Elevated circulating levels of phenylacetylglutamine in stroke patients with T2D are linked to specific gut microbiota

Elevated circulating levels of phenylacetylglutamine in stroke patients with T2D are linked to specific gut microbiota | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Elevated circulating levels of phenylacetylglutamine in stroke patients with T2D are linked to specific gut microbiota Minping Wei, Qin Huang, Fang Yu, Yuanlin Ying, Yunfang Luo, Xianjing Feng, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1245321/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Objective Type 2 diabetes (T2D) aggravates the injury of ischemic stroke (IS). The alterations of gut microbiota and its metabolite phenylacetylglutamine (PAGln) levels in stroke patients with T2D remain unclear. Therefore, our study aimed to explore the differences in gut microbiota and its metabolite PAGln between IS patients with and without T2D. Methods In our study, 35 IS with T2D (IS-T2D group), 50 IS patients without T2D (IS-NT2D group), and 29 healthy controls (HC group) were recruited. Fecal samples were collected and analyzed using high-throughput sequencing of 16S rRNA genes, and plasma samples were subjected to targeted metabolomics to detect metabolite PAGln. Plasma PAGln levels of rats with transplantation of fecal microbes from patients were assessed. Results Our results showed that the plasma PAGln levels in IS-T2D patients were significantly higher than those in IS-NT2D patients. Correlation analysis showed that plasma PAGln levels were significantly correlated with the relative abundance of Enterobacteriaceae, Verrucomicrobiota , and Klebsiella , which were enriched in IS-T2D patients. Further studies demonstrated that plasma PAGln levels were positively correlated with the concentration of neutrophil extracellular traps (NETs), and NETs levels were increased in a dose-dependent manner according to PAGln levels. Moreover, the rats transplanted with fecal microbes from IS-T2D patients developed more severe brain injury and higher plasma PAGln levels compared to the rats transplanted with fecal microbes from IS-NT2D patients. Conclusions Our results suggest that T2D may contribute to aggravation in stroke patients via NETs, mediated in part by gut microbiota and its metabolite PAGln. gut microbiota phenylacetylglutamine ischemic stroke type 2 diabetes neutrophil extracellular traps Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Stroke is an important public health problem affecting more than 10 million people every year around the world 1 . In China, the mortality rate of stroke is 149.49/100,000, accounting for 22% of the national overall mortality rate 2 . It is widely known that type 2 diabetes (T2D) is an independent risk factor for ischemic stroke (IS) 3 . Surveys have shown that among IS patients, up to 23.5% of patients had diabetes mellitus, accompanied by higher risks of stroke recurrence, disability, and mortality 4 , 5 . As the prevalence of IS with T2D (IS-T2D) has seen a steady increase globally, it is increasingly important for finding new pathophysiological mechanisms and therapeutic targets for IS-T2D. In recent years, increasing evidence suggests that changes in the gut microbiota and its metabolites participate in the pathophysiological mechanism of IS and T2D 6 – 13 . A cohort study of more than 5000 people showed that patients with T2D and patients who developed incident adverse cardiovascular events after 3-year follow-up had higher plasma phenylacetylglutamine (PAGln) levels, which were proved to be an independent predictor of adverse cardiovascular events 14 . The metabolite, PAGln, is produced by gut microbiota via catabolizing the essential amino acid phenylalanine (PHE) in the distal colon 15 . As a gut microbiota-derived metabolite, PAGln is not only related to elevated blood glucose but also closely related to the risk of thrombotic events, such as coronary heart disease (CHD) 16 , 17 . Similarly, IS is also a dreadful thrombotic event. Multiple studies have shown that gut microbiota and its metabolites changed after stroke and then exacerbated cerebral infarction in turn 18 , 19 . Nevertheless, the relationship among gut microbiota, its metabolite PAGln, and stroke with T2D remains unknown. A study showed that neutrophils (NE) mediated inflammation to amplify cerebral microvascular damage in the early stage of T2D, resulting in more severe brain edema and nerve damage after ischemia 20 . Furthermore, NE also participated in inflammatory damage by releasing neutrophil extracellular traps (NETs), which are network structures of DNA fibers, composed of histones and active particles 21 , 22 . Extensive research has confirmed the formation of NETs in blood circulation and thrombosis in patients with IS 23 , 24 . Moreover, high glucose levels or hyperglycemia in diabetic patients could increase the release of NETs 22 , 23 . In inflammatory bowel disease, NETs are involved in inflammation after intestinal dysbiosis 25 . Therefore, we hypothesized that gut microbiota and its metabolites were disrupted in stroke patients with T2D, leading to NET-related immune imbalance. Here, we evaluated the characteristics of gut microbiota in IS-T2D patients, IS patients without T2D (IS-NT2D), and healthy control people (HC) via 16S ribosomal RNA sequencing and determined the plasma PAGln concentration in those groups by targeted liquid chromatography-mass spectrometry. We also analyzed the relationship between PAGln levels and NETs. In addition, we constructed a prediction model based on the differential relative abundances of microbiota, plasma PAGln levels, and NETs levels for discriminating between IS-T2D patients and IS-NT2D patients. Finally, we validated whether the fecal microbes from IS-T2D patients could exacerbate brain infarction and elevate circulating PAGln levels in IS-T2D patients via fecal microbiota transplantation (FMT) in animal experiments. Methods Study population We recruited 85 patients with IS in the Department of Neurology, Xiangya Hospital, Central South University (Changsha, China) from December 2019 to December 2020. And the patients were divided into 50 patients without T2D (IS-NT2D group) and 35 patients with T2D (IS-T2D group) (T2D was defined as T2D medical history or typical diabetes symptoms with either random blood glucose ≥ 11.1 mmol/L, fasting blood glucose ≥ 7.0mmol/L, or blood glucose at 2h after glucose load ≥ 11.1mmol/L 26 ). Meanwhile, 29 healthy controls (HC group) were recruited. Inclusion criteria of patients were as follows: (1) age between 18 and 80 years old; (2) first diagnosed acute IS (stroke was defined as a rapid clinical onset of a neurological impairment lasting more than 24 hours or resulting in death, with no cause other than that of vascular origin 27 . IS was further confirmed by comprehensive neurological physical examination, head computed tomography, and/or magnetic resonance imaging); (3) admitted within two weeks of IS onset. In addition, patients with T2D treated with metformin or acarbose were excluded. HC matched by age and sex were recruited. Exclusion criteria of patients and HC were as follows: (1) used antibiotics or probiotics before admission or after admission within 1 month; (2) suffered from acute inflammatory disease, severe infective disease and/or cancer, severe liver and kidney failure, hepatic impairment, autoimmune disease, severe mental illness, and gut disease (i.e., inflammatory bowel disease, ulcerative colitis, and Crohn’s disease); (3) had a history of intestinal surgery. For all participants, plasma samples were collected within 24h, and fecal samples were obtained within 48h of admission. At the same time, baseline characteristics including demographic and cerebrovascular risk factors (histories of hypertension, dyslipidemia, diabetes mellitus, CHD, and smoking) were collected and recorded at admission. Each participant in this study provided written informed consent, and the study protocol complied with the principles of the 1975 Declaration of Helsinki and was approved by the Ethics Committee of Xiangya Hospital, Central South University, China. All patients were assessed with the National Institutes of Health Stroke Scale (NIHSS) scores on admission and evaluated by the modified Rankin scale (mRS) scores 3 months after onset. Both NIHSS and mRS scores were rated by two trained clinical staffs who were blinded to the study protocol. 16S rRNA amplification and sequencing of fecal microbiota samples Fecal samples of all research objects were stored at -80°C within 30 minutes once obtained. Total genome DNA from the samples was extracted using a DNA kit (Magnetic Soil And Stool DNA Kit, TIANGEN, DP712, China) according to the manufacturer’s instructions. We chose the V3-V4 region of the 16S rRNA gene to finish polymerase chain reaction (PCR) amplification and used specific primer 341F (5’-CCTAYGGGRBGCASCAG-3’) and 806R (5’-GGACTACNNGGGTATCTAAT-3’). The mixture of PCR products was purified using Qiagen Gel Extraction Kit (Qiagen, Germany) and sequencing libraries were generated with the TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA). At last, the library was sequenced on an Illumina NovaSeq platform. Bioinformatics and Biostatistics We used Quantitative Insights into Microbial Ecology (QIIME) V1.9.1 software to analyze microbial data. The Effective Tags of all samples were clustered by the Uparse algorithm (Uparse v7.0.1001). Sequences were clustered into operational taxonomic units (OTUs) with 97% identity, and species annotations were made to the OTUs sequence (the threshold was set to 0.8 ~ 1). QIIME was used for species alpha and beta diversity analysis, and R software (2.15.3) was used for drawing the species accumulation curve. The linear discriminant analysis (LDA) effect size (LEfSe) was used to determine the difference among the three groups of bacteria, with a threshold of 4. PICRUSt was used to predict the metagenome function of 16S rRNA biological information. According to the 16S rRNA sequencing data, the function prediction based on the KEGG database was performed. Laboratory tests 12h fasting blood samples were collected on admission, centrifuged at 3000 RPM for 10 minutes, and stored at -80°C. A routine blood test was analyzed with an automatic biochemical analyzer. Blood biochemistry such as blood urea nitrogen (BUR), serum creatinine (Scr), triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), glucose, glycosylated hemoglobin (HbA1C), homocysteine (Hcy) was analyzed with automated enzymatic analysis. Plasma PAGln concentrations were quantified by targeted liquid chromatography-mass spectrometry. The plasma was diluted 10-fold with ddH2O. 48µL diluted plasma was mixed with 2µL internal standard (1ppm D5-PAGln), diluted 3-fold with cold methanol, then centrifuged (21,000 x g; 4°Cfor 15 min), and then transferred to a clean glass bottle for testing in AB SCIEX TripleTOF 6500 System (AB SCIEX, Foster City, CA, USA). Finally, 1 uL supernatant was analyzed by injecting Acquity UPLC BEH C18 column for analysis (50×2.1 mm, 1.7 µm) at a column temperature of 40°C, a flow rate of 0.3 mL/min, mobile phase A containing 0.1% acetic acid, mobile phase B containing 0.1% acetic acid. The concentration of PAGln was measured by establishing a standard curve based on the known PAGln concentration. Quantification of NETs markers Citrullinated histone H3 (CitH3) is currently considered to be the most specific marker of NETs 23 . Therefore, we evaluated the concentration of CitH3 in the plasma to represent the level of NETs. CitH3 was measured with the CitH3 Detection ELISA kit (Cayman Chemical, 501620, Ann Arbor, MI, USA) according to the manufacturer’s instructions. Animals The experimental protocols were approved by the Experimental Animal Welfare Ethics Committee of Central South University on September 7, 2021 (Approval No. 2019-0004). All experimental procedures were conducted following the Care and Use of Laboratory Animals by the guidelines of the National Institutes of Health (NIH). Sprague-Dawley (SD) rats (5–6 weeks old) (purchased from Hunan SJA Laboratory Animal Co. Ltd) were placed in a specific pathogen-free (SPF) environment and raised under the conditions of cycles of 12h light/dark, 50%-55% humidity, and 100–200 Lux of light intensity. The animals were randomly divided into the FMT-IS-NT2D group (transplanted with stool samples from IS-NT2D patients) and the FMT-IS-T2D group (transplanted with stool samples from IS-T2D patients). Fecal microbiota transplantation (FMT) Before FMT, rats in both groups were fed with drinking water containing antibiotics (vancomycin 500mg/L, neomycin 1g/L, ampicillin 1g/L, metronidazole 1g/L) for 1 week. The drinking water containing antibiotics was replaced every 1–2 days 28 . Then, stool samples from 5 IS-T2D patients and 5 IS-NT2D patients were selected. Next, the stool homogenate was centrifuged at 1000 RPM and the supernatant was collected. Fecal supernatant from the two patient groups was gavaged to the two rat groups (2ml/d for each rat), respectively, for 1 week, and then modeling was made 29 . Middle cerebral artery occlusion (MCAO) procedure Before inducing the MCAO model, 1–2 ml orbital blood of anesthetized rats was collected to measure the PAGln concentration. The collected blood was immediately centrifuged at 3000 RPM for 10 minutes, and the plasma was separated and stored at -80°C for testing. After blood collection, rats were subjected to middle cerebral artery occlusion as previously described 30 . Briefly, rats were placed in a supine position, and a midline incision was made in the neck to expose the external carotid artery (ECA) and common carotid artery (CCA). The ECA was ligated and the internal carotid artery (ICA) was separated. A 4 − 0 silicon-coated monofilament suture was inserted into ICA after cutting on ECA. After 90min of occlusion, the suture was taken out and the ECA was ligated. The neck wound was sutured and the animals were recovered and reared for 24 hours before being killed. After anesthesia, the blood of stroke rats was collected through the cardiac puncture, and the plasma was centrifuged as described above. The separated plasma was placed in a refrigerator at -80°C for testing. The plasma PAGln concentration of rats was measured with the rat PAGln ELISA assay kit (Shanghai Jianglai Biotechnology, JL51250, Shanghai, China) according to the manufacturer’s instructions. Neurological evaluation Garcia neurofunctional score was used to evaluate the neurological function of rats at 24h after the MCAO procedure 31 . The score includes six tests: spontaneous movement, symmetry of limb movement, symmetry of forelimb extension, climbing, body proprioception, and response to whiskers. The score ranges from 3 to 18, and the lower the score, the more serious the neurological deficit. The score was evaluated by two trained experimenters who were blinded to the study protocol. Measurement of infarct volume After anesthesia, rats were sacrificed to extract brain tissue. Brain slices with a thickness of 2mm were taken from a coronal plane, and 5 slices were cut from each brain. Staining was then performed by soaking in a 2% TTC solution at 37°C for 30 min 32 . Infarct volume was calculated using Image-Pro Plus Image software. The results were expressed as infarct volume percentage: infarct volume percentage = total infarct volume/total brain volume x 100% 33 . Statistical analyses The data were statistically analyzed and managed by SPSS 26.0 and GraphPad Prism 8.0. For continuous data, it was expressed as mean ± standard error mean (SEM) if conformed to the normal distribution and was expressed as the median and interquartile range (IQR) if not conformed to the normal distribution. For categorical data, it was expressed as percentages. The mean was analyzed by the t-test and the median was analyzed by the Mann-Whitney u-test in comparison between the two groups. The mean was analyzed by the ANOVA and the median was analyzed by the Kruskal-Wallis test in comparison among the three groups. The percentage was analyzed by the chi-square or Fisher’s exact test. Spearman correlation analysis was used to analyze the relationship among PAGln level, gut microbiota, and biochemical indexes. Receiver operator characteristic (ROC) was used to evaluate the diagnostic performance of plasma PAGln levels, gut microbiota, and NETs levels for IS-T2D. A value of P < 0.05 was considered significant. Univariate and multivariate logistics regression was used to evaluate whether the plasma PAGln level was a risk factor for IS-T2D and IS-NT2D. The relative risk was shown as the odds ratio (OR) with the 95% confidence interval. Result Participant characteristics A total of 114 subjects were recruited in our study, including 35 patients with IS-T2D, 50 patients with IS-NT2D, and 29 healthy individuals. As showed by the data in Table 1 , there were no significant differences in age and sex among the three groups. Risk factors such as hypertension, dyslipidemia, and CHD were not statistically significantly different between the IS-T2D and IS-NT2D groups. The severity of infarction (admission NIHSS score) and short-term prognosis (90-day mRS score) of patients with IS-T2D were worse than those with IS-NT2D ( P < 0.05). IS-T2D and IS-NT2D groups had significantly higher levels of leukocyte (LEU), NE, blood urea nitrogen (BUN), and serum creatinine (Scr) when compared with the HC group, and the IS-T2D group had the highest levels among the three groups (P < 0.05). Moreover, glucose and HbA1c of the IS-T2D group were significantly higher than those of the other two groups ( P < 0.001). Table 1 Characteristics of the study participants Baseline characteristics HC(n = 29) IS-NT2D(n = 50) IS-T2D(n = 35) P- value Gender, n (M/F) 12/17 31/19 24/11 0.696 Age (years) 57.79 ± 14.11 57.62 ± 12.08 60.14 ± 11.95 0.629 Risk factors, n (%) Hypertension 15(51.7) b 36(72.0) 31(88.6) 0.005 Dyslipidemia 8(27.6) 13(26.0) 13(37.1) 0.518 Diabetes 0(0) 0(0) 35(100) / CHD 3(10.3) 5(10.0) 6(17.1) 0.574 Current smoking 9(31.0) 26(52.0) 16(45.7) 0.194 Clinical findings Admission NIHSS score / 3(1, 6) b 6(4, 8) / 90-Day mRS score / 1(0.75, 2) b 2(2, 3) / Biochemical data LEU (x10^9/µL) 5.8(5.0, 6.6) 6.5(5.3, 8.2) 7.3(6.2, 8.8) 0.002 NE (x10^9/µL) 3.4(2.9, 4.0) 4.4(3.4, 4.9) 5.0(3.8, 6.0) < 0.001 LY (x10^9/µL) 1.5(1.3, 1.8) 1.4(1.1, 2.1) 1.6(1.2, 2.0) 0.883 NLR 2.1(1.8, 3.1) 2.4(2.0, 3.8) 3.3(2.3, 4.4) 0.032 BUN, mmol/L 4.36(3.51, 5.36) 4.64(3.94, 5.93) 5.35(4.45, 6.54) 0.030 Scr(mmol/L) 70.0(62.0, 84.0) 78.0(65.4, 78.6) 80.1(71.0, 93.7) 0.360 TG (mmol/L) 1.51(1.23, 2.01) 1.47(1.06, 2.22) 1.54(1.26, 2.04) 0.730 TC (mmol/L) 4.15(3.61, 4.94) 4.22(3.38, 4.92) 3.55(3.06, 5.02) 0.180 HDL (mmol/L) 2.67(2.30, 3.09) 2.68(1.99, 3.15) 2.23(1.84, 3.00) 0.220 Glu (mmol/L) 4.98(4.81, 5.41) 5.23(4.81, 5.67) 9.20(6.24, 10.82) < 0.001 HbA1c (%) 5.8(5.4, 5.9) 5.6(5.5, 5.9) 7.8(6.7, 8.4) < 0.001 Hcy (µmol/L) 11.7(8.4, 14.7) 12.9(9.8, 17.8) 13.9(10.8, 15.5) 0.335 M/F, male/female; CHD, coronary heart disease; LEU, leukocytes; NE, neutrophils; LY, lymphocytes; NLR, neutrophil to lymphocyte ratio; BUN, blood urea nitrogen; Scr, serum creatinine; TG, triglycerides; TC, total cholesterol; HDL, high-density lipoprotein; Glu, glucose; HbA1c, glycated hemoglobin; Hcy, homocysteine. The three groups were compared by the Kruskal-Wallis test or the ANOVA. b,c Refers to Mann-Whitney U test; b p < 0.05 vs IS-T2D, c p < 0.05 vs IS-NT2D. PAGln concentration was an independent risk factor of IS-T2D We measured the plasma PAGln concentration of the subjects by the targeted liquid chromatography-mass spectrometry. The plasma PAGln levels of the patients with IS-T2D were significantly higher than IS-NT2D patients and HC group (median, 2.77 vs. 1.77µmol/L, P = 0.001; median, 2.77 vs. 1.04µmol/L, P < 0.001) (Fig. 1 A). Furthermore, the PAGln levels of the IS-NT2D group were also higher than the HC group (median, 1.77 vs. 1.04µmol/L, P = 0.027) (Fig. 1 A). Next, we sought to explore the associations among IS, T2D, and the PAGln levels. The univariate logistic regression analysis showed that PAGln levels (OR 1.68, 95%CI 1.04 to 2.72, P = 0.04) were a risk factor for patients with IS (Fig. 1 B). This significance still existed after adjusting for gender, age, hypertension, CHD, smoking, LEU, NE, BUN, Scr, TG, TC, Hcy (OR 2.34, 95%CI 1.08 to 5.09, P = 0.03) (Fig. 1 B). That could translate to an increase of 2.34-times in the risk of IS for every 1 µmol/L increase in plasma PAGln levels. More importantly, elevated PAGln levels increased the risk of T2D for stroke patients by both univariate (OR 1.56, 95%CI 1.13 to 2.17, P = 0.01) and multivariate logistic regression analyses (adjustment for gender, age, hypertension, CHD, smoking, LEU, NE, BUN, Scr, TG, TC, Hcy) (OR 1.53, 95% CI 1.01 to 2.30, P = 0.044) (Fig. 1 C). This also means an increase of 1.53-times in the risk of T2D in stroke patients for every 1 µmol/L increase in plasma