Alcohol consumption may be associated with postoperative delirium in the elderly: the PNDABLE study.

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 (Aß40, Aß42, 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, Aß42 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 Aß42 (ß = -0.638, P < 0.001) in POD patients. Mediation analyses showed that alcohol consumption is likely to partially mediate POD through Aß42 (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 Aß42 (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 Aß42.

Physical activity may a probably protective factor for postoperative delirium: the PNDABLE study.

Objective: This study aims to explore the relationship between physical activity (PA) and postoperative delirium (POD). Methods: We selected 400 patients from the Perioperative Neurocognitive Disorder and Biomarkers Lifestyle (PNDABLE) database, and the patients in the PNDABLE database were sampled and tested Alzheimer's biomarkers. The diagnosis of POD was made using the Confusion Assessment Scale (CAM) and the severity was assessed using Memorial Delirium Assessment Scale (MDAS). Mini-Mental State Examination (MMSE) scale was used to detect the mental state of the patients. Enzyme-linked immunosorbent assay (ELISA) was used to detect the level of preoperative cerebrospinal fluid (CSF) biomarkers, such as amyloid ß plaque 42 (Aß42), total tau protein (T-tau), and phosphorylated tau protein (P-tau). Logistic regression, sensitivity analysis, and post hoc analysis were used to explore the relationship between risk and protective factors on POD. We used the mediating effect to explore whether PA mediates the occurrence of POD through CSF biomarkers. Results: The incidence of POD was 17.5%. According to our research, the consequence prompted that PA might be the protective factor for POD [odds ratio (OR): 0.336, 95% confidence interval (95 CI) 0.206-0.548, P < 0.001]. The result of logistic regression revealed that CSF biomarker Aß42 (OR: 0.997, 95 CI 0.996-0.999, P < 0.001) might be a protective factor against POD, and the T-tau (OR: 1.006, 95 CI 1.003-1.009, P = 0.001) and P-tau (OR: 1.039, 95 CI 1.018-1.059, P < 0.001) might risk factors for POD. Sensitivity analysis confirmed the correlation between PA and CSF biomarkers in the patients with POD. Mediation effect analysis showed that PA may reduce the occurrence of POD partly through CSF biomarkers, such as Aß42 (proportion: 11%, P < 0.05), T-tau (proportion: 13%, P < 0.05), and P-tau (proportion: 12%, P < 0.05). Conclusion: Physical activity is probably a protective factor for POD and may exert a mediating effect through CSF biomarkers.

Relationship between preoperative neutrophil-to-lymphocyte ratio and postoperative delirium: The PNDABLE and the PNDRFAP cohort studies.

OBJECTIVES: In this study, the relationship between preoperative neutrophil-to-lymphocyte ratio (NLR) and Alzheimer-related biomarkers in cerebrospinal fluid (CSF) was investigated to determine whether high NLR is a potential risk factor for postoperative delirium (POD) and to evaluate its predictive efficacy. METHODS: We selected 1000 patients from the perioperative neurocognitive disorder risk factor and prognosis (PNDRFAP) database and 999 patients from the perioperative neurocognitive disorder and biomarker lifestyle (PNDABLE) database. Patients in the PNDABLE database have been measured for Alzheimer-related biomarkers in CSF (Aß40 , Aß42 , P-tau, and tau protein). Mini-mental state examination was used to assess the preoperative mental status of patients. POD was diagnosed using the confusion assessment method and assessed for severity using the memorial delirium assessment scale. Logistic regression analysis was utilized to explore the association of preoperative NLR with POD. What's more, we also performed sensitivity analysis by adding corrected confounders, and the results were almost unchanged. Spearman's rank correlation was used to determine the associations between NLR and Alzheimer-related biomarkers. Mediation analyses with 10,000 bootstrapped iterations were used to explore the mediation effects. Finally, we use decision curves and the nomogram model to evaluate the efficacy of preoperative NLR in predicting POD; we also performed external validation using data from Qilu Hospital. RESULT: Logistic regression results showed that an elevated preoperative NLR was a risk factor for the development of POD in patients (PNDRFAP: OR = 1.067, 95% CI 1.020-1.116; PNDABLE: OR = 1.182, 95% CI 1.048-1.335, p < .05). Spearman's rank correlation analysis showed a positive but weak correlation between NLR and P-tau/T-tau (R = .065). The mediating effect results indicate that NLR likely mediates the occurrence of POD through elevated tau protein levels (proportion: 47.47%). The results of the box plots showed statistically significant NLR and CSF biomarkers between the POD and non-POD (NPOD) groups (p < .05), with higher NLR, P-tau, and T-tau in the POD group than in the NPOD group. In contrast, the NPOD group had higher Aß42 levels compared to the POD group. In addition, we used R package to plot the decision curve and nomogram both suggesting a good predictive effect of preoperative NLR on the occurrence of POD. CONCLUSION: Elevated preoperative NLR levels may be a risk factor for POD and likely mediate the development of POD through elevated P-tau/T-tau levels.

Potential Value of Serum Uric Acid in the Identification of Postoperative Delirium in Geriatric Patients Undergoing Knee Replacement.

Objectives: The relationship between preoperative serum uric acid (SUA) and cerebrospinal fluid (CSF) Alzheimer-related biomarkers were investigated to determine whether high SUA is a potential risk factor for postoperative delirium (POD) and to evaluate its predictive efficacy. Methods: The participants were selected from the Perioperative Neurocognitive Disorder Risk Factor and Prognosis (PNDRFAP) study and the Perioperative Neurocognitive Disorder and Biomarker Lifestyle (PNDABLE) study. The logistic regression equation was used to analyze the risk factors and protective factors of POD. The interaction term (SUA × Sex) was introduced into the linear model to explore the potential modification effects of sex on the identified correlations. We analyzed the mediating effects of Alzheimer-related biomarkers. Finally, we constructed the receiver operating characteristic (ROC) curve and the nomogram model to evaluate the efficacy of SUA and Alzheimer-related biomarkers in predicting POD. Results: Patients with POD had elevated SUA level (PNDRFAP: p = 0.002, PNDABLE: p < 0.001). Preoperative SUA level was positively correlated with CSF phosphorylated tau (P-tau) (p = 0.027) and ß-amyloid42 (Aß42)/P-tau (p = 0.023). Interaction analysis did not find any modification effect of sex. The relationship between SUA and POD was partially mediated by CSF P-tau (15.3%). ROC curve showed that the model combining SUA and Alzheimer-related biomarkers had better performance in predicting POD [area under the curve (AUC) = 0.880; p < 0.001], and the predictive model is accurate. Conclusions: High SUA may enhance CSF P-tau level, thus increasing the risk of POD, and the model combining SUA and Alzheimer-related biomarkers can accurately predict the occurrence of POD.