Behavioural Neurology

Behavioural Neurology / 2021 / Article
Special Issue

Communication, Feeding and Swallowing Disorders in Neurological Diseases

View this Special Issue

Research Article | Open Access

Volume 2021 |Article ID 6696806 | https://doi.org/10.1155/2021/6696806

Ying Yu, Jie Gao, Shasha Wang, Heng Lv, Liping Xiao, Hengyuan Shi, Xianjie Jia, "Synergistic Effects of Aldehyde Dehydrogenase 2 Polymorphisms and Alcohol Consumption on Cognitive Impairment after Ischemic Stroke in Han Chinese", Behavioural Neurology, vol. 2021, Article ID 6696806, 10 pages, 2021. https://doi.org/10.1155/2021/6696806

Synergistic Effects of Aldehyde Dehydrogenase 2 Polymorphisms and Alcohol Consumption on Cognitive Impairment after Ischemic Stroke in Han Chinese

Academic Editor: Lambros Messinis
Received01 Apr 2021
Revised16 May 2021
Accepted07 Jun 2021
Published25 Jun 2021

Abstract

Aldehyde dehydrogenase 2 (ALDH2) polymorphisms are related to both stroke risk and alcohol consumption. However, the influence of ALDH2 polymorphisms and alcohol consumption on cognitive impairment after ischemic stroke remains unknown, as do the possible mechanisms. We enrolled 180 Han Chinese ischemic stroke patients from four community health centers in Bengbu, China. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), and two different MoCA cutoff scores were used to define cognitive impairment in ischemic stroke patients. The ALDH2 genotypes were determined using polymerase chain reaction and direct sequencing. To assess the associations of ALDH2 polymorphisms and alcohol consumption with cognitive impairment after ischemic stroke, we performed binary logistic regression analysis with odds ratios. We revealed that individuals with the ALDH2 wild-type genotype were more likely to have high MoCA scores than those with the mutant and heterozygous types (). In addition, using two MoCA cutoff scores, the percentage of moderate to excessive alcohol consumption in the cognitive impairment group was higher than that in the nonimpairment group (). The levels of 4-hydroxy-2-nonenal () and swallowing function () were also higher in the cognitive impairment group than in the nonimpairment group. Moreover, after adjusting for other potential risk factors, ALDH2 polymorphisms and alcohol consumption had a significant synergistic effect on cognitive impairment (). Specifically, the ALDH22 mutant allele and higher alcohol consumption were associated with cognitive impairment and swallowing ability after ischemic stroke. Targeting ALDH2 may be a useful biomarker for cognitive rehabilitation following ischemic stroke.

1. Introduction

Stroke is an important problem in public health and is the leading cause of adult disability worldwide [1, 2]. It has been reported that approximately 15 million people per year have a stroke [3]. Most patients experience some disturbance in cognitive function following stroke [4], and cognitive function is a significant focus in stroke rehabilitation [5]. Alcohol consumption, which is common worldwide, affects the development of stroke and cognitive performance [6, 7]. Epidemiological evidence has revealed that excessive drinking is a major risk factor for all stroke subtypes, but especially for ischemic stroke [8, 9]. Additionally, some cohort studies have suggested that light to moderate drinking may have a protective effect on cardiovascular disease and ischemic stroke [9, 10]. In contrast, more recent studies have indicated that alcohol consumption is roughly linearly associated with stroke risk [11, 12]. However, a U-shaped relationship has also been reported between regular alcohol consumption and cognitive function in several major epidemiological studies [13]; thus, the relationship between alcohol consumption and cognitive impairment after ischemic stroke remains uncertain.

Alcohol metabolism represents a key biological determinant that can impact drinking behavior. Aldehyde dehydrogenase 2 (ALDH2) is the primary enzyme involved in this metabolic process [14]. Previous research has examined the potential effects of ALDH2 on alcohol consumption and health outcomes. However, the relationship between ALDH2, alcohol consumption, and cognitive function in patients with ischemic stroke is unclear. The main function of ALDH2 is to detoxify acetaldehyde, which is a toxic chemical product of ethanol metabolism [15]. Moreover, ALDH2 also removes other toxic aldehydes, such as 4-hydroxy-2-nonenal (4-HNE). As a potential substrate of ALDH2, 4-HNE is commonly considered a specific marker of ischemic stroke injury [15, 16].

ALDH2 is abundant in the brain, heart, lungs, and other organs with high mitochondrial contents [17, 18]. The ALDH2 gene consists of 13 exons and 12 introns. In exon 12, a polymorphism exists, in the form of a G-to-A missense mutation. The glutamate at position 504 is substituted by lysine (Glu504Lys). This polymorphism is also known as rs671, or the ALDH22 form, while the more common wild-type form is known as ALDH21. There are thus three possible allele combinations in the population: wild-type (), heterozygote (), and mutant () [19, 20]. Approximately 40% of the East Asian population carries an ALDH22 mutant allele, with a resulting marked reduction in enzymatic activity [15]. Recent studies have reported that the Glu504Lys polymorphism may affect ischemic stroke risk in the Han Chinese population and that carriers of the ALDH22 allele have increased 4-HNE levels after stroke [21]. However, there has been little previous research into the association between ALDH2 genotypes and cognitive impairment after ischemic stroke.

Therefore, the aim of this study was to evaluate the association of ALDH2 genotypes and alcohol consumption with cognitive function after ischemic stroke. Cognitive function can be tested briefly using the Montreal Cognitive Assessment (MoCA), and this test is recommended for screening cognitive impairment in patients with ischemic stroke [22]. In many previous studies, cognitive impairment has been defined as a MoCA [23]. However, some researchers have recommended that a MoCA cutoff score of 22/23 points might be more suitable for detecting cognitive impairment [24]. In addition, swallowing deficits are also commonly reported in patients with ischemic stroke [25], and cognitive dysfunction is related to dysphagia [26]. In the present study, we used these two different MoCA cutoff scores to investigate the relationship between alcohol consumption, 4-HNE levels, swallowing function, and cognitive impairment after ischemic stroke, respectively. We further investigated the synergistic effects of ALDH2 genotype and alcohol consumption on the MoCA score and swallowing ability in ischemic stroke patients. Finally, we sought to explore the underlying mechanisms that might influence these associations.

