Ether-linked phospholipidomic profiling unveils novel lipid fingerprints and algorithm predicts the future development of carotid plaques in postmenopausal women: a population-based cohort study

Ether-linked phospholipidomic profiling unveils novel lipid fingerprints and algorithm predicts the future development of carotid plaques in postmenopausal women: a population-based cohort study | 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 Ether-linked phospholipidomic profiling unveils novel lipid fingerprints and algorithm predicts the future development of carotid plaques in postmenopausal women: a population-based cohort study Qiuhui Xuan, Qihang Li, Yitong Lu, Xiaoqian Du, Xiaotong Ma, Yunyun Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5738378/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 Serum ether-linked phospholipids (ePLs) have gained attention in metabolic disease research. Postmenopausal women face a higher risk of developing atherosclerosis (AS), yet current methods for risk prediction and understanding of AS pathophysiology remain limited. This study aimed to identify ePL biomarkers linked to AS, assess their chemical composition in relation to AS risk, and explore whether their impact is mediated by clinical risk factors in postmenopausal women. Methods Here, this research was conducted within the Rose Asymptomatic Intracranial Artery Stenosis (RICAS) prospective study and included 203 postmenopausal women without carotid plaques at baseline. After a median follow-up of 3.8 years, 51 participants developed new carotid plaques. Baseline serum ePLs were semi-quantitated using liquid chromatography-mass spectrometry. Results The mean age of participants was 60.51 (±7.83) years. A total of 85 unique ePL species across five lipid subclasses were identified and quantified according to lipid internal standards. Multivariate models indicated global metabolic disruptions in ePLs preceding carotid plaque formation. Six ePLs were identified as potential biomarkers associated with AS risk (VIP > 1, p < 0.05, FDR 1, p < 0.05). Causal mediation analysis indicated that LDL-C mediated the effects of these ePLs on AS. A machine-learning model incorporating these ePLs with clinical parameters significantly improved carotid plaque prediction (AUC: 0.835, Net Reclassification Improvement > 0, p < 0.001). Conclusion This study highlighted importance of metabolic disruption in PUFA-ePLs for the development of AS. Our findings support the notion that metabolic disruption of PUFA-ePLs can affect LDL-C levels, which is the primary driver of AS in postmenopausal women. Ether-linked phospholipidome as a valuable phenotype hold potential clinical utility in the prediction of AS. Carotid plaques Ether-linked phospholipids with polyunsaturated fatty acyl chains Predictive model Causal mediation Postmenopausal women Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Over the past 30 years, the prevalence of cardiovascular disease (CVD) in Chinese women has risen by 10%, making it the leading cause of death among women and accounting for nearly 35% of all female deaths 1 . Atherosclerosis (AS) demonstrates a higher prevalence in postmenopausal women, surpassing that of age-matched males 2 , 3 . Menopause is closely associated with dyslipidemia, a major risk factor of AS 4 – 7 . Extensive studies have focused on conventional blood lipids such as triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) 6 , 8 , 9 . Even balancing these traditional and modifiable CVD risk factors, such as LDL-C, the results are only partially successful, only 23% decrease in major CVD events 10 . These conventional lipid biomarkers may not fully capture the complex alterations in lipid metabolism leading to CVD events 11 , 12 . Therefore, there is a need for investigating novel risk factors that could contribute to AS risk, help in identifying those at the highest risk of CVD events. An important hallmark of atherosclerosis (AS) is disrupted lipid metabolism, which frequently leads to lipid accumulation in various cells and tissues. Different circulating lipid types and their subcategories, such as sphingolipids, phospholipids, and cholesteryl esters, have been associated with the development of AS 13 – 15 . Ether-linked phospholipids (ePLs) are a naturally occurring glycerophospholipid primarily present as phosphatidylcholine (PC) and phosphatidylethanolamine (PE) species 16 . Differing from traditional phospholipids (PC and PE) by the presence of 1-O-alkyl-2-acyl- or 1-O-alk-1′-enyl bonds at the sn-1 position of the glycerol backbone and an ester bond at the sn-2 position 17 – 19 , ePLs represent a distinct subclass of lipid compounds and impart unique biological roles, inducing a panoply of events, such as oxidative stress and inflammation, which contribute to disease pathogenesis 17 , 18 , 20 . Stuart L. Schreiber has reported that ePLs, particularly noteworthy within these polyunsaturated fatty acid ePLs, played a crucial role in oxidative stress, helping mitigate oxidative damage and maintain cellular integrity 18 , 21 . Furthermore, incorporating newly identified lipid species alongside traditional risk factors has been shown to enhance the prediction of CVD events 14 , 22 . Advances in mass spectrometry (MS)-based lipidomics, a methodology enabling the evaluation of hundreds of lipid species across various pathways, have proven invaluable in identifying novel lipid biomarkers associated with CVD 22 – 26 . MS-based ether-linked phospholipidomics not only help understand the biological processes of AS onset and development, but also help identify more novel risk factors from the standpoint of metabolites. In order to better understand the shared causes and drivers of AS, we included 203 postmenoapausal women without carotid plaques at baseline from the multi-community-based prospective study: the Rose Asymptomatic Intracranial Artery Stenosis (RICAS) study. We conducted semi-quantitative profiling of the ether-phospholipidome in baseline serum using ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS), which provides precise quality and quantitation of molecular ePL species. We investigated the associations of baseline levels of ePLs and their chemical compositions with the incident carotid plaques with a follow-up of approximately 3.8 years (51 cases with new carotid plaques). We build a predictive model of incident carotid plaques and also established a comprehensive catalog of associations among ePL-clinical risk factor- carotid plaque risk, helping contextualize our findings and provide future directions in ePL studies. Methods Study Participants The RICAS study was a community-based cohort study that enrolled 2,474 rural residents aged 40 and above from Kongcun Town, Pingyin County, Shandong, China (2017) 27 , 28 . Initially, 2,016 participants underwent carotid ultrasound measurements, provided overnight fasting blood samples, and completed basic demographic surveys, physical examinations, and blood tests at baseline. At baseline, a total of 1230 participants have no carotid plaques. After a follow-up of median 3.8 years (2021), the follow-up examination and were re-examined for carotid plaques were conducted. This study, part of the RICAS prospective study, included 203 postmenopausal participants without carotid plaques at baseline. After a follow-up duration of median 3.8 years, 51 participants developed new carotid plaques. The primary exclusion criteria were: 1) incomplete baseline interview and clinical data, 2) carotid plaques detected at baseline, 3) lost to follow-up, 4) taking drugs affecting carotid plaque or lipidome at baseline or during follow-up, such as lipid-lowering medications, 5) incomplete carotid ultrasonography data at follow-up, 6) the participants are male, and 7) pre- and perimenopausal females at baseline or during follow-up. Detailed design of this study was provided (Fig. 1). The RICAS study was approved by the Ethics Committee of Shandong Provincial Hospital. All participants provided written informed consent. This study was conducted in accordance with the principles expressed in the Declaration of Helsinki. Carotid ultrasonography examination and definition of carotid plaque during follow‑up Carotid ultrasound examinations were conducted by two experienced physicians using a 7-MHz linear transducer (Siemens ACUSON P500). Both bilateral carotid arteries were scanned longitudinally to measure the carotid intima-media thickness (cIMT) and identify atherosclerotic plaques within the artery wall, including the common carotid artery, internal carotid artery, and external carotid artery was defined as the presence of plaques with a cIMT value ≥ 1.5 mm in any segment of the carotid arteries 29 , 30 . Further details regarding the carotid ultrasound examination can be found in a previous study 28 . Serum ether-linked phospholipidome profiling Serum ePL species were profiled on stored frozen serum specimens that had been collected at baseline. The semi-quantitative ether-phospholipidome were accomplished using the UHPLC-HRMS in our lab. Details on sample extraction, separation and MS detection are described previously 31 . Briefly, 300 µL of cold, internal standard-containing methanol was added into 40 µL of sera. The extraction methanol contained the following internal standards for quantity and retention time normalization: LPC (19:0) at 0.67 µg/mL, PC (19:0/19:0) at 1.67µg/mL, PE (17:0/17:0) at 0.83 µg/mL. The homogenate was vortexed for 30 s followed by 1000 µL of cold methyl tert-butyl ether was added. Then, the mixture was vortexed for 10 s and vibrated for 15 min. Next, 300 µL of ultrapure water was added and vortexed for 10 s to induce phase separation. After centrifugation for 10 min at 14,000×prm, 400 µL of the upper non-polar phase was collected and freeze-dried for ether-phospholipidome analysis. The non-polar phase employed for ether-phospholipidome was resuspended in a mixture of acetonitrile/isopropanol/water (65:30:5, v/v/v) before injection. 