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

doi:10.21203/rs.3.rs-2482427/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-2482427/v1

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published 23 Jun, 2023

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Objectives: The aim of this study was to reveal the relationship between alcohol consumption and Postoperative delirium (POD) in the elderly.

Methods: A total of 284 patients were enrolled in this study (mean age was 69.38 (65-90) years and 7.1% were female). Those who had measurements of CSF Aβ₄₀, Aβ₄₂，P-tau, and tau protein and drinking characteristics were included from the Perioperative Neurocognitive Disorder And Biomarker Lifestyle (PNDABLE ) study. Mini-Mental State Examination (MMSE) was used to assess 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). The two independent samples t-test was used for comparison between the two groups. Logistic regression analysis was utilized to explore the association of alcohol consumption with POD. What’s more, We also performed sensitivity analysis by adding corrected confounders and the results were almost unchanged. Linear regression analysis was used to study the relationship between alcohol consumption and CSF biomarkers. Mediation analyses with 10000 bootstrapped iterations were used to explore the mediation effects. A two-factor ANOVA was used to explore the impact of gender and alcohol consumption on CSF biomarkers. 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% (44/252). Logistic regression showed that alcohol consumption (OR = 1.016, 95%CI 1.009-1.024, P < 0.001) is a risk factor for POD. In particular, alcohol consumption >24 g (heavy) is more likely to cause POD. What’s more, Aβ₄₂is 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. The two independent samples t-test showed that the non-postoperative delirium (NPOD) group had higher Aβ₄₂ levels than the POD group; the POD group had more alcohol consumption than the NPOD group (P < 0.001). Mediation analyses show that alcohol consumption is likely to mediate POD through Aβ₄₂(proportion:14.21%) partially. ROC curve showed that alcohol consumption (AUC=0.904; P<0.001) exhibited a relative better discriminatory ability in POD prediction compared to Aβ₄₂ (AUC=0.798; P<0.001). The calibration curve indicated good prediction of the nomogram(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 Aβ_42.

elderly

alcohol consumption

β-Amyloid protein

biomarkers

cerebrospinal fluid

Postoperative delirium (POD) can be defined as an acute cerebral dysfunction or failure and fluctuating consciousness, accompanied by obvious impairment of concentration and cognitive function.^1,2 It often occurs in the first week after surgery (or before discharge) with a higher incidence 1–3 days postoperatively.³ POD may lead to higher health resource costs and mortality and longer hospitalization. ⁴POD remains a common postoperative clinical complication in the elderly, previous studies have shown that the incidence of POD is about 17.6%⁵ and its pathological mechanism has not been fully understood.

With the rapid growth of the Chinese economy, alcohol consumption of China is greatly rising.⁶ According to epidemiological findings, alcohol consumption may increase the risk of cognitive impairment in the elderly.⁷ Studies have also shown that alcohol consumption is considered to be an independent risk factor for cognitive impairment.⁸ Because POD is often accompanied by cognitive impairment,⁴ thus, a possible correlation between alcohol consumption and POD can be inferred. Considering the current drinking status, actively exploring the relationship between alcohol consumption and POD to prevent the occurrence of POD in China is imperative.

Previous studies have shown that lower preoperative CSF Aβ levels and higher tau levels are associated with a higher incidence of POD and more severe symptoms .^9,10 CSF Aβ₄₀, Aβ₄₂, T-tau and P-tau have been reported in numerous studies to help predict cognitive impairment. It was also found that patients with POD had a lower Aβ₄₂/P-tau ratio and a lower CSF Aβ₄₂ level.^9,11,12 Thus, a conclusion that there is a correlation between CSF biomarkers and cognitive dysfunction can be made.

There is little research on the relationship between alcohol consumption and POD in the elderly and their related mechanisms. In order to reveal the relationship between them, this study analyzed from the following three perspectives. At first, whether alcohol consumption was an independent influence on POD. Second, Is there a correlation between CSF biomarkers and alcohol consumption. Third, if a correlation does exist, whether alcohol consumption will lead to POD through CSF biomarkers. If the above conjecture was confirmed, the study would hopefully provide a preemptive strategy to decrease the incidence of POD in the elderly and reduce the burden it takes to families and the society.

