Higher p-Cresol sulfate and tumor necrosis factor (TNF)-α levels are associated with cognitive improvement through increased TNF receptor 2 in individuals with mild cognitive impairment: A cross-sectional mediation analysis

Abstract

Background

Neuroinflammation in neurocognitive disorders is driven by the release of tumor necrosis factor (TNF)-α from brain immune cells in response to injury, infection, or p-Cresol sulfate (p-CS)—a metabolite associated with chronic kidney disease and linked to TNF-α activity. However, the underlying mechanisms through which TNF-α and p-CS influence cognitive performance remain unclear.

Objective

This study investigated the impact of TNF-α and p-CS on cognition, focusing on the role of TNF Receptor 2 (TNFR2) in cognitively normal individuals (CN; n = 36), Alzheimer's disease patients (AD; n = 85), and those with mild cognitive impairment (MCI; n = 219).

Methods

Cognitive status was assessed with ADAS-Cog 13, p-CS measured via MxP^® Quant 500, and TNF-α/TNFR2 quantified using Human DiscoveryMAP^®. Mediation analysis explored TNFR2's role in linking p-CS, TNF-α, and cognition, with significance set at p < 0.05 and FDR controlled by the Benjamini–Hochberg method.

Results

The results showed that TNF-α levels were slightly higher in AD than in MCI, while TNFR2 levels were lowest in MCI, higher in CN, and highest in AD. After adjusting for age, gender, and APOE ɛ3/ɛ4 status, higher TNF-α levels were associated with higher TNFR2 levels in both MCI and AD. In MCI, elevated TNFR2 correlated with better cognitive function, indicating a possible neuroprotective role at this stage of cognitive decline. Further analysis revealed that both p-CS and TNF-α contributed to increased TNFR2 levels, which in turn supported cognitive performance.

Conclusions

In short, p-CS and TNF-α may improve cognitive performance via TNFR2 in individuals with MCI.

Keywords

Alzheimer's disease cognitive performance mild cognitive impairment neuroinflammation p-Cresyl sulfate TNF receptor 2 tumor necrosis factor-alpha

Introduction

Neuroinflammation is a central mechanism in the pathophysiology of various neurocognitive disorders, including Alzheimer's disease (AD) dementia,^1,2 mild cognitive impairment (MCI),^3,4 Parkinson's disease (PD),⁵ schizophrenia,⁶ and cognitive impairments linked to conditions such as chronic kidney disease (CKD).^7,8 In these conditions, the activation of immune cells in the brain triggers the release of pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6), contributing to neuronal damage and cognitive decline.^9–12

Among these, TNF-α has drawn significant attention due to its dual roles in both promoting and regulating inflammation. TNF-α exerts its effects by binding to two distinct receptors, TNFR1 and TNFR2, which have contrasting functions. TNFR1 (TNFRSF1A, CD120a), widely expressed across tissues, primarily mediates pro-inflammatory and apoptotic pathways.^13,14 In contrast, TNFR2, also referred to as Tumor Necrosis Factor Receptor Superfamily Member 1B (TNFRSF1B) or CD120b, is expressed on specific cell types, including endothelial cells, fibroblasts, and subsets of neurons and immune cells.^15,16 It has been shown to have anti-inflammatory impacts¹⁷ and play a key role in promoting neuroprotection and supporting neuronal survival in brain cells.^18–21

In AD dementia, TNFR1 has been linked to inflammation and cell death, whereas TNFR2 plays a crucial role in counteracting the harmful effects of TNFR1. Specifically, TNFR2 has been found to mitigate damage from chronic microglial activation and excessive pro-inflammatory cytokines like TNF-α, which can disrupt neuronal integrity and impair amyloid-β (Aβ) clearance. Studies have shown that, unlike TNFR1, TNFR2 activation can enhance immune regulation, promote synaptic repair, and strengthen neuronal resilience, positioning it as a promising therapeutic target for neurodegeneration.^{18,19,21–23} In mouse models, TNFR2 overexpression alleviated AD dementia pathology, while its deletion exacerbated the disease.²⁴ Moreover, activating TNFR2 in a transgenic AD dementia mouse model (J20xhuTNFR2-k/i) with the TNFR2 agonist (NewStar2) reduced Aβ plaque load, increased microglial phagocytosis, and improved cognitive function and synaptic plasticity.²⁵ Thus, these findings suggested that TNFR2 activation could help alleviate AD dementia neuropathology, further supporting TNFR2 as a viable therapeutic target for AD dementia.

However, further research is needed to fully understand the mechanisms through which TNFR2 exerts its neuroprotective effects and to determine its therapeutic potential in human populations, as most evidence comes from preclinical studies. Specifically, investigating TNFR2 in individuals with MCI could be beneficial, as this stage represents an early window for intervention, where modulating TNFR2 activity might help delay or prevent the progression to AD dementia. In addition, since TNFR2 is a receptor for TNF-α, a key pro-inflammatory cytokine involved in neuroinflammation, it is crucial to explore how TNFR2 activation influences the balance between protective and pathological TNF-α signaling. Thus, understanding these dynamics could provide valuable insights into whether targeting TNFR2 might help mitigate neuroinflammation while preserving its beneficial effects on neuronal repair and immune regulation.

