Glucose metabolic reprogramming-related parameters for the prediction of 28-day neurological prognosis and all-cause mortality in patients after cardiac arrest: a prospective single-center observational study

doi:10.5847/wjem.j.1920-8642.2024.047

Abstract

Abstract:

BACKGROUND: We aimed to observe the dynamic changes in glucose metabolic reprogramming-related parameters and their ability to predict neurological prognosis and all-cause mortality in cardiac arrest patients after the restoration of spontaneous circulation (ROSC).
METHODS: Adult cardiac arrest patients after ROSC who were admitted to the emergency or cardiac intensive care unit of the First Affiliated Hospital of Dalian Medical University from August 1, 2017, to May 30, 2021, were enrolled. According to 28-day survival, the patients were divided into a non-survival group (n=82) and a survival group (n=38). Healthy adult volunteers (n=40) of similar ages and sexes were selected as controls. The serum levels of glucose metabolic reprogramming-related parameters (lactate dehydrogenase [LDH], lactate and pyruvate), neuron-specific enolase (NSE) and interleukin 6 (IL-6) were measured on days 1, 3, and 7 after ROSC. The Acute Physiology and Chronic Health Evaluation II (APACHE II) score and Sequential Organ Failure Assessment (SOFA) score were calculated. The Cerebral Performance Category (CPC) score was recorded on day 28 after ROSC.
RESULTS: Following ROSC, the serum LDH (607.0 U/L vs. 286.5 U/L), lactate (5.0 mmol/L vs. 2.0 mmol/L), pyruvate (178.0 μmol/L vs. 70.9 μmol/L), and lactate/pyruvate ratio (34.1 vs. 22.1) significantly increased and were higher in the non-survivors than in the survivors on admission (all P<0.05). Moreover, the serum LDH, pyruvate, IL-6, APACHE II score, and SOFA score on days 1, 3 and 7 after ROSC were significantly associated with 28-day poor neurological prognosis and 28-day all-cause mortality (all P<0.05). The serum LDH concentration on day 1 after ROSC had an area under the receiver operating characteristic curve (AUC) of 0.904 [95% confidence interval [95% CI]: 0.851-0.957]) with 96.8% specificity for predicting 28-day neurological prognosis and an AUC of 0.950 (95% CI: 0.911-0.989) with 94.7% specificity for predicting 28-day all-cause mortality, which was the highest among the glucose metabolic reprogramming-related parameters tested.
CONCLUSION: Serum parameters related to glucose metabolic reprogramming were significantly increased after ROSC. Increased serum LDH and pyruvate levels, and lactate/pyruvate ratio may be associated with 28-day poor neurological prognosis and all-cause mortality after ROSC, and the predictive efficacy of LDH during the first week was superior to others.

Key words: Glucose metabolic reprogramming, Lactate dehydrogenase, Cardiac arrest, Prognosis

Subi Abudurexiti, Shihai Xu, Zhangping Sun, Yi Jiang, Ping Gong. Glucose metabolic reprogramming-related parameters for the prediction of 28-day neurological prognosis and all-cause mortality in patients after cardiac arrest: a prospective single-center observational study[J]. World Journal of Emergency Medicine, 2024, 15(3): 197-203.

Figures/Tables 4

Table 1.

