Lipid remodeling in serum and correlation with stroke in patients with leukoaraiosis

Lipid remodeling in serum and correlation with stroke in patients with leukoaraiosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Lipid remodeling in serum and correlation with stroke in patients with leukoaraiosis Feng Lin, Yige Song, Hongi Cao, Wangting Song, Fengye Liao, Yanping Deng, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4422937/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Despite the identification of many hub lipids for stroke, the underlying pathophysiology of stroke in elderly patients with leukoaraiosis (LA) remains poorly understood, which is important for the administration of antithrombotic therapy for LA patients. This study aims to illuminate the preliminary lipid metabolic process associated with stroke in LA patients (LS). Methods The study cohort consisted of 215 individuals undergoing magnetic resonance imaging(MRI), from which a subset 13 patients with stroke matched with a control group, and 48 LS patients matched with 40 LA patients were selected for further investigation after exclusion. Serum lipidome was profiled by UPLC-TOF. OPLS-DA was used for classification and identifying differential metabolites. Customizing structural equation (CSE) model was applied to assess the pathway weight of novel metabolites in stroke incidence. Linear regression and matrix correlation were used to investigate the relationships between differentiated metabolites and outcomes. Results Using lipid profiling and multivariate statistical analysis, we screened 168 different compounds between LA and LS. Based on the enrichment and Sankey diagram of pathway, 52 lipid molecules were regarded as differential metabolites associated with glycerolipid, glycerophospholipid, and sphingolipid metabolism. After CSE weighted the pathway node molecules, we finally identified 11 key metabolites achieving a prediction, in which DG(14:0/22:4) (OR = 5.33) and Cer(d18:1/24:1) (OR = 21.44) were significant risk factors for LS. All 11 metabolites exhibited correlations with the outcome (LS incidence), with particularly heightened metabolic disruption in the presence of high blood pressure. We conducted linear regression analysis and found changes in FA16:1; O, DG(12:0/17:2) and DG(14:0/22:4) out of 11 metabolites correlated with Fazekas scores between CK and LS group. Similarly, compared with LA group, DG(14:0/22:4) (OR = 5.33, p = 0.02) and Cer(d18:1/24:1) (OR = 21.44, p = 0.068) are risk factors for LS. Especially, Cer(d18:1/24:1) and PI(22:1/20:1) were significantly associated with the LS incidence. Conclusion This study identified 11 metabolites as key metabolites for stroke incidence in LA patients, including subgroups divided by Fazekas scores. This study provides novel insights into lipid metabolic process from LA to LS, in which the lipid disturbance in glycolipids and glycerophospholipids, as well as the regulatory role of Cer(18:1/24:1), which are valuable for further studies of LS. Leukoaraiosis lipidomics plasma metabolic pathway stroke Cer(18:1/24:1) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Stroke is a significant contributor to global morbidity and mortality, primarily arising from atherosclerosis affecting the cervical or proximal intracranial vessels[ 1 – 3 ]. Leukoaraiosis(LA) exhibits a close correlation with ischemic stroke[ 4 ]. Increasing research has elucidated a heightened prevalence of LA in individuals with a history of stroke, with LA identified as a risk factor for both initial and subsequent occurrences of stroke[ 5 – 7 ]. Previous research has established LA as an imaging marker for small vessel disease, potentially serving as a risk factor for stroke and unfavorable clinical outcomes[ 8 , 9 ]. Furthermore, a study has identified the existence of white matter hyperintensities as a robust prognostic indicator for early recurrence after acute ischemic stroke[ 10 ]. Lacunar cerebral infarcts and LA are commonly encountered in patients presenting with transient ischemic attack (TIA) and ischemic stroke. LA shares similar risk factors with cerebral infarction, including factors such as age and hypertension[ 11 ]. A study suggested that antiplatelet therapy might help prevent recurrent strokes in patients with white matter hyperintensities, while the risk of bleeding needs careful consideration[ 12 ]. These studies indicate that the use of antiplatelet and anticoagulant drugs should be carefully considered, and personalized treatment and medication plans may be needed for stroke patients with LA[ 13 – 15 ]. Therefore, screening LA patients at risk of stroke through biomarkers can decrease the incidence of cerebral hemorrhage in LA patients who might use antithrombotic drugs without the risk of stroke. Traylor et al. employed a genetic risk score methodology to evaluate the impact of gene variants associated with LA on the risk of lacunar stroke[ 16 ]. Their study revealed that genetic factors influencing LA were also correlated with the risk of lacunar infarction, while no significant association was observed with other stroke subtypes[ 16 ]. Concurrently, advancements in lipidomics technology have facilitated a deeper understanding of the relationship between lipid metabolism and these pathologies[ 17 ]. Some studies have suggested that dyslipidemia may promote the occurrence and development of stroke and LA by affecting multiple biological processes. Dyslipidemia may lead to stroke through mechanisms such as atherosclerosis, thrombosis, inflammation, and cerebrovascular damage[ 18 , 19 ]. Meanwhile, some studies have found a close relationship between dyslipidemia and cerebral LA. A lipidomics-based study revealed that certain lipid substances were associated with the progression of white matter hyperintensities[ 20 ]. These research results suggest that dyslipidemia may be a common pathological mechanism for stroke and LA[ 1 , 21 – 23 ]. The regulation of lipid metabolism may be an important strategy for the prevention and treatment of these diseases. Our previous studies have revealed significant potential in the application of lipidomics and metabolomics for in-depth investigations into the mechanisms of cerebrovascular diseases and the identification of biomarkers. Through lipidomics, we uncovered a potential association between carotid artery stenosis induced by radiotherapy and specific triglycerides[ 24 ]. Additionally, metabolomic analysis of cerebral thrombi highlighted metabolic differences in thrombi of diverse origins[ 25 ]. Another study focuses on the metabolic profile during the treatment process of LTC (Longxue Tongluo Capsule). It was found that LTC benefits stroke rats by regulating glycerophospholipid and sphingolipid metabolism[ 26 ]. In this study, by utilizing lipidomics techniques to identify early key metabolites of stroke in patients with LA, we aim to enhance our understanding of the underlying lipid metabolic processes involved in stroke with LA. Additionally, we seek to potentially develop early warning signs for stroke in patients with LA, aiming to improve clinical guidance for their management. Materials and methods Patients and Study Design A total of 499 patients were enrolled into the database for patients with leukoaraiosis(LA) and stroke in LA patients. We selected individuals admitted to Sanming First Hospital Affiliated with Fujian Medical University in China from 2021 to 2022. Patients with severe heart, lung, liver, or kidney diseases; central nervous system tumors; and acute cerebral hemorrhage were specifically excluded. Eligible participants for the study were required to demonstrate no, mild, or severe subcortical white-matter changes on cranial magnetic resonance imaging(MRI), and to have no hyperlipidemia or any other conditions that could potentially influence lipid metabolism. To ensure the accuracy of diagnosis, specific inclusion and exclusion criteria were established based on our laboratory's redefinition and classification scheme for LA and stroke (Fig. 1 ). After applying exclusion criteria, a total of 215 consecutive neurology outpatients and inpatients were ultimately included in the study, and underwent clinical brain MRI. Following propensity matching for sex and age, 88 subjects were recruited and divided into two distinct groups: the LA group (LA, n = 40) and the stroke in patients with LA group (LS, n = 48). Additionally, the study encompassed a control cohort comprising 100 individuals. After matching for sex, age, systolic blood pressure (SBP), diastolic blood pressure (DBP), glucose levels (GLU), and hemoglobin A1c (HBA1c) a control group with 13 individuals (Control group) and a group comprising 13 patients with Stroke (Stroke group) were established (Table S1 ). This clinical research trial was approved by the Fujian Sanming Hospital ethical committee and registered, and all participants provided written informed consent. Our study was approved by the Ethics Committee of the Sanming First Hospital Affiliated with Fujian Medical University (Ethics Approval Number: 2022-44). Serum Lipidome Extraction Blood samples from patients initially hospitalized for LA, LS or stroke were collected in centrifuge tubes. The fresh blood samples were transported to the laboratory for 20 minutes by cold chain (4°C). The serum was isolated by centrifugation at 9000 g at 4°C for 10 minutes. The serum samples were stored in -80°C freezer. The lipids from serum samples were extracted with isopropanol (IPA). The procedure includes: (1) 200 µL of serum extracted with 600 µL of precooled IPA, (2) vortexed for 1 minute, (3) incubated at room temperature for 10 minutes, (4) storing the extraction mixture overnight at -20°C, (5) centrifuged samples at 14000 rpm for 20 minutes, (6) transferred the supernatants into a new centrifuge tube and diluted to 1:10 with IPA/ACN/H2O (2.5:1:1, v/ v/ v). The extracted samples were stored at -80°C before the LC/MS analysis. In addition, 50 µL was extracted from each serum sample to prepare a quality control (QC) sample. LC-QTOF for Lipidomics Analysis The samples were analyzed by ACQUITYUPLC (Waters) and XEVO-G2XS quadrupole time-of-flight (QTOF) mass spectrometry (Waters) with ESI. Lipid separation was performed on an Acquity UPLC charged surface hybrid C18 column (2.1 × 100 mm, 1.7 µm, Waters), and the gradient mobile phase was composed of 10 mM ammonium formate and 0.1% formic acid in an acetonitrile/aqueous solution (A, 60:40, v/v) and 10 mM ammonium formate and 0.1% formic acid in an isopropanol/acetonitrile solution (B, 90:10, v/v). A 20-minute accelerated elution curve was employed with the flow rate of the mobile phase 0.4 mL/min. The injected 1 µL sample was initially eluted with 40% B, graded linearly to 43% B in 2 minutes, and then increased to 50% B in 0.1 minutes. Over the next 9.9 minutes, the gradient was further increased to 54% B and then to 70% in 0.1 minutes. In the last part of the gradient, the amount of B was increased to 99% in 5.9 minutes. Finally, solution B returned to 40% in 0.1 minutes, and the column was balanced for 1.9 minutes before the next injection. The lipids in both positive and negative modes were detected by a Xevo-G2XS QTOF mass spectrometer, which was operated in MSE mode from m/z 50–1200, and the collection time for each scan was 0.2 seconds. The source temperature was set to 120 ℃. The desolvation temperature was 550 ℃, the gas flow rate was 1000 L/h, and nitrogen was used as the flowing gas. The capillary voltage was set to 2.0 kV (+)/1.5 kV (-), and the cone voltage was 20 V. Leucine encephalin (molecular weight = 555.62 × 200 pg/µL, 1:1 acetonitrile: water) was used as the locking mass for accurate mass determination and corrected with 0.5 mm sodium formate solution. The samples were randomly sorted, and 5 quality control samples were initially injected to adjust the conditions of the column. A QC sample was injected in every 10 samples for analysis to investigate the repeatability of the data. Data Processing and Statistical Analysis Statistical analysis was conducted on clinical data; gender variables were analyzed using the chi-square test, and an independent t-test was used for age variables. Metabolic changes in plasma extract were analyzed by using a UPLC-Q-TOF MS system and the equipped software Progenesis QI (Waters). The original data was pre-processed and adjusted by the LOESS linear model. