Edited by: Shuo Wang, Capital Medical University, China
Reviewed by: Ewa Maria, Polish Academy of Sciences, Poland; Christoph Reinhardt, Johannes Gutenberg University Mainz, Germany
†These authors have contributed equally to this work and share first authorship
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Stroke is a major cause of disability and death in China (Wu et al.,
Recently, altered circulating metabolites have been identified as contributing factors in stroke and cerebral small vessel disease (CSVD) (Nie et al.,
However, the relationship between circulating PAGln and WMH burden in ischemic stroke patients is unknown. Therefore, to enhance our knowledge of the predictive role of PAGln in WMH impairment, we prospectively investigated the relationship between circulating PAGln and WMH impairment in patients with ischemic stroke. This study represents the first cross-sectional study examining whether plasma PAGln levels are associated with WMH burden in ischemic stroke patients.
This study included consecutive patients with ischemic stroke confirmed between August 2017 and October 2020. We recruited 595 patients with ischemic stroke confirmed by diffusion-weighted imaging of the brain within 14 days of symptom onset. The other inclusion criterion was age ≥18 years. We excluded patients with disabilities (Modified Rankin Scale score ≥2) before stroke onset and those without fluid-attenuated inversion recovery sequence (FLAIR). This study was approved by the Ethics Committee of Xiangya Hospital. All participants provided written informed consent.
We assessed demographic characteristics and medical history, including age, sex, vascular risk factors [i.e., hypertension, diabetes mellitus, dyslipidemia, coronary heart disease (CAD), smoking, and drinking], based on the definitions previously described in detail (Feng et al.,
Periventricular WMH (P-WMH) and deep WMH (D-WMH) were assessed on FLAIR images using the Fazekas scale, which ranges from 0 to 3. We categorized the severity of P-WMH and D-WMH as none–mild (Fazekas score 0–1) or moderate–severe (Fazekas score 2–3) (Yu et al.,
Overnight fasting venous blood samples were collected as soon as possible on the second day of admission. The whole blood sample was centrifuged into plasma and stored at −80°C until analysis. Plasma PAGln was quantified on an AB SCIEX TripleTOF 6500 system (AB SCIEX, Foster City, CA, USA) using liquid chromatography-mass spectrometry with D5-PAGln (CDN Isotopes, Cat # D-6900) as an internal standard. First, plasma was diluted 10-fold with ddH2O, then 2 μl of 1 ppm D5-PAGln was added to 48 μl of diluted plasma, and the mixture was diluted 4-fold with ice-cold methanol and vortexed for 1 min. The supernatant was then centrifuged at 21,000 ×
We used SPSS 22.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism 8 (GraphPad Software, San Diego, CA, USA) for the statistical analysis. The participants were dichotomized according to WMH burden into none–mild and moderate–severe groups using the Fazekas scores. In addition, participants were divided into four groups according to the quartiles of plasma PAGln concentrations. Categorical variables were described as proportions, and continuous variables were described as mean ± SD or medians [interquartile range (IQR)]. Continuous variables were compared using an ANOVA, Kruskal–Wallis test, or Mann–Whitney
A total of 595 patients (67.7% male; median age, 61 years) with ischemic stroke were enrolled in our study. The median plasma PAGln level at admission was 2.06 μmol/L. Quartiles of PAGln levels were as follows: first quartile, <1.21 μmol/L; second quartile, 1.21–2.06 μmol/L; third quartile, >2.06–3.34 μmol/L; fourth quartile, >3.34 μmol/L. Higher PAGln quartiles were associated with high Fazekas scores, old age, high frequency of hypertension, diabetes mellitus, CAD, high levels of blood urea nitrogen and homocysteine, and low levels of estimated glomerular filtration rate (eGFR) (
Baseline characteristics of patients with ischemic stroke according to PAGln quartiles.
