Abstract
Current methods lack high‑risk identification for acute kidney injury (AKI) after acute myocardial infarction (AMI). This study aimed to develop a metabolic‑biomarker‑based predictive system. 124 AMI patients (July 2023–October 2024) were enrolled prospectively. Logistic regression, ROC curves, and Pearson correlation were used to assess predictive values. Post-PCI kidney injury incidence was 19.39% (n = 19). The injury group showed higher LVEF, FFA, and Killip ≥ 2 rates (P < 0.05), but lower 5-MTP and UMOD (P < 0.05). FFA, 5-MTP, and UMOD were independent risk factors (P < 0.05), with combined AUC = 0.931 (superior to single markers, P < 0.05). BUN, UA, SCr, and eGFR correlated strongly with these metabolites (P < 0.05). LVEF, 5‑MTP, and UMOD are key metabolic indicators for early AKI risk identification. The integrated “biomarker+nursing” pathway improves early warning and outcomes in AMI patients.
Keywords: Acute myocardial infarction, AKI, Urocortin, Rehabilitation care pathway, 5-methoxytryptophan
Introduction
Acute myocardial infarction is a common and serious cardiovascular disease. Its incidence has been increasing in recent years, showing a trend toward younger patients. According to data from the World Health Organization [1], it is shown that about 7 million people are affected by acute myocardial infarction every year worldwide. Up to 2020, the number of deaths due to acute myocardial infarction reached 4.51 million worldwide, a 19% increase compared to 2010 [2]. This means that clinical preventive and therapeutic measures for cardiovascular diseases should be strengthened as early as possible to reduce the burden of acute myocardial infarction. Coronary intervention (PCI) is an important way of clinical treatment for acute myocardial infarction, which is mainly through cardiac catheterization technology to dredge the narrowed or occluded coronary artery lumen, playing a role in improving myocardial blood perfusion. This therapy can control disease progression and improve patients’ survival and quality of life [3].
AKI is a more frequent complication after PCI in patients with acute myocardial infarction, which is also one of the factors associated with poor prognosis of patients. Relevant reports [4] pointed out that the incidence of acute AKI after PCI was 11.56%. Another study [5] showed that the incidence of AKI during hospitalization in patients with acute myocardial infarction was about 7.1%−29.3%, and the in-hospital mortality rate of patients with AKI was 20 times higher compared with that of Non-AKI patients. In addition, the development of AKI during acute myocardial infarction is closely associated with increased long-term morbidity and mortality, continued loss of kidney function, and the risk of progression to end-stage nephrosis, which has attracted clinical attention. Therefore, it is crucial to identify the high-risk factors for the occurrence of AKI in patients with acute myocardial infarction in order to provide a reference for the early development of corresponding interventions. In the past, serum creatinine (SCr) was often taken to evaluate AKI in the clinic, but its sensitivity and specificity were limited, which could not accurately reflect the degree of AKI in time, and even affected the early detection and diagnosis of AKI [6]. In recent years, with the deepening of clinical research, metabolomics has been found to have made significant progress in predicting the occurrence of AKI associated with cardiac surgery. According to relevant reports [7], lipids are the products produced in the process of lipid metabolism, and abnormal lipid metabolism can be involved in the occurrence and development of AKI through a variety of mechanisms, and there is a close relationship between the elevation of lipid metabolism-related indexes and the rapid decline in kidney function, so it can be accurately predicted through the lipid metabolism indexes whether the patient will experience acute AKI. It has also been suggested that metabolite indicators are also associated with AKI such as 5-methoxytryptophan (5-MTP), which is a metabolite of anti-inflammatory and antifibrotic effects; and urinary regulator (UMOD), which is a protein secreted by specific cells of kidney and closely related to metabolic regulation [8, 9].
In this regard, an early understanding of the value of different metabolites in predicting the occurrence of AKI in patients with acute myocardial infarction could provide a guiding direction in the formulation of countermeasures for rehabilitation care pathway interventions. The rehabilitation care pathway is a targeted, evidence-based nursing intervention that primarily focuses on the patient, gradually guides them through rehabilitation training, and promotes gradual improvement in their prognosis. Therefore, this study provided guidance for clinical practice by providing insight into the high-risk factors for AKI in patients with acute myocardial infarction and exploring the relationship between changes in metabolites and AKI in patients, in order to explore more precise rehabilitation care pathways and interventions.
Methods
Research Object
We conducted a prospective study and selected 124 patients with acute myocardial infarction, who were included from July 2023 to October 2024. The study was approved by the hospital ethics committee and the relevant provisions of the Declaration of Helsinki were strictly adhered to; at the same time, the purpose, process, risks and potential benefits of this study were explained in detail to the patients, and the patients and their families were informed (Fig. 1).
Fig. 1.
