Skip to main content

Decreased blood CD4+ T lymphocyte helps predict cognitive impairment in patients with amyotrophic lateral sclerosis

Abstract

Background

ALS patients have changed peripheral immunity. It is unknown whether peripheral immunity is related to cognitive dysfunction in ALS patients.

Objective

To explore the relationship between the peripheral blood lymphocyte subsets and the cognitive status in ALS patients.

Methods

Among 81 ALS patients, we compared the demographic, clinical, and peripheral levels of total T lymphocyte, CD4+ T lymphocyte, CD8+ T lymphocyte, B lymphocyte, and NK cell between those with cognitive impairment (ALS-ci) and those without (ALS-nci). The cognitive status was evaluated via the Chinese version of the Edinburgh cognitive and behavioral screen (ECAS). Significant predictors of cognitive impairment in univariate logistic regression analysis were further examined using multivariate logistic regression analysis.

Results

39.5% of all ALS patients had cognitive impairment. The ALS-ci group had shorter education time, older age at both symptom onset and testing, longer disease duration, and lower levels of peripheral total, CD4+, and CD8+ T lymphocyte and B lymphocyte than the ALS-nci group. Frequency of behavioral impairment did not differ between the two groups. While parameters with significant differences identified by group comparison were also significant predictors of cognitive impairment in univariate logistic regression analysis except the level of B lymphocyte, only older age at testing, education time less than 9 years, and lower level of CD4+ T lymphocyte remained significant in multivariate logistic regression analysis. The predictive model combining these three parameters had an area under the receiver operating characteristic curve value of 0.842 with a sensitivity of 90.6% and a specificity of 67.3%.

Conclusion

In Chinese ALS patients, blood CD4+ T lymphocyte might help evaluate cognitive impairment along with age and education level.

Peer Review reports

Introduction

Amyotrophic lateral sclerosis (ALS) is a fatal and progressive neurodegenerative disease characterized by loss of upper and lower motor neurons [1]. While ALS was initially considered to selectively involve the motor system, evidence suggests that ALS could involve multiple systems including the cognitive function [2]. Nearly half of ALS patients have mild cognitive or behavioral impairment [3], and around 5 to 15% have cognitive and behavioral changes that fulfill the diagnostic criteria for frontotemporal dementia (FTD) [4,5,6].

Cognitive impairment may influence patient survival [7, 8], increase caregiver burden [8, 9], and affect decision-making processes during treatment [10]. Hence, it is of great importance to accurately identify the cognitive status of ALS patients. At present, this process largely relies on comprehensive neuropsychological assessment [11]. In ALS patients, studies have detected deficits in various cognitive domains including executive function, social cognition, language, and working memory [11]. It is time-consuming to assess all the domains comprehensively via non-ALS-specific neuropsychological batteries [12]. Moreover, many screening tools are inappropriate for patients with severe physical deficits in speech, writing, and drawing [12, 13]. Therefore, the Edinburgh cognitive and behavioral screen (ECAS), a rapid neuropsychological screening tool, was specifically developed to identify cognitive and behavioral changes of ALS patients [13]. Remarkably, ECAS has been widely implemented in both clinical and research settings with different language versions and has proven to be an efficient and reliable screening tool for ALS patients [12, 14,15,16,17,18].

Previous studies have investigated the changes in peripheral blood lymphocyte subsets in ALS patients [19,20,21,22,23,24,25]. Studies showed significantly higher [19] or similar [20] level of T lymphocyte, similar level of B lymphocyte [19, 20], and significant increase [19, 21, 22] or no change [20] in the number of NK cell in ALS patients compared to healthy controls. While most studies found increased percentage of CD4+ T lymphocyte in ALS patients [19, 20, 23], several studies found decrease [24] or no change [21] in CD4+ T lymphocyte. Level of CD8+ T lymphocyte was found to be increased [19, 22], reduced [20, 25], or unchanged in ALS patients [21, 23]. Although these studies did not provide conclusive results, they strongly supported changes in the peripheral immunity in ALS patients.

Importantly, changed peripheral immunity was found to be related to cognitive impairment of patients with various neurodegenerative diseases, such as Alzheimer’s disease (AD) [26, 27] and Parkinson’s disease (PD) [28, 29]. Nevertheless, there has been no study exploring the relationship between the peripheral blood lymphocyte subsets and the cognitive status in ALS patients.

In this study, we evaluated the peripheral blood lymphocyte subsets in ALS patients with and without cognitive impairment, and analyzed the predictive value of lymphocyte subsets for cognitive impairment in ALS patients.

