Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Non-linear association of liver enzymes with cognitive performance in the elderly: A cross-sectional study

  • Yan-Li Zhang,

    Roles Conceptualization, Writing – original draft

    Affiliation Department of Neurological Intensive Care Unit, Sixth Hospital of Shanxi Medical University (General Hospital of Tisco), Taiyuan, Shanxi, China

  • Shi-Ying Jia,

    Roles Writing – original draft

    Affiliation Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China

  • Bo Yang,

    Roles Formal analysis, Methodology

    Affiliation Department of Hernia and Abdominal Wall Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China

  • Jie Miao,

    Roles Formal analysis

    Affiliation Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China

  • Chen Su,

    Roles Formal analysis

    Affiliation Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China

  • Zhi-Gang Cui,

    Roles Formal analysis

    Affiliation Department of Neurology, The Third People’s Hospital of Datong, Datong, Shanxi, China

  • Li-Ming Yang ,

    Roles Conceptualization, Supervision, Writing – review & editing

    neuroguo@163.com (J-HG); tgylm1970@163.com (L-MY)

    Affiliation Department of Neurological Intensive Care Unit, Sixth Hospital of Shanxi Medical University (General Hospital of Tisco), Taiyuan, Shanxi, China

  • Jun-Hong Guo

    Roles Conceptualization, Methodology, Supervision, Writing – review & editing

    neuroguo@163.com (J-HG); tgylm1970@163.com (L-MY)

    Affiliation Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China

Abstract

Background

Although liver metabolic dysfunction has been found to potentially elevate susceptibility to cognitive impairment and dementia, there is still insufficient evidence to explore the non-linear association of liver enzymes with cognitive performance. Therefore, we aimed to elucidate the non-linear relationship between liver enzymes and cognitive performance.

Methods

In this cross-sectional study, 2764 individuals aged ≥ 60 who participated in the National Health and Nutrition Survey (NHANES) between 2011 and 2014 were included. The primary data comprised liver enzyme levels (alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), AST/ALT ratio, and gamma-glutamyl transferase (GGT)), and cognitive performance was the major measured outcome. The associations were analyzed using weighted multivariate logistic regression, subgroup analysis, a generalized additive model, smooth fitting curves, and threshold effects.

Results

The results of the fully adjusted model indicated that ALP was negatively associated with the animal fluency test (AFT) score (OR = 1.48, 95% CI: 1.11–1.98), whereas ALT demonstrated a positive association with the consortium to establish a registry for Alzheimer’s disease (CERAD) test score (OR = 0.72, 95% CI: 0.53–0.97). Additionally, the AST/ALT ratio was negatively associated with the global cognitive test (OR = 2.39, 95% CI: 1.53–3.73), CERAD (OR = 2.61, 95% CI: 1.77–3.84), and digit symbol substitution test (DSST) scores (OR = 2.51, 95% CI: 1.57–4.02). GGT was also negatively associated with the AFT score (OR = 1.16, 95% CI: 1.01–1.33) in unadjusted model. A non-linear relationship was observed between liver enzymes and the risk of cognitive impairment as assessed by the global cognitive test. Specifically, when ALP > 60 U/L, 0.77 < AST/ALT < 1.76, and 25 < GGT < 94 U/L, higher liver enzyme levels were significantly associated with an elevated cognitive impairment risk, while a lower cognitive impairment risk when ALT level was > 17 U/L.

Conclusions

There is a non-linear relationship between liver enzymes and cognitive performance, indicating that liver enzyme levels should be maintained within a certain level to mitigate the risk of cognitive impairment.

1 Introduction

The decline in cognitive performance among older adults has emerged as a significant global public health concern due to an increased life expectancy and the rise in chronic comorbidities [1]. According to the World Alzheimer Report of 2022, the global prevalence of individuals living with dementia is projected to rise to 139 million by 2050 (available online: https://www.who.int/news-room/fact-sheets/detail/dementia). Cognitive impairment manifests as diverse symptoms, like memory disorders, language deterioration, and impaired executive function, and is regarded as a substantial predictor for dementia development [2,3]. Consequently, it is imperative to identify modifiable factors that potentially contribute to cognitive decline to facilitate the development of effective preventive interventions [46].

Alzheimer’s disease (AD) is the most common form of senile dementia [7]. Multiple studies have demonstrated that metabolic dysfunctions, including disturbances in energy metabolism, chronic inflammation, oxidative stress, and neuronal insulin resistance, play a crucial role in the pathogenesis of AD [710]. AD is a systemic metabolic disorder, and metabolic processes in the peripheral organs are also significant [9,10]. Hepatic metabolic activity determines the metabolic readout in the peripheral circulation [11], as reported in a previous study, where hepatic dysfunction is proposed to contribute to AD due to failure to maintain Aβ homeostasis in the periphery, consequently resulting in the release of proinflammatory cytokines due to chronic inflammation or metabolic dysfunction [12]. Therefore, impairments in hepatic metabolic activities are associated with the development of cognitive impairment and dementia [7,12,13].

Evaluation of hepatic function entails quantifying the levels of alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), AST/ALT ratio, and gamma-glutamyl transferase (GGT) in the peripheral blood. A significant correlation has been reported between elevated ALP levels and impaired cognitive function [7,14]. Besides, ALT and AST are associated with cardiovascular and metabolic disorders [14,15] which are recognized risk factors for AD and cognitive decline [16,17]. Similarly, lower ALT levels and elevated AST/ALT ratios were reported to be associated with diminished cerebral glucose metabolism, impaired neurotransmitter production and synaptic transmission, and elevated amyloid-β deposition [7,8]. GGT has been recognized as a biomarker for oxidative stress and an atherosclerosis prognosticator [18,19]. The initial GGT level and GGT variability were positively and independently linked to the prospective risk of dementia [2022].

However, the extent to which cognitive impairment is linked to altered liver enzymes remains inadequately investigated, and a definitive threshold for the impact of each liver enzyme on cognitive function has yet to be established. Consequently, we conducted a cross-sectional study utilizing the National Health and Nutrition Examination Survey (NHANES) 2011–2014 database to investigate the association between various liver enzymes and cognitive function and evaluate the potential non-linear associations between different liver enzymes and cognitive function in geriatrics.

2 Methods

2.1 Study population

The data used in this study were obtained from the 2011–2014 cycles of the NHANES, a comprehensive nationwide cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC). Comprehensive information regarding the survey contents and sampling methods is given elsewhere [23,24]. The NHANES cycles were approved by the Research Ethics Review Board (ERB) of the National Center for Health Statistics (NCHS) at the CDC. Written consent was obtained from all participants in the survey [25].

