Risk Assessment of Serum Biomarkers with Perioperative Neurocognitive Dysfunction in Elderly Patients Undergoing Thoracic Surgery: A Prospective Cohort Study

Abstract

Perioperative neurocognitive disorder (PND) is a common complication following thoracic surgery and often leading to poor outcomes. Despite ongoing research, effective treatments for late PND remain limited. Identifying reliable biomarkers for early diagnosis is, therefore, essential. A prospective cohort study was conducted with 60 elderly patients undergoing thoracic surgery. Serum samples were collected within 10 minutes prior to anesthesia and following extubation to measure adiponectin (APN), cyclic adenosine monophosphate (cAMP), protein kinase A (PKA), aquaporin-4 (AQP4) and brain-derived neurotrophic factor (BDNF). Among PND patients, serum APN, PKA, AQP4, and BDNF levels were markedly decreased compared with the normal group. While serum cAMP (HR = 1.087, p = 0.695, 95% CI [0.284–4.166]) and PKA (HR = 0.996, p = 0.09, 95% CI [0.491–0.947]) were not significantly correlated with PND, serum APN (HR = 0.307, 95% CI [0.113–0.835], p = 0.021), AQP4 (HR = 0.204, 95% CI [0.060–0.697], p = 0.011), and BDNF (HR = 0.382, 95% CI [0.177–0.823], p = 0.014) were protective factors against PND. ROC analysis demonstrated that APN (AUC = 0.68, 95% CI [0.51–0.87]), AQP4 (AUC = 0.73, 95% CI [0.59–0.87]), BDNF (AUC = 0.73, 95% CI [0.59–0.87]), and the model of combining those biomarkers (AUC = 0.91, 95% CI [0.83–0.99]) could predict PND. PND patients exhibited a lower protective stress response to surgical trauma. High serum APN, AQP4, and BDNF levels were independent protective factors for PND, and a combined model of these biomarkers showed predictive potential for PND.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10571-025-01636-z.

Introduction

Perioperative neurocognitive disorder (PND) encompasses cognitive abnormalities emerging preoperatively or within 12 months post-surgery, manifesting primarily as memory decline, attention deficits, and impaired language comprehension (Evered et al. 2018). PND can prolong hospital stays, increase readmission rates, and even raise mortality rates, making it a significant concern in perioperative care (Vacas et al. 2021; Cusimano et al. 2019). PND arises from complex interactions between patient-specific vulnerabilities (e.g., advanced age, preexisting cognitive decline) and perioperative stressors (surgical trauma, anesthetic neurotoxicity) (Foley and Djaiani 2025; Wang et al. 2024). Although the exact mechanism has not been fully elucidated, neuroinflammation has been confirmed to be a core pathological factor. Inhibiting the inflammatory response triggered by surgery can reduce the incidence and severity of cognitive impairment (Glumac et al. 2017). Among these, thoracic surgery, due to its extensive trauma and stress responses, can provoke significant systemic inflammatory responses. With the rising incidence and detection rates of lung cancer (Leiter et al. 2023), PND has emerged as a notable complication in elderly patients undergoing thoracic surgery, who often have frail health and diminished physiological reserves (Khan et al. 2024), with an incidence reported up to 39.6% within 7 days after surgery (Wang et al. 2023).

Prompt intervention for PND is essential, as the duration of cognitive deficit correlates with poorer patient outcomes (Wyrobek et al. 2017). Early resolution of PND can help reduce healthcare costs and conserve healthcare resources (Suraarunsumrit et al. 2024). However, early detection and intervention are often hindered by the high rate of undiagnosed cases. Identifying a panel of reliable biomarkers could facilitate early diagnosis and monitoring of PND in clinical practice and research (Weng et al. 2024).

