Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects

Next-generation biomarkers for alcohol consumption and alcohol use disorder diagnosis, prognosis, and treatment: A critical review

This critical review summarizes the current state of omics-based biomarkers in the alcohol research field. We first provide definitions and background information on alcohol and alcohol use disorder (AUD), biomarkers, and "omic" technologies. We next summarize using (1) genetic information as risk/prognostic biomarkers for the onset of alcohol-related problems and the progression from regular drinking to problematic drinking (including AUD), (2) epigenetic information as diagnostic biomarkers for AUD and risk biomarkers for alcohol consumption, (3) transcriptomic information as diagnostic biomarkers for AUD, risk biomarkers for alcohol consumption, and (4) metabolomic information as diagnostic biomarkers for AUD, risk biomarkers for alcohol consumption, and predictive biomarkers for response to acamprosate in subjects with AUD. In the final section, the clinical implications of the findings are discussed, and recommendations are made for future research.

Molecular mechanisms involved in alcohol craving, IRF3, and endoplasmic reticulum stress: a multi-omics study

Alcohol use disorder (AUD) is the most prevalent substance use disorder worldwide. Acamprosate and naltrexone are anti-craving drugs used in AUD pharmacotherapy. However, molecular mechanisms underlying their anti-craving effect remain unclear. This study utilized a patient-derived induced pluripotent stem cell (iPSC)-based model system and anti-craving drugs that are used to treat AUD as "molecular probes" to identify possible mechanisms associated with alcohol craving. We examined the pathophysiology of craving and anti-craving drugs by performing functional genomics studies using iPSC-derived astrocytes and next-generation sequencing. Specifically, RNA sequencing performed using peripheral blood mononuclear cells from AUD patients with extreme values for alcohol craving intensity prior to treatment showed that inflammation-related pathways were highly associated with alcohol cravings. We then performed a genome-wide assessment of chromatin accessibility and gene expression profiles of induced iPSC-derived astrocytes in response to ethanol or anti-craving drugs. Those experiments identified drug-dependent epigenomic signatures, with IRF3 as the most significantly enriched motif in chromatin accessible regions. Furthermore, the activation of IRF3 was associated with ethanol-induced endoplasmic reticulum (ER) stress which could be attenuated by anti-craving drugs, suggesting that ER stress attenuation might be a target for anti-craving agents. In conclusion, we found that craving intensity was associated with alcohol consumption and treatment outcomes. Our functional genomic studies suggest possible relationships among craving, ER stress, IRF3 and the actions of anti-craving drugs.

The Exposome and Nutritional Pharmacology and Toxicology: A New Application for Metabolomics

The exposome refers to all of the internal and external life-long exposures that an individual experiences. These exposures, either acute or chronic, are associated with changes in metabolism that will positively or negatively influence the health and well-being of individuals. Nutrients and other dietary compounds modulate similar biochemical processes and have the potential in some cases to counteract the negative effects of exposures or enhance their beneficial effects. We present herein the concept of Nutritional Pharmacology/Toxicology which uses high-information metabolomics workflows to identify metabolic targets associated with exposures. Using this information, nutritional interventions can be designed toward those targets to mitigate adverse effects or enhance positive effects. We also discuss the potential for this approach in precision nutrition where nutrients/diet can be used to target gene-environment interactions and other subpopulation characteristics. Deriving these "nutrient cocktails" presents an opportunity to modify the effects of exposures for more beneficial outcomes in public health.

The oxytocin receptor rs2254298 polymorphism and alcohol withdrawal symptoms: a gene-environment interaction in mood disorders

Objective

Alcohol use disorder (AUD) is a common mental disorder characterized by repeated withdrawal episodes. Negative emotions during withdrawal are the primary factors affecting successful abstinence. Oxytocin is a critical modulator of emotions. OXTR, the oxytocin receptor, may also be a promising candidate for treating alcohol withdrawal symptoms. Previous studies indicated that people with different genotypes of OXTR rs2254298 were reported to suffer from more significant depressive or heightened anxiety symptoms when experiencing early adversity. The present study aims to explore the modulatory role of the polymorphism OXTR rs2254298 on mood disorders during alcohol withdrawal and to help researchers better understand and develop effective relapse prevention and interventions for alcohol use disorders.