Among the 19,931 individuals in the NHANES 2011–2014 database, a subset of 3632 individuals were identified as being ≥ 60 years old and thus potentially suitable for cognitive functioning evaluation. After excluding 868 individuals due to incomplete cognitive function tests or liver enzyme measurements, 2764 participants were included in this study, representing a weighted population estimate of approximately 50.1 million non-institutionalized U.S. adults aged ≥ 60 (Fig 1).

thumbnail
Fig 1. Flow chart for screening study participants from NHANES 2011–2014.

https://doi.org/10.1371/journal.pone.0306839.g001

2.2 Liver Enzymes measurement

Beckman Coulter UniCel DxC800 (Beckman Coulter) was employed for detecting and measuring levels of liver enzymes, including albumin, ALP, AST, ALT, GGT, and total bilirubin (TB) [26]. ALP and ALT activities were measured using a kinetic rate method, while AST and GGT were determined using an enzymatic rate method. Albumin concentration was measured using the bichromatic digital endpoint method, while TB concentration was determined using a timed-endpoint diazo method known as Jendrassik-Grof. More information is available at https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/BIOPRO_G.htm and https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/BIOPRO_G.htm.

2.3 Assessment of cognitive performance

The cognitive function of participants aged ≥ 60 was assessed using three tests (available online: https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/CFQ_G.htm and https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/CFQ_G.htm), i.e., word learning and recall modules from the consortium to establish a registry for Alzheimer’s disease (CERAD) test, the animal fluency test (AFT), and the digit symbol substitution test (DSST).

CERAD test evaluates the capacity to acquire and retain new verbal information [27,28], comprising three consecutive learning trials (CERAD-IR, consortium to establish a registry for Alzheimer’s Disease immediate recall) and a delayed recall task (CERAD-DR, consortium to establish a registry for Alzheimer’s Disease delayed recall). During the learning trials (CERAD-IR), participants were prompted to recall as many words as possible immediately after being instructed to articulate 10 unrelated words. The delayed word-recall task (CERAD-DR) occurred after the completion of the AFT and DSST. The overall score was determined by summing the results of CERAD-IR and CERAD-DR. The AFT test assesses categorical verbal fluency, a constituent of executive function [27,28]. Participants were instructed to name as many animals as possible within one minute, and the final score was determined based on the cumulative number of accurate answers. The DSST evaluates cognitive abilities such as processing speed, sustained attention, and working memory [29,30]. Each participant strived to replicate the corresponding symbols accurately in 133 boxes adjacent to the numbers within 2 min, and the DSST score referred to the total number of correct matches, where a higher score on each cognitive test indicated superior cognitive performance.

Considering the lack of an acknowledged standard for determining low cognitive performance in the CERAD, AFT, and DSST assessments, the lowest quartile as a reference point was selected to use for discerning different cognitive impairment types, which was consistent with the approaches adopted in previous studies [27,29,30]. Moreover, given the observed significance of age’s impact on cognitive performance, the three cognitive test scores were subsequently adjusted based on age categories (≥ 60 and ≥ 70 years) [27,28,30], as indicated in S1 Table. Participants whose scores were at or below the cut-off points were categorized as having poor cognitive performance, whereas those with scores above the cut-off points were classified as having normal cognitive performance. Finally, considering the potential floor and ceiling effects in individual cognitive scores [31,32], a global cognitive performance score was derived by averaging the standardized z-scores obtained from the three cognitive tests. The z-score was calculated using the formula z = (x-m)/σ, where x represents the raw score, m denotes the overall mean, and σ is the standard deviation (SD) [27,28,32].

2.4 Covariates

Our study incorporated previously reported variables affecting liver and cognitive function and other variables accumulated through clinical experience [27,28,33]. Following the guidelines provided by the STROBE statement, introducing covariates results in a modification of the basic model by more than 10% [34]. Hence, we included the subsequent covariables, i.e., gender, race (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other race), age (≥ 60 and ≥ 70 years), education level (below high school, high school, and above high school), poverty–income ratio (PIR) (< 1.3 and ≥ 1.3), physical activity (vigorous (≥ the recommended level of activity), moderate (< the recommended level of activity) and no activity), as supported by evidence suggesting that Americans advised to engage in at least 75 min of vigorous or 150 min of moderate physical activity/week [35], smoking (current smokers (smoke ≥ 100 cigarettes and continue), never smokers (< 100 cigarettes in their lifetime) and former smokers (smoked ≥ 100 cigarettes in their lifetime but currently no smoking)), body mass index (BMI) (< 25 kg/m2; 25 to 30 kg/m2; ≥ 30 kg/m2), alcohol consumption (none, moderate (defined as > 0 to ≤ 2 drinks/day for men or > 0 to ≤ 1 drink/day for women), or heavy (defined as > 2 to < 5 drinks/day for men or > 1 to < 4 drinks/day for women)) [36], and chronic disease conditions including diabetes (defined by questionnaire "Doctor told you have diabetes" and/or laboratory measured glycated hemoglobin A1c (HbA1c) ≥ 6.5%), hypertension (defined by questionnaire "Have you ever been diagnosed with hypertension by a healthcare professional?" Have you been advised to take prescribed medication for high blood pressure?" "Are you currently taking prescribed medication for hypertension?" and systolic blood pressure ≥ 140 and/or diastolic blood pressure ≥ 90 mmHg), stroke (defined by a questionnaire "Have you ever received a medical diagnosis of stroke?"); coronary heart disease (CHD, determined by a questionnaire "Has a medical professional ever told you that you had a CHD?"), and liver disease (determined by the questionnaire "Have you ever been told by a doctor about the presence of any liver ailment?"). Total cholesterol (TC, mmol/L), triglyceride (TG, mmol/L), and serum uric acid (SUA, μmol/L) levels were measured in the laboratory.

2.5 Statistical analysis

Statistical analysis was conducted on the original datasets obtained from NHANES. Following the NHANES Weight Analytical Guide [37], the MEC exam sample weights (WTMEC2YR) were employed to calculate the new sample weights after merging the datasets from 2011 to 2014. After log2 transformation, liver enzymes exhibited a normal distribution [7]. Continuous variables were assessed by calculating the weighted mean±SD, whereas comparisons between groups with low and normal cognitive performance were performed using a weighted linear regression model. Categorical variables were presented as n (%) and were compared using the weighted Chi-square test.