2. Methods

2.1. Patients

From June 2015 through August 2015, patients with ischemic stroke from four community health centers located in the Longzihu District of Bengbu (Anhui Province, China) were recruited in our study. We visited each community and held a free health checkup for participants. Each participant completed a self-reported questionnaire relating to their lifestyle and medical history, including information on prior stroke and baseline disease status. The inclusion criteria for ischemic stroke patients were as follows: (i) stroke diagnosis as per the revised diagnostic criteria of the 4th National Cerebrovascular Disease Conference in China [27], (ii) stroke diagnosis based on computed tomography or magnetic resonance imaging brain scans, (iii) within 3 months after stroke onset, (iv) permanent residents of Han Chinese ethnicity in selected communities, and (v) informed consent provided. The exclusion criteria were as follows: (i) previous history of cerebral vascular malformation, transient ischemic attack, intracranial hemorrhage, stroke mimics (i.e., seizures or migraines), or neurological deficits; (ii) previous history of bleeding diathesis, anticoagulation therapy, illicit drug use, or serious medical illness; (iii) previous history of illiteracy or any major mental or physical condition that may interfere with cognitive assessments; and (iv) a diagnosis of coronary artery disease [19, 22, 28].

Data were initially obtained from 200 participants. We excluded patients who were unable to participate in the interview because of serious cognitive impairment () and those with missing medical records (). We also excluded subjects who had missing information regarding alcohol habits, such as alcohol status and the amount and frequency of alcohol consumption (), and regarding recurrence and death (). Thus, a total of 180 patients with ischemic stroke were enrolled in this study. A detailed flowchart showing participant selection is provided in Figure 1.

2.2. Demographic and Clinical Characteristics and Measurements

We obtained patient demographic information from patients’ medical charts and self-reported data. According to the medical charts, patients were divided into two subtypes using a simple clinical scheme with the Oxfordshire Community Stroke Project (OCSP) classification and included the following: posterior circulation infarction (PCI) and anterior circulation infarction (ACI) [29]. In the PCI group, the infarcts involved the brainstem, posterior cerebral artery area, thalamus, or cerebellum; in the ACI group, the infarcts occurred in the region of the middle cerebral artery, anterior cerebral artery, or anterior choroidal artery [30].

After fasting for 8 to 12 hours, we measured each patient’s blood pressure, height, and weight and calculated their body mass index (BMI). In addition, fasting venous blood samples were collected at approximately the same time of day, in the morning, to minimize diurnal variations [31]. Each blood sample was drawn into a tube containing ethylenediaminetetraacetic acid as an anticoagulant and a tube without anticoagulant. The obtained plasma and serum were preserved at -80°C until assays were performed. Routine blood and biochemistry tests were analyzed, including fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). This part of the study was approved by the ethics committee of Bengbu Medical College. The cognitive assessments were conducted at the same time as the blood samples were taken.

2.3. Cognitive Assessments

The MoCA is a cognitive screening tool that can be used to distinguish healthy cognitive aging from mild cognitive impairment [32]. It is simple to conduct, sensitive, and valid. Since its introduction into clinical practice, it has been repeatedly demonstrated to be suitable for the initial assessment of mental status and for follow-up assessments [33]. The MoCA was administered by trained physicians in each community.

The MoCA comprises eight subtests that involve visuospatial/executive, naming, memory, attention, language, abstraction, delayed recall, and orientation with respect to time and place. MoCA scores range from 0 to 30. A higher score indicates better cognitive performance [34, 35]. With a cutoff of 26 (which we used as Method 1 in our study), the sensitivity and specificity of MoCA have been reported as 90% and 87%, respectively, when administered to screen patients with mild cognitive impairment in Canada [32]. However, subsequent clinical studies have demonstrated that some patients with normal cognitive ability have MoCA scores below 26 [36]. The MoCA cutoff scores of ischemic stroke patients by the educational level have been reported as follows: 24/25 for individuals with ≥7 years of education, 19/20 for individuals with 1 to 6 years of education, and 13/14 for illiterate individuals. Therefore, we also used a cutoff point of 22/23 (which we used as Method 2 in our study) for MoCA scores [24, 31, 37].

2.4. Water-Swallowing Test

The water-swallowing test (WST) is frequently used in clinical practice as a functional assessment to evaluate swallowing function [38]. Swallowing performance was assessed with the 30 mL water swallowing test which is cheap, easy to use, and with the highest reliability [39]. A total of 30 mL water was put on a plastic cup. The patient was ordered to drink the water “as quickly as comfortably possible” in an upright seated position. The time to drink and presence or absence of coughing were recorded. The results included the following five levels: level I (drink once, no coughing), level II (drinking more than two times of interruption, no coughing), level III (drinking once, with coughing), level IV (drinking more than two times of interruption, with coughing), and level V (coughing frequently and cannot drink the water successfully). After examination, swallowing ability was classified as normal (level I within 5 s), possible abnormality (level I over 5 s or level II), and abnormality (levels III to V). Possible abnormality and abnormality are considered dysphagia [39].

2.5. Alcohol Consumption Measurements

Data regarding alcohol consumption were collected via a self-administered questionnaire [40]. The questionnaire included a range of drinking variables in the past 12 months before the stroke. The type of alcohol (liquor, beer, or wine), quantity of consumption, and frequency of consumption (never or occasionally, daily, weekly, or monthly) were all assessed. The average daily intake of absolute alcohol was estimated based on the quantity and frequency of consumption. The content of ethanol (pure alcohol) was assumed to be 15.1 g for a drink of liquor, 13.2 g for a can of beer, and 10.8 g for a standard glass of wine [41, 42]. For each participant, total ethanol intake was converted to standard units per week (1  g ethanol). Each participant’s drinking status was then classified as one of four distinct categories: nondrinker, light drinker, moderate drinker, or excessive drinker [41]. Nondrinkers consumed <1 unit of ethanol per week. Light drinkers consumed 1-10 units/week for men and 1-7 units/week for women. Moderate drinkers consumed 11-21 units/week for men and 8-14 units/week for women. Excessive drinkers consumed >21 units/week for men and >14 units/week for women.