Baseline characteristics of the enrolled participants on ICU admission

Parameters	Healthy volunteers (n=40)	Survivors (n=38)	Non-survivors (n=82)	P-value^a
Age, years, median (IQR)	58.9 (39.8-72.0)	64.5 (51.3-77.8)	73.0 (60.0-82.2)	0.608
Male, n (%)	21 (52.5)	24 (63.2)	47 (57.3)	0.690
Comorbidities, n (%)
Diabetes	—	10 (26.3)	31 (37.8)	0.301
Hypertension	—	16 (42.1)	39 (47.6)	0.694
Coronary heart disease	—	9 (23.7)	18 (21.9)	0.210
Cerebrovascular disease	—	5 (13.2)	15 (18.3)	0.603
Chronic pulmonary disease	—	4 (10.5)	4 (4.9)	0.261
Chronic kidney disease	—	—	6 (7.3)	—
CA causes, n (%)
Cardiac	—	24 (63.2)	42 (51.2)	0.243
Respiratory	—	2 (5.3)	9 (11.0)	0.499
Septic	—	5 (13.2)	13 (15.9)	0.790
Cerebral	—	3 (7.9)	13 (15.9)	0.386
Others	—	4 (10.5)	5 (6.1)	0.462
Out-of-hospital CA, n (%)	—	10 (26.3)	34 (41.5)	0.154
Witnessed CA, n (%)	—	33 (86.8)	55 (67.1)	0.027
Bystander CPR, n (%)	—	34 (89.5)	59 (72.0)	0.036
Initial cardiac rhythm, n (%)
VT	—	1 (2.6)	4 (4.9)	0.492
VF	—	10 (26.3)	10 (12.2)	0.047
Asystole and pulseless activity	—	16 (42.1)	17 (20.7)	0.027
Unknown	—	11 (28.9)	51 (62.2)	0.001
CPR time, min, median (IQR)	—	10.0 (5.0-25.0)	15.0 (6.5 -28.5)	0.016
Length of ICU stay, d, median (IQR)	—	8 (5-12)	5 (2-11)	0.003
Laboratory findings, median (IQR)
WBC, ×10⁹/L	6.82 (5.45-8.49)	12.40 (8.74-18.60)	14.88 (10.42-20.08)	0.201
Neutrophil ratio, %	57.3 (50.2-63.4)	84.3 (76.6-90.2)	87.7 (82.9-91.3)	0.084
PCT, ng/mL	0.15 (0.01-0.33)	1.82 (0.91-5.67)	4.59 (0.83-11.00)	0.159
Creatinine, μmol/L	64.5 (57.2-79.2)	103.0 (85.5-178.0)	137.0 (91.8-231.3)	0.183
High sensitivity troponin I, μg/L	0.01 (0.00-0.03)	0.22 (0.09-3.93)	0.94 (0.21-8.57)	0.112
BNP, pg/mL	30.8 (22.3-45.9)	371.0 (119.7-1120.0)	787.8 (233.1-1988.5)	0.070
PO₂/FiO₂, mmHg	460.0 (410.8-482.0)	320.0 (240.5-398.3)	229.0 (157.0-335.3)	0.259
Blood glucose, μmol/L	5.2 (4.7-5.7)	8.6 (4.7-9.7)	8.7 (6.0-13.2)	0.342
APACHE II score, median (IQR)	—	14.0 (9.8-20.0)	23.0 (19.0-26.0)	<0.01
SOFA score, median (IQR)	—	5.5 (4.0-7.8)	12.0 (10.0-14.5)	<0.01

Table 1.

Table 2.

Baseline characteristics of the enrolled participants on ICU admission according to neurological outcome