PNDABLE database

Volunteers were recruited from the Perioperative Neurocognitive Disorder And Biomarker Lifestyle (PNDABLE) study, which is an ongoing large-scale cohort study launched in 2018 and volunteers included in the database were between 40 and 90 years of age, concentrating on the risk factors and biomarkers of perioperative neurocognitive disorder (PND) in the Han population of northern China. The purpose of PNDABLE is to determine the genetic and environmental factors of PND biomarkers and the lifestyle factors that might change the risk of PND in the non-demented northern Chinese Han population so that the basis for disease prevention and early diagnosis can be formed. All participants were provided an informed consent, and they could also decide to stop participatingat any time and for any reasonsc. Their CSF and blood samples could be used for research purposes in the future.

Participants

We selected patients who underwent elective surgery under combined spinal and epidural anesthesia between June 2020 and June 2021 in Qingdao Municipal Hospital. The inclusion criteria of this study include (1) aged between 65 and 90; (2) Drinking frequency ≥1 time per week and history of drinking ≥1 year ¹³. (3) American Society of Anesthesiologists (ASA)Ⅰ~ Ⅱ; (4) preoperative cognitive status was intact with no language communication barrier. The exclusion criteria included: (1) central nervous system infection, head trauma, multiple sclerosis, neurodegenerative diseases (such as epilepsy, Parkinson's disease), or other notable neurological diseases; (2) severe visual and hearing impairment (3) non-drinkers, abstainers, or those who used to drink regularly but have not consumed alcohol in the past year; (4) history of severe mental or neurological disorders, such as Alzheimer's disease, Parkinson's syndrome, cerebrovascular accidents and cerebrovascular disease (5) preoperative Mini-Mental State Examination scale (MMSE) ≤ 23 points (6) drug abuse or psychotropic substance abuse, long-term use of steroids and hormonal drugs, (7) family history of genetic disorders (e.g., early-onset familial AD, hereditary ataxia, hereditary spastic paraplegia, etc.).

We eventually included 252 patients for statistical analysis. (see Figure 1, ﬂow diagram).

Neuropsychological Testing

All participants accepted meticulous clinical and neuropsychological assessments and MMSE the day before the scheduled operation. Patients were followed up on postoperative day 1-7 days or before they were discharged from the hospital at 10 a.m. and 2 p.m. twice a day. At the same time, the presence or absence of POD was recorded. The presence of POD was defined according to Confusion Assessment Scale (CAM), those with POD were classified as POD group and those with POD negative were classified as non-POD group(NPOD). The severity of POD was defined according to the Memorial Delirium Assessment Scale (MDAS).^14,15 All of the above assessments were performed by an anesthesiologist and a neurologist who had no knowledge of the patient's perioperative management（The anaesthetist and neurologist who visit preoperatively and postoperatively are different）. The CAM and MDAS has been proven to apply to the patients with good credibility and practicality. ^16,17

Anesthesia and Surgery

All participants were performed elective surgery under combined spinal and epidural anesthesia. The participants did not receive preoperative medications, and they were instructed not to drink for 6 hours and not to eat for 8 hours before surgery. After entering the operating room, we routinely monitored ECG, SpO2, NBP and opened vein access. The anesthesia position was lateral decubitus, with the space between the spinous processes of lumbar 3-4 (L₃-L₄) as the puncture site. After successful puncture, 2ml of cerebrospinal fluid was extracted from the subarachnoid space, followed by injection of 2-2.5ml Ropivacaine (0.66%) for about 30 seconds. The patient's anesthesia level was controlled below the thoracic 8 (T₈) level. The patient's oxygen saturation, pulse, blood pressure, electrolytes, etc. were checked at regular intervals (every 3 minutes) during anesthesia and surgery.

After the operation, the patient was sent to the anesthesia recovery room for observation for thirty minutes and returned to the ward if there was no abnormality. Post-operatively, the Numerical Rating Scale (NRS) was used to assess the pain. Patient-controlled intravenous analgesia (PCIA) was used in postoperative pain management. (Butorphanol tartrate injection 10mg + Toranisetron hydrochloride injection 5mg + 0.9% sodium chloride solution 89ml maintained NRS <3 points).

