Pharmacometabolomic mapping of early biochemical changes induced by sertraline and placebo

The Association Between Branched-Chain Amino Acid Concentrations and the Risk of Autism Spectrum Disorder in Preschool-Aged Children

Several studies have linked branched-chain amino acid (BCAA) metabolism disorders with autism spectrum disorder (ASD), but the results have been inconsistent. The purpose of this study was to explore the association between BCAA concentrations and the risk of ASD. A total of 313 participants were recruited from two tertiary referral hospitals from May 2018 to July 2021. Concentrations of BCAAs in dried blood spots were analyzed using liquid chromatography-tandem mass spectrometry-based analysis. Multivariate analyses and restricted cubic spline models were used to identify the association between BCAAs and the risk of ASD, and a nomogram was developed by using multivariate logistic regression and the risk was determined by receiver operating characteristic curve analysis and calibration curve analysis. Concentrations of total BCAA, valine, and leucine/isoleucine were higher in the ASD group, and all of them were positively and non-linearly associated with the risk of ASD even after adjusting for potential confounding factors such as age, gender, body mass index, and concentrations of BCAAs (P < 0.05). The nomogram integrating total BCAA and valine showed a good discriminant AUC value of 0.756 (95% CI 0.676-0.835). The model could yield net benefits across a reasonable range of risk thresholds. In the stratified analysis, the diagnostic ability of the model was more pronounced in children older than 3 years. We provide evidence that increased levels of BCAAs are associated with the risk of ASD, and the nomogram model of BCAAs presented here can serve as a marker for the early diagnosis of ASD.

Citalopram exposure of hESCs during neuronal differentiation identifies dysregulated genes involved in neurodevelopment and depression

Selective serotonin reuptake inhibitors (SSRIs), including citalopram, are widely used antidepressants during pregnancy. However, the effects of prenatal exposure to citalopram on neurodevelopment remain poorly understood. We aimed to investigate the impact of citalopram exposure on early neuronal differentiation of human embryonic stem cells using a multi-omics approach. Citalopram induced time- and dose-dependent effects on gene expression and DNA methylation of genes involved in neurodevelopmental processes or linked to depression, such as BDNF, GDF11, CCL2, STC1, DDIT4 and GAD2. Single-cell RNA-sequencing analysis revealed distinct clusters of stem cells, neuronal progenitors and neuroblasts, where exposure to citalopram subtly influenced progenitor subtypes. Pseudotemporal analysis showed enhanced neuronal differentiation. Our findings suggest that citalopram exposure during early neuronal differentiation influences gene expression patterns associated with neurodevelopment and depression, providing insights into its potential neurodevelopmental impact and highlighting the importance of further research to understand the long-term consequences of prenatal SSRI exposure.

A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation

BACKGROUND

Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathological mechanisms underlying depression and candidate clinical biomarkers.

METHODS

Depression-associated metabolomics was studied in 2 datasets from the UK Biobank database: participants with lifetime depression (N = 123,459) and participants with current depression (N = 94,921). The Whitehall II cohort (N = 4744) was used for external validation. CatBoost machine learning was used for modeling, and Shapley additive explanations were used to interpret the model. Fivefold cross-validation was used to validate model performance, training the model on 3 of the 5 sets with the remaining 2 sets for validation and testing, respectively. Diagnostic performance was assessed using the area under the receiver operating characteristic curve.

RESULTS

In the lifetime depression and current depression datasets and sex-specific analyses, 24 significantly associated metabolic biomarkers were identified, 12 of which overlapped in the 2 datasets. The addition of metabolic features slightly improved the performance of a diagnostic model using traditional (nonmetabolomics) risk factors alone (lifetime depression: area under the curve 0.655 vs. 0.658 with metabolomics; current depression: area under the curve 0.711 vs. 0.716 with metabolomics).

CONCLUSIONS

The machine learning model identified 24 metabolic biomarkers associated with depression. If validated, metabolic biomarkers may have future clinical applications as supplementary information to guide early and population-based depression detection.

Metabolomics in Depression: What We Learn from Preclinical and Clinical Evidences

Depression is one of the predominant common mental illnesses that affects millions of people of all ages worldwide. Random mood changes, loss of interest in routine activities, and prevalent unpleasant senses often characterize this common depreciated mental illness. Subjects with depressive disorders have a likelihood of developing cardiovascular complications, diabesity, and stroke. The exact genesis and pathogenesis of this disease are still questionable. A significant proportion of subjects with clinical depression display inadequate response to antidepressant therapies. Hence, clinicians often face challenges in predicting the treatment response. Emerging reports have indicated the association of depression with metabolic alterations. Metabolomics is one of the promising approaches that can offer fresh perspectives into the diagnosis, treatment, and prognosis of depression at the metabolic level. Despite numerous studies exploring metabolite profiles post-pharmacological interventions, a quantitative understanding of consistently altered metabolites is not yet established. The article gives a brief discussion on different biomarkers in depression and the degree to which biomarkers can improve treatment outcomes. In this review article, we have systemically reviewed the role of metabolomics in depression along with current challenges and future perspectives.

Characterization of "microbiome-metabolome-immunity" in depressed rats with divergent responses to Paroxetine

OBJECTIVES

Selective serotonin reuptake inhibitors (SSRIs) are the first-line anti-depressants. Unfortunately, about 30 % depressed patients do not effectively respond to SSRIs. It is still unclear that the gastrointestinal characteristics of responders and non-responders, and the differences.

METHODS

Herein, we characterized gut microbiome and metabolome of depressed rats with differential responses to Paroxetine (PX) by 16S rRNA sequencing and 1H NMR-based metabolomics, respectively. On top of this, we constructed both inter- and inner-layer networks, intuitively showing the correlations among behavioral indicators, immune factors, intestinal bacteria, and differential metabolites.

RESULTS

Consequently, we found that depressed rats differently responded to PX, which could be divided into PX responsive (PX-R) and non-responsive (PX-N) groups. Firstly, the depressive behaviors of PX-R rats and PX-N rats significantly differed. Meanwhile, inflammatory balance was also characterized for depressed rats with different responses to PX. Overall, PX-R rats and PX-N rats exhibited differential gut microbiome and metabolome, including intestinal structures, intestinal functions, metabolic profiles, metabolites, and metabolic pathways.

LIMITATIONS

Metabolites that identified by metabolomics based on 1H NMR are not comprehensive enough.

CONCLUSIONS

Taken together, our study demonstrated that gut microbiome and metabolome, as well as related functions, are of significance in differential responses of depressed rats to PX, which might be novel insights in uncovering the mechanisms of differences in efficacies of antidepressants.

Dysfunction of neurotransmitter metabolism is associated with the severity of depression in first-diagnosed, drug-naïve depressed patients

BACKGROUND & AIMS

Biochemical changes of neurotransmitters underlying major depressive disorder (MDD) are unknown. This study preliminarily explored the association between neurotransmitters with MDD and the possibility of objective laboratory prediction of neurotransmitter involvement in MDD.

METHODS

A total of 87 first-diagnosed, drug-naïve patients with depression and 50 healthy controls (HCs) were included in the cross-sectional study. The levels and turnovers of neurotransmitters (glutamine (GLN), glutamic acid (GLU), γ-2Aminobutiric acid (GABA), kainate (KA), vanillylmandelic acid (VMA), 3-methoxy 4-hydroxyphenyl ethylene glycol (MHPG), noradrenaline (NE), homovanillic acid (HVA), dihydroxy-phenyl acetic acid (DOPAC), dopamine (DA), tryptophane (TRP), kynurenine (KYN), serotonin (5-HT), 5-hydroxyindoleacetic acid (5-HIAA)) were determined and the confounding factors were adjusted. Then a correlation and a predictive analysis towards neurotransmitters for MDD were performed.