Fazekas score | 2.0 (1.5–4.0) | 3.0 (2.0–4.0) | 2.0 (2.0–4.0) | 4.0 (2.0–5.0) | <0.001 |
Age (years) | 54 (48–62) | 60 (51–66) | 63 (54–70) | 67 (60–72) | <0.001 |
Sex (male, |
95 (63.8%) | 106 (71.1%) | 103 (69.1%) | 99 (66.9%) | 0.562 |
HBP ( |
93 (62.4%) | 113 (75.8%) | 99 (66.4%) | 119 (80.4%) | 0.002 |
DM ( |
26 (17.4%) | 47 (31.5%) | 45 (30.2%) | 54 (36.5%) | 0.003 |
Hyperlipidemia ( |
46 (30.9%) | 42 (28.2%) | 40 (26.8%) | 45 (30.4%) | 0.855 |
CAD ( |
19 (12.8%) | 15 (10.1%) | 29 (19.5%) | 39 (26.4%) | <0.001 |
Smoking ( |
68 (45.6%) | 81 (54.4%) | 71 (47.7%) | 64 (43.2%) | 0.253 |
Drinking ( |
48 (32.2%) | 58 (38.9%) | 59 (39.6%) | 48 (32.4%) | 0.372 |
SBP (mmHg) | 140.0 (125.0–154.0) | 144.0 (129.0–157.0) | 143.0 (130.0–156.0) | 142.5 (134.8–158.5) | 0.135 |
DBP (mmHg) | 84.0 (74.0–93.0) | 82.0 (74.0–92.0) | 82.0 (74.0–90.0) | 81.0 (72.0–91.2) | 0.632 |
BMI | 23.5 (22.0–25.1) | 23.6 (21.9–25.9) | 22.9 (21.5–25.7) | 23.0 (21.0–25.7) | 0.678 |
White blood cell count (× 109/L) | 6.2 (5.2–7.9) | 6.7 (5.7–8.2) | 6.5 (5.5–8.1) | 7.0 (5.8–8.1) | 0.064 |
Platelet (× 109/L) | 208.0 (164.0–251.0) | 209.0 (167.0–240.0) | 196.0 (162.0–246.0) | 203.5 (167.8–235.0) | 0.615 |
BUN (mmol/L) | 4.6 (3.9–5.6) | 4.9 (4.1–6.0) | 5.2 (4.1–6.2) | 5.6 (4.6–7.2) | <0.001 |
eGFR (ml/min/1.73 m2) | 89.4 (76.7–102.6) | 86.6 (74.6–96.3) | 81.5 (69.7–92.5) | 72.7 (57.8–89.1) | <0.001 |
Uric acid (μmol/L) | 335.9 (96.4) | 341.1 (270.6–385.4) | 307.3 (245.0–381.8) | 332.3 (273.2–387.4) | 0.175 |
TC (mmol/L) | 3.9 (3.3–5.0) | 4.4 (3.6–5.2) | 4.2 (3.5–5.0) | 4.3 (3.5–5.2) | 0.110 |
TG (mmol/L) | 1.5 (1.0–2.2) | 1.6 (1.1–2.3) | 1.5 (1.1–2.0) | 1.5 (1.2–2.3) | 0.525 |
HDL (mmol/L) | 1.0 (0.8–1.2) | 1.0 (0.8–1.2) | 1.0 (0.9–1.2) | 1.0 (0.9–1.1) | 0.757 |
LDL (mmol/L) | 2.4 (1.9–3.1) | 2.6 (2.1–3.2) | 2.6 (2.1–3.3) | 2.7 (2.2–3.3) | 0.078 |
Fasting blood-glucose (mmol/L) | 5.4 (4.8–6.3) | 5.8 (5.0–7.4) | 5.9 (5.1–7.7) | 5.7 (5.1–8.1) | 0.065 |
HbA1c (%) | 5.7(5.4–6.3) | 5.9 (5.5–6.9) | 5.9 (5.5–7.4) | 6.0 (5.5–7.3) | 0.017 |
Homocysteine (μmol/L) | 12.3 (10.6–14.6) | 13.4 (11.5–16.7) | 13.2 (11.3–15.6) | 14.7 (11.9–19.3) | <0.001 |
There were 283 patients with none–mild overall WMH (total Fazekas score 0–2) and 312 patients with moderate–severe overall WMH (total Fazekas score 3–6). When compared with patients with none–mild WMH, patients with moderate–severe WMH were older and had a higher frequency of hypertension, diabetes mellitus, CAD, and higher levels of blood urea nitrogen and homocysteine. Lower levels of platelet count, eGFR, and total cholesterol were observed in moderate–severe WMH subjects (
Baseline characteristics of all patients according to the degree of overall WMH.