Methods

Participants

From April 2018 to October 2020, patients who visited the Department of Neurology, Tongji Hospital, Wuhan and who were diagnosed with possible, probable, or definite ALS according to the revised El Escorial criteria [30] were included in our study. Exclusion criteria included a history of autoimmune diseases or hematological disorders, concurrent infectious diseases, use of immunosuppressive agents, anti-inflammatory medications or corticosteroids that might affect the function of the immune system, and other neurological diseases such as cerebrovascular diseases, epilepsy, traumatic brain injury, and dementia. Additionally, none of the ALS patients enrolled since the start of the pandemic had a history of COVID-19. Our study was approved by the Ethics Committee of Tongji Hospital (TJ-IRB20201219), and all participants signed written informed consent before the enrollment.

Demographic and clinical data acquisition

Demographic data including gender, age at testing, age at onset, disease duration and education time were collected during the patient visit. The cognitive function of patients was evaluated with the Chinese version of ECAS [12], using a cut-off ECAS total score of 81.92 out of 136 calculated as the mean value subtracted by two standard deviations obtained by the healthy Chinese population, where several subdomains additionally underwent Chinese language specific modifications [12]. ALS patients were consequently divided into the group of patients with cognitive impairment (ALS-ci) and the group of patients without cognitive impairment (ALS-nci). Behavioral impairment in ALS patients was defined as at least one behavioral abnormality determined by the behavioral part of ECAS. The severity of physical disability was evaluated by the ALS Functional Rating Scale-Revised (ALSFRS-R) [31].

Flow cytometry

Venous blood sampling was performed between 5:00 a.m. and 7:00 a.m. during the hospital stay for each patient. Peripheral blood lymphocyte subsets including peripheral total T lymphocyte, CD4+ T and CD8+ T lymphocyte, B lymphocyte, and NK cell were quantified by flow cytometry (flow cytometer: BD FACSCantoTM II; antibodies: BD Multitest™ 6-color TBNK) based on their forward scattering characteristics and lateral scattering characteristics after immunostaining. Results of each lymphocyte subset were expressed as absolute number (/μL) as well as percentage value divided by total lymphocyte (%). For T lymphocyte, CD4+ /CD8+ ratio was also calculated.

Statistical analysis

Shapiro-Wilk test was used to determine the distribution of continuous data. To compare the parameters between the two groups, independent t-test was used for normally distributed continuous data that were expressed as mean ± standard deviation and Mann-Whitney U test was used for non-normally distributed continuous data that were expressed as median [interquartile range]. Chi-square test was used for categorical data that were reported as frequencies.

Demographic, clinical and lymphocyte parameters were examined using univariate regression analysis for the prediction of cognitive impairment in ALS patients. Significant parameters were further included as independent variables in a multivariate logistic analysis with cognitive impairment as a dependent variable. Independent continuous variables that were not normally distributed were transformed into normalized data when possible. Predictive power of these parameters was assessed via the area under the receiver operating characteristic (ROC) curve value with sensitivity and specificity values calculated based on the maximal Youden Index. Pearson correlation or Spearman correlation analysis was performed between each significant independent variable from the multivariate regression analysis and the ECAS total score in case of normal or non-normal data distribution. All statistical analyses were performed using the SPSS Software (version 25.0) with the significant threshold set as p < 0.05.

Results

Demographic and clinical characteristics

Eighty-one ALS patients were enrolled in this study (age 54.9 ± 11.2 years; 48 males). Demographic and clinical characteristics of the participants were summarized in Table 1. Of all ALS patients, mean age at onset was 54.2 ± 11.1 years, median disease duration was 12 months (ranging from 2 to 64 months), and median ALSFRS-R score was 42 (ranging from 15 to 48). 64 (79.0%) patients had limb onset, 15 (18.5%) patients had bulbar onset, and 2 (2.5%) patients had mixed onset (Table 1).

Table 1 Demographic and clinical characteristics of ALS patients

32 (39.5%) ALS patients had cognitive impairment (ALS-ci) and 49 (60.5%) ALS patients had no cognitive impairment (ALS-nci). Two patients in the ALS-ci group and three patients in the ALS-nci group did not have the behavioral part of ECAS since they were neither accompanied by caregivers during hospitalization nor had they close contacts during post-hospitalization call. 3 (10%) patients had disinhibition, 6 (20.0%) patients had apathy, 5 (16.7%) patients had loss of sympathy, 1 (3.3%) patient had perseveration, and 2 (6.7%) patients had changes in eating behaviour in the ALS-ci group, while 1 (2.2%) patient had disinhibition, 4 (8.7%) patients had apathy, 4 (8.7%) patients had loss of sympathy, 2 (4.3%) patients had perseveration, and 2 (4.3%) patients had changes in eating behaviour in the ALS-nci group. Behavioral abnormalities did not differ between the two groups (Supplemental Table 1).