5 µL of the resuspended non-polar phase was injected into a Shimadzu UHPLC (Columbia, MD, USA) coupled to AB SCIEX Triple Q-TOF 5600 Plus (Concord, Canada) system. A Waters Acquity UPLC BEH C8 (100mm × 2.1mm i.d.; 1.7 µm) was used for ePL separation. The mobile phases consisted of 6:4 (v/v) acetonitrile/water (10 mM ammonium acetate, phase A) and 9:1 (v/v) isopropanol/acetonitrile (10 mM ammonium acetate, phase B). The flow rate was set at 0.26 mL/min and the column temperature was at 55°C. The elution gradient started with 32% B, held for 1.5 min, and was linearly increased to 85% B at 15.5 min, and then reached 100% B at 15.6 min, kept for 2.4 min. At last, it returned to 32% B within 0.1 min and held for 1.9 min for column equilibration. The total run time was 20 min. The AB SCIEX Triple Q-TOF 5600 Plus with an ESI ion source was used to collect spectra with a data dependent MS/MS spectra acquisition method. The MS parameters conducted for lipid detection were summarized as follows: the ion spray voltage of MS, 5.5 kV in ESI (+) and − 4.5 kV in ESI (-); interface heater temperature, 500°C in ESI (+) and 550°C in ESI (-); ion source gas 1, 2 and curtain gas, 50, 50, and 35 psi in ESI (+) and 55, 55, and 35 psi in ESI (-). The MS scan range 300–1250 Da in ESI (+) and 150–1250 Da in ESI (-). Differing from traditional phospholipids (PC and PE) by the presence of 1-O-alkyl-2-acyl- or 1-O-alk-1′-enyl bonds at the sn-1 position of the glycerol backbone and an ester bond at the sn-2 position (Fig. 2A), ePLs have specific MS fragments and retention behaviors. Based on m/z , retention time and MS fragments, a total of 93 ePL species were identified. After raw data were quantified on MultiQuant software (version 3.0, AB SCIEX, Framingham, U.S.A.), the intensities of ePLs in each sample were normalized to those of the corresponding lipid internal standards. The median relative standard deviation (RSD) of these ePLs in all quality control (QC) samples was 11.46% (range, 3.83%-45.11%). We excluded 8 ePL species with RSD exceeding 30%. These 85 ePLs were defined into 5 lipid subclasses according to the LipidMaps classification scheme, i.e., lyso-phosphatidylcholine with alkyl substituent (LPC O), lyso-phosphatidylethanolamine with alkyl substituent (LPE O), phosphatidylcholine with alkyl substituent (PC O), phosphatidylethanolamine with alkyl substituent (PE O) and PE-plasmalogen (PE p). The number and percentage of ePLs in each lipid category are shown (Fig. 2B). In all QC samples, 44% of the annotated ePLs demonstrated biological reproducibility with an RSD% < 10%, 80% showed reproducibility with an RSD% < 15%, and 89% exhibited reproducibility with an RSD% < 20% during the ether-phospholipidome analysis (Fig. 2C). These findings indicate that the data we obtained are robust and reliable. Distributions at baseline levels of 5 lipid subclasses were shown among individuals with incident carotid plaques and those controls and no outlets were observed (Fig. 2D). Detailed information on 85 ePL species and 5 ePL subclasses was shown (Table S1 ). Covariates Serum triglycerides were determined through a direct colorimetric assay, while LDL cholesterol was estimated using the Friedewald equation if triglyceride levels were below 4 mmol/L. For higher triglyceride levels, LDL cholesterol was measured directly. Body mass index (BMI) was calculated as the ratio of measured weight (in kilograms) to height (in meters) squared. Information on lipid-lowering therapy was based on self-reports. Statistical Analysis Baseline clinical characteristics between participants with incident carotid plaque and those controls without carotid plaques were compared using the unpaired t-test for continuous variables and the χ2-test for categorical variables. Partial least squares difference analysis (PLS-DA) was performed by SIMCA-P software (Umetrics, Umeå, Sweden) to maximize the distance and assess the holistically ePL metabolic alteration between groups 32 . Variable importance in the projection (VIP) generated from PLS-DA model was used for defining ePLs that contribute to the classification between groups. Nonparametric tests for individual ePLs were performed using the open-source software MultiExperiment Viewer (MeV, version 4.9.0, Dana-Farber Cancer Institute, MA) in Wilcoxon, Mann-Whitney test mode with the significant level of p < 0.05 and false discovery rate (FDR) < 0.05. Logistic regression model was used to assess odds ratios (ORs) and 95% confidence intervals (CIs) for associations between baseline levels of the potential differential ePLs and their chemical composition with carotid plaque risk, with or without adjustment for age, BMI, TG and LDL-C. All the adjusted p were adusted by Benjamini-Hochberg (BH). Data on covariates, including age, BMI, TG and LDL-C, was over 99% complete. Missing values for covariates were addressed using multiple imputations with chained equations, yielding results consistent with those obtained without imputation. The receiver-operating characteristic curve (ROC) curve was employed to assess the predictive ability of baseline routine clinical parameters and their panel composition with ePLs on the incidence of carotid plaques at follow-up, calculating the corresponding area under the receiver-operating characteristic curve (AUC). To assess the incremental predictive capacity of adding ePLs to the baseline routine clinical parameters for AS, we employed Net Reclassification Improvement (NRI) and p values. Values of NRI > 0 indicated that the new model has improved predictive capacity compared to the old model. The causal mediation associations of the identified differential ePL species with carotid plaque risk through some key clinical risk parameters were explored by causal mediation analysis using the bootstrapping method 33 in the ‘mediation’ package in R (version 4.5.0). Two thousand bootstraps were run to estimate the confidence intervals. The remaining settings were set default. Baseline ePL Pearson correlation networks in the control group and in the cases with incident carotid plaques were performed to explore the potential interactions among these ePLs (the cutoff Pearson correlation coefficient was 0.8). Ethics statement All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (the Ethics Committee of Shandong Provincial Hospital, China) and with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all patients for being included in the study. Results Baseline clinical characteristics of study participants According to the primary exclusion criteria, a total of 203 postmenopausal women without carotid plaques at baseline were included (Fig. 1). The baseline demographic, clinical and laboratory characteristics were collected. Over a median 3.8-year follow-up, 51 cases developed new carotid plaque. The baseline characteristics of all participants as well as of the incident carotid plaque cases and those without the incident carotid plaque were provide separately (Table 1 ). The mean ± SD age of all participants was 60.51 ± 7.83 years. The incident carotid plaque cases were older and had higher baseline levels of total cholesterol (TC), LDL-C, high-density lipoprotein cholesterol (HDL-C) than those control subjects. The higher prevalence of dyslipidemia, hypertension and diabetes mellitus were reported in incident carotid plaque cases (50.98%, 72.55%, 17.65% vs. 43.42%, 58.55%, 12.50%) (Table 1 ), consistent with. previous studies 34 , 35 . No difference was observed for baseline BMI, TG, alanineaminotransferase (AST), creatinine (CREA) and blood urea nitrogen (BUN) between the two groups. Table 1 Baseline characteristics of study participants Characteristics Overall (n = 203) Incident carotid plaques at follow-up Non-incidence (n = 152) Incidence (n = 51) p Age, years 60.51 ± 7.83 58.58 ± 6.88 66.27 ± 7.70 1.38E-08 Body mass index, kg/m2 25.83 ± 3.66 26.01 ± 3.76 25.28 ± 3.33 1.92E-01 Blood pressure, mm Hg SBP 146.64 ± 21.82 144.95 ± 22.21 151.67 ± 19.96 4.64E-02 DBP 87.83 ± 12.16 87.86 ± 11.61 87.75 ± 13.81 9.59E-01 Comorbidity Dyslipidemia, n (%) 92 (45.32%) 66 (43.42%) 26 (50.98%) 4.17E-01 Hypertension, n (%) 126 (62.07%) 89 (58.55%) 37 (72.55%) 9.52E-02 Diabetes mellitus, n (%) 28 (13.79%) 19 (12.50%) 9 (17.65%) 3.55E-01 Laboratory data TG, mmoL/L 1.54 ± 0.90 1.58 ± 0.93 1.4 ± 0.76 1.59E-01 TC, mmoL/L 5.48 ± 0.97 5.32 ± 0.92 5.96 ± 0.99 1.02E-04 LDL-C, mmoL/L 3.06 ± 0.64 2.96 ± 0.59 3.35 ± 0.69 6.43E-04 HDL-C, mmoL/L 1.63 ± 0.36 1.58 ± 0.33 1.77 ± 0.41 3.79E-03 AST 25.29 ± 7.06 25.43 ± 7.34 24.86 ± 6.17 5.87E-01 CREA 53.7 ± 10.11 52.69 ± 8.03 56.73 ± 14.35 6.03E-02 BUN 4.84 ± 1.27 4.75 ± 1.02 5.11 ± 1.81 1.85E-01 Data are n (%) and mean ± SD. p for difference of baseline characteristics between participants with or without incident carotid plaques at follow-up were calculated using un-paired t-test for continuous variables and the χ2-test for categorical variables. SBP: systolic blood pressure, DBP: diastolic blood pressure, TG: triglycerides, TC: total cholesterol, LDL-C: low-density lipoprotein cholesterol, HDL-C: high-density lipoprotein cholesterol, AST: alanineaminotransferase, CREA: creatinine, BUN: blood urea nitrogen. Ether-linked phospholipidomic fingerprints and carotid plaque risk We first applied a supervised PLS-DA model to holistically assess the ePL metabolic alterations at baseline level. We observed the discrimination trends from ether-phospholipidome data in the PLS-DA score plots between cases with the incident AS and controls (Fig. S1 A). Principal component 1 of this PLS-DA model showed cumulative Q2 > 0, which means that our model is not overfitted and is predictive (Fig. S1 B). These results suggested that baseline profound ePL metabolic perturbations were closely related with incident carotid plaques. To investigate the associations between individual ePL species and incident carotid plaques, firstly, based on above the PLS-DA model, we found out the vital variables to distinguish cases with incident carotid plaques from