Our preliminary data have revealed that thoracic surgery in elderly patients triggers decreased serum adiponectin (APN), cyclic adenosine monophosphate (cAMP), protein kinase A (PKA), and brain-derived neurotrophic factor (BDNF), alongside increased aquaporin-4 (AQP4) (Suppl Fig. 1). APN, a plasma protein secreted by adipose tissue (Straub and Scherer 2019), is associated with cognitive function in patients undergoing thoracic surgery (Xie et al. 2019). We have demonstrated the anti-neuroinflammatory effect of APN in PND animal models (Zhang et al. 2023). The cAMP/PKA signaling pathway, known for its anti-inflammatory effects, also plays a role in cognitive and learning processes (Fu et al. 2019). Hao et al. reported that APN confers neuroprotection by activating the cAMP/PKA/BDNF signaling pathway (Bai et al. 2018), which is implicated in neurotoxicity, neuronal apoptosis, and synaptic function (Wang et al. 2022). Furthermore, AQP4 is the most abundant and efficient subtype of aquaporins in the central nervous system (Mannan et al. 2024). In a depression model, AQP4 gene knockout mediated hippocampal neuronal remodeling by modulating cAMP/PKA activity (Kong et al. 2009). It suggests a possible regulatory relationship between the cAMP/PKA/BDNF and AQP4 pathways, which may play an essential role in the APN-mediated neuroprotective effects in perioperative neurocognitive disorders.

To date, however, no studies have explored this relationship. We hypothesized that the serum levels of APN, cAMP, PKA, AQP4 and BDNF are correlated with postoperative cognitive performance. This study aimed to investigate the impact of serum levels of APN, cAMP, PKA, AQP4 and BDNF on PND and to assess the predictive value of these biomarkers in identifying PND risk. By identifying valuable biomarkers, our study seeks to advance early diagnostic capabilities, thereby reducing the healthcare burden associated with PND.

Patients and Methods

Study Design and Population

This prospective cohort study enrolled patients who underwent elective video-assisted thoracic surgery at Dongguan People’s Hospital between August 2023 and February 2024. This study was approved by the Dongguan People’s Hospital Ethics Committee (KYKT2023-026) and registered in the Chinese Clinical Trial Registry (ChiCTR2300073901; date of registration: 07-25-2023). The written informed consent was obtained from all participants.

Inclusion Criteria

(1) 65–85 years old, with complete clinical data; (2) American Society of Anesthesiologists (ASA) physical status classification of I or II; (3) no history of malignant tumors or central nervous system/mental disorders; (4) no long-term use of hypnotics, sedatives, analgesics, or antidepressants; and (5) a preoperative Mini-Mental State Examination (MMSE) score of ≥ 26.

Exclusion Criteria

(1) required a change in surgery or anesthesia plan after enrollment, (2) had communication barriers preventing assessment completion, or (3) experienced severe perioperative complications, such as cardiorespiratory arrest or anaphylactic shock.

Sample Size Calculation

The sample size was calculated using the formula for comparing two proportions under a cohort study design:

Z_α is the standard normal deviate corresponding to the desired level of significance (α = 0.05), which is 1.96. Z_β is the standard normal deviate corresponding to the desired power (1 − β = 0.8), which is 0.84. p1 is the incidence of PND in the exposed group (39.8%) according to previous report (Wang et al. 2023). p2 is the incidence in the control group with preoperative MMSE score < 26 points (7.5%) according to our pre-experiment. p and q are set to 0.5 for conservative estimation. Using these parameters, the required sample size was calculated to be approximately 54 participants. Taking into account a 10% rate of potential dropouts and data loss, we aimed to recruit 60 participants.

Outcomes

The study endpoints were the associations of preoperative and postoperative serum APN, cAMP, PKA, AQP4, and BDNF levels with PND. Cognitive function was assessed using the Chinese version of MMSE on the first, third, and fifth postoperative days, and those with a decrease in score of > 2 points were included in the PND group (Labaste et al. 2023; Lu et al. 2021), while the rest were included in the normal group (Fig. 1). The MMSE was administered by the same trained researcher (X.S) to reduce systematic errors.

