Large-scale plasma metabolome analysis reveals alterations in HDL metabolism in migraine

Improving Stroke Outcome Prediction Using Molecular and Machine Learning Approaches in Large Vessel Occlusion

Introduction: Predicting stroke outcomes in acute ischemic stroke (AIS) can be challenging, especially for patients with large vessel occlusion (LVO). Available tools such as infarct volume and the National Institute of Health Stroke Scale (NIHSS) have shown limited accuracy in predicting outcomes for this specific patient population. The present study aimed to confirm whether sudden metabolic changes due to blood-brain barrier (BBB) disruption during LVO reflect differences in circulating metabolites and RNA between small and large core strokes. The second objective was to evaluate whether integrating molecular markers with existing neurological and imaging tools can enhance outcome predictions in LVO strokes. Methods: The infarction volume in patients was measured using magnetic resonance diffusion-weighted images, and the 90-day stroke outcome was defined by a modified Rankin Scale (mRS). Differential expression patterns of miRNAs were identified by RNA sequencing of serum-driven exosomes. Nuclear magnetic resonance (NMR) spectroscopy was used to identify metabolites associated with AIS with small and large infarctions. Results: We identified 41 miRNAs and 11 metabolites to be significantly associated with infarct volume in a multivariate regression analysis after adjusting for the confounders. Eight miRNAs and ketone bodies correlated significantly with infarct volume, NIHSS (severity), and mRS (outcome). Through integrative analysis of clinical, radiological, and omics data using machine learning, our study identified 11 top features for predicting stroke outcomes with an accuracy of 0.81 and AUC of 0.91. Conclusions: Our study provides a future framework for advancing stroke therapeutics by incorporating molecular markers into the existing neurological and imaging tools to improve predictive efficacy and enhance patient outcomes.

Deep and unbiased proteomics, pathway enrichment analysis, and protein-protein interaction of biomarker signatures in migraine

Background

Currently, there are no biomarkers for migraine.

Objectives

We aimed to identify proteomic biomarker signatures for diagnosing, subclassifying, and predicting treatment response in migraine.

Design

This is a cross-sectional and longitudinal study of untargeted serum and cerebrospinal fluid (CSF) proteomics in episodic migraine (EM; n = 26), chronic migraine (CM; n = 26), and healthy controls (HC; n = 26).

Methods

We developed classification models for biomarker identification and natural clusters through unsupervised classification using agglomerative hierarchical clustering (AHC). Pathway analysis of differentially expressed proteins was performed.

Results

Of 405 CSF proteins, the top five proteins that discriminated between migraine patients and HC were angiotensinogen, cell adhesion molecule 3, immunoglobulin heavy variable (IGHV) V-III region JON, insulin-like growth factor binding protein 6 (IGFBP-6), and IGFBP-7. The top-performing classifier demonstrated 100% sensitivity and 75% specificity in differentiating the two groups. Of 229 serum proteins, the top five proteins in classifying patients with migraine were immunoglobulin heavy variable 3-74 (IGHV 3-74), proteoglycan 4, immunoglobulin kappa variable 3D-15, zinc finger protein (ZFP)-814, and mediator of RNA polymerase II transcription subunit 12. The best-performing classifier exhibited 94% sensitivity and 92% specificity. AHC separated EM, CM, and HC into distinct clusters with 90% success. Migraine patients exhibited increased ZFP-814 and calcium voltage-gated channel subunit alpha 1F (CACNA1F) levels, while IGHV 3-74 levels decreased in both cross-sectional and longitudinal serum analyses. ZFP-814 remained upregulated during the CM-to-EM reversion but was suppressed when CM persisted. CACNA1F was pronounced in CM persistence. Pathway analysis revealed immune, coagulation, glucose metabolism, erythrocyte oxygen and carbon dioxide exchange, and insulin-like growth factor regulation pathways.

Conclusion

Our data-driven study provides evidence for identifying novel proteomic biomarker signatures to diagnose, subclassify, and predict treatment responses for migraine. The dysregulated biomolecules affect multiple pathways, leading to cortical spreading depression, trigeminal nociceptor sensitization, oxidative stress, blood-brain barrier disruption, immune response, and coagulation cascades.

Trial registration

NCT03231241, ClincialTrials.gov.

Sex-Specific Association of Cardiovascular Risk Factors With Migraine: The Population-Based Rotterdam Study

BACKGROUND AND OBJECTIVES

Although several lines of evidence suggest a link between migraine and cardiovascular events, less is known about the association between cardiovascular risk factors (CVRFs) and migraine. This knowledge is clinically important to provide directions on mitigating the cardiovascular risk in patients with migraine. We hypothesized that CVRFs are associated with a higher migraine prevalence. Therefore, our primary objective was to investigate sex-specific associations between CVRFs and lifetime prevalence of migraine.

METHODS

We performed cross-sectional analyses within an ongoing population-based cohort study (Rotterdam Study), including middle-aged and elderly individuals. By means of (structured) interviews, physical examinations, and blood sampling, we obtained information on the lifetime prevalence of migraine and the following traditional CVRFs: current smoking, obesity, hypercholesterolemia, hypertension, and diabetes mellitus. Similarly, we obtained information on quantitative component data on these CVRFs, including pack-years of smoking, lipid levels, systolic and diastolic blood pressure (BP), body mass index, and fasting glucose levels. Patients with migraine were age-matched to individuals without migraine, and we performed conditional logistic regression analyses to investigate the sex-stratified association of CVRFs with migraine.

RESULTS

In total, 7,266 community-dwelling middle-aged and elderly persons were included (median age 66.6 [IQR 56.4-74.8] years, 57.5% females). The lifetime prevalence of migraine was 14.9%. In females, current smoking (odds ratio (OR) 0.72, 95% CI 0.58-0.90), more pack-years (OR per SD increase 0.91, 95% CI 0.84-1.00), diabetes mellitus (OR 0.74, 95% CI 0.56-0.98), and higher fasting glucose levels (OR per SD increase in glucose 0.90, 95% CI 0.82 - 0.98) were all related to a lower migraine prevalence while a higher diastolic BP related to a higher migraine prevalence (OR per SD increase 1.16, 95% CI 1.04-1.29). In males, no significant associations between CVRFs and migraine were observed.

DISCUSSION

Traditional CVRFs were either unrelated or inversely related to migraine in middle-aged and elderly individuals, but only in females. In males, we did not find any association between CVRFs and migraine. Because only an increased diastolic BP was related to a higher migraine prevalence in females, our study contributes to the hypothesis that migraine is not directly associated with traditional CVRFs. Future studies are warranted to extrapolate these findings to younger populations.

A cross-tissue transcriptome-wide association study reveals novel susceptibility genes for migraine

BACKGROUND

Migraine is a common neurological disorder with a strong genetic component. Despite the identification of over 100 loci associated with migraine susceptibility through genome-wide association studies (GWAS), the underlying causative genes and biological mechanisms remain predominantly elusive.