CSF core biomarkers measurements and collection

2 ml CSF was collected in a polypropylene centrifugal tube, then centrifuging at 2000 × g for 10 minutes at room temperature ^18,19 as well as separated and stored in an enzyme-free EP (Eppendorf) tube (oxygen bottle, PCR-02-C) at -80 °C for further use in the following steps of this study. These samples were subjected to at most two freeze-thaw cycles.

ELISA was used to detect the level of Aβ₄₀, Aβ₄₂, T-tau and P-tau, which were detected from 2 ml CSF, using Aβ₄₀ (BioVendor, Ghent, Belgium Lot: No. 292-6230), Aβ₄₂ (BioVendor, Ghent, Belgium Lot: No. 296-64401), P-tau (BioVendor, Ghent, Belgium Lot: QY-PF9092), and tau (BioVendor, Ghent, Belgium Lot: No.EK-H12242) assay kit under the manufacturer's instructions. Finally, using an enzyme marker (EnSpire, PerkinElmer, Waltham, MA, USA) ^18,19 to measure the optical density value (OD value) of each hole at the wavelength of 450 m. All samples were measured by the same laboratory personnel, and they were blinded with the group assignment.

Classification of alcohol intake

We traced the patient's drinking history and calculated the average daily alcohol intake according to the formula : amount of alcohol consumed (g) = amount of alcohol consumed (ml) × alcoholic concentration (%) × 0.8 (The density of alcohol is known to be 0.8 g/cm³). The patients' drinking history was investigated and classified according to the following criteria:²⁰

1. Average daily intake of alcohol < 12 g (mild)

2. Average daily intake of alcohol 12-23 g (moderate)

3. Average daily intake of alcohol >24 g (heavy)

Sample size estimation

The preliminary test in this study explored that 4 covariates（alcohol consumption, Aβ₄₀, Aβ₄₂, and P-tau）were included in the Logistic regression. According to previous studies, the POD incidence was 17.6%,⁵ and the loss of follow-up rate was assumed to be 20%. Thus, the required sample size was calculated to be 284 cases (4×10÷0.176÷0.8=284).

Statistical Analysis

The Kolmogorov-Smirnov (KS) test was used to determine the normality of the samples. Data that conformed to the normal distribution were expressed as mean ± standard deviation (SD) and data that did not conform to the normal distribution were expressed as the median and 25–75 percentile (M,(Q₂₅,Q₇₅)) or number (%). The two independent samples t-test was used to test whether there was a significant difference in the levels of CSF biomarkers and alcohol consumption between the POD and NPOD groups. The difference was considered statistically significant at P < 0.05.

Binary logistic regression was used to discuss whether the alcohol consumption was an independent influence on POD. Moreover, in order to investigate the range of alcohol intake that predisposes to POD, the average daily alcohol intake of participants was also categorized according to the above average daily alcohol consumption and included sequentially in a logistic regression for the study. Linear regression models were used to examine the relationship between CSF biomarkers with alcohol consumption. The covariates in the binary logistic regression include average daily alcohol intake, levels of Aβ₄₀, Aβ₄₂, P-Tau protein, because they were significantly correlated with POD in the univariate analysis (P < 0.05). Subsequently, to improve the accuracy of the results, we further corrected for the effect of confounding factors, including age, gender, years of education, cigarette use (yes or no), hypertension (yes or no), Coronary heart disease(yes or no), diabetes(yes or no) and MMSE, which showed that the results were barely changed in this analysis (OR = 1.016, 95%CI 1.009-1.024, P < 0.001); A two-way ANOVA was used to investigate the effects of gender and alcohol consumption on POD .

Moreover, linear regression models covering three equations were performed to examine whether the association between Alcohol consumption and POD was mediated by the CSF biomarkers. Mediation effects were established if the following criteria were simultaneously reached: (1) Changes in Alcohol consumption were significantly affect the CSF biomarkers; (2) Changes in the CSF biomarkers were account for variations in the POD; (3) Changes in Alcohol consumption were significantly or not significantly related to POD; (4) The association between Alcohol consumption and POD was attenuated when the CSF biomarkers were added in the regression model. Furthermore, the attenuation or indirect effect was estimated, with the significance determined using 10,000 bootstrapped iterations. The indirect effect (IE) was P < 0.05, which was considered to be significant.