PAGln levels. Therefore, increased plasma PAGln levels were an independent risk factor for IS patients and stroke patients with T2D. The gut microbiota was disturbed in stroke patients withT2D Previous studies have demonstrated that PAGln was a metabolite of gut microbiota. Moreover, PAGln was elevated in the plasma of patients with T2D 3 4 , 35 . Therefore, to investigate whether PAGln-producing bacteria were enriched in the gut microbiota of stroke patients with T2D, we conducted the 16S rRNA gene sequencing. The species accumulation boxplot showed that our fecal samples were sufficient and the species were abundant (Fig. 2 A). The Shannon index and the Simpson index were calculated to evaluate the richness and evenness of intestinal microorganisms of each group. However, we found that there were no significant differences in α diversity among the three groups (Fig. 2 B − 2C). Next, the β diversity of the three groups was analyzed by principal coordinate analysis (PCoA) based on the unweighted UniFrac distance. The results showed that the microbiome structures of both IS-T2D and IS-NT2D patients were separated from the HC group, but community structure was similar between IS-NT2D and IS-T2D groups (IS-T2D vs. HC, R 2 = 0.050, P = 0.001; IS-NT2D vs. HC, R 2 = 0.034, P = 0.002; IS-T2D vs. IS-NT2D, R 2 = 0.017, P = 0.059, Adonis test) (Fig. 2 D). A total of 5360 OTUs were obtained from 114 fecal samples by 16S rRNA gene sequencing and classified into 63 bacterial phyla, 455 bacterial families, and 778 bacterial genera. The gut microbiota differed among the three groups at the phylum, family, and genus levels. At the phylum level, the gut microbiota was mainly composed of Firmicutes (51.0%), Proteobacteria (18.7%), Bacteroidota (21.3%), Actinobacteriota (4.2%), Fusobacteriota (1.7%), and Verrucomicrobiota (3.1%) (Fig. 2 E). The relative abundance of Firmicutes in patients with IS-T2D was significantly lower than that in the IS-NT2D and HC groups (43% vs. 52%, P = 0.027; 43% vs. 55%, P = 0.005) (Fig. 2 F). The relative abundance of Proteobacteria and Verrucomicrobiota in the IS-T2D group was higher than that in the IS-NT2D group (23% vs. 17%, P = 0.024; 4.3% vs. 1.4%, P = 0.05) (Fig. 2 F). At the family level, the harmful bacteria Enterobacteriaceae were enriched in IS-T2D patients with significant differences compared with IS-NT2D and HC groups (21.6% vs. 16.5%, P = 0.027; 21.6% vs. 13.0%, P = 0.019) (Fig. 2 G). The relative abundance of Akkermansiaceae in the IS-T2D group was the highest among the three groups, but there was a significant difference only between the IS-NT2D and HC groups (1.4% vs. 3.3%, P = 0.046) (Fig. 2 G). At the genus level, the beneficial bacteria Faecalibacterium, Dialister , and Roseburia were all significantly reduced in IS-T2D and IS-NT2D groups when compared with the HC group ( Faecalibacterium , 7.4% vs. 15.4%, P < 0.001; 9.6% vs. 15.4%, P = 0.003; Dialister , 3.0% vs. 3.7%, P < 0.001; 2.0% vs. 3.7%, P = 0.005; Roseburia , 0.9% vs 3.4%, P < 0.001, 1.4% vs 3.4%, P = 0.001) (Fig. 2 H). And the relative abundance of harmful bacteria Klebsiella significantly increased in the IS-T2D group compared with the HC and IS-NT2D groups (4.5 vs. 1.8, P = 0.002; 4.5 vs. 2.5, P = 0.01) (Fig. 2 H). In summary, the diversity of gut microbiota in patients with IS-T2D decreased, with decreased beneficial bacteria and increased harmful bacteria. In addition, to better identify the microbial markers among the three groups, we used the LEfSe tool. The results showed that there was a total of 20 gut microbiota taxa from phylum to species, which were differentially abundant bacterial taxa (LDA score > 4) in three groups (Fig. 2 I). Specifically, there were 6 increased abundant taxa in the IS-T2D group, including o_Enterobacterales, f_Enterobacteriaceae, p_Verrucomicrobiota, c_Verrucomicrobiota, s_Klebsiella_pneumoniae , and g_Klebsiella. Next, we performed PICRUSt analysis to predict potential functional pathways of the microbiome communities and compared the differences between IS-T2D and IS-NT2D groups (Fig. 2 J). In total, there were 300 differential KEGG pathways (level 3), of which 49 were significantly different with the average relative abundance of one of the two groups no less than 0.1%. There were 29 pathways enriched, but 20 pathways were poor in the IS-T2D group. Comparing with the IS-NT2D group, we found that amino acid metabolism (such as glycine, serine, and threonine metabolism, P = 0.033; tyrosine metabolism, P = 0.014; beta-alanine metabolism, P = 0.020; tryptophan metabolism, P = 0.002) was significantly more abundant in the IS-T2D group (Fig. 2 J). PAGln levels were associated with characteristic microbiota related to IS-T2D, poor prognosis, and NETs-related inflammation To investigate the association between PAGln levels and IS-T2D, we performed Spearman correlation analysis. As showed in Table 2 , the PAGln levels were positively correlated with age, BUN, Scr, Glu, HbA1c, and Hcy in all subjects, and the correlations between age and PAGln were especially pronounced in stroke patients with T2D. Furthermore, it is worth noting that the PAGln levels were positively correlated with mRS score of 90 days after the onset in all stroke patients. In addition, we found that the PAGln levels were positively correlated with NE in stroke patients with T2D, suggesting an association between inflammation and PAGln levels in stroke patients with T2D. Table 2 Correlations between clinical indexes, biochemical indexes, and PAGln Total population (n = 114) All IS patients (n = 85) IS-T2D group (n = 35) PAGln PAGln PAGln r P r P r P NIHSS / / 0.095 0.386 0.012 0.238 90d-mRS / / 0.314 0.003 0.947 0.169 Age 0.469 < 0.001 0.477 < 0.001 0.624 < 0.001 LEU 0.136 0.149 0.042 0.704 0.186 0.284 NE 0.181 0.054 0.147 0.181 0.345 0.042 BUN 0.448 < 0.001 0.438 < 0.001 0.503 0.002 Scr 0.259 0.005 0.25 0.021 0.184 0.289 Glu 0.437 < 0.001 0.437 < 0.001 0.117 0.505 HbA1c 0.319 0.001 0.289 0.007 0.125 0.473 Hcy 0.267 0.004 0.167 0.127 0.194 0.264 LEU, leukocytes; NE, neutrophils; BUN, blood urea nitrogen; Scr, serum creatinine; Glu, glucose; HbA1c, glycated hemoglobin; Hcy, homocysteine. NETs are bactericidal substances, released extracellularly after NE activation, and their dysregulation can lead to inflammation 36 . Therefore, we measured the plasma NETs concentration of all subjects. We evaluated the concentration of CitH3 in the plasma to represent the level of NETs 37 . Our results showed that the plasma CitH3 levels in IS-T2D patients were significantly higher than those in IS-NT2D and HC groups (6.34 vs. 5.57ng/ml, P < 0.001; 6.34 vs. 4.73ng/ml, P < 0.001). The plasma CitH3 levels in the IS-NT2D group were also significantly higher than those in the HC group (5.57 vs. 4.73ng/ml, P < 0.001) (Fig. 3 A). Moreover, Spearman correlation analysis revealed a significant correlation between PAGln and CitH3 levels (r = 0.41, P < 0.001) (Fig. 3 B). We further analyzed the distribution of CitH3 levels based on PAGln levels as assessed by quartiles. The concentration of plasma CitH3 showed a dose-dependent increase according to PAGln levels with the highest levels being observed in subjects with the highest PAGln concentrations (Q4: 6.15 vs Q1: 5.27ng/ml, P < 0.01) (Fig. 3 C). Next, we explored the relationships between PAGln, clinical indicators, and gut microbiota. We performed Spearman correlation analysis on the data of all stroke patients. Spearman correlation analysis showed that PAGln level was significantly positively correlated with 6 microbial markers ( o_Enterobacterales, f_Enterobacteriaceae, p_Verrucomicrobiota, c_Verrucomicrobiota, s_Klebsiella_pneumoniae , and g_Klebsiella ) related to IS-T2D. Interestingly, o_Enterobacterales, f_Enterobacteriaceae, p_Verrucomicrobiota , and c_Verrucomicrobiota were also positively correlated with NE. Meanwhile, o_Enterobacterales and f_Enterobacteriaceae were positively correlated with Glu and Hcy; s_Klebsiella_pneumoniae and g_Klebsiella were positively correlated with HbA1c (Fig. 3 D). PAGln levels, NETs levels, and gut microbiota were diagnostic indexes for IS-T2D. We next explored whether the plasma PAGln, gut microbiota, and NETs could be used as biomarkers of stroke with T2D. Based on the plasma PAGln levels, differential microbiota (defined as the relative abundance of bacteria in gut microbiota with LDA score > 4), and NETs levels, we conducted ROC analysis. As shown in Fig. 4 , plasma PAGln levels (AUC: 0.7160, 95%CI: 0.6074–0.8246; P = 0.0007), different gut microbiota (AUC: 0.8757, 95%CI: 0.8016–0.9462; P < 0.0001) and NETs levels (AUC: 0.8229, 95%CI: 0.7353–0.9105; P < 0.0001) could well distinguish patients with IS-T2D from stroke patients (Fig. 4 ). Notably, when plasma PAGln levels and the differential microbiota were incorporated into the model construction, the AUC increased to 88.7 ± 3.54% ( P < 0.0001). And when the PAGln and NETs levels were incorporated into the model construction, the AUC increased to 83.4 ± 4.38% ( P < 0.0001). Finally, we included PAGln levels, NETs levels, and differential microbiota into the model construction, the area under the ROC curve was up to 94.7 ± 2.26% ( P < 0.0001). In summary, the intestinal metabolite PAGln, differential microbiota, and inflammatory indicator NET could all be used as diagnostic indicators for stroke with T2D. Moreover, the combination of these biomarkers might improve diagnostic efficiency. Elevated PAGln levels in IS-T2D patients could be transmitted through the gut microbiota To investigate whether the gut microbiota of stroke patients with T2D contributed to the elevation of plasma PAGln levels, we treated antibiotic-treated rats with a fecal transplant from IS-T2D and IS-NT2D patients (Fig. 5 A). Results demonstrated that compared with the rats receiving fecal microbes from patients with IS-NT2D (preFMT-IS-NT2D group), the PAGln concentration of the rats receiving fecal microbes from patients with IS-T2D (preFMT-IS-T2D group) was significantly increased and nearly doubled (preFMT-IS-T2D vs. preFMT-IS-NT2D, P = 0.02) (Fig. 5 D). MCAO model was established in rats after FMT. 24 hours after stroke, the rats receiving fecal microbes from IS-T2D patients had more severe stroke than the rats receiving fecal bacteria from IS-NT2D patients, with decreased neurological function scores (FMT-IS-T2D vs. FMT-IS-NT2D, P = 0.042) (Fig. 5 B) and increased infarct volume (FMT-IS-T2D vs. FMT-IS-NT2D, P = 0.042) (Fig. 5 C). Meanwhile, after stroke, PAGln levels in rats receiving fecal microbes from patients with IS-T2D were also nearly twice as high as those receiving fecal microbes from patients with IS-NT2D (FMT-IS-T2D vs. FMT-IS-NT2D, P = 0.03) (Fig. 5 D). Notably, PAGln levels in rats receiving fecal microbes from patients with IS-T2D were further elevated after stroke (preFMT-IS-T2D vs. FMT-IS-T2D, P = 0.021), while the PAGln levels in rats receiving fecal microbes from IS-NT2D patients were in an increasing tendency with no significant differences (preFMT-IS-NT2D vs. FMT-IS-NT2D, P = 0.31). Discussion This study is the first time for exploring the relationship between gut microbiota and its metabolite PAGln in stroke patients with T2D. Our data suggest that: (i) in the IS-T2D group, the gut microbiota was significantly imbalanced and the plasma PAGln levels increased partly caused by the disorder of gut microbiota, which was further confirmed by animal studies (Fig. 6 ); (ii) elevated PAGln levels were associated with poor functional outcomes and plasma NETs levels; (iii) PAGln levels, gut microbiota, and NETs levels could be used as combined diagnostic indicators for IS-T2D. Previous research has established that PAGln was associated with CHD, peripheral artery disease, heart failure, and other cardiovascular diseases 17 , 38 – 40 . In addition, PAGln was also closely related to obesity, diabetes, prediabetes, and other metabolic diseases 16 , 34 , 41 – 43 . Research shows that metabolic disorders such as insulin resistance, dyslipidemia, and fatty can increase platelet activity and aggregation via dysregulation of the NO-mediated signaling pathway, leading to thrombosis and atherosclerotic lesion formation 44 . And PAGln also has the effect of driving platelet invasiveness 14 . A metabolic disorder like diabetes mellitus may promote vascular injury via PAGln-mediated molecular mechanisms. Our results showed that IS-NT2D caused an increase in plasma PAGln levels. More importantly, PAGln levels were higher in IS patients with the complication of T2D. Although the plasma clearance of PAGln is closely related to renal function, that is, if renal function is impaired, the plasma PAGln clearance will be severely reduced, there was no significant difference in BUN or Scr levels between IS-T2D and IS-NT2D groups, indicating that the elevated PAGln levels in IS-T2D patients were mainly influenced by T2D 3 8 , 45 . Except for stroke, Tang et al. found that plasma PAGln levels were significantly higher in patients with heart failure and diabetes mellitus than in those with heart failure alone, which suggested the elevation of plasma PAGln is associated with diabetes mellitus 40 . Our results also revealed that elevated PAGln was an independent risk factor for the patient with stroke and T2D, indicating that PAGln may be an important molecule of T2D in contributing to an exacerbation in stroke injury, of which the mechanism remains obscure and needs to be further explored. PAGln is a metabolite of phenylalanine degradation by gut microbiota. Previous studies have revealed that, except for abnormal metabolism, people with diabetes mellitus had severely disturbed gut microbiota 11 , 46 . Therefore, increased plasma PAGln levels in IS-T2D patients indicate that they might not only use more amino acids as energy sources, but have more gut microbiota to degrade phenylalanine compared with stroke patients, which was confirmed by our results. We found that the gut microbiota of IS-T2D patients was disordered, and there was more PAGln-related gut microbiota in these patients, including o_Enterobacterales, f_Enterobacteriaceae, p_Verrucomicrobiota, c_Verrucomicrobiota, s_Klebsiella_pneumoniae , and g_Klebsiella . Enterobacteriaceae is recognized as harmful gut microorganisms and showed a growth advantage in both stroke and T2D 1 8 , 47 . A study has reported that Enterobacteriaceae was closely related to the mortality of stroke patients, increasing inflammatory factors such as TNF-α and IL-1β through the LPS-TLR4 pathway, thus accelerating systemic inflammation and exacerbating cerebral infarction 18 . In addition, studies have shown that Escherichia coli belonging to Enterobacteriaceae can catabolize aromatic compounds such as phenylalanine and phenylacetic acid 48 – 50 . The phenylacetic acid is the middle product that PHE is metabolized to PAGln. Our results showed that Enterobacteriaceae was one of the major differential bacteria in the IS-T2D group, which was positively correlated with PAGln, indicating that Enterobacteriaceae might cause the aggravation of brain injury by diabetes mellitus via metabolites PAGln. Consistent with Ottosson et al., a positive correlation was also found between plasma PAGln levels and Verrucomicrobiota in a CHD risk cohort 17 . In our study, c_Verrucomicrobiota was dominated by g_Akkermansia (99.4%). It is generally believed that Akkermansia is a kind of beneficial bacteria to maintain the health of the intestinal epithelium, which can enhance intestinal barrier function, produce short-chain fatty acids (SCFAs), improve metabolic disorders, and it has even been considered the new probiotics which could be developed and utilized 51 , 52 . However, its role in the development of stroke and diabetes mellitus remains elusive. Studies have reported that Akkermansia was elevated in patients with prediabetes and diabetes as well as in diabetic animal models 53 , 54 . In addition, it has been suggested that a significant increase in Akkermansia could contribute to the erosion of the mucous layer and enhancement of pathogen filtration in the intestinal epithelial layer when the dietary fiber was lacking in the host gut 55 . The latest study found that Akkermansia strains could be divided into five distinct candidate species with different host preferences and functions, of which only Akkermansia mucinophilus had the health-associated properties referring to being associated with low body mass index in the host 56 . In our study, Akkermansia was significantly decreased in IS-NT2D patients but increased in IS-T2D patients, and c_Verrucomicrobiota , dominated by Akkermansia , was significantly associated with PAGln. We speculated that T2D could increase a harmful species of Akkermansia , which is linked with PAGln metabolism. Therefore, it is necessary to further explore the relationship between each candidate species of Akkermansia and PAGln. Consistent with the study of Li et al., our results suggested that PAGln was significantly correlated with Klebsiella 53 . Klebsiella is widely believed to be a pathogenic bacterium and a strain with the TMA gene 57 – 59 . TMA is an essential precursor of the gut microbiota metabolite TMAO, which has been found to promote thrombosis via increasing platelet reactivity 60 . And TMAO is closely associated with atherosclerosis and poor prognosis in IS 61 , 62 . Therefore, it is reasonable to hypothesize that Klebsiella is a strain with the characteristics of promoting thrombosis. In addition to the above bacteria, previous research also reported that PAGln was associated with Enterocoocus , Streptocoocus , Escherichia_ Shigella , G_f_o_SHA.98c_clostridia , and Eggerthella lenta 17 , 53 , 63 . And finally, we transplanted the fecal microbiota from patients with IS-T2D into rodents, ultimately resulting in increased plasma PAGln levels, which provided direct evidence that elevated PAGln levels in IS-T2D patients were partly caused by intestinal microorganism disorders. It is noteworthy that the PAGln level of rats receiving the fecal microbes from IS-T2D patients further increased after stroke. A possible explanation for this might be that under the acute stress of stroke, this part of microbiota further grew and multiplied, resulting in an increase of PAGln levels. Additionally, our study indicated that gut bacteria functions such as amino acid metabolism were enhanced and abnormal in patients with IS-T2D. Further work should be undertaken to search for strains related to PAGln metabolism and improve the prognosis of IS-T2D by targeting PAGln-producing bacteria. The formation of NETs is originally identified as an important antimicrobial phenomenon, including resisting microbial invasion, stopping the microbial spread, and killing pathogens 64 . However, mounting evidence indicates that NETs play a negative role in many diseases such as endocrine diseases, nervous system diseases, and respiratory diseases 65 – 67 . Consistent with previous research, our results showed an increase in NETs levels in stroke patients and a further increase in stroke patients with T2D. Platelet-neutrophil interactions are a critical pathophysiological process of thromboinflammation 68 . In the arterial microenvironment of thrombosis, activated platelets trigger NE to form and release NETs containing thrombotic tissue factor (TF) through several mechanisms, including NE autophagy induced by the presentation of high mobility group frame 1 (HMGB1) protein, release of the NETs inducer–inorganic polyphosphate (polyP), and activation of the platelet Toll-like receptor 4(TLR 4) 69 – 71 . In addition, NETs, in turn, can further aggravate thromboinflammation via promoting thrombin and fibrin formation and binding to platelet-derived microparticles (PMPs) and coagulation factors 72 . Recently, a study had indicated the effects of the gut microbiota metabolite PAGln in driving platelet invasiveness and thrombosis via β adrenergic receptors 14 . According to our data, plasma PAGln levels were positively correlated with NETs, and NETs showed a dose-dependent increase according to PAGln levels. Therefore, we speculated that the gut microbiota metabolite PAGln might contribute to platelet-neutrophil interactions via enhancing platelet reactivity, which ultimately promotes the formation of NETs. Meanwhile, our data showed a positive correlation between PAGln and IS-T2D-associated gut microbiota, of which were positively correlated with NE. Dysregulation of gut microbiota in patients with arteriosclerotic cerebral small vessel disease has been reported to independently enhance the proinflammatory property of NE 73 . We thus inferred that IS-T2D-associated gut microbiota could exacerbate inflammation status of NE, and then combine with elevated PAGln in plasma to promote the formation of NETs, which may ultimately contribute to the aggravation of thromboinflammatory. In the meantime, our data suggest that elevated plasma PAGln levels were correlated with a 90-day poor prognosis in stroke patients, further supporting that the