METHODS

The FinnGen R10 dataset, consisting of 333,711 subjects (20,908 cases and 312,803 controls), was utilized in conjunction with the Genotype-Tissue Expression Project (GTEx) v8 EQTls files to conduct cross-tissue transcriptome association studies (TWAS). Functional Summary-based Imputation (FUSION) was employed to validate these findings in single tissues. Additionally, candidate susceptibility genes were screened using Gene Analysis combined with Multi-marker Analysis of Genomic Annotation (MAGMA). Subsequent Mendelian randomization (MR) and colocalization analyses were conducted. Furthermore, GeneMANIA analysis was employed to enhance our understanding of the functional implications of these susceptibility genes.

RESULTS

We identified a total of 19 susceptibility genes associated with migraine in the cross-tissue TWAS analysis. Two novel susceptibility genes, REV1 and SREBF2, were validated through both single tissue TWAS and MAGMA analysis. Mendelian randomization and colocalization analyses further confirmed these findings. REV1 may reduce the migraine risk by regulating DNA damage repair, while SREBF2 may increase the risk of migraine by regulating cholesterol metabolism.

CONCLUSION

Our study identified two novel genes whose predicted expression was associated with the risk of migraine, providing new insights into the genetic framework of migraine.

Poor healthy lifestyle and life's essential 8 are associated with higher risk of new-onset migraine: a prospective cohort study

BACKGROUND

Lifestyle are closely related to migraine. However, there is a lack of studies investigating the association between Healthy lifestyle or Life's Essential 8 (LE8) and the risk of migraine. The objective of this research was to investigate the relationship between Healthy lifestyle scores and Life's essential 8 scores, and migraine.

METHODS

332,895 UK Biobank participants without migraine were included. Healthy lifestyle were assessed using seven lifestyle factors, and categorized as poor, intermediate, or ideal. LE8, based on the American Heart Association (AHA) Guidelines for Cardiovascular Health (CVH), consist of eight indicators classified as low, moderate, or high CVH. The Cox proportional hazard model was employed to examine the association between Healthy lifestyle scores, LE8 scores, and migraine, with calculations for population-attributable fraction (PAF) and cumulative incidence.

RESULTS

During a median follow-up of 13.58 years, participants in intermediate (HR: 0.91; 95% CI: 0.85, 0.99) or ideal category of Healthy lifestyle (HR: 0.81; 95% CI: 0.73, 0.91) significantly reduced migraine risk compared to the poor category. Similarly, high CVH (HR: 0.73; 95% CI: 0.58, 0.92) also lowered migraine risk, while moderate CVH (HR: 0.93; 95% CI: 0.85, 1.02) did not show a difference compared to low CVH. If all individuals adhered to higher categories of Healthy lifestyle and LE8, approximately 11.38% and 22.05% of migraine cases could be prevented. Among individual lifestyle factors, maintaining an ideal body mass index (BMI), physical activity, sleep duration, sleep pattern, and sedentary time were associated with substantial reductions in migraine risk, by 5.65%, 0.81%, 10.16%, 16.39%, and 6.57%, respectively.

CONCLUSION

Our study provides evidence that poor Healthy lifestyle and Life's Essential 8 are associated with higher risk of new-onset migraine.

The association between plasma trans-fatty acids level and migraine: A cross-sectional study from NHANES 1999-2000

OBJECTIVES

Trans-fatty acid (TFA) has been linked to an increased risk of a variety of diseases, such as cardiovascular disease (CVD), diabetes, and cancer. However, the relationship between plasma TFAs and migraine is little known. The current study aimed to determine the association between plasma TFAs and migraine in a large cross-sectional study among U.S. adults.

METHODS

The participants from the US National Health and Nutrition Examination Survey (NHANES) were included during the period 1999-2000. The plasma concentrations of four major TFAs, including palmitelaidic acid (C16:1n-7t), elaidic acid (C18:1n-9t), vaccenic acid (C18:1n-7t), and linolelaidic acid (C18:2n-6t, 9t) were measured by gas chromatography/mass spectrometry (GC/MS). The presence of migraine headache was determined by self-report questionnaire. Weighted multivariable logistic regressions and restricted cubic spline (RCS) regressions were explored to assess the relationship between plasma TFAs and migraine. Furthermore, stratified analysis and testing of interaction terms were used to evaluate the effect modification by sex, age, race/ethnicity, family income, and BMI.

RESULTS

A total of 1534 participants were included. The overall weighted prevalence of severe headache or migraine was 21.2 %. After adjusting for all potential covariates, plasma levels of elaidic acid and linolelaidic acid were positively associated with migraine. The adjusted OR values were 1.18 (95 %CI: 1.08-1.29, p=0.014, per 10 units increase) and 1.24 (95 %CI: 1.07-1.44, p=0.024). Then the included participants were divided into 2-quantiles by plasma TFA levels. Compared with participants with lower plasma levels of elaidic acid and linolelaidic acid (Q1 groups), those in the Q2 group had a higher prevalence of migraine when adjusted for all covariates in Model 2. The adjusted OR values were 2.43 (95 %CI: 1.14-5.18, p=0.037) for elaidic acid, and 2.18 (95 %CI: 1.14-4.20, p=0.036) for linolelaidic acid. Results were robust when analyses were stratified by sex, age, race/ethnicity, family income, and BMI, and no effect modification on the association was found.

CONCLUSIONS

Our results demonstrated a positive association between migraine prevalence and plasma levels of elaidic acid and linolelaidic acid in US adults. These results highlight the connection between circulating TFAs and migraine.

Metabolite Biomarkers of Prolonged and Intensified Pain and Distress in Head and Neck Cancer Patients Undergoing Radio- or Chemoradiotherapy by Means of NMR-Based Metabolomics-A Preliminary Study

Treatment of head and neck squamous cell carcinoma (HNSCC) has a detrimental impact on patient quality of life. The rate of recognized distress/depression among HNSCC patients ranges from 9.8% to 83.8%, and the estimated prevalence of depression among patients receiving radiotherapy is 63%. Shorter overall survival also occurs in preexisting depression or depressive conditions. The present study analyzes the nuclear magnetic resonance (NMR) blood serum metabolic profiles during radio-/chemoradiotherapy and correlates the detected alterations with pain and/or distress accumulated with the disease and its treatment. NMR spectra were acquired on a Bruker 400 MHz spectrometer and analyzed using multivariate methods. The results indicate that distress and/or pain primarily affect the serum lipids and metabolites of energy (glutamine, glucose, lactate, acetate) and one-carbon (glycine, choline, betaine, methanol, threonine, serine, histidine, formate) metabolism. Sparse disturbances in the branched-chain amino acids (BCAA) and in the metabolites involved in protein metabolism (lysine, tyrosine, phenylalanine) are also observed. Depending on the treatment modality-radiotherapy or concurrent chemoradiotherapy-there are some differences in the altered metabolites.