2.6. 4-HNE Concentration Measurements

The plasma levels of 4-HNE were estimated using ELISA kits (Elabscience Biotechnology, Wuhan, China) according to the manufacturer’s instructions [21].

2.7. ALDH2 Genotyping Measurements

Genomic DNA samples were obtained from blood samples using commercial DNA extraction kits (Tiangen Biotech, Beijing, China). The primer sequences were as follows: forward primer, 5-GTCAACTGCTATGATGTGTTTGG-3 and reverse primer, 5-CCACCAGCAGACCCTCAAG-3. The 50 μL polymerase chain reaction (PCR) mixture consisted of 2 μL DNA template, 2 μL forward and 2 μL reverse primers, 25 μL TaqMan Master Mix, and 19 μL double-distilled H2O. The PCR was conducted with predenaturation for 3 min at 94°C, followed by 35 cycles of amplification (94°C for 45 s, 53°C for 30 s, and 72°C for 45 s), and extension for 5 min at 72°C [20]. The PCR products were purified using commercial kits (Axygen Biosciences, Corning, NY, USA) and sent to GenScript Corporation (Nanjing, China) for sequencing.

2.8. Statistical Analyses

The results of continuous variables are presented as the , whereas the results of categorical variables are expressed as numbers of patients and percentages. Two-tailed Student’s -test or one-way ANOVA was performed for continuous variables, and the test was performed for categorical variables. Binary logistic regression analysis was performed to determine the associations of ALDH2 polymorphisms and alcohol consumption with cognitive impairment and swallowing ability after ischemic stroke in a Han Chinese population by estimating the odds ratios (ORs) with 95% confidence intervals (CIs) [43]. We used two different cognitive impairment assessment methods (Method 1 and Method 2) to estimate the correlations between ALDH2 polymorphism, alcohol consumption, and cognitive impairment. All missing values of predictors were imputed. Statistical analyses were conducted using SPSS version 24.0 software (IBM Corporation, Chicago, USA). All values of less than 0.05 were taken as statistically significant.

3. Results

3.1. Baseline Characteristics

Table 1 shows the demographic characteristics of all participants, grouped by ALDH2 polymorphism. We combined the heterozygotes () and mutant homozygotes () into one category and compared them with the wild-type homozygotes () in our analyses. There were no significant differences between the two categories in age or education. However, the levels of alcohol consumption were significantly higher in the ALDH2 wild-type genotype group than in the mutant and heterozygous genotype group (). We further compared alcohol consumption between two ALDH2 genotypes by gender; the results showed that there was no statistical difference between genotype and alcohol consumption in males. However, the levels of alcohol consumption were significantly higher in the ALDH2 wild-type genotype group than in the mutant and heterozygous genotype group in females (, Supplementary Table 1).


CharacteristicsALDH2 genotypes/chi square value
() ()

Age (years)1.4900.138
Males, (%)38 (43.7%)49 (52.7%)8.5360.003
Education7.0130.071
 <6 years18 (20.7%)22 (23.7%)
 6-9 years27 (31.0%)19 (20.4%)
 9-12 years21 (24.1%)37 (39.8%)
 >12 years21 (24.1%)15 (16.1%)
Alcohol consumption12.4640.006
 Nondrinkers12.6%21.5%
 Light drinkers8.0%22.6%
 Moderate drinkers36.8%30.1%
 Excessive drinkers42.5%25.8%

Continuous variables are expressed as the when normally distributed, and categorical variables are expressed as percentages.
3.2. Clinical Characteristics

The clinical characteristics of participants according to ALDH2 polymorphism are shown in Table 2. There were no significant differences between the two groups in BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), or FPG, TG, HDL-C, or LDL-C levels. However, 4-HNE levels were higher in patients with mutant alleles ( ng/L) than in patients with wild-type alleles ( ng/L; ), whereas TC levels were lower in patients with mutant alleles ( mmol/L) than in patients with wild-type alleles ( mmol/L; ).


CharacteristicsALDH2 genotypes value
() ()

BMI (kg/m2)1.8210.070
SBP (mmHg)1.5950.113
DBP (mmHg)0.0980.922
FPG (mmol/L)0.9110.363
TC (mmol/L)2.1060.037
TG (mmol/L)1.2520.214
HDL-C (mmol/L)0.5830.561
LDL-C (mmol/L)0.2340.815
4-HNE (ng/mL)4.0960.001

Continuous variables are expressed as the when normally distributed, and categorical variables are expressed as percentages. BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; FPG: fasting plasma glucose; TC: total cholesterol; TG: triglycerides; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; 4-HNE: 4-hydroxy-trans-2-nonenal.
3.3. Analysis of Cognitive Performance in Subjects with Different ALDH2 Genotypes

MoCA was used in ischemic stroke patients as a dependent variable to assess the extent of early vascular cognitive dysfunction. We compared the MoCA scores of three genotypes by analysis of variance (, ). Figure 2 shows that the MoCA scores of the ALDH2 wild-type genotype group (, ) were higher than those of the heterozygous group (, ; ). The MoCA scores of the ALDH2 wild-type genotype group () were higher than that of the mutant genotype group (, ; ). And there was no difference between the heterozygous and mutant genotype groups () (Figure 2).