PAGln plays a negative role in the development of stroke. Above all, our study revealed that the presence of T2D could increase the inflammatory reaction in IS patients, mediated in part by the altered composition of gut microbiota and the effects of bacterial metabolite PAGln. In this study, we not only confirmed the dysbiosis of gut microbiota and the increase of harmful molecular PAGln in IS-T2D patients, but also found that the gut microbiota dysbiosis, the increased plasma PAGln, and NETs could serve as important diagnostic markers for stroke with T2D. Limitations of the study: Firstly, the stroke patients included in our study were within 2 weeks of onset, which was enough to cause significant differences in microbiota between samples in group 18 . In addition, the NIHSS scores of the patients included were low, limiting the representativeness of the study. Secondly, our sample size was relatively small, which needs to be further expanded. Meanwhile, we need to exclude confounding factors like dietary habits, lifestyle, and fecal status, and increase the T2D group in the future, to enhance persuasion of the experimental results. Finally, in the animal experiment, we only conducted a simple FMT experiment, and the transplanted fecal samples included bacteria, fungi, and other microorganisms, which is hard to determine which strain plays a role. Further experimental studies need to be carried out in the future. Conclusions Collectively, we found that stroke patients with T2D had gut microbiota disorders and increased plasma PAGln levels, a microbiota metabolite, which was positively correlated with NETs. Our findings contribute to the understanding of the role of gut microbial metabolite-related mechanisms in the progression of stroke with T2D and provide a new therapeutic target --- PAGln for the treatment of stroke with T2D. The causal relationship between PAGln and IS with diabetes mellitus and its mechanism should be further explored. Abbreviations T2D type 2 diabetes IS ischemic stroke IS-T2D IS with T2D IS-NT2D IS without T2D PAGln phenylacetylglutamine PHE acid-phenylalanine CHD coronary heart disease NE neutrophils NETs neutrophil extracellular traps HC healthy control NIHSS National Institutes of Health Stroke Scale mRS modified Rankin scale PCR polymerase chain reaction OTUs operational taxonomic units LDA linear discriminant analysis LEfSe linear discriminant analysis (LDA) effect size BUN blood urea nitrogen Scr serum creatinine TG triglyceride TC total cholesterol HDL high-density lipoprotein HbA1C glycosylated hemoglobin Hcy homocysteine CitH3 citrullinated histone H3 NIH National Institutes of Health SD Sprague-Dawley SPF specific pathogen-free FMT fecal microbiota transplantation MCAO middle cerebral artery occlusion ECA external carotid artery CCA common carotid artery ICA internal carotid artery SEM standard error mean IQR interquartile range ROC receiver operator characteristic LEU leukocyte OR odds ratio PCoA principal coordinate analysis AUC the area under the curve SCFAs short-chain fatty acids TF tissue factor HMGB1 high mobility group box 1 Polyp polyphosphoric TLR 4 Toll-like receptor 4 PMPs platelet-derived microparticles Declarations Acknowledgments We would like to thank the National Clinical Research Center for Geriatric Disorders (Xiangya hospital Central South University) for the experimental platform and the following fundings: the Project Program of National Clinical Research Center for Geriatric Disorders (Xiangya Hospital, Grant No. 2020LNJJ16), the Provincial Key Plan for Research and Development of Hunan (Grant No. 2020SK2067; No. 2020SK2069), the Natural Science Foundation of Hunan Province (Grant No. 2021JJ31109; No. 2020JJ4875), and the Fundamental Research Funds for the Central Universities of Central South University [Grant No. 2021zzts1029; No. 2020zzts269]. Ethics Approval Each participant in this study provided written informed consent, and the study protocol complied with the principles of the 1975 Declaration of Helsinki and approved by the Ethics Committee of Xiangya Hospital, Central South University, China. The animal experimental protocols were approved by the Experimental Animal Welfare Ethics Committee of Central South University. Consent to Participate Not applicable Consent to Publish All the authors verify that they concur with the present submission and that the material submitted has not been previously reported in any other journal. Competing Interests All authors declare that they have no conflict of interest. Availability of data and materials FASTQ files of the 16S rRNA gene sequencing are available under SRA accession number PRJNA 820272 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA820272). Funding Sources of funding: This study was supported by the Project Program of National Clinical Research Center for Geriatric Disorders (Xiangya Hospital, Grant No. 2020LNJJ16), the Provincial Key Plan for Research and Development of Hunan (Grant No. 2020SK2067; No. 2020SK2069), the Natural Science Foundation of Hunan Province (Grant No. 2021JJ31109; No. 2020JJ4875), and the Fundamental Research Funds for the Central Universities of Central South University [Grant No. 2021zzts1029; No. 2020zzts269]. Author Contributions Experimental design, overall data analysis, and writing original draft: JX and MPW; patients’ blood and fecal samples collection and testing: MPW, YFL, and TTZ; animal experiments: MPW and DL; technical support, data collection, and data interpretation: QH, FY, XJF, JF, and QH; manuscript revision: QH and YLY. All authors have read and agreed to the published version of the manuscript. Acknowledgments We would like to thank the National Clinical Research Center for Geriatric Disorders (Xiangya hospital Central South University) for the experimental platform. Preprint A previous version of this manuscript was published as a preprint 74 . References Wafa HA, Wolfe CDA, Bhalla A, Wang Y (2020) Long-term trends in death and dependence after ischaemic strokes: A retrospective cohort study using the South London Stroke Register (SLSR). PLoS Med 17:e1003048. https://doi.org/10.1371/journal.pmed.1003048 Wang YJ, Li ZX, Gu HQ, Zhai Y, Jiang Y, Zhao XQ et al (2020) China Stroke Statistics 2019: A Report From the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Non-communicable Disease Control and Prevention. Stroke Vasc Neurol 5:211–239. Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations https://doi.org/10.1136/svn-2020-000457 Tuttolomondo A, Pinto A, Salemi G, Di Raimondo D, Di Sciacca R, Fernandez P et al (2008) Diabetic and non-diabetic subjects with ischemic stroke: differences, subtype distribution and outcome. Nutr Metab Cardiovasc Dis 18:152–157. https://doi.org/10.1016/j.numecd.2007.02.003 Yao T, Zhan Y, Shen J, Xu L, Peng B, Cui Q et al (2020) Association between fasting blood glucose and outcomes and mortality in acute ischaemic stroke patients with diabetes mellitus: a retrospective observational study in Wuhan, China. BMJ Open 10:e037291. https://doi.org/10.1136/bmjopen-2020-037291 Szlachetka WA, Pana TA, Tiamkao S, Clark AB, Kongbunkiat K, Sawanyawisuth K et al (2020) Impact of Diabetes on Complications, Long Term Mortality and Recurrence in 608,890 Hospitalised Patients with Stroke. Glob Heart 15:2. https://doi.org/10.5334/gh.364 Spychala MS, Venna VR, Jandzinski M, Doran SJ, Durgan DJ, Ganesh BP et al (2018) Age-related changes in the gut microbiota influence systemic inflammation and stroke outcome. Ann Neurol 84:23–36. https://doi.org/10.1002/ana.25250 Li N, Wang X, Sun C, Wu X, Lu M, Si Y et al (2019) Change of intestinal microbiota in cerebral ischemic stroke patients. BMC Microbiol 19:191. https://doi.org/10.1186/s12866-019-1552-1 Yamashiro K, Tanaka R, Urabe T, Ueno Y, Yamashiro Y, Nomoto K et al (2017) Gut dysbiosis is associated with metabolism and systemic inflammation in patients with ischemic stroke. PLoS ONE 12:e0171521. https://doi.org/10.1371/journal.pone.0171521 Singh V, Roth S, Llovera G, Sadler R, Garzetti D, Stecher B et al (2016) Microbiota Dysbiosis Controls the Neuroinflammatory Response after Stroke. J Neurosci 36:7428–7440. https://doi.org/10.1523/JNEUROSCI.1114-16.2016 Xia GH, You C, Gao XX, Zeng XL, Zhu JJ, Xu KY et al (2019) Stroke Dysbiosis Index (SDI) in Gut Microbiome Are Associated With Brain Injury and Prognosis of Stroke. Front Neurol 10:397. https://doi.org/10.3389/fneur.2019.00397 Wu H, Tremaroli V, Schmidt C, Lundqvist A, Olsson LM, Krämer M et al (2020) The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study. Cell Metab 32 :379 – 90.e3. https://doi.org/10.1016/j.cmet.2020.06.011 Allin KH, Tremaroli V, Caesar R, Jensen BAH, Damgaard MTF, Bahl MI et al (2018) Aberrant intestinal microbiota in individuals with prediabetes. Diabetologia 61:810–820. https://doi.org/10.1007/s00125-018-4550-1 Massier L, Tabei S, Crane A, Didt KD, Fallmann J, Bergen MV et al (2020) Adipose tissue derived bacteria are associated with inflammation in obesity and type 2 diabetes. Gut 69:1796–1806. https://doi.org/10.1136/gutjnl-2019-320118 Nemet I, Saha PP, Gupta N, Zhu W, Romano KA, Skye SM et al (2020) A Cardiovascular Disease-Linked Gut Microbial Metabolite Acts via Adrenergic Receptors. Cell. 180:862 – 77 e22 https://doi.org/10.1016/j.cell.2020.02.016 Loo RL, Zou X, Appel LJ, Nicholson JK, Holmes E (2018) Characterization of metabolic responses to healthy diets and association with blood pressure: application to the Optimal Macronutrient Intake Trial for Heart Health (OmniHeart), a randomized controlled study. Am J Clin Nutr 107:323–334. https://doi.org/10.1093/ajcn/nqx072 Urpi-Sarda M, Almanza-Aguilera E, Llorach R, Vazquez-Fresno R, Estruch R, Corella D et al (2019) Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: A cross-sectional study of PREDIMED trial participants. Diabetes Metab 45:167–174. https://doi.org/10.1016/j.diabet.2018.02.006 Ottosson F, Brunkwall L, Smith E, Orho-Melander M, Nilsson PM, Fernandez C et al (2020) The gut microbiota-related metabolite phenylacetylglutamine associates with increased risk of incident coronary artery disease. J Hypertens 38:2427–2434. https://doi.org/10.1097/HJH.0000000000002569 Xu K, Gao X, Xia G, Chen M, Zeng N, Wang S et al Rapid gut dysbiosis induced by stroke exacerbates brain infarction in turn. Gut 2021:gutjnl-2020-323263. https://doi.org/10.1136/gutjnl-2020-323263 Brea D, Poon C, Benakis C, Lubitz G, Murphy M, Iadecola C et al (2021) Stroke affects intestinal immune cell trafficking to the central nervous system. Brain Behav Immun 96:295–302. https://doi.org/10.1016/j.bbi.2021.05.008 Zhang F, Zhao Q, Jiang Y, Liu N, Liu Q, Shi FD et al (2019) Augmented Brain Infiltration and Activation of Leukocytes After Cerebral Ischemia in Type 2 Diabetic Mice. Front Immunol 10:2392. https://doi.org/10.3389/fimmu.2019.02392 Marta-Enguita J, Navarro-Oviedo M, Rubio-Baines I, Aymerich N, Herrera M, Zandio B et al (2021) Association of calprotectin with other inflammatory parameters in the prediction of mortality for ischemic stroke. J Neuroinflamm 18. https://doi.org/10.1186/s12974-020-02047-1 Deng J, Zhao F, Zhang Y, Zhou Y, Xu X, Zhang X et al (2020) Neutrophil extracellular traps increased by hyperglycemia exacerbate ischemic brain damage. Neurosci Lett 738:135383. https://doi.org/10.1016/j.neulet.2020.135383 Valles J, Lago A, Santos MT, Latorre AM, Tembl JI, Salom JB et al (2017) Neutrophil extracellular traps are increased in patients with acute ischemic stroke: prognostic significance. Thromb Haemost 117:1919–1929. https://doi.org/10.1160/TH17-02-0130 Laridan E, Denorme F, Desender L, François O, Andersson T, Deckmyn H et al (2017) Neutrophil extracellular traps in ischemic stroke thrombi. Ann Neurol 82:223–232. https://doi.org/10.1002/ana.24993 Li G, Lin J, Zhang C, Gao H, Lu H, Gao X et al (2021) Microbiota metabolite butyrate constrains neutrophil functions and ameliorates mucosal inflammation in inflammatory bowel disease. Gut Microbes 13:1968257. https://doi.org/10.1080/19490976.2021.1968257 Society CD (2018) Guidelines for the prevention and control of type 2 diabetes in China (2017 Edition). Chin J Practical Intern Med 2018 38:292–344 Hatano S (1976) Experience from a multicentre stroke register: a preliminary report. Bull World Health Organ 54:541–553 Simon DW, Rogers MB, Gao Y, Vincent G, Firek BA, Janesko-Feldman K et al (2020) Depletion of gut microbiota is associated with improved neurologic outcome following traumatic brain injury. Brain Res 1747:147056. https://doi.org/10.1016/j.brainres.2020.147056 Chen R, Xu Y, Wu P, Zhou H, Lasanajak Y, Fang Y et al (2019) Transplantation of fecal microbiota rich in short chain fatty acids and butyric acid treat cerebral ischemic stroke by regulating gut microbiota. Pharmacol Res 148:104403. https://doi.org/10.1016/j.phrs.2019.104403 Zhou Z, Xu N, Matei N, McBride DW, Ding Y, Liang H et al (2021) Sodium butyrate attenuated neuronal apoptosis via GPR41/Gβγ/PI3K/Akt pathway after MCAO in rats. J Cereb Blood Flow Metab 41:267–281. https://doi.org/10.1177/0271678X20910533 Garcia JH, Wagner S, Liu KF, Hu XJ (1995) Neurological deficit and extent of neuronal necrosis attributable to middle cerebral artery occlusion in rats. Statistical validation.Stroke;26 Liu Q, Jin Z, Xu Z, Yang H, Li L, Li G et al (2019) Antioxidant effects of ginkgolides and bilobalide against cerebral ischemia injury by activating the Akt/Nrf2 pathway in vitro and in vivo. Cell Stress Chaperones 24:441–452. https://doi.org/10.1007/s12192-019-00977-1 Chai Z, Gong J, Zheng P, Zheng J (2020) Inhibition of miR-19a-3p decreases cerebral ischemia/reperfusion injury by targeting IGFBP3 in vivo and in vitro. Biol Res 53:17. https://doi.org/10.1186/s40659-020-00280-9 Li J, Morrow C, Barnes S, Wilson L, Womack ED, McLain A et al (2021) Gut Microbiome Composition and Serum Metabolome Profile Among Individuals With Spinal Cord Injury and Normal Glucose Tolerance or Prediabetes/Type 2 Diabetes. Arch Phys Med Rehabil 702–710. https://doi.org/10.1016/j.apmr.2021.03.043 Tan YM, Gao Y, Teo G, Koh HWL, Tai ES, Khoo CM et al (2021) Plasma Metabolome and Lipidome Associations with Type 2 Diabetes and Diabetic Nephropathy. Metabolites 11. https://doi.org/10.3390/metabo11040228 Lin EY, Lai HJ, Cheng YK, Leong KQ, Cheng LC, Chou YC et al (2020) Neutrophil Extracellular Traps Impair Intestinal Barrier Function during Experimental Colitis. Biomedicines 8:275. https://doi.org/10.3390/biomedicines8080275 Thålin C, Daleskog M, Göransson SP, Schatzberg D, Lasselin J, Laska A-C et al (2017) Validation of an enzyme-linked immunosorbent assay for the quantification of citrullinated histone H3 as a marker for neutrophil extracellular traps in human plasma. Immunol Res 65:706–712. https://doi.org/10.1007/s12026-017-8905-3 Poesen R, Claes K, Evenepoel P, de Loor H, Augustijns P, Kuypers D et al (2016) Microbiota-Derived Phenylacetylglutamine Associates with Overall Mortality and Cardiovascular Disease in Patients with CKD. J Am Soc Nephrol 27:3479–3487 Azab SM, Zamzam A, Syed MH, Abdin R, Qadura M, Britz-McKibbin P (2020) Serum Metabolic Signatures of Chronic Limb-Threatening Ischemia in Patients with Peripheral Artery Disease. J Clin Med 9:1877. https://doi.org/10.3390/jcm9061877 Tang H-Y, Wang C-H, Ho H-Y, Lin J-F, Lo C-J, Huang C-Y et al (2020) Characteristic of Metabolic Status in Heart Failure and Its Impact in Outcome Perspective. Metabolites 10:437. https://doi.org/10.3390/metabo10110437 Elliott P, Posma JM, Chan Q, Garcia-Perez I, Wijeyesekera A, Bictash M et al (2015) Urinary metabolic signatures of human adiposity. Sci Transl Med 7:285ra62. https://doi.org/10.1126/scitranslmed.aaa5680 Arnoriaga-Rodríguez M, Mayneris-Perxachs J, Burokas A, Contreras-Rodríguez O, Blasco G, Coll C et al (2020) Obesity Impairs Short-Term and Working Memory through Gut Microbial Metabolism of Aromatic Amino Acids. Cell Metab. 32:548 – 60.e7 https://doi.org/10.1016/j.cmet.2020.09.002 Wijeyesekera A, Clarke PA, Bictash M, Brown IJ, Fidock M, Ryckmans T et al (2012) Quantitative UPLC-MS/MS analysis of the gut microbial co-metabolites phenylacetylglutamine, 4-cresyl sulphate and hippurate in human urine: INTERMAP Study. Anal Methods 4:65–72 Suslova TE, Sitozhevskii AV, Ogurkova ON, Kravchenko ES, Kologrivova IV, Anfinogenova Y et al (2014) Platelet hemostasis in patients with metabolic syndrome and type 2 diabetes mellitus: cGMP- and NO-dependent mechanisms in the insulin-mediated platelet aggregation. Front Physiol 5:501. https://doi.org/10.3389/fphys.2014.00501 Wang X, Tseng J, Mak C, Poola N, Vilchez RA (2021) Exposures of Phenylacetic Acid and Phenylacetylglutamine Across Different Subpopulations and Correlation with Adverse Events. Clin Pharmacokinet 60:1557–1567. https://doi.org/10.1007/s40262-021-01047-5 Gruneck L, Kullawong N, Kespechara K, Popluechai S (2020) Gut microbiota of obese and diabetic Thai subjects and interplay with dietary habits and blood profiles. PeerJ 8:e9622. https://doi.org/10.7717/peerj.9622 Chen Q, Ma X, Li C, Shen Y, Zhu W, Zhang Y et al (2020) Enteric Phageome Alterations in Patients With Type 2 Diabetes. Front Cell Infect Microbiol 10:575084. https://doi.org/10.3389/fcimb.2020.575084 Miller DA, Simmonds S (1957) Phenylalanine and tyrosine metabolism in E. coli strain K-12. Science 126:445–446 Teufel R, Mascaraque V, Ismail W, Voss M, Perera J, Eisenreich W et al (2010) Bacterial phenylalanine and phenylacetate catabolic pathway revealed. Proc Natl Acad Sci U S A 107:14390–14395. https://doi.org/10.1073/pnas.1005399107 Ferrández A, Miñambres B, García B, Olivera ER, Luengo JM, García JL et al (1998) Catabolism of phenylacetic acid in Escherichia coli. Characterization of a new aerobic hybrid pathway. J Biol Chem 273:25974–25986 Zhou K (2017) Strategies to promote abundance of, an emerging probiotics in the gut, evidence from dietary intervention studies. J Funct Foods 33:194–201. https://doi.org/10.1016/j.jff.2017.03.045 Cani PD (2019) Microbiota and metabolites in metabolic diseases. Nat Rev Endocrinol 15:69–70. https://doi.org/10.1038/s41574-018-0143-9 Li JV, Ashrafian H, Sarafian M, Homola D, Rushton L, Barker G et al (2021) Roux-en-Y gastric bypass-induced bacterial perturbation contributes to altered host-bacterial co-metabolic phenotype. Microbiome 9:139. https://doi.org/10.1186/s40168-021-01086-x Yu F, Han W, Zhan G, Li S, Jiang X, Wang L et al (2019) Abnormal gut microbiota composition contributes to the development of type 2 diabetes mellitus in db/db mice. Aging 11:10454–10467. https://doi.org/10.18632/aging.102469 Desai MS, Seekatz AM, Koropatkin NM, Kamada N, Hickey CA, Wolter M et al (2016) A Dietary Fiber-Deprived Gut Microbiota Degrades the Colonic Mucus Barrier and Enhances Pathogen Susceptibility. Cell 167:1339–1353. .e21 .. https://doi.org/10.1016/j.cell.2016.10.043 Karcher N, Nigro E, Punčochář M, Blanco-Míguez A, Ciciani M, Manghi P et al (2021) Genomic diversity and ecology of human-associated Akkermansia species in the gut microbiome revealed by extensive metagenomic assembly. Genome Biol 22:209. https://doi.org/10.1186/s13059-021-02427-7 Heng X, Liu W, Chu W (2021) Identification of choline-degrading bacteria from healthy human feces and used for screening of trimethylamine (TMA)-lyase inhibitors. Microb Pathog 152:104658. https://doi.org/10.1016/j.micpath.2020.104658 Kuka J, Videja M, Makrecka-Kuka M, Liepins J, Grinberga S, Sevostjanovs E et al (2020) Metformin decreases bacterial trimethylamine production and trimethylamine N-oxide levels in db/db mice. Sci Rep 10:14555. https://doi.org/10.1038/s41598-020-71470-4 Jameson E, Doxey AC, Airs R, Purdy KJ, Murrell JC, Chen Y (2016) Metagenomic data-mining reveals contrasting microbial populations responsible for trimethylamine formation in human gut and marine ecosystems. Microb Genom 2:e000080. https://doi.org/10.1099/mgen.0.000080 Zhu W, Gregory JC, Org E, Buffa JA, Gupta N, Wang Z et al (2016) Gut Microbial Metabolite TMAO Enhances Platelet Hyperreactivity and Thrombosis Risk. Cell. https://doi.org/10.1016/j.cell.2016.02.011 . 