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

Open Access

Peer-reviewed

Research Article

Depression as a risk factor for Alzheimer’s disease: A human post-mortem study

  • Mizuki Morisaki ,

    Roles Data curation, Formal analysis, Investigation, Supervision, Writing – original draft, Writing – review & editing

    mizuki.morisaki@bristol.ac.uk

    Affiliation Dementia Research Group, University of Bristol, Bristol, United Kingdom

    ⨯
  • Farnoosh Rezaali,

    Roles Data curation

    Affiliation Dementia Research Group, University of Bristol, Bristol, United Kingdom

    ⨯
  • Laurie C. Lau,

    Roles Data curation, Investigation, Methodology

    Affiliation Clinical and Experimental Sciences, Faculty of Medicine, Sir Henry Wellcome Laboratories, University of Southampton, Southampton, United Kingdom

    ⨯
  • Delphine Boche,

    Roles Conceptualization, Data curation, Methodology, Writing – review & editing

    Affiliation Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

    ⨯
  • Golam M. Khandaker,

    Roles Conceptualization, Writing – review & editing

    Affiliations MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom, Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, Bristol, United Kingdom, NIHR Bristol Biomedical Research Centre, Bristol, United Kingdom

    ⨯
  • Gustavo Turecki,

    Roles Conceptualization, Writing – review & editing

    Affiliation Douglas Bell Brain Bank, McGill University, Montreal, Canada

    ⨯
  • Lindsey I. Sinclair

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – review & editing

    Affiliation Dementia Research Group, University of Bristol, Bristol, United Kingdom

    ⨯

Correction

5 Jan 2026: Morisaki M, Rezaali F, Lau LC, Boche D, Khandaker GM, et al. (2026) Correction: Depression as a risk factor for Alzheimer’s disease: A human post-mortem study. PLOS ONE 21(1): e0340296. https://doi.org/10.1371/journal.pone.0340296 View correction

Abstract

Depression is associated with persistent low mood. In mid to late life, it has been identified as a risk factor for Alzheimer’s disease (AD) with evidence that depression might be an early manifestation of AD. Although the underlying mechanisms by which depression enhances AD development remain unknown, there are several features commonly seen in both diseases such as the presence of neuroinflammation. In this study, we aimed to identify whether neuroinflammation is increased in depression as observed in the early stages of AD by examining post-mortem human brain tissue. Post-mortem human brain tissue from 54 cases with depression and 37 controls without depression were retrieved from the Douglas Bell Canada Brain Bank. Sixteen early-stage AD cases defined as a Braak stage III-IV and 15 controls were sourced from the South West Dementia Brain Bank. Frozen tissue from the dorsal prefrontal cortex was obtained for all cases in order to measure inflammatory proteins (IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, and TNF-α) and endothelial markers (ICAM-1, VCAM) using ELISA and MesoScale Multiplex Assays. In the depression group, increase of IL-6 and IL-10, and decrease of IL-1β were observed compared to controls, with no changes detected for the other cytokines and the endothelial markers. In early-stage AD cases, only increased ICAM-1 expression was found compared to controls, indicating endothelial activation as an early feature of AD. None of the cytokines measured showed alteration of their expression in early-stage AD cases. Depression, but not AD, was associated with evidence of neuroinflammation. Depression may increase AD risk through different mechanism(s) than inflammation.

Citation: Morisaki M, Rezaali F, Lau LC, Boche D, Khandaker GM, Turecki G, et al. (2025) Depression as a risk factor for Alzheimer’s disease: A human post-mortem study. PLoS ONE 20(4): e0320561. https://doi.org/10.1371/journal.pone.0320561

Editor: Mu-Hong Chen, Taipei Veterans General Hospital, Taiwan

Received: August 23, 2024; Accepted: February 20, 2025; Published: April 3, 2025

Copyright: © 2025 Morisaki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly as this dataset contains potentially identifiable clinical information on rare events (suicide). Phenotypic information on individuals who donated tissue to the Douglas Bell brain bank is available on request by bona fide researchers. Contact details for the data are: South West Dementia Brain Bank (Professor PG Kehoe: Patrick.Kehoe@bristol.ac.uk) Douglas Bell Brain Bank (brain.bank@douglas.mcgill.ca)

Funding: We are grateful to the Alzheimer’s Society (grant 518) and the Elizabeth Blackwell Institute at the University of Bristol and the Wellcome trust institutional strategic support fund (204813/Z/16/Z) who supported Dr. Sinclair and Dr. Morisaki during this research project. Prof. Khandaker is supported by the Wellcome Trust (Grant No. 201486/Z/16/Z), the Medical Research Council, UK (Grant No. MC_UU_00011/1); Grant No. MR/S037675/1; and Grant No. MR/W014416/1), and the National Institute of Health Research Bristol Biomedical Research Centre, UK (Grant No. NIHR203315). The South West Dementia Brain Bank is funded by BRACE, the Alzheimer’s Society & Alzheimer’s Research UK as part of Brains for Dementia Research. The Douglas Bell Brain Bank is funded by the fonds du recherche du Quebec-Santé through the Réseau québécois sur le suicide, les troubles de l’humeur et les troubles associés, as well as Brain Canada Foundation. Parts of this work contributed to an MSc in neuroscience for Miss Rezali. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Depression (most commonly known as major depressive disorder: MDD) is a mental disorder associated with continuous low mood and loss of pleasure [1,2]. MDD is common throughout life (i.e., lifetime prevalence 4.4% - 20% [3,4]), but mid to late life MDD has been specifically identified as a risk factor for developing dementia later in life [5–7]. Individuals who have had MDD in mid to late life are twice as much as likely to develop dementia [7]. Occurrence of MDD is also common after diagnosis of dementia and comorbidity with dementia is estimated to be 30% [8,9]. Depression in mid to later life has been hence hypothesised to be an early manifestation of dementia [10], but the exact mechanism by which MDD increases risk of developing dementia remains unknown. Despite the fact that depression is a risk factor for AD, our previous study showed no evidence of increased Aβ/tau in the brain tissue from patients with depression in either early (18–50 years old) and late (51–90 years old) stage of life [11], suggesting that depression may increase risk of AD through other factors.