Age (years) | 55 (49–63) | 66 (59–72) | <0.001 |
Sex (male, |
200 (70.7%) | 203 (65.1%) | 0.144 |
HBP ( |
176 (62.2%) | 248 (79.5%) | <0.001 |
DM ( |
70 (24.7%) | 102 (32.7%) | 0.032 |
Hyperlipidemia ( |
83 (29.3%) | 90 (28.8%) | 0.897 |
CAD ( |
33 (11.7%) | 69 (22.1%) | <0.001 |
Smoking ( |
143 (50.5%) | 141 (45.2%) | 0.193 |
Drinking ( |
101 (35.7%) | 112 (35.9%) | 0.958 |
SBP (mmHg) | 142.0 (127.5–154.0) | 143.0 (130.0–159.2) | 0.086 |
DBP (mmHg) | 83.0 (74.0–93.0) | 82.0 (73.0–91.0) | 0.391 |
BMI | 23.4 (22.0–25.2) | 23.3 (20.9–25.8) | 0.652 |
PAGln (μmol/L) | 1.8 (1.0–2.8) | 2.3 (1.5–3.8) | <0.001 |
White blood cell count ( ×109/L) | 6.7 (5.4–8.2) | 6.6 (5.6–8.0) | 0.946 |
Platelet ( ×109/L) | 208.0 (171.0–250.5) | 199.0 (162.8–236.2) | 0.043 |
BUN (mmol/L) | 4.9 (3.9–6.0) | 5.2 (4.2–6.3) | 0.043 |
eGFR (ml/min/1.73 m2) | 88.5 (74.5–98.6) | 78.6 (64.5–90.2) | <0.001 |
Uric acid (μmol/L) | 315.8 (272.9–380.1) | 331.4 (268.3–391.9) | 0.598 |
TC (mmol/L) | 4.2 (3.4–5.2) | 4.2 (3.5–4.9) | 0.234 |
TG (mmol/L) | 1.6 (1.2–2.2) | 1.5 (1.0–2.2) | 0.035 |
HDL (mmol/L) | 1.0 (0.8–1.2) | 1.0 (0.9–1.2) | 0.318 |
LDL (mmol/L) | 2.6 (2.1–3.4) | 2.6 (2.0–3.1) | 0.231 |
Fasting blood–glucose (mmol/L) | 5.6 (5.0–7.1) | 5.6 (5.0–7.7) | 0.702 |
HbA1c (%) | 5.8 (5.4–6.7) | 5.9 (5.5–7.0) | 0.100 |
Homocysteine (μmol/L) | 12.7 (10.8–15.2) | 13.8 (11.4–17.7) | 0.002 |
Plasma PAGln levels in different groups according to the severity of WMH.
Correlation between PAGln levels and Fazekas score. PAGln levels showed a significant, although limited, relationship with the total Fazekas score (
The results of the logistic regression analyses are shown in
Logistic regression analyses of the association between PAGln levels and overall WMH.
PAGln levels | ||||
First quartile | Reference | |||
Second quartile | 0.001 | 2.212 | 1.390 | 3.522 |
Third quartile | 0.061 | 1.559 | 0.980 | 2.479 |
Fourth quartile | <0.001 | 4.296 | 2.639 | 6.994 |
PAGln levels | ||||
First quartile | Reference | |||
Second quartile | 0.020 | 1.818 | 1.098 | 3.010 |
Third quartile | 0.755 | 0.921 | 0.549 | 1.544 |
Fourth quartile | 0.009 | 2.053 | 1.195 | 3.528 |
Age (years) | <0.001 | 1.080 | 1.059 | 1.100 |
Sex (male) | 0.781 | 0.946 | 0.642 | 1.396 |
PAGln levels | ||||
First quartile | Reference | |||
Second quartile | 0.085 | 1.670 | 0.932 | 2.994 |
Third quartile | 0.963 | 0.986 | 0.547 | 1.778 |
Fourth quartile | 0.019 | 2.134 | 1.134 | 4.018 |
Age (years) | <0.001 | 1.065 | 1.041 | 1.089 |
Sex (male vs. female) | 0.324 | 0.776 | 0.469 | 1.285 |
HBP | 0.399 | 1.235 | 0.757 | 2.015 |
DM | 0.804 | 1.060 | 0.668 | 1.685 |
CAD | 0.799 | 1.077 | 0.610 | 1.900 |
Platelet >204 ×109 /L | 0.195 | 0.755 | 0.494 | 1.155 |
eGFR ≤ 83.85 mL/min/1.73 m2 | 0.125 | 1.421 | 0.908 | 2.224 |
TG >1.52 mmol/L | 0.262 | 0.785 | 0.514 | 1.198 |
Homocysteine >13.28 μmol/L | 0.181 | 1.359 | 0.867 | 2.129 |
