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. 2023 Jun 13;2023:2590115. doi: 10.1155/2023/2590115

Association between Changes in Plasma Metabolism and Clinical Outcomes of Sepsis

Xin Li 1, Zhongnan Yin 2,3, Wei Yan 1, Meng Wang 1, Chun Chang 1, Chenglin Guo 1, Lixiang Xue 2,3, Qingtao Zhou 1,✉, Yongchang Sun 1,✉
PMCID: PMC10281824  PMID: 37346225

Abstract

Current prognostic biomarkers for sepsis have limited sensitivity and specificity. This study aimed to investigate dynamic lipid metabolomics and their association with septic immune response and clinical outcomes of sepsis. This prospective cohort study included patients with sepsis who met the Sepsis 3.0 criteria. On hospitalization days 1 (D1) and 7 (D7), plasma samples were collected, and patients underwent liquid chromatography with tandem mass spectrometry. A total of 40 patients were enrolled in the study, 24 (60%) of whom were men. The median age of the enrolled patients was 81 (68–84) years. Thirty-one (77.5%) patients had a primary infection site of the lung. Participants were allocated to the survivor (25 cases) and nonsurvivor (15 cases) groups based on their 28-day survival status. Ultimately, a total of 113 lipids were detected in plasma samples on D 1 and D 7, of which 42 lipids were most abundant in plasma samples. The nonsurvival group had significantly lower lipid expression levels in lysophosphatidylcholine (LysoPC) (16 : 0, 17 : 0,18 : 0) and 18 : 1 SM than those in the survival group (p <  0.05) on D7–D1. The correlation analysis showed that D7–D1 16 : 0 LysoPC (r = 0.367, p = 0.036),17 : 0 LysoPC (r = 0.389, p = 0.025) and 18 : 0 LysoPC(r = 0.472, p = 0.006) levels were positively correlated with the percentage of CD3+ T cell in the D7–D1. Plasma LysoPC and SM changes may serve as prognostic biomarkers for sepsis, and lipid metabolism may play a role in septic immune disturbances.

1. Introduction

Sepsis is an infection-induced systemic inflammatory response involving multiple mediators and cytokines. It is a commonly encountered life-threatening condition in intensive care units (ICUs) worldwide [1]. Despite medical advances, sepsis remains a serious global health problem with an estimated 48.9 million cases and 11 million deaths annually [2]. Martin et al. [3] found that over a period of 24 years (1979–2002), the rate of increase in sepsis incidence was 20.4% higher in those aged ≥65 years than that in their counterparts. The sepsis risk in 65-year-old individuals grew by an average of 9.5% per year over 24 years, and the incidence rate for people aged 65 years and older increased by 11.5% annually. Currently, the Acute Physiology and Chronic Health Evaluation (APACHE) II score is commonly used in the ICU to assess the severity of hospitalized patients [4]. However, this method has some limitations and fails to reflect prognosis in patients whose disease peak is not observed within the first 24 hours after admission.

Research interest in the association between lipid metabolism and sepsis is increasing [5, 6]. Recent studies suggest that poor prognosis is related to increased free fatty acid levels, changes in polyunsaturated fatty acid metabolism [7], decreased lysophosphatidylcholine (LysoPC) levels [8], and elevated plasma ceramide levels [9]. In addition, Park et al. [10] conducted a serum lipid analysis of 74 ICU patients and found that LysoPC levels in the survival group were higher than those in the mortality group and that LysoPC monitoring helped predict the 28-day mortality rate of patients with sepsis and septic shock. These studies suggest that lipidomic analysis may help develop prognostic markers for sepsis.

To further examine the role of lipid metabolism in sepsis, this study aimed to investigate dynamic lipid metabolomics and their association with T cells and the septic immune response and clinical outcomes of sepsis.

2. Materials and Methods

2.1. Study Design and Period

This prospective cohort study was conducted at the medical ICU (MICU) of a university-affiliated urban teaching hospital from December 2019 to February 2022. The study protocol was approved by the medical science research ethics committee of our hospital (approval no. M2019396). All patients or their legally authorized representatives provided written informed consent to participate in the study.

2.2. Study Population

A total of 40 patients with sepsis admitted to the MICU were included and subsequently divided into the survivor and nonsurvivor groups based on their 28-day survival status. The inclusion criteria were as follows: (1) patients who met the Sepsis 3.0 diagnostic criteria [11] and (2) were aged ≥18 years. The exclusion criteria were as follows: (1) pregnancy, (2) human immunodeficiency virus or tuberculosis infection, and (3) a “do-not-resuscitate” or discontinuation of tracheal intubation order.

2.3. Clinical and Laboratory Data

Data on the following variables were collected: age, sex, body weight, height, body mass index (BMI), days of mechanical ventilation, and length of stay at discharge or death. Finally, plasma (4 mL) samples were collected on day 1 (D1) and day 7 (D7) of hospitalization. The collected samples were aliquoted into multiple tubes to avoid repeated thawing and freezing. Samples were stored in a −80°C freezer until use.

2.4. Flow Cytometry

Whole blood was collected directly into anticoagulant tubes and centrifuged at 1,800 × g for 10 min. The cells were treated with ammonium-chloride-potassium lysing buffer and stained with fluorescence-labeled antibodies for 15 min in 1 × phosphate-buffered saline. All experiments were performed using the Beckman CytoFLEX S flow cytometer (Beckman Coulter, Brea, CA, USA). Kaluza Analysis 2.1 software was used for data analysis. The following antibodies were used in this experiment: CD45-allophycocyanin (clone HI30), CD3-fluorescein-5-isothiocyanate (clone HIT3a), CD4-phycoerythrin (PE) (clone RPA-T4), CD8-peridinin chlorophyll protein/cyanine 5.5 (clone SK1), and CD8-PE/Dazzle™ (clone SK1). All antibodies were purchased from BioLegend (San Diego, CA, USA).

2.5. Lipid Analysis

Ammonium acetate (purity ≥99%) and formic acid (purity ≥98%) were purchased from Fluka (Charlotte, NC, USA); acetonitrile, water and isopropyl alcohol from Thermo Fisher Scientific (Waltham, MA, USA); and saline from Dubang Pharmaceuticals Co., Ltd. (Changchun, China).