RESULTS

After adjusting confounding factors, GLU (OR = 1.159), (GLU+ GABA)/GLN (OR = 1.217), DOPAC (OR = 1.106), DOPAC/DA (OR = 1.089) and (DOPAC+ HVA)/DA (OR = 1.026) enacted as risk factors of MDD, while KYN (OR = 0.992) was a protective factor. GABAergic and TRPergic pathways were associated with severity of depressive and anxiety symptoms in patients with depression. The predictive model for MDD (AUC = 0.775, 95%CI 0.683-0.860) consisted of KYN (OR = 0.990) and (GLU + GABA)/GLN (OR = 4.101).

CONCLUSIONS

First-diagnosed, drug-naïve depression patients showed abnormal neurotransmitter composition. GLU, (GLU + GABA)/GLN, DOPAC, DOPAC/DA and (DOPAC + HVA)/DA were risk factors of MDD, while KYN was a protective factor. GABAergic and TRPergic pathways were correlated with MDD clinical characteristics. KYN and (GLU + GABA)/GLN may have a predictive value for MDD.

Circulating metabolites may illustrate relationship of alcohol consumption with cardiovascular disease

BACKGROUND

Metabolite signatures of long-term alcohol consumption are lacking. To better understand the molecular basis linking alcohol drinking and cardiovascular disease (CVD), we investigated circulating metabolites associated with long-term alcohol consumption and examined whether these metabolites were associated with incident CVD.

METHODS

Cumulative average alcohol consumption (g/day) was derived from the total consumption of beer, wine, and liquor on average of 19 years in 2428 Framingham Heart Study Offspring participants (mean age 56 years, 52% women). We used linear mixed models to investigate the associations of alcohol consumption with 211 log-transformed plasma metabolites, adjusting for age, sex, batch, smoking, diet, physical activity, BMI, and familial relationship. Cox models were used to test the association of alcohol-related metabolite scores with fatal and nonfatal incident CVD (myocardial infarction, coronary heart disease, stroke, and heart failure).

RESULTS

We identified 60 metabolites associated with cumulative average alcohol consumption (p < 0.05/211 ≈ 0.00024). For example, 1 g/day increase of alcohol consumption was associated with higher levels of cholesteryl esters (e.g., CE 16:1, beta = 0.023 ± 0.002, p = 6.3e - 45) and phosphatidylcholine (e.g., PC 32:1, beta = 0.021 ± 0.002, p = 3.1e - 38). Survival analysis identified that 10 alcohol-associated metabolites were also associated with a differential CVD risk after adjusting for age, sex, and batch. Further, we built two alcohol consumption weighted metabolite scores using these 10 metabolites and showed that, with adjustment age, sex, batch, and common CVD risk factors, the two scores had comparable but opposite associations with incident CVD, hazard ratio 1.11 (95% CI = [1.02, 1.21], p = 0.02) vs 0.88 (95% CI = [0.78, 0.98], p = 0.02).

CONCLUSIONS

We identified 60 long-term alcohol consumption-associated metabolites. The association analysis with incident CVD suggests a complex metabolic basis between alcohol consumption and CVD.

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.

Targeting PDK2 rescues stress-induced impaired brain energy metabolism

Depression is a mental illness frequently accompanied by disordered energy metabolism. A dysregulated hypothalamus pituitary adrenal axis response with aberrant glucocorticoids (GCs) release is often observed in patients with depression. However, the associated etiology between GCs and brain energy metabolism remains poorly understood. Here, using metabolomic analysis, we showed that the tricarboxylic acid (TCA) cycle was inhibited in chronic social defeat stress (CSDS)-exposed mice and patients with first-episode depression. Decreased mitochondrial oxidative phosphorylation was concomitant with the impairment of the TCA cycle. In parallel, the activity of pyruvate dehydrogenase (PDH), the gatekeeper of mitochondrial TCA flux, was suppressed, which is associated with the CSDS-induced neuronal pyruvate dehydrogenase kinase 2 (PDK2) expression and consequently enhanced PDH phosphorylation. Considering the well-acknowledged role of GCs in energy metabolism, we further demonstrated that glucocorticoid receptors (GR) stimulated PDK2 expression by directly binding to its promoter region. Meanwhile, silencing PDK2 abrogated glucocorticoid-induced PDH inhibition, restored the neuronal oxidative phosphorylation, and improved the flux of isotope-labeled carbon (U-13C] glucose) into the TCA cycle. Additionally, in vivo, pharmacological inhibition and neuron-specific silencing of GR or PDK2 restored CSDS-induced PDH phosphorylation and exerted antidepressant activities against chronic stress exposure. Taken together, our findings reveal a novel mechanism of depression manifestation, whereby elevated GCs levels regulate PDK2 transcription via GR, thereby impairing brain energy metabolism and contributing to the onset of this condition.

A multiplatform metabolomics approach for comprehensive analysis of GIST xenografts with various KIT mutations

Metabolites in biological matrices belong to diverse chemical groups, ranging from non-polar long-chain fatty acids to small polar molecules. The goal of untargeted metabolomic analysis is to measure the highest number of metabolites in the sample. Nevertheless, from an analytical point of view, no single technique can measure such a broad spectrum of analytes. Therefore, we selected a method based on GC-MS and LC-MS with two types of stationary phases for the untargeted profiling of gastrointestinal stromal tumours. The procedure was applied to GIST xenograft samples (n = 71) representing four different mutation models, half of which were treated with imatinib. We aimed to verify the method coverage and advantages of applying each technique. RP-LC-MS measured most metabolites due to a significant fraction of lipid components of the tumour tissue. What is unique and worth noting is that all applied techniques were able to distinguish between different mutation models. However, for detecting imatinib-induced alterations in the GIST metabolome, RP-LC-MS and GC-MS proved to be more relevant than HILIC-LC-MS, resulting in a higher number of significantly changed metabolites in four treated models. Undoubtedly, the inclusion of all mentioned techniques makes the method more comprehensive. Nonetheless, for green chemistry and time and labour saving, we assume that RP-LC-MS and GC-MS analyses are sufficient to cover the global GIST metabolome.

Circulating Metabolites May Illustrate Relationship of Alcohol Consumption with Cardiovascular Disease

Background

Metabolite signatures of long-term alcohol consumption are lacking. To better understand the molecular basis linking alcohol drinking and cardiovascular disease (CVD), we investigated circulating metabolites associated with long-term alcohol consumption and examined whether these metabolites were associated with incident CVD.

Methods

Cumulative average alcohol consumption (g/day) was derived from the total consumption of beer, wine and liquor on average of 19 years in 2,428 Framingham Heart Study Offspring participants (mean age 56 years, 52% women). We used linear mixed models to investigate the associations of alcohol consumption with 211 log-transformed plasma metabolites, adjusting for age, sex, batch, smoking, diet, physical activity, BMI, and familial relationship. Cox models were used to test the association of alcohol-related metabolite scores with fatal and nonfatal incident CVD (myocardial infarction, coronary heart disease, stroke, and heart failure).

Results

We identified 60 metabolites associated with cumulative average alcohol consumption (p<0.05/211≈0.00024). For example, one g/day increase of alcohol consumption was associated with higher levels of cholesteryl esters (e.g., CE 16:1, beta=0.023±0.002, p=6.3e-45) and phosphatidylcholine (e.g., PC 32:1, beta=0.021±0.002, p=3.1e-38). Survival analysis identified that 10 alcohol-associated metabolites were also associated with a differential CVD risk after adjusting for age, sex, and batch. Further, we built two alcohol consumption weighted metabolite scores using these 10 metabolites and showed that, with adjustment age, sex, batch, and common CVD risk factors, the two scores had comparable but opposite associations with incident CVD, hazard ratio 1.11(95% CI=[1.02, 1.21],p=0.02) vs 0.88 (95% CI=[0.78, 0.98], p=0.02).

