Serum Metabolomic Profiling of In-Stent Neoatherosclerosis Confirmed by Optical Coherence Tomography in a Rabbit Model

doi:10.21203/rs.3.rs-3084723/v1

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Serum Metabolomic Profiling of In-Stent Neoatherosclerosis Confirmed by Optical Coherence Tomography in a Rabbit Model

https://doi.org/10.21203/rs.3.rs-3084723/v1

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Introduction:

In-stent neoatherosclerosis (ISNA) leads to late stent thrombosis (LST) and very late stent thrombosis (VLST), which are major limitations of drug-eluting stents (DESs) application. Metabolic heterogeneity among individuals poses challenges for ISNA prevention.

Objectives

To explore the specific metabolic molecular dysregulation underlying ISNA.

Methods

After DES implantation in the iliac artery, eight rabbits were received a high cholesterol diet (HCD), while another eight control rabbits were fed a normal diet. Serum samples were collected at baseline and after treatments for untargeted metabolomic analysis. Optical coherence tomography (OCT) and histological examination were used to classify the different outcomes of ISNA progression.

Results

Among the rabbits fed the HCD, six developed ISNA and were categorized as Group A, while the remaining two rabbits on the HCD without ISNA were classified as Group B. Additionally, eight control rabbits on a normal diet were designated as Group C. Metabolomics analysis detected a total of 36883 metabolic peaks, leading to the identification of 279 metabolites. Dysregulation of 67, 2, and 40 metabolites was observed in Groups A, B, and C, respectively. Eight dysregulated pathways were enriched based on the 67 differential metabolites in the ISNA group. Notably, eight metabolites, including 9-trans-palmitelaidic acid, biliverdin, palmitoleic acid, choline, cystine, pipecolinic acid, histidine, and L-proline, exhibited specific changing trends in the ISNA group compared to Groups B and C.

Conclusion

This study provides comprehensive insights into the metabolic molecular mechanisms underlying ISNA, and emphasizes the significance of personalized prevention strategies based on individual metabolic profiles.

Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases

Health sciences/Biomarkers/Diagnostic markers

Biological sciences/Biochemistry/Metabolomics

Atherosclerosis

Metabolomics

Drug-eluting stent

Optical coherence tomography

Rabbit

Percutaneous coronary intervention (PCI) in the form of stent implantation has become a preferred choice for the treatment of obstructive coronary artery disease (Mehta et al. 2019; Palmerini et al. 2015). With the development of drug-eluting stents (DESs), the incidence of coronary restenosis has been significantly decreased compared with bare metal stents (BMSs). However, at the same time, the risk of late stent thrombosis (LST) and very late stent thrombosis (VLST) have been recognized as major limitations of DES, which usually portends a poor prognosis (Nakazawa et al. 2011; Palmerini et al. 2012; Valgimigli et al. 2018). In-stent neoatherosclerosis (ISNA) is characterized by the formation of plaques and has been identified as a major cause of LST and VLST after DES implantation (Park, Kang, Virmani, Nakano, & Ueda 2012; Taniwaki, Windecker, & Raber 2014; Xia et al. 2018). Unfortunately, although clinical treatment strictly adheres to standardized therapy and secondary prevention strategies for coronary artery disease, the incidence of ISNA in patients after DES implantation is still as high as 20%-50% (Lee et al. 2015; Nakamura et al. 2019; Song et al. 2017). There is heterogeneity in metabolism among individuals, which may be a critical reason for the unsatisfactory control of ISNA (Ouyang et al. 2021). Consequently, understanding the pathological and metabolic mechanisms of ISNA development has become a hot spot of recent clinical attention.

Metabolomics provides insights into terminal biological systems and can reflect phenotypic changes in disease processes (Newgard 2017; Ussher, Elmariah, Gerszten, & Dyck 2016; Z. Wang et al. 2019; Wishart 2019). Previous studies have identified potential biomarkers for predicting outcomes after DES implantation. For example, Mahmud et al. revealed a panel of six metabolites in plasma 1-month post-PCI, which can be used to predict in-stent restenosis 6 months after PCI with a diagnostic accuracy rate of 90% (Cui et al. 2017). Hirata et al. investigated the serum metabolic profiles of 86 male patients after stent implantation and found that eight metabolites were altered specifically according to the development of stent thrombosis 6 months after PCI (Hasokawa et al. 2012). However, little is known about the specific metabolic dysregulation underlying ISNA, which may contribute to a precise clinical treatment for improving the prognosis of patients after DES implantation.