Increased peripheral inflammatory response has been associated with both depression and dementia [12–15]. However, cytokine expression in peripheral fluids such as serum can be affected by many different environmental factors [16] and may not fully reflect the neuroinflammatory status present in the central nervous system (CNS). This study took a novel approach using human post-mortem brain tissue from the region particularly affected by depression (i.e., dorsolateral prefrontal cortex (DLPFC)) [17–19] to investigate whether depression mirrors the neuroinflammatory status found in the early stages of Alzheimer’s disease (AD). Depression is a known factor to increase the risk of AD [20], and neuroinflammation could be one of the factors that drives AD disease development.

Alzheimer’s disease (AD) is the most common form of dementia accounting for 60 – 70% of all cases [21]. AD shares several biological features with MDD; including shared genetic variation [22,23], structural changes in the hippocampus [24], and increased inflammatory response [13,25]. Increased peripheral inflammation has been reported in both diseases and studies have suggested that chronic inflammation may play a role in their respective pathophysiology [26–28]. In MDD, prolonged activation of immune system [29–31] was identified with increased pro-inflammatory markers such as cytokines (interleukin (IL)-1, IL-2, IL-6, IL-8, IL-12, interferon (IFN)-γ, tumour necrosis factor (TNF)α) and adhesion molecules (intercellular molecule (ICAM)-1), while anti-inflammatory markers (e.g., IL-10) were reduced [32–34]. Additionally, a meta-analysis evaluating 438 MDD cases and 350 non-MDD cases reported significant elevation of TNFα and IL-6 in MDD [4], consistent with another systematic review showing increased TNFα and IL-6 in MDD, suggesting an overall elevation of peripheral inflammation [35]. However, this systematic review also reported an increase of anti-inflammatory cytokine IL-10 [35], highlighting the complexity of MDD as a condition associated with a dysregulated immune system[35]. Of note, MDD often co-occurs with other inflammatory diseases such as inflammatory bowel disease where it has been reported that 44.4% of patients with inflammatory bowel disease experienced depression [36].

Chronic inflammation is a well characterised phenomenon of AD [12,25]. Dysregulated inflammatory response caused by imbalanced pro- and anti-inflammatory cytokines is key to AD pathology. Some authors argue that increased inflammation could be detected years before the AD diagnosis [37], with studies reporting increased inflammation associated with the onset of AD. For example, proinflammatory cytokine IL-1β in serum was significantly elevated in patients with mild cognitive impairment (MCI) compared to controls [38]. Moreover, patients aged > 65 years revealed increased serum IL-1β and TNFα in people who developed AD after 2 years of follow up [17]. These results suggest inflammation is associated with AD, particularly at its early stages.

Neuroinflammation is a recognised hallmark of AD pathology [12,39–41], but whether dysregulation of the immune system is the common mechanism by which MDD could contribute to increase the risk of AD remains unknown. Furthermore, limited knowledge is available regarding neuroinflammation in MDD as most studies only examined peripheral inflammatory markers.

In this study, we explored whether neuroinflammation and downstream vascular markers of increased inflammation are increased in MDD and in the early stages of AD using post-mortem human brain tissue from individuals with MDD or AD prior to their demise compared to age- and sex-matched controls. We hypothesise that individuals with MDD will show a similar increase of neuroinflammation to that seen in early-stage AD. We focused on inflammation markers in the human post-mortem brain tissue rather than microglia (main immune cells in the brain) itself as our previous study showed no difference between MDD and AD groups [42].

Materials and methods

Cases

Human brain tissue was obtained from the South West Dementia Brain Bank (Bristol, UK) and the Douglas Bell Brain Bank (Montreal, Canada). Controls brains were defined as non-neurological non-neuropathological normal brains and were age, gender and post-mortem delay matched as far as possible with the disease groups. The depression cohort was composed from 54 cases with depression and 37 controls who did not suffer with depression. The early-stage AD cohort consisted of 16 early-stage AD cases based on a Braak stage of III/IV and 15 controls (See Table 1 for more details). pH values are presented in Table 1 to ensure the quality of tissue is adequate for further analysis. The data was first accessed for research purposes on 30th March 2018. The data was anonymised at all time, but it contains potentially identifiable clinical information on rare events (i.e., suicide).

thumbnail
Download:
Table 1. Demographic and clinical data of the cases.

https://doi.org/10.1371/journal.pone.0320561.t001

For the depression and control cohorts, detailed information on psychiatric illness ante-mortem was available, as the Douglas Bell Brain Bank performed detailed interviews with relatives of the donor at a suitable time after brain donation which is known as psychological autopsies [43]. Criteria retained within the MDD group include: individuals with depression meeting DSM-IV/ DSM-V criteria either at the time of death or in the 2 years preceding their demise. Individuals were excluded if they had evidence of a dementing illness either ante-mortem or on post-mortem examination, or major neuropathological abnormalities (e.g., cancer), or evidence of a widespread neurological disease process other than those under investigation (e.g., multiple sclerosis). While information was available as whether individuals had been taking antidepressants, this was not the case regarding the anti-inflammatory medication. A full description of this cohort has been previously published [44].

In the early-stage AD group, individuals had to have a diagnosis of MCI/AD made in the 2 years before death and a Braak stage of IV or less. Cases were excluded if the following events were reported in the clinical reports: (i) history of a major depressive disorder severe enough to require psychiatric treatment; (ii) presence of a major neuropathological or inflammatory abnormality (e.g., cancer, bacterial infection); or (iii) evidence of a widespread disease process other than those under investigation (e.g., multiple sclerosis).

We focused on the dorsolateral prefrontal cortex (DLPFC) to investigate neuroinflammation. This is an area known to be affected in MDD and AD [17–19]. Particularly, DLPFC dysfunction is seen at early stage of AD [19,45], and is considered to be crucial due to its compensatory mechanism for neuropathological alteration [19]. Prefrontal cortex is also one of the brain areas which is most associated with MDD pathology [46] and both structural and functional abnormality are characterised in DLPFC of MDD patients [46–50].

Ethical approval

Ethical approval for this work was provided by the Greater Manchester East NRES committee (ref 17/NW/0126). Informed written consent was given by the next of kin at the point of brain donation to the brain banks which supplied tissue for this study for the tissue to be used in research studies.

Sample homogenisation

4°C RIPA lysis buffer (Merck) was combined with protease inhibitor (complete MINI protease inhibitor, Merck) and phosphatase inhibitor (phosSTOP, Merck) and added into 2ml homogenate tube containing fresh frozen brain tissue with 5-10 zirconia beads. All tubes were placed in a Precellys homogeniser for 2 x 15 sec at 6000 g. Then, tissue homogenates were centrifuged at 13,000 g for 15 minutes at 4°C. The supernatant was aliquoted into non-binding 96-well storage plates (Thermo Scientific) and total protein concentration was assessed using the Coomassie protein assay kit (Thermo Scientific). The storage plate was stored at -80°C until further analysis was conducted.

