Comprehensive plasma lipidomic pro les reveal a lipid-based signature panel as a diagnostic and predictive biomarker for cerebral aneurysms

Yong-Dong Li (  dr_liyongdong@sina.com ) Shanghai Jiao Tong University A liated Sixth People's Hospital, Shanghai, China Yue-Qi Zhu Shanghai Jiao Tong University A liated Sixth People's Hospital, Shanghai, China Bing Zhao Renji Hospital, Shanghai Jiao Tong University, School of Medicine Yu He Shanghai Jiao Tong University A liated Sixth People’s Hospital Bin-Xian Gu Shanghai Jiao Tong University A liated Sixth People’s Hospital Hao-Tao Lu Shanghai Jiao Tong University A liated Sixth People’s Hospital Yi Gu Shanghai Jiao Tong University A liated Sixth People’s Hospital Li-Ming Wei Shanghai Jiao Tong University A liated Sixth People’s Hospital Yao-Hua Pan Renji Hospital, Shanghai Jiao Tong University, School of Medicine Zheng-Nong Chen Shanghai Jiao Tong University A liated Sixth People’s Hospital Yong-Ning Sun Shanghai Jiao Tong University A liated Sixth People’s Hospital Wu Wang Shanghai Jiao Tong University A liated Sixth People’s Hospital

lipidomic technologies have provided new insights into this complex area. Plasma lipid species and classes/subclasses have been identi ed as associated with type 2 diabetes mellitus (T2DM) 16 and cardiovascular diseases (CVDs) 17,18 , suggesting that these lipid species might be useful biomarkers for these diseases. However, to the best of our knowledge, no studies have investigated the plasma lipid pro le and biomarkers associated with CAs.
Accordingly, we performed an untargeted lipidomics evaluation using the plasma from HCs, patients with UCAs, and those with RCAs, to identify a plasma lipid pro le for patients with CA using a LC-MS platform in a large case-control study. First, we analyzed the lipidomic pro les comprehensively in the three groups and reported an in-depth analysis of the plasma lipid alterations that could be used to differentiate among them. Subsequently, we built a four-lipid biomarker signature that could not only diagnose and predict UCAs/RCAs, but also predicted subtype patients with severe RCA or high-risk UCA.

Baseline characteristics
The brief study designs are shown in Fig.1, A-B. The study rst recruited 360 patients (cohort 1, 144 men and 216 women, median age: 55.5 years; age range: 17-87 years), including 120 HCs, 120 patients with UCA, and 120 age-and sex-matched patients with RCA. The baseline characteristics of the human subjects in the three groups, based on their diagnostic status, are shown in Extended Data 1 Table S1. There were no signi cant differences in sex, age, hypertension, diabetes mellitus, hyperlipemia, coronary heart disease, smoking, alcohol consumption, and body mass index (BMI) among the three groups of participants. The duration of plasma storage at measurement did not differ among the three groups.
There were 147 aneurysms in the 120 patients with UCA and 145 aneurysms in the 120 patients with RCA.
Next, we enrolled 72 men and 108 women for trend testing of the primary results from the 180 patients (cohort 2), including 60 HCs, 60 patients with UCA, and 60 age-and sex-matched patients with RCA. Baseline characteristics of the human subjects in the three groups are shown in Extended Data 2 Table  S1.
Overview of the distribution of lipid species and subclass intensities in the three groups To enable comprehensive plasma lipidomic pro ling of CAs, lipidomic analysis was performed with an untargeted LC-MS method using a CSH C18 column and using the same LC-MS with consistent quality control in a total of 360 participants from one center and 68 QC samples (Extended Data 1 Fig. S1).
After QC and support vector regression (SVR), LC-MS detected 1312 lipids (972 ESI+ and 340 ESI-) covering 8 lipid categories and 29 subclasses in all three groups, in which TG was the most abundant lipid in all three groups, followed by phosphatidylcholine (PC), sphingomyelin (SM), phosphatidylethano-lamine (PE), and ceramides (Cer) (Fig.2 A). In the UCA and RCA samples, the numbers of identi ed lipids were the same as those identi ed in the HC samples after case-by-case review. We then performed PCA to analyze the lipid data set and identify the characteristics of each group. QC samples, shown as calamus ellipses, were center clustered, which indicated good reproducibility of the instruments and stability during the lipidomics study (Fig. 2B).
The lipid intensity of every sample accompanied by sex and age is shown in Fig. 2 C. The total lipid intensity comparisons of the three groups, with and without sex and/or age data, is shown in Fig. 2 D-I. The trend of total lipid intensity decreased from HC to UCA to RCA, and a signi cant difference among the three groups was observed (Fig. 2 D, ANOVA test, p < 0.001). The same decreased trend was also observed in the F (female) or M (male) subgroups ( Fig. 2 G, ANOVA test, p < 0.001) and age subgroups ( Fig. 2 I, ANOVA test, p < 0.05) among the three groups. There were no signi cant differences between M and F in each of the three groups ( Fig. 2 E), among the four age subgroups in each of the three groups ( Fig. 2 F), and among the four age subgroups in the F or M subgroups (Fig. 2 H). In addition, the same decreased trend was observed in 26 of the 29 subclasses among the three groups ( Fig. 2 J, ANOVA test, p < 0.05).