control group. A total of 17 ePLs with VIP > 1.0 both for PCA 1 and PCA 2 were selected for subsequent univariate analysis to determine whether they were significantly altered in baseline sera for cases with incident carotid plaques (Fig. S1 C and S1D, Fig. 3A). In total, 6 ePLs exhibited p < 0.05 and FDP < 0.05 (Fig. 3A) and were regarded as the potential differential biomarker candidates. Details on differential ePLs were provided (Table S2). Subsequently, a logistic regression analysis was used to determine ORs with 95%CI of baseline levels of the 6 potential ePLs for newly incident carotid plaques. Among these 6 individual ePLs, all showed significant associations with carotid plaque risk (All ORs > 1, p < 0.05; Fig. 3B). After adjustments for age, BMI, TG and LDL-C, 4 ePLs, including PE O-18:2_18:1, PE O-18:0_20:4, PE O-18:1_22:4 and PE O-20:1_20:4, remained closely associated with increased carotid plaque risk (adjusted p < 0.05) (Fig. 3C-E). Baseline levels of these 4 ePLs were shown and significantly elevated in cases with incident carotid plaques compared with controls (Fig. S2A-D). Interestingly, we observed that the 4 ePLs mainly involved in polyunsaturated fatty acyl-chain (PUFA)-containing ePLs (e.g., 18:2, 20:4, 22:4). To further discern PUFA-containing ePLs more associated with incident carotid plaques, we systematically explored how the chemical composition of ePLs influenced their association with the incident carotid plaques. We summed all species according to the different unsaturation of two certain fatty acyl-chains attached to the ePLs base (e.g., all species with 0, 1, 2, 3, 4, 5, 6, 7 and 8 of carbon-carbon double bonds (C = C) in two certain fatty acyl-chains), independent of fatty acyl-chain length. We found that the PUFA-containing ePLs (‘C = C’ > 2) were still strongly associated with the risk of carotid plaques, before and after adjustments for age, BMI and TG (Fig. 4A-C). But, After adjustment for LDL-C, only PUFA-containing ePLs (‘C = C’ = 3) showed a significantly association with the future development of carotid plaques (Fig. 4D). Collectively, these results highlighted those alterations in baseline levels of ether-linked phospholipidomic fingerprints, including individual ePL species with PUFA, and their chemical compositions with high unsaturation, were closely linked to the incidence of carotid plaques in postmenopausal women. This suggested that PUFA-ePLs might be potential biomarkers for future carotid plaque incidence, aiding in identifying individuals at risk for AS before overt pathology develops. A putative causal role of the ePLs in future carotid plaques mediated by LDL-C We systematically evaluated the putative causal relationships between the 4 ePLs (PE O-18:2_18:1, PE O-18:0_20:4, PE O-18:1_22:4 and PE O-20:1_20:4), clinical risk factors (BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), TG, TC, LDL-C and HDL-C) and future incident carotid plaques. A total of 28 mediation links were established for the contribution of ePLs to incident carotid plaques mediated by clinical risk factors (Fig. 5A). The results showed that only four mediation pathways mediated by LDL-C were significant ( p _FDRmediation 0.05). LDL-C mediated 25%, 26%, 25% and 24% of the effect of PE O-18:2_18:1, PE O-18:0_20:4, PE O-18:1_22:4 and PE O-20:1_20:4 on carotid plaque incidence, respectively (Fig. 5B-E, all p _FDRmediation 0.05). Association of carotid plaque-associated ePL species and major clinical risk parameters Spearman correlation analysis was performed to build up a correlation matrix among carotid plaque-associated ePL species and major clinical parameters-related AS risk. Among all participants, the baseline levels of these carotid plaque-associated ePL species were closely correlated with a range of biochemical measurements and metabolic parameters (Fig. 5F). For example, most of these ePLs were more highly, positively associated with LDL-C and BUN, but inversely associated with TG and BMI. Correlation network analysis of baseline ePLs for controls and cases with incident carotid plaques Correlation network is a useful tool for integrating information from the high throughput experiment to show the relationships among the metabolites. In this study, correlation network was constructed and analyzed on total 85 baseline ePLs in controls (Fig. S3A) and cases with incident carotid plaques (Fig. S3B). The metabolic correlation network in cases with incident carotid plaques was similar to that in controls, implying that potential interactions among ePLs had no change in the development of carotid plaques. ePL metabolic signatures predicting carotid plaque identified by machine learning To define the potential prediction of the identified 4 ePLs for new carotid plaques, the ROC curve was employed to assess the predictive ability of baseline routine clinical parameters and their panel composition with ePLs on the incidence of carotid plaques at follow-up, calculating the corresponding AUCs. Firstly, we separately evaluated the predictive effects of the 9 routine clinical parameters, including age, BMI, blood pressure (SBP and DBP), AST and CREA and BUN, TG and LDL-C on carotid plaque risk. The AUCs of the routine clinical parameters, in turn, were 0.774, 0.550, 0.624, 0.569, 0.561 and 0.671, respectively. Subsequently, taking into account the predictive capability of the 9 routine clinical parameters and the 4 ePLs as a whole, we separately constructed 2 different prediction models: the model 1 (basal model) including all 9 routine clinical parameters alone; the model 2 including all 9 routine clinical parameters and 4 ePLs. When addition of 4 ePLs, the model 2 performed better than the model 1 (AUC 0.835 vs 0.824, p 0, p < 0.05). Altogether, the addition of 4 ePLs on the top of the routine clinical parameter exhibited the greatest predictive benefit for carotid plaque detection. In the future clinical practice, the ePLs may be taken into risk assessment model to improved prediction performance of AS. Discussion MS-based ether-linked phospholipidomics enables a broad range of ePL metabolites to be characterized. This rich source of information could facilitate the elucidation of the underlying pathological progression of the disease. In this prospective, population-based longitudinal cohort, part from the RICAS study, we observed a profound pro-atherogenic ePL metabolic characterization preceding the development of carotid plaques, allowing for an in-depth understanding of metabolic changes in postmenopausal women. The altered ether-linked phospholipidome, including plasmalogens of PEs, indicated the presence of lipid dysfunction, ROS-induced oxidation and peroxisomal disorder in incident carotid plaque individuals 36 , 37 . The associations between ePLs, particularly PUFA-containing ePLs, and CVD are controversial 38 – 41 . Nevertheless, our finding on PUFA-containing ePLs is supported by the study that showed elevated ePLs content in human sera of CVD and the carotid artery plaque tissues 41 , 42 . Understanding the role of ePL species, especially highly unsaturated ePLs in health and disease holds promise for understanding pathogenesis of CVD. Our finding revealed the association between the ether-linked phospholipidome and the development of carotid plaque, which was independent of confounders, such as age, BMI. Previous research has indicated ongoing focus regarding whether ether-linked phospholipidome impacts clinical risk factors responsible for CVD development. In the current study, we demonstrated that LDL-C rather than TG strongly mediated the effect ePLs on AS risk. Both are key components of cardiovascular risk assessment 43 , 44 . LDL-C is mainly involved in liver cholesterol metabolism and has been found to be strongly associated with cholesterol transport and is more directly linked AS 45 , whereas TG is mainly related to fat storage and energy metabolism and showed more correlation with obesity and insulin resistance 46 . This observation suggests that ePL metabolic perturbation before carotid plaque development showed more correlations with cholesterol metabolism than energy metabolism in Chinese postmenopausal women. Results from causal mediation analysis supplemented knowledge on the potential function of metabolites influencing the onset of AS. However, as these findings are mostly generated from observational data, further functional experiments focusing on specific ePL metabolites are needed to confirm the causality. Risk stratification is a critical component in AS prevention. We developed a prediction model for future carotid plaque development in postmenopausal women. The combination of metabolomic information and conventional clinical risk factors in the prediction model showed good predictive performance and classification accuracy. This finding emphasized that metabolites could reveal subtle biochemical changes and risk factors that traditional markers cannot capture 47 , 48 . New metabolic biomarkers are linked to AS, offering the potential for personalized risk assessment. However, despite the improved predictive power from metabolite integration, further research is needed to validate their widespread use and standardization in clinical practice. Our study has a few limitations. The sample size of the case-control study within a prospective cohort is relatively small. Additionally, our 4 ePL metabolites-based prediction model for carotid plaque has not been validated in an independent longitudinal cohort. Conclusions The current study identified a panel of ePL metabolic biomarkers that were able to predict carotid plaque onset, which may be potentially used for the precision management of AS. To our knowledge, this is the first prospective ether-phospholipidomic study of AS risk among postmenopausal individuals, highlighting importance of the PUFA-containing ePLs in AS risk. Furthermore, our findings support the notion that the ePL metabolic disruption drive LDL-C rather than TG in turn cause AS. Declarations