Screening process for patients
Diagnostic Criteria
Compliance with the new recommendations of the American College of Cardiology/American Heart Association guidelines for acute myocardial infarction [10], the diagnostic criteria had an elevated or regressive myocardial enzyme profile along with at least any of the following: (1) development of pathologic Q waves, (2) presence of symptoms of ischemic chest pain, (3) imaging evidence of new regional myocardial ventricular wall motion abnormalities, (4) new ischemic electrocardiographic changes; (5) angiographic demonstrations of coronary thrombus.
Inclusion and Exclusion Criteria
Inclusion criteria: (1) complete baseline information and relevant examination data; (2) no chronic kidney disease or AKI prior to admission; (3) meeting the indications for coronary intervention (PCI) and undergoing emergency PCI within the time window; (4) hospitalization days > 1 day.
Exclusion criteria: (1) the presence of coagulation disorders, malignant tumors, or immune system diseases; (2) death within 24 h of admission; (3) age < 18 years; (4) renal transplantation or preoperative renal substitution therapy; (5) severe systemic reactions or acute and chronic infections.
General Situation
A questionnaire with baseline information was designed according to the results of the literature review which included gender, age, body mass index (BMI), education, smoking history, drinking history, diabetes, hypertension, and Killip’s classification (Grade 1: no symptoms of heart failure, unrestricted activity, normal cardiac function; Grade 2: mild heart failure, pulmonary rales with less than 1/2 lung field, mild chest tightness or shortness of breath; Grade 3: moderate to severe heart failure, pulmonary rales with greater than 1/2 lung field, accompanied by marked wet rales, gallop rhythm or pulmonary stasis, severe chest tightness and shortness of breath; Grade 4: cardiogenic shock, with hypotension and symptoms of peripheral vasoconstriction, or even a state of shock [11]), multiple vessel lesions, infarct location, time from onset to PCI, PCI pathway, type of contrast medium, and dose of contrast medium.
Grouping Approach
All patients were counted for the presence or absence of AKI within 1 week after undergoing PCI, and grouped according to the results, with those who experienced renal injury included in the renal injury group and those who did not included in the Non-AKI group. The criteria for AKI were in accordance with the criteria of the 2012 guidelines for acute kidney injury [12]: (1) a 50% increase in SCr from the basal value within 7 days was confirmed or presumed; (2) the SCr level increased to 26.5µmol/L within 48–72 h after contrast medium exposure; (3) a decrease in urine output of 0.5 ml/kg/h for more than 6 h.
Echocardiography
On the morning of the second day after admission, a color Doppler ultrasound diagnostic instrument (Shenzhen Huaxia Healthcare Medical Technology Co., Ltd, HDDZ) was selected to carry out echocardiography, and the patient was instructed to take a lying position, with the lead electrodes placed on the corresponding parts of the wrists, anterior thorax, and ankles of patients. The intensity of the homophony in the chambers and ventricular walls was observed, with the left ventricular ejection fraction (LVEF), and the left ventricular end-diastolic internal diameter (LVEDD) recorded.
Complete Blood Count
On the morning of the second day after admission, fasting venous blood (3 ml) was collected from all patients on the day of examination, with centrifuged at 3000 r/min for 10 min, with the upper layer of serum taken to determine the white blood cell count (WBC) and platelet count (PLT) by using a fully automated blood cell analyzer (Shenzhen Dymind Bio-technology Co. Ltd., D5-CRP) with the supporting reagents.
Physiological Indicators
On the morning of the second day after admission, an electronic sphygmomanometer model HBP-1320 from Omron (Dalian) Co., Ltd. was selected to measure systolic and diastolic blood pressure, with mean arterial pressure (MAP) calculated according to the formula (1/3 systolic + 2/3 diastolic); with an electrocardiogram machine (Siemens, Germany, model 9130 K) selected, patients were instructed to take the supine position, after routine clean of skin, and recorded their heart rate in the resting state.
Renal Function
On the morning of the second day after admission, fasting venous blood (3 ml) of patients was collected and placed in EDTA anticoagulation tube, centrifuged at 3000r/min (radius 15 cm), with the serum separated after 10 min and placed in −80℃ cryogenic refrigerator for storage. With a fully automated biochemical analyzer (Hyson Mechem, Japan, BX-3010) selected, the blood urea nitrogen (BUN), uric acid (UA) and serum creatinine (SCr) were detected according to the instruction manual of the reagent kit (Nanjing Sembega Biotechnology Co., Ltd.), and the estimated glomerular filtration rate (eGFR) was obtained by calculating according to the formula [175×SCr− 1.234×Age− 0.179 (females×0.79)].
Metabolites
On the morning of the second day after admission, fasting venous blood (3 ml) and fresh morning urine (10 ml) of patients were collected and then placed in EDTA anticoagulation tube, centrifuged at the speed of 3000r/min (radius 15 cm), separated the serum after 10 min, and placed in a −80℃ low temperature refrigerator for storage and spare. Atellica CH930 automatic biochemical analyzer from Siemens was chosen to detect 5-MTP and UMOD by enzyme-linked immunosorbent assay and the accompanying kit, with free fatty acid (FFA) detected by double-lift colorimetric assay and the accompanying kit.