According to the STROBE statement [34], a multivariate test was conducted using three models, i.e., Model 1: no covariates adjusted, Model 2: adjusted for age, gender, race, education status, and PIR, and Model 3: adjusted for all covariates. Weighted multivariate logistic regression models were developed to examine the association between liver enzyme levels and cognitive performance. Subsequently, liver enzyme levels were categorized into four groups based on quartiles, ranging from the lowest (Q1) to the highest (Q4). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for all three models. Moreover, weighted multivariate logistic regression was employed to conduct a subgroup analysis. If the interaction P-value yielded insignificance, the outcomes of the distinct strata could be deemed reliable. However, if significance was observed, it indicated the presence of a distinct population subset. In addition, we employed a generalized additive model (GAM) based on fitted smooth curves and a threshold effects analysis model to examine the non-linear relationships and identify inflection points between liver enzymes and the risk of cognitive impairment. All statistical analyses were performed using EmpowerStats (version 2.0, www.empowerstats.com) and Stata v17.0, with P < 0.05 indicated statistically significant.

3 Results

3.1 Demographic characteristics of study participants

The demographic characteristics of the included participants in the liver enzyme survey concerning different dimensions of cognitive performance are presented in Tables 1 and S2. A total of 2764 individuals aged ≥ 60 participated in this study, with 2045 exhibiting normal cognitive function and 719 demonstrating decreased cognitive function, assessed by global cognitive performance. Results indicated a significant correlation between cognitive function and various factors, including age, race, educational status, income level, BMI (except for CREAD), physical activity, alcohol consumption, presence of chronic diseases (including diabetes, hypertension, and stroke), ALB, ALP, ALT, AST/ALT ratio, GGT (in globe cognitive performance and AFT), TC and serum creatinine (SCr) levels (P < 0.05). The underlying reason for cognitive impairment lies in various sociological, disease-related, and lifestyle factors. Among the sociological factors, advanced age, male, and low educational level play a significant role. Disease-related factors, including diabetes, hypertension, and coronary heart disease, also contribute to this condition. Furthermore, unhealthy lifestyle habits such as excessive drinking and inadequate physical activity are closely associated with the development of cognitive impairment. In our study, the proportion of cognitive impairment is higher in participants who were male, older (≥70 years), Mexican American, other Hispanic, non-Hispanic Black; have lower educational level, lower poverty–income ratio, and lower BMI; not engaged in physical activity; current smokers and higher alcohol consumers. Additionally, this group displayed a higher prevalence of chronic conditions such as diabetes, hypertension, and stroke. In terms of biochemical markers, individuals with cognitive impairment demonstrated reduced concentrations of ALB, ALT, and SCr, alongside elevated levels of ALP, AST/ALT ratio, GGT, and TC. However, no noteworthy disparities were noted in AST levels between the normal and impaired cognitive groups. For CREAD and DSST, participants who were male, with coronary heart and liver disease had lower cognitive performance.

thumbnail
Table 1. Characteristics of liver enzymes and the globle cognitive performance (assessed by z-score) among participants from NHANES 2011–2014 (N = 2764).

https://doi.org/10.1371/journal.pone.0306839.t001

3.2 Association between liver enzymes and cognitive performance

3.2.1 Relationship between ALP and cognitive performance.

The association results between ALP and various dimensions of cognitive performance in geriatric participants are shown in Tables 2, 3 and S3. After adjusting for all covariates, ALP levels between 55-65U/L were associated with a lower risk of cognitive impairment assessed by global cognitive performance with OR (95%CI) being 0.66 (0.44–0.99) (Table 2) and ALP levels between 66-79U/L were associated with a lower risk of cognitive impairment, as assessed by DSST with OR (95%CI) being 0.64 (0.42–0.97) (Table 3). While the fully adjusted model revealed a noteworthy negative correlation between ALP and AFT, with an OR (95% CI) of 1.48 (1.11–1.98) (Table 3). Furthermore, when treated as a categorical variable based on quartiles, ALP levels higher than 80U/L were associated with an elevated risk of cognitive impairment assessed by AFT with OR (95%CI) being 1.50 (1.06–2.12) after adjusting for age, gender, race, education status, and PIR (S3 Table). These contradictory findings suggest a non-linear association between ALP and cognitive function, indicating an inflection point in their relationship.

thumbnail
Table 2. The associations between liver enzymes and Global Cognitive Performance among participants from NHANES 2011–2014 (N = 2764).

https://doi.org/10.1371/journal.pone.0306839.t002

thumbnail
Table 3. The associations between liver enzymes and different dimensions of cognitive performance (CERAD Test, AFT and DSST) in Model 3 among participants from NHANES 2011–2014 (N = 2764).

https://doi.org/10.1371/journal.pone.0306839.t003

In the sub-groups analysis (S4 Table) stratified by gender, age, race, education, physical activity, alcohol consumption, smoking, and chronic diseases (hypertension, diabetes, stroke, and CHD), the findings revealed a consistent association between ALP and global cognitive performance in individuals who were identified as Mexican American and those with a low level of education (P for trend < 0.01). The influence of race on the relationship between ALP and global cognitive performance was found to be significant (P for interaction < 0.001). Specifically, Mexican Americans demonstrated a strong correlation between ALP and the likelihood of cognitive impairment compared to those of other racial backgrounds.

3.2.2 Relationship between ALT and cognitive performance.

Tables 2, 3 and S3 present the association outcomes between ALT and diverse aspects of cognitive performance among geriatric individuals. In the fully adjusted model, a significant positive association was observed between ALT and CERAD test, yielding an OR (95% CI) of 0.72 (0.53–0.97) (Table 3). Moreover, the statistical significance of this trend remained evident even when ALT was analyzed as a categorical variable (quartile). Adjusting for all covariates, the highest ALT quartile consistently demonstrated a lower likelihood of impaired cognitive function compared to the lowest quartile, with OR (95%CI) values of 0.55 (0.37–0.82) for global cognitive performance (Table 2), 0.53 (0.37–0.76) for CERAD (Table 3), and 0.53 (0.34–0.80) for DSST (Table 3) (P for trend < 0.01). These findings reveal a strong dose-dependent association between cognitive performance and ALT levels, indicating that a higher ALT dosage conferred a more pronounced cognitive protective effect.

In the subgroup analysis (S5 Table), a stable association between ALT and global cognitive performance was observed among specific demographic groups, including males, individuals ≥ 70 years, individuals of other Hispanic and non-Hispanic White ethnicities, those with lower and higher levels of education, individuals with no physical activity, moderate alcohol consumption, and with chronic diseases (P for trend < 0.05). However, it was found that chronic disease conditions, particularly stroke, significantly influenced (P for interaction < 0.05) the association between ALT and global cognitive performance.

