Abstract
Background
Sepsis-associated liver injury (SALI) is an independent risk factor for multiple organ dysfunction and high mortality in septic patients, which is often associated with a poor prognosis. Currently, there is still a lack of early diagnostic biomarkers of Sepsis-Associated Liver Injury in clinical practice. YAP1 (Yes1 Associated Transcriptional Regulator) has been demonstrated to correlate with hepatic inflammation, nevertheless, its exact function and mechanism in sepsis-associated liver injury have not been conclusively determined.
Methods
Clinical data of septic patients in the ICU of Northern Jiangsu People’s Hospital (Oct. 2023 - Dec. 2025) were reviewed. Patients were classified into sepsis non-liver injury (SNLI) and SALI groups according to the presence or absence of liver injury at admission. Logistic regression was used to identify independent risk factors for SALI. Receiver operating characteristic (ROC) analysis assessed predictive performance. Patients were stratified by plasma YAP1 quartiles to evaluate its association with disease progression, and significant variables were further analyzed by logistic regression. Correlations were examined using Spearman analysis. A Cox proportional hazards model was applied to assess the association between YAP1 and 28-day mortality in SALI patients.
Results
A total of 199 patients were included (SNLI, n = 121; SALI, n = 78). Plasma YAP1 was an independent protective factor for SALI (OR = 0.97, P < 0.001), while BMI (OR = 1.35, P = 0.015) and day 1 lactate (OR = 1.48, P = 0.002) were independent risk factors. YAP1 showed good predictive performance (AUC = 0.86). Higher YAP1 levels were independently associated with 72-hour SOFA score reduction (OR = 7.55, P = 0.009), indicating improved early organ function. No significant association was found between YAP1 and 28-day mortality.
Conclusion
Plasma YAP1 is inversely associated with SALI occurrence and demonstrates good predictive performance. Higher YAP1 levels are associated with early organ function improvement but not with 28-day mortality.
Keywords: Sepsis, Liver injury, YAP1, Risk factors, Biomarker
Introduction
Sepsis is defined as life-threatening organ dysfunction caused by the host’s uncontrolled response to infection [1]. The Global Burden of Disease (GBD) study in 2017 indicated that there were 48.9 million cases of sepsis and 11 million sepsis-related deaths globally [2]. These staggering figures underscore that sepsis represents a significant public health challenge warranting urgent attention. As a vital immunoregulatory organ, the liver often suffers significant damage during sepsis onset [3, 4]. Research indicates that sepsis-associated liver injury is an independent risk factor for multiple organ dysfunction and high mortality in septic patients, significantly impacting both short-term and long-term survival outcomes in intensive care unit patients [5, 6]. Consequently, it is extremely urgent to identify and intervene in SALI early and improve the quality of life of survivors.
The pathophysiological mechanisms of sepsis-associated liver injury are complex, including inflammation, oxidative stress, mitochondrial dysfunction, and more. Among these mechanisms, excessive inflammatory cascades is the dominant mechanism, and intervention of inflammation can significantly reduce hepatic injury [7, 8]. Therefore, we propose that reducing the inflammatory response constitutes a crucia step in alleviating sepsis-associated liver injury. Previous studies have confirmed that, in addition to core axes such as TLR4-NF-κB and JAK/STAT playing pivotal roles in hepatic inflammation, pathways including Hippo-YAP1 also participate in the hepatic inflammatory response. For example, the co-translocation of YAP1 and β-catenin into the nucleus has been shown to alleviate inflammation following ischemia-reperfusion (I/R) [9]. Moreover, YAP1 is also closely linked to hepatocyte proliferation and regeneration, alleviation of hepatic fibrosis, and repair of hepatic ischaemia-reperfusion injury [10–12]. Although these studies have provided a basis for the protective effect of YAP1 in the liver to a certain extent, the specific role and mechanism of YAP1 in sepsis-associated liver injury have not been determined, and whether YAP1 can be used for the assessment and treatment of SALI is also unknown, indicating that the relationship between YAP1 and SALI worths further extensive investigation.
This study aims to investigate the relationship between YAP1 and sepsis-associated liver injury, and to speculate the possible mechanism of YAP1 in SALI from a clinical perspective, with the objective of providing novel insights for the early diagnosis and treatment of SALI.
Subjects and methods
Study population
This prospective observational study enrolled patients with sepsis admitted to the Department of Critical Care Medicine at Northern Jiangsu People’s Hospital form October 2023 to December 2025. The study was approved by the Ethics Review Committee of Northern Jiangsu People’s Hospital (Approval No.: 2024ky293).
Research methods
Inclusion Criteria: (1) Patients diagnosed with sepsis on the basis of the Third International Consensus Definitions for Sepsis (Sepsis-3), i.e., a Sequential Organ Failure Assessment score ≥ 2 and suspected or confirmed infection [1]; (2) Age ≥ 18 years old; and (3) Patients who were informed about the process and purpose of this study, voluntarily participated, and signed an informed consent form.
Exclusion Criteria: (1) Patients with chronic liver diseases or acute exacerbations of chronic hepatic dysfunction; (2) Patients with drug-induced liver injury; (3) Patients with a history of liver transplantation; (4) Patients diagnosed with sepsis more than 24 h prior to enrolment; (5) Patients with a history of blood transfusion; and (6) Patients with an expected survival time of less than 24 h; (7) Patients with missing data or loss to follow-up.
Grouping: Patients were classified into the sepsis non-liver injury (SNLI) and sepsis-associated liver injury (SALI) groups based on liver injury status at admission. SALI was defined as sepsis with either total bilirubin > 34.1 µmol/L or alanine aminotransferase (ALT) > 80 U/L [13–15]. For progression analysis, SALI patients were stratified according to plasma YAP1 levels. Changes in organ function were assessed using the difference in SOFA score (ΔSOFA), defined as the baseline SOFA score minus the value at each time point. Patients with ΔSOFA ≥ 1 were considered to have improved organ function.
Data collection: The following indicators were collected and compared form the electronic medical record system: ① Patient demographics, including age, gender, height, weight, smoking history, alcohol consumption history, and underlying medical conditions. ②Laboratory results from Days 1 and 3 following ICU admission (with Day 1 defined as the 24-hour period commencing admission), including lactate, complete blood count (CBC; white blood cell count, neutrophil percentage, etc.), hepatic and renal function, coagulation profile, and other clinically relevant parameters. ③The infected area is determined based on clinical manifestations, imaging examinations, and microbiological culture results, and is classified as pulmonary infection, abdominal infection, skin and soft tissue infection, urinary tract infection, and other sites.④Total length of stay and ICU (Intensive Care Unit) stay duration during the patient’s admission to our institution.
Treatment protocol: This study was observational in design, and no unified intervention or stratified analysis of specific treatment protocols was performed. All patients with sepsis (including both SALI and SNLI groups) received standardized management according to the 2021 Surviving Sepsis Campaign (SSC) guidelines, including antimicrobial therapy, fluid resuscitation, hemodynamic support, and organ function support. Specific treatment decisions were individualized by the attending physicians based on patient condition.
Sample collection and measurement: Peripheral blood samples were collected from patients within 24 h of ICU admission. Plasma was separated and stored at − 80 ℃ until analysis. The levels of target factors in plasma were measured using a commercially available enzyme-linked immunosorbent assay (ELISA) kit according to the manufacturer’s instructions.
Statistical analysis
Statistical analyses were performed using R software (version 4.3.0). Continuous variables are presented as mean ± standard deviation or median (interquartile range), and categorical variables as counts and percentages. Group comparisons were conducted using the Student’s t-test or Mann–Whitney U test for continuous variables, and the χ² test for categorical variables, as appropriate.Univariable and multivariable logistic regression analyses were performed to identify independent factors associated with SALI. Variables with potential collinearity were assessed and managed to ensure model stability. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of YAP1.For disease progression, logistic regression and Spearman correlation analyses were used to assess the association between plasma YAP1 levels and changes in SOFA scores. Cox proportional hazards models were applied to evaluate the association between YAP1 and 28-day mortality in SALI patients. P < 0.05 was considered statistically significant.
Results
Baseline data
As shown in Fig. 1 and 217 patients with sepsis were enrolled in the study. Eleven cases were excluded due to chronic liver disease or acute exacerbation of chronic liver dysfunction. Three cases were excluded because sepsis was diagnosed more than 24 h before enrollment. Two patient was excluded due to an exceped survival time of less than 24 h, and two patients were excluded due to missing data. Ultimately, 78 SALI patients and 121 SNLI patients were enrolled, totalling 199 subjects. Among these, 127 were male and 72 were female; median age was 73.00 (61.00, 79.00) years; with a mean body mass index (BMI) of 23.18 ± 3.76 kg/m². The most common source of infection at admission was pulmonary (51.26%), followed by abdominal infection (28.64%). Among comorbidities, hypertension was the most prevalent, affecting 114 patients (57.29%).
Fig. 1.
Patient inclusion and exclusion flowchart. SALI: Sepsis-Associated Liver Injury; SNLI: Sepsis-Non- Liver Injury
Clinical characteristic analysis of the sepsis-associated liver injury group and the non-sepsis-associated liver injury group
Comparisons of demographic characteristics, clinical variables, and outcomes between the SNLI and SALI groups showed significant differences in BMI (P = 0.033), age (P = 0.049), smoking history (28.93% vs. 44.87%, P = 0.021), alcohol use (22.31% vs. 39.74%, P = 0.008), YAP1 (P < 0.001), SOCS1 (P = 0.037), day 1 lactate (P < 0.001), platelet count (P = 0.039), ALT (P < 0.001), AST (P < 0.001), total bilirubin (P < 0.001), indirect bilirubin (P < 0.001), direct bilirubin (P < 0.001), NT-proBNP (P = 0.010), PT (P < 0.001), INR (P < 0.001), as well as day 3 lactate (P < 0.001), platelet count (P = 0.004), ALT (P < 0.001), AST (P < 0.001), total bilirubin (P < 0.001), indirect bilirubin (P < 0.001), direct bilirubin (P < 0.001), and in-hospital mortality (P = 0.025) (all P < 0.05). No significant differences were observed for the remaining variables. See Tables 1 and 2, and 3 for details.
Table 1.