Observation on Indicators
All patients were grouped according to whether AKI occurred within 1 week after PCI, with basic demographic and clinical characteristics, laboratory measurements, and metabolite levels compared between the two groups to analyze the value of different metabolites in predicting the occurrence of AKI in patients with acute myocardial infarction by using Logistic regression equations, receiver operating characteristic (ROC) curves and Pearson correlation models. An additional 40 patients at high risk for AKI (characterized by elevated expression levels of LVEF, 5-MTP, and UMOD) were selected, among whom 20 patients received the nursing intervention pathway and were assigned to the intervention group, while the remaining 20 patients received routine nursing care and were assigned to the control group. The nursing outcomes between the two groups were subsequently compared.
Statistical Processing
Statistical analyses were performed using SPSS 25.0. Categorical data were expressed as n (%) and compared using the χ² test. Continuous data were tested for normality using the Shapiro-Wilk method. Normally distributed data were presented as mean ± SD, with comparisons between groups conducted using independent samples t-tests and within groups using paired t-tests. Non-normally distributed data were expressed as median (P25, P75) and analyzed using the Mann-Whitney U test. A p-value < 0.05 was considered statistically significant.
Results
Analysis of AKI in Patients with Acute Myocardial Infarction
A total of 124 patients with acute myocardial infarction admitted to the CCU were initially enrolled in this study. During the data screening phase, nursing staff strictly reviewed each patient’s demographic information, clinical characteristics, laboratory findings, and imaging examination reports in accordance with the predefined inclusion and exclusion criteria. As a result, 26 patients were excluded due to incomplete baseline data or missing examination results that failed to meet the requirements for clinical research analysis. Ultimately, 98 patients were included in the final study cohort. The nursing team subsequently developed individualized CCU nursing care plans for all enrolled patients, and the occurrence of AKI was assessed within one week after treatment. Among these patients, 19 developed AKI and were assigned to the AKI group, whereas the remaining 79 patients without AKI were assigned to the Non-AKI group.
Comparison of Basic Demographic and Clinical Characteristics of the Two Groups
Comparison of the baseline demographic and clinical characteristics between the AKI and Non-AKI groups revealed no statistically significant differences in sex, age, BMI, educational level, smoking history, alcohol consumption history, diabetes mellitus, hypertension, multivessel disease, infarction location, LVEDD, time from symptom onset to PCI, PCI approach, contrast agent type, or contrast agent dosage (P > 0.05). However, the proportion of patients with Killip classification ≥ grade 2 was significantly higher in the AKI group than in the Non-AKI group, while LVEF was significantly lower in the AKI group (P < 0.05). Through hourly urine output monitoring conducted by the nursing team, it was observed that the daily urine volume in the AKI group (1654.26 mL/d) was lower than that in the Non-AKI group (1810.25 mL/d), and a decline in urine output was detected approximately 6 h before the onset of AKI. Detailed results are presented in Table 1.
Table 1.
Comparison of basic demographic and clinical characteristics of the two groups (n = 98)
| Clinical information | AKI group (n = 19) | Non-AKI group (n = 79) | χ2/t | P | |
|---|---|---|---|---|---|
| Gender | Male | 11 | 44 | 0.030 | 0.862 |
| Female | 8 | 35 | |||
| Age (years) | 56.37 ± 6.82 | 57.12 ± 6.45 | 0.450 | 0.654 | |
| BMI (kg/m2) | 22.94 ± 1.30 | 23.05 ± 1.47 | 0.299 | 0.766 | |
| Education level | Junior high school and below | 7 | 24 | 0.296 | 0.587 |
| High school and above | 12 | 55 | |||
| Smoking history | Yes | 10 | 48 | 0.419 | 0.518 |
| No | 9 | 31 | |||
| Drinking history | Yes | 8 | 36 | 0.074 | 0.785 |
| No | 11 | 43 | |||
| Diabetes | Yes | 7 | 22 | 0.595 | 0.441 |
| No | 12 | 57 | |||
| Hypertension | Yes | 9 | 36 | 0.020 | 0.888 |
| No | 10 | 43 | |||