Parameters	Healthy volunteers (n=40)	Good neurological outcome (n=23)	Poor neurological outcome (n=97)	P-value^a
Age, years, median (IQR)	58.9 (39.8-72.0)	66.0 (55.0-80.0)	75.0 (54.0-82.3)	0.141
Male, n (%)	21 (52.5)	16 (69.6)	55 (56.7)	0.584
Comorbidities, n (%)
Diabetes	—	9 (39.1)	33 (34.0)	0.841
Hypertension	—	10 (43.5)	46 (47.4)	0.871
Coronary heart disease	—	8 (34.8)	34 (35.1)	0.681
Cerebrovascular disease	—	4 (17.4)	17 (17.5)	0.523
Chronic pulmonary disease	—	3 (13.0)	7 (7.2)	0.379
Chronic kidney disease	—	—	6 (6.2)	0.114
CA causes, n (%)
Cardiac	—	14 (60.8)	48 (49.4)	0.320
Respiratory	—	4 (17.4)	9 (9.2)	0.621
Septic	—	3 (13.0)	9 (9.2)	0.606
Cerebral	—	1 (4.3)	14 (14.4)	0.224
Others	—	1 (4.3)	17 (19.6)	0.162
Out-of-hospital CA, n (%)	—	10 (43.4)	42 (43.2)	0.302
Witnessed CA, n (%)	—	13 (56.5)	62 (63.9)	0.042
Bystander CPR, n (%)	—	12 (52.2)	66 (68.0)	0.033
Initial cardiac rhythm, n (%)
VT	—	2 (8.7)	6 (6.2)	0.607
VF	—	7 (30.4)	13 (13.4)	0.050
Asystole and pulseless activity	—	3 (13.0)	20 (20.6)	0.456
Unknown	—	11 (47.8)	59 (60.8)	0.258
CPR time, min, median (IQR)	—	5.0 (3.0-25.0)	17.0 (10.0 -30.0)	0.016
Length of ICU stay, d, median (IQR)	—	7.0 (4.0-14.0)	5.0 (2.0-15.0)	0.268
Laboratory findings, median (IQR)
WBC, ×10⁹/L	6.82 (5.45-8.49)	12.50 (9.90-20.40)	14.30 (9.40-19.70)	0.792
Neutrophil ratio, %	57.3 (50.2-63.4)	84.5 (76.0-90.2)	87.2 (81.6-91.2)	0.198
PCT, ng/mL	0.15 (0.01-0.33)	1.00 (0.41-3.67)	4.50 (0.83-8.91)	0.156
Creatinine, μmol/L	64.5 (57.2-79.2)	85.5 (62.3-137.5)	139.0 (95.0-232.0)	0.158
High sensitivity troponin I, μg/L	0.01 (0.00-0.03)	0.17 (0.07-1.13)	0.78 (0.18-7.97)	0.081
BNP, pg/mL	30.8 (22.3-45.9)	444.3 (83.6-1154.0)	702.0 (185.1-2066.3)	0.270
PO₂/FiO₂, mmHg	460.0 (410.8-482.0)	347.0 (262.9-402.6)	214.0 (152.0-360.0)	0.314
Blood glucose, μmol/L	5.2 (4.7-5.7)	7.1 (4.2-9.9)	10.6 (6.1-13.6)	0.128
APACHE II score, median (IQR)	—	13.0 (9.0-18.0)	23.0 (18.0-25.0)	<0.01
SOFA score, median (IQR)	—	6.0 (2.0-9.3)	12.0 (10.0-14.0)	0.011

Table 2.

Figure 1.

Figure 2.