The same trend was well tested in cohort 2 (Extended Data 2 and Extended Data 2 Fig. S2.)

Plasma lipidomic pro ling of HCs, UCAs, and RCAs
To investigate the lipidomic changes associated with CAs, three paired comparisons were performed. The differential lipids that satis ed the criterion of variable importance in the projection (VIP) of > 1.0 and p value < 0.05 were considered as potential differential lipids. An Orthogonal partial least squares discriminant analysis (OPLS-DA) model was employed to further investigate lipid changes and differential lipids. OPLS-DA score plots revealed that all three groups could be discriminated ( Fig. 3 A-C). Parameters for the explained variation (R2), an indicator of model robustness, and the cross-validated predictive ability (Q2) were obtained, as shown in Fig. 3A-C. The heatmap depicts the relative abundance of all lipids in all three groups (Fig. 3 D). Among these three paired comparisons, the lipid pro les of RCA were better distinguished from those of the HC group (Fig. 3 D).
As summarized in Extended Data 1 Table S2-3, 75 and 130 differential lipids were identi ed from the comparisons of UCA vs. HC and RCA vs. HC participants, respectively. Compared with the HC group, 5.5% (75) of the identi ed lipids were signi cantly different in the UCA group (p value < 0.05; VIP > 1.0), while, 9.9% (130) of identi ed lipids were signi cantly altered in the RCA group (p value < 0.05; VIP > 1.0), and the number of altered lipids were greater in the RCA group than in the UCA group (c2 test, p < 0.05). From this trend, we found that the number of altered lipids correlated positively with the severity of the clinical CA status (UCA and RCA). We hypothesized that increased lipid alteration in patients with a CA resulted in increased likelihood of CA rupture.
Interestingly, compared with HCs, except for 10 lipids in UCA group, the other altered lipids were underrepresented, with lower levels in the UCA and RCA groups (Extended Data 1 Table S2-3). This indicated that most altered lipids were decreased in the plasma of the CA group, and the degree of the decreased lipids was much lower in the RCA group than that in UCA the group. Using a combination of our results and published research (13,14), we hypothesized that the decreased lipids in plasma probably accumulated in the CA and normal artery wall, which resulted in the formation, development, and rupture of CAs. This trend was also tested in cohort 2 (Extended Data 2 and Extended Data 2 Fig. S3).
In addition, as shown in Extended Data 1 Tables S2-5, there were 35 differential lipids including 4 lipid subclasses (1 LPC, 4 SMs, 7 PCs, and 23 TGs) that were signi cantly altered among the three groups (Fig. 3E), which could be used as potential biomarkers to diagnose or to discriminate CAs from HCs. The relative levels of these 35 differential lipid pro les were presented as a heatmap and a differential lipid pro le was observed when comparing UCA to HC, RCA to HC, and RCA to UCA (Fig. 3 F). The lipids exhibited the same decreasing trends from HC to UCA to RCA, and a signi cant difference was observed among the three groups and between each of the two groups ( Fig. 3 G, p < 0.001).
In patients with RCAs, all the differential lipids were decreased as compared with HCs (Extended Data 1 Table S3 and Fig.4B). 75 out of 130 altered lipids were signi cantly decreased (FC< 0.75 or log2FC < -0.42), and 24 lipids showed a less than 0.5-fold decrease (log2FC < -1). Of the 75 signi cantly decreased lipids, 86.7% (65/75) were TGs, and all the 24 lipids with a less than 0.5-fold decrease were TGs (Extended Data 1 Table S3, and Fig.4B). Therefore, although there were many lipids altered from UCA to RCA, we only identi ed one distinct lipid subclass (TGs) with respect to clinical classi cation.
In addition, as shown in Extended Data 1 Table S4 and Fig.4C, there were 119 identi able lipids exhibiting statistically signi cant differential abundance between RCAs and UCAs, and the majorities (115 lipids) were decreased in RCAs as compared with UCAs. Of these, 46 out of 119 altered lipids were signi cantly decreased (FC< 0.75 or log2FC < -0.42) and 16 lipids showed a less than 0.5-fold decrease (log2FC < -1). Of the 46 signi cantly decreased lipids, 95.7% (44/46) were TGs, and all the 16 lipids were TGs (Extended Data 1 Table S4 and Fig.4C). Of note, as compared with HCs, we observed that TG became the predominant and distinct pro le of altered lipid, which indicated that TG metabolism was severely disrupted in patients with CAs.