Another factor that has received limited research attention in the context of neuroinflammation is p-Cresol sulfate (p-CS).²⁶ p-CS is a sulfur-containing metabolite formed by the sulfation of p-cresol, which is produced during the breakdown of amino acids, particularly tyrosine, by gut bacteria. p-Cresol is generated during the fermentation of dietary proteins in the intestines, and after its formation, it is converted into p-cresol sulfate by sulfotransferase enzymes in the liver.²⁷

p-CS has primarily been studied in the context of CKD, where elevated levels result from impaired elimination and accumulate in the bloodstream.²⁸ Emerging evidence, however, suggests that p-CS may also contribute to neurodegenerative diseases such as PD and cognitive decline. For instance, a study demonstrated that p-CS levels were significantly elevated in the cerebrospinal fluid (CSF) of patients with PD compared to healthy controls, despite similar or lower plasma concentrations. This suggests that p-CS may cross the blood–brain barrier (BBB) and exert neurotoxic effects within the central nervous system (CNS), potentially influencing disease progression.²⁹ Specifically, in unilateral nephrectomized C57BL/6 mice, an animal model of kidney dysfunction, p-CS has been shown to reduce neuronal markers like microtubule-associated protein 2 (MAP-2), stem cell markers, and proteins involved in cell proliferation, while increasing caspase 3 activity. Further, it lowered brain-derived neurotrophic factor (BDNF) and serotonin levels, raised corticosterone, and disrupted key signaling pathways related to BDNF and serotonin, thereby promoting neuroinflammation and potentially accelerating neurodegenerative processes.²⁶

Nonetheless, despite its association with increased inflammation and pro-inflammatory cytokines, no study has yet explored the role of p-CS in relation to TNF- α and its receptor, such as TNFR2, particularly in the context of cognitive performance in individuals with disorders like MCI. Given the potential interaction between p-CS and inflammatory pathways, further research is needed to investigate whether p-CS influence TNF-α signaling, and vice versa, as well as their impact on neuroinflammation and neurodegeneration in these populations. Little is known about how TNFR2 mediates its neuroprotective effects, particularly in the early stages of neurodegeneration in MCI. Former studies have focused on tissue-based approaches or animal models, limiting understanding of TNFR2's effects in humans and, specifically, in individuals with MCI and AD dementia. In short, while p-CS is linked to inflammation, its role in TNF-α signaling and TNFR2 activation remains unexplored. Given TNFR2's neuroprotective function, understanding its mediation between p-CS and TNF-α could provide key insights into inflammation-driven cognitive impairment.

The current study, to our knowledge, was the first to investigate the association between p-CS, TNF-α, and TNFR2 in relation to cognitive performance in individuals with cognitively normal (CN), AD dementia, and MCI, and highlights the potential role of TNFR2 in modulating cognitive performance. These findings will offer novel insights into the therapeutic potential of TNFR2 in early-stage neurodegeneration.

Materials

The data for this study was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://www.adni.loni.usc.edu), which was launched in 2003 under the leadership of Principal Investigator Michael W. Weiner, MD. ADNI's primary goal is to determine if MRI, PET scans, biomarkers, and neuropsychological assessments can effectively track the progression of MCI and early AD dementia.

Participants recruitment

This cross-sectional study classified participants into CN, MCI, and AD dementia groups based on ADNI's standardized criteria (http://www.adni.loni.usc.edu). Group classification was determined using ADAS-Cog 13 and CDR-SB scores. CN individuals had ADAS-Cog 13 scores of 10 or lower, MCI participants scored above 10 but remained below the dementia threshold, while those with AD dementia typically scored above 18. Similarly, CDR-SB scores of 1 or lower indicated CN status, scores between 1 and 4 corresponded to MCI, and scores exceeding 4 were indicative of AD dementia. These cutoffs were used to distinguish cognitive status alongside physician evaluations and biomarker analyses to ensure accurate group assignments.

To ensure a homogeneous cohort and minimize confounding factors affecting cognitive performance, participants with major neurological or psychiatric disorders, including schizophrenia, major depressive disorder, epilepsy, and substance abuse, were excluded. Conditions known to impact cognition, such as traumatic brain injury and uncontrolled diabetes, were also criteria for exclusion. Consistent with the ADNI protocol, which prioritizes precise tracking of cognitive decline and neurodegeneration, the study further excluded individuals with neurodegenerative diseases like PD and Huntington's disease. Additionally, participants using medications that could influence cognitive function, including benzodiazepines, antipsychotics, anticonvulsants, and strong opioids, were not included. These strict criteria strengthened the study's reliability, supporting the development of accurate diagnostic tools and targeted interventions.

Cognitive assessment

Cognitive status in participants was evaluated using two key tools: the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog)^30,31 and the Clinical Dementia Rating Scale—Sum of Boxes (CDR-SB).³² The ADAS-Cog, widely used for monitoring cognitive decline in conditions such as MCI and AD, measures cognitive impairment through tasks like word recall, object naming, and geometric figure copying. Scores range from 0 to 70, with higher scores indicating greater impairment. The ADAS-Cog 13 is a more detailed version, allowing for a comprehensive assessment of cognitive function.³¹ In this study, the ADAS-Cog 13 was used for cognitive testing to analyze the relationship between biomarkers such as p-CS, TNF-α, and TNFR2 and cognitive function. The CDR-SB, derived from the CDR scale, assesses dementia severity across six domains, including memory, orientation, and judgment, by gathering input from both the participant and an informant. Each domain is rated on a scale from 0 (no impairment) to 3 (severe impairment), with scores summed to provide a detailed quantification of cognitive and functional deficits.^33,34

Plasma p-cresol sulfate measurement

The measurement of p-CS was conducted using the MxP^® Quant 500 kit (Biocrates, Austria), designed to analyze over 600 metabolites. This method utilized a combination of Flow Injection Analysis-Mass Spectrometry (FIA-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS), both employing Multiple Reaction Monitoring (MRM) for selective detection and quantification of metabolites.