Predictive value of Alcohol consumptionand the CSF biomarkers was described with a receiver-operating characteristics (ROC) curve and the area under the curve (AUC) reported the discriminatory ability. Nomogram will be used to visualize the predicted results and the calibration curve will be used to verify the predicted model.

The data were analyzed with R4.4.1 (R Foundation for Statistical Computing, Vienna, Austria), GraphPad Prism version 8.0 (GraphPad Software, Inc, LaJolla, CA, USA) and Stata MP16.0 (Solvusoft Corporation, Inc, Chicago, Illinois, USA).

Participant Characteristics

284 patients were recruited in total. Among them, 252 met the requirements and 32 were excluded. Reasons for exclusion included surgery plan changes and patients’ unwillingness to be enrolled. Of the 252 patients included in the study, 44 subjects experienced POD during the 1-7 day postoperative follow-up (positive rate of POD: 17.5%). The average age of participants was 69.38 (SD = 4.066); 18 participants (7.1%) were female. Specific information for all participants was presented in Table 1.

Comparison between POD and NPOD groups

Participants were divided into two groups according to the occurrence of POD (POD and NPOD), and two independent samples t-test was performed in order to compare the levels of CSF biomarkers and alcohol consumption. The results suggested a significant difference in the levels of CSF Aβ₄₂ protein and alcohol consumption between the POD and NPOD groups (P < 0.001). The NPOD group had higher Aβ₄₂ levels while the POD group had more alcohol consumption. However, there was no significant difference in Aβ₄₀, T-tau, and P-tau. For the results of the study, please refer to Figure 2.

The relationship between alcohol consumption and POD

Binary logistic regression showed that alcohol consumption (OR = 1.016, 95%CI 1.009 - 1.024, P < 0.001) was a risk factor for POD, and the positive rate of POD would increase with the daily intake (please refer to Table 2A). Subsequently, alcohol intake was further classified according to the Classification of alcohol intake and sequentially included in a logistic regression for the study. The reason is that POD did not occur in patients with an average daily alcohol intake of less than 12 g, and patients with a rank variable of 1 was not included in this regression analysis. Results showed that an average daily alcohol intake >24 g (heavy) was significantly associated with the incidence of POD ( OR = 1.014, 95%CI 1.008 - 1.021, P < 0.05 ) (please refer to Table 2B). What’s more, Aβ₄₂was a protective factor for POD (OR = 0.993, 95%CI 0.989 - 0.997, P < 0.05); P-Tau was a risk factor for POD (OR = 1.093, 95%CI 1.022 - 1.168, P < 0.05).

Sensitivity analysis

In order to minimize the influence of confounding factors and enhance the reliability of the results, a sensitivity analysis was conducted: adding more confounding factors, including age, gender, years of education, cigarette use (yes or no), hypertension (yes or no), coronary heart disease(yes or no), diabetes(yes or no) and MMSE. The conclusion was that the results were barely changed in this analysis (OR = 1.016, 95%CI 1.009 - 1.024, P < 0.001).

The relationship between alcohol consumption and CSF biomarkers

Linear regression analysis showed a significantly negative association between alcohol consumption and CSF Aβ₄₂ (β = -0.638, P < 0.001) in POD patients while no such relationship was found in NPOD patients(P > 0.05). At the same time, the same relationship was not found for the other three CSF biomarkers(Aβ₄₀, P-tau, T-tau P>0.05), regardless of the POD or NPOD group. Rrelevant graphs of the linear regression results were shown in Figure 3.

The mediation analyses

Mediation analyses showed that alcohol consumption was likely to cause POD through Aβ₄₂ (proportion:14.21%, P < 0.05). Relevant results were shown in Figure 4.