Patients from the ALS-ci group had shorter education time (9 years vs. 12 years, P < 0.001), older age at testing (60.1 years vs. 51.6 years, P < 0.001), as well as at symptom onset (59.3 years vs. 51.0 years, P < 0.001), and longer disease duration (14 months vs. 11 months, P = 0. 0.025) than those in the ALS-nci group. Gender ratio, BMI values, site of onset, and ALSFRS-R scores did not differ significantly between the two groups (Table 1).

Changes in the peripheral blood lymphocyte subsets

Compared to the ALS-nci group, patients in the ALS-ci group had significantly lower numbers of total T lymphocyte (996.50/μL vs 1247.00/μL, P = 0.005), CD4+ T lymphocyte (652.47/μL vs. 767.63/μL, P = 0.019), CD8+ T lymphocyte (262.00/μL vs. 360.00/μL, P = 0.019), and B lymphocyte (158.50/μL vs. 219.00/μL, P = 0.035). Number of NK cell, percentages of total T lymphocyte, CD4+ T lymphocyte, CD8+ T lymphocyte, B lymphocyte, and NK cell, and CD4+/CD8+ ratio were similar between the two groups (P > 0.05) (Table 2).

Table 2 Comparison of peripheral blood lymphocyte subsets between the ALS-ci group and the ALS-nci group

The predictive model of cognitive impairment in ALS patients

Univariate logistic regression showed that older age at onset (OR 1.030, 95% CI 1.085–1.143, P = 0.002) or at testing (OR 1.090, 95% CI 1.034–1.149, P = 0.001), education time less than 9 years (OR 5.318, 95% CI 1.859–15.215, P = 0.002), longer disease duration (OR 1.769, 95% CI 1.072–2.919, P = 0.026), and higher levels of total T lymphocyte (OR 0.530, 95% CI 0.318–0.882, P = 0.015), CD4+ T lymphocyte (OR 0.997, 95% CI 0.995–0.999, P = 0.024), and CD8+ T lymphocyte (OR 0.582, 95% CI 0.355–0.955, P = 0.032) were significant predictors of cognitive impairment of ALS patients (Tables 3 and 4). Only older age at testing (OR 1.107, 95% CI 1.041–1.177, P = 0.001), education time less than 9 years (OR 6.995, 95% CI 2.068–23.663, P = 0.002), and lower level of CD4+ T lymphocyte (OR 0.997, 95% CI 0.995–0.999, p = 0.049) remained significant in multivariate logistic regression analysis (Table 5). The model combining older age at testing, lower education level, and lower number of CD4+ T lymphocyte predicted a higher risk of cognitive impairment of ALS patients, with an area under the ROC curve value of 0.842 (95% CI 0.775–0.933, P < 0.001), a sensitivity of 90.6%, and a specificity of 67.3% (Fig. 1).

Table 3 Univariate logistic regression by including demographic and clinical parameters
Table 4 Univariate logistic regression by including lymphocyte parameters
Table 5 Multivariate logistic regression analysis for the prediction of cognitive impairment in ALS patients
Fig. 1
figure1

ROC curve for cognitive impairment in ALS patients. Legend: The area under the curve was 0.842 (95% CI 0.775–0.933, P < 0.001), with a sensitivity of 90.6% and a specificity of 67.3%.

Correlation analysis between demographic, clinical, and lymphocyte parameters and the ECAS total score

Significant predictors of cognitive impairment in the multivariate regression analysis were further examined by correlation analysis to explore their relationship with the ECAS total score. There was a negative correlation between age at testing and the ECAS total score (r = − 0.397, P < 0.001) and a positive correlation between education time and the ECAS total score (r = 0.691, P < 0.001), while no significant correlation between the number of CD4+ T lymphocyte and the ECAS total score was found (r = 0.180, P = 0.107).

Discussion

Our study showed that ALS patients with cognitive impairment (ALS-ci group) displayed a different peripheral immune profile compared to those without (ALS-nci group). The numbers of total T lymphocyte, CD4+ T lymphocyte, CD8+ T lymphocyte, and B lymphocyte were all decreased in the ALS-ci group. Although the number of CD4+ T lymphocyte was not correlated with the ECAS total score, it was a significant predictor of cognitive impairment in ALS patients along with older age at testing and lower education level. To the best of our knowledge, this is the first study investigating the relationship between peripheral blood lymphocyte subsets and cognitive status in ALS patients.

