Purpose: We aimed to investigate the relevance of body mass index (BMI) to postoperative delirium (POD), and to test whether the influences of BMI on POD were mediated by cerebrospinal fluid (CSF) biomarkers.Patients and methods: Our study recruited 682 and 761 cognitively intact individuals from the perioperative neurocognitive disorder risk factor and prognosis (PNDRFAP) study and the perioperative neurocognitive disorder and biomarker lifestyle (PNDABLE) study, respectively. The incidence of POD was evaluated by using Confusion Assessment Method (CAM), and POD severity was measured by using the Memorial Delirium Assessment Scale (MDAS). Logistic regression was used to analyze the relationship between BMI and POD. The levels of Aβ40, Aβ42, T‐tau, and P‐tau in preoperative CSF were measured by enzyme‐linked immune‐sorbent assay (ELISA) in the PNDABLE study. Mediation analysis with 5000 bootstrapped iterations was used to explore the mediation effects.Results: In the PNDRFAP study, the incidence of POD was 16.3%, with logistic regression analysis showing that BMI (odds ratio [OR] = 0.900, 95% confidence interval [CI] 0.823–0.985, p = .022) is a protective factor of POD. In the PNDABLE study, the incidence of POD was 18.7%, and regression analysis confirmed that BMI (OR = 0.832, 95% CI 0.761–0.910, p < .001) is a protective factor of POD, while T‐tau (OR = 1.005, 95% CI 1.003–1.006, p < .001) and P‐tau (OR = 1.037, 95% CI 1.024–1.050, p < .001) were risk factors of POD. Mediation analyses revealed that the association between BMI and POD was partially mediated by T‐tau (proportion: 36%) and P‐tau (proportion: 24%).Conclusion: Higher BMI mediated protective effects on POD through CSF biomarkers (T‐tau and P‐tau).

Objectives: This study aimed to reveal the relationship between alcohol consumption and Postoperative delirium (POD) in the elderly. Methods: We selected 252 patients from the Perioperative Neurocognitive Disorder And Biomarker Lifestyle (PNDABLE ) study. Patients in the PNDABLE database have been measured for Alzheimer-related biomarkers in CSF (Aβ₄₀, Aβ₄₂, P-tau, and tau protein). Mini-Mental State Examination (MMSE) was used to assess the preoperative mental status of patients. POD was diagnosed using the Confusion Assessment Method (CAM) and assessed for severity using the Memorial Delirium Assessment Scale (MDAS). Logistic regression analysis was utilized to explore the association of alcohol consumption with POD. Linear regression analysis was used to study the relationship between alcohol consumption and CSF biomarkers. Mediation analyses with 10,000 bootstrapped iterations were used to explore the mediation effects. Finally, we constructed the receiver operating characteristic (ROC) curve and the nomogram model to evaluate the efficacy of alcohol consumption and CSF biomarkers in predicting POD. Result: The incidence of POD was 17.5%. Logistic regression showed that alcohol consumption (OR = 1.016, 95%CI 1.009–1.024, P < 0.001) is a risk factor for POD. What’s more, Aβ₄₂ is a protective factor for POD (OR = 0.993, 95%CI 0.989–0.997, P < 0.05), and P-Tau was a risk factor for POD (OR = 1.093, 95%CI 1.022–1.168, P < 0.05). Linear regression analysis revealed that alcohol consumption was negatively associated with CSF Aβ₄₂ (β = -0.638, P < 0.001) in POD patients. Mediation analyses showed that alcohol consumption is likely to partially mediate POD through Aβ42 (proportion:14.21%). ROC curve showed that alcohol consumption (AUC = 0.904; P < 0.001) exhibited a relatively better discriminatory ability in POD prediction compared to Aβ₄₂ (AUC = 0.798; P < 0.001). The calibration curve indicated a good nomogram prediction (P = 0.797). Conclusion: Alcohol consumption is a risk factor for POD (particularly for those with > 24 g a day on average) in the elderly, and contributes to POD through the mediation of Aβ₄₂.

Objective: We aimed to investigate the relationship between preoperative lipid level and postoperative delirium (POD) and explore whether lipid’s effect on POD is mediated by POD core protein.Methods: A total of 635 patients who were planned to undergo knee/hip arthroplasty under combined spinal-epidural anesthesia, regardless of gender, were selected. The patients were aged 40–90 years with American Society of Anesthesiologists physical status I II. The Mini-Mental State Examination (MMSE) was completed 1 day before the operation. Five milliliter elbow venous blood was taken from the patients before anesthesia, and serum levels of total cholesterol (TG), triglyceride (TC), low-density lipoprotein (LDL-C), and high-density lipoprotein (HDL-C) were detected. Cerebrospinal fluid (CSF) was extracted after successful spinal-epidural combined puncture, and amyloid beta₄₀ (Aβ₄₀), amyloid beta₄₂ (Aβ₄₂), total Tau (t-Tau), and phosphorylated Tau (p-Tau) in the CSF were measured by enzyme-linked immunosorbent assays (ELISA). After the operation, the occurrence and severity of POD were assessed using the Confusion Assessment Method and the Memorial Delirium Assessment Scale (MDAS), respectively. Patients were categorized into POD group and NPOD group. Logistic regression was used to analyze the relationship between POD and TC, TG, LDL-C, and HDL-C, and the mediating effect was used to analyze the role of POD core proteins in the relationship between lipid and MDAS. We used the receiver operating characteristic (ROC) and the precision-recall curve (PRC) analysis to assess the ability of TC, TG, LDL-C, and HDL-C ability to predict POD. Finally, we performed a sensitivity analysis to assess the stability of the results.Results: A total of 562 patients were finally enrolled in this study, and 66 patients developed POD, with an incidence of 11.7%. Logistic regression analysis showed that high concentration of TC (OR = 3.148, 95%CI 1.858∼5.333, P < 0.001), TG (OR = 2.483, 95%CI 1.573∼3.918, P < 0.001), and LDL-C (OR = 2.469, 95%CI 1.310∼4.656, P = 0.005) in serum were risk factors for POD. A high concentration of HDL-C (OR = 0.258, 95%CI 0.112∼0.594, P = 0.001) was a protective factor for POD after adjusted for age, sex, education, and MMSE score. ROC curves showed that HDL-C have the highest sensitivity and specificity in predicting POD. For these four lipid markers, the PRC range from 0.602 to 0.731, respectively. The mediating analysis showed that POD core proteins could partially mediate the relationship between lipid and POD (effect value: 16.19∼91.04%). The results were barely changed in the sensitivity analysis, and the sensitivity analysis has shown that the results were stable.Conclusion: The increase of serum TG, TC, and LDL-C concentration is a risk factor for POD development, while high HDL-C concentration is a protective factor for POD, and the occurrence of POD is caused by hyperlipidemia may be caused by POD core proteins.Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [Chictr200033439].