Two-factor ANOVA

A two-factor ANOVA was used to explore the effects of gender and alcohol consumption on Aβ₄₂. The results showed a significant interaction between gender and alcohol consumption (P < 0.05), and a simple effect analysis performed showed that the level of Aβ₄₂ in CSF decreased with increasing alcohol intake when “gender” was fixed (please refer to Table 3A). When the amount of alcohol consumed was fixed, there was no significant effect of gender on the level of CSF Aβ₄₂.However, when light alcohol consumption (average daily intake of alcohol < 12 g) was present, women had higher Aβ₄₂ levels than men (please refer to Table 3B).

Predictive model

ROC curve showed that two factors were effective in predicting POD, alcohol consumption (AUC=0.904; P<0.001) exhibited a relative better discriminatory ability in POD prediction compared to Aβ₄₂ (AUC=0.798; P<0.001) (Fig.5). The efficacy of each predictor is shown in the nomogram (Fig.6). The calibration curve indicated good prediction of the nomogram(P=0.797) (Fig.7).

The result primarily showed that alcohol consumption is a risk factor for POD in the elderly and possibly mediated by Aβ₄₂.

The study showed a positive rate of 17.5% for POD, which was close to previous results.^21,22 POD can be defined as an acute brain dysfunction,²³ Which can present as hypoactive (decreased alertness, motor activity and anhedonia), hyperactive (agitated and combative) or mixed forms.^24,25 As POD is associated with longer hospitalization, poorer functional recovery and higher healthcare costs, 72.4% patients will die within 5 years.^26–28 Thus, identifying individuals with high risk of POD and developing early prevention and intervention strategies are of great significance on public significance.

There are many theories about the mechanism of POD : (1) tau protein phosphorylation and Aβ pathological deposition theory, (2) inflammatory response theory, (3) stress response theory, and (4) cholinergic theory. Studies have demonstrated that CSF Aβ₄₂, Aβ₄₀, T-tau, and P-tau in patients are associated with cognitive decline.^29,30 The results of this study shows that alcohol consumption is a risk factor for POD and mediates the development of POD through Aβ₄₂. Therefore, this article will mainly discuss the mechanism of Aβ pathological deposition leading to POD.

With the development of economy, alcohol as a globally popular beverage was consumed more often, which makes the relationship between alcohol consumption and cognitive impairment more pronounced.^6,7 Results of this study prove that alcohol consumption is a risk factor for POD. Alcohol consumption also has been shown to be associated with cognitive decline in previous studies.³¹ Cognitive decline was well documented in alcohol drinkers,¹³ which is consistent with this trial conclusion, but the underlying mechanisms are still unclear currently.

This study showed that Aβ₄₂ was reduced in POD patients. Epidemiological studies have shown that excessive alcohol consumption increases the accumulation of Aβ and tau protein phosphorylation.³² What’s more, CSF indicators of amyloid accumulation were higher for those with regular alcohol consumption rather than with infrequent alcohol consumption.¹³ A few years ago, a study also showed a decrease in Aβ₄₂ in patients with POD.⁹ All of the above findings strongly support the results of this trial.

According to the study results, alcohol consumption is a risk factor for POD, and alcohol intake > 24 g (heavy) is more likely to cause POD, which is similar to the conclusion of Richards and Sabia;^31,33 Aβ₄₂ is a protective factor for POD, which also echoed Idland’s study;⁹ There is a negative correlation between the alcohol consumption and Aβ₄₂ in POD patients and regular alcohol drinkers have increased pathological accumulation of Aβ₄₂, leading to a decreased CSF Aβ₄₂ concentration.^13,32 The results were further validated using sensitivity analysis, which were barely changed in this analysis. Combining the findings, a hypothesis that alterations in CSF Aβ₄₂ might be one of the mechanisms that alcohol consumption leads to POD was made. Therefore, a mediation analysis with 10000 bootstrapped iterations was performed to explore the mediation effects. The result shows that alcohol consumption is likely to cause POD through Aβ₄₂ (14.21%). In addition, the prediction models constructed for the high-risk factors of logistic regression analysis also indicated good sensitivity and predictability of the results of this study.