Prophylactic treatment can modify vascular risk biomarkers in high-frequency episodic and chronic migraine patients: a pilot study

To evaluate whether preventive treatment can modify endothelial and oxidative biomarkers of vascular disease risk in patients with high-frequency episodic and chronic migraine. In this observational, prospective pilot study, 88 prophylactic treatment-naïve patients with episodic and chronic migraine and 56 healthy sex/age matched controls underwent ultrasonography exams and blood tests at baseline, and again in the migraine patients after 3 months' treatment with metoprolol or topiramate. Biomarkers for endothelial function and oxidative stress were analyzed. At baseline, patients with migraine in the low-frequency episodic group had differences exclusively in nitrates 17.6 versus 27.33 µM; p = 0.046 compared to the controls. However, when comparing the group comprised of patients with high-frequency episodic migraine and chronic migraine versus controls, statistically significant differences appeared in hsCRP 2.68 versus 1.64 mg/dL; p = 0.049, vWF antigen (133% vs. 110%; p = 0.020, vWF activity (111% vs. 90%; p = 0.010) and isoprostane levels (181 vs. 238 µM; p = 0.05). Only in the chronic migraine subgroup did we found statistically significant differences in CIMT (0.60 vs. 0.54 mm; p = 0.042) which were significantly greater than in the controls. After treatment, patients who respond to preventive treatment exhibited significantly higher levels of nitrates (24.2-13.8 µM; p = 0.022) and nitrites (10.4-3.43 µM; p = 0.002) compared than non-responders. Moreover, biomarker levels improved in treatment-responsive patients with migraine; hsCRP levels decreased from 2.54 to 1.69 mg/dL (p < 0.05), vWF activity levels decreased from 124 to 103 IU/dL (p = 0.003) and prothrombin activity decreased from 1.01 to 0.93 (p = 0.01). These differences were also observed in the high-frequency and chronic migraine subgroup and reach statistical significance in the case of hsCRP, which decreased from 2.12 to 0.83 mg/dL (p = 0.048). Patients with migraines have differences in biomarker levels compared to controls, suggesting endothelial and oxidative dysfunction. The greatest differences in biomarker levels compared to controls are observed in migraine patients in the high-frequency and chronic migraine subgroups. Based on our results, preventive treatment is capable of modifying markers of endothelial dysfunction and oxidative stress in migraine patients, even in cases of chronic and high-frequency migraine.

Low levels of small HDL particles predict but do not influence risk of sepsis

BACKGROUND

Low levels of high-density lipoprotein (HDL) cholesterol have been associated with higher rates and severity of infection. Alterations in inflammatory mediators and infection are associated with alterations in HDL cholesterol. It is unknown whether the association between HDL and infection is present for all particle sizes, and whether the observed associations are confounded by IL-6 signalling.

METHODS

In the UK Biobank, ~ 270,000 individuals have data on HDL subclasses derived from nuclear magnetic resonance analysis. We estimated the association of particle count of total HDL and HDL subclasses (small, medium, large, and extra-large HDL) with sepsis, sepsis-related death, and critical care admission in a Cox regression model. We subsequently utilised genetic data from UK Biobank and FinnGen to perform Mendelian randomisation (MR) of each HDL subclass and sepsis to test for a causal relationship. Finally, we explored the role of IL-6 signalling as a potential causal driver of changes in HDL subclasses.

RESULTS

In observational analyses, higher particle count of small HDL was associated with protection from sepsis (Hazard ratio, HR 0.80; 95% CI 0.74-0.86, p = 4 × 10-9 comparing Quartile 4, highest quartile of HDL to Quartile 1, lowest quartile of HDL), sepsis-related death (HR 0.80; 95% CI 0.74-0.86, p = 2 × 10-4), and critical care admission with sepsis (HR 0.72 95% CI 0.60-0.85, p = 2 × 10-4). Parallel associations with other HDL subclasses were likely driven by changes in the small HDL compartment. MR analyses did not strongly support causality of small HDL particle count on sepsis incidence (Odds ratio, OR 0.98; 95% CI 0.89-1.07, p = 0.6) or death (OR 0.94, 95% CI 0.75-1.17, p = 0.56), although the estimate on critical care admission with sepsis supported protection (OR 0.73, 95% CI 0.57-0.95, p = 0.02). Bidirectional MR analyses suggested that increased IL-6 signalling was associated with reductions in both small (beta on small HDL particle count - 0.16, 95% CI - 0.10 to - 0.21 per natural log change in SD-scaled CRP, p = 9 × 10-8).and total HDL particle count (beta - 0.13, 95% CI - 0.09 to - 0.17, p = 7 × 10-10), but that the reverse effect of HDL on IL-6 signalling was largely null.

CONCLUSIONS

Low number of small HDL particles are associated with increased hazard of sepsis, sepsis-related death, and sepsis-related critical care admission. However, genetic analyses did not strongly support this as causal. Instead, we demonstrate that increased IL-6 signalling, which is known to alter infection risk, could confound associations with reduced HDL particle count, and suggest this may explain part of the observed association between (small) HDL particle count and sepsis.

Towards precision medicine in migraine: Recent therapeutic advances and potential biomarkers to understand heterogeneity and treatment response

After 35 years since the introduction of the International Classification of Headache Disorders (ICHD), we are living in the era of the second great revolution in migraine therapies. First, discoveries of triptans provided a breakthrough in acute migraine treatment utilizing bench-to-bedside research results on the role of serotonin in migraine. Next, the discovery of the role of neuropeptides, more specifically calcitonin gene-related peptide (CGRP) in migraine attack led to the development of anti-CGRP therapies that are effective both in acute and preventive treatment, and are also able to reduce migraine-related burden. Here, we reviewed the most recent clinical studies and real-world data on available migraine-specific medications, including triptans, ditants, gepants and anti-CGRP monoclonal antibodies. Novel drug targets, such as PACAP and amylins were also discussed. To address the main challenges of migraine therapy, the high heterogeneity of people with migraine, the prevalent presence of various comorbid disorders, and the insufficient medical care of migraine patients were covered. Promising novel approaches from the fields of omics, blood and saliva biomarker, imaging and provocation studies might bring solutions for these challenges with the potential to identify further drug targets, distinguish more homogeneous patient subgroups, contribute to more optimal drug selection strategies, and detect biomarkers in association with headache features or predicting treatment efficacy. In the future, the combined analysis of data of different biomarker modalities with machine learning algorithms may serve precision medicine in migraine treatment.

Discovery and validation of circulating stroke metabolites by NMR-based analyses using patients from the MISS and UK Biobank

BACKGROUND

Stroke is a significant health issue in the United States, and identifying biomarkers for the prevention and functional recovery after an acute stroke remains the highest priority. This study aims to identify circulating metabolite signatures that may be associated with stroke pathophysiology by performing discovery and validation studies.

METHODS

We performed targeted metabolomics profiling of 420 participants of the discovery dataset of Metabolome in an Ischemic Stroke Study (MISS) using high-throughput nuclear magnetic resonance (NMR) spectroscopy. A validation study of significantly altered metabolites was conducted using an independent cohort of 117,988 participants from the UK Biobank, whose metabolomics profiles were generated using the same NMR technology.