For sample preparation, 10 µL of plasma was added to a 96-well extraction plate preloaded with internal standards. After drying the samples under nitrogen for 30 min, they were derivatized using phenyl isothiocyanate and eluted with 5 mM ammonium acetate in methanol. For FIA-MS analysis, samples were diluted in a proprietary running solvent at a 50:1 ratio. For LC-MS, samples were diluted in an 80:20 water-to-methanol solution at a 1:1 ratio. FIA-MS was used to measure lipophilic compounds such as phosphatidylcholines and ceramides, providing semiquantitative results using a single-point calibration standard. LC-MS focused on hydrophilic analytes like p-CS, using reversed-phase chromatography and a 7-point calibration curve with 1/x² regression for precise quantification.

To ensure data reliability, quality control included Biocrates QC samples, NIST SRM-1950 reference plasma, and pooled study samples, which were analyzed at regular intervals to monitor assay performance and identify batch effects. Stable isotope-labeled internal standards further enhanced accuracy and reproducibility. The kit's validation in Biocrates’ laboratory ensured standardization of results across projects, enabling accurate and consistent measurement of p-CS and related metabolites.

Plasma TNF-α and TNFR 2 measurement

To measure TNF-α and TNFR2, the study used a multiplex immunoassay platform provided by Rules-Based Medicine (RBM) as part of the ADNI Plasma Biomarker Project. Plasma samples were collected in EDTA tubes, processed per ADNI protocols, aliquoted, and frozen within 120 min to preserve biomarker stability. Samples were stored at −80°C and later shipped to RBM for analysis.

At RBM, the Human DiscoveryMAP^® platform, based on Luminex xMAP^® technology, typically operated on Luminex 100™ or 200™ systems, was employed. This bead-based multiplex immunoassay used antibody-coated beads to bind TNF-α and TNFR2 in the plasma. After washing, secondary antibodies tagged with fluorescent dyes were added to detect the bound analytes, with signals quantified via a Luminex analyzer. Internal calibration standards ensured data consistency, and outlier detection rules were applied to identify anomalies like potential serum contamination.

Samples were randomized, and analysis was blinded to clinical data to avoid bias. This method provided sensitive and specific detection of TNF-α and TNFR2, enabling a reliable investigation of their roles in cognitive function and neurodegeneration. The multiplex platform's efficiency in analyzing multiple biomarkers from small plasma volumes further supported the study's robustness. This robust methodology allowed for sensitive and specific detection of TNF-α and TNFR2, enabling the researchers to explore their roles in cognitive function and neurodegenerative processes with high reliability.

Statistical analysis

Statistical analyses were performed using Python (v3.11) with the following libraries: pandas (v2.1.4) for data manipulation, statsmodels (v0.14.0) and pingouin (v0.5.4) for statistical modeling, scipy (v1.12.0) for inferential tests, and numpy (v1.26.4) for numerical operations. The normality of continuous variables was assessed using the Shapiro–Wilk test. Normally distributed variables were summarized as mean (standard deviation) and compared using one-way analysis of variance (ANOVA), while nonnormally distributed variables analyzed using the Kruskal–Wallis test. Categorical variables were reported as frequencies and percentages. We consider post hoc pairwise comparisons using Tukey's HSD test when ANOVA is used and pairwise Mann-Whitney U tests when the Kruskal-Wallis test is used. Statistical significance was defined as p < 0.05 (α = 0.05) for all comparisons.

Mediation analysis was conducted to examine the mechanistic role of TNFR2 in the relationships between plasma p-CS, TNF-α, and cognitive outcomes measured by the ADAS-Cog 13. Three primary pathways were evaluated: (1) the association between p-CS and TNF-α, with TNFR2 as the mediator; (2) the association between TNF-α and ADAS-Cog 13, with TNFR2 as the mediator; and (3) the association between p-CS and ADAS-Cog 13, with TNFR2 as the mediator. Each mediation model was analyzed under two conditions to account for potential confounders: Model 1 adjusted for age, gender, and APOE ɛ3 carrier status, while Model 2 adjusted for age, gender, and APOE ɛ4 carrier status. To avoid multicollinearity, two separate models were used—one adjusting for APOE ε3 and the other for APOE ε4 status—since these alleles are mutually exclusive. This allowed clearer interpretation of their distinct effects. Heterozygous and homozygous carriers were not separated due to sample size constraints. Direct and indirect effects were estimated using bootstrapping with 10,000 resamples.

To account for multiple comparisons, the false discovery rate (FDR) was controlled using the Benjamini–Hochberg procedure, and a significance threshold of p < 0.05 was applied. All analyses were conducted in Python, with an emphasis on reproducibility and transparency. Detailed scripts and datasets used in the analysis are available upon request to facilitate independent validation of the findings.

Results

This study included 340 participants, comprising 36 CN individuals, 219 individuals with MCI, and 85 individuals with AD dementia. Table 1 summarizes the characteristics of the study population. The mean age did not significantly differ among groups (p = 0.636, CN versus MCI; p = 0.882, CN versus AD dementia; p = 0.882, MCI versus AD dementia). Similarly, years of education were comparable, although a trend toward lower education levels was observed in the AD dementia group (p = 0.140). The distribution of gender and handedness did not differ significantly between groups (p = 0.268 and p = 0.706, respectively). A significantly higher proportion of APOE ɛ4 carriers was observed in the AD group (52.94%) compared to MCI (44.75%) and CN (11.11%) (p < 0.001). Conversely, APOE ɛ3 carriers were more prevalent in the CN group (p = 0.018). Cognitive performance significantly declined across groups. CDR-SB scores progressively increased from CN (0.0 ± 0.0) to MCI (2.19 ± 1.41) and AD dementia (5.26 ± 2.3) (p < 0.001 for all comparisons). Likewise, ADAS-Cog 13 scores were significantly lower in AD dementia (15.58 ± 6.88) compared to MCI (22.7 ± 6.63) and CN (26.33 ± 3.96) (p < 0.001 for all comparisons) (Table 1).