Comparison of baseline characteristics between patient groups
| Variables | Total (n = 199) | SNLI (n = 121) | SALI (n = 78) | Statistic | P |
|---|---|---|---|---|---|
| BMI, kg/m2 | 23.18 ± 3.76 | 22.73 ± 3.64 | 23.89 ± 3.86 | t=−2.15 | 0.033 * |
| Age, y | 73.00 (61.00, 79.00) | 74.00 (63.00, 80.00) | 69.50 (60.00, 76.00) | Z=−1.97 | 0.049 * |
| Female, n(%) | 72 (36.18) | 46 (38.02) | 26 (33.33) | χ²=0.45 | 0.502 |
| Pulmonary infection, n(%) | 102 (51.26) | 66 (54.55) | 36 (46.15) | χ²=1.34 | 0.248 |
| Abdominal infection, n(%) | 57 (28.64) | 30 (24.79) | 27 (34.62) | χ²=2.24 | 0.135 |
| Urinary tract infection, n(%) | 20 (10.05) | 16 (13.22) | 4 (5.13) | χ²=3.44 | 0.064 |
| Other infections, n(%)† | 20 (10.05) | 9 (7.44) | 11 (14.10) | χ²=2.33 | 0.127 |
| Smoking history, n(%) | 70 (35.18) | 35 (28.93) | 35 (44.87) | χ²=5.29 | 0.021 * |
| Drinking history, n(%) | 58 (29.15) | 27 (22.31) | 31 (39.74) | χ²=6.98 | 0.008 * |
| Hypertension, n(%) | 114 (57.29) | 72 (59.50) | 42 (53.85) | χ²=0.62 | 0.431 |
| Diabetes, n(%) | 58 (29.15) | 36 (29.75) | 22 (28.21) | χ²=0.05 | 0.815 |
BMI denotes body mass index. †Other infection sites included skin and soft tissue infection (n = 11), biliary tract infection (n = 4), central nervous system infection (n = 1), pelvic infection (n = 1), and primary bloodstream infection (n = 3). *P < 0.05.
Table 2.
Comparison of clinical data between the two patient groups
| Variables | Total (n = 199) | SNLI (n = 121) | SALI (n = 78) | Statistic | P |
|---|---|---|---|---|---|
| YAP1 level, pg/ml | 215.94 (162.97, 269.34) | 255.94 (211.97, 272.59) | 156.91 (144.90, 173.31) | Z=−8.49 | < 0.001 * |
| D1Lactate, mmol/L | 1.70 (1.10, 2.90) | 1.40 (1.00, 2.10) | 2.55 (1.60, 3.95) | Z=−5.65 | < 0.001 * |
| D1Hemoglobin, g/L | 104.73 ± 28.10 | 102.64 ± 26.33 | 107.97 ± 30.54 | t=−1.31 | 0.192 |
| D1White blood cells, 109/L | 11.48 (6.76, 16.41) | 11.16 (6.49, 16.36) | 12.35 (8.26, 16.44) | Z=−0.84 | 0.400 |
| D1Percentage of neutrophils | 0.89 (0.83, 0.94) | 0.89 (0.82, 0.93) | 0.89 (0.84, 0.94) | Z=−1.12 | 0.263 |
| D1Platelet count, 109/L | 145.00 (90.50, 203.50) | 155.00 (102.00, 214.00) | 127.50 (74.25, 183.25) | Z=−2.07 | 0.039* |
| D1ALT, U/L | 26.00 (17.50, 68.00) | 21.00 (15.00, 30.00) | 81.50 (28.50, 206.50) | Z=−7.67 | < 0.001 * |
| D1AST, U/L | 40.00 (25.00, 114.50) | 30.00 (23.00, 55.00) | 134.50 (38.75, 354.00) | Z=−7.16 | < 0.001 * |
| D1TBIL, µmol/L | 18.90 (12.00, 33.80) | 13.70 (10.00, 20.30) | 41.70 (22.65, 70.60) | Z=−8.60 | < 0.001 * |
| D1IBIL, µmol/L | 14.10 (8.30, 25.75) | 11.50 (6.90, 16.20) | 30.20 (15.40, 42.38) | Z=−7.10 | < 0.001 * |
| D1DBIL, µmol/L | 0.00 (0.00, 3.65) | 0.00 (0.00, 0.00) | 3.65 (0.00, 19.35) | Z=−6.99 | < 0.001 * |
| D1ALP, U/L | 77.00 (57.00, 115.50) | 73.00 (55.00, 108.00) | 82.00 (59.25, 118.00) | Z=−1.25 | 0.210 |
| D1Serum creatinine, µmol/L | 103.00 (73.45, 200.50) | 97.60 (69.80, 185.30) | 114.15 (79.40, 215.70) | Z=−1.41 | 0.160 |
| D1 Urea, mmol/L | 11.22 (7.54, 18.80) | 10.94 (7.69, 19.13) | 11.64 (7.02, 16.94) | Z=−0.01 | 0.993 |
| D1PCT, ng/ml | 8.11 (1.34, 10.43) | 7.23 (1.24, 10.41) | 8.67 (2.07, 10.54) | Z=−1.02 | 0.310 |
| D1NT-proBNP, pg/ml | 3733.00 (1063.50, 5247.40) | 2970.00 (587.00, 5064.82) | 4772.70 (1935.50, 5426.55) | Z=−2.57 | 0.010 * |
| D1PT, s | 14.50 (13.10, 16.75) | 14.00 (12.80, 15.70) | 15.80 (13.65, 18.62) | Z=−4.01 | < 0.001 * |
| D1APTT, s | 32.60 (28.80, 38.20) | 32.20 (28.50, 37.30) | 33.20 (29.43, 40.38) | Z=−1.15 | 0.250 |
| D1INR | 1.28 (1.13, 1.49) | 1.23 (1.11, 1.38) | 1.41 (1.19, 1.68) | Z=−3.96 | < 0.001 * |
| D3Lactate, mmol/L | 1.50 (1.05, 2.00) | 1.30 (1.00, 1.80) | 1.81 (1.33, 2.45) | Z=−4.36 | < 0.001 * |
| D3Haemoglobin, g/L | 92.88 ± 22.51 | 90.92 ± 20.76 | 95.91 ± 24.81 | t=−1.53 | 0.127 |
| D3White blood cells, 109/L | 9.81 (7.06, 14.40) | 9.78 (6.85, 15.50) | 10.00 (7.28, 12.90) | Z=−0.52 | 0.601 |
| D3Percentage of neutrophils | 0.88 (0.82, 0.92) | 0.87 (0.81, 0.91) | 0.88 (0.84, 0.92) | Z=−0.88 | 0.380 |
| D3Platelet count, 109/L | 115.00 (72.00, 164.50) | 130.00 (87.00, 180.00) | 92.00 (53.00, 148.66) | Z=−2.87 | 0.004 * |
| D3ALT, U/L | 42.00 (19.00, 109.83) | 27.00 (16.40, 72.04) | 74.85 (39.00, 166.66) | Z=−5.03 | < 0.001 * |
| D3AST, U/L | 47.00 (25.00, 133.80) | 36.00 (23.00, 106.00) | 78.50 (43.27, 165.50) | Z=−4.19 | < 0.001 * |
| D3TBIL, µmol/L | 24.30 (13.35, 34.13) | 18.50 (11.60, 30.40) | 34.15 (19.27, 65.62) | Z=−5.99 | < 0.001 * |
| D3IBIL, µmol/L | 16.50 (9.20, 24.23) | 14.40 (8.50, 21.40) | 22.00 (12.65, 39.83) | Z=−4.36 | < 0.001 * |
| D3DBIL, µmol/L | 0.00 (0.00, 9.04) | 0.00 (0.00, 7.10) | 8.10 (0.00, 28.85) | Z=−5.63 | < 0.001 * |
| D3ALP, U/L | 88.00 (59.50, 98.91) | 81.00 (57.00, 97.94) | 93.00 (63.00, 106.75) | Z=−1.89 | 0.058 |
| D3Serum creatinine, µmol/L | 128.60 (71.85, 147.75) | 122.00 (72.60, 146.00) | 137.60 (71.75, 150.30) | Z=−0.45 | 0.651 |
| D3PT, s | 13.70 (12.60, 15.20) | 13.50 (12.60, 14.65) | 14.10 (12.72, 16.70) | Z=−1.74 | 0.081 |
| D3APTT, s | 33.90 (29.85, 38.55) | 33.20 (29.10, 37.71) | 34.40 (30.50, 40.85) | Z=−1.40 | 0.161 |
| D3INR | 1.22 (1.10, 1.50) | 1.21 (1.10, 1.45) | 1.24 (1.08, 1.58) | Z=−0.52 | 0.604 |
| ICU length of stay, d | 6.68 (4.00, 11.03) | 6.97 (4.00, 11.00) | 6.15 (4.00, 11.05) | Z=−0.04 | 0.972 |
| Total length of stay, d | 15.00 (8.00, 22.95) | 15.39 (7.72, 24.49) | 14.78 (9.00, 21.33) | Z=−0.00 | 0.999 |
| Septic shock, n(%) | 108 (54.27) | 64 (52.89) | 44 (56.41) | χ²=0.24 | 0.627 |
| Death during hospitalisation, n(%) | 47 (23.62) | 22 (18.18) | 25 (32.05) | χ²=5.06 | 0.025 * |
| 14-day case fatality rate, n(%) | 31 (15.58) | 14 (11.57) | 17 (21.79) | χ²=3.77 | 0.052 |
| 28-day case fatality rate, n(%) | 41 (20.60) | 20 (16.53) | 21 (26.92) | χ²=3.13 | 0.077 |
YAP1 denotes Yes-associated protein 1, ALT denotes alanine aminotransferase, AST denotes aspartate aminotransferase, DBIL denotes direct bilirubin, IBIL denotes indirect bilirubin, TBIL denotes total bilirubin, ALP denotes alkaline phosphatase, PCT denotes Procalcitonin, NT-proBNP denotes N-terminal pro-B-type natriuretic peptide, PT denotes prothrombin time, APTT denotes activated partial thromboplastin time, INR denotes international normalised ratio, ICU denotes intensive care unit. *P < 0.05.
Table 3.