Methods

We recruited 265 adult Chinese Han men with AUD. Anxiety and depressive symptoms were measured using the Self-Rating Anxiety Scale and Self-Rating Depression Scale. Alcohol dependence levels were measured using Michigan Alcoholism Screening Test. Genomic DNA extraction and genotyping from participants' peripheral blood samples.

Result

First, a multiple linear regression was used to set the alcohol dependence level, OXTR.rs2254298, interaction terms as the primary predictor variable, and depression or anxiety as an outcome; age and educational years were covariates. There was a significant interaction between OXTR rs2254298 and alcohol dependence level on anxiety (B = 0.23, 95% confidence interval [CI]: 0.01-0.45) but not on depression (B = -0.06, 95% CI: -0.30 - 0.18). The significance region test showed that alcohol-dependent men who are GG homozygous were more likely to experience anxiety symptoms than subjects with the A allele (A allele: β = 0.27, p < 0.001; GG homozygote: β = 0.50, p < 0.001). Finally, re-parameterized regression analysis demonstrated that this gene-environment interaction of OXTR rs2254298 and alcohol dependence on anxiety fits the weak differential susceptibility model (R² = 0.17, F (5,259) = 13.46, p < 0.001).

Conclusion

This study reveals a gene-environment interactive effect between OXTR rs2254298 and alcohol withdrawal on anxiety but not depression. From the perspective of gene-environment interactions, this interaction fits the differential susceptibility model; OXTR rs2254298 GG homozygote carriers are susceptible to the environment and are likely to experience anxiety symptoms of alcohol withdrawal.

RNA biomarkers for alcohol use disorder

Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.

Plasma TNFSF10 levels associated with acamprosate treatment response in patients with alcohol use disorder

Acamprosate is an anti-craving drug used in alcohol use disorder (AUD) pharmacotherapy. However, only a subset of patients achieves optimal treatment outcomes. The identification of predictive biomarkers of acamprosate treatment response in patients with AUD would be a substantial advance in addiction medicine. We designed this study to use proteomics data as a quantitative biological trait as a step toward identifying inflammatory modulators that might be associated with acamprosate treatment outcomes. The NIAAA-funded Mayo Clinic Center for the Individualized Treatment of Alcoholism study had previously recruited 442 AUD patients who received 3 months of acamprosate treatment. However, only 267 subjects returned for the 3-month follow-up visit and, as a result, had treatment outcome information available. Baseline alcohol craving intensity was the most significant predictor of acamprosate treatment outcomes. We performed plasma proteomics using the Olink target 96 inflammation panel and identified that baseline plasma TNF superfamily member 10 (TNFSF10) concentration was associated with alcohol craving intensity and variation in acamprosate treatment outcomes among AUD patients. We also performed RNA sequencing using baseline peripheral blood mononuclear cells from AUD patients with known acamprosate treatment outcomes which revealed that inflammation-related pathways were highly associated with relapse to alcohol use during the 3 months of acamprosate treatment. These observations represent an important step toward advancing our understanding of the pathophysiology of AUD and molecular mechanisms associated with acamprosate treatment response. In conclusion, applying omics-based approaches may be a practical approach for identifying biologic markers that could potentially predict alcohol craving intensity and acamprosate treatment response.

Genome-wide association study for circulating FGF21 in patients with alcohol use disorder: Molecular links between the SNHG16 locus and catecholamine metabolism

Objective

Alcohol consumption can increase circulating levels of fibroblast growth factor 21 (FGF21). The effects of FGF21 in the central nervous system are associated with the regulation of catecholamines, neurotransmitters that play a crucial role in reward pathways. This study aims to identify genetic variants associated with FGF21 levels and evaluate their functional role in alcohol use disorder (AUD).

Methods

We performed a genome-wide association study (GWAS) using DNA samples from 442 AUD subjects recruited from the Mayo Clinic Center for the Individualized Treatment of Alcoholism Study. Plasma FGF21 levels were measured using Olink proximity extension immunoassays. Alcohol consumption at time of entry into the study was measured using the self-reported timeline followback method. Functional genomic studies were performed using HepG2 cells and induced pluripotent stem cell (iPSC)-derived brain organoids.