| Killip classification | ≥ 2 grades | 12 | 24 | 7.081 | 0.008 |
| <2 grades | 7 | 55 | |||
| Multiple vessel lesions | Yes | 9 | 47 | 0.919 | 0.338 |
| No | 10 | 32 | |||
| Infarct location | Inferior wall | 11 | 51 | 0.293 | 0.589 |
| Anterior wall | 8 | 28 | |||
| LVEF (%) | 51.35 ± 6.04 | 57.28 ± 7.41 | 3.235 | 0.002 | |
| LVEDD (mm) | 49.52 ± 4.82 | 50.13 ± 5.10 | 0.473 | 0.637 | |
| Time from onset to PCI (h) | 5.03 ± 0.64 | 5.18 ± 0.52 | 1.078 | 0.284 | |
| PCI pathway | Brachial artery | 9 | 44 | 0.428 | 0.513 |
| Radial artery | 10 | 35 | |||
| Type of contrast medium | Iodixanol | 12 | 59 | 1.019 | 0.313 |
| Iohexol | 7 | 20 | |||
| Dose of contrast medium (ml) | 164.87 ± 9.65 | 169.20 ± 10.11 | 1.690 | 0.094 | |
|
Urine output (ml/d) |
1654.26 ± 121.44 | 1810.25 ± 140.29 | 4.458 | <0.001 | |
Comparison of Laboratory Indicators between the Two Groups
Comparation on the laboratory indexes between the two groups, it was found that the complete blood count [WBC (11.34 ± 3.68 vs. 10.85 ± 3.51), PLT (276.38 ± 12.45 vs. 281.46 ± 13.12)], physiological indexes [systolic blood pressure (76.58 ± 8.31 vs. 76.08 ± 8.82), diastolic blood pressure (143.24 ± 10.96 vs. 147.51 ± 11.65), heart rate (74.56 ± 7.73 vs. 75.08 ± 8.04), and MAP (61.77 ± 5.91 vs. 62.13 ± 6.11)] in the AKI group were not significantly different from those of the Non-AKI group (P > 0.05 Table 2.).
Table 2.
Comparison of laboratory indicators between the two groups (øverline X ± s)
| Inspection indicators | n | AKI group (n = 19) | Non-AKI group (n = 79) | t/Z | P |
|---|---|---|---|---|---|
| Complete blood count | |||||
| WBC (×109/L) | 98 | 11.34 ± 3.68 | 10.85 ± 3.51 | 0.541 | 0.590 |
| PLT (×109/L) | 98 | 276.38 ± 12.45 | 281.46 ± 13.12 | 1.530 | 0.129 |
| Physiological indexes | |||||
| Systolic blood pressure | 98 | 76.58 ± 8.31 | 76.08 ± 8.82 | 0.224 | 0.823 |
| Diastolic blood pressure | 98 | 143.24 ± 10.96 | 147.51 ± 11.65 | 1.450 | 0.150 |
| Heart rate (beats/min) | 98 | 74.56 ± 7.73 | 75.08 ± 8.04 | 0.255 | 0.799 |
| MAP (mmHg) | 98 | 61.77 ± 5.91 | 62.13 ± 6.11 | 0.232 | 0.817 |
Comparison of Renal Function Indexes between the Two Groups
Comparison of the renal function indexes between the two groups, it was found that BUN (5.59 ± 1.76 vs. 5.36 ± 1.68), UA (276.41 ± 10.35 vs. 270.54 ± 11.20), SCr (85.32 ± 9.37 vs. 86.77 ± 9.65), and eGFR (47.96 ± 5.22 vs.48.41 ± 5.76) in the AKI group were not statistically different compared with the Non-AKI group (P > 0.05 Fig. 2).
Fig. 2.
Comparison of renal function indexes between the two groups (A was the comparison of BUN between the AKI group and Non-AKI group, unit: mmol/L; B was the comparison of UA between the two groups, unit: µmol/L; C was the comparison of SCr between the two groups, unit: µmol/L; D was the comparison of eGFR between the groups, unit: mL/min/1.73m2)
Comparison of Metabolite Indices between the Two Groups
Comparison of the metabolite indexes between the two groups, the results showed that FFA (1.12 ± 0.36) mmol/L in the AKI group was higher compared to FFA (0.84 ± 0.27) mmol/L in the Non-AKI group, whereas the levels of UMOD (103.58 ± 3.45) mg/L and 5-MTP (17.27 ± 2.34) pg/ml in the AKI group were lower than those in the Non-AKI group of UMOD (108.46 ± 3.95) mg/L and 5-MTP (21.39 ± 3.13) pg/ml (P < 0.05 Fig. 3).
Fig. 3.
Comparison of metabolite indexes between the two groups (A was the comparison of FFA between the AKI group and Non-AKI group, unit: mmol/L; B was the comparison of UMOD between the two groups, unit: mg/L; C was the comparison of 5-MTP between the two groups, unit: pg/ml)
Correlations between Metabolic Biomarkers and Renal Function Indicators
Further correlation analyses were conducted in patients with acute myocardial infarction complicated by AKI. The results demonstrated that FFA was positively correlated with BUN, UA, and SCr, but negatively correlated with eGFR (r = 0.51, 0.48, 0.70, and − 0.67, respectively; P < 0.05). In contrast, 5-MTP was negatively correlated with BUN, UA, and SCr, while showing a positive correlation with eGFR (r = − 0.72, − 0.55, − 0.61, and 0.72, respectively; P < 0.05). Similarly, UMOD was negatively correlated with BUN, UA, and SCr, but positively correlated with eGFR (r = − 0.59, − 0.68, − 0.74, and 0.53, respectively; P < 0.05). Detailed results are presented in Fig. 4.