Summary

We identified 60 long-term alcohol consumption-associated metabolites. The association analysis with incident CVD suggests a complex metabolic basis between alcohol consumption and CVD.

The Antidepressant Effect of Deoiled Sunflower Seeds on Chronic Unpredictable Mild Stress in Mice Through Regulation of Microbiota–Gut–Brain Axis

Objectives

Sunflower seeds provide tryptophan-rich proteins with the potential to protect against depression. Tryptophan is a precursor of serotonin and a substrate for the production of indole derivatives by gut microbiota. This study aimed to investigate the association between the depression-alleviating effects of deoiled and dechlorogenic sunflower seeds (DSFS) and regulation of gut microbiota.

Materials and Methods

Male C57BL/6J mice were fed a diet comprising a source of soy protein (normal and model control), DSFS or whey protein concentrate (positive control) for 7 weeks, and chronic stress-induced depression was induced.

Results

Feeding the DSFS diet prevented depression-like behaviors, intestinal barrier damage, elevated plasma corticosterone, and reduced hippocampal serotonin levels in mice. Meanwhile, Feeding the DSFS diet significantly altered the gut microbiota structure, characterized by elevated relative abundances of Ileibacterium valens, Ruminococcus flavefaciens, Clostridium scindens, and Olsenella massiliensis, which were inversely associated with depressive behaviors and markers of mucosal barrier damage. DSFS also altered the gut metabolite profile, prevented depression-induced gut L-tryptophan depletion, and upregulated its metabolite indoleacetaldehyde.

Conclusion

Feeding the DSFS diet prevented depression in mice by remodeling the gut microbiota and bacterial tryptophan metabolism.

Therapeutic drug monitoring of sertraline in children and adolescents: A naturalistic study with insights into the clinical response and treatment of obsessive-compulsive disorder

BACKGROUND

Sertraline is a selective serotonin reuptake inhibitor with specific indications in child and adolescent psychiatry. Notwithstanding its frequent use and clinical benefits, the relationship between pharmacokinetics, pharmacodynamics, efficacy, and tolerability of sertraline across indications, particularly in non-adult patients, is not fully understood.

METHOD

This naturalistic therapeutic drug monitoring (TDM) study was conducted in a transdiagnostic sample of children and adolescents treated with sertraline (n = 78; mean age, 14.22 ± 2.39; range, 7-18 years) within the prospective multicenter "TDM-VIGIL" project. Associations between dose, serum concentration, and medication-specific therapeutic and side effects based on the Clinical Global Impression scale were examined. Tolerability was measured qualitatively with the 56-item Pediatric Adverse Event Rating Scale.

RESULTS

A strong linear positive dose-serum concentration relationship (with dose explaining 45% of the variance in concentration) and significant effects of weight and co-medication were found. Neither dose nor serum concentration were associated with side effects. An overall mild-to-moderate tolerability profile of sertraline was observed. In contrast with the transdiagnostic analysis that did not indicate an effect of concentration, when split into depression (MDD) and obsessive-compulsive disorder (OCD) diagnoses, the probability of clinical improvement significantly increased as both dose and concentration increased for OCD, but not for MDD.

CONCLUSIONS

This TDM-flexible-dose study revealed a significant diagnosis-specific effect between sertraline serum concentration and clinical efficacy for pediatric OCD. While TDM already guides clinical decision-making regarding compliance, dose calibration, and drug-drug interactions, combining TDM with other methods, such as pharmacogenetics, may facilitate a personalized medicine approach in psychiatry.

Metabolomic and inflammatory signatures of symptom dimensions in major depression

BACKGROUND

Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions.

METHODS

Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism.

RESULTS

The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids.

CONCLUSION

The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

A Preliminary Nuclear Magnetic Resonance Metabolomics Study Identifies Metabolites that Could Serve as Diagnostic Markers of Major Depressive Disorder

BACKGROUND

The evaluation of metabolites that are directly involved in the physiological process, few steps short of phenotypical manifestation, remains vital for unravelling the biological moieties involved in the development of the (MDD) and in predicting its treatment outcome.

METHODOLOGY

Eight (8) urine and serum samples each obtained from consenting healthy controls (HC), twenty-five (25) urine and serum samples each from first episode treatment naïve MDD (TNMDD) patients, and twenty (22) urine and serum samples each s from treatment naïve MDD patients 2 weeks after SSRI treatment (TWMDD) were analysed for metabolites using proton nuclear magnetic resonance (1HNMR) spectroscopy. The evaluation of patients' samples was carried out using Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Square- Discriminant Analysis (OPLSDA) models.

RESULTS

In the serum, decreased levels of lactate, glucose, glutamine, creatinine, acetate, valine, alanine, and fatty acid and an increased level of acetone and choline in TNMDD or TWMDD irrespective of whether an OPLSDA or PLSDA evaluation was used were identified. A test for statistical validations of these models was successful.

CONCLUSION

Only some changes in serum metabolite levels between HC and TNMDD identified in this study have potential values in the diagnosis of MDD. These changes included decreased levels of lactate, glutamine, creatinine, valine, alanine, and fatty acid, as well as an increased level of acetone and choline in TNMDD. The diagnostic value of these changes in metabolites was maintained in samples from TWMDD patients, thus reaffirming the diagnostic nature of these metabolites for MDD.

Metabolomics of Major Depressive Disorder: A Systematic Review of Clinical Studies

Although the understanding of the pathophysiology of major depressive disorder (MDD) has advanced greatly, this has not been translated into improved outcomes. To date, no biomarkers have been identified for the diagnosis, prognosis, and therapeutic management of MDD. Thus, we aim to review the biomarkers that are differentially expressed in MDD. A systematic review was conducted in January 2022 in the PubMed/MEDLINE, Scopus, Embase, PsycINFO, and Gale Academic OneFile databases for clinical studies published from January 2001 onward using the following terms: "Depression" OR "Depressive disorder" AND "Metabolomic." Multiple metabolites were found at altered levels in MDD, demonstrating the involvement of cellular signaling metabolites, components of the cell membrane, neurotransmitters, inflammatory and immunological mediators, hormone activators and precursors, and sleep controllers. Kynurenine and acylcarnitine were identified as consistent with depression and response to treatment. The most consistent evidence found was regarding kynurenine and acylcarnitine. Although the data obtained allow us to identify how metabolic pathways are affected in MDD, there is still not enough evidence to propose changes to current diagnostic and therapeutic actions. Some limitations are the heterogeneity of studies on metabolites, methods for detection, analyzed body fluids, and treatments used. The experiments contemplated in the review identified increased or reduced levels of metabolites, but not necessarily increased or reduced the activity of the associated pathways. The information acquired through metabolomic analyses does not specify whether the changes identified in the metabolites are a cause or a consequence of the pathology.

Effects of dexmedetomidine, propofol, sevoflurane and S-ketamine on the human metabolome: A randomised trial using nuclear magnetic resonance spectroscopy

BACKGROUND

Pharmacometabolomics uses large-scale data capturing methods to uncover drug-induced shifts in the metabolic profile. The specific effects of anaesthetics on the human metabolome are largely unknown.

OBJECTIVE

We aimed to discover whether exposure to routinely used anaesthetics have an acute effect on the human metabolic profile.

DESIGN

Randomised, open-label, controlled, parallel group, phase IV clinical drug trial.

SETTING

The study was conducted at Turku PET Centre, University of Turku, Finland, 2016 to 2017.

PARTICIPANTS

One hundred and sixty healthy male volunteers were recruited. The metabolomic data of 159 were evaluable.

INTERVENTIONS

Volunteers were randomised to receive a 1-h exposure to equipotent doses (EC50 for verbal command) of dexmedetomidine (1.5 ng ml-1; n = 40), propofol (1.7 μg ml-1; n = 40), sevoflurane (0.9% end-tidal; n = 39), S-ketamine (0.75 μg ml-1; n = 20) or placebo (n = 20).