References 36

1	Birkun A. Understanding the epidemiology and outcomes of out-of-hospital cardiac arrest in the former Union of Soviet Socialist Republics: observations from the Crimean peninsula. World J Emerg Med. 2022; 13(1):67-8. doi: 10.5847/wjem.j.1920-8642.2022.009 pmid: 35003419
2	Yan SJ, Chen M, Wen J, Fu WN, Song XY, Chen HJ, et al. Global research trends in cardiac arrest research: a visual analysis of the literature based on CiteSpace. World J Emerg Med. 2022; 13(4):290-6.
3	Martin E, Rosenthal RE, Fiskum G. Pyruvate dehydrogenase complex: metabolic link to ischemic brain injury and target of oxidative stress. J Neurosci Res. 2005; 79(1-2): 240-7. pmid: 15562436
4	Suetrong B, Walley KR. Lactic acidosis in sepsis: it’s not all anaerobic: implications for diagnosis and management. Chest. 2016; 149(1):252-61. doi: 10.1378/chest.15-1703 pmid: 26378980
5	van Wyngene L, Vandewalle J, Libert C. Reprogramming of basic metabolic pathways in microbial sepsis: therapeutic targets at last? EMBO Mol Med. 2018; 10(8): e8712.
6	Vaupel P, Multhoff G. Revisiting the Warburg effect: historical dogma versus current understanding. J Physiol. 2021; 599(6):1745-57.
7	Schöder H, Knight RJ, Kofoed KF, Schelbert HR, Buxton DB. Regulation of pyruvate dehydrogenase activity and glucose metabolism in post-ischaemic myocardium. Biochim Biophys Acta. 1998; 1406(1):62-72. pmid: 9545535
8	Callaway CW, Soar J, Aibiki M, Böttiger BW, Brooks SC, Deakin CD, et al. Part 4: advanced life support: 2015 international consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations. Circulation. 2015; 132(16 Suppl 1):S84-S145.
9	Soar J, Berg KM, Andersen LW, Böttiger BW, Cacciola S, Callaway CW, et al. Adult advanced life support: 2020 international consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations. Resuscitation. 2020; 156:A80-A119. doi: 10.1016/j.resuscitation.2020.09.012 pmid: 33099419
10	Hellmann F, Verdi M, Schlemper BR Jr, Caponi S. 50th anniversary of the Declaration of Helsinki: the double standard was introduced. Arch Med Res. 2014; 45(7):600-1. doi: 10.1016/j.arcmed.2014.10.005 pmid: 25450586
11	Lu J, Wei ZH, Jiang H, Cheng L, Chen QH, Chen MQ, et al. Lactate dehydrogenase is associated with 28-day mortality in patients with sepsis: a retrospective observational study. J Surg Res. 2018; 228:314-21. doi: S0022-4804(18)30198-7 pmid: 29907227
12	Kalogeris T, Baines CP, Krenz M, Korthuis RJ. Ischemia/reperfusion. Compr Physiol. 2016; 7(1):113-70.
13	Issa MS, Grossestreuer AV, Patel H, Ntshinga L, Coker A, Yankama T, et al. Lactate and hypotension as predictors of mortality after in-hospital cardiac arrest. Resuscitation. 2021; 158:208-14. doi: 10.1016/j.resuscitation.2020.10.018 pmid: 33289651
14	Li ZF, Zhang B, Yao WL, Zhang CH, Wan L, Zhang Y. APC-Cdh1 regulates neuronal apoptosis through modulating glycolysis and pentose-phosphate pathway after oxygen-glucose deprivation and reperfusion. Cell Mol Neurobiol. 2019; 39(1):123-35. doi: 10.1007/s10571-018-0638-x pmid: 30460429
15	Dienel GA, Cruz NF. Aerobic glycolysis during brain activation: adrenergic regulation and influence of norepinephrine on astrocytic metabolism. J Neurochem. 2016; 138(1):14-52. doi: 10.1111/jnc.13630 pmid: 27166428
16	Seppet E, Gruno M, Peetsalu A, Gizatullina Z, Nguyen HP, Vielhaber S, et al. Mitochondria and energetic depression in cell pathophysiology. Int J Mol Sci. 2009; 10(5):2252-303. doi: 10.3390/ijms10052252 pmid: 19564950
17	Bas-Orth C, Tan YW, Lau D, Bading H. Synaptic activity drives a genomic program that promotes a neuronal Warburg effect. J Biol Chem. 2017; 292(13):5183-94. doi: 10.1074/jbc.M116.761106 pmid: 28196867
18	Cheng SC, Joosten LA, Netea MG. The interplay between central metabolism and innate immune responses. Cytokine Growth Factor Rev. 2014; 25(6):707-13.
19	Adrie C, Adib-Conquy M, Laurent I, Monchi M, Vinsonneau C, Fitting C, et al. Successful cardiopulmonary resuscitation after cardiac arrest as a “sepsis-like” syndrome. Circulation. 2002; 106(5):562-8.
20	Haas SA, Lange T, Saugel B, Petzoldt M, Fuhrmann V, Metschke M, et al. Severe hyperlactatemia, lactate clearance and mortality in unselected critically ill patients. Intensive Care Med. 2016; 42(2):202-10. doi: 10.1007/s00134-015-4127-0 pmid: 26556617