Fig. 1.

Depicts the flowchart of patient enrollment

Preoperative Evaluation

On the day prior to surgery, we conducted interviews with all patients to gather their baseline information, which included age, gender, educational years, and medical histories such as hypertension, diabetes mellitus, and coronary artery disease (CAD). The data collection, physical examinations, and cognitive assessments related to PND were all conducted by a qualified anesthesiologist (X.S).

Anesthesia and Surgery

All patients received general anesthesia (double-lumen tracheal intubation) with paravertebral nerve block (T4-5, 0.25% ropivacaine 25 ml). Peripheral venous blood (5 mL) was collected within 10 min prior to anesthesia and within 10 min following extubation. All patients were induced by the same method of routine intravenous rapid anesthesia: cis-atracurium (0.15 mg/kg), sufentanil (0.3 mcg/kg), and etomidate (0.3 mg/kg) and maintained with cis-atracurium, remifentanil, and propofol. Administered through a face mask under positive pressure ventilation for a duration of 3 min, oxygen was supplied to the patient prior to intubation with a double-lumen endotracheal tube, which was guided into place using a visual laryngoscope. Following intubation, a fiberoptic bronchoscope was utilized to confirm the accurate positioning of the endotracheal tube. Subsequently, the patient was connected to an anesthetic machine for mechanical ventilation, with settings adjusted to deliver a tidal volume within the range of 6–8 ml per kilogram of body weight and a breathing rate (BR) of 12 breaths per minute. Commencing surgical procedures involved the implementation of lung isolation techniques to facilitate unilateral lung ventilation. Throughout this process, ventilation parameters were fine-tuned to maintain end-tidal carbon dioxide levels within the target range of 35–45 mmHg. The dosages of remifentanil (0.05–0.3 mcg/kg/min), propofol (4–8 mg/kg/h), and norepinephrine were adjusted intra-operatively based on the baseline blood pressure (± 20% of baseline) and Bispectral Index (BIS) values (40–60). Patients were transferred to the anesthetic recovery room after surgery and received intravenous analgesic treatment with sufentanil (1 mcg/kg/day) for two days postoperatively.

ELISA

After collection, peripheral blood was centrifuged and the supernatant was stored at – 80 °C. The concentrations of APN (Ruixinbio, Quanzhou, China. Lot: E20240202- 104694H), cAMP (Ruixinbio, Quanzhou, China. Lot: E20240202- J105815H), PKA (E20240202- 100795H), AQP4 (Ruixinbio, Quanzhou, China. Lot: E20240202-105229H) and BDNF (Ruixinbio, Quanzhou, China. Lot: E20240202- 104552H) were detected using Enzyme Linked Immunosorbent Assay (ELISA) kit. Samples and standards were assessed in duplicate, with the average values of these duplicates utilized for statistical analysis. To mitigate batch-to-batch variability, all antibodies and assay plates were sourced from the identical production lot. Furthermore, the within-batch Coefficient of Variation (CV) did not exceed 5%, and the inter-batch CV remained below 15%.

Blinding

The researchers responsible for the ELISA assays were blinded to the PND group assignments. Additionally, the researchers involved in the MMSE follow-up groupings were blinded to the biomarker testing outcomes.

Statistical Analysis

Statistical analysis was conducted using SPSS version 25.0, with a two-tailed p-value of less than 0.05 considered statistically significant. Descriptive statistics were applied to continuous variables, presented as means with standard deviations (SDs) or medians with quartiles (Qs), while categorical variables were reported as frequencies and percentages. Kolmogorov–Smirnov tests were used to assess normal distribution. Differences among groups were evaluated using unpaired t-tests for normally distributed continuous variables and Mann–Whitney U tests for categorical variables (e.g., years of education) and non-normally distributed continuous variables (such as postoperative cAMP, preoperative APN in the PND group, preoperative cAMP in the normal group, and postoperative BDNF in the normal group).

