
==== Front
Mol Neurobiol
Mol Neurobiol
Molecular Neurobiology
0893-7648
1559-1182
Springer US New York

38459364
4093
10.1007/s12035-024-04093-9
Original Article
Plasmalogens and Octanoylcarnitine Serve as Early Warnings for Central Retinal Artery Occlusion
Wang Chuansen 1
Li Ying 1
Feng Jiaqing 1
Liu Hang 2
Wang Yuedan 1
Wan Yuwei 1
Zheng Mengxue 1
Li Xuejie 1
Chen Ting ct19870629@hotmail.com

1
http://orcid.org/0000-0001-7762-8689
Xiao Xuan xiaoxuan1111@whu.edu.cn

12
1 https://ror.org/03ekhbz91 grid.412632.0 0000 0004 1758 2270 Department of Ophthalmology, Renmin Hospital of Wuhan University, No. 238 Jie Fang Road, Wuhan, 430060 Hubei China
2 https://ror.org/03ekhbz91 grid.412632.0 0000 0004 1758 2270 Department of Clinical Laboratory, Institute of Translational Medicine, Renmin Hospital of Wuhan University, Wuhan, China
8 3 2024
8 3 2024
2024
61 10 80268037
19 10 2023
3 3 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Central retinal artery occlusion (CRAO) is a kind of ophthalmic emergency which may cause loss of functional visual acuity. However, the limited treatment options emphasize the significance of early disease prevention. Metabolomics has the potential to be a powerful tool for early identification of individuals at risk of CRAO. The aim of the study was to identify potential biomarkers for CRAO through a comprehensive analysis. We employed metabolomics analysis to compare venous blood samples from CRAO patients with cataract patients for the venous difference, as well as arterial and venous blood from CRAO patients for the arteriovenous difference. The analysis of metabolites showed that PC(P-18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PC(P-18:0/20:4(5Z,8Z,11Z,14Z)) and octanoylcarnitine were strongly correlated with CRAO. We also used univariate logistic regression, random forest (RF), and support vector machine (SVM) to screen clinical parameters of patients and found that HDL-C and ApoA1 showed significant predictive efficacy in CRAO patients. We compared the predictive performance of the clinical parameter model with combined model. The prediction efficiency of the combined model was significantly better with area under the receiver operating characteristic curve (AUROC) of 0.815. Decision curve analysis (DCA) also exhibited a notably higher net benefit rate. These results underscored the potency of these three substances as robust predictors of CRAO occurrence.

Keywords

Central retinal artery occlusion
Metabolomics
Differential metabolites
Prediction model
Clinical parameter
Key research and development project of Hubei Province2022BCA009 Xiao Xuan http://dx.doi.org/10.13039/501100012226 Fundamental Research Funds for the Central Universities 2042023gf0013 Xiao Xuan issue-copyright-statement© Springer Science+Business Media, LLC, part of Springer Nature 2024
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pmcIntroduction

Central retinal artery occlusion (CRAO) is an ophthalmological emergency which mainly refer that the interruption of blood flow of central retinal artery occlusion [1–3]. The outcome of CRAO is very serious, which may cause loss of functional visual acuity and damage retinal cells irreversibly within hours [4, 5]. In addition, there is an increased risk of stroke and myocardial ischemia after CRAO[6–8]. Therefore, early identification of possible CRAO patients and early intervention is extremely important to save vision of patient. A 10-year follow-up of the cohort showed that AV incision, arteriole wall opacity, and retinal vein obstruction predicted the occurrence of retinal embolism, but no subsequent studies supported this view [9]. Currently, studies of biomarkers for CRAO have focused on hematological markers such as Neutrophil/lymphocyte Ratio, but these markers are primarily used to assess a patient's systemic inflammatory status. And exhibits greater prognostic value in assessing the clinical outcomes of individuals with CRAO [10–12]. However, there is still no suitable biomarker to predict the occurrence of CRAO. New biomarkers and targets are needed for CRAO.

Metabolomics is mainly a discipline that studies the types, quantities, and their changes of metabolites (<1500 Da) caused by organisms caused by external stimuli, pathophysiological changes, and genetic mutations [13, 14]. Because metabolites can directly reflect the metabolic pathway at a certain moment, they have the potential to magnify even subtle changes at the gene or protein level [15, 16]. At present, metabolomics has shown a great role in the identification of biomarkers in the field of other cardiovascular and cerebrovascular diseases, such as myocardial infarction and stroke [17–20]. Given the similarities in pathogenesis between retinal artery occlusion and conditions like stroke and myocardial infarction, metabolomics may be helpful for the discovery of potential biomarkers for CRAO [21, 22]. Employing serum as sample may easily reflect systemic changes of human body, making it potentially valuable for uncovering the underlying mechanisms of CRAO, a type of vascular disorder. However, the metabolomics of CRAO based on serum is still an unexplored territory for researchers.

In this study, we conducted a comparison between the venous blood of CRAO patients and control patients utilizing non-targeted metabolomics. This approach allowed us to discern alterations in metabolite profiles among CRAO patients. Additionally, we compared arterial blood collecting from the embolization site in the anterior segment of CRAO patients before thrombolytic surgery and venous blood. This dual approach aimed to provide a more comprehensive understanding of the lesions and possible information about emboli associated with CRAO. Our aim is to investigate the potential underlying mechanisms of CRAO through the analysis of the metabolome and identifying potential biomarkers.

Material and Methods

Patient Cohort

This study was approved by the Ethical Committee Board of Renmin Hospital of Wuhan University and followed the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants enrolled in this study. Inclusion criteria: (1) age 18–75 years; (2) CRAO patients were diagnosed with existing guidelines, including sudden vision loss, positive RAPD, retinal ischemic edema, and delayed arterial filling on angiography; (3) the patient or surrogate consent to take part in the study; (4) blood samples were collected within 72 h of symptom onset. Exclusion criteria include the following: (1) the patient had a history of CRAO; (2) the patient had no coronary artery disease, stroke, malignancy, severe renal insufficiency (eGFR<30 mL/min), liver disease, or lung disease.

Demographic, clinical, and laboratory data of patients were obtained, including factors such as age and gender. Venous blood samples were prospectively collected from patients after overnight fasting in the morning at first admission. Arterial blood samples were taken from upstream of the embolization site of CRAO near the embolus prior to interventional injection of thrombolytic agents. In total, venous blood samples were collected from 37 patients with CRAO, and arterial blood samples were collected from 28 of these patients. At the same time, we collected blood samples of 24 cataract patients as the control group.

Patients with hypertension were defined as those with blood pressure above 140/90 mmHg measured on two or more separate occasions or who had a hypertension history. Hematological indicators, including red blood cell count (RBC) and hemoglobin (Hb), were measured using XS-1000i fully automated hematology analyzer (Sysmex) and accompanying reagents. The lipid profiles, triglycerides (TG), total cholesterol (TCh), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), Lipoprotein A (LPA), Apolipoprotein A1 (ApoA1), Apolipoprotein B (ApoB) and Apolipoprotein A1b (ApoA1b), were measured by ADVIA 2400 biochemical analyzer (Siemens). Non-high-density lipoprotein cholesterol (Non-LDL-C) was calculated as TCh minus HDL-C.

The levels of prothrombin time (PT), thrombin time (TT), activated coagulation time (ACT), activated partial thromboplastin time (APTT), international normalized ratio (INR), fibrinogen (Fib), fibrin degradation products (FDP) and D-dimer were detected by CA7000 automatic hemagglutination analyzer (Sysmex) and proprietary reagents. The operation process was carried out in strict accordance with the instructions, and the detection was completed within 4h.

Samples were centrifuged for 10 min at 1000 g at room temperature and serum was aliquoted and stored at -80°C until use for metabolomic analyses.