In addition, TGs were also the distinct pro le of lipids in patients with UCA and RCA as compared with HCs in cohort 2 (Extended Data 2 and Extended Data 2 Fig. 4.) Lipid-based diagnostic prediction model for CA vs. HC To investigate the lipid-based diagnostic prediction model for CAs (UCAs + RCAs), we rst discriminated CAs from HCs, and then differentiated UCAs from RCAs. We assigned cohort 1 (n = 360; HCs = 120, CAs = 240) to the training cohort, and cohort 2 (n = 180; HCs = 60, CAs =120) to the validation cohort. Therefore, the ratio of samples in the training and validation sets was 2:1. The baseline characteristics of subjects in the two cohorts are shown in Extended Data 1 Table S6.
To discriminate CAs from HCs, the two cohorts were subjected to an independent and comprehensive analysis to discover biomarkers. First, the lipid pro les between the HCs and the patients with CAs were compared in the two cohorts, which identi ed 61 differentially abundant (variable importance in projection (VIP) of > 1.0 and p value < 0.05) candidates in both cohorts. Secondly, we used random forest algorithms and the least absolute shrinkage and selection operator (LASSO) to decrease the number of lipid biomarkers, which produced 12 overlapping biomarkers from the two algorithms.
First, the training cohort was used to train the 4-lipid prediction model. In the training cohort, the nomogram's calibration plot demonstrated good agreement between observation and prediction (Fig. 5B). The Hosmer-Lemeshow (HL) test statistic was not signi cant (P = 0.244), indicating a good t to the model. The receiver operator characteristic (ROC) analysis for the nomogram of the four biomarkers yielded areas under the ROC curve (AUCs) of 0.814 (95% con dence interval (CI): 0.767-0.861, Fig. 5 C, D) for the training cohort. The dp-score showed speci city and sensitivity of 60.0% and 87.5, respectively (Fig. 5D), using the best cutoff value.
Next, the four-lipid signature and the same statistical model were applied in the validation cohort (60 HC and 120 CA cases) to assess the accuracy of the signature. In the validation cohort, the lipid biomarkers showed excellent diagnostic accuracy to the identify patients with CAs. As it did in the training cohort, the nomogram showed favorable calibration in the validation cohort (Fig. 5E). The HL test result was not signi cant (p = 0.387), and in the validation cohort, the AUCs were 0.803 (95% CI: 0.735-0.871, Fig. 5F, G) for the nomogram. The speci city and sensitivity of the dp-score were 85 % and 69.2%, respectively (Fig.  5G).
Next, decision curve analysis (DCA) was used to compare the lipid performance of the model in the training and validation cohorts. To diagnose CAs in the validation and training cohorts, the developed model showed the highest net bene t within the ranges of most of the potential thresholds (Fig. 5H). Based on the lipid nomogram, the net bene ts were similar, with several overlaps, within this range. This suggested the possibility of using the nomogram in clinical practice for the diagnosis and prediction of CAs.
A lipid-based combination diagnostic prediction model for CA vs. HC Sex, age, and hypertension are known to be associated with CAs; therefore, whether a model combining our lipid signature with these three preoperative clinical features could improve the diagnostic accuracy to detect CA in clinic was assessed. The results showed that the diagnostic accuracy for CA of the combined signature was slightly better in both the validation and training cohorts (AUC values of 0.802 and 0.836, respectively, Fig. 5 I, J, and Extended Data 1 Fig. S2). Moreover, compared with the preoperative clinical features alone, including gender, age and hypertension, the combination signature demonstrated signi cantly improved diagnostic accuracy. Finally, using the cutoffs determined using the Youden index from this four-lipid signature model, all patients were categorized into high-and low-risk groups. The univariate and multivariate logistic regression analyses results are shown in Extended Data 1 Table S8. In both clinical cohorts, multivariate analysis showed that the four-lipid signature was an independent predictor to discriminate CAs from HCs, (training cohort: odds ratio [OR], 10.13; 95% CI, 5.93-17.74; p < 0 .001; validation cohort: OR, 12.66; 95% CI, 5.84-30.28, p < 0.001, Extended Data 1 Table  S8).