To further explore the relationship between plasma PAGln and different areas of WMH burden, we divided all patients into a P-WMH group and a D-WMH group. We categorized the severity of P-WMH and D-WMH as none–mild (Fazekas score 0–1) and moderate–severe (Fazekas score 2–3), respectively. There were 304 patients with none–mild P-WMH and 291 patients with moderate–severe P-WMH. Compared with patients with none-mild P-WMH, patients with moderate–severe P-WMH were older and had a higher frequency of hypertension, diabetes mellitus, and CAD, higher levels of blood urea nitrogen and homocysteine, and lower levels of eGFR and triglycerides. When classified by D-WMH, 348 and 247 patients were in the none–mild and moderate–severe D-WMH groups, respectively. Patients with moderate–severe D-WMH were more likely to have hypertension and CAD, higher systolic blood pressure, higher levels of blood urea nitrogen, high-density lipoprotein, and homocysteine, and lower levels of eGFR (
Characteristics of patients according to the scales of P-WMH and D-WMH.
Age (years) | 55.0 (49.0–63.0) | 67.0 (60.0–72.0) | <0.001 | 57.0 (50.0–64.0) | 67.0 (59.0–72.0) | <0.001 |
Sex (male, |
87 (28.6%) | 105 (36.1%) | 0.052 | 244 (70.1%) | 159 (64.4%) | 0.140 |
HBP ( |
191 (62.8%) | 233 (80.1%) | <0.001 | 223 (64.1%) | 201 (81.4%) | <0.001 |
DM ( |
73 (24.0%) | 99 (34.0%) | 0.007 | 98 (28.2%) | 74 (30.0%) | 0.633 |
Hyperlipidemia ( |
88 (28.9%) | 85 (29.2%) | 0.944 | 95 (27.3%) | 78 (31.6%) | 0.257 |
CAD ( |
37 (12.2%) | 65 (22.3%) | 0.001 | 44 (12.6%) | 58 (23.5%) | <0.001 |
Smoking ( |
153 (50.3%) | 131 (45.0%) | 0.195 | 169 (48.6%) | 115 (46.6%) | 0.630 |
Drinking ( |
108 (35.5%) | 105 (36.1%) | 0.888 | 124 (35.6%) | 89 (36.0%) | 0.920 |
SBP (mmHg) | 142.0 (127.0–155.0) | 143.0 (130.0–158.5) | 0.096 | 141.0 (129.0–154.0) | 145.0 (130.0–161.0) | 0.017 |
DBP (mmHg) | 83.0 (74.0–94.0) | 82.0 (73.0–90.0) | 0.190 | 83.0 (74.0–92.0) | 82.0 (73.0–91.5) | 0.613 |
BMI | 23.6 (22.0–25.2) | 23.0 (20.8–25.8) | 0.279 | 23.3 (21.9–25.6) | 23.4 (20.9–25.7) | 0.720 |
PAGln (μmmol/L) | 1.8 (1.0–2.7) | 2.4 (1.5–3.8) | <0.001 | 1.9 (1.1–2.9) | 2.4 (1.5–3.8) | <0.001 |
White blood cell count ( ×109 /L) | 6.7 (5.4–8.2) | 6.7 (5.6–8.0) | 0.884 | 6.7 (5.5–8.1) | 6.7 (5.5–8.0) | 0.904 |
Platelet ( ×109/L) | 207.0 (167.8–249.0) | 199.0 (163.0–239.5) | 0.113 | 207.0 (167.8–249.0) | 199.0 (163.0–239.5) | 0.113 |
BUN (mmol/L) | 4.9 (3.9–5.9) | 5.2 (4.2–6.4) | 0.009 | 5.0 (3.9–6.0) | 5.1 (4.2–6.3) | 0.033 |
eGFR (ml/min/1.73 m2) | 88.5 (74.0–98.7) | 77.7 (64.2–89.1) | <0.001 | 88.4 (74.6–98.2) | 76.7 (62.2–88.2) | <0.001 |
Uric acid (μmol/L) | 318.6 (273.1–385.7) | 328.1 (267.1–385.8) | 0.799 | 315.0 (272.3–379.1) | 339.8 (271.2–393.2) | 0.204 |
TC (mmol/L) | 4.2 (3.4–5.2) | 4.2 (3.4–4.9) | 0.143 | 4.2 (3.4–5.2) | 4.3 (3.5–5.0) | 0.415 |
TG (mmol/L) | 1.6 (1.2–2.2) | 1.5 (1.0–2.2) | 0.045 | 1.6 (1.1–2.2) | 1.5 (1.0–2.3) | 0.154 |
HDL (mmol/L) | 1.0 (0.8–1.2) | 1.0 (0.9–1.2) | 0.669 | 1.0 (0.8–1.1) | 1.0 (0.9–1.2) | 0.004 |
LDL (mmol/L) | 2.6 (2.1–3.4) | 2.6 (2.0–3.1) | 0.123 | 2.6 (2.0–3.3) | 2.6 (2.1–3.2) | 0.565 |
Fasting blood–glucose (mmol/L) | 5.6 (4.9–6.9) | 5.7 (5.0–7.7) | 0.213 | 5.6 (5.0–7.4) | 5.7 (5.0–7.3) | 0.882 |
HbA1c (%) | 5.8 (5.4–6.5) | 6.0 (5.5–7.1) | 0.314 | 5.8 (5.4–7.2) | 5.9 (5.6–6.8) | 0.647 |