Comparison of clinical score-related data between the two patient groups
| Variables | Total (n = 199) | SNLI (n = 121) | SALI (n = 78) | Statistic | P |
|---|---|---|---|---|---|
| APACHE II score | 21.00 (17.00, 26.00) | 21.00 (17.00, 25.00) | 21.00 (16.00, 27.75) | Z=−0.78 | 0.438 |
| SOFA (excluding liver function) | 8.00 (5.00, 10.00) | 7.00 (5.00, 9.00) | 9.00 (5.00, 10.00) | Z=−1.05 | 0.294 |
| qSOFA score | χ²=4.14 | 0.247 | |||
| 0 | 16 (8.04) | 10 (8.26) | 6 (7.69) | ||
| 1 | 50 (25.13) | 25 (20.66) | 25 (32.05) | ||
| 2 | 105 (52.76) | 70 (57.85) | 35 (44.87) | ||
| 3 | 28 (14.07) | 16 (13.22) | 12 (15.38) | ||
| Blood pressure ≤ 100 mmHg, n(%) | 102 (51.26) | 60 (49.59) | 42 (53.85) | χ²=0.34 | 0.557 |
| Respiratory rate ≥ 22 breaths per minute, n(%) | 119 (59.80) | 74 (61.16) | 45 (57.69) | χ²=0.24 | 0.627 |
| Altered consciousness on admission, n(%) | 124 (62.31) | 79 (65.29) | 45 (57.69) | χ²=1.17 | 0.280 |
APACHE II score denotes the Acute Physiology and Chronic Health Evaluation score, SOFA score denotes the Sequential Organ Failure Assessment score, qSOFA denotes the quick sequential organ failure assessment score
Analysis of factors associated with the development of sepsis-associated liver injury
To explore the relationship between YAP1 and disease severity, we performed Spearman correlation analysis. Spearman correlation analysis revealed that plasma YAP1 levels were negatively correlated with markers of liver injury, including ALT (r = −0.368, P<0.001), AST (r = −0.371, P<0.001), and total bilirubin (r = −0.433, P<0.001). Although YAP1 also showed negative correlations with inflammatory parameters such as white blood cell count (r = −0.040, P = 0.573) and neutrophil percentage (r = −0.059, P = 0.411), these associations did not reach statistical significance. These findings indicate a significant inverse relationship between YAP1 levels and the degree of liver injury, but not directly with systemic inflammatory markers in this cohort.
The single-factor logistic regression analysis showed that there were statistically significant differences between the two groups in terms of body weight (P = 0.033), BMI (P = 0.035), other infections (P = 0.022), previous history of alcohol consumption (P = 0.009), plasma YAP1 level (P < 0.001), D1 lactate (P < 0.001), D1 NT-proBNP (P = 0.011), D3 lactate (P = 0.015), and D3 platelet count (P = 0.030) (P < 0.05). (Table 4).
Table 4.
Univariate logistic regression for sepsis-associated liver injury in septic patients
| Variables | P | OR | 95%CI |
|---|---|---|---|
| Female | 0.502 | 0.82 | 0.45 ~ 1.48 |
| Age, y | 0.094 | 0.98 | 0.96 ~ 1.00 |
| Height, cm | 0.542 | 1.01 | 0.98 ~ 1.05 |
| Weight, kg | 0.033 * | 1.03 | 1.01 ~ 1.05 |
| BMI, kg/m2 | 0.035 * | 1.09 | 1.01 ~ 1.18 |
| Pulmonary infection | 0.248 | 0.71 | 0.40 ~ 1.26 |
| Abdominal infection | 0.136 | 1.61 | 0.86 ~ 2.99 |
| Urinary tract infection | 0.074 | 0.35 | 0.11 ~ 1.10 |
| Other infections | 0.022 * | 2.00 | 1.10 ~ 3.62 |
| Smoking history | 0.133 | 2.04 | 0.80 ~ 5.19 |
| Drinking history | 0.009 * | 2.30 | 1.23 ~ 4.28 |
| Hypertension | 0.431 | 0.79 | 0.45 ~ 1.41 |
| Diabetes | 0.815 | 0.93 | 0.49 ~ 1.74 |
| Respiratory rate ≥ 22 breaths per minute | 0.627 | 0.87 | 0.49 ~ 1.55 |
| Altered consciousness on admission | 0.281 | 0.72 | 0.40 ~ 1.30 |
| Blood pressure ≤ 100 mmHg | 0.557 | 1.19 | 0.67 ~ 2.10 |
| APACHE Ⅱ | 0.491 | 1.01 | 0.98 ~ 1.05 |
| SOFA (excluding liver function) | 0.296 | 1.05 | 0.96 ~ 1.16 |
| YAP1 level, pg/ml | < 0.001 * | 0.97 | 0.96 ~ 0.98 |
| D1Lactate, mmol/L | < 0.001 * | 1.74 | 1.37 ~ 2.19 |
| D1Hemoglobin, g/L | 0.192 | 1.01 | 1.00 ~ 1.02 |
| D1White blood cells, 109/L | 0.793 | 1.00 | 0.98 ~ 1.03 |
| D1Percentage of neutrophils | 0.335 | 1.68 | 0.58 ~ 4.83 |
| D1Platelet count, 109/L | 0.141 | 1.00 | 1.00 ~ 1.00 |
| D1Serum creatinine, µmol/L | 0.629 | 1.00 | 1.00 ~ 1.00 |
| D1 Urea, mmol/L | 0.762 | 1.00 | 0.98 ~ 1.03 |
| D1ALP, U/L | 0.381 | 1.00 | 1.00 ~ 1.00 |
| D1PT, s | 0.197 | 1.03 | 0.98 ~ 1.08 |
| D1APTT, s | 0.441 | 1.01 | 0.99 ~ 1.03 |
| D1INR | 0.957 | 1.00 | 0.89 ~ 1.12 |
| D1PCT, ng/ml | 0.514 | 1.01 | 0.98 ~ 1.03 |
| D1NT-proBNP, pg/ml | 0.011 * | 1.01 | 1.01 ~ 1.01 |
| D3Lactate, mmol/L | 0.015 * | 1.27 | 1.05 ~ 1.54 |
| D3Haemoglobin, g/L | 0.129 | 1.01 | 1.00 ~ 1.02 |
| D3White blood cells, 109/L | 0.319 | 0.98 | 0.93 ~ 1.02 |
| D3Percentage of neutrophils | 0.361 | 4.37 | 0.18 ~ 103.55 |
| D3Platelet count, 109/L | 0.030 * | 0.99 | 0.99 ~ 0.99 |
| D3Serum creatinine, µmol/L | 0.367 | 1.00 | 1.00 ~ 1.00 |
| D3PT, s | 0.088 | 1.08 | 0.99 ~ 1.18 |
| D3APTT, s | 0.763 | 1.00 | 0.98 ~ 1.01 |
| D3INR | 0.663 | 1.21 | 0.51 ~ 2.88 |
BMI denotes body mass index, APACHE II score denotes the Acute Physiology and Chronic Health Evaluation score, SOFA score denotes the Sequential Organ Failure Assessment score, YAP1 denotes Yes-associated protein 1, ALP denotes alkaline phosphatase, PCT denotes Procalcitonin, NT-proBNP denotes N-terminal pro-B-type natriuretic peptide, PT denotes prothrombin time, APTT denotes activated partial thromboplastin time, INR denotes international normalised ratio. *P < 0.05.
The factors with P < 0.05 identified in the univariate logistic regression analysis and the potential related factors mentioned in the existing studies were included in the multivariate logistic regression model. To ensure the early predictive ability of the model, only the baseline (day 1) parameters were evaluated as candidate predictors. Although the variables measured on the third day had statistical significance in the univariate analysis, they reflected the dynamic changes after treatment rather than baseline characteristics, and thus their inclusion might introduce lead-time bias. Therefore, they were not included in the multivariate model. The results showed that plasma YAP1 level (OR = 0.97, 95%CI: 0.96–0.98, P < 0.001) was an independent protective factor for SALI in patients with sepsis, and body mass index (OR = 1.35, 95%CI: 1.06–1.72, P = 0.015) and lactate level on day 1 (OR = 1.48, 95%CI: 1.16–1.90, P = 0.002) were independent risk factors for Sali in sepsis patients. Detailed results are presented in Table 5.
Table 5.
Multivariate logistic regression analysis for sepsis-associated liver injury in septic patients
| Variables | β | S.E | Z | P | OR (95% CI) |
|---|---|---|---|---|---|
| Smoking history | 0.32 | 0.54 | 0.60 | 0.551 | 1.38 (0.48 ~ 3.94) |
| Drinking history | 0.92 | 0.55 | 1.68 | 0.093 | 2.52 (0.86 ~ 7.41) |
| Weight, kg | −0.06 | 0.04 | −1.66 | 0.096 | 0.94 (0.87 ~ 1.01) |
| BMI, kg/m2 | 0.30 | 0.12 | 2.43 | 0.015 * | 1.35 (1.06 ~ 1.72) |
| YAP1 level, pg/ml | −0.03 | 0.00 | −6.54 | < 0.001 * | 0.97 (0.96 ~ 0.98) |
| D1Lactate, mmol/L | 0.39 | 0.13 | 3.10 | 0.002* | 1.48 (1.16 ~ 1.90) |
| D1NT-proBNP, pg/ml | 0.00 | 0.00 | 1.25 | 0.212 | 1.00 (1.00 ~ 1.00) |
BMI denotes body mass index, YAP1 denotes Yes-Associated Protein 1, NT-proBNP denotes N-terminal pro-B-type natriuretic peptide. *P < 0.05.
Predictive value of YAP1 levels for sepsis-associated liver injury
The significant variables selected from the multivariate logistic regression analysis were further included in the ROC curve analysis. The results showed that D1 lactate, BMI, and YAP1 levels all had predictive significance for the occurrence of SALI. The results indicated that the D1 lactate level and BMI were risk factors for the occurrence of SALI. When the optimal cut-off value of D1 lactate was 2.5 mmol/L, the area under the curve (AUC) was 0.74 (95% CI: 0.67–0.81), with a sensitivity of 79% and a specificity of 56%; when the optimal cut-off value of BMI was 25.25 kg/m2, the AUC was 0.58 (95% CI: 0.50–0.66), with a sensitivity of 76% and a specificity of 40%. Plasma YAP1 level was an independent protective factor for the occurrence of SALI and had a high predictive value for SALI. The optimal cut-off value of plasma YAP1 level was 177.18 pg/ml, with an AUC of 0.86 (95% CI: 0.79–0.92), a sensitivity of 93%, and a specificity of 78%.
We further developed a combined logistic regression model integrating YAP1, day 1 lactate, and BMI. This combined model achieved an AUC of 0.88 (95% CI: 0.83 ~ 0.94), with a sensitivity of 88% and specificity of 82%, demonstrating superior predictive performance compared with YAP1 alone (Fig. 2; Table 6).
Fig. 2.
ROC curve for D1 lactate, BMI, YAP1 levels and Combined model prediction of SALI occurrence
Table 6.