Lipid-based diagnostic prediction model for UCA vs. RCA To differentiate UCAs from RCAs, we assigned cohort 1 (n = 240; UCAs = 120, RCAs = 120) to the training cohort, and cohort 2 (n = 120; UCAs = 60, RCAs = 60) to the validation cohort. The baseline characteristics of cohort 1 and cohort 2 subjects are shown in Extended Data 1 Table S9. Consistent with the model for CAs vs. HCs, we still used the four biomarkers as the diagnostic prediction model for UCAs vs. RCAs. We constructed a nomogram and a dp-score that was obtained according to the coe cients and the constant derived from multinomial logistic regression (Extended Data 1 Table S10). The risk score model was obtained as follow: -0.0412+ (0.0320*PE (20:1p/18:2)+H) + (-0.7768* CerG1(d40:4)+NH4) +(-0.3816* TG(18:0p/16:0/16:1)+NH4) + (-1.2405* TG(54:2e)+NH) . The DCA for the lipid nomogram of UCAs vs. RCAs is presented in Fig. 6 H. The decision curve indicated that at a threshold probability of 20-85% for a patient or doctor, then using the developed lipid nomogram for the diagnosis and prediction UCAs from RCAs would add more bene t than either the treat-none scheme or the treat-all-patients scheme. The net bene t of the test cohort was somewhat lower than the net bene t of the training cohort based on the lipid nomogram within this range. This suggested possibility of using the nomogram in clinical practice to diagnose and predict UCAs from RCAs A lipid-based combination diagnostic prediction model for UCAs vs RCAs To further increase the accuracy of diagnosis of URCs or RCAs in the clinic, we also assessed a combination the three preoperative clinical features and the developed model lipid signature. In the validation and training cohorts, the combination signature demonstrated slightly better diagnostic accuracy for URCs or RCAs (AUCs of 0.719 and 0.78, respectively, Fig. 6 I, J and Extended Data 1 Fig. S3). Moreover, in terms of diagnostic accuracy, compared that that of the preoperative clinical features sex, age, and hypertension, the combination signature demonstrated a signi cant improvement. Finally, using the Youden index-derived cutoffs from the four-lipid signature model, all patients were categorized into high-and low-risk groups. The results of for the univariate and multivariate logistic regression analyses results are shown in Extended Data 1 Table S11. In both cohorts, multivariate analysis identi ed the fourlipid signature as an independent predictor to discriminate UCA form RCA (training cohort: OR, 6.44; 95% CI, 3.68-11.56; p < 0.001; validation cohort: OR, 4.65; 95% CI, 2.10-10.84, p < 0.001, Extended Data 1 Table S11).
Lipid-based subtyping of RCA A lower lipid intensity in RCA was associated with patients with severe RCA To explore the lipid-de ned speci c subtypes within CA, strati cation analysis was carried out using nonnegative matrix factorization (NMF) consensus-clustering 19,20 . NMF consensus-clustering was rst performed for RCA in cohort 1, and two major lipid subtypes (R-I and R-II) were identi ed among the RCA samples ( Fig. 7 A, Extended Data 1 Fig. S4), with 65 cases belonging to R-I and 55 belonging to R-II. The total lipid intensity of R-II was signi cantly lower than that of R-I (Fig. 7 B, p < 0.001). The relative abundance of the two subtypes according to age, sex, etc., is shown in Fig. 7C.
To explore the clinical characteristics between the R-I and R-II subgroups, the baseline characteristics of the subjects in the two subgroups were compared. There were no signi cant differences in sex, age, hypertension, diabetes mellitus, hyperlipemia, smoking, alcohol consumption, and BMI between R-I and R-II (Extended Data 1 Table S12). Subsequently, we compared the aneurysm characteristics associated with RCA, such as aneurysm size, location, aneurysm neck, single or multiple, regular or irregular, bifurcation or sidewall aneurysm, modi ed sher grade (MFG) 21 , Glasgow Coma Scale (GCS), the Coma number at onset, ventricular drainage (VD), and hospital day. There were signi cant differences in MFG, GCS, and the number of Coma at onset between R-I and R-II (Fig. 7 D); no other signi cant differences were observed (Extended Data 1 Table S12). Fig. 7 E-F show typical cases in the R-I subtype, and Fig. 7 G-H show typical cases in the R-II subtype. These results indicated that patients in the R-II subgroup were associated with severe RCA.
Diagnostic prediction potential of the four-lipid signature for patients with severe RCA As mentioned-above, patients in R-II are often associated with severe or poor outcomes in the clinic; therefore, we next evaluated the diagnostic predictive potential of the four-lipid biomarker signature for patients with severe RCA. First, the Youden index-derived cutoffs from the four-lipid signature were used to separate the patients with RCA into high-and low-risk groups, which were then analyzed as independent predictors using univariate and multivariate logistic regression. Multivariate logistic regression analysis identi ed the four-lipid signature as an independent predictor of detection severe RCA, in the two clinical cohorts (training cohort: OR, 6.46; 95% CI, 2.71-16.55; p < 0.001; validation cohort: OR, 4.33; 95% CI, 1.30-16.06, p = 0.01, Extended Data 1 Table S13). Thus, in addition to diagnosing RCA, the four-lipid signature could also predict patients with severe RCA.
Lipid-based subtyping of UCA UCA subtypes predict slow or rapid aneurysm growth Next, NMF consensus-clustering was performed for UCA in cohort 1, and two major lipid subtypes (U-I and U-II) were identi ed among the UCA samples (Fig. 8A, Extended Data 1 Fig. S5) with 54 belonging to subtype U-I and 66 cases belonging to U-II. The total lipid intensity of U-II was signi cantly lower than that of U-I (Fig. 8B, p < 0.001). The relative abundance of the two subtypes by age, sex, and etc., is shown in Fig. 8C.
