A prospective study of serum metabolites and risk of ischemic stroke

Associations of Plasma Metabolites With Risks of Incident Stroke and Its Subtypes in Chinese Adults

BACKGROUND

Metabolomics studies have identified various metabolic markers associated with stroke risk, yet much uncertainty persists regarding heterogeneity in these associations between different stroke subtypes. We aimed to examine metabolic profiles associated with incident stroke and its subtypes in Chinese adults.

METHODS AND RESULTS

We performed a nested case-control study within the Dongfeng-Tongji cohort, including 1029 and 266 incident cases of ischemic stroke (IS) and hemorrhagic stroke (HS), respectively, with a mean follow-up period of 6.1±2.3 years. Fifty-five metabolites in fasting plasma were measured by ultra-high-performance liquid chromatography-mass spectrometry. We examined the associations of metabolites with the risks of total stroke, IS, and HS, with a focus on the comparison of associations of plasma metabolite with IS and HS, using conditional logistic regression. We found that increased levels of asymmetrical/symmetrical dimethylarginine and glutamate were significantly associated with elevated risk of total stroke (odds ratios and 95%, 1.20 [1.08-1.34] and 1.22 [1.09-1.36], respectively; both Benjamini-Hochberg-adjusted P <0.05). When examining stroke subtypes, asymmetrical/symmetrical dimethylarginine was nominally associated with both IS and HS (odds ratios [95% CIs]: 1.16 [1.03-1.31] and 1.39 [1.07-1.81], respectively), while glutamate was associated with only IS (odds ratios [95% CI]: 1.26 [1.11-1.43]). The associations of glutamate with IS risk were significantly stronger among participants with hypertension and diabetes than among those without these diseases (both P for interaction <0.05).

CONCLUSIONS

This study validated the positive associations of asymmetrical/symmetrical dimethylarginine and glutamate with stroke risk, mainly that of IS, in a Chinese population, and revealed a novel unanimous association of with both IS and HS. Our findings provided potential intervention targets for stroke prevention.

Assessing the causal association between human blood metabolites and the risk of gout

BACKGROUND

The occurrence of gout is closely related to metabolism, but there is still a lack of evidence on the causal role of metabolites in promoting or preventing gout.

METHODS

We applied a two-sample Mendelian randomization (MR) analysis to assess the association between 486 serum metabolites and gout using genome-wide association study statistics. The inverse variance weighting method was used to generate the main results, while sensitivity analyses using MR-Egger, weighted median, Cochran's Q test, Egger intercept test, and leave-one-out analysis, were performed to assess the stability and reliability of the results. We also performed a metabolic pathway analysis to identify potential metabolic pathways.

RESULTS

After screening, 486 metabolites were retained for MR analysis. After screening by IVW and sensitivity analysis, 14 metabolites were identified with causal effect on gout (P < 0.05), among which hexadecanedioate was the most significant candidate metabolite associated with a lower risk of gout (IVW OR = 0.50; 95% CI = 0.38-0.67; P = 1.65 × 10-6 ). Metabolic pathway analysis identified one pathway that may be associated with the disease.

CONCLUSION

This MR study combining genomics with metabolomics provides a novel insight into the causal role of blood metabolites in the risk of gout, which implies that examination of certain blood metabolites would be a feasible strategy for screening populations with a higher risk of gout.

Causal relationship between human blood metabolites and risk of ischemic stroke: a Mendelian randomization study

Background: Ischemic stroke (IS) is a major cause of death and disability worldwide. Previous studies have reported associations between metabolic disorders and IS. However, evidence regarding the causal relationship between blood metabolites and IS lacking. Methods: A two-sample Mendelian randomization analysis (MR) was used to assess the causal relationship between 1,400 serum metabolites and IS. The inverse variance-weighted (IVW) method was employed to estimate the causal effect between exposure and outcome. Additionally, MR-Egger regression, weighted median, simple mode, and weighted mode approaches were employed as supplementary comprehensive evaluations of the causal effects between blood metabolites and IS. Tests for pleiotropy and heterogeneity were conducted. Results: After rigorous selection, 23 known and 5 unknown metabolites were identified to be associated with IS. Among the 23 known metabolites, 13 showed significant causal effects with IS based on 2 MR methods, including 5-acetylamino-6-formylamino-3-methyluracil, 1-ribosyl-imidazoleacetate, Behenoylcarnitine (C22), N-acetyltyrosine, and N-acetylputrescine to (N (1) + N (8))-acetate,these five metabolites were positively associated with increased IS risk. Xanthurenate, Glycosyl-N-tricosanoyl-sphingadienine, Orotate, Bilirubin (E,E), Bilirubin degradation product, C17H18N2O, Bilirubin (Z,Z) to androsterone glucuronide, Bilirubin (Z,Z) to etiocholanolone glucuronide, Biliverdin, and Uridine to pseudouridine ratio were associated with decreased IS risk. Conclusion: Among 1,400 blood metabolites, this study identified 23 known metabolites that are significantly associated with IS risk, with 13 being more prominent. The integration of genomics and metabolomics provides important insights for the screening and prevention of IS.

Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension

INTRODUCTION

The burden of stroke in patients with hypertension is very high, and its prediction is critical.

OBJECTIVES

We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.

METHODS

This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension.

RESULTS

Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957).

CONCLUSIONS

We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.

A comparative study of plasma and dried blood spot metabolomics and its application to diabetes mellitus

Metabolomics has become a promising method for understanding pathological mechanisms. Plasma (PLS) is the most common sample type used for metabolomics studies, and dried blood spot (DBS) sampling has been regarded as a good strategy due to its unique characteristics. However, how results obtained from DBS can be correlated to results obtained from PLS remains unclear. To bridge the results and to investigate the feasibility of using DBS to study metabolomics, we performed a comparative study using 64 paired PLS and DBS samples. The number of features extracted from the two different sample types was investigated. The concentration correlations of the identified metabolites between the DBS and PLS were individually studied. Approximately 47 % showed a strong correlation, 19 % showed a moderate correlation, and 34 % showed a low or even negligible correlation. Finally, we applied both PLS- and DBS-based metabolomics to explore the dysregulated metabolites in diabetes mellitus (DM) patients. Thirty-two non-DM subjects and 32 DM patients were enrolled, and 2 significant metabolites were found in both PLS and DBS samples. In summary, detailed correlation information between PLS and DBS metabolites was first explored in this study, and it is anticipated that these results could facilitate future applications in DBS-based metabolomics.

Soyasaponin I alleviates hypertensive intracerebral hemorrhage by inhibiting the renin-angiotensin-aldosterone system

BACKGROUND

Hypertensive intracerebral hemorrhage (HICH) is a life-threatening disease and lacks effective treatments. Previous studies have confirmed that metabolic profiles altered after ischemic stroke, but how brain metabolism changes after HICH was unclear. This study aimed to explore the metabolic profiles after HICH and the therapeutic effects of soyasaponin I on HICH.

METHODS

HICH model was established first. Hematoxylin and eosin staining was used to estimate the pathological changes after HICH. Western blot and Evans blue extravasation assay were applied to determine the integrity of the blood-brain barrier (BBB). Enzyme-linked immunosorbent assay was used to detect the activation of the renin-angiotensin-aldosterone system (RAAS). Next, liquid chromatography-mass spectrometry-untargeted metabolomics was utilized to analyze the metabolic profiles of brain tissues after HICH. Finally, soyasaponin I was administered to HICH rats, and the severity of HICH and activation of the RAAS were further assessed.