ELISA

ICAM-1 and VCAM-2 were measured using ELISA to determine the permeability of blood brain barrier (R&D systems) as a marker of the downstream consequences of neuroinflammation. The samples were diluted in 1 in 200 for ICAM-1 and 1 in 80 for VCAM-1 before the ELISA to be conducted following the manufacturer’s protocol. The samples were measured in duplicate on each plate and the ELISA repeated twice. Expression of ICAM-1 and VCAM-1 was normalised against absolute protein level using Optima data analysis software (BMG Labtech). ELISA absorbance was measured at 450nm with the FLUOstar OPTIMA microplate reader (BMG Labtech).

Multiplex assay

Inflammation associated marker proteins (IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, and TNF-α) were measured by the V-Plex MesoScale Discovery (MSD) electrochemiluminescence multi-spot assay platform (MesoScale Diagnostics with the Proinflammatory Panel 1 Human Kit (cat. No. K15049D)). The samples were first diluted 1 in 2 and incubated overnight. Following this step, the rest of the analysis was conducted following the manufacturer’s protocol. The protein levels of inflammation associated marker were measured by Meso Quickplex SQ120 (MSD) and obtained values were normalised against total protein concentration (pg/mL/mg).

Statistical analysis

The Shapiro-Wilk’s test was conducted to assess the normality of the data. If the data was normally distributed, linear regression was carried out including demographic variables as co-variates (i.e., age, sex, post-mortem delay and use of antidepressant if it is known). Those co-variates were included in the analysis as they may affect expression of neuroinflammatory markers independent from disease conditions. In addition to these, marital status, educational level and methods of suicide were included as co-variates for MDD and MDD control analysis. Suicidal methods were included as a co-variate as hanging (which is the most popular suicidal method) could potentially increase inflammation in the brain [51–53] and subsequently the level of cytokines and endothelial markers. Although we have included factors which could have affected inflammatory status independent from disease condition itself as much as possible, some factors were not included as data on these was not available, such as environmental conditions at the time of demise, smoking status, diet, or the presence of infections prior to death. We were able to include cause of death, life style (e.g., smoking and frequency of exercises) use of anti-inflammatory drugs or any other disease conditions (chronic inflammatory diseases). If the data were not normally distributed, Mann-Whitney U-test was conducted. A p <  0.05 was considered statistically significant. All values are shown as mean ±  standard deviation. Statistical analysis was conducted using JASP Team (Version 0.16. 3) and stata (version 17). The graphs created with GraphPad Prism 9.4. Statistical comparison was made in 2x2 design (MDD vs. MDD control; AD vs. AD control) to as control groups were diverse (e.g., age) to allow us to compare the group difference in more accurate manner (See S1 for 4x4 design (all cases of MDD, AD and controls).

Results

The MDD vs. MDD controls

The characteristics of the cohorts.

Please note that in the results the term MDD group includes both those with MDD and depressive disorder NOS. All MDD cases died by suicide, with most of them under psychotropic medications at the time of their demise, suggesting the presence of a severe depressive illness. Unfortunately, no clinical information was available on the number of depressive episodes experienced by individuals, nor the timing of depressive episodes in relation to the time of their demise. The cause of death implies that the patients were suffering with low mood at the time. As expected, there was a significant difference in cause of death (p < 0.001) as shown in the Table 1. Similarly to the cause of death, there was a significant difference in antidepressant medication use in last 3 months (p < 0.001) and in antipsychotic medication (p < 0.001) across the groups, with the MDD having the highest use, as expected (Table 1). Although there was overall significance in age (X23 = 59.36, p < 0.001), there was no significant age difference between MDD and MDD controls. As expected, age of MDDs and MDD control groups were significantly different from early-stage AD and early-stage AD control groups (p < 0.001).

Endothelial cell activation

Neither ICAM-1 (p = 0.998, beta = 0.000163, 95% Cl: -0.114 to 0.115) or VCAM-1 (p = 0.522, beta = -0.0476, 95% Cl: - 0.196 to 0.100) were significantly different between groups, indicating that depression was unlikely to affect endothelial cell activation in the DLPFC (Fig 1A-B ).

thumbnail
Download:
Fig 1. Expression level of endothelial activation markers and cytokine between MDD and control cohorts including endothelial activation

(A) ICAM and (B) VCAM; anti-inflammatory markers (C) IL-4, (D) IL-10 and (E) IL-13; and pro-inflammatory markers (F) IFN-γ, (G) IL-1β, (H) IL-2, (I) IL-6, (J) IL-8 (K) IL-12p70 and (L) TNFα. p < 0.05 * , p < 0.01**.

https://doi.org/10.1371/journal.pone.0320561.g001

Cytokine expression

A significant increase in the MDD vs. control groups was detected for IL-6 (MDD: 0.624 (2.262), controls: 0.338 (0.634); W = 1243, p = 0.032) and IL-10 (MDD: 0.0343 (0.0228), controls: 0.0288 (0.01500), p = 0.001, beta = 0.219, 95% CI: 0.0926 to 0.346). While IL-1β expression showed a significant decrease (MDD:0.035 (0.047), controls: 0.098 (0.26); W = 1225, p = 0.045) in the MDD cases. No difference was observed for the other cytokines (IL-4: W = 1014, p = 0.787, IL-13: W = 1112, p = 0.283, IFN-γ: W = 1086, p = 0.304, IL-2:p = 0.1, beta = 0.105, 95% Cl: -0.0205 to 0.231, IL-8: W = 1086, p = 0.389, IL-12p70: W = 813, p = 0.389, TNFα: W = 1045, p = 0.600) (Fig 1C-L).

Early-stage AD vs early-stage AD controls

The characteristics of the cohorts.

All early-stage AD cases had neuropathological diagnosis of AD as stated in Table 1. As previously mentioned, the MDD cohorts differed in age compared to the early-stage AD cohorts (X23 = 59.36, p < 0.001), meaning that early-stage AD cohort and their controls were approximately 35 years older in their age at death (mean age of 85 and 83.3 respectively) than MDD and their respective controls (mean age of 49.6 and 49.1). Neither age between early-stage AD and early-stage AD controls or sex ratio (p = 0.42) were significantly different.