To explore the clinical characteristics between U-I and U-II, the baseline characteristics of the human subjects and the aneurysm characteristics in the two subgroups were compared. However, there were no signi cant differences in sex, age, hypertension, diabetes mellitus, hyperlipemia, smoking, alcohol consumption, BMI, aneurysm size, location, aneurysm neck, single or multiple, regular or irregular, bifurcation or sidewall, and hospital day between the U-I and U-II subtypes (Extended Data 1 Table S14).
We next checked the patients using MRA follow-up. Although, most patients with UCAs were treated when detected, we still enrolled 23 UCA patients in this study, among whom 20 were followed-up using MRA for more than seven years. According to NMF consensus-clustering, 10 patients were classi ed in the U-I subgroup and 10 were in the U-II subgroup. Other than the lipid intensity of U-II being signi cantly lower than that of U-I, we noticed that seven of ten UCAs (70%) in the U-II subtype were enlarged, while only two of ten UCAs (20%) in the U-I subtype were enlarged. Therefore, the aneurysm growth rate was higher in the U-II than in the U-I subtype (c2 test, p < 0.05), which indicated that the lower lipid intensity in the U-II subtype was associated with rapid CA progression, when compared to those in the U-I subtype. Fig. 8 D-E show the typical cases in U-I, and Fig. 8 F-G show the typical cases in U-II. These illustrated that rapid aneurysm growth in the U-II subtype group increased the chance of CA rupture, while the slow growth observed in the U-I subtype group might be associated with slower aneurysm progression and resistance to CA rupture.
Diagnostic prediction potential of the four-lipid signature for high-risk patients Patients in the U-II subtype group are associated with rapid aneurysm growth and the chance of CA rupture; therefore, we next evaluated the diagnostic predictive potential of our lipid biomarkers for patients with UCA with rapid growth. First, the Youden index-derived cutoffs from four-lipid signature model were used to separate the patients with RCA into high-and low-risk groups, which were then assessed as independent predictor using univariate and multivariate logistic regression analyses. In both cohorts, multivariate logistic regression analysis identi ed the four-lipid signature an independent predictor to detect patients with UCA with rapid growth (training cohort: OR, 6.04; 95% CI, 2.73-14.05; p < 0.001; validation cohort: OR, 145.3, 95% CI, 21.3-3203.3, p < 0.001, Extended Data 1 Table S15).
In addition, when we extracted 20 UCA cases from cohort 1 and analyzed them separately. Surprisingly, using the Youden index-derived cutoffs from the dp-score, we observed a high consistency between NMF subtype and the risk groups. Among the 20 patients, multivariate analysis identi ed the 4-lipid signature as an independent predictor to detect patients with UCA with rapid growth (OR, 12.91; 95% CI, 1.61-180.5; p < 0.05, Extended Data 1 Table S16). These results showed that the lipid signature had a signi cant predictive potential to detect patients with UCA with rapid growth (high-risk UCA patients).

Discussion
In the present study, we performed a comprehensive lipidomic analysis of human plasma from HCs, and from patients with UCA and RCA, to investigate altered lipidomic features and identify lipid signatures associated with CAs. We found: 1) The total lipid intensity and most lipid classes from the HC, UCA, and RCA groups decreased signi cantly (p < 0.05).
2) The number of the altered lipids increased and correlated with the severity of the CAs, with low abundance in most altered lipids from all three comparisons (UCA vs. HC, RCA vs. HC, and RCA vs. UCA). 3) TGs were the distinct pro le of lipids in plasma from CA samples. 4) The NMF-de ned lipidspeci c subtypes could not only discriminate between minor (R-I) and severe (R-II) states of patients with RCA, but also predicted slow (U-I) or rapid (U-II) aneurysm growth in patients with UCA. 5) A model incorporating four selected lipid signatures showed good calibration and diagnostic prediction for CA vs. HC and UCA vs. RCA in both the training and validation clinical cohorts. 6) The four-lipid signature was demonstrated as an independent predictor of discrimination of CA from HC, UCA from RCA, and RCA or UCA subtypes in both the training and validation clinical cohorts.
Decreased plasma lipid levels appeared to correlate negatively with lipid accumulation in the CA wall, as described by Frösen et al. 13,14 , who revealed that a lipid accumulation was associated with SMCderived foam cell formation, in ammation, mural SMC loss, and CA wall degenerative remodeling, eventually leading to a ruptured CA wall. We hypothesized that lipid accumulation in the CA wall and the normal artery led to decreased plasma lipid levels. Although Frösen et al. found that the plasma total cholesterol and triglyceride levels were often normal, they did not further examine the plasma lipid species and classes/subclasses. Therefore, our results support the theory that lipids and their oxidation products accumulate in the CA wall and lead to the formation, development, and rupture of CA.
In addition, another important result in this study was that TGs represent pivotal lipids associated with CAs. In normal situations, circulating very-low-density lipoprotein (VLDL), intermediate density lipoproteins (IDL) and LDL, which contain TGs, normally ux into and out of the endothelium via transcytosis [23][24][25][26] . However, many CA walls have experienced total erosion of their endothelium or do not possess a functionally intact endothelium 7 , which increases lipoprotein in ux into the vessel wall and causes their accumulation in the CA wall 27 . However, further study is required to determine whether TGs are the most notably altered lipids in CA walls, and how CA formation, development, and rupture are induced by TGs.