Fig. 4.
Correlation between renal function indicators and metabolic biomarkers in patients with acute myocardial infarction complicated by AKI
LASSO Regression Analysis of AKI Occurrence in Patients With Acute Myocardial Infarction
Given the correlations between FFA, 5-MTP, UMOD, and renal function parameters, this study further applied a two-step LASSO-logistic regression approach to determine whether these indicators were independently associated with the occurrence of AKI. AKI occurrence in patients with acute myocardial infarction was set as the dependent variable, and LASSO regression was employed for variable selection from all potential influencing factors. Ten-fold cross-validation was subsequently performed to determine the optimal λ value. As the penalty coefficient λ increased, the regression coefficients of the independent variables were gradually compressed, as illustrated in Fig. 5(A). Ultimately, the λ value corresponding to the minimum cross-validation error (λ = 0.039) was selected as the optimal parameter, as shown in Fig. 5(B). Seven significant influencing factors were identified: BUN, UA, LVEF, Killip classification, FFA, 5-MTP, and UMOD, with no evidence of multicollinearity observed among these variables.
Fig. 5.
Clinical feature selection based on the LASSO regression model
Influencing Factors for AKI Occurrence in Patients With Acute Myocardial Infarction
Based on the baseline differences observed between groups and the significant correlations between metabolic biomarkers and renal function parameters, multivariate logistic regression analysis was further performed to identify independent risk factors for AKI following PCI in patients with acute myocardial infarction. The results demonstrated that LVEF (OR: 0.837, 95% CI: 0.716–0.980), 5-MTP (OR: 0.642, 95% CI: 0.441–0.933), and UMOD (OR: 0.679, 95% CI: 0.512–0.900) were all independent influencing factors for AKI occurrence in patients with acute myocardial infarction (P < 0.05). Detailed results are presented in Table 3.
Table 3.
Multifactorial analysis of AKI in patients with acute myocardial infarction
| Variables | β | S.E | Z | P | OR(95%CI) |
|---|---|---|---|---|---|
| BUN | 0.528 | 0.376 | 1.970 | 0.160 | 1.695(0.811–3.543) |
| UA | 0.105 | 0.060 | 3.080 | 0.079 | 1.111(0.988–1.250) |
| LVEF | −0.177 | 0.080 | 4.908 | 0.027 | 0.837(0.716–0.980) |
| Killip classification | −1.212 | 0.971 | 1.558 | 0.212 | 0.298(0.044–1.996) |
| FFA | 4.192 | 2.186 | 3.676 | 0.055 | 4.151(0.911–8.668) |
| 5-MTP | −0.443 | 0.191 | 5.386 | 0.020 | 0.642(0.441–0.933) |
| UMOD | −0.387 | 0.144 | 7.236 | 0.007 | 0.679(0.512–0.900) |
Early Identification of AKI in Patients with Acute Myocardial Infarction based on different Metabolic Biomarkers
ROC curves were constructed for LVEF, 5-MTP, and UMOD to evaluate their performance in the early identification of AKI in patients with acute myocardial infarction. The results demonstrated that the AUC values of LVEF, 5-MTP, and UMOD for predicting AKI occurrence were 0.744, 0.855, and 0.835, respectively, indicating that all three biomarkers possessed predictive value for AKI in patients with acute myocardial infarction. Notably, the combined predictive model incorporating LVEF, 5-MTP, and UMOD achieved an AUC of 0.921, which was significantly superior to that of each individual predictor alone (P < 0.05). From a nursing perspective, LVEF, UMOD, and 5-MTP may serve as valuable early screening indicators and are recommended for inclusion in AKI risk assessment tools for ICU patients with myocardial infarction. Detailed results are presented in Fig. 6.
Fig. 6.
ROC curves of LVEF, 5-MTP, and UMOD for predicting AKI in patients with acute myocardial infarction
Nursing Intervention
An additional 40 patients at high risk for AKI (characterized by abnormal expression levels of LVEF, 5-MTP, and UMOD) were selected. Among them, 20 patients received the nursing intervention pathway and were assigned to the intervention group, while the remaining 20 patients received routine nursing care and were assigned to the control group. Comparative analysis of nursing outcomes between the two groups demonstrated that the incidences of AKI and adverse events were significantly lower in the intervention group than in the control group, while the duration of hospitalization was also significantly shorter in the intervention group (P < 0.05). Detailed results are presented in Table 4.
Table 4.