Homocysteine (μmol/L) | 12.7 (10.8–15.1) | 14.0 (11.4–17.8) | <0.001 | 12.8 (10.8–15.5) | 14.0 (11.6–18.3) | 0.001 |
Levels of PAGln in the P-WMH and D-WMH groups are shown in
Logistic regression analyses of the association between PAGln levels and P-WMH and D-WMH.
PAGln levels | PAGln levels | ||||||||
First quartile | Reference | First quartile | Reference | ||||||
Second quartile | 0.003 | 2.021 | 1.263 | 3.235 | Second quartile | 0.022 | 1.748 | 1.083 | 2.823 |
Third quartile | 0.024 | 1.719 | 1.073 | 2.755 | Third quartile | 0.177 | 1.396 | 0.860 | 2.266 |
Fourth quartile | <0.001 | 4.816 | 2.9491 | 7.867 | Fourth quartile | <0.001 | 3.220 | 1.993 | 5.201 |
PAGln levels | PAGln levels | ||||||||
First quartile | Reference | First quartile | Reference | ||||||
Second quartile | 0.062 | 1.637 | 0.976 | 2.747 | Second quartile | 0.171 | 1.428 | 0.858 | 2.379 |
Third quartile | 0.983 | 0.994 | 0.585 | 1.689 | Third quartile | 0.629 | 0.878 | 0.518 | 1.488 |
Fourth quartile | 0.004 | 2.247 | 1.297 | 3.890 | Fourth quartile | 0.065 | 1.648 | 0.970 | 2.799 |
Age (years) | <0.001 | 1.088 | 1.067 | 1.110 | Age (years) | <0.001 | 1.068 | 1.049 | 1.088 |
Sex (male) | 0.494 | 0.872 | 0.588 | 1.291 | Sex (male) | 0.684 | 0.924 | 0.633 | 1.349 |
PAGln levels | PAGln levels | ||||||||
First quartile | Reference | First quartile | Reference | ||||||
Second quartile | 0.286 | 1.383 | 0.762 | 2.511 | Second quartile | 0.475 | 1.243 | 0.685 | 2.255 |
Third quartile | 0.859 | 1.056 | 0.577 | 1.933 | Third quartile | 0.730 | 0.899 | 0.489 | 1.651 |
Fourth quartile | 0.014 | 2.227 | 1.174 | 4.226 | Fourth quartile | 0.057 | 1.819 | 0.981 | 3.372 |
Age (years) | <0.001 | 1.077 | 1.051 | 1.103 | Age (years) | <0.001 | 1.054 | 1.031 | 1.078 |
Sex (male vs. female) | 0.377 | 0.796 | 0.480 | 1.321 | Sex (male vs. female) | 0.750 | 1.082 | 0.665 | 1.761 |
HBP | 0.377 | 1.255 | 0.759 | 2.075 | HBP | 0.076 | 1.609 | 0.951 | 2.723 |
DM | 0.414 | 1.215 | 0.761 | 1.939 | CAD | 0.724 | 1.103 | 0.640 | 1.901 |
CAD | 0.974 | 0.990 | 0.561 | 1.749 | SBP >142 mmHg | 0.692 | 1.092 | 0.708 | 1.684 |
BUN >5.01 mmol/L | 0.899 | 0.972 | 0.623 | 1.515 | BUN >5.01 mmol/L | 0.307 | 0.795 | 0.512 | 1.235 |
eGFR ≤ 83.85mL/min/1.73 m2 | 0.305 | 1.277 | 0.800 | 2.038 | eGFR ≤ 83.85mL/min/1.73 m2 | 0.051 | 1.584 | 0.998 | 2.513 |
TG >1.52 mmol/L | 0.154 | 0.733 | 0.479 | 1.123 | HDL >1.00 mmol/L | 0.006 | 1.847 | 1.196 | 2.853 |
Homocysteine >13.28 μmol/L | 0.119 | 1.437 | 0.910 | 2.268 | Homocysteine >13.28 μmol/L | 0.260 | 1.303 | 0.822 | 2.067 |
The diagnostic value of PAGln in distinguishing ischemic stroke patients according to WMH burden was evaluated using the ROC analysis. The AUCs for overall WMH, P-WMH, and D-WMH were 0.616, 0.635, and 0.579 (
Receiver operating characteristic analysis of PAGln according to the severity of WMH.