ROC curve analysis of D1 lactate, BMI, YAP1 levels and Combined model for predicting SALI occurrence
| AUC | Sensitivity | Specificity | Cut-off | 95% CI | |
|---|---|---|---|---|---|
| D1lactate, mmol/L | 0.74 | 0.79 | 0.56 | 2.5 | 0.67 ~ 0.81 |
| BMI, kg/m2 | 0.58 | 0.76 | 0.4 | 25.25 | 0.5 ~ 0.66 |
| YAP1 level‡, pg/ml | 0.86 | 0.93 | 0.78 | 177.18 | 0.79 ~ 0.92 |
| Combined model | 0.88 | 0.88 | 0.82 | 0.481 | 0.83 ~ 0.94 |
BMI body mass index, YAP1 Yes-associated protein 1. ‡ indicates adjusted values for YAP1 levels. Day 1 lactate and BMI were positively associated with the occurrence of SALI, whereas plasma YAP1 levels were negatively associated with SALI occurrence
Analysis of the association between plasma YAP1 levels and the progression of SALI
To further evaluate the association between plasma YAP1 levels and SALI progression, patients were stratified into four groups according to YAP1 quartiles. The incidence of septic shock and the proportions of SOFA score reduction at 24, 48, and 72 h were compared across groups. No statistically significant differences were observed in septic shock incidence or in SOFA score reduction at 24 and 48 h (all P > 0.05).In contrast, a significant difference was found in the proportion of SOFA score reduction at 72 h among the four groups (P = 0.021), suggesting that higher plasma YAP1 levels may be associated with improved organ function within 72 h in SALI patients.Based on these findings, 72-hour SOFA score reduction was selected as the primary progression endpoint for subsequent correlation and regression analyses.
We further stratified SALI patients based on 72-hour SOFA score reduction into an improvement group (n = 39) and a non-improvement group (n = 39). Comparison of baseline characteristics and clinical parameters between the two groups revealed significant differences in plasma YAP1 levels, day 1 lactate levels, and APACHE II scores (all P < 0.05), whereas no statistically significant differences were observed for the remaining variables. Details are presented in the Tables 7 and 8.
Table 7.
Comparison of baseline data between the two groups of patients
| Variables | Total(n = 78) | Non-improved group(n = 39) | Improved group(n = 39) | Statistic | P |
|---|---|---|---|---|---|
| Weight, kg | 66.72 ± 13.61 | 64.33 ± 13.74 | 69.12 ± 13.22 | t=−1.57 | 0.122 |
| BMI, kg/m2 | 23.89 ± 3.86 | 23.19 ± 3.77 | 24.59 ± 3.87 | t=−1.61 | 0.112 |
| Age, y | 69.50 (60.00, 76.00) | 72.00 (66.50, 77.50) | 67.00 (57.50, 75.00) | Z=−1.68 | 0.094 |
| Height, cm | 168.00 (160.00, 172.00) | 165.00 (159.50, 173.00) | 170.00 (161.50, 172.00) | Z=−0.79 | 0.430 |
| Female, n(%) | 26 (33.33) | 15 (38.46) | 11 (28.21) | χ²=0.92 | 0.337 |
| Pulmonary infection, n(%) | 38 (48.72) | 22 (56.41) | 16 (41.03) | χ²=1.85 | 0.174 |
| Abdominal infection, n(%) | 28 (35.90) | 13 (33.33) | 15 (38.46) | χ²=0.22 | 0.637 |
| Urinary tract infection, n(%) | 8 (10.26) | 5 (12.82) | 3 (7.69) | χ²=0.14 | 0.709 |
| Other infections, n(%) | 11 (14.10) | 4 (10.26) | 7 (17.95) | χ²=0.95 | 0.329 |
| Smoking history, n(%) | 35 (44.87) | 15 (38.46) | 20 (51.28) | χ²=1.30 | 0.255 |
| Drinking history, n(%) | 31 (39.74) | 12 (30.77) | 19 (48.72) | χ²=2.62 | 0.105 |
| Hypertension, n(%) | 42 (53.85) | 20 (51.28) | 22 (56.41) | χ²=0.21 | 0.650 |
| Diabetes, n(%) | 22 (28.21) | 12 (30.77) | 10 (25.64) | χ²=0.25 | 0.615 |
BMI denotes body mass index
Table 8.
Comparison of laboratory indicators between the two groups of patients
| Variables | Total (n = 78) | Non-improved group(n = 39) | Improved group(n = 39) | Statistic | P |
|---|---|---|---|---|---|
| Interquartile YAP1, pg/ml | χ²=9.75 | 0.021 * | |||
| Q1 | 19 (24.36) | 13 (33.33) | 6 (15.38) | ||
| Q2 | 20 (25.64) | 12 (30.77) | 8 (20.51) | ||
| Q3 | 20 (25.64) | 10 (25.64) | 10 (25.64) | ||
| Q4 | 19 (24.36) | 4 (10.26) | 15 (38.46) | ||
| D1Lactate, mmol/L | 2.55 (1.60, 3.95) | 3.00 (1.70, 6.75) | 2.20 (1.45, 3.20) | Z=−2.21 | 0.027 * |
| D1Hemoglobin, g/L | 107.97 ± 30.54 | 102.36 ± 32.22 | 113.59 ± 28.05 | t=−1.64 | 0.105 |
| D1White blood cells, 109/L | 12.35 (8.26, 16.44) | 12.34 (6.54, 16.05) | 12.36 (8.93, 17.96) | Z=−0.77 | 0.442 |
| D1Percentage of neutrophils | 0.89 (0.84, 0.94) | 0.89 (0.83, 0.93) | 0.91 (0.86, 0.94) | Z=−1.01 | 0.315 |
| D1ALT, U/L | 81.50 (28.50, 206.50) | 70.10 (25.00, 211.00) | 98.00 (37.50, 201.00) | Z=−0.47 | 0.639 |
| D1AST, U/L | 134.50 (38.75, 354.00) | 144.00 (35.50, 511.00) | 133.00 (44.00, 279.50) | Z=−0.21 | 0.834 |
| D1TBIL, µmol/L | 41.70 (22.65, 70.60) | 46.00 (31.40, 72.20) | 38.80 (20.05, 59.55) | Z=−1.10 | 0.272 |
| D1IBIL, µmol/L | 30.20 (15.40, 42.38) | 29.00 (13.80, 43.40) | 31.70 (16.05, 42.25) | Z=−0.28 | 0.776 |
| D1DBIL, µmol/L | 3.65 (0.00, 19.35) | 4.60 (0.00, 21.15) | 0.00 (0.00, 12.65) | Z=−1.00 | 0.319 |
| D1ALP, U/L | 82.00 (59.25, 118.00) | 69.00 (57.50, 124.00) | 95.00 (60.00, 114.50) | Z=−0.66 | 0.506 |
| D1Serum creatinine, µmol/L | 114.15 (79.40, 215.70) | 131.90 (81.50, 218.00) | 105.00 (79.05, 211.30) | Z=−0.17 | 0.865 |
| D1Urea, mmol/L | 11.64 (7.02, 16.94) | 11.94 (8.46, 18.46) | 11.33 (6.50, 15.13) | Z=−1.00 | 0.315 |
| D1PCT, ng/ml | 7.65 (1.65, 39.82) | 8.19 (2.12, 36.05) | 6.89 (1.19, 38.91) | Z=−0.10 | 0.920 |
| D1PT, s | 15.80 (13.65, 18.62) | 16.30 (13.70, 20.05) | 15.20 (13.85, 17.30) | Z=−0.83 | 0.407 |
| D1APTT, s | 33.20 (29.43, 40.38) | 33.70 (28.80, 44.00) | 33.20 (30.10, 37.20) | Z=−0.62 | 0.536 |
| D1INR | 1.41 (1.19, 1.68) | 1.43 (1.19, 1.80) | 1.36 (1.21, 1.57) | Z=−0.44 | 0.656 |
| APACHE Ⅱ | 21.00 (16.00, 27.75) | 25.00 (19.00, 29.50) | 19.00 (15.00, 25.00) | Z=−2.05 | 0.041 * |
YAP1 denotes Yes-associated protein 1, ALT denotes alanine aminotransferase, AST denotes aspartate aminotransferase, DBIL denotes direct bilirubin, IBIL denotes indirect bilirubin, TBIL denotes total bilirubin, ALP denotes alkaline phosphatase, PCT denotes Procalcitonin, PT denotes prothrombin time, APTT denotes activated partial thromboplastin time, INR denotes international normalised ratio, APACHE II score denotes the Acute Physiology and Chronic Health Evaluation score. Q1 to Q4 were divided into four groups based on the quartiles of plasma YAP1 level. The grouping intervals were as follows: Q1: ≤144.53 pg/ml, Q2:144.54-156.91 pg/ml, Q3:156.92-174.48 pg/ml, Q4: > 174.48 pg/ml. *P < 0.05.
Further multivariable logistic regression analysis including statistically significant variables demonstrated that higher plasma YAP1 levels (> 174.48 pg/mL) remained an independent protective factor for 72-hour SOFA score reduction. Patients in the highest quartile of YAP1 showed a significantly increased likelihood of SOFA improvement at 72 h (OR = 7.55, 95% CI: 1.66–34.42, P = 0.009). In contrast, day 1 lactate level remained an independent risk factor (OR = 0.80, 95% CI: 0.66–0.98, P = 0.030) (Table 9).
Table 9.
Multivariate logistic regression analysis of the 72-hour SOFA score decline in SALI patients
| Variables | β | S.E | Z | P | OR (95%CI) |
|---|---|---|---|---|---|
| Interquartile YAP1, pg/ml | |||||
| Q1 | 1.00 (Reference) | ||||
| Q2 | 0.27 | 0.70 | 0.39 | 0.697 | 1.31 (0.34 ~ 5.13) |
| Q3 | 0.84 | 0.69 | 1.22 | 0.222 | 2.33 (0.60 ~ 9.01) |
| Q4 | 2.02 | 0.77 | 2.61 | 0.009 * | 7.55 (1.66 ~ 34.42) |
| D1Lactate, mmol/L | −0.22 | 0.10 | −2.17 | 0.030 * | 0.80 (0.66 ~ 0.98) |
YAP1 denotes Yes-associated protein 1. Q1 to Q4 were divided into four groups based on the quartiles of plasma YAP1 level. The grouping intervals were as follows: Q1: ≤144.53 pg/ml, Q2:144.54-156.91 pg/ml, Q3:156.92-174.48 pg/ml, Q4: > 174.48 pg/ml. *P < 0.05.
Spearman correlation analysis demonstrated a positive association between plasma YAP1 levels and SOFA score reduction (r = 0.383, P < 0.001), indicating a statistically significant correlation. These findings suggest that higher plasma YAP1 levels are associated with a greater reduction in SOFA scores within 72 h, reflecting more pronounced improvement in organ function.
Analysis of factors associated with 28-day mortality in SALI patients in relation to plasma YAP1 levels.
To further explore the association between plasma YAP1 levels and clinical outcomes in SALI patients, 28-day all-cause mortality was used as the primary endpoint. Clinical characteristics were compared between survivors and non-survivors, and the predictive value of plasma YAP1 for 28-day mortality was assessed.