Supplementary Information The online version contains supplementary material available at Acknowledgements We are indebted to the study participants for their input and interface. We greatly appreciate the assistance of nurses, laboratory technicians, clinical research assistants and data managers at Department of Endocrinology. Authors’ contributions Q.X.: Formal analysis, Investigation, Methodology, Data curation, Writing-original draft, Visualization, Funding acquisition. Q.L.: Formal analysis, Investigation, Data curation, Validation. Q.S.: Conceptualization, Resources, Writing - review & editing, Supervision, Project administration. Y.X.: Data curation, Writing - review & editing. Y.L., X.D., X.M.: Writing - review & editing, Supervision. Funding This work was supported by the National Natural Science Foundation of China (22204090); Natural Science Foundation of Shandong Province (ZR2021QB089); Taishan Scholar Foundation of Shandong Province (to Qiuhui Xuan). Data availability The Chinese Data protection Agency does not allow open access to our data; however, upon reasonable request the steering committee of the RICAS Population Study may allow further follow-up analyses. Ethics approval and consent to participate Approval from the local ethics committee was obtained before the onset of the study and all the participants provided their consent before starting the study. Consent for publication Not applicable. Competing interests The authors have no competing interests References Vogel B, Acevedo M, Appelman Y, Bairey Merz CN, Chieffo A, Figtree GA, Guerrero M, Kunadian V, Lam CSP, Maas A, Mihailidou AS, Olszanecka A, Poole JE, Saldarriaga C, Saw J, Zuhlke L, Mehran R. 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Boren J, Chapman MJ, Krauss RM, Packard CJ, Bentzon JF, Binder CJ, Daemen MJ, Demer LL, Hegele RA, Nicholls SJ, Nordestgaard BG, Watts GF, Bruckert E, Fazio S, Ference BA, Graham I, Horton JD, Landmesser U, Laufs U, Masana L, Pasterkamp G, Raal FJ, Ray KK, Schunkert H, Taskinen MR, van de Sluis B, Wiklund O, Tokgozoglu L, Catapano AL, Ginsberg HN. Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2020;41:2313–30. Ginsberg HN, Packard CJ, Chapman MJ, Boren J, Aguilar-Salinas CA, Averna M, Ference BA, Gaudet D, Hegele RA, Kersten S, Lewis GF, Lichtenstein AH, Moulin P, Nordestgaard BG, Remaley AT, Staels B, Stroes ESG, Taskinen MR, Tokgozoglu LS, Tybjaerg-Hansen A, Stock JK, Catapano AL. 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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-5738378","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":400141140,"identity":"81020e41-f930-4eb4-bf8c-a403f9972b21","order_by":0,"name":"Qiuhui Xuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYLCCigIQycbA8MHAxo44LWcMGCRAWhhnFKQlk6aFmefDIcYGQqoNbiQ/e3DA4HAdP/+xNGkbgwPMDOyHj27Ap0VyRpq5AVCLBJBxTDrH4A4fA09a2g18WvglEsykPwC1GNxgbwNqecbMIMFjhlcLm0T6NwmQLQbnj7dJWxgcZmwgpIVfIscMouUA0GEMxGiR7HlTBtSSLjlzRlqyZY9BWjIbIb8YHE/fJnGgwpofGGKGN378sbHjZz98DK8WKGgGESwSYN8RoRwE6kAE8wciVY+CUTAKRsEIAwAhFkh2b8TvdgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2996-6111","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Qiuhui","middleName":"","lastName":"Xuan","suffix":""},{"id":400141141,"identity":"6b8b7104-6213-4fe1-ac35-ca1958dd0e3e","order_by":1,"name":"Qihang Li","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qihang","middleName":"","lastName":"Li","suffix":""},{"id":400141142,"identity":"b84c10fa-1271-4b97-a2b3-eaf6d46679d8","order_by":2,"name":"Yitong Lu","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yitong","middleName":"","lastName":"Lu","suffix":""},{"id":400141143,"identity":"cbf7bfa4-d48a-4270-8080-137e8f0e7171","order_by":3,"name":"Xiaoqian Du","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqian","middleName":"","lastName":"Du","suffix":""},{"id":400141144,"identity":"56acc908-1285-49f6-9646-d0091ef2dd60","order_by":4,"name":"Xiaotong Ma","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Ma","suffix":""},{"id":400141145,"identity":"f66ce0da-ec79-4316-b025-7344d2016eb2","order_by":5,"name":"Yunyun Xu","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yunyun","middleName":"","lastName":"Xu","suffix":""},{"id":400141146,"identity":"57a27140-9845-4141-9043-31f32048d7ad","order_by":6,"name":"Qinjian Sun","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University: Shandong Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qinjian","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-12-31 01:53:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5738378/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5738378/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73784686,"identity":"bae39f38-93ed-4eb5-9c5a-60d4530431fe","added_by":"auto","created_at":"2025-01-14 16:01:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33980,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart of the participants from the multi-community-based RICAS study (n = 203 at baseline).\u003c/p\u003e","description":"","filename":"Figures1.png","url":"https://assets-eu.researchsquare.com/files/rs-5738378/v1/696e2fa27d317ee2ebeea568.png"},{"id":73784687,"identity":"75332d81-2adc-4d0a-920f-33f2cb12ebc6","added_by":"auto","created_at":"2025-01-14 16:01:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49142,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the serum ether-phospholipid dataset. A, Schematic showing the distinct structures of phospholipids and the two subtypes of ether-linked phospholipids. B, Number of the detected ether-phospholipids in lipid subclass using LipidMaps classification scheme and the percentage of each lipid subclass in the ether-phospholipids detected. C, Reproducibility of the detected \u0026nbsp;etherphospholipids with 22 serum replicates. D, Baseline subclass levels of serum ether-phospholipids \u0026nbsp;with 95% CI in controls and cases with incident carotid plaques, respectively.\u003c/p\u003e","description":"","filename":"Figures2.png","url":"https://assets-eu.researchsquare.com/files/rs-5738378/v1/91c0a3717029d979bbe51d67.png"},{"id":73784688,"identity":"29386af1-22b3-4621-a3de-c2a796f6d2cd","added_by":"auto","created_at":"2025-01-14 16:01:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54894,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential ether-phospholipids at baseline between controls (n = 152) and cases with incident carotid plaques (n = 51). A, Venn diagram of significantly differential ether-phospholipids at baseline between controls and cases with incident carotid plaques. VIP score \u0026gt; 1 for both PCA 1 and PCA 2 of the PLS-DA model; p \u0026lt; 0.05 \u0026amp; false discovery rate (FDR) \u0026lt; 0.05 from Nonparametric Test (the Mann-Withney-Wilcoxon model). B-E, Forest plot of OR (95% CI) for incident carotid plaques per standard deviation (SD) of baseline ether-phospholipids from the unadjusted, adjusted (Age, BMI), adjusted (TG) and adjusted (LDL-C) logistic regression models, respectively.\u003c/p\u003e","description":"","filename":"Figures3.png","url":"https://assets-eu.researchsquare.com/files/rs-5738378/v1/ff834d62ca3cc3754253c2a7.png"},{"id":73785611,"identity":"9646c509-a102-4093-96c8-cb5904c08b1b","added_by":"auto","created_at":"2025-01-14 16:09:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49442,"visible":true,"origin":"","legend":"\u003cp\u003eOR (95% CI) of the incident carotid plaques (n =51) per SD of the chemical compositions of ether-phospholipids.A-D, Forest plot of OR (95% CI) for the incident carotid plaques per SD of summed all ether-phospholipids according to the different unsaturation of two certain fatty acyl-chains attached to the ether-phospholipid base from the unadjusted, adjusted (Age, BMI), adjusted (TG) and adjusted (LDL-C) logistic regression models, respectively.\u003c/p\u003e","description":"","filename":"Figures4.png","url":"https://assets-eu.researchsquare.com/files/rs-5738378/v1/3ce08e014d390baa900e8812.png"},{"id":73784693,"identity":"f1609a0e-ae10-430e-a5e3-25618e09863e","added_by":"auto","created_at":"2025-01-14 16:01:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":99193,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of the potential differential ether-phospholipids and some key clinical parameters and their prediction performance of incident carotid plaques (n = 51). A, Sankey plot showing the mediation effects of serum ether-phospholipids on incident carotid plaques through clinical risk factors. Colors corresponded to the mediation linkages mediated by different clinical risk factors. B-E, Analysis \u0026nbsp;of the effect of PE O-18:2_18:1, PE O-18:0_20:4, PE O-18:1_22:4 and PE O-20:1_20:4 on incident \u0026nbsp;carotid plaques as mediated by LDL-C, respectively. Mediation effects of serum etherphospholipids on \u0026nbsp;incident carotid plaques through clinical risk factors were shown by red arrows and reverse mediations \u0026nbsp;were represented by blue arrows. F, Spearman’s correlation analysis of the association of significantly \u0026nbsp;differential ether-phospholipids with the main clinical parameters. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001. \u0026nbsp;The color key represents the regression coefficients of the independent variables. G, ROC curves for the \u0026nbsp;baseline models of 9 key clinical parameters and the 4 potential differential ether-phospholipids for the \u0026nbsp;incident carotid plaques, respectively. Panel 1 including age, BMI, blood pressure (SBP and DBP), AST \u0026nbsp;and CREA and BUN, TG and LDL-C; Panel 2 including age, BMI, blood pressure (SBP and DBP), AST \u0026nbsp;and CREA and BUN, TG and LDL-C, and the 4 ePLs identified in this study.