Considering that there may be an interaction between gender and alcohol consumption, a two-way ANOVA was conducted, the finding indicates an interaction does exist. Thus, by a simple effects analysis we found that the level of Aβ₄₂ in CSF would decrease with increasing alcohol intake when “gender” was fixed. However, when the variable “daily alcohol intake” was the same, there would be no significant difference in the level of CSF Aβ₄₂. Last but not list, when average daily intake of alcohol < 12 g (light alcohol consumption) was present, women had higher Aβ₄₂ levels than men. This may suggest that light alcohol consumption in women could increase Aβ₄₂ to reduce the incidence of POD. It is possibly a new and preventive perspective and can reduce the incidence of POD in clinic operations. However, the exact mechanism needs to be further explored.

The characteristic of this study is that it quantified daily drinking as alcohol intake, which increased accuracy and reliability of the experimental results while previous studies have only explored the current frequency of alcohol consumption but not considering specific doses. But at the same time, this study also has some limitations: on one hand, it is a single-center experiment and a multicenter study still needs to be conducted. On the other hand, the sample size of this study was slightly small. Expanding the sample size can further promote the applicability and credibility of the experiment .

In conclusion, alcohol consumption is a risk factor for POD in the elderly and will lead to POD through the mediation of Aβ₄₂., especially for patients with an average daily alcohol intake > 24 g. Comparing with patients with other levels of alcohol intake, their risk of POD will increase. It provides a new insight into the prevention of POD, which is expected to provide guidance for POD prevention by regulating or controlling alcohol consumption. Early prevention of POD could improve life quality of patients, shorten the duration of hospitalization and relieve the burden on families and the society.

Ethics Statement

Ethical approval for this study (Ethical Committee N◦2020 PRO FORMA Y number 005) was provided by the Ethical Committee Qingdao Municipal Hospital, Qingdao, China (Chairman Prof Yang) on 21 May 2020. All patients/participants enrolled had provided their written informed consent. And written informed consent had also been obtained from the individual(s) for any potentially identiﬁable images or data included in this article that may be published. Statements of their signed consent are available onrequest. All methods comply with the Declaration of Helsinki guidelines.

Consent for publication

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article from the study will be made available by the corresponding authors, without undue reservation. All the raw data are attached in supplementary file "standard data" and have been submitted with the article.

Conflict of Interest

The authors declare that they have no competing interests.

Funding

This study was supported by grants from the National Natural Science Foundation of China (No. 91849126).

Author Contributions

Xiaoyue Wu was the first authors of this article, responsible for the design and implementation of this project. Ning Zhang, Bin Zhou, Siyu Liu responsible for the data statistics, Fei Wang, Jiahan Wang, Xinhui Tang, Rui Dong were responsible for the data collection. Xu Lin, Bin Wang and Yanlin Bi modified the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors thank the colleagues who have made contributions to build the PNDABLE Study. The authors also thank the subjects and their family for their cooperation in this study.

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TABLE 1.Characteristics of participants
	POD	Non-POD	t-values/z/χ2		P-values
Gender (male/female)	0/44	18/190	2.900		0.089
Age (year),（mean±SD）	70.20±5.201	69.21±3.776	-1.201		0.235
BMI（mean±SD,Kg/m²)	25.02±3.099	25.45±3.10	0.911		0.363
Education (M,(Q₂₅,Q₇₅))	9(6,12)	9(9,12)	2.197		0.029
ASA grade，(N(%))			-0.150		0.881
I	10(22.7%)	57(27.4%)
II	34(77.3%)	151(72.6%)
NRS, (M,(Q₂₅,Q₇₅))	3(2,3)	2(2,3)	-1.050		0.295
MMSE, (M,(Q₂₅,Q₇₅))	27.5(26,29)	29(27,30)	2.838		0.005
MDAS, (M,(Q₂₅,Q₇₅))	13(11,15.75)	3(0,5)	-25.169		0.000
Cigarette use, yes (%) No(%) *	28(63.6%) 16(36.4%)	129(62%) 79(38%)	0.040		0.841
Hypertension, yes (%) No(%)*	19(43.2%) 25(56.8)	61(29.3%) 147(70.7%)	3.217		0.073
Diabetes mellitus, yes (%) No(%)*	3(6.8%) 41(93.2%)	19(9.1%) 189(90.9%)	0.04		0.841
CHD, yes (%) No(%)*	3(6.8%) 41(93.2%)	27(13%) 181(87%)	1.315		0.251
mean daily alcohol intake(N,(%))				-8.321	0.000
<12g		74(35.6%)
12~23g	3(6.8%)	61(29.3%)
>24g	41(93.1%)	73(35.1%)