RESULTS AND CONCLUSION

Our study identified 16 metabolites to be significantly perturbed during acute stroke. Amino acid phenylalanine was significantly increased, while glutamine and histidine were significantly lowered in stroke. Serum levels of apolipoprotein A-1, HDL particles, small HDL particles, essential fatty acids, and phosphatidylcholine were reduced, while ketone bodies like 3-hydroxybutyrate and acetoacetate were markedly increased in stroke. Based on the robust validation in a large independent UK Biobank dataset, some of these analytes may become clinically meaningful biomarkers to predict or prevent stroke in humans.

Sex-specific metabolic profiling to explain the increased CVD risk in women with migraine: a narrative review

BACKGROUND

Migraine is a disabling neurological disorder whose diagnosis is based on clinical criteria. A shortcoming of these criteria is that they do not fully capture the underlying neurobiological factors and sex-specific complications in migraine such as cardio- and cerebrovascular disease. Biomarker research can help to improve disease characterization and identify pathophysiological mechanism underlying these comorbidities.

OBJECTIVE

In this narrative review we searched for sex-specific metabolomics research to identify markers that may explain the migraine-cardiovascular disease (CVD) relationship.

DISCUSSION

Large-scale plasma metabolome analyses revealed alterations in migraine. Sex-specific findings showed a less CVD-protective HDL metabolism as well as the ApoA1 lipoprotein, especially for women with migraine. To explore other possible pathophysiological pathways, we expanded our review to include inflammatory markers, endothelial and vascular markers and sex hormones. Biological sex differences may affect the pathophysiology of migraine and its complications.

CONCLUSIONS

There is no general large dyslipidemia profile in migraine patients, in line with findings that the increased risk of CVD in migraine patients seems not to be due to (large artery) atherosclerosis. Sex-specific associations are indicative towards a less CVD-protective lipoprotein profile in women with migraine. Future studies into the pathophysiology of CVD and migraine need to take sex specific factors into account. By establishing the overlapping pathophysiological mechanism of migraine and CVD, and unraveling the associated effects these diseases exert on each other, better preventative measures can be identified.

Neurological and psychiatric comorbidities of migraine: Concepts and future perspectives

BACKGROUND

This narrative review aims to discuss several common neurological and psychiatric disorders that show comorbidity with migraine. Not only can we gain pathophysiological insights by studying these disorders, comorbidities also have important implications for treating migraine patients in clinical practice.

METHODS

A literature search on PubMed and Embase was conducted with the keywords "comorbidity", "migraine disorders", "migraine with aura", "migraine without aura", "depression", "depressive disorders", "epilepsy", "stroke", "patent foramen ovale", "sleep wake disorders", "restless legs syndrome", "genetics", "therapeutics".

RESULTS

Several common neurological and psychiatric disorders show comorbidity with migraine. Major depression and migraine show bidirectional causality and have shared genetic factors. Dysregulation of both hypothalamic and thalamic pathways have been implicated as a possibly cause. The increased risk of ischaemic stroke in migraine likely involves spreading depolarizations. Epilepsy is not only bidirectionally related to migraine, but is also co-occurring in monogenic migraine syndromes. Neuronal hyperexcitability is an important overlapping mechanism between these conditions. Hypothalamic dysfunction is suggested as the underlying mechanism for comorbidity between sleep disorders and migraine and might explain altered circadian timing in migraine.

CONCLUSION

These comorbid conditions in migraine with distinct pathophysiological mechanisms have important implications for best treatment choices and may provide clues for future approaches.

Migraine genetics: Status and road forward

BACKGROUND

Migraine is considered a multifactorial genetic disorder. Different platforms and methods are used to unravel the genetic basis of migraine. Initially, linkage analysis in multigenerational families followed by Sanger sequencing of protein-coding parts (exons) of genes in the genomic region shared by affected family members identified high-effect risk DNA mutations for rare Mendelian forms of migraine, foremost hemiplegic migraine. More recently, genome-wide association studies testing millions of DNA variants in large groups of patients and controls have proven successful in identifying many dozens of low-effect risk DNA variants for the more common forms of migraine with the number of associated DNA variants increasing steadily with larger sample sizes. Currently, next-generation sequencing, utilising whole exome and whole genome sequence data, and other omics data are being used to facilitate their functional interpretation and the discovery of additional risk factors. Various methods and analysis tools, such as genetic correlation and causality analysis, are used to further characterise genetic risk factors.

FINDINGS

We describe recent findings in genome-wide association studies and next-generation sequencing analysis in migraine. We show that the combined results of the two most recent and most powerful migraine genome-wide association studies have identified a total of 178 LD-independent (r² < 0.1) genome-wide significant single nucleotide polymorphisms (SNPs), of which 99 were unique to Hautakangas et al., 11 were unique to Choquet et al., and 68 were identified by both studies. When considering that Choquet et al. also identified three SNPs in a female-specific genome-wide association studies then these two recent studies identified 181 independent SNPs robustly associated with migraine. Cross-trait and causal analyses are beginning to identify and characterise specific biological factors that contribute to migraine risk and its comorbid conditions.

CONCLUSION

This review provides a timely update and overview of recent genetic findings in migraine.

Metabolomics dissection of depression heterogeneity and related cardiometabolic risk

BACKGROUND

A recent hypothesis postulates the existence of an 'immune-metabolic depression' (IMD) dimension characterized by metabolic dysregulations. Combining data on metabolomics and depressive symptoms, we aimed to identify depressions associated with an increased risk of adverse metabolic alterations.

METHOD

Clustering data were from 1094 individuals with major depressive disorder in the last 6 months and measures of 149 metabolites from a ¹H-NMR platform and 30 depressive symptoms (IDS-SR30). Canonical correlation analyses (CCA) were used to identify main independent metabolite-symptom axes of variance. Then, for the replication, we examined the association of the identified dimensions with metabolites from the same platform and cardiometabolic diseases in an independent population-based cohort (n = 6572).

RESULTS

CCA identified an overall depression dimension and a dimension resembling IMD, in which symptoms such as sleeping too much, increased appetite, and low energy level had higher relative loading. In the independent sample, the overall depression dimension was associated with lower cardiometabolic risk, such as (i.e. per s.d.) HOMA-1B -0.06 (95% CI -0.09 - -0.04), and visceral adipose tissue -0.10 cm² (95% CI -0.14 - -0.07). In contrast, the IMD dimension was associated with well-known cardiometabolic diseases such as higher visceral adipose tissue 0.08 cm² (95% CI 0.04-0.12), HOMA-1B 0.06 (95% CI 0.04-0.09), and lower HDL-cholesterol levels -0.03 mmol/L (95% CI -0.05 - -0.01).