RESULTS

We successfully constructed HICH model. HICH significantly impaired BBB integrity and activated RAAS. HICH increased PE(14:0/24:1(15Z)), arachidonoyl serinol, PS(18:0/22:6(4Z, 7Z, 10Z, 13Z, 16Z, and 19Z)), PS(20:1(11Z)/20:5(5Z, 8Z, 11Z, 14Z, and 17Z)), glucose 1-phosphate, etc., in the brain, whereas decreased creatine, tripamide, D-N-(carboxyacetyl)alanine, N-acetylaspartate, N-acetylaspartylglutamic acid, and so on in the hemorrhagic hemisphere. Cerebral soyasaponin I was found to be downregulated after HICH and supplementation of soyasaponin I inactivated the RAAS and alleviated HICH.

CONCLUSION

The metabolic profiles of the brains changed after HICH. Soyasaponin I alleviated HICH via inhibiting the RAAS and may serve as an effective drug for the treatment of HICH in the future.

High-Protein Supplementation and Neuromuscular Electric Stimulation after Aneurysmal Subarachnoid Hemorrhage Increases Systemic Amino Acid and Oxidative Metabolism: A Plasma Metabolomics Approach

Background The INSPIRE randomized clinical trial demonstrated that a high protein diet (HPRO) combined with neuromuscular electrical stimulation (NMES) attenuates muscle atrophy and may improve functional outcomes after aSAH. Using an untargeted metabolomics approach, we sought to identify specific metabolites mediating these effects. Methods Blood samples were collected from subjects on admission prior to randomization to either standard of care (SOC; N=12) or HPRO+NMES (N=12) and at 7 days as part of the INSPIRE protocol. Untargeted metabolomics were performed for each plasma sample. Paired fold changes were calculated for each metabolite among subjects in the HPRO+NMES group at baseline and 7 days after intervention. Changes in metabolites from baseline to 7 days were compared for the HPRO+NMES and SOC groups. Sparse partial least squared discriminant analysis (sPLS-DA) identified metabolites discriminating each group. Pearson's correlation coefficients were calculated between each metabolite and total protein per day, nitrogen balance, and muscle volume Multivariable models were developed to determine associations between each metabolite and muscle volume. Results A total of 18 unique metabolites were identified including pre and post treatment and differentiating SOC vs HPRO+NMES. Of these, 9 had significant positive correlations with protein intake: N-acetylserine (ρ=0.61, P =1.56x10 -3 ), N-acetylleucine (ρ=0.58, P =2.97x10 -3 ), β-hydroxyisovaleroylcarnitine (ρ=0.53, P =8.35x10 -3 ), tiglyl carnitine (ρ=0.48, P =0.0168), N-acetylisoleucine (ρ=0.48, P =0.0183), N-acetylthreonine (ρ=0.47, P =0.0218), N-acetylkynurenine (ρ=0.45, P =0.0263), N-acetylvaline (ρ=0.44, P =0.0306), and urea (ρ=0.43, P =0.0381). In multivariable regression models, N-acetylleucine was significantly associated with preserved temporalis [OR 1.08 (95%CI 1.01, 1.16)] and quadricep [OR 1.08 (95%CI 1.02, 1.15)] muscle volume. Quinolinate was also significantly associated with preserved temporalis [OR 1.05 (95%CI 1.01, 1.09)] and quadricep [OR 1.04 (95%CI 1.00, 1.07)] muscle volume. N-acetylserine, N-acetylcitrulline, and b-hydroxyisovaleroylcarnitine were also associated with preserved temporalis or quadricep volume. Conclusions Metabolites defining the HPRO+NMES intervention mainly consisted of amino acid derivatives. These metabolites had strong correlations with protein intake and were associated with preserved muscle volume.

Searching for Metabolic Markers of Stroke in Human Plasma via NMR Analysis

More than 12 million people around the world suffer a stroke every year, one every 3 s. Stroke has a variety of causes and is often the result of a complex interaction of risk factors related to age, genetics, gender, lifestyle, and some cardiovascular and metabolic diseases. Despite this evidence, it is not possible to prevent the onset of stroke. The use of innovative methods for metabolite analysis has been explored in the last years to detect new stroke biomarkers. We use NMR spectroscopy to identify small molecule variations between different stages of stroke risk. The Framingham Stroke Risk Score was used in people over 63 years of age living in long-term care facilities (LTCF) to calculate the probability of suffering a stroke. Using this parameter, three study groups were formed: low stroke risk (LSR, control), moderate stroke risk (MSR) and high stroke risk (HSR). Univariate statistical analysis showed seven metabolites with increasing plasma levels across different stroke risk groups, from LSR to HSR: isoleucine, asparagine, formate, creatinine, dimethylsulfone and two unidentified molecules, which we termed "unknown-1" and "unknown-3". These metabolic markers can be used for early detection and to detect increasing stages of stroke risk more efficiently.

Plasma metabonomic study on the effect of Para‑hydroxybenzaldehyde intervention in a rat model of transient focal cerebral ischemia

Gastrodia elata Blume has been widely used to treat various central and peripheral nerve diseases, and Para‑hydroxybenzaldehyde (PHBA) is one of the indicated components suggested to provide a neuroprotective effect. In our previous, it was shown that PHBA protected mitochondria against cerebral ischemia‑reperfusion (I/R) injury in rats. In the present study, how PHBA regulated the metabolic mechanism in blood following cerebral I/R was assessed to identify an effective therapeutic target for the prevention and treatment of ischemic stroke (IS). First, a rat model of cerebral ischemia‑reperfusion injury was established via middle cerebral artery occlusion/reperfusion (MCAO/R). The therapeutic effect of PHBA on brain I/R was evaluated by assessing the neurological function score, triphenyl tetrazolium chloride, hematoxylin and eosin, and Nissl staining. Next, a non‑targeted metabolomic based on high‑performance liquid chromatography quadrupole time‑of‑flight mass spectrometry was established to identify differential metabolites. Finally, a targeted metabolic spectrum was analyzed and the potential therapeutic targets were verified by Western blotting. The results showed that the neurological function score, cerebral infarction area, hippocampal morphology, and the number of neurons in the PHBA group were significantly improved compared with the model group. Metabonomic analysis showed that 13 different metabolites were identified between the model and PHBA group, which may be involved in the 'tricarboxylic acid cycle', 'glutathione metabolism', and 'mutual transformation of pentose and glucuronates', amongst others. Among these, the levels of the most significant differential metabolite, dGMP, decreased significantly following PHBA treatment. Western blotting was used to verify the expression of membrane‑associated guanosine kinase PSD‑95 and the subunit of glutamate AMPA receptor GluA1, which significantly increased after PHBA treatment. In addition, it was also found that PHBA increased the expression of the light chain‑3 protein and autophagy effector protein 1, whilst the expression of sequestosome‑1 decreased, indicating that PHBA promoted autophagy. Similarly, in TUNEL staining and detection of apoptosis‑related proteins, it was found that MCAO/R upregulated the expression of Bax and cleaved‑caspase‑3 whilst downregulating the expression of Bcl‑2 and increasing the apoptosis of hippocampal neurons; PHBA reversed this situation. These results suggest that cerebral I/R causes postsynaptic dysfunction by disrupting the interaction between PSD‑95 and AMPARs, and the inhibition of the autophagy system eventually leads to the apoptosis of hippocampal neurons.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Serum Metabolomic Markers of Dairy Consumption: Results from the Atherosclerosis Risk in Communities Study and the Bogalusa Heart Study

BACKGROUND

Dairy consumption is related to chronic disease risk; however, the measurement of dairy consumption has largely relied upon self-report. Untargeted metabolomics allows for the identification of objective markers of dietary intake.

OBJECTIVES

We aimed to identify associations between dietary dairy intake (total dairy, low-fat dairy, and high-fat dairy) and serum metabolites in 2 independent study populations of United States adults.

METHODS

Dietary intake was assessed with food frequency questionnaires. Multivariable linear regression models were used to estimate cross-sectional associations between dietary intake of dairy and 360 serum metabolites analyzed in 2 subgroups of the Atherosclerosis Risk in Communities study (ARIC; n = 3776). Results from the 2 subgroups were meta-analyzed using fixed effects meta-analysis. Significant meta-analyzed associations in the ARIC study were then tested in the Bogalusa Heart Study (BHS; n = 785).

