Preoperative sleep disturbance and Postoperative delirium in older Adults: A Mediation Analysis via Insulin-Like Growth Factor-1

Preoperative sleep disturbance and Postoperative delirium in older Adults: A Mediation Analysis via Insulin-Like Growth Factor-1 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preoperative sleep disturbance and Postoperative delirium in older Adults: A Mediation Analysis via Insulin-Like Growth Factor-1 Chen Wang, Zhengzhen Huang, Jing Wang, Ziyu Zhu, Meimei Zhu, Yan Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8754243/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Postoperative delirium (POD) is a clinically important complication in older surgical patients. Preoperative sleep disturbance is common and potentially modifiable, yet the biological pathways linking sleep quality to POD remain incompletely understood. We examined the association between preoperative sleep quality and POD, tested whether insulin-like growth factor-1 (IGF-1) partially mediates this relationship, and evaluated the incremental discriminatory value of adding IGF-1 to a clinical prediction model. Methods: This single-center prospective study screened 260 patients aged ≥60 years undergoing elective surgery under general anesthesia at the Naval Medical Center (May–July 2025); 254 were included in the final analysis. Sleep quality in the preceding month was assessed by Pittsburgh Sleep Quality Index (PSQI) and fasting serum IGF-1 was measured. POD was screened daily on postoperative days 1–3 using the Confusion Assessment Method (CAM). Multivariable logistic regression identified independent POD factors; mediation analysis quantified IGF-1 mediation of the PSQI–POD association. Models with vs without IGF-1 were compared using receiver operating characteristic (ROC) curves; areas under the curve (AUCs) were compared with DeLong’s test. Results: Among 254 patients, POD occurred in 45/254 (17.7%). In multivariable logistic regression, higher PSQI scores (OR 1.49, 95% CI 1.26–1.77; p<0.001), older age (OR 1.13, 95% CI 1.06–1.21; p<0.001), and higher C-reactive protein (CRP) levels (OR 1.28, 95% CI 1.14–1.45; p<0.001) were independently associated with increased POD risk, whereas higher IGF-1 levels were independently protective (OR 0.98, 95% CI 0.96–0.99; p=0.038). Mediation analysis indicated a significant partial mediation by IGF-1, accounting for 25% of the total effect (95% CI 0.09–0.74; p=0.002). In ROC analyses, the model without IGF-1 achieved an AUC of 0.82 (95% CI 0.76–0.88), and the model including IGF-1 achieved an AUC of 0.83 (95% CI 0.77–0.89); the difference was not significant (DeLong Z=−1.52, p=0.129). Conclusions : Poor preoperative sleep quality was independently associated with POD in older patients, with IGF-1 partially mediating this relationship. Adding IGF-1 provided limited incremental discrimination. Trial registration: Chinese Clinical Trial Registry (ChiCTR), ChiCTR2500109251 . Registered on 16 September 2025. Retrospectively registered. Sleep disturbance Postoperative delirium Insulin-like growth factor-1 (IGF-1) Mediation analysis Figures Figure 1 Figure 2 Figure 3 1. Introduction Postoperative delirium (POD) is a common perioperative neuropsychiatric syndrome of acute brain dysfunction, characterized by acute, fluctuating disturbances in attention and cognition, and is associated with adverse recovery trajectories and poorer longer-term outcomes.[1, 2]Older adults are at substantially higher risk than younger patients; POD incidence varies widely by surgical population and can be high after major surgery.[3, 4] Therefore, clarifying modifiable mechanisms and improving perioperative risk stratification are clinically important for optimizing the care of older surgical patients. Sleep disturbance is highly prevalent in older adults, and preoperative anxiety, environmental disruption, and perioperative stress often further worsen sleep quality. Increasing evidence suggests that preoperative sleep disturbance (PSD) is associated with POD, although findings may differ across populations. [5, 6] Mechanistically, poor preoperative sleep has been implicated in enhanced neuroinflammatory responses, disrupted neurotransmitter homeostasis, and impaired neural repair processes, potentially increasing vulnerability to POD. [7, 8] Insulin-like growth factor-1 (IGF-1) is a neurotrophic factor with immunomodulatory properties in the central nervous system, and altered IGF-1 signaling have been proposed as a potential biological pathway linking PSD to POD. [9, 10]Prior studies suggest that sleep loss may be accompanied by reduced IGF-1 activity, with downstream effects on neuronal survival, synaptic plasticity, and cognitive function. [11, 12] In preclinical models, exogenous IGF-1 has shown to mitigate sleep disturbance–related neural injury via PI3K/Akt pathway activation. [13, 14] Collectively, these observations support IGF-1 as a plausible mechanistic bridge between PSD and POD, while its translational value in perioperative delirium warrants further clarification. Against this background, we conducted a prospective clinical observational study to examine the relationship between preoperative sleep quality and POD in older surgical patients, focusing on the potential mediating role of IGF-1. This work aims to advance mechanistic understanding and inform perioperative risk stratification and future preventive strategies. 2. Methods This study was conducted in accordance with the Declaration of Helsinki and relevant regulations .The study design, implementation, and data collection adhered to these principles throughout . Written informed consent was obtained from all participants, and participant confidentiality was maintained. Ethical approval was granted by the Ethics Committee of the Naval Medical Center (approval No. AF-HEC-010), and the study was registered with the Chinese Clinical Trial Registry (ChiCTR2500109251). 2.1 Participants We enrolled inpatients patients aged ≥60 years who underwent elective surgery under general anesthesia at the Naval Medical Center. Eligible procedures included thoracic surgery, urological laparoscopic surgery, orthopedic spine surgery, or general abdominal surgery. These surgical categories were selected to capture a cohort with relatively substantial perioperative stress exposure and broadly comparable perioperative management, thereby reducing heterogeneity related to surgical complexity and improving internal consistency of perioperative care pathways. Patients were excluded if they: (1) had a prior diagnosis of delirium, cognitive impairment, or severe psychiatric disease; (2) were receiving medications that could influence delirium risk or alter IGF-1 levels (e.g., growth hormone or antipsychotic agents); (3) had severe comorbidities likely to materially affect sleep or metabolic function (e.g., end-stage hepatic or renal disease); (4) required an intensive care unit (ICU) stay >72 hours; or (5) declined participation or were unable to complete follow-up assessments as scheduled. 2.2 Preoperative assessment and data collection Preoperatively, sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), a validated self-report questionnaire assessing sleep quality over the preceding month . [15, 16]The PSQI comprises seven components derived from 18 items, yielding a global score ranging from 0 to 21; a global score ≥5 was used to define PSD, with higher scores indicating poorer sleep quality. [15] In this study, PSQI reflected sleep quality during the month prior to admission and was modeled as a continuous variable in the primary analyses to preserve information and statistical power; PSD (a dichotomized form of PSQI) was not included concurrently with PSQI to avoid redundancy and potential collinearity. We collected preoperative demographic and clinical characteristics (age, sex, body mass index, ASA physical status, years of education, and preoperative hemoglobin), comorbidities (e.g., hypertension, diabetes mellitus, coronary artery disease, and cerebrovascular disease), and surgical category. Perioperative variables included intraoperative anesthetic drug doses (sufentanil, ciprofol, and remifentanil), the Minimum mean arterial pressure, operative time, and time to awakening. Laboratory measurements included preoperative serum IGF-1 and preoperative C-reactive protein (CRP) levels. Peripheral venous blood was collected preoperatively; serum was separated by centrifugation and stored at −80 °C until analysis. Serum IGF-1 was quantified using an enzyme-linked immunosorbent assay (ELISA) kit (Human Insulin-like Growth Factor-1 [IGF-1] ELISA Research Kit; Jiangsu Sumeike Biotechnology Co., Ltd., China) and is reported in ng/mL. Preoperative CRP was measured by the hospital laboratory using an immunoturbidimetric assay and is reported in mg/L. 2.3 Anesthesia management All enrolled patients received a standardized anesthetic protocol. General anesthesia was induced with midazolam (0.05–0.10 mg/kg), sufentanil (0.10–0.30 μg/kg), cisatracurium besylate (0.15 mg/kg), and ciprofol (0.3–0.4 mg/kg). Anesthesia was maintained with combined intravenous and inhalational agents, including ciprofol (continuous infusion, 0.8–2.4 mg/kg/h), remifentanil (continuous infusion, 0.05–0.30 μg/kg/min), cisatracurium besylate (continuous infusion, 1–3 μg/kg/min; or intermittent boluses of 0.02 mg/kg as needed), and sevoflurane (end-tidal concentration, 0.5–3.0%). Anesthetic depth was monitored using the Cerebral State Index (CSI), and agents were titrated to maintain CSI at 40–60. Hemodynamic management targeted mean arterial pressure (MAP) within ±20% of baseline and generally ≥65 mmHg. Baseline MAP was defined as the pre-induction value measured after the patient had been resting quietly upon arrival in the operating room. Hypotension (MAP 20% below baseline) was managed with protocolized adjustments including intravenous fluids, optimization of anesthetic depth/analgesia, and vasopressors when needed. Hypertension (>20% above baseline) was managed by deepening anesthesia and/or optimizing analgesia, with antihypertensive agents when clinically indicated. Normothermia was maintained throughout surgery, with core temperature monitored via a nasopharyngeal probe and maintained at ≥36.0°C. 2.4 Outcomes and adverse events The primary outcome was the occurrence of POD. Delirium was assessed using the Confusion Assessment Method (CAM). [17]Assessments were performed at 2 hours postoperatively, on postoperative day 1, and on the morning of postoperative day 2 (three assessments in total) by anesthesiologists who were blinded to the study data, and findings were recorded prospectively. POD was considered present if delirium was detected at any assessment time point. Postoperative adverse events, including nausea, vomiting, and somnolence, were recorded using predefined criteria. Nausea was defined as a subjective feeling of the urge to vomit. Vomiting was defined as any episode of emesis or retching. Somnolence was defined as clinically significant drowsiness requiring repeated verbal or tactile stimulation to maintain arousal. The incidence of these events was documented at prespecified postoperative time points. 