In our patients, we found significant reduction of total T lymphocyte, CD4+ T and CD8+ T lymphocyte, and total B lymphocyte in the ALS patients with cognitive impairment compared to those without. Additionally, lowered number of CD4+ T lymphocyte seemed to be an independent risk factor of cognitive impairment in ALS patients. Alterations of the peripheral immune system have been reported to be related to cognitive impairment in other neurodegenerative disease [26,27,28,29]. Reduction of B and T lymphocytes was detected in patients with dementia of different patterns including AD, vascular dementia, and FTD [26]. One study found that the number of CD4+ T lymphocyte was positively associated with MMSE scores in AD patients [27]. In PD patients with cognitive impairment, Hu et al. detected significant lower numbers of CD4+, CD8+, and total T lymphocyte, and decreased CD4+/CD8+ T ratio [28]. Magistrelli et al. found that PD patients with cognitive impairment had increased activated regulatory T-lymphocyte (Treg) and Th1 lymphocytes [29]. The imbalance between CD4+ T lymphocyte subpopulations may lead to a proinflammatory state with an overproduction of proinflammatory cytokines that could impair the blood-brain barrier, reach the central nervous system (CNS), and further aggravate neurodegeneration [32]. Thus, it is possible that a fine-tuned balance of immune cells plays a major role in both the intactness of blood-brain barrier [33] as well as the maintenance of cross-talk between immune cells, glia and neurons [34], and abnormal peripheral immunity might be related to pathophysiological processes of cognitive impairment in different neurodegenerative diseases.

Recent basic studies might provide some clues on the periphery immune abnormality in ALS patients with cognitive impairment. Animal studies indicated that adaptive immunity affects cognitive performance, and T lymphocytes are the major immune players in this process [35,36,37]. Early work demonstrated that mice with severe combined immune deficient (SCID, deficient in both T cell and B cell responses) performed poorly in spatial learning and memory tasks compared with wild type (WT) mice [35, 36], while cognitive deficit can be reversed by reconstituting the T lymphocyte compartment in SCID mice [35]. Additionally, depletion of adaptive immunity also led to impaired learning behavior in WT mice, while cognitive dysfunction following immunity ablation could be restored by passive transfer of autologous T lymphocytes in WT mice [35]. Interestingly, learning behavior was not impaired in mice that were specifically depleted of B lymphocytes [37]. While the blood-brain barrier efficiently precludes lymphocyte entry into the brain parenchyma, a large number of T lymphocytes reside in the meninges and are considered to influence the brain function [38]. Mice exhibit learning deficits when T cell migration was blocked from the peripheral blood to the meninges [38]. These studies suggested that the deficit of peripheral T lymphocytes could lead to cognitive impairment. In comparison, disruption of the blood-brain barrier that could permit the entry of proinflammatory cells to the CNS were identified both in ALS animal models [39,40,41] and ALS patients [39, 42,43,44]. In particular, T lymphocytes were found to contribute to the neuro-inflammatory processes and infiltrate the CNS during disease progression of ALS [45]. However, the role of T lymphocytes in cognitive impairment might be different from its role in the neuro-degenerating processes in ALS patients.

Our study had several limitations. The study had a cross-sectional design and a small sample size. Thus, caution should be raised when interpreting the “predictors” of cognitive impairment identified by the logistic regression analysis performed in our study. In addition, detailed subsets of peripheral blood lymphocytes, including naïve T, memory T, and Treg lymphocytes were not evaluated in our study. While the revised Strong criteria [6] helps classify ALS patients with cognitive or/and behavioral impairment, our methodology only compared the percentage of patients with behavioral impairment in the ALS-ci group and the ALS-nci group based on the behavioral part of ECAS and was insufficient to categorize ALS patients set by the revised Strong criteria [6]. Additionally, cut-off scores for behavioral abnormalities defined in ECAS might need further exploration and more studies that fulfill the methodology requirements set by the revised Strong criteria are needed to determine whether changed immunity is related to behavioral abnormalities in ALS patients. Importantly, the C9orf72 gene mutation, which is strongly related to ALS/FTD and could impact the cognitive status [46], was not examined in our study, although it had been shown that frequency of C9orf72 mutation in the Chinese population is rather low [47]. Moreover, since all enrolled participants are Chinese and the genetic contribution to innate immunity is not clearly known, caution is warranted when findings are extrapolated to the general ALS population. Furthermore, emotional [48], respiratory [49], metabolic factors [50,51,52] were not evaluated in our study.

