Skip to main content

Gamma-glutamyl transferase levels are associated with the occurrence of post-stroke cognitive impairment: a multicenter cohort study



Gamma-glutamyl transferase (GGT) is involved in maintenance of physiological concentrations of glutathione in cells, and protects them from oxidative stress-induced damage. However, its role in post-stroke cognitive impairment (PSCI) remains unknown. Here, we investigated the effects of serum GGT on PSCI.


We conducted a prospective, multicenter cohort study. A total of 1, 957 participants with a minor ischemic stroke or transient ischemic attack whose baseline GGT levels were measured were enrolled from the Impairment of Cognition and Sleep (ICONS) study of the China National Stroke Registry-3 (CNSR-3). They were categorized into four groups according to quartiles of baseline GGT levels. Cognitive functions were assessed using the Montreal Cognitive Assessment (MoCA) approach. Multiple logistic regression models were performed to evaluate the relationship between GGT and PSCI at 3 months follow-up.


Among the 1957 participants, 671 (34.29%) patients suffered PSCI at 3 months follow-up. The highest GGT level quartile group exhibited a lower risk of PSCI in the fully adjusted model [OR (95% CI): 0.69 (0.50-0.96)], relative to the lowest group. Moreover, incorporation of GGT to the conventional model resulted in slight improvements in PSCI outcomes after 3 months (NRI: 12.00%; IDI: 0.30%).


Serum GGT levels are inversely associated with the risk of PSCI, with extremely low levels being viable risk factors for PSCI.

Peer Review reports


Globally, stroke is a leading cause of disabilities and mortalities, affecting one in every four people [13]. Cognitive impairment, which is a common stroke complication, has attracted numerous research attention. According to the Vascular Impairment of Cognition Classification Consensus Study (VICCCS), vascular cognitive impairment (VCI) refers to cognitive disorders caused by underlying vascular factors and can be associated with obvious cerebrovascular diseases [4]. Vascular cognitive impairment no-dementia (VCI-ND) is based on the proposed criteria of small vessel ischemic disease and cognitive deficits without dementia [5]. Furthermore, vascular depression can lead to decreased intracortical facilitation and disruption of glutamate neurotransmission, which plays a major role in synaptic plasticity, and might contribute to the cognitive deterioration [68]. These cognitive and mood symptoms are associated with vascular damage in the white matter connecting the prefrontal cortex and basal ganglia as well as those connecting the prefrontal cortex and cerebellum. Transcranial doppler ultrasound revealed a hemodynamic pattern of cerebral hypoperfusion and increased vascular resistance [9]. Cognitive impairment manifests as memory decline, abstract thinking, and judgment impairment, but, the ability for daily life is normal. However, it presents a higher risk for more severe cognitive impairments, especially after recurrent strokes, which can seriously affect a patient’s quality of life. As a subtype of VCI, post-stroke cognitive impairment (PSCI) emphasizes that stroke events trigger cognitive dysfunction. Approximately 50% of stroke survivors manifest cognitive dysfunctions, 6 months after stroke, and are more likely to develop dementia within the following 3 years, which significantly affects their quality of life [10, 11]. Moreover, a community-based epidemiological survey in China reported that incidences of PSCI and dementia were 56.6 and 23.2%, respectively, 3 months after stroke [12].

Currently, the diagnosis of PSCI is mainly based on clinical manifestations and on structural changes in brains of patients. This diagnostic criteria formed the basis for construction of SIGNAL2 and CHANGE risk models [13, 14]. The Leukoaraiosis and Disability Study (LADIS) revealed that the severity of changes in white matter is associated with worse performances on overall cognitive tests [15]. Alterrations in mean diffusivity of normal-appearing white matter, corpus callosum atrophy, the presence of lacunes in the thalamus, gray matter, and hippocampal volumes are significantly associated with speed, memory performance, and executive functions. Combined measurement of these imaging metrics can be used as a comprehensive neuroimaging marker for predicting vascular cognitive impairment [1619]. However, the use of biomarkers for the diagnosis and prognosis of PSCI remains a challenge [20, 21].

Gamma-glutamyl transferase (GGT) is a serum metabolic biomarker that is mainly used to assess liver function [22, 23]. GGT is involved in maintenance of physiological concentrations of glutathione in cells and reflects the oxidation-antioxidant balance in the body [24, 25]. It has been reported that GGT levels are correlated with decreased cognitive function in diabetics [26, 27]. In addition, a Korean retrospective study found that GGT variability is associated with Alzheimer’s disease, implying that serum GGT levels are potential predictors of cognitive decline [28]. Moreover, serum metabolites, including GGT, have been shown to be differentially expressed in patients with PSCI and post-stroke non-cognitive impairment [29, 30], suggesting that GGT may affect PSCI occurrence.