Endothelial cell activation

VCAM-1 did not show statistical difference between the 2 groups (early-stage AD: 2.071 (1.273), early-stage AD controls: 1.201 (0.544); p = 0.055, beta = -0.477, 95% CI: -0.966 to 0.012). Early-stage AD group (17.742 (9.997)) showed significantly higher ICAM-1 expression (p = 0.001** beta = -0.564, 95% CI: -0.873 to -0.255) compared to early-stage AD controls (8.877 (3.569)), indicating increased endothelial activation in the early stage of AD (Fig 2A-B).

thumbnail
Download:
Fig 2. Expression level of endothelial activation markers and cytokine between early-stage AD and early-stage AD control cohorts including

(A) ICAM and (B) VCAM; anti-inflammatory markers (C) IL-4, (D) IL-10 and (E) IL-13; and pro-inflammatory markers (F) IFN-γ, (G) IL-1β, (H) IL-2, (I) IL-6, (J) IL-8 (K) IL-12p70 and (L) TNFα. p < 0.05 * , p < 0.01**.

https://doi.org/10.1371/journal.pone.0320561.g002

Cytokine expression

None of the cytokines were significantly different between early-stage AD and early-stage AD controls (IL-4: p = 0.994, beta = 0.001, 95% CI: -0.313 to 0.315, IL-10: p = 0.323, beta = 0.13, 95%CI: -0.139 to 0.405, IL-13: W = 141, p = 0.423, IFN-γ: p = 0.134, beta = 4.000, 95%CI: -1.323 to 9.33, IL-2: p = 0.245, beta = 4.443, 95%CI: -3.250 to 12.137, IL-8: p = 0.273, beta = -0.409, 95%CI: -1.162 to 0.343, IL-12p70: p = 0.498, beta = -2.601, 95%CI: -10.409 to 5.207, TNFα: p = 0.076, beta = -0.255, 95%CI: -0.539 to 0.029; Fig 2C-L)

Discussion

Our study is the first post-mortem study investigating the potential neuroinflammatory link between depression and early-stage AD in the DLPFC. Both depression and AD are well associated with increased neuroinflammation, but this is traditionally assessed by biomarkers detected in CSF and serum or by positron emission tomography[54]. Very few studies have investigated the inflammatory brain environment in post-mortem human brain tissue. We observe that depression was not associated with changes in the inflammatory cytokine response as observed in early stage of AD with increased ICAM-1 expression. Instead, depression showed a different neuroinflammatory response with lower IL-1β and higher IL-6 and IL-10 expression compared to the controls. The inflammatory findings suggest that depression may have a different pattern of neuroinflammatory change to early-stage AD, implying that neuroinflammation is unlikely to be the mechanism by which depression might contribute to the development or progression of AD or at least not via a neuroinflammatory pathway similar to AD.

Neuroinflammation in depression patients

Contradictory to many past studies, we showed a downregulation of IL-1β in depression patients. Although IL-1β is a proinflammatory cytokine and is considered to play a pivotal role in depression [55], increased inflammatory cytokines associated with depression is often derived from peripheral measure such as blood or CSF. The level of inflammatory cytokines found in these peripheral compartments does not always reflect the neuroinflammatory state in the brain. Interestingly, even in human blood, cumulative meta-analysis revealed that there is no consistent association with IL-1β and TNFα whilst IL-6 showed significance with depression in humans [56]. This means that despite meta-analyses reporting increases of peripheral IL-1β, TNFα and IL-6, only IL-6 levels reach statistical significance in the human brain. In fact, another human study using blood samples reported decreased IL-1β in middle aged male with depressive symptomatology[57]. IL-1β level in human CSF also showed inconsistent result with a meta-analysis describing one study with increased IL-1β in depression group while two studies showed no change in IL-1β level [58].

In contrast, level of IL-10 particularly in serum is generally increased with depression [59,60], which was also consistent with our findings. IL-10 is a primary anti-inflammatory cytokine and suggested to be increased in a response to the increase of IL-6 which is associated with suicide [61,62]. As IL-10 level is also known to be positively correlated with the severity of the disease [59], this may explain the increased IL-10 concentration found in depression brains compared to controls, similar to IL-6 in serum considering that all of the depression patients whose brains were used in this study committed suicide. Increased IL-10 may act as a compensatory mechanism against increased proinflammatory cytokines such as IL-6. However, IL-10 can also activate immune response in certain circumstances[61]. Furthermore, increased IL-10 level does not always reflect depression disease severity or suicide, suggesting that it may not always act as a compensatory mechanism. For example, a review article reported that suicidal attempt and suicidal idealisation did not affect IL-10 level in plasma [63]. Another study found that mRNA analysis in whole blood indicated that IL-10 was in fact decreased with suicidal idealisation and disease severity, while IL-6 level was increased [64]. These studies again show the complexity in immune response associated in depression. As far as we are aware, due to paucity of previous studies, there is no systematic review or meta-analysis conducted using post-mortem human brain studies of inflammatory cytokines in depression [58], which highlights the importance of our study. One previous study reported that expression of IL-1β was increased as well as IL-6 and TNF-α in PFC of the teenage suicide depression patients [65], consistent with peripheral findings. However, it remains unknown whether this also applies to older adults. Moreover, a rodent depression model, showed a lack of increased IL-1β but elevation of IL-6 [66], similar to our results in the human DLPFC, strongly indicating that studies are required to identify how IL-1β level changes in the brain with ageing and its subsequent effects. Although it remains controversial [58], increased activation of microglia (approximately 26%) in PFC has been shown in depression patients using positron emission tomography (PET) [67], implying that depression could be associated with chronic microglial activation. As IL-1β release can initiate microglia and astrocyte activation leading to the downstream synthesis of other proinflammatory cytokines in CNS[68,69] (e.g., IL-6), the chronic microglial activation reported in depression may activate a negative feedback to supress IL-1β. Consistent with other studies investigating peripheral inflammation, our study showed an increase of IL-6 in depression brains. IL-6 exhibits transient anti- and pro-inflammatory effects which are mainly determined by its receptor binding: proinflammatory effects are activated via IL-6 trans-signalling though the soluble IL-6 receptor (sIL-6R), while binding to membrane IL-6 receptor (IL-6R) exhibits anti-inflammatory effects[70,71]. Although previous studies have reported in depression, association with elevated peripheral inflammation [58] particularly IL-6 [64], whether IL-6 has pro- or anti-inflammatory effects in the disease remains unclear. An In vitro study using human hippocampal progenitor cells (HPC0A07/03C), showed that IL-6 inflammatory effect is concentration dependent as well as the concentration of IL-1β [72]. Decreased IL-1β and increased IL-6 may act as a compensatory mechanism in depression to counter act dysregulated neuroinflammatory response.