3.2.3 Relationship between AST/ALT ratio and cognitive performance.

Tables 2, 3 and S3 present the association between AST/ALT levels and various cognitive outcomes among geriatric subjects. In the fully adjusted model, AST/ALT levels were significantly associated with an increased risk of cognitive impairment, with an OR (95% CI) 2.39 (1.53–3.73) for global cognitive performance (Table 2), 2.61 (1.77–3.84) for CERAD (Table 3), and 2.51 (1.57–4.02) for DSST (Table 3). The statistical significance of the trend persisted when analyzing AST/ALT levels as a categorical variable (quartiles), with the highest quartile exhibiting a progressively increased risk of cognitive impairment compared to the lowest quartile in the fully adjusted model. Specifically, the OR (95%CI) were 2.00 (1.28–3.11) for global cognitive performance (Table 2), 2.21 (1.49–3.29) for CERAD (Table 3), and 2.16 (1.34–3.46) for DSST (Table 3) (P for trend < 0.01). These findings suggested that a lower dosage of AST/ALT conferred a more significant cognitive protective advantage.

In the subgroup analysis (S6 Table), a consistent association between AST/ALT ratio and global cognitive performance was observed in specific demographic groups, including males, individuals ≥ 70 years, those of other Hispanic and non-Hispanic White ethnicity, individuals with a higher education level, moderate physical activity, moderate alcohol consumption, non-smokers and former smokers, and those with hypertension and diabetes (P for trend < 0.05).

3.2.4 Relationship between GGT and cognitive performance.

The results in S3 Table demonstrate a noteworthy negative association between GGT and cognitive performance in the unadjusted model for AFT, with an OR (95% CI) being 1.16 (1.01–1.33). Moreover, when considered as a categorical variable (quartiles), GGT levels in the highest quartile exhibited a negative link with AFT (OR = 1.51, 95% CI: 1.08–2.12) in unadjusted model (S3 Table). Conversely, GGT levels ranging from 19-27U/L displayed a positive correlation with DSST (OR = 0.56, 95% CI: 0.37–0.85) in the fully adjusted model (Table 3). The conflicting results hint at a complex, non-linear link between GGT and cognitive function, pointing to a potential inflection point in their association.

In the analysis of subgroups (S7 Table), a stable association between GGT and global cognitive performance was observed among individuals of other Hispanic origin with a low level of education and moderate alcohol consumption (P for trend < 0.05).

3.3 Non-linear relationships between liver enzymes and cognitive performance

Outliers were identified and excluded for each liver enzyme marker if their values deviated by more than 4 SDs from the mean value [7]. A sensitivity analysis was conducted in order to ascertain the robustness of the findings by excluding the outlier values of liver enzymes (S8S11 Tables). Subsequently, the GAM and smooth curve fitting technique were employed to identify the non-linear associations between various liver enzymes and the risk of cognitive impairment, as assessed by global cognitive performance.

In the fully adjusted model, examining the relationship between ALP and the risk of cognitive impairment revealed a non-linear association (Fig 2A), with an inflection point of 60 by threshold effect analysis (Table 4). Before reaching the inflection point, a significant negative correlation was observed between ALP level and the risk of cognitive impairment, with an OR (95% CI) of 0.97 (0.96–0.99). Conversely, following the inflection point, a significant positive correlation was identified with an OR (95% CI) of 1.01 (1.00–1.02). In other words, when ALP levels exceed 60 U/L, there is a significant association with an increased risk of cognitive impairment.

thumbnail
Fig 2. Smooth curve fitting to evaluate the nonlinear relationship between liver enzymes and the risk of cognitive impairment based on the globle cognitive performance.

(A) The relationship between ALP and the risk of cognitive impairment. (B) The relationship between ALT and the risk of cognitive impairment. (C) The relationship between AST/ALT and the risk of cognitive impairment. (D) The relationship between GGT and the risk of cognitive impairment. The red solid line represents the probability of cognitive impairment occurrence and the blue dotted line represents the 95% CI curve.

https://doi.org/10.1371/journal.pone.0306839.g002

thumbnail
Table 4. Threshold effect analysis of liver enzymes on cognitive impairment.

https://doi.org/10.1371/journal.pone.0306839.t004

Similarly, a non-linear relationship was observed between ALT and the risk of cognitive impairment in the fully adjusted model (Fig 2B), further supported by the threshold effect analysis, which identified two inflection points at 17 and 40 (Table 4). A higher incidence of cognitive impairment was observed when ALT levels were below 17. Prior to this inflection point, there was a significant negative correlation between ALT levels and the risk of cognitive impairment, with an OR (95% CI) of 0.88 (0.83–0.93). However, no significant association was found between ALT levels and cognitive impairment risk within the range of 17 to 40 (OR: 1.00; 95% CI: 0.98–1.02) or beyond the inflection point of 40 (OR: 0.96; 95% CI: 0.91–1.01). That is to say, within the normal physiological range, a lower risk of cognitive impairment is associated with an ALT level exceeding 17 U/L.

Furthermore, a non-linear association was observed between the AST/ALT ratio and the risk of cognitive impairment in the fully adjusted model (Fig 2C). The threshold effect analysis revealed two inflection points at 0.77 and 1.76 (Table 4). Before the inflection point of 0.77, a significant negative correlation was found between the AST/ALT ratio and the risk of cognitive impairment, with an OR (95% CI) of 0.01 (0.00–0.59). Conversely, a significant positive correlation was observed between the inflection points of 0.77 and 1.76, with an OR (95% CI) of 2.27 (1.43–3.59). Therefore, it can be inferred that AST/ALT levels significantly correlate with increased cognitive impairment risk when the ratio falls within the range of 0.77 to 1.76.

Moreover, a non-linear association between GGT levels and the risk of cognitive impairment was observed in the fully adjusted model (Fig 2D), while the threshold effect analysis revealed two inflection points at 25 and 94 (Table 4). Before the inflection point at 25, a significant negative correlation was observed between GGT level and the risk of cognitive impairment, with an OR (95% CI) of 0.97 (0.95–0.99). Conversely, a significant positive correlation was observed between inflection points at 25 and 94, with an OR (95% CI) of 1.02 (1.01–1.03). Therefore, when GGT levels fall within the range of 25 to 94 U/L, a significant association with an increased risk of cognitive impairment can be inferred.

