Serum L-selectin levels as predictive markers for chronic major depressive disorder progression.

BACKGROUND: Major depressive disorder (MDD) exhibits a recurrence rate of up to 70%. Frequent recurrence can lead to chronic depression, which has considerable personal and societal consequences. This study aims to identify a serum protein biomarker to predict MDD recurrence and progression to chronicity. METHODS: Serum samples from the MDD with single episode group (MDD-S), MDD with recurrence group (MDD-R), and a healthy control group were collected. Non-targeted analysis of the serum proteome was conducted using liquid chromatography-tandem mass spectrometry. Statistically significant common proteins when comparing the three groups were chosen. The selected marker candidates were subsequently validated through multiple response monitoring (MRM), incorporating a healthy control, MDD-S, MDD-R(2) (two episodes), and MDD-R(> 2) (more than two episodes) groups. RESULTS: L-selectin levels showed an upward trend in the MDD-R group compared to the healthy control and MDD-S groups. MRM validation revealed a decreased tendency for L-selectin in the MDD-R(> 2) group, indicative of a chronic state, versus the healthy control and MDD-S groups. The receiver operating characteristic analysis highlighted L-selectin as the chosen biomarker due to its classification efficacy for the MDD-R(> 2) group. CONCLUSION: L-selectin emerged as a predictive biomarker for MDD recurrence and its potential evolution into chronic depression. This marker offers insights into changes in leukocyte-mediated inflammatory responses characteristic of chronic depression. Consequently, it may forecast the transition from acute to chronic inflammation in depressive patients.

Serum proteomic changes related to residual impairment in remittent depression are associated with immune and inflammatory processes.

In patients with major depressive disorder, various functional areas are impaired, negatively impacting the quality of life. Remission can restore pre-depression functions; however, some patients may still have residual impairments. Distinguishing between near-normal recovery and residual impairment helps identify those at a high risk of relapse risk and helps tailor treatment. Accordingly, we aimed to discover and validate biomarkers that distinguish between near-normal recovery and residual impairment in remission states through serum proteome analysis. Pooled serum and individual serum samples from three groups (depression status, remission status with residual impairment, and remission status with normal recovery) were analyzed using liquid chromatography-tandem mass spectrometry. The combination of four proteins-antithrombin-III, serum amyloid A4 protein, C1q subcomponent subunit B, and serum amyloid P-component-was selected as a candidate biomarker. The trend of protein changes suggests complement C1q subcomponent subunit B and serum amyloid P-component as potential biomarkers for distinguishing remission from residual impairment. Changes in complement C1q subcomponent subunit B and serum amyloid P-component suggest that the complement system and inflammation-related immune mechanisms are associated with residual impairment in remittent major depressive disorder.

Common protein networks for various drug regimens of major depression are associated with complement and immunity.

BACKGROUND: Major depressive disorder (MDD) can present a variety of clinical presentations and has high inter-individual heterogeneity. Multiple studies have suggested various subtype models related to symptoms, etiology, sex, and treatment response. Employing different regimens is common when treating MDD, and identifying effective therapeutics requires time. Frequent treatment attempts and failures can lead to a diagnosis of treatment resistance, and the heterogeneity of treatment responses among individuals makes it difficult to understand and interpret the biological mechanisms underlying MDD. AIM: This study explored the differentially expressed proteins and commonly altered protein networks across drug treatments by comparing the serum proteomes of patients with MDD treated with drug regimens (T-MDD, n = 20) and untreated patients (NT-MDD, n = 20). METHODS: Differentially expressed proteins were profiled in non-drug-treated and drug-treated patients with depression using liquid chromatography-mass spectrometry. The common protein networks affected by different medications were studied. RESULTS: Of the proteins profiled, 12 were significantly differentially expressed between the T-MDD and NT-MDD groups. Commonly altered proteins and networks of various drug treatments for depression were related to the complement system and immunity. CONCLUSIONS: Our results provide information on common biological changes across different pharmacological treatments employed for depression and provide an alternative perspective for improving our understanding of the biological mechanisms of drug response in MDD with great heterogeneity in the background of the disease.

Introduction to the forensic research via omics markers in environmental health vulnerable areas (FROM) study.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas-including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter-along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

Serum protein profiling reveals mechanism of activated thrombus formation in patients with stroke and atrial fibrillation.