However, the role of GGT in PSCI has not been conclusively determined, and to date, only a handful of models for predicting PSCI have been constructed. Notably, these models are mainly constructed based on cerebrovascular risk factors, with the effects of non-cerebrovascular risk factors on PSCI remaining unclear. Therefore, the relationship between GGT and PSCI should be evaluated further. In addition, expert consensus states that the diagnosis of PSCI refers to cognitive dysfunction after a stroke event in 6 months, and most patients suffer cognitive impairment within 3 months after stroke [46]. Therefore, we aimed to investigate the association of serum GGT with PSCI during 3 months of follow-up. This study is presented in accordance with the STROBE reporting checklist.


Study population

All participants with a minor ischemic stroke or transient ischemic attack were selected from the Impairment of Cognition and Sleep (ICONS) study of the China National Stroke Registry-3 (CNSR-3). Patient selection was performed from 2015 to 2018 [31]. ICONS is a large national, multi-center, and prospective cohort involving about 40 hospitals in China [32]. Acute ischemic stroke (AIS) and transient ischemic attack (TIA) are the most common cerebrovascular events in China. PSCI includes cognitive impairment caused by AIS and TIA. Studies report that 3 months after TIA, more than one-third of patients exhibit cognitive dysfunction [3335]. Therefore, we continuously recruited patients with AIS and TIA, with no history of cognitive disorders before stroke. Generally, according to the World Health Organization criteria, AIS and TIA are diagnosed based on symptomatic presentations (acute onset of neurological deficits, which persist for > 24 h in the case of AIS, or for < 24 h in the case of TIA), physical signs, scale evaluations, and are confirmed by neuroimages (magnetic resonance or brain computed tomography) [36,37,38].

The inclusion criteria for patients in this study were: (i) Diagnosed with AIS or TIA and hospitalized upon symptomatic onset within 7 days; (ii) The absence of any history of cognitive dysfunctions, serious mental disorders such as psychosis or schizophrenia (documented in medical records); (iii) The absence of any other factors that affect cognitive or sleep assessments, for instance, severe aphasia defined as National Institutes of Health Stroke Scale (NIHSS) item 9 (Best Language) > 2, consciousness disorders defined as NIHSS item 1a (Level of Consciousness) > 1 or 1b (LOC Questions) > 1, hearing loss, visual impairment, hard to cooperate, severe unilateral neglect or dyslexia; (iv) Muscle strength of handedness ≥ level 4 after Manual Muscle Testing; and (v) Those whose baseline GGT levels were accessible and who had completed the standard cognitive function evaluation at 3 months of follow-up. Eventually, a total of 1957 participants were enrolled in our study.

This study was performed in accordance with the guidelines described by the Helsinki Declaration and was approved by the Ethical Committee of Beijing Tiantan Hospital (No. KY2015-001-01). Prior to their inclusion in the study, all participants signed written informed consents.

Data collection

The data collection protocol and statistical analyses were performed as previously described [36, 37]. Confounding variables in this study were selected based on the findings of studies on risk factors for PSCI [12, 3943]. Upon admission, all participants were comprehensively and precisely assessed, which included the collection of their demographic information (age, sex, body mass index, smoking, and educational level, among others), and evaluation of their medical histories (stroke, hypertension, dyslipidemia, diabetes mellitus, coronary artery disease, atrial fibrillation, heart failure, fatty liver disease, epilepsy, and cancer). The 7-item Generalized Anxiety Disorder Scale was used to assess participants’ anxiety status. In addition, they were subjected to a detailed physical examination, and several parameters, including the modified Rankin Scale, Trial of ORG 10172 in Acute Stroke Treatment (TOAST) type, NIHSS score, ABCD2score, Glasgow Coma Scale, and Manual Muscle Testing were assessed. Moreover, exposure to various medications during hospitalization (antiplatelet aggregation therapy, antihypertensive therapy, lipid-lowering therapy, hypoglycemic therapy, antidepressant therapy, sedative-hypnotic therapy) was assessed. Then, fasting blood samples were obtained, for laboratory analysis of serum GGT, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides (TG), total cholesterol (TC), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum albumin, effective glomerular filtration rate (eGFR), albumin, and serum uric acid (UA) levels. These samples were collected in EDTA anticoagulation blood collection and serum-separation tubes within 24 h of admission.