4 Discussion

Despite the longstanding association between liver enzyme status and cognitive function, a consensus regarding the specific threshold of liver enzymes for evaluating their effects on cognition is yet to be reached. In our study, following covariable screening, we utilized a multivariate bivariate logistic regression model to construct three distinct models, aiming to determine the presence of a linear relationship between cognitive function and liver enzymes. Certain findings indicated a potential correlation between elevated liver enzyme levels and a higher risk of cognitive impairment when treating liver enzymes as categorical variables. Notably, statistically significant differences were exclusively observed in the Q4 group, suggesting that the presumed linear relationship between liver enzyme levels and cognitive impairment risk may not be significant and pointing towards a potential non-linear association. Moreover, across various models, the association between varying levels of liver enzymes and cognitive impairment risk may exhibit contrasting patterns, also indicating a nonlinear relationship and an inflection point exists between liver enzyme levels and cognitive impairment risk. Furthermore, we utilize the GAM to fit a smoothing curve, allowing us to delve deeper into the nonlinear connection between liver enzyme levels and cognitive impairment risk. Additionally, we conduct an analysis of the threshold effect to identify specific threshold values for liver enzymes worthy of consideration, specifically when ALP > 60 U/L, the AST/ALT ratio fell within the range of 0.77 to 1.76, and GGT ranged from 25 to 94 U/L, a higher levels of liver enzymes were found to be significantly associated with an increased risk of cognitive impairment. Conversely, a lower incidence of cognitive impairment was observed when ALT levels > 17 U/L. These findings suggested that liver enzymes possess the potential to serve as a monitoring indicator for poor cognitive performance, emphasizing the importance of maintaining liver enzyme levels within a specific range to mitigate the risk of cognitive impairment. Further investigation is warranted in subsequent studies to reach a sound consensus regarding the suitable thresholds of liver enzyme levels concerning cognitive function.

The ALT and AST enzymes are frequently used for liver status screening [38]. Our study revealed a significant positive correlation between ALT and cognitive performance, as assessed through the global cognitive performance, CERAD test, and DSST in the fully adjusted model. Conversely, we found a negative association between AST/ALT ratio and cognitive function, evident in the same cognitive tests after adjusting for all confounders. The findings indicated that individuals with cognitive impairment exhibited lower levels of ALT (but not AST) and a higher AST/ALT ratio than those with normal cognitive function. A subsequent stratified analysis revealed that this disparity was more pronounced among males. The present findings align with those of a previous study, wherein individuals with dementia exhibited decreased ALT levels (while AST remained unaffected) and elevated AST/ALT ratios within the normal physiological range compared to control subjects. These discrepancies were also observed when comparing men with and without dementia but were not evident among women [39], suggesting gender disparities, potentially influenced by distinct hormonal profiles [40], may contribute to liver-related metabolic processes [41].

However, contrary to prior cross-sectional studies that found strong positive links between ALT and AST levels with cognitive performance, as well as a substantial negative association between the AST/ALT ratio and cognitive function, our research revealed no statistically significant difference in AST levels between those with low and normal cognitive performance. This may be explained by the fact that AST is not exclusively produced in the liver, but also in numerous other tissues, like skeletal muscles, heart, brain. Conversely, ALT is predominantly localized in the cytoplasm of hepatocytes, making it widely regarded as the most liver-specific enzyme [8,39]. Furthermore, a previous study indicated that the AST/ALT ratio was higher in the group with cognitive impairment than in the normal cognitive function group; however, the difference in ALT levels between the two groups was insignificant [42]. Different from our findings, the ALT level of the cognitive impairment group was lower than that of the normal cognitive function group. Additionally, a recent prospective study provided evidence that low levels of plasma aminotransferases, particularly ALT, are associated with an elevated long-term risk of dementia in middle-aged patients [8].

The altered enzyme levels observed in individuals with cognitive impairments might be explained by two mechanisms. First of all, the decline in aminotransferase levels leads to a subsequent decrease in pyruvate levels, thereby reducing hepatic gluconeogenesis and glucose production [8,42], adversely affecting energy homeostasis in body tissues, including the brain [8,42]. Notably, reduced brain glucose metabolism, a distinctive feature of AD and cognitive impairment during the prodromal phase, has been documented [7]. Furthermore, it has been consistently demonstrated that elevated AST/ALT ratios and decreased ALT levels are significantly associated with cerebral glucose hypometabolism, specifically in regions that play a role in memory and executive function [7]. Second, altered ALT and AST levels may affect the production of glutamate, a major excitatory neurotransmitter involved in synaptic transmission in the cortical and hippocampal regions, consequently influencing memory and cognition through the mechanism of long-term potentiation [43,44].

The present study observed a significant negative association between serum ALP levels and cognitive performance, aligning with prior research findings [7,14,45]. However, it is pertinent to mention that our results indicated a negative association between higher ALP levels and lower executive functioning scores assessed by AFT in the fully adjusted model, partially deviating from the observed association between higher ALP levels and lower memory scores [7]. However, after adjusting for all covariates, ALP levels in the second quartile were linked to a reduced risk of cognitive impairment in global cognitive performance, while levels in the third quartile were associated with a lower risk of cognitive impairment in DSST. These conflicting results suggest a non-linear relationship between ALP and cognitive function, indicating a potential turning point in their association. Although ALP is predominantly expressed in the liver and kidneys, it is also available in the brain, specifically within endothelial cells, synaptic contacts, and the cerebral cortex [46,47]. The neuronal form of ALP plays a role in developmental plasticity and cortical function through its involvement in γ-aminobutyric acid metabolism [48,49]. Alterations in plasma ALP levels may arise from central nervous system injury [50]. Tissue nonspecific ALP facilitates the conversion of extracellular hyperphosphorylated monomeric tau into dephosphorylated tau, which in turn activates muscarinic receptors, leading to a sustained influx of calcium, ultimately contributing to further neuronal degeneration [5153].

GGT level was examined and adopted to evaluate liver function [20,22]. Our findings regarding the association between GGT levels and cognitive performance were consistent with prior investigations conducted in diverse cohorts [2022]. Futher, our findings indicate that higher GGT levels were negatively associated with executive functioning scores assessed by AFT in the unadjusted model, whereas moderate GGT levels positively correlated with DSST scores in the fully adjusted model. These conflicting observations suggest a complex, non-linear relationship between GGT and cognitive function, indicating a potential turning point in their association. GGT plays a significant role in the metabolism of glutathione and facilitates pro-oxidant and pro-inflammatory mechanisms that contribute to the development of age-related neurodegenerative disorders, including dementia [21,22,54]. Moreover, increased GGT levels have been linked to the development of atherosclerosis through their direct participation in the formation of atheromatous plaques, which have also been identified as a potential underlying factor in dementia pathogenesis [19,55]. Consequently, GGT holds promise as a potential contributor to the pathogenesis of dementia, given its role as an indicator of oxidative stress and atherosclerosis.