RESULTS

In the ARIC study and BHS, the mean age was 54 and 48 years, 61% and 29% were Black, and the mean dairy intake was 1.7 and 1.3 servings/day, respectively. Twenty-nine significant associations between dietary intake of dairy and serum metabolites were identified in the ARIC study (total dairy, n = 14; low-fat dairy, n = 10; high-fat dairy, n = 5). Three associations were also significant in BHS: myristate (14:0) was associated with high-fat dairy, and pantothenate was associated with total dairy and low-fat dairy, but 23 of the 27 associations significant in the ARIC study and tested in BHS were not associated with dairy in BHS.

CONCLUSIONS

We identified metabolomic associations with dietary intake of dairy, including 3 associations found in 2 independent cohort studies. These results suggest that myristate (14:0) and pantothenate (vitamin B5) are candidate biomarkers of dairy consumption.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Lipidomic signature of stroke recurrence after transient ischemic attack

While TIA patients have transient symptoms, they should not be underestimated, as they could have an underlying pathology that may lead to a subsequent stroke: stroke recurrence (SR). Previously, it has been described the involvement of lipids in different vascular diseases. The aim of the current study was to perform a lipidomic analysis to identify differences in the lipidomic profile between patients with SR and patients without. Untargeted lipidomic analysis was performed in plasma samples of 460 consecutive TIA patients recruited < 24 h after the onset of symptoms. 37 (8%) patients suffered SR at 90 days. Lipidomic profiling disclosed 7 lipid species differentially expressed between groups: 5 triacylglycerides (TG), 1 diacylglyceride (DG), and 1 alkenyl-PE (plasmalogen) [specifically, TG(56:1), TG(63:0), TG(58:2), TG(50:5), TG(53:7, DG(38:5)) and PE(P-18:0/18:2)]. 6 of these 7 lipid species belonged to the glycerolipid family and a plasmalogen, pointing to bioenergetics pathways, as well as oxidative stress response. In this context, it was proposed the PE(P-18:0/18:2) as potential biomarker of SR condition.The observed changes in lipid patterns suggest pathophysiological mechanisms associated with lipid droplets metabolism and antioxidant protection that is translated to plasma level as consequence of a more intensive or high-risk ischemic condition related to SR.

Gut microbiota-associated metabolites and risk of ischemic stroke in REGARDS

Several metabolite markers are independently associated with incident ischemic stroke. However, prior studies have not accounted for intercorrelated metabolite networks. We used exploratory factor analysis (EFA) to determine if metabolite factors were associated with incident ischemic stroke. Metabolites (n = 162) were measured in a case-control cohort nested in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study, which included 1,075 ischemic stroke cases and 968 random cohort participants. Cox models were adjusted for age, gender, race, and age-race interaction (base model) and further adjusted for the Framingham stroke risk factors (fully adjusted model). EFA identified fifteen metabolite factors, each representing a well-defined metabolic pathway. Of these, factor 3, a gut microbiome metabolism factor, was associated with an increased risk of stroke in the base (hazard ratio per one-unit standard deviation, HR = 1.23; 95%CI = 1.15-1.31; P = 1.98 × 10-10) and fully adjusted models (HR = 1.13; 95%CI = 1.06-1.21; P = 4.49 × 10-4). The highest tertile had a 45% increased risk relative to the lowest (HR = 1.45; 95%CI = 1.25-1.70; P = 2.24 × 10-6). Factor 3 was also associated with the Southern diet pattern, a dietary pattern previously linked to increased stroke risk in REGARDS (β = 0.11; 95%CI = 0.03-0.18; P = 8.75 × 10-3). These findings highlight the role of diet and gut microbial metabolism in relation to incident ischemic stroke.

The biochemistry and physiology of long-chain dicarboxylic acid metabolism

Mitochondrial β-oxidation is the most prominent pathway for fatty acid oxidation but alternative oxidative metabolism exists. Fatty acid ω-oxidation is one of these pathways and forms dicarboxylic acids as products. These dicarboxylic acids are metabolized through peroxisomal β-oxidation representing an alternative pathway, which could potentially limit the toxic effects of fatty acid accumulation. Although dicarboxylic acid metabolism is highly active in liver and kidney, its role in physiology has not been explored in depth. In this review, we summarize the biochemical mechanism of the formation and degradation of dicarboxylic acids through ω- and β-oxidation, respectively. We will discuss the role of dicarboxylic acids in different (patho)physiological states with a particular focus on the role of the intermediates and products generated through peroxisomal β-oxidation. This review is expected to increase the understanding of dicarboxylic acid metabolism and spark future research.

Metabolomics of Dietary Intake of Total, Animal, and Plant Protein: Results from the Atherosclerosis Risk in Communities (ARIC) Study

Background

Dietary consumption has traditionally been studied through food intake questionnaires. Metabolomics can be used to identify blood markers of dietary protein that may complement existing dietary assessment tools.

Objectives

We aimed to identify associations between 3 dietary protein sources (total protein, animal protein, and plant protein) and serum metabolites using data from the Atherosclerosis Risk in Communities Study.

Methods

Participants' dietary protein intake was derived from a food frequency questionnaire administered by an interviewer, and fasting serum samples were collected at study visit 1 (1987-1989). Untargeted metabolomic profiling was performed in 2 subgroups (subgroup 1: n = 1842; subgroup 2: n = 2072). Multivariable linear regression models were used to assess associations between 3 dietary protein sources and 360 metabolites, adjusting for demographic factors and other participant characteristics. Analyses were performed separately within each subgroup and meta-analyzed with fixed-effects models.

Results

In this study of 3914 middle-aged adults, the mean (SD) age was 54 (6) y, 60% were women, and 61% were Black. We identified 41 metabolites significantly associated with dietary protein intake. Twenty-six metabolite associations overlapped between total protein and animal protein, such as pyroglutamine, creatine, 3-methylhistidine, and 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid. Plant protein was uniquely associated with 11 metabolites, such as tryptophan betaine, 4-vinylphenol sulfate, N-δ-acetylornithine, and pipecolate.

Conclusions

The results of 17 of the 41 metabolites (41%) were consistent with those of previous nutritional metabolomic studies and specific protein-rich food items. We discovered 24 metabolites that had not been previously associated with dietary protein intake. These results enhance the validity of candidate markers of dietary protein intake and introduce novel metabolomic markers of dietary protein intake.

Identification of three potential novel biomarkers for early diagnosis of acute ischemic stroke via plasma lipidomics

INTRODUCTION

Acute ischemic stroke (AIS) accounts for the majority of all stroke, globally the second leading cause of death. Due to its rapid development after onset, its early diagnosis is crucial.

OBJECTIVES

We aim to identify potential highly reliable blood-based biomarkers for early diagnosis of AIS using quantitative plasma lipid profiling via a machine learning approach.

METHODS

Lipidomics was used for quantitative plasma lipid profiling, based on ultra-performance liquid chromatography tandem mass spectrometry. Our samples were divided into a discovery and a validation set, each containing 30 AIS patients and 30 health controls (HC). Differentially expressed lipid metabolites were screened based on the criteria VIP > 1, p < 0.05, and fold change > 1.5 or < 0.67. The least absolute shrinkage and selection operator (LASSO) and random forest algorithms in machine learning were used to select differential lipid metabolites as potential biomarkers.

RESULTS

Three key differential lipid metabolites, CarnitineC10:1, CarnitineC10:1-OH and Cer(d18:0/16:0), were identified as potential biomarkers for early diagnosis of AIS. The former two, associated with thermogenesis, were down-regulated, whereas the latter, associated with necroptosis and sphingolipd metabolism, was upregulated. Univariate and multivariate logistic regressions showed that these three lipid metabolites and the resulting diagnostic model exhibited a strong ability in discriminating between AIS patients and HCs in both the discovery and validation sets, with an area under the curve above 0.9.