Stroke is an acute cerebrovascular disease in which blood flow to the brain is suddenly disrupted, causing damage to nerve cells. It involves complex and diverse pathophysiological processes and the treatment strategies are also diverse. The treatment for patients with stroke and atrial fibrillation (AF) is aimed at suppressing thrombus formation and migration. However, information regarding the protein networking involved in different thrombus formation pathways in patients with AF and stroke is insufficient. We performed protein profiling of patients with ischemic stroke with and without AF to investigate the mechanisms of thrombus formation and its pathophysiological association while providing helpful information for treating and managing patients with AF. These two groups were compared to identify the protein networks related to thrombus formation in AF. We observed that patients with ischemic stroke and AF had activated inflammatory responses induced by C-reactive protein, lipopolysaccharide-binding protein, and alpha-1-acid glycoprotein 1. In contrast, thyroid hormones were increased due to a decrease in transthyretin and retinol-binding protein 4 levels. The mechanism underlying enhanced cardiac activity, vasodilation, and the resulting thrombosis pathway were confirmed in AF. These findings will play an essential role in improving the prevention and treatment of AF-related stroke.

Uncovering the health implications of abandoned mines through protein profiling of local residents.

Residents in areas with abandoned mines risk significant exposure to abundant heavy metals in the environment. However, current clinical indicators cannot fully reflect the health changes associated with abandoned mine exposure. The aim of this study was to identify biological changes in the residents of abandoned mine areas via proteomic analysis of their blood. Blood samples were collected from abandoned mine and control areas, and mass spectrometry was used for protein profiling. A total of 138 unique or common proteins that were differentially expressed in low-exposure abandoned mine area (LoAMA) or high-exposure abandoned mine area (HiAMA) compared to non-exposure control area (NEA) were analyzed, and identified 4 clusters based on functional similarity. Among the 10 proteins that showed specific change in LoAMA, 4 proteins(Apolipoprotein M, Apolipoprotein E, Apolipoprotein L1, and Cholesteryl ester transfer protein) were cluded in cluster 1(plasma lipoprotein remodeling), and linked to proteins that showed specific change in protein expression in HiAMA. Therefore, it is suggested that 4 proteins are changed at low exposure to an abandoned mine (or initial exposure), and then at high exposure, changes in various proteins involved in linked plasma lipoprotein remodeling are induced, which might triggered by the 4 proteins. Interestingly, in addition to plasma lipoprotein remodeling, proteins involved in other functional networks were changed in the high exposure group. These were all directly or indirectly linked to the 4 biomarkers(Apolipoprotein M, Apolipoprotein E, Apolipoprotein L1, and Cholesteryl ester transfer protein) that changed during low exposure. This suggests their potential utility in identifying areas impacted by abandoned mines. Especially, proteins involved in lipid metabolism and renal function-related diseases in individuals exposed to heavy metals in abandoned mine areas were correlated. Chronic kidney disease is predominantly instigated by cardiovascular disease and is commonly accompanied by dyslipidemia.

Cadmium-associated protein changes in residents of contaminated areas: Abandoned mine and smelter.

Cadmium (Cd), a serious environmental contaminant, is associated with adverse health effects. However, the specific changes that the human body experiences in response to exposure to varying concentrations of cadmium remain unknown. The high levels of heavy metal contamination, especially Cd, in abandoned mines and smelter sites make them ideal locations to investigate the physiological manifestations of Cd exposure. This study found that individuals inhabiting abandoned mine and smelter areas had higher concentrations of Cd in their urine and blood compared to those living outside these areas (i.e., the controls). Furthermore, proteomic profiling of blood samples from all study groups was performed to identify proteomic biomarkers associated with chronic and severe Cd exposure. This analysis showed statistically significant correlations between urine Cd levels and sixteen proteins. Among these proteins, seven exhibited significantly altered expressions in samples from contaminated areas compared with those from control areas. Therefore, these proteins were selected as potential markers representing Cd-related protein alterations. Multiple reaction monitoring analysis was performed to validate the expression patterns of the proteins and four proteins were found to exhibit consistent trends. The findings show that Cd exposure significantly affects the expression of certain proteins in the human body. Understanding the underlying mechanisms and diseases associated with Cd-induced protein alterations can aid in the development of effective preventive and therapeutic strategies for individuals exposed to Cd-linked pollution.