The primary advantage of our study lies in utilizing the NHANES database, which facilitates the acquisition of a sufficiently large sample size. Additionally, we employed three standard cognitive tests to generate a comprehensive cognitive score, thereby mitigating the influence of ceiling and floor effects and enabling the determination of cut-off values (specifically, the lowest quartile) for each age-stratified group. Furthermore, we conducted a stratified analysis to investigate the potential influence of confounding variables on the observed associations. Finally, we employed smoothed fitting curves and conducted a threshold effects analysis to investigate non-linear associations and determine the inflection points between liver enzymes and the risk of cognitive impairment.

However, it is imperative to recognize the potential limitations of this study. First, using a cross-sectional design precludes the establishment of causality, necessitating a cautious interpretation of the current findings. Second, while the extensive data collection conducted by NHANES enables the examination of established confounding variables, residual confounders cannot be entirely ruled out. Furthermore, it is important to note that our study lacked a clinical examination capable of diagnosing and further categorizing cognitive impairment. Finally, some studies have indicated that longitudinal alterations in liver enzymes may predict cognitive impairment [20,21]. Consequently, additional prospective investigations are required to explore the influence of both baseline liver enzyme levels and variability on the progression of cognitive impairment.

5 Conclusion

This study examined the independent relationship between liver enzymes and cognitive performance. Negative associations were observed between ALP, AST/ALT ratio, and GGT levels and cognitive performance. Conversely, a positive association was found between ALT levels and cognitive performance. Furthermore, this study identified non-linear relationships and their respective thresholds with cognitive performance. Both physical and nutritional interventions have the potential to exert a significant impact on liver enzymes and mitigate their adverse effects on cognitive function [39,56]. Future research should incorporate interventional studies to evaluate the potential role of liver enzymes in cognitive decline.

Supporting information

S1 Table. The cutoff points of the CERAD test, AFT, and DSST adjusted based on age.

https://doi.org/10.1371/journal.pone.0306839.s001

(DOCX)

S2 Table. Characteristics of liver enzymes and the CERAD test, AFT, and DSST among participants from NHANES 2011–2014 (N = 2764).

https://doi.org/10.1371/journal.pone.0306839.s002

(DOCX)

S3 Table. The associations between liver enzymes and different dimensions of cognitive performance (CERAD Test, AFT and DSST) in Model 1 and Model 2 among participants from NHANES 2011–2014 (N = 2764).

https://doi.org/10.1371/journal.pone.0306839.s003

(DOCX)

S4 Table. Subgroup analysis of the association between quartiles of ALP and cognitive performance.

https://doi.org/10.1371/journal.pone.0306839.s004

(DOCX)

S5 Table. Subgroup analysis of the association between quartiles of ALT and cognitive performance.

https://doi.org/10.1371/journal.pone.0306839.s005

(DOCX)

S6 Table. Subgroup analysis of the association between quartiles of AST/ALT ratio and cognitive performance.

https://doi.org/10.1371/journal.pone.0306839.s006

(DOCX)

S7 Table. Subgroup analysis of the association between quartiles of GGT and cognitive performance.

https://doi.org/10.1371/journal.pone.0306839.s007

(DOCX)

S8 Table. The associations between ALP and different dimensions of cognitive performance (N = 2747, sensitivity analysis).

https://doi.org/10.1371/journal.pone.0306839.s008

(DOCX)

S9 Table. The associations between ALT and different dimensions of cognitive performance (N = 2746, sensitivity analysis).

https://doi.org/10.1371/journal.pone.0306839.s009

(DOCX)

S10 Table. The associations between AST/ALT and different dimensions of cognitive performance (N = 2753, sensitivity analysis).

https://doi.org/10.1371/journal.pone.0306839.s010

(DOCX)

S11 Table. The associations between GGT and different dimensions of cognitive performance (N = 2736, sensitivity analysis).

https://doi.org/10.1371/journal.pone.0306839.s011

(DOCX)

Acknowledgments

We express our profound gratitude to all individuals who participated in this study and all those who contributed their efforts for this study.