165:111 – 24 Ding L, Chang M, Guo Y, Zhang L, Xue C, Yanagita T et al (2018) Trimethylamine-N-oxide (TMAO)-induced atherosclerosis is associated with bile acid metabolism. Lipids Health Dis 17:286. https://doi.org/10.1186/s12944-018-0939-6 Wu C, Xue F, Lian Y, Zhang J, Wu D, Xie N et al (2020) Relationship between elevated plasma trimethylamine N-oxide levels and increased stroke injury. Neurology 94:e667–e77. https://doi.org/10.1212/wnl.0000000000008862 Wang X, Yang S, Li S, Zhao L, Hao Y, Qin J et al (2020) Aberrant gut microbiota alters host metabolome and impacts renal failure in humans and rodents. Gut 69:2131–2142. https://doi.org/10.1136/gutjnl-2019-319766 Jorch SK, Kubes P (2017) An emerging role for neutrophil extracellular traps in noninfectious disease. Nat Med 23:279–287. https://doi.org/10.1038/nm.4294 de Vries JJ, Hoppenbrouwers T, Martinez-Torres C, Majied R, Özcan B, van Hoek M et al (2020) Effects of Diabetes Mellitus on Fibrin Clot Structure and Mechanics in a Model of Acute Neutrophil Extracellular Traps (NETs) Formation. Int J Mol Sci 21:7107. https://doi.org/10.3390/ijms21197107 Guo Y, Zeng H, Gao C (2021) ; 2021:9931742. https://doi.org/10.1155/2021/9931742 Dicker AJ, Crichton ML, Pumphrey EG, Cassidy AJ, Suarez-Cuartin G, Sibila O et al (2018) Neutrophil extracellular traps are associated with disease severity and microbiota diversity in patients with chronic obstructive pulmonary disease. J Allergy Clin Immunol 141:117–127. https://doi.org/10.1016/j.jaci.2017.04.022 Pircher J, Engelmann B, Massberg S, Schulz C (2019) Platelet-Neutrophil Crosstalk in Atherothrombosis. Thromb Haemost 119:1274–1282. https://doi.org/10.1055/s-0039-1692983 Maugeri N, Campana L, Gavina M, Covino C, De Metrio M, Panciroli C et al (2014) Activated platelets present high mobility group box 1 to neutrophils, inducing autophagy and promoting the extrusion of neutrophil extracellular traps. J Thromb Haemost 12:2074–2088. https://doi.org/10.1111/jth.12710 Chrysanthopoulou A, Kambas K, Stakos D, Mitroulis I, Mitsios A, Vidali V et al (2017) Interferon lambda1/IL-29 and inorganic polyphosphate are novel regulators of neutrophil-driven thromboinflammation. J Pathol 243:111–122. https://doi.org/10.1002/path.4935 Peña-Martínez C, Durán-Laforet V, García-Culebras A, Ostos F, Hernández-Jiménez M, Bravo-Ferrer I et al (2019) Pharmacological Modulation of Neutrophil Extracellular Traps Reverses Thrombotic Stroke tPA (Tissue-Type Plasminogen Activator) Resistance. Stroke 50:3228–3237. https://doi.org/10.1161/STROKEAHA.119.026848 Zhou P, Li T, Jin J, Liu Y, Li B, Sun Q et al (2020) Interactions between neutrophil extracellular traps and activated platelets enhance procoagulant activity in acute stroke patients with ICA occlusion. EBioMedicine 53:102671. https://doi.org/10.1016/j.ebiom.2020.102671 Cai W, Chen X, Men X, Ruan H, Hu M, Liu S et al (2021) Gut microbiota from patients with arteriosclerotic CSVD induces higher IL-17A production in neutrophils via activating RORγt. Sci Adv 7:eabe4827. https://doi.org/10.1126/sciadv.abe4827 Minping Wei QH, Yu F, Ying Y, Luo Y, Feng X, Liao D, Zhao T, Huang Q (2022) Elevated Circulating Levels of Phenylacetylglutamine in Stroke Patients With T2D Are Linked to Specific Gut Microbiota. https://doi.org/https://doi.org/10.21203/rs.3.rs-1245321/v1 . Jian Xia Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1245321","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":115378562,"identity":"ddaa6103-be91-4fdc-a08b-13174fc3c14b","order_by":0,"name":"Minping Wei","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Minping","middleName":"","lastName":"Wei","suffix":""},{"id":115378563,"identity":"960a02b1-17e5-4949-8e5b-4c5c164b6fdf","order_by":1,"name":"Qin Huang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Huang","suffix":""},{"id":115378564,"identity":"7eaa9ede-437f-468f-8099-692b15bb1014","order_by":2,"name":"Fang Yu","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Yu","suffix":""},{"id":115378565,"identity":"c021acfa-12d0-4bc6-bdff-efd8a0ee4427","order_by":3,"name":"Yuanlin Ying","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yuanlin","middleName":"","lastName":"Ying","suffix":""},{"id":115378566,"identity":"9e2ac071-2c6d-4877-9977-62a5c9064ad0","order_by":4,"name":"Yunfang Luo","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yunfang","middleName":"","lastName":"Luo","suffix":""},{"id":115378567,"identity":"b2954979-66ff-4aab-92a0-ed73539c1db3","order_by":5,"name":"Xianjing Feng","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xianjing","middleName":"","lastName":"Feng","suffix":""},{"id":115378568,"identity":"4e530b7a-6000-4ede-8919-9686011a876e","order_by":6,"name":"Di Liao","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Liao","suffix":""},{"id":115378569,"identity":"7d3fb276-fd6a-4c0b-b213-03bd94d7e81a","order_by":7,"name":"Tingting Zhao","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Zhao","suffix":""},{"id":115378570,"identity":"6c8c43ff-9b39-4d63-8d8b-f4e567c2bf1f","order_by":8,"name":"Qing Huang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Huang","suffix":""},{"id":115378571,"identity":"50e842ee-1625-4350-a729-fbb9f68add82","order_by":9,"name":"Jie Feng","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Feng","suffix":""},{"id":115378572,"identity":"a6212d2d-44c7-4735-bacd-10c6013e5479","order_by":10,"name":"jian xia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie2PsWoCQRCGJyyszQ/X3nLxHQaEk+BhXsUgXGVhmXLlwEpSG/Qh7hH22MImD6DYBA6sLARLA2ZNZ7N3pZD9YIqB+WbmJwoEHpJKE3GGqKP/2ifdTpnmz2phWis3Tjbj5ailwpWdnacswKquz6CsWxpx+PYpSldFsmSJfpKnCSjvlUb22adELksCBl5WEylA9q00kLFPkVQVF3AM3n0J99i1WXFX5u4Kg7cg95hpVlyW+QA8glrkqVrzuPdpZepVeLup9/i5vkYdW5+O78Pux6Y4eBWKzd0GV8I7fwujmyYCgUDg3/MLEzxFWrmvK6oAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9154-131X","institution":"Xiangya Hospital Central South University","correspondingAuthor":true,"prefix":"","firstName":"jian","middleName":"","lastName":"xia","suffix":""}],"badges":[],"createdAt":"2022-01-10 07:59:17","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-1245321/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-1245321/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22941626,"identity":"e8ded5b3-1ec5-4ccf-b694-e090a1c86c0f","added_by":"auto","created_at":"2022-06-22 15:07:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of plasma PAGln levels in three groups\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Plasma PAGln levels were increased in IS-T2D and IS-NT2D groups and the highest in the IS-T2D group. \u003cstrong\u003e(B)\u003c/strong\u003e Logistic regression analysis of risk factors for stroke patients (black curve and black characters represent multivariate analysis; red curve and red characters represent univariate analysis). \u003cstrong\u003e(C)\u003c/strong\u003e Logistic regression analysis of risk factors for T2D of stroke patients (black curve, black characters, red curve, and red characters are the same as Fig. B). (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; (A) by Mann-Whitney u-test)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1245321/v2/d359ec9caa22967963def8d2.png"},{"id":22941629,"identity":"b042ae4f-763e-40b5-8213-58b60ebc25f3","added_by":"auto","created_at":"2022-06-22 15:07:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":226595,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe composition and differences of gut microbiota in three study groups\u003c/strong\u003e \u003cstrong\u003e(A) \u003c/strong\u003eThe species accumulation boxplot. \u003cstrong\u003e(B)\u003c/strong\u003e Shannon index (By Mann-Whitney u-test). \u003cstrong\u003e(C)\u003c/strong\u003e Simpson index (By Mann-Whitney u-test). \u003cstrong\u003e(D) \u003c/strong\u003eThe β-diversity via PCoA based on the Bray-Curtis dissimilarity index (IS-T2D vs HC, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e= 0.050, \u003cem\u003eP\u003c/em\u003e=0.001; IS-NT2D vs HC, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e= 0.034, \u003cem\u003eP\u003c/em\u003e=0.002; IS-T2D vs IS-NT2D, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e= 0.017, \u003cem\u003eP\u003c/em\u003e=0.059, Adonis test). \u003cstrong\u003e(E) \u003c/strong\u003eConstitution of bacterial phyla in three study groups. \u003cstrong\u003e(F) \u003c/strong\u003eComparison of the relative abundance of gut microbiota at phylum-level among three study groups. \u003cstrong\u003e(G) \u003c/strong\u003eComparison of the relative abundance of gut microbiota at family-level among three study groups. \u003cstrong\u003e(H) \u003c/strong\u003eComparison of the relative abundance of gut microbiota at genus-level among three study groups. \u003cstrong\u003e(I) \u003c/strong\u003eCharacteristics of gut microbiota in three study groups were evaluated with LEfSe (LDA score \u0026gt;4). \u003cstrong\u003e(J) \u003c/strong\u003ePrediction of significantly changed metabolic pathways in the IS-T2D group compared to the IS-NT2D group (KEGG pathway annotations in level 3). ( *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001, \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e =0.05; (A-C) by Mann-Whitney u-test).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1245321/v2/ccf20fb76380e80c9d0fab61.png"},{"id":22942322,"identity":"4446ad13-2d05-499c-bd13-26921f2a06a6","added_by":"auto","created_at":"2022-06-22 15:12:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116129,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of NETs levels in three groups, and the correlation analysis (A)\u003c/strong\u003e Plasma NETs levels were increased in IS-T2D and IS-NT2D groups and the highest in the IS-T2D group. \u003cstrong\u003e(B)\u003c/strong\u003e Positive correlation between plasma PAGln level and plasma NETs level (r=0.41, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). \u003cstrong\u003e(C)\u003c/strong\u003e Comparison of the NETs levels between patients with increasing quartiles of PAGln levels (** indicate \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01 as compared to NETs in patients from the first quartiles, \u003csup\u003e#\u003c/sup\u003e indicate \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 as compared to NETs in patients from the second quartiles). \u003cstrong\u003e(D) \u003c/strong\u003eAssociation of the altered bacterial composition (LDA score \u0026gt;4) and the clinical and biochemical information was evaluated with Spearman correlation analysis\u0026nbsp;( *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; (A) (C)\u003cstrong\u003e \u003c/strong\u003eby Mann-Whitney u-test).\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1245321/v2/c01f67dd7e97cfb531f043bf.png"},{"id":22941631,"identity":"d6f209f5-2212-410e-a977-8ac8eaac1863","added_by":"auto","created_at":"2022-06-22 15:07:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":89903,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC analysis of predicting efficiency of PAGln levels, altered bacterial composition, and NETs levels for IS-T2D \u003c/strong\u003eOrange curve, orange characters, AUC: 0.7160 ± 0.0554, \u003cem\u003eP\u003c/em\u003e =0.0007; purple curve, purple characters, AUC: 0.8757 ± 0.0377, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.0001; green curve, green characters, AUC: 0.8229 ± 0.0447, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.0001; blue curve, blue characters, AUC: 0.8337 ± 0.0438, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.0001; red curve, red characters, AUC: 0.8869 ± 0.0354, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.0001; black curve, black characters, AUC: 0.9466 ± 0.0226, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1245321/v2/3de2293c22e94364e1d6f66f.png"},{"id":22942323,"identity":"09281b4f-5ca8-471d-ac5b-1bac49c9262c","added_by":"auto","created_at":"2022-06-22 15:12:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96088,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFecal transplantation from IS-T2D patients resulted in increased neurological impairment and plasma PAGln levels in rats (A)\u003c/strong\u003e Animal experimental design. \u003cstrong\u003e(B)\u003c/strong\u003e Rats receiving fecal microbes from IS-T2D patients had decreased neurological function scores 24h after cerebral ischemia-reperfusion. \u003cstrong\u003e(C)\u003c/strong\u003e Rats receiving fecal microbes from IS-T2D patients had increased infarct volume 24h after cerebral ischemia-reperfusion. \u003cstrong\u003e(D)\u003c/strong\u003e Rats receiving fecal microbes from IS-T2D patients had increased plasma PAGln levels regardless of whether they had experienced an ischemic stroke. Shown is the mean (±SEM) of PAGln levels of rats. n= 5 to 8 rats per group. (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; by Mann-Whitney u-test) .\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-1245321/v2/ea21aa8b6a138d09c4182978.png"},{"id":22941627,"identity":"495e6eff-9dbf-45f4-9f56-20467e3a4b5a","added_by":"auto","created_at":"2022-06-22 15:07:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":84217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolite PAGln may involve in exacerbation of injury of IS-T2D via gut microbiota \u003c/strong\u003eGut microbiota disordered and plasma PAGln levels increased in IS-T2D patients. Increased plasma PAGln levels were positively correlated with NETs. The neurological dysfunctions and infarct volume of the rats receiving fecal microbes from IS-T2D patients increased significantly under the action of the gut-brain axis. In addition, plasma PAGln levels were significantly increased in rats with or without stroke.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-1245321/v2/9ebf8cf69e22e437f3b893b3.png"},{"id":23067962,"identity":"d2214aa4-696d-4d12-b0e7-c09966e60a75","added_by":"auto","created_at":"2022-06-24 21:31:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1358589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1245321/v2/5ce51ee3-80b0-4191-b688-14e33599ed4f.pdf"}],"financialInterests":"","formattedTitle":"Elevated circulating levels of phenylacetylglutamine in stroke patients with T2D are linked to specific gut microbiota","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is an important public health problem affecting more than 10\u0026nbsp;million people every year around the world\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In China, the mortality rate of stroke is 149.49/100,000, accounting for 22% of the national overall mortality rate\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It is widely known that type 2 diabetes (T2D) is an independent risk factor for ischemic stroke (IS)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Surveys have shown that among IS patients, up to 23.5% of patients had diabetes mellitus, accompanied by higher risks of stroke recurrence, disability, and mortality\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. As the prevalence of IS with T2D (IS-T2D) has seen a steady increase globally, it is increasingly important for finding new pathophysiological mechanisms and therapeutic targets for IS-T2D.\u003c/p\u003e \u003cp\u003eIn recent years, increasing evidence suggests that changes in the gut microbiota and its metabolites participate in the pathophysiological mechanism of IS and T2D\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. A cohort study of more than 5000 people showed that patients with T2D and patients who developed incident adverse cardiovascular events after 3-year follow-up had higher plasma phenylacetylglutamine (PAGln) levels, which were proved to be an independent predictor of adverse cardiovascular events\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The metabolite, PAGln, is produced by gut microbiota via catabolizing the essential amino acid phenylalanine (PHE) in the distal colon\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. As a gut microbiota-derived metabolite, PAGln is not only related to elevated blood glucose but also closely related to the risk of thrombotic events, such as coronary heart disease (CHD)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Similarly, IS is also a dreadful thrombotic event. Multiple studies have shown that gut microbiota and its metabolites changed after stroke and then exacerbated cerebral infarction in turn\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the relationship among gut microbiota, its metabolite PAGln, and stroke with T2D remains unknown.\u003c/p\u003e \u003cp\u003eA study showed that neutrophils (NE) mediated inflammation to amplify cerebral microvascular damage in the early stage of T2D, resulting in more severe brain edema and nerve damage after ischemia\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Furthermore, NE also participated in inflammatory damage by releasing neutrophil extracellular traps (NETs), which are network structures of DNA fibers, composed of histones and active particles\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Extensive research has confirmed the formation of NETs in blood circulation and thrombosis in patients with IS\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Moreover, high glucose levels or hyperglycemia in diabetic patients could increase the release of NETs\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In inflammatory bowel disease, NETs are involved in inflammation after intestinal dysbiosis\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Therefore, we hypothesized that gut microbiota and its metabolites were disrupted in stroke patients with T2D, leading to NET-related immune imbalance.\u003c/p\u003e \u003cp\u003eHere, we evaluated the characteristics of gut microbiota in IS-T2D patients, IS patients without T2D (IS-NT2D), and healthy control people (HC) via 16S ribosomal RNA sequencing and determined the plasma PAGln concentration in those groups by targeted liquid chromatography-mass spectrometry. We also analyzed the relationship between PAGln levels and NETs. In addition, we constructed a prediction model based on the differential relative abundances of microbiota, plasma PAGln levels, and NETs levels for discriminating between IS-T2D patients and IS-NT2D patients. Finally, we validated whether the fecal microbes from IS-T2D patients could exacerbate brain infarction and elevate circulating PAGln levels in IS-T2D patients via fecal microbiota transplantation (FMT) in animal experiments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eWe recruited 85 patients with IS in the Department of Neurology, Xiangya Hospital, Central South University (Changsha, China) from December 2019 to December 2020. And the patients were divided into 50 patients without T2D (IS-NT2D group) and 35 patients with T2D (IS-T2D group) (T2D was defined as T2D medical history or typical diabetes symptoms with either random blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L, fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0mmol/L, or blood glucose at 2h after glucose load\u0026thinsp;\u0026ge;\u0026thinsp;11.1mmol/L\u003csup\u003e26\u003c/sup\u003e). Meanwhile, 29 healthy controls (HC group) were recruited. Inclusion criteria of patients were as follows: (1) age between 18 and 80 years old; (2) first diagnosed acute IS (stroke was defined as a rapid clinical onset of a neurological impairment lasting more than 24 hours or resulting in death, with no cause other than that of vascular origin\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. IS was further confirmed by comprehensive neurological physical examination, head computed tomography, and/or magnetic resonance imaging); (3) admitted within two weeks of IS onset. In addition, patients with T2D treated with metformin or acarbose were excluded. HC matched by age and sex were recruited. Exclusion criteria of patients and HC were as follows: (1) used antibiotics or probiotics before admission or after admission within 1 month; (2) suffered from acute inflammatory disease, severe infective disease and/or cancer, severe liver and kidney failure, hepatic impairment, autoimmune disease, severe mental illness, and gut disease (i.e., inflammatory bowel disease, ulcerative colitis, and Crohn\u0026rsquo;s disease); (3) had a history of intestinal surgery. For all participants, plasma samples were collected within 24h, and fecal samples were obtained within 48h of admission. At the same time, baseline characteristics including demographic and cerebrovascular risk factors (histories of hypertension, dyslipidemia, diabetes mellitus, CHD, and smoking) were collected and recorded at admission. Each participant in this study provided written informed consent, and the study protocol complied with the principles of the 1975 Declaration of Helsinki and was approved by the Ethics Committee of Xiangya Hospital, Central South University, China.