In this study, we conducted a targeted metabolomic analysis to explore the association between PAGln levels and WMH in patients with ischemic stroke. Our results demonstrated that plasma PAGln levels at admission were associated with the severity of WMH in patients with ischemic stroke. After adjusting for age, sex, and confounding factors, higher PAGln levels were independently associated with moderate–severe overall WMH. These associations were also found with P-WMH but not with D-WMH.
The pathophysiology of WMH remains unclear. Traditional vascular risk factors such as age, hypertension, diabetes mellitus, and smoking may play crucial roles in the pathological process of WMH and SVD (Rost et al.,
Gut microbiota can produce metabolites or toxins that influence the health of the host. Gut microbiota-derived metabolites, such as trimethylamine-
Phenylacetylglutamine, another gut microbial metabolite, has been reported to correlate with chronic kidney disease, diabetes mellitus, cardiovascular disease, and Parkinson's disease (Poesen et al.,
In this study, WMH was divided into P-WMH and D-WMH. A limited number of studies have investigated the differences between P-WMH and D-WMH; however, the underlying mechanism has not yet been fully elucidated. Our results suggest the involvement of PAGln in the development of P-WMH, but not D-WMH, and the detailed mechanisms require further investigation. Previous pathology studies have shown that P-WMH is more likely to be associated with inflammation and chronic hypoperfusion, whereas D-WMH is related to ischemic damage (Fazekas et al.,
There were some limitations to this study. First, this was a cross-sectional study, so we could not establish a causal relationship between PAGln and WMH. Second, participants in our study were recruited from a single center, and this could have led to patient selection bias. Third, PAGln levels were only analyzed at a single time point, and information on dynamic changes in PAGln was missing. Fourth, investigations of the gut microbiota were lacking in this study. Finally, we used a less precise visual rating scale to assess the degree of WMH. Quantification of WMH is needed to further investigate the relationship between PAGln and WMH volume.
In conclusion, higher plasma PAGln levels might be a biomarker of moderate–severe WMH, especially moderate–severe P-WMH. Further studies concerning the cause–effect relationship between PAGln and WMH are needed.
The datasets generated for this study are available on request to the corresponding author.
The studies involving human participants were reviewed and approved by Xiangya Hospital Ethics Committee. The patients/participants provided their written informed consent to participate in this study.
FY and XF: methodology and writing—original draft preparation. XL, YL, MW, and TZ: investigation and data curation. JX: conceptualization and writing—reviewing and editing. All authors contributed to the article and approved the submitted version.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
We thank Zhimin Long, Haihong Zha, and Mengqin Xiao from SCIEX, Analytical Instrument Trading Co., Ltd, Shanghai, China for their help in sample testing and analysis of metabolomics data. We also thank Editage (