3.4. Association of Alcohol Consumption and 4-HNE Levels with Cognitive Impairment

We used two different cognitive impairment assessment methods to compare alcohol consumption between the cognitive impairment and nonimpairment groups (Table 3). For both of the MoCA cutoff scores, the percentage of moderate to excessive alcohol consumption was higher in the cognitive impairment group than in the nonimpairment group. According to the MoCA subscores of visuospatial/executive, naming, memory, attention, language, abstraction, delayed recall, and orientation, MoCA subscores were compared with alcohol consumption in two ALDH2 genotypes. Intergroup differences were assessed using the single factor analysis of variance. Among the seven subscores, we found that two subscores (language and delayed recall) have a significant difference in alcohol consumption using univariate analysis (Supplementary Table 2).


Alcohol consumptionMethod 1Method 2
ImpairmentNonimpairmentImpairmentNonimpairment

Nondrinkers21 (13.4%)10 (43.5%)12 (9.9%)19 (32.2%)
Light drinkers24 (15.3%)4 (17.4%)18 (14.9%)10 (16.9%)
Moderate drinkers52 (33.1%)8 (34.8%)44 (36.4%)16 (27.1%)
Excessive drinkers60 (38.2%)1 (4.3%)47 (38.8%)14 (23.7%)
Chi square17.41915.238
value0.0010.002

Method 1: MoCA cutoff score of 26. Method 2: MoCA cutoff score of 23.

We also used the two different MoCA cutoff scores to compare 4-HNE levels between the cognitive impairment and nonimpairment groups (Figure 3(a)). With a cutoff score of 26, the levels of 4-HNE were higher in the cognitive impairment group (, ) than in the nonimpairment group (, ). Furthermore, when we considered MoCA scores and educational levels of the ischemic stroke patients, with a cutoff score of 23 (Figure 3(b)), the levels of 4-HNE were also higher in the cognitive impairment group (, ) than in the nonimpairment group (, ).

3.5. Association between Swallowing Function and Cognitive Impairment

We used two different cognitive impairment assessment methods, to compare swallowing levels between the cognitive impairment and nonimpairment groups (Table 4). For both of the MoCA cutoff scores, the level of swallowing function was higher in the cognitive impairment group than in the nonimpairment group.


SwallowingMethod 1Method 2
ImpairmentNonimpairmentImpairmentNonimpairment

Level I9 (5.7%)23 (100.0%)0.0010 (0.0%)32 (54.2%)0.001
Level II44 (28.0%)0 (0.0%)17 (14.0%)27 (45.8%)
Level III53 (33.8%)0 (0.0%)53 (43.8%)0 (0.0%)
Level IV29 (18.5%)0 (0.0%)29 (24.0%)0 (0.0%)
Level V22 (14.0%)0 (0.0%)22 (18.2%)0 (0.0%)

3.6. Separate Effects of ALDH2 Polymorphisms and Alcohol Consumption on Cognitive Impairment

To test the possible association of ALDH2 polymorphism and alcohol consumption with cognitive impairment, we assessed the separate effects of ALDH2 polymorphism and alcohol consumption on cognitive impairment in ischemic stroke patients. The association between ALDH2 polymorphisms and cognitive impairment risk in ischemic stroke patients is shown in Table 5. At a cutoff MoCA score of 26, participants carrying the mutant ALDH2 allele had a higher risk of cognitive impairment (, , ). Similarly, at a cutoff MoCA score of 23, participants carrying the mutant ALDH2 allele also had a higher risk of cognitive impairment (, , ).


Method 1Method 2

ALDH2 genotypes
RefRef
3.29 (1.03-10.50)2.65 (1.19-5.92)
Alcohol consumption
 NondrinkersRefRef
 Light drinkers0.59 (0.16-0.89)#0.68 (0.33-0.89)#
 Moderate drinkers0.77 (0.21-1.06)1.08 (0.83-2.19)
 Excessive drinkers1.22 (1.16-1.49)#1.78 (1.03-2.15)#

aModels were adjusted for age, gender, and education. vs. ALDH2 wild-type genotype group (). # vs. nondrinkers.

Further analysis of these results revealed a clear association between alcohol consumption and cognitive impairment risk in ischemic stroke patients. At a MoCA cutoff score of 26, there was an OR of 0.59 () in light drinkers and an OR of 1.22 () in excessive drinkers compared with nondrinkers. The OR values using a MoCA cutoff score of 23 were similar to the values using a cutoff score of 26.

3.7. ORs of Alcohol Consumption on Cognitive Impairment, Stratified by ALDH2 Polymorphism

Table 6 shows the ORs of alcohol consumption on cognitive impairment, stratified by ALDH2 polymorphism, after taking into account other potential risk factors. At a MoCA cutoff score of 26, the multivariate OR of cognitive impairment risk was 7.75 (95% CI: 1.03–113.78) for the ALDH2 wild-type genotype in excessive drinkers compared with nondrinkers. In patients with the ALDH2 heterozygous group, the multivariate OR (95% CI) of cognitive impairment risk compared with nondrinkers was 10.95 (1.04-114.88) in moderate drinkers. The OR values had a similar trend using a cutoff MoCA score of 23. However, in patients with the ALDH2 heterozygous group, the multivariate OR (95% CI) of cognitive impairment risk compared with nondrinkers was 13.74 (1.96-96.51) in light drinkers, 22.36 (3.69-135.54) in moderate drinkers, and 20.93 (2.77-158.45) in excessive drinkers.


ALDH2 genotypesAlcohol consumptionMethod 1Method 2

NondrinkersRefRef
Light drinkers1.75 (0.20-15.19)0.45 (0.05-3.91)
Moderate drinkers2.97 (0.60-14.68)2.43 (0.54-11.02)
Excessive drinkers7.75 (1.03-113.78)3.93 (0.84-18.44)

NondrinkersRefRef
Light drinkers15.99 (0.96-266.02)13.74 (1.96-96.51)#
Moderate drinkers10.95 (1.04-114.88)#22.36 (3.69-135.54)#
Excessive drinkers20.93 (2.77-158.45)#

Nondrinkers
Light drinkers
Moderate drinkers
Excessive drinkers

aModels were adjusted for age, gender, education, and subtype. vs. nondrinkers with ALDH2 wild-type genotype (). # vs. nondrinkers with ALDH2 mutant genotype ().