CONCLUSIONS

Combining metabolomics and clinical symptoms we identified a replicable depression dimension associated with adverse metabolic alterations, in line with the IMD hypothesis. Patients with IMD may be at higher cardiometabolic risk and may benefit from specific treatment targeting underlying metabolic dysregulations.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Alterations in metabolic flux in migraine and the translational relevance

BACKGROUND

Migraine is a highly prevalent disorder with significant economical and personal burden. Despite the development of effective therapeutics, the causes which precipitate migraine attacks remain elusive. Clinical studies have highlighted altered metabolic flux and mitochondrial function in patients. In vivo animal experiments can allude to the metabolic mechanisms which may underlie migraine susceptibility. Understanding the translational relevance of these studies are important to identifying triggers, biomarkers and therapeutic targets in migraine.

MAIN BODY

Functional imaging studies have suggested that migraineurs feature metabolic syndrome, exhibiting hallmark features including upregulated oxidative phosphorylation yet depleted available free energy. Glucose hypometabolism is also evident in migraine patients and can lead to altered neuronal hyperexcitability such as the incidence of cortical spreading depression (CSD). The association between obesity and increased risk, frequency and worse prognosis of migraine also highlights lipid dysregulation in migraine pathology. Calcitonin gene related peptide (CGRP) has demonstrated an important role in sensitisation and nociception in headache, however its role in metabolic regulation in connection with migraine has not been thoroughly explored. Whether impaired metabolic function leads to increased release of peptides such as CGRP or excessive nociception leads to altered flux is yet unknown.

CONCLUSION

Migraine susceptibility may be underpinned by impaired metabolism resulting in depleted energy stores and altered neuronal function. This review discusses both clinical and in vivo studies which provide evidence of altered metabolic flux which contribute toward pathophysiology. It also reviews the translational relevance of animal studies in identifying targets of biomarker or therapeutic development.

Role of Omics in Migraine Research and Management: A Narrative Review

Migraine is a neurological disorder defined by episodic attacks of chronic pain associated with nausea, photophobia, and phonophobia. It is known to be a complex disease with several environmental and genetic factors contributing to its susceptibility. Risk factors for migraine include head or neck injury (Arnold, Cephalalgia 38(1):1-211, 2018). Stress and high temperature are known to trigger migraine, while sleep disorders and anxiety are considered to be the comorbid conditions with migraine. Studies have reported various biomarkers, including genetic variants, proteins, and metabolites implicated in migraine's pathophysiology. Using the "omics" approach, which deals with genetics, transcriptomics, proteomics, and metabolomics, more specific biomarkers for various migraine can be identified. On account of its multifactorial nature, migraine is an ideal study model focusing on integrated omics approaches, including genomics, transcriptomics, proteomics, and metabolomics. The current review has been compiled with an aim to focus on the genomic alterations especially involved in the regulation of glutamatergic neurotransmission, cortical excitability, ion channels, solute carrier proteins, or receptors; their expression in migraine patients and also specific proteins and metabolites, including some inflammatory biomarkers that might represent the migraine phenotype at the molecular level. The systems biology approach holds the promise to understand the pathophysiology of the disease at length and also to identify the specific therapeutic targets for novel interventions.

Genetic analyses identify pleiotropy and causality for blood proteins and highlight Wnt/β-catenin signalling in migraine

Migraine is a common complex disorder with a significant polygenic SNP heritability (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{{SNP}}^{2}$$\end{document}hSNP2). Here we utilise genome-wide association study (GWAS) summary statistics to study pleiotropy between blood proteins and migraine under the polygenic model. We estimate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{{SNP}}^{2}$$\end{document}hSNP2 for 4625 blood protein GWASs and identify 325 unique proteins with a significant \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{{SNP}}^{2}$$\end{document}hSNP2 for use in subsequent genetic analyses. Pleiotropy analyses link 58 blood proteins to migraine risk at genome-wide, gene and/or single-nucleotide polymorphism levels—suggesting shared genetic influences or causal relationships. Notably, the identified proteins are largely distinct from migraine GWAS loci. We show that higher levels of DKK1 and PDGFB, and lower levels of FARS2, GSTA4 and CHIC2 proteins have a significant causal effect on migraine. The risk-increasing effect of DKK1 is particularly interesting—indicating a role for downregulation of β-catenin-dependent Wnt signalling in migraine risk, suggesting Wnt activators that restore Wnt/β-catenin signalling in brain could represent therapeutic tools against migraine.

Understanding of the causes and treatment of migraine is incomplete. Here, the authors detect pleiotropic genetic effects and causal relationships between migraine and 58 proteins that are largely distinct from migraine-associated loci identified by GWAS.

Classical prescription Dachuanxiong Formula delays nitroglycerin-induced pain response in migraine mice through reducing endothelin-1 level and regulating fatty acid biosynthesis

ETHNOPHARMACOLOGICAL RELEVANCE

Dachuanxiong Formula (DCXF) is a classical Chinese medicine prescription and is composed of dried rhizomes from Ligusticum striatum DC. (Chuanxiong Rhizoma) and Gastrodia elata Bl. (Gastrodiae Rhizoma) at the ratio of 4:1 (w/w). It has been used as Chinese medicine prescription for thousands of years. DCXF is used traditionally to treat many diseases, including migraine, atherosclerosis and ischemic stroke.

AIM OF THE STUDY

This study aimed to investigate the effects of DCXF on pain response in migraine mice, and the underlying mechanisms using proteomics and bioinformatics analyses.

MATERIALS AND METHODS

DCXF extract was prepared by mixing Chuanxiong Rhizoma and Gastrodiae Rhizoma at a mass ratio of 4:1 (w/w). After extraction, the extract was filtered prior to high performance liquid chromatography (HPLC) analysis. Nitroglycerin (NTG) was used to establish a mouse migraine model, and a behaviour study was conducted by hot plate test. In addition, proteomics and bioinformatics studies were conducted to investigate the mechanisms of DCXF-mediating anti-migraine treatment.

RESULTS

Our results showed that there were significant differences in the latencies between NTG-treated and DCXF low dose- and high doses-treated groups at 30 min after NTG injection, this suggested that DCXF could ameliorate pain response in migraine mice. Besides, the plasma levels of endothelin-1 were also measured. NTG group significantly enhanced the endothelin-1 level compared to the control group. In contrast, DCXF low dose and high dose groups significantly reduced this level compared to NTG group. In addition, the underlying mechanisms were also investigated. Our results demonstrated that the anti-migraine treatment of DCXF was highly associated with fatty acid synthesis, suggesting that DCXF ameliorated pain response through reducing endothelin-1 level and regulating fatty acid synthesis.

CONCLUSIONS

The present study revealed the anti-migraine effect of DCXF in migraine mice and provided insights into the mechanisms of DCXF-mediating anti-migraine treatment.