Conclusion

A relationship between the peripheral immune changes and the occurrence of cognitive decline in ALS patients was identified. In particular. ALS patients with a reduced peripheral CD4+ T lymphocyte level may be more vulnerable to cognitive impairment apart from aging processes and lower educational level. Further studies are required to investigate the pathophysiological mechanisms underlying this observation.

Availability of data and materials

Data are preserved locally and are available from the corresponding author on reasonable request.

Abbreviations

AD:

Alzheimer’s diseas

ALS:

Amyotrophic lateral sclerosis

ALSFRS-R:

The ALS Functional Rating Scale-Revised

CNS:

Central nervous system

ECAS:

Edinburgh cognitive and behavioral screen

FTD:

Frontotemporal dementia

PD:

Parkinson’s disease

ROC:

Receiver operating characteristic curve

SCID:

Severe combined immune deficient

Treg:

Regulatory T lymphocyte

WT:

Wild type

References

  1. 1.

    Gordon PH. Amyotrophic lateral sclerosis: an update for 2013 clinical features, pathophysiology, management and therapeutic trials. Aging Dis. 2013;4(5):295–310. https://doi.org/10.14336/ad.2013.0400295.

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Silani V, Ludolph A, Fornai F. The emerging picture of ALS: a multisystem, not only a motor neuron disease. Arch Ital Biol. 2017;155(4):99–109. https://doi.org/10.12871/00039829201741.

    Article  PubMed  Google Scholar 

  3. 3.

    Grossman AB, Woolley-Levine S, Bradley WG, Miller RG. Detecting neurobehavioral changes in amyotrophic lateral sclerosis. Amyotroph Lateral Scler. 2007;8(1):56–61. https://doi.org/10.1080/17482960601044106.

    Article  PubMed  Google Scholar 

  4. 4.

    Murphy J, Henry R, Lomen-Hoerth C. Establishing subtypes of the continuum of frontal lobe impairment in amyotrophic lateral sclerosis. Arch Neurol. 2007;64(3):330–4. https://doi.org/10.1001/archneur.64.3.330.

    Article  PubMed  Google Scholar 

  5. 5.

    Phukan J, Pender NP, Hardiman O. Cognitive impairment in amyotrophic lateral sclerosis. Lancet Neurol. 2007;6(11):994–1003. https://doi.org/10.1016/s1474-4422(07)70265-x.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Strong MJ, Abrahams S, Goldstein LH, Woolley S, McLaughlin P, Snowden J, et al. Amyotrophic lateral sclerosis - frontotemporal spectrum disorder (ALS-FTSD): revised diagnostic criteria. Amyotroph Lateral Scler Frontotemporal Degener. 2017;18(3–4):153–74. https://doi.org/10.1080/21678421.2016.1267768.

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Olney RK, Murphy J, Forshew D, Garwood E, Miller BL, Langmore S, et al. The effects of executive and behavioral dysfunction on the course of ALS. Neurology. 2005;65(11):1774–7. https://doi.org/10.1212/01.wnl.0000188759.87240.8b.

  8. 8.

    Chiò A, Vignola A, Mastro E, Giudici AD, Iazzolino B, Calvo A, et al. Neurobehavioral symptoms in ALS are negatively related to caregivers' burden and quality of life. Eur J Neurol. 2010;17(10):1298–303. https://doi.org/10.1111/j.1468-1331.2010.03016.x.

  9. 9.

    Pagnini F, Rossi G, Lunetta C, Banfi P, Castelnuovo G, Corbo M, et al. Burden, depression, and anxiety in caregivers of people with amyotrophic lateral sclerosis. Psychol Health Med. 2010;15(6):685–93. https://doi.org/10.1080/13548506.2010.507773.

  10. 10.

    Khin Khin E, Minor D, Holloway A, Pelleg A. Decisional capacity in amyotrophic lateral sclerosis. J Am Acad Psychiatry Law. 2015;43(2):210–7.

    PubMed  Google Scholar 

  11. 11.

    Goldstein LH, Abrahams S. Changes in cognition and behaviour in amyotrophic lateral sclerosis: nature of impairment and implications for assessment. Lancet Neurol. 2013;12(4):368–80. https://doi.org/10.1016/s1474-4422(13)70026-7.

    Article  PubMed  Google Scholar 

  12. 12.

    Ye S, Ji Y, Li C, He J, Liu X, Fan D. The Edinburgh cognitive and behavioural ALS screen in a Chinese amyotrophic lateral sclerosis population. PLoS One. 2016;11 5:e0155496; doi: https://doi.org/10.1371/journal.pone.0155496.