Table 1.

Demographic characteristics of participants.

	CN (N = 36)	MCI (N = 219)	AD dementia (N = 85)	Comparison	p*	p
Age, year	74.99 (6.14)	74.19 (7.36)	75.04 (7.98)	CN versus MCI	0.882	0.636^a
				CN versus AD	0.882
				MCI versus AD	0.882
Gender						0.268^c
Male	18 (50%)	135 (61.64%)	46 (54.12%)
Female	18 (50%)	84 (38.36%)	39 (45.88%)
Handedness						0.706^c
Right	33 (91.67%)	204 (93.15%)	81 (95.29%)
Left	3 (8.33%)	15 (6.85%)	4 (4.71%)
Education, year	15.72 (2.84)	15.83 (2.97)	15 (3.11)	CN versus MCI	0.700	0.140^a
				CN versus AD	0.439
				MCI versus AD	0.089
APOE ɛ3 carrier						0.018 ^c
Negative	6 (16.67%)	52 (23.74%)	32 (37.65%)
Positive	30 (83.33%)	167 (76.26%)	53 (62.35%)
APOE ɛ4 carrier						<0.001 ^c
Negative	32 (88.89%)	121 (55.25%)	40 (47.06%)
Positive	4 (11.11%)	98 (44.75%)	45 (52.94%)
CDR-SB	0 (0)	2.19 (1.41)	5.26 (2.3)	CN versus MCI	<0.001	<0.001 ^a
				CN versus AD	<0.001
				MCI versus AD	<0.001
ADAS-Cog 13	26.33 (3.96)	22.7 (6.63)	15.58 (6.88)	CN versus MCI	<0.001	<0.001 ^b
				CN versus AD	<0.001
				MCI versus AD	<0.001

CN: cognitively normal; MCI: mild cognitive impairment; AD dementia: Alzheimer's disease dementia; CDR-SB: Clinical Dementia Rating – Sum of Boxes; ADAS-Cog 13: Alzheimer's Disease Assessment Scale – Cognitive Subscale (13 items); APOE ɛ3 carrier: presence of Apolipoprotein E epsilon 3 allele; APOE ɛ4 carrier: Presence of Apolipoprotein E epsilon 4 allele; Continuous variables are reported as Mean (Standard Deviation); Categorical variables are reported as Number (Percentage);. * Tukey's HSD test when ANOVA is used and pairwise Mann-Whitney U tests when the Kruskal-Wallis test is used; ^aKruskal-Wallis test; ^bANOVA test; ^cChi-Square test. Bold values indicate statistical significance.

Table 2 summarizes the plasma analytes levels across the diagnostic groups. Plasma levels of p-CS were comparable across diagnostic groups (CN: 26.3 ± 13.88; MCI: 28.32 ± 15.86; AD dementia: 28.16 ± 13.32), with no significant differences observed (p = 0.614, CN versus MCI; p = 0.628, CN versus AD dementia; p = 0.628, MCI versus AD dementia). Plasma TNF-α levels exhibited significant group differences (CN: 0.75 ± 0.28 pg/mL; MCI: 0.74 ± 0.25 pg/mL; AD dementia: 0.79 ± 0.23 pg/mL). A modest but significant higher level was observed in MCI compared to AD dementia (p = 0.017), while differences between CN and MCI approached significance (p = 0.010). No significant differences were noted between CN and AD dementia (p = 0.822). Levels of TNFR2 significantly differed across groups (CN: 0.81 ± 0.11 pg/mL; MCI: 0.75 ± 0.15 pg/mL; AD dementia: 0.82 ± 0.15 pg/mL). MCI participants exhibited significantly lower levels compared to both CN (p < 0.001) and AD dementia (p < 0.001), while AD dementia levels were higher than those in CN (p = 0.003). (Table 2)

Table 2.

Plasma analytes levels across diagnostic groups.

	CN (N = 36)	MCI (N = 219)	AD dementia (N = 85)	Comparison	p*	P
p-CS	26.3 (13.88)	28.32 (15.86)	28.16 (13.32)	CN versus MCI	0.628	0.614^a
				CN versus AD	0.628
				MCI versus AD	0.628
TNF-α	0.75 (0.28)	0.74 (0.25)	0.79 (0.23)	CN versus MCI	0.045	0.010 ^a
				CN versus AD	0.822
				MCI versus AD	0.017
TNFR2	0.81 (0.11)	0.75 (0.15)	0.82 (0.15)	CN versus MCI	<0.001	0.001 ^b
				CN versus AD	0.003
				MCI versus AD	<0.001

CN: cognitively normal; MCI: mild cognitive impairment; AD dementia: Alzheimer's disease dementia; TNF-α: tumor necrosis factor-alpha; p-CS: p-Cresol sulfate, TNFR2: TNF receptor 2; Results are reported as Mean (Standard Deviation); * Tukey's HSD test when ANOVA is used and pairwise Mann-Whitney U tests when the Kruskal-Wallis test is used. ^a Kruskal-Wallis test; ^b ANOVA test. Bold values indicate statistical significance.