\u003c/p\u003e \u003cp\u003eAll patients were assessed with the National Institutes of Health Stroke Scale (NIHSS) scores on admission and evaluated by the modified Rankin scale (mRS) scores 3 months after onset. Both NIHSS and mRS scores were rated by two trained clinical staffs who were blinded to the study protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e16S rRNA amplification and sequencing of fecal microbiota samples\u003c/h2\u003e \u003cp\u003eFecal samples of all research objects were stored at -80\u0026deg;C within 30 minutes once obtained. Total genome DNA from the samples was extracted using a DNA kit (Magnetic Soil And Stool DNA Kit, TIANGEN, DP712, China) according to the manufacturer\u0026rsquo;s instructions. We chose the V3-V4 region of the 16S rRNA gene to finish polymerase chain reaction (PCR) amplification and used specific primer 341F (5\u0026rsquo;-CCTAYGGGRBGCASCAG-3\u0026rsquo;) and 806R (5\u0026rsquo;-GGACTACNNGGGTATCTAAT-3\u0026rsquo;). The mixture of PCR products was purified using Qiagen Gel Extraction Kit (Qiagen, Germany) and sequencing libraries were generated with the TruSeq\u0026reg; DNA PCR-Free Sample Preparation Kit (Illumina, USA). At last, the library was sequenced on an Illumina NovaSeq platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics and Biostatistics\u003c/h2\u003e \u003cp\u003eWe used Quantitative Insights into Microbial Ecology (QIIME) V1.9.1 software to analyze microbial data. The Effective Tags of all samples were clustered by the Uparse algorithm (Uparse v7.0.1001). Sequences were clustered into operational taxonomic units (OTUs) with 97% identity, and species annotations were made to the OTUs sequence (the threshold was set to 0.8\u0026thinsp;~\u0026thinsp;1). QIIME was used for species alpha and beta diversity analysis, and R software (2.15.3) was used for drawing the species accumulation curve. The linear discriminant analysis (LDA) effect size (LEfSe) was used to determine the difference among the three groups of bacteria, with a threshold of 4. PICRUSt was used to predict the metagenome function of 16S rRNA biological information. According to the 16S rRNA sequencing data, the function prediction based on the KEGG database was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory tests\u003c/h2\u003e \u003cp\u003e12h fasting blood samples were collected on admission, centrifuged at 3000 RPM for 10 minutes, and stored at -80\u0026deg;C. A routine blood test was analyzed with an automatic biochemical analyzer. Blood biochemistry such as blood urea nitrogen (BUR), serum creatinine (Scr), triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), glucose, glycosylated hemoglobin (HbA1C), homocysteine (Hcy) was analyzed with automated enzymatic analysis.\u003c/p\u003e \u003cp\u003ePlasma PAGln concentrations were quantified by targeted liquid chromatography-mass spectrometry. The plasma was diluted 10-fold with ddH2O. 48\u0026micro;L diluted plasma was mixed with 2\u0026micro;L internal standard (1ppm D5-PAGln), diluted 3-fold with cold methanol, then centrifuged (21,000 x g; 4\u0026deg;Cfor 15 min), and then transferred to a clean glass bottle for testing in AB SCIEX TripleTOF 6500 System (AB SCIEX, Foster City, CA, USA). Finally, 1 uL supernatant was analyzed by injecting Acquity UPLC BEH C18 column for analysis (50\u0026times;2.1 mm, 1.7 \u0026micro;m) at a column temperature of 40\u0026deg;C, a flow rate of 0.3 mL/min, mobile phase A containing 0.1% acetic acid, mobile phase B containing 0.1% acetic acid. The concentration of PAGln was measured by establishing a standard curve based on the known PAGln concentration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of NETs markers\u003c/h2\u003e \u003cp\u003eCitrullinated histone H3 (CitH3) is currently considered to be the most specific marker of NETs\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Therefore, we evaluated the concentration of CitH3 in the plasma to represent the level of NETs. CitH3 was measured with the CitH3 Detection ELISA kit (Cayman Chemical, 501620, Ann Arbor, MI, USA) according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003eThe experimental protocols were approved by the Experimental Animal Welfare Ethics Committee of Central South University on September 7, 2021 (Approval No. 2019-0004). All experimental procedures were conducted following the Care and Use of Laboratory Animals by the guidelines of the National Institutes of Health (NIH).\u003c/p\u003e \u003cp\u003eSprague-Dawley (SD) rats (5\u0026ndash;6 weeks old) (purchased from Hunan SJA Laboratory Animal Co. Ltd) were placed in a specific pathogen-free (SPF) environment and raised under the conditions of cycles of 12h light/dark, 50%-55% humidity, and 100\u0026ndash;200 Lux of light intensity. The animals were randomly divided into the FMT-IS-NT2D group (transplanted with stool samples from IS-NT2D patients) and the FMT-IS-T2D group (transplanted with stool samples from IS-T2D patients).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFecal microbiota transplantation (FMT)\u003c/h2\u003e \u003cp\u003eBefore FMT, rats in both groups were fed with drinking water containing antibiotics (vancomycin 500mg/L, neomycin 1g/L, ampicillin 1g/L, metronidazole 1g/L) for 1 week. The drinking water containing antibiotics was replaced every 1\u0026ndash;2 days \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Then, stool samples from 5 IS-T2D patients and 5 IS-NT2D patients were selected. Next, the stool homogenate was centrifuged at 1000 RPM and the supernatant was collected. Fecal supernatant from the two patient groups was gavaged to the two rat groups (2ml/d for each rat), respectively, for 1 week, and then modeling was made \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMiddle cerebral artery occlusion (MCAO) procedure\u003c/h2\u003e \u003cp\u003eBefore inducing the MCAO model, 1\u0026ndash;2 ml orbital blood of anesthetized rats was collected to measure the PAGln concentration. The collected blood was immediately centrifuged at 3000 RPM for 10 minutes, and the plasma was separated and stored at -80\u0026deg;C for testing. After blood collection, rats were subjected to middle cerebral artery occlusion as previously described\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Briefly, rats were placed in a supine position, and a midline incision was made in the neck to expose the external carotid artery (ECA) and common carotid artery (CCA). The ECA was ligated and the internal carotid artery (ICA) was separated. A 4\u0026thinsp;\u0026minus;\u0026thinsp;0 silicon-coated monofilament suture was inserted into ICA after cutting on ECA. After 90min of occlusion, the suture was taken out and the ECA was ligated. The neck wound was sutured and the animals were recovered and reared for 24 hours before being killed.\u003c/p\u003e \u003cp\u003eAfter anesthesia, the blood of stroke rats was collected through the cardiac puncture, and the plasma was centrifuged as described above. The separated plasma was placed in a refrigerator at -80\u0026deg;C for testing. The plasma PAGln concentration of rats was measured with the rat PAGln ELISA assay kit (Shanghai Jianglai Biotechnology, JL51250, Shanghai, China) according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNeurological evaluation\u003c/h2\u003e \u003cp\u003eGarcia neurofunctional score was used to evaluate the neurological function of rats at 24h after the MCAO procedure\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The score includes six tests: spontaneous movement, symmetry of limb movement, symmetry of forelimb extension, climbing, body proprioception, and response to whiskers. The score ranges from 3 to 18, and the lower the score, the more serious the neurological deficit. The score was evaluated by two trained experimenters who were blinded to the study protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of infarct volume\u003c/h2\u003e \u003cp\u003eAfter anesthesia, rats were sacrificed to extract brain tissue. Brain slices with a thickness of 2mm were taken from a coronal plane, and 5 slices were cut from each brain. Staining was then performed by soaking in a 2% TTC solution at 37\u0026deg;C for 30 min\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Infarct volume was calculated using Image-Pro Plus Image software. The results were expressed as infarct volume percentage: infarct volume percentage\u0026thinsp;=\u0026thinsp;total infarct volume/total brain volume x 100%\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe data were statistically analyzed and managed by SPSS 26.0 and GraphPad Prism 8.0. For continuous data, it was expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error mean (SEM) if conformed to the normal distribution and was expressed as the median and interquartile range (IQR) if not conformed to the normal distribution. For categorical data, it was expressed as percentages. The mean was analyzed by the t-test and the median was analyzed by the Mann-Whitney u-test in comparison between the two groups. The mean was analyzed by the ANOVA and the median was analyzed by the Kruskal-Wallis test in comparison among the three groups. The percentage was analyzed by the chi-square or Fisher\u0026rsquo;s exact test. Spearman correlation analysis was used to analyze the relationship among PAGln level, gut microbiota, and biochemical indexes. Receiver operator characteristic (ROC) was used to evaluate the diagnostic performance of plasma PAGln levels, gut microbiota, and NETs levels for IS-T2D. A value of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Univariate and multivariate logistics regression was used to evaluate whether the plasma PAGln level was a risk factor for IS-T2D and IS-NT2D. The relative risk was shown as the odds ratio (OR) with the 95% confidence interval.\u003c/p\u003e "},{"header":"Result","content":"\u003cdiv id=\"Sec14\" type=\"Results\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eA total of 114 subjects were recruited in our study, including 35 patients with IS-T2D, 50 patients with IS-NT2D, and 29 healthy individuals. As showed by the data in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there were no significant differences in age and sex among the three groups. Risk factors such as hypertension, dyslipidemia, and CHD were not statistically significantly different between the IS-T2D and IS-NT2D groups. The severity of infarction (admission NIHSS score) and short-term prognosis (90-day mRS score) of patients with IS-T2D were worse than those with IS-NT2D (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). IS-T2D and IS-NT2D groups had significantly higher levels of leukocyte (LEU), NE, blood urea nitrogen (BUN), and serum creatinine (Scr) when compared with the HC group, and the IS-T2D group had the highest levels among the three groups \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, glucose and HbA1c of the IS-T2D group were significantly higher than those of the other two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIS-NT2D(n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIS-T2D(n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (M/F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.79\u0026thinsp;\u0026plusmn;\u0026thinsp;14.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.62\u0026thinsp;\u0026plusmn;\u0026thinsp;12.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.14\u0026thinsp;\u0026plusmn;\u0026thinsp;11.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRisk factors, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(51.7)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(88.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical findings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission NIHSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(1, 6)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(4, 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90-Day mRS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.75, 2)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemical data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEU (x10^9/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8(5.0, 6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5(5.3, 8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3(6.2, 8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNE (x10^9/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4(2.9, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4(3.4, 4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0(3.8, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLY (x10^9/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5(1.3, 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4(1.1, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6(1.2, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1(1.8, 3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4(2.0, 3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3(2.3, 4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.36(3.51, 5.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.64(3.94, 5.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.35(4.45, 6.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0(62.0, 84.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.0(65.4, 78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.1(71.0, 93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51(1.23, 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47(1.06, 2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54(1.26, 2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.15(3.61, 4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.22(3.38, 4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.55(3.06, 5.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.67(2.30, 3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68(1.99, 3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.23(1.84, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.98(4.81, 5.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.23(4.81, 5.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.20(6.24, 10.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8(5.4, 5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6(5.5, 5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.8(6.7, 8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHcy (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.7(8.4, 14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.9(9.8, 17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.9(10.8, 15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eM/F, male/female; CHD, coronary heart disease; LEU, leukocytes; NE, neutrophils; LY, lymphocytes; NLR, neutrophil to lymphocyte ratio; BUN, blood urea nitrogen; Scr, serum creatinine; TG, triglycerides; TC, total cholesterol; HDL, high-density lipoprotein; Glu, glucose; HbA1c, glycated hemoglobin; Hcy, homocysteine. The three groups were compared by the Kruskal-Wallis test or the ANOVA. \u003csup\u003eb,c\u003c/sup\u003e Refers to Mann-Whitney U test; \u003csup\u003eb\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 vs IS-T2D, \u003csup\u003ec\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 vs IS-NT2D.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePAGln concentration was an independent risk factor of IS-T2D\u003c/h2\u003e \u003cp\u003eWe measured the plasma PAGln concentration of the subjects by the targeted liquid chromatography-mass spectrometry. The plasma PAGln levels of the patients with IS-T2D were significantly higher than IS-NT2D patients and HC group (median, 2.77 vs. 1.77\u0026micro;mol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; median, 2.77 vs. 1.04\u0026micro;mol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Furthermore, the PAGln levels of the IS-NT2D group were also higher than the HC group (median, 1.77 vs. 1.04\u0026micro;mol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we sought to explore the associations among IS, T2D, and the PAGln levels. The univariate logistic regression analysis showed that PAGln levels (OR 1.68, 95%CI 1.04 to 2.72, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.04) were a risk factor for patients with IS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). This significance still existed after adjusting for gender, age, hypertension, CHD, smoking, LEU, NE, BUN, Scr, TG, TC, Hcy (OR 2.34, 95%CI 1.08 to 5.09, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). That could translate to an increase of 2.34-times in the risk of IS for every 1 \u0026micro;mol/L increase in plasma PAGln levels. More importantly, elevated PAGln levels increased the risk of T2D for stroke patients by both univariate (OR 1.56, 95%CI 1.13 to 2.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and multivariate logistic regression analyses (adjustment for gender, age, hypertension, CHD, smoking, LEU, NE, BUN, Scr, TG, TC, Hcy) (OR 1.53, 95% CI 1.01 to 2.30, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.044) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). This also means an increase of 1.53-times in the risk of T2D in stroke patients for every 1 \u0026micro;mol/L increase in plasma PAGln levels. Therefore, increased plasma PAGln levels were an independent risk factor for IS patients and stroke patients with T2D.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eThe gut microbiota was disturbed in stroke patients withT2D\u003c/h2\u003e \u003cp\u003ePrevious studies have demonstrated that PAGln was a metabolite of gut microbiota. Moreover, PAGln was elevated in the plasma of patients with T2D\u003csup\u003e3\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Therefore, to investigate whether PAGln-producing bacteria were enriched in the gut microbiota of stroke patients with T2D, we conducted the 16S rRNA gene sequencing.\u003c/p\u003e \u003cp\u003eThe species accumulation boxplot showed that our fecal samples were sufficient and the species were abundant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The Shannon index and the Simpson index were calculated to evaluate the richness and evenness of intestinal microorganisms of each group. However, we found that there were no significant differences in α diversity among the three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB \u0026minus;\u0026thinsp;2C). Next, the β diversity of the three groups was analyzed by principal coordinate analysis (PCoA) based on the unweighted UniFrac distance. The results showed that the microbiome structures of both IS-T2D and IS-NT2D patients were separated from the HC group, but community structure was similar between IS-NT2D and IS-T2D groups (IS-T2D vs. HC, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.050, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; IS-NT2D vs. HC, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.034, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; IS-T2D vs. IS-NT2D, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.017, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.059, Adonis test) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 5360 OTUs were obtained from 114 fecal samples by 16S rRNA gene sequencing and classified into 63 bacterial phyla, 455 bacterial families, and 778 bacterial genera. The gut microbiota differed among the three groups at the phylum, family, and genus levels. At the phylum level, the gut microbiota was mainly composed of \u003cem\u003eFirmicutes\u003c/em\u003e (51.0%), \u003cem\u003eProteobacteria\u003c/em\u003e (18.7%), \u003cem\u003eBacteroidota\u003c/em\u003e (21.3%), \u003cem\u003eActinobacteriota\u003c/em\u003e (4.2%), \u003cem\u003eFusobacteriota\u003c/em\u003e (1.7%), and \u003cem\u003eVerrucomicrobiota\u003c/em\u003e (3.1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The relative abundance of \u003cem\u003eFirmicutes\u003c/em\u003e in patients with IS-T2D was significantly lower than that in the IS-NT2D and HC groups (43% vs. 52%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027; 43% vs. 55%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). The relative abundance of \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eVerrucomicrobiota\u003c/em\u003e in the IS-T2D group was higher than that in the IS-NT2D group (23% vs. 17%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024; 4.3% vs. 1.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). At the family level, the harmful bacteria \u003cem\u003eEnterobacteriaceae\u003c/em\u003e were enriched in IS-T2D patients with significant differences compared with IS-NT2D and HC groups (21.6% vs. 16.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027; 21.6% vs. 13.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). The relative abundance of \u003cem\u003eAkkermansiaceae\u003c/em\u003e in the IS-T2D group was the highest among the three groups, but there was a significant difference only between the IS-NT2D and HC groups (1.4% vs. 3.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). At the genus level, the beneficial bacteria \u003cem\u003eFaecalibacterium, Dialister\u003c/em\u003e, and \u003cem\u003eRoseburia\u003c/em\u003e were all significantly reduced in IS-T2D and IS-NT2D groups when compared with the HC group (\u003cem\u003eFaecalibacterium\u003c/em\u003e, 7.4% vs. 15.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 9.6% vs. 15.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; \u003cem\u003eDialister\u003c/em\u003e, 3.0% vs. 3.7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 2.0% vs. 3.7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; \u003cem\u003eRoseburia\u003c/em\u003e, 0.9% vs 3.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 1.4% vs 3.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). And the relative abundance of harmful bacteria \u003cem\u003eKlebsiella\u003c/em\u003e significantly increased in the IS-T2D group compared with the HC and IS-NT2D groups (4.5 vs. 1.8, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; 4.5 vs. 2.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). In summary, the diversity of gut microbiota in patients with IS-T2D decreased, with decreased beneficial bacteria and increased harmful bacteria.