Samples stored at −80°C were thawed in a 4°C refrigerator. Subsequently, a 10 µL sample aliquot was sequentially supplemented with 10 µL of an internal standard mix solution, 10 µL of water, and 100 µL of chloroform: methanol (2 : 1, v/v) extracting solution. The resulting mixture was vortexed for 20 s, placed in a 4°C refrigerator for 30 min, and then centrifuged at 7,800 × g for 3 min. Subsequently, the supernatant was collected using a 1-mL syringe and placed into a 0.5-mL EP tube. Thereafter, 40 µL of the supernatant was immediately placed into a clean EP tube and evaporated until dry via nitrogen gas blowdown. The tube was sealed and stored in a −20°C freezer until use. Before injection, the sample was dissolved in 50 µL of acetonitrile and isopropyl alcohol (1 : 1, v/v) and vortexed for 60 s.

All samples underwent liquid chromatography (LC) analysis using an Ekspert UltraLC 100 System (Eksigent Technologies, Silicon Valley, CA, USA), followed by mass spectrometry analysis using an AB SCIEX TripleTOF 5600 System (Eksigent Technologies, Silicon Valley, CA, USA). The standard was 19 : 0 LysoPC, 19 : 0 phosphatidylcholine (PC), 17 : 0 phosphatidylethanolamine (PE), 12 : 0 SM, and 19 : 0 ceramide. The optimized mass spectrometer settings were as follows: gas, 50 psi; gas 2, 50 psi; curtain gas, 30 psi; ion source temperature, 550°C; ion spray voltage floating, −4,500 V; and mass range, 50−1,200 Da. An acquisition method was established in the negative ion mode to ensure the accurate identification of each analyte. The LC conditions were as follows: chromatography column, Waters (Milford, MA, USA) XBridge Peptide BEH C18 (2.1 × 100 mm, 3.5 μm; Waters); mobile phase: A, 10 mM ammonium acetate + 0.1% formic acid + 99.9% water; mobile phase: B, 10 mM ammonium acetate + 0.1% formic acid + 49.95% acetonitrile + 49.95% isopropyl alcohol; operating temperature, 40°C; flow rate, 0.4 mL/min; and run time, 20 min. The gradient program for the initial solvent B was as follows: 0.01–2.00 min, 35–80% B; 2.0–9.0 min, 80–100% B; 9.0–15.0 min, 100% B; 15.0–16.0 min, 100−35% B (hold until 20.0 min for the next run; and injection volume, 2 μL. Regarding the lipid standard database, the PeakView v.1.2 software (SCIEX, Framingham, MA, USA) was used to characterize the sample lipids, and MultiQuant v.2.1 (SCIEX) was used to quantify qualitative lipids.

2.6. Statistical Analyses

Clinical data were compared between the survivor and nonsurvivor groups. SPSS software v.21.0 (IBM Corp., Armonk, NY, USA) was used for statistical analysis. Continuous variables are expressed as medians (range), while categorical variables are expressed as frequencies (%). Continuous variables were compared using the nonparametric Mann–Whitney U-test, and categorical variables were compared using the χ2 test. The degree of correlation was expressed using Pearson's correlation coefficient “r.” A p  <  0.05 indicated statistical significance.

3. Results

3.1. Patient Characteristics

We included 40 patients with sepsis, 24 (60%) of whom were men. Participants were allocated to the survivor (n = 25) and nonsurvivor (n = 15) groups based on their 28-day survival status. The median age of the enrolled patients was 81 (68–84) years. Thirty-one (77.5%) patients had a pulmonary infection. Table 1 shows the detailed characteristics of the participants.

Table 1.

Clinical characteristics of the participants.

Variable Survivors (n = 25) Nonsurvivors (n = 15) p-value
Age, years 78 (52–84) 83 (76–85) 0.105
Men 12 (48) 12 (80) 0.046∗
BMI (kg/m2) 25 (20–29) 22 (19–28) 0.224
Primary site of infection
 Lung 20 (80) 11 (73.3) 0.922
 Other site 5(20) 4 (27) 0.922
Comorbidities
 COPD 1 (4) 1 (6.67) 1.000
 DM 9 (36) 3 (20) 0.476
 Hypertension 13 (52) 10 (66.7) 0.364
 Cerebrovascular disease 12 (48) 6 (40) 0.622
ARDS 13 (52) 10 (67) 0.364
Duration of MV (days) 6 (2–10) 7 (2–14) 0.594
Length of hospital stay (days) 17 (12–25) 15 (6–20) 0.088
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Values are expressed as n (%) or median (25%–75%) unless otherwise stated. BMI, body mass index; COPD, chronic obstructive pulmonary disease; DM, diabetes mellitus; ARDS, acute respiratory distress syndrome; MV, mechanical ventilation.

3.2. Lipidomics Analysis

In total, 113 lipids were detected in plasma samples on day 1 and day 7, of which 42 lipids were most abundant in plasma samples (Table S1 shows Rt and m/z of 42 kinds of lipids). Table 2 shows all lipid results in D1, D7, and D7–D1 survival and nonsurvival groups. We found that the difference in lipid concentration between groups was significant for D7-D1. Figure 1 shows the scatter plot of D7-D1(changes between D7 and D1) differential metabolites between the survival and nonsurvival groups.

Table 2.

Plasma lipid data of survivor and nonsurvivor groups.