Outcome evaluation

Clinical outcome has involved the assessment of PSCI occurrence after 3 months of follow-up. We applied the Montreal Cognitive Assessment (MoCA) approach to assess cognitive functions and adopted a MoCA cut-off point of <23/30, which has previously been shown to have the best sensitivity and specificity for detecting PSCI in Chinese patients [12, 35, 4446]. Baseline MOCA evaluation was performed by a certified neuropsychologist, while follow-up MoCA evaluation was performed by a neurologist who was blinded to baseline assessment.

Statistical analyses

All statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Participants who were lost to follow-up were excluded from the study. Continuous variables are presented as median (interquartile range) and were compared using the Kruskal–Wallis test. Categorical variables are expressed as numbers (proportions) and were compared using the χ2 or Fisher’s exact tests. First, we categorized all recruited participants into four groups according to baseline GGT quartiles, then, we collected their characteristics upon admission. Thereafter, we analyzed the association between GGT levels and PSCI using multivariable logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) after adjusting for confounding factors. In addition, since GGT is a metabolic index, many factors independent of cognitive status may affect it, notably liver problems. Thus, potential confounders related to liver functions were also taken into account. Restricted cubic spline analyses were performed to assess the association while C statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the degree to which the model predicted PSCI after the addition of GGT. We established the conventional model using various parameters, such as age, sex, educational level, BMI, smoking, drinking, NIHSS score at admission, history of stroke, hypertension, dyslipidemia, diabetes mellitus, coronary artery disease, atrial fibrillation, heart failure, and laboratory TC, TG, WBC, as well as UA levels [12, 3942]. Finally, we performed subgroup analyses, considering age, sex, body mass index, alcohol drinking, stroke type as interaction factors.

All analyses were two-sided, and P<0.05 was considered statistically significant.


Baseline characteristics

Among the 2625 participants in the ICONS study, 1957 participants with complete baseline GGT levels and 3-months follow-up were enrolled (Fig. 1). They were divided into four groups according to the quartile of GGT levels, namely < 17, 17 ~ 24, 24 ~ 37, and ≥ 37 U/L. A summary of baseline characteristics of the recruited participants is presented in Tables 1 and 2, Supplementary Table 1. The analysis of these characteristics revealed a significant correlation between GGT levels and age, sex, educational levels, smoking, alcohol drinking, body mass index (BMI), diabetes mellitus, hypoglycemic therapy, antidepressant therapy, sedative-hypnotic therapy, TOAST type, HDL, TG, TC, AST, ALT, UA, eGFR, and albumin.

Fig. 1
figure 1

Flowchart in this study. MoCA, The Montreal Cognitive Assessment; GGT, gamma-glutamyl transferase

Table 1 Baseline characteristics of the enrolled participants based on their GGT quartiles
Table 2 Baseline characteristics of the enrolled participants based on their GGT quartiles

Clinical outcomes

Among the eligible participants, 671 (34.29%) patients suffered PSCI at 3 months follow-up, and the characteristics of participants with PSCI were shown in Supplementary Table 2. The correlation between GGT and PSCI is presented in Table 3 and Fig. 2. In Summary, patients in the highest quartile group recorded a 31% decrease in PSCI risk at 3 months follow-up, after adjusting for confounding factors [OR: 0.69 (95%CI: 0.50-0.96)], relative to the lower quartile group.

Table 3 Association between GGT levels and PSCI incidence at 3 months follow-up
Fig. 2
figure 2

Forest plots of ORs for incident PSCI according to GGT quartile level. The ORs for PSCI incidence according to GGT quartile levels were adjusted for variables of model 3 in Table 3. PSCI, post-stroke cognitive impairment; Ref, reference

Notably, restricted cubic spline analysis revealed that GGT levels were inversely associated with PSCI at 3 months (Fig. 3). However, once GGT increased by over 60 U/L, PSCI incidence no longer decreased.

Fig. 3
figure 3

Spline models about the association between GGT levels and clinical outcomes. The association between GGT levels and PSCI occurrence at 3 months. The ORs from the logistic regression model were adjusted for variables of model 3 in Table 3. Red lines indicate adjusted OR, while the blue lines indicate 95%CI. GGT, gamma-glutamyl transferase; PSCI, post-stroke cognitive impairment; OR, odds ratio; CI, confidence interval

After incorporating GGT into the conventional model to predict PSCI occurrence, there was a slight improvement in discriminatory power and reclassification after 3 months of follow-up[NRI: 12.00% (P = 0.01); IDI: 0.30% (P = 0.02)]. Due to the inverse correlation between GGT and PSCI, we set the OR of the highest quartile as the reference (Table 4).