  13. 13.

    Abrahams S, Newton J, Niven E, Foley J, Bak TH. Screening for cognition and behaviour changes in ALS. Amyotrophic Lateral Scler Frontotemporal Degener. 2014;15(1–2):9–14. https://doi.org/10.3109/21678421.2013.805784.

    Article  Google Scholar 

  14. 14.

    Niven E, Newton J, Foley J, Colville S, Swingler R, Chandran S, et al. Validation of the Edinburgh cognitive and Behavioural amyotrophic lateral sclerosis screen (ECAS): a cognitive tool for motor disorders. Amyotrophic Lateral Scler Frontotemporal Degener. 2015;16(3–4):172–9. https://doi.org/10.3109/21678421.2015.1030430.

  15. 15.

    Lulé D, Burkhardt C, Abdulla S, Böhm S, Kollewe K, Uttner I, et al. The Edinburgh cognitive and Behavioural amyotrophic lateral sclerosis screen: a cross-sectional comparison of established screening tools in a German-Swiss population. Amyotrophic Lateral Scler Frontotemporal Degener. 2015;16(1–2):16–23. https://doi.org/10.3109/21678421.2014.959451.

  16. 16.

    Poletti B, Solca F, Carelli L, Madotto F, Lafronza A, Faini A, et al. The validation of the Italian Edinburgh cognitive and Behavioural ALS screen (ECAS). Amyotrophic Lateral Scler Frontotemporal Degener. 2016;17(7–8):489–98. https://doi.org/10.1080/21678421.2016.1183679.

  17. 17.

    Siciliano M, Trojano L, Trojsi F, Greco R, Santoro M, Basile G, et al. Edinburgh cognitive and behavioural ALS screen (ECAS)-Italian version: regression based norms and equivalent scores. Neurol Sci. 2017;38(6):1059–68. https://doi.org/10.1007/s10072-017-2919-4.

  18. 18.

    Mora JS, Salas T, Fernández MC, Rodríguez-Castillo V, Marín S, Chaverri D, et al. Spanish adaptation of the Edinburgh cognitive and behavioral amyotrophic lateral sclerosis screen (ECAS). Amyotrophic Lateral Scler Frontotemporal Degener. 2018;19(1–2):74–9. https://doi.org/10.1080/21678421.2017.1406952.

  19. 19.

    Gustafson MP, Staff NP, Bornschlegl S, Butler GW, Maas ML, Kazamel M, et al. Comprehensive immune profiling reveals substantial immune system alterations in a subset of patients with amyotrophic lateral sclerosis. PLoS One. 2017;12(7):e0182002. https://doi.org/10.1371/journal.pone.0182002.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Mantovani S, Garbelli S, Pasini A, Alimonti D, Perotti C, Melazzini M, et al. Immune system alterations in sporadic amyotrophic lateral sclerosis patients suggest an ongoing neuroinflammatory process. J Neuroimmunol. 2009;210(1–2):73–9. https://doi.org/10.1016/j.jneuroim.2009.02.012.

  21. 21.

    Murdock BJ, Zhou T, Kashlan SR, Little RJ, Goutman SA, Feldman EL. Correlation of peripheral immunity with rapid amyotrophic lateral sclerosis progression. JAMA Neurol. 2017;74(12):1446–54. https://doi.org/10.1001/jamaneurol.2017.2255.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Rentzos M, Evangelopoulos E, Sereti E, Zouvelou V, Marmara S, Alexakis T, et al. Alterations of T cell subsets in ALS: a systemic immune activation? Acta Neurol Scand. 2012;125(4):260–4. https://doi.org/10.1111/j.1600-0404.2011.01528.x.

  23. 23.

    Zhang R, Gascon R, Miller RG, Gelinas DF, Mass J, Hadlock K, et al. Evidence for systemic immune system alterations in sporadic amyotrophic lateral sclerosis (sALS). J Neuroimmunol. 2005;159(1–2):215–24. https://doi.org/10.1016/j.jneuroim.2004.10.009.

  24. 24.

    Chen X, Feng W, Huang R, Guo X, Chen Y, Zheng Z, et al. Evidence for peripheral immune activation in amyotrophic lateral sclerosis. J Neurol Sci. 2014;347(1–2):90–5. https://doi.org/10.1016/j.jns.2014.09.025.

  25. 25.