As shown in Table 3, in Model 1, adjusted for age, gender, and APOE ɛ3 status, no significant association was found between p-CS and TNFR2 in CN (β = 0, p = 0.785) or AD dementia (β = 0, p = 0.868). However, in MCI, higher p-CS levels were significantly associated with higher TNFR2 levels (β = 0.002, p = 0.006). Additionally, higher TNF-α levels were significantly associated with TNFR2 in AD dementia (β = 0.546, p = 0.004) and MCI (β = 0.475, p < 0.001). The total and direct effects of p-CS on TNF-α through TNFR2 were not significant in any of the groups. However, the indirect effect, indicating mediation through TNFR2, was significant only in MCI (β = 0.001, p < 0.001), suggesting that greater p-CS may influence TNF-α levels by upregulating TNFR2 in MCI.

Table 3.

Association between p-CS and TNF-α mediated by TNFR2.

	CN (N = 36)		MCI (N = 219)		AD dementia (N = 85)
	β (S.E.)	p	β (S.E.)	p	β (S.E.)	p
Model 1
TNFR2 ∼ p-CS	0 (0.001)	0.785	0.002 (0.001)	0.006	0 (0.001)	0.868
TNF-α ∼ TNFR2	0.473 (0.224)	0.215	0.475 (0.112)	<0.001	0.546 (0.155)	0.004
Total	0.002 (0.002)	0.61	0 (0.001)	0.995	−0.001 (0.002)	0.868
Direct	0.002 (0.002)	0.61	−0.001 (0.001)	0.497	−0.001 (0.002)	0.868
Indirect	0 (0.001)	0.785	0.001 (0)	<0.001	0 (0.001)	0.868
Model 2
TNFR2 ∼ p-CS	0 (0.001)	0.864	0.002 (0.001)	<0.001	0 (0.001)	0.811
TNF-α ∼ TNFR2	0.438 (0.223)	0.293	0.481 (0.111)	<0.001	0.534 (0.157)	0.005
Total	0.002 (0.002)	0.655	0 (0.001)	0.970	−0.001 (0.002)	0.811
Direct	0.002 (0.002)	0.655	−0.001 (0.001)	0.488	−0.001 (0.002)	0.811
Indirect	0 (0.001)	0.864	0.001 (0)	<0.001	0 (0.001)	0.811

CN: cognitively normal; MCI: mild cognitive impairment; AD dementia: Alzheimer's disease dementia; TNF-α: tumor necrosis factor-alpha, p-CS: p-Cresol sulfate; TNFR2: TNF receptor 2; Model 1 was adjusted for age, gender and APOE ɛ3 status; Model 2 was adjusted for age, gender and APOE ɛ4 status. Bold values indicate statistical significance.

In Model 2, adjusted for age, gender, and APOE ɛ4 status, similar patterns were observed. Greater p-CS level was significantly associated with greaterTNFR2 levels in MCI (β = 0.002, p < 0.001), but not in CN (β = 0, p = 0.864) or AD dementia (β = 0, p = 0.811). Higher TNF-α levels were significantly associated with greater TNFR2 levels in MCI (β = 0.481, p < 0.001) and AD dementia (β = 0.534, p = 0.005), but not in CN (β = 0.438, p = 0.293). As with the previous model, neither the total nor the direct effects were significant in CN or AD dementia, while the indirect effect was significant only in MCI (β = 0.001, p < 0.001), indicating that the mediation through TNFR2 is prominent in MCI (Table 3).

Further analysis focused on the association between TNF-α and cognitive function, as measured by the ADAS-Cog 13, mediated by TNFR2. The analysis found that in Model 1, adjusted for age, gender, and APOE ɛ3 status, TNF-α levels were significantly associated with TNFR2 in both MCI (β = 0.165, p < 0.001) and AD dementia (β = 0.246, p = 0.004), but not in CN (β = 0.266, p = 0.215). Additionally, in MCI, higher TNFR2 levels were significantly associated with greater cognitive performance (β = -9.157, p = 0.009), whereas no such relationship was observed in CN or AD dementia. However, the indirect effect of TNF-α on cognitive function through TNFR2 was significant only in MCI (β = -1.609, p = 0.009), suggesting that higher TNF-α levels may slow or partially prevent disease-related cognitive performance deterioration in MCI by upregulating TNFR2-mediated processes. In Model 2, adjusted for age, gender, and APOE ɛ4 status, similar patterns were observed, with the indirect effect remaining significant only in MCI (β = -1.49, p = 0.027) (Table 4).

Table 4.

Association between TNF-α and ADAS-Cog 13 mediated by TNFR2.

	CN (N = 36)		MCI (N = 219)		AD dementia (N = 85)
	β (S.E.)	p	β (S.E.)	p	β (S.E.)	p
Model 1
TNFR2 ∼ TNF-α	0.266 (0.126)	0.215	0.165 (0.039)	<0.001	0.246 (0.07)	0.004
ADAS-Cog 13 ∼ TNFR2	0.47 (6.025)	0.938	−9.157 (3.238)	0.009	5.281 (5.415)	0.464
Total	−6.195 (4.374)	0.278	−0.401 (1.944)	0.837	4.349 (3.628)	0.464
Direct	−7.228 (4.725)	0.278	1.207 (1.992)	0.681	3.521 (3.916)	0.464
Indirect	1.033 (1.927)	0.630	−1.609 (0.781)	0.009	0 (0.001)	0.600
Model 2
TNFR2 ∼ TNF-α	0.252 (0.129)	0.259	0.17 (0.039)	<0.001	0.236 (0.07)	0.005
ADAS-Cog 13 ∼ TNFR2	1.154 (5.951)	0.848	−8.33 (3.215)	0.026	5.839 (5.164)	0.466
Total	−6.366 (4.374)	0.259	−0.538 (1.938)	0.781	3.742 (3.437)	0.466
Direct	−7.486 (4.674)	0.259	0.952 (1.995)	0.781	2.703 (3.686)	0.466
Indirect	1.119 (1.993)	0.640	−1.49 (0.827)	0.027	1.039 (1.296)	0.466

CN: cognitively normal; MCI: mild cognitive impairment; AD dementia: Alzheimer's disease dementia; TNF-α: tumor necrosis factor-alpha; TNFR2: TNF receptor 2; ADAS-Cog 13: Alzheimer's Disease Assessment Scale – Cognitive Subscale (13 items); Model 1 was adjusted for age, gender and APOE ɛ3 status; Model 2 was adjusted for age, gender and APOE ɛ4 status. Bold values indicate statistical significance.