4. Discussion

Previous studies have revealed that ALDH2 polymorphisms are closely related to the incidence of ischemic stroke. The current study was the first case-cohort study describing the effects of ALDH2 polymorphisms and alcohol consumption on cognitive impairment after ischemic stroke. We demonstrated that ALDH2 polymorphisms and alcohol consumption had a synergistic effect on cognitive impairment, even after taking other potential risk factors into account (age, gender, education, and subtype). Our results indicate that the association between alcohol consumption and cognitive impairment is stronger in the ALDH2 heterozygous group than in the wild-type genotype group, as well as with swallowing ability.

Cognitive impairment after stroke can affect the quality of life and long-term prognosis (higher mortality and more disability) of stroke survivors [44]. Several studies have confirmed that the common functional single nucleotide polymorphism (SNP) in exon 12 of ALDH2 is a risk indicator for ischemic stroke [21]. SNPs are the most abundant and stable genetic variations that exist in genomes [45, 46]. In the present study, the ALDH22 polymorphism was associated with cognitive impairment after ischemic stroke, although there was a lack of evidence of this in previous studies. Our data revealed that patients in the ALDH2 wild-type genotype group were significantly more likely to have higher MoCA scores than patients in the mutant and heterozygous genotype group, which suggests that the ALDH22 polymorphism is associated with cognitive impairment after stroke.

The MoCA can be used as a dependent variable to assess the extent of early cognitive dysfunction [22]. In previous studies, cognitive impairment was defined by a MoCA cutoff score of <26 [23, 32]. However, some researchers have recommended that a cutoff score of 22/23 points might be more suitable to detect cognitive impairment [24, 31, 37]. To test the possible association between ALDH2 polymorphisms and cognitive impairment in patients with ischemic stroke, we therefore used two different MoCA cutoff scores to investigate interaction effects. We found that the ALDH2 mutant allele carried a higher risk of cognitive impairment using both MoCA cutoff scores. Furthermore, 4-HNE levels were higher in the cognitive impairment group than in the nonimpairment group using both MoCA cutoff scores.

Several studies have demonstrated that 4-HNE is a potential substrate for ALDH2. The levels of 4-HNE are elevated following ischemic stroke injury. Guo et al. reported that 4-HNE plays an important role in the pathogenesis of neurological diseases and is a potential biomarker for ischemic stroke [15, 16]. Our data revealed that 4-HNE levels were significantly lower in patients with the ALDH2 wild-type genotype than in patients carrying mutant ALDH2 alleles. Meanwhile, patients with the ALDH2 wild-type genotype were significantly more likely to have a higher MoCA score compared with those carrying mutant alleles. This finding may explain why the ALDH2 mutant allele carries a significantly higher risk of cognitive impairment.

Swallowing deficits are also commonly reported in patients with ischemic stroke [25]. Several studies revealed that cognitive dysfunction was associated with dysphagia [25, 26]. Therefore, we investigated the association between swallowing function and cognitive impairment using two MoCA cutoff scores. In this study, the severity of dysphagia might contribute to cognitive impairment for both MoCA cutoff scores. In addition, considering that the lesion site may affect the swallowing function, we further adopted subtypes of ischemic stroke to investigate the interaction of ALDH2 and alcohol consumption on swallowing. Our data described that both ALDH2 genotypes showed a higher risk of dysphagia in excessive drinkers compared to nondrinkers, but the risk of dysphagia was higher in carriers of the mutant ALDH2 allele than in noncarriers.

As an important determinant of drinking behavior, ALDH2 has a well-known role in ethanol metabolism [47]. Various longitudinal studies have reported a link between moderate alcohol consumption and improved cognitive performance [48, 49]. In our study, we also explored the association between alcohol consumption and cognitive function in patients with ischemic stroke. Considerable evidence has emerged suggesting that alcohol consumption behaviors are related to the ALDH22 polymorphism in Asian populations [50, 51]. We divided the ischemic stroke patients in our study into four subgroups based on their history of alcohol consumption. Alcohol consumption in the ALDH2 wild-type genotype group was significantly higher than that in the mutant and heterozygous genotype group. This may be because carriers of ALDH2 mutant alleles are more sensitive to alcohol, which reportedly makes them less inclined to engage in excessive drinking [52]. We used two different cognitive impairment cutoff points to analyze the association between alcohol consumption and cognitive impairment. The univariate analysis revealed a higher percentage of moderate to excessive alcohol consumption in the cognitive impairment group than in the nonimpairment group, which suggests that alcohol consumption may have an effect on cognitive impairment after ischemic stroke. We also applied a binary logistic regression model to evaluate the synergistic effects of ALDH2 polymorphisms and alcohol consumption on cognitive impairment. Adjusted for age, gender, education, and subtype, we demonstrated that the multivariate risk of cognitive impairment was higher in excessive drinkers than in nondrinkers with the ALDH2 wild-type genotype, while the risk of cognitive impairment was higher in light to excessive drinkers than nondrinkers with the ALDH2 mutant or heterozygous genotype. These findings further suggest that ALDH2 might be involved in the pathogenesis and progression of cognitive impairment after ischemic stroke, as well as having a role in alcohol metabolism.

There were several limitations in the present study. First, some participants refused to participate, while blood samples were unable to be obtained from some patients; this may have caused sampling bias. Second, the MoCA scale is commonly used as a screening scale for cognitive function but fails to assess global disability after ischemic stroke. So, in this study, we also evaluated the swallowing function. The current findings are therefore considered preliminary and require validation. Third, the plasma 4-HNE levels were detected only once, at baseline, and potential fluctuations in plasma 4-HNE levels were not evaluated; we were therefore unable to adjust for this effect. Finally, although the ALHD22 allele is an important risk factor for ischemic stroke, results have been inconsistent over different ethnic groups, different countries, and different genders. Because the sample size was relatively small, we did not perform a stratified analysis by lesion location. We adopted subtypes of ischemic stroke to replace lesion location, and this might have affected our study results (Supplementary Table 3). To better understand the relationship between ischemic stroke impairment and ALDH2 genotypes, future studies need to enroll larger sample sizes across multiple communities. To this end, the current study represents an ongoing effort, and we will continue to regularly update the analysis, with the aim of providing a comprehensive and easily accessible review, as well as facilitating a best-practice approach to ischemic stroke rehabilitation.