References

  1. 1. Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology. 2013;80(19):1778–83. pmid:23390181
  2. 2. Pei H, Ma L, Cao Y, Wang F, Li Z, Liu N, et al. Traditional Chinese Medicine for Alzheimer’s Disease and Other Cognitive Impairment: A Review. Am J Chin Med. 2020;48(3):487–511. pmid:32329645
  3. 3. Sherman JC, Henderson CR, Jr., Flynn S, Gair JW, Lust B. Language Decline Characterizes Amnestic Mild Cognitive Impairment Independent of Cognitive Decline. J Speech Lang Hear Res. 2021;64(11):4287–307. pmid:34699277
  4. 4. Kivipelto M, Mangialasche F, Snyder HM, Allegri R, Andrieu S, Arai H, et al. World-Wide FINGERS Network: A global approach to risk reduction and prevention of dementia. Alzheimer’s & dementia: the journal of the Alzheimer’s Association. 2020;16(7):1078–94. pmid:32627328
  5. 5. Sanz-Blasco R, Ruiz-Sánchez de León JM, Ávila-Villanueva M, Valentí-Soler M, Gómez-Ramírez J, Fernández-Blázquez MA. Transition from mild cognitive impairment to normal cognition: Determining the predictors of reversion with multi-state Markov models. Alzheimer’s & dementia: the journal of the Alzheimer’s Association. 2022;18(6):1177–85. pmid:34482637
  6. 6. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413–46. pmid:32738937
  7. 7. Nho K, Kueider-Paisley A, Ahmad S, MahmoudianDehkordi S, Arnold M, Risacher SL, et al. Association of Altered Liver Enzymes With Alzheimer Disease Diagnosis, Cognition, Neuroimaging Measures, and Cerebrospinal Fluid Biomarkers. JAMA network open. 2019;2(7):e197978. pmid:31365104
  8. 8. Lu Y, Pike JR, Selvin E, Mosley T, Palta P, Sharrett AR, et al. Low Liver Enzymes and Risk of Dementia: The Atherosclerosis Risk in Communities (ARIC) Study. Journal of Alzheimer’s disease: JAD. 2021;79(4):1775–84. pmid:33459646
  9. 9. Clarke JR, Ribeiro FC, Frozza RL, De Felice FG, Lourenco MV. Metabolic Dysfunction in Alzheimer’s Disease: From Basic Neurobiology to Clinical Approaches. Journal of Alzheimer’s disease: JAD. 2018;64(s1):S405–s26. pmid:29562518
  10. 10. Kapogiannis D, Mattson MP. Disrupted energy metabolism and neuronal circuit dysfunction in cognitive impairment and Alzheimer’s disease. The Lancet Neurology. 2011;10(2):187–98. pmid:21147038
  11. 11. Zheng H, Cai A, Shu Q, Niu Y, Xu P, Li C, et al. Tissue-Specific Metabolomics Analysis Identifies the Liver as a Major Organ of Metabolic Disorders in Amyloid Precursor Protein/Presenilin 1 Mice of Alzheimer’s Disease. J Proteome Res. 2019;18(3):1218–27. pmid:30592618
  12. 12. Estrada LD, Ahumada P, Cabrera D, Arab JP. Liver Dysfunction as a Novel Player in Alzheimer’s Progression: Looking Outside the Brain. Frontiers in aging neuroscience. 2019;11:174. pmid:31379558
  13. 13. Li W, Yue L, Sun L, Xiao S. An Increased Aspartate to Alanine Aminotransferase Ratio Is Associated With a Higher Risk of Cognitive Impairment. Frontiers in medicine. 2022;9:780174. pmid:35463002
  14. 14. Kellett KA, Williams J, Vardy ER, Smith AD, Hooper NM. Plasma alkaline phosphatase is elevated in Alzheimer’s disease and inversely correlates with cognitive function. Int J Mol Epidemiol Genet. 2011;2(2):114–21. pmid:21686125
  15. 15. Goessling W, Massaro JM, Vasan RS, D’Agostino RB Sr., Ellison RC, Fox CS. Aminotransferase levels and 20-year risk of metabolic syndrome, diabetes, and cardiovascular disease. Gastroenterology. 2008;135(6):1935–44, 44.e1. pmid:19010326
  16. 16. Santos CY, Snyder PJ, Wu WC, Zhang M, Echeverria A, Alber J. Pathophysiologic relationship between Alzheimer’s disease, cerebrovascular disease, and cardiovascular risk: A review and synthesis. Alzheimer’s & dementia (Amsterdam, Netherlands). 2017;7:69–87. pmid:28275702
  17. 17. Sattar N, Scherbakova O, Ford I, O’Reilly DS, Stanley A, Forrest E, et al. Elevated alanine aminotransferase predicts new-onset type 2 diabetes independently of classical risk factors, metabolic syndrome, and C-reactive protein in the west of Scotland coronary prevention study. Diabetes. 2004;53(11):2855–60. pmid:15504965
  18. 18. Corti A, Belcastro E, Dominici S, Maellaro E, Pompella A. The dark side of gamma-glutamyltransferase (GGT): Pathogenic effects of an ’antioxidant’ enzyme. Free radical biology & medicine. 2020;160:807–19. pmid:32916278
  19. 19. Ruban A, Daya N, Schneider ALC, Gottesman R, Selvin E, Coresh J, et al. Liver Enzymes and Risk of Stroke: The Atherosclerosis Risk in Communities (ARIC) Study. Journal of stroke. 2020;22(3):357–68.
  20. 20. Lee YB, Han K, Park S, Kim SM, Kim NH, Choi KM, et al. Gamma-glutamyl transferase variability and risk of dementia: A nationwide study. Int J Geriatr Psychiatry. 2020;35(10):1105–14. pmid:32392636
  21. 21. Kunutsor SK, Laukkanen JA. Gamma glutamyltransferase and risk of future dementia in middle-aged to older Finnish men: A new prospective cohort study. Alzheimer’s & dementia: the journal of the Alzheimer’s Association. 2016;12(9):931–41.
  22. 22. Praetorius Björk M, Johansson B. Gamma-Glutamyltransferase (GGT) as a biomarker of cognitive decline at the end of life: contrasting age and time to death trajectories. Int Psychogeriatr. 2018;30(7):981–90. pmid:29108523
  23. 23. Marruganti C, Baima G, Aimetti M, Grandini S, Sanz M, Romandini M. Periodontitis and low cognitive performance: A population-based study. J Clin Periodontol. 2023;50(4):418–29. pmid:36644802
  24. 24. Dye BA, Afful J, Thornton-Evans G, Iafolla T. Overview and quality assurance for the oral health component of the National Health and Nutrition Examination Survey (NHANES), 2011–2014. BMC Oral Health. 2019;19(1):95. pmid:31142316
  25. 25. Zipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and operations, 1999–2010. Vital Health Stat 1. 2013 (56):1–37. pmid:25078429
  26. 26. Song L, Li H, Fu X, Cen M, Wu J. Association of the Oxidative Balance Score and Cognitive Function and the Mediating Role of Oxidative Stress: Evidence from the National Health and Nutrition Examination Survey (NHANES) 2011–2014. J Nutr. 2023;153(7):1974–83. pmid:37187352
  27. 27. Pang K, Liu C, Tong J, Ouyang W, Hu S, Tang Y. Higher Total Cholesterol Concentration May Be Associated with Better Cognitive Performance among Elderly Females. Nutrients. 2022;14(19). pmid:36235850