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was first used for classification discrimination. The reliability of the model was verified by cross-validation and displacement test. The parameters R2 and Q2 were used to evaluate the interpretability and predictability of the model, respectively. By p -value ( p < 0.05), Variable Importance Projection (VIP ≥ 1) and False Discovery Rate (FDR < 0.05) a standard potential difference marker is selected. The best truncation value was determined by using the Youden index. All statistical analyses, as well as Customizing Structural Equation (CSE) model, Linear regression model, Logical regression model, and N-fold cross-validation were performed using R version 4.3.2, and p < 0.05 was considered statistically significant. Results Clinical Characteristics of the Subjects We enrolled 499 patients into database of SanMing First Hospital from 2020 to 2022. Of these, 284 patients were excluded because they did not undergo magnetic resonance imaging (MRI). Among the remaining 215, 99 were further excluded due to medical conditions (Fig. 1 ). After propensity matching of sex and age, 40 patients with leukoaraiosis (LA) and 48 patients with stroke in leukoaraiosis (LS) remained for further analysis. The demographics and clinical characteristics of these two groups were summarized in Table 1 . Compared to the LA group, the LS group exhibited significant increases in SBP, DBP, GLU, and HBA1c, along with a significant decrease in Fazekas scores. To explore the specificity of key metabolites between LA and LS groups, we also included 13 patients who had suffered stroke (Stroke group) and 13 control participants (Control group). These groups were propensity matched for sex, age, BMI, SBP, DBP, GLU and HBA1c. The demographics and clinical characteristics of the Control and Stroke groups were summarized in Supplementary Table 1. Table 1 Baseline characteristics of the participants. Name Levels LA (N = 40) LS (N = 48) p Sex Female 22 (55%) 18 (37.5%) 0.154 Male 18 (45%) 30 (62.5%) Age Mean ± SD 68.6 ± 11.6 70.3 ± 11.0 0.487 BMI Mean ± SD 24.4 ± 3.7 23.3 ± 2.9 0.115 SBP Mean ± SD 129.4 ± 16.0 146.8 ± 27.0 < .001*** DBP Mean ± SD 75.6 ± 12.7 87.0 ± 15.2 < .001*** TG Mean ± SD 1.5 ± 0.9 1.5 ± 0.9 0.802 CHO Mean ± SD 4.1 ± 0.9 4.4 ± 1.2 0.213 HDL Mean ± SD 1.2 ± 0.3 1.1 ± 0.3 0.236 LDL Mean ± SD 2.2 ± 0.8 2.5 ± 1.0 0.109 GLU Mean ± SD 4.9 ± 0.9 6.3 ± 2.5 0.001** Fazekas Mean ± SD 4.0 ± 1.4 3.2 ± 1.4 0.019* HCY Mean ± SD 12.5 ± 6.9 12.9 ± 5.3 0.734 HBA1c Mean ± SD 6.2 ± 0.8 7.0 ± 2.2 0.029* * p < .05; ** p < .01; *** p < .001. Comparative Lipid Profiling between LA and LS Groups To identify key metabolite of stroke in patients with LA, we employed lipidomic profiling to compared serum lipid molecules between LA and LS patients, as well as those in Control and Stroke groups. Using OPLS-DA, the score plots distinctly displayed the separation between the Control and Stroke groups, as well as between the LA and LS groups (Fig. 2 A and D). Additionally, the volcano plot revealed metabolites with significant differences ( p 1) between the two groups (Fig. 2 C and F). Between Control and Stroke groups, among the 2647 metabolites, 80 different metabolites were selected based on criteria (Supplementary Table 2). Between LA and LS groups, among the 2649 metabolites, 168 different metabolites were selected based on criteria (Supplementary Table 3). The pathway of 80 and 168 different metabolites were documented using KEGG (Supplementary Tables 4 and 5). The enrichment analysis of the 80 different metabolites between the Control and Stroke groups was mainly in the Adipocytokine signaling pathway, Glycerophospholipid metabolism, Ether lipid metabolism, and Glycerolipid metabolism (Table 2 ). The pathways of 168 different metabolites between LA and LS were primarily enriched in Fat digestion and absorption, Glycerolipid metabolism, Glycerophospholipid metabolism Vitamin digestion and absorption, and Ether lipid metabolism (Fig. 2 G and Table 2 ). Using Venny analysis of differential substances between the Control vs. Stroke and LA vs. LS groups, we identified 157 uniquely differential compounds between LA and LS groups. Notably, two compounds Cer(d18:0/24:1(15Z)) and Cer(d18:1/24:1(15Z)), exhibited an increasing trend between the Control and Stroke group, while displaying a decreasing trend between the LA and LS groups (Fig. 2 J). Among these 157 compounds, a Sankey analysis was conducted to further focus on key metabolites in the important pathways. The diagram displayed a concentrated distribution of 52 lipid metabolites to the enriched pathways, with triglyceride and monoglyceride showing a pronounced tendency to flow towards Glycerolipid metabolism, while phosphatidylinositol, phosphatidylethanolamine, and phosphatidylcholine predominantly channeled into Glycerophospholipid metabolism, and ceramide followed a distinct trajectory along the Sphingolipid metabolism (Fig. 2 H). These 52 lipid molecules have been confirmed as candidate metabolites associated with stroke in patients with LA (Table 3 ). Table 2 KEGG pathway enrichment Pathway Name Match Status p FDR CK vs DIS Biosynthesis of unsaturated fatty acids 1/74 0.0299 0.0299 Glycerolipid metabolism 2/38 0.0003 0.0009 Phosphonate and phosphinate metabolism 1/56 0.0177 0.0191 Glycerophospholipid metabolism 3/56 0.0000 0.0003 Choline metabolism in cancer 2/11 0.0000 0.0001 Sphingolipid metabolism 1/35 0.0072 0.0091 Fatty acid elongation 1/40 0.0093 0.0108 LA vs LS Glycerolipid metabolism 2/38 0.0010 0.0025 Glycerophospholipid metabolism 3/56 0.0002 0.0007 Fatty acid biosynthesis 1/58 0.0389 0.0423 Sphingolipid metabolism 1/35 0.0151 0.0191 Cholesterol metabolism 2/10 0.0000 0.0002 Adipocytokine signaling pathway 2/7 0.0000 0.0001 Fatty acid elongation 1/40 0.0195 0.0239 Table 3 Identified selected differentiating lipids between LA and LS LA LS FC p Glycerolipid metabolism DG O-38:8 1 ± 0.55 1.28 ± 0.7 1.28 0.02 DG O-40:8 0.78 ± 0.43 1.17 ± 0.58 1.49 0 DG O-36:4 0.88 ± 0.45 1.11 ± 0.52 1.27 0.01 DG(12:0/17:2(9Z,12Z)/0:0) 0.97 ± 0.54 0.69 ± 0.51 0.71 0.01 DG(14:0/17:2(9Z,12Z)/0:0) 0.89 ± 0.99 0.53 ± 0.92 0.59 0.04 DG(14:0/22:4(7Z,10Z,13Z,16Z)/0:0) 0.93 ± 0.56 1.29 ± 0.81 1.4 0.01 DG(16:0/18:3(9Z,12Z,15Z)/0:0) 1.08 ± 1.11 0.72 ± 0.88 0.66 0.05 DG(18:2(9Z,12Z)/18:2(9Z,12Z)/0:0) 0.97 ± 0.62 1.37 ± 0.87 1.41 0.01 DG(19:1(9Z)/20:1(11Z)/0:0) 1.22 ± 1.25 0.64 ± 0.87 0.52 0.01 DG(19:1(9Z)/22:3(10Z,13Z,16Z)/0:0) 1.31 ± 1.25 0.87 ± 1.03 0.66 0.04 DG(P-14:0/18:1(9Z)) 0.95 ± 0.97 0.59 ± 0.77 0.62 0.03 MG(0:0/18:1(9Z)/0:0) 0.89 ± 0.33 1.14 ± 0.48 1.28 0 TG(14:1(9Z)/18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) 1.17 ± 0.53 0.93 ± 0.59 0.79 0.02 TG(15:1(9Z)/16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) 0.84 ± 0.3 1.02 ± 0.41 1.21 0.01 TG(18:3(9Z,12Z,15Z)/20:1(11Z)/22:1(11Z)) 1.54 ± 1.33 0.98 ± 1.3 0.64 0.03 Glycerophospholipid metabolism PE dO-40:0 1.22 ± 0.96 0.76 ± 0.89 0.63 0.01 PC(18:1(11Z)/0:0) 1.54 ± 0.71 1.21 ± 0.64 0.79 0.01 PC(18:3(6Z,9Z,12Z)/0:0) 1.26 ± 0.62 0.9 ± 0.39 0.72 0 PC(18:3(9Z,12Z,15Z)/0:0) 1.35 ± 0.54 0.97 ± 0.53 0.72 0 PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/18:1(9Z)) 1.3 ± 0.69 0.96 ± 0.52 0.74 0.01 PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) 1.25 ± 0.54 0.96 ± 0.47 0.77 0.01 PC(O-18:0/1:0) 1.24 ± 0.36 0.96 ± 0.32 0.77 0 PC(P-16:0/0:0) 1.04 ± 0.34 0.83 ± 0.28 0.8 0 PC(P-16:0/18:4(6Z,9Z,12Z,15Z)) 1.16 ± 0.9 0.78 ± 0.84 0.67 0.02 PC(P-18:1(9Z)/22:2(13Z,16Z)) 0.74 ± 0.28 1.02 ± 0.59 1.39 0 PE(0:0/20:1(11Z)) 1.24 ± 0.4 0.96 ± 0.41 0.77 0 PE(17:0/18:0) 1.5 ± 1.39 0.87 ± 1.11 0.58 0.01 PE(20:0/17:2(9Z,12Z)) 1.33 ± 1.02 0.96 ± 1.06 0.73 0.05 PE(20:2(11Z,14Z)/17:0) 1.29 ± 0.92 0.91 ± 0.99 0.71 0.04 PE(20:2(11Z,14Z)/18:3(9Z,12Z,15Z)) 1.11 ± 0.64 0.66 ± 0.56 0.59 0 PE(20:4(5Z,8Z,11Z,14Z)/18:0) 0.93 ± 0.37 1.2 ± 0.51 1.29 0 PE(22:2(13Z,16Z)/15:0) 1.29 ± 0.93 0.9 ± 0.97 0.7 0.03 PE(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/16:0) 0.99 ± 0.52 1.2 ± 0.46 1.21 0.03 PE(O-18:0/22:4(7Z,10Z,13Z,16Z)) 0.81 ± 0.25 1.04 ± 0.45 1.28 0 PE(O-20:0/22:0) 1.07 ± 0.5 0.84 ± 0.54 0.78 0.02 PE(P-18:0/17:2(9Z,12Z)) 1.31 ± 1.12 0.81 ± 1.02 0.62 0.02 PE(P-18:0/22:4(7Z,10Z,13Z,16Z)) 0.83 ± 0.36 1.06 ± 0.48 1.29 0.01 PE(P-20:0/17:0) 1.11 ± 1.06 0.68 ± 0.92 0.62 0.03 PE(P-20:0/19:0) 1.03 ± 0.29 1.24 ± 0.47 1.2 0.01 PE(P-20:0/20:2(11Z,14Z)) 1.24 ± 0.66 0.91 ± 0.55 0.73 0.01 PE(P-20:0/21:0) 0.96 ± 0.34 1.22 ± 0.64 1.28 0.01 PI(20:2(11Z,14Z)/16:1(9Z)) 1.19 ± 0.61 0.88 ± 0.44 0.74 0.01 PI(22:1(11Z)/21:0) 1.32 ± 0.86 0.76 ± 0.6 0.57 0 PI(P-20:0/18:3(9Z,12Z,15Z)) 0.75 ± 0.37 1.09 ± 0.57 1.45 0 Sphingolipid metabolism 1-O-behenoyl-Cer(d18:1/18:0) 0.78 ± 0.34 1.01 ± 0.41 1.3 0 Cer(d18:0/24:1(15Z)) 0.84 ± 0.35 1.04 ± 0.35 1.24 0 Cer(d18:0/26:1(17Z)) 0.69 ± 0.52 0.89 ± 0.5 1.3 0.03 Cer(d18:0/h26:0) 1.03 ± 0.73 0.72 ± 0.83 0.7 0.03 Cer(d18:1/24:1(15Z)) 0.79 ± 0.27 1.03 ± 0.31 1.31 0 Cer(d18:1/25:0) 0.77 ± 0.26 0.96 ± 0.37 1.25 0 Cer(m18:0/22:0) 0.95 ± 0.42 1.17 ± 0.48 1.24 0.01 Cer(t18:0)/24:0(2OH[R])) 1.27 ± 1.06 0.8 ± 0.89 0.63 0.01 Cer(t20:0/22:0) 1.04 ± 0.71 0.71 ± 0.74 0.69 0.02 Cer(t20:0/26:0) 1.09 ± 0.93 0.75 ± 0.91 0.69 0.05 CerP(d18:1/22:0) 1.23 ± 0.52 1.55 ± 0.61 1.26 0.01 Fatty acid elongation FA 16:1;O 0.98 ± 0.81 0.51 ± 0.49 0.52 0 Classification of Altered Lipids between LA and LS Groups To further investigate the metabolism network and series of alternations in metabolites between the LA and LS groups, we conducted a classification analysis of lipid skeleton and fatty acid chains. Compared to the LA group, there was a significant increase in phosphatidylinositol, phosphatidylethanolamine, and monoglyceride, while fatty acid, phosphatidylcholine, and triglyceride exhibited a significant decrease (Fig. 3 A). Additionally, we analyzed the alternations of ω-3, -6, and − 9 fatty acid chains, with significant changes noted in the LS group compared to the LA group. DG 18:2, DG 22:4, PE 20:4, and PE 22:4 within the ω-6 category exhibited significant decreases. Monoglyceride and ceramide showed alterations exclusively within the ω-9 fatty acid chains, notably characterized by changes in MG 18:1, Cer24:1, and 26:1. In comparison to ω-3 and − 9, variations in glycerolipid (TG, DG, MG) within the ω-6 fatty acid chains were not significant, particularly fatty acid chains from 18:3 to 22:2 (Fig. 3 B). In the heatmap of altered lipid molecules, we observed a clustering of diglycerid, triglyceride, phosphatidylethanolamine and ceramide in different classifications between the two groups, which contributed to distinguishing the LS from LA group (Fig. 3 C). To further investigate the roles of altered metabolites, partial least squares path modeling (PLS path modeling) was conducted, providing a deeper understanding of the different lipid classifications impact on and association with outcomes (LA or LS group). With a modeling goodness of fit (GOF) of 0.6018, the results showed that ω-6 (r = |-0.5817|, p < 0.001) and glycerophospholipid metabolism (r =|-0.565|, p < 0.001) played significant roles in impacting outcomes related to fatty acid chains and lipid skeletons. It is noteworthy that sphingolipid metabolism (r = 0.4428, p < 0.001) had a positive impact on outcomes in terms of fatty acid chains (C24:1, C26:1) and lipid skeleton (ceramide) (Fig. 3 D). Key Metabolites Identified Based on Pathways Nodes Based on the enriched pathway nodes of Fatty acid elongation, Glycerophospholipid, Glycerolipid, and Sphingolipid metabolism, a series of lipid molecules were selected with alterations consistent from pathway downstream to upstream (Fig. 4 A). Furthermore, through PLS model loading analysis, we identified compounds with the highest loading value in each pathway node, indicating more important contribution to the outcomes. These metabolites included DG(12:0/17:2); DG(14:0/22:4); MG(0:0/18:1); TG(18:3/20:1/22:1); PC(18:3/0:0); PC(22:6/22:6); PE(0:0/20:1); PE(22:2/15:0); PI(22:1/21:0); Cer(d18:1/24:1); and FA 16:1;O (Fig. 4 A). Among these 11 metabolites, there were significant increases in MG(0:0/18:1(9Z)), DG(14:0/22:4/0:0), and Cer(d18:1/24:1) in the LS group, while other