5. Conclusions

The present study demonstrated that ALDH2 polymorphisms and alcohol consumption were associated with cognitive impairment and dysphagia in patients after ischemic stroke, mainly in patients with the mutant allele. ALDH2 may be involved in the pathogenesis and progression of ischemic stroke in the Han Chinese population. ALDH2 might therefore be a useful biomarker to target for cognitive rehabilitation following ischemic stroke. However, the underlying mechanisms need to be further explored.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethical Approval

This study was conducted with approval from the Ethics Committee of Bengbu Medical College.

Disclosure

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Acknowledgments

This work was supported by the Centers for Disease Control and Prevention of Bengbu, China. The work was also supported by the Natural Science Foundation of the Education Department of Anhui Province (KJ2019A0306), the Transformation of Medicine Research Projects of Bengbu Medical College (BYTM2019004), the 512 Talent Cultivation Plan of Bengbu Medical College (by51201202 and by51201307), and the Chronic Noncommunicable Disease Prevention and Control Program of Bengbu Medical College (BYKC201901), China. We are grateful to all of the professionals and participants involved in the study. We also thank Bronwen Gardner, PhD, from Liwen Bianji, Edanz Editing China (http://www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

Supplementary Materials

Supplementary Table 1: comparison of MoCA subscores based on ALDH2 polymorphisms and alcohol consumption. Supplementary Table 2: comparison of MoCA subscores based on ALDH2 polymorphisms and alcohol consumption. Supplementary Table 3: multivariate odds ratios (95% confidence intervals) for alcohol consumption on swallowing ability in stroke patients, stratified by ALDH2 polymorphism. (Supplementary Materials)