CONCLUSIONS

Our work provides valuable information on the pathophysiology of AIS and constitutes an important step toward clinical application of blood-based biomarkers for diagnosing AIS.

Advancing Stroke Research on Cerebral Thrombi with Omic Technologies

Cerebrovascular diseases represent a leading cause of disability, morbidity, and death worldwide. In the last decade, the advances in endovascular procedures have not only improved acute ischemic stroke care but also conceded a thorough analysis of patients' thrombi. Although early anatomopathological and immunohistochemical analyses have provided valuable insights into thrombus composition and its correlation with radiological features, response to reperfusion therapies, and stroke etiology, these results have been inconclusive so far. Recent studies applied single- or multi-omic approaches-such as proteomics, metabolomics, transcriptomics, or a combination of these-to investigate clot composition and stroke mechanisms, showing high predictive power. Particularly, one pilot studies showed that combined deep phenotyping of stroke thrombi may be superior to classic clinical predictors in defining stroke mechanisms. Small sample sizes, varying methodologies, and lack of adjustments for potential confounders still represent roadblocks to generalizing these findings. However, these techniques hold the potential to better investigate stroke-related thrombogenesis and select secondary prevention strategies, and to prompt the discovery of novel biomarkers and therapeutic targets. In this review, we summarize the most recent findings, overview current strengths and limitations, and present future perspectives in the field.

Metabolomics as a potential tool for monitoring patients with aneurysmal subarachnoid hemorrhage

Metabolomics has evolved into a particularly useful tool to study interactions between metabolites and serves as an aid in unraveling the complexity of entire metabolomes. Nonetheless, it is increasingly viewed as a methodology with practical applications in the clinical setting, where identifying and quantifying biomarkers of interest could prove useful for diagnostics. Starting from a concise overview of the most prominent analytical techniques employed in metabolomics, herein we present a review of its application in studies of brain metabolism and cerebrovascular diseases, paying most attention to its uses in researching aneurysmal subarachnoid hemorrhage. Both animal models and human studies are considered, and metabolites identified as potential biomarkers are highlighted.

Potential preventive markers in the intracerebral hemorrhage process are revealed by serum untargeted metabolomics in mice using hypertensive cerebral microbleeds

Hypertensive cerebral microbleeds (HCMB) may be the early stage of hypertensive intracerebral hemorrhage (HICH), which is a serious threat to health due to its high mortality and disability rates. The early clinical symptoms of HCMB may not be significant. Moreover, it is difficult to achieve early diagnosis and intervention for targeted prevention of HICH. Although hypertension (HTN) is a predisposition for HCMB, it remains unclear whether there is any difference between hypertensive patients with or without HCMB. Therefore, we carried out liquid chromatography-mass spectrometry (LC-MS) to analyze early biomarkers for HCMB in mice with hypertension and to lay the foundation for early prevention of HICH in hypertensive patients. In total, 18 C57 male mice were randomly divided into the HCMB (n = 6), HTN (n = 6), and control groups (CON, n = 6). Hematoxylin-eosin and diaminobenzidine staining were used to assess the reliability of the model. The metabolite expression level and sample category stability were tested using the displacement test of orthogonal partial least squares discriminant analysis (OPLS-DA). Significant differences in metabolites were screened out using variable importance in the projection (VIP > 1), which were determined using the OPLS-DA model and the P-value of the t-test (P < 0.05) combined with the nonparametric rank-sum test. With an area under the curve (AUC) > 0.85 and a P-value of 0.05, the receiver operating characteristic curve (ROC) was used to further screen the distinct metabolites of HCMB. Compared with the HTN and CON groups, the HCMB group had significantly higher blood pressure and lower average body weight (P < 0.05). Through untargeted LC-MS analysis, 93 distinct metabolites were identified in the HCMB (P < 0.05, VIP > 1) group. Among these potential biomarkers, six significantly decreased and eight significantly increased differential metabolites were found. Meanwhile, we found that the HCMB group had statistically distinct arginine and purine metabolism pathways (P < 0.05), and citrulline may be the most significant possible biomarker of HCMB (AUC > 0.85, P < 0.05). All of these potential biomarkers may serve as early biomarkers for HICH in hypertension.

Targeted metabolomics reveals serum changes of amino acids in mild to moderate ischemic stroke and stroke mimics

Background

The pathophysiological processes linked to an acute ischemic stroke (IS) can be reflected in the circulating metabolome. Amino acids (AAs) have been demonstrated to be one of the most significant metabolites that can undergo significant alteration after a stroke.

Methods

We sought to identify the potential biomarkers for the early detection of IS using an extensive targeted technique for reliable quantification of 27 different AAs based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). A cohort with 216 participants was enrolled, including 70 mild to moderate ischemic stroke patients (National Institutes of Health Stroke Scale < 15, MB group), 76 stroke mimics (MM group) and 70 healthy controls (NC group).

Results

It was found that upon comparing MB and MM to control patients, AAs shifts were detected via partial least squares discrimination analysis (PLS-DA) and pathway analysis. Interestingly, MB and MM exhibited similar AAs pattern. Moreover, ornithine, asparagine, valine, citrulline, and cysteine were identified for inclusion in a biomarker panel for early-stage stroke detection based upon an AUC of 0.968 (95% CI 0.924-0.998). Levels of ornithine were positively associated with infract volume, 3 months mRS score, and National Institutes of Health Stroke Scale (NIHSS) score in MB. In addition, a metabolites biomarker panel, including ornithine, taurine, phenylalanine, citrulline, cysteine, yielded an AUC of 0.99 (95% CI 0.966-1) which can be employed to effectively discriminate MM patients from control.

Conclusion

Overall, alternations in serum AAs are characteristic metabolic features of MB and MM. AAs could serve as promising biomarkers for the early diagnosis of MB patients since mild to moderate IS patients were enrolled in the study. The metabolism of AAs can be considered as a key indicator for both the prevention and treatment of IS.

Integrated Metabolomics and Lipidomics Approach for the Study of Metabolic Network and Early Diagnosis in Cerebral Infarction

Cerebral infarction (CI) remains a major cause of high mortality and long-term disability worldwide. The exploration of biomarkers and pathogenesis is crucial for the early diagnosis of CI. Although the understanding of metabolic perturbations underlying CI has increased in recent years, the relationship between altered metabolites and disease pathogenesis has only been partially elucidated and requires further investigation. In this study, we performed an integrated metabolomics and lipidomics analysis on 59 healthy subjects and 47 CI patients. Ultimately, 49 metabolite and 68 lipid biomarkers were identified and enriched in 24 disturbed pathways. The metabolic network revealed a significant interaction between altered lipids and other metabolites. Using receiver operating characteristic curve (ROC) analysis, a panel of three polar metabolites and seven lipids was optimized in the training set, which included taurine, oleoylcarnitine, creatinine, PE(22:6/P-18:0), Cer 34:2, GlcCer(d18:0/18:0), DG 44:0, LysoPC(16:0), 22:6-OH/LysoPC, and TAG58:7-FA22:4. Subsequently, a support vector machine (SVM) model was constructed and validated, which showed excellent predictive ability in the validation set. Thereby, the integrated metabolomics and lipidomics approach could contribute to a comprehensive understanding of the metabolic dyshomeostasis associated with the pathogenesis of underlying CI. The present research may promote a deeper understanding and early diagnosis of CI in the clinic. All raw data were deposited in PRIDE (PXD036199).