Changes in Plasma Lipid Levels Following Cortical Spreading Depolarization in a Transgenic Mouse Model of Familial Hemiplegic Migraine

Metabolite levels in peripheral body fluids can correlate with attack features in migraine patients, which underscores the potential of plasma metabolites as possible disease biomarkers. Migraine headache can be preceded by an aura that is caused by cortical spreading depolarization (CSD), a transient wave of neuroglial depolarization. We previously identified plasma amino acid changes after CSD in familial hemiplegic migraine type 1 (FHM1) mutant mice that exhibit increased neuronal excitability and various migraine-related features. Here, we aimed to uncover lipid metabolic pathways affected by CSD, guided by findings on the involvement of lipids in hemiplegic migraine pathophysiology. Using targeted lipidomic analysis, we studied plasma lipid metabolite levels at different time points after CSD in wild-type and FHM1 mutant mice. Following CSD, the most prominent plasma lipid change concerned a transient increase in PGD₂, which lasted longer in mutant mice. In wild-type mice only, levels of anti-inflammatory lipid mediators DPAn-3, EPA, ALA, and DHA were elevated 24 h following CSD compared to Sham-treated animals. Given the role of PGs and neuroinflammation in migraine pathophysiology, our findings underscore the potential of monitoring peripheral changes in lipids to gain insight in central brain mechanisms.

Migraine: A Review on Its History, Global Epidemiology, Risk Factors, and Comorbidities

Migraine affects more than one billion individuals each year across the world, and is one of the most common neurologic disorders, with a high prevalence and morbidity, especially among young adults and females. Migraine is associated with a wide range of comorbidities, which range from stress and sleep disturbances to suicide. The complex and largely unclear mechanisms of migraine development have resulted in the proposal of various social and biological risk factors, such as hormonal imbalances, genetic and epigenetic influences, as well as cardiovascular, neurological, and autoimmune diseases. This review presents a comprehensive review of the most up-to-date literature on the epidemiology, and risk factors, as well as highlighting the gaps in our knowledge.

1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints

Background

Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples, i.e. ‘metabolomics’, is steadily gaining popularity, as it is highly informative for various phenotypical characteristics. Here we aim to leverage metabolomics to impute missing data in clinical variables routinely assessed in large epidemiological and clinical studies.

Methods

To this end, we have employed ∼26,000 ¹H-NMR metabolomics samples from 28 Dutch cohorts collected within the BBMRI-NL consortium, to create 19 metabolomics-based predictors for clinical variables, including diabetes status (AUC_{5-Fold CV} = 0·94) and lipid medication usage (AUC_{5-Fold CV} = 0·90).

Findings

Subsequent application in independent cohorts confirmed that our metabolomics-based predictors can indeed be used to impute a wide array of missing clinical variables from a single metabolomics data resource. In addition, application highlighted the potential use of our predictors to explore the effects of totally unobserved confounders in omics association studies. Finally, we show that our predictors can be used to explore risk factor profiles contributing to mortality in older participants.

Interpretation

To conclude, we provide ¹H-NMR metabolomics-based models to impute clinical variables routinely assessed in epidemiological studies and illustrate their merit in scenarios when phenotypic variables are partially incomplete or totally unobserved.

Funding

BBMRI-NL, X-omics, VOILA, Medical Delta and the Dutch Research Council (NWO-VENI).

Phenotypic and Genotypic Associations Between Migraine and Lipoprotein Subfractions

BACKGROUND AND OBJECTIVE

To evaluate phenotypic and genetic relationships between migraine and lipoprotein subfractions.

METHODS

We evaluated phenotypic associations between migraine and 19 lipoprotein subfraction measures in the Women's Genome Health Study (n = 22,788). We then investigated genetic relationships between these traits using summary statistics from the International Headache Genetics Consortium for migraine (n_case = 54,552, n_control = 297,970) and combined summary data for lipoprotein subfractions (n up to 47,713).

RESULTS

There was a significant phenotypic association (odds ratio 1.27 [95% confidence interval 1.12-1.44]) and a significant genetic correlation at 0.18 (p = 0.001) between migraine and triglyceride-rich lipoproteins (TRLPs) concentration but not for low-density lipoprotein or high-density lipoprotein subfractions. Mendelian randomization (MR) estimates were largely null, implying that pleiotropy rather than causality underlies the genetic correlation between migraine and lipoprotein subfractions. Pleiotropy was further supported in cross-trait meta-analysis, revealing significant shared signals at 4 loci (chr2p21 harboring THADA, chr5q13.3 harboring HMGCR, chr6q22.31 harboring HEY2, and chr7q11.23 harboring MLXIPL) between migraine and lipoprotein subfractions. Three of these loci were replicated for migraine (p < 0.05) in a smaller sample from the UK Biobank. The shared signal at chr5q13.3 colocalized with expression of HMGCR, ANKDD1B, and COL4A3BP in multiple tissues.

CONCLUSIONS

The study supports the association between certain lipoprotein subfractions, especially for TRLP, and migraine in populations of European ancestry. The corresponding shared genetic components may help identify potential targets for future migraine therapeutics.

CLASSIFICATION OF EVIDENCE

This study provides Class I evidence that migraine is significantly associated with some lipoprotein subfractions.

Metabolic profile changes in serum of migraine patients detected using 1H-NMR spectroscopy

Background

Migraine is a common brain disorder but reliable diagnostic biomarkers in blood are still lacking. Our aim was to identify, using proton nuclear magnetic resonance (¹H-NMR) spectroscopy, metabolites in serum that are associated with lifetime and active migraine by comparing metabolic profiles of patients and controls.

Methods

Fasting serum samples from 313 migraine patients and 1512 controls from the Erasmus Rucphen Family (ERF) study were available for ¹H-NMR spectroscopy. Data was analysed using elastic net regression analysis.

Results

A total of 100 signals representing 49 different metabolites were detected in 289 cases (of which 150 active migraine patients) and 1360 controls. We were able to identify profiles consisting of 6 metabolites predictive for lifetime migraine status and 22 metabolites predictive for active migraine status. We estimated with subsequent regression models that after correction for age, sex, BMI and smoking, the association with the metabolite profile in active migraine remained. Several of the metabolites in this profile are involved in lipid, glucose and amino acid metabolism.

Conclusion

This study indicates that metabolic profiles, based on serum concentrations of several metabolites, including lipids, amino acids and metabolites of glucose metabolism, can distinguish active migraine patients from controls.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10194-021-01357-w.

Genetic overlap and causality between blood metabolites and migraine

The availability of genome-wide association studies (GWASs) for human blood metabolome provides an excellent opportunity for studying metabolism in a heritable disease such as migraine. Utilizing GWAS summary statistics, we conduct comprehensive pairwise genetic analyses to estimate polygenic genetic overlap and causality between 316 unique blood metabolite levels and migraine risk. We find significant genome-wide genetic overlap between migraine and 44 metabolites, mostly lipid and organic acid metabolic traits (FDR < 0.05). We also identify 36 metabolites, mostly related to lipoproteins, that have shared genetic influences with migraine at eight independent genomic loci (posterior probability > 0.9) across chromosomes 3, 5, 6, 9, and 16. The observed relationships between genetic factors influencing blood metabolite levels and genetic risk for migraine suggest an alteration of metabolite levels in individuals with migraine. Our analyses suggest higher levels of fatty acids, except docosahexaenoic acid (DHA), a very long-chain omega-3, in individuals with migraine. Consistently, we found a causally protective role for a longer length of fatty acids against migraine. We also identified a causal effect for a higher level of a lysophosphatidylethanolamine, LPE(20:4), on migraine, thus introducing LPE(20:4) as a potential therapeutic target for migraine.