MAIN OUTCOME MEASURES

Metabolite subgroups of apolipoproteins and lipoproteins, cholesterol, glycerides and phospholipids, fatty acids, glycolysis, amino acids, ketone bodies, creatinine and albumin and the inflammatory marker GlycA, were analysed with nuclear magnetic resonance spectroscopy from arterial blood samples collected at baseline, after anaesthetic administration and 70 min postanaesthesia.

RESULTS

All metabolite subgroups were affected. Statistically significant changes vs. placebo were observed in 11.0, 41.3, 0.65 and 3.9% of the 155 analytes in the dexmedetomidine, propofol, sevoflurane and S-ketamine groups, respectively. Dexmedetomidine increased glucose, decreased ketone bodies and affected lipoproteins and apolipoproteins. Propofol altered lipoproteins, fatty acids, glycerides and phospholipids and slightly increased inflammatory marker glycoprotein acetylation. Sevoflurane was relatively inert. S-ketamine increased glucose and lactate, whereas branched chain amino acids and tyrosine decreased.

CONCLUSION

A 1-h exposure to moderate doses of routinely used anaesthetics led to significant and characteristic alterations in the metabolic profile. Dexmedetomidine-induced alterations mirror α2-adrenoceptor agonism. Propofol emulsion altered the lipid profile. The inertness of sevoflurane might prove useful in vulnerable patients. S-ketamine induced amino acid alterations might be linked to its suggested antidepressive properties.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02624401. URL: https://clinicaltrials.gov/ct2/show/NCT02624401.

Metabolomic signatures associated with depression and predictors of antidepressant response in humans: A CAN-BIND-1 report

One of the biggest challenges in treating depression is the heterogeneous and qualitative nature of its clinical presentations. This highlights the need to find quantitative molecular markers to tailor existing treatment strategies to the individual's biological system. In this study, high-resolution metabolic phenotyping of urine and plasma samples from the CAN-BIND study collected before treatment with two common pharmacological strategies, escitalopram and aripiprazole, was performed. Here we show that a panel of LDL and HDL subfractions were negatively correlated with depression in males. For treatment response, lower baseline concentrations of apolipoprotein A1 and HDL were predictive of escitalopram response in males, while higher baseline concentrations of apolipoprotein A2, HDL and VLDL subfractions were predictive of aripiprazole response in females. These findings support the potential of metabolomics in precision medicine and the possibility of identifying personalized interventions for depression.

The effects of polyphenols-rich tropical fruit juice on cognitive function and metabolomics profile - a randomized controlled trial in middle-aged women

Objectives: Polyphenols, particularly anthocyanins, have received attention in improving health issues during old age, including decline in cognitive function and other health parameters. We aimed to determine the effects of polyphenols-rich tropical fruit TP 3-in-1™ juice towards improving cognitive function, oxidative stress and metabolomics profiles among middle-aged women.Methods: This clinical trial involved 31 subjects with signs of poor cognitive function, as assessed using the Rey Auditory Verbal Learning Test (RAVLT). They were randomized to receive either TP 3-in-1™ juice (n = 16) or placebo (n = 15). Study subjects consumed 500 ml of beverages for three times per day, three days per week, for a period of ten weeks. Juice supplementation provided 9135 mg GAE of total phenolic content and 194.1 mg cyanidin-3-glucoside of total anthocyanin monomer.Results: There was a significant interaction effects on RAVLT immediate recall (p < 0.05) and Comprehensive Trail Making Test (CTMT) Trail 4 (p < 0.05). Metabolomics analysis showed the presence of metabolites related to polyphenols intake and cognitive functions with the intervention group showed increased urinary excretion of thyroxine and 3-methyladenine. Thyroxine and 3-methyladenine give stability to human transthyretin (TTR) and activate autophagy, respectively, which are associated with the pathogenesis of Alzheimer's disease.Conclusion: The result shows the potential of TP 3-in-1™ juice which is rich in anthocyanins in improving cognitive function, particularly learning, memory, processing speed, sequencing, mental flexibility and visual-motor skills domains, among middle-aged women.

Metabolomics, metabolic flux analysis and cancer pharmacology

Metabolic reprogramming is a hallmark of cancer and increasing evidence suggests that reprogrammed cell metabolism supports tumor initiation, progression, metastasis and drug resistance. Understanding metabolic dysregulation may provide therapeutic targets and facilitate drug research and development for cancer therapy. Metabolomics enables the high-throughput characterization of a large scale of small molecule metabolites in cells, tissues and biofluids, while metabolic flux analysis (MFA) tracks dynamic metabolic activities using stable isotope tracer methods. Recent advances in metabolomics and MFA technologies make them powerful tools for metabolic profiling and characterizing metabolic activities in health and disease, especially in cancer research. In this review, we introduce recent advances in metabolomics and MFA analytical technologies, and provide the first comprehensive summary of the most commonly used isotope tracing methods. In addition, we highlight how metabolomics and MFA are applied in cancer pharmacology studies particularly for discovering targetable metabolic vulnerabilities, understanding the mechanisms of drug action and drug resistance, exploring potential strategies with dietary intervention, identifying cancer biomarkers, as well as enabling precision treatment with pharmacometabolomics.

Alterations in acylcarnitines, amines, and lipids inform about the mechanism of action of citalopram/escitalopram in major depression

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), yet their mechanisms of action are not fully understood and their therapeutic benefit varies among individuals. We used a targeted metabolomics approach utilizing a panel of 180 metabolites to gain insights into mechanisms of action and response to citalopram/escitalopram. Plasma samples from 136 participants with MDD enrolled into the Mayo Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) were profiled at baseline and after 8 weeks of treatment. After treatment, we saw increased levels of short-chain acylcarnitines and decreased levels of medium-chain and long-chain acylcarnitines, suggesting an SSRI effect on β-oxidation and mitochondrial function. Amines-including arginine, proline, and methionine sulfoxide-were upregulated while serotonin and sarcosine were downregulated, suggesting an SSRI effect on urea cycle, one-carbon metabolism, and serotonin uptake. Eighteen lipids within the phosphatidylcholine (PC aa and ae) classes were upregulated. Changes in several lipid and amine levels correlated with changes in 17-item Hamilton Rating Scale for Depression scores (HRSD₁₇). Differences in metabolic profiles at baseline and post-treatment were noted between participants who remitted (HRSD₁₇≤ 7) and those who gained no meaningful benefits (<30% reduction in HRSD₁₇). Remitters exhibited (a) higher baseline levels of C3, C5, alpha-aminoadipic acid, sarcosine, and serotonin; and (b) higher week-8 levels of PC aa C34:1, PC aa C34:2, PC aa C36:2, and PC aa C36:4. These findings suggest that mitochondrial energetics-including acylcarnitine metabolism, transport, and its link to β-oxidation-and lipid membrane remodeling may play roles in SSRI treatment response.

Metabolic features of recurrent major depressive disorder in remission, and the risk of future recurrence

Recurrent major depressive disorder (rMDD) is a relapsing-remitting disease with high morbidity and a 5-year risk of recurrence of up to 80%. This was a prospective pilot study to examine the potential diagnostic and prognostic value of targeted plasma metabolomics in the care of patients with rMDD in remission. We used an established LC-MS/MS platform to measure 399 metabolites in 68 subjects with rMDD (n = 45 females and 23 males) in antidepressant-free remission and 59 age- and sex-matched controls (n = 40 females and 19 males). Patients were then followed prospectively for 2.5 years. Metabolomics explained up to 43% of the phenotypic variance. The strongest biomarkers were gender specific. 80% of the metabolic predictors of recurrence in both males and females belonged to 6 pathways: (1) phospholipids, (2) sphingomyelins, (3) glycosphingolipids, (4) eicosanoids, (5) microbiome, and (6) purines. These changes traced to altered mitochondrial regulation of cellular redox, signaling, energy, and lipid metabolism. Metabolomics identified a chemical endophenotype that could be used to stratify rrMDD patients at greatest risk for recurrence with an accuracy over 0.90 (95%CI = 0.69-1.0). Power calculations suggest that a validation study of at least 198 females and 198 males (99 cases and 99 controls each) will be needed to confirm these results. Although a small study, these results are the first to show the potential utility of metabolomics in assisting with the important clinical challenge of prospectively identifying the patients at greatest risk of recurrence of a depressive episode and those who are at lower risk.