Comparative analysis showed that patients in the non-survivor group had significantly higher age, SOFA scores, APACHE II scores, day 1 lactate, day 1 PT, day 1 INR, day 3 lactate, day 3 neutrophil percentage, day 3 PT, and day 3 INR compared with survivors (all P < 0.05). In addition, total hospital stay was significantly shorter in the non-survivor group (P < 0.001), suggesting that mortality tended to occur in the early phase of hospitalization. No significant differences were observed between the two groups in sex, site of infection, comorbidities, admission consciousness status, plasma YAP1 levels, or ICU length of stay (all P > 0.05). Detailed results are presented in Table 10.
Table 10.
Table for comparison of baseline data of SALI patients between 28-day survival group and death group
| Variables | Total(n = 78) | 28-day survival group(n = 57) | 28 days Dead Group(n = 21) | Statistic | P |
|---|---|---|---|---|---|
| BMI, kg/m2 | 23.89 ± 3.86 | 24.38 ± 3.69 | 22.55 ± 4.09 | t = 1.89 | 0.062 |
| Age, y | 69.50 (60.00, 76.00) | 68.00 (57.00, 75.00) | 76.00 (70.00, 78.00) | Z=−2.71 | 0.007 * |
| Height, cm | 168.00 (160.00, 172.00) | 168.00 (160.00, 174.00) | 165.00 (160.00, 170.00) | Z=−0.76 | 0.447 |
| Weight, kg | 65.00 (55.75, 75.00) | 67.50 (60.00, 78.00) | 60.00 (55.00, 70.00) | Z=−1.57 | 0.117 |
| SOFA | 10.00 (7.00, 12.00) | 9.00 (6.00, 12.00) | 11.00 (10.00, 13.00) | Z=−2.08 | 0.037 * |
| APACHE II | 21.00 (16.00, 27.75) | 19.00 (15.00, 26.00) | 26.00 (21.00, 31.00) | Z=−2.35 | 0.019 * |
| YAP1 level, pg/ml | 156.91 (144.90, 173.31) | 158.84 (145.44, 173.82) | 149.69 (142.38, 165.01) | Z=−0.97 | 0.330 |
| D1Lactate, mmol/L | 2.55 (1.60, 3.95) | 2.30 (1.50, 3.50) | 4.60 (2.10, 9.20) | Z=−2.83 | 0.005 * |
| D1Hemoglobin, g/L | 107.97 ± 30.54 | 110.42 ± 31.11 | 101.33 ± 28.60 | t = 1.17 | 0.246 |
| D1White blood cells, 109/L | 12.35 (8.26, 16.44) | 12.62 (8.08, 16.30) | 11.11 (9.54, 19.32) | Z=−0.16 | 0.875 |
| D1Percentage of neutrophils | 0.89 (0.84, 0.94) | 0.88 (0.84, 0.94) | 0.90 (0.87, 0.92) | Z=−0.02 | 0.982 |
| D1Platelet count, 109/L | 127.50 (74.25, 183.25) | 122.00 (73.00, 181.00) | 146.00 (84.00, 184.00) | Z=−0.28 | 0.778 |
| D1ALT, U/L | 81.50 (28.50, 206.50) | 72.60 (33.00, 190.00) | 110.00 (25.00, 232.00) | Z=−0.38 | 0.702 |
| D1AST, U/L | 134.50 (38.75, 354.00) | 124.00 (38.00, 250.00) | 169.00 (45.00, 609.00) | Z=−0.95 | 0.341 |
| D1TBIL, µmol/L | 41.70 (22.65, 70.60) | 40.10 (22.50, 59.40) | 50.70 (27.70, 80.20) | Z=−0.96 | 0.335 |
| D1IBIL, µmol/L | 30.20 (15.40, 42.38) | 29.00 (17.00, 42.90) | 32.50 (14.00, 42.00) | Z=−0.29 | 0.774 |
| D1DBIL, µmol/L | 3.65 (0.00, 19.35) | 2.90 (0.00, 11.90) | 13.00 (0.00, 40.30) | Z=−1.56 | 0.118 |
| D1ALP, U/L | 82.00 (59.25, 118.00) | 79.00 (58.00, 115.00) | 94.00 (61.00, 125.00) | Z=−0.79 | 0.430 |
| D1Serum creatinine, µmol/L | 114.15 (79.40, 215.70) | 107.00 (80.60, 204.60) | 131.90 (79.00, 227.20) | Z=−0.11 | 0.910 |
| D1Urea, mmol/L | 11.64 (7.02, 16.94) | 10.83 (6.97, 15.73) | 12.91 (8.65, 17.34) | Z=−0.66 | 0.506 |
| D1PCT, ng/ml | 7.65 (1.65, 39.82) | 10.14 (1.61, 36.21) | 6.89 (1.76, 42.49) | Z=−0.21 | 0.834 |
| D1PT, s | 15.80 (13.65, 18.62) | 15.10 (13.30, 17.40) | 17.20 (15.20, 21.60) | Z=−2.86 | 0.004 * |
| D1APTT, s | 33.20 (29.43, 40.38) | 33.10 (28.70, 37.90) | 34.90 (30.50, 44.40) | Z=−1.35 | 0.176 |
| D1INR | 1.41 (1.19, 1.68) | 1.35 (1.17, 1.57) | 1.52 (1.34, 1.99) | Z=−2.32 | 0.021 * |
| D3Lactate, mmol/L | 1.85 (1.33, 2.53) | 1.50 (1.10, 2.10) | 3.20 (2.53, 5.10) | Z=−5.11 | < 0.001 * |
| D3Hemoglobin, g/L | 96.04 ± 24.80 | 97.48 ± 25.48 | 92.15 ± 22.99 | t = 0.84 | 0.404 |
| D3White blood cells, 109/L | 10.00 (7.28, 12.90) | 10.14 (7.30, 14.13) | 9.24 (7.11, 11.70) | Z=−0.35 | 0.727 |
| D3Percentage of neutrophils | 0.88 (0.84, 0.92) | 0.88 (0.80, 0.91) | 0.89 (0.86, 0.93) | Z=−1.97 | 0.049 * |
| D3Platelet count, 109/L | 92.00 (53.00, 135.00) | 96.00 (56.00, 155.00) | 84.00 (40.00, 118.00) | Z=−1.04 | 0.300 |
| D3PT, s | 14.10 (12.72, 16.70) | 13.70 (12.20, 15.40) | 15.70 (13.30, 19.60) | Z=−2.61 | 0.009 * |
| D3APTT, s | 34.40 (30.50, 40.85) | 33.30 (30.50, 38.00) | 35.83 (34.00, 42.30) | Z=−1.56 | 0.120 |
| D3INR | 1.24 (1.08, 1.59) | 1.21 (1.06, 1.46) | 1.64 (1.17, 1.91) | Z=−2.55 | 0.011 * |
| ICU length of stay, d | 6.15 (4.00, 11.05) | 6.40 (4.00, 12.00) | 6.00 (3.55, 8.75) | Z=−0.79 | 0.430 |
| Total length of stay, d | 14.78 (9.00, 21.33) | 17.02 (11.00, 24.00) | 7.08 (4.45, 13.73) | Z=−3.93 | < 0.001 * |
| Female, n(%) | 26 (33.33) | 16 (28.07) | 10 (47.62) | χ²=2.64 | 0.104 |
| Pulmonary infection, n(%) | 38 (48.72) | 24 (42.11) | 14 (66.67) | χ²=3.71 | 0.054 |
| Abdominal infection, n(%) | 28 (35.90) | 21 (36.84) | 7 (33.33) | χ²=0.08 | 0.774 |
| Urinary tract infection, n(%) | 8 (10.26) | 4 (7.02) | 4 (19.05) | χ²=1.28 | 0.257 |
| Other infections, n(%) | 11 (14.10) | 11 (19.30) | 0 (0.00) | χ²=3.26 | 0.071 |
| Smoking history, n(%) | 35 (44.87) | 27 (47.37) | 8 (38.10) | χ²=0.53 | 0.465 |
| Drinking history, n(%) | 31 (39.74) | 24 (42.11) | 7 (33.33) | χ²=0.49 | 0.483 |
| Hypertension, n(%) | 42 (53.85) | 32 (56.14) | 10 (47.62) | χ²=0.45 | 0.503 |
| Diabetes, n(%) | 22 (28.21) | 16 (28.07) | 6 (28.57) | χ²=0.00 | 0.965 |
| Blood pressure ≤ 100 mmHg, n(%) | 42 (53.85) | 29 (50.88) | 13 (61.90) | χ²=0.75 | 0.386 |
| Respiratory rate ≥ 22 breaths per minute, n(%) | 45 (57.69) | 34 (59.65) | 11 (52.38) | χ²=0.33 | 0.564 |
| Altered consciousness on admission, n(%) | 45 (57.69) | 30 (52.63) | 15 (71.43) | χ²=2.22 | 0.136 |
BMI denotes body mass index, SOFA score denotes the Sequential Organ Failure Assessment score, APACHE II score denotes the Acute Physiology and Chronic Health Evaluation score, YAP1 denotes Yes-associated protein 1, ALT denotes alanine aminotransferase, AST denotes aspartate aminotransferase, DBIL denotes direct bilirubin, IBIL denotes indirect bilirubin, TBIL denotes total bilirubin, ALP denotes alkaline phosphatase, PCT denotes Procalcitonin, PT denotes prothrombin time, APTT denotes activated partial thromboplastin time, INR denotes international normalised ratio, ICU denotes intensive care unit. *P< 0.05.
Using 28-day mortality as the dependent variable, univariable Cox regression was first performed, followed by multivariable Cox regression including variables identified in the univariable analysis. The results showed that age (HR = 1.05, 95% CI: 1.01–1.09, P = 0.035) and day 1 lactate (HR = 1.19, 95% CI: 1.01–1.41, P = 0.035) were significantly associated with 28-day mortality in SALI patients. In contrast, plasma YAP1 levels were not significantly associated with 28-day mortality (HR = 1.00, 95% CI: 0.99–1.01, P = 0.808). Detailed results are presented in Table 11.
Table 11.
Multivariate Cox proportional hazards regression analysis was used to analyze the influencing factors of 28-day mortality in SALI patients
| Variables | β | S.E | Z | P | HR (95%CI) |
|---|---|---|---|---|---|
| Age | 0.05 | 0.02 | 2.11 | 0.035 * | 1.05 (1.01 ~ 1.09) |
| SOFA | 0.07 | 0.09 | 0.69 | 0.491 | 1.07 (0.89 ~ 1.29) |
| APACHE Ⅱ | 0.00 | 0.03 | 0.04 | 0.967 | 1.00 (0.94 ~ 1.07) |
| YAP1 level | −0.00 | 0.00 | −0.24 | 0.808 | 1.00 (0.99 ~ 1.01) |
| D1Lactate | 0.18 | 0.08 | 2.11 | 0.035 * | 1.19 (1.01 ~ 1.41) |
| PT | 0.02 | 0.06 | 0.31 | 0.754 | 1.02 (0.90 ~ 1.16) |
SOFA score denotes the Sequential Organ Failure Assessment score, APACHE II score denotes the Acute Physiology and Chronic Health Evaluation score, YAP1 denotes Yes-associated protein 1, PT denotes prothrombin time. *P < 0.05.