    Provinciali L, Laurenzi MA, Vesprini L, Giovagnoli AR, Bartocci C, Montroni M, et al. Immunity assessment in the early stages of amyotrophic lateral sclerosis: a study of virus antibodies and lymphocyte subsets. Acta Neurol Scand. 1988;78(6):449–54. https://doi.org/10.1111/j.1600-0404.1988.tb03686.x.

  26. 26.

    Busse M, Michler E, von Hoff F, Dobrowolny H, Hartig R, Frodl T, et al. Alterations in the peripheral immune system in dementia. J Alzheimers Dis. 2017;58(4):1303–13. https://doi.org/10.3233/jad-161304.

  27. 27.

    Bonotis K, Krikki E, Holeva V, Aggouridaki C, Costa V, Baloyannis S. Systemic immune aberrations in Alzheimer's disease patients. J Neuroimmunol. 2008;193(1–2):183–7. https://doi.org/10.1016/j.jneuroim.2007.10.020.

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Hu ZX, Song WN, Lu XD, Zhou ML, Shao JH. Peripheral T lymphocyte immunity and l-dopamine in patients with Parkinson's disease. J Biol Regul Homeost Agents. 2018;32(3):687–91.

    CAS  PubMed  Google Scholar 

  29. 29.

    Magistrelli L, Storelli E, Rasini E, Contaldi E, Comi C, Cosentino M, et al. Relationship between circulating CD4+ T lymphocytes and cognitive impairment in patients with Parkinson's disease. Brain Behav Immun. 2020;89:668–74. https://doi.org/10.1016/j.bbi.2020.07.005.

  30. 30.

    Brooks BR, Miller RG, Swash M, Munsat TL. El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord. 2000;1(5):293–9. https://doi.org/10.1080/146608200300079536.

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Cedarbaum JM, Stambler N, Malta E, Fuller C, Hilt D, Thurmond B, et al. The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III). J Neurol Sci. 1999;169(1–2):13–21. https://doi.org/10.1016/s0022-510x(99)00210-5.

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Brugger F, Erro R, Balint B, Kägi G, Barone P, Bhatia KP. Why is there motor deterioration in Parkinson's disease during systemic infections-a hypothetical view. NPJ Parkinsons Dis. 2015;1(1):15014. https://doi.org/10.1038/npjparkd.2015.14.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Sonar SA, Lal G. Blood-brain barrier and its function during inflammation and autoimmunity. J Leukoc Biol. 2018;103(5):839–53. https://doi.org/10.1002/jlb.1ru1117-428r.

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Neumann H, Wekerle H. Neuronal control of the immune response in the central nervous system: linking brain immunity to neurodegeneration. J Neuropathol Exp Neurol. 1998;57(1):1–9. https://doi.org/10.1097/00005072-199801000-00001.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Brynskikh A, Warren T, Zhu J, Kipnis J. Adaptive immunity affects learning behavior in mice. Brain Behav Immun. 2008;22(6):861–9. https://doi.org/10.1016/j.bbi.2007.12.008.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Kipnis J, Cohen H, Cardon M, Ziv Y, Schwartz M. T cell deficiency leads to cognitive dysfunction: implications for therapeutic vaccination for schizophrenia and other psychiatric conditions. Proc Natl Acad Sci U S A. 2004;101(21):8180–5. https://doi.org/10.1073/pnas.0402268101.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Radjavi A, Smirnov I, Kipnis J. Brain antigen-reactive CD4+ T cells are sufficient to support learning behavior in mice with limited T cell repertoire. Brain Behav Immun. 2014;35:58–63. https://doi.org/10.1016/j.bbi.2013.08.013.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Filiano AJ, Gadani SP, Kipnis J. How and why do T cells and their derived cytokines affect the injured and healthy brain? Nat Rev Neurosci. 2017;18(6):375–84. https://doi.org/10.1038/nrn.2017.39.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Garbuzova-Davis S, Sanberg PR. Blood-CNS barrier impairment in ALS patients versus an animal model. Front Cell Neurosci. 2014;8:21. https://doi.org/10.3389/fncel.2014.00021.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Nicaise C, Mitrecic D, Demetter P, De Decker R, Authelet M, Boom A, et al. Impaired blood-brain and blood-spinal cord barriers in mutant SOD1-linked ALS rat. Brain Res. 2009;1301:152–62. https://doi.org/10.1016/j.brainres.2009.09.018.

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Garbuzova-Davis S, Haller E, Saporta S, Kolomey I, Nicosia SV, Sanberg PR. Ultrastructure of blood-brain barrier and blood-spinal cord barrier in SOD1 mice modeling ALS. Brain Res. 2007;1157:126–37. https://doi.org/10.1016/j.brainres.2007.04.044.