Peripheral Markers of Depression

Major Depressive Disorder (MDD) is a leading cause of disability worldwide, creating a high medical and socioeconomic burden. There is a growing interest in the biological underpinnings of depression, which are reflected by altered levels of biological markers. Among others, enhanced inflammation has been reported in MDD, as reflected by increased concentrations of inflammatory markers-C-reactive protein, interleukin-6, tumor necrosis factor-α and soluble interleukin-2 receptor. Oxidative and nitrosative stress also plays a role in the pathophysiology of MDD. Notably, increased levels of lipid peroxidation markers are characteristic of MDD. Dysregulation of the stress axis, along with increased cortisol levels, have also been reported in MDD. Alterations in growth factors, with a significant decrease in brain-derived neurotrophic factor and an increase in fibroblast growth factor-2 and insulin-like growth factor-1 concentrations have also been found in MDD. Finally, kynurenine metabolites, increased glutamate and decreased total cholesterol also hold promise as reliable biomarkers for MDD. Research in the field of MDD biomarkers is hindered by insufficient understanding of MDD etiopathogenesis, substantial heterogeneity of the disorder, common co-morbidities and low specificity of biomarkers. The construction of biomarker panels and their evaluation with use of new technologies may have the potential to overcome the above mentioned obstacles.

Kynurenic acid is a potential overlapped biomarker between diagnosis and treatment response for depression from metabolome analysis

Since optimal treatment at an early stage leads to remission of symptoms and recovery of function, putative biomarkers leading to early diagnosis and prediction of therapeutic responses are desired. The current study aimed to use a metabolomic approach to extract metabolites involved in both the diagnosis of major depressive disorder (MDD) and the prediction of therapeutic response for escitalopram. We compared plasma metabolites of MDD patients (n = 88) with those in healthy participants (n = 88) and found significant differences in the concentrations of 20 metabolites. We measured the Hamilton Rating Scale for Depression (HRSD) on 62 patients who completed approximately six-week treatment with escitalopram before and after treatment and found that kynurenic acid and kynurenine were significantly and negatively associated with HRSD reduction. Only one metabolite, kynurenic acid, was detected among 73 metabolites for overlapped biomarkers. Kynurenic acid was lower in MDD, and lower levels showed a better therapeutic response to escitalopram. Kynurenic acid is a metabolite in the kynurenine pathway that has been widely accepted as being a major mechanism in MDD. Overlapping biomarkers that facilitate diagnosis and prediction of the treatment response may help to improve disease classification and reduce the exposure of patients to less effective treatments in MDD.

Blues in the Brain and Beyond: Molecular Bases of Major Depressive Disorder and Relative Pharmacological and Non-Pharmacological Treatments

Despite the extensive research conducted in recent decades, the molecular mechanisms underlying major depressive disorder (MDD) and relative evidence-based treatments remain unclear. Various hypotheses have been successively proposed, involving different biological systems. This narrative review aims to critically illustrate the main pathogenic hypotheses of MDD, ranging from the historical ones based on the monoaminergic and neurotrophic theories, through the subsequent neurodevelopmental, glutamatergic, GABAergic, inflammatory/immune and endocrine explanations, until the most recent evidence postulating a role for fatty acids and the gut microbiota. Moreover, the molecular effects of established both pharmacological and non-pharmacological approaches for MDD are also reviewed. Overall, the existing literature indicates that the molecular mechanisms described in the context of these different hypotheses, rather than representing alternative ones to each other, are likely to contribute together, often with reciprocal interactions, to the development of MDD and to the effectiveness of treatments, and points at the need for further research efforts in this field.

Interactive effects between cadmium stabilized by palygorskite and mobilized by siderophores from Pseudomonas fluorescens

The application of palygorskite (PAL) for potentially toxic trace elements (Cd²⁺, Ni²⁺, etc.) remediation in polluted soil can substantially reduce the bioavailability and toxicity of these hazard materials. However, the secretion of organic acids and siderophores by microorganisms might result in the re-mobilization of cadmium (Cd) in PAL-bound forms (PAL-Cd). In this study, the interactive effects between Cd stabilized by PAL and mobilized by siderophores from Pseudomonas fluorescens were performed with four flask-shaking experimental treatments, namely, strain with or without an ability of siderophores production respectively associated with or without PAL-Cd. The GC-MS and UHPLC-MS test methods were used to analyze the concentrations of metabolites. Results showed that the Cd mobilized by strain with siderophores production was 22.1% higher than that of strain without the ability of siderophores production (p < 0.05). The mobilization of Cd in PAL in turn significantly reduced the siderophores production of Pseudomonas fluorescens by 25.1% (p < 0.05). The numbers of metabolites significantly up-regulated and down-regulated were 9 and 22 in strain groups with PAL-Cd addition compared with the groups without PAL-Cd, respectively. Metabolomics analysis revealed that the mobilized Cd affects the signal transduction pathway and primary metabolic processes, reduces the metabolic capacity of pentose phosphate pathway, glycolysis and tricarboxylic acid cycle pathway. These changes inhibit the ability of strain to biosynthesize amino acids during the mobilization processes, further reducing the capacity of Pseudomonas fluorescens to produce siderophores. This study provides a useful information on how to select soil Cd-stabilizing materials in a targeted manner and how to avoid Cd re-mobilization by siderophores.

Pilot Study of Metabolomic Clusters as State Markers of Major Depression and Outcomes to CBT Treatment

Major depressive disorder (MDD) is a common and disabling syndrome with multiple etiologies that is defined by clinically elicited signs and symptoms. In hopes of developing a list of candidate biological measures that reflect and relate closely to the severity of depressive symptoms, so-called “state-dependent” biomarkers of depression, this pilot study explored the biochemical underpinnings of treatment response to cognitive behavior therapy (CBT) in medication-free MDD outpatients. Plasma samples were collected at baseline and week 12 from a subset of MDD patients (N = 26) who completed a course of CBT treatment as part of the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Targeted metabolomic profiling using the AbsoluteIDQ^® p180 Kit and LC-MS identified eight “co-expressed” metabolomic modules. Of these eight, three were significantly associated with change in depressive symptoms over the course of the 12-weeks. Metabolites found to be most strongly correlated with change in depressive symptoms were branched chain amino acids, acylcarnitines, methionine sulfoxide, and α-aminoadipic acid (negative correlations with symptom change) as well as several lipids, particularly the phosphatidlylcholines (positive correlation). These results implicate disturbed bioenergetics as an important state marker in the pathobiology of MDD. Exploratory analyses contrasting remitters to CBT versus those who failed the treatment further suggest these metabolites may serve as mediators of recovery during CBT treatment. Larger studies examining metabolomic change patterns in patients treated with pharmacotherapy or psychotherapy will be necessary to elucidate the biological underpinnings of MDD and the -specific biologies of treatment response.