Results

Plasma FGF21 levels were positively correlated with recent alcohol consumption and gamma-glutamyl transferase levels, a commonly used marker for heavy alcohol use. One variant, rs9914222, located 5’ of SNHG16 on chromosome 17 was associated with plasma FGF21 levels (p = 4.60E-09). This variant was also associated with AUD risk (β: −3.23; p:0.0004). The rs9914222 SNP is an eQTL for SNHG16 in several brain regions, i.e., the variant genotype was associated with decreased expression of SNHG16. The variant genotype for the rs9914222 SNP was also associated with higher plasma FGF21 levels. Knockdown of SNHG16 in HepG2 cells resulted in increased FGF21 concentrations and decreased expression and enzyme activity for COMT, an enzyme that plays a key role in catecholamine metabolism. Finally, we demonstrated that ethanol significantly induced FGF21, dopamine, norepinephrine, and epinephrine concentrations in iPSC-derived brain organoids.

Conclusions

GWAS for FGF21 revealed a SNHG16 genetic variant associated with FGF21 levels which are associated with recent alcohol consumption. Our data suggest that SNHG16 can regulate FGF21 concentrations and decrease COMT expression and enzyme activity which, in turn, have implications for the regulation of catecholamines.

(The ClinicalTrials.gov Identifier: NCT00662571)

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Plasma FGF21 levels are positively associated with recent alcohol use.

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GWAS for FGF21 revealed a SNHG16 genetic variant which might be associated with AUD.

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The rs9914222 SNP was an eQTL for SNHG16 in several brain regions.

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Ethanol could regulate SNHG16 expression, FGF21 levels and catecholamines.

Identification of cerebrospinal fluid and serum metabolomic biomarkers in first episode psychosis patients

Psychotic disorders are currently diagnosed by examining the patient's mental state and medical history. Identifying reliable diagnostic, monitoring, predictive, or prognostic biomarkers would be useful in clinical settings and help to understand the pathophysiology of schizophrenia. Here, we performed an untargeted metabolomics analysis using ultra-high pressure liquid chromatography coupled with time-of-flight mass spectroscopy on cerebrospinal fluid (CSF) and serum samples of 25 patients at their first-episode psychosis (FEP) manifestation (baseline) and after 18 months (follow-up). CSF and serum samples of 21 healthy control (HC) subjects were also analyzed. By comparing FEP and HC groups at baseline, we found eight CSF and 32 serum psychosis-associated metabolites with non-redundant identifications. Most remarkable was the finding of increased CSF serotonin (5-HT) levels. Most metabolites identified at baseline did not differ between groups at 18-month follow-up with significant improvement of positive symptoms and cognitive functions. Comparing FEP patients at baseline and 18-month follow-up, we identified 20 CSF metabolites and 90 serum metabolites that changed at follow-up. We further utilized Ingenuity Pathway Analysis (IPA) and identified candidate signaling pathways involved in psychosis pathogenesis and progression. In an extended cohort, we validated that CSF 5-HT levels were higher in FEP patients than in HC at baseline by reversed-phase high-pressure liquid chromatography. To conclude, these findings provide insights into the pathophysiology of psychosis and identify potential psychosis-associated biomarkers.

Genetic Variants Associated with Acamprosate Treatment Response in Alcohol Use Disorder Patients: A Multiple Omics Study

Background and Purpose

Acamprosate is an anti‐craving drug used for the pharmacotherapy of alcohol use disorder (AUD). However, only some patients achieve optimal therapeutic outcomes. This study was designed to explore differences in metabolomic profiles between patients who maintained sobriety and those who relapsed, to determine whether those differences provide insight into variation in acamprosate treatment response phenotypes.

Experimental Approach

We previously conducted an acamprosate trial involving 442 AUD patients, and 267 of these subjects presented themselves for a 3‐month follow‐up. The primary outcome was abstinence. Clinical information, genomic data and metabolomics data were collected. Baseline plasma samples were assayed using targeted metabolomics.

Key Results

Baseline plasma arginine, threonine, α‐aminoadipic acid and ethanolamine concentrations were associated with acamprosate treatment outcomes and baseline craving intensity, a measure that has been associated with acamprosate treatment response. We next applied a pharmacometabolomics‐informed genome‐wide association study (GWAS) strategy to identify genetic variants that might contribute to variations in plasma metabolomic profiles that were associated with craving and/or acamprosate treatment outcome. Gene expression data for induced pluripotent stem cell‐derived forebrain astrocytes showed that a series of genes identified during the metabolomics‐informed GWAS were ethanol responsive. Furthermore, a large number of those genes could be regulated by acamprosate. Finally, we identified a series of single nucleotide polymorphisms that were associated with acamprosate treatment outcomes.