Variable (nM) D1 D7 D7-D1
Survivors (n = 25) Nonsurvivors (n = 15) p-value Survivors (n = 25) Nonsurvivors (n = 8) p-value Survivors (n = 25) Nonsurvivors (n = 8) p-value
16 : 0 LysoPC 47645.79 (31495.28–89381.93) 54143.53 (24175.21–110959.19) 0.900 117402.86 (76388.19–153896.00) 38892.97 (26863.80–109655.06) 0.017∗ 45344.39 (22450.94–88171.33) 5273.77 (−24878.11–57746.44) 0.048∗
14 : 0 LysoPC 562.61 (443.30–625.22) 544.81 (387.80–674.52) 0.727 854.10 (691.57–1058.04) 731.14 (515.61–927.74) 0.193 292.74 (98.52–512.49) 89.55 (0.80–163.51) 0.153
15 : 0 LysoPC 566.46 (442.74–668.70) 594.59 (412.87–720.05) 0.769 889.98 (635.55–985.57) 716.97 (553.37–812.88) 0.153 206.08 (75.76–464.47) 83.36 (−14.53–159.54) 0.110
17 : 0 LysoPC 554.96 (380.61–739.33) 517.30 (341.62–855.38) 0.685 858.26 (672.48–1149.97) 574.04 (430.60–686.53) 0.005∗ 275.04 (203.05–549.00) −32.92 (−211.46–124.50) 0.001∗
18 : 0 LysoPC 6778.18 (3811.50–10874.18) 7025.67 (2518.06–9248.98) 0.586 13450.31 (9228.13–17360.12) 5259.48 (3231.33–10666.63) 0.004∗ 5718.02 (2554.51–9075.89) 120.06 (−2361.27–2198.87) 0.010∗
18 : 1 LysoPC 6022.53 (3553.98–8608.39) 6224.57 (2203.90–10340.38) 0.922 12912.09 (8657.25–17139.27) 6056.01 (2494.34–14707.32) 0.040∗ 5435.26 (1390.30–12651.51) 750.78 (−804.26–8620.02) 0.179
20 : 4 LysoPI 722.37 (445.65–1308.57) 780.50 (451.08–2158.25) 0.349 699.68 (438.84–1293.19) 651.92 (391.67–1176.63) 0.501 −38.40 (−187.64–78.05) −25.59 (−57.42–344.32) 0.294
18 : 0 PE 1493.12 (1201.51–2745.27) 2280.28 (1445.53–3225.21) 0.167 2142.35 (1595.05–3186.86) 3664.51 (2875.74–6476.39) 0.036∗ 507.00 (139.72–4095.54) 729.71 (−298.53–4177.34) 0.867
16:0–18 : 1 PE 3491.63 (2551.85–5749.08) 6750.93 (3238.72–8458.05) 0.158 3725.58 (2051.79–7734.99) 10951.61 (4649.92–16254.12) 0.019∗ 447.28 (−1202.71–4095.54) 6806.34 (489.33–9295.82) 0.059
16:0–18 : 2 PE 5971.65 (4667.43–12012.34) 14213.70 (10082.27–19008.73) 0.026∗ 6335.59 (3940.83–19357.27) 17851.65 (9616.94–41152.45) 0.053 1834.57 (−1665.37–8588.23) 8366.01 (1089.32–21130.06) 0.193
16:0–20 : 4 PE 6264.81 (4058.03–12709.27) 9581.70 (6905.14–13060.32) 0.150 7692.52 (4141.69–14919.92) 12910.23 (7285.30–22963.00) 0.223 734.46 (−1914.44–5549.51) 5083.14 (−955.67–8134.60) 0.334
16:0–22 : 6 PE 19348.42 (12947.13–46886.62) 37492.20 (23092.63–50996.44) 0.214 28178.89 (13008.68–59176.73) 59446.43 (22453.56–73603.91) 0.223 5614.04 (−5768.61–35450.20) 22605.58 (4309.01–38569.88) 0.257
18:0–18 : 2 PE 12733.97 (10594.57–20815.69) 23814.87 (20595.80–46621.78) 0.017∗ 15494.19 (10115.09–33258.62) 38422.61 (20754.93–59676.60) 0.053 3390.22 (−1419.69–14017.09) 5849.92 (−3689.90–34346.29) 0.737
18:0–20 : 4 PE 195308.16 (144022.72–316482.95) 322636.03 (234243.32–401924.15) 0.142 248414.01 (120406.60–394477.35) 343228.72 (185468.10–448939.29) 0.294 3271.57 (−48274.87–173536.48) 21496.88 (−60103.70–115631.77) 0.933
18:0–22 : 6 PE 7224.19 (5100.28–16136.85) 11880.36 (4813.88–16899.79) 0.426 9161.84 (4877.97–18786.07) 17835.56 (12013.79–22761.61) 0.141 1937.65 (−2091.67–11920.19) 6545.06 (803.73–10169.62) 0.501
14 : 0 LysoPE 555.99 (334.27–3484.52) 353.11 (259.18–1532.81) 0.476 443.99 (193.15–2703.23) 2742.10 (456.81–13156.82) 0.101 −119.46 (−405.53–513.47) 1835.86 (−78.69–8644.08) 0.085
16 : 0 LysoPE 4453.28 (2364.01–8896.05) 5100.30 (2747.04–9349.86) 0.856 8210.14 (4510.54–10939.53) 8208.28 (5078.70–10758.13) 0.966 3461.40 (703.63–7092.41) 2596.76 (1621.38–5808.26) 0.900
18 : 0 LysoPE 1826.52 (1413.53–3211.28) 1809.18 (1016.44–3238.54) 0.511 3826.56 (2331.47–4850.50) 2180.73 (1250.18–3159.32) 0.036∗ 1006.70 (17.66–2485.23) 605.07 (−270.03–1262.71) 0.425
18 : 1 LysoPE 2097.79 (939.33–4891.90) 2431.32 (653.89–4078.73) 0.944 3360.97 (2614.74–5520.26) 5256.52 (2591.47–9697.28) 0.529 1998.87 (−399.72–2639.20) 1542.73 (288.15–7518.27) 0.834
15 : 0 PC 4085.07 (2926.55–5553.55) 4349.65 (2461.94–5276.59) 0.989 3801.10 (2327.55–5570.09) 4288.43 (2942.47–6122.28) 0.674 −131.06 (−1102.35–1590.94) 1229.49 (−1355.13–1870.44) 0.401
16 : 0 PC 29703.76 (25474.95–42230.58) 35894.33 (22077.55–48070.41) 0.494 30432.44 (24713.70–40188.12) 30571.81 (20737.63–36444.16) 0.585 609.53 (−4776.65–4269.97) −2842.23 (−9211.40–1481.85) 0.355
17 : 0 PC 22393.56 (6104.38–39570.25) 31940.92 (4004.49–44439.10) 0.605 25331.06 (7159.97–42717.74) 22169.66 (3396.93–54568.61) 0.705 871.32 (−2114.08–3755.21) 601.81 (−118.18–11814.62) 0.585