Discovery and validation of protein biomarkers for monitoring the effectiveness of drug treatment for major depressive disorder.

Major depressive disorder (MDD) has a high prevalence worldwide. Although the economic burden of depression increases annually, the proportion of patients with MDD receiving treatment did not increase between 2010 and 2018, suggesting an unmet treatment need. The burden of long-term treatment for depression is borne by patients. In this context, biomarkers associated with drug-treatment responses can be used as reference indicators to reduce unnecessary treatment and costs. Changes in biomolecules in response to drug treatment for depression and drug-treatment response markers have been studied extensively. The Hamilton Depression Rating Scale (HAM-D) is mainly used as an indicator of response and remission; however, it is difficult to determine whether the medication contributes to recovery when evaluating the effect of drug treatment for depression based on this assessment. Therefore, it is necessary to monitor the effect of medication compared to normal health conditions. Here, serum protein levels were compared using liquid chromatography-tandem mass spectrometry among a group of patients with depression who did not receive medication, a group of patients receiving medication, and a control group. Eight selected biomarkers, including Apolipoproteins A-I, Complement factor H, Complement C5, Complement C1q subcomponent subunit B, Alpha-2-HS-glycoprotein, Complement C1q subcomponent subunit C, Vitamin D-binding protein and Corticosteroid-binding globulin were distinguished between disease states, and protein levels in the drug-treated group were similar to those in the control group. These markers can be used to monitor the effectiveness of drug treatment.

Serum Protein Profiling Reveals a Decrease in Apolipoprotein A-IV During a Clinical Depressive Mood State.

Purpose: Depressive mood is a major psychiatric symptom that causes serious disturbances in daily life. Unlike physical symptoms, psychiatric symptoms are more difficult to evaluate objectively. Therefore, we aimed to discover biomarkers that reflect changes in serum protein metabolism during a clinical depressive mood. Methods: Serum protein profiling was conducted in participants who were not experiencing a current depressive episode (healthy individuals and patients in remission). Serum proteins were identified and quantified using liquid chromatography-tandem mass spectrometry. Differentially expressed proteins with a p-value <0.05 were selected, and candidate biomarkers were verified using multiple reaction monitoring analysis for absolute quantification. Results: Apolipoprotein A-IV levels were lower in the group with a current episode of depression than in the remission and healthy control groups. Further, fibronectin levels were also lower in the group with a current episode of depression than in the healthy control group but not in the remission group. Conclusion: We found that apolipoprotein A-IV-mediated inflammation is involved in clinical depressive moods, possibly by inducing neurological changes in the brain. Therefore, apolipoprotein A-IV and fibronectin levels may be explored as potentially novel biomarkers for detecting a current episode of depression.

Potential Biomarkers to Distinguish Type 1 Myocardial Infarction in Troponin-Elevated Diseases.

Classifying myocardial infarction by subtype is crucial for appropriate patient management. Although troponin is currently the most commonly used biomarker, it is not a specific marker for myocardial infarction and cannot distinguish subtypes. Furthermore, previous studies have confirmed that proteins known as myocardial infarction markers could function to distinguish the type of myocardial infarction. Therefore, we identify a marker that can distinguish type 1 myocardial infarction from other diseases with elevated troponin. We used mass spectrometry to compare type 1 myocardial infarction with other conditions characterized by troponin elevation and identified new candidate markers for disease classification. We then verified these markers, along with those already known to be associated with cardiovascular disease and plaque rupture. We identified α-1 acid glycoprotein 2, corticosteroid-binding globulin, and serotransferrin as potential distinguishing markers. The presence of these markers and other parameters, such as chest pain, electrocardiogram, and troponin levels from the complementary diagnostic processes, could provide valuable information to specifically diagnose type 1 myocardial infarction.

Validation of the Metabolite Ergothioneine as a Forensic Marker in Bloodstains.