Neuroinflammation associated with early-stage AD

Unlike depression, significant elevation of ICAM-1 was detected in our early-stage AD cohort. Although ICAM-1 is a vascular marker and not directly regarded as an inflammatory marker, it is a regulator of cellular response in inflammation and increase of its expression can be used as indicator of neuroinflammation in the brain [73]. Cell adhesion molecules (CAMs) elevation including ICAM-1 can be triggered by vascular deposition of amyloid β (Aβ), and CAMs could aid circulation of leukocytes [74]. Although the exact role of ICAM-1 in AD pathology remains unknown, ICAM-1 is found in the frontal cortex of AD patients and localised near Aβ plaques [75]. ICAM-1 forms extravascular aggregation [75,76], which may suggest an early defence mechanism against AD. In fact, increased ICAM-1 is observed in CSF across the different stages of AD including preclinical period, while markers of neuroinflammation such as IL-6 did not [77]. Moreover, an increase of ICAM-1 was observed to protect neurons from Aβ neurotoxicity in both in vivo and in vitro models and can improve cognitive deficit in Aβ-infused rat and 5xFAD mouse, further suggesting a potential protective role of ICAM-1 in early-stage AD pathology defence mechanism.

However, it is important to note that blood-brain barrier (BBB) failure, which is seen at the early stage of AD [78], can be caused by increased ICAM-1 expression [79]. Increased ICAM-1 in endothelial cells could lead to subsequent recruitment of inflammatory cytokines which again enhances neuroinflammation [79]. In fact, knockdown of ICAM-1 in human brain microvascular endothelial cells (HBMVECs) upregulated Aβ degradation by increasing neprilysin [80], while protective role of ICAM-1 was reported from AD model [81]. Although increased ICAM-1 associated with early-stage AD has been reported in various studies including ours, the role of ICAM-1 does not seem to be a simple driver for neuroinflammation, but seems to be specific the pathophysiomechanism of AD.

Strengths and limitations

Unlike studies using peripheral measures of cytokines, our use of post-mortem brain tissue provides us with a more valid measure of neuroinflammatory status in the brain for both depression and AD. Our study uniquely addresses the neuroinflammatory status in a brain region known to be involved in the control of mood and affected in depression [82] It is possible that general increase in neuroinflammation was not observed in DLPFC at the early-stage AD group as none or only small amounts of neurofibrillary tangles are present at this stage. However, it is important to highlight that neuroinflammatory change observed in depression was not reflected in early-stage AD, despite the fact that general increase of inflammation is reported in both diseases. Future studiesshould examine brain areas which would have relatively advanced neurofibrillary tangles such as the medial temporal cortex area, which would improve our understanding of the role of neuroinflammation. It would also be helpful to examine brain areas with shared influence from AD and depression such as the limbic system, but tissue was not available from these brain areas for this study.

We controlled for co-variates as much as possible, but there are limitations such as age of each cohort and limited clinical information. Indeed, no information was available on whether the individuals in the depression group and their controls were taking anti-inflammatory medications or whether co-morbid inflammatory illnesses were present, which could have affected the inflammatory status measured in the brain. Finally, it is possible that the cytokines could have been affected by post-mortem delay, although we controlled for this as far as we were able to.

Although there were 35 years of mean age difference between depression and early-stage AD groups, which could have influenced the baseline neuroinflammation, none of the inflammatory markers were different between early-stage AD control and depression control (Supplement 1), indicating that age does not seem to play a major role in the neuroinflammatory status. ICAM-1 was the only marker which early-stage AD control had significantly more than depression control, showing that ICAM-1 level in the DLPFC is increased with age, which is also consistently with the previous finding in the orbitofrontal cortex [83]. Although increase of ICAM-1 could lead to further recruitment of inflammatory cytokines which can result in an increase inflammation [79], such an effect was not seen in our study. Indeed, increased baseline neuroinflammation has been suggested in many studies, but age-related differences in cytokines are often reported using rodent models and also increased neuroinflammation is most pronounced in the hippocampus compared to the other brain regions [84]. These suggest that age-related neuroinflammatory could be negligible in the DLPFC of human brains and it is likely to be affected by disease conditions.

It is important to note that all of the brains from depression cases were obtained from people who had died by suicide, which strongly indicates the results of severe depression and therefore the findings in this study may only reflect severe depression cases. This could explain the inconsistency of the finding in this study compared to others as mentioned previously. However, it is crucial to highlight that most of past studies have only examined peripheral samples (i.e., CSF and blood), and therefore our finding is still useful. In addition to this, restricted sample sizes may contribute the limited findings.

Conclusions

Our hypothesis that depression and early-stage AD share common neuroinflammatory characteristics was not validated by our findings, suggesting that depression might not increase AD risk via the same neuroinflammatory pathway. However, the increased neuroinflammation reported in the early stage of AD might be induced by BBB disruption whilst the neuroinflammation associated with depression appear to independent from BBB changes. It is important to identify specific neuroinflammatory pathways associated with each disease and to determine downstream effects of neuroimmune dysregulation. Future studies should include larger numbers of individuals and tissue from a greater number of brain areas in order to characterise in-depth the neuroinflammatory milieu in depression.

Supporting information

S1.

Expression level of endothelial activation markers and cytokine between early-stage AD, early-stage AD control, MDD and MDD control cohorts across 4 groups.

https://doi.org/10.1371/journal.pone.0320561.s001

(DOCX)

Acknowledgments

We are very grateful to the donors and families who generously donated the samples used in this research and to the staff of both brain banks. Tissue for this study was kindly supplied by the South West Dementia Brain Bank, University of Bristol and the Douglas Bell Brain Bank at McGill University, Montreal.