16 : 1 (Δ9–Cis) PC 2035.07 (1139.23–3367.16) 2219.49 (1406.17–4142.06) 0.364 2347.80 (1563.95–4028.63) 2473.91 (1485.48–5075.80) 0.933 725.71 (−297.81–1478.72) 499.71 (−226.39–1347.20) 0.644
18 : 1 (Δ6–Cis) PC 261178.44 (195560.70–355721.48) 339413.93 (2493.18–443771.49) 0.150 329095.33 (235899.83–400610.19) 221229.71 (184490.45–391672.19) 0.257 56856.96 (−50362.91–129076.83) −62696.57 (−257199.48–2990.53) 0.017∗
18 : 2 (Cis) PC (DLPC) 184199.14 (13610.08–301147.62) 202776.66 (29317.93–379207.71) 0.459 176871.95 (25541.39–301606.26) 59002.20 (10548.48–240050.72) 0.193 −267.98 (−34042.76–14970.49) 325.01 (−49320.78–43124.56) 0.933
20 : 4 (CisPC 950.81 (711.96–1284.71) 1017.86 (880.51–1352.09) 0.748 1291.29 (1074.31–1633.81) 764.50 (344.66–984.90) 0.001∗ 359.83 (−64.11–658.44) −9.08 (−134.27–43.44) 0.032∗
16:0–18 : 1 PC 367968.93 (293044.87–478156.82) 407921.85 (339975.21–611837.27) 0.258 393292.77 (244105.72–609542.74) 503048.65 (373013.97–624819.66) 0.334 59498.46 (−68655.93–183371.08) 58029.25 (30467.89–201823.68) 0.450
18:0–22 : 6 PC 12707.22 (8572.14–15919.73) 11393.47 (7586.36–14668.03) 0.426 12586.54 (7885.99–17052.51) 9246.98 (3893.92–11631.45) 0.032∗ −424.36 (−3051.63–5472.86) −3238.85 (−5093.89–182.75) 0.093
18:1–18 : 0 PC 30919.73 (24618.95–39336.74) 31332.57 (27753.23–43794.64) 0.567 46915.50 (34774.84–61301.82) 48831.19 (37985.70–54468.56) 0.769 7093.81 (1054.35–28166.38) 11643.09 (−876.27–18644.37) 0.737
16 : 0 SM 240873.95 (76481.33–311554.44) 305080.43 (85113.52–361243.22) 0.194 238174.68 (76941.20–334210.42) 131647.18 (43982.20–223070.37) 0.053 −8772.97 (−49228.81–22084.70) −23679.46 (−36682.05–−7178.98) 0.401
17 : 0 SM 3509.81 (2278.21–4340.59) 4147.07 (2107.43–5222.32) 0.335 3581.18 (1931.35–4417.85) 2358.71 (985.82–3560.52) 0.110 −57.00 (−841.94–441.83) −569.16 (−724.59–70.64) 0.208
18 : 0 SM 36630.75 (18728.18–48618.78) 34702.66 (16191.61–56476.49) 0.685 28706.78 (15548.77–55309.55) 16192.68 (5140.87–33252.71) 0.110 −3066.21 (−8039.50–6457.16) −4126.86 (−6741.35–392.36) 0.867
18 : 1 SM 2868.66 (2164.78–5085.49) 3437.40 (2846.27–4393.62) 0.349 4009.61 (2562.18–5334.50) 3056.94 (2224.28–3962.18) 0.141 392.19 (−930.29–1880.62) −1358.22 (−3028.30–328.19) 0.023∗
24 : 0 SM 17360.63 (11729.22–34205.97) 30746.35 (15078.40–38704.38) 0.185 20905.22 (12342.47–32776.33) 19001.57 (5103.74–28461.75) 0.378 70.05 (−5114.18–5536.13) −4431.31 (−5523.38–8206.74) 0.401
24 : 1 SM 77329.53 (48739.03–150692.35) 120008.44 (56023.29–171201.33) 0.235 116073.97 (58571.47–165057.54) 68616.14 (34768.06–154764.03) 0.257 5636.92 (−17507.22–29374.29) −11454.39 (−25321.07–60237.72) 0.401
16 : 0 ceramide 2934.78 (1677.44–4628.57) 4020.45 (1231.47–5189.63) 0.769 2661.30 (1284.48–3143.01) 2554.08 (906.93–4054.96) 0.801 −641.42 (−1570.97–71.92) −575.98 (−1272.81–−137.78) 0.966
18 : 0 ceramide 2001.39 (1061.58–3242.56) 2212.33 (782.03–3023.55) 0.791 1780.69 (1082.96–2499.12) 1697.29 (720.56–3060.19) 0.933 −216.07 (−844.71–282.25) −93.96 (−1404.07–360.25) 0.867
18 : 1 ceramide 911.56 (550.07–1300.82) 1006.46 (536.90–1338.42) 0.878 893.39 (549.48–1121.13) 786.71 (526.98–1207.20) 0.769 −29.78 (−201.78–21.02) −8.96 (−243.81–19.14) 0.674
20 : 0 ceramide 1658.83 (986.15–2092.05) 1764.86 (920.03–2798.69) 0.379 1355.39 (950.57–1830.90) 1306.95 (743.49–2167.06) 0.501 −109.40 (−422.72–158.68) −134.37 (−1159.48–−18.95) 0.179
22 : 0 ceramide 304.84 (267.70–443.87) 337.13 (296.71–530.06) 0.175 362.14 (254.03–527.38) 376.95 (300.57–574.25) 0.585 −19.40 (−84.89–201.51) −34.03 (−164.26–251.51) 0.556
24 : 0 ceramide 31362.79 (25605.08–42431.63) 30447.84 (20865.83–45588.97) 0.922 29859.38 (24106.03–40325.47) 35167.62 (22076.35–45854.22) 0.475 −1021.46 (−8034.04–11936.68) 2016.01 (−5911.50–5192.63) 0.801
24 : 1 ceramide 5527.79 (3948.79–7456.97) 6419.72 (3379.74–11735.65) 0.158 4532.09 (3229.22–7040.63) 4173.49 (1575.18–10255.01) 0.674 174.54 (−1881.11–1654.20) −418.20 (−3031.20–912.74) 0.529
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P  <  0.05 was considered statistically significant. D1, day 1; D7, day 7; DLPC, dioleoylphosphatidylcholine; PC, phosphatidylcholine; PE, phosphatidylethanolamines; PI, phosphatidylinositol; SM, sphingomyelin.