2.5 Sample size estimation For a priori sample size planning, we assumed an anticipated POD incidence of approximately 10%. To ensure the stability of the regression model, we applied the events-per-variable (EPV) principle, requiring at least 10 outcome events per predictor. Given that the core objectives were to evaluate the PSQI–POD association and to examine the role of IGF-1, we prespecified a minimal core model including two key variables (PSQI and IGF-1). Accordingly, the minimum required sample size was estimated as participants. 2.6 Statistical analysis All analyses were performed using R (version 4.4.2). Categorical variables are presented as counts (percentages) and were compared using the χ² test or Fisher’s exact test, as appropriate. Continuous variables are presented as mean ± standard deviation (SD) for approximately normally distributed data or as median (interquartile range [IQR]) for non-normally distributed data; between-group comparisons were performed using the independent-samples t test or the Mann–Whitney U test, as appropriate. To identify factors associated with POD, univariable logistic regression was first performed to estimate odds ratios (ORs) with 95% confidence intervals (CIs), and variables with p<0.10 were considered candidates for multivariable modeling. To reduce the risk of overfitting, the number of predictors retained in the multivariable model was constrained by the EPV principle based on the available number of POD events; when the candidate set exceeded the allowable range, variables were prioritized according to clinical importance and biological plausibility, with PSQI and IGF-1 prespecified as key variables of interest. The final model was then constructed using an AIC-based forward stepwise selection procedure (R function step ). Mediation analysis was then conducted to assess whether preoperative IGF-1 mediated the association between sleep disturbance and POD. A nonparametric bootstrap procedure (1,000 simulations) was implemented using the R package mediation to estimate the direct, indirect, and total effects, along with the proportion mediated and corresponding 95% CIs. [18] To evaluate the incremental predictive value of IGF-1, two multivariable logistic prediction models (with vs without IGF-1) were developed and compared using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Differences between AUCs were assessed using DeLong’s test. 3. Results 3.1 Participant characteristics We enrolled 260 older adults (≥60 years) undergoing elective surgery, which met the prespecified sample size requirement for model development; after excluding six patients lost to follow-up, 254 participants were included in the final analysis. POD occurred in 45 patients (17.7%) within the first 3 postoperative days. Compared with patients without POD, those who developed POD were older, had higher preoperative PSQI scores, had lower preoperative serum IGF-1 levels, and had higher preoperative CRP levels (all p<0.05). No significant between-group differences were observed in sex, comorbidities (e.g., cardiovascular disease, diabetes mellitus, and cerebrovascular disease), surgical category, perioperative medication use, or total sufentanil consumption within 48 hours postoperatively (all p>0.05) (Table 1). Table 1: Baseline characteristics of patients with and without postoperative delirium Variables Non-delirium patients (N=209 ) Delirium patients (N=45 ) P-value Age, years 73.42±7.36 77.38±4.93 <0.001* Sex, N(%) 0.861 Male 108(51.7) 22(48.9) Female 101(48.3) 23(51.1) BMI, kg/m² 23.65±3.70 23.21±3.32 0.432 Education, years 9.10±2.01 8.98±2.09 0.721 ASA 0.975 I 97（46.4） 20(44.4) II 79（37.8） 17(37.8) III 30(14.4) 7(15.6) Ⅳ 3(1.4) 1(2.2) PSQI 3.0(3.0,4.0) 5.0(4.0,7.0) <0.001* Preoperative sleep disturbance, N(%) <0.001* Yes 44(21.1) 32(71.1) No 165(78.9) 13(28.9) Cardiovascular disease, N(%) 0.782 Yes 39(18.7) 7(15.6) No 170(81.3) 38(84.4) Diabetes mellitus, N(%) 0.597 Yes 28(13.4) 8(17.8) No 181(86.6) 37(82.2) Cerebrovascular disease, N(%) 0.466 Yes 19(9.1) 2(4.4) No 190(90.9) 43(95.6) Hemoglobin, g/dl 12.98±0.75 12.96±0.80 0.878 IGF-1,ng/ml 122.27±21.12 110.93±19.31 <0.001* CRP,mg/L 2.80(2.13,4.42) 3.23(2.62,6.24) 0.012* Surgical type, N(%) 0.627 Thoracic surgery 45(21.5) 9(20.0) Urological laparoscopic surgery 46(22.0) 14(31.1) Orthopedic spinal surgery 61(29.2) 11(24.4) General abdominal surgery 57(27.3) 11(34.4) Operative time, min 120.65±27.45 128.31±24.41 0.066 Anesthesia time, min 131.03±27.75 139.38±25.04 0.051 Minimum mean arterial pressure, mmHg 68.49±9.73 65.62±10.11 0.087 Ciprofol dose, mg 70.89±2.77 71.24±3.00 0.463 Remifentanil dose, mg 1.69±0.30 1.77±0.31 0.137 Sufentanil dose, µg 40.31±9.47 38.56±9.15 0.250 Wake-up time, min 10.99±2.98 11.04±3.19 0.917 SpO2<90% Yes 7(3.3) 1(2.2) 1.000 No 202(96.7) 44(97.8) Postoperative nausea and vomiting (PONV) 1.000 Yes 7(3.3) 2(4.4) No 202(96.7) 43(95.6) Remedial analgesia ,N(%) 1.000 Yes 28(13.4) 6(13.3) No 181(86.6) 39(86.6) 48-h postoperative sufentanil consumption, µg 118.05±11.73 116.91±13.29 0.597 Notes: * P < 0.05 indicates a statistically significant difference. 3.2 Univariable analysis Univariable logistic regression was used to examine associations between candidate variables and POD (Table 2). Older age (OR 1.09, 95% CI 1.04-1.15; p=0.001), higher PSQI score (OR 1.40, 95% CI 1.21-1.63; p<0.001), and higher CRP level (OR 1.17, 95% CI 1.06-1.28; p=0.002) were associated with increased odds of POD, whereas higher IGF-1 level was associated with reduced odds of POD (OR 0.97, 95% CI 0.96-0.99; p=0.001). Operative time (p=0.086), anesthesia time (p=0.066), and minimum mean arterial pressure (p=0.077) met the prespecified screening threshold (p0.05). Table 2: Univariable logistic regression analysis of factors associated with postoperative delirium Variable OR 95% CI P-value Age (per year) 1.09 1.04-1.15 0.001*† Female Ref - - Male 1.12 0.59-2.13 0.735 BMI 0.97 0.88-1.06 0.460 Education (per year) 0.97 0.83-1.14 0.711 ASA Ⅰ Ref - - ASA Ⅱ 1.04 0.51-2.13 0.906 ASA Ⅲ 1.13 0.44-2.94 0.799 ASA Ⅳ 1.62 0.16-16.35 0.684 PSQI (per point) 1.40 1.21-1.63 <0.001*† Cardiovascular disease 0.80 0.31-1.84 0.624 Diabetes mellitus 1.40 0.56-3.19 0.446 Cerebrovascular disease 0.47 0.07-1.68 0.315 Hemoglobin (g/dl) 0.97 0.63-1.48 0.877 IGF-1 (ng/mL) 0.97 0.96-0.99 0.001*† CRP (mg/L) 1.17 1.06-1.28 0.002*† General abdominal surgery Ref - - Thoracic surgery 0.47 0.10-2.07 0.315 Urological laparoscopic surgery 1.52 0.60-3.87 0.378 Orthopedic spinal surgery 0.90 0.34-2.36 0.833 Operative time (min) 1.01 1.00-1.02 0.086† Anesthesia time (min) 1.01 1.00-1.02 0.066† Minimum mean arterial pressure (mmHg) 0.97 0.94-1.00 0.077† Ciprofol dose (mg) 1.05 0.93-1.18 0.437 Remifentanil dose (mg) 2.41 0.80-7.69 0.126 Sufentanil dose (µg) 0.98 0.95-1.01 0.257 Wake-up time (min) 1.01 0.90-1.12 0.913 SpO2<90% 0.66 0.08-5.47 0.697 Postoperative nausea and vomiting (PONV) 1.34 0.27-6.69 0.719 Remedial analgesia 0.99 0.35-2.42 0.991 48-h postoperative sufentanil consumption (µg) 0.99 0.97-1.02 0.564 Notes: †p<0.10 in univariable analysis (screening threshold; candidate for multivariable modeling). *p<0.05. ORs were estimated using univariable logistic regression and are presented with 95% CIs. “Ref” indicates the reference category. For binary variables, ORs compare presence vs absence. 3.3 Multivariable logistic regression Following the prespecified EPV constraint and variable prioritization strategy, a parsimonious multivariable logistic regression model was constructed, with PSQI and IGF-1 prespecified as key variables of interest. In the final model, higher PSQI score (OR 1.49, 95% CI 1.26–1.77; p<0.001), older age (OR 1.13, 95% CI 1.06–1.21; p<0.001), and higher CRP level (OR 1.28, 95% CI 1.14–1.45; p<0.001) were independently associated with increased odds of POD, whereas higher preoperative IGF-1 level was independently protective (OR 0.98, 95% CI 0.96–0.99; p=0.038) (Figure 1). 3.4 Mediation analysis To explore potential mechanisms linking preoperative sleep quality to POD, causal mediation analysis was performed to evaluate the mediating role of IGF-1 in the association between PSQI and POD. The average causal mediation effect (ACME) was statistically significant, indicating partial mediation by IGF-1. The indirect effect accounted for approximately 25.4% of the total effect (proportion mediated 0.25, 95% CI 0.09–0.74; p=0.002). After accounting for mediation through IGF-1, the direct effect of PSQI on POD remained statistically significant. The mediation pathway is illustrated in Figure 2. 3.5 ROC analysis ROC curves were used to evaluate the predictive performance of the multivariable models (Figure 3). The model without IGF-1 yielded an AUC of 0.82 (95% CI 0.76–0.88), whereas the model including IGF-1 yielded an AUC of 0.83 (95% CI 0.77–0.89). DeLong’s test showed no significant difference between the AUCs (Z=−1.52, p=0.129), indicating limited incremental discrimination from adding IGF-1 to the existing model. 4. Discussion In this prospective observational study, poorer preoperative sleep quality was associated with a higher risk of POD in older surgical patients, and this association remained independently significant after adjustment for key clinical covariates. Our findings further suggest that IGF-1 may be involved in the PSD–POD association: higher IGF-1 levels were associated with a lower POD risk and showed evidence of partial mediation. Notably, although IGF-1 may offer mechanistic insight, adding IGF-1 to routine clinical variables provided only a modest (and statistically non-significant) improvement in model discrimination, suggesting limited incremental predictive value as a single added biomarker while potentially informing mechanistic interpretation. Based on these observations, we next discuss potential biological explanations through which PSD may influence POD, with particular attention to IGF-1–related pathways and inflammatory mechanisms. In our cohort, the prevalence of preoperative sleep disturbance among older patients was 29.9%, underscoring its relevance as a potentially modifiable factor and a clinically feasible target for preoperative assessment. [6] In univariable analyses, higher PSQI score, older age, and higher CRP level were associated with increased odds of POD, whereas higher IGF-1 level was associated with reduced odds of POD. In addition, anesthesia time, operative time, and minimum intraoperative MAP showed borderline associations (all p<0.10), suggesting that perioperative exposure and hemodynamic factors may also contribute. After multivariable adjustment, the overall direction of the main associations