Multivariate binary logistic regression analyses were performed to explore associations between PND and serum levels of APN, cAMP, PKA, AQP4, and BDNF, confirming no multicollinearity among the independent variables (VIF < 1.5). The predictive value of serum levels of APN, AQP4, and BDNF for PND was further assessed using receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) calculated to determine diagnostic accuracy.

Results

Participant Characteristics

A total of 60 participants were initially recruited. After excluding seven participants, 53 were included in the final analysis (Fig. 1).

Of the 53 patients included, 12 (22.64%) experienced PND within 7 days postoperatively, consistent with prior studies (Khan et al. 2018). Demographic and clinical characteristics of the PND and normal groups are summarized in Table 1. No significant differences were observed between the groups of age, gender, education level, preoperative MMSE score, duration of surgery, or preoperative serum biomarker levels.

Table 1.

Characteristics and preoperative serum biomarkers of included participants

Characteristics	Normal (n = 41)	PND (n = 12)	p value*
Age, year	69.75 ± 4.22	69.85 ± 4.45	0.95
Male, yes (%)	33 (64.7%)	5 (41.7%)	0.14
Education, year	9.0 (8.0–9.0)	8.5 (8.0–10.0)	0.99
Diabetes mellitus, yes (%)	11 (26.9%)	2 (16.7%)	0.71
Hypertension, yes (%)	22 (53.7%)	6 (50.0%)	0.82
Body mass index (kg/m2)	22.5 ± 2.22	24.1 ± 3.03	0.11
CAD, yes (%)	2 (4.9%)	2 (16.7%)	0.21
Creatinine (μmol/L)	59.5 (53.5, 61.6)	72 (54, 91.5)	0.18
Hemoglobin (g/L)	122.8 ± 13.72	128.5 ± 14	0.30
hs-CRP > 5 mg/L (%)	5 (12.2%)	2 (16.7%)	0.44
Bleeding (ml)	24.29 ± 12.2	52.43 ± 16.68	0.22
Preoperative MMSE score	28.15 ± 0.69	27.9 ± 0.52	0.25
Operating time (min)	178.54 ± 55.01	157.92 ± 51.85	0.25
Total amount of propofol (mg)	104.64 ± 13.75	99.48 ± 15.63	0.39
Total amount of remifentanil (mcg)	1248.1 ± 180.32	1072.2 ± 155.19	0.27
Preoperative serum biomarkers
APN (μg/ml)	4.790 (4.090–5.410)	5.565 (3.858–5.953)	0.6786
cAMP (nmol/L)	2.550 (2.300–3.160)	2.635 (2.145–2.903)	0.2297
PKA (pg/ml)	661.7 ± 193.4	699.3 ± 172.2	0.5476
AQP4 (ng/ml)	7.822 ± 1.397	7.771 ± 1.520	0.9138
BDNF (ng/ml)	6.970 ± 1.999	6.720 ± 2.048	0.7068

CAD coronary artery disease, hs-CRP Hypersensitive C-reactive protein, MMSE Mini-Mental State Examination, APN adiponectin, cAMP cyclic adenosine monophosphate, PKA protein kinase A, AQP4 aquaporin 4, BDNF brain-derived neurotrophic factor

PND Patients Exhibited a Lower Protective Stress Response to Surgical Trauma

We further investigated the factors contributing to variations in postoperative cognitive function by comparing the changes in these serum biomarkers before and after surgery. In the normal group, we observed a significant increase in serum APN, cAMP, PKA, and BDNF levels (p < 0.001, Fig. 2A–C, E, along with a significant decrease in serum AQP4 levels (p < 0.001, Fig. 2D) following surgery, indicating a robust stress response. While the PND group exhibited increased serum cAMP and BDNF levels (p < 0.05, Fig. 2G and J) and decreased serum AQP4 levels (p < 0.001, Fig. 2I), these changes were less pronounced than those in the normal group.