Figure 1.

Figure 1

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Scatter Plot of D7–D1 lipid levels for the survival and nonsurvival groups. ∗ means p < 0.05 ∗∗ means p < 0.01.D7–D1 LysoPC level (16 : 0, 17 : 0,18 : 0) and 18 : 1 SM levels were significantly higher in the survivor group than in the nonsurvivor group. D1, day 1; D7, day 7; D7–D1, change between D7 and D1; LysoPC, lysophosphatidylcholine.

3.3. Relationship between T Lymphocyte Subsets and Lipids

In order to reveal the correlation between changes of lipid metabolism with lymphocyte disturbance in sepsis, we analyzed the lymphocyte subsets on D1 and D7 in survivor and nonsurvivor groups, and found that there were significant differences in CD3+ T lymphocytes in the blood on D1 and D7 in both survivor and nonsurvivor groups (Table 3). According to the results in Table 2, the between-group difference in plasma lipid levels was greater for D7–D1 than for D1 or D7. We analyzed the correlation between D7–D1 plasma lipid levels and D7–D1 lymphocyte subsets, and found that D7–D1 plasma 16 : 0 LysoPC (r = 0.367, p=0.036),17 : 0 LysoPC(r = 0.389, p=0.025) and 18 : 0 LysoPC(r = 0.472, p=0.006) levels were positively correlated with the percentage of CD3+ T cell in the D7–D1 (Figure 2).

Table 3.

Results of D1 and D7 immune cell subsets in sepsis patients.

Variable (%) Survivors (n = 25) Nonsurvivors (n = 15) p-value
D1
CD3+T 66.68 (60.73–72.89) 46.75 (41.92–62.88) 0.007∗
CD4+T 57.48 (45.25–66.51) 66.77 (56.28–71.22) 0.093
CD8+T 38.58 (27.22–48.92) 27.64 (23.87–34.99) 0.081
NKT 6.77 (3.48–9.65) 3.28 (2.14–6.28) 0.028∗
NK 18.52 (11.79–32.85) 24.99 (15.55–36.18) 0.426
B 9.16 (5.09–18.69) 13.72 (8.04–32.46) 0.166
Neu 83.89 (68.46–87.61) 85.94 (74.54–93.37) 0.361
Monocyte 3.92 (2.83–6.26) 3.94 (1.82–6.79) 0.952
MDSCs 0.51 (0.17–3.65) 0.95 (0.34–4.17) 0.469
D7 Survivors (n = 25) Non-survivors (n = 8) p-value
CD3+T 72.23 (61.91–77.52) 47.40 (35.90–58.93) 0.000∗
CD4+T 57.74 (50.80–62.87) 48.23 (31.83–72.79) 0.298
CD8+T 37.63 (23.85–42.07) 37.20 (20.43–48.17) 0.853
NKT 7.38 (3.84–10.27) 5.53 (2.92–7.26) 0.334
NK 15.87 (8.99–22.31) 20.47 (13.16–35.76) 0.176
B 8.14 (5.00–16.44) 13.73 (5.07–35.23) 0.352
Neu 79.38 (66.92–84.84) 89.58 (76.13–92.94) 0.074
Monocyte 3.68 (2.10–5.95) 4.03 (1.86–5.48) 1.000
MDSCs 0.72 (0.21–3.16) 0.17 (0.11–4.22) 0.344
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Values are expressed as n (%) or median (25%–75%) unless otherwise stated.

Figure 2.

Figure 2

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Correlation between D7–D1 plasma lipid levels and D7–D1 lymphocyte subsets. ∗p  <  0.05 indicated that the difference was statistically significant. (a) D7–D1 CD3+T lymphocyte percentage correlates with D7–D1 16 : 0 LysoPC. (b) D7–D1 CD3+T lymphocyte percentage correlates with D7–D1 17 : 0 LysoPC. (c) D7–D1 CD3+T lymphocyte percentage correlates with D7-D1 18 : 0 LysoPC.

4. Discussion

In this study, we investigated differences in lipid metabolism between surviving and nonsurviving patients with sepsis based on their 28-day survival status. There are 42 lipids in plasma, including LysoPC PC, PE, LysoPE LysoPI, SM, and ceramide. Scatter plots were used to represent the above-given differential metabolites (Figure 1), and we found that LysoPC and SM of the D7–D1 survival group were higher than those of the nonsurvival group, and the differences were statistically significant.

LysoPC, a lipid mediator and major phospholipid component, is involved in immune cell recruitment, stimulation, and infection [12–14]. Kamisoglu et al. [9] reported that five LysoPC type levels were significantly lower in patients with sepsis than in healthy controls. In our study, the LysoPC concentrations in the nonsurvivor group decreased significantly by D7. Drobnik et al. [12] demonstrated that the LysoPC level was significantly lower in patients with sepsis than those in healthy people. Moreover, samples obtained from patients with sepsis on days 4 and 7 revealed an association between LysoPC concentrations and sepsis-related mortality. Park et al. [10] noted that serial measurements of LysoPC concentrations predicted 28-day mortality in ICU patients with severe sepsis and septic shock.