remained consistent. Collectively, these findings support modeling IGF-1 as a mediator and motivate further investigation of IGF-1–related pathways in perioperative brain health. Our mediation analysis provided evidence consistent with partial mediation by IGF-1 in the association between PSD and POD. IGF-1 is a key neurotrophic factor involved in neuroplasticity, synaptogenesis, and the maintenance of cognitive function, and prior studies have linked alterations in IGF-1 signaling to adverse neurological outcomes, including Alzheimer’s disease and postoperative neurocognitive disorders. [19-21]Consistent with the observations of Baranowska-Bik and colleagues, reduced IGF-1 levels may increase structural and functional vulnerability of the central nervous system. [22] Mechanistically, chronic sleep disturbance may disrupt the hypothalamic–pituitary–liver axis and has been proposed to reduce IGF-1 synthesis and peripheral release, providing a plausible pathway through which sleep disruption may influence postoperative cognitive outcomes. [23] In addition, IGF-1 deficiency has been associated with heightened inflammatory responses and increased oxidative stress, which may further exacerbate neurotoxic burden and promote delirium. [24-26] In our study, IGF-1 accounted for approximately 25% of the total effect in the “PSQI–POD association” pathway, supporting a partial mechanistic contribution and providing further evidence for a biological link between sleep disturbance and POD. On this basis, IGF-1 may complement preoperative risk assessment, although its incremental predictive value appeared limited when added as a single biomarker; it may also inform future mechanistic studies and the development of potential preventive strategies. [27]Notably, preoperative CRP levels were independently associated with POD, suggesting that systemic inflammation may represent an additional key pathway linking sleep disturbance to perioperative brain vulnerability. [28] Together with the finding of only partial mediation by IGF-1, these results support the concept that the impact of sleep disturbance on POD is likely driven by multiple concurrent mechanisms, including neurotrophic and inflammatory pathways. We focused on hospitalized older adults undergoing elective surgery under general anesthesia (thoracic surgery, urological laparoscopic surgery, orthopedic spine surgery, and general abdominal surgery), given that this population is at high risk for POD and has a substantial burden of sleep disturbance. [5] Epidemiologic data suggest that approximately 30–50% of hospitalized older adults experience sleep problems to varying degrees, implying that perioperative brain outcomes in this group may be particularly susceptible to sleep dysregulation. [6, 29]Compared with prior work that largely examined IGF-1 in relation to cognitive outcomes, our study extends this line of inquiry to the perioperative setting and, through mediation analysis, supports a potential mechanistic pathway linking preoperative sleep disturbance to POD via IGF-1. In ROC analyses, adding IGF-1 increased the AUC modestly from 0.82 to 0.83; however, the incremental improvement was not statistically significant, which may reflect the strong discrimination of the baseline model and limited statistical power for detecting small AUC differences. From a pragmatic clinical perspective, the PSQI may facilitate rapid preoperative screening and initial risk stratification, [30]whereas the translational value of IGF-1 as an adjunct biomarker or intervention target warrants prospective validation. Several limitations should be acknowledged. First, this was a single-center prospective observational study with a relatively limited sample size, and the study population was restricted to older inpatients undergoing four categories of elective surgery under general anesthesia; thus, the generalizability of our findings to other surgical types or shorter procedures requires further validation. Second, POD was assessed using the CAM. Although CAM is practical for clinical use, delirium is inherently fluctuating and transient episodes may have been missed, potentially leading to an underestimation of the true incidence. Third, POD assessments were confined to the early postoperative period (2 hours postoperatively, postoperative day 1, and the morning of postoperative day 2) and did not cover a longer high-risk window, which may have resulted in missed late-onset delirium events. Finally, the PSQI is a subjective instrument and may not fully capture objective sleep characteristics; the absence of objective sleep monitoring limited further characterization of key sleep parameters (e.g., sleep efficiency and nocturnal awakenings). Future multicenter studies with larger samples, extended follow-up, and incorporation of objective sleep measures are warranted to validate and refine these findings. 5. Conclusions In older adults undergoing elective surgery, poorer preoperative sleep quality was independently associated with a higher risk of POD. Older age and higher preoperative CRP were independent risk factors, whereas higher preoperative IGF-1 was independently protective; mediation analysis further suggested that IGF-1 partially mediated the association between sleep quality and POD. The incremental improvement in model discrimination with the addition of IGF-1 was limited, and larger studies are needed to confirm its added predictive value. Declarations Ethics approval and consent to participate: This study was approved by the Ethics Committee of the Naval Medical Center (Approval No. AF-HEC-010) and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants before enrollment. Consent for publication: Not applicable. Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions: Chen Wang, Zhengzhen Huang, and Jing Wang contributed equally to this work. Chen Wang, Zhengzhen Huang, Jing Wang, Xiaoyong Miao, and Jianping Cao conceived and designed the study. Chen Wang, Zhengzhen Huang, Jing Wang, Ziyu Zhu, Meimei Zhu, Yan Li, and Ying Yao collected the data. Chen Wang and Zhengzhen Huang performed the statistical analysis. Chen Wang drafted the manuscript. Xiaoyong Miao and Jianping Cao supervised the study and critically revised the manuscript. All authors reviewed and approved the final manuscript. Acknowledgements: Not applicable. Trial registration: Chinese Clinical Trial Registry (ChiCTR2500109251). References Oliveira FR, Oliveira VH, Oliveira ÍM, Lima JW, Calderaro D, Gualandro DM, Caramelli B: Hypertension, mitral valve disease, atrial fibrillation and low education level predict delirium and worst outcome after cardiac surgery in older adults . BMC Anesthesiol 2018, 18 (1):15. Wong J, Doherty HR, Singh M, Choi S, Siddiqui N, Lam D, Liyanage N, Tomlinson G, Chung F: The prevention of delirium in elderly surgical patients with obstructive sleep apnea (PODESA): a randomized controlled trial . BMC Anesthesiol 2022, 22 (1):290. Li Y-W, Li H-J, Li H-J, Feng Y, Yu Y, Guo X-Y, Li Y, Zhao B-J, Hu X-Y, Zuo M-Z et al : Effects of two different anesthesia-analgesia methods on incidence of postoperative delirium in elderly patients undergoing major thoracic and abdominal surgery: study rationale and protocol for a multicenter randomized controlled trial . BMC Anesthesiol 2015, 15 :144. Wang Y, Xue X, Liu Y, Fan Q, Wang X, Li Y, Yan F, Zhang X: Can individualized blood pressure control prevent delirium after surgery in high-risk patients with sleep disorders? BMC Anesthesiol 2025, 25 (1):569. Liu Y, Zhang X, Jiang M, Zhang Y, Wang C, Sun Y, Shi Z, Wang B: Impact of Preoperative Sleep Disturbances on Postoperative Delirium in Patients with Intracranial Tumors: A Prospective, Observational, Cohort Study . Nat Sci Sleep 2023, 15 :1093-1105. Fadayomi AB, Ibala R, Bilotta F, Westover MB, Akeju O: A Systematic Review and Meta-Analysis Examining the Impact of Sleep Disturbance on Postoperative Delirium . Crit Care Med 2018, 46 (12):e1204-e1212. Irwin MR: Sleep and inflammation: partners in sickness and in health . Nat Rev Immunol 2019, 19 (11):702-715. Wang X, Hua D, Tang X, Li S, Sun R, Xie Z, Zhou Z, Zhao Y, Wang J, Li S et al : The Role of Perioperative Sleep Disturbance in Postoperative Neurocognitive Disorders . Nat Sci Sleep 2021, 13 :1395-1410. Sonntag WE, Ramsey M, Carter CS: Growth hormone and insulin-like growth factor-1 (IGF-1) and their influence on cognitive aging . Ageing Res Rev 2005, 4 (2):195-212. Labandeira-Garcia JL, Costa-Besada MA, Labandeira CM, Villar-Cheda B, Rodríguez-Perez AI: Insulin-Like Growth Factor-1 and Neuroinflammation . Front Aging Neurosci 2017, 9 :365. Naismith SL, Mowszowski L: Sleep disturbance in mild cognitive impairment: a systematic review of recent findings . Curr Opin Psychiatry 2018, 31 (2):153-159. Krueger JM, Frank MG, Wisor JP, Roy S: Sleep function: Toward elucidating an enigma . Sleep Med Rev 2016, 28 :46-54. Wan Y, Gao W, Zhou K, Liu X, Jiang W, Xue R, Wu W: Role of IGF-1 in neuroinflammation and cognition deficits induced by sleep deprivation . Neurosci Lett 2022, 776 :136575. Inouye SK, Westendorp RGJ, Saczynski JS: Delirium in elderly people . Lancet 2014, 383 (9920):911-922. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ: The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research . Psychiatry Res 1989, 28 (2):193-213. Mollayeva T, Thurairajah P, Burton K, Mollayeva S, Shapiro CM, Colantonio A: The Pittsburgh sleep quality index as a screening tool for sleep dysfunction in clinical and non-clinical samples: A systematic review and meta-analysis . Sleep Med Rev 2015, 25 :52-73. Inouye SK, van Dyck CH, Alessi CA, Balkin S, Siegal AP, Horwitz RI: Clarifying confusion: the confusion assessment method. A new method for detection of delirium . Ann Intern Med 1990, 113 (12):941-948. Imai K, Keele L, Tingley D: A general approach to causal mediation analysis . Psychol Methods 2010, 15 (4):309-334. Kaur N, Aran KR: Uncovering the intricacies of IGF-1 in Alzheimer's disease: new insights from regulation to therapeutic targeting . Inflammopharmacology 2025, 33 (3):1311-1330. O'Neill C, Kiely AP, Coakley MF, Manning S, Long-Smith CM: Insulin and IGF-1 signalling: longevity, protein homoeostasis and Alzheimer's disease . Biochem Soc Trans 2012, 40 (4):721-727. Jiang J, Lv X, Liang B, Jiang H: Circulating TNF-α levels increased and correlated negatively with IGF-I in postoperative cognitive dysfunction . Neurol Sci 2017, 38 (8):1391-1392. Baranowska-Bik A, Bik W: Insulin and brain aging . Prz Menopauzalny 2017, 16 (2):44-46. Chennaoui M, Léger D, Gomez-Merino D: Sleep and the GH/IGF-1 axis: Consequences and countermeasures of sleep loss/disorders . Sleep Med Rev 2020, 49 :101223. Cerejeira J, Batista P, Nogueira V, Vaz-Serra A, Mukaetova-Ladinska EB: The stress response to surgery and postoperative delirium: evidence of hypothalamic-pituitary-adrenal axis hyperresponsiveness and decreased suppression of the GH/IGF-1 Axis . J Geriatr Psychiatry Neurol 2013, 26 (3):185-194. Egberts A, Wijnbeld EHA, Fekkes D, van der Ploeg MA, Ziere G, Hooijkaas H, van der Cammen TJM, Mattace-Raso FUS: Neopterin: a potential biomarker for delirium in elderly patients . Dement Geriatr Cogn Disord 2015, 39 (1-2):116-124. Piñeiro-Hermida S, López IP, Alfaro-Arnedo E, Torrens R, Iñiguez M, Alvarez-Erviti L, Ruíz-Martínez C, Pichel JG: IGF1R deficiency attenuates acute inflammatory response in a bleomycin-induced lung injury mouse model . Sci Rep 2017, 7 (1):4290. Shen H, Shao Y, Chen J, Guo J: Insulin-Like Growth Factor-1, a Potential Predicative Biomarker for Postoperative Delirium Among Elderly Patients with Open Abdominal Surgery . Curr Pharm Des 2016, 22 (38):5879-5883. Irwin MR, Olmstead R, Carroll JE: Sleep Disturbance, Sleep Duration, and Inflammation: A Systematic Review and Meta-Analysis of Cohort Studies and Experimental Sleep Deprivation . Biol Psychiatry 2015, 80 (1):40-52. Salis F, Lecca R, Belfiori M, Figorilli M, Casaglia E, Congiu P, Mulas M, Puligheddu MMF, Mandas A: Sleep quality, daytime sleepiness, and risk of falling: results from an exploratory cross-sectional study . Eur Geriatr Med 2025, 16 (1):197-204. Curcio G, Tempesta D, Scarlata S, Marzano C, Moroni F, Rossini PM, Ferrara M, De Gennaro L: Validity of the Italian version of the Pittsburgh Sleep Quality Index (PSQI) . Neurol Sci 2013, 34 (4):511-519. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8754243","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594815490,"identity":"cf3a19e0-dd13-42d4-8d78-dfc54fa1f923","order_by":0,"name":"Chen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYLACxgYo44OBjR0pWpgZGGcUpCWTpoWZ58MhuI04gcHxs4df/Nxhk8cg3X9M2sbgADMD++GjG/BqOZOXZtl7Jq2YQeYwm3SOwR0+Bp60tBv4tJgdyDEzZmw7nNggkQzS8oyZQYLHDL+W829AWv5DtFgYHGZsIKjlRo7xY8a2AxAtDMRosb/xxoyxty0ZpMXYsscgLZmNkF8k+3OMP/xsswNqSXx448cfGzt+9sPH8GoBAjYJsHUHYFwCykGA+QMRikbBKBgFo2AkAwBQskif+UF5DgAAAABJRU5ErkJggg==","orcid":"","institution":"Naval Medical Center, Naval Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Wang","suffix":""},{"id":594815491,"identity":"8b95f36d-bb12-4cbb-8af2-f953f382d95e","order_by":1,"name":"Zhengzhen Huang","email":"","orcid":"","institution":"Naval Medical Center, Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhengzhen","middleName":"","lastName":"Huang","suffix":""},{"id":594815492,"identity":"52a2b50c-0a50-4271-ac89-9cf7758488d5","order_by":2,"name":"Jing Wang","email":"","orcid":"","institution":"Naval Medical Center, Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":594815493,"identity":"e6d314a7-5d88-425e-9813-481c15bc0d25","order_by":3,"name":"Ziyu Zhu","email":"","orcid":"","institution":"Naval Medical Center, Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziyu","middleName":"","lastName":"Zhu","suffix":""},{"id":594815494,"identity":"5d3b352e-af88-4117-9e4b-597492c369fe","order_by":4,"name":"Meimei Zhu","email":"","orcid":"","institution":"Naval Medical Center, Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meimei","middleName":"","lastName":"Zhu","suffix":""},{"id":594815495,"identity":"c454f197-f6b0-4092-bdd8-5c79280d7848","order_by":5,"name":"Yan Li","email":"","orcid":"","institution":"Naval Medical Center, Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""},{"id":594815496,"identity":"58a8c43f-3d7f-4d92-a3d4-a87724dc17e5","order_by":6,"name":"Ying Yao","email":"","orcid":"","institution":"Naval Medical Center, Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Yao","suffix":""},{"id":594815497,"identity":"bcc30fd3-25e5-46b9-bb24-841c51a51dde","order_by":7,"name":"Xiaoyong Miao","email":"","orcid":"","institution":"Naval Medical Center, Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyong","middleName":"","lastName":"Miao","suffix":""},{"id":594815498,"identity":"bc8f5642-2a2b-4924-9772-6b7cee837c9c","order_by":8,"name":"Jianping Cao","email":"","orcid":"","institution":"Naval Medical Center, Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianping","middleName":"","lastName":"Cao","suffix":""}],"badges":[],"createdAt":"2026-02-01 06:54:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8754243/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8754243/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103321548,"identity":"e98c40d6-b928-458c-9459-1730cf4749d2","added_by":"auto","created_at":"2026-02-24 11:57:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10049,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of multivariable logistic regression analysis for postoperative delirium\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8754243/v1/4320c7d65790fcf71f7e6f35.png"},{"id":103321568,"identity":"f5f3bdf3-1b3a-426d-bfac-6bea6c726c95","added_by":"auto","created_at":"2026-02-24 11:57:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23007,"visible":true,"origin":"","legend":"\u003cp\u003eMediation model of the effect of preoperative sleep quality on postoperative delirium via IGF-1\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8754243/v1/b92249bb4d629c503d6e6823.png"},{"id":103321550,"identity":"b485226b-e9f2-42ca-8eed-3a45bb2cc177","added_by":"auto","created_at":"2026-02-24 11:57:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9603,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves of multivariable models with and without IGF-1\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8754243/v1/656f2638e8d43449f049763b.png"},{"id":105566594,"identity":"30a83882-9a44-40ff-9b67-e73edb411c35","added_by":"auto","created_at":"2026-03-27 12:56:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2202827,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8754243/v1/eed76be9-e192-44d5-87c6-62e4676dc97d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preoperative sleep disturbance and Postoperative delirium in older Adults: A Mediation Analysis via Insulin-Like Growth Factor-1","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePostoperative delirium (POD) is a common perioperative neuropsychiatric syndrome of acute brain dysfunction, characterized by acute, fluctuating disturbances in attention and cognition, and is associated with adverse recovery trajectories and poorer longer-term outcomes.[1, 2]Older adults are at substantially higher risk than younger patients; POD incidence varies widely by surgical population and can be high after major surgery.[3, 4] Therefore, clarifying modifiable mechanisms and improving perioperative risk stratification are clinically important for optimizing the care of older surgical patients.\u003c/p\u003e\n\u003cp\u003eSleep disturbance is highly prevalent in older adults, and preoperative anxiety, environmental disruption, and perioperative stress often further worsen sleep quality. Increasing evidence suggests that preoperative sleep disturbance (PSD) is associated with POD, although findings may differ across populations. [5, 6] Mechanistically, poor preoperative sleep has been implicated in enhanced neuroinflammatory responses, disrupted neurotransmitter homeostasis, and impaired neural repair processes, potentially increasing vulnerability to POD. [7, 8] Insulin-like growth factor-1 (IGF-1) is a neurotrophic factor with immunomodulatory properties in the central nervous system, and altered IGF-1 signaling have been proposed as a potential biological pathway linking PSD to POD. [9, 10]Prior studies suggest that sleep loss may be accompanied by reduced IGF-1 activity, with downstream effects on neuronal survival, synaptic plasticity, and cognitive function. [11, 12]\u0026nbsp;In preclinical models, exogenous IGF-1 has shown to mitigate sleep disturbance\u0026ndash;related neural injury via PI3K/Akt pathway activation.\u0026nbsp;[13, 14]\u0026nbsp;Collectively, these observations support IGF-1 as a plausible mechanistic bridge between PSD and POD, while its translational value in perioperative delirium warrants further clarification.\u003c/p\u003e\n\u003cp\u003eAgainst this background, we conducted a prospective clinical observational study to examine the relationship between preoperative sleep quality and POD in older surgical patients, focusing on the potential mediating role of IGF-1. This work aims to advance mechanistic understanding and inform perioperative risk stratification and future preventive strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis study was conducted in accordance with \u003cstrong\u003ethe Declaration of Helsinki and relevant regulations\u003c/strong\u003e.The study design, implementation, and data collection \u003cstrong\u003eadhered to these principles throughout\u003c/strong\u003e. Written informed consent was obtained from all participants, and participant confidentiality was maintained. Ethical approval was granted by the Ethics Committee of the Naval Medical Center (approval No. AF-HEC-010), and the study was registered with the Chinese Clinical Trial Registry (ChiCTR2500109251).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe enrolled inpatients patients aged \u0026ge;60 years who underwent elective surgery under general anesthesia at the Naval Medical Center. Eligible procedures included thoracic surgery, urological laparoscopic surgery, orthopedic spine surgery, or general abdominal surgery. These surgical categories were selected to capture a cohort with relatively substantial perioperative stress exposure and broadly comparable perioperative management, thereby reducing heterogeneity related to surgical complexity and improving internal consistency of perioperative care pathways.\u003c/p\u003e\n\u003cp\u003ePatients were excluded if they: (1) had a prior diagnosis of delirium, cognitive impairment, or severe psychiatric disease; (2) were receiving medications that could influence delirium risk or alter IGF-1 levels (e.g., growth hormone or antipsychotic agents); (3) had severe comorbidities likely to materially affect sleep or metabolic function (e.g., end-stage hepatic or renal disease); (4) required an intensive care unit (ICU) stay \u0026gt;72 hours; or (5) declined participation or were unable to complete follow-up assessments as scheduled.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Preoperative assessment and data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreoperatively, sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), \u003cstrong\u003ea validated self-report questionnaire assessing sleep quality over the preceding month\u003c/strong\u003e. [15, 16]The PSQI comprises seven components derived from 18 items, yielding a global score ranging from 0 to 21; a global score \u0026ge;5 was used to define PSD, with higher scores indicating poorer sleep quality. [15] In this study, PSQI reflected sleep quality during the month prior to admission and was modeled as a continuous variable in the primary analyses to preserve information and statistical power; PSD (a dichotomized form of PSQI) \u003cstrong\u003ewas not included concurrently with PSQI\u003c/strong\u003eto avoid redundancy and potential collinearity.