Metabolomics of ischemic stroke: insights into risk prediction and mechanisms

Ischemic stroke (IS) is the most prevalent type of stroke. The early diagnosis and prognosis of IS are crucial for successful therapy and early intervention. Metabolomics, a tool in systems biology based on several innovative technologies, can be used to identify disease biomarkers and unveil underlying pathophysiological processes. Accordingly, in recent years, an increasing number of studies have identified metabolites from cerebral ischemia patients and animal models that could improve the diagnosis of IS and prediction of its outcome. In this paper, metabolomic research is comprehensively reviewed with a focus on describing the metabolic changes and related pathways associated with IS. Most clinical studies use biofluids (e.g., blood or plasma) because their collection is minimally invasive and they are ideal for analyzing changes in metabolites in patients of IS. We review the application of animal models in metabolomic analyses aimed at investigating potential mechanisms of IS and developing novel therapeutic approaches. In addition, this review presents the strengths and limitations of current metabolomic studies on IS, providing a reference for future related studies.

Dynamic patterns of postprandial metabolic responses to three dietary challenges

Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.

Detection of acute ischemic stroke and backtracking stroke onset time via machine learning analysis of metabolomics

The time window from stroke onset is critical for the treatment decision. However, in unknown onset stroke, it is often difficult to determine the exact onset time because of the lack of assessment methods, which can result in controversial and random treatment decisions. Previous studies have shown that serum biomarkers, in addition to imaging assessment, are useful for determining the stroke onset time. However, as yet there are no specific biomarkers or corresponding methodologies that are accurate and effective for determining the onset time of unknown onset stroke. Herein, we describe our novel advanced metabolites-based machine learning method (termed extreme gradient boost [XGBoost]) combined with recursive feature elimination, which accurately screened five metabolites from 1124 metabolites detected in serum. These metabolites were capable of both detecting acute ischemic stroke and backtracking the acute ischemic stroke onset time. To further investigate the pathological mechanisms of acute ischemic stroke, we also examined characteristic metabolites in different brain regions, and found two metabolites that could distinguish the core infarct area from the ischemic penumbra. Although this study is based on animal experiments, our machine learning framework and selected metabolites may provide a basis for clinical stroke evaluation and treatment.

Serum Metabolites and Kidney Outcomes: The Atherosclerosis Risk in Communities Study

Rationale & Objective

Novel metabolite biomarkers of kidney failure with replacement therapy (KFRT) may help identify people at high risk for adverse kidney outcomes and implicated pathways may aid in developing targeted therapeutics.

Study Design

Prospective cohort.

Setting & Participants

The cohort included 3,799 Atherosclerosis Risk in Communities study participants with serum samples available for measurement at visit 1 (1987-1989).

Exposure

Baseline serum levels of 318 metabolites.

Outcomes

Incident KFRT, kidney failure (KFRT, estimated glomerular filtration rate <15 mL/min/1.73 m², or death from kidney disease).

Analytical Approach

Because metabolites are often intercorrelated and represent shared pathways, we used a high dimension reduction technique called Netboost to cluster metabolites. Longitudinal associations between clusters of metabolites and KFRT and kidney failure were estimated using a Cox proportional hazards model.

Results

Mean age of study participants was 53 years, 61% were African American, and 13% had diabetes. There were 160 KFRT cases and 357 kidney failure cases over a mean of 23 years. The 314 metabolites were grouped in 43 clusters. Four clusters were significantly associated with risk of KFRT and 6 were associated with kidney failure (including 3 shared clusters). The 3 shared clusters suggested potential pathways perturbed early in kidney disease: cluster 5 (15 metabolites involved in alanine, aspartate, and glutamate metabolism as well as 5-oxoproline and several gamma-glutamyl amino acids), cluster 26 (6 metabolites involved in sugar and inositol phosphate metabolism), and cluster 34 (21 metabolites involved in glycerophospholipid metabolism). Several individual metabolites were also significantly associated with both KFRT and kidney failure, including glucose and mannose, which were associated with higher risk of both outcomes, and 5-oxoproline, gamma-glutamyl amino acids, linoleoylglycerophosphocholine, 1,5-anhydroglucitol, which were associated with lower risk of both outcomes.

Limitations

Inability to determine if the metabolites cause or are a consequence of changes in kidney function.

Conclusions

We identified several clusters of metabolites reproducibly associated with development of KFRT. Future experimental studies are needed to validate our findings as well as continue unraveling metabolic pathways involved in kidney function decline.

Nucleosides Associated With Incident Ischemic Stroke in the REGARDS and JHS Cohorts

BACKGROUND AND OBJECTIVES

Both genetic and environmental factors contribute to stroke risk. We sought to identify novel metabolites associated with incident stroke in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort and determine whether they reflected genetic or environmental variation.

METHODS

This was a stroke case-cohort observational study nested in REGARDS. Cases were defined as incident stroke and metabolomic profiles were compared to a randomly selected control cohort. In baseline plasma samples, 162 metabolites were measured using liquid chromatography-tandem mass spectrometry. Cox proportional hazards models were adjusted for age, sex, race, and age by race in the base model. Fully adjusted models included traditional stroke risk factors. Mediation analyses conducted for these stroke risk factors used the metabolite as mediator. Genome-wide associations with the leading candidate metabolites were calculated using array data. Replication analyses in the Jackson Heart Study (JHS) were conducted using random effects meta-analysis.

RESULTS

There were 2,043 participants who were followed over an average period of 7.1 years, including 1,075 stroke cases and 968 random controls. Nine metabolites were associated with stroke in the base model, 8 of which were measured and remained significant in meta-analysis with JHS. In the fully adjusted model in REGARDS, guanosine (hazard ratio [HR] 1.34, 95% CI 1.18-1.53; p = 7.26 × 10^-6) and pseudouridine (HR 1.28, 95% CI 1.13-1.45; p = 1.03 × 10^-4) were associated with incident ischemic stroke following Bonferroni adjustment. Guanosine also partially mediated the relationship between hypertension and stroke (17.6%) and pseudouridine did not mediate any risk factor. Genome-wide association analysis identified loci rs34631560 and rs34631560 associated with pseudouridine, but these did not explain the association of pseudouridine with stroke.

DISCUSSION

Guanosine and pseudouridine are nucleosides associated with incident ischemic stroke independently of other risk factors. Genetic and mediation analyses suggest that environmental exposures rather than genetic variation link nucleoside levels to stroke risk.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that guanosine and pseudouridine are associated with incident stroke.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Metabolomic Profiles Associated With Incident Ischemic Stroke

BACKGROUND AND OBJECTIVES

Women have higher lifetime risk of stroke than men, and metabolic factors seem more strongly associated with stroke for women than men. However, few studies in either men or women have evaluated metabolomic profiles and incident stroke.

METHODS

We applied liquid chromatography-tandem mass spectrometry to measure 519 plasma metabolites in a discovery set of women in the Nurses' Health Study (NHS; 454 incident ischemic stroke cases, 454 controls) with validation in 2 independent, prospective cohorts: Prevención con Dieta Mediterránea (PREDIMED; 118 stroke cases, 791 controls) and Nurses' Health Study 2 (NHS2; 49 ischemic stroke cases, 49 controls). We applied logistic regression models with stroke as the outcome to adjust for multiple risk factors; the false discovery rate was controlled through the q value method.

RESULTS

Twenty-three metabolites were significantly associated with incident stroke in NHS after adjustment for traditional risk factors (q < 0.05). Of these, 14 metabolites were available in PREDIMED and 3 were significantly associated with incident stroke: methionine sulfoxide, N6-acetyllysine, and sucrose (q < 0.05). In NHS2, one of the 23 metabolites (glucuronate) was significantly associated with incident stroke (q < 0.05). For all 4 metabolites, higher levels were associated with increased risk. These 4 metabolites were used to create a stroke metabolite score (SMS) in the NHS and tested in PREDIMED. Per unit of standard deviation of SMS, the odds ratio for incident stroke was 4.12 (95% confidence interval [CI] 2.26-7.51) in PREDIMED, after adjustment for risk factors. In PREDIMED, the area under the receiver operating characteristic curve (AUC) for the model including SMS and traditional risk factors was 0.70 (95% CI 0.75-0.79) vs the AUC for the model including the traditional risk factors only of 0.65 (95% CI 0.70-0.75), corresponding to a 5% improvement in risk prediction with SMS (p < 0.005).