References

  1. W. J. Tu, H. C. Qiu, Y. Zhang et al., “Lower serum retinoic acid level for prediction of higher risk of mortality in ischemic stroke,” Neurology, vol. 92, no. 15, pp. e1678–e1687, 2019. View at: Publisher Site | Google Scholar
  2. W. J. Tu, X. Dong, S. J. Zhao, D.-G. Yang, and H. Chen, “Prognostic value of plasma neuroendocrine biomarkers in patients with acute ischaemic stroke,” Journal of Neuroendocrinology, vol. 25, no. 9, pp. 771–778, 2013. View at: Publisher Site | Google Scholar
  3. W. Johnson, O. Onuma, M. Owolabi, and S. Sachdev, “Stroke: a global response is needed,” Bulletin of the World Health Organization, vol. 94, no. 9, pp. 634–634A, 2016. View at: Publisher Site | Google Scholar
  4. Y. Qu, L. Zhuo, N. Li et al., “Prevalence of post-stroke cognitive impairment in china: a community-based, cross-sectional study,” PLoS One, vol. 10, no. 4, p. e0122864, 2015. View at: Publisher Site | Google Scholar
  5. K. Tokairin, T. Osanai, T. Abumiya, K. Kazumata, K. Ono, and K. Houkin, “Regional transarterial hypothermic infusion in combination with endovascular thrombectomy in acute ischaemic stroke with cerebral main arterial occlusion: protocol to investigate safety of the clinical trial,” BMJ Open, vol. 7, no. 8, p. e016502, 2017. View at: Publisher Site | Google Scholar
  6. C.-T. Yao, C.-A. Cheng, H.-K. Wang et al., “The role of ALDH2 and ADH1B polymorphism in alcohol consumption and stroke in Han Chinese,” Hum Genomics, vol. 5, no. 6, pp. 569–576, 2011. View at: Publisher Site | Google Scholar
  7. Y. L. Zheng, F. Lian, Q. Shi et al., “Alcohol intake and associated risk of major cardiovascular outcomes in women compared with men: a systematic review and meta-analysis of prospective observational studies,” BMC Public Health, vol. 15, no. 1, p. 773, 2015. View at: Publisher Site | Google Scholar
  8. J. Patra, B. Taylor, H. Irving et al., “Alcohol consumption and the risk of morbidity and mortality for different stroke types--a systematic review and meta-analysis,” BMC Public Health, vol. 10, no. 1, p. 258, 2010. View at: Google Scholar
  9. C. Zhang, Y. Y. Qin, Q. Chen et al., “Alcohol intake and risk of stroke: A dose–response meta-analysis of prospective studies,” International Journal of Cardiology, vol. 174, no. 3, pp. 669–677, 2014. View at: Publisher Site | Google Scholar
  10. M. V. Holmes, C. E. Dale, L. Zuccolo et al., “Association between C reactive protein and coronary heart disease: mendelian randomisation analysis based on individual participant data,” BMJ, vol. 349, p. g4164, 2014. View at: Google Scholar
  11. I. Y. Millwood, R. G. Walters, X. W. Mei et al., “Conventional and genetic evidence on alcohol and vascular disease aetiology: a prospective study of 500 000 men and women in China,” The Lancet, vol. 393, no. 10183, pp. 1831–1842, 2019. View at: Publisher Site | Google Scholar
  12. A. M. Wood, S. Kaptoge, A. S. Butterworth et al., “Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies,” The Lancet, vol. 391, no. 10129, pp. 1513–1523, 2018. View at: Google Scholar
  13. I. Lang, R. B. Wallace, F. A. Huppert, and D. Melzer, “Moderate alcohol consumption in older adults is associated with better cognition and well-being than abstinence,” Age Ageing, vol. 36, no. 3, pp. 256–261, 2007. View at: Publisher Site | Google Scholar
  14. P. J. Brooks, M.-A. Enoch, D. Goldman, T.-K. Li, and A. Yokoyama, “The alcohol flushing response: an unrecognized risk dactor for esophageal cancer from alcohol consumption,” PLoS Medicine, vol. 6, no. 3, pp. 258–263, 2009. View at: Publisher Site | Google Scholar
  15. J. M. Guo, A. J. Liu, P. Zang et al., “ALDH2 protects against stroke by clearing 4-HNE,” Cell Research, vol. 23, no. 7, pp. 915–930, 2013. View at: Publisher Site | Google Scholar
  16. W. C. Lee, H. Y. Wong, Y. Y. Chai et al., “Lipid peroxidation dysregulation in ischemic stroke: plasma 4-HNE as a potential biomarker?” Biochemical and Biophysical Research Communications, vol. 425, no. 4, pp. 842–847, 2012. View at: Publisher Site | Google Scholar
  17. C. H. Chen, G. R. Budas, E. N. Churchill, M.-H. Disatnik, T. D. Hurley, and D. Mochly-Rosen, “Activation of aldehyde dehydrogenase-2 reduces ischemic damage to the heart,” Science, vol. 321, no. 5895, pp. 1493–1495, 2008. View at: Publisher Site | Google Scholar
  18. M. J. Stewart, K. Malek, and D. W. Crabb, “Distribution of messenger RNAs for aldehyde dehydrogenase 1, aldehyde dehydrogenase 2, and aldehyde dehydrogenase 5 in human tissues,” Journal of investigative medicine: the official publication of the American Federation for Clinical Research, vol. 44, no. 2, pp. 42–46, 1996. View at: Google Scholar
  19. Y. Qu, H. L. Zhang, L. M. Yu, Y. Sun, H.-l. Wu, and Y.-g. Chen, “Aldehyde dehydrogenase 2 polymorphism as a protective factor for intracranial vascular stenosis in ischemic stroke in Han Chinese,” International Journal of Neuroscience, vol. 126, no. 4, pp. 342–347, 2016. View at: Publisher Site | Google Scholar
  20. F. Xu, Y. Chen, R. Lv et al., “ALDH2 genetic polymorphism and the risk of type II diabetes mellitus in CAD patients,” Hypertens Res, vol. 33, no. 1, pp. 49–55, 2010. View at: Google Scholar
  21. H. Yang, Z. Song, G. P. Yang et al., “The ALDH2 rs671 polymorphism affects post-stroke epilepsy susceptibility and plasma 4-HNE levels,” PLoS One, vol. 9, no. 10, p. e109634, 2014. View at: Publisher Site | Google Scholar
  22. L. Zuo, Y. Dong, R. Zhu et al., “Screening for cognitive impairment with the Montreal Cognitive Assessment in Chinese patients with acute mild stroke and transient ischaemic attack: a validation study,” BMJ Open, vol. 6, no. 7, p. e011310, 2016. View at: Publisher Site | Google Scholar
  23. L. Chen, C. Yu, N. Zhang, J. Liu, and W. Liu, “Cognitive impairment in patients with Parkinson’s disease: a 30-month follow-up study,” Clinical neurology and neurosurgery, vol. 151, pp. 65–69, 2016. View at: Publisher Site | Google Scholar
  24. N. Carson, L. Leach, and K. J. Murphy, “A re-examination of Montreal Cognitive Assessment (MoCA) cutoff scores,” International journal of geriatric psychiatry, vol. 33, no. 2, pp. 379–388, 2018. View at: Publisher Site | Google Scholar
  25. M. A. Crary, G. D. Carnaby, I. Sia, A. Khanna, and M. F. Waters, “Spontaneous swallowing frequency has potential to identify dysphagia in acute stroke,” Stroke, vol. 44, no. 12, pp. 3452–3457, 2013. View at: Publisher Site | Google Scholar
  26. S. Y. Jo, J.-W. Hwang, and S.-B. Pyun, “Relationship between cognitive function and dysphagia after stroke,” Annals of rehabilitation medicine, vol. 41, no. 4, p. 564, 2017. View at: Publisher Site | Google Scholar
  27. G. Wang, X. Cheng, and X. Zhang, “Use of various CT imaging methods for diagnosis of acute ischemic cerebrovascular disease,” Neural regeneration research, vol. 8, no. 7, p. 655, 2013. View at: Google Scholar