References

  1. 1. Ribeiro Â, Ribeiro J, von Doellinger O. Depression and psychodynamic psychotherapy. Revista Brasileira de Psiquiatria. 2017;40(1):105–9.
  2. 2. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 2013.
    • 3. Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, Murray CJL, et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med. 2013;10(11):e1001547. pmid:24223526
    • 4. Dowlati Y, Herrmann N, Swardfager W, Liu H, Sham L, Reim E. A meta-analysis of cytokines in major depression. Biological Psychiatry. 2010;67(5):446–57.
    • 5. Diniz BS, Butters MA, Albert SM, Dew MA, Reynolds CF 3rd. Late-life depression and risk of vascular dementia and Alzheimer’s disease: systematic review and meta-analysis of community-based cohort studies. Br J Psychiatry. 2013;202(5):329–35. pmid:23637108
    • 6. Green R, Cupples L, Kurz A, Auerbach S, Go R, Sadovnick D, et al. Depression as a risk factor for Alzheimer disease: The MIRAGE study. Archives of Neurology. 2003;60(5):753–9.
    • 7. Ly M, Karim HT, Becker JT, Lopez OL, Anderson SJ, Aizenstein HJ, et al. Late-life depression and increased risk of dementia: a longitudinal cohort study. Transl Psychiatry. 2021;11(1):147. pmid:33654078
    • 8. Kitching D. Depression in dementia. Australian Prescriber. 2015;38(6):209.
    • 9. Byers AL, Yaffe K. Depression and risk of developing dementia. Nat Rev Neurol. 2011;7(6):323–31. pmid:21537355
    • 10. Byers A, Yaffe K. Depression and risk of developing dementia. Nature Reviews Neurology. 2011;7(6):323.
    • 11. Sinclair LI, Mohr A, Morisaki M, Edmondson M, Chan S, Bone-Connaughton A, et al. Is later-life depression a risk factor for Alzheimer’s disease or a prodromal symptom: a study using post-mortem human brain tissue?. Alzheimers Res Ther. 2023;15(1):153. pmid:37700368
    • 12. Kinney J, Bemiller S, Murtishaw A, Leisgang A, Salazar A, Lamb B. Inflammation as a central mechanism in Alzheimer’s disease. Alzheimer’s and Dementia: Translational Research and Clinical Interventions. 2018;4(1):575–90.
    • 13. Murakami M, Stofkova A, Lürding R, Giuliani F, Lee C. The role of inflammation in depression and fatigue. Frontiers in Immunology. 2019;1:1696.
    • 14. Mahdavi M, Karima S, Rajaei S, Aghamolaii V, Ghahremani H, Ataei R, et al. Plasma Cytokines Profile in Subjects with Alzheimer’s Disease: Interleukin 1 Alpha as a Candidate for Target Therapy. Galen Med J. 2021;10:e1974. pmid:35434157
    • 15. Ye G, Yin G, Tang Z, Fu J, Chen J, Chen S, et al. Association between increased serum interleukin-6 levels and sustained attention deficits in patients with major depressive disorder. Psychological Medicine. 2018;48(15):2508–14. [cited 2023 Oct 31]. Available from: https://www.cambridge.org/core/journals/psychological-medicine/article/association-between-increased-serum-interleukin6-levels-and-sustained-attention-deficits-in-patients-with-major-depressive-disorder/1F2E1C1103F04254851BFE527B88EE5F
    • 16. Hayley S, Hakim A, Albert P. Depression, dementia and immune dysregulation. Brain. 2021;144(3):746–57.
    • 17. Kim J, Stewart R, Kang H, Bae K, Kim S, Shin I. Longitudinal associations between serum cytokine levels and dementia. Frontiers in Psychiatry. 2018;9:606.
    • 18. Swardfager W, Lanctôt K, Rothenburg L, Wong A, Cappell J, Herrmann N. A meta-analysis of cytokines in Alzheimer’s disease. Biol Psychiatry. 2010;68(10):930–41.
    • 19. Kumar S, Zomorrodi R, Ghazala Z, Goodman M, Blumberger D, Cheam A. Extent of dorsolateral prefrontal cortex plasticity and its association with working memory in patients with Alzheimer disease. JAMA Psychiatry. 2017;74(12):1266.
    • 20. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet. 2020;396(10248):413–46.
    • 21. Zhang T, Ma S, Lv J, Wang X, Afewerky H, Li H. The emerging role of exosomes in Alzheimer’s disease. Ageing Research Reviews. 2021;68:101321.
    • 22. Luykx J, Ashbrook D, Manchia M, Monereo-Sánchez J, van der Meer D, Monereo-Sánchez J. Genetic overlap between Alzheimer’s disease and depression mapped onto the brain. Frontiers in Neuroscience. 2021.
    • 23. Lutz MW, Sprague D, Barrera J, Chiba-Falek O. Shared genetic etiology underlying Alzheimer’s disease and major depressive disorder. Transl Psychiatry. 2020;10(1):88. pmid:32152295
    • 24. Dafsari FS, Jessen F. Depression-an underrecognized target for prevention of dementia in Alzheimer’s disease. Transl Psychiatry. 2020;10(1):160. pmid:32433512
    • 25. Xie J, Van Hoecke L, Vandenbroucke R. The impact of systemic inflammation on Alzheimer’s disease pathology. Frontiers in Immunology. 2022;12:796867.
    • 26. Dafsari F, Jessen F. Depression – an underrecognized target for prevention of dementia in Alzheimer’s disease. Translational Psychiatry. 2020;10(1):1–13.
    • 27. Leonard BE. Inflammation, depression and dementia: are they connected?. Neurochem Res. 2007;32(10):1749–56. pmid:17705097
    • 28. Caraci F, Copani A, Nicoletti F, Drago F. Depression and Alzheimer’s disease: neurobiological links and common pharmacological targets. Eur J Pharmacol. 2010;626(1):64–71.
    • 29. Leonard E, . The concept of depression as a dysfunction of the immune system. Current Immunology Reviews. 2010;6(3):205–12.
    • 30. Miller A, Raison C. Immune system contributions to the pathophysiology of depression. Focus. 2008;6(1):36–45.
    • 31. Lee C, Giuliani F. The role of inflammation in depression and fatigue. Frontiers in Immunology. 2019;10:1696.
    • 32. Müller N. The Role of Intercellular Adhesion Molecule-1 in the Pathogenesis of Psychiatric Disorders. Front Pharmacol. 2019;10:1251. pmid:31824303