In this study, the dp-score, as a diagnostic prediction model, was developed using a four-lipid marker signature. The model could discriminate patients with CA from HCs accurately. The dp-score could differentiate patients with CA with different prognoses effectively and was identi ed using multivarite analysis as an independent predictive risk factor. The dp-score had a better discriminatory potential than other predictive risk factors (age, gender and hypertension). Moreover, a nomogram was developed comprising the dp-score, age, gender, and hypertension, which showed a slightly superior diagnostic accuracy for CAs in the validation and training cohorts. Thus, the developed nomogram demonstrated a good predictive performance and could be used to predict the prognosis of CA.
Ruptured cerebral aneurysms, which are the most common etiology of nontraumatic subarachnoid hemorrhage (SAH), can cause a catastrophic event with a mortality rate of 25 to 50%, while permanent disability occurs in nearly 50% of survivors; therefore, only approximately one-third of patients who suffer from SAH have a positive outcome [2][3][4][5] . In general, the hemorrhage stage is the key factor in determining illness severity in patients with RCA. Patients with considerable SAH are often associated with poor outcomes or disability/mortality. By contrast, patients with minor SAH are often associated with positive outcomes. Using NMF consensusclustering analysis, the patients with RCAs were classi ed into two major lipid subtypes (R-I and R-II). The patients in R-I exhibited minor hemorrhage and were associated with improved outcomes, while the patients in R-II exhibited considerable hemorrhage and were associated with poor outcomes. The predominant difference between the two subtypes was the difference in lipid intensity. The lipid intensity in RII was lower than that in R-I (p < 0.05). while, the 4-lipid signature could act as an independent predictor of distinguish R-I from R-II and detection severe RCA patients in both training and validation clinical cohorts. Therefore, we could distinguish between the two situations or suggest the severity of RCA patients using plasma lipidomics. Furthermore, many catastrophic events, such as disability/mortality, might be rescued if plasma lipidomics or plasma lipid biomarkers could be routinely applied for physical examinations in the future.
With the development of modern CT and MR technology, more and more UCAs can be detected. For patients with UCAs in the clinical settings, we need to de nitively resolve whether the patient should undergo treatment or observation. Simply, UCAs could be classi ed according to their lower or higher risk status. In general, we could discriminate a UCA at lower or higher risk status from its aneurysm characteristics, such as size, shape (regular or irregular), location (bifurcation or sidewall) or aneurysm neck at MRA, CTA, or DSA. In this study, although the lipid intensity in U-II was lower than that in U-I (p < 0.05), there were no signi cant differences in these aneurysm characteristics between the two subgroups, according to NMF consensus-clustering, which was also consistent with our results in the RCA group. Aneurysm rupture did not correlate with size, shape, location, or aneurysm neck in the clinic. Fortunately, we found that U-II subgroup aneurysms were associated with rapid CA progression compared to those in the U-I subgroup. while, the 4-lipid signature demonstrated as an independent predictor of distinguish U-I from U-II and detection UCA patients with rapid growth (high-risk UCA patients) in both cohorts. From this point of view, we might explain the phenomenon observed in the clinic: Why do certain smaller aneurysms rupture easily, while larger aneurysms are more resistant to rupture to some degree. The essential disparity might lie in the difference in lipid accumulation in the CA wall or in the rate of decrease in plasma lipids. Therefore, we hypothesized that the faster the decrease in plasma lipids, the faster the progression in aneurysm growth and ease of rupture.
Currently, except for MRA and/or CTA, there is no other valid approach that can be used for the early diagnosis of UCAs in the clinic. In addition, these techniques are often time-consuming and hospitals with few cases would have limited access, and there are still a large number of cases that could not be detected. Therefore, there is an urgent need for accurate noninvasive biomarkers for the early and differential diagnosis of UCAs. In this study, the 4 lipid signatures not only showed good calibration and diagnostic prediction for CA vs HC and UCA vs RCR, but also demonstrated as an independent predictor of discrimination CA from HC, UCA form RCA, and RCA or UCA subtypes.
Several limitations were associated with this study. First, although we observed speci c lipid decreases in plasma, we did not use CA tissue to test whether these decreased lipid species accumulated in the CA wall, because CA tissues are di cult to obtain. Although Frösen et al. [13][14] demonstrated that lipids accumulate in all cerebral vasculature and CA walls, they did not indicate which lipids accumulated in the CA wall. Therefore, this detail deserves further investigation. Second, insu cient numbers of participants in the UCA group were subjected to MRA long-term follow-up, and more cases are needed to support our conclusions. Lastly, this study focused on the results observed via lipidomics analysis; however, did not thoroughly investigate the mechanisms or possible signaling pathways. Furthermore, our study did not include aspects related to systemic immunity, which deserves further study.