Conclusion and Implications

These results serve as an important step towards advancing our understanding of disease pathophysiology and drug action responsible for variation in acamprosate response and alcohol craving in AUD patients.

Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?

Alcohol dependence (AD) is a condition of alcohol use disorder in which the drinkers frequently develop emotional symptoms associated with a continuous alcohol intake. AD characterized by metabolic disturbances can be quantitatively analyzed by metabolomics to identify the alterations in metabolic pathways. This study aimed to: i) compare the plasma metabolic profiling between healthy and AD-diagnosed individuals to reveal the altered metabolic profiles in AD, and ii) identify potential biological correlates of alcohol-dependent inpatients based on metabolomics and interpretable machine learning. Plasma samples were obtained from healthy (n = 42) and AD-diagnosed individuals (n = 43). The plasma metabolic differences between them were investigated using liquid chromatography-tandem mass spectrometry (AB SCIEX^® QTRAP 4500 system) in different electrospray ionization modes with scheduled multiple reaction monitoring scans. In total, 59 and 52 compounds were semi-quantitatively measured in positive and negative ionization modes, respectively. In addition, 39 metabolites were identified as important variables to contribute to the classifications using an orthogonal partial least squares-discriminant analysis (OPLS-DA) (VIP > 1) and also significantly different between healthy and AD-diagnosed individuals using univariate analysis (p-value < 0.05 and false discovery rate < 0.05). Among the identified metabolites, indole-3-carboxylic acid, quinolinic acid, hydroxy-tryptophan, and serotonin were involved in the tryptophan metabolism along the indole, kynurenine, and serotonin pathways. Metabolic pathway analysis revealed significant changes or imbalances in alanine, aspartate, glutamate metabolism, which was possibly the main altered pathway related to AD. Tryptophan metabolism interactively influenced other metabolic pathways, such as nicotinate and nicotinamide metabolism. Furthermore, among the OPLS-DA-identified metabolites, normetanephrine and ascorbic acid were demonstrated as suitable biological correlates of AD inpatients from our model using an interpretable, supervised decision tree classifier algorithm. These findings indicate that the discriminatory metabolic profiles between healthy and AD-diagnosed individuals may benefit researchers in illustrating the underlying molecular mechanisms of AD. This study also highlights the approach of combining metabolomics and interpretable machine learning as a valuable tool to uncover potential biological correlates. Future studies should focus on the global analysis of the possible roles of these differential metabolites and disordered metabolic pathways in the pathophysiology of AD.

Classical blood biomarkers identify patients with higher risk for relapse 6 months after alcohol withdrawal treatment

This naturalistic study among patients with alcohol dependence examined whether routine blood biomarkers could help to identify patients with high risk for relapse after withdrawal treatment. In a longitudinal study with 6-month follow-up among 133 patients with alcohol dependence who received inpatient alcohol withdrawal treatment, we investigated the usefulness of routine blood biomarkers and clinical and sociodemographic factors for potential outcome prediction and risk stratification. Baseline routine blood biomarkers (gamma-glutamyl transferase [GGT], alanine aminotransferase [ALT/GPT], aspartate aminotransferase [AST/GOT], mean cell volume of erythrocytes [MCV]), and clinical and sociodemographic characteristics were recorded at admission. Standardized 6 months' follow-up assessed outcome variables continuous abstinence, days of continuous abstinence, daily alcohol consumption and current abstinence. The combined threshold criterion of an AST:ALT ratio > 1.00 and MCV > 90.0 fl helped to identify high-risk patients. They had lower abstinence rates (P = 0.001), higher rates of daily alcohol consumption (P < 0.001) and shorter periods of continuous abstinence (P = 0.027) compared with low-risk patients who did not meet the threshold criterion. Regression analysis confirmed our hypothesis that the combination criterion is an individual baseline variable that significantly predicted parts of the respective outcome variances. Routinely assessed indirect alcohol biomarkers help to identify patients with high risk for relapse after alcohol withdrawal treatment. Clinical decision algorithms to identify patients with high risk for relapse after alcohol withdrawal treatment could include classical blood biomarkers in addition to clinical and sociodemographic items.

Changes in Circulating Metabolome Precede Alcohol-Related Diseases in Middle-Aged Men: A Prospective Population-Based Study With a 30-Year Follow-Up

BACKGROUND

Heavy alcohol use has been associated with altered circulating metabolome. We investigated whether changes in the circulating metabolome precede incident diagnoses of alcohol-related diseases.