Metabolomic Profiling

The samples were separated by Agilent 1290 Infinity LC ultra-high performance liquid chromatography HILIC column and analyzed by Triple TOF6600 mass spectrometer (AB SCIEX) and Q Exactive series mass spectrometer (Thermo). Positive and negative ion modes of electrospray ionization (ESI) were respectively detected. The original data was converted by ProteoWizard, and then XCMS was used for peak alignment, retention time correction and peak area extraction. Metabolite structure identification and data preprocessing were conducted on the data extracted through XCMS. Subsequently, an assessment of the quality of the experimental data was performed, followed by data analysis. Normalized total peak intensity levels were imported into the ropls R package (version 1.16.0) and the orthogonal partial least squares discriminant analysis (OPLS-DA) algorithm was used for multivariate data analysis, including R2 and Q2 quality indicators, and the calculation of projected variable importance (VIP) values. VIP reflects variability in pathological stimulus responses explained by specified metabolites.

Statistical Analysis

IBM SPSS Statistics for Windows software, Version 22.0 (IBM Corp., Armonk, N.Y., USA) was used for statistical analysis. Continuous data of clinical parameters were described as median ± SD and tested by Kolmogorov-Smirnov test to assess the normal distribution of the data. If the data followed a normal distribution, we proceeded to test for homogeneity of variances. If the variances were found to be equal, we conducted independent sample t-tests. In cases where the assumption of homogeneity of variances was not met, we performed tests to assess the homogeneity of variances. For data that did not exhibit a normal distribution, we employed the Mann-Whitney U test. Frequency variables were recorded as numbers and percentages and compared by χ2 test. In addition, univariate logistic regression was used for statistical analysis of clinical parameters. R (Version 4.3.0) is used for building the machine learning model of random forest (RF) and support vector machine (SVM) to evaluate the importance of variables. R package randomForest, caret and e1071 were used. Venn diagram was plotted by R package VennDiagram.

Receiver operating characteristic (ROC) was plot by R package pROC. It was performed to calculate the sensitivity and specificity of the significant clinical parameters and differential metabolites and investigate the optimal cutoff value for predictions. The optimal cutoff value for sensitivity and specificity was calculated according to the maximal Youden Index. AUROC curves were used to demonstrate the predictive validity. DCA was plotted by R package rmda. By comparing the threshold value to the net benefit, the DCA curve can evaluate the pros and cons of different decision strategies.

Results

Metabolomic Profiling

The overall workflow of the study is shown in Fig. 1. Non-targeted metabolomics analysis was conducted using liquid chromatography-mass spectrometry. Following quality control, data filtering, and normalization, a total of 1259 metabolites were identified through the combination of positive and negative ion models. Among these, 793 metabolites were identified in the positive ion mode, and 466 metabolites were identified in the negative ion mode. All identified metabolites in this study were categorized based on their chemical taxonomy. The lipids and lips-like molecules accounted for the largest proportion, reaching 30.183%. Organic acids and derivatives, as well as organoheterocyclic compounds, also represented a significant proportion of the identified molecular species.Fig. 1 General workflow for this study Clinical parameters of CRAO patients and control patients were screened by statistical methods, and then different clinical parameters was identified. On the other hand, we identified differential metabolites by comparing venous differences between CRAO patients and controls by LC-MS/MS. In addition, we also obtained arteriovenous metabolomics differences between CRAO patients to identify differential metabolites. We then combined the differential metabolites to obtain the co-express differential metabolites. Finally, we combined with the clinical parameters obtained through statistical analysis to build a predictive model and evaluate the model

To compare the differences in systemic metabolic profile between CRAO patients and control patients, the venous blood samples from CRAO patients were analyzed and subsequently compared with those from the control patients.

OPLS-DA was conducted, revealing apparent discrimination among samples (Fig. 2A, B). The supervised classification of OPLS-DA was validated by 200-fold cross-validation. Negative ion model was estimated with a R2 value of 0.8345 and a Q2 value of 0.4412 at the first component and positive ion model was estimated with a R2 value of 0.5893 and a Q2 value of 0.3309 (Fig. 2C, D). For the two models, the intercept between Q2 regression line and Y-axis is less than 0.05, and with the decrease of replacement retention, R2 and Q2 decline, and the regression line shows an upward trend, indicating that the model is robust and reliable without overfitting. The criteria for significant differential metabolites involved OPLS-DA VIP>1 and unpaired t test with unequal group variance at a threshold of P<0.05. Using univariate analysis, we conducted differential analysis on all detected metabolites in both positive and negative ion modes. Metabolites with a fold change (FC) greater than 1.5 or less than 0.67, and a p-value less than 0.05, were selected as differentially expressed metabolites. These differentially expressed metabolites were visually presented using volcano plots (Fig. 2E, F).Fig. 2 Metabolomics analysis of venous blood differences between CRAO group and control group A, B: Constructing OPLS-DA model for distinguishing CRAO and control group were constructed in negative and positive ion mode. The models show that the two groups can be clearly distinguished; C, D: Permutation test of the models. The model was tested by 200 permutation tests to ensure the validity of the model; E, F: Volcano plot of differential metabolites in negative and positive ion mode. Metabolites with a FC more than 1.5 or less than 0.67, and a p-value less than 0.05, were selected as differentially expressed metabolites. Significantly upregulated metabolites are depicted in red, significantly downregulated metabolites are depicted in blue

Differential metabolites were screened comprehensively by univariate statistical analysis and multidimensional statistical analysis (Fig. 3A, B). A total of 54 differential metabolites were identified, of which 34 were identified under the anion model and 20 were identified under the anion model. Among these differential metabolites, 27 substances were significantly upregulated, and 27 substances were significantly downregulated in CRAO patients compared to control patients. Through metabolite network mapping, we observed a higher degree of alterations in lipid metabolites among CRAO patients (Fig. 3C). Pathway enrichment analysis revealed significant enrichment of cholesterol metabolism pathway, with different metabolites contributing to a substantial proportion of the overall pathway changes in CRAO patients. Furthermore, the pathway of primary bile acid biosynthesis and bile secretion exhibited considerable alterations in the metabolic profile of CRAO patients (Fig. 3D).Fig. 3 Pathway analysis of venous blood differences between CRAO group and control group. A, B: Differential metabolites between CRAO group and control group in negative and positive ion mode. Red represents upregulated differential metabolites and green represents downregulated differential metabolites. C: Network map of the differential metabolites. D: Pathway enrichment analysis of differential metabolites. Rich factor was the ratio of the number of differentially expressed metabolites located in this pathway to the total number of metabolites located in this pathway among all annotated metabolites

To identify more meaningful metabolites related to CRAO, we compared arterial and venous blood samples from a group of CRAO patients. samples were collected from the site upstream of the obstruction in patients with CRAO who underwent an interventional procedure prior to selective ophthalmic thrombolysis. We hypothesized that the arterial blood collected from the obstruction site might contain differential metabolites that reflect the characteristics of the emboli in comparison to venous blood. Compared with the comparison between venous blood that can reflect the difference in the systemic metabolic status of patients, the comparison between arterial blood and venous blood may be more targeted to reflect the details of embolism. Therefore, we performed metabolomic analysis and comparison of arterial and venous blood sample of CRAO patients.