\u003c/p\u003e\n\u003cp\u003eWe collected preoperative demographic and clinical characteristics (age, sex, body mass index, ASA physical status, years of education, and preoperative hemoglobin), comorbidities (e.g., hypertension, diabetes mellitus, coronary artery disease, and cerebrovascular disease), and surgical category. Perioperative variables included intraoperative anesthetic drug doses (sufentanil, ciprofol, and remifentanil), the Minimum mean arterial pressure, operative time, and time to awakening. Laboratory measurements included preoperative serum IGF-1 and preoperative C-reactive protein (CRP) levels.\u0026nbsp;Peripheral venous blood was collected preoperatively; serum was separated by centrifugation and stored at \u0026minus;80 \u0026deg;C until analysis. Serum IGF-1 was quantified using an enzyme-linked immunosorbent assay (ELISA) kit (Human Insulin-like Growth Factor-1 [IGF-1] ELISA Research Kit; Jiangsu Sumeike Biotechnology Co., Ltd., China) and is reported in ng/mL. Preoperative CRP was measured by the hospital laboratory using an immunoturbidimetric assay and is reported in mg/L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Anesthesia management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll enrolled patients received a standardized anesthetic protocol. General anesthesia was induced with midazolam (0.05\u0026ndash;0.10 mg/kg), sufentanil (0.10\u0026ndash;0.30 \u0026mu;g/kg), cisatracurium besylate (0.15 mg/kg), and ciprofol (0.3\u0026ndash;0.4 mg/kg). Anesthesia was maintained with combined intravenous and inhalational agents, including ciprofol (continuous infusion, 0.8\u0026ndash;2.4 mg/kg/h), remifentanil (continuous infusion, 0.05\u0026ndash;0.30 \u0026mu;g/kg/min), cisatracurium besylate (continuous infusion, 1\u0026ndash;3 \u0026mu;g/kg/min; or intermittent boluses of 0.02 mg/kg as needed), and sevoflurane (end-tidal concentration, 0.5\u0026ndash;3.0%). Anesthetic depth was monitored using the Cerebral State Index (CSI), and agents were titrated to maintain CSI at 40\u0026ndash;60. Hemodynamic management targeted mean arterial pressure (MAP) within \u0026plusmn;20% of baseline and generally \u0026ge;65 mmHg. Baseline MAP was defined as the pre-induction value measured after the patient had been resting quietly upon arrival in the operating room. Hypotension (MAP \u0026lt;65 mmHg or \u0026gt;20% below baseline) was managed with protocolized adjustments including intravenous fluids, optimization of anesthetic depth/analgesia, and vasopressors when needed. Hypertension (\u0026gt;20% above baseline) was managed by deepening anesthesia and/or optimizing analgesia, with antihypertensive agents when clinically indicated. Normothermia was maintained throughout surgery, with core temperature monitored via a nasopharyngeal probe and maintained at \u0026ge;36.0\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Outcomes and adverse events\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was the occurrence of POD. Delirium was assessed using the Confusion Assessment Method (CAM). [17]Assessments were performed at 2 hours postoperatively, on postoperative day 1, and on the morning of postoperative day 2 (three assessments in total) by anesthesiologists who were blinded to the study data, and findings were recorded prospectively. POD was considered present if delirium was detected at any assessment time point.\u003c/p\u003e\n\u003cp\u003ePostoperative adverse events, including nausea, vomiting, and somnolence, were recorded using predefined criteria. \u003cstrong\u003eNausea\u003c/strong\u003e was defined as a subjective feeling of the urge to vomit. \u003cstrong\u003eVomiting\u003c/strong\u003e was defined as any episode of emesis or retching. \u003cstrong\u003eSomnolence\u003c/strong\u003e was defined as clinically significant drowsiness requiring repeated verbal or tactile stimulation to maintain arousal. The incidence of these events was documented at prespecified postoperative time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Sample size estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor a priori sample size planning, we assumed an anticipated POD incidence of approximately 10%. To ensure the stability of the regression model, we applied the events-per-variable (EPV) principle, requiring at least 10 outcome events per predictor. Given that the core objectives were to evaluate the PSQI\u0026ndash;POD association and to examine the role of IGF-1, we prespecified a minimal core model including two key variables (PSQI and IGF-1). Accordingly, the minimum required sample size was estimated as\u0026nbsp;\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\" width=\"217\" height=\"52\"\u003e\u0026nbsp;participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using R (version 4.4.2). Categorical variables are presented as counts (percentages) and were compared using the \u0026chi;\u0026sup2; test or Fisher\u0026rsquo;s exact test, as appropriate. Continuous variables are presented as mean \u0026plusmn; standard deviation (SD) for approximately normally distributed data or as median (interquartile range [IQR]) for non-normally distributed data; between-group comparisons were performed using the independent-samples t test or the Mann\u0026ndash;Whitney U test, as appropriate.\u003c/p\u003e\n\u003cp\u003eTo identify factors associated with POD, univariable logistic regression was first performed to estimate odds ratios (ORs) with 95% confidence intervals (CIs), and variables with p\u0026lt;0.10 were considered candidates for multivariable modeling. To reduce the risk of overfitting, the number of predictors retained in the multivariable model was constrained by the EPV principle based on the available number of POD events; when the candidate set exceeded the allowable range, variables were prioritized according to clinical importance and biological plausibility, with PSQI and IGF-1 prespecified as key variables of interest. The final model was then constructed using an AIC-based forward stepwise selection procedure (R function\u0026nbsp;\u003ccode\u003estep\u003c/code\u003e).\u003c/p\u003e\n\u003cp\u003eMediation analysis was then conducted to assess whether preoperative IGF-1 mediated the association between sleep disturbance and POD. A nonparametric bootstrap procedure (1,000 simulations) was implemented using the R package \u003cem\u003emediation\u003c/em\u003e to estimate the direct, indirect, and total effects, along with the proportion mediated and corresponding 95% CIs. [18]\u003c/p\u003e\n\u003cp\u003eTo evaluate the incremental predictive value of IGF-1, two multivariable logistic prediction models (with vs without IGF-1) were developed and compared using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Differences between AUCs were assessed using DeLong\u0026rsquo;s test.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Participant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe enrolled 260 older adults (\u0026ge;60 years) undergoing elective surgery, which met the prespecified sample size requirement for model development; after excluding six patients lost to follow-up, 254 participants were included in the final analysis. POD occurred in 45 patients (17.7%) within the first 3 postoperative days. Compared with patients without POD, those who developed POD were older, had higher preoperative PSQI scores, had lower preoperative serum IGF-1 levels, and had higher preoperative CRP levels (all p\u0026lt;0.05). No significant between-group differences were observed in sex, comorbidities (e.g., cardiovascular disease, diabetes mellitus, and cerebrovascular disease), surgical category, perioperative medication use, or total sufentanil consumption within 48 hours postoperatively (all p\u0026gt;0.05) (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1:\u0026nbsp;Baseline characteristics of patients with and without postoperative delirium\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-delirium patients\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N=209 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDelirium patients\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(N=45 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73.42\u0026plusmn;7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77.38\u0026plusmn;4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex, N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e108(51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22(48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e101(48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23(51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.65\u0026plusmn;3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.21\u0026plusmn;3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducation, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.10\u0026plusmn;2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.98\u0026plusmn;2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97（46.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20(44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79（37.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17(37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30(14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7(15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePSQI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0(3.0,4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0(4.0,7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePreoperative sleep disturbance, N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44(21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32(71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e165(78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13(28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiovascular disease, N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39(18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7(15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e170(81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38(84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus, N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28(13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8(17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e181(86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37(82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCerebrovascular disease, N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e190(90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43(95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHemoglobin, g/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.98\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.96\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIGF-1,ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e122.27\u0026plusmn;21.