Further, the analysis focused on the relationship between p-CS and cognitive function, mediated by TNFR2. It found that in MCI, greater p-CS levels were significantly associated with higher TNFR2 levels (β = 0.002, p = 0.013 in Model 1 and β = 0.002, p = 0.014 in Model 2). Notably, TNFR2 levels were significantly linked to cognitive improvement (β = -9.157, p = 0.013 in Model 1 and β = -8.33, p = 0.026 in Model 2). The indirect effect of p-CS on cognitive function through TNFR2 was significant in Model 1 (β = -0.016, p = 0.040) and approached significance in Model 2 (β = -0.015, p = 0.053), suggesting that higher p-CS levels may improve cognitive function via mediating increased TNFR2 levels solely in MCI. However, no significant associations were observed in CN or AD dementia groups (Table 5).

Table 5.

Association between p-CS and ADAS-Cog 13 mediated by TNFR2.

	CN (N = 36)		MCI (N = 219)		AD dementia (N = 85)
	β (S.E.)	p	β (S.E.)	p	β (S.E.)	p
Model 1
TNFR2 ∼ p-CS	0 (0.001)	0.976	0.002 (0.001)	0.013	0 (0.001)	0.816
ADAS-Cog 13 ∼ TNFR2	0.47 (6.025)	0.976	−9.157 (3.238)	0.013	5.281 (5.415)	0.816
Total	0.05 (0.044)	0.661	−0.009 (0.029)	0.799	−0.034 (0.056)	0.816
Direct	0.051 (0.045)	0.661	0.007 (0.029)	0.799	−0.035 (0.056)	0.816
Indirect	0 (0.012)	0.976	−0.016 (0.009)	0.040	0.002 (0.007)	0.816
Model 2
TNFR2 ∼ p-CS	0 (0.001)	0.9	0.002 (0.001)	0.014	0 (0.001)	0.828
ADAS-Cog 13 ∼ TNFR2	1.154 (5.951)	0.9	−8.33 (3.215)	0.026	5.839 (5.164)	0.828
Total	0.063 (0.045)	0.454	−0.006 (0.029)	0.826	−0.035 (0.054)	0.828
Direct	0.063 (0.046)	0.454	0.009 (0.029)	0.826	−0.036 (0.054)	0.828
Indirect	0 (0.01)	0.9	−0.015 (0.009)	0.053	0.002 (0.008)	0.828

CN: cognitively normal; MCI: mild cognitive impairment; AD dementia: Alzheimer's disease dementia; TNF-α: tumor necrosis factor-alpha; p-CS: p-Cresol sulfate: TNFR2: TNF receptor-like 2; ADAS-Cog 13: Alzheimer's Disease Assessment Scale – Cognitive Subscale (13 items); Model 1 was adjusted for age, gender and APOE ɛ3 status; Model 2 was adjusted for age, gender and APOE ɛ4 status. Bold values indicate statistical significance.

Discussion

To our knowledge, this is the first study to explore the association between p-CS, TNF-α, and TNFR2 with cognitive performance in individuals with CN, MCI, and AD dementia.

The primary finding was that TNF-α levels were slightly higher in AD dementia than MCI, with a trend toward significance between CN and MCI. In contrast, TNFR2 levels were lowest in MCI (0.75), higher in CN (0.81), and highest in AD dementia (0.82). These results underscored the distinct roles of TNF-α and its receptor—TNFR2—in patients with cognitive impairment diseases, such as MCI and AD. The differential regulation of TNF-α and TNFR2 may reflect distinct pathological processes, highlighting the need for further investigation in the context of neurodegenerative disease progression. Further analysis, adjusted for age, gender, and APOE ɛ3/ɛ4 status, revealed a significant positive association between TNF-α levels and TNFR2 expression in both MCI and AD dementia across all models. In MCI group, higher TNFR2 levels were significantly associated with better cognitive function, suggesting that TNFR2 may play a role in preserving cognitive function during this stage of the disease.

This study, solely in individuals with MCI, identified a significant interplay between p-CS, TNFR2, TNF-α, and cognitive function. The analysis revealed that greater p-CS and TNF-α levels were significantly associated with higher TNFR2 levels, which in turn were linked to better cognitive function. These findings suggest a dynamic mechanism in which p-CS activates TNFR2, leading to an increase in TNF-α, which further amplifies TNFR2 expression. This positive feedback loop may appear to enhance cognitive performance in MCI, suggesting a potential neuroprotective role.

Most importantly, the current study found that higher TNFR2 levels mediated the relationship between both TNF-α and p-CS with cognitive function in MCI. Specifically, higher levels of TNF-α and p-CS were significantly associated with better cognitive performance through the mediation of elevated TNFR2 levels in individuals with MCI. These findings underscored the neuroprotective potential of TNFR2 and its ability to facilitate the cognitive benefits of both p-CS and TNF-α. In essence, TNFR2 emerged as a pivotal target for improving cognitive function. Its activation—whether through interaction with TNF-α or via the effects of p-CS—may enhance cognitive performance. Moreover, these findings emphasized the critical role of the p-CS-TNFR2-TNF-α axis in supporting cognitive health in the early stages of cognitive decline. However, it is of note that further in vitro and in vivo studies are necessary to confirm the involvement of p-CS-TNFR2-TNF-α axis in cognitive function and elucidate their underlying mechanisms.