Ergothioneine, which is a naturally occurring metabolite, generally accumulates in tissues and cells subjected to oxidative stress, owing to its structural stability at physiological pH; therefore, it has been attracting attention in various biomedical fields. Ergothioneine has also been suggested as a potential forensic marker, but its applicability has not yet been quantitatively validated. In this study, quantitative analysis of ergothioneine in bloodstains was conducted to estimate the age of bloodstains and that of bloodstain donors. Blood from youth and elderly participants was used to generate bloodstains. After extracting metabolites from the bloodstains under prevalent age conditions, ergothioneine levels were quantified by mass spectrometry via multiple reaction monitoring. The concentration of ergothioneine in day 0 bloodstains (fresh blood), was significantly higher in the elderly group than in the youth group, but it did not differ by sex. Statistically significant differences were observed between the samples from the two age groups on days 0, 5 and 7, and on days 2 and 3 compared with day 0. The findings suggest that ergothioneine can be used to estimate the age of bloodstains and of the donor; it could be useful as a potential marker in reconstructing crime scenes.

Discovery and validation of acetyl-L-carnitine in serum for diagnosis of major depressive disorder and remission status through metabolomic approach.

Major depressive disorder (MDD) is one of the most common psychiatric disorders that accompany psychophysiological and mood changes. However, the pathophysiology-based disease mechanism of MDD is not yet fully understood, and diagnosis is also conducted through interviews with clinicians and patients. Diagnosis and treatment of MDD are limited due to the absence of biomarkers underlying the pathophysiological mechanisms of MDD. Although various attempts have been made to discover metabolite biomarkers for the diagnosis and treatment response of MDD, problems with sample size and consistency of results have limited clinical application. In addition, it was reported that future biomarker studies must consider exposure to antidepressants, which is the main cause of heterogeneity in depression subgroups. Therefore, the purpose of this study is to discover and validate biomarkers for the diagnosis of depression in consideration of exposure to drug treatment including antidepressants that contribute to the heterogeneity of the MDD subgroup. In the biomarker discovery and validation set, the disease group consisted of a mixture of patients exposed and unexposed to drug treatment including antidepressants for the treatment of MDD. The serum metabolites that differed between the MDD patients and the control group were profiled using mass spectrometry. The validation set including the remission group was used to verify the effectiveness as a biomarker for the diagnosis of depression and determination of remission status. The presence of different metabolites between the two groups was confirmed through serum metabolite profiling between the MDD patient group and the control group. Finally, Acetylcarnitine was selected as a biomarker. In validation, acetylcarnitine was significantly decreased in MDD and was distinguished from remission status. This study confirmed that the discovered acetylcarnitine has potential as a biomarker for diagnosing depression and determining remission status, regardless of exposure to drug treatment including antidepressants.

Serum proteomic analysis of major depressive disorder patients and their remission status: Novel biomarker set of zinc-alpha-2-glycoprotein and keratin type II cytoskeletal 1.

Major depressive disorder (MDD) is the most common mood disorder, and causes various mental, physical and cognitive symptoms. Clinicians diagnose MDD using multiple interviews and overall impression during the interviews, which makes MDD diagnosis highly subjective. To overcome this, we investigated novel protein biomarker for MDD. Serum from each subject were analyzed using nano liquid chromatography-triple time-of-flight mass spectrometry. We identified two proteins, zinc-alpha-2-glycoprotein (ZA2G) and keratin type II cytoskeletal 1 (K2C1), as final biomarkers. These biomarkers were downregulated during depression (p < 0.05, AUC of ROC >0.7). ZA2G is related to tryptophan metabolism, which is a main serotonin synthesis pathway. K2C1 is involved in the kinin-kallikrein system, which produces bradykinin, an anti-inflammatory mediator in the brain. Our results suggest that the two protein candidates are related to inflammation and that MDD is highly associated with inflammation. Finally, since all subjects in the two groups were taking antidepressants, our results suggest that the identified biomarkers could determine the presence or absence of illness and could be used to monitor therapeutic effects.

Serum biomarker discovery related to pathogenesis in acute coronary syndrome by proteomic approach.