lipid compounds exhibited a substantial decrease (Fig. 4 B-K). Relationships of 11 Key Metabolites with Outcomes The relationships of 11 metabolites were conducted with the outcome were analyzed. The results showed that PE(22:2/15:0) and TG(18:2/20:1/22:1) exhibited no significant correlation with outcomes, while the other metabolites presented significant correlations. Particularly, both Cer(d18:1/24:1) and PI(22:1/20:1) exhibited high correlation with the outcomes with p-values less than 0.001 (Fig. 5 A). Blood pressure has been identified as an independent risk factor for both LA and LS[ 27 ]. To investigate the relationships between key metabolites and outcomes associated with blood pressure, we conducted the correlation analysis of 11 key metabolites with outcomes in normal or abnormal blood pressure conditions. Cer(d18:1/24:1) showed a positive correlation with outcomes in both normal and abnormal blood pressure conditions, although the correlation weakened under abnormal blood pressure. MG(0:0/18:1), PC(18:3/0:0), PC(22:6/22:6), PE(0:0/20:1), PI(22:1/21:0), and FA16:1,O exhibited no significant correlation with outcomes in the presence of normal blood pressure. However, in cases of abnormal blood pressure, with the exception of MG(0:0/18:1), all these factors demonstrated significant negative correlations. Simultaneously, triglycerides and monoglycerides displayed different correlations with phosphatidylcholine, phosphatidylethanolamine, and phosphatidylinositol in abnormal blood pressure compared with those in normal blood pressure (Fig. 5 B). To better elucidate the underlying relationship between the LA and LS groups, multivariable regression analysis of the 11 key metabolites was conducted. Under the criterion of a p -value less than 0.1, four components were marked: DG(14:0/22:4), PC(18:3/0:0), Cer(d18:1/24:1), and FA 16:1;O. Among these, DG(14:0/22:4) (OR = 5.33, p = 0.02), and Cer(d18:1/24:1) (OR = 21.44, p = 0.068) were considered risk factors (Fig. 5 C). Relationship of 11 Key Metabolites with Fazekas Score between LA and LS Groups We employed linear regression analysis to assess the relationship between 11 compounds and Fazekas scores. We observed that in the CK group and LS group, the p -values for FA16:1, O, DG(12:0/17:2), and DG(14:0/22:4) were less than 0.1, indicating a significant negative correlation with changes in MRI (Fig. 5 D-F). Subsequently, we subdivided the patients into two groups by Fazekas < 4 and Fazekas ≥ 4. The results of the CSE analysis revealed a positive impact of DG(14:0/22:4), MG(0:0/18:1), and PI(22:1/21:0) on the outcomes in both subgroups (Fig. 6 A-B). Furthermore, significant correlations were found between MG(0:0/18:1), PC(18:3/0:0), PC(22:6/22:6), PE(0:0/20:1), PI(22:1/21:0), Cer(d18:1/24:1) and the incidence of LS when Fazekas was less than 4. However, these correlations were no longer significant when Fazekas was 4 or more (Fig. 6 C). Based on our research findings, We analyzed 11 key metabolites, and indicated changed pathways in glycerolipid, glycerophospholipid, fatty acid elongation, and sphingolipid associated with stroke in patients with LA (Fig. 7 ). Discussion This study employed a UPLC-MS/MS platform for lipidomic analysis to identify features associated with the occurrence of stroke in patients with LA. Previous investigations have indicated that severe leukoaraiosis (sLA) could serve as a significant adverse factor affecting early neurological recovery following mechanical thrombectomy (MT), potentially diminishing favorable outcomes in acute ischemic stroke (AIS) patients undergoing MT[ 28 – 30 ]. The recent BEST trial demonstrated that moderate to severe leukoaraiosis (LA) was linked to poorer outcomes among patients receiving endovascular treatment[ 31 ]. Significant alterations in lipids between LA and LS included glycerolipids, glycerophospholipids, sphingolipids, and fatty acids (Fig. 3 A). Through structural equation modeling and pathway analysis, we selected 11 key metabolites that exhibited excellent performance in distinguishing between LS and LA (AUC = 0.840) (Fig. S1 B). Additionally, these key metabolites also demonstrated efficacy within subgroups (Table S6 and Fig. S1 C-D). White matter damage in the brain is often linked to the aging process and chronic conditions such as hypertension, as well as various diseases[ 32 ]. The impact of white matter lesions becomes particularly significant in patients diagnosed with LA, who faced an elevated risk of stroke[ 33 ]. Kongbunkiat et al. conducted a meta-analysis involving 15 studies with a total of 5,967 participants[ 34 ]. They reported a relative risk (RR) of 1.65 (95% confidence interval [CI] 1.26–2.16, p = 0.001) for the occurrence of symptomatic intracerebral hemorrhage (SICH) in patients with LA, translating to an absolute risk (AR) increase of 2.5% compared to those without LA[ 34 ]. This consistent association indicates that the presence and severity of LA are linked to a heightened risk of SICH following thrombolysis for acute ischemic stroke (AIS). The elevated risk of intracerebral hemorrhage (ICH) post-thrombolysis in acute cerebral infarction patients with LA may be attributed to factors such as vascular endothelial injury, increased platelet activation, and hypercoagulability[ 34 ]. Additionally, in individuals undergoing anticoagulant treatment, the presence of LA was correlated with an augmented likelihood of recurrent stroke and intracranial hemorrhage[ 35 ]. Nevertheless, conventional risk factors exhibit limitations in fully predicting LA in patients who have experienced a stroke. Lipidomics, an analytical approach focusing on the detection of lipid metabolites at the systemic level, holds promise in uncovering potential biomarkers, identifying lipid metabolic pathways, and constructing networks of lipid metabolism[ 36 ]. A systematic review comprehensively outlined potential metabolic biomarkers and pathways associated with ischemic stroke, underscoring the consistent identification of several metabolites with biomarker potential[ 37 ]. Additionally, metabolomics analysis using machine learning techniques was performed on plasma samples from ischemic stroke patients and controls, revealing three key differential lipid metabolites[ 38 ]. Furthermore, our previous research has identified lipid biomarkers associated with cardioembolic and atherosclerotic stroke[ 26 ]. Pathway analysis indicated that glycerophospholipid metabolism actively participate in both LA and LS groups (Fig. 2 H). Concurrently, we observed a significant decrease in glycerophospholipids in the LS group (Fig. 3 ). Glycerophospholipids are an important part of the neuronal cell membrane structure and are involved in cell recognition and signal transduction. Therefore, the degradation of glycerophospholipids, which produces polyunsaturated fatty acids such as docosahexaenoicacid and arachidonic acid, may be a sign of brain damage[ 39 , 40 ]. A previous study has indicated that glycerophospholipid metabolism was significantly disrupted in stroke rats. These findings suggest that the decreased levels of glycerophospholipids in the hippocampus may play a role in the pathophysiology of stroke[ 41 ]. Our study confirmed that during the progression of cerebral white matter stroke, glycerophospholipid metabolism was abnormally inhibited (Fig. 3 ). Through pathway analysis and SCE model, we selected 11 key metabolites between LA and LS groups. There were DG(12:0/17:2), DG(14:0/22:4), MG(0:0/18:1), TG(18:3/20:1/22:1), PC(18:3/0:0), PC(22:6/22:6), PE(0:0/20:1), PE(22:2/15:0), PI(22:1/21:0), Cer(d18:1/24:1), FA16:1 O (Fig. 4 ). We identified these key metabolites to be concentrated in monoglyceride, diglyceride, triglyceride, phosphatidylinositol, phosphatidylethanolamine, phosphatidylcholine, ceramide, and fatty acids. The primary role of triglycerides is for the storage and provision of energy[ 42 ]. Triglycerides consist of saturated fatty acids (SFAs), monounsaturated fatty acids (MFAs), and polyunsaturated fatty acids (PFAs)[ 43 ]. The decreased of TG(18:3/20:1/22:1) observed in LS compared to LA may be associated with an activated inflammatory response (Fig. 4 D)[ 44 ]. Our findings suggest that the perturbed equilibrium in triglyceride metabolism could be linked to an inflammatory response. The demyelinating effects of LA impair the network connectivity within the brain to differing extents, influencing specific neurotransmitter conduction pathways in the white matter (WM)[ 45 ]. The preservation of normal cognitive functions is intricately tied to the integrity of the cholinergic pathway in the brain, which forms intricate connections with regions such as the prefrontal cortex, ventral striatum, and hippocampus/amygdala[ 45 ]. Our investigation results also indicated a noteworthy association between the dysregulation of diglyceride, lysophosphatidylcholine (LPC), and phosphatidylcholine, and an increased susceptibility to stroke (Fig. 4 . C and J-K). We also found that higher levels of Cer(d18:2/24:1) were associated with LS (Fig. E4). Neuroinflammatory processes, characteristic of demyelinating diseases like multiple sclerosis (MS), contribute to myelin sheath damage[ 46 ]. Previous investigations have demonstrated heightened cerebrospinal fluid (CSF) concentrations of sphingomyelins and ceramides in individuals with multiple sclerosis and other demyelinating conditions[ 47 , 48 ]. Based on biological significance, we conducted multivariable regression analysis and identified DG(14:0/22:4) (OR = 5.33, p = 0.02) and Cer(d18:1/24:1) (OR = 21.44, p = 0.068) as risk factors for stroke with LA. Concurrently, linear regression analysis revealed a significant negative correlation between FA16:1, O, DG(12:0/17:2), and DG(14:0/22:4) and Fazekas scores (Fig. 5 C-F). We also conducted a 7-fold cross-validation, revealing strong predictive performance for these combinations (Fig. S1 A-B). We ultimately confirmed the involvement of glycerolipid metabolism, glycerophospholipid metabolism, and sphingolipid metabolism in the pathway of associated with stroke in patients with LA (Fig. 7 ). Potential mechanisms by which LA exacerbates the impact of ischemic stroke encompass hypoxia, vascular endothelial injury, disruption of the blood-brain barrier, and impairment of brain connectivity[ 4 ]. Hypoxia-ischemia is believed to contribute to the etiology of neurotrophic acid (LA)[ 49 , 50 ]. Brain hypoxia primarily arises from small vessel disease, particularly affecting arteries like the thalamostriate and other perforating arteries[ 51 , 52 ]. Neuropathological investigations have revealed that LA, as observed on CT or MRI scans, correlates with demyelination, astrocytic gliosis, arteriolosclerosis, dilated perivascular spaces, and frequently coexists with lacunar infarcts[ 53 ]. Our previous research found that phosphatidylcholine (PC) significantly decreases under hypoxic conditions[ 54 ], which is consistent with the results of this study(Fig. 7 ). Some studies discovered significant blood-brain barrier leakage in the normal white matter of stroke patients, which became more severe the closer they were to white matter hyperintensity (WMHs), while blood-brain barrier dysfunction increased with increasing WMH load[ 55 – 57 ]. Based on the Fazekas score, patients in the LA and LS groups were divided into two subgroups (Table S4). Through logistic regression model analysis, a specific combination (PC(18:3/0:0), Cer(d18:1/24:1), FA 16:1;O, and clinical information) was found to have high predictive value for adverse cardiovascular events under conditions of lower Fazekas scores(Fig. S1 C-D). Some studies have confirmed the significant role of plasma Cer(24:1) concentration in predicting referrals for coronary angiography in different populations[ 58 – 61 ]. These results indicated a notable association between elevated plasma sphingolipid concentrations and adverse cardiovascular events[ 59 – 61 ]. Harshfield's investigation had revealed a positive correlation between higher levels of SM(d18:2/24:1) and measures such as fissure count, WMH volume, and cognitive abilities[ 62 ]. Our study further substantiated a significant elevation in Cer(d18:1/24:1) within the LS group, regardless of whether the Fazekas score is less