DISCUSSION

Metabolites associated with stroke included 2 amino acids, a carboxylic acid, and sucrose. A composite SMS including these metabolites was associated with ischemic stroke and showed improvement in risk prediction beyond traditional risk factors.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that a SMS accurately predicts incident ischemic stroke risk.

Risk Factors, Lifestyle Behaviors, and Vascular Brain Health

Although a relationship between traditional cardiovascular risk factors and stroke has long been recognized, these risk factors likely play a role in other aspects of brain health. Clinical stroke is only the tip of the iceberg of vascular brain injury that includes covert infarcts, white matter hyperintensities, and microbleeds. Furthermore, an individual's risk for not only stroke but poor brain health includes not only these traditional vascular risk factors but also lifestyle and genetic factors. The purpose of this narrative review is to summarize the state of the evidence on traditional and nontraditional vascular risk factors and their contributions to brain health. Additionally, we will review important modifiers that interact with these risk factors to increase, or, in some cases, reduce risk of adverse brain health outcomes, with an emphasis on genes and biomarkers associated with Alzheimer disease. Finally, we will consider the importance of social determinants of health in brain health outcomes.

Metabolomic Profiles of Men and Women Ischemic Stroke Patients

Background: Stroke is known to affect both men and women; however, incidence and outcomes differ between them. Therefore, the discovery of novel, sex-specific, blood-based biomarkers for acute ischemic stroke (AIS) patients has the potential to enhance the understanding of the etiology of this deadly disease in the content of sex. The objective of this study was to identify serum metabolites associated with male and female AIS patients. Methods: Metabolites were measured with the use of untargeted, reverse-phase ultra-performance liquid chromatography-tandem mass spectrometry quantification from blood specimens collected from AIS patients. Samples were collected from 36 patients comprising each of 18 men and women with matched controls. Metabolic pathway analysis and principal component analysis (PCA) was used to differentiate metabolite profiles for male and female AIS patients from the control, while logistic regression was used to determine differences in metabolites between male and female AIS patients. Results: In female AIS patients, 14 distinct altered metabolic pathways and 49 corresponding metabolites were identified, while 39 metabolites and 5 metabolic pathways were identified in male patients. Metabolites that are predictive of ischemic stroke in female patients were 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4) (AUC = 0.914, 0.765–1.000), 1-(1-enyl-palmitoyl)-2-palmitoyl-GPC (P-16:0/16:0) (AUC = 0.840, 0.656–1.000), and 5,6-dihydrouracil (P-16:0/20:2) (AUC = 0.815, 0.601–1.000). Significant metabolites that were predictive of stroke in male patients were 5alpha-androstan-3alpha,17beta-diol disulfate (AUC = 0.951, 0.857–1.000), alpha-hydroxyisocaproate (AUC = 0.938, 0.832–1.000), threonate (AUC = 0.877, 0.716–1.000), and bilirubin (AUC = 0.817, 0.746–1.000). Conclusions: In the current study, the untargeted serum metabolomics platform identified multiple pathways and metabolites associated with male and female AIS patients. Further research is necessary to characterize how these metabolites are associated with the pathophysiology in male and female AIS patients.

Metabolomics and metabolites in ischemic stroke

Stroke is a major reason for disability and the second highest cause of death in the world. When a patient is admitted to a hospital, it is necessary to identify the type of stroke, and the likelihood for development of a recurrent stroke, vascular dementia, and depression. These factors could be determined using different biomarkers. Metabolomics is a very promising strategy for identification of biomarkers. The advantage of metabolomics, in contrast to other analytical techniques, resides in providing low molecular weight metabolite profiles, rather than individual molecule profiles. Technically, this approach is based on mass spectrometry and nuclear magnetic resonance. Furthermore, variations in metabolite concentrations during brain ischemia could alter the principal neuronal functions. Different markers associated with ischemic stroke in the brain have been identified including those contributing to risk, acute onset, and severity of this pathology. In the brain, experimental studies using the ischemia/reperfusion model (IRI) have shown an impaired energy and amino acid metabolism and confirmed their principal roles. Literature data provide a good basis for identifying markers of ischemic stroke and hemorrhagic stroke and understanding metabolic mechanisms of these diseases. This opens an avenue for the successful use of identified markers along with metabolomics technologies to develop fast and reliable diagnostic tools for ischemic and hemorrhagic stroke.

Association of SUMOylation Pathway Genes With Stroke in a Genome-Wide Association Study in India

OBJECTIVE

To undertake a genome-wide association study (GWAS) to identify genetic variants for stroke in an Indian population.

METHODS

In a hospital-based case-control study, 8 teaching hospitals in India recruited 4,088 participants, including 1,609 stroke cases. Imputed genetic variants were tested for association with stroke subtypes using both single-marker and gene-based tests. Association with vascular risk factors was performed with logistic regression. Various databases were searched for replication, functional annotation, and association with related traits. Status of candidate genes previously reported in the Indian population was also checked.

RESULTS

Associations of vascular risk factors with stroke were similar to previous reports and show modifiable risk factors such as hypertension, smoking, and alcohol consumption as having the highest effect. Single-marker-based association revealed 2 loci for cardioembolic stroke (1p21 and 16q24), 2 for small vessel disease stroke (3p26 and 16p13), and 4 for hemorrhagic stroke (3q24, 5q33, 6q13, and 19q13) at p < 5 × 10^-8. The index single nucleotide polymorphism of 1p21 is an expression quantitative trait locus (p _lowest = 1.74 × 10^-58) for RWDD3 involved in SUMOylation and is associated with platelet distribution width (1.15 × 10^-9) and 18-carbon fatty acid metabolism (p = 7.36 × 10^-12). In gene-based analysis, we identified 3 genes (SLC17A2, FAM73A, and OR52L1) at p < 2.7 × 10^-6. Eleven of 32 candidate gene loci studied in an Indian population replicated (p < 0.05), and 21 of 32 loci identified through previous GWAS replicated according to directionality of effect.

CONCLUSIONS

This GWAS of stroke in an Indian population identified novel loci and replicated previously known loci. Genetic variants in the SUMOylation pathway, which has been implicated in brain ischemia, were identified for association with stroke.

Uric Acid and Gluconic Acid as Predictors of Hyperglycemia and Cytotoxic Injury after Stroke

Hyperglycemia is a feature of worse brain injury after acute ischemic stroke, but the underlying metabolic changes and the link to cytotoxic brain injury are not fully understood. In this observational study, we applied regression and machine learning classification analyses to identify metabolites associated with hyperglycemia and a neuroimaging proxy for cytotoxic brain injury. Metabolomics and lipidomics were carried out using liquid chromatography-tandem mass spectrometry in admission plasma samples from 381 patients presenting with an acute stroke. Glucose was measured by a central clinical laboratory, and a subgroup of patients (n = 201) had apparent diffusion coefficient (ADC) imaging quantified on magnetic resonance imaging (MRI) to estimate cytotoxic injury. Uric acid was the leading metabolite in univariate analysis of both hyperglycemia (OR 19.6, 95% CI 8.6-44.7, P = 1.44 × 10-12) and ADC (OR 5.3, 95% CI 2.2-13.0, P = 2.42 × 10-4). To further prioritize model features and account for non-linear correlation structure, a random forest machine learning algorithm was applied to separately model hyperglycemia and ADC. The statistical techniques used have identified uric acid and gluconic acids as leading candidate markers common to all models (R2 = 68%, P = 2.2 × 10-10 for uric acid; R2 = 15%, P = 8.09 × 10-10 for gluconic acid). Both uric acid and gluconic acid were associated with hyperglycemia and cytotoxic brain injury. Both metabolites are linked to oxidative stress, which highlights two candidate targets for limiting brain injury after stroke.