Comparison of nursing outcomes between the intervention group and the control group
| Group | AKI incidence [n (%)] | Length of hospital stay (d) | Adverse events [n (%)] |
|---|---|---|---|
| Intervention group (n = 20) | 2(10.00) | 7.39 ± 2.04 | 20.00%(1/2/1/0) |
| Control group (n = 20) | 8(40.00) | 8.95 ± 2.67 | 50.00%(3/4/1/2) |
| χ2/t | 4.800 | 2.076 | 3.956 |
| P | 0.028 | 0.045 | 0.047 |
Adverse events included pulmonary infection, gastrointestinal discomfort, puncture-site hematoma, and other complications
Discussion
PCI is a key treatment for acute myocardial infarction. When performed within the optimal time window, it can reopen infarcted vessels, restore blood flow, and improve prognosis. However, some patients develop AKI post‑PCI, which increases hospital stay and one‑year mortality, leading to poor outcomes [13]. Thus, early identification of AKI risk factors is crucial for improving prognosis. Among 98 patients, 19 (19.39%) developed AKI after PCI, consistent with clinical data, confirming that AKI is a notable risk post‑PCI in AMI patients and requires focused attention.
The pathogenesis of AKI in acute myocardial infarction is complex and multifactorial, involving synergistic mechanisms, with cardiac dysfunction closely linked to renal injury [14]. Among cardiac function indicators, LVEF is a key parameter of systolic function, reflecting myocardial contractility, pumping efficiency, and overall cardiac status [15]. Previous clinical studies have reported that, compared with conventional cardiac functional indicators, LVEF can detect impaired cardiac function at an earlier stage [16]. In this study, LVEF was significantly lower in the AKI group and was identified as an independent risk factor. Mechanistically, reduced LVEF indicates impaired systolic function and decreased cardiac output, which may lead to inadequate renal perfusion and ischemic‑hypoxic injury [17, 18]. In addition, cardiac insufficiency may activate the neuroendocrine system, leading to water and sodium retention, microcirculatory dysfunction, and inflammatory responses. Collectively, these pathological alterations may jointly contribute to the initiation and progression of AKI [19].
In cardiology, 5-MTP is associated with ventricular remodeling and plays a key role in myocardial fibrosis by regulating extracellular matrix remodeling and influencing cardiac stress responsiveness [20]. 5-MTP, a 5-methoxyindole metabolite of L-tryptophan, has antifibrotic and anti-inflammatory properties and provides protective effects in acute myocardial infarction and renal injury [21]. Our results showed that 5‑MTP levels were lower in the AKI group and served as both a risk factor and a predictive marker. Mechanistically, 5‑MTP protects against oxidative stress by preserving mitochondrial antioxidant enzymes and reducing ROS‑generating NADPH oxidases, while also alleviating inflammation by controlling cytokines/chemokines and limiting macrophage/T‑cell infiltration [22]. In a mouse unilateral ureteral obstruction model, renal 5‑MTP levels were reduced; 5‑MTP supplementation alleviated renal interstitial fibrosis and suppressed inflammatory signaling [19]. Thus, 5‑MTP exerts anti‑inflammatory and antifibrotic effects, reducing epithelial and glomerular injury. Its levels correlate with BUN, UA, SCr, and eGFR. Decreased 5‑MTP may induce AKI via NF‑κB activation and Nrf2 inhibition, serving as a biomarker for early CKD and predicting renal damage in AMI [23, 24].
UMOD is a multifunctional glycoprotein expressed in epithelial cells of renal distal tubules and collecting ducts, playing a key role in maintaining urine concentration and dilution. There was a retrospective study [25] which analyzed the predictive value of UMOD < 120 mg/L for the occurrence of AKI after PCI in patients with acute myocardial infarction, and it found that this index was a risk factor for AKI, with some predictive value at the same time. This conclusion was similar to the results of this study, and the UMOD level in the AKI group was significantly lower than that in Non-AKI group, further confirming the value of UMOD in predicting AKI as well as related to the renal function of patients. At this stage, several clinical studies have demonstrated that UMOD levels change in the serum and urine of patients with AKI and can be used as one of the markers for early diagnosis of AKI [26, 27]. Analysis of the causes found that once AKI occurred, it could be implicated in acute myocardial infarction patients with renal tubular epithelial cell damage, and activate the inflammatory mediators of body, causing renal inflammatory response, which damaged to not only the renal tubular epithelial cells, but also a certain degree of impact on the synthesis and release of UMOD; the cardiac function abnormalities of patients with acute myocardial infarction could affect the renal perfusion situation, coupled with the AKI after the production of nephrotoxicity substances, making the renal tubular epithelial cells and endothelial cells damaged, directly reducing the expression level of UMOD [28, 29].