than 4 or more than or equal to 4. LA and patients with acute cerebral infarction share common risk factors, including advanced age, hypertension, diabetes, smoking, and alcohol consumption[ 4 ]. With the exception of age, which cannot be controlled, targeting the aforementioned risk factors is of paramount importance for both primary and secondary prevention strategies. Our study, through an in-depth exploration of metabolic biological processes, provides a theoretical basis and assistance for the future prevention and treatment from LA to LS. However, this study has some limitations. The current validation is limited to self-validation, and further studies are needed to validate and optimize the discovered biomarkers and evaluate their effectiveness in terms of clinical application. Therefore, in future studies, we should select purer samples and expand the sample size to further investigate the biomarkers of these findings and evaluate their clinical applicability in screening and diagnosing stroke in individuals and patients with LA. This will contribute to the improvement of screening and diagnostic methods for individuals with LA and stroke in patients with LA, enabling clinicians to develop more personalized treatment strategies. Conclusion This study identified 11 novel metabolites as potential key metabolites for stroke incidence in patients with leukoaraiosis, spanning various Fazekas scores subgroups. The research contributes novel insights into potential distinctive mechanisms from leukoaraiosis to leukoaraiosis with stroke, emphasizing disruptions in glycolipids and glycerophospholipids, along with the regulatory role of Cer(18:1/24:1). These findings provide a comprehensive foundation for further investigations into the mechanistic intricacies of leukoaraiosis with stroke. Declarations Acknowledgments We would like to express our sincere gratitude to Dr. Chen from Bao Feng Key Laboratory of Genetics and Metabolism for his valuable guidance and insightful discussions throughout this study. We are thankful to the technical staff at Bao Feng Key Laboratory of Genetics and Metabolism for their assistance with data collection and analysis. Funding This work was supported by the Fujian Sanming Science and Technological innovation Project: Screening of novel lipid markers for cerebral infarction in patients with leukoaraiosis and construction of prediction model [grant numbers NO: 2023-S-70], and Fujian Provincial Science and Technology Project: Nervonic acid intervenes in the Process of Alzheimer's disease by regulating the intestinal flora and metabolism [grant number 2020J011271]. Data availability statement The article and supplementary material contain the original contributions discussed in this study; further questions should be addressed to the corresponding author. Ethics approval and consent to participate This study was reviewed and approved by the Ethics Committee of the Sanming First Hospital Affiliated with Fujian Medical University (Ethics Approval Number: 2022-44). The participants provided written informed consent to participate in this study. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Consent for publication Before participating in the study, all participants signed up with informed permission. References Adams HP Jr., Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, Marsh EE 3. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4422937","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304442112,"identity":"def463b8-b52b-4324-b56c-a76da28f35a7","order_by":0,"name":"Feng Lin","email":"","orcid":"","institution":"Sanming First Hospital Affiliated to Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Lin","suffix":""},{"id":304442116,"identity":"435be483-364a-4f12-b231-819e39f91dc9","order_by":1,"name":"Yige Song","email":"","orcid":"","institution":"Bao Feng Key Laboratory of Genetics and Metabolism","correspondingAuthor":false,"prefix":"","firstName":"Yige","middleName":"","lastName":"Song","suffix":""},{"id":304442118,"identity":"23de224b-5d2c-41e4-b047-3bb4b3c1787a","order_by":2,"name":"Hongi Cao","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongi","middleName":"","lastName":"Cao","suffix":""},{"id":304442119,"identity":"fcddbcc2-8798-4c74-9045-7b472e55aa55","order_by":3,"name":"Wangting Song","email":"","orcid":"","institution":"Bao Feng Key Laboratory of Genetics and Metabolism","correspondingAuthor":false,"prefix":"","firstName":"Wangting","middleName":"","lastName":"Song","suffix":""},{"id":304442121,"identity":"5377ebc0-39e7-41a4-9e65-8cfc942f6772","order_by":4,"name":"Fengye Liao","email":"","orcid":"","institution":"Sanming First Hospital Affiliated to Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fengye","middleName":"","lastName":"Liao","suffix":""},{"id":304442123,"identity":"4839e1dc-9c15-40bb-b25b-5396af7baf8d","order_by":5,"name":"Yanping Deng","email":"","orcid":"","institution":"Sanming First Hospital Affiliated to Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanping","middleName":"","lastName":"Deng","suffix":""},{"id":304442124,"identity":"8b9a681c-4558-4dce-95c7-0536267c8391","order_by":6,"name":"Qinyu Wei","email":"","orcid":"","institution":"Sanming First Hospital Affiliated to Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qinyu","middleName":"","lastName":"Wei","suffix":""},{"id":304442125,"identity":"17ffaec8-b9c4-4a40-8288-30ae2b09eced","order_by":7,"name":"Weimin Hong","email":"","orcid":"","institution":"Sanming First Hospital Affiliated to Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Hong","suffix":""},{"id":304442126,"identity":"2df38158-0fd6-47dd-9bad-82a0fda55d43","order_by":8,"name":"Guifeng Yao","email":"","orcid":"","institution":"Sanming First Hospital Affiliated to Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guifeng","middleName":"","lastName":"Yao","suffix":""},{"id":304442127,"identity":"e455d761-d555-4b12-96fb-6aa2dff25fb6","order_by":9,"name":"Fat Tin Agassi Sze","email":"","orcid":"","institution":"Graduate Institute of Bioresources, National Pingtung University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Fat","middleName":"Tin Agassi","lastName":"Sze","suffix":""},{"id":304442128,"identity":"9301877f-1b53-4675-a53d-6ace9c9e7545","order_by":10,"name":"Chunguang Ding","email":"","orcid":"","institution":"National Center for Occupational Safety and Health, NHC","correspondingAuthor":false,"prefix":"","firstName":"Chunguang","middleName":"","lastName":"Ding","suffix":""},{"id":304442129,"identity":"c65cf734-86f0-4e8b-b0c7-665817e9d7f2","order_by":11,"name":"Xianyang Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYJCCwyCCn5n58APStEi2s6UZEK2FGUQYnOdRkCBKOf+MHMPDBTV29saHeRgMGGpsoglqkThzxuDwjGPJidsO8x54wHAsLbeBkBYD9h6Dw7wNzAlmh/kSDBgbDhOhhZkHpKXe3riZx0CCOC0QWw4zbmAmVovEmWMFQL8cT5xxGBjICcT4hX9G8ubPBTXV9vz9hw8/+FBjQ1gLAwMHUgQmEFYOAuwPiFM3CkbBKBgFIxcAACPuPSDuA8CYAAAAAElFTkSuQmCC","orcid":"","institution":"Bao Feng Key Laboratory of Genetics and Metabolism","correspondingAuthor":true,"prefix":"","firstName":"Xianyang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-05-15 06:21:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4422937/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4422937/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57728101,"identity":"500bce1e-5553-41b3-8080-da4f0f6ecf89","added_by":"auto","created_at":"2024-06-04 21:42:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74852,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for the recruitment processes of study subjects in a study on Chinese stroke patients with leukoaraiosis.\u003c/p\u003e","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4422937/v1/9a68aa2683d54f0b3e2daa02.png"},{"id":57728106,"identity":"80143884-4c66-4250-a93e-033e8eb5a70b","added_by":"auto","created_at":"2024-06-04 21:42:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":312895,"visible":true,"origin":"","legend":"\u003cp\u003eAlterations in lipid molecules between LA and LS as well as between Control and Stroke group. \u003cstrong\u003e(A-C) \u003c/strong\u003eOPLS-DA score scatter plot between Control and Stroke groups; S-plot for serum metabolite selection based on covariance and correlation. p1 means covariance of projection-based OPLS-DA model and pcorr1 means correlation of projection-based OPLS-DA model; Volcano plots showing both the P-value and fold change of metabolites. TRUE (blue) or FALSE (red) represents the metabolite that matched the filter criteria or not. \u003cstrong\u003e(D-F) \u003c/strong\u003eOPLS-DA score scatter plot between LA and LS groups; S-plot; Volcano plots. \u003cstrong\u003e(G) \u003c/strong\u003eKEGG enrichment analysis of LA and LS groups. Scatter plots present the enriched metabolic pathways. \u003cstrong\u003e(H) \u003c/strong\u003eSankey Diagram concentrating lipid molecules to metabolic pathways. \u003cstrong\u003e(I) \u003c/strong\u003eVenn Analysis: Comparing similarities and differences in lipid molecules among groups. Control vs. Stroke down or up means that lipid molecules exhibit a significant decrease or increase in Stroke compared to Control group. LA vs. LS down or up means lipid molecules exhibit a significant decrease or increase in LS compared to LA group.\u003c/p\u003e","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4422937/v1/cf806abedcd862d18a3006c9.png"},{"id":57728105,"identity":"41993982-b6db-41f3-a66b-44b98ccf4084","added_by":"auto","created_at":"2024-06-04 21:42:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":427396,"visible":true,"origin":"","legend":"\u003cp\u003eAlternations in lipid classification between LA and LS groups. \u003cstrong\u003e(A)\u003c/strong\u003e Lipid skeleton alteration between LA and LS groups. \u003cstrong\u003e(B)\u003c/strong\u003e Variations in the fatty acid chains. \u003cstrong\u003e(C)\u003c/strong\u003e Cluster heat map of changed lipids between LA and LS groups.\u003cstrong\u003e (D)\u003c/strong\u003e Customizing structural equation analysis of ω-3, ω-6 and ω-9 family fatty acids and lipid metabolism pathway for the outcome (“Group” in the middle) of LA or LS. *p \u0026lt; .05; **p \u0026lt; .01; ***p \u0026lt; .001.\u003c/p\u003e","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4422937/v1/e4555b4f665b1c40553a2d42.png"},{"id":57728109,"identity":"7c7f2a01-058a-4260-97d3-13b73ab9f395","added_by":"auto","created_at":"2024-06-04 21:42:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125405,"visible":true,"origin":"","legend":"\u003cp\u003eScreening for the key metabolites for LS. \u003cstrong\u003e(A)\u003c/strong\u003e Diagram showing the weights of node molecules in pathway enriched by changed metabolites between LA and LS groups. \u003cstrong\u003e(B-K) \u003c/strong\u003eThe corresponding key metabolites in LS group compared to the LA group.\u003c/p\u003e","description":"","filename":"OnlineFig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4422937/v1/957b948185f86d6d8ec8731f.png"},{"id":57728886,"identity":"7f4c1cdc-0348-4a29-b815-93f31ad5024c","added_by":"auto","created_at":"2024-06-04 21:50:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":111689,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of 11 key metabolites with the outcomes. \u003cstrong\u003e(A)\u003c/strong\u003e Correlation analysis of DG(12:0/17:2), DG(14:0/22:4), MG(0:0/18:1), TG(18:3/20:1/22:1), PC(18:3/0:0), PC(22:6/22:6), PE(0:0/20:1), PE(22:2/15:0), PI(22:1/21:0), Cer(d18:1/24:1), FA 16:1;O with the outcome.\u003cstrong\u003e (B)\u003c/strong\u003e Correlation analysis of key metabolites with the outcome under normal or abnormal blood pressure condition. \u003cstrong\u003e(C)\u003c/strong\u003e Multivariable regression analysis. The odds ratios with 95% confidence intervals (CIs) and \u003cem\u003ep\u003c/em\u003e-values between LA and LS groups. \u003cstrong\u003e(D-E) \u003c/strong\u003eLinear regression was performed for 11 key metabolites and Fazekas scores between CK and LS groups.