Table 4 Reclassification and disclination statistics for PSCI prediction by GGT levels

Subgroup analysis

According to previous studies, some demographic and physiological factors may influence GGT levels, which might result in different effects of GGT on PSCI [24, 28, 47]. Thus, in this study, we further conducted the interaction analysis. Odds ratios for GGT and PSCI were stratified by age, sex, BMI, alcohol drinking, and stroke type. Notably, low- and high-GGT levels refer to the lowest (25%) and highest (75%) quartiles, respectively. P values from interaction analyses between GGT and age, sex, BMI, alcohol drinking were 0.91, 0.68, 0.09, and 0.96, stroke type were 0.81, 0.44, 0.09, 0.93, and 0.58, respectively at 3 months of follow-up (Table 5, Fig. 4). This result indicated the underlying interaction effect between BMI and GGT, while other subgroup analyses revealed no significant interactions.

Table 5 Subgroup analysis indicating the correlations between GGT levels and PSCI
Fig. 4
figure 4

Forest maps of ORs for incident PSCI stratified by different subgroups. Odds ratios for GGT and PSCI were stratified by age, sex, BMI, alcohol drinking, and stroke type. Low-GGT refers to the lowest quartile of 25%, while High-GGT refers to the remaining 75% of the quartile. ORs for incidence of PSCI were adjusted for variables of model 3 in Table 3. GGT, gamma-glutamyl transferase; PSCI, post-stroke cognitive impairment; BMI, body mass index; OR, odds ratio; CI, confidence interval


This large prospective cohort study demonstrated that baseline GGT levels were inversely associated with PSCI occurrence. Specifically, extremely low GGT levels were established to be risk factors for PSCI, even after adjusting for confounding factors including age, sex, educational level, smoking, drinking, BMI, some laboratory indicators, and medical history. Interestingly, the incorporation of GGT into the conventional model resulted in an 11.87% increase in predicting PSCI. Furthermore, PSCI showed a stronger inverse association with GGT especially in individuals with a lower BMI. Correlations between GGT and PSCI in other subgroups revealed no significant change after testing for interactions. This finding indicates that GGT exerted a consistent effect on PSCI, regardless of patients’ age, sex, alcohol drinking habits, and stroke type [48].

As a common complication after stroke, PSCI is associated with serious disabilities. Studies have found that a variety of serum biomarkers are associated with PSCI. Moreover, previous studies reported contrasting findings in terms of the relationship between GGT and cognitive impairment. However, studies have not evaluated the role of GGT in PSCI. According to previous studies, oxidative stress is one of the pathogenic mechanisms of PSCI [30, 49]. After cerebral ischemia and hypoxia, endogenous antioxidants are decreased and oxygen free radicals are overproduced during perfusion of low cerebral blood flow. The body’s oxidative and antioxidant systems are out of balance. Free radicals lead to cell death by damaging proteins, fats, and DNA, which in turn leads to systemic vascular endothelial dysfunction, increases the permeability of the blood-brain barrier and leads to extravasation of blood substances and leakage of serum proteins. These abnormalities are thought to lead to subsequent neuronal damage, such as grey matter atrophy and cortical thinning, leading to cognitive dysfunction [50]. GGT as a biomarker reflecting the oxidation-antioxidant balance in the body should be paid attention to in PSCI related studies. In this multicenter cohort study, we established an association between GGT and PSCI.

This result may be explained by the following mechanism. Biologically, GGT is critical for antioxidant defenses [18]. It is involved in the maintenance of physiological concentrations of glutathione and plays a vital role in protecting cells from oxidative stress damage. GGT induction can be used as a protective adaptation mechanism in physiological and pathological processes [51]. In the initial development of stroke, inflammatory cytokines levels in the body increase, and GGT levels can compensatory increase when catabolism of inflammatory cytokines containing glutathione. In this process, the glutamic acid and the strong reducing agent (dipeptide cysteinyl glycine) are produced. The latter is hydrolyzed by dipeptidase to cysteine and glycine, which are then taken up by cells for intracellular glutathione resynthesis [22, 25]. Among the catabolic products mentioned above, glutamate can be used as the energy material of brain tissue to improve and maintain neurological function. Both glycine and cysteine are constituent amino acids of endogenous antioxidant reduced glutathione, which can protect nerve cells from oxidative stress and reduce the oxidative damage caused by Aβ deposition. The amino acid neurotransmitter is an important transmitter system in the brain. GGT is thought to contribute to the transport process of amino acids across the blood-brain barrier due to the tight junctions of the endothelium cells that prevent the free diffusion of substances [52, 53]. In the brain, GGT mainly exists in the microvascular endothelial cells and in the choroid plexus where the blood-cerebrospinal fluid barrier exists. This enzyme plays a role in regulating the uptake and transport of amino acids, facilitating amino acid transport across the blood-brain barrier and intracellular glutathione regeneration. GGT has been shown to have a protective effect on brain cells. The main deficiency of the vascular endothelial barrier is its inability to prevent the entrance of lipophilic xenobiotic substances, while the intracellular glutathione synthesis catalyzed by GGT can detoxify such substances. Therefore, GGT plays an important role in defending cells from oxidative-induced damage. Moreover, increased GGT levels in the normal range can also indicate that the liver is better at dealing with oxidative stress. When the degree of oxidative stress in the body decreases, PSCI incidence decrease accordingly. Notably, very low GGT levels are indicators of poor liver functions. In patients with more severe chronic liver disease, especially in advanced cirrhosis, the GGT level is continuously at a low value, possibly due to the loss of glutamyl transpeptidase synthesis in hepatocytes. Deficiency or absence of GGT leads to impaired glutamate cycle, which affects the absorption, transport, and utilization of amino acids causes glutathione resynthesis disorder, and progressive neurological symptoms [54]. Therefore, a better GGT reserve is essential for generating enough glutathione to maintain the redox balance in the body.