Fig. 2.

Comparison of serum APN, cAMP, PKA, AQP4 and BDNF before and after operation in normal patients and PND patients. The level of serum APN (A), cAMP (B), PKA (C), AQP4 (D) and BDNF (E) in normal group before and after operation. The level of serum APN (F), cAMP (G), PKA (H), AQP4 (I) and BDNF (J) in PND group before and after operation. The data are shown as the means ± SD. *p < 0.05, ***p < 0.001 vs the before surgery group

Next, we compared the postoperative serum biomarkers between normal and PND patients, aiming to uncover the factors contributing to PND. Among PND patients, the expressions of serum APN (p < 0.05, Fig. 3A), PKA (p < 0.05, Fig. 3C), AQP4 (p < 0.05, Fig. 3D), and BDNF (p < 0.05, Fig. 3E) were markedly decreased compared with the normal group, suggesting a less effective stress response and potential pathogenic mechanisms in PND.

Fig. 3.

Comparison of postoperative serum APN, cAMP, PKA, AQP4, and BDNF levels between normal and PND patients. Postoperative serum APN (A), cAMP (B), PKA (C), AQP4 (D) and BDNF (E) levels for the normal and PND groups are shown. Data are presented as means ± SD. *p < 0.05 vs. the normal group

Serum APN, AQP4 and BDNF were Independent Protective Factors for PND in Elderly Thoracic Surgery Patients

Subsequently, univariate logistic regression analysis was conducted to estimate the effect of these biomarkers on the risk of PND (Fig. 4A. and Suppl Table 1). Our findings indicated that serum APN (Hazard Ratio [HR] = 0.555, p = 0.044, 95% confidence interval [CI] [0.313–0.985]), AQP4 (HR = 0.202, p = 0.01, 95% CI [0.058–0.702]), and BDNF (HR = 0.682, p = 0.022, 95% CI [0.491–0.947]) were independent protective factors for PND. In contrast, serum cAMP (HR = 1.087, p = 0.695, 95% CI [0.284–4.166]) and PKA (HR = 0.996, p = 0.09, 95% CI [0.491–0.947]) were not significantly correlated with PND. Furthermore, in multivariate logistic regression analysis (Fig. 4B and Suppl Table 2), the aforementioned associations remained significant. High serum APN (HR = 0.307, p = 0.021, 95%CI [0.113–0.835]), AQP4 (HR = 0.204, p = 0.011, 95% CI [0.060–0.697]), and BDNF (HR = 0.382, p = 0.014, 95% CI [0.177–0.823]) remained protective factors for PND.

Fig. 4.

Univariate and multivariate logistic regression analyses of postoperative serum APN, cAMP, PKA, AQP4, and BDNF levels. A Univariate logistic regression analysis of postoperative serum APN, cAMP, PKA, AQP4, and BDNF levels. B Multivariate logistic regression analyses of postoperative serum APN, AQP4, and BDNF levels

Model Combining Serum APN, AQP4, and BDNF Levels Could Predict PND in Elderly Thoracic Surgery Patients

Next, we analyzed the predictive value of these biomarkers in PND through receiver operator characteristic (ROC) curve analysis. Areas under the curve (AUC) for APN (0.68, 95%CI [0.51–0.87]), AQP4 (0.73, 95%CI [0.59–0.87]), and BDNF (0.73, 95%CI [0.59–0.88]) for PND are presented in Fig. 5  and Suppl Table 3. Serum APN, AQP4, and BDNF levels emerged as predictive biomarkers for PND, and the model combining them was even more sensitive (0.91, 95%CI [0.83–0.99]).

Fig. 5.