\u003c/p\u003e","description":"","filename":"OnlineFig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4422937/v1/0221f68d65c230426e3fc72b.png"},{"id":57728108,"identity":"222a7ec0-e288-4243-964b-627fa52a559c","added_by":"auto","created_at":"2024-06-04 21:42:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":160019,"visible":true,"origin":"","legend":"\u003cp\u003eKey metabolites for LS in subgroups divided by Fazekas Scores. \u003cstrong\u003e(A-B)\u003c/strong\u003e Customizing structural equation analysis for the outcomes under Fazekas scores less than 4 or more than and equal to 4 conditions. \u003cstrong\u003e(C) \u003c/strong\u003eCorrelation analysis of key metabolites under Fazekas scores less than 4 (lower) or more than and equal to 4 (upper) conditions.\u003c/p\u003e","description":"","filename":"OnlineFig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4422937/v1/3fdba2899aa6e474e48e32c2.png"},{"id":57728104,"identity":"284d69e4-8745-4862-84ea-b1d0eb14c3be","added_by":"auto","created_at":"2024-06-04 21:42:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":135297,"visible":true,"origin":"","legend":"\u003cp\u003eThe metabolic processes involved in the 11 key metabolites associated with stroke in patients with (LA). The 11 key metabolites were indicated in bold.\u003c/p\u003e","description":"","filename":"OnlineFig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4422937/v1/5b8a980fbadfd49b2699f783.png"},{"id":59086411,"identity":"df52961e-f7aa-4599-9f0f-4201e41db1fa","added_by":"auto","created_at":"2024-06-26 08:02:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2682110,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4422937/v1/baa7aa28-cb50-40cc-a288-473e4f97209a.pdf"},{"id":57728103,"identity":"1771e1ef-d001-4af4-9912-a9bdfcf4a3ee","added_by":"auto","created_at":"2024-06-04 21:42:02","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":147858,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4422937/v1/70df74a31b92618e8428ba96.jpg"},{"id":57728885,"identity":"39bbd777-9eb4-46d6-98bd-2794104c67d4","added_by":"auto","created_at":"2024-06-04 21:50:02","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":133323,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4422937/v1/b97ccc5277730eae0d5a2e70.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lipid remodeling in serum and correlation with stroke in patients with leukoaraiosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is a significant contributor to global morbidity and mortality, primarily arising from atherosclerosis affecting the cervical or proximal intracranial vessels[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Leukoaraiosis(LA) exhibits a close correlation with ischemic stroke[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Increasing research has elucidated a heightened prevalence of LA in individuals with a history of stroke, with LA identified as a risk factor for both initial and subsequent occurrences of stroke[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previous research has established LA as an imaging marker for small vessel disease, potentially serving as a risk factor for stroke and unfavorable clinical outcomes[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, a study has identified the existence of white matter hyperintensities as a robust prognostic indicator for early recurrence after acute ischemic stroke[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Lacunar cerebral infarcts and LA are commonly encountered in patients presenting with transient ischemic attack (TIA) and ischemic stroke. LA shares similar risk factors with cerebral infarction, including factors such as age and hypertension[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A study suggested that antiplatelet therapy might help prevent recurrent strokes in patients with white matter hyperintensities, while the risk of bleeding needs careful consideration[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These studies indicate that the use of antiplatelet and anticoagulant drugs should be carefully considered, and personalized treatment and medication plans may be needed for stroke patients with LA[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, screening LA patients at risk of stroke through biomarkers can decrease the incidence of cerebral hemorrhage in LA patients who might use antithrombotic drugs without the risk of stroke.\u003c/p\u003e \u003cp\u003eTraylor et al. employed a genetic risk score methodology to evaluate the impact of gene variants associated with LA on the risk of lacunar stroke[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Their study revealed that genetic factors influencing LA were also correlated with the risk of lacunar infarction, while no significant association was observed with other stroke subtypes[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Concurrently, advancements in lipidomics technology have facilitated a deeper understanding of the relationship between lipid metabolism and these pathologies[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Some studies have suggested that dyslipidemia may promote the occurrence and development of stroke and LA by affecting multiple biological processes. Dyslipidemia may lead to stroke through mechanisms such as atherosclerosis, thrombosis, inflammation, and cerebrovascular damage[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Meanwhile, some studies have found a close relationship between dyslipidemia and cerebral LA. A lipidomics-based study revealed that certain lipid substances were associated with the progression of white matter hyperintensities[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These research results suggest that dyslipidemia may be a common pathological mechanism for stroke and LA[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe regulation of lipid metabolism may be an important strategy for the prevention and treatment of these diseases. Our previous studies have revealed significant potential in the application of lipidomics and metabolomics for in-depth investigations into the mechanisms of cerebrovascular diseases and the identification of biomarkers. Through lipidomics, we uncovered a potential association between carotid artery stenosis induced by radiotherapy and specific triglycerides[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, metabolomic analysis of cerebral thrombi highlighted metabolic differences in thrombi of diverse origins[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Another study focuses on the metabolic profile during the treatment process of LTC (Longxue Tongluo Capsule). It was found that LTC benefits stroke rats by regulating glycerophospholipid and sphingolipid metabolism[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, by utilizing lipidomics techniques to identify early key metabolites of stroke in patients with LA, we aim to enhance our understanding of the underlying lipid metabolic processes involved in stroke with LA. Additionally, we seek to potentially develop early warning signs for stroke in patients with LA, aiming to improve clinical guidance for their management.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and Study Design\u003c/h2\u003e \u003cp\u003eA total of 499 patients were enrolled into the database for patients with leukoaraiosis(LA) and stroke in LA patients. We selected individuals admitted to Sanming First Hospital Affiliated with Fujian Medical University in China from 2021 to 2022. Patients with severe heart, lung, liver, or kidney diseases; central nervous system tumors; and acute cerebral hemorrhage were specifically excluded. Eligible participants for the study were required to demonstrate no, mild, or severe subcortical white-matter changes on cranial magnetic resonance imaging(MRI), and to have no hyperlipidemia or any other conditions that could potentially influence lipid metabolism. To ensure the accuracy of diagnosis, specific inclusion and exclusion criteria were established based on our laboratory's redefinition and classification scheme for LA and stroke (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After applying exclusion criteria, a total of 215 consecutive neurology outpatients and inpatients were ultimately included in the study, and underwent clinical brain MRI. Following propensity matching for sex and age, 88 subjects were recruited and divided into two distinct groups: the LA group (LA, n\u0026thinsp;=\u0026thinsp;40) and the stroke in patients with LA group (LS, n\u0026thinsp;=\u0026thinsp;48). Additionally, the study encompassed a control cohort comprising 100 individuals. After matching for sex, age, systolic blood pressure (SBP), diastolic blood pressure (DBP), glucose levels (GLU), and hemoglobin A1c (HBA1c) a control group with 13 individuals (Control group) and a group comprising 13 patients with Stroke (Stroke group) were established (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This clinical research trial was approved by the Fujian Sanming Hospital ethical committee and registered, and all participants provided written informed consent. Our study was approved by the Ethics Committee of the Sanming First Hospital Affiliated with Fujian Medical University (Ethics Approval Number: 2022-44).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSerum Lipidome Extraction\u003c/h2\u003e \u003cp\u003eBlood samples from patients initially hospitalized for LA, LS or stroke were collected in centrifuge tubes. The fresh blood samples were transported to the laboratory for 20 minutes by cold chain (4\u0026deg;C). The serum was isolated by centrifugation at 9000 g at 4\u0026deg;C for 10 minutes. The serum samples were stored in -80\u0026deg;C freezer. The lipids from serum samples were extracted with isopropanol (IPA). The procedure includes: (1) 200 \u0026micro;L of serum extracted with 600 \u0026micro;L of precooled IPA, (2) vortexed for 1 minute, (3) incubated at room temperature for 10 minutes, (4) storing the extraction mixture overnight at -20\u0026deg;C, (5) centrifuged samples at 14000 rpm for 20 minutes, (6) transferred the supernatants into a new centrifuge tube and diluted to 1:10 with IPA/ACN/H2O (2.5:1:1, v/ v/ v). The extracted samples were stored at -80\u0026deg;C before the LC/MS analysis. In addition, 50 \u0026micro;L was extracted from each serum sample to prepare a quality control (QC) sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLC-QTOF for Lipidomics Analysis\u003c/h2\u003e \u003cp\u003eThe samples were analyzed by ACQUITYUPLC (Waters) and XEVO-G2XS quadrupole time-of-flight (QTOF) mass spectrometry (Waters) with ESI. Lipid separation was performed on an Acquity UPLC charged surface hybrid C18 column (2.1 \u0026times; 100 mm, 1.7 \u0026micro;m, Waters), and the gradient mobile phase was composed of 10 mM ammonium formate and 0.1% formic acid in an acetonitrile/aqueous solution (A, 60:40, v/v) and 10 mM ammonium formate and 0.1% formic acid in an isopropanol/acetonitrile solution (B, 90:10, v/v). A 20-minute accelerated elution curve was employed with the flow rate of the mobile phase 0.4 mL/min. The injected 1 \u0026micro;L sample was initially eluted with 40% B, graded linearly to 43% B in 2 minutes, and then increased to 50% B in 0.1 minutes. Over the next 9.9 minutes, the gradient was further increased to 54% B and then to 70% in 0.1 minutes. In the last part of the gradient, the amount of B was increased to 99% in 5.9 minutes. Finally, solution B returned to 40% in 0.1 minutes, and the column was balanced for 1.9 minutes before the next injection. The lipids in both positive and negative modes were detected by a Xevo-G2XS QTOF mass spectrometer, which was operated in MSE mode from m/z 50\u0026ndash;1200, and the collection time for each scan was 0.2 seconds. The source temperature was set to 120 ℃. The desolvation temperature was 550 ℃, the gas flow rate was 1000 L/h, and nitrogen was used as the flowing gas. The capillary voltage was set to 2.0 kV (+)/1.5 kV (-), and the cone voltage was 20 V. Leucine encephalin (molecular weight\u0026thinsp;=\u0026thinsp;555.62 \u0026times; 200 pg/\u0026micro;L, 1:1 acetonitrile: water) was used as the locking mass for accurate mass determination and corrected with 0.5 mm sodium formate solution. The samples were randomly sorted, and 5 quality control samples were initially injected to adjust the conditions of the column. A QC sample was injected in every 10 samples for analysis to investigate the repeatability of the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Processing and Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted on clinical data; gender variables were analyzed using the chi-square test, and an independent t-test was used for age variables. Metabolic changes in plasma extract were analyzed by using a UPLC-Q-TOF MS system and the equipped software Progenesis QI (Waters). The original data was pre-processed and adjusted by the LOESS linear model.