Acute coronary syndrome (ACS) results from inadequate supply of blood flow from the coronary arteries to the heart or ischemia. ACS has an extremely high morbidity and mortality. The levels of biomarkers currently used for detection of ACS also increase in response to myocardial necrosis and other diseases and are not elevated immediately after symptoms appear, thus limiting their diagnostic capacity. Therefore, we aimed to discover new ACS diagnostic biomarkers with high sensitivity and specificity that are specifically related to ACS pathogenesis. Sera from 50 patients with ACS and healthy controls (discovery cohort) each were analyzed using mass spectrometry (MS) to identify differentially expressed proteins, and protein candidates were evaluated as ACS biomarkers in 120 people in each group (validation cohort). α-1-acid glycoprotein 1 (AGP1), complement C5 (C5), leucine-rich α-2-glycoprotein (LRG), and vitronectin (VN) were identified as biomarkers whose levels increase and gelsolin (GSN) as a biomarker whose levels decrease in patients with ACS. We concluded that these biomarkers are associated with the pathogenesis of ACS and can predict the onset of ACS prior to the appearance of necrotic biomarkers.

Discovery of Screening Biomarkers for Major Depressive Disorder in Remission by Proteomic Approach.

Major depressive disorder (MDD) is a common disorder involving depressive mood and decreased motivation. Due to its high heterogeneity, novel biomarkers are required to diagnose MDD. In this study, a proteomic method was used to identify a new MDD biomarker. Using sequential window acquisition of all theoretical mass spectra acquisitions and multiple reaction monitoring analysis via mass spectrometry, relative and absolute quantification of proteins in the sera was performed. The results of the relative quantitation by sequential window acquisition for all theoretical mass spectra data showed that seven proteins were significantly differently expressed between MDD patients and other patients with remission status. However, absolute quantification by multiple reaction monitoring analysis identified prothrombin as the only significantly upregulated protein in the depressive state compared to remission (p < 0.05) and was, thus, subsequently selected as an MDD biomarker. The area under the curve for prothrombin was 0.66. Additionally, increased prothrombin/thrombin induced hyper-activation of platelets via activating protease-activated receptors, a feature associated with MDD; specifically, activated platelets secrete various molecules related to MDD, including brain-derived neurotropic factors and serotonin. Therefore, prothrombin is a potential screening, prognostic, and diagnostic marker for MDD.

Biomarker Discovery of Acute Coronary Syndrome Using Proteomic Approach.

Acute coronary syndrome (ACS) is a condition in which the coronary artery supplying blood to the heart is infarcted via formation of a plaque and thrombus, resulting in abnormal blood supply and high mortality and morbidity. Therefore, the prompt and efficient diagnosis of ACS and the need for new ACS diagnostic biomarkers are important. In this study, we aimed to identify new ACS diagnostic biomarkers with high sensitivity and specificity using a proteomic approach. A discovery set with samples from 20 patients with ACS and 20 healthy controls was analyzed using mass spectrometry. Among the proteins identified, those showing a significant difference between each group were selected. Functional analysis of these proteins was conducted to confirm their association with functions in the diseased state. To determine ACS diagnostic biomarkers, standard peptides of the selected protein candidates from the discovery set were quantified, and these protein candidates were validated in a validation set consisting of the sera of 50 patients with ACS and 50 healthy controls. We showed that hemopexin, leucine-rich α-2-glycoprotein, and vitronectin levels were upregulated, whereas fibronectin level was downregulated, in patients with ACS. Thus, the use of these biomarkers may increase the accuracy of ACS diagnosis.

Serum biomarker panel for the diagnosis of rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune disease of inflammatory joint damage, wherein C-reactive protein and autoantibodies including rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) are rapidly elevated. These serological factors are diagnostic markers of RA; however, their sensitivity and specificity for prediction warrant improvement for an early and accurate diagnosis. METHODS: We aimed to identify alternative biomarkers by serum protein profiling using LC-MS/MS. We performed statistical and functional analysis of differentially expressed proteins to identify biomarker candidates complementing conventional serological tests. RESULTS: Seven biomarker candidates were verified through multiple reaction monitoring-based quantitative analysis, of which angiotensinogen (AGT), serum amyloid A-4 protein (SAA4), vitamin D-binding protein (VDBP), and retinol-binding protein-4 (RBP4) had an area under the curve over 0.8, thus distinguishing RA patients, including seronegative (RF- and anti-CCP-negative) RA patients, from healthy controls. CONCLUSIONS: Therefore, among seronegative RA patients, a four-biomarker panel (AGT, SAA4, VDBP, and RBP4) can prevent false negatives and help diagnose RA accurately.