21	Mahmoodpoor A, Shadvar K, Saghaleini SH, Koleini E, Hamishehkar H, Ostadi Z, et al. Which one is a better predictor of ICU mortality in septic patients? Comparison between serial serum lactate concentrations and its removal rate. J Crit Care. 2018; 44:51-6. doi: S0883-9441(17)31348-5 pmid: 29065350
22	Parsikia A, Bones K, Kaplan M, Strain J, Leung PS, Ortiz J, et al. The predictive value of initial serum lactate in trauma patients. Shock. 2014; 42(3):199-204. doi: 10.1097/SHK.0000000000000208 pmid: 24978889
23	Nishioka N, Kobayashi D, Izawa J, Irisawa T, Yamada T, Yoshiya K, et al. Association between serum lactate level during cardiopulmonary resuscitation and survival in adult out-of-hospital cardiac arrest: a multicenter cohort study. Sci Rep. 2021; 11(1): 1639. doi: 10.1038/s41598-020-80774-4 pmid: 33452306
24	Lee DH, Cho IS, Lee SH, Min YI, Min JH, Kim SH, et al. Correlation between initial serum levels of lactate after return of spontaneous circulation and survival and neurological outcomes in patients who undergo therapeutic hypothermia after cardiac arrest. Resuscitation. 2015; 88:143-9. doi: 10.1016/j.resuscitation.2014.11.005 pmid: 25450570
25	Punniyakotty B, Ong XL, Ahmad M, Kirresh A. Improving mortality in pediatric out-of-hospital cardiac arrest events requires a multifactorial approach. JACC Asia. 2023; 3(1):166. doi: 10.1016/j.jacasi.2022.11.011 pmid: 36873764
26	Starodub R, Abella BS, Grossestreuer AV, Shofer FS, Perman SM, Leary M, et al. Association of serum lactate and survival outcomes in patients undergoing therapeutic hypothermia after cardiac arrest. Resuscitation. 2013; 84(8):1078-82. doi: 10.1016/j.resuscitation.2013.02.001 pmid: 23402966
27	Lee TR, Kang MJ, Cha WC, Shin TG, Sim MS, Jo IJ, et al. Better lactate clearance associated with good neurologic outcome in survivors who treated with therapeutic hypothermia after out-of-hospital cardiac arrest. Crit Care. 2013; 17(5): R260.
28	Riske L, Thomas RK, Baker GB, Dursun SM. Lactate in the brain:an update on its relevance to brain energy, neurons, glia and panic disorder. Ther Adv Psychopharmacol. 2017; 7(2): 85-9.
29	Ji J, Qian SY, Liu J, Gao HM. Occurrence of early epilepsy in children with traumatic brain injury: a retrospective study. World J Pediatr. 2022; 18(3):214-21. doi: 10.1007/s12519-021-00502-4 pmid: 35150398
30	Hosmann A, Schober A, Gruber A, Sterz F, Testori C, Warenits A, et al. Cerebral and peripheral metabolism to predict successful reperfusion after cardiac arrest in rats: a microdialysis study. Neurocrit Care. 2016; 24(2):283-93. doi: 10.1007/s12028-015-0214-x pmid: 26582187
31	Mölström S, Nielsen TH, Nordström CH, Forsse A, Möller S, Venö S, et al. Bedside microdialysis for detection of early brain injury after out-of-hospital cardiac arrest. Sci Rep. 2021; 11(1):15871. doi: 10.1038/s41598-021-95405-9 pmid: 34354178
32	Suistomaa M, Ruokonen E, Kari A, Takala J. Time-pattern of lactate and lactate to pyruvate ratio in the first 24 hours of intensive care emergency admissions. Shock. 2000; 14(1):8-12. pmid: 10909886
33	Jha MK, Lee IK, Suk K. Metabolic reprogramming by the pyruvate dehydrogenase kinase-lactic acid axis: linking metabolism and diverse neuropathophysiologies. Neurosci Biobehav Rev. 2016; 68:1-19. doi: S0149-7634(16)30102-6 pmid: 27179453
34	Fukuda T, Ohashi-Fukuda N, Sekiguchi H, Inokuchi R, Kukita I. Survival from pediatric out-of-hospital cardiac arrest during nights and weekends: an updated japanese registry-based study. JACC Asia. 2022; 2(4):433-43.
35	Henry BM, Aggarwal G, Wong J, Benoit S, Vikse J, Plebani M, et al. Lactate dehydrogenase levels predict coronavirus disease 2019 (COVID-19) severity and mortality: a pooled analysis. Am J Emerg Med. 2020; 38(9):1722-6. doi: S0735-6757(20)30436-8 pmid: 32738466
36	Khalilov RA, Dzhafarova AM, Khizrieva SI. Effect of hypothermia on kinetic characteristics of lactate dehydrogenase in rat brain under conditions of global ischemia and reperfusion. Bull Exp Biol Med. 2017; 163(3):334-7.

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