In the present study, untargeted metabolomic analysis was conducted on serum samples from 16 adult male New Zealand White rabbits subjected to different treatments, to investigate the specific metabolic profiles of ISNA progression. Optical coherence tomography (OCT) continuously collects intravascular images, and can accurately observe the state of neointima in vivo. Therefore, as a supplement to histopathological examination, OCT was used in this study to judge the progression of ISNA in rabbit models. As a result, eight rabbits in the control group were free of ISNA, and six out of eight rabbits in the treatment group developed ISNA. Notably, the remaining two rabbits in the treatment group did not develop ISNA, highlighting the existence of metabolic heterogeneity among individuals and will provide more information for clinical considerations. In summary, this study provides comprehensive insights into the metabolic molecular mechanisms underlying the occurrence and development of ISNA. The findings emphasize the importance of considering individual metabolic profiles for precise clinical treatment after DES implantation.

2.1 Ethical approval

The study protocol was approved by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University (Harbin, China). All animal experimental procedures were conducted following the administrative acts for experimental animals in China, and performed by the same operation team.

2.2 Animal experiment

Sixteen adult male New Zealand White rabbits (2.5-3.5 kg, 3-4 months) were assigned to receive sirolimus-eluting stent (Lepu Medical, Beijing, China) implantation into the iliac artery, according to a previously published protocol (G. Wang et al. 2019). The animals were excluded if the animal died prematurely, preventing the collection of behavioral and histological data. However, all of the 16 rabbits were included as they underwent successful DES implantation. The implanted stent size was 3 mm×15 mm. Subsequently, 8 rabbits were randomly sampled and fed a high cholesterol diet (HCD) containing 1% cholesterol for 8 weeks until euthanasia, while the other 8 rabbits were fed a normal diet and defined as the control group. An independent investigator (SF) administered the diet treatment based on the randomization table. This investigator was the only person aware of the treatment group allocation. A second investigator (HL, YF) was responsible for the following OCT imaging, whereas a third investigator (YY) performed histology analysis.

2.3 OCT imaging and analyses

At 8 weeks after stent placement and initiating the HCD diet, OCT was performed, and the stented segments were harvested for histomorphometric analysis to assess the progression of ISNA. OCT was carried out after administration of 100 μg of nitroglycerin using an OCT System (LightLab Imaging, Westford, MA, USA) with an imaging wire (crossing profile, 0.016 inches; LightLab Imaging). The OCT image wire was advanced to the distal end of the stent through a 3-F occlusion balloon catheter. The entire length of the stent and 5 mm from both stent ends were imaged with an automatic pullback device at a pullback speed of 2 mm/s during flushing with heparinized lactate Ringer’s solution through the guiding catheter to transiently displace blood. Cross-sectional OCT images were analyzed at 1-mm intervals. Mean neointimal thickness (NIT), stent area, and lumen area for each cross-sectional OCT image were semiautomatically calculated using OCT offline analysis software (LightLab Imaging, Westford, Massachusetts). NIT was measured from the luminal border to the inner border of the struts. The neointimal area (NA) was calculated as the stent area minus the lumen area. The restenosis rate was calculated as NA divided by stent area. As described previously, the stent was considered to have ISNA when lipid-laden neointima was present. The OCT feature of ISNA was defined as a signal poor region with a diffuse border inside the stent.

2.4 Stent harvest and histology analysis

Stents were harvested and cut into three 5-mm segments (distal, middle, and proximal). For light microscopy, specimens were embedded in methyl methacrylate resin, sectioned into 8 μm sections, and stained with hematoxylin-eosin (H&E) as previously described. ISNA was deﬁned as peristrut foamy macrophage clusters with or without fibroatheromas and ruptures with thrombosis.

2.5 Sample collection and preparation

Blood samples were collected via the rabbit ear vein at two time points (baseline and 8 weeks after feeding with 1% HCD) and then gently shaken and centrifuged at 3000 r for 10 minutes to extract the serum supernatant. Then, each 50 μL of serum sample was extracted, weighted in a frozen EP tube, and immediately stored at -80 ℃ before metabolite extraction.