Maternal Obesity/Diabetes, Plasma Branched-Chain Amino Acids, and Autism Spectrum Disorder Risk in Urban Low-Income Children: Evidence of Sex Difference

Maternal metabolic conditions are known risk factors for child autism spectrum disorder (ASD). Branched-chain amino acids (BCAAs) are also associated with ASD. We examined the joint associations of maternal metabolic conditions and BCAAs on the risk of child ASD and whether the associations differed by child's sex. We analyzed 789 mother-infant pairs, a subset of the Boston Birth Cohort, from a predominantly urban, low-income, minority population. Maternal plasma BCAAs were measured by liquid chromatography-tandem mass spectrometry in samples collected 24-72 hr postpartum. A composite BCAA score was created using factor analysis, and prepregnancy obesity and diabetes (ob/DM) were combined into one variable. Logistic regression was used to explore the role of BCAAs as mediators or cofactors with ob/DM and child's sex on ASD risk. BCAA-ob/DM and BCAA-sex interactions were also examined. Maternal BCAAs alone were not associated with ASD and did not mediate the path between ob/DM and ASD. In the presence of maternal ob/DM, BCAA score was significantly associated with ASD (adjusted OR 2.33, 95% CI 1.18, 4.60). Interactions were present for valine with ob/DM and for valine and isoleucine with male sex on ASD risk. The odds ratio (OR) for risk of ASD was the greatest with all three risk factors combined-male sex, above median BCAA score, and ob/DM (OR 10.79, 95% CI 4.40, 26.42). Similar patterns were found for other developmental disorders, though not as strong as for ASD. Additional studies are warranted to clarify the role of maternal BCAAs, ob/DM, and child's sex in ASD. Autism Res 2019, 12: 1562-1573. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: This study investigated whether maternal obesity/diabetes and maternal circulating branched-chain amino acids (BCAAs) can jointly affect child ASD risk and whether the associations differ by child's sex. We found that the risk of ASD was greater among mothers with obesity/diabetes who also had elevated concentrations of BCAAs and that this risk was even greater for male children. These findings provide new evidence on fetal origins of ASD and sex difference and warrant additional investigation.

Co-Expression Network Analysis Revealed That the ATP5G1 Gene Is Associated With Major Depressive Disorder

Major depressive disorder (MDD) is a leading cause of disability worldwide, although its etiology and mechanism remain unknown. The aim of our study was to identify hub genes associated with MDD and to illustrate the underlying mechanisms. A weighted gene co-expression network analysis (WGCNA) was performed to identify significant gene modules and hub genes associated with MDD in peripheral blood mononuclear cells (PBMCs) (n = 45). In the blue module (R ² = 0.95), five common hub genes in both co-expression network and protein-protein interaction (PPI) network were regarded as "real" hub genes. In another independent dataset, GSE52790, four genes were still significantly down-regulated in PBMCs from MDD patients compared with the controls. Furthermore, these four genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in PBMCs from 33 MDD patients and 41 healthy controls. The qRT-PCR analysis showed that ATP synthase membrane subunit c locus 1 (ATP5G1) was significantly down-regulated in samples from MDD patients than in control samples (t = -2.89, p-value = 0.005). Moreover, this gene was significantly differentially expressed between patients and controls in the prefrontal cortex (z = -2.83, p-value = 0.005). Highly significant differentially methylated positions were identified in the Brodmann area 25 (BA25), with probes in the ATP5G1 gene being significantly associated with MDD: cg25495775 (t = 2.82, p-value = 0.008), cg25856120 (t = -2.23, p-value = 0.033), and cg23708347 (t = -2.24, p-value = 0.032). These findings indicate that the ATP5G1 gene is associated with the pathogenesis of MDD and that it could serve as a peripheral biomarker for MDD.

Moving pharmacoepigenetics tools for depression toward clinical use

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide, and over half of patients do not achieve symptom remission following an initial antidepressant course. Despite evidence implicating a strong genetic basis for the pathophysiology of MDD, there are no adequately validated biomarkers of treatment response routinely used in clinical practice. Pharmacoepigenetics is an emerging field that has the potential to combine both genetic and environmental information into treatment selection and further the goal of precision psychiatry. However, this field is in its infancy compared to the more established pharmacogenetics approaches.

METHODS

We prepared a narrative review using literature searches of studies in English pertaining to pharmacoepigenetics and treatment of depressive disorders conducted in PubMed, Google Scholar, PsychINFO, and Ovid Medicine from inception through January 2019. We reviewed studies of DNA methylation and histone modifications in both humans and animal models of depression.

RESULTS

Emerging evidence from human and animal work suggests a key role for epigenetic marks, including DNA methylation and histone modifications, in the prediction of antidepressant response. The challenges of heterogeneity of patient characteristics and loci studied as well as lack of replication that have impacted the field of pharmacogenetics also pose challenges to the development of pharmacoepigenetic tools. Additionally, given the tissue specific nature of epigenetic marks as well as their susceptibility to change in response to environmental factors and aging, pharmacoepigenetic tools face additional challenges to their development.

LIMITATIONS

This is a narrative and not systematic review of the literature on the pharmacoepigenetics of antidepressant response. We highlight key studies pertaining to pharmacoepigenetics and treatment of depressive disorders in humans and depressive-like behaviors in animal models, regardless of sample size or methodology. While we discuss DNA methylation and histone modifications, we do not cover microRNAs, which have been reviewed elsewhere recently.

CONCLUSIONS

Utilization of genome-wide approaches and reproducible epigenetic assays, careful selection of the tissue assessed, and integration of genetic and clinical information into pharmacoepigenetic tools will improve the likelihood of developing clinically useful tests.

Emerging Applications of Metabolomics in Traditional Chinese Medicine Treating Hypertension: Biomarkers, Pathways and More

Hypertension is a prevalent, complex, and polygenic cardiovascular disease, which is associated with increased mortality and morbidity. Across the world, traditional Chinese medicine (TCM) constituted by herbal medicine and non-pharmacological therapies is used to assist blood pressure management. Though widely accepted in daily practice, its mechanism remains largely unknown. Recent years saw a number of studies utilizing metabolomics technologies to elucidate the biological foundation of the antihypertensive effect of TCM. Metabolomics is a relatively "young" omics approach that has gained enormous attention recently in cardiovascular drug discovery and pharmacology studies of natural products. In this review, we described the use of metabolomics in deciphering TCM diagnostic codes for hypertension and in revealing molecular events that drive the antihypertensive effect. By corroborating the diagnostic rules, there's accumulating evidence showing that metabolic profile could be the signature of different syndromes/patterns of hypertension, which offers new perspectives for disease diagnosis and efficacy optimization. Moreover, TCM treatment significantly altered the metabolic perturbations associated with hypertension, which could be a crucial mechanism of the therapeutic effect of TCM. Not only significantly rebalances the dynamics of metabolic flux, TCM but also elicits metabolic network reorganization through restoring the functions of key metabolites, and metabolic pathways. The role of TCM in regulating metabolic perturbations will be informative to researchers seeking new leads for drug discovery. This review further envisioned the promises of employing metabolomics to explore network pharmacology, host-gut microbiota interactions and metabolic reprogramming in TCM, and possible herb-drug interactions in this field in future.

Can targeted metabolomics predict depression recovery? Results from the CO-MED trial

Metabolomics is a developing and promising tool for exploring molecular pathways underlying symptoms of depression and predicting depression recovery. The AbsoluteIDQ™ p180 kit was used to investigate whether plasma metabolites (sphingomyelins, lysophosphatidylcholines, phosphatidylcholines, and acylcarnitines) from a subset of participants in the Combining Medications to Enhance Depression Outcomes (CO-MED) trial could act as predictors or biologic correlates of depression recovery. Participants in this trial were assigned to one of three pharmacological treatment arms: escitalopram monotherapy, bupropion-escitalopram combination, or venlafaxine-mirtazapine combination. Plasma was collected at baseline in 159 participants and again 12 weeks later at study exit in 83 of these participants. Metabolite concentrations were measured and combined with clinical and sociodemographic variables using the hierarchical lasso to simultaneously model whether specific metabolites are particularly informative of depressive recovery. Increased baseline concentrations of phosphatidylcholine C38:1 showed poorer outcome based on change in the Quick Inventory of Depressive Symptoms (QIDS). In contrast, an increased ratio of hydroxylated sphingomyelins relative to non-hydroxylated sphingomyelins at baseline and a change from baseline to exit suggested a better reduction of symptoms as measured by QIDS score. All metabolite-based models performed superior to models only using clinical and sociodemographic variables, suggesting that metabolomics may be a valuable tool for predicting antidepressant outcomes.

Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling

Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of twenty-first century healthcare.

Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics

The pathogenesis of ME/CFS, a disease characterized by fatigue, cognitive dysfunction, sleep disturbances, orthostatic intolerance, fever, irritable bowel syndrome (IBS), and lymphadenopathy, is poorly understood. We report biomarker discovery and topological analysis of plasma metabolomic, fecal bacterial metagenomic, and clinical data from 50 ME/CFS patients and 50 healthy controls. We confirm reports of altered plasma levels of choline, carnitine and complex lipid metabolites and demonstrate that patients with ME/CFS and IBS have increased plasma levels of ceramide. Integration of fecal metagenomic and plasma metabolomic data resulted in a stronger predictive model of ME/CFS (cross-validated AUC = 0.836) than either metagenomic (cross-validated AUC = 0.745) or metabolomic (cross-validated AUC = 0.820) analysis alone. Our findings may provide insights into the pathogenesis of ME/CFS and its subtypes and suggest pathways for the development of diagnostic and therapeutic strategies.

Peripheral biomarkers of major depression and antidepressant treatment response: Current knowledge and future outlooks

BACKGROUND

In recent years, we have accomplished a deeper understanding about the pathophysiology of major depressive disorder (MDD). Nevertheless, this improved comprehension has not translated to improved treatment outcome, as identification of specific biologic markers of disease may still be crucial to facilitate a more rapid, successful treatment. Ongoing research explores the importance of screening biomarkers using neuroimaging, neurophysiology, genomics, proteomics, and metabolomics measures.

RESULTS

In the present review, we highlight the biomarkers that are differentially expressed in MDD and treatment response and place a particular emphasis on the most recent progress in advancing technology which will continue the search for blood-based biomarkers.

LIMITATIONS

Due to space constraints, we are unable to detail all biomarker platforms, such as neurophysiological and neuroimaging markers, although their contributions are certainly applicable to a biomarker review and valuable to the field.

CONCLUSIONS

Although the search for reliable biomarkers of depression and/or treatment outcome is ongoing, the rapidly-expanding field of research along with promising new technologies may provide the foundation for identifying key factors which will ultimately help direct patients toward a quicker and more effective treatment for MDD.

Effects of long-term cadmium exposure on urinary metabolite profiles in mice

Cadmium (Cd) is a common environmental pollutant with known toxic effects on the kidney. Urinary metabolomics is a promising approach to study mechanism by which Cd-induced nephrotoxicity. The aim of this study was to elucidate the mechanism of Cd toxicity and to develop specific biomarkers by identifying urinary metabolic changes after a long-term of Cd exposure and with the critical concentration of Cd in the kidney. Urine samples were collected from wild-type 129/Sv mice after 67 weeks of 300 ppm Cd exposure and analyzed by ultra performance liquid chromatography connected with quadrupole time of flight mass spectrometer (UPLC-QTOF-MS) based metabolomics approach. A total of 40 most differentiated metabolites (9 down-regulated and 31 up-regulated) between the control and Cd-exposed group were identified. The majority of the regulated metabolites are amino acids (glutamine, L-aspartic acid, phenylalanine, tryptophan, and D-proline) indicating that amino acid metabolism pathways are affected by long-term exposure of Cd. However, there are also some nucleotides (guanosine, guanosine monophosphate, cyclic AMP, uridine), amino acid derivatives (homoserine, N-acetyl-L-aspartate, N-acetylglutamine, acetyl-phenylalanine, carboxymethyllysine), and peptides. Results of pathway analysis showed that the arginine and proline metabolism, purine metabolism, alanine, aspartate and glutamate metabolism, and aminoacyl-tRNA biosynthesis were affected compared to the control. This study demonstrates that metabolomics is useful to elucidate the metabolic responses and biological effects induced by Cd-exposure.

Beta-defensin 1, aryl hydrocarbon receptor and plasma kynurenine in major depressive disorder: metabolomics-informed genomics

Major depressive disorder (MDD) is a heterogeneous disease. Efforts to identify biomarkers for sub-classifying MDD and antidepressant therapy by genome-wide association studies (GWAS) alone have generally yielded disappointing results. We applied a metabolomics-informed genomic research strategy to study the contribution of genetic variation to MDD pathophysiology by assaying 31 metabolites, including compounds from the tryptophan, tyrosine, and purine pathways, in plasma samples from 290 MDD patients. Associations of metabolite concentrations with depressive symptoms were determined, followed by GWAS for selected metabolites and functional validation studies of the genes identified. Kynurenine (KYN), the baseline plasma metabolite that was most highly associated with depressive symptoms, was negatively correlated with severity of those symptoms. GWAS for baseline plasma KYN concentrations identified SNPs across the beta-defensin 1 (DEFB1) and aryl hydrocarbon receptor (AHR) genes that were cis-expression quantitative trait loci (eQTLs) for DEFB1 and AHR mRNA expression, respectively. Furthermore, the DEFB1 locus was associated with severity of MDD symptoms in a larger cohort of 803 MDD patients. Functional studies demonstrated that DEFB1 could neutralize lipopolysaccharide-stimulated expression of KYN-biosynthesizing enzymes in monocytic cells, resulting in altered KYN concentrations in the culture media. In addition, we demonstrated that AHR was involved in regulating the expression of enzymes in the KYN pathway and altered KYN biosynthesis in cell lines of hepatocyte and astrocyte origin. In conclusion, these studies identified SNPs that were cis-eQTLs for DEFB1 and AHR and, which were associated with variation in plasma KYN concentrations that were related to severity of MDD symptoms.

The metabonomics study of P-selectin glycoprotein ligand-1 (PSGL-1) deficiency inhibiting the progression of atherosclerosis in LDLR-/- mice

Atherosclerosis (AS) is a multi-factorial chronic disease commonly associated with the mechanisms of metabolism disorder, endothelial dysfunction and chronic inflammation. AS an inflammatory molecule, p-selectin glycoprotein ligand-1 (PSGL-1) played an important role in the inflammatory process of atherogenesis involving the recruitment of leukocyte and transmitting signals to activate leukocyte during the adhesion process. So far, there has been little study regarding the effects of PSGL-1 on AS progression and the metabolic regulation. In this report, we studied the effect of PSGL-1 deficiency on the formation and progression of AS and the metabolic regulation by use of LDLR^-/-, PSGL-1^-/- transgenic mice based on metabonomics. It was found that the PSGL-1 deficiency reduced the atherosclerotic plaque area, inflammatory cells infiltration and fiber hyperplasia during the AS development. The serum metabonomics study showed that the LDLR^-/- ,PSGL-1^-/- mice had higher levels of HDL, valine, acetate, pyruvate, choline, PC, GPC and glycine, and lower levels of LDL+VLDL and lactate at the early stage of atherosclerosis, while lactate, citrate and glutamine showed statistical significance at the late stage of atherosclerosis. These results showed that the PSGL-1 deficiency inhibited the AS progression and regulated glucose metabolism, lipid metabolism, amino acid and phospholipid metabolism in LDLR^-/- mice.