    • 33. Schiepers OJG, Wichers MC, Maes M. Cytokines and major depression. Prog Neuropsychopharmacol Biol Psychiatry. 2005;29(2):201–17. pmid:15694227
    • 34. Maes M, Yirmyia R, Noraberg J, Brene S, Hibbeln J, Perini G, et al. The inflammatory & neurodegenerative (I&ND) hypothesis of depression: leads for future research and new drug developments in depression. Metab Brain Dis. 2009;24(1):27–53. pmid:19085093
    • 35. Köhler CA, Freitas TH, Maes M, de Andrade NQ, Liu CS, Fernandes BS, et al. Peripheral cytokine and chemokine alterations in depression: a meta-analysis of 82 studies. Acta Psychiatr Scand. 2017;135(5):373–87. pmid:28122130
    • 36. Navabi S, Gorrepati VS, Yadav S, Chintanaboina J, Maher S, Demuth P, et al. Influences and Impact of Anxiety and Depression in the Setting of Inflammatory Bowel Disease. Inflamm Bowel Dis. 2018;24(11):2303–8. pmid:29788469
    • 37. Culjak M, Nikolac Perkovic M, Uzun S, Svob Strac D, Nedic Erjavec G, Leko MB, et al. The Association between TNF-alpha, IL-1 alpha and IL-10 with Alzheimer’s Disease. Vol. 17, Current Alzheimer Research. 2020.
    • 38. Forlenza O, Diniz B, Talib L, Mendonça V, Ojopi E, Gattaz W. Increased serum IL-1β level in Alzheimer’s disease and mild cognitive impairment. Dementia and Geriatric Cognitive Disorders. 2009;28(6):507–12.
    • 39. Heneka MT, Carson MJ, El Khoury J, Landreth GE, Brosseron F, Feinstein DL, et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol. 2015;14(4):388–405. pmid:25792098
    • 40. Osimo E, Pillinger T, Mateos Rodriguez I, Khandaker G, Pariante C, Howes O. Inflammatory markers in depression: A meta-analysis of mean differences and variability in 5,166 patients and 5,083 controls. Journal Name. 2020;Volume Number(Issue Number):Page Range.
    • 41. Sobue A, Komine O, Yamanaka K. Neuroinflammation in Alzheimer’s disease: microglial signature and their relevance to disease. Inflamm Regen. 2023;43(1):26. pmid:37165437
    • 42. Lin J, Morisaki M, Sampathkumar S, Lau L, Boche D, Khandaker G. Neuroinflammation in comorbid depression in Alzheimer’s disease: A pilot study using post-mortem brain tissue. Neuroscience Applied. 2024;3(1):104051.
    • 43. Bhushan D, Yadav J, Rozatkar A, Moirangthem S, Arora A. The psychological autopsy: An overview of its utility and methodology. J Neurosci Rural Pract. 2023;14(3):447.
    • 44. Sinclair LI, Mohr A, Morisaki M, Edmondson M, Chan S, Bone-Connaughton A, et al. Is later-life depression a risk factor for Alzheimer’s disease or a prodromal symptom: a study using post-mortem human brain tissue?. Alzheimers Res Ther. 2023;15(1):153. pmid:37700368
    • 45. Doe J. Understanding the Challenges of Citation. 2023.
      • 46. Pizzagalli DA, Roberts AC. Prefrontal cortex and depression. Neuropsychopharmacology. 2022;47(1):225–46. pmid:34341498
      • 47. Shen T, Li C, Wang B, Yang W-M, Zhang C, Wu Z, et al. Increased cognition connectivity network in major depression disorder: a FMRI study. Psychiatry Investig. 2015;12(2):227–34. pmid:25866524
      • 48. Kinou M, Takizawa R, Marumo K, Kawasaki S, Kawakubo Y, Fukuda M, et al. Differential spatiotemporal characteristics of the prefrontal hemodynamic response and their association with functional impairment in schizophrenia and major depression. Schizophr Res. 2013;150(2–3):459–67. pmid:24016725
      • 49. Siegle GJ, Thompson W, Carter CS, Steinhauer SR, Thase ME. Increased Amygdala and Decreased Dorsolateral Prefrontal BOLD Responses in Unipolar Depression: Related and Independent Features. 2007 [cited 2024 Feb 28. ]; Available from: www.sobp.org/journal
        • 50. Wang L, Zhao Y, Edmiston E, Womer F, Zhang R, Zhao P, et al. Structural and functional abnormities of amygdala and prefrontal cortex in major depressive disorder with suicide attempts. Frontiers in Psychiatry. 2020;10:923.
        • 51. Kim TH, Lee SH, Kim DH, Kim SC, Jin W, Kim SY, et al. Non-aneurysmal and non-traumatic subarachnoid hemorrhage after attempted suicide by incomplete hanging. Clin Exp Emerg Med. 2017;4(1):56–9. pmid:28435903
        • 52. Morris C, Reid M. The Journal of the Intensive Care Society. Vol. 6. 2005.
        • 53. Namas R, Ghuma A, Zamora R, Do O, Tr B, Vodovotz Y. The Acute Inflammatory Response in Trauma/ Hemorrhage and Traumatic Brain Injury: Current State and Emerging Prospects. [cited 2024 Feb 28]; Available from: www.ljm.org.ly
          • 54. Oestreich LKL, O’sullivan MJ. Review Transdiagnostic In Vivo Magnetic Resonance Imaging Markers of Neuroinflammation. [cited 2023 Aug 31]; Available from: https://doi.org/10.1016/j.bpsc.2022.01.003
            • 55. Farooq RK, Asghar K, Kanwal S, Zulqernain A. Role of inflammatory cytokines in depression: Focus on interleukin-1β. Biomed Rep. 2017;6(1):15–20. pmid:28123701
            • 56. Haapakoski R, Mathieu J, Ebmeier KP, Alenius H, Kivimäki M. Cumulative meta-analysis of interleukins 6 and 1β, tumour necrosis factor α and C-reactive protein in patients with major depressive disorder. Brain Behav Immun. 2015;49:206–15. pmid:26065825
            • 57. Ovaskainen Y, Koponen H, Jokelainen J, Keinänen-Kiukaanniemi S, Kumpusalo E, Vanhala M. Depressive symptomatology is associated with decreased interleukin-1 beta and increased interleukin-1 receptor antagonist levels in males. Psychiatry Res. 2009;167(1–2):73–9. pmid:19346005
            • 58. Enache D, Pariante CM, Mondelli V. Markers of central inflammation in major depressive disorder: A systematic review and meta-analysis of studies examining cerebrospinal fluid, positron emission tomography and post-mortem brain tissue. Brain Behav Immun. 2019;81:24–40. pmid:31195092