\u003c/p\u003e","description":"","filename":"Figures5.png","url":"https://assets-eu.researchsquare.com/files/rs-5738378/v1/98c8ac39223313eac4f02a1b.png"},{"id":76037285,"identity":"c288d93e-87f5-41c7-86d8-e0196b55a261","added_by":"auto","created_at":"2025-02-11 16:21:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1183041,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5738378/v1/91f0d2a8-3ac8-43ad-899d-d832ded3a40c.pdf"},{"id":73784702,"identity":"3a4a9169-b2b6-4f77-9c6b-8ec95246d79d","added_by":"auto","created_at":"2025-01-14 16:01:52","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1552309,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5738378/v1/66bca5add5840e3a817fe2f9.docx"}],"financialInterests":"","formattedTitle":"Ether-linked phospholipidomic profiling unveils novel lipid fingerprints and algorithm predicts the future development of carotid plaques in postmenopausal women: a population-based cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eOver the past 30 years, the prevalence of cardiovascular disease (CVD) in Chinese women has risen by 10%, making it the leading cause of death among women and accounting for nearly 35% of all female deaths\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Atherosclerosis (AS) demonstrates a higher prevalence in postmenopausal women, surpassing that of age-matched males\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Menopause is closely associated with dyslipidemia, a major risk factor of AS\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Extensive studies have focused on conventional blood lipids such as triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Even balancing these traditional and modifiable CVD risk factors, such as LDL-C, the results are only partially successful, only 23% decrease in major CVD events\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. These conventional lipid biomarkers may not fully capture the complex alterations in lipid metabolism leading to CVD events\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Therefore, there is a need for investigating novel risk factors that could contribute to AS risk, help in identifying those at the highest risk of CVD events.\u003c/p\u003e \u003cp\u003eAn important hallmark of atherosclerosis (AS) is disrupted lipid metabolism, which frequently leads to lipid accumulation in various cells and tissues. Different circulating lipid types and their subcategories, such as sphingolipids, phospholipids, and cholesteryl esters, have been associated with the development of AS\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Ether-linked phospholipids (ePLs) are a naturally occurring glycerophospholipid primarily present as phosphatidylcholine (PC) and phosphatidylethanolamine (PE) species\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Differing from traditional phospholipids (PC and PE) by the presence of 1-O-alkyl-2-acyl- or 1-O-alk-1\u0026prime;-enyl bonds at the sn-1 position of the glycerol backbone and an ester bond at the sn-2 position\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, ePLs represent a distinct subclass of lipid compounds and impart unique biological roles, inducing a panoply of events, such as oxidative stress and inflammation, which contribute to disease pathogenesis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Stuart L. Schreiber has reported that ePLs, particularly noteworthy within these polyunsaturated fatty acid ePLs, played a crucial role in oxidative stress, helping mitigate oxidative damage and maintain cellular integrity\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Furthermore, incorporating newly identified lipid species alongside traditional risk factors has been shown to enhance the prediction of CVD events\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Advances in mass spectrometry (MS)-based lipidomics, a methodology enabling the evaluation of hundreds of lipid species across various pathways, have proven invaluable in identifying novel lipid biomarkers associated with CVD\u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. MS-based ether-linked phospholipidomics not only help understand the biological processes of AS onset and development, but also help identify more novel risk factors from the standpoint of metabolites.\u003c/p\u003e \u003cp\u003eIn order to better understand the shared causes and drivers of AS, we included 203 postmenoapausal women without carotid plaques at baseline from the multi-community-based prospective study: the Rose Asymptomatic Intracranial Artery Stenosis (RICAS) study. We conducted semi-quantitative profiling of the ether-phospholipidome in baseline serum using ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS), which provides precise quality and quantitation of molecular ePL species. We investigated the associations of baseline levels of ePLs and their chemical compositions with the incident carotid plaques with a follow-up of approximately 3.8 years (51 cases with new carotid plaques). We build a predictive model of incident carotid plaques and also established a comprehensive catalog of associations among ePL-clinical risk factor- carotid plaque risk, helping contextualize our findings and provide future directions in ePL studies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eThe RICAS study was a community-based cohort study that enrolled 2,474 rural residents aged 40 and above from Kongcun Town, Pingyin County, Shandong, China (2017)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Initially, 2,016 participants underwent carotid ultrasound measurements, provided overnight fasting blood samples, and completed basic demographic surveys, physical examinations, and blood tests at baseline. At baseline, a total of 1230 participants have no carotid plaques. After a follow-up of median 3.8 years (2021), the follow-up examination and were re-examined for carotid plaques were conducted.\u003c/p\u003e \u003cp\u003eThis study, part of the RICAS prospective study, included 203 postmenopausal participants without carotid plaques at baseline. After a follow-up duration of median 3.8 years, 51 participants developed new carotid plaques. The primary exclusion criteria were: 1) incomplete baseline interview and clinical data, 2) carotid plaques detected at baseline, 3) lost to follow-up, 4) taking drugs affecting carotid plaque or lipidome at baseline or during follow-up, such as lipid-lowering medications, 5) incomplete carotid ultrasonography data at follow-up, 6) the participants are male, and 7) pre- and perimenopausal females at baseline or during follow-up. Detailed design of this study was provided (Fig.\u0026nbsp;1). The RICAS study was approved by the Ethics Committee of Shandong Provincial Hospital. All participants provided written informed consent. This study was conducted in accordance with the principles expressed in the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCarotid ultrasonography examination and definition of carotid plaque during follow‑up\u003c/h3\u003e\n\u003cp\u003eCarotid ultrasound examinations were conducted by two experienced physicians using a 7-MHz linear transducer (Siemens ACUSON P500). Both bilateral carotid arteries were scanned longitudinally to measure the carotid intima-media thickness (cIMT) and identify atherosclerotic plaques within the artery wall, including the common carotid artery, internal carotid artery, and external carotid artery was defined as the presence of plaques with a cIMT value\u0026thinsp;\u0026ge;\u0026thinsp;1.5 mm in any segment of the carotid arteries\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Further details regarding the carotid ultrasound examination can be found in a previous study\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eSerum ether-linked phospholipidome profiling\u003c/h3\u003e\n\u003cp\u003eSerum ePL species were profiled on stored frozen serum specimens that had been collected at baseline. The semi-quantitative ether-phospholipidome were accomplished using the UHPLC-HRMS in our lab. Details on sample extraction, separation and MS detection are described previously\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Briefly, 300 \u0026micro;L of cold, internal standard-containing methanol was added into 40 \u0026micro;L of sera. The extraction methanol contained the following internal standards for quantity and retention time normalization: LPC (19:0) at 0.67 \u0026micro;g/mL, PC (19:0/19:0) at 1.67\u0026micro;g/mL, PE (17:0/17:0) at 0.83 \u0026micro;g/mL. The homogenate was vortexed for 30 s followed by 1000 \u0026micro;L of cold methyl tert-butyl ether was added. Then, the mixture was vortexed for 10 s and vibrated for 15 min. Next, 300 \u0026micro;L of ultrapure water was added and vortexed for 10 s to induce phase separation. After centrifugation for 10 min at 14,000\u0026times;prm, 400 \u0026micro;L of the upper non-polar phase was collected and freeze-dried for ether-phospholipidome analysis.\u003c/p\u003e \u003cp\u003eThe non-polar phase employed for ether-phospholipidome was resuspended in a mixture of acetonitrile/isopropanol/water (65:30:5, v/v/v) before injection. 5 \u0026micro;L of the resuspended non-polar phase was injected into a Shimadzu UHPLC (Columbia, MD, USA) coupled to AB SCIEX Triple Q-TOF 5600 Plus (Concord, Canada) system. A Waters Acquity UPLC BEH C8 (100mm \u0026times; 2.1mm i.d.; 1.7 \u0026micro;m) was used for ePL separation. The mobile phases consisted of 6:4 (v/v) acetonitrile/water (10 mM ammonium acetate, phase A) and 9:1 (v/v) isopropanol/acetonitrile (10 mM ammonium acetate, phase B). The flow rate was set at 0.26 mL/min and the column temperature was at 55\u0026deg;C. The elution gradient started with 32% B, held for 1.5 min, and was linearly increased to 85% B at 15.5 min, and then reached 100% B at 15.6 min, kept for 2.4 min. At last, it returned to 32% B within 0.1 min and held for 1.9 min for column equilibration. The total run time was 20 min. The AB SCIEX Triple Q-TOF 5600 Plus with an ESI ion source was used to collect spectra with a data dependent MS/MS spectra acquisition method. The MS parameters conducted for lipid detection were summarized as follows: the ion spray voltage of MS, 5.5 kV in ESI (+) and \u0026minus;\u0026thinsp;4.5 kV in ESI (-); interface heater temperature, 500\u0026deg;C in ESI (+) and 550\u0026deg;C in ESI (-); ion source gas 1, 2 and curtain gas, 50, 50, and 35 psi in ESI (+) and 55, 55, and 35 psi in ESI (-). The MS scan range 300\u0026ndash;1250 Da in ESI (+) and 150\u0026ndash;1250 Da in ESI (-).