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110.93\u0026plusmn;19.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRP,mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.80(2.13,4.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.23(2.62,6.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurgical type, N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThoracic surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45(21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9(20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrological laparoscopic surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46(22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14(31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOrthopedic spinal surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61(29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11(24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeneral abdominal surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57(27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11(34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOperative time, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120.65\u0026plusmn;27.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e128.31\u0026plusmn;24.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnesthesia time, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131.03\u0026plusmn;27.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139.38\u0026plusmn;25.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMinimum mean arterial pressure, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.49\u0026plusmn;9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.62\u0026plusmn;10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCiprofol dose, mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70.89\u0026plusmn;2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.24\u0026plusmn;3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRemifentanil dose, mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.69\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.77\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSufentanil dose, \u0026micro;g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.31\u0026plusmn;9.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.56\u0026plusmn;9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWake-up time, min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.99\u0026plusmn;2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.04\u0026plusmn;3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpO2\u0026lt;90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e202(96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44(97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostoperative nausea and vomiting (PONV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e202(96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43(95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRemedial analgesia ,N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28(13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6(13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e181(86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39(86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48-h postoperative sufentanil consumption, \u0026micro;g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e118.05\u0026plusmn;11.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e116.91\u0026plusmn;13.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes:\u003cstrong\u003e*\u003c/strong\u003e P \u0026lt; 0.05 indicates a statistically significant difference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Univariable analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariable logistic regression was used to examine associations between candidate variables and POD (Table 2). Older age (OR 1.09, 95% CI 1.04-1.15; p=0.001), higher PSQI score (OR 1.40, 95% CI 1.21-1.63; p\u0026lt;0.001), and higher CRP level (OR 1.17, 95% CI 1.06-1.28; p=0.002) were associated with increased odds of POD, whereas higher IGF-1 level was associated with reduced odds of POD (OR 0.97, 95% CI 0.96-0.99; p=0.001). Operative time (p=0.086), anesthesia time (p=0.066), and minimum mean arterial pressure (p=0.077) met the prespecified screening threshold (p\u0026lt;0.10) and were carried forward as candidates for multivariable modeling; no other variables were associated with POD (all p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eTable 2:\u0026nbsp;Univariable logistic regression analysis of factors associated with postoperative delirium\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;(per year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.04-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001*\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59-2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.88-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducation (per year)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.83-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eASA Ⅰ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eASA Ⅱ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51-2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eASA Ⅲ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44-2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eASA Ⅳ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16-16.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePSQI\u0026nbsp;(per point)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21-1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31-1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.56-3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07-1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHemoglobin (g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63-1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIGF-1\u0026nbsp;(ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.96-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001*\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRP\u0026nbsp;(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.06-1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002*\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeneral abdominal surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThoracic surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10-2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrological laparoscopic surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.60-3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOrthopedic spinal surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.34-2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOperative time\u0026nbsp;(min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.086\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnesthesia time (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.066\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMinimum mean arterial pressure\u0026nbsp;\u0026nbsp;(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.94-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.077\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCiprofol dose (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.93-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRemifentanil dose (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.80-7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSufentanil dose (\u0026micro;g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.95-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWake-up time (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.90-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpO2\u0026lt;90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08-5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePostoperative nausea and vomiting (PONV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27-6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRemedial analgesia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.35-2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48-h postoperative sufentanil consumption\u0026nbsp;(\u0026micro;g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: \u0026dagger;p\u0026lt;0.10 in univariable analysis (screening threshold; candidate for multivariable modeling). *p\u0026lt;0.05. ORs were estimated using univariable logistic regression and are presented with 95% CIs. \u0026ldquo;Ref\u0026rdquo; indicates the reference category. For binary variables, ORs compare presence vs absence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;3.3 Multivariable logistic regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the prespecified EPV constraint and variable prioritization strategy, a parsimonious multivariable logistic regression model was constructed, with PSQI and IGF-1 prespecified as key variables of interest. In the final model, higher PSQI score (OR 1.49, 95% CI 1.26\u0026ndash;1.77; p\u0026lt;0.001), older age (OR 1.13, 95% CI 1.06\u0026ndash;1.21; p\u0026lt;0.001), and higher CRP level (OR 1.28, 95% CI 1.14\u0026ndash;1.45; p\u0026lt;0.001) were independently associated with increased odds of POD, whereas higher preoperative IGF-1 level was independently protective (OR 0.98, 95% CI 0.96\u0026ndash;0.99; p=0.038) (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Mediation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore potential mechanisms linking preoperative sleep quality to POD, causal mediation analysis was performed to evaluate the mediating role of IGF-1 in the association between PSQI and POD. The average causal mediation effect (ACME) was statistically significant, indicating partial mediation by IGF-1. The indirect effect accounted for approximately 25.4% of the total effect (proportion mediated 0.25, 95% CI 0.09\u0026ndash;0.74; p=0.002). After accounting for mediation through IGF-1, the direct effect of PSQI on POD remained statistically significant. The mediation pathway is illustrated in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 ROC analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curves were used to evaluate the predictive performance of the multivariable models (Figure 3). The model without IGF-1 yielded an AUC of 0.82 (95% CI 0.76\u0026ndash;0.88), whereas the model including IGF-1 yielded an AUC of 0.83 (95% CI 0.77\u0026ndash;0.89). DeLong\u0026rsquo;s test showed no significant difference between the AUCs (Z=\u0026minus;1.52, p=0.129), indicating limited incremental discrimination from adding IGF-1 to the existing model.