Former research on TNFR2 activation has revealed significant therapeutic potential in AD models. For instance, the administration of NewStar2, a TNFR2-specific agonist fusion protein composed of two trimeric murine TNF domains linked to an Fc-silenced IgG1 heavy chain, designed for enhanced in vivo stability and targeted immune modulation, has been shown to improve synaptic plasticity, as demonstrated by increased hippocampal synapsin-1 expression, a critical marker of synaptic function.^25,35,36 Unlike TNFR1, which is associated with synaptic dysfunction through excessive glutamate release,³⁷ TNFR2 activation exerted neuroprotective effects, restoring synaptic balance and promoting neuronal health. Moreover, TNFR2 activation has been shown to reduce Aβ plaque burden and downregulate BACE-1, a key enzyme in Aβ production. These outcomes are consistent with findings that TNFR2-mediated activation of the PI3 K/Akt pathway inhibits BACE-1 expression, thereby mitigating Aβ accumulation. Collectively, these studies highlighted the potential of TNFR2-targeted strategies to counteract neurodegeneration by leveraging its neuroprotective and homeostatic properties. TNFR2 activation also offered opportunities to modulate inflammation, enhance microglial function, and support neuronal survival in AD contexts.^{25,35,36,38–43}

Of note, enhanced Aβ clearance following NewStar2 treatment further supports its therapeutic potential; increased levels of soluble Aβ₄₀ and Aβ₄₂ in CSF suggest that TNFR2 activation improves Aβ clearance, potentially through mechanisms such as restored CSF absorption or the degradation of existing plaques, which release soluble Aβ into interstitial fluid and CSF.^44,45 Additionally, studies showed that TNFR2 stimulation enhances microglial activation, as evidenced by a rise in Iba1-positive cells in the hippocampus. This activation shifts microglial behavior toward a more phagocytic state, evidenced by increased CD68-positive microglia around Aβ plaques, indicating that TNFR2 activation enhances microglial efficiency in clearing Aβ deposits while simultaneously reducing neuroinflammation.^{25,35,43,46,47} In short, these findings highlight TNFR2's therapeutic potential and call for further exploration in human AD research.

The precise mechanisms underlying TNFR2's therapeutic effects remain incompletely understood, but several plausible pathways have been identified. One key mechanism involves TNFR2-mediated activation of the PI3 K/Akt signaling pathway,^48–51 which supports neuronal survival and inhibits BACE-1 expression, thereby directly reducing Aβ production.^51,52 TNFR2 activation has also been associated with modulation of the nuclear factor kappa B (NF-κB) signaling pathway, a critical regulator of inflammation. Specifically, by selectively engaging the non-canonical NF-κB pathway, TNFR2 may suppress excessive neuroinflammation while promoting cell survival and repair processes in both neurons and glial cells. This dual regulatory effect could further enhance microglial functionality, facilitating Aβ phagocytosis and maintaining overall tissue homeostasis.⁵³

TNFR2 plays a pivotal role in protecting oligodendrocytes and their precursors, promoting the survival, proliferation, and differentiation of oligodendrocyte precursor cells. These cells are essential for remyelination and the repair of damaged myelin sheaths, a process that is particularly critical in diseases like multiple sclerosis, where demyelination and neuroinflammation result in significant functional deficits.^54,55 Beyond its role in supporting oligodendrocytes, TNFR2 regulates microglia, the brain's resident immune cells. Through TNFR2 signaling, microglia transition from a proinflammatory state to an anti-inflammatory, neuroprotective phenotype.^56,57 This shift helps mitigate neuroinflammation, a key feature of neurodegenerative conditions such as AD, multiple sclerosis, and other CNS disorders.⁵⁸ Further, TNFR2 activation reduces excitotoxic damage, as demonstrated in models of N-methyl-D-aspartate (NMDA)-induced brain injury, where it decreased lesion size and inflammation, effectively protecting neurons from further damage.^59–61

In addition to the aforementioned, TNFR2 activation induces astrocytes to produce neurotrophic factors such as BDNF and nerve growth factor (NGF),^62,63 which are essential for neuronal survival, growth, and repair. BDNF promotes synaptic plasticity and cognitive function,^64–66 while NGF supports cholinergic neuron survival and aids in axonal regeneration and myelin repair.^67,68 Thus, through TNFR2 signaling, astrocytes produce neurotrophic factors that create a neuroprotective environment, aiding recovery from neuroinflammation, excitotoxicity, and neurodegeneration. These effects highlight TNFR2 as a promising neuroprotective target, with further research needed to clarify its mechanisms and clinical potential (Figure 1).

Figure 1.

TNF-α, through its receptor TNFR1, plays a crucial role in mediating both inflammation and apoptosis, which contribute to neurodegeneration in the brain. Upon TNF-α binding to TNFR1, the receptor recruits the adaptor protein TRADD, which then activates caspase-8. This activation triggers a cascade leading to the activation of caspase-3, a key mediator of apoptosis, resulting in cell death. In addition, TRADD activates TRAF2, which recruits cIAPs (cellular inhibitors of apoptosis proteins) to inhibit apoptotic signals and simultaneously activates the NF-κB pathway, a central regulator of inflammatory responses. TRAF1, another adaptor protein associated with TNFR1, activates the MEKK1/4 kinase complex and the JNK pathway, further promoting inflammation and apoptosis. These pathways collectively enhance neuroinflammation and contribute to the loss of neurons and tissue integrity in neurodegenerative diseases. In contrast, signaling through TNFR2 leads to a more protective and reparative response in brain cells. Upon TNF-α binding to TNFR2, the receptor recruits TRAF2, which then activates IKK (IκB kinase) and NIK (NF-κB-inducing kinase). These activations stimulate the NF-κB and MAPK pathways, which are involved in promoting cell survival, proliferation, and tissue regeneration. This signaling is critical for protecting neuronal cells from damage, supporting neurogenesis, and facilitating tissue repair. Therefore, while TNFR1-mediated signaling promotes apoptosis and inflammation, TNFR2 activation plays a protective role, contributing to neuroprotection and tissue regeneration. This balance between TNFR1 and TNFR2 signaling represents a complex mechanism governing the response to neuroinflammation and neuronal damage in the brain.