Similarly, we construct an OPLS-DA model to distinguish sample s and test the model (Fig. 4A, B, C, D). The results of univariate statistical analysis and multidimensional statistical analysis were then used to screen the differential metabolites (Fig. 4E, F). In total, 182 differential metabolites were identified, with 74 identified in positive ion mode and 108 identified in negative ion mode. Among these metabolites, 61 metabolites exhibited an upward trend, and 121 metabolites showed a downward trend (Fig. 4G, H).Fig. 4 Metabolomics analysis of difference of arterial blood group and venous blood group in CRAO patients. A, B: Constructing OPLS-DA model for distinguishing arterial blood group and venous blood group in negative and positive ion mode. The models show that the two groups can be clearly distinguished; C, D: Permutation test of the models. The model was tested by 200 permutation tests to ensure the validity of the model; E, F: Volcano plot of differential metabolites in negative and positive ion mode. Metabolites with a FC greater than 1.5 or less than 0.67, and a p-value less than 0.05, were selected as differentially expressed metabolites. Significantly upregulated metabolites are depicted in red, significantly downregulated metabolites are depicted in blue; G, H: Differential metabolites between arterial blood group and venous blood group in CRAO patients in negative and positive ion mode. Red represents upregulated differential metabolites and green represents downregulated differential metabolites

To screen for differential metabolites in CRAO patients, we compared venous blood from CRAO patients and control group. We then selected metabolites that displayed the same trend in the comparison of the arterial blood of CRAO patients with the venous blood. Venn diagram represents same trend metabolites among the differential metabolites in the venous blood of CRAO patients compared to the control group and those in the arterial and venous blood of CRAO patients. A total of 7 kinds of substances were identified (Fig. 5A, B). Among the upregulated metabolites, we found tris(hydroxymethyl)aminomethane, fenpropidin, and 4-hydroxybenzaldehyde. In the downregulated category, there were 4 substances: PC(P-18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PC(P-18:0/20:4(5Z,8Z,11Z,14Z)), octanoylcarnitine, and trans-3-hydroxycotinine-β-glucuronide. The normalized charge-mass ratio, representing relative concentration, is employed to illustrate the concentration gradient of the selected metabolites post-screening (Fig. 5C).Fig. 5 Screening for representative differential metabolites A: Differential metabolites showed an upward trend in the analysis. The red part represents 60 kinds of upregulated differential metabolites comparing the arterial blood of CRAO patients with the venous blood. The blue part represents 26 kinds of upregulated differential metabolites comparing the venous blood of CRAO patients with the venous blood of control patients. The intersection of the Venn diagram represents co-upregulated metabolites among the differential metabolites in the venous blood of CRAO patients compared to the control group and those in the arterial and venous blood of CRAO patients. B: Differential metabolites showed a downward trend in the analysis. The red part represents 121 kinds of downregulated differential metabolites comparing the arterial blood of CRAO patients with the venous blood. The blue part represents 27 kinds of downregulated differential metabolites comparing the venous blood of CRAO patients with the venous blood of control patients. The intersection of the Venn diagram represents co-downregulated metabolites among the differential metabolites in the venous blood of CRAO patients compared to the control group and those in the arterial and venous blood of CRAO patients. C: Box plots represent the relative content of 7 metabolites screened in Fig. 5A and Fig. 5B

After excluding some non-human metabolites and those metabolites that are clearly not related to CRAO based on prior knowledge, we narrowed down the selection to three metabolites: PC(P-18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PC(P-18:0/20:4(5Z,8Z,11Z,14Z)) and octanoylcarnitine. We hypothesize that these metabolites could potentially be biomarkers of CRAO.

Clinical Parameters Analysis

We conducted an analysis of the clinical parameters of the patients from whom blood samples were collected (Table 1). The p-value of gender, Hb, HDL-C, ApoA1 and AT-III was less than 0.05, which can be statistically different between the CRAO group and the control group.Table 1 Clinical Characteristics of CRAO patients and control patients

	CRAO (n = 37)	Control (n = 24)	P value	
Gender (male)	26(70.27%)	7(29.17%)	0.002	
Age (years)	61.08±8.64	65.50±8.10	0.050	
Hypertension (%)	18(48.65%)	10(41.67%)	0.593	
RBC (×1012/L)	4.60±0.57	4.42±0.52	0.251	
Hb (g/L)	138.84±15.42	130.21±14.43	0.031	
TCh (mmol/L)	4.49±0.92	4.71±1.08	0.404	
TG (mmol/L)	1.54(1.22-2.10)	1.72±0.90	0.560	
HDL-C (mmol/L)	1.03±0.23	1.25±0.27	0.001	
LDL-C (mmol/L)	2.63±0.84	2.71±0.95	0.740	
Non-HDL-C (mmol/L)	3.46±0.89	3.45±1.06	0.973	
ApoA1 (g/L)	1.22±0.15	1.37±0.15	0.001	
ApoB (g/L)	0.87±0.21	0.88±0.25	0.861	
ApoA1b (g/L)	1.42(1.20-1.76)	1.62(1.35-2.06)	0.122	
PT (s)	10.77±0.67	11.40±2.95	0.756	
TT (s)	16.20(15.60-17.15)	15.95(1.5.50-16.55)	0.231	
ACT (s)	113.25±12.99	108.51±24.83	0.334	
APTT (s)	27.90(26.30-29.10)	27.80(25.45-28.70)	0.521	
INR	0.91(0.89-0.97)	0.92(0.88-1.00)	0.652	
FIB (g/L)	3.04±0.73	2.89±0.44	0.369	
FDP (mg/L)	1.30(1.00-1.80)	1.12(0.81-1.80)	0.595	
D-Dimer (mg/L)	0.27(0.23-0.42)	0.32(0.21-0.39)	0.768	
AT-III (g/L)	106.49±13.81	94.65±14.09	0.002	
RBC red blood cell, Hb hemoglobin, TCh total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, Non-HDL-C non-high-density lipoprotein cholesterol, ApoA1 apolipoprotein A1, ApoB apolipoprotein B, ApoA1b apolipoprotein A1b, PT prothrombin time, TT thrombin time, ACT activated coagulation time, APTT activated partial thromboplastin time, INR international normalized ratio, FIB fibrinogen, FDP fibrin degradation products, AT-III antithrombin-III

To further screen the clinical parameters, univariate logistic regression analysis was conducted on the clinical data. The p-value of gender, Hb, HDL-C, ApoA1 and AT-III was less than 0.05, which was considered statistically significant. In terms of the odds ratio (OR) value, gender, HDL-C and Apoa1 are less than 1, indicating a protective effect against CRAO onset. Conversely, the ORs for Hb and AT-III were greater than 1, implying that they might contribute as risk factors for CRAO. However, it is worth noting that the OR values for Gender, Hb, and AT-III were very close to 1, suggesting that their effects might not be substantial. RF and SVM analyzed these parameters and ranked the variables with statistically significant differences as described above. The results are as follows (Table 2).Table 2 Summary of the important clinical parameter for the CRAO patients and non-CRAO patients in various analysis

	Univariate logistic regression	RF	SVM	
Odds Ratio (95%CI)	P-Value	
Gender	0.94 (0.88-1)	0.002	Rank 3	Rank 6	
Hb	1.04 (1-1.08)	0.040	Rank 9	Rank 9	
HDL-C	0.03 (0-0.31)	0.004	Rank 4	Rank 4	
ApoA1	0 (0-0.13)	0.003	Rank 1	Rank 2	
AT-III	1.07 (1.02-1.12)	0.005	Rank 2	Rank 1	

Finally, we evaluated parameters with the OR value of univariate logistic regression as far away from 1 as possible and the P-value less than 0.05. Also, RF and SVM were used to rank these clinical parameters. We combined the results of the three methods to screen the variables, and obtained two variables, HDL-C and ApoA1.

Diagnostic Performance of Combined Model of CRAO

We constructed a clinical model and a combined model of clinical parameters and metabolites for the prediction of CRAO. The clinical model contains two screened clinical parameters. The predictive capacity of the clinical model, as indicated by the AUROC value, was 0.757, reflecting its reasonable ability to discriminate between instances of CRAO and non-CRAO (Fig. 5A).