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this prospective observational study, poorer preoperative sleep quality was associated with a higher risk of POD in older surgical patients, and this association remained independently significant after adjustment for key clinical covariates. Our findings further suggest that IGF-1 may be involved in the PSD\u0026ndash;POD association: higher IGF-1 levels were associated with a lower POD risk and showed evidence of partial mediation. Notably, although IGF-1 may offer mechanistic insight, adding IGF-1 to routine clinical variables provided only a modest (and statistically non-significant) improvement in model discrimination, suggesting limited incremental predictive value as a single added biomarker while potentially informing mechanistic interpretation. Based on these observations, we next discuss potential biological explanations through which PSD may influence POD, with particular attention to IGF-1\u0026ndash;related pathways and inflammatory mechanisms.\u003c/p\u003e\n\u003cp\u003eIn our cohort, the prevalence of preoperative sleep disturbance among older patients was 29.9%, underscoring its relevance as a potentially modifiable \u003cstrong\u003efactor\u003c/strong\u003e and a clinically feasible target for preoperative assessment. [6] In univariable analyses, higher PSQI score, older age, and higher CRP level were associated with increased odds of POD, whereas higher IGF-1 level was associated with reduced odds of POD. In addition, anesthesia time, operative time, and minimum intraoperative MAP showed borderline associations (all p\u0026lt;0.10), suggesting that perioperative exposure and hemodynamic factors may also contribute. After multivariable adjustment, the overall direction of the main associations remained consistent. Collectively, these findings support modeling IGF-1 as a mediator and motivate further investigation of IGF-1\u0026ndash;related pathways in perioperative brain health.\u003c/p\u003e\n\u003cp\u003eOur mediation analysis provided evidence consistent with partial mediation by IGF-1 in the association between PSD and POD. IGF-1 is a key neurotrophic factor involved in neuroplasticity, synaptogenesis, and the maintenance of cognitive function, and prior studies have linked alterations in IGF-1 signaling to adverse neurological outcomes, including Alzheimer\u0026rsquo;s disease and postoperative neurocognitive disorders. [19-21]Consistent with the observations of Baranowska-Bik and colleagues, reduced IGF-1 levels may increase structural and functional vulnerability of the central nervous system. [22]\u0026nbsp;Mechanistically, chronic sleep disturbance may disrupt the hypothalamic\u0026ndash;pituitary\u0026ndash;liver axis and has been proposed to reduce IGF-1 synthesis and peripheral release, providing a plausible pathway through which sleep disruption may influence postoperative cognitive outcomes.\u0026nbsp;[23]\u0026nbsp;In addition, IGF-1 deficiency has been associated with heightened inflammatory responses and increased oxidative stress, which may further exacerbate neurotoxic burden and promote delirium.\u0026nbsp;[24-26]\u0026nbsp;In our study, IGF-1 accounted for approximately 25% of the total effect in the \u0026ldquo;PSQI\u0026ndash;POD association\u0026rdquo; pathway, supporting a partial mechanistic contribution and providing further evidence for a biological link between sleep disturbance and POD. On this basis, IGF-1 may complement preoperative risk assessment, although its incremental predictive value appeared limited when added as a single biomarker; it may also inform future mechanistic studies and the development of potential preventive strategies.\u0026nbsp;[27]Notably, preoperative CRP levels were independently associated with POD, suggesting that systemic inflammation may represent an additional key pathway linking sleep disturbance to perioperative brain vulnerability.\u0026nbsp;[28]\u0026nbsp;Together with the finding of only partial mediation by IGF-1, these results support the concept that the impact of sleep disturbance on POD is likely driven by multiple concurrent mechanisms, including neurotrophic and inflammatory pathways.\u003c/p\u003e\n\u003cp\u003eWe focused on hospitalized older adults undergoing elective surgery under general anesthesia (thoracic surgery, urological laparoscopic surgery, orthopedic spine surgery, and general abdominal surgery), given that this population is at high risk for POD and has a substantial burden of sleep disturbance. [5] Epidemiologic data suggest that approximately 30\u0026ndash;50% of hospitalized older adults experience sleep problems to varying degrees, implying that perioperative brain outcomes in this group may be particularly susceptible to sleep dysregulation. [6, 29]Compared with prior work that largely examined IGF-1 in relation to cognitive outcomes, our study extends this line of inquiry to the perioperative setting and, through mediation analysis, supports a potential mechanistic pathway linking preoperative sleep disturbance to POD via IGF-1. In ROC analyses, adding IGF-1 increased the AUC modestly from 0.82 to 0.83; however, the incremental improvement was not statistically significant, which may reflect the strong discrimination of the baseline model and limited statistical power for detecting small AUC differences. From a pragmatic clinical perspective, the PSQI may facilitate rapid preoperative screening and initial risk stratification, [30]whereas the translational value of IGF-1 as an adjunct biomarker or intervention target warrants prospective validation.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be acknowledged. First, this was a single-center prospective observational study with a relatively limited sample size, and the study population was restricted to older inpatients undergoing four categories of elective surgery under general anesthesia; thus, the generalizability of our findings to other surgical types or shorter procedures requires further validation. Second, POD was assessed using the CAM. Although CAM is practical for clinical use, delirium is inherently fluctuating and transient episodes may have been missed, potentially leading to an underestimation of the true incidence. Third, POD assessments were confined to the early postoperative period (2 hours postoperatively, postoperative day 1, and the morning of postoperative day 2) and did not cover a longer high-risk window, which may have resulted in missed late-onset delirium events. Finally, the PSQI is a subjective instrument and may not fully capture objective sleep characteristics; the absence of objective sleep monitoring limited further characterization of key sleep parameters (e.g., sleep efficiency and nocturnal awakenings). Future multicenter studies with larger samples, extended follow-up, and incorporation of objective sleep measures are warranted to validate and refine these findings.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn older adults undergoing elective surgery, poorer preoperative sleep quality was independently associated with a higher risk of POD. Older age and higher preoperative CRP were independent risk factors, whereas higher preoperative IGF-1 was independently protective; mediation analysis further suggested that IGF-1 partially mediated the association between sleep quality and POD. The incremental improvement in model discrimination with the addition of IGF-1 was limited, and larger studies are needed to confirm its added predictive value.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003eThis study was approved by the Ethics Committee of the Naval Medical Center (Approval No. AF-HEC-010) and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants before enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003eThis study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e Chen Wang, Zhengzhen Huang, and Jing Wang contributed equally to this work. Chen Wang, Zhengzhen Huang, Jing Wang, Xiaoyong Miao, and Jianping Cao conceived and designed the study. Chen Wang, Zhengzhen Huang, Jing Wang, Ziyu Zhu, Meimei Zhu, Yan Li, and Ying Yao collected the data. Chen Wang and Zhengzhen Huang performed the statistical analysis. Chen Wang drafted the manuscript. Xiaoyong Miao and Jianping Cao supervised the study and critically revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003eChinese Clinical Trial Registry (ChiCTR2500109251).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOliveira FR, Oliveira VH, Oliveira \u0026Iacute;M, Lima JW, Calderaro D, Gualandro DM, Caramelli B: \u003cstrong\u003eHypertension, mitral valve disease, atrial fibrillation and low education level predict delirium and worst outcome after cardiac surgery in older adults\u003c/strong\u003e. \u003cem\u003eBMC Anesthesiol \u003c/em\u003e2018, \u003cstrong\u003e18\u003c/strong\u003e(1):15.\u003c/li\u003e\n\u003cli\u003eWong J, Doherty HR, Singh M, Choi S, Siddiqui N, Lam D, Liyanage N, Tomlinson G, Chung F: \u003cstrong\u003eThe prevention of delirium in elderly surgical patients with obstructive sleep apnea (PODESA): a randomized controlled trial\u003c/strong\u003e. \u003cem\u003eBMC Anesthesiol \u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(1):290.\u003c/li\u003e\n\u003cli\u003eLi Y-W, Li H-J, Li H-J, Feng Y, Yu Y, Guo X-Y, Li Y, Zhao B-J, Hu X-Y, Zuo M-Z\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eEffects of two different anesthesia-analgesia methods on incidence of postoperative delirium in elderly patients undergoing major thoracic and abdominal surgery: study rationale and protocol for a multicenter randomized controlled trial\u003c/strong\u003e. \u003cem\u003eBMC Anesthesiol \u003c/em\u003e2015, \u003cstrong\u003e15\u003c/strong\u003e:144.\u003c/li\u003e\n\u003cli\u003eWang Y, Xue X, Liu Y, Fan Q, Wang X, Li Y, Yan F, Zhang X: \u003cstrong\u003eCan individualized blood pressure control prevent delirium after surgery in high-risk patients with sleep disorders?\u003c/strong\u003e \u003cem\u003eBMC Anesthesiol \u003c/em\u003e2025, \u003cstrong\u003e25\u003c/strong\u003e(1):569.\u003c/li\u003e\n\u003cli\u003eLiu Y, Zhang X, Jiang M, Zhang Y, Wang C, Sun Y, Shi Z, Wang B: \u003cstrong\u003eImpact of Preoperative Sleep Disturbances on Postoperative Delirium in Patients with Intracranial Tumors: A Prospective, Observational, Cohort Study\u003c/strong\u003e. \u003cem\u003eNat Sci Sleep \u003c/em\u003e2023, \u003cstrong\u003e15\u003c/strong\u003e:1093-1105.\u003c/li\u003e\n\u003cli\u003eFadayomi AB, Ibala R, Bilotta F, Westover MB, Akeju O: \u003cstrong\u003eA Systematic Review and Meta-Analysis Examining the Impact of Sleep Disturbance on Postoperative Delirium\u003c/strong\u003e. \u003cem\u003eCrit Care Med \u003c/em\u003e2018, \u003cstrong\u003e46\u003c/strong\u003e(12):e1204-e1212.\u003c/li\u003e\n\u003cli\u003eIrwin MR: \u003cstrong\u003eSleep and inflammation: partners in sickness and in health\u003c/strong\u003e. \u003cem\u003eNat Rev Immunol \u003c/em\u003e2019, \u003cstrong\u003e19\u003c/strong\u003e(11):702-715.