approach combining metabolomics and gene-expression analyses

We explored the acute multifunctional effects of polyphenols from Hibiscus sabdariffa in humans to assess possible consequences on the host's health. The expected dynamic response was studied using a combination of transcriptomics and metabolomics to integrate specific functional pathways through network-based methods and to generate hypotheses established by acute metabolic effects and/or modifications in the expression of relevant genes. Data were obtained from healthy male volunteers after 3 hours of ingestion of an aqueous Hibiscus sabdariffa extract. The data were compared with data obtained prior to the ingestion, and the overall findings suggest that these particular polyphenols had a simultaneous role in mitochondrial function, energy homeostasis and protection of the cardiovascular system. These findings suggest beneficial actions in inflammation, endothelial dysfunction, and oxidation, which are interrelated mechanisms. Among other effects, the activation of the heme oxygenase-biliverdin reductase axis, the systemic inhibition of the renin-angiotensin system, the inhibition of the angiotensin-converting enzyme, and several actions mirroring those of the peroxisome proliferator-activated receptor agonists further support this notion. We also found concordant findings in the serum of the participants, which include a decrease in cortisol levels and a significant increase in the active vasodilator metabolite of bradykinin (des-Arg(9)-bradykinin). Therefore, our data support the view that polyphenols from Hibiscus sabdariffa play a regulatory role in metabolic health and in the maintenance of blood pressure, thus implying a multi-faceted impact in metabolic and cardiovascular diseases.

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.