Subsequently, we incorporated the three previously identified significantly differential metabolites and clinical parameters into the logistic regression model. and draw the ROC curve together with the clinical model. We then plotted the ROC curve for this combined model, alongside the clinical model. The results showed that the prediction effect of the model was significantly improved after adding metabolites with the AUROC of 0.815, signifying a significant advancement over the clinical model (Fig. 6A).Fig. 6 Evaluation of clinical and combined model. A: ROC curves for prediction of CRAO progression based on clinical parameters or a combination of clinical parameters and the differential metabolites in all CRAO and non-CRAO patients. Clinical model include two clinical parameters: HDL-C and ApoA1. Combined model includes two clinical parameters and three differential metabolites: HDL-C, ApoA1, PC (P-18:0/20:4 (5Z,8Z,11Z,14Z), PC (P-18:0/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)) and octanoylcarnitine; B: DCA curve of clinical and combined model

To investigate the impact of the combined model on individual patient classification, net reclassification index (NRI) was also calculated. The analysis showed that the combined model outperformed the clinical model in reclassifying non-responders with an NRI of 0.2083. This underscores a meaningful improvement in risk prediction. Additionally, we calculated the integrated discrimination improvement (IDI) to gauge the overall improvement of the model. An IDI value of 0.131 indicated that the new model significantly bolstered its predictive prowess compared to the old model. We also generated a DCA curve to assess the clinical utility of the models. The DCA curve demonstrated that within the High-Risk Threshold range of 0.4 to 1, the combined model consistently exhibited a notably higher net benefit rate compared to the clinical model (Fig 6B). This suggests that the combined model offers enhanced clinical value across this range of risk thresholds.

Discussion

The aim of this study was to investigate the potential underlying mechanisms of CRAO through the analysis of the metabolome and identifying potential biomarkers. Through screening clinical parameters, we found two blood indexes that may have predictive effects on CRAO occurrence: ApoA1 and HDL-C. And more importantly, by comparing the venous differences between CRAO patients and controls, as well as the arteriovenous differences between CRAO patients, we identified three metabolites: PC (P-18:0/20:4 (5Z,8Z,11Z,14Z)), PC (P-18:0/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)), and octanylcarnitine. We consider these metabolites having the potential to serve as biomarkers of CRAO.

For CRAO, there are currently no suitable biomarkers to predict the onset of CRAO. Current research has focused on markers of inflammation and platelet, such as Neutrophil/Lymphocyte Ratio and platelet distribution width [10, 11, 23]. However, one problem with these indicators is that they can only represent systemic inflammation or abnormal blood clotting. The specificity of such indicators is not high, and their predictive capability for CRAO is not robust. Given that much of the embolus in CRAO patients originates from carotid atherosclerotic plaque, markers of hyperlipidemia serve as better indicators of the systemic condition of patients [24]. Recently, blood lipid indicators, which were widely used in the field of stroke and myocardial infarction, have demonstrated potential in predicting the onset and progression of CRAO [25]. A nationally based cohort study showed a strong association between high HDL-C levels and reduced incidence of CRAO [26]. Also, our study found that HDL-C and ApoA1 can be used as factors to predict the development of CRAO in patients. This can be attributed to the beneficial effects of HDL-C on cardiovascular health [27]. HDL can transfer excess cholesterol from peripheral tissues to the liver in addition, impeding the accumulation of cholesterol in the artery wall and preventing the progression of atherosclerosis [28]. Studies have also shown that the level of HDL-C levels are associated with patient vision outcomes in RAO [29]. ApoA1 is the main component of HDL-C, which also showed a clear correlation with atherosclerosis [30, 31].

Venous blood comparisons between patients and controls may be a good way to explore changes of systemic metabolic characteristics for CRAO [32, 33]. However, better metabolite-specific analyses are required to identify differential metabolites that truly reflect the characteristics of CRAO. During the circulation of blood through organs, the substances it carries can enter cells to participate in metabolism, and the metabolic waste generated enters the bloodstream for removal [34]. Consequently, there can be differences in the levels of metabolites between arterial blood entering organs and venous blood exiting organs, referred to as arteriovenous metabolite differences [35–37]. These differences can reflect the metabolic status of organs, helping understand normal organ homeostasis and potential organ pathologies. Murashige et al. conducted a metabolomic analysis of blood samples taken from the radial artery, coronary sinus, and femoral veins, revealing arteriovenous gradients of circulating metabolites across the heart and leg [38]. They found that the heart mainly consumes fatty acids and secretes glutamine and other nitrogen-rich amino acids. A failing heart consumes more ketones and lactic acid and has a higher rate of proteolysis. Lindeman et.al. analyzed the delayed graft function caused by ischemia-reperfusion injury after kidney transplantation by comparing the differences of arteriovenous metabolites after kidney transplantation [39]. This method has high organ specificity and can reflects the characteristics of local lesion metabolism better than the comparison of venous blood between patients and controls.

For CRAO, alterations in metabolite concentrations upon reaching the embolus site can provide insights into the characteristics of local lesions. Consequently, we analyzed arterial blood near the embolus and compared it with the venous blood of patients. As this analysis inherently encompasses metabolic information for specific organs, we employed Venn diagrams to identify differential metabolites in the venous blood analysis. We propose that these specific metabolites likely play a significant role in the pathogenesis and progression of CRAO.

Studies have shown that the elevated PC(P-18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) and PC(P-18:0/20:4(5Z,8Z,11Z,14Z)) were inversely associated with adiposity indicators and Acceptance and Action Questionnaire for Weight-related difficulties [40].Obesity and overweight have been shown to be important risk factors for CRAO patients [41]. They are also classified as plasmalogens [40]. Plasmalogens have been recognized as crucial constituents of cell membranes and play a significant role in antioxidant processes due to their chemical bonds that are susceptible to attack by reactive oxygen species [42, 43]. In addition, plasmalogens are involved in cholesterol metabolism regulation and transport through a variety of mechanisms [44]. Lipidomics studies have found that plasmalogens is negatively correlated with cardiovascular disease [45]. In addition, the level of plasmalogens in human serum is positively related with serum HDL-C levels and decrease with aging [46].

Our findings revealed that two distinct metabolites, PC(P-18:0/20:4 (5Z,8Z,11Z,14Z)) and PC(P-18:0/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)), exhibited reduced expression levels in CRAO patients compared to control patients. This reduced expression of plasmalogens correlates with the decreased levels of HDL-C. Our results confirm this conclusion. This suggests that plasmalogens may be involved in the pathogenesis of CRAO in patients, possibly through plasmalogens exerting their antioxidant role in atherosclerosis. Concurrently, studies have demonstrated that increased plasmalogen can inhibit cholesterol biosynthesis [47]. The reduced expression of plasmalogens in CRAO patients may result in the accumulation of cholesterol and promote the development of atherosclerosis, which may also be a possible cause of CRAO. Further studies are needed to investigate the potential association between plasmalogens and CRAO.

Octanoylcarnitine is categorized as a kind of medium-chain acylcarnitine which play a role in the β-oxidation of fatty acid [48, 49]. When the rate of β-oxidation exceeds the rate of tricarboxylic acid cycle oxidation, it results in the accumulation of medium-chain acylcarnitine [50]. It is available in dried blood spots for newborn screening to identify those at high risk of medium-chain acyl-CoA dehydrogenase deficiency [51]. Currently, studies based on metabolomics have found that medium-chain acylcarnitine may be related to a variety of diseases, such as cardiovascular and cerebrovascular diseases, insulin resistance and acquired immune deficiency syndrome [52–54].