However, it still had some limitations. First, we adopted a relatively short follow-up period which may have influenced the observed outcomes. Previous studies mainly focused on the relationship between GGT with AD and cognitive decline in later stages of life over 10 year follow-up periods. However, the relationship between GGT and PSCI has not been conclusively determined. Since PSCI is closely associated with stroke and is characterized by its fluctuations, the outcome of the association between GGT and PSCI depends on the length of follow-up. Second, we found that in the population with high GGT levels, levels of biochemical indicators for liver functions, such as ALT and AST were also high. It is possible that this population paid more attention to their health conditions, and on their own, they could be adopting certain measures to protect their liver functions during follow-up. This may have weakened the potentially dangerous relationship between GGT and PSCI. Third, due to missing data for some variables of interest, the potential impact of residual confounders such as a more accurate assessment of the emotional state, partial drug use and dosage, and location of stroke lesions on the results were not excluded completely. Moreover, this study only included patients with minor stroke (low NIHSS score) and TIA, which could not represent all stroke cohorts. To give greater relevance and breadth to the interesting results of this research, further research should also take into account patients with stroke of greater severity and longer follow-up. Furthermore, as an index of biological metabolism, GGT is affected by many factors and presents a dynamic change characteristic. Nevertheless, we only assessed the relationship between GGT at baseline and PSCI. Since baseline GGT levels may be temporarily altered by stroke events, we cannot rule out the possibility of changes in physical health state. The effect of GGT dynamic change on PSCI should be further studied in the future.


In summary, our results revealed that baseline GGT levels are inversely associated with PSCI, with extremely low GGT levels considered to be a risk factor for PSCI. However, GGT levels dynamically change and it plays a two-sided role in vivo. Therefore, relying solely on GGT to predict PSCI should be carefully considered and further longitudinal studies are needed to clarify the mechanism of GGT affecting neuroplasticity.

Availability of data and materials

The data supporting the findings of this study are available from the corresponding author upon reasonable request.



Gamma-glutamyl transferase


Post-stroke cognitive impairment


Vascular cognitive impairment-no dementia


The Montreal Cognitive Assessment


Body mass index


Acute ischemic stroke


Transient ischemic attack


Trial of ORG 10172 in Acute Stroke Treatment


High-density lipoprotein


Low-density lipoprotein




Total cholesterol


Alanine aminotransferase


Aspartate aminotransferase


Effective glomerular filtration rate


Uric acid


Interquartile range


Odds ratios


Confidence interval


Net reclassification improvement


Integrated discrimination improvement


The Impairment of Cognition and Sleep study


The China National Stroke Registry-3


  1. Campbell BCV, Khatri P. Stroke. Lancet. 2020;396:129–42.

    Article  PubMed  Google Scholar 

  2. Wang Y-J, Li Z-X, Hong-Qiu G, Zhai Y, Jiang Y, Zhao X-Q, et al. China stroke statistics 2019: a report from the National Center for healthcare quality Management in Neurological Diseases, China National Clinical Research Center for neurological Diseases, the Chinese Stroke Association, National Center for chronic and noncommunicable disease control and prevention, Chinese Center for Disease Control and Prevention and institute for global neuroscience and stroke collaborations. Stroke Vasc Neurol. 2020;5(3):211–39.

    Article  PubMed  PubMed Central  Google Scholar 

  3. GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the global burden of disease study 2016. Lancet. 2017;390(10100):1151–210.