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    Donnenfeld H, Kascsak RJ, Bartfeld H. Deposits of IgG and C3 in the spinal cord and motor cortex of ALS patients. J Neuroimmunol. 1984;6(1):51–7. https://doi.org/10.1016/0165-5728(84)90042-0.

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Garbuzova-Davis S, Hernandez-Ontiveros DG, Rodrigues MC, Haller E, Frisina-Deyo A, Mirtyl S, et al. Impaired blood-brain/spinal cord barrier in ALS patients. Brain Res. 2012;1469:114–28. https://doi.org/10.1016/j.brainres.2012.05.056.

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Kawamata T, Akiyama H, Yamada T, McGeer PL. Immunologic reactions in amyotrophic lateral sclerosis brain and spinal cord tissue. Am J Pathol. 1992;140(3):691–707.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    McGeer PL, McGeer EG. Inflammatory processes in amyotrophic lateral sclerosis. Muscle Nerve. 2002;26(4):459–70. https://doi.org/10.1002/mus.10191.

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Byrne S, Elamin M, Bede P, Shatunov A, Walsh C, Corr B, et al. Cognitive and clinical characteristics of patients with amyotrophic lateral sclerosis carrying a C9orf72 repeat expansion: a population-based cohort study. Lancet Neurol. 2012;11(3):232–40. https://doi.org/10.1016/S1474-4422(12)70014-5.

  47. 47.

    Jiao B, Tang B, Liu X, Yan X, Zhou L, Yang Y, et al. Identification of C9orf72 repeat expansions in patients with amyotrophic lateral sclerosis and frontotemporal dementia in mainland China. Neurobiol Aging. 2014;35(4):936 e19–22. https://doi.org/10.1016/j.neurobiolaging.2013.10.001.

    CAS  Article  Google Scholar 

  48. 48.

    Carelli L, Solca F, Faini A, Madotto F, Lafronza A, Monti A, et al. The complex interplay between depression/anxiety and executive functioning: insights from the ECAS in a large ALS population. Front Psychol. 2018;9:450. https://doi.org/10.3389/fpsyg.2018.00450.

  49. 49.

    Kim SM, Lee KM, Hong YH, Park KS, Yang JH, Nam HW, et al. Relation between cognitive dysfunction and reduced vital capacity in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2007;78(12):1387–9. https://doi.org/10.1136/jnnp.2006.111195.

  50. 50.

    Llewellyn D, Langa K, Friedland R, Lang I. Serum albumin concentration and cognitive impairment. Curr Alzheimer Res. 2010;7(1):91–6. https://doi.org/10.2174/156720510790274392.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Ng TP, Feng L, Niti M, Yap KB. Albumin, haemoglobin, BMI and cognitive performance in older adults. Age Ageing. 2008;37(4):423–9. https://doi.org/10.1093/ageing/afn102.

    Article  PubMed  Google Scholar 

  52. 52.

    Ahmed RM, Highton-Williamson E, Caga J, Thornton N, Ramsey E, Zoing M, et al. Lipid metabolism and survival across the Frontotemporal dementia-amyotrophic lateral sclerosis Spectrum: relationships to eating behavior and cognition. J Alzheimers Dis. 2018;61(2):773–83. https://doi.org/10.3233/jad-170660.

Download references

Acknowledgments

We thank our colleagues in the Department of Neurology, Tongji Hospital for assistance with the study. We sincerely thank all study participants.

Funding

This study was supported by the Bethune Charitable Foundation.

Author information

Affiliations

Authors

Contributions

YY contributed to data collection, analysis and interpretation, and draft writing. ZG, JT, ZL contributed to data collection. FD contributed to study design. ML contributed to study design, data analysis and interpretation, and manuscript revision. MZ and DP contributed to study design, data interpretation, and manuscript revision. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Mao Liu or Min Zhang.

Ethics declarations

Ethics approval and consent to participate

Our study was approved by the Ethics Committee of Tongji Hospital (TJ-IRB20201219) and was performed following the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All patients consented to participate and signed the informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

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

Supplementary Information

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 http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) 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

Yang, Y., Pan, D., Gong, Z. et al. Decreased blood CD4+ T lymphocyte helps predict cognitive impairment in patients with amyotrophic lateral sclerosis. BMC Neurol 21, 157 (2021). https://doi.org/10.1186/s12883-021-02185-w

Download citation

Keywords

  • Amyotrophic lateral sclerosis
  • Cognitive impairment
  • ECAS
  • Peripheral blood lymphocyte subsets