ROC analysis for serum APN, AQP4, and BDNF levels and the model combining these biomarkers. The ROC curves for APN (A), AQP4 (B), and BDNF (C) and the model based on their combination (D) are presented

Discussion

Our study demonstrated that thoracic surgery is associated with increased APN, cAMP, PKA, BDNF and decreased AQP4 observed postoperatively. These alterations were particularly more notable in normal patients than in PND patients. The traditional view holds that a decrease in protective factors before surgery will lead to PND (Miniksar et al. 2021). Here, we propose that the impaired protective stress response to trauma in PND patients, may also be one of the mechanisms contributing to its pathogenesis. Among them, APN, BDNF and AQP4 have protective value in PND, and a model composed of these three factors can predict the occurrence of PND. It simultaneously reveals the potential pathogenic mechanisms of BBB integrity, neuroinflammation, and synaptic plasticity in PND.

Currently, the diagnosis of PND largely depends on subjective cognitive tests, which lack objective biological markers, leading to difficulties in early intervention. In a recently published review, the best predictive strategy has yet to be identified (Liu et al. 2025). Because cerebrospinal fluid collection is not feasible in most perioperative settings, attention has turned to blood-based markers for individual risk assessment, which may be more practical and clinically useful (Schaefer et al. 2019).

In this study, we focused on the following three major categories of peripheral blood markers that showed significant changes after thoracic surgery in our previous results: (1) APN, known for its antioxidant, anti-inflammatory, and anti-apoptotic properties(Zhang et al. 2023), along with cAMP and PKA, which are involved in classic anti-inflammatory pathways and are linked to learning and memory(Fu et al. 2019); (2) AQP4, which plays a role in blood–brain barrier integrity and neuroinflammation (Dang and Wang 2023); and (3) BDNF, which is a key regulator of synaptic transmission and plasticity (Mobed et al. 2023).

Despite animal studies indicating a link between the cAMP/PKA pathway and neural function (Mo et al. 2024; Chen et al. 2023), our findings showed no direct association between preoperative or postoperative serum levels of cAMP, PKA and PND in patients undergoing thoracic surgery. While the clinical value of the cAMP/PKA pathway in cognitive function remains uncertain, the significant postoperative alterations in this pathway suggest that it is highly responsive to surgical stress and trauma.

In contrast, higher serum levels of APN, AQP4, and BDNF in response to surgical stress emerged as independent protective factors against PND, with odds ratios smaller than one. Contrary to reports of postoperative APN/BDNF decline (Wyrobek et al. 2017; Xie and Yao 2023; Xie et al. 2016), we observed significant increases. APN, produced primarily in peripheral white adipose tissue, can cross the blood–brain barrier and exert central effects (Zhang et al. 2025), while BDNF, abundant in the hippocampus and cerebral cortex, is also expressed in skeletal muscle, liver, and heart. Blood levels of APN and BDNF may serve as accessible biomarkers for PND, correlating well with cerebrospinal fluid and brain levels (Pan et al. 1998). We speculate that the observed differences in the expression levels of APN and BDNF may be attributed to variations in surgical procedures, anesthesia regimens, the conservative management of intraoperative fluids during single-lung ventilation of thoracic surgery or some other reason we haven’t found yet.

A decrease in AQP4 postoperatively indicated that there is also disruption of brain water balance and an increase in neuroinflammation in clinical PND patients, which is consistent with an animal finding by Dong et al. (Dong et al. 2024). Polarized expression of AQP4 at astrocytic endfeet facilitates convective influx of CSF into the brain parenchyma, driving clearance of inflammatory proteins accumulated during surgical stress (Dong et al. 2024). Concurrently, AQP4 maintains water/ion homeostasis by mitigating cytotoxic edema through rapid water efflux and regulating potassium spatial buffering, thereby preventing neuronal hyperexcitability (Eugenin von Bernhardi and Dimou 2022).