  28. 28. Li S, Sun W, Zhang D. Association of Zinc, Iron, Copper, and Selenium Intakes with Low Cognitive Performance in Older Adults: A Cross-Sectional Study from National Health and Nutrition Examination Survey (NHANES). Journal of Alzheimer’s disease: JAD. 2019;72(4):1145–57. pmid:31683474
  29. 29. Chen SP, Bhattacharya J, Pershing S. Association of Vision Loss With Cognition in Older Adults. JAMA ophthalmology. 2017;135(9):963–70. pmid:28817745
  30. 30. Wang R, Wang W, Hu P, Zhang R, Dong X, Zhang D. Association of Dietary Vitamin D Intake, Serum 25(OH)D(3), 25(OH)D(2) with Cognitive Performance in the Elderly. Nutrients. 2021;13(9).
  31. 31. Lim CR, Harris K, Dawson J, Beard DJ, Fitzpatrick R, Price AJ. Floor and ceiling effects in the OHS: an analysis of the NHS PROMs data set. BMJ open. 2015;5(7):e007765. pmid:26216152
  32. 32. Li W, Li S, Shang Y, Zhuang W, Yan G, Chen Z, et al. Associations between dietary and blood inflammatory indices and their effects on cognitive function in elderly Americans. Frontiers in neuroscience. 2023;17:1117056. pmid:36895419
  33. 33. Dong X, Li S, Sun J, Li Y, Zhang D. Association of Coffee, Decaffeinated Coffee and Caffeine Intake from Coffee with Cognitive Performance in Older Adults: National Health and Nutrition Examination Survey (NHANES) 2011–2014. Nutrients. 2020;12(3). pmid:32245123
  34. 34. Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Ann Intern Med. 2007;147(8):W163–94. pmid:17938389
  35. 35. Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, et al. The Physical Activity Guidelines for Americans. JAMA. 2018;320(19):2020–8. pmid:30418471
  36. 36. Xie ZQ, Li HX, Tan WL, Yang L, Ma XW, Li WX, et al. Association of Cholecystectomy With Liver Fibrosis and Cirrhosis Among Adults in the USA: A Population-Based Propensity Score-Matched Study. Frontiers in medicine. 2021;8:787777. pmid:34917640
  37. 37. Johnson CL, Paulose-Ram R, Ogden CL, Carroll MD, Kruszon-Moran D, Dohrmann SM, et al. National health and nutrition examination survey: analytic guidelines, 1999–2010. Vital Health Stat 2. 2013 (161):1–24. pmid:25090154
  38. 38. Pacifico L, Ferraro F, Bonci E, Anania C, Romaggioli S, Chiesa C. Upper limit of normal for alanine aminotransferase: quo vadis? Clin Chim Acta. 2013;422:29–39. pmid:23566931
  39. 39. Ferri E, Rossi PD, Scichilone M, Lucchi TA, Arosio B. Liver Enzymes in a Cohort of Community-Dwelling Older Persons: Focus on Sex Contribution. Nutrients. 2022;14(23).
  40. 40. Mauvais-Jarvis F, Clegg DJ, Hevener AL. The role of estrogens in control of energy balance and glucose homeostasis. Endocr Rev. 2013;34(3):309–38. pmid:23460719
  41. 41. Petroff D, Bätz O, Jedrysiak K, Kramer J, Berg T, Wiegand J. Age Dependence of Liver Enzymes: An Analysis of Over 1,300,000 Consecutive Blood Samples. Clin Gastroenterol Hepatol. 2022;20(3):641–50. pmid:33524594
  42. 42. Wu K, Xu C, Qiu G, Guo Q, Chen C, Liu W, et al. Association of lower liver function with cognitive impairment in the Shenzhen ageing-related disorder cohort in China. Frontiers in aging neuroscience. 2022;14:1012219. pmid:36313027
  43. 43. Cassano T, Pace L, Bedse G, Lavecchia AM, De Marco F, Gaetani S, et al. Glutamate and Mitochondria: Two Prominent Players in the Oxidative Stress-Induced Neurodegeneration. Current Alzheimer research. 2016;13(2):185–97. pmid:26679860
  44. 44. Ribeiro FM, Vieira LB, Pires RG, Olmo RP, Ferguson SS. Metabotropic glutamate receptors and neurodegenerative diseases. Pharmacological research. 2017;115:179–91. pmid:27872019
  45. 45. Han SW, Park YH, Jang ES, Nho K, Kim S. Implications of Liver Enzymes in the Pathogenesis of Alzheimer’s Disease. Journal of Alzheimer’s disease: JAD. 2022;88(4):1371–6. pmid:35786657
  46. 46. Moss DW. Physicochemical and pathophysiological factors in the release of membrane-bound alkaline phosphatase from cells. Clin Chim Acta. 1997;257(1):133–40. pmid:9028630
  47. 47. Goldstein DJ, Rogers CE, Harris H. Expression of alkaline phosphatase loci in mammalian tissues. Proceedings of the National Academy of Sciences of the United States of America. 1980;77(5):2857–60. pmid:6930672
  48. 48. Fonta C, Négyessy L, Renaud L, Barone P. Areal and subcellular localization of the ubiquitous alkaline phosphatase in the primate cerebral cortex: evidence for a role in neurotransmission. Cereb Cortex. 2004;14(6):595–609. pmid:15054075
  49. 49. Langer D, Ikehara Y, Takebayashi H, Hawkes R, Zimmermann H. The ectonucleotidases alkaline phosphatase and nucleoside triphosphate diphosphohydrolase 2 are associated with subsets of progenitor cell populations in the mouse embryonic, postnatal and adult neurogenic zones. Neuroscience. 2007;150(4):863–79. pmid:18031938
  50. 50. Yamashita M, Sasaki M, Mii K, Tsuzuki M, Takakura K, Yoshinoya S, et al. Measurement of serum alkaline phosphatase isozyme I in brain-damaged patients. Neurol Med Chir (Tokyo). 1989;29(11):995–8. pmid:2483867
  51. 51. Gómez-Ramos A, Díaz-Hernández M, Rubio A, Miras-Portugal MT, Avila J. Extracellular tau promotes intracellular calcium increase through M1 and M3 muscarinic receptors in neuronal cells. Mol Cell Neurosci. 2008;37(4):673–81. pmid:18272392
  52. 52. Pooler AM, Hanger DP. Functional implications of the association of tau with the plasma membrane. Biochem Soc Trans. 2010;38(4):1012–5. pmid:20658995
  53. 53. Vardy ER, Kellett KA, Cocklin SL, Hooper NM. Alkaline phosphatase is increased in both brain and plasma in Alzheimer’s disease. Neurodegener Dis. 2012;9(1):31–7. pmid:22024719
  54. 54. Forrest SL, Kril JJ, Kovacs GG. Association Between Globular Glial Tauopathies and Frontotemporal Dementia-Expanding the Spectrum of Gliocentric Disorders: A Review. JAMA neurology. 2021;78(8):1004–14. pmid:34152367
  55. 55. Romero-Cabrera JL, Ankeny J, Fernández-Montero A, Kales SN, Smith DL. A Systematic Review and Meta-Analysis of Advanced Biomarkers for Predicting Incident Cardiovascular Disease among Asymptomatic Middle-Aged Adults. International journal of molecular sciences. 2022;23(21). pmid:36362325
  56. 56. Ogino E, Manly JJ, Schupf N, Mayeux R, Gu Y. Current and past leisure time physical activity in relation to risk of Alzheimer’s disease in older adults. Alzheimer’s & dementia: the journal of the Alzheimer’s Association. 2019;15(12):1603–11. pmid:31587996