\u003c/p\u003e \u003cp\u003eDiffering from traditional phospholipids (PC and PE) by the presence of 1-O-alkyl-2-acyl- or 1-O-alk-1\u0026prime;-enyl bonds at the sn-1 position of the glycerol backbone and an ester bond at the sn-2 position (Fig.\u0026nbsp;2A), ePLs have specific MS fragments and retention behaviors. Based on \u003cem\u003em/z\u003c/em\u003e, retention time and MS fragments, a total of 93 ePL species were identified.\u003c/p\u003e \u003cp\u003eAfter raw data were quantified on MultiQuant software (version 3.0, AB SCIEX, Framingham, U.S.A.), the intensities of ePLs in each sample were normalized to those of the corresponding lipid internal standards. The median relative standard deviation (RSD) of these ePLs in all quality control (QC) samples was 11.46% (range, 3.83%-45.11%). We excluded 8 ePL species with RSD exceeding 30%. These 85 ePLs were defined into 5 lipid subclasses according to the LipidMaps classification scheme, i.e., lyso-phosphatidylcholine with alkyl substituent (LPC O), lyso-phosphatidylethanolamine with alkyl substituent (LPE O), phosphatidylcholine with alkyl substituent (PC O), phosphatidylethanolamine with alkyl substituent (PE O) and PE-plasmalogen (PE p). The number and percentage of ePLs in each lipid category are shown (Fig.\u0026nbsp;2B). In all QC samples, 44% of the annotated ePLs demonstrated biological reproducibility with an RSD% \u0026lt; 10%, 80% showed reproducibility with an RSD% \u0026lt; 15%, and 89% exhibited reproducibility with an RSD% \u0026lt; 20% during the ether-phospholipidome analysis (Fig.\u0026nbsp;2C). These findings indicate that the data we obtained are robust and reliable. Distributions at baseline levels of 5 lipid subclasses were shown among individuals with incident carotid plaques and those controls and no outlets were observed (Fig.\u0026nbsp;2D). Detailed information on 85 ePL species and 5 ePL subclasses was shown (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eSerum triglycerides were determined through a direct colorimetric assay, while LDL cholesterol was estimated using the Friedewald equation if triglyceride levels were below 4 mmol/L. For higher triglyceride levels, LDL cholesterol was measured directly. Body mass index (BMI) was calculated as the ratio of measured weight (in kilograms) to height (in meters) squared. Information on lipid-lowering therapy was based on self-reports.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eBaseline clinical characteristics between participants with incident carotid plaque and those controls without carotid plaques were compared using the unpaired t-test for continuous variables and the χ2-test for categorical variables.\u003c/p\u003e \u003cp\u003ePartial least squares difference analysis (PLS-DA) was performed by SIMCA-P software (Umetrics, Ume\u0026aring;, Sweden) to maximize the distance and assess the holistically ePL metabolic alteration between groups\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Variable importance in the projection (VIP) generated from PLS-DA model was used for defining ePLs that contribute to the classification between groups. Nonparametric tests for individual ePLs were performed using the open-source software MultiExperiment Viewer (MeV, version 4.9.0, Dana-Farber Cancer Institute, MA) in Wilcoxon, Mann-Whitney test mode with the significant level of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eLogistic regression model was used to assess odds ratios (ORs) and 95% confidence intervals (CIs) for associations between baseline levels of the potential differential ePLs and their chemical composition with carotid plaque risk, with or without adjustment for age, BMI, TG and LDL-C. All the adjusted \u003cem\u003ep\u003c/em\u003e were adusted by Benjamini-Hochberg (BH). Data on covariates, including age, BMI, TG and LDL-C, was over 99% complete. Missing values for covariates were addressed using multiple imputations with chained equations, yielding results consistent with those obtained without imputation.\u003c/p\u003e \u003cp\u003eThe receiver-operating characteristic curve (ROC) curve was employed to assess the predictive ability of baseline routine clinical parameters and their panel composition with ePLs on the incidence of carotid plaques at follow-up, calculating the corresponding area under the receiver-operating characteristic curve (AUC). To assess the incremental predictive capacity of adding ePLs to the baseline routine clinical parameters for AS, we employed Net Reclassification Improvement (NRI) and \u003cem\u003ep\u003c/em\u003e values. Values of NRI\u0026thinsp;\u0026gt;\u0026thinsp;0 indicated that the new model has improved predictive capacity compared to the old model.\u003c/p\u003e \u003cp\u003eThe causal mediation associations of the identified differential ePL species with carotid plaque risk through some key clinical risk parameters were explored by causal mediation analysis using the bootstrapping method\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e in the \u0026lsquo;mediation\u0026rsquo; package in R (version 4.5.0). Two thousand bootstraps were run to estimate the confidence intervals. The remaining settings were set default.\u003c/p\u003e \u003cp\u003eBaseline ePL Pearson correlation networks in the control group and in the cases with incident carotid plaques were performed to explore the potential interactions among these ePLs (the cutoff Pearson correlation coefficient was 0.8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (the Ethics Committee of Shandong Provincial Hospital, China) and with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all patients for being included in the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline clinical characteristics of study participants\u003c/h2\u003e \u003cp\u003eAccording to the primary exclusion criteria, a total of 203 postmenopausal women without carotid plaques at baseline were included (Fig.\u0026nbsp;1). The baseline demographic, clinical and laboratory characteristics were collected. Over a median 3.8-year follow-up, 51 cases developed new carotid plaque. The baseline characteristics of all participants as well as of the incident carotid plaque cases and those without the incident carotid plaque were provide separately (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD age of all participants was 60.51\u0026thinsp;\u0026plusmn;\u0026thinsp;7.83 years. The incident carotid plaque cases were older and had higher baseline levels of total cholesterol (TC), LDL-C, high-density lipoprotein cholesterol (HDL-C) than those control subjects. The higher prevalence of dyslipidemia, hypertension and diabetes mellitus were reported in incident carotid plaque cases (50.98%, 72.55%, 17.65% \u003cem\u003evs.\u003c/em\u003e 43.42%, 58.55%, 12.50%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), consistent with. previous studies\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. No difference was observed for baseline BMI, TG, alanineaminotransferase (AST), creatinine (CREA) and blood urea nitrogen (BUN) between the two groups.\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 study 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=\"left\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;203)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eIncident carotid plaques at follow-up\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-incidence\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;152)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncidence\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;51)\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\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.51\u0026thinsp;\u0026plusmn;\u0026thinsp;7.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.58\u0026thinsp;\u0026plusmn;\u0026thinsp;6.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.27\u0026thinsp;\u0026plusmn;\u0026thinsp;7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood pressure, mm Hg\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\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146.64\u0026thinsp;\u0026plusmn;\u0026thinsp;21.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144.95\u0026thinsp;\u0026plusmn;\u0026thinsp;22.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151.67\u0026thinsp;\u0026plusmn;\u0026thinsp;19.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.64E-02\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\u003e87.83\u0026thinsp;\u0026plusmn;\u0026thinsp;12.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.86\u0026thinsp;\u0026plusmn;\u0026thinsp;11.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.75\u0026thinsp;\u0026plusmn;\u0026thinsp;13.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.59E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\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\u003eDyslipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (45.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (43.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (50.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.17E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (62.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (58.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (72.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.52E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (13.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (12.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (17.