A discovery of screening markers for rheumatoid arthritis by liquid chromatography mass spectrometry: A metabolomic approach.

AIM: This study aimed to discover serum metabolite biomarkers for potential use in screening for rheumatoid arthritis (RA). METHODS: The sera from 43 healthy controls (HCs) and 49 RA patients were globally analyzed using high-performance liquid chromatography- tandem mass spectrometry. Molecular features (MFs) from samples were analyzed using volcano plots, partial least squares discriminant analysis, and variable importance in projection scores to select candidates. The spectra of candidate MFs were matched with the METLIN database. We confirmed the association between candidates and RA and analyzed the receiver-operating characteristic (ROC) curves. RESULTS: We selected a total of 57 candidate MFs that had a fold change ≥1.5, P value ≤.05, and over 80% of frequency. Among them, 18 MFs were identified as metabolites with the METLIN database. Six metabolites (dehydroepiandrosterone sulfate, androsterone sulfate, γ-linolenic acid, 9[E],11[E]-conjugated linoleic acid, docosahexaenoic acid, and docosapentaenoic acid [22n-3]) out of the 18 were associated with mechanisms of RA and were selected as final candidates. ROC curve analysis revealed their area under the curve (AUC) values were all above 0.75 and the combined AUC of the six candidates was 0.89. CONCLUSION: Using six candidates as a marker set showed potential in distinguishing RA patients from HCs, based on high AUC values. Therefore, we propose that a marker set of these six candidates has potential clinical application in RA screening.

Proteomics Reveals Plasma Biomarkers for Ischemic Stroke Related to the Coagulation Cascade.

Stroke has a high incidence rate and often leads to permanent disability, particularly if it is not treated promptly. However, no blood biomarkers for early diagnosis are available to date. Therefore, we sought to detect stroke-specific blood biomarkers by identifying proteins associated with the underlying coagulation mechanism, which accounts for more than 80% of all stroke cases. Protein profiling was performed using blood samples from 16 healthy controls and 18 patients who suffered a stroke as the discovery set. We identified upregulated proteins (> 1.5-fold change and p value < 0.05) in patients who suffered a stroke relative to the corresponding levels in healthy controls by nano-liquid chromatography-tandem mass spectrometry using data-independent acquisition based on sequential window acquisition of all theoretical mass spectra, which was developed to improve the consistency and accuracy of candidate proteins. Pathway analysis confirmed that the upregulated proteins were mainly involved in blood coagulation. Among these, we selected prothrombin, plasminogen, fibrinogen alpha-chain, and histidine-rich glycoprotein as candidate biomarkers. Multiple reaction monitoring analysis was performed on a validation set of 61 serum samples (31 healthy controls and 30 stroke patients) to assess the diagnostic value of the candidate biomarkers. All four proteins showed higher expression levels in patients with stroke than in healthy controls. The areas under the receiver operating characteristic curve were greater than 0.9, confirming their clinical value. These four blood coagulation proteins may help in diagnosing stroke more accurately and quickly.

Proteomics-Based Identification of Diagnostic Biomarkers Related to Risk Factors and Pathogenesis of Ischemic Stroke.

Ischemic stroke is caused by blood clot formation and consequent vessel blockage. Proteomic approaches provide a cost-effective alternative to current diagnostic methods, including computerized tomography (CT) scans and magnetic resonance imaging (MRI). To identify diagnostic biomarkers associated with ischemic stroke risk factors, we performed individual proteomic analysis of serum taken from 20 healthy controls and 20 ischemic stroke patients. We then performed SWATH analysis, a data-independent method, to assess quantitative changes in protein expression between the two experimental conditions. Our analysis identified several candidate protein biomarkers, 11 of which were validated by multiple reaction monitoring (MRM) analysis as novel diagnostic biomarkers associated with ischemic stroke risk factors. Our study identifies new biomarkers associated with the risk factors and pathogenesis of ischemic stroke which, to the best of our knowledge, were previously unknown. These markers may be effective in not only the diagnosis but also the prevention and management of ischemic stroke.