Association between serum lipid levels and severe headache or migraine in representative American population: A cross-sectional study

BACKGROUND

The relationship between serum lipids and migraine remains controversial.

OBJECTIVE

This study aimed to analyze the association between serum lipids and severe headache or migraine in the general population.

METHOD

Data were collected from a nationally representative sample of participants in the National Health and Nutrition Examination Survey from 1999 to 2004. Interviewers recorded self-reported severe headaches or migraines and whether pain lasted for more than 24 h in three months. A weighted general linear model was used to estimate the association between serum lipids and severe headache or migraine. Regression analyses were performed after adjusting for age, sex, race, energy intake, sodium intake, etc. Subgroup analyses were performed using the same regression model.

RESULTS

We included 5,937 individuals in the study, with a weighted mean age of 45.8 years. Males accounted for 47.6% of the participants. After adjusting for covariates, a non-significant association was found between migraine and total cholesterol (odds ratio=0.96, 95% confidence interval=0.85, 1.05; P=0.32), low-density lipoprotein cholesterol (odds ratio=0.96; 95% confidence interval= 0.75, 1.17, P=0.55), and high-density lipoprotein cholesterol (odds ratio=0.99; 95% confidence interval=0.49, 1.59, P=0.58) in the continuous form. In subgroup analyses, no significant association was found between total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and self-reported severe headache or migraine.

CONCLUSION

Total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol were not significantly associated with severe headache or migraine in the general American population after adjusting for covariates. The supporting information for measuring common serum lipids in general headaches and migraines is insufficient.

Metabolomic profiles discriminating anxiety from depression

OBJECTIVE

Depression has been associated with metabolomic alterations. Depressive and anxiety disorders are often comorbid diagnoses and are suggested to share etiology. We investigated whether differential metabolomic alterations are present between anxiety and depressive disorders and which clinical characteristics of these disorders are related to metabolomic alterations.

METHODS

Data were from the Netherlands Study of Depression and Anxiety (NESDA), including individuals with current comorbid anxiety and depressive disorders (N = 531), only a current depression (N = 304), only a current anxiety disorder (N = 548), remitted depressive and/or anxiety disorders (N = 897), and healthy controls (N = 634). Forty metabolites from a proton nuclear magnetic resonance lipid-based metabolomics panel were analyzed. First, we examined differences in metabolites between disorder groups and healthy controls. Next, we assessed whether depression or anxiety clinical characteristics (severity and symptom duration) were associated with metabolites.

RESULTS

As compared to healthy controls, seven metabolomic alterations were found in the group with only depression, reflecting an inflammatory (glycoprotein acetyls; Cohen's d = 0.12, p = 0.002) and atherogenic-lipoprotein-related (e.g., apolipoprotein B: Cohen's d = 0.08, p = 0.03, and VLDL cholesterol: Cohen's d = 0.08, p = 0.04) profile. The comorbid group showed an attenuated but similar pattern of deviations. No metabolomic alterations were found in the group with only anxiety disorders. The majority of metabolites associated with depression diagnosis were also associated with depression severity; no associations were found with anxiety severity or disease duration.

CONCLUSION

While substantial clinical overlap exists between depressive and anxiety disorders, this study suggests that altered inflammatory and atherogenic-lipoprotein-related metabolomic profiles are uniquely associated with depression rather than anxiety disorders.

Association and genetic overlap between clinical chemistry tests and migraine

INTRODUCTION

In this paper, we studied several serum clinical chemistry tests of cardiovascular disease (CVD), iron deficiency anemia, liver and kidney disorders in migraine.

METHODS

We first explored the association of 22 clinical chemistry tests with migraine risk in 697 migraine patients and 2722 controls. To validate and interpret association findings, cross-trait genetic analyses were conducted utilising genome-wide association study (GWAS) data comprising 23,986 to 452,264 individuals.

RESULTS

Significant associations with migraine risk were identified for biomarkers of CVD risk, iron deficiency and liver dysfunction (odds ratios = 0.86-1.21; 1 × 10^-4 < p < 3 × 10^-2). Results from cross-trait genetic analyses corroborate the significant biomarker associations and indicate their relationship with migraine is more consistent with biological pleiotropy than causality. For example, association and genetic overlap between a lower level of HDL-C and increased migraine risk are due to shared biology rather than a causal relationship. Furthermore, additional genetic analyses revealed shared genetics among migraine, the clinical chemistry tests, and heart problems and iron deficiency anemia, but not liver disease.

CONCLUSIONS

These findings highlight common biological mechanisms underlying migraine, heart problems and iron deficiency anemia and provide support for their investigation in the development of novel therapeutic and dietary interventions.

An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data

Epilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological comorbidities (e.g., anxiety, migraine, and stroke). While general comorbidity prevalences and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient-specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting risks of future comorbidities for individual epilepsy patients. In this work, we use inpatient and outpatient administrative health claims data of around 19,500 U.S. epilepsy patients. We suggest a dedicated multimodal neural network architecture (Deep personalized LOngitudinal convolutional RIsk model-DeepLORI) to predict the time-dependent risk of six common comorbidities of epilepsy patients. We demonstrate superior performance of DeepLORI in a comparison with several existing methods. Moreover, we show that DeepLORI-based predictions can be interpreted on the level of individual patients. Using a game theoretic approach, we identify relevant features in DeepLORI models and demonstrate that model predictions are explainable in light of existing knowledge about the disease. Finally, we validate the model on independent data from around 97,000 patients, showing good generalization and stable prediction performance over time.

Network Pharmacology and Metabolomics Studies on Antimigraine Mechanisms of Da Chuan Xiong Fang (DCXF)

Background

Da Chuan Xiong Fang (DCXF) is a traditional Chinese medicine (TCM) formula used to treat migraines. Previously, we uncovered partial mechanisms involved in the therapeutic actions of DCXF on migraines.

Methods

In this study, we further elucidated its antimigraine mechanisms in vivo by using an integrated strategy coupling with network pharmacology and metabolomics techniques.

Results

Network pharmacology identified 33 genes linked with both migraine and DCXF, most of which were 5-hydroxytryptamine receptors, dopamine, and peptide receptors. The results of GO and KEGG enrichment analysis showed that DCXF significantly regulated tyrosine metabolism, tryptophan metabolism, dopamine metabolic process, glucose transmembrane transport, lipid metabolism, and fatty acid transport. The results of metabolomics analysis found that the metabolism of tryptophan and tyrosine in the brain tissue and energy and lipid metabolism of rats tended towards normal and reached normal levels after administering DCXF. The metabolomics and network pharmacology approaches demonstrated similar antimigraine effects of DCXF on endogenous neurotransmitters and overall trends in serum and brain tissue. Using both approaches, 62 hub genes were identified from the protein-protein interaction (PPI) network of DCXF and gene-metabolite interaction network, with hub genes and different metabolites in serum and brain tissue. The hub genes of DCXF, which were mostly linked with inflammation, might affect mainly neurotransmitters in serum and brain tissue metabolisms.