2.6 Metabolite extraction

First, the experimental order was randomized before metabolite extraction and LC-MS analyses. Subsequently, the EP tube was thawed at 4 ℃ on ice, vortexed for 30 s after the addition of 1000 μL of extract solvent (acetonitrile-methanol-water, 2:2:1), sonicated for 10 min in an ice-water bath, and incubated for 1 h at -20 ℃ to precipitate proteins. Then, the sample tubes were centrifuged at 12000 rpm for 15 min at 4 ℃, and the supernatants were transferred to LC-MS vials and stored at -80 ℃ until UHPLC-QE Orbitrap/MS analysis. A quality control (QC) sample was prepared by pooling small aliquots of the supernatants from each serum sample.

2.7 LC-MS/MS analysis

LC-MS/MS analyses were performed using a UHPLC system (U3000, Thermo) with a UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm) coupled to Q Exactive (Orbitrap MS, Thermo). Mobile phase A was 0.1% formic acid in water for positive-ion mode, 5 mmol/L ammonium acetate in water for negative-ion mode, and the mobile phase B was acetonitrile. The elution gradient was set as follows: 0 min, 1% B; 1 min, 1% B; 8 min, 99% B; 10 min, 99% B; 10.1 min, 1% B; 12 min, 1% B. The flow rate was 0.5 mL/min, and the injection volume was 2 μL. A QE mass spectrometer was used for its ability to acquire MS/MS spectra on an information-dependent basis (IDA) during an LC/MS experiment. In this mode, the acquisition software (Xcalibur 4.0.27, Thermo) continuously evaluates the full scan survey MS data as it collects and triggers the acquisition of MS/MS spectra depending on preselected criteria. ESI source conditions were set as follows: sheath gas flow rate of 45 Arb, aux gas flow rate of 15 Arb, capillary temperature of 320 ℃, full mass resolution of 70000, MS/MS resolution of 17500, collision energy of 20/40/60 eV in the NCE model, and spray voltage of 3.8 kV (positive) or -3.1 kV (negative).

2.8 Data procession and annotation

MS raw data files were converted to the abf format using “ABFconvert” and processed by “MS dial” (version 4.6). The preprocessing results generated a data matrix that consisted of the retention time (RT), mass-to-charge ratio (m/z) values, and peak intensity. MassBank of North America (MoNA) was used for peak annotation after MS dial data processing with an in-house MS/MS database.

2.9 Statistical analysis

The data matrix was imported into the R platform (version 3.6.2) for statistical analysis. Paired t-test was performed for self-paired samples to identify the differential metabolites in each treatment group. The metabolic fold-change (FC) was calculated by comparing the median value after dietary intervention to the baseline level. Metabolites with a p value less than 0.05 and absolute FC larger than 1.5 were further defined as dysregulated metabolites and then mapped into the KEGG database (http://www.genome.jp/kegg/) for pathway enrichment analysis using the hypergeometric test.

3.1 Group-specific ISNA progression

As presented in Fig. 1, ISNA was induced in eight rabbits by the combination of HCD and DES implantation into the iliac artery, while the other eight rabbits received a normal diet along with DES implantation. Ultimately, six rabbits fed the HCD exhibited ISNA progression based on OCT and histomorphometric tests and defined as Group A, and the remaining two rabbits fed the HCD free of ISNA were defined as Group B. Additionally, the control rabbits fed a normal diet free of ISNA were defined as Group C (Fig. 2). After excluding blurred images, 92, 32, and 127 frames of OCT images were scanned and analyzed for Groups A, B, and C, respectively. ISNA plaques were detected in 14 frames in Group A, while no plaques were observed in Groups B and C. The average NITs of Groups A, B, and C were 0.22±0.10, 0.15±0.06, and 0.13±0.10 mm, respectively. The average NAs of Groups A, B, and C were 1.60±0.59, 1.10±0.46, and 0.89±0.60 mm², respectively. The average restenosis rates of Group A, B, and C were 30.7%, 21.7% and 20.0%, respectively. These results, in addition to the pathological diagnosis, confirm that ISNA occurred only in the six rabbits of Group A.