Improved analytical methods should be considered to explore metabolomic changes in CRAO patients before and after onset. Additionally, we did not classify patients based on embolus type and analyze them separately. The metabolomic characteristics of CRAO patients with different types of emboli may be different, which may have an impact on the analysis. Furthermore, variations in the time from onset to treatment and the evolving metabolic status over time after onset might also impact the outcomes. There is still a difference in the time between the onset of disease and the collection of blood samples, which may cause some metabolites that can reflect the characteristics of the disease to be difficult to identify due to time.

Abbreviations

CRAO central retinal artery occlusion

AUROC area under the receiver operating characteristic curve

RBC red blood cell

Hb hemoglobin

TCh total cholesterol

TG triglyceride

HDL-C high-density lipoprotein cholesterol

LDL-C low-density lipoprotein cholesterol

Non-HDL-C non-high-density lipoprotein cholesterol

ApoA1 apolipoprotein A1

ApoB apolipoprotein B

ApoA1b apolipoprotein A1b

PT prothrombin time

TT thrombin time

ACT activated coagulation time

APTT activated partial thromboplastin time

INR international normalized ratio

FIB fibrinogen

FDP fibrin degradation products

AT-III antithrombin-III

ROC receiver operating characteristic

DCA decision curve analysis

OR odds ratio

NRI net reclassification index

IDI integrated discrimination improvement

Acknowledgements

We would like to acknowledge participants from the Renmin Hospital of Wuhan University who donated samples.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

Author Contributions

Author contributions are as follow: Chuansen Wang conceived, analyzed the data, prepared figures and tables, authored or reviewed drafts of the paper, approved the final draft. Ying Li, Jiaqing Feng and Hang Liu designed and analyzed process, reviewed drafts of the paper, approved the final draft. Yuedan Wang, Yuwei Wan and Mengxue Zheng collected the blood samples. Xuejie Li gave guidance on statistical methods. Xuan Xiao and Ting Chen conceived, reviewed drafts of the paper, approved the final draft.

Funding

This work was supported by the Key research and development project of Hubei Province (2022BCA009); Fundamental Research Funds for the Central Universities (2042023gf0013).

Declarations

Ethics Approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Renmin Hospital of Wuhan University (protocol code, WDRY2022-K278 and approval date, November 30, 2022). Informed consent was obtained from all individual participants included in the study.

Consent to Participate

Written informed consent for publication was obtained from all participants.

Consent for Publication

All authors approved the final manuscript and the submission to this journal.

Competing Interests

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Chuansen Wang and Ying Li contributed equally.
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References