In summary, comprehensive lipidomic analysis identi ed decreased lipids as a prominent feature of CAs, and a four-lipid biomarker signature could not only better diagnose and predict UCAs/RCAs from HCs, but also predicted subtypes of patients with severe RCA or high-risk UCA. On the one hand, these results highlight a possible key role of plasma lipidomics to support the theory of lipid accumulation in the CA wall. On the other hand, the results highlight the favorable predictive capability of the four-lipid biomarker signature as a diagnostic predictive potential tool to assess the prognosis of CA. Although lipidomic data of the CA wall are lacking and long-term follow-up with more case are needed, our data provides important biological insights and clear clinical implications for the future.

Materials And Methods
Study design and patient recruitment A case-control study with a matched sex and age design was used. Samples were selected (n = 540) from the 1388 participants with available blood samples according to the following order: We rst divided these patients into three groups according to their diagnostic status (HC, UCA, and RCA); and then four subgroups according to age [≤ 45 (n = 135), 46 to 55 (n = 135), 56 to 65 (n = 135), and > 65 years (n = 135)] were classi ed in each group; nally, men (n = 216) and women (n = 324), with a ratio of 1:1.5, were allocated to each subgroup. Blood samples were taken from an arm vein or from the femoral artery with no intravenous transfusion. Peripheral blood samples (10 mL) were collected from each patient into EDTA-vacutainers (BD, Franklin Lakes, NJ, USA). Plasma was centrifuged at 3000 × g for 10 min to remove cells within three hours and then stored at −80 °C until use.
Patients with CA diagnosed using computed tomography angiography (CTA), magnetic resonance angiography (MRA), and/or digit subtraction angiography (DSA), and patients without CA (diagnosed by MRA) with normal liver and renal function, and electrolyte levels served as a comparison group. Patients were referred for these procedures for numerous reasons, including acute processes, such as subarachnoid hemorrhage (SAH) and intracranial hemorrhage, as well as for nonacute indications to rule out cerebrovascular diseases, such as headache, dizziness, or no symptoms. Patients with abnormal liver functions caused by a malignant tumor, hepatitis, hepatic cirrhosis, liver function failure, and other hepatic diseases were excluded from the study.
The collected baseline data included clinical information, biochemical characteristics, and the demographics of all participants.
Image acquisition and image review MRA, CTA, and/or DSA examinations were described previously and are not presented here (6). The aneurysm type was classi ed as saccular and fusiform. Aneurysm size was recorded as the maximum 2D angiographic or MRA dimension: (1) < 3 mm, (2) 3-5 mm, (3) > 5-10 mm, or (4) > 10 mm. The number of aneurysms was classi ed into two groups: Single and multiple aneurysms. Aneurysm locations were grouped into four categories: The anterior communicating artery (ACA), the middle cerebral artery (MCA), the internal carotid artery (ICA), and the vertebral and basal artery system (VBAS), or into bifurcation and sidewall aneurysms. Aneurysm shape was classi ed as either regular or irregular (with daughter sac or lobulated). The aneurysm neck was de ned as either narrow or wide (≥ 4 mm or fundus:neck ratio ≤ 2). Aneurysm growth was de ned as an aneurysm that increased by > 1 mm in maximum diameter during follow-ups, as compared with the initial examination.
Three observers, who were highly experienced in neurointerventional radiology and had previously tested the application common-standard interpretation techniques, were blinded to all clinical, CTA, MRA, and DSA results. They analyzed all datasets independently on an o ine-workstation from multiple on-screen viewing angles. In the event of interobserver discrepancies in the detection of intracranial aneurysms, consensus was achieved or a majority decision was obtained. , followed by incubation at room temperature for 30 min. The solution was centrifuged at 14000 × g for 15 min at 10 °C, and the upper organic solvent layer was obtained and dried under nitrogen. For LC-MS analysis, the samples were re-dissolved and vortexed in 200 μL of an isopropanol solution. To monitor the stability and repeatability of the instrument analyses, quality control (QC) samples were prepared by pooling each sample, and these were analyzed together with the other samples. The sample queue was randomly tested to remove bias.
The initial mobile phase was 30% solvent B at a ow rate of 300 μL/min. It was held for 2 min, and then linearly increased to 100% solvent B in 23 min, followed by equilibration at 5% solvent B for 10 min.
Mass spectra were acquired by Q Exactive Plus in positive and negative mode. Electrospray ionization (ESI) parameters were optimized and preset for all measurements as follows: Source temperature, 300 °C; Capillary Temp, 350 °C. In positive ion mode, the ion spray voltage was set at 3000 V, the S-Lens RF level was set at 50%, and the scan range of the instrument was set at m/z 200-1800. In negative ion mode, the ion spray voltage was set at -2500 V, the S-Lens RF level was set at 60%, and the scan range of the instrument was set at m/z 250-1800.