Discussion
This study found that plasma YAP1 levels were significantly lower in the SALI group than in the SNLI group (P < 0.001). YAP1 was identified as an independent protective factor for SALI, whereas BMI and day 1 lactate were independent risk factors. YAP1 also showed good predictive performance for SALI (AUC = 0.86). Regarding disease progression, higher YAP1 levels were positively associated with 72-hour SOFA score reduction and served as an independent protective factor for early organ function recovery. However, no significant association was observed between YAP1 levels and 28-day mortality in SALI patients.
Sepsis, as a severe systemic infectious disease, exerts a profound impact on patients’ immediate and long-term quality of life. As early as 2020, the WHO identified sepsis as the cause of one in five deaths globally (approximately 20%), with 85% of these occurring in low- and middle-income countries. Survivors of sepsis often experience varying degrees of physical, cognitive, and psychological impairment. This imposes an invisible burden on their work and daily lives [16]. Despite healthcare professionals increasingly prioritising sepsis diagnosis and treatment with more standardised management protocols in recent years, mortality rates and associated healthcare costs remain persistently high. Among these, multiple organ dysfunction syndrome (MODS) represents a pivotal stage in sepsis progression and a primary cause of mortality. Carolina Lorencio Cárdenas et al. analysed mortality rates associated with multi-organ dysfunction in sepsis, finding statistically significant reductions in mortality rates linked to organ failure across all organs except the liver in sepsis patients [17]. Furthermore, Wang Lu et al. confirmed the “lung-kidney-liver-heart” sequence of organ damage in sepsis by analysing SOFA scores from 25,077 patients [18]. These studies collectively indicate that sepsis-associated liver injury carries a relatively poorer prognosis compared to other organs, presenting a significant challenge to healthcare. Consequently, the diagnosis and management of sepsis-associated liver injury hold significant research potential and play a crucial role in improving the overall prognosis of sepsis patients. Given the rapid onset and irreversibility of the inflammatory storm, combined with existing research findings, early identification and intervention are pivotal in delaying the progression of sepsis-associated liver injury and enhancing patient survival rates [19, 20]. Regrettably, to date, there remains a clinical lack of effective markers for the early identification of sepsis-associated liver injury.
YAP1 serves as the core co-activator of transcription within the Hippo signalling pathway, with its activity regulated by phosphorylation and subcellular localisation [21]. In 2010, YAP1 was first demonstrated in vivo to function as the liver’s “organ size switch“ [22–24]. Subsequently, its tumour suppressor properties were identified, leading to its widespread application in hepatocellular carcinoma research and treatment [25, 26]. In recent years, as the mechanisms of major pathways have been further explored and supplemented, researchers have discovered that the Hippo-YAP1 pathway runs parallel to and cross-talks with the six classical major inflammatory pathways within the liver. YAP1, in particular, can act as a key factor within this pathway to regulate hepatic inflammation and liver injury [27, 28]. It is noteworthy that the role of YAP1 in hepatic inflammation remains controversial. Some studies suggest YAP1 promotes inflammation progression; for instance, Ari Kwon et al. demonstrated that YAP1 activation enhances inflammation via mitochondrial stress and macrophage polarisation pathways [9, 29, 30]. Conversely, Yu Xue et al. observed that insufficient YAP1 expression induces hepatic inflammation. Additionally, Wei Xuan et al. indicated that YAP1 activation inhibits macrophage and neutrophil infiltration [31–33]. These findings demonstrate that YAP1 engages in multiple pathways during inflammatory progression, reflecting a complex mechanism worthing further investigation.
Although the specific molecular mechanism of YAP1 regulating liver inflammation in sepsis has not been fully elucidated, its clinical translational value cannot be ignored. Finally, the findings of basic research need to be verified by population-based cohort studies to better guide clinical practice. At present, there is still a lack of clinical research on YAP1 in sepsis-associated acute liver injury (SALI). This study verified the feasibility of plasma YAP1 as a biomarker of SALI through a prospective cohort, which filled the research gap in this field to a certain extent. By confirming the independent association between YAP1 and SALI, determining the optimal clinical cut-off value, and further analyzing the relationship between YAP1 and disease progression and short-term prognosis, this study will help to bridge the gap between basic experimental findings and clinical application. Since it is difficult for animal models to fully simulate the complex pathological process and individual heterogeneity of human sepsis, such clinical validation studies are of great practical significance.
Consistent with prior studies, pulmonary infection remains the most common source of infection in sepsis. Furthermore, we found this conclusion to hold true in SALI [34–36]. Unfortunately, upon further collection of microbiological data, we found that the availability of definitive etiological evidence was limited. A considerable proportion of patients lacked confirmed pathogen culture results, largely attributable to factors such as prior antibiotic exposure before admission or the inherent limitations of conventional culture techniques in detecting fastidious organisms. Given the substantial amount of missing data, we determined that including specific pathogen types (e.g., bacterial, viral, fungal) in our analysis would compromise the reliability and validity of the findings. Therefore, to maintain scientific rigor, we focused our analysis on infection sites rather than specific pathogens.
To determine the distribution of other clinical characteristics between the two groups, independent of the liver injury status that defined the subgroups, we compared clinical data such as age, gender, past medical history, infection site, APACHE II score and modified SOFA score (excluding liver function). Results indicated no significant differences between groups in these parameters, suggesting that apart from the defining condition of liver injury, the cohorts were well-balanced in terms of demographics, comorbidities, and non-hepatic organ dysfunction severity.
It is worth noting that although the baseline characteristics of the two groups of cohorts seemed comparable, the relatively limited sample size (n = 199) inevitably limited the statistical power of the regression model. Although multivariate adjustments were made and variance inflation factors were evaluated to ensure model stability, it was still impossible to completely rule out the possibility of residual confounding factors and type II errors. Some clinical relevant variables that did not reach statistical significance in this cohort may become predictive indicators in a larger population.
Furthermore, as this investigation was conducted in a single tertiary medical centre, regional clinical practice patterns, case-mix structure, and therapeutic strategies may have influenced the observed associations. Sepsis is a highly heterogeneous syndrome, and variations in pathogen spectrum, resource allocation, and organ support modalities across institutions may modify the relationship between YAP1 expression and SALI development. Therefore, while our findings provide preliminary clinical evidence supporting the protective role of YAP1, validation in larger, multicentre cohorts encompassing diverse healthcare settings is warranted to enhance external validity and generalisability.
Given the central role of inflammation in sepsis, researchers have sought to explore the relationship between YAP1 and sepsis, though existing studies remain largely fundamental in nature. Wang et al. discovered that YAP1 competitively inhibits the E3 ubiquitin ligase from ubiquitinating NLRP3, thereby preventing degradation of the NLRP3 protein. This leads to its substantial accumulation and hyperactivation, ultimately triggering the characteristic “cytokine storm” and multi-organ damage observed in sepsis [37]. In models of sepsis-induced organ damage, YAP1 acts as a protective factor, alleviating symptoms and organ involvement in mice. For instance, within sepsis-associated lung injury models, YAP1 has been demonstrated to regulate endothelial cell activation and suppress pulmonary vasoinflammation by preventing TRAF6-mediated NF-κB activation [38]. In SAE, YAP1 suppresses ferritin autophagy-mediated ferroptosis in the hippocampus, thereby alleviating cognitive dysfunction in mice [39].
However, the effects and mechanisms of YAP1 in regulating inflammation during sepsis-associated liver injury remain unclear. Li Changyong et al., approaching from the perspective of cellular programmed death, discovered that YAP1 inhibits ferroptosis, thereby mitigating sepsis-associated liver injury [40]. This aligns with our findings: baseline data from patients in the SALI group and SNLI group revealed significantly lower YAP1 levels in SALI patients compared to SNLI patients. We therefore hypothesise that YAP1 may exert a protective role in SALI. In addition, this study also found that the history of alcohol consumption showed statistically significant differences between the two groups, while long-term alcohol consumption has been recognized as a definitive risk factor for liver injury [41].
In addition to the above factors, there were statistically significant differences between the two groups in terms of BMI, age, previous smoking history, D1 lactate, D1 platelet count, D1 ALT, D1 AST, D1 TBIL, D1 IBIL, D1 DBIL, D1 NT-proBNP, D1 PT, D1 INR, D3 lactate, D3 platelet count, D3 ALT, D3 AST, D3 TBIL, D3 IBIL, and D3 DBIL, as well as in terms of in-hospital mortality.
It is important to acknowledge the timing of YAP1 measurement in the present study. Blood samples were collected within 24 h of ICU admission, and plasma YAP1 levels were measured to assess its early predictive value for the development of SALI. However, this single time-point measurement precludes evaluation of the temporal dynamics of YAP1 throughout the course of SALI. SALI is a dynamic process characterized by fluctuations in inflammatory responses, metabolic alterations, and complex inter-organ crosstalk. Serial monitoring of YAP1 and liver injury markers may offer valuable insights. Such an approach could help determine whether fluctuations in YAP1 levels precede biochemical deterioration or instead reflect compensatory mechanisms during recovery. This important question warrants further investigation in future studies.
To further elucidate the relationship between YAP1 and sepsis-associated liver injury, we conducted a risk factor analysis. Existing literature indicates that risk factors for SALI include age, interleukin-6, history of alcohol consumption, serum iron, lactate, and total iron-binding capacity. Integrating these studies with data available at our centre, we incorporated demographic and hospitalisation records into regression analysis. Univariable logistic regression analysis showed statistically significant differences between the two groups in body weight, BMI, other infection, history of alcohol use, plasma YAP1 levels, day 1 lactate, day 1 NT-proBNP, day 3 lactate, and day 3 platelet count. However, following further multivariate logistic regression analysis, only BMI, plasma YAP1 levels, and day 1 lactate levels were identified as independent influencing factors for SALI.