3.2 Global metabolic profiles of different treatment groups

LC-MS based untargeted metabolomic analysis was conducted to characterize the metabolic disorders in the three different treatment groups. Overall, 25643 and 11240 metabolic peaks were detected in positive-ion mode and negative-ion mode, respectively. After peak annotation and MS/MS identification, a total of 279 metabolites were finally identified, with 189 metabolites in positive-ion mode and 130 metabolites in negative-ion mode (Fig. 3A-C). Subsequently, a univariate analysis based on the combination of paired t-test and FC calculation was performed to identify dysregulated metabolites. The volcano plots revealed that 67, 2, and 40 metabolites were dysregulated at the statistically significant levels in Group A, B, and C, respectively. However, the Venn diagram demonstrated that 14 differential metabolites were shared by Group A and C, whereas no differentially regulated metabolites were shared between Group A or Group C with Group B (Fig. 3D-G).

3.3 Dysregulated metabolic profile of the ISNA group

Detailed information on the 67 differential metabolites in the ISNA group is listed in Supplementary Table 1. As the metabolic profile was drastically changed along with the progression of ISNA in Group A, an unsupervised hierarchical cluster analysis (HCA) was further performed to visualize the result of discrimination by the 67 differential metabolites. The metabolic profile of serum samples after the dietary intervention was clustered tightly and separated from the metabolic profile of baseline samples (Fig. 4A). Furthermore, pathway enrichment analysis based on the 67 differential metabolites in the ISNA group identified eight dysregulated pathways that reached statistical significance, including glutathione metabolism, arginine and proline metabolism, glycine, serine and threonine metabolism, arginine biosynthesis, citrate cycle [TCA cycle], glyoxylate and dicarboxylate metabolism, aminoacyl-tRNA biosynthesis, valine, leucine, and isoleucine biosynthesis (Table 1). In particular, the impact of dysregulated metabolites on glutathione metabolism, and arginine and proline metabolism exhibited effect sizes larger than 0.300 (Fig. 4B).

3.4 Specific dysregulated metabolites in the ISNA group

We finally investigated the changing trends to identify the specific metabolic disorders of the ISNA group compared with Groups B and C. Among the 67 differential metabolites, only 8 metabolites displayed a specific changing trend in the ISNA group, while no significant differences in changing trends were observed for the remaining 59 metabolites (Supplementary Table 1). As depicted in Fig. 4C, an increase of 9-trans-palmitelaidic acid, biliverdin, and palmitoleic acid was observed after treatment along with the progression of ISNA compared to baseline but was decreased after treatment in Groups B and C. Conversely, choline, cystine, pipecolinic acid, histidine and L-proline were reduced after treatment along with the progression of ISNA compared to baseline, but were upregulated after treatment in Groups B and C (Fig. 4C). More detailed information on the eight metabolites associated with ISNA is provided in Table 2.

The development of ISNA after DES implantation poses tough challenges for the clinical prevention of LST and VLST. In addition to physical factors such as hemodynamics, the characteristics of plaque also play an important role in the development of ISNA and LST/VLST (Hoshino et al. 2019; Kang et al. 2011). While previous studies have primarily focused on diagnostic approaches and major contributors to LST/VLST, the metabolic profile and molecular mechanisms of ISNA progression have been relatively understudied. In the current study, we conducted a comprehensive exploration of the global and specific metabolic profiles of ISNA using a rabbit model, detecting over 30000 metabolic peaks and identifying approximately 300 metabolites. To the best of our knowledge, this is the first report providing such a comprehensive overview of ISNA development.