METHODS

This is a prospective population-based cohort study where the participants were 42- to 60-year-old males at baseline (years 1984 to 1989). Subjects who received a diagnosis for an alcohol-related disease during the follow-up were defined as cases (n = 92, mean follow-up of 13.6 years before diagnosis). Diagnoses were obtained through linkage with national health registries. We used 2 control groups: controls who self-reported similar levels of alcohol use as compared to cases at baseline (alcohol-controls, n = 92), and controls who self-reported only light drinking at baseline (control-controls, n = 90). A nontargeted metabolomics analysis of baseline serum samples was performed.

RESULTS

There were significant differences between the study groups in the baseline serum levels of 64 metabolites: in amino acids (e.g., glutamine [FDR-corrected q-value = 0.0012]), glycerophospholipids (e.g., lysophosphatidylcholine 16:1 [q = 0.0008]), steroids (e.g., cortisone [q = 0.00001]), and fatty acids (e.g., palmitoleic acid [q = 0.0031]). The main finding was that after controlling for baseline levels of self-reported alcohol use and the biomarker of alcohol use, gamma-glutamyl transferase, and when compared to both alcohol-control and control-control group, the alcohol-case group had lower serum levels of asparagine (Cohen's d = -0.48 [95% CI -0.78 to -0.19] and d = -0.49 [-0.78 to -0.19], respectively) and serotonin (d = -0.45 [-0.74 to -0.15], and d = -0.46 [-0.75 to -0.16], respectively), with no difference between the two control groups (asparagine d = 0.00 [-0.29 to 0.29] and serotonin d = -0.01 [-0.30 to 0.29]).

CONCLUSIONS

Changes in the circulating metabolome, especially lower serum levels of asparagine and serotonin, are associated with later diagnoses of alcohol-related diseases, even after adjustment for the baseline level of alcohol use.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.

Changes in the Human Metabolome Associated With Alcohol Use: A Review

AIMS

The metabolome refers to the functional status of the cell, organ or the whole body. Metabolomic methods measure the metabolome (metabolite profile) which can be used to examine disease progression and treatment responses. Here, our aim was to review metabolomics studies examining effects of alcohol use in humans.

METHODS

We performed a literature search using PubMed and Web of Science for reports on changes in the human metabolite profile associated with alcohol use; we found a total of 23 articles published before end of 2018.

RESULTS

Most studies had investigated plasma, serum or urine samples; only four studies had examined other sample types (liver, faeces and broncho-alveolar lavage fluid). Levels of 51 metabolites were altered in two or more of the reviewed studies. Alcohol use was associated with changes in the levels of lipids and amino acids. In general, levels of fatty acids, phosphatidylcholine diacyls and steroid metabolites tended to increase, whereas those of phosphatidylcholine acyl-alkyls and hydroxysphingomyelins declined. Common alterations in circulatory levels of amino acids included decreased levels of glutamine, and increased levels of tyrosine and alanine.

CONCLUSIONS

More studies, especially with a longitudinal study design, or using more varied sample materials (e.g. organs or saliva), are needed to clarify alcohol-induced diseases and alterations at a target organ level. Hopefully, this will lead to the discovery of new treatments, improved recognition of individuals at high risk and identification of those subjects who would benefit most from certain treatments.

Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study

A comprehensive screening method using machine learning and many factors (biological characteristics, Helicobacter pylori infection status, endoscopic findings and blood test results), accumulated daily as data in hospitals, could improve the accuracy of screening to classify patients at high or low risk of developing gastric cancer. We used XGBoost, a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. Longitudinal and comprehensive medical check-up data were collected from 25,942 participants who underwent multiple endoscopies from 2006 to 2017 at a single facility in Japan. The participants were classified into a case group (y = 1) or a control group (y = 0) if gastric cancer was or was not detected, respectively, during a 122-month period. Among 1,431 total participants (89 cases and 1,342 controls), 1,144 (80%) were randomly selected for use in training 10 classification models; the remaining 287 (20%) were used to evaluate the models. The results showed that XGBoost outperformed logistic regression and showed the highest area under the curve value (0.899). Accumulating more data in the facility and performing further analyses including other input variables may help expand the clinical utility.