\u003c/p\u003e \u003cp\u003eOrthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was first used for classification discrimination. The reliability of the model was verified by cross-validation and displacement test. The parameters R2 and Q2 were used to evaluate the interpretability and predictability of the model, respectively. By \u003cem\u003ep\u003c/em\u003e-value (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Variable Importance Projection (VIP\u0026thinsp;\u0026ge;\u0026thinsp;1) and False Discovery Rate (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) a standard potential difference marker is selected. The best truncation value was determined by using the Youden index.\u003c/p\u003e \u003cp\u003eAll statistical analyses, as well as Customizing Structural Equation (CSE) model, Linear regression model, Logical regression model, and N-fold cross-validation were performed using R version 4.3.2, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics of the Subjects\u003c/h2\u003e \u003cp\u003eWe enrolled 499 patients into database of SanMing First Hospital from 2020 to 2022. Of these, 284 patients were excluded because they did not undergo magnetic resonance imaging (MRI). Among the remaining 215, 99 were further excluded due to medical conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After propensity matching of sex and age, 40 patients with leukoaraiosis (LA) and 48 patients with stroke in leukoaraiosis (LS) remained for further analysis. The demographics and clinical characteristics of these two groups were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared to the LA group, the LS group exhibited significant increases in SBP, DBP, GLU, and HBA1c, along with a significant decrease in Fazekas scores. To explore the specificity of key metabolites between LA and LS groups, we also included 13 patients who had suffered stroke (Stroke group) and 13 control participants (Control group). These groups were propensity matched for sex, age, BMI, SBP, DBP, GLU and HBA1c. The demographics and clinical characteristics of the Control and Stroke groups were summarized in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLA (N\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLS (N\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFazekas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05; **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01; ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eComparative Lipid Profiling between LA and LS Groups\u003c/h2\u003e \u003cp\u003eTo identify key metabolite of stroke in patients with LA, we employed lipidomic profiling to compared serum lipid molecules between LA and LS patients, as well as those in Control and Stroke groups. Using OPLS-DA, the score plots distinctly displayed the separation between the Control and Stroke groups, as well as between the LA and LS groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and D). Additionally, the volcano plot revealed metabolites with significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and high variable importance in projection (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1) between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBetween Control and Stroke groups, among the 2647 metabolites, 80 different metabolites were selected based on criteria (Supplementary Table\u0026nbsp;2). Between LA and LS groups, among the 2649 metabolites, 168 different metabolites were selected based on criteria (Supplementary Table\u0026nbsp;3). The pathway of 80 and 168 different metabolites were documented using KEGG (Supplementary Tables\u0026nbsp;4 and 5). The enrichment analysis of the 80 different metabolites between the Control and Stroke groups was mainly in the Adipocytokine signaling pathway, Glycerophospholipid metabolism, Ether lipid metabolism, and Glycerolipid metabolism (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pathways of 168 different metabolites between LA and LS were primarily enriched in Fat digestion and absorption, Glycerolipid metabolism, Glycerophospholipid metabolism Vitamin digestion and absorption, and Ether lipid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Using Venny analysis of differential substances between the Control vs. Stroke and LA vs. LS groups, we identified 157 uniquely differential compounds between LA and LS groups. Notably, two compounds Cer(d18:0/24:1(15Z)) and Cer(d18:1/24:1(15Z)), exhibited an increasing trend between the Control and Stroke group, while displaying a decreasing trend between the LA and LS groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003eAmong these 157 compounds, a Sankey analysis was conducted to further focus on key metabolites in the important pathways. The diagram displayed a concentrated distribution of 52 lipid metabolites to the enriched pathways, with triglyceride and monoglyceride showing a pronounced tendency to flow towards Glycerolipid metabolism, while phosphatidylinositol, phosphatidylethanolamine, and phosphatidylcholine predominantly channeled into Glycerophospholipid metabolism, and ceramide followed a distinct trajectory along the Sphingolipid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). These 52 lipid molecules have been confirmed as candidate metabolites associated with stroke in patients with LA (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKEGG pathway enrichment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatch Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK vs DIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiosynthesis of unsaturated fatty acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerolipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphonate and phosphinate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerophospholipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholine metabolism in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingolipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatty acid elongation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA vs LS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerolipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerophospholipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatty acid biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingolipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdipocytokine signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatty acid elongation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdentified selected differentiating lipids between LA and LS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerolipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG O-38:8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG O-40:8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG O-36:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG(12:0/17:2(9Z,12Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG(14:0/17:2(9Z,12Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG(14:0/22:4(7Z,10Z,13Z,16Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG(16:0/18:3(9Z,12Z,15Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG(18:2(9Z,12Z)/18:2(9Z,12Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG(19:1(9Z)/20:1(11Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG(19:1(9Z)/22:3(10Z,13Z,16Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG(P-14:0/18:1(9Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMG(0:0/18:1(9Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(14:1(9Z)/18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(15:1(9Z)/16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(18:3(9Z,12Z,15Z)/20:1(11Z)/22:1(11Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerophospholipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE dO-40:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC(18:1(11Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC(18:3(6Z,9Z,12Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC(18:3(9Z,12Z,15Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/18:1(9Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC(O-18:0/1:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC(P-16:0/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC(P-16:0/18:4(6Z,9Z,12Z,15Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC(P-18:1(9Z)/22:2(13Z,16Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(0:0/20:1(11Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(17:0/18:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(20:0/17:2(9Z,12Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(20:2(11Z,14Z)/17:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(20:2(11Z,14Z)/18:3(9Z,12Z,15Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(20:4(5Z,8Z,11Z,14Z)/18:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(22:2(13Z,16Z)/15:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/16:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(O-18:0/22:4(7Z,10Z,13Z,16Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(O-20:0/22:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(P-18:0/17:2(9Z,12Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(P-18:0/22:4(7Z,10Z,13Z,16Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(P-20:0/17:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(P-20:0/19:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(P-20:0/20:2(11Z,14Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(P-20:0/21:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI(20:2(11Z,14Z)/16:1(9Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI(22:1(11Z)/21:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI(P-20:0/18:3(9Z,12Z,15Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingolipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-O-behenoyl-Cer(d18:1/18:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:0/24:1(15Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:0/26:1(17Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:0/h26:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/24:1(15Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(d18:1/25:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(m18:0/22:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(t18:0)/24:0(2OH[R]))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(t20:0/22:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCer(t20:0/26:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerP(d18:1/22:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatty acid elongation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFA 16:1;O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClassification of Altered Lipids between LA and LS Groups\u003c/h2\u003e \u003cp\u003eTo further investigate the metabolism network and series of alternations in metabolites between the LA and LS groups, we conducted a classification analysis of lipid skeleton and fatty acid chains. Compared to the LA group, there was a significant increase in phosphatidylinositol, phosphatidylethanolamine, and monoglyceride, while fatty acid, phosphatidylcholine, and triglyceride exhibited a significant decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, we analyzed the alternations of ω-3, -6, and \u0026minus;\u0026thinsp;9 fatty acid chains, with significant changes noted in the LS group compared to the LA group. DG 18:2, DG 22:4, PE 20:4, and PE 22:4 within the ω-6 category exhibited significant decreases. Monoglyceride and ceramide showed alterations exclusively within the ω-9 fatty acid chains, notably characterized by changes in MG 18:1, Cer24:1, and 26:1. In comparison to ω-3 and \u0026minus;\u0026thinsp;9, variations in glycerolipid (TG, DG, MG) within the ω-6 fatty acid chains were not significant, particularly fatty acid chains from 18:3 to 22:2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In the heatmap of altered lipid