\u003c/p\u003e \u003cp\u003eIn addition, to better identify the microbial markers among the three groups, we used the LEfSe tool. The results showed that there was a total of 20 gut microbiota taxa from phylum to species, which were differentially abundant bacterial taxa (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;4) in three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Specifically, there were 6 increased abundant taxa in the IS-T2D group, including \u003cem\u003eo_Enterobacterales, f_Enterobacteriaceae, p_Verrucomicrobiota, c_Verrucomicrobiota, s_Klebsiella_pneumoniae\u003c/em\u003e, and \u003cem\u003eg_Klebsiella.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eNext, we performed PICRUSt analysis to predict potential functional pathways of the microbiome communities and compared the differences between IS-T2D and IS-NT2D groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ). In total, there were 300 differential KEGG pathways (level 3), of which 49 were significantly different with the average relative abundance of one of the two groups no less than 0.1%. There were 29 pathways enriched, but 20 pathways were poor in the IS-T2D group. Comparing with the IS-NT2D group, we found that amino acid metabolism (such as glycine, serine, and threonine metabolism, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033; tyrosine metabolism, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014; beta-alanine metabolism, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020; tryptophan metabolism, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) was significantly more abundant in the IS-T2D group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePAGln levels were associated with characteristic microbiota related to IS-T2D, poor prognosis, and NETs-related inflammation\u003c/h2\u003e \u003cp\u003eTo investigate the association between PAGln levels and IS-T2D, we performed Spearman correlation analysis. As showed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the PAGln levels were positively correlated with age, BUN, Scr, Glu, HbA1c, and Hcy in all subjects, and the correlations between age and PAGln were especially pronounced in stroke patients with T2D. Furthermore, it is worth noting that the PAGln levels were positively correlated with mRS score of 90 days after the onset in all stroke patients. In addition, we found that the PAGln levels were positively correlated with NE in stroke patients with T2D, suggesting an association between inflammation and PAGln levels in stroke patients with T2D.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between clinical indexes, biochemical indexes, and PAGln\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal population (n\u0026thinsp;=\u0026thinsp;114)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAll IS patients (n\u0026thinsp;=\u0026thinsp;85)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eIS-T2D group (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePAGln\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePAGln\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePAGln\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90d-mRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHcy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eLEU, leukocytes; NE, neutrophils; BUN, blood urea nitrogen; Scr, serum creatinine; Glu, glucose; HbA1c, glycated hemoglobin; Hcy, homocysteine.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNETs are bactericidal substances, released extracellularly after NE activation, and their dysregulation can lead to inflammation \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Therefore, we measured the plasma NETs concentration of all subjects. We evaluated the concentration of CitH3 in the plasma to represent the level of NETs \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Our results showed that the plasma CitH3 levels in IS-T2D patients were significantly higher than those in IS-NT2D and HC groups (6.34 vs. 5.57ng/ml, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 6.34 vs. 4.73ng/ml, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The plasma CitH3 levels in the IS-NT2D group were also significantly higher than those in the HC group (5.57 vs. 4.73ng/ml, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Moreover, Spearman correlation analysis revealed a significant correlation between PAGln and CitH3 levels (r\u0026thinsp;=\u0026thinsp;0.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). We further analyzed the distribution of CitH3 levels based on PAGln levels as assessed by quartiles. The concentration of plasma CitH3 showed a dose-dependent increase according to PAGln levels with the highest levels being observed in subjects with the highest PAGln concentrations (Q4: 6.15 vs Q1: 5.27ng/ml, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we explored the relationships between PAGln, clinical indicators, and gut microbiota. We performed Spearman correlation analysis on the data of all stroke patients. Spearman correlation analysis showed that PAGln level was significantly positively correlated with 6 microbial markers (\u003cem\u003eo_Enterobacterales, f_Enterobacteriaceae, p_Verrucomicrobiota, c_Verrucomicrobiota, s_Klebsiella_pneumoniae\u003c/em\u003e, and \u003cem\u003eg_Klebsiella\u003c/em\u003e) related to IS-T2D. Interestingly, \u003cem\u003eo_Enterobacterales, f_Enterobacteriaceae, p_Verrucomicrobiota\u003c/em\u003e, and \u003cem\u003ec_Verrucomicrobiota\u003c/em\u003e were also positively correlated with NE. Meanwhile, \u003cem\u003eo_Enterobacterales\u003c/em\u003e and \u003cem\u003ef_Enterobacteriaceae\u003c/em\u003e were positively correlated with Glu and Hcy; \u003cem\u003es_Klebsiella_pneumoniae\u003c/em\u003e and \u003cem\u003eg_Klebsiella\u003c/em\u003e were positively correlated with HbA1c (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePAGln levels, NETs levels, and gut microbiota were diagnostic indexes for IS-T2D.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe next explored whether the plasma PAGln, gut microbiota, and NETs could be used as biomarkers of stroke with T2D. Based on the plasma PAGln levels, differential microbiota (defined as the relative abundance of bacteria in gut microbiota with LDA score\u0026thinsp;\u0026gt;\u0026thinsp;4), and NETs levels, we conducted ROC analysis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, plasma PAGln levels (AUC: 0.7160, 95%CI: 0.6074\u0026ndash;0.8246; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0007), different gut microbiota (AUC: 0.8757, 95%CI: 0.8016\u0026ndash;0.9462; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and NETs levels (AUC: 0.8229, 95%CI: 0.7353\u0026ndash;0.9105; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) could well distinguish patients with IS-T2D from stroke patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, when plasma PAGln levels and the differential microbiota were incorporated into the model construction, the AUC increased to 88.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). And when the PAGln and NETs levels were incorporated into the model construction, the AUC increased to 83.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.38% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Finally, we included PAGln levels, NETs levels, and differential microbiota into the model construction, the area under the ROC curve was up to 94.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In summary, the intestinal metabolite PAGln, differential microbiota, and inflammatory indicator NET could all be used as diagnostic indicators for stroke with T2D. Moreover, the combination of these biomarkers might improve diagnostic efficiency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eElevated PAGln levels in IS-T2D patients could be transmitted through the gut microbiota\u003c/h2\u003e \u003cp\u003eTo investigate whether the gut microbiota of stroke patients with T2D contributed to the elevation of plasma PAGln levels, we treated antibiotic-treated rats with a fecal transplant from IS-T2D and IS-NT2D patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Results demonstrated that compared with the rats receiving fecal microbes from patients with IS-NT2D (preFMT-IS-NT2D group), the PAGln concentration of the rats receiving fecal microbes from patients with IS-T2D (preFMT-IS-T2D group) was significantly increased and nearly doubled (preFMT-IS-T2D vs. preFMT-IS-NT2D, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). MCAO model was established in rats after FMT. 24 hours after stroke, the rats receiving fecal microbes from IS-T2D patients had more severe stroke than the rats receiving fecal bacteria from IS-NT2D patients, with decreased neurological function scores (FMT-IS-T2D vs. FMT-IS-NT2D, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and increased infarct volume (FMT-IS-T2D vs. FMT-IS-NT2D, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Meanwhile, after stroke, PAGln levels in rats receiving fecal microbes from patients with IS-T2D were also nearly twice as high as those receiving fecal microbes from patients with IS-NT2D (FMT-IS-T2D vs. FMT-IS-NT2D, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Notably, PAGln levels in rats receiving fecal microbes from patients with IS-T2D were further elevated after stroke (preFMT-IS-T2D vs. FMT-IS-T2D, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), while the PAGln levels in rats receiving fecal microbes from IS-NT2D patients were in an increasing tendency with no significant differences (preFMT-IS-NT2D vs. FMT-IS-NT2D, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first time for exploring the relationship between gut microbiota and its metabolite PAGln in stroke patients with T2D. Our data suggest that: (i) in the IS-T2D group, the gut microbiota was significantly imbalanced and the plasma PAGln levels increased partly caused by the disorder of gut microbiota, which was further confirmed by animal studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e); (ii) elevated PAGln levels were associated with poor functional outcomes and plasma NETs levels; (iii) PAGln levels, gut microbiota, and NETs levels could be used as combined diagnostic indicators for IS-T2D.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious research has established that PAGln was associated with CHD, peripheral artery disease, heart failure, and other cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In addition, PAGln was also closely related to obesity, diabetes, prediabetes, and other metabolic diseases\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Research shows that metabolic disorders such as insulin resistance, dyslipidemia, and fatty can increase platelet activity and aggregation via dysregulation of the NO-mediated signaling pathway, leading to thrombosis and atherosclerotic lesion formation\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. And PAGln also has the effect of driving platelet invasiveness\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A metabolic disorder like diabetes mellitus may promote vascular injury via PAGln-mediated molecular mechanisms. Our results showed that IS-NT2D caused an increase in plasma PAGln levels. More importantly, PAGln levels were higher in IS patients with the complication of T2D. Although the plasma clearance of PAGln is closely related to renal function, that is, if renal function is impaired, the plasma PAGln clearance will be severely reduced, there was no significant difference in BUN or Scr levels between IS-T2D and IS-NT2D groups, indicating that the elevated PAGln levels in IS-T2D patients were mainly influenced by T2D\u003csup\u003e3\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Except for stroke, Tang et al. found that plasma PAGln levels were significantly higher in patients with heart failure and diabetes mellitus than in those with heart failure alone, which suggested the elevation of plasma PAGln is associated with diabetes mellitus\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Our results also revealed that elevated PAGln was an independent risk factor for the patient with stroke and T2D, indicating that PAGln may be an important molecule of T2D in contributing to an exacerbation in stroke injury, of which the mechanism remains obscure and needs to be further explored.\u003c/p\u003e \u003cp\u003ePAGln is a metabolite of phenylalanine degradation by gut microbiota. Previous studies have revealed that, except for abnormal metabolism, people with diabetes mellitus had severely disturbed gut microbiota\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Therefore, increased plasma PAGln levels in IS-T2D patients indicate that they might not only use more amino acids as energy sources, but have more gut microbiota to degrade phenylalanine compared with stroke patients, which was confirmed by our results. We found that the gut microbiota of IS-T2D patients was disordered, and there was more PAGln-related gut microbiota in these patients, including \u003cem\u003eo_Enterobacterales, f_Enterobacteriaceae, p_Verrucomicrobiota, c_Verrucomicrobiota, s_Klebsiella_pneumoniae\u003c/em\u003e, and \u003cem\u003eg_Klebsiella\u003c/em\u003e. \u003cem\u003eEnterobacteriaceae\u003c/em\u003e is recognized as harmful gut microorganisms and showed a growth advantage in both stroke and T2D\u003csup\u003e1\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. A study has reported that \u003cem\u003eEnterobacteriaceae\u003c/em\u003e was closely related to the mortality of stroke patients, increasing inflammatory factors such as TNF-α and IL-1β through the LPS-TLR4 pathway, thus accelerating systemic inflammation and exacerbating cerebral infarction\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In addition, studies have shown that \u003cem\u003eEscherichia coli\u003c/em\u003e belonging to Enterobacteriaceae can catabolize aromatic compounds such as phenylalanine and phenylacetic acid\u003csup\u003e\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The phenylacetic acid is the middle product that PHE is metabolized to PAGln. Our results showed that \u003cem\u003eEnterobacteriaceae\u003c/em\u003e was one of the major differential bacteria in the IS-T2D group, which was positively correlated with PAGln, indicating that \u003cem\u003eEnterobacteriaceae\u003c/em\u003e might cause the aggravation of brain injury by diabetes mellitus via metabolites PAGln. Consistent with Ottosson et al., a positive correlation was also found between plasma PAGln levels and \u003cem\u003eVerrucomicrobiota\u003c/em\u003e in a CHD risk cohort\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In our study, \u003cem\u003ec_Verrucomicrobiota\u003c/em\u003e was dominated by \u003cem\u003eg_Akkermansia\u003c/em\u003e (99.4%). It is generally believed that \u003cem\u003eAkkermansia\u003c/em\u003e is a kind of beneficial bacteria to maintain the health of the intestinal epithelium, which can enhance intestinal barrier function, produce short-chain fatty acids (SCFAs), improve metabolic disorders, and it has even been considered the new probiotics which could be developed and utilized\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. However, its role in the development of stroke and diabetes mellitus remains elusive. Studies have reported that \u003cem\u003eAkkermansia\u003c/em\u003e was elevated in patients with prediabetes and diabetes as well as in diabetic animal models\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In addition, it has been suggested that a significant increase in \u003cem\u003eAkkermansia\u003c/em\u003e could contribute to the erosion of the mucous layer and enhancement of pathogen filtration in the intestinal epithelial layer when the dietary fiber was lacking in the host gut\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The latest study found that \u003cem\u003eAkkermansia\u003c/em\u003e strains could be divided into five distinct candidate species with different host preferences and functions, of which only \u003cem\u003eAkkermansia mucinophilus\u003c/em\u003e had the health-associated properties referring to being associated with low body mass index in the host\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. In our study, \u003cem\u003eAkkermansia\u003c/em\u003e was significantly decreased in IS-NT2D patients but increased in IS-T2D patients, and \u003cem\u003ec_Verrucomicrobiota\u003c/em\u003e, dominated by \u003cem\u003eAkkermansia\u003c/em\u003e, was significantly associated with PAGln. We speculated that T2D could increase a harmful species of \u003cem\u003eAkkermansia\u003c/em\u003e, which is linked with PAGln metabolism. Therefore, it is necessary to further explore the relationship between each candidate species of \u003cem\u003eAkkermansia\u003c/em\u003e and PAGln. Consistent with the study of Li et al., our results suggested that PAGln was significantly correlated with \u003cem\u003eKlebsiella\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eKlebsiella\u003c/em\u003e is widely believed to be a pathogenic bacterium and a strain with the TMA gene\u003csup\u003e\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. TMA is an essential precursor of the gut microbiota metabolite TMAO, which has been found to promote thrombosis via increasing platelet reactivity\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. And TMAO is closely associated with atherosclerosis and poor prognosis in IS\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Therefore, it is reasonable to hypothesize that \u003cem\u003eKlebsiella\u003c/em\u003e is a strain with the characteristics of promoting thrombosis. In addition to the above bacteria, previous research also reported that PAGln was associated with \u003cem\u003eEnterocoocus\u003c/em\u003e, \u003cem\u003eStreptocoocus\u003c/em\u003e, \u003cem\u003eEscherichia_ Shigella\u003c/em\u003e, \u003cem\u003eG_f_o_SHA.98c_clostridia\u003c/em\u003e, and \u003cem\u003eEggerthella lenta\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. And finally, we transplanted the fecal microbiota from patients with IS-T2D into rodents, ultimately resulting in increased plasma PAGln levels, which provided direct evidence that elevated PAGln levels in IS-T2D patients were partly caused by intestinal microorganism disorders. It is noteworthy that the PAGln level of rats receiving the fecal microbes from IS-T2D patients further increased after stroke. A possible explanation for this might be that under the acute stress of stroke, this part of microbiota further grew and multiplied, resulting in an increase of PAGln levels. Additionally, our study indicated that gut bacteria functions such as amino acid metabolism were enhanced and abnormal in patients with IS-T2D. Further work should be undertaken to search for strains related to PAGln metabolism and improve the prognosis of IS-T2D by targeting PAGln-producing bacteria.\u003c/p\u003e \u003cp\u003eThe formation of NETs is originally identified as an important antimicrobial phenomenon, including resisting microbial invasion, stopping the microbial spread, and killing pathogens\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. However, mounting evidence indicates that NETs play a negative role in many diseases such as endocrine diseases, nervous system diseases, and respiratory diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Consistent with previous research, our results showed an increase in NETs levels in stroke patients and a further increase in stroke patients with T2D. Platelet-neutrophil interactions are a critical pathophysiological process of thromboinflammation\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. In the arterial microenvironment of thrombosis, activated platelets trigger NE to form and release NETs containing thrombotic tissue factor (TF) through several mechanisms, including NE autophagy induced by the presentation of high mobility group frame 1 (HMGB1) protein, release of the NETs inducer\u0026ndash;inorganic polyphosphate (polyP), and activation of the platelet Toll-like receptor 4(TLR 4)\u003csup\u003e\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. In addition, NETs, in turn, can further aggravate thromboinflammation via promoting thrombin and fibrin formation and binding to platelet-derived microparticles (PMPs) and coagulation factors\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Recently, a study had indicated the effects of the gut microbiota metabolite PAGln in driving platelet invasiveness and thrombosis via β adrenergic receptors\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. According to our data, plasma PAGln levels were positively correlated with NETs, and NETs showed a dose-dependent increase according to PAGln levels. Therefore, we speculated that the gut microbiota metabolite PAGln might contribute to platelet-neutrophil interactions via enhancing platelet reactivity, which ultimately promotes the formation of NETs. Meanwhile, our data showed a positive correlation between PAGln and IS-T2D-associated gut microbiota, of which were positively correlated with NE. Dysregulation of gut microbiota in patients with arteriosclerotic cerebral small vessel disease has been reported to independently enhance the proinflammatory property of NE\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. We thus inferred that IS-T2D-associated gut microbiota could exacerbate inflammation status of NE, and then combine with elevated PAGln in plasma to promote the formation of NETs, which may ultimately contribute to the aggravation of thromboinflammatory. In the meantime, our data suggest that elevated plasma PAGln levels were correlated with a 90-day poor prognosis in stroke patients, further supporting that the PAGln plays a negative role in the development of stroke. Above all, our study revealed that the presence of T2D could increase the inflammatory reaction in IS patients, mediated in part by the altered composition of gut microbiota and the effects of bacterial metabolite PAGln.