    Article  Google Scholar 

  4. Skrobot OA, O'Brien J, Black S, Chen C, DeCarli C, Erkinjuntti T, et al. The vascular impairment of cognition classification consensus study. Alzheimers Dement. 2017;13:624–33.

    Article  PubMed  Google Scholar 

  5. Tang Y, Xing Y, Zhu Z, He Y, Li F, Yang J, et al. The effects of 7-week cognitive training in patients with vascular cognitive impairment, no dementia (the cog-vaccine study): a randomized controlled trial. Alzheimers Dement. 2019;15:605–14.

    Article  PubMed  Google Scholar 

  6. Bella R, Ferri R, Cantone M, Pennisi M, Lanza G, Malaguarnera G, et al. Motor cortex excitability in vascular depression. Int J Psychophysiol. 2011;82:248–53.

    Article  PubMed  Google Scholar 

  7. Bella R, Pennisi G, Cantone M, Palermo F, Pennisi M, Lanza G, et al. Clinical presentation and outcome of geriatric depression in subcortical ischemic vascular disease. Gerontology. 2010;56:298–302.

    CAS  Article  PubMed  Google Scholar 

  8. Pennisi M, Lanza G, Cantone M, Ricceri R, Spampinato C, Pennisi G, et al. Correlation between motor cortex excitability changes and cognitive impairment in vascular depression: pathophysiological insights from a longitudinal tms study. Neural Plast. 2016;2016:8154969.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. Vinciguerra L, Lanza G, Puglisi V, Pennisi M, Cantone M, Bramanti A, et al. Transcranial doppler ultrasound in vascular cognitive impairment-no dementia. PLoS One. 2019;14:e0216162.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Mijajlović MD, Pavlović A, Brainin M, Heiss WD, Quinn TJ, Ihle-Hansen HB, et al. Post-stroke dementia - a comprehensive review. BMC Med. 2017;15(1):11.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Merriman NA, Sexton E, McCabe G, Walsh ME, Rohde D, Gorman A, et al. Addressing cognitive impairment following stroke: systematic review and meta-analysis of non-randomised controlled studies of psychological interventions. BMJ Open. 2019;9:e024429.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kandiah N, Chander RJ, Lin X, Ng A, Poh YY, Cheong CY, et al. Cognitive impairment after mild stroke: development and validation of the SIGNAL2 risk score. J Alzheimers Dis. 2016;49(4):1169–77.

    Article  PubMed  Google Scholar 

  13. Chander RJ, Lam BYK, Lin X, Ng AYT, Wong APL, Mok VCT. Development and validation of a risk score (CHANGE) for cognitive impairment after ischemic stroke. Sci Rep. 2017;7(1):12441.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 2001–2011: a decade of the ladis (leukoaraiosis and disability) study: what have we learned about white matter changes and small-vessel disease? Cerebrovasc Dis. 2011;32:577–88.

  15. Nieminen T, Brander A, et al. Global burden of small vessel disease-related brain changes on mri predicts cognitive and functional decline. Stroke. 2020;51:170–8.

    Article  PubMed  Google Scholar 

  16. Jokinen H, Kalska H, Ylikoski R, Madureira S, Verdelho A, van der Flier WM, et al. Longitudinal cognitive decline in subcortical ischemic vascular disease--the ladis study. Cerebrovasc Dis. 2009;27:384–91.

    Article  PubMed  Google Scholar 

  17. Zhao L, Biesbroek JM, Shi L, Liu W, Kuijf HJ, Chu WW, et al. Strategic infarct location for post-stroke cognitive impairment: a multivariate lesion-symptom mapping study. J Cereb Blood Flow Metab. 2018;38(8):1299–311.

    Article  PubMed  Google Scholar 

  18. Casolla B, Caparros F, Cordonnier C, Bombois S, Hénon H, Bordet R, et al. Biological and imaging predictors of cognitive impairment after stroke:a systematic review. J Neurol. 2019;266(11):2593–604.

    Article  PubMed  Google Scholar 

  19. Chinese Stroke Society Vascular Cognitive Disorder Branch. Expert consensus on the prevention and treatment of cognitive impairment after stroke in China (in Chinese). Chin J Stroke. 2021;16(4):376–89.

    Article  Google Scholar 

  20. Li C-H, Chang Y-H, Chou M-C, Chen C-H, Ho B-L, Hsieh S-W, et al. Factors of post-stroke dementia:a nationwide cohort study in Taiwan. Geriatr Gerontol Int. 2019;19(8):815–22.

    CAS  Article  PubMed  Google Scholar 

  21. He M, Wang J'e, Liu N, Xiao X, Geng S, Meng P, et al. Effects of blood pressure in the early phase of ischemic stroke and stroke subtype on poststroke cognitive impairment. Stroke. 2018;49(7):1610–7.