Statistical Workflow for Feature Selection in Human Metabolomics Data

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

Changes in the serum metabolite profile correlate with decreased brain gray matter volume in moderate-to-heavy drinking young adults

Our aim was to analyze metabolite profile changes in serum associated with moderate-to-heavy consumption of alcohol in young adults and to evaluate whether these changes are connected to reduced brain gray matter volumes. These study population consisted of young adults with a 10-year history of moderate-to-heavy alcohol consumption (n = 35) and light-drinking controls (n = 27). We used the targeted liquid chromatography mass spectrometry method to measure concentrations of metabolites in serum, and 3.0 T magnetic resonance imaging to assess brain gray matter volumes. Alterations in amino acid and energy metabolism were observed in the moderate-to-heavy drinking young adults when compared to the controls. After correction for multiple testing, the group of moderate-to-heavy drinking young adults had increased serum concentrations of 1-methylhistamine (p = 0.001, d = 0.82) when compared to the controls. Furthermore, concentrations of 1-methylhistamine (r = -0.48, p = 0.004) and creatine (r = -0.52, p = 0.001) were negatively correlated with the brain gray matter volumes in the females. Overall, our results show association between moderate-to-heavy use of alcohol and altered metabolite profile in young adults as well as suggesting that some of these changes could be associated with the reduced brain gray matter volume.

Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning

Combined single cell mass spectrometry and machine learning methods is demonstrated for the first time to achieve rapid and reliable prediction of the phenotype of unknown single cells based on their metabolomic profiles, with experimental validation. This approach can be potentially applied towards prediction of drug-resistant phenotypes prior to chemotherapy.

Precision Psychiatry and Dual Disorders

OBJECTIVE

Addiction co-occurs with distinct pathological personality traits, other psychiatric disorders or symptoms and cognitive impairment, which are known as dual disorders or co-occurring disorders. This symptomatic high concurrency suggests that both conditions are in some ways causally linked. Research is ongoing to identify distinctive neurobehavioral mechanisms and endophenotypes that predispose individuals to compulsive drug use and other mental disorders. Research is also providing new revelations about the diverse effects of substances on individuals, including differences according to sex. Today we know that the same substance may give rise to different behavioral, affective, cognitive, and sensory effects across different individuals.

METHODS

This state-of the art review tends to address the concept of precision psychiatry and dual disorders. The PubMed database was searched for the last 15 years to identify those articles that reported neurobiological perspectives on dual disorders, addiction and other mental disorders, precision medicine, and precision psychiatry.

RESULTS

There has been considerable progress made in recent years in relation to the study of addiction and dual disorders. The concept of dual disorders attempts to capture not only the persistence of substance use and substance seeking but also the evident vulnerability of specific subpopulations to switch from controlled to compulsive drug use. Precision medicine is focused on identifying this individual vulnerability to illness as much as the individual response to treatment. Psychiatry is fully committed to this goal. Regarding addiction, essential precision medicine advances will be possible if concerted efforts are made in the discovery of biological variations and environmental factors that contribute to individual vulnerability to addictive disorders and dual disorders, together with the identification of moderators of treatment response.

CONCLUSIONS

Here we survey the discoveries, future research directions, and translational relevance of the concept of precision psychiatry for dual disorders. The review may offer new perspectives on this issue and highlight a new way to see and to think about dual disorders.

Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition

In this paper, the stability of the plasma metabolome at −20 °C for up to 30 days was evaluated using liquid chromatography-high resolution mass spectrometric metabolomics analysis. To follow the time-series deterioration of the plasma metabolome, the use of an elastic net regularized regression model for the prediction of storage time at −20 °C based on the plasma metabolomic profile, and the selection and ranking of metabolites with high temporal changes was demonstrated using the glmnet package in R. Out of 1229 (positive mode) and 1483 (negative mode) metabolite features, the elastic net model extracted 32 metabolites of interest in both positive and negative modes. L-gamma-glutamyl-L-(iso)leucine (tentative identification) was found to have the highest time-dependent change and significantly increased proportionally to the storage time of plasma at −20 °C (R² = 0.6378 [positive mode], R² = 0.7893 [negative mode], p-value < 0.00001). Based on the temporal profiles of the extracted metabolites by the model, results show only minimal deterioration of the plasma metabolome at −20 °C up to 1 month. However, majority of the changes appeared at around 12–15 days of storage. This allows scientists to better plan logistics and storage strategies for samples obtained from low-resource settings, where −80 °C storage is not guaranteed.