  28. Y. Yamada, J. Sakuma, I. Takeuchi et al., “Identification of six polymorphisms as novel susceptibility loci for ischemic or hemorrhagic stroke by exome-wide association studies,” International Journal of Molecular Medicine, vol. 39, no. 6, pp. 1477–1491, 2017. View at: Publisher Site | Google Scholar
  29. W.-D. Tao, M. Liu, M. Fisher et al., “Posterior versus anterior circulation infarction: how different are the neurological deficits?” Stroke, vol. 43, no. 8, pp. 2060–2065, 2012. View at: Publisher Site | Google Scholar
  30. Q. Zeng, W. Tao, C. Lei, W. Dong, and M. Liu, “Etiology and risk factors of posterior circulation infarction compared with anterior circulation infarction,” Journal of Stroke and Cerebrovascular Diseases, vol. 24, no. 7, pp. 1614–1620, 2015. View at: Publisher Site | Google Scholar
  31. K. L. Chen, Y. Xu, A. Q. Chu et al., “Validation of the Chinese version of Montreal Cognitive Assessment basic for screening mild cognitive impairment,” Journal of the American Geriatrics Society, vol. 64, no. 12, pp. e285–e290, 2016. View at: Publisher Site | Google Scholar
  32. Z. S. Nasreddine, N. A. Phillips, V. Bédirian et al., “The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment,” Journal of the American Geriatrics Society, vol. 53, no. 4, pp. 695–699, 2005. View at: Publisher Site | Google Scholar
  33. M. Ihara, Y. Okamoto, and R. Takahashi, “Suitability of the Montreal cognitive assessment versus the mini-mental state examination in detecting vascular cognitive impairment,” Journal of Stroke and Cerebrovascular Diseases, vol. 22, no. 6, pp. 737–741, 2013. View at: Publisher Site | Google Scholar
  34. J. C. Davis, S. Bryan, L. C. Li et al., “Mobility and cognition are associated with wellbeing and health related quality of life among older adults: a cross-sectional analysis of the Vancouver Falls Prevention Cohort.,” BMC Geriatrics, vol. 15, no. 1, p. 75, 2015. View at: Publisher Site | Google Scholar
  35. S. Freitas, M. R. Simoes, L. Alves, and I. Santana, “Montreal Cognitive Assessment (MoCA): normative study for the Portuguese population,” Journal of Clinical and Experimental Neuropsychology, vol. 33, no. 9, pp. 989–996, 2011. View at: Publisher Site | Google Scholar
  36. L. Zhang, “Determination of the cut-off point of the Chinese version of the Montreal Cognitive Assessment among Chinese elderly in Guangzhou,” Chinese Mental Health Journal, vol. 22, no. 2, p. 123, 2008. View at: Google Scholar
  37. M. Nara, M. Sugie, T. Takahashi et al., “Japanese version of the Montreal Cognitive Assessment cut-off score to clarify improvement of mild cognitive impairment after exercise training in community-dwelling older adults,” Geriatrics & gerontology international, vol. 18, no. 6, pp. 833–838, 2018. View at: Publisher Site | Google Scholar
  38. A. Osawa and S. Maeshima, “Swallowing Disorders in Patients with Stroke,” in Voice and Swallowing Disorders, IntechOpen., 2019. View at: Google Scholar
  39. T. Wang, Y. Zhao, and A. Guo, “Association of swallowing problems with frailty in Chinese hospitalized older patients,” International journal of nursing sciences, vol. 7, no. 4, pp. 408–412, 2020. View at: Publisher Site | Google Scholar
  40. I. Y. Millwood, L. Li, M. Smith et al., “Alcohol consumption in 0.5 million people from 10 diverse regions of China: prevalence, patterns and socio-demographic and health-related correlates,” International journal of epidemiology, vol. 42, no. 3, pp. 816–827, 2013. View at: Google Scholar
  41. W. Huang, C. Qiu, B. Winblad, and L. Fratiglioni, “Alcohol consumption and incidence of dementia in a community sample aged 75 years and older,” Journal of Clinical Epidemiology, vol. 55, no. 10, pp. 959–964, 2002. View at: Publisher Site | Google Scholar
  42. E. Glovannucci, G. Colditz, M. J. Stampfer et al., “The assessment of alcohol consumption by a simple self-administered questionnaire,” American journal of epidemiology, vol. 133, no. 8, pp. 810–817, 1991. View at: Publisher Site | Google Scholar
  43. A. Jaywant, J. Toglia, F. M. Gunning, and M. W. O’Dell, “The diagnostic accuracy of the Montreal Cognitive Assessment in inpatient stroke rehabilitation,” Neuropsychological Rehabilitation, vol. 29, no. 8, pp. 1163–1176, 2019. View at: Publisher Site | Google Scholar
  44. E. Douven, S. H. Schievink, F. R. Verhey et al., “The Cognition and Affect after Stroke - a Prospective Evaluation of Risks (CASPER) study: rationale and design,” BMC Neurology, vol. 16, no. 1, p. 65, 2016. View at: Publisher Site | Google Scholar
  45. J. Zhang, J. Yang, L. Zhang et al., “A new SNP genotyping technology target SNP-seq and its application in genetic analysis of cucumber varieties,” Scientific Reports, vol. 10, no. 1, p. 5623, 2020. View at: Publisher Site | Google Scholar
  46. B. Lehne, C. M. Lewis, and T. Schlitt, “From SNPs to genes: disease association at the gene level,” PLoS One, vol. 6, no. 6, p. e20133, 2011. View at: Publisher Site | Google Scholar
  47. S. E. Luczak, T. Liang, and T. L. Wall, “Age of drinking initiation as a risk factor for alcohol use disorder symptoms is moderated by ALDH22 and ethnicity,” Alcoholism: Clinical and Experimental Research, vol. 41, no. 10, pp. 1738–1744, 2017. View at: Publisher Site | Google Scholar
  48. G. E. Bond, R. L. Burr, S. M. McCurry, M. M. Rice, A. R. Borenstein, and E. B. Larson, “Alcohol and cognitive performance: a longitudinal study of older Japanese Americans. The Kame Project,” International Psychogeriatrics, vol. 17, no. 4, pp. 653–668, 2005. View at: Publisher Site | Google Scholar
  49. K. J. Mukamal, L. H. Kuller, and A. L. Fitzpatrick, “Prospective study of alcohol consumption and risk of dementia in older adults,” JAMA, vol. 289, no. 11, pp. 1405–1413, 2003. View at: Publisher Site | Google Scholar
  50. L. Wu, Y. He, B. Jiang et al., “The association between the prevalence, treatment and control of hypertension and the risk of mild cognitive impairment in an elderly urban population in China,” Hypertension Research, vol. 39, no. 5, pp. 367–375, 2016. View at: Publisher Site | Google Scholar
  51. C. Shin, K. Kwack, N. H. Cho, S. H. Kim, and I. Baik, “Sex-specific differences in the association of a common aldehyde dehydrogenase 2 gene polymorphism and alcohol consumption with stroke risk in a Korean population: a prospective cohort study,” Nutrition Research and Practice, vol. 9, no. 1, pp. 79–86, 2015. View at: Publisher Site | Google Scholar
  52. T. Takeshita and K. Morimoto, “Self-reported alcohol-associated symptoms and drinking behavior in three LDH2 genotypes among Japanese university students,” Alcoholism: Clinical and Experimental Research, vol. 23, no. 6, pp. 1065–1069, 1999. View at: Publisher Site | Google Scholar

Copyright © 2021 Ying Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views200
Downloads754
Citations

Related articles

No related content is available yet for this article.

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.