    Article  PubMed  Google Scholar 

  22. Ndrepepa G, Kastrati A. Gamma-glutamyl transferase and cardiovascular disease. Ann Transl Med. 2016;4:481.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Koenig G, Seneff S. Gamma-Glutamyltransferase: a predictive biomarker of cellular antioxidant inadequacy and disease risk. Dis Markers. 2015;2015:818570.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Kunutsor SK. Gamma-glutamyltransferase-friend or foe within? Liver Int. 2016;36:1723–34.

    CAS  Article  PubMed  Google Scholar 

  25. Foyer CH, Nocto G. Redox homeostasis and antioxidant signaling: a metabolic interface between stress perception and physiological responses. Plant Cell. 2005;17(7):1866–75.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Gasecka A, Siwik D, Gajewska M, Jaguszewski MJ, Mazurek T, Filipiak KJ, et al. Early biomarkers of neurodegenerative and neurovascular disorders in diabetes. J Clin Med. 2020;9(9):2807.

    CAS  Article  PubMed Central  Google Scholar 

  27. Hong SH, Han K, Park S, Kim SM, Kim NH, Choi KM, et al. Gamma-Glutamyl Transferase variability and risk of dementia in diabetes mellitus: a Nationwide population-based study. J Clin Endocrinol Metab. 2020;105(3):dgaa019.

    Article  PubMed  Google Scholar 

  28. 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;35:1105–14.

    Article  PubMed  Google Scholar 

  29. Praetorius Bjork 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;30:981–90.

    Article  PubMed  Google Scholar 

  30. Liu M, Zhou K, Li H, Dong X, Tan G, Chai Y, et al. Potential of serum metabolites for diagnosing post-stroke cognitive impairment. Mol BioSyst. 2015;11:3287–96.

    CAS  Article  PubMed  Google Scholar 

  31. Wang Y, Jing J, Meng X, Pan Y, Wang Y, Zhao X, et al. The third China National Stroke Registry (CNSR-III) for patients with acute ischaemic stroke or transient ischaemic attack: design, rationale and baseline patient characteristics. Stroke Vasc Neurol. 2019;4:158–64.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Wang Y, Liao X, Wang C, Zhang N, Zuo L, Yang Y, et al. Impairment of cognition and sleep after acute ischemic stroke or transient ischemic attack in Chinese patients: design, rationale and baseline patient characteristics of a nationwide multicenter prospective registry. Stroke Vasc Neurol. 2021;6(1):139–44.

    Article  PubMed  Google Scholar 

  33. Van Rooij FG, Schaapsmeerders P, Maaijwee NA, van Duijnhoven DA, de Leeuw FE, Kessels RP, et al. Persistent cognitive impairment after transient ischemic attack. Stroke. 2014;45:2270–4.

    Article  PubMed  Google Scholar 

  34. Pendlebury ST, Wadling S, Silver LE, Mehta Z, Rothwell PM. Transient cognitive impairment in tia and minor stroke. Stroke. 2011;42:3116–21.

    Article  PubMed  Google Scholar 

  35. Liao XL, Zuo LJ, Zhang N, Yang Y, Pan YS, Xiang XL, et al. The occurrence and longitudinal changes of cognitive impairment after acute ischemic stroke. Neuropsychiatr Dis Treat. 2020;16:807–14.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Hurford R, Li L, Lovett N, Kubiak M, Kuker W, Rothwell PM. Prognostic value of “tissue-based” definitions of TIA and minor stroke: population-based study. Neurology. 2019;92(21):e2455–61.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. Stroke--1989. Recommendations on stroke prevention, diagnosis, and therapy. Report of the WHO task force on stroke and other cerebrovascular disorders. Stroke. 1989;20:1407–31.

    Article  Google Scholar 

  38. Sachdev PS, Lipnicki DM, Crawford JD, Wen W, Brodaty H. Progression of cognitive impairment in stroke/tia patients over 3 years. J Neurol Neurosurg Psychiatry. 2014;85:1324–30.

    Article  PubMed  Google Scholar 

  39. Pendlebury ST, Rothwell PM. Prevalence, incidence, and factors associated with pre-stroke and post-stroke dementia: a systematic review and meta-analysis. Lancet Neurol. 2009;8:1006–18.

    Article  PubMed  Google Scholar 

  40. Jia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020;5:e661–71.

    Article  PubMed  Google Scholar 

  41. Molad J, Hallevi H, Korczyn AD, Kliper E, Auriel E, Bornstein NM, et al. Vascular and neurodegenerative markers for the prediction of post-stroke cognitive impairment: results from the TABASCO study. J Alzheimers Dis. 2019;70:889–98.