The results of this study were largely consistent with those of previous studies. However, previous studies only analyzed on D1 and D7, and our study emphasized the significance of dynamic monitoring. Repeated measurements indicated that the decrease in LysoPC concentration was more evident in the nonsurvivor than that in the survivor group. This decrease may be attributed to the reduced expression of lipopolysaccharide-induced tissue factor in monocytes owing to changes in LysoPC [15], which can attenuate the release of proinflammatory cytokines and promote the release of anti-inflammatory cytokines [16]. The persistently decreased LysoPC levels in patients with sepsis result primarily from disruptions to metabolic homeostasis [17]. In our study, the decreased LysoPC levels in the nonsurvivor group were associated with sepsis-induced immune overreaction, possibly leading to LysoPC consumption. A study [18] using 1H nuclear magnetic resonance to evaluate sepsis-induced acute lung injury (ALI) in 13 patients reported that sphingolipid levels were lower in patients with sepsis-induced ALI than in healthy controls. Although the specific mechanisms remain unclear, our results indicate that LysoPC could be a prognostic sepsis biomarker.

SM lipids, including sphingosines, fatty acids, phosphoric acid, and nitrogenous bases, are major membrane constituents [19]. Moreover, SM lipids and their metabolites are essential signaling molecules that regulate key signal transduction processes, including cell growth, differentiation, senescence, and apoptosis [20]. Previous studies have reported that SM is involved in sepsis-induced lung injury [21, 22]. Mecatti et al. [5] performed a lipidomics analysis of plasma and erythrocytes in 20 patients with sepsis, and lower levels of SM, PC, and LysoPC were found in these patients than in healthy controls. Arshad et al. [23] also observed low SM, PC, and LysoPC levels and high acid sphingomyelinase (aSMAse) activity in patients with community-acquired pneumonia, with lipid levels gradually returning to the normal range with clinical improvement. Sepsis involves increases in aSMAse plasma levels and activity, which are correlated with disease severity [24]. The aforementioned studies suggest an association between SM and sepsis severity, possibly due to SM involvement in the inflammatory response. Similarly, we observed a decrease in plasma SM levels in patients with sepsis, which was more apparent in the nonsurvivor than in the survivor group. This difference may be attributed to SM hydrolysis to ceramide, which exerts anti-inflammatory effects by inhibiting reactive oxygen species, mitogen-activated protein kinases, phosphatidylinositol-3-kinase, protein kinase B, Janus kinase/signal transducer, and activators of transcription pathways, while upregulating protein kinase A and heme oxygenase-1 expression [25].

Sepsis is a host response disorder to infection that leads to life-threatening organ dysfunction [11] and reflects the complex response of the host immune system to pathogens [26]. During sepsis, CD4+ T cells are activated in response to antigen presentation by dendritic cells or monocytes, releasing immunomodulators and coordinating cytotoxic CD8+ T cells [27]. Moreover, low lymphocyte levels and nonrecovery are associated with sepsis mortality risk [28]. LysoPC is produced by the action of phospholipase A2 on PC and promotes inflammatory effects, including endothelial cell adhesion, monocyte chemotactic activation, and macrophage activation [29]. Lin et al. [30] demonstrated that the most abundant plasma LysoPC species (16 : 0, 18 : 0, and 18 : 1) inhibited the production of reactive oxygen species and activation of neutrophils, and LysoPC prevented neutrophil-mediated pulmonary vascular injury in an in vitro lung perfusion model. Asaoka et al. [31] found that LysoPC specifically enhanced T lymphocyte activation. Furthermore, LysoPC enhances interferon-γ secretion, activates CD4+ and CD8+ T cells, and increases CD40L and C-X-C chemokine receptor type 4 expression in CD4+ T cells [32]. In addition, Ni et al. [33] observed that plasmalogen lysophosphatidylethanolamine was a type of autoantigen that stimulated natural killer T cell production and activation. In our preliminary analysis, the D7–D1 LysoPC (16 : 0, 17 : 0,18 : 0) plasma concentration was positively correlated with the percentage of CD3+ T cell in the D7–D1. These results suggest that lipids play a role in the disturbance of lymphocyte homeostasis during sepsis, although further investigation is required.

The aging process is characterized by decreased immune function and increased stress response. Oxidative and metabolic stress may lead to changes in sphingolipid metabolism and increase the risk of age-related diseases. However, an association was reported between age and 18:0–22 : 6 PC lipids in women and between age and eicosapentaenoic acid-containing lipids (e.g., 16:0–20 : 5 PC and 20 : 5 cholesteryl ester) and 18:1–22 : 0 ceramide in men; however, no other lipid values were correlated with age [34]. A study involving 800 healthy volunteers reported no correlation between total LysoPC levels and age [35]. Another study reported that the in-hospital mortality risk of sepsis was significantly associated with advanced age [36]; furthermore, the survivor group had higher BMIs than the nonsurvivor group, although the difference was nonsignificant. While obesity is associated with an inflammatory response, the underlying mechanisms remain unclear. High BMI was previously reported as a protective factor against sepsis [37]. Recently, an inverse association between LysoPCs (17 : 0, 18 : 1, and 18 : 2) and BMI was reported [38]. Bagheri et al. demonstrated that LysoPC (18 : 1 and 18 : 2) values, although not SM values, were negatively associated with obesity in Iranian adults [39]. No such correlations were observed in our study, likely because of differences in the study populations. Further studies are required to clarify whether sepsis differentially affects lipid metabolism in patients with and without obesity.

This study had limitations. It did not include a healthy control group since previous studies have repeatedly demonstrated lipid variations in patients with sepsis. Moreover, given the lack of an external validation cohort and small sample size, our findings require validation.

5. Conclusions

In conclusion, our findings indicate that repeated monitoring of lipids in plasma LysoPC and SM may help predict the prognosis of patients with sepsis. Furthermore, the correlation between changes in lipids and lymphocyte subsets suggests that lipid metabolism plays a role in the immune disturbance that occurs during sepsis.

Acknowledgments

This study was funded as a clinical cohort construction program of Peking University Third Hospital (Grant no. BYSYDL2021019).

Contributor Information

Qingtao Zhou, Email: qtzhou75@163.com.

Yongchang Sun, Email: suny@bjmu.edu.cn.

Data Availability

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

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this article.

Authors' Contributions

Xin Li and Zhongnan Yin are contributed equally to this work.