molecules, we observed a clustering of diglycerid, triglyceride, phosphatidylethanolamine and ceramide in different classifications between the two groups, which contributed to distinguishing the LS from LA group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). To further investigate the roles of altered metabolites, partial least squares path modeling (PLS path modeling) was conducted, providing a deeper understanding of the different lipid classifications impact on and association with outcomes (LA or LS group). With a modeling goodness of fit (GOF) of 0.6018, the results showed that ω-6 (r = |-0.5817|, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and glycerophospholipid metabolism (r =|-0.565|, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) played significant roles in impacting outcomes related to fatty acid chains and lipid skeletons. It is noteworthy that sphingolipid metabolism (r\u0026thinsp;=\u0026thinsp;0.4428, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had a positive impact on outcomes in terms of fatty acid chains (C24:1, C26:1) and lipid skeleton (ceramide) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eKey Metabolites Identified Based on Pathways Nodes\u003c/h2\u003e \u003cp\u003eBased on the enriched pathway nodes of Fatty acid elongation, Glycerophospholipid, Glycerolipid, and Sphingolipid metabolism, a series of lipid molecules were selected with alterations consistent from pathway downstream to upstream (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Furthermore, through PLS model loading analysis, we identified compounds with the highest loading value in each pathway node, indicating more important contribution to the outcomes. These metabolites included DG(12:0/17:2); DG(14:0/22:4); MG(0:0/18:1); TG(18:3/20:1/22:1); PC(18:3/0:0); PC(22:6/22:6); PE(0:0/20:1); PE(22:2/15:0); PI(22:1/21:0); Cer(d18:1/24:1); and FA 16:1;O (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Among these 11 metabolites, there were significant increases in MG(0:0/18:1(9Z)), DG(14:0/22:4/0:0), and Cer(d18:1/24:1) in the LS group, while other lipid compounds exhibited a substantial decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-K).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRelationships of 11 Key Metabolites with Outcomes\u003c/h2\u003e \u003cp\u003eThe relationships of 11 metabolites were conducted with the outcome were analyzed. The results showed that PE(22:2/15:0) and TG(18:2/20:1/22:1) exhibited no significant correlation with outcomes, while the other metabolites presented significant correlations. Particularly, both Cer(d18:1/24:1) and PI(22:1/20:1) exhibited high correlation with the outcomes with p-values less than 0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBlood pressure has been identified as an independent risk factor for both LA and LS[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To investigate the relationships between key metabolites and outcomes associated with blood pressure, we conducted the correlation analysis of 11 key metabolites with outcomes in normal or abnormal blood pressure conditions. Cer(d18:1/24:1) showed a positive correlation with outcomes in both normal and abnormal blood pressure conditions, although the correlation weakened under abnormal blood pressure. MG(0:0/18:1), PC(18:3/0:0), PC(22:6/22:6), PE(0:0/20:1), PI(22:1/21:0), and FA16:1,O exhibited no significant correlation with outcomes in the presence of normal blood pressure. However, in cases of abnormal blood pressure, with the exception of MG(0:0/18:1), all these factors demonstrated significant negative correlations. Simultaneously, triglycerides and monoglycerides displayed different correlations with phosphatidylcholine, phosphatidylethanolamine, and phosphatidylinositol in abnormal blood pressure compared with those in normal blood pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo better elucidate the underlying relationship between the LA and LS groups, multivariable regression analysis of the 11 key metabolites was conducted. Under the criterion of a \u003cem\u003ep\u003c/em\u003e-value less than 0.1, four components were marked: DG(14:0/22:4), PC(18:3/0:0), Cer(d18:1/24:1), and FA 16:1;O. Among these, DG(14:0/22:4) (OR\u0026thinsp;=\u0026thinsp;5.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), and Cer(d18:1/24:1) (OR\u0026thinsp;=\u0026thinsp;21.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.068) were considered risk factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRelationship of 11 Key Metabolites with Fazekas Score between LA and LS Groups\u003c/h2\u003e \u003cp\u003eWe employed linear regression analysis to assess the relationship between 11 compounds and Fazekas scores. We observed that in the CK group and LS group, the \u003cem\u003ep\u003c/em\u003e-values for FA16:1, O, DG(12:0/17:2), and DG(14:0/22:4) were less than 0.1, indicating a significant negative correlation with changes in MRI (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F).\u003c/p\u003e \u003cp\u003eSubsequently, we subdivided the patients into two groups by Fazekas\u0026thinsp;\u0026lt;\u0026thinsp;4 and Fazekas\u0026thinsp;\u0026ge;\u0026thinsp;4. The results of the CSE analysis revealed a positive impact of DG(14:0/22:4), MG(0:0/18:1), and PI(22:1/21:0) on the outcomes in both subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Furthermore, significant correlations were found between MG(0:0/18:1), PC(18:3/0:0), PC(22:6/22:6), PE(0:0/20:1), PI(22:1/21:0), Cer(d18:1/24:1) and the incidence of LS when Fazekas was less than 4. However, these correlations were no longer significant when Fazekas was 4 or more (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on our research findings, We analyzed 11 key metabolites, and indicated changed pathways in glycerolipid, glycerophospholipid, fatty acid elongation, and sphingolipid associated with stroke in patients with LA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employed a UPLC-MS/MS platform for lipidomic analysis to identify features associated with the occurrence of stroke in patients with LA. Previous investigations have indicated that severe leukoaraiosis (sLA) could serve as a significant adverse factor affecting early neurological recovery following mechanical thrombectomy (MT), potentially diminishing favorable outcomes in acute ischemic stroke (AIS) patients undergoing MT[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The recent BEST trial demonstrated that moderate to severe leukoaraiosis (LA) was linked to poorer outcomes among patients receiving endovascular treatment[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Significant alterations in lipids between LA and LS included glycerolipids, glycerophospholipids, sphingolipids, and fatty acids (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Through structural equation modeling and pathway analysis, we selected 11 key metabolites that exhibited excellent performance in distinguishing between LS and LA (AUC\u0026thinsp;=\u0026thinsp;0.840) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Additionally, these key metabolites also demonstrated efficacy within subgroups (Table S6 and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC-D).\u003c/p\u003e \u003cp\u003eWhite matter damage in the brain is often linked to the aging process and chronic conditions such as hypertension, as well as various diseases[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The impact of white matter lesions becomes particularly significant in patients diagnosed with LA, who faced an elevated risk of stroke[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Kongbunkiat et al. conducted a meta-analysis involving 15 studies with a total of 5,967 participants[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. They reported a relative risk (RR) of 1.65 (95% confidence interval [CI] 1.26\u0026ndash;2.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) for the occurrence of symptomatic intracerebral hemorrhage (SICH) in patients with LA, translating to an absolute risk (AR) increase of 2.5% compared to those without LA[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This consistent association indicates that the presence and severity of LA are linked to a heightened risk of SICH following thrombolysis for acute ischemic stroke (AIS). The elevated risk of intracerebral hemorrhage (ICH) post-thrombolysis in acute cerebral infarction patients with LA may be attributed to factors such as vascular endothelial injury, increased platelet activation, and hypercoagulability[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, in individuals undergoing anticoagulant treatment, the presence of LA was correlated with an augmented likelihood of recurrent stroke and intracranial hemorrhage[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Nevertheless, conventional risk factors exhibit limitations in fully predicting LA in patients who have experienced a stroke. Lipidomics, an analytical approach focusing on the detection of lipid metabolites at the systemic level, holds promise in uncovering potential biomarkers, identifying lipid metabolic pathways, and constructing networks of lipid metabolism[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A systematic review comprehensively outlined potential metabolic biomarkers and pathways associated with ischemic stroke, underscoring the consistent identification of several metabolites with biomarker potential[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, metabolomics analysis using machine learning techniques was performed on plasma samples from ischemic stroke patients and controls, revealing three key differential lipid metabolites[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Furthermore, our previous research has identified lipid biomarkers associated with cardioembolic and atherosclerotic stroke[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePathway analysis indicated that glycerophospholipid metabolism actively participate in both LA and LS groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Concurrently, we observed a significant decrease in glycerophospholipids in the LS group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Glycerophospholipids are an important part of the neuronal cell membrane structure and are involved in cell recognition and signal transduction. Therefore, the degradation of glycerophospholipids, which produces polyunsaturated fatty acids such as docosahexaenoicacid and arachidonic acid, may be a sign of brain damage[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A previous study has indicated that glycerophospholipid metabolism was significantly disrupted in stroke rats. These findings suggest that the decreased levels of glycerophospholipids in the hippocampus may play a role in the pathophysiology of stroke[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Our study confirmed that during the progression of cerebral white matter stroke, glycerophospholipid metabolism was abnormally inhibited (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThrough pathway analysis and SCE model, we selected 11 key metabolites between LA and LS groups. There were DG(12:0/17:2), DG(14:0/22:4), MG(0:0/18:1), TG(18:3/20:1/22:1), PC(18:3/0:0), PC(22:6/22:6), PE(0:0/20:1), PE(22:2/15:0), PI(22:1/21:0), Cer(d18:1/24:1), FA16:1 O (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We identified these key metabolites to be concentrated in monoglyceride, diglyceride, triglyceride, phosphatidylinositol, phosphatidylethanolamine, phosphatidylcholine, ceramide, and fatty acids. The primary role of triglycerides is for the storage and provision of energy[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Triglycerides consist of saturated fatty acids (SFAs), monounsaturated fatty acids (MFAs), and polyunsaturated fatty acids (PFAs)[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The decreased of TG(18:3/20:1/22:1) observed in LS compared to LA may be associated with an activated inflammatory response (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD)[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our findings suggest that the perturbed equilibrium in triglyceride metabolism could be linked to an inflammatory response. The demyelinating effects of LA impair the network connectivity within the brain to differing extents, influencing specific neurotransmitter conduction pathways in the white matter (WM)[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The preservation of normal cognitive functions is intricately tied to the integrity of the cholinergic pathway in the brain, which forms intricate connections with regions such as the prefrontal cortex, ventral striatum, and hippocampus/amygdala[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Our investigation results also indicated a noteworthy association between the dysregulation of diglyceride, lysophosphatidylcholine (LPC), and phosphatidylcholine, and an increased susceptibility to stroke (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. C and J-K). We also found that higher levels of Cer(d18:2/24:1) were associated with LS (Fig. E4). Neuroinflammatory processes, characteristic of demyelinating diseases like multiple sclerosis (MS), contribute to myelin sheath damage[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Previous investigations have demonstrated heightened cerebrospinal fluid (CSF) concentrations of sphingomyelins and ceramides in individuals with multiple sclerosis and other demyelinating conditions[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Based on biological significance, we conducted multivariable regression analysis and identified DG(14:0/22:4) (OR\u0026thinsp;=\u0026thinsp;5.