OCT, also known as an optical biopsy, was used in this study to inspect stent segments with high resolution, providing detailed information on plaque characteristics and avoiding misdiagnosis of ISNA (Fahed & Jang 2021). In this study, based on the ISNA diagnostic criteria applicable to humans, OCT and pathological analysis were performed to group the rabbit models. The results showed that two out of eight rabbits in the HCD group did not develop ISNA, mirroring the clinical scenario. This suggests the reliability of the established rabbit models and grouping results, thus highlighting the metabolomics results are representative for clinical consideration. Compared to the ISNA group, rabbits in Group B exhibited fewer metabolic disorders, indicating that individual metabolic capacity plays a crucial role in ISNA progression (Fig. 3G). These findings further indicate that the occurrence of ISNA was affected not only by environmental intervention but also by individual metabolism. A total of 67 metabolites were dysregulated in the ISNA group, with 54 downregulated metabolites and 13 upregulated metabolites after treatment (Fig. 3D and Table 1). When comparing the ISNA group with rabbit groups without ISNA development (Groups B and C), eight metabolites were found to be specifically associated with ISNA. Two trans fatty acids (TFAs), 9-trans-palmitelaidic acid and palmitoleic acid were evaluated in the ISNA group with statistical significance (p=0.003 and p=0.014, respectively). The positive correlation between TFAs and cardiovascular diseases, including atherosclerosis, is consistent with the conclusions of previous human studies (Hirata et al. 2017; Oteng & Kersten 2020). Biliverdin, as a byproduct of the breakdown of hemoglobin, has been reported to regulate antioxidant defenses and pivotal cellular processes such as inflammation, cell proliferation and apoptosis (Ayer, Zarjou, Agarwal, & Stocker 2016). Consistently, the results of this study indicate that elevated biliverdin is related to the occurrence and development of ISNA (FC=2.757, p=0.008). Dietary choline intake was reported to be associated with a lower risk of cardiovascular events in many previous studies (Millard et al. 2018; Zhong et al. 2021). However, a large-scale cohort study in Japan demonstrated that the level of choline intake does not affect the risk of cardiovascular death (Nagata et al. 2015). Additionally, it is unclear whether there is a correlation between dietary choline intake and plasma choline concentrations (Konstantinova et al. 2008; Van Parys et al. 2020). Specifically, several studies have indicated that higher plasma choline concentrations are associated with unfavorable cardiovascular risk (Roe et al. 2017; Ueland 2011; Zuo et al. 2018). However, our findings provided contradictory evidence that the reduction of serum choline is associated with the risk of ISNA. Considering that the p value of 0.049 is close to the significance level, this conclusion should be viewed with cautious. Cystine helps to eliminate oxidants and can be converted into cysteine. Cysteine can oxidize and increase the risk of vascular disease (El-Khairy, Ueland, Refsum, Graham, & Vollset 2001; Go & Jones 2011; Suzuki et al. 2018). In agreement, we found that the decrease in cystine is related to ISNA, suggesting a higher conversion rate of cystine to cysteine. As a metabolite of lysine, pipecolinic acid exists in human physiological fluids, and was reported to be associated with type II diabetes and cancer (Assad et al. 2020; Guerra et al. 2015; Ouyang et al. 2021). Here, it is reported for the first time that the reduction of pipecolinic acid will increase the risk of ISNA, which could extend the current understanding of ISNA metabolism. We found that serum histidine concentrations were inversely related to the risk of ISNA, which is in agreement with the discovery of a secondary comprehensive study based on the population of Atherosclerosis Risk in Communities (ARIC), The Framingham Heart Study (FHS) Offspring Cohort, and The Cardiovascular Health Study (CHS) Cohort (Yu et al. 2015). Proline-rich proteins (such as proline-rich tyrosine kinase [PYK2] and a proline/serine-rich coiled-coil protein 1 [Psrc1]) have been reported to play an important role in the development of atherosclerosis (Katsume et al. 2011; Segedy et al. 2014; Xi et al. 2018). Similarly, our study also demonstrated that the decrease in proline concentration was related to ISNA, which is consistent with the findings of Chwilkowska et al. (Kuzan et al. 2021). In summary, these eight dysregulated metabolites should be specific and closely related to the occurrence and development of ISNA.

However, there are several limitations in this study that need to be clarified. Firstly, the small sample size due to the high cost of DES implantation and the challenges associated with histopathological examination may limit the statistical significance of dysregulated metabolites among the rabbit models. Additionally, while we identified eight dysregulated pathways in ISNA rabbits, further research is required to elucidate the underlying regulatory mechanisms.

In conclusion, our global metabolomic study has provided valuable insights into the impact of both environmental intervention and individual metabolism on the occurrence of ISNA. We have identified eight dysregulated metabolic pathways and eight specific dysregulated metabolites associated with ISNA, and will provide novel insights into the specific metabolic molecular mechanisms involved. These findings underscore the importance of personalized prevention strategies that consider individual metabolic profiles.