1. Ardila Jurado E Sturm V Brugger F Nedeltchev K Arnold M Bonati LH Carrera E Michel P Central retinal artery occlusion: current practice, awareness and prehospital delays in Switzerland Front Neurol 2022 13 888456 10.3389/fneur.2022.888456 35677327
Ardila Jurado E, Sturm V, Brugger F, Nedeltchev K, Arnold M, Bonati LH, Carrera E, Michel P et al (2022) Central retinal artery occlusion: current practice, awareness and prehospital delays in Switzerland. Front Neurol 13:888456. 10.3389/fneur.2022.88845635677327
2. Scott IU Campochiaro PA Newman NJ Biousse V Retinal vascular occlusions Lancet 2020 396 1927 1940 10.1016/s0140-6736(20)31559-2 33308475
Scott IU, Campochiaro PA, Newman NJ, Biousse V (2020) Retinal vascular occlusions. Lancet 396:1927–1940. 10.1016/s0140-6736(20)31559-233308475
3. Chen T Wang Y Li X Feng J Yang H Li Y Feng H Xiao X Sex differences in major adverse cardiovascular and cerebrovascular event risk among central retinal artery occlusion patients Sci Rep 2023 13 14930 10.1038/s41598-023-42247-2 37696870
Chen T, Wang Y, Li X, Feng J, Yang H, Li Y, Feng H, Xiao X (2023) Sex differences in major adverse cardiovascular and cerebrovascular event risk among central retinal artery occlusion patients. Sci Rep 13:14930. 10.1038/s41598-023-42247-237696870
4. Mac Grory B Schrag M Biousse V Furie KL Gerhard-Herman M Lavin PJ Sobrin L Tjoumakaris SI Management of central retinal artery occlusion: a scientific statement from the American Heart Association Stroke 2021 52 e282 e294 10.1161/str.0000000000000366 33677974
Mac Grory B, Schrag M, Biousse V, Furie KL, Gerhard-Herman M, Lavin PJ, Sobrin L, Tjoumakaris SI et al (2021) Management of central retinal artery occlusion: a scientific statement from the American Heart Association. Stroke 52:e282–e294. 10.1161/str.000000000000036633677974
5. Tobalem S Schutz JS Chronopoulos A Central retinal artery occlusion - rethinking retinal survival time BMC Ophthalmol 2018 18 101 10.1186/s12886-018-0768-4 29669523
Tobalem S, Schutz JS, Chronopoulos A (2018) Central retinal artery occlusion - rethinking retinal survival time. BMC Ophthalmol 18:101. 10.1186/s12886-018-0768-429669523
6. Varma DD Cugati S Lee AW Chen CS A review of central retinal artery occlusion: clinical presentation and management Eye (Lond) 2013 27 688 697 10.1038/eye.2013.25 23470793
Varma DD, Cugati S, Lee AW, Chen CS (2013) A review of central retinal artery occlusion: clinical presentation and management. Eye (Lond) 27:688–697. 10.1038/eye.2013.2523470793
7. Al Jarallah O Risk of acute stroke in patients with retinal artery occlusion: a systematic review and meta-analysis Eur Rev Med Pharmacol Sci 2023 27 5627 5635 10.26355/eurrev_202306_32803 37401301
Al Jarallah O (2023) Risk of acute stroke in patients with retinal artery occlusion: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci 27:5627–5635. 10.26355/eurrev_202306_3280337401301
8. Roskal-Wałek J, Wałek P, Biskup M, Sidło J, Cieśla E, Odrobina D, Mackiewicz J, Wożakowska-Kapłon B (2022) Retinal Artery Occlusion and Its Impact on the Incidence of Stroke, Myocardial Infarction, and All-Cause Mortality during 12-Year Follow-Up. J Clin Med 11. 10.3390/jcm11144076
9. Klein R Klein BE Jensen SC Moss SE Meuer SM Retinal emboli and stroke: the Beaver Dam Eye Study Arch Ophthalmol 1999 117 1063 1068 10.1001/archopht.117.8.1063 10448750
Klein R, Klein BE, Jensen SC, Moss SE, Meuer SM (1999) Retinal emboli and stroke: the Beaver Dam Eye Study. Arch Ophthalmol 117:1063–1068. 10.1001/archopht.117.8.106310448750
10. Zhang Y Xing Z Deng A Unveiling the predictive capacity of inflammatory and platelet markers for central retinal artery occlusion Thromb Res 2023 232 108 112 10.1016/j.thromres.2023.11.004 37976730
Zhang Y, Xing Z, Deng A (2023) Unveiling the predictive capacity of inflammatory and platelet markers for central retinal artery occlusion. Thromb Res 232:108–112. 10.1016/j.thromres.2023.11.00437976730
11. Qin G He F Zhang H Pazo EE Dai G Yao Q He W Xu L Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) are more prominent in retinal artery occlusion (RAO) compared to retinal vein occlusion (RVO) PLoS One 2022 17 e0263587 10.1371/journal.pone.0263587 35113973
Qin G, He F, Zhang H, Pazo EE, Dai G, Yao Q, He W, Xu L et al (2022) Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) are more prominent in retinal artery occlusion (RAO) compared to retinal vein occlusion (RVO). PLoS One 17:e0263587. 10.1371/journal.pone.026358735113973
12. Elbeyli A Kurtul BE Ozcan DO Ozcan SC Dogan E Assessment of red cell distribution width, platelet/lymphocyte ratio, systemic immune-inflammation index, and neutrophil/lymphocyte ratio values in patients with central retinal artery occlusion Ocul Immunol Inflamm 2022 30 1940 1944 10.1080/09273948.2021.1976219 34524949
Elbeyli A, Kurtul BE, Ozcan DO, Ozcan SC, Dogan E (2022) Assessment of red cell distribution width, platelet/lymphocyte ratio, systemic immune-inflammation index, and neutrophil/lymphocyte ratio values in patients with central retinal artery occlusion. Ocul Immunol Inflamm 30:1940–1944. 10.1080/09273948.2021.197621934524949
13. Johnson CH Ivanisevic J Siuzdak G Metabolomics: beyond biomarkers and towards mechanisms Nat Rev Mol Cell Biol 2016 17 451 459 10.1038/nrm.2016.25 26979502
Johnson CH, Ivanisevic J, Siuzdak G (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17:451–459. 10.1038/nrm.2016.2526979502
14. Rinschen MM Ivanisevic J Giera M Siuzdak G Identification of bioactive metabolites using activity metabolomics Nat Rev Mol Cell Biol 2019 20 353 367 10.1038/s41580-019-0108-4 30814649
Rinschen MM, Ivanisevic J, Giera M, Siuzdak G (2019) Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol 20:353–367. 10.1038/s41580-019-0108-430814649
15. Wishart DS Metabolomics for Investigating Physiological and Pathophysiological Processes Physiol Rev 2019 99 1819 1875 10.1152/physrev.00035.2018 31434538
Wishart DS (2019) Metabolomics for Investigating Physiological and Pathophysiological Processes. Physiol Rev 99:1819–1875. 10.1152/physrev.00035.201831434538
16. Danzi F Pacchiana R Mafficini A Scupoli MT Scarpa A Donadelli M Fiore A To metabolomics and beyond: a technological portfolio to investigate cancer metabolism Signal Transduct Target Ther 2023 8 137 10.1038/s41392-023-01380-0 36949046
Danzi F, Pacchiana R, Mafficini A, Scupoli MT, Scarpa A, Donadelli M, Fiore A (2023) To metabolomics and beyond: a technological portfolio to investigate cancer metabolism. Signal Transduct Target Ther 8:137. 10.1038/s41392-023-01380-036949046
17. Montaner J Ramiro L Simats A Tiedt S Makris K Jickling GC Debette S Sanchez JC Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke Nat Rev Neurol 2020 16 247 264 10.1038/s41582-020-0350-6 32322099
Montaner J, Ramiro L, Simats A, Tiedt S, Makris K, Jickling GC, Debette S, Sanchez JC et al (2020) Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke. Nat Rev Neurol 16:247–264. 10.1038/s41582-020-0350-632322099
18. Ruiz-Canela M, Hruby A, Clish CB, Liang L, Martínez-González MA, Hu FB (2017) Comprehensive metabolomic profiling and incident cardiovascular disease: a systematic review. J Am Heart Assoc 6. 10.1161/jaha.117.005705
19. Holmes MV Millwood IY Kartsonaki C Hill MR Bennett DA Boxall R Guo Y Xu X Lipids, lipoproteins, and metabolites and risk of myocardial infarction and stroke J Am Coll Cardiol 2018 71 620 632 10.1016/j.jacc.2017.12.006 29420958
Holmes MV, Millwood IY, Kartsonaki C, Hill MR, Bennett DA, Boxall R, Guo Y, Xu X et al (2018) Lipids, lipoproteins, and metabolites and risk of myocardial infarction and stroke. J Am Coll Cardiol 71:620–632. 10.1016/j.jacc.2017.12.00629420958
20. Cambray S Portero-Otin M Jové M Torreguitart N Colàs-Campàs L Sanz A Benabdelhak I Yemisci M Metabolomic estimation of the diagnosis and onset time of permanent and transient cerebral ischemia Mol Neurobiol 2018 55 6193 6200 10.1007/s12035-017-0827-5 29270918