\u003c/li\u003e\n\u003cli\u003eWang X, Hua D, Tang X, Li S, Sun R, Xie Z, Zhou Z, Zhao Y, Wang J, Li S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe Role of Perioperative Sleep Disturbance in Postoperative Neurocognitive Disorders\u003c/strong\u003e. \u003cem\u003eNat Sci Sleep \u003c/em\u003e2021, \u003cstrong\u003e13\u003c/strong\u003e:1395-1410.\u003c/li\u003e\n\u003cli\u003eSonntag WE, Ramsey M, Carter CS: \u003cstrong\u003eGrowth hormone and insulin-like growth factor-1 (IGF-1) and their influence on cognitive aging\u003c/strong\u003e. \u003cem\u003eAgeing Res Rev \u003c/em\u003e2005, \u003cstrong\u003e4\u003c/strong\u003e(2):195-212.\u003c/li\u003e\n\u003cli\u003eLabandeira-Garcia JL, Costa-Besada MA, Labandeira CM, Villar-Cheda B, Rodr\u0026iacute;guez-Perez AI: \u003cstrong\u003eInsulin-Like Growth Factor-1 and Neuroinflammation\u003c/strong\u003e. \u003cem\u003eFront Aging Neurosci \u003c/em\u003e2017, \u003cstrong\u003e9\u003c/strong\u003e:365.\u003c/li\u003e\n\u003cli\u003eNaismith SL, Mowszowski L: \u003cstrong\u003eSleep disturbance in mild cognitive impairment: a systematic review of recent findings\u003c/strong\u003e. \u003cem\u003eCurr Opin Psychiatry \u003c/em\u003e2018, \u003cstrong\u003e31\u003c/strong\u003e(2):153-159.\u003c/li\u003e\n\u003cli\u003eKrueger JM, Frank MG, Wisor JP, Roy S: \u003cstrong\u003eSleep function: Toward elucidating an enigma\u003c/strong\u003e. \u003cem\u003eSleep Med Rev \u003c/em\u003e2016, \u003cstrong\u003e28\u003c/strong\u003e:46-54.\u003c/li\u003e\n\u003cli\u003eWan Y, Gao W, Zhou K, Liu X, Jiang W, Xue R, Wu W: \u003cstrong\u003eRole of IGF-1 in neuroinflammation and cognition deficits induced by sleep deprivation\u003c/strong\u003e. \u003cem\u003eNeurosci Lett \u003c/em\u003e2022, \u003cstrong\u003e776\u003c/strong\u003e:136575.\u003c/li\u003e\n\u003cli\u003eInouye SK, Westendorp RGJ, Saczynski JS: \u003cstrong\u003eDelirium in elderly people\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2014, \u003cstrong\u003e383\u003c/strong\u003e(9920):911-922.\u003c/li\u003e\n\u003cli\u003eBuysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ: \u003cstrong\u003eThe Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research\u003c/strong\u003e. \u003cem\u003ePsychiatry Res \u003c/em\u003e1989, \u003cstrong\u003e28\u003c/strong\u003e(2):193-213.\u003c/li\u003e\n\u003cli\u003eMollayeva T, Thurairajah P, Burton K, Mollayeva S, Shapiro CM, Colantonio A: \u003cstrong\u003eThe Pittsburgh sleep quality index as a screening tool for sleep dysfunction in clinical and non-clinical samples: A systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eSleep Med Rev \u003c/em\u003e2015, \u003cstrong\u003e25\u003c/strong\u003e:52-73.\u003c/li\u003e\n\u003cli\u003eInouye SK, van Dyck CH, Alessi CA, Balkin S, Siegal AP, Horwitz RI: \u003cstrong\u003eClarifying confusion: the confusion assessment method. A new method for detection of delirium\u003c/strong\u003e. \u003cem\u003eAnn Intern Med \u003c/em\u003e1990, \u003cstrong\u003e113\u003c/strong\u003e(12):941-948.\u003c/li\u003e\n\u003cli\u003eImai K, Keele L, Tingley D: \u003cstrong\u003eA general approach to causal mediation analysis\u003c/strong\u003e. \u003cem\u003ePsychol Methods \u003c/em\u003e2010, \u003cstrong\u003e15\u003c/strong\u003e(4):309-334.\u003c/li\u003e\n\u003cli\u003eKaur N, Aran KR: \u003cstrong\u003eUncovering the intricacies of IGF-1 in Alzheimer\u0026apos;s disease: new insights from regulation to therapeutic targeting\u003c/strong\u003e. \u003cem\u003eInflammopharmacology \u003c/em\u003e2025, \u003cstrong\u003e33\u003c/strong\u003e(3):1311-1330.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Neill C, Kiely AP, Coakley MF, Manning S, Long-Smith CM: \u003cstrong\u003eInsulin and IGF-1 signalling: longevity, protein homoeostasis and Alzheimer\u0026apos;s disease\u003c/strong\u003e. \u003cem\u003eBiochem Soc Trans \u003c/em\u003e2012, \u003cstrong\u003e40\u003c/strong\u003e(4):721-727.\u003c/li\u003e\n\u003cli\u003eJiang J, Lv X, Liang B, Jiang H: \u003cstrong\u003eCirculating TNF-\u0026alpha; levels increased and correlated negatively with IGF-I in postoperative cognitive dysfunction\u003c/strong\u003e. \u003cem\u003eNeurol Sci \u003c/em\u003e2017, \u003cstrong\u003e38\u003c/strong\u003e(8):1391-1392.\u003c/li\u003e\n\u003cli\u003eBaranowska-Bik A, Bik W: \u003cstrong\u003eInsulin and brain aging\u003c/strong\u003e. \u003cem\u003ePrz Menopauzalny \u003c/em\u003e2017, \u003cstrong\u003e16\u003c/strong\u003e(2):44-46.\u003c/li\u003e\n\u003cli\u003eChennaoui M, L\u0026eacute;ger D, Gomez-Merino D: \u003cstrong\u003eSleep and the GH/IGF-1 axis: Consequences and countermeasures of sleep loss/disorders\u003c/strong\u003e. \u003cem\u003eSleep Med Rev \u003c/em\u003e2020, \u003cstrong\u003e49\u003c/strong\u003e:101223.\u003c/li\u003e\n\u003cli\u003eCerejeira J, Batista P, Nogueira V, Vaz-Serra A, Mukaetova-Ladinska EB: \u003cstrong\u003eThe stress response to surgery and postoperative delirium: evidence of hypothalamic-pituitary-adrenal axis hyperresponsiveness and decreased suppression of the GH/IGF-1 Axis\u003c/strong\u003e. \u003cem\u003eJ Geriatr Psychiatry Neurol \u003c/em\u003e2013, \u003cstrong\u003e26\u003c/strong\u003e(3):185-194.\u003c/li\u003e\n\u003cli\u003eEgberts A, Wijnbeld EHA, Fekkes D, van der Ploeg MA, Ziere G, Hooijkaas H, van der Cammen TJM, Mattace-Raso FUS: \u003cstrong\u003eNeopterin: a potential biomarker for delirium in elderly patients\u003c/strong\u003e. \u003cem\u003eDement Geriatr Cogn Disord \u003c/em\u003e2015, \u003cstrong\u003e39\u003c/strong\u003e(1-2):116-124.\u003c/li\u003e\n\u003cli\u003ePi\u0026ntilde;eiro-Hermida S, L\u0026oacute;pez IP, Alfaro-Arnedo E, Torrens R, I\u0026ntilde;iguez M, Alvarez-Erviti L, Ru\u0026iacute;z-Mart\u0026iacute;nez C, Pichel JG: \u003cstrong\u003eIGF1R deficiency attenuates acute inflammatory response in a bleomycin-induced lung injury mouse model\u003c/strong\u003e. \u003cem\u003eSci Rep \u003c/em\u003e2017, \u003cstrong\u003e7\u003c/strong\u003e(1):4290.\u003c/li\u003e\n\u003cli\u003eShen H, Shao Y, Chen J, Guo J: \u003cstrong\u003eInsulin-Like Growth Factor-1, a Potential Predicative Biomarker for Postoperative Delirium Among Elderly Patients with Open Abdominal Surgery\u003c/strong\u003e. \u003cem\u003eCurr Pharm Des \u003c/em\u003e2016, \u003cstrong\u003e22\u003c/strong\u003e(38):5879-5883.\u003c/li\u003e\n\u003cli\u003eIrwin MR, Olmstead R, Carroll JE: \u003cstrong\u003eSleep Disturbance, Sleep Duration, and Inflammation: A Systematic Review and Meta-Analysis of Cohort Studies and Experimental Sleep Deprivation\u003c/strong\u003e. \u003cem\u003eBiol Psychiatry \u003c/em\u003e2015, \u003cstrong\u003e80\u003c/strong\u003e(1):40-52.\u003c/li\u003e\n\u003cli\u003eSalis F, Lecca R, Belfiori M, Figorilli M, Casaglia E, Congiu P, Mulas M, Puligheddu MMF, Mandas A: \u003cstrong\u003eSleep quality, daytime sleepiness, and risk of falling: results from an exploratory cross-sectional study\u003c/strong\u003e. \u003cem\u003eEur Geriatr Med \u003c/em\u003e2025, \u003cstrong\u003e16\u003c/strong\u003e(1):197-204.\u003c/li\u003e\n\u003cli\u003eCurcio G, Tempesta D, Scarlata S, Marzano C, Moroni F, Rossini PM, Ferrara M, De Gennaro L: \u003cstrong\u003eValidity of the Italian version of the Pittsburgh Sleep Quality Index (PSQI)\u003c/strong\u003e. \u003cem\u003eNeurol Sci \u003c/em\u003e2013, \u003cstrong\u003e34\u003c/strong\u003e(4):511-519.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sleep disturbance, Postoperative delirium, Insulin-like growth factor-1 (IGF-1), Mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-8754243/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8754243/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePostoperative delirium (POD) is a clinically important complication in older surgical patients. Preoperative sleep disturbance is common and potentially modifiable, yet the biological pathways linking sleep quality to POD remain incompletely understood. We examined the association between preoperative sleep quality and POD, tested whether insulin-like growth factor-1 (IGF-1) partially mediates this relationship, and evaluated the incremental discriminatory value of adding IGF-1 to a clinical prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis single-center prospective study screened 260 patients aged ≥60 years undergoing elective surgery under general anesthesia at the Naval Medical Center (May–July 2025); 254 were included in the final analysis. Sleep quality in the preceding month was assessed by Pittsburgh Sleep Quality Index (PSQI) and fasting serum IGF-1 was measured. POD was screened daily on postoperative days 1–3 using the Confusion Assessment Method (CAM). Multivariable logistic regression identified independent POD factors; mediation analysis quantified IGF-1 mediation of the PSQI–POD association. Models with vs without IGF-1 were compared using receiver operating characteristic (ROC) curves; areas under the curve (AUCs) were compared with DeLong’s test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eAmong 254 patients, POD occurred in 45/254 (17.7%). In multivariable logistic regression, higher PSQI scores (OR 1.49, 95% CI 1.26–1.77; p\u0026lt;0.001), older age (OR 1.13, 95% CI 1.06–1.21; p\u0026lt;0.001), and higher C-reactive protein (CRP) levels (OR 1.28, 95% CI 1.14–1.45; p\u0026lt;0.001) were independently associated with increased POD risk, whereas higher IGF-1 levels were independently protective (OR 0.98, 95% CI 0.96–0.99; p=0.038). Mediation analysis indicated a significant partial mediation by IGF-1, accounting for 25% of the total effect (95% CI 0.09–0.74; p=0.002). In ROC analyses, the model without IGF-1 achieved an AUC of 0.82 (95% CI 0.76–0.88), and the model including IGF-1 achieved an AUC of 0.83 (95% CI 0.77–0.89); the difference was not significant (DeLong Z=−1.52, p=0.129).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Poor preoperative sleep quality was independently associated with POD in older patients, with IGF-1 partially mediating this relationship. Adding IGF-1 provided limited incremental discrimination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e Chinese Clinical Trial Registry (ChiCTR), \u003cstrong\u003eChiCTR2500109251\u003c/strong\u003e. Registered on \u003cstrong\u003e16 September 2025. Retrospectively registered.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Preoperative sleep disturbance and Postoperative delirium in older Adults: A Mediation Analysis via Insulin-Like Growth Factor-1","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 11:56:29","doi":"10.21203/rs.3.rs-8754243/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3776da7-1d9a-479b-8535-605a32d096ca","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T02:25:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 11:56:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8754243","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8754243","identity":"rs-8754243","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}