Multi-omic analysis of stroke recurrence in African Americans from the Vitamin Intervention for Stroke Prevention (VISP) clinical trial

African Americans endure a nearly two-fold greater risk of suffering a stroke and are 2–3 times more likely to die from stroke compared to those of European ancestry. African Americans also have a greater risk of recurrent stroke and vascular events, which are deadlier and more disabling than incident stroke. Stroke is a multifactorial disease with both heritable and environmental risk factors. We conducted an integrative, multi-omic study on 922 plasma metabolites, 473,864 DNA methylation loci, and 556 variants from 50 African American participants of the Vitamin Intervention for Stroke Prevention clinical trial to help elucidate biomarkers contributing to recurrent stroke rates in this high risk population. Sixteen metabolites, including cotinine, N-delta-acetylornithine, and sphingomyelin (d17:1/24:1) were identified in t-tests of recurrent stroke outcome or baseline smoking status. Serum tricosanoyl sphingomyelin (d18:1/23:0) levels were significantly associated with recurrent stroke after adjusting for covariates in Cox Proportional Hazards models. Weighted Gene Co-expression Network Analysis identified moderate correlations between sphingolipid markers and clinical traits including days to recurrent stroke. Integrative analyses between genetic variants in sphingolipid pathway genes identified 29 nominal associations with metabolite levels in a one-way analysis of variance, while epigenomic analyses identified xenobiotics, predominately smoking-associated metabolites and pharmaceutical drugs, associated with methylation profiles. Taken together, our results suggest that metabolites, specifically those associated with sphingolipid metabolism, are potential plasma biomarkers for stroke recurrence in African Americans. Furthermore, genetic variation and DNA methylation may play a role in the regulation of these metabolites.

Serum metabolomic patterns in young patients with ischemic stroke: a case study

BACKGROUND

Ischemic stroke is one of the leading causes of death and adult disability. The incidence of ischemic stroke continues to rise in young adults. This study aimed to provide a comprehensive evaluation of metabolic changes and explore possible mechanisms in young ischemic stroke patients without common risk factors.

METHODS

This study investigated serum metabolomics in 50 young patients with newly suffered ischemic stroke and 50 age-, sex-, and body mass index-matched healthy controls. Liquid chromatography coupled with a Waters Xevo TQ-S mass spectrometer with an electrospray ionization (ESI) source was used to analyze amino acid or bile acid, and free fatty acid or lipid was analyzed by liquid chromatography coupled with a Qtrap5500 mass spectrometer with an ESI source. The metabolomic data were analyzed by performing a multivariate statistical analysis.

RESULTS

A total of 197 metabolites, including amino acids, bile acids, free fatty acids, and lipids, were identified in all participants. Multivariate models showed significant differences in serum metabolomic patterns between young patients with ischemic stroke and healthy controls. The stroke patients had increased L-methionine, homocysteine, glutamine, uric acid, GCDCA, and PE (18:0/20:4, 16:0/22:5), and decreased levels of L-citrulline, taurine, PC (16:2/22:6, 16:2/20:5, 15:0/18:2), and SM (d18:1/23:0, d20:0/19:1, d18:1/22:0, d16:0/26:1, d16:0/18:0, d16:0/22:1, d18:1/19:1, d16:0/17:1, d16:1/24:1, d18:1/19:0). Based on the identified metabolites, the metabolic pathways of arginine biosynthesis, glycerophospholipid metabolism, and taurine and hypotaurine metabolism were significantly enriched in the young patients with ischemic stroke.

CONCLUSIONS

Serum metabolomic patterns were significantly different between young patients with ischemic stroke and healthy controls. Our study is beneficial in providing a further view into the pathophysiology of young patients with ischemic stroke.

Epigenetic factors in atherosclerosis: DNA methylation, folic acid metabolism, and intestinal microbiota

Atherosclerosis is a complex disease, influenced by both genetic and non-genetic factors. The most important epigenetic mechanism in the pathogenesis of atherosclerosis is DNA methylation, which involves modification of the gene without changes in the gene sequence. Nutrients involved in one-carbon metabolism interact to regulate DNA methylation, especially folic acid and B vitamins. Deficiencies in folic acid and other nutrients, such as vitamins B6 and B12, can increase homocysteine levels, induce endothelial dysfunction, and accelerate atherosclerotic pathological processes. Supplemented nutrients can improve DNA methylation status, reduce levels of inflammatory factors, and delay the process of atherosclerosis. In this review, the influence of intestinal flora on folate metabolism and epigenetics is also considered.

Genomic Determinants of Hypertension With a Focus on Metabolomics and the Gut Microbiome

Epidemiologic and genomic studies have progressively improved our understanding of the causation of hypertension and the complex relationship with diet and environment. The majority of Mendelian forms of syndromic hypotension and hypertension (HTN) have all been linked to mutations in genes whose encoded proteins regulate salt-water balance in the kidney, supporting the primacy of the kidneys in blood pressure regulation. There are more than 1,477 single nucleotide polymorphisms associated with blood pressure and hypertension and the challenge is establishing a causal role for these variants. Hypertension is a complex multifactorial phenotype and it is likely to be influenced by multiple factors including interactions between diet and lifestyle factors, microbiome, and epigenetics. Given the finite genetic variability that is possible in humans, it is likely that incremental gains from single marker analyses have now plateaued and a greater leap in our understanding of the genetic basis of disease will come from integration of other omics and the interacting environmental factors. In this review, we focus on emerging results from the microbiome and metabolomics and discuss how leveraging these findings may facilitate a deeper understanding of the interrelationships between genomics, diet, and microbial ecology in humans in the causation of essential hypertension.

Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke

Despite many years of research, no biomarkers for stroke are available to use in clinical practice. Progress in high-throughput technologies has provided new opportunities to understand the pathophysiology of this complex disease, and these studies have generated large amounts of data and information at different molecular levels. The integration of these multi-omics data means that thousands of proteins (proteomics), genes (genomics), RNAs (transcriptomics) and metabolites (metabolomics) can be studied simultaneously, revealing interaction networks between the molecular levels. Integrated analysis of multi-omics data will provide useful insight into stroke pathogenesis, identification of therapeutic targets and biomarker discovery. In this Review, we detail current knowledge on the pathology of stroke and the current status of biomarker research in stroke. We summarize how proteomics, metabolomics, transcriptomics and genomics are all contributing to the identification of new candidate biomarkers that could be developed and used in clinical stroke management.

Urine dicarboxylic acids change in pre-symptomatic Alzheimer's disease and reflect loss of energy capacity and hippocampal volume

Non-invasive biomarkers will enable widespread screening and early diagnosis of Alzheimer’s disease (AD). We hypothesized that the considerable loss of brain tissue in AD will result in detection of brain lipid components in urine, and that these will change in concert with CSF and brain biomarkers of AD. We examined urine dicarboxylic acids (DCA) of carbon length 3–10 to reflect products of oxidative damage and energy generation or balance that may account for changes in brain function in AD. Mean C4-C5 DCAs were lower and mean C7-C10 DCAs were higher in the urine from AD compared to cognitively healthy (CH) individuals. Moreover, mean C4-C5 DCAs were lower and mean C7-C9 were higher in urine from CH individuals with abnormal compared to normal CSF amyloid and Tau levels; i.e., the apparent urine changes in AD also appeared to be present in CH individuals that have CSF risk factors of early AD pathology. In examining the relationship between urine DCAs and AD biomarkers, we found short chain DCAs positively correlated with CSF Aβ₄₂, while C7-C10 DCAs negatively correlated with CSF Aβ₄₂ and positively correlated with CSF Tau levels. Furthermore, we found a negative correlation of C7-C10 DCAs with hippocampal volume (p < 0.01), which was not found in the occipital volume. Urine measures of DCAs have an 82% ability to predict cognitively healthy participants with normal CSF amyloid/Tau. These data suggest that urine measures of increased lipoxidation and dysfunctional energy balance reflect early AD pathology from brain and CSF biomarkers. Measures of urine DCAs may contribute to personalized healthcare by indicating AD pathology and may be utilized to explore population wellness or monitor the efficacy of therapies in clinical trials.