Supplementary Materials

Supplementary Materials

All the measured lipids (incl Rt and measured ion (m/z).

Click here for additional data file. (21.4KB, docx)

References

  • 1.Fleischmann C., Scherag A., Adhikari N. K., et al. Assessment of global incidence and mortality of hospital-treated sepsis. Current estimates and limitations. American Journal of Respiratory and Critical Care Medicine . 2016;193(3):259–272. doi: 10.1164/rccm.201504-0781oc. [DOI] [PubMed] [Google Scholar]
  • 2.Kim W. Y., Hong S. B. Sepsis and acute respiratory distress syndrome: recent update. Tuberculosis and Respiratory Diseases . 2016;79(2):53–57. doi: 10.4046/trd.2016.79.2.53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Martin G. S., Mannino D. M., Moss M. The effect of age on the development and outcome of adult sepsis. Critical Care Medicine . 2006;34(1):15–21. doi: 10.1097/01.ccm.0000194535.82812.ba. [DOI] [PubMed] [Google Scholar]
  • 4.Knaus W. A., Zimmerman J. E., Wagner D. P., Draper E. A., Lawrence D. E. Apache-acute physiology and chronic health evaluation: a physiologically based classification system. Critical Care Medicine . 1981;9(8):591–597. doi: 10.1097/00003246-198108000-00008. [DOI] [PubMed] [Google Scholar]
  • 5.Mecatti G. C., Fernandes Messias M. C., Sant’Anna Paiola R. M., et al. Lipidomic profiling of plasma and erythrocytes from septic patients reveals potential biomarker candidates. Biomarker Insights . 2018;13 doi: 10.1177/1177271918765137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Li J., Xia K., Xiong M., Wang X., Yan N. Effects of sepsis on the metabolism of sphingomyelin and cholesterol in mice with liver dysfunction. Experimental and Therapeutic Medicine . 2017;14(6):5635–5640. doi: 10.3892/etm.2017.5226. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Idrovo J. P., Yang W. L., Jacob A., et al. Inhibition of lipogenesis reduces inflammation and organ injury in sepsis. Journal of Surgical Research . 2016;200(1):242–249. doi: 10.1016/j.jss.2015.06.059. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Rival T., Cinq-Frais C., Silva-Sifontes S., et al. Alteration of plasma phospholipid fatty acid profile in patients with septic shock. Biochimie . 2013;95(11):2177–2181. doi: 10.1016/j.biochi.2013.08.006. [DOI] [PubMed] [Google Scholar]
  • 9.Kamisoglu K., Sleight K. E., Calvano S. E., Coyle S. M., Corbett S. A., Androulakis I. P. Temporal metabolic profiling of plasma during endotoxemia in humans. Shock . 2013;40(6):519–526. doi: 10.1097/shk.0000000000000063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Park D. W., Kwak D. S., Park Y. Y., et al. Impact of serial measurements of lysophosphatidylcholine on 28-day mortality prediction in patients admitted to the intensive care unit with severe sepsis or septic shock. Journal of Critical Care . 2014;29(5):882.e5–11. doi: 10.1016/j.jcrc.2014.05.003. [DOI] [PubMed] [Google Scholar]
  • 11.Singer M., Deutschman C. S., Seymour C. W., et al. The Third international consensus definitions for sepsis and septic shock (Sepsis-3) JAMA . 2016;315(8):801–810. doi: 10.1001/jama.2016.0287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Drobnik W., Liebisch G., Audebert F. X., et al. Plasma ceramide and lysophosphatidylcholine inversely correlate with mortality in sepsis patients. Journal of Lipid Research . 2003;44(4):754–761. doi: 10.1194/jlr.m200401-jlr200. [DOI] [PubMed] [Google Scholar]
  • 13.Bulger E. M., Maier R. V. Lipid mediators in the pathophysiology of critical illness. Critical Care Medicine . 2000;28:N27–N36. doi: 10.1097/00003246-200004001-00004. [DOI] [PubMed] [Google Scholar]
  • 14.Mesquita R. D., Carneiro A. B., Bafica A., et al. Trypanosoma cruzi infection is enhanced by vector saliva through immunosuppressant mechanisms mediated by lysophosphatidylcholine. Infection and Immunity . 2008;76(12):5543–5552. doi: 10.1128/iai.00683-08. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Engelmann B., Zieseniss S., Brand K., et al. Tissue factor expression of human monocytes is suppressed by lysophosphatidylcholine. Arteriosclerosis, Thrombosis, and Vascular Biology . 1999 Jan;19(1):47–53. doi: 10.1161/01.atv.19.1.47. [DOI] [PubMed] [Google Scholar]
  • 16.Smani Y., Domínguez-Herrera J., Ibáñez-Martínez J., Pachón J. Therapeutic efficacy of lysophosphatidylcholine in severe infections caused by Acinetobacter baumannii. Antimicrobial Agents and Chemotherapy . 2015;59(7):3920–3924. doi: 10.1128/aac.04986-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Law S. H., Chan M. L., Marathe G. K., Parveen F., Chen C. H., Ke L. Y. An updated review of lysophosphatidylcholine metabolism in human diseases. International Journal of Molecular Sciences . 2019;20(5):p. 1149. doi: 10.3390/ijms20051149. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Stringer K. A., Serkova N. J., Karnovsky A., Guire K., Paine R., 3rd, Standiford T. J. Metabolic consequences of sepsis-induced acute lung injury revealed by plasma 1H-nuclear magnetic resonance quantitative metabolomics and computational analysis. American Journal of Physiology - Lung Cellular and Molecular Physiology . 2011;300(1):L4–L11. doi: 10.1152/ajplung.00231.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Endapally S., Frias D., Grzemska M., Gay A., Tomchick D. R., Radhakrishnan A. Molecular discrimination between two conformations of sphingomyelin in plasma membranes. Cell . 2019;176(5):1040–1053.e17. doi: 10.1016/j.cell.2018.12.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Cartier A., Hla T. Sphingosine 1-phosphate: lipid signaling in pathology and therapy. Science . 2019;366(6463) doi: 10.1126/science.aar5551.eaar5551 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Anjum F., Joshi K., Grinkina N., Gowda S., Cutaia M., Wadgaonkar R. Role of sphingomyelin synthesis in pulmonary endothelial cell cytoskeletal activation and endotoxin-induced lung injury. American Journal of Respiratory Cell and Molecular Biology . 2012;47(1):94–103. doi: 10.1165/rcmb.2010-0458oc. [DOI] [PubMed] [Google Scholar]