Cambray S, Portero-Otin M, Jové M, Torreguitart N, Colàs-Campàs L, Sanz A, Benabdelhak I, Yemisci M et al (2018) Metabolomic estimation of the diagnosis and onset time of permanent and transient cerebral ischemia. Mol Neurobiol 55:6193–6200. 10.1007/s12035-017-0827-529270918
21. Fallico M Lotery AJ Longo A Avitabile T Bonfiglio V Russo A Murabito P Palmucci S Risk of acute stroke in patients with retinal artery occlusion: a systematic review and meta-analysis Eye (Lond) 2020 34 683 689 10.1038/s41433-019-0576-y 31527762
Fallico M, Lotery AJ, Longo A, Avitabile T, Bonfiglio V, Russo A, Murabito P, Palmucci S et al (2020) Risk of acute stroke in patients with retinal artery occlusion: a systematic review and meta-analysis. Eye (Lond) 34:683–689. 10.1038/s41433-019-0576-y31527762
22. Jia J Zhang H Liang X Dai Y Liu L Tan K Ma R Luo J Application of metabolomics to the discovery of biomarkers for ischemic stroke in the murine model: a comparison with the clinical results Mol Neurobiol 2021 58 6415 6426 10.1007/s12035-021-02535-2 34532786
Jia J, Zhang H, Liang X, Dai Y, Liu L, Tan K, Ma R, Luo J et al (2021) Application of metabolomics to the discovery of biomarkers for ischemic stroke in the murine model: a comparison with the clinical results. Mol Neurobiol 58:6415–6426. 10.1007/s12035-021-02535-234532786
23. Guven S Kilic D Neutrophil to lymphocyte ratio (NLR) is a better tool rather than monocyte to high-density lipoprotein ratio (mhr) and platelet to lymphocyte ratio (PLR) in central retinal artery occlusions Ocul Immunol Inflamm 2021 29 997 1001 10.1080/09273948.2020.1712433 32078399
Guven S, Kilic D (2021) Neutrophil to lymphocyte ratio (NLR) is a better tool rather than monocyte to high-density lipoprotein ratio (mhr) and platelet to lymphocyte ratio (PLR) in central retinal artery occlusions. Ocul Immunol Inflamm 29:997–1001. 10.1080/09273948.2020.171243332078399
24. Hong JH Sohn SI Kwak J Yoo J Ahn SJ Woo SJ Jung C Yum KS Retinal artery occlusion and associated recurrent vascular risk with underlying etiologies PLoS One 2017 12 e0177663 10.1371/journal.pone.0177663 28570629
Hong JH, Sohn SI, Kwak J, Yoo J, Ahn SJ, Woo SJ, Jung C, Yum KS et al (2017) Retinal artery occlusion and associated recurrent vascular risk with underlying etiologies. PLoS One 12:e0177663. 10.1371/journal.pone.017766328570629
25. Chen T, Li Y, Wang Y, Li X, Wan Y, Xiao X (2023) ApoB, non-HDL-C, and LDL-C Are more prominent in retinal artery occlusion compared to retinal vein occlusion. Ocul Immunol Inflamm:1–7. 10.1080/09273948.2023.2173245
26. Hwang S Kang SW Choi KJ Son KY Lim DH Shin DW Kim K Kim SJ High-density lipoprotein cholesterol and the risk of future retinal artery occlusion development: a nationwide cohort study Am J Ophthalmol 2022 235 188 196 10.1016/j.ajo.2021.09.027 34624247
Hwang S, Kang SW, Choi KJ, Son KY, Lim DH, Shin DW, Kim K, Kim SJ (2022) High-density lipoprotein cholesterol and the risk of future retinal artery occlusion development: a nationwide cohort study. Am J Ophthalmol 235:188–196. 10.1016/j.ajo.2021.09.02734624247
27. Soppert J Lehrke M Marx N Jankowski J Noels H Lipoproteins and lipids in cardiovascular disease: from mechanistic insights to therapeutic targeting Adv Drug Deliv Rev 2020 159 4 33 10.1016/j.addr.2020.07.019 32730849
Soppert J, Lehrke M, Marx N, Jankowski J, Noels H (2020) Lipoproteins and lipids in cardiovascular disease: from mechanistic insights to therapeutic targeting. Adv Drug Deliv Rev 159:4–33. 10.1016/j.addr.2020.07.01932730849
28. Ouimet M Barrett TJ Fisher EA HDL and reverse cholesterol transport Circ Res 2019 124 1505 1518 10.1161/circresaha.119.312617 31071007
Ouimet M, Barrett TJ, Fisher EA (2019) HDL and reverse cholesterol transport. Circ Res 124:1505–1518. 10.1161/circresaha.119.31261731071007
29. Yasuda M Sato H Hashimoto K Osada U Hariya T Nakayama H Asano T Suzuki N Carotid artery intima-media thickness, HDL cholesterol levels, and gender associated with poor visual acuity in patients with branch retinal artery occlusion PLoS One 2020 15 e0240977 10.1371/journal.pone.0240977 33091078
Yasuda M, Sato H, Hashimoto K, Osada U, Hariya T, Nakayama H, Asano T, Suzuki N et al (2020) Carotid artery intima-media thickness, HDL cholesterol levels, and gender associated with poor visual acuity in patients with branch retinal artery occlusion. PLoS One 15:e0240977. 10.1371/journal.pone.024097733091078
30. Zivanovic Z Divjak I Jovicevic M Rabi-Zikic T Radovanovic B Ruzicka-Kaloci S Popovic D Stokic E Association between apolipoproteins AI and B and ultrasound indicators of carotid atherosclerosis Curr Vasc Pharmacol 2018 16 376 384 10.2174/1570161115666171010123157 29032752
Zivanovic Z, Divjak I, Jovicevic M, Rabi-Zikic T, Radovanovic B, Ruzicka-Kaloci S, Popovic D, Stokic E et al (2018) Association between apolipoproteins AI and B and ultrasound indicators of carotid atherosclerosis. Curr Vasc Pharmacol 16:376–384. 10.2174/157016111566617101012315729032752
31. Abi-Ayad M Abbou A Abi-Ayad FZ Behadada O Benyoucef M HDL-C, ApoA1 and VLDL-TG as biomarkers for the carotid plaque presence in patients with metabolic syndrome Diabetes Metab Syndr 2018 12 175 179 10.1016/j.dsx.2017.12.017 29338972
Abi-Ayad M, Abbou A, Abi-Ayad FZ, Behadada O, Benyoucef M (2018) HDL-C, ApoA1 and VLDL-TG as biomarkers for the carotid plaque presence in patients with metabolic syndrome. Diabetes Metab Syndr 12:175–179. 10.1016/j.dsx.2017.12.01729338972
32. Sharma RA Dattilo M Newman NJ Biousse V Treatment of nonarteritic acute central retinal artery occlusion Asia Pac J Ophthalmol (Phila) 2018 7 235 241 10.22608/apo.201871 29717825
Sharma RA, Dattilo M, Newman NJ, Biousse V (2018) Treatment of nonarteritic acute central retinal artery occlusion. Asia Pac J Ophthalmol (Phila) 7:235–241. 10.22608/apo.20187129717825
33. Chen CS Varma D Lee A Arterial occlusions to the eye: from retinal emboli to ocular ischemic syndrome Asia Pac J Ophthalmol (Phila) 2020 9 349 357 10.1097/apo.0000000000000287 32459696
Chen CS, Varma D, Lee A (2020) Arterial occlusions to the eye: from retinal emboli to ocular ischemic syndrome. Asia Pac J Ophthalmol (Phila) 9:349–357. 10.1097/apo.000000000000028732459696
34. Claassen J Thijssen DHJ Panerai RB Faraci FM Regulation of cerebral blood flow in humans: physiology and clinical implications of autoregulation Physiol Rev 2021 101 1487 1559 10.1152/physrev.00022.2020 33769101
Claassen J, Thijssen DHJ, Panerai RB, Faraci FM (2021) Regulation of cerebral blood flow in humans: physiology and clinical implications of autoregulation. Physiol Rev 101:1487–1559. 10.1152/physrev.00022.202033769101
35. Bae H Lam K Jang C Metabolic flux between organs measured by arteriovenous metabolite gradients Exp Mol Med 2022 54 1354 1366 10.1038/s12276-022-00803-2 36075951
Bae H, Lam K, Jang C (2022) Metabolic flux between organs measured by arteriovenous metabolite gradients. Exp Mol Med 54:1354–1366. 10.1038/s12276-022-00803-236075951
36. Park G Haley JA Le J Jung SM Fitzgibbons TP Korobkina ED Li H Fluharty SM Quantitative analysis of metabolic fluxes in brown fat and skeletal muscle during thermogenesis Nat Metab 2023 5 1204 1220 10.1038/s42255-023-00825-8 37337122
Park G, Haley JA, Le J, Jung SM, Fitzgibbons TP, Korobkina ED, Li H, Fluharty SM et al (2023) Quantitative analysis of metabolic fluxes in brown fat and skeletal muscle during thermogenesis. Nat Metab 5:1204–1220. 10.1038/s42255-023-00825-837337122
37. Cumpstey AF Minnion M Fernandez BO Mikus-Lelinska M Mitchell K Martin DS Grocott MPW Feelisch M Pushing arterial-venous plasma biomarkers to new heights: A model for personalised redox metabolomics? Redox Biol 2019 21 101113 10.1016/j.redox.2019.101113 30738322