The study suggests that LVEF, 5‑MTP, and UMOD may serve as adjunctive biomarkers for early prediction of perioperative AKI risk in AMI patients undergoing PCI. Their clinical utility lies in identifying high‑risk patients rather than replacing existing treatments. Based on these biomarkers, a nursing intervention pathway was developed, including comprehensive assessment, regular monitoring of renal function, bed rest, positioning to avoid renal compression, low‑salt/low‑fat diet with controlled protein intake, urine monitoring, lipid management, and progressive rehabilitation. In a pilot study of 40 high‑risk patients (20 receiving the nursing pathway vs. 20 routine care), the intervention group showed significantly lower AKI incidence, fewer adverse events, and shorter hospital stay (P < 0.05). This “metabolic biomarker + nursing assessment” pathway may enable early risk stratification and proactive preventive care.
However, limitations include single‑center design, small sample size (only 19 AKI events, raising overfitting risk), variability in biomarker detection methods, lack of long‑term follow‑up, and the exploratory nature of the nursing pathway (only 40 patients). Future multicenter studies with larger samples, standardized protocols, long‑term follow‑up, and randomized controlled trials are needed to validate the findings and establish optimized rehabilitation pathways.
In conclusion, LVEF, 5-MTP, and UMOD may serve as important metabolic indicators for the early identification of patients at high risk for AKI in the CCU setting. The establishment of a “metabolic biomarker + nursing assessment” pathway may significantly improve early warning efficiency, reduce the incidence of AKI and adverse events, and shorten hospitalization duration, thereby providing evidence-based support for individualized nursing interventions in patients with acute myocardial infarction.
Author Contributions
Yan Yao (First Author): Conceptualization, Methodology, Investigation, Formal Analysis, Writing – Original Draft. Fei Liu (Co-First Author): Investigation, Data Curation, Validation, Writing – Review & Editing. Jie Meng (Second Author): Literature Review, Software, Visualization. Jinjing Zhang (Third Author): Data Collection, Statistical Analysis, Proofreading. Mingxing Chen (Corresponding Author): Supervision, Funding Acquisition, Writing – Final Approval. Tianqing Cao (Co-Corresponding Author): Methodology Optimization, Ethical Compliance, Academic Oversight.
Funding
Not applicable.
Data Availability
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Declarations
Ethics approval
This study was reviewed and approved by the Ethics Committee of Northern Jiangsu People’s Hospital (Clinical Trial Number: Not Applicable).
Consent to participate
Written informed consent was obtained from all participants, and all procedures followed the principles of the Declaration of Helsinki.
Conflict of interest
No potential conflict of interest was reported by the authors.
Footnotes
Yan Yao and Fei Liu these authors contributed equally to this work and share first authorship.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
MingXing Chen, Email: chenmx0919@163.com.
Tianqing Cao, Email: ctq19890924@126.com.
References
- 1.Ando H, Sawano M. Cardiac arrest and post-discharge mortality in patients with myocardial infarction: A large-scale nationwide registry analysis. Resusc Plus. 2024;18:100647. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Fan J, Liu J, Wang G, et al. Dynamic changes in the renal function of acute myocardial infarction patients with reduced eGFR after emergency percutaneous coronary intervention. Int Heart J. 2023;64(5):798–806. [DOI] [PubMed] [Google Scholar]
- 3.Chang YT, Chung MC, Chang CH, et al. Anti-EMT and anti-fibrosis effects of protocatechuic aldehyde in renal proximal tubular cells and the unilateral ureteral obstruction animal model. Pharm Biol. 2022;60(1):1198–206. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Chao CT. Neutrophil percentage as a potential biomarker of acute kidney injury risk and short-term prognosis in patients with acute myocardial infarction in the elderly [Letter]. Clin Interv Aging. 2024;19:761–2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Feng W, Zhou J, Lun Z, et al. A comparison between two different definitions of contrast-associated acute kidney injury for long-term mortality in patients with chronic kidney disease undergoing coronary angiography. Clin Interv Aging. 2024;19:303–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Gao S, Chao Y, Li N, et al. An integrated proteomics and metabolomics strategy for the mechanism of calcium oxalate crystal-induced kidney injury. Front Med (Lausanne). 2022;9:805356. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Huang K, Wen XQ, Zhang W, et al. Predictive value of 5-methoxytryptophan on long-term clinical outcome after PCI in patients with acute myocardial infarction-a prospective cohort study. J Cardiovasc Transl Res. 2024;17(5):1036–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Huang X, Mulasihan M, Tudi M, et al. A single-center clinical study of acute kidney injury associated with acute myocardial infarction. Int Urol Nephrol. 2024;56(1):325–34. [DOI] [PubMed] [Google Scholar]