This may be related to the following mechanisms: (1) High BMI leads to a persistent, low-grade inflammatory state in the body, rendering the liver more susceptible to injury from inflammation, ischemia, and hypoxia. Obesity is also frequently accompanied by metabolic disturbances and impaired mitochondrial function, which reduce the tolerance of hepatocytes. Furthermore, high BMI disrupts the intestinal barrier and promotes endotoxin translocation, which further exacerbates liver injury during sepsis via the gut–liver axis, ultimately increasing the risk of hepatic damage [42, 43]. (2) The Hippo-YAP1 pathway regulates hepatic inflammation and, under specific conditions, exerts anti-inflammatory and hepatoprotective effects. The essence of sepsis-associated liver injury remains secondary hepatic dysfunction mediated by systemic inflammation, thus retaining a strong inflammatory association [44]. Although the underlying mechanisms remain incompletely elucidated, existing literature provides theoretical support for YAP1’s protective role. Consequently, patients with SALI exhibiting more severe inflammation demonstrate lower YAP1 levels. (3) Lactate, recommended as a core component of sepsis treatment in the Save Sepsis Campaign (SSC) guidelines, is frequently associated with increased mortality and indicates potential adverse outcomes [45–48]. Brian M Fuller and colleagues noted that lactate can be used for risk stratification in critical illness. Among patients with severe sepsis, even mildly elevated lactate (2–3.9 mmol/L) can identify those at higher risk of poorer outcomes [49]. Within the sepsis population, SALI patients typically exhibit worse clinical outcomes. Combining these findings, SALI is associated with higher lactate levels. Therefore, to promptly identify and delay the onset of sepsis-associated liver injury, healthcare professionals should closely monitor the mental status of septic patients. For those with potential or established altered consciousness, ensure adequate airway management and nutritional support, while remaining vigilant against further infection exacerbation through brain-liver axis dysregulation and metabolic substrate deprivation. Furthermore, where feasible, early monitoring of YAP1 and lactate levels should be initiated. Prompt intervention and dynamic monitoring should be implemented for patients with abnormal markers, with organ protection strategies initiated proactively to improve patient prognosis.
To further explore the predictive value of YAP1 levels for sepsis-associated liver injury, we conducted an analysis using a ROC curve. Results indicate that YAP1 serves as a protective factor against SALI development, demonstrating significant predictive accuracy and sensitivity. In addition, we constructed a joint prediction model integrating YAP1, day 1 lactate level, and BMI. The AUC of the combined model was 0.88 (95% CI: 0.83 ~ 0.94), which was slightly higher than that of using YAP1 alone, suggesting that the predictive value could be improved by combining other clinical indicators to a certain extent. However, due to the limited improvement, YAP1 itself is still a simple and easily available independent biomarker with good clinical application prospects.
Of note, given the limited clinical evidence linking YAP1 to SALI, we provide a brief clinical interpretation of its potential mechanism based on existing literature. As previously noted, in mouse models of SALI, YAP1 mitigates sepsis-associated liver injury by antagonising ferroptosis. Ferroptosis exacerbates hepatic inflammation by regulating the production and release of inflammatory cytokines [40, 50]. Furthermore, Yu Hailong et al. observed that in a mouse cerebral haemorrhage model, YAP1 inhibition activated the TLR4-NF-κB and JAK2-STAT3 pathways. This manifested as decreased SOCS1/SOCS3 levels alongside significantly upregulated transcriptional activity of inflammatory cytokines and chemokines (including IL-1β, IL-6, TNF-α, and MCP-1) [51]. Therefore, based on the previous literature suggesting a potential association between the YAP1-related signaling pathway and SOCS1, we conducted an exploratory test of the plasma SOCS1 levels in two groups of patients. The results showed that compared with the SNLI group [60.89 (55.34, 66.18) pg/mL], the SOCS1 level in the SALI group was significantly lower [56.44 (52.70, 63.50) pg/mL] (P = 0.037), suggesting that there is a potential clinical correlation between SOCS1 and SALI. However, since this result is based solely on cross-sectional clinical data, it cannot be used for mechanism-based inference; and the core purpose of this study is to evaluate the clinical value of YAP1, so the results related to SOCS1 are only reported as exploratory findings. The specific role and mechanism of SOCS1 still need to be further verified by subsequent basic experiments.
Regarding disease progression, changes in SOFA scores at 24, 48, and 72 h were used to assess organ function recovery. A significant independent association between plasma YAP1 levels and SOFA score reduction was observed only at 72 h, whereas no significant associations were found at 24–48 h. This pattern is consistent with the time-dependent nature of sepsis-induced injury and the delayed activation of the Hippo-YAP pathway, suggesting that the 72-hour time point more reliably reflects YAP1-related organ recovery effects.Multivariable analysis further confirmed that higher YAP1 levels were an independent protective factor for 72-hour SOFA score reduction. Mechanistically, YAP1 may promote early organ function recovery by attenuating oxidative stress and inflammatory responses, thereby alleviating liver injury.
In terms of 28-day mortality, no significant difference in YAP1 levels was observed between survivors and non-survivors. After adjustment for confounding factors such as age, disease severity, and lactate in Cox regression models, YAP1 remained not independently associated with 28-day mortality. These findings suggest that long-term outcomes in SALI are influenced by multiple factors, including age, comorbidities, systemic inflammation, and tissue perfusion. YAP1 primarily reflects local hepatic injury and early recovery capacity, and therefore is more suitable as a biomarker for early identification and evaluation of organ function recovery rather than for long-term prognostic prediction.
In summary, YAP1 may represent a potential strategy for the early diagnosis and treatment of SALI.
Limitations
This study has several limitations. As a single-center observational study, the relatively small sample size and lack of dynamic monitoring of plasma YAP1 may have limited the statistical power and generalizability of the findings. Although 28-day mortality in SALI patients was analyzed, the relatively small number of cases and limited number of outcome events may have restricted the robustness of the long-term survival assessment. In addition, this study primarily provides a preliminary clinical exploration of YAP1 expression patterns without further elucidating its underlying regulatory mechanisms in disease pathophysiology. Therefore, the proposed conclusions require further validation in future basic and translational research.
Conclusion
This study was a single-center study with a limited sample size, which affected the statistical power and the generalizability of the conclusions, and was unable to comprehensively reflect the disease onset and treatment characteristics of different populations. Moreover, the study did not conduct continuous and dynamic monitoring of YAP1 and liver injury-related markers, and thus could not clearly determine their dynamic correlations with disease progression and outcome. In terms of mechanism, this study only discovered the correlation of relevant molecules at the clinical level and did not deeply explore their specific regulatory mechanisms in the disease. The relevant conclusions still need to be further verified by future basic research.
Authors’ contributions
Hai-long Yu, Nian-Fang Lu and Rui-qiang Zheng conceptualized the study. Jun Shao, Lu-lu Zhou, and Hao-ran Wang contributed equally to study design, data analysis, and manuscript drafting. Xiao-hua Gu, Tian-wei Wang, Ting-ting Yu, Ji-chao Zhai, Ai-peng Hu, Yuan-yuan Zhu and Wei Lei assisted in data collection and study coordination. Hai-long Yu, Nian-Fang Lu and Rui-qiang Zheng supervised the project and critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.
Funding
This study was supported by the following grants: the National Natural Science Foundation of China (No. 82371336); the Chinese Postdoctoral Science Foundation (No. 2022M711426); the Yangzhou Municipal Science and Technology Bureau (No. YZ2024091, No. YZ2024098, and No. YZ2025076); the Eighth Batch Support Program for Key Clinical Technologies of Northern Jiangsu People’s Hospital (FCJS202509); the Clinical Research Program of Northern Jiangsu People’s Hospital (SBLC25003); the Young Talent Support Program of Northern Jiangsu People’s Hospital (SBQN25009).
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was performed in conformance with the Declaration of Helsinki ethical guidelines and was approved by the Northern Jiangsu People’s Hospital (2024ky293). The Clinical Trial Number is ChiCTR2500105578. Informed consent was obtained from each participant.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jun Shao, Lulu Zhou and Haoran Wang contributed equally to this work.
Contributor Information
Hailong Yu, Email: hailongyu1982tg@163.com.
Nianfang Lu, Email: lunianfangbj@126.com.
Ruiqiang Zheng, Email: zhengruiqiang2021@163.com.