Conclusion

Network pharmacology and metabolomics study may help identify hub genes, metabolites, and possible pathways of disease and treatment. Additionally, two parts of the results were integrated to confirm each other. Their combination may help elucidate the relationship between hub genes and metabolites and provide the further understanding of TCM mechanisms.

A rapid GC method coupled with quadrupole or time of flight mass spectrometry for metabolomics analysis

Gas chromatography-mass spectrometry (GC-MS) is an ideal tool for analyzing the intermediates of tricarboxylic acid cycle and glycolysis, sugars, organic acids and amino acids, etc. High-throughput metabolomics methods are required by large-scale clinical researches, and time of flight mass spectrometry (TOF MS) having fast scanning rate is preferable for rapid GC. Quadrupole MS (qMS) instruments have 95% market share, and their potential in rapid metabolomics is worth being studied. In this work, a within 15-min GC program was established and matched by qMS scanning for plasma metabolome analysis after N-methyl-N-(trimethylsilyl)-trifluoroacetamide derivatization. Compared to the longer-time program GC-qMS method, the rapid GC-qMS method had nearly no metabolome information loss, and it had excellent profile performance in repeatability, intra-day and inter-day precision, sampling range, linearity and extraction recovery. Compared to TOF MS, qMS achieved similar results in investigating lung cancer serum metabolic disruptions. Partial least squares-discriminant analysis revealed that the two datasets acquired by qMS and TOF MS had very similar model parameters, and most of top ranked differential metabolites were the same. This study provides a rapid and economical GC-qMS metabolomics method for researchers. Still, MS having faster scanning rate and higher sensitivity are recommended, if possible, to detect more small peaks and some co-eluted peaks.

The Metabolomics of Chronic Pain Conditions: A Systematic Review

BACKGROUND

Chronic pain is a significant public health problem in the United States, affecting approximately 100 million people. Yet there is a lack of robust biomarkers for clinical use in chronic pain conditions. Downstream effects of environmental, genomic, and proteomic variations in individuals with chronic pain conditions can be identified and quantified using a metabolomic approach.

AIM/DESIGN

The purpose of this systematic review was to examine the literature for reports of potential metabolomic signatures associated with chronic pain conditions.

METHODS

We searched relevant electronic databases for published studies that used various metabolomic approaches to investigate chronic pain conditions among subjects of all ages.

RESULTS

Our search identified a total of 586 articles, 18 of which are included in this review. The reviewed studies used metabolomics to investigate fibromyalgia (n = 5), osteoarthritis (n = 4), migraine (n = 3), musculoskeletal pain (n = 2), and other chronic pain conditions (n = 1/condition). Results show that several known and newly identified metabolites differ in individuals with chronic pain conditions compared to those without these conditions. These include amino acids (e.g., glutamine, serine, and phenylalanine) and intermediate products (e.g., succinate, citrate, acetylcarnitine, and N-acetylornithine) of pathways that metabolize various macromolecules.

CONCLUSION

Though more high-quality research is needed, this review provides insights into potential biomarkers for future metabolomics studies in people with chronic pain conditions.

NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data

Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.

Metabolomics in Central Sensitivity Syndromes

Central sensitization syndromes are a collection of frequently painful disorders that contribute to decreased quality of life and increased risk of opiate abuse. Although these disorders cause significant morbidity, they frequently lack reliable diagnostic tests. As such, technologies that can identify key moieties in central sensitization disorders may contribute to the identification of novel therapeutic targets and more precise treatment options. The analysis of small molecules in biological samples through metabolomics has improved greatly and may be the technology needed to identify key moieties in difficult to diagnose diseases. In this review, we discuss the current state of metabolomics as it relates to central sensitization disorders. From initial literature review until Feb 2020, PubMed, Embase, and Scopus were searched for applicable studies. We included cohort studies, case series, and interventional studies of both adults and children affected by central sensitivity syndromes. The majority of metabolomic studies addressing a CSS found significantly altered metabolites that allowed for differentiation of CSS patients from healthy controls. Therefore, the published literature overwhelmingly supports the use of metabolomics in CSS. Further research into these altered metabolites and their respective metabolic pathways may provide more reliable and effective therapeutics for these syndromes.

Metabolomics Profile in Depression: A Pooled Analysis of 230 Metabolic Markers in 5283 Cases With Depression and 10,145 Controls

BACKGROUND

Depression has been associated with metabolic alterations, which adversely impact cardiometabolic health. Here, a comprehensive set of metabolic markers, predominantly lipids, was compared between depressed and nondepressed persons.

METHODS

Nine Dutch cohorts were included, comprising 10,145 control subjects and 5283 persons with depression, established with diagnostic interviews or questionnaires. A proton nuclear magnetic resonance metabolomics platform provided 230 metabolite measures: 51 lipids, fatty acids, and low-molecular-weight metabolites; 98 lipid composition and particle concentration measures of lipoprotein subclasses; and 81 lipid and fatty acids ratios. For each metabolite measure, logistic regression analyses adjusted for gender, age, smoking, fasting status, and lipid-modifying medication were performed within cohort, followed by random-effects meta-analyses.

RESULTS

Of the 51 lipids, fatty acids, and low-molecular-weight metabolites, 21 were significantly related to depression (false discovery rate q < .05). Higher levels of apolipoprotein B, very-low-density lipoprotein cholesterol, triglycerides, diglycerides, total and monounsaturated fatty acids, fatty acid chain length, glycoprotein acetyls, tyrosine, and isoleucine and lower levels of high-density lipoprotein cholesterol, acetate, and apolipoprotein A1 were associated with increased odds of depression. Analyses of lipid composition indicators confirmed a shift toward less high-density lipoprotein and more very-low-density lipoprotein and triglyceride particles in depression. Associations appeared generally consistent across gender, age, and body mass index strata and across cohorts with depressive diagnoses versus symptoms.

CONCLUSIONS

This large-scale meta-analysis indicates a clear distinctive profile of circulating lipid metabolites associated with depression, potentially opening new prevention or treatment avenues for depression and its associated cardiometabolic comorbidity.

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

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

Heritability estimates for 361 blood metabolites across 40 genome-wide association studies

Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes.

New discoveries in migraine mechanisms and therapeutic targets

Migraine is among the most common and most disabling disorders worldwide, yet its underlying pathophysiology is among the most poorly understood. New information continues to emerge on mechanisms within the central and peripheral nervous systems that may contribute to migraine attacks. Additionally, new therapeutics have recently become available and along with much needed relief for many patients, these drugs provide insight into the disorder based on their mechanism of action. This review will cover new findings within the last several years that add to the understanding of migraine pathophysiology, including those related to the vasculature, calcitonin gene-related peptide (CGRP), and mechanisms within the cortex and meninges that may contribute to attacks. Discussion will also cover recent findings on novel therapeutic targets, several of which continue to show promise in new preclinical studies, including acid-sensing ion channels (ASICs) and the delta-opioid receptor (DOR).