    CAS  Article  PubMed  Google Scholar 

  42. Zhang X, Bi X. Post-stroke cognitive impairment: a review focusing on molecular biomarkers. J Mol Neurosci. 2020;70:1244–54.

    CAS  Article  PubMed  Google Scholar 

  43. Sun JH, Tan L, Yu JT. Post-stroke cognitive impairment: epidemiology, mechanisms and management. Ann Transl Med. 2014;2(8):80.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Salvadori E, Pasi M, Poggesi A, Chiti G, Inzitari D, Pantoni L. Predictive value of MoCA in the acute phase of stroke on the diagnosis of mid-term cognitive impairment. J Neurol. 2013;260(9):2220–7.

    CAS  Article  PubMed  Google Scholar 

  45. Lees R, Selvarajah J, Fenton C, Pendlebury ST, Langhorne P, Stott DJ, et al. Test accuracy of cognitive screening tests for diagnosis of dementia and multidomain cognitive impairment in stroke. Stroke. 2014;45:3008–18.

    Article  PubMed  Google Scholar 

  46. Zuo L, Dong Y, Zhu R, Jin Z, Li Z, Wang Y, 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. 2016;6:e011310.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Whitfield JB. Gamma glutamyl transferase. Crit Rev Clin Lab Sci. 2001;38(4):263–355.

    CAS  Article  PubMed  Google Scholar 

  48. Sun X, Ioannidis JP, Agoritsas T, Alba AC, Guyatt G. How to use a subgroup analysis: users' guide to the medical literature. JAMA. 2014;311(4):405–11.

    CAS  Article  PubMed  Google Scholar 

  49. Zhang X, Yuan M, Yang S, Chen X, Wu J, Wen M, et al. Enriched environment improves post-stroke cognitive impairment and inhibits neuroinflammation and oxidative stress by activating Nrf2-ARE pathway. Int J Neurosci. 2021;131(7):641–9.

    CAS  Article  PubMed  Google Scholar 

  50. Liu H, Zhang J. Cerebral hypoperfusion and cognitive impairment: the pathogenic role of vascular oxidative stress. Int J Neurosci. 2012;122(9):494–9.

    CAS  Article  PubMed  Google Scholar 

  51. Jean JC, Liu Y, Joyce-Brady M. The importance of gamma-glutamyl transferase in lung glutathione homeostasis and antioxidant defense. Biofactors. 2003;17(1-4):161–73.

    CAS  Article  PubMed  Google Scholar 

  52. Frey A, Meckelein B, Weiler-Güttler H, Möckel B, Flach R, Gassen HG. Pericytes of the brain microvasculature express gamma-glutamyl transpeptidase. Eur J Biochem. 1991;202(2):421–9.

    CAS  Article  PubMed  Google Scholar 

  53. Risau W, Dingler A, Albrecht U, Dehouck MP, Cecchelli R. Blood-brain barrier pericytes are the main source of gamma-glutamyltranspeptidase activity in brain capillaries. J Neurochem. 1992;58(2):667–72.

    CAS  Article  PubMed  Google Scholar 

  54. Ristoff E, Larsson A. Inborn errors in the metabolism of glutathione. Orphanet J Rare Dis. 2007;2:16.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


We thank the staff and participants of the ICONS (the Impairment of Cognition and Sleep study of the China National Stroke Registry-3) study for their contribution.


This work was supported by the National Key Technology Research and Development Program of China (2018YFC2002300, 2018YFC2002302, 2020YFC2004102), National Natural Science Foundation of China (NSFC: 81972144, 81870905).

Author information

Authors and Affiliations



SL and YZ made contributions to the conception and design of the study and substantively revised the manuscript; SL, YP, and XX developed the statistical analyzing procedure and helped in the interpretation of the data; XL and YP were involved in the acquisition of the data. SL and XX have drafted the manuscript. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Yumei Zhang.

Ethics declarations

Ethics approval and consent to participate

This study was performed in accordance with the principles of the Declaration of Helsinki. Approval was granted by the Ethical Committee of Beijing Tiantan Hospital (No.KY2015-001-01). Written informed consents were obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Supplementary Table 1.

Baseline characteristics of the included and excluded participants. Supplementary Table 2. The demographic or neurological differences between PSCI and non-PSCI in included patients.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, S., Liao, X., Pan, Y. et al. Gamma-glutamyl transferase levels are associated with the occurrence of post-stroke cognitive impairment: a multicenter cohort study. BMC Neurol 22, 65 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Association
  • Gamma-glutamyl transferase
  • Post-stroke cognitive impairment