            • 59. Anjum S, Qusar M, Shahriar M, Islam S, Bhuiyan M, Islam M. Altered serum interleukin-7 and interleukin-10 are associated with drug-free major depressive disorder. Therapeutic Advances in Psychopharmacology. 2020;10.
            • 60. Al-Fadhel SZ, Al-Hakeim HK, Al-Dujaili AH, Maes M. IL-10 is associated with increased mu-opioid receptor levels in major depressive disorder. European Psychiatry. 2019;57:46–51.
            • 61. Carlini V, Noonan D, Abdalalem E, Goletti D, Sansone C, Calabrone L. The multifaceted nature of IL-10: regulation, role in immunological homeostasis and its relevance to cancer, COVID-19 and post-COVID conditions. Frontiers in Immunology. 2023;14:1161067.
            • 62. Bengoechea-Fortes S de la P, Ramírez-Expósito MJ, Martínez-Martos JM. Suicide, neuroinflammation and other physiological alterations. Eur Arch Psychiatry Clin Neurosci. 2024;274(5):1037–49. pmid:36913003
            • 63. Marini S, Vellante F, Matarazzo I, De Berardis D, Serroni N, Gianfelice D, et al. Inflammatory markers and suicidal attempts in depressed patients: A review. Int J Immunopathol Pharmacol. 2016;29(4):583–94. pmid:26729403
            • 64. Kelly KM, Smith JA, Mezuk B. Depression and interleukin-6 signaling: A Mendelian Randomization study. Brain Behav Immun. 2021;95:106–14. pmid:33631287
            • 65. Pandey GN, Rizavi HS, Ren X, Fareed J, Hoppensteadt DA, Roberts RC, et al. Proinflammatory cytokines in the prefrontal cortex of teenage suicide victims.
              • 66. Sukoff Rizzo SJ, Neal SJ, Hughes ZA, Beyna M, Rosenzweig-Lipson S, Moss SJ, et al. Evidence for sustained elevation of IL-6 in the CNS as a key contributor of depressive-like phenotypes. Transl Psychiatry. 2012;2(12):e199. pmid:23212583
              • 67. Setiawan E, Wilson AA, Mizrahi R, Rusjan PM, Miler L, Rajkowska G, et al. Role of translocator protein density, a marker of neuroinflammation, in the brain during major depressive episodes. JAMA Psychiatry. 2015;72(3):268–75. pmid:25629589
              • 68. Mendiola AS, Cardona AE. The IL-1β phenomena in neuroinflammatory diseases. J Neural Transm (Vienna). 2018;125(5):781–95. pmid:28534174
              • 69. Shaftel SS, Griffin WST, O’Banion MK. The role of interleukin-1 in neuroinflammation and Alzheimer disease: an evolving perspective. J Neuroinflammation. 2008;5:7. pmid:18302763
              • 70. Maes M, Anderson G, Kubera M, Berk M. Targeting classical IL-6 signalling or IL-6 trans-signalling in depression?. Expert Opin Ther Targets. 2014;18(5):495–512. pmid:24548241
              • 71. Rossetti AC, Paladini MS, Brüning CA, Spero V, Cattaneo MG, Racagni G, et al. Involvement of the IL-6 signaling pathway in the anti-anhedonic effect of the antidepressant agomelatine in the chronic mild stress model of depression. International Journal of Molecular Sciences. 2022;23(20).
              • 72. Borsini A, Di Benedetto MG, Giacobbe J, Pariante CM. Pro- and anti-inflammatory properties of interleukin in vitro: Relevance for major depression and human hippocampal neurogenesis. Int J Neuropsychopharmacol. 2020;23(11):738–50.
              • 73. Bui TM, Wiesolek HL, Sumagin R. ICAM-1: A master regulator of cellular responses in inflammation, injury resolution, and tumorigenesis. J Leukoc Biol. 2020;108(3):787–99. pmid:32182390
              • 74. Shawkatová I, Javor J, Párnická Z, Minárik G, Vašečková B, Králová M, et al. ICAM1 gene polymorphism in late-onset Alzheimer’s disease. Biologia. 2019;74(11):1531–8.
              • 75. Verbeek MM, Otte-Höller I, Westphal JR, Wesseling P, Ruiter DJ, de Waal RM. Accumulation of intercellular adhesion molecule-1 in senile plaques in brain tissue of patients with Alzheimer’s disease. Am J Pathol. 1994;144(1):104–16. pmid:7904796
              • 76. Müller N. The role of intercellular adhesion molecule-1 in the pathogenesis of psychiatric disorders. Frontiers in Psychiatry. 2019;10:1251.
              • 77. Janelidze S, Mattsson N, Stomrud E, Lindberg O, Palmqvist S, Zetterberg H, et al. CSF biomarkers of neuroinflammation and cerebrovascular dysfunction in early Alzheimer disease. Neurology. 2018;91(9):e867–77.
              • 78. Montagne A, Zhao Z, Zlokovic BV. Alzheimer’s disease: A matter of blood-brain barrier dysfunction?. J Exp Med. 2017;214(11):3151–69. pmid:29061693
              • 79. Müller N. The role of intercellular adhesion molecule-1 in the pathogenesis of psychiatric disorders. Frontiers in Pharmacology. 2019;10(11):462654.
              • 80. Otgongerel D, Lee H-J, Jo SA. Induction of ICAM1 in Brain Vessels is Implicated in an Early AD Pathogenesis by Modulating Neprilysin. Neuromolecular Med. 2023;25(2):193–204. pmid:35948857
              • 81. Guha S, Paidi RK, Goswami S, Saha P, Biswas SC. ICAM-1 protects neurons against Amyloid-β and improves cognitive behaviors in 5xFAD mice by inhibiting NF-κB. Brain Behav Immun. 2022;100:194–210. pmid:34875346
              • 82. Koenigs M, Grafman J. The functional neuroanatomy of depression: distinct roles for ventromedial and dorsolateral prefrontal cortex. Behav Brain Res. 2009;201(2):239–43. pmid:19428640
              • 83. Miguel-Hidalgo J, Nithuairisg S, Stockmeier C, Rajkowska G. Distribution of ICAM-1 immunoreactivity during aging in the human orbitofrontal cortex. J Neurosci. 2023;83(1):1–10.
              • 84. Porcher L, Bruckmeier S, Burbano SD, Finnell JE, Gorny N, Klett J, et al. Aging triggers an upregulation of a multitude of cytokines in the male and especially the female rodent hippocampus but more discrete changes in other brain regions. J Neuroinflammation. 2021;18(1):219. pmid:34551810