  • 22.Hu S., Ding Y., Gong J., Yan N. Sphingomyelin synthase 2 affects CD14-associated induction of NF-κB by lipopolysaccharides in acute lung injury in mice. Molecular Medicine Reports . 2016;14(4):3301–3306. doi: 10.3892/mmr.2016.5611. [DOI] [PubMed] [Google Scholar]
  • 23.Arshad H., Alfonso J. C. L., Franke R., et al. Decreased plasma phospholipid concentrations and increased acid sphingomyelinase activity are accurate biomarkers for community-acquired pneumonia. Journal of Translational Medicine . 2019;17(1):p. 365. doi: 10.1186/s12967-019-2112-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Chung H. Y., Hupe D. C., Otto G. P., et al. Acid sphingomyelinase promotes endothelial stress response in systemic inflammation and sepsis. Molecular Medicine . 2016;22(1):412–423. doi: 10.2119/molmed.2016.00140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Jung J. S., Shin K. O., Lee Y. M., et al. Anti-inflammatory mechanism of exogenous C2 ceramide in lipopolysaccharide-stimulated microglia. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids . 2013;1831(6):1016–1026. doi: 10.1016/j.bbalip.2013.01.020. [DOI] [PubMed] [Google Scholar]
  • 26.Horiguchi H., Loftus T. J., Hawkins R. B., et al. Innate immunity in the persistent inflammation, immunosuppression, and catabolism syndrome and its implications for therapy. Frontiers in Immunology . 2018;9:p. 595. doi: 10.3389/fimmu.2018.00595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Ryan T., Coakley J. D., Martin-Loeches I. Defects in innate and adaptive immunity in patients with sepsis and health care associated infection. Annals of Translational Medicine . 2017;5(22):p. 447. doi: 10.21037/atm.2017.09.21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Hohlstein P., Gussen H., Bartneck M., et al. Prognostic relevance of altered lymphocyte subpopulations in critical illness and sepsis. Journal of Clinical Medicine . 2019;8(3):p. 353. doi: 10.3390/jcm8030353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Yamamoto N., Homma S., Millman I. Identification of the serum factor required for in vitro activation of macrophages Role of vitamin D3-binding protein (group specific component, Gc) in lysophospholipid activation of mouse peritoneal macrophages. The Journal of Immunology . 1991;147(1):273–280. doi: 10.4049/jimmunol.147.1.273. [DOI] [PubMed] [Google Scholar]
  • 30.Lin P., Welch E. J., Gao X. P., Malik A. B., Ye R. D. Lysophosphatidylcholine modulates neutrophil oxidant production through elevation of cyclic AMP. The Journal of Immunology . 2005;174(5):2981–2989. doi: 10.4049/jimmunol.174.5.2981. [DOI] [PubMed] [Google Scholar]
  • 31.Asaoka Y., Oka M., Yoshida K., Sasaki Y., Nishizuka Y. Role of lysophosphatidylcholine in T-lymphocyte activation: involvement of phospholipase A2 in signal transduction through protein kinase C. Proceedings of the National Academy of Sciences of the U S A . 1992;89(14):6447–6451. doi: 10.1073/pnas.89.14.6447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Han K. H., Hong K. H., Ko J., et al. Lysophosphatidylcholine up-regulates CXCR4 chemokine receptor expression in human CD4 T cells. Journal of Leukocyte Biology . 2004;76(1):195–202. doi: 10.1189/jlb.1103563. [DOI] [PubMed] [Google Scholar]
  • 33.Ni G., Li Z., Liang K., Wu T., De Libero G., Xia C. Synthesis and evaluation of immunostimulant plasmalogen lysophosphatidylethanolamine and analogues for natural killer T cells. Bioorganic & Medicinal Chemistry . 2014;22(11):2966–2973. doi: 10.1016/j.bmc.2014.04.012. [DOI] [PubMed] [Google Scholar]
  • 34.Ishikawa M., Maekawa K., Saito K., et al. Plasma and serum lipidomics of healthy white adults shows characteristic profiles by subjects’ gender and age. PLoS One . 2014;9(3) doi: 10.1371/journal.pone.0091806.e91806 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Trabado S., Al-Salameh A., Croixmarie V., et al. The human plasma-metabolome: reference values in 800 French healthy volunteers; impact of cholesterol, gender and age. PLoS One . 2017;12(3) doi: 10.1371/journal.pone.0173615.e0173615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Ward H. H., Kiernan E. A., Deschler C. L., et al. Clinical and demographic parameters of patients treated using a sepsis protocol. Clinical Therapeutics . 2019;41(6):1020–1028. doi: 10.1016/j.clinthera.2019.03.016. [DOI] [PubMed] [Google Scholar]
  • 37.Zhou Q., Wang M., Li S., et al. Impact of body mass index on survival of medical patients with sepsis: a prospective cohort study in a university hospital in China. BMJ Open . 2018;8(9) doi: 10.1136/bmjopen-2018-021979.e021979 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Tulipani S., Palau-Rodriguez M., Miñarro Alonso A., et al. Biomarkers of morbid obesity and prediabetes by metabolomic profiling of human discordant phenotypes. Clinica Chimica Acta . 2016;463:53–61. doi: 10.1016/j.cca.2016.10.005. [DOI] [PubMed] [Google Scholar]
  • 39.Bagheri M., Djazayery A., Farzadfar F., et al. Plasma metabolomic profiling of amino acids and polar lipids in Iranian obese adults. Lipids in Health and Disease . 2019;18(1):p. 94. doi: 10.1186/s12944-019-1037-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Materials

All the measured lipids (incl Rt and measured ion (m/z).

Click here for additional data file. (21.4KB, docx)

Data Availability Statement

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

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