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

Metabolic profiling by NMR spectroscopy or hyphenated mass spectrometry, known as metabonomics or metabolomics, is an important tool for systems-based approaches in biology and medicine. The experiments are typically done in a diagnostic fashion where changes in metabolite profiles are interpreted as a consequence of an intervention or event; be that a change in diet, the administration of a drug, physical exertion or the onset of a disease. By contrast, pharmacometabonomics takes a prognostic approach to metabolic profiling, in order to predict the effects of drug dosing before it occurs. Differences in pre-dose metabolite profiles between groups of subjects are used to predict post-dose differences in response to drug administration. Thus the paradigm is inverted and pharmacometabonomics is the metabolic equivalent of pharmacogenomics. Although the field is still in its infancy, it is expected that pharmacometabonomics, alongside pharmacogenomics, will assist with the delivery of personalised or precision medicine to patients, which is a critical goal of 21st century healthcare.

The steroid metabolome in women with premenstrual dysphoric disorder during GnRH agonist-induced ovarian suppression: effects of estradiol and progesterone addback

Clinical evidence suggests that symptoms in premenstrual dysphoric disorder (PMDD) reflect abnormal responsivity to ovarian steroids. This differential steroid sensitivity could be underpinned by abnormal processing of the steroid signal. We used a pharmacometabolomics approach in women with prospectively confirmed PMDD (n=15) and controls without menstrual cycle-related affective symptoms (n=15). All were medication-free with normal menstrual cycle lengths. Notably, women with PMDD were required to show hormone sensitivity in an ovarian suppression protocol. Ovarian suppression was induced for 6 months with gonadotropin-releasing hormone (GnRH)-agonist (Lupron); after 3 months all were randomized to 4 weeks of estradiol (E2) or progesterone (P4). After a 2-week washout, a crossover was performed. Liquid chromatography/tandem mass spectrometry measured 49 steroid metabolites in serum. Values were excluded if >40% were below the limit of detectability (n=21). Analyses were performed with Wilcoxon rank-sum tests using false-discovery rate (q<0.2) for multiple comparisons. PMDD and controls had similar basal levels of metabolites during Lupron and P4-derived neurosteroids during Lupron or E2/P4 conditions. Both groups had significant increases in several steroid metabolites compared with the Lupron alone condition after treatment with E2 (that is, estrone-SO4 (q=0.039 and q=0.002, respectively) and estradiol-3-SO4 (q=0.166 and q=0.001, respectively)) and after treatment with P4 (that is, allopregnanolone (q=0.001 for both PMDD and controls), pregnanediol (q=0.077 and q=0.030, respectively) and cortexone (q=0.118 and q=0.157, respectively). Only sulfated steroid metabolites showed significant diagnosis-related differences. During Lupron plus E2 treatment, women with PMDD had a significantly attenuated increase in E2-3-sulfate (q=0.035) compared with control women, and during Lupron plus P4 treatment a decrease in DHEA-sulfate (q=0.07) compared with an increase in controls. Significant effects of E2 addback compared with Lupron were observed in women with PMDD who had significant decreases in DHEA-sulfate (q=0.065) and pregnenolone sulfate (q=0.076), whereas controls had nonsignificant increases (however, these differences did not meet statistical significance for a between diagnosis effect). Alterations of sulfotransferase activity could contribute to the differential steroid sensitivity in PMDD. Importantly, no differences in the formation of P4-derived neurosteroids were observed in this otherwise highly selected sample of women studied under controlled hormone exposures.

Altered KYN/TRP, Gln/Glu, and Met/methionine sulfoxide ratios in the blood plasma of medication-free patients with major depressive disorder

Capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS) is a comprehensive, quantitative, and high throughput tool used to analyze metabolite profiles. In the present study, we used CE-TOFMS to profile metabolites found in the blood plasma of 33 medication-free patients with major depressive disorder (MDD) and 33 non-psychiatric control subjects. We then investigated changes which occurred in the metabolite levels during an 8-week treatment period. The medication-free MDD patients and control subjects showed significant differences in their mean levels of 33 metabolites, including kynurenine (KYN), glutamate (Glu), glutamine (Gln), methionine sulfoxide, and methionine (Met). In particular, the ratios of KYN to tryptophan (TRP), Gln to Glu, and Met to methionine sulfoxide were all significantly different between the two groups. Among the 33 metabolites with altered levels in MDD patients, the levels of KYN and Gln, as well as the ratio of Gln to Glu, were significantly normalized after treatment. Our findings suggest that imbalances in specific metabolite levels may be involved in the pathogenesis of MDD, and provide insight into the mechanisms by which antidepressant agents work in MDD patients.

The Personalization of Clopidogrel Antiplatelet Therapy: The Role of Integrative Pharmacogenetics and Pharmacometabolomics

Dual antiplatelet therapy of aspirin and clopidogrel is pivotal for patients undergoing percutaneous coronary intervention. However, the variable platelets reactivity response to clopidogrel may lead to outcome failure and recurrence of cardiovascular events. Although many genetic and nongenetic factors are known, great portion of clopidogrel variable platelets reactivity remain unexplained which challenges the personalization of clopidogrel therapy. Current methods for clopidogrel personalization include CYP2C19 genotyping, pharmacokinetics, and platelets function testing. However, these methods lack precise prediction of clopidogrel outcome, often leading to insufficient prediction. Pharmacometabolomics which is an approach to identify novel biomarkers of drug response or toxicity in biofluids has been investigated to predict drug response. The advantage of pharmacometabolomics is that it does not only predict the response but also provide extensive information on the metabolic pathways implicated with the response. Integrating pharmacogenetics with pharmacometabolomics can give insight on unknown genetic and nongenetic factors associated with the response. This review aimed to review the literature on factors associated with the variable platelets reactivity response to clopidogrel, as well as appraising current methods for the personalization of clopidogrel therapy. We also aimed to review the literature on using pharmacometabolomics approach to predict drug response, as well as discussing the plausibility of using it to predict clopidogrel outcome.

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients' (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.

TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics

Millions of patients suffer from major depressive disorder (MDD), but many do not respond to selective serotonin reuptake inhibitor (SSRI) therapy. We used a pharmacometabolomics-informed pharmacogenomics research strategy to identify genes associated with metabolites that were related to SSRI response. Specifically, 306 MDD patients were treated with citalopram or escitalopram and blood was drawn at baseline, 4 and 8 weeks for blood drug levels, genome-wide single nucleotide polymorphism (SNP) genotyping and metabolomic analyses. SSRI treatment decreased plasma serotonin concentrations (P<0.0001). Baseline and plasma serotonin concentration changes were associated with clinical outcomes (P<0.05). Therefore, baseline and serotonin concentration changes were used as phenotypes for genome-wide association studies (GWAS). GWAS for baseline plasma serotonin concentrations revealed a genome-wide significant (P=7.84E-09) SNP cluster on chromosome four 5' of TSPAN5 and a cluster across ERICH3 on chromosome one (P=9.28E-08) that were also observed during GWAS for change in serotonin at 4 (P=5.6E-08 and P=7.54E-07, respectively) and 8 weeks (P=1.25E-06 and P=3.99E-07, respectively). The SNPs on chromosome four were expression quantitative trait loci for TSPAN5. Knockdown (KD) and overexpression (OE) of TSPAN5 in a neuroblastoma cell line significantly altered the expression of serotonin pathway genes (TPH1, TPH2, DDC and MAOA). Chromosome one SNPs included two ERICH3 nonsynonymous SNPs that resulted in accelerated proteasome-mediated degradation. In addition, ERICH3 and TSPAN5 KD and OE altered media serotonin concentrations. Application of a pharmacometabolomics-informed pharmacogenomic research strategy, followed by functional validation, indicated that TSPAN5 and ERICH3 are associated with plasma serotonin concentrations and may have a role in SSRI treatment outcomes.

From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments.

Metabolomics enables precision medicine: "A White Paper, Community Perspective"

INTRODUCTION BACKGROUND TO METABOLOMICS

Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.

OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE

We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.

CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE

Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.