Notes: Categorical variables are reported as numbers and percentages; continuous variables are reported as means ± SD, whereas non-normal data are expressed as the M(Q₂₅,Q₇₅);

Abbreviations: POD, postoperative delirium; NPOD, no postoperative delirium; MDAS, Memorial Delirium Assessment Scale; NRS, Numerical Rating Scale; kg, kilogram; M,median; SD, standard deviation; Q,quartile; N,number; g, gram; *,control group.

TABLE 2.Logistic regression analysis

		Unadjusted			Adjusted
	OR	95%CI	P value	OR	95%CI	P value
mean daily alcohol intake	1.016	1.009-1.023	P<0.001	1.015	1.009-1.023	P < 0.001
Aβ₄₀（pg/ml）	1.000	0.999-1.00	P < 0.05	1.000	0.999-1.000	P < 0.05
Aβ₄₂（pg/ml）	0.992	0.988-0.997	P < 0.05	0.992	0.987-0.997	P < 0.05
P-tau（pg/ml）	1.076	1.011-1.144	P < 0.05	1.073	1.008-1.142	P < 0.05
T-tau（pg/ml）	1.005	0.997-1.013	P > 0.05	-	-	-

Notes: Binary logistic regression was used to discuss whether the amount of alcohol intake was an independent influence on POD. Unadjusted：covariates including mean daily alcohol intake，Aβ₄₀，Aβ₄₂，P-tau，T-tau；Adjusted: through adding more covariates, including age, gender, years of education, BMI, which showed that the results were barely changed in this analysis

Abbreviations: OR, odds ratio; CI, confidence interval;

mean daily alcohol intake		Unadjusted			Adjusted
mean daily alcohol intake	OR	95%CI	P value	OR	95%CI	P value
< 12g	-
12-23g	0.746	0.482-1.155	P > 0.05	0.000	0.000-0.000	P > 0.05
>24g	1.014	1.008-1.021	P < 0.05	1.011	1.003-1.019	P < 0.05

Notes:The average daily alcohol intake was transformed into a rank variable according to the above classification, Unadjusted：Relationship between alcohol classification and POD of the same grade; Adjusted: through adding more covariates, including age, gender, years of education, cigarette use (yes or no), hypertension (yes or no), Coronary heart disease(yes or no), diabetes(yes or no) and MMSE, which showed that the results were barely changed in this analysis

Abbreviations: OR, odds ratio; CI, confidence interval;

TABLE 3. The simple effect analysis

	I	J	I-J	P values	95%CI
Female	< 12 g	12-23	152.701	0.038	8.822	296.580
	12-23 g	< 12	-152.701	0.038	-296.580	-8.822
male	< 12 g	12-23	-19.629	0.858	-86.026	46.768
		> 24	73.248	0.007	16.228	130.268
	12-23 g	< 12	19.629	0.858	-46.768	86.026
		> 24	92.877	0.001	33.956	151.799
	> 24 g	< 12	-73.248	0.007	-130.268	-16.228
		12-23	-92.877	0.001	-151.799	-33.956

Notes: The result of simple effect: Fixed gender ; 3B: I-J, the difference in Aβ₄₂ between the same gender and different average daily alcohol intake. In this study, women drank within 23g of alcohol, so alcohol levels >24g are not listed in the table.

	I	J	I-J	P values	95%CI
< 12 g	Female	male	113.797	0.022	16.554	211.039
	male	Female	-113.797	0.022	-211.039	-16.554
12-23 g	Female	male	-58.534	0.334	-177.716	60.648
	male	Female	58.534	0.334	-60.648	177.716

Notes: The result of simple effect: Fixed alcohol consumption; 3A: I-J, the difference in Aβ₄₂ between different genders with the same average daily alcohol intake. In this study, those who drink > 24g are men, so they are not listed in the table.

No competing interests reported.

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

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