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.55E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory data\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\u003eTG, mmoL/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmoL/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mmoL/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.43E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmoL/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.79E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.29\u0026thinsp;\u0026plusmn;\u0026thinsp;7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.43\u0026thinsp;\u0026plusmn;\u0026thinsp;7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.86\u0026thinsp;\u0026plusmn;\u0026thinsp;6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.87E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCREA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.69\u0026thinsp;\u0026plusmn;\u0026thinsp;8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.73\u0026thinsp;\u0026plusmn;\u0026thinsp;14.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.03E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are n (%) and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. \u003cem\u003ep\u003c/em\u003e for difference of baseline characteristics between participants with or without incident carotid plaques at follow-up were calculated using un-paired t-test for continuous variables and the χ2-test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSBP: systolic blood pressure, DBP: diastolic blood pressure, TG: triglycerides, TC: total cholesterol, LDL-C: low-density lipoprotein cholesterol, HDL-C: high-density lipoprotein cholesterol, AST: alanineaminotransferase, CREA: creatinine, BUN: blood urea nitrogen.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEther-linked phospholipidomic fingerprints and carotid plaque risk\u003c/h2\u003e \u003cp\u003eWe first applied a supervised PLS-DA model to holistically assess the ePL metabolic alterations at baseline level. We observed the discrimination trends from ether-phospholipidome data in the PLS-DA score plots between cases with the incident AS and controls (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Principal component 1 of this PLS-DA model showed cumulative Q2\u0026thinsp;\u0026gt;\u0026thinsp;0, which means that our model is not overfitted and is predictive (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). These results suggested that baseline profound ePL metabolic perturbations were closely related with incident carotid plaques.\u003c/p\u003e \u003cp\u003eTo investigate the associations between individual ePL species and incident carotid plaques, firstly, based on above the PLS-DA model, we found out the vital variables to distinguish cases with incident carotid plaques from control group. A total of 17 ePLs with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.0 both for PCA 1 and PCA 2 were selected for subsequent univariate analysis to determine whether they were significantly altered in baseline sera for cases with incident carotid plaques (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC and S1D, Fig.\u0026nbsp;3A). In total, 6 ePLs exhibited \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDP\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;3A) and were regarded as the potential differential biomarker candidates. Details on differential ePLs were provided (Table S2).\u003c/p\u003e \u003cp\u003eSubsequently, a logistic regression analysis was used to determine ORs with 95%CI of baseline levels of the 6 potential ePLs for newly incident carotid plaques. Among these 6 individual ePLs, all showed significant associations with carotid plaque risk (All ORs\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;3B). After adjustments for age, BMI, TG and LDL-C, 4 ePLs, including PE O-18:2_18:1, PE O-18:0_20:4, PE O-18:1_22:4 and PE O-20:1_20:4, remained closely associated with increased carotid plaque risk (adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;3C-E). Baseline levels of these 4 ePLs were shown and significantly elevated in cases with incident carotid plaques compared with controls (Fig. S2A-D). Interestingly, we observed that the 4 ePLs mainly involved in polyunsaturated fatty acyl-chain (PUFA)-containing ePLs (e.g., 18:2, 20:4, 22:4).\u003c/p\u003e \u003cp\u003eTo further discern PUFA-containing ePLs more associated with incident carotid plaques, we systematically explored how the chemical composition of ePLs influenced their association with the incident carotid plaques. We summed all species according to the different unsaturation of two certain fatty acyl-chains attached to the ePLs base (e.g., all species with 0, 1, 2, 3, 4, 5, 6, 7 and 8 of carbon-carbon double bonds (C\u0026thinsp;=\u0026thinsp;C) in two certain fatty acyl-chains), independent of fatty acyl-chain length. We found that the PUFA-containing ePLs (\u0026lsquo;C\u0026thinsp;=\u0026thinsp;C\u0026rsquo; \u0026gt; 2) were still strongly associated with the risk of carotid plaques, before and after adjustments for age, BMI and TG (Fig.\u0026nbsp;4A-C). But, After adjustment for LDL-C, only PUFA-containing ePLs (\u0026lsquo;C\u0026thinsp;=\u0026thinsp;C\u0026rsquo; = 3) showed a significantly association with the future development of carotid plaques (Fig.\u0026nbsp;4D).\u003c/p\u003e \u003cp\u003eCollectively, these results highlighted those alterations in baseline levels of ether-linked phospholipidomic fingerprints, including individual ePL species with PUFA, and their chemical compositions with high unsaturation, were closely linked to the incidence of carotid plaques in postmenopausal women. This suggested that PUFA-ePLs might be potential biomarkers for future carotid plaque incidence, aiding in identifying individuals at risk for AS before overt pathology develops.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eA putative causal role of the ePLs in future carotid plaques mediated by LDL-C\u003c/h2\u003e \u003cp\u003eWe systematically evaluated the putative causal relationships between the 4 ePLs (PE O-18:2_18:1, PE O-18:0_20:4, PE O-18:1_22:4 and PE O-20:1_20:4), clinical risk factors (BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), TG, TC, LDL-C and HDL-C) and future incident carotid plaques. A total of 28 mediation links were established for the contribution of ePLs to incident carotid plaques mediated by clinical risk factors (Fig.\u0026nbsp;5A). The results showed that only four mediation pathways mediated by LDL-C were significant (\u003cem\u003ep\u003c/em\u003e_FDRmediation\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while 24 mediation causal effects mediated by the other six clinical risk factors did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e_FDRmediation\u0026thinsp;\u0026gt;\u0026thinsp;0.05). LDL-C mediated 25%, 26%, 25% and 24% of the effect of PE O-18:2_18:1, PE O-18:0_20:4, PE O-18:1_22:4 and PE O-20:1_20:4 on carotid plaque incidence, respectively (Fig.\u0026nbsp;5B-E, all \u003cem\u003ep\u003c/em\u003e_FDRmediation\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the other hand, no significant mediation effect of 4 ePLs on the link between LDL-C and carotid plaques risk was observed, respectively (Fig.\u0026nbsp;5B-E, all \u003cem\u003ep\u003c/em\u003e_FDRinvese mediation\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of carotid plaque-associated ePL species and major clinical risk parameters\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis was performed to build up a correlation matrix among carotid plaque-associated ePL species and major clinical parameters-related AS risk. Among all participants, the baseline levels of these carotid plaque-associated ePL species were closely correlated with a range of biochemical measurements and metabolic parameters (Fig.\u0026nbsp;5F). For example, most of these ePLs were more highly, positively associated with LDL-C and BUN, but inversely associated with TG and BMI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation network analysis of baseline ePLs for controls and cases with incident carotid plaques\u003c/h2\u003e \u003cp\u003eCorrelation network is a useful tool for integrating information from the high throughput experiment to show the relationships among the metabolites. In this study, correlation network was constructed and analyzed on total 85 baseline ePLs in controls (Fig. S3A) and cases with incident carotid plaques (Fig. S3B). The metabolic correlation network in cases with incident carotid plaques was similar to that in controls, implying that potential interactions among ePLs had no change in the development of carotid plaques.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eePL metabolic signatures predicting carotid plaque identified by machine learning\u003c/h2\u003e \u003cp\u003eTo define the potential prediction of the identified 4 ePLs for new carotid plaques, the ROC curve was employed to assess the predictive ability of baseline routine clinical parameters and their panel composition with ePLs on the incidence of carotid plaques at follow-up, calculating the corresponding AUCs.\u003c/p\u003e \u003cp\u003eFirstly, we separately evaluated the predictive effects of the 9 routine clinical parameters, including age, BMI, blood pressure (SBP and DBP), AST and CREA and BUN, TG and LDL-C on carotid plaque risk. The AUCs of the routine clinical parameters, in turn, were 0.774, 0.550, 0.624, 0.569, 0.561 and 0.671, respectively. Subsequently, taking into account the predictive capability of the 9 routine clinical parameters and the 4 ePLs as a whole, we separately constructed 2 different prediction models: the model 1 (basal model) including all 9 routine clinical parameters alone; the model 2 including all 9 routine clinical parameters and 4 ePLs. When addition of 4 ePLs, the model 2 performed better than the model 1 (AUC 0.835 vs 0.824, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;5G). The Net Reclassification Improvement (NRI) analysis showed that the inclusion of the four ePLs significantly improved classification accuracy compared to the basal model (NRI\u0026thinsp;\u0026gt;\u0026thinsp;0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Altogether, the addition of 4 ePLs on the top of the routine clinical parameter exhibited the greatest predictive benefit for carotid plaque detection. In the future clinical practice, the ePLs may be taken into risk assessment model to improved prediction performance of AS.