Based on the results of this study, we speculate that APN, BDNF, and AQP4 form an integrated ‘activate-clearance-repair’ axis essential for neuroprotection in PND. APN serves as the hub by enhancing BDNF synthesis via PPARγ (Guo et al. 2017), and preserving AQP4 polarity through inhibiting MMP-9 transcription-a mechanism validated in our previous cohort (Xie et al. 2016). In turn, BDNF promotes neuronal resilience by upregulating AQP4 transcription (Albini et al. 2023), while AQP4-dependent glymphatic clearance prevents neurotoxic accumulation that impairs APN/BDNF function (Dong et al. 2024). This triad operates synergistically to maintain BBB integrity, suppress neuroinflammation, and support synaptic plasticity. Disruption of any component-exacerbated by aging or anesthesia-amplifies PND vulnerability. Combinatorial strategies demonstrate superior efficacy in rescuing cognitive deficits versus monotherapies, highlighting the therapeutic promise of targeting this axis.

Meanwhile, single biomarker may not adequately predict PND in elderly patients, but a carefully selected panel of biomarkers could offer greater sensitivity and specificity for risk assessment. Our ROC curve analysis indicated that a model combining serum APN, AQP4, and BDNF levels is a strong predictor of PND. Also, we established biomarker cut-off values in this study (Suppl Table 3). Further research could integrate these values into perioperative care protocols and stratify patients into risk categories and potentially guide preoperative assessments and interventions. For instance, patients identified as high risk based on these biomarkers could receive more intensive perioperative monitoring, cognitive support, or even modifications to their anesthetic and surgical plans to minimize the risk of delirium. Furthermore, these biomarkers could also be valuable in clinical trials assessing new treatments for PND to some extent. They could serve as objective measures of treatment efficacy and help in the development of strategies to prevent or treat postoperative cognitive impairment.

Additionally, we propose that certain preexisting individual characteristics may determine PND risk. For example, we previously demonstrated higher preoperative inflammatory levels in aged mice than in adults (Zhang et al. 2020), and aging itself is a well-known risk factor for PND. While surgical trauma may induce a protective stress response, this response appeared attenuated in the PND group compared with the normal group, suggesting that an inadequate peripheral stress response may contribute to PND development in some patients.

This study has several limitations. It was conducted at a single center with a small sample size. Meanwhile, MMSE has limitations in differentiating between specific subtypes of PND, which limits the generalizability of the results. Second, we did not monitor dynamic changes in serum APN, AQP4, and BDNF levels, and we lacked long-term cognitive outcome data. Future studies with larger cohorts are warranted to examine the association between cognitive deficit types and these biomarkers in PND. Additionally, animal studies may help confirm the efficacy of these biomarkers in predicting cognitive outcomes.

Conclusions

Our study suggested that a suppressed protective stress response to surgical trauma may be a pathogenic mechanism in PND. Elevated serum APN, AQP4, and BDNF levels might serve as independent predictors of PND, and a model combining these biomarkers offers promise for predicting PND in clinical settings.

Supplementary Information

Below is the link to the electronic supplementary material.

Author Contributions

ZZ, DW, RZ, YC, and XL: Methodology. XS, SL, and HC: formal analysis and investigation. ZZ, DW and RZ: writing—original draft preparation. HX, ZZ: writing—review and editing. HX, ZZ: resources. HX: supervision.

Funding

This work was supported by Basic and Applied Basic Research Foundation of Guangdong Province (Grant No. 2023A1515012456), Dongguan Science and Technology of Social Development Program (Grant No. 2021800905202), Guangdong Medical Research Foundation (Grant No. A2024387), and Guangdong Provincial Medical Association Anesthesiology Branch Research Foundation (Grant No. GDSA202202004).

Data Availability

The datasets used and/or analyzed in this study may be provided at the reasonable request of the corresponding author.

Declarations

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Ethical Approval

This study was approved by the Dongguan People’s Hospital Ethics Committee (KYKT2023-026). The patients/participants provided their written informed consent to participate in this study.

Consent for Publication

Not applicable.