References
- 1.Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801–10. 10.1001/jama.2016.0287. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200–11. 10.1016/S0140-6736(19)32989-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Kobashi H, Toshimori J, Yamamoto K. Sepsis-associated liver injury: incidence, classification and the clinical significance. Hepatol Res. 2013;43(3):255–66. 10.1111/j.1872-034X.2012.01069.x. [DOI] [PubMed] [Google Scholar]
- 4.Strnad P, Tacke F, Koch A, Trautwein C. Liver - guardian, modifier and target of sepsis. Nat Rev Gastroenterol Hepatol. 2017;14(1):55–66. 10.1038/nrgastro.2016.168. [DOI] [PubMed] [Google Scholar]
- 5.Beyer D, Hoff J, Sommerfeld O, Zipprich A, Gassler N, Press AT. The liver in sepsis: molecular mechanism of liver failure and their potential for clinical translation. Mol Med. 2022;28(1):84. 10.1186/s10020-022-00510-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Sun J, Zhang J, Wang X, Ji F, Ronco C, Tian J, Yin Y. Gut-liver crosstalk in sepsis-induced liver injury. Crit Care. 2020;24(1):614. 10.1186/s13054-020-03327-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Guo Y, Guo W, Chen H, Sun J, Yin Y. Mechanisms of sepsis-induced acute liver injury: a comprehensive review. Front Cell Infect Microbiol. 2025;15:1504223. 10.3389/fcimb.2025.1504223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Wang J, Tao X, Liu Z, Yan Y, Cheng P, Liu B, et al. Noncoding RNAs in sepsis-associated acute liver injury: roles, mechanisms, and therapeutic applications. Pharmacol Res. 2025;212:107596. 10.1016/j.phrs.2025.107596. [DOI] [PubMed] [Google Scholar]
- 9.Li C, Jin Y, Wei S, Sun Y, Jiang L, Zhu Q, et al. Hippo signaling controls NLR family pyrin domain containing 3 activation and governs immunoregulation of mesenchymal stem cells in mouse liver injury. Hepatology. 2019;70(5):1714–31. 10.1002/hep.30700. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Liu Y, Lu T, Zhang C, Xu J, Xue Z, Busuttil RW, et al. Activation of YAP attenuates hepatic damage and fibrosis in liver ischemia-reperfusion injury. J Hepatol. 2019;71(4):719–30. 10.1016/j.jhep.2019.05.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Rigual MDM, Angulo-Aguado M, Zagorac S, Alvarez-Diaz R, Benitez-Mondejar M, Yi F, et al. Macrophages harness hepatocyte glutamate to boost liver regeneration. Nature. 2025;641(8064):1005–16. 10.1038/s41586-025-08778-6. [DOI] [PubMed] [Google Scholar]
- 12.Zhang S, Sun Z, Chen Z, Bi Y, Wei S, Mao Z, Jin J, Ding Y, Wang W. Endothelial YAP/TEAD1-CXCL17 signaling recruits myeloid-derived suppressor cells against liver ischemia-reperfusion injury. Hepatology. 2025;81(3):888–902. 10.1097/HEP.0000000000000773. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Hu JJ, Deng F, Sun QS, Xiong QM, Min Y, Feng SY, et al. Time-restricted feeding protects against septic liver injury by reshaping gut microbiota and metabolite 3-hydroxybutyrate. Gut Microbes. 2025;17(1):2486515. 10.1080/19490976.2025.2486515. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kwo PY, Cohen SM, Lim JK. ACG clinical guideline: evaluation of abnormal liver chemistries. Am J Gastroenterol. 2017;112(1):18–35. 10.1038/ajg.2016.517. [DOI] [PubMed] [Google Scholar]
- 15.Wang T-W, Zhou L-L, Yuan J, Zhou W-X, Wang H-R, Yu T-T, et al. Study of the relationship between iron metabolism disorders and sepsis-associated liver injury: a prospective observational study. World J Gastroenterol. 2025. 10.3748/wjg.v31.i14.104584. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.La Via L, Sangiorgio G, Stefani S, Marino A, Nunnari G, Cocuzza S, et al. The global burden of sepsis and septic shock. Epidemiologia (Basel). 2024;5(3):456–78. 10.3390/epidemiologia5030032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Lorencio Cardenas C, Yebenes JC, Vela E, Cleries M, Sirvent JM, Fuster-Bertolin C, et al. Trends in mortality in septic patients according to the different organ failure during 15 years. Crit Care. 2022;26(1):302. 10.1186/s13054-022-04176-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Wang L, Chen J, Zhou X, China National Critical Care Quality Control Centre, G. Multiple organ dysfunction and the mortality in sepsis: a multicenter retrospective study. Chin Med J (Engl). 2025;138(16):2037–9. 10.1097/CM9.0000000000003673. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Rhee C, Dantes R, Epstein L, Murphy DJ, Seymour CW, Iwashyna TJ, et al. Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009–2014. JAMA. 2017;318(13):1241–9. 10.1001/jama.2017.13836. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Whiles BB, Deis AS, Simpson SQ. Increased time to initial antimicrobial administration is associated with progression to septic shock in severe sepsis patients. Crit Care Med. 2017;45(4):623–9. 10.1097/CCM.0000000000002262. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Ma S, Meng Z, Chen R, Guan KL. The Hippo Pathway: biology and pathophysiology. Annu Rev Biochem. 2019;88:577–604. 10.1146/annurev-biochem-013118-111829. [DOI] [PubMed] [Google Scholar]
- 22.Driskill JH, Pan D. The Hippo Pathway in liver homeostasis and pathophysiology. Annu Rev Pathol. 2021;16:299–322. 10.1146/annurev-pathol-030420-105050. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Lu L, Li Y, Kim SM, Bossuyt W, Liu P, Qiu Q, et al. Hippo signaling is a potent in vivo growth and tumor suppressor pathway in the mammalian liver. Proc Natl Acad Sci U S A. 2010;107(4):1437–42. 10.1073/pnas.0911427107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Pibiri M, Simbula G. Role of the Hippo pathway in liver regeneration and repair: recent advances. Inflamm Regen. 2022;42(1):59. 10.1186/s41232-022-00235-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Zhang C, Wei W, Tu S, Liang B, Li C, Li Y, et al. Upregulation of CYR61 by TGF-β and YAP signaling exerts a counter-suppression of hepatocellular carcinoma. J Biol Chem. 2024;300(4):107208. 10.1016/j.jbc.2024.107208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Zhou D, Conrad C, Xia F, Park JS, Payer B, Yin Y, et al. Mst1 and Mst2 maintain hepatocyte quiescence and suppress hepatocellular carcinoma development through inactivation of the Yap1 oncogene. Cancer Cell. 2009;16(5):425–38. 10.1016/j.ccr.2009.09.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Fu M, Hu Y, Lan T, Guan KL, Luo T, Luo M. The Hippo signalling pathway and its implications in human health and diseases. Signal Transduct Target Ther. 2022;7(1):376. 10.1038/s41392-022-01191-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Meli VS, Veerasubramanian PK, Downing TL, Wang W, Liu WF. Mechanosensation to inflammation: roles for YAP/TAZ in innate immune cells. Sci Signal. 2023;16(783):eadc9656. 10.1126/scisignal.adc9656. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Kwon A, Lee NY, Yu JH, Choi MG, Park J, Koo JH. Mitochondrial stress activates YAP/TAZ through RhoA oxidation to promote liver injury. Cell Death Dis. 2024;15(1):51. 10.1038/s41419-024-06448-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Yang Y, Ni M, Zong R, Yu M, Sun Y, Li J, et al. Targeting Notch1-YAP circuit reprograms macrophage polarization and alleviates acute liver injury in mice. Cell Mol Gastroenterol Hepatol. 2023;15(5):1085–104. 10.1016/j.jcmgh.2023.01.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Liu Y, Lu T, Zhang C, Xu J, Xue Z, Busuttil RW, et al. Activation of YAP attenuates hepatic damage and fibrosis in liver ischemia-reperfusion injury. J Hepatol. 2019;71(4):719–30. 10.1016/j.jhep.2019.05.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Xuan W, Song D, Hou J, Meng X. Regulation of Hippo-YAP1/TAZ pathway in metabolic dysfunction-associated steatotic liver disease. Front Pharmacol. 2025;16:1505117. 10.3389/fphar.2025.1505117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Xue Y, Lu J, Liu Y, Gao Y, Gong Y, Yang Y, et al. Dihydroartemisinin modulated arachidonic acid metabolism and mitigated liver inflammation by inhibiting the activation of 5-LOX and COX-2. Heliyon. 2024;10(13):e33370. 10.1016/j.heliyon.2024.e33370. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Guo Q, Qu P, Cui W, Liu M, Zhu H, Chen W, Sun N, Geng S, Song W, Li X, Lou A. Organism type of infection is associated with prognosis in sepsis: an analysis from the MIMIC-IV database. BMC Infect Dis. 2023;23(1):431. 10.1186/s12879-023-08387-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Moskowitz A, Omar Y, Chase M, Lokhandwala S, Patel P, Andersen LW, et al. Reasons for death in patients with sepsis and septic shock. J Crit Care. 2017;38:284–8. 10.1016/j.jcrc.2016.11.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Xie J, Wang H, Kang Y, Zhou L, Liu Z, Qin B, et al. The epidemiology of sepsis in Chinese ICUs: a national cross-sectional survey. Crit Care Med. 2020;48(3):e209–18. 10.1097/CCM.0000000000004155. [DOI] [PubMed] [Google Scholar]
- 37.Wang D, Zhang Y, Xu X, Wu J, Peng Y, Li J, et al. YAP promotes the activation of NLRP3 inflammasome via blocking K27-linked polyubiquitination of NLRP3. Nat Commun. 2021;12(1):2674. 10.1038/s41467-021-22987-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Lv Y, Kim K, Sheng Y, Cho J, Qian Z, Zhao YY, et al. YAP controls endothelial activation and vascular inflammation through TRAF6. Circ Res. 2018;123(1):43–56. 10.1161/CIRCRESAHA.118.313143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Yang X, Duan H, Li S, Zhang J, Dong L, Ding J, et al. Yap1 alleviates sepsis associated encephalopathy by inhibiting hippocampus ferroptosis via maintaining mitochondrial dynamic homeostasis. J Cell Mol Med. 2024;28(19):e70156. 10.1111/jcmm.70156. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Wang J, Zhu Q, Li R, Zhang J, Ye X, Li X. YAP1 protects against septic liver injury via ferroptosis resistance. Cell Biosci. 2022;12(1):163. 10.1186/s13578-022-00902-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Petermann-Rocha F, Zhou Z, Mathers JC, Celis-Morales C, Raubenheimer D, Sattar N, et al. Diet modifies the association between alcohol consumption and severe alcohol-related liver disease incidence. Nat Commun. 2024;15(1):6880. 10.1038/s41467-024-51314-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Choi W, Woo GH, Kwon TH, Jeon JH. Obesity-driven metabolic disorders: the interplay of inflammation and mitochondrial dysfunction. Int J Mol Sci. 2025;26(19):9715. 10.3390/ijms26199715. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Fei M, Xu Y, Jin P, Wang Y, Zhou M. Mitochondrial dysfunction in sepsis-induced liver injury: from pathophysiology to preclinical therapeutic targets. J Transl Med. 2025;23(1):1339. 10.1186/s12967-025-07369-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Xu X, Yang T, An J, Li B, Dou Z. Liver injury in sepsis: manifestations, mechanisms and emerging therapeutic strategies. Front Immunol. 2025;16:1575554. 10.3389/fimmu.2025.1575554. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Desposito L, Bascara C. Review: sepsis guidelines and core measure bundles. Postgrad Med. 2024;136(7):702–11. 10.1080/00325481.2024.2388021. [DOI] [PubMed] [Google Scholar]
- 46.Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, Machado FR, McIntyre L, Ostermann M, Prescott HC, Schorr C, Simpson S, Wiersinga WJ, Alshamsi F, Angus DC, Arabi Y, Azevedo L, Beale R, Beilman G, Levy M. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181–247. 10.1007/s00134-021-06506-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Pino RM, Singh J. Appropriate clinical use of lactate measurements. Anesthesiology. 2021;134(4):637–44. 10.1097/ALN.0000000000003655. [DOI] [PubMed] [Google Scholar]
- 48.Villar J, Short JH, Lighthall G. Lactate predicts both short- and long-term mortality in patients with and without sepsis. Infect Dis (Auckl). 2019;12:1178633719862776. 10.1177/1178633719862776. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Fuller BM, Dellinger RP. Lactate as a hemodynamic marker in the critically ill. Curr Opin Crit Care. 2012;18(3):267–72. 10.1097/MCC.0b013e3283532b8a. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Xu W, Hou H, Yang W, Tang W, Sun L. Immunologic role of macrophages in sepsis-induced acute liver injury. Int Immunopharmacol. 2024;143(Pt 2):113492. 10.1016/j.intimp.2024.113492. [DOI] [PubMed] [Google Scholar]
- 51.Yu H, Cao X, Li W, Liu P, Zhao Y, Song L, et al. Targeting connexin 43 provides anti-inflammatory effects after intracerebral hemorrhage injury by regulating YAP signaling. J Neuroinflammation. 2020;17(1):322. 10.1186/s12974-020-01978-z. [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.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.