Proteomics Analysis of Brain Tissue in a Rat Model of Ischemic Stroke in the Acute Phase

Background: Stroke is a leading health issue, with high morbidity and mortality rates worldwide. Of all strokes, approximately 80% of cases are ischemic stroke (IS). However, the underlying mechanisms of the occurrence of acute IS remain poorly understood because of heterogeneous and multiple factors. More potential biomarkers are urgently needed to reveal the deeper pathogenesis of IS.

Methods: We identified potential biomarkers in rat brain tissues of IS using an iTRAQ labeling approach coupled with LC-MS/MS. Furthermore, bioinformatrics analyses including GO, KEGG, DAVID, and Cytoscape were used to present proteomic profiles and to explore the disease mechanisms. Additionally, Western blotting for target proteins was conducted for further verification.

Results: We identified 4,578 proteins using the iTRAQ-based proteomics method. Of these proteins, 282 differentiated proteins, comprising 73 upregulated and 209 downregulated proteins, were observed. Further bioinformatics analysis suggested that the candidate proteins were mainly involved in energy liberation, intracellular protein transport, and synaptic plasticity regulation during the acute period. KEGG pathway enrichment analysis indicated a series of representative pathological pathways, including energy metabolite, long-term potentiation (LTP), and neurodegenerative disease-related pathways. Moreover, Western blotting confirmed the associated candidate proteins, which refer to oxidative responses and synaptic plasticity.

Conclusions: Our findings highlight the identification of candidate protein biomarkers and provide insight into the biological processes involved in acute IS.

Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus

Gestational Diabetes Mellitus (GDM), which is correlated with changes in the gut microbiota, is a risk factor for neonatal inborn errors of metabolism (IEMs). Maternal hyperglycemia exerts epigenetic effects on genes that encode IEM-associated enzymes, resulting in changes in the neonatal blood metabolome. However, the relationship between maternal gut microbiota and the neonatal blood metabolome remains poorly understood. This study aimed at understanding the connections between maternal gut microbiota and the neonatal blood metabolome in GDM. 1H-NMR-based untargeted metabolomics was performed on maternal fecal samples and targeted metabolomics on the matched neonatal dry blood spots from a cohort of 40 pregnant women, including 22 with GDM and 18 controls. Multi-omic association methods (including Co-Inertia Analysis and Procrustes Analysis) were applied to investigate the relationship between maternal fecal metabolome and the neonatal blood metabolome. Both maternal fecal metabolome and the matched neonatal blood metabolome could be separated along the vector of maternal hyperglycemia. A close relationship between the maternal and neonatal metabolomes was observed by multi-omic association approaches. Twelve out of thirty-two maternal fecal metabolites with altered abundances from 872 1H- NMR features (Bonferroni-adjusted P < 0.05) in women with GDM and the controls were identified, among which 8 metabolites contribute (P < 0.05 in a 999-step permutation test) to the close connection between maternal and the neonatal metabolomes in GDM. Four of these eight maternal fecal metabolites, including lysine, putrescine, guanidinoacetate, and hexadecanedioate, were negatively associated (Spearman rank correlation, coefficient value < −0.6, P < 0.05) with maternal hyperglycemia. Biotin metabolism was enriched (Bonferroni-adjusted P < 0.05 in the hypergeometric test) with the four-hyperglycemia associated fecal metabolites. The results of this study suggested that maternal fecal metabolites contribute to the connections between maternal fecal metabolome and the neonatal blood metabolome and may further affect the risk of IEMs.

Recruiting Control Participants into Stroke Biomarker Studies

The number of scientists using -omics technologies to investigate biomarkers with the potential to gauge risk and aid in the diagnosis, treatment, and prognosis of stroke continues to rise, yet there are few resources to aid investigators in recruiting control participants. In this review, we describe two major strategies to match control participants to a stroke cohort-propensity score matching and one-to-one matching-including statistical approaches to gauge the balance between groups. We then explore the advantages and disadvantages of traditional recruitment methods including approaching spouses of enrolled stroke participants, direct recruitment from clinics, community outreach events, approaching retirement communities, and buying samples from a 3rd party vendor. Newer methods to identify controls by screening the electronic health record and using an online screening questionnaire are also described. Finally, we cover compensation for control participants and special considerations. The hope is that this review will serve as a roadmap whereby an investigator can successfully tailor their control recruitment strategy to the research question at hand and the local research environment. While this review is focused on blood-based biomarker studies, the principles will apply to investigators studying a broad range of biological materials.

Biomarker Application for Precision Medicine in Stroke

Stroke remains one of the leading causes of long-term disability and mortality despite recent advances in acute thrombolytic therapies. In fact, the global lifetime risk of stroke in adults over the age of 25 is approximately 25%, with 24.9 million cases of ischemic stroke and 18.7 million cases of hemorrhagic stroke reported in 2015. One of the main challenges in developing effective new acute therapeutics and enhanced long-term interventions for stroke recovery is the heterogeneity of stroke, including etiology, comorbidities, and lifestyle factors that uniquely affect each individual stroke survivor. In this comprehensive review, we propose that future biomarker studies can be designed to support precision medicine therapeutic interventions after stroke. The current challenges in defining ideal biomarkers for stroke are highlighted, including consideration of disease course, age, lifestyle factors, and subtypes of stroke. This overview of current clinical trials includes biomarker collection, and concludes with an example of biomarker design for aneurysmal subarachnoid hemorrhage. With the advent of “-omics” studies, neuroimaging, big data, and precision medicine, well-designed stroke biomarker trials will greatly advance the treatment of a disease that affects millions globally every year.

Metabolomics facilitates the discovery of metabolic biomarkers and pathways for ischemic stroke: a systematic review

INTRODUCTION

Ischemic stroke (IS) is a major contributor to the global disease burden, and effective biomarkers for IS management in clinical practice are urgently needed. Metabolomics can detect metabolites that are small enough to cross the blood-brain barrier in a high-throughput manner, and thus represents a powerful tool for discovering biomarkers of IS.

OBJECTIVES

In this study, we conducted a systematic review to identify potential metabolic biomarkers and pathways that might facilitate risk predictions, clinical diagnoses, the recognition of complications, predictions of recurrence and an understanding of the pathogenesis of IS.

METHODS

The PubMed and Web of Science databases were searched for relevant studies published between January 2000 and July 2019. The study objectives, study designs and reported metabolic biomarkers were systematically examined and compared. Pathway analysis was performed using the MetaboAnalyst online software.

RESULTS

Twenty-eight studies were included in this systematic review. Many consistent metabolites, including isoleucine, leucine, valine, glycine, lysine, glutamate, LysoPC(16:0), LysoPC(18:2), serine, uric acid, citrate and palmitic acid, possess potential as biomarkers of IS. Metabolic pathways and dysregulations that are implicated in excitotoxicity, inflammation, apoptosis, oxidative stress, neuroprotection, energy failure, and elevation of intracellular Ca²⁺ levels, were indicated as playing important roles in the development and progression of IS.

CONCLUSIONS

This systematic review summarizes potential metabolic biomarkers and pathways related to IS, which may provide opportunities for the construction of diagnostic or predictive models for IS and the discovery of novel therapeutic targets.