- 9.Ider M, Ok M, Naseri A, et al. Acute kidney injury is associated with higher serum Cys-C and NGAL concentrations, and risk of mortality in premature calves with respiratory distress syndrome. Animals (Basel). 2023. 10.3390/ani13020232. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Spinler SA, Hilleman DE, Cheng JW, et al. New recommendations from the 1999 American College of Cardiology/American Heart Association acute myocardial infarction guidelines. Ann Pharmacother. 2001;35(5):589–617. [DOI] [PubMed] [Google Scholar]
- 11.Jiang Y, Zhu X, Jordan K, et al. Dyslipidemia-induced renal fibrosis related to ferroptosis and endoplasmic reticulum stress. J Lipid Res. 2024;65(9):100610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Kaur J, Bhardwaj N, Reddy S, et al. Acute kidney injury in acute myocardial infarction and its outcome at 3 and 6 months. Saudi J Kidney Dis Transpl. 2023;34(4):297–304. [DOI] [PubMed] [Google Scholar]
- 13.Kim KA, Lee JE, Choi IJ, et al. Effect of renin-angiotensin system blockers in acute myocardial infarction patients with acute kidney injury. Cardiorenal Med. 2024;14(1):178–90. [DOI] [PubMed] [Google Scholar]
- 14.Nakamura T, Watanabe M, Sugiura J, et al. Prognostic impact and predictors of persistent renal dysfunction in acute kidney injury after percutaneous coronary intervention for acute myocardial infarction. Sci Rep. 2024;14(1):6299. 10.1038/s41598-024-56929-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Kristensen AMD, Atar D, Yndigegn T, et al. Beta-Blockers after Myocardial Infarction with Normal Ejection Fraction[J]. N Engl J Med. 2026;394(6):540–50. [DOI] [PubMed] [Google Scholar]
- 16.Eggers KM, Eggers KM, Hamilton E, et al. Predictors of depressed left-ventricular ejection fraction after acute myocardial infarction. Int J Cardiol. 2026;445:134040. [DOI] [PubMed] [Google Scholar]
- 17.Smarz K, Tysarowski M, Zioło J, et al. Low chronotropic response in post-myocardial infarction exercise test predicts worse prognosis in patients with preserved or mildly reduced left ventricular ejection fraction. Int J Cardiol. 2025;433:133320. [DOI] [PubMed] [Google Scholar]
- 18.Fonseca AC. Stroke and recent myocardial infarction, reduced left ventricular ejection fraction, left ventricular thrombus, and wall motion abnormalities. Curr Cardiol Rep. 2023;25(12):1687–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Bae MW, Moon SG, Jung KT, et al. Prognostic impact of changes in left ventricular ejection fraction and wall motion score index in patients with myocardial infarction[J]. Front Cardiovasc Med. 2025;12:1530006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Ye J, Liu C, Deng Z, et al. Risk factors associated with contrast-associated acute kidney injury in ST-segment elevation myocardial infarction patients: a systematic review and meta-analysis. BMJ Open. 2023;13(6):e070561. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Knott JD, Ola O, DE Michieli L, et al. Diagnosis of acute myocardial infarction in patients with renal failure using high-sensitivity cardiac troponin T. Eur Heart J Acute Cardiovasc Care. 2024;13(7):546–58. [DOI] [PubMed] [Google Scholar]
- 22.Zhang P, Wu L, Zou TT, et al. Machine learning for early prediction of major adverse cardiovascular events after first percutaneous coronary intervention in patients with acute myocardial infarction: retrospective cohort study JMIR Form Res. 2024;8:e48487. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Sacco A, Montalto C, Bravi F, et al. Non-ST-elevation acute coronary syndrome in chronic kidney disease: prognostic implication of an early invasive strategy. Minerva Cardiol Angiol. 2023;71(1):44–50. [DOI] [PubMed] [Google Scholar]
- 24.Sonoda T, Wada H, Ogita M, et al. Clinical features and predictors of outcome in patients with acute myocardial infarction complicated by out-of-hospital cardiac arrest. BMC Cardiovasc Disord. 2022;22(1):185. 10.1186/s12872-022-02628-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Sun X, Wang H, Liu Y, et al. 5-Methoxytryptophan alleviates lipopolysaccharide-induced acute kidney injury by regulating Nrf2-mediated mitophagy. J Inflamm Res. 2024;17:9857–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Tanriover C, Copur S, Mutlu A, et al. Early aging and premature vascular aging in chronic kidney disease. Clin Kidney J. 2023;16(11):1751–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Turan A, RIvas E Devereauxp J, et al. Relative contributions of anaemia and hypotension to myocardial infarction and renal injury: Post hoc analysis of the POISE-2 trial [J]. Eur J Anaesthesiol. 2023;40(5):365–71. [DOI] [PubMed] [Google Scholar]
- 28.Vukmirovic PM, Bukumiric Z. ILINCIC B, Serum uromodulin, a potential biomarker of tubulointerstitial damage, correlates well with measured GFR and ERPF in patients with obstructive nephropathy. Med (Kaunas), 2022;58(12). [DOI] [PMC free article] [PubMed]
- 29.Wu JY, Lee GL, Chueh YF, et al. 5-Methoxytryptophan protects against Toll-like receptor 2-mediated renal tissue inflammation and fibrosis in a murine unilateral ureteral obstruction model. J Innate Immun. 2025;17(1):78–94. [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 data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.