Ethics statement

The study protocol was approved by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University (Harbin, China), and all animal experimental procedures were conducted following the administrative acts for experimental animals in China. All experiments were carried out in compliance with the ARRIVE guidelines.

Acknowledgements

We thank Dr. Gang Wang for the recommendation on DES implantation experiment.

Author contributions

Zhuozhong Wang: Conceptualization, Investigation, Data management, Statistical analysis, Writing - Original draft, Writing - Review & Editing. Lei Fan: Conceptualization, Investigation, Data management, Writing - Original draft, Writing - Review & Editing. Yue Yang: Conceptualization, Investigation, Pathological analysis, Writing - Original draft, Writing - Review & Editing. Shi Feng: Formal analysis, Data management, Investigation, Writing - Review & Editing. Huimin Liu: Animal model establishment, Data management, Investigation, Writing - Review & Editing. Shaohong Fang: Data management, Investigation, Writing - Review & Editing. Yan Fang: Conceptualization, Investigation, Data management, OCT analysis, Writing - Original draft, Writing - Review & Editing, Supervision. All authors have read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 82204147 to ZW), Fund of Key Laboratory of Myocardial Ischemia, Ministry of Education (Grant No. KF202201 to ZW), Fellowship of China Postdoctoral Science Foundation (Grant No. 2020M670923 to ZW), Fundamental Research Funds for the Provincial Universities (Grant No. 2019-KYYWF-0352 to YF), Fund of Key Laboratory of Myocardial Ischemia, Ministry of Education (Grant No. KF201902 to YF), and the Scientific Research Foundation of 2nd Affiliated Hospital of Harbin Medical University (Grant No. KYBS2015-34 to YF).

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at XXXX.

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TABLE 1. Dysregulated metabolic pathways in the ISNA group (Group A).

No.	Pathway name	Impact	p value	Pathway length	Overlap
1	Glutathione metabolism	0.379	0.002	28	5
2	Arginine and proline metabolism	0.317	0.008	38	5
3	Glycine, serine and threonine metabolism	0.295	0.027	34	4
4	Arginine biosynthesis	0.193	0.011	14	3
5	Citrate cycle (TCA cycle)	0.170	0.029	20	3
6	Glyoxylate and dicarboxylate metabolism	0.161	0.004	32	5
7	Aminoacyl-tRNA biosynthesis	0.000	0.000	48	9
8	Valine, leucine and isoleucine biosynthesis	0.000	0.002	8	3

TABLE 2. Information of ISNA related metabolites after fed with high cholesterol diet.

No.	Metabolites	p value (Group A)	Fold change^a
No.	Metabolites	p value (Group A)	Group A	Group B	Group C
1	9-Trans-Palmitelaidic acid	0.003	2.095	0.859	0.808
2	Biliverdin	0.008	2.757	0.766	0.647
3	Choline	0.049	0.651	1.489	1.230
4	Cystine	0.011	0.549	1.292	1.251
5	Histidine	0.003	0.511	1.215	1.275
6	L-Proline	0.005	0.552	1.347	1.328
7	Palmitoleic acid	0.014	1.759	0.745	0.745
8	Pipecolinic acid	0.017	0.565	1.100	1.157

^aMedian ratio of metabolites after fed with high cholesterol diet versus before fed.

No competing interests reported.

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Serum Metabolomic Profiling of In-Stent Neoatherosclerosis Confirmed by Optical Coherence Tomography in a Rabbit Model

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1 Introduction

2 Materials and methods

2.1 Ethical approval

2.2 Animal experiment

2.3 OCT imaging and analyses

2.4 Stent harvest and histology analysis

2.5 Sample collection and preparation

2.6 Metabolite extraction

2.7 LC-MS/MS analysis

2.8 Data procession and annotation

2.9 Statistical analysis

3 Results

3.1 Group-specific ISNA progression

3.2 Global metabolic profiles of different treatment groups

3.3 Dysregulated metabolic profile of the ISNA group

3.4 Specific dysregulated metabolites in the ISNA group

4 Discussion

5 Limitations

6 Conclusion

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