Identi cation by lipid Search
LipidSearch software (Thermo Scienti c™) was employed to conduct lipid identi cation and data processing on the raw data, which included peak extraction, lipid identi cation, peak alignment, and quanti cation. This software contains MS2 & MS3 databases of 8 categories, 300 subclasses, and about 1.7 million lipid molecules.

Normalization of lipid data
For large-scale lipidomics studies, we used the support vector regression (SVR) normalization method to normalize lipid data to effectively remove the intra-batch and inter-batch variations in the LC-MS analysis. In brief, the intensities of 360 (120 healthy, 120 Non-SAH, and 120 SAH) samples were extracted from the lipid data, resulting in a 1312 × 360 lipid-expression matrix. Then, QC-SVR, implemented in the R/Bioconductor package, MetNormalizer, was used to normalize the expression matrix. For subsequent quantitative analyses, the normalized intensities were log2-transformed. In addition, the samples were removed when the missing values was more than 30%. The remaining missing values were imputed by the nearest 10 neighbors using the k-Nearest Neighbor algorithm.

Lipid difference analysis
The data was rst log2 scaled before multivariate data analysis (MVDA), computed with the ropls R package. This package implements unsupervised principal component analysis (PCA) analysis, with supervised partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) based on the original, NIPALS-based, versions of the algorithms. Using this package, the R2 and Q2 quality metrics, the score and orthogonal distances, the permutation diagnostics, as well as the variable importance for projection (VIP) values could be calculated.
Permutation testing was performed 200 times. Score plots, loadings, and permutation plots were generated with the calculated results using R. The variables were standardized (mean-centered and Pareto scaled, which is the same as the Par scaling method in software SIMCA) prior to model building. The ropls package is available from the Bioconductor repository. Univariate statistical analysis includes Student's t-test and variance multiple analysis.
Lipidomic subtype identi cation in aneurysm patients: To identify the lipidomic subtypes in the lipid expression matrix from the 240 aneurysm samples, we used the NMF v.0.21.0 consensus cluster method from the R package. NMF is a machine learning method that can e ciently identify distinct molecular patterns and molecular classi cations. For each lipid in the samples, we rst calculated the coe cient of variation, which was used produce a descending order of ranked metabolites. NMF v.0.20.6 in R v.3.6.1 unsupervised consensus-clustering was then used to analyze the top 30% most-variant lipids. The nsNMF algorithm was carried out using 469 iterations for the clustering runs and 200 iterations for the rank survey. The average silhouette width for clustering solutions between 2 and 6 clusters and the pro les of the cophenetic score were used to select the preferred cluster result. From the lipidomic data, the silhouette width, rank survey pro les of the cophenetic score, together with the consensus membership heat maps, suggested a two-subtype solution for patients with aneurysm.
The categorical basic and demographic characteristic variables are shown as numbers and percentages, and c2 test was used for their comparisons. Continuous variables, shown as the mean (± SD), if normally distributed, were compared using an unpaired t-test. Non-normally distributed data are presented as the median (interquartile range), and differences were determined using one-way analysis of variance (ANOVA) and a Wilcoxon rank sum text. Statistical signi cance was indicated by a p value £ 0.05.
To select biomarkers, least absolute shrinkage and selection operator (LASSO) was performed using the "glmnet" package and package caret was used to perform random forest algorithms. Subsequently, the generalized linear model (glm, regressions) was constructed to analyze the biomarkers. The "car" package was used to calculated the VIP values. The "pROC" package was used to plot the ROC curve. The "rms" package was used to construct the nomogram and produce the calibration plots. the was conducted using the "HLtest.R." was used to perform the Hosmer-Lemeshow test, and "ggDCA.R." was used to perform the DCA.
In addition, to build the cd-score, we tted a logistic regression model. The performance of the cd-scorebased classi er was assessed using a ROC curve, which provided assigned cases to high-and low-risk groups using Youden index-derived cutoff thresholds from the four-lipid signature model. The Wilcoxon test was used to examine the distribution of the cd-score between clinical categories. To compare the discriminatory performance of the dp-score, age, sex, hypertension, and other factors, ROC was used. To determine the effects of potential risk factors, multivariate logistic regression analysis was used. R software, version 3.6.1 was used to perform all the analyses.
Note that cohort 1 and cohort 2 were analyzed independently (without being normalized together) for comprehensive lipidomic pro le analysis, and the training cohort (cohort 1) and validation cohort (cohort 2) were normalized together for biomarker analysis. Brief study designs. (A) step 1, work ow for lipidomic analysis and subtype analysis (RI and RII for RCA, UI and UII for UCA). (B) step 2, work ow for building the diagnostic prediction model for CA vs HC and RCA vs UCA, and in addition to lipid signature with diagnostic prediction (dp) score for CA vs HC, UCA vs RCA, and RCA or UCA subtypes. Note: QC = Quality control; SVR = Support vector regression; HC = Health control; UCA = Unruptured cerebral aneurysm; RCA = Ruptured cerebral aneurysm.