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) and Cer(d18:1/24:1) (OR\u0026thinsp;=\u0026thinsp;21.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.068) as risk factors for stroke with LA. Concurrently, linear regression analysis revealed a significant negative correlation between FA16:1, O, DG(12:0/17:2), and DG(14:0/22:4) and Fazekas scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-F). We also conducted a 7-fold cross-validation, revealing strong predictive performance for these combinations (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B). We ultimately confirmed the involvement of glycerolipid metabolism, glycerophospholipid metabolism, and sphingolipid metabolism in the pathway of associated with stroke in patients with LA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePotential mechanisms by which LA exacerbates the impact of ischemic stroke encompass hypoxia, vascular endothelial injury, disruption of the blood-brain barrier, and impairment of brain connectivity[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Hypoxia-ischemia is believed to contribute to the etiology of neurotrophic acid (LA)[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Brain hypoxia primarily arises from small vessel disease, particularly affecting arteries like the thalamostriate and other perforating arteries[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Neuropathological investigations have revealed that LA, as observed on CT or MRI scans, correlates with demyelination, astrocytic gliosis, arteriolosclerosis, dilated perivascular spaces, and frequently coexists with lacunar infarcts[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Our previous research found that phosphatidylcholine (PC) significantly decreases under hypoxic conditions[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], which is consistent with the results of this study(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome studies discovered significant blood-brain barrier leakage in the normal white matter of stroke patients, which became more severe the closer they were to white matter hyperintensity (WMHs), while blood-brain barrier dysfunction increased with increasing WMH load[\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Based on the Fazekas score, patients in the LA and LS groups were divided into two subgroups (Table S4). Through logistic regression model analysis, a specific combination (PC(18:3/0:0), Cer(d18:1/24:1), FA 16:1;O, and clinical information) was found to have high predictive value for adverse cardiovascular events under conditions of lower Fazekas scores(Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC-D). Some studies have confirmed the significant role of plasma Cer(24:1) concentration in predicting referrals for coronary angiography in different populations[\u003cspan additionalcitationids=\"CR59 CR60\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. These results indicated a notable association between elevated plasma sphingolipid concentrations and adverse cardiovascular events[\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Harshfield's investigation had revealed a positive correlation between higher levels of SM(d18:2/24:1) and measures such as fissure count, WMH volume, and cognitive abilities[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Our study further substantiated a significant elevation in Cer(d18:1/24:1) within the LS group, regardless of whether the Fazekas score is less than 4 or more than or equal to 4.\u003c/p\u003e \u003cp\u003eLA and patients with acute cerebral infarction share common risk factors, including advanced age, hypertension, diabetes, smoking, and alcohol consumption[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. With the exception of age, which cannot be controlled, targeting the aforementioned risk factors is of paramount importance for both primary and secondary prevention strategies. Our study, through an in-depth exploration of metabolic biological processes, provides a theoretical basis and assistance for the future prevention and treatment from LA to LS. However, this study has some limitations. The current validation is limited to self-validation, and further studies are needed to validate and optimize the discovered biomarkers and evaluate their effectiveness in terms of clinical application. Therefore, in future studies, we should select purer samples and expand the sample size to further investigate the biomarkers of these findings and evaluate their clinical applicability in screening and diagnosing stroke in individuals and patients with LA. This will contribute to the improvement of screening and diagnostic methods for individuals with LA and stroke in patients with LA, enabling clinicians to develop more personalized treatment strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified 11 novel metabolites as potential key metabolites for stroke incidence in patients with leukoaraiosis, spanning various Fazekas scores subgroups. The research contributes novel insights into potential distinctive mechanisms from leukoaraiosis to leukoaraiosis with stroke, emphasizing disruptions in glycolipids and glycerophospholipids, along with the regulatory role of Cer(18:1/24:1). These findings provide a comprehensive foundation for further investigations into the mechanistic intricacies of leukoaraiosis with stroke.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to Dr. Chen from Bao Feng Key Laboratory of Genetics and Metabolism for his valuable guidance and insightful discussions throughout this study. We are thankful to the technical staff at Bao Feng Key Laboratory of Genetics and Metabolism for their assistance with data collection and analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Fujian Sanming Science and Technological innovation\u0026nbsp;Project: Screening of novel lipid markers for cerebral infarction in patients with leukoaraiosis and construction of prediction model [grant numbers NO: 2023-S-70], and Fujian Provincial Science and Technology Project: Nervonic acid intervenes in the Process of Alzheimer\u0026apos;s disease by regulating the intestinal flora and metabolism [grant number 2020J011271].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe article and supplementary material contain the original contributions discussed in this study; further questions should be addressed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the \u0026nbsp;Ethics Committee of the Sanming First Hospital Affiliated with Fujian Medical University \u0026nbsp; (Ethics Approval Number: 2022-44). The participants provided written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore participating in the study, all participants signed up with informed permission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams HP Jr., Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, Marsh EE 3. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. 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Brain. 2022;145:2461\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Leukoaraiosis, lipidomics, plasma, metabolic pathway, stroke, Cer(18:1/24:1)","lastPublishedDoi":"10.21203/rs.3.rs-4422937/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4422937/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite the identification of many hub lipids for stroke, the underlying pathophysiology of stroke in elderly patients with leukoaraiosis (LA) remains poorly understood, which is important for the administration of antithrombotic therapy for LA patients. This study aims to illuminate the preliminary lipid metabolic process associated with stroke in LA patients (LS).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study cohort consisted of 215 individuals undergoing magnetic resonance imaging(MRI), from which a subset 13 patients with stroke matched with a control group, and 48 LS patients matched with 40 LA patients were selected for further investigation after exclusion. Serum lipidome was profiled by UPLC-TOF. OPLS-DA was used for classification and identifying differential metabolites. Customizing structural equation (CSE) model was applied to assess the pathway weight of novel metabolites in stroke incidence. Linear regression and matrix correlation were used to investigate the relationships between differentiated metabolites and outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUsing lipid profiling and multivariate statistical analysis, we screened 168 different compounds between LA and LS. Based on the enrichment and Sankey diagram of pathway, 52 lipid molecules were regarded as differential metabolites associated with glycerolipid, glycerophospholipid, and sphingolipid metabolism. After CSE weighted the pathway node molecules, we finally identified 11 key metabolites achieving a prediction, in which DG(14:0/22:4) (OR\u0026thinsp;=\u0026thinsp;5.33) and Cer(d18:1/24:1) (OR\u0026thinsp;=\u0026thinsp;21.44) were significant risk factors for LS. All 11 metabolites exhibited correlations with the outcome (LS incidence), with particularly heightened metabolic disruption in the presence of high blood pressure. We conducted linear regression analysis and found changes in FA16:1; O, DG(12:0/17:2) and DG(14:0/22:4) out of 11 metabolites correlated with Fazekas scores between CK and LS group. Similarly, compared with LA group, DG(14:0/22:4) (OR\u0026thinsp;=\u0026thinsp;5.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) and Cer(d18:1/24:1) (OR\u0026thinsp;=\u0026thinsp;21.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.068) are risk factors for LS. Especially, Cer(d18:1/24:1) and PI(22:1/20:1) were significantly associated with the LS incidence.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study identified 11 metabolites as key metabolites for stroke incidence in LA patients, including subgroups divided by Fazekas scores. This study provides novel insights into lipid metabolic process from LA to LS, in which the lipid disturbance in glycolipids and glycerophospholipids, as well as the regulatory role of Cer(18:1/24:1), which are valuable for further studies of LS.\u003c/p\u003e","manuscriptTitle":"Lipid remodeling in serum and correlation with stroke in patients with leukoaraiosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 21:41:57","doi":"10.21203/rs.3.rs-4422937/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7214f57e-7bca-494b-8686-117a4fb55926","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-26T07:54:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-04 21:41:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4422937","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4422937","identity":"rs-4422937","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}