\u003c/p\u003e \u003cp\u003eIn this study, we not only confirmed the dysbiosis of gut microbiota and the increase of harmful molecular PAGln in IS-T2D patients, but also found that the gut microbiota dysbiosis, the increased plasma PAGln, and NETs could serve as important diagnostic markers for stroke with T2D.\u003c/p\u003e \u003cp\u003eLimitations of the study: Firstly, the stroke patients included in our study were within 2 weeks of onset, which was enough to cause significant differences in microbiota between samples in group\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In addition, the NIHSS scores of the patients included were low, limiting the representativeness of the study. Secondly, our sample size was relatively small, which needs to be further expanded. Meanwhile, we need to exclude confounding factors like dietary habits, lifestyle, and fecal status, and increase the T2D group in the future, to enhance persuasion of the experimental results. Finally, in the animal experiment, we only conducted a simple FMT experiment, and the transplanted fecal samples included bacteria, fungi, and other microorganisms, which is hard to determine which strain plays a role. Further experimental studies need to be carried out in the future.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCollectively, we found that stroke patients with T2D had gut microbiota disorders and increased plasma PAGln levels, a microbiota metabolite, which was positively correlated with NETs. Our findings contribute to the understanding of the role of gut microbial metabolite-related mechanisms in the progression of stroke with T2D and provide a new therapeutic target --- PAGln for the treatment of stroke with T2D. The causal relationship between PAGln and IS with diabetes mellitus and its mechanism should be further explored.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eT2D \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003etype 2 diabetes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eischemic stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eIS-T2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eIS with T2D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eIS-NT2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eIS without T2D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003ePAGln\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003ephenylacetylglutamine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003ePHE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eacid-phenylalanine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003ecoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eneutrophils\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eNETs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eneutrophil extracellular traps\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003ehealthy control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eNIHSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eNational Institutes of Health Stroke Scale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003emRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003emodified Rankin scale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003ePCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003epolymerase chain reaction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eOTUs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eoperational taxonomic units\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eLDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003elinear discriminant analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eLEfSe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003elinear discriminant analysis (LDA) effect size\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eblood urea nitrogen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eScr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eserum creatinine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003etriglyceride\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003etotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003ehigh-density lipoprotein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eHbA1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eglycosylated hemoglobin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eHcy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003ehomocysteine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eCitH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003ecitrullinated histone H3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eNIH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eNational Institutes of Health\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eSprague-Dawley\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eSPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003especific pathogen-free\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eFMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003efecal microbiota transplantation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eMCAO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003emiddle cerebral artery occlusion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eECA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eexternal carotid artery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eCCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003ecommon carotid artery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eICA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003einternal carotid artery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eSEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003estandard error mean\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003einterquartile range\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003ereceiver operator characteristic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eLEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eleukocyte\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eodds ratio\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003ePCoA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eprincipal coordinate analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003ethe area under the curve\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eSCFAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eshort-chain fatty acids\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003etissue factor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eHMGB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003ehigh mobility group box 1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003ePolyp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003epolyphosphoric\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003eTLR 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eToll-like receptor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.90685413005272%\"\u003e\n \u003cp\u003ePMPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"63.09314586994728%\"\u003e\n \u003cp\u003eplatelet-derived microparticles\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the National Clinical Research Center for Geriatric Disorders (Xiangya hospital Central South University) for the experimental platform and the following fundings: the Project Program of National Clinical Research Center for Geriatric Disorders (Xiangya Hospital, Grant No. 2020LNJJ16), the Provincial Key Plan for Research and Development of Hunan (Grant No. 2020SK2067; No. 2020SK2069), the Natural Science Foundation of Hunan Province (Grant No. 2021JJ31109; No. 2020JJ4875), and the Fundamental Research Funds for the Central Universities of Central South University [Grant No. 2021zzts1029; No. 2020zzts269].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u0026nbsp;\u003c/strong\u003eEach participant in this study provided written informed consent, and the study protocol complied with the principles of the 1975 Declaration of Helsinki and approved by the Ethics Committee of Xiangya Hospital, Central South University, China. The animal experimental protocols were approved by the Experimental Animal Welfare Ethics Committee of Central South University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u0026nbsp;\u003c/strong\u003eAll the authors verify that they concur with the present submission and that the material submitted has not been previously reported in any other journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003eAll authors declare that they have no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFASTQ files of the 16S rRNA gene sequencing are available under SRA accession number PRJNA 820272 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA820272).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSources of funding: This study was supported by the Project Program of National Clinical Research Center for Geriatric Disorders (Xiangya Hospital, Grant No. 2020LNJJ16), the Provincial Key Plan for Research and Development of Hunan (Grant No. 2020SK2067; No. 2020SK2069), the Natural Science Foundation of Hunan Province (Grant No. 2021JJ31109; No. 2020JJ4875), and the Fundamental Research Funds for the Central Universities of Central South University [Grant No. 2021zzts1029; No. 2020zzts269].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental design, overall data analysis, and writing original draft: JX and MPW; patients\u0026rsquo; blood and fecal samples collection and testing: MPW, YFL, and TTZ; animal experiments: MPW and DL; technical support, data collection, and data interpretation: QH, FY, XJF, JF, and QH; manuscript revision: QH and YLY. All authors have read and agreed to the published version of the manuscript. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the National Clinical Research Center for Geriatric Disorders (Xiangya hospital Central South University) for the experimental platform.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreprint\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA previous version of this manuscript was published as a preprint\u003csup\u003e74\u003c/sup\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWafa HA, Wolfe CDA, Bhalla A, Wang Y (2020) Long-term trends in death and dependence after ischaemic strokes: A retrospective cohort study using the South London Stroke Register (SLSR). PLoS Med 17:e1003048. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmed.1003048\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.1003048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang YJ, Li ZX, Gu HQ, Zhai Y, Jiang Y, Zhao XQ et al (2020) China Stroke Statistics 2019: A Report From the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Non-communicable Disease Control and Prevention. Stroke Vasc Neurol 5:211\u0026ndash;239. Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/svn-2020-000457\u003c/span\u003e\u003cspan address=\"10.1136/svn-2020-000457\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuttolomondo A, Pinto A, Salemi G, Di Raimondo D, Di Sciacca R, Fernandez P et al (2008) Diabetic and non-diabetic subjects with ischemic stroke: differences, subtype distribution and outcome. Nutr Metab Cardiovasc Dis 18:152\u0026ndash;157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.numecd.2007.02.003\u003c/span\u003e\u003cspan address=\"10.1016/j.numecd.2007.02.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao T, Zhan Y, Shen J, Xu L, Peng B, Cui Q et al (2020) Association between fasting blood glucose and outcomes and mortality in acute ischaemic stroke patients with diabetes mellitus: a retrospective observational study in Wuhan, China. BMJ Open 10:e037291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2020-037291\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2020-037291\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzlachetka WA, Pana TA, Tiamkao S, Clark AB, Kongbunkiat K, Sawanyawisuth K et al (2020) Impact of Diabetes on Complications, Long Term Mortality and Recurrence in 608,890 Hospitalised Patients with Stroke. Glob Heart 15:2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5334/gh.364\u003c/span\u003e\u003cspan address=\"10.5334/gh.364\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpychala MS, Venna VR, Jandzinski M, Doran SJ, Durgan DJ, Ganesh BP et al (2018) Age-related changes in the gut microbiota influence systemic inflammation and stroke outcome. Ann Neurol 84:23\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ana.25250\u003c/span\u003e\u003cspan address=\"10.1002/ana.25250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi N, Wang X, Sun C, Wu X, Lu M, Si Y et al (2019) Change of intestinal microbiota in cerebral ischemic stroke patients. BMC Microbiol 19:191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12866-019-1552-1\u003c/span\u003e\u003cspan address=\"10.1186/s12866-019-1552-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamashiro K, Tanaka R, Urabe T, Ueno Y, Yamashiro Y, Nomoto K et al (2017) Gut dysbiosis is associated with metabolism and systemic inflammation in patients with ischemic stroke. PLoS ONE 12:e0171521. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0171521\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0171521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh V, Roth S, Llovera G, Sadler R, Garzetti D, Stecher B et al (2016) Microbiota Dysbiosis Controls the Neuroinflammatory Response after Stroke. J Neurosci 36:7428\u0026ndash;7440. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1523/JNEUROSCI.1114-16.2016\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.1114-16.2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia GH, You C, Gao XX, Zeng XL, Zhu JJ, Xu KY et al (2019) Stroke Dysbiosis Index (SDI) in Gut Microbiome Are Associated With Brain Injury and Prognosis of Stroke. Front Neurol 10:397. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fneur.2019.00397\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2019.00397\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu H, Tremaroli V, Schmidt C, Lundqvist A, Olsson LM, Kr\u0026auml;mer M et al (2020) The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study. Cell Metab 32 :379 \u0026ndash; 90.e3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cmet.2020.06.011\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2020.06.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllin KH, Tremaroli V, Caesar R, Jensen BAH, Damgaard MTF, Bahl MI et al (2018) Aberrant intestinal microbiota in individuals with prediabetes. Diabetologia 61:810\u0026ndash;820. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-018-4550-1\u003c/span\u003e\u003cspan address=\"10.1007/s00125-018-4550-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassier L, Tabei S, Crane A, Didt KD, Fallmann J, Bergen MV et al (2020) Adipose tissue derived bacteria are associated with inflammation in obesity and type 2 diabetes. Gut 69:1796\u0026ndash;1806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/gutjnl-2019-320118\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2019-320118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNemet I, Saha PP, Gupta N, Zhu W, Romano KA, Skye SM et al (2020) A Cardiovascular Disease-Linked Gut Microbial Metabolite Acts via Adrenergic Receptors. Cell. 180:862 \u0026ndash; 77 e22 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2020.02.016\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2020.02.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoo RL, Zou X, Appel LJ, Nicholson JK, Holmes E (2018) Characterization of metabolic responses to healthy diets and association with blood pressure: application to the Optimal Macronutrient Intake Trial for Heart Health (OmniHeart), a randomized controlled study. Am J Clin Nutr 107:323\u0026ndash;334. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ajcn/nqx072\u003c/span\u003e\u003cspan address=\"10.1093/ajcn/nqx072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrpi-Sarda M, Almanza-Aguilera E, Llorach R, Vazquez-Fresno R, Estruch R, Corella D et al (2019) Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: A cross-sectional study of PREDIMED trial participants. Diabetes Metab 45:167\u0026ndash;174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.diabet.2018.02.006\u003c/span\u003e\u003cspan address=\"10.1016/j.diabet.2018.02.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOttosson F, Brunkwall L, Smith E, Orho-Melander M, Nilsson PM, Fernandez C et al (2020) The gut microbiota-related metabolite phenylacetylglutamine associates with increased risk of incident coronary artery disease. J Hypertens 38:2427\u0026ndash;2434. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/HJH.0000000000002569\u003c/span\u003e\u003cspan address=\"10.1097/HJH.0000000000002569\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu K, Gao X, Xia G, Chen M, Zeng N, Wang S et al Rapid gut dysbiosis induced by stroke exacerbates brain infarction in turn. Gut 2021:gutjnl-2020-323263. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/gutjnl-2020-323263\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2020-323263\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrea D, Poon C, Benakis C, Lubitz G, Murphy M, Iadecola C et al (2021) Stroke affects intestinal immune cell trafficking to the central nervous system. Brain Behav Immun 96:295\u0026ndash;302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbi.2021.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.bbi.2021.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang F, Zhao Q, Jiang Y, Liu N, Liu Q, Shi FD et al (2019) Augmented Brain Infiltration and Activation of Leukocytes After Cerebral Ischemia in Type 2 Diabetic Mice. Front Immunol 10:2392. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2019.02392\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2019.02392\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarta-Enguita J, Navarro-Oviedo M, Rubio-Baines I, Aymerich N, Herrera M, Zandio B et al (2021) Association of calprotectin with other inflammatory parameters in the prediction of mortality for ischemic stroke. J Neuroinflamm 18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12974-020-02047-1\u003c/span\u003e\u003cspan address=\"10.1186/s12974-020-02047-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng J, Zhao F, Zhang Y, Zhou Y, Xu X, Zhang X et al (2020) Neutrophil extracellular traps increased by hyperglycemia exacerbate ischemic brain damage. Neurosci Lett 738:135383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neulet.2020.135383\u003c/span\u003e\u003cspan address=\"10.1016/j.neulet.2020.135383\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValles J, Lago A, Santos MT, Latorre AM, Tembl JI, Salom JB et al (2017) Neutrophil extracellular traps are increased in patients with acute ischemic stroke: prognostic significance. Thromb Haemost 117:1919\u0026ndash;1929. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1160/TH17-02-0130\u003c/span\u003e\u003cspan address=\"10.1160/TH17-02-0130\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaridan E, Denorme F, Desender L, Fran\u0026ccedil;ois O, Andersson T, Deckmyn H et al (2017) Neutrophil extracellular traps in ischemic stroke thrombi. Ann Neurol 82:223\u0026ndash;232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ana.24993\u003c/span\u003e\u003cspan address=\"10.1002/ana.24993\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi G, Lin J, Zhang C, Gao H, Lu H, Gao X et al (2021) Microbiota metabolite butyrate constrains neutrophil functions and ameliorates mucosal inflammation in inflammatory bowel disease. Gut Microbes 13:1968257. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/19490976.2021.1968257\u003c/span\u003e\u003cspan address=\"10.1080/19490976.2021.1968257\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSociety CD (2018) Guidelines for the prevention and control of type 2 diabetes in China (2017 Edition). Chin J Practical Intern Med 2018 38:292\u0026ndash;344\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHatano S (1976) Experience from a multicentre stroke register: a preliminary report. Bull World Health Organ 54:541\u0026ndash;553\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimon DW, Rogers MB, Gao Y, Vincent G, Firek BA, Janesko-Feldman K et al (2020) Depletion of gut microbiota is associated with improved neurologic outcome following traumatic brain injury. Brain Res 1747:147056. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.brainres.2020.147056\u003c/span\u003e\u003cspan address=\"10.1016/j.brainres.2020.147056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen R, Xu Y, Wu P, Zhou H, Lasanajak Y, Fang Y et al (2019) Transplantation of fecal microbiota rich in short chain fatty acids and butyric acid treat cerebral ischemic stroke by regulating gut microbiota. Pharmacol Res 148:104403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phrs.2019.104403\u003c/span\u003e\u003cspan address=\"10.1016/j.phrs.2019.104403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Z, Xu N, Matei N, McBride DW, Ding Y, Liang H et al (2021) Sodium butyrate attenuated neuronal apoptosis via GPR41/Gβγ/PI3K/Akt pathway after MCAO in rats. J Cereb Blood Flow Metab 41:267\u0026ndash;281. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0271678X20910533\u003c/span\u003e\u003cspan address=\"10.1177/0271678X20910533\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia JH, Wagner S, Liu KF, Hu XJ (1995) Neurological deficit and extent of neuronal necrosis attributable to middle cerebral artery occlusion in rats. Statistical validation.Stroke;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Q, Jin Z, Xu Z, Yang H, Li L, Li G et al (2019) Antioxidant effects of ginkgolides and bilobalide against cerebral ischemia injury by activating the Akt/Nrf2 pathway in vitro and in vivo. Cell Stress Chaperones 24:441\u0026ndash;452. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12192-019-00977-1\u003c/span\u003e\u003cspan address=\"10.1007/s12192-019-00977-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChai Z, Gong J, Zheng P, Zheng J (2020) Inhibition of miR-19a-3p decreases cerebral ischemia/reperfusion injury by targeting IGFBP3 in vivo and in vitro. Biol Res 53:17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40659-020-00280-9\u003c/span\u003e\u003cspan address=\"10.1186/s40659-020-00280-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Morrow C, Barnes S, Wilson L, Womack ED, McLain A et al (2021) Gut Microbiome Composition and Serum Metabolome Profile Among Individuals With Spinal Cord Injury and Normal Glucose Tolerance or Prediabetes/Type 2 Diabetes. Arch Phys Med Rehabil 702\u0026ndash;710. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apmr.2021.03.043\u003c/span\u003e\u003cspan address=\"10.1016/j.apmr.2021.03.043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan YM, Gao Y, Teo G, Koh HWL, Tai ES, Khoo CM et al (2021) Plasma Metabolome and Lipidome Associations with Type 2 Diabetes and Diabetic Nephropathy. Metabolites 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/metabo11040228\u003c/span\u003e\u003cspan address=\"10.3390/metabo11040228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin EY, Lai HJ, Cheng YK, Leong KQ, Cheng LC, Chou YC et al (2020) Neutrophil Extracellular Traps Impair Intestinal Barrier Function during Experimental Colitis. Biomedicines 8:275. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biomedicines8080275\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines8080275\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTh\u0026aring;lin C, Daleskog M, G\u0026ouml;ransson SP, Schatzberg D, Lasselin J, Laska A-C et al (2017) Validation of an enzyme-linked immunosorbent assay for the quantification of citrullinated histone H3 as a marker for neutrophil extracellular traps in human plasma. Immunol Res 65:706\u0026ndash;712. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12026-017-8905-3\u003c/span\u003e\u003cspan address=\"10.1007/s12026-017-8905-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoesen R, Claes K, Evenepoel P, de Loor H, Augustijns P, Kuypers D et al (2016) Microbiota-Derived Phenylacetylglutamine Associates with Overall Mortality and Cardiovascular Disease in Patients with CKD. J Am Soc Nephrol 27:3479\u0026ndash;3487\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzab SM, Zamzam A, Syed MH, Abdin R, Qadura M, Britz-McKibbin P (2020) Serum Metabolic Signatures of Chronic Limb-Threatening Ischemia in Patients with Peripheral Artery Disease. J Clin Med 9:1877. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jcm9061877\u003c/span\u003e\u003cspan address=\"10.3390/jcm9061877\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang H-Y, Wang C-H, Ho H-Y, Lin J-F, Lo C-J, Huang C-Y et al (2020) Characteristic of Metabolic Status in Heart Failure and Its Impact in Outcome Perspective. Metabolites 10:437. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/metabo10110437\u003c/span\u003e\u003cspan address=\"10.3390/metabo10110437\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElliott P, Posma JM, Chan Q, Garcia-Perez I, Wijeyesekera A, Bictash M et al (2015) Urinary metabolic signatures of human adiposity. Sci Transl Med 7:285ra62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/scitranslmed.aaa5680\u003c/span\u003e\u003cspan address=\"10.1126/scitranslmed.aaa5680\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnoriaga-Rodr\u0026iacute;guez M, Mayneris-Perxachs J, Burokas A, Contreras-Rodr\u0026iacute;guez O, Blasco G, Coll C et al (2020) Obesity Impairs Short-Term and Working Memory through Gut Microbial Metabolism of Aromatic Amino Acids. Cell Metab. 32:548 \u0026ndash; 60.e7 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cmet.2020.09.002\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2020.09.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWijeyesekera A, Clarke PA, Bictash M, Brown IJ, Fidock M, Ryckmans T et al (2012) Quantitative UPLC-MS/MS analysis of the gut microbial co-metabolites phenylacetylglutamine, 4-cresyl sulphate and hippurate in human urine: INTERMAP Study. Anal Methods 4:65\u0026ndash;72\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuslova TE, Sitozhevskii AV, Ogurkova ON, Kravchenko ES, Kologrivova IV, Anfinogenova Y et al (2014) Platelet hemostasis in patients with metabolic syndrome and type 2 diabetes mellitus: cGMP- and NO-dependent mechanisms in the insulin-mediated platelet aggregation. Front Physiol 5:501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphys.2014.00501\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2014.00501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Tseng J, Mak C, Poola N, Vilchez RA (2021) Exposures of Phenylacetic Acid and Phenylacetylglutamine Across Different Subpopulations and Correlation with Adverse Events. Clin Pharmacokinet 60:1557\u0026ndash;1567. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40262-021-01047-5\u003c/span\u003e\u003cspan address=\"10.1007/s40262-021-01047-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGruneck L, Kullawong N, Kespechara K, Popluechai S (2020) Gut microbiota of obese and diabetic Thai subjects and interplay with dietary habits and blood profiles. PeerJ 8:e9622. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7717/peerj.9622\u003c/span\u003e\u003cspan address=\"10.7717/peerj.9622\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Q, Ma X, Li C, Shen Y, Zhu W, Zhang Y et al (2020) Enteric Phageome Alterations in Patients With Type 2 Diabetes. Front Cell Infect Microbiol 10:575084. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcimb.2020.575084\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2020.575084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller DA, Simmonds S (1957) Phenylalanine and tyrosine metabolism in E. coli strain K-12. Science 126:445\u0026ndash;446\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeufel R, Mascaraque V, Ismail W, Voss M, Perera J, Eisenreich W et al (2010) Bacterial phenylalanine and phenylacetate catabolic pathway revealed. Proc Natl Acad Sci U S A 107:14390\u0026ndash;14395. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1005399107\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1005399107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerr\u0026aacute;ndez A, Mi\u0026ntilde;ambres B, Garc\u0026iacute;a B, Olivera ER, Luengo JM, Garc\u0026iacute;a JL et al (1998) Catabolism of phenylacetic acid in Escherichia coli. Characterization of a new aerobic hybrid pathway. J Biol Chem 273:25974\u0026ndash;25986\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou K (2017) Strategies to promote abundance of, an emerging probiotics in the gut, evidence from dietary intervention studies. J Funct Foods 33:194\u0026ndash;201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jff.2017.03.045\u003c/span\u003e\u003cspan address=\"10.1016/j.jff.2017.03.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCani PD (2019) Microbiota and metabolites in metabolic diseases. Nat Rev Endocrinol 15:69\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41574-018-0143-9\u003c/span\u003e\u003cspan address=\"10.1038/s41574-018-0143-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi JV, Ashrafian H, Sarafian M, Homola D, Rushton L, Barker G et al (2021) Roux-en-Y gastric bypass-induced bacterial perturbation contributes to altered host-bacterial co-metabolic phenotype. Microbiome 9:139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40168-021-01086-x\u003c/span\u003e\u003cspan address=\"10.1186/s40168-021-01086-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu F, Han W, Zhan G, Li S, Jiang X, Wang L et al (2019) Abnormal gut microbiota composition contributes to the development of type 2 diabetes mellitus in db/db mice. Aging 11:10454\u0026ndash;10467. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/aging.102469\u003c/span\u003e\u003cspan address=\"10.18632/aging.102469\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesai MS, Seekatz AM, Koropatkin NM, Kamada N, Hickey CA, Wolter M et al (2016) A Dietary Fiber-Deprived Gut Microbiota Degrades the Colonic Mucus Barrier and Enhances Pathogen Susceptibility. Cell 167:1339\u0026ndash;1353. \u003cdiv class=\"ExternalRefDOI\"\u003e.e21\u003c/div\u003e.. https://doi.org/10.1016/j.cell.2016.10.043\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarcher N, Nigro E, Punčoch\u0026aacute;ř M, Blanco-M\u0026iacute;guez A, Ciciani M, Manghi P et al (2021) Genomic diversity and ecology of human-associated Akkermansia species in the gut microbiome revealed by extensive metagenomic assembly. Genome Biol 22:209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-021-02427-7\u003c/span\u003e\u003cspan address=\"10.1186/s13059-021-02427-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeng X, Liu W, Chu W (2021) Identification of choline-degrading bacteria from healthy human feces and used for screening of trimethylamine (TMA)-lyase inhibitors. Microb Pathog 152:104658. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.micpath.2020.104658\u003c/span\u003e\u003cspan address=\"10.1016/j.micpath.2020.104658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuka J, Videja M, Makrecka-Kuka M, Liepins J, Grinberga S, Sevostjanovs E et al (2020) Metformin decreases bacterial trimethylamine production and trimethylamine N-oxide levels in db/db mice. Sci Rep 10:14555. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-71470-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-71470-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJameson E, Doxey AC, Airs R, Purdy KJ, Murrell JC, Chen Y (2016) Metagenomic data-mining reveals contrasting microbial populations responsible for trimethylamine formation in human gut and marine ecosystems. Microb Genom 2:e000080. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1099/mgen.0.000080\u003c/span\u003e\u003cspan address=\"10.1099/mgen.0.000080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu W, Gregory JC, Org E, Buffa JA, Gupta N, Wang Z et al (2016) Gut Microbial Metabolite TMAO Enhances Platelet Hyperreactivity and Thrombosis Risk. Cell. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2016.02.011\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2016.02.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 165:111 \u0026ndash; 24\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing L, Chang M, Guo Y, Zhang L, Xue C, Yanagita T et al (2018) Trimethylamine-N-oxide (TMAO)-induced atherosclerosis is associated with bile acid metabolism. Lipids Health Dis 17:286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12944-018-0939-6\u003c/span\u003e\u003cspan address=\"10.1186/s12944-018-0939-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu C, Xue F, Lian Y, Zhang J, Wu D, Xie N et al (2020) Relationship between elevated plasma trimethylamine N-oxide levels and increased stroke injury. Neurology 94:e667\u0026ndash;e77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/wnl.0000000000008862\u003c/span\u003e\u003cspan address=\"10.1212/wnl.0000000000008862\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Yang S, Li S, Zhao L, Hao Y, Qin J et al (2020) Aberrant gut microbiota alters host metabolome and impacts renal failure in humans and rodents. Gut 69:2131\u0026ndash;2142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/gutjnl-2019-319766\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2019-319766\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJorch SK, Kubes P (2017) An emerging role for neutrophil extracellular traps in noninfectious disease. Nat Med 23:279\u0026ndash;287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nm.4294\u003c/span\u003e\u003cspan address=\"10.1038/nm.4294\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Vries JJ, Hoppenbrouwers T, Martinez-Torres C, Majied R, \u0026Ouml;zcan B, van Hoek M et al (2020) Effects of Diabetes Mellitus on Fibrin Clot Structure and Mechanics in a Model of Acute Neutrophil Extracellular Traps (NETs) Formation. Int J Mol Sci 21:7107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms21197107\u003c/span\u003e\u003cspan address=\"10.3390/ijms21197107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Y, Zeng H, Gao C (2021) ; 2021:9931742. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2021/9931742\u003c/span\u003e\u003cspan address=\"10.1155/2021/9931742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDicker AJ, Crichton ML, Pumphrey EG, Cassidy AJ, Suarez-Cuartin G, Sibila O et al (2018) Neutrophil extracellular traps are associated with disease severity and microbiota diversity in patients with chronic obstructive pulmonary disease. J Allergy Clin Immunol 141:117\u0026ndash;127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jaci.2017.04.022\u003c/span\u003e\u003cspan address=\"10.1016/j.jaci.2017.04.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePircher J, Engelmann B, Massberg S, Schulz C (2019) Platelet-Neutrophil Crosstalk in Atherothrombosis. Thromb Haemost 119:1274\u0026ndash;1282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1055/s-0039-1692983\u003c/span\u003e\u003cspan address=\"10.1055/s-0039-1692983\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaugeri N, Campana L, Gavina M, Covino C, De Metrio M, Panciroli C et al (2014) Activated platelets present high mobility group box 1 to neutrophils, inducing autophagy and promoting the extrusion of neutrophil extracellular traps. J Thromb Haemost 12:2074\u0026ndash;2088. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jth.12710\u003c/span\u003e\u003cspan address=\"10.1111/jth.12710\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChrysanthopoulou A, Kambas K, Stakos D, Mitroulis I, Mitsios A, Vidali V et al (2017) Interferon lambda1/IL-29 and inorganic polyphosphate are novel regulators of neutrophil-driven thromboinflammation. J Pathol 243:111\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/path.4935\u003c/span\u003e\u003cspan address=\"10.1002/path.4935\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePe\u0026ntilde;a-Mart\u0026iacute;nez C, Dur\u0026aacute;n-Laforet V, Garc\u0026iacute;a-Culebras A, Ostos F, Hern\u0026aacute;ndez-Jim\u0026eacute;nez M, Bravo-Ferrer I et al (2019) Pharmacological Modulation of Neutrophil Extracellular Traps Reverses Thrombotic Stroke tPA (Tissue-Type Plasminogen Activator) Resistance. Stroke 50:3228\u0026ndash;3237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/STROKEAHA.119.026848\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.119.026848\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou P, Li T, Jin J, Liu Y, Li B, Sun Q et al (2020) Interactions between neutrophil extracellular traps and activated platelets enhance procoagulant activity in acute stroke patients with ICA occlusion. EBioMedicine 53:102671. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ebiom.2020.102671\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2020.102671\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai W, Chen X, Men X, Ruan H, Hu M, Liu S et al (2021) Gut microbiota from patients with arteriosclerotic CSVD induces higher IL-17A production in neutrophils via activating RORγt. Sci Adv 7:eabe4827. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/sciadv.abe4827\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.abe4827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinping Wei QH, Yu F, Ying Y, Luo Y, Feng X, Liao D, Zhao T, Huang Q (2022) Elevated Circulating Levels of Phenylacetylglutamine in Stroke Patients With T2D Are Linked to Specific Gut Microbiota. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.21203/rs.3.rs-1245321/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-1245321/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Jian Xia\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gut microbiota, phenylacetylglutamine, ischemic stroke, type 2 diabetes, neutrophil extracellular traps","lastPublishedDoi":"10.21203/rs.3.rs-1245321/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1245321/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eType 2 diabetes (T2D) aggravates the injury of ischemic stroke (IS). The alterations of gut microbiota and its metabolite phenylacetylglutamine (PAGln) levels in stroke patients with T2D remain unclear. Therefore, our study aimed to explore the differences in gut microbiota and its metabolite PAGln between IS patients with and without T2D.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn our study, 35 IS with T2D (IS-T2D group), 50 IS patients without T2D (IS-NT2D group), and 29 healthy controls (HC group) were recruited. Fecal samples were collected and analyzed using high-throughput sequencing of 16S rRNA genes, and plasma samples were subjected to targeted metabolomics to detect metabolite PAGln. Plasma PAGln levels of rats with transplantation of fecal microbes from patients were assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur results showed that the plasma PAGln levels in IS-T2D patients were significantly higher than those in IS-NT2D patients. Correlation analysis showed that plasma PAGln levels were significantly correlated with the relative abundance of \u003cem\u003eEnterobacteriaceae, Verrucomicrobiota\u003c/em\u003e, and \u003cem\u003eKlebsiella\u003c/em\u003e, which were enriched in IS-T2D patients. Further studies demonstrated that plasma PAGln levels were positively correlated with the concentration of neutrophil extracellular traps (NETs), and NETs levels were increased in a dose-dependent manner according to PAGln levels. Moreover, the rats transplanted with fecal microbes from IS-T2D patients developed more severe brain injury and higher plasma PAGln levels compared to the rats transplanted with fecal microbes from IS-NT2D patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur results suggest that T2D may contribute to aggravation in stroke patients via NETs, mediated in part by gut microbiota and its metabolite PAGln.\u003c/p\u003e","manuscriptTitle":"Elevated circulating levels of phenylacetylglutamine in stroke patients with T2D are linked to specific gut microbiota","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-06-22 15:07:34","doi":"10.21203/rs.3.rs-1245321/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2022-01-17 14:02:00","doi":"10.21203/rs.3.rs-1245321/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a2022429-1f99-457d-88a6-a87089322c87","owner":[],"postedDate":"June 22nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-06-24T21:31:12+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-22 15:07:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-1245321","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1245321","identity":"rs-1245321","version":["v2"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}