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMS-based ether-linked phospholipidomics enables a broad range of ePL metabolites to be characterized. This rich source of information could facilitate the elucidation of the underlying pathological progression of the disease. In this prospective, population-based longitudinal cohort, part from the RICAS study, we observed a profound pro-atherogenic ePL metabolic characterization preceding the development of carotid plaques, allowing for an in-depth understanding of metabolic changes in postmenopausal women. The altered ether-linked phospholipidome, including plasmalogens of PEs, indicated the presence of lipid dysfunction, ROS-induced oxidation and peroxisomal disorder in incident carotid plaque individuals\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The associations between ePLs, particularly PUFA-containing ePLs, and CVD are controversial \u003csup\u003e\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Nevertheless, our finding on PUFA-containing ePLs is supported by the study that showed elevated ePLs content in human sera of CVD and the carotid artery plaque tissues\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Understanding the role of ePL species, especially highly unsaturated ePLs in health and disease holds promise for understanding pathogenesis of CVD.\u003c/p\u003e \u003cp\u003eOur finding revealed the association between the ether-linked phospholipidome and the development of carotid plaque, which was independent of confounders, such as age, BMI. Previous research has indicated ongoing focus regarding whether ether-linked phospholipidome impacts clinical risk factors responsible for CVD development. In the current study, we demonstrated that LDL-C rather than TG strongly mediated the effect ePLs on AS risk. Both are key components of cardiovascular risk assessment\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. LDL-C is mainly involved in liver cholesterol metabolism and has been found to be strongly associated with cholesterol transport and is more directly linked AS\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, whereas TG is mainly related to fat storage and energy metabolism and showed more correlation with obesity and insulin resistance\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. This observation suggests that ePL metabolic perturbation before carotid plaque development showed more correlations with cholesterol metabolism than energy metabolism in Chinese postmenopausal women. Results from causal mediation analysis supplemented knowledge on the potential function of metabolites influencing the onset of AS. However, as these findings are mostly generated from observational data, further functional experiments focusing on specific ePL metabolites are needed to confirm the causality.\u003c/p\u003e \u003cp\u003eRisk stratification is a critical component in AS prevention. We developed a prediction model for future carotid plaque development in postmenopausal women. The combination of metabolomic information and conventional clinical risk factors in the prediction model showed good predictive performance and classification accuracy. This finding emphasized that metabolites could reveal subtle biochemical changes and risk factors that traditional markers cannot capture\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. New metabolic biomarkers are linked to AS, offering the potential for personalized risk assessment. However, despite the improved predictive power from metabolite integration, further research is needed to validate their widespread use and standardization in clinical practice.\u003c/p\u003e \u003cp\u003eOur study has a few limitations. The sample size of the case-control study within a prospective cohort is relatively small. Additionally, our 4 ePL metabolites-based prediction model for carotid plaque has not been validated in an independent longitudinal cohort.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe current study identified a panel of ePL metabolic biomarkers that were able to predict carotid plaque onset, which may be potentially used for the precision management of AS. To our knowledge, this is the first prospective ether-phospholipidomic study of AS risk among postmenopausal individuals, highlighting importance of the PUFA-containing ePLs in AS risk. Furthermore, our findings support the notion that the ePL metabolic disruption drive LDL-C rather than TG in turn cause AS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online version contains supplementary material available at\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are indebted to the study participants for their input and interface. We greatly appreciate the assistance of nurses, laboratory technicians, clinical research assistants and data managers at Department of Endocrinology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQ.X.: Formal analysis, Investigation, Methodology, Data curation, Writing-original draft, Visualization, Funding acquisition. Q.L.: Formal analysis, Investigation, Data curation, Validation. Q.S.: Conceptualization, Resources, Writing - review \u0026amp; editing, Supervision, Project administration. Y.X.: Data curation, Writing - review \u0026amp; editing. Y.L., X.D., X.M.: Writing - review \u0026amp; editing, Supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (22204090); Natural Science Foundation of Shandong Province (ZR2021QB089); Taishan Scholar Foundation of Shandong Province (to Qiuhui Xuan).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Chinese Data protection Agency does not allow open access to our data; however, upon reasonable request the steering committee of the RICAS Population Study may allow further follow-up analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval from the local ethics committee was obtained before the onset of the study and all the participants provided their consent before starting the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVogel B, Acevedo M, Appelman Y, Bairey Merz CN, Chieffo A, Figtree GA, Guerrero M, Kunadian V, Lam CSP, Maas A, Mihailidou AS, Olszanecka A, Poole JE, Saldarriaga C, Saw J, Zuhlke L, Mehran R. 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Circulation. 2020;142:2110\u0026ndash;27.\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":"Carotid plaques, Ether-linked phospholipids with polyunsaturated fatty acyl chains, Predictive model, Causal mediation, Postmenopausal women","lastPublishedDoi":"10.21203/rs.3.rs-5738378/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5738378/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum ether-linked phospholipids (ePLs) have gained attention in metabolic disease research. Postmenopausal women face a higher risk of developing atherosclerosis (AS), yet current methods for risk prediction and understanding of AS pathophysiology remain limited. This study aimed to identify ePL biomarkers linked to AS, assess their chemical composition in relation to AS risk, and explore whether their impact is mediated by clinical risk factors in postmenopausal women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHere, this research was conducted within the Rose Asymptomatic Intracranial Artery Stenosis (RICAS) prospective study and included 203 postmenopausal women without carotid plaques at baseline. After a median follow-up of 3.8 years, 51 participants developed new carotid plaques. Baseline serum ePLs were semi-quantitated using liquid chromatography-mass spectrometry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean age of participants was 60.51 (±7.83) years. A total of 85 unique ePL species across five lipid subclasses were identified and quantified according to lipid internal standards. Multivariate models indicated global metabolic disruptions in ePLs preceding carotid plaque formation. Six ePLs were identified as potential biomarkers associated with AS risk (VIP \u0026gt; 1, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, FDR \u0026lt; 0.05). After adjusting for age, BMI, TG, and LDL-C, four polyunsaturated fatty acid (PUFA)-containing ePLs remained significantly associated with carotid plaque risk (OR \u0026gt; 1, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Causal mediation analysis indicated that LDL-C mediated the effects of these ePLs on AS. A machine-learning model incorporating these ePLs with clinical parameters significantly improved carotid plaque prediction (AUC: 0.835, Net Reclassification Improvement \u0026gt; 0, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study highlighted importance of metabolic disruption in PUFA-ePLs for the development of AS. Our findings support the notion that metabolic disruption of PUFA-ePLs can affect LDL-C levels, which is the primary driver of AS in postmenopausal women. Ether-linked phospholipidome as a valuable phenotype hold potential clinical utility in the prediction of AS.\u003c/p\u003e","manuscriptTitle":"Ether-linked phospholipidomic profiling unveils novel lipid fingerprints and algorithm predicts the future development of carotid plaques in postmenopausal women: a population-based cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-14 16:01:47","doi":"10.21203/rs.3.rs-5738378/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":"10623991-e5ee-4157-a7fd-6e0ba4bb82f9","owner":[],"postedDate":"January 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-11T16:13:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-14 16:01:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5738378","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5738378","identity":"rs-5738378","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}