Cumpstey AF, Minnion M, Fernandez BO, Mikus-Lelinska M, Mitchell K, Martin DS, Grocott MPW, Feelisch M (2019) Pushing arterial-venous plasma biomarkers to new heights: A model for personalised redox metabolomics? Redox Biol 21:101113. 10.1016/j.redox.2019.10111330738322
38. Murashige D Jang C Neinast M Edwards JJ Cowan A Hyman MC Rabinowitz JD Frankel DS Comprehensive quantification of fuel use by the failing and nonfailing human heart Science 2020 370 364 368 10.1126/science.abc8861 33060364
Murashige D, Jang C, Neinast M, Edwards JJ, Cowan A, Hyman MC, Rabinowitz JD, Frankel DS et al (2020) Comprehensive quantification of fuel use by the failing and nonfailing human heart. Science 370:364–368. 10.1126/science.abc886133060364
39. Lindeman JH Wijermars LG Kostidis S Mayboroda OA Harms AC Hankemeier T Bierau J Sai Sankar Gupta KB Results of an explorative clinical evaluation suggest immediate and persistent post-reperfusion metabolic paralysis drives kidney ischemia reperfusion injury Kidney Int 2020 98 1476 1488 10.1016/j.kint.2020.07.026 32781105
Lindeman JH, Wijermars LG, Kostidis S, Mayboroda OA, Harms AC, Hankemeier T, Bierau J, Sai Sankar Gupta KB et al (2020) Results of an explorative clinical evaluation suggest immediate and persistent post-reperfusion metabolic paralysis drives kidney ischemia reperfusion injury. Kidney Int 98:1476–1488. 10.1016/j.kint.2020.07.02632781105
40. Noerman S Klåvus A Järvelä-Reijonen E Karhunen L Auriola S Korpela R Lappalainen R Kujala UM Plasma lipid profile associates with the improvement of psychological well-being in individuals with perceived stress symptoms Sci Rep 2020 10 2143 10.1038/s41598-020-59051-x 32034255
Noerman S, Klåvus A, Järvelä-Reijonen E, Karhunen L, Auriola S, Korpela R, Lappalainen R, Kujala UM et al (2020) Plasma lipid profile associates with the improvement of psychological well-being in individuals with perceived stress symptoms. Sci Rep 10:2143. 10.1038/s41598-020-59051-x32034255
41. Dziedzic R Zaręba L Iwaniec T Kubicka-Trząska A Romanowska-Dixon B Bazan-Socha S Dropiński J High prevalence of thrombophilic risk factors in patients with central retinal artery occlusion Thromb J 2023 21 81 10.1186/s12959-023-00525-z 37507715
Dziedzic R, Zaręba L, Iwaniec T, Kubicka-Trząska A, Romanowska-Dixon B, Bazan-Socha S, Dropiński J (2023) High prevalence of thrombophilic risk factors in patients with central retinal artery occlusion. Thromb J 21:81. 10.1186/s12959-023-00525-z37507715
42. Messias MCF Mecatti GC Priolli DG de Oliveira CP Plasmalogen lipids: functional mechanism and their involvement in gastrointestinal cancer Lipids Health Dis 2018 17 41 10.1186/s12944-018-0685-9 29514688
Messias MCF, Mecatti GC, Priolli DG, de Oliveira CP (2018) Plasmalogen lipids: functional mechanism and their involvement in gastrointestinal cancer. Lipids Health Dis 17:41. 10.1186/s12944-018-0685-929514688
43. Jové M, Mota-Martorell N, Obis È, Sol J, Martín-Garí M, Ferrer I, Portero-Otin M et al (2023) Ether Lipid-Mediated Antioxidant Defense in Alzheimer's Disease. Antioxidants (Basel) 12. 10.3390/antiox12020293
44. Paul S Lancaster GI Meikle PJ Plasmalogens: A potential therapeutic target for neurodegenerative and cardiometabolic disease Prog Lipid Res 2019 74 186 195 10.1016/j.plipres.2019.04.003 30974122
Paul S, Lancaster GI, Meikle PJ (2019) Plasmalogens: A potential therapeutic target for neurodegenerative and cardiometabolic disease. Prog Lipid Res 74:186–195. 10.1016/j.plipres.2019.04.00330974122
45. Meikle PJ Wong G Tsorotes D Barlow CK Weir JM Christopher MJ MacIntosh GL Goudey B Plasma lipidomic analysis of stable and unstable coronary artery disease Arterioscler Thromb Vasc Biol 2011 31 2723 2732 10.1161/atvbaha.111.234096 21903946
Meikle PJ, Wong G, Tsorotes D, Barlow CK, Weir JM, Christopher MJ, MacIntosh GL, Goudey B et al (2011) Plasma lipidomic analysis of stable and unstable coronary artery disease. Arterioscler Thromb Vasc Biol 31:2723–2732. 10.1161/atvbaha.111.23409621903946
46. Maeba R Maeda T Kinoshita M Takao K Takenaka H Kusano J Yoshimura N Takeoka Y Plasmalogens in human serum positively correlate with high- density lipoprotein and decrease with aging J Atheroscler Thromb 2007 14 12 18 10.5551/jat.14.12 17332687
Maeba R, Maeda T, Kinoshita M, Takao K, Takenaka H, Kusano J, Yoshimura N, Takeoka Y et al (2007) Plasmalogens in human serum positively correlate with high- density lipoprotein and decrease with aging. J Atheroscler Thromb 14:12–18. 10.5551/jat.14.1217332687
47. Honsho M Abe Y Fujiki Y Dysregulation of plasmalogen homeostasis impairs cholesterol biosynthesis J Biol Chem 2015 290 28822 28833 10.1074/jbc.M115.656983 26463208
Honsho M, Abe Y, Fujiki Y (2015) Dysregulation of plasmalogen homeostasis impairs cholesterol biosynthesis. J Biol Chem 290:28822–28833. 10.1074/jbc.M115.65698326463208
48. Ferreira GC McKenna MC L-Carnitine and Acetyl-L-carnitine Roles and Neuroprotection in Developing Brain Neurochem Res 2017 42 1661 1675 10.1007/s11064-017-2288-7 28508995
Ferreira GC, McKenna MC (2017) L-Carnitine and Acetyl-L-carnitine Roles and Neuroprotection in Developing Brain. Neurochem Res 42:1661–1675. 10.1007/s11064-017-2288-728508995
49. Violante S Ijlst L Te Brinke H Tavares de Almeida I Wanders RJ Ventura FV Houten SM Carnitine palmitoyltransferase 2 and carnitine/acylcarnitine translocase are involved in the mitochondrial synthesis and export of acylcarnitines Faseb j 2013 27 2039 2044 10.1096/fj.12-216689 23322164
Violante S, Ijlst L, Te Brinke H, Tavares de Almeida I, Wanders RJ, Ventura FV, Houten SM (2013) Carnitine palmitoyltransferase 2 and carnitine/acylcarnitine translocase are involved in the mitochondrial synthesis and export of acylcarnitines. Faseb j 27:2039–2044. 10.1096/fj.12-21668923322164
50. Panov AV, Mayorov VI, Dikalova AE, Dikalov SI (2022) Long-Chain and Medium-Chain Fatty Acids in Energy Metabolism of Murine Kidney Mitochondria. Int J Mol Sci 24. 10.3390/ijms24010379
51. Khalid JM Oerton J Besley G Dalton N Downing M Green A Henderson M Krywawych S Relationship of octanoylcarnitine concentrations to age at sampling in unaffected newborns screened for medium-chain acyl-CoA dehydrogenase deficiency Clin Chem 2010 56 1015 1021 10.1373/clinchem.2010.143891 20413428
Khalid JM, Oerton J, Besley G, Dalton N, Downing M, Green A, Henderson M, Krywawych S et al (2010) Relationship of octanoylcarnitine concentrations to age at sampling in unaffected newborns screened for medium-chain acyl-CoA dehydrogenase deficiency. Clin Chem 56:1015–1021. 10.1373/clinchem.2010.14389120413428
52. Seo WK Jo G Shin MJ Oh K Medium-chain acylcarnitines are associated with cardioembolic stroke and stroke recurrence Arterioscler Thromb Vasc Biol 2018 38 2245 2253 10.1161/atvbaha.118.311373 30026276
Seo WK, Jo G, Shin MJ, Oh K (2018) Medium-chain acylcarnitines are associated with cardioembolic stroke and stroke recurrence. Arterioscler Thromb Vasc Biol 38:2245–2253. 10.1161/atvbaha.118.31137330026276
53. Nowak C Hetty S Salihovic S Castillejo-Lopez C Ganna A Cook NL Broeckling CD Prenni JE Glucose challenge metabolomics implicates medium-chain acylcarnitines in insulin resistance Sci Rep 2018 8 8691 10.1038/s41598-018-26701-0 29875472
Nowak C, Hetty S, Salihovic S, Castillejo-Lopez C, Ganna A, Cook NL, Broeckling CD, Prenni JE et al (2018) Glucose challenge metabolomics implicates medium-chain acylcarnitines in insulin resistance. Sci Rep 8:8691. 10.1038/s41598-018-26701-029875472
54. Waagsbø B Svardal A Ueland T Landrø L Øktedalen O Berge RK Flo TH Aukrust P Low levels of short- and medium-chain acylcarnitines in HIV-infected patients Eur J Clin Invest 2016 46 408 417 10.1111/eci.12609 26913383
Waagsbø B, Svardal A, Ueland T, Landrø L, Øktedalen O, Berge RK, Flo TH, Aukrust P et al (2016) Low levels of short- and medium-chain acylcarnitines in HIV-infected patients. Eur J Clin Invest 46:408–417. 10.1111/eci.1260926913383