Another marker related to neuroinflammation and the focus of this study is p-CS.^69–71 Little is known about its role in neurodegenerative diseases and cognitive decline. p-CS is a protein-bound uremic toxin produced by gut microbiota through tyrosine and phenylalanine metabolism. Due to its high albumin-binding capacity, PCS accumulates in the blood of CKD patients, whose impaired renal function prevents its excretion. This accumulation is linked to various complications, particularly CNS disorders, including depression, anxiety, PD, and biomarkers like Aβ levels that is related to AD.^27,29,69

The current study's findings suggested that p-CS may increase TNFR2, thereby improving cognitive function in individuals with MCI. In contrast, a study on PD examined the role of uremic toxins, including p-CS, in the CSF and plasma of 27 participants (18 with PD, 9 controls). The study found higher CSF-to-plasma ratios for indoxyl sulfate and p-CS in PD patients, with p-CS levels notably elevated in the CSF, suggesting that elevated levels of p-CS in the CSF may play a role in the pathogenesis and progression of PD.²⁹ In addition, another study, in unilateral nephrectomized C57/BL/6 mice, has shown that p-CS can reduce neuronal markers like MAP-2 and stem cell markers, while increasing caspase 3 activity. It also lowered BDNF and serotonin levels, increased corticosterone levels, and disrupted key signaling pathways, which may promote neuroinflammation and accelerate neurodegenerative processes.⁶⁹

The exact mechanisms by which p-CS induces neurological changes are not fully understood, but several pathways have been identified. Uremic toxins appear to compromise the integrity of the BBB, allowing neurotoxic molecules to enter the brain, thereby worsening CNS complications.^72,73 They also stimulate microglial activation, increasing the production of inflammatory mediators like IL-1β, TNF-α, and IL-6. For instance, a study in C57BL/6 mice found that uremic toxins in CKD activate microglia, disrupting neuronal potassium and calcium balance, which impairs cognition. CKD also alters the blood-brain barrier, increasing vascular permeability. Uremic conditions cause microglial potassium efflux via the KCa3.1 channel and IL-1β maturation through p38-MAPK signaling. Restoring potassium balance or inhibiting IL-1β signaling improves cognitive function in CKD. The study suggests that targeting microglial activation and IL-1β signaling may offer therapeutic strategies to prevent or reverse CKD-related cognitive impairment.⁷⁴

In rodent models, specifically the 5/6 nephrectomy CKD model, protein-bound uremic toxins (like p-CS) accumulation in brain tissues was linked to depression-like and anxiety-like behaviors, cognitive deficits, and neuroinflammation. Behavioral tests revealed anxiety and depression-like symptoms, and they found spatial learning and memory impairments among rodents. These findings mirror clinical observations of depression, anxiety, and cognitive decline in CKD patients.^75–77 Moreover, the accumulation of p-CS was alleviated by administering AST-120, a uremic toxin absorbent, which improved behavioral outcomes and reduced neuroinflammation, further supporting the causative role of p-CS in CNS complications.⁶⁹

The strength of this study lied in its novel exploration of TNFR2's role in mediating the effects of TNF-α and p-CS on cognitive function in individuals with MCI. It identifies a feedback loop where p-CS increases TNFR2, amplifying TNF-α signaling and enhancing cognitive performance. Targeting TNFR2 offers neuroprotective insights, particularly for individuals with CKD, inflammation-related diseases, AD, or MCI, where elevated biomarkers due to inflammation and immune response could benefit from such strategies.

Notwithstanding these strengths, there were several limitations. One limitation of our study is its cross-sectional design, which restricts the ability to infer causality; future research should employ longitudinal and mechanistic approaches to better elucidate temporal dynamics and underlying biological processes. Additionally, the study focused on a specific population (individuals with neurodegenerative conditions), and further research is needed to assess whether the findings can be generalized to other conditions. Besides, the long-term effects of targeting TNFR2 for cognitive improvement were not explored, and the precise mechanisms underlying the observed effects require further investigation. Another limitation of this study was the heterogeneity and variability in sample size, which may have contributed to the large variance within groups and reduced the statistical power of our findings. Differences in demographic and clinical characteristics among participants could have further influenced the results. Future studies should aim for larger, more homogeneous cohorts to minimize variability and improve the reliability of conclusions regarding the role of p-CS in neurocognitive disorders. Importantly, it is acknowledged that the use of the three-way method for mediation analysis is a limitation of our study, as it may not fully account for time-varying confounders or causal relationships. Future studies should consider employing advanced causal inference techniques such as marginal structural models to more robustly examine mediation pathways and better capture the dynamic interactions between inflammatory markers and cognitive outcomes. The study also did not account for potential confounding factors, such as other medications, genetic factors related to cognitive decline, or family history, which could influence the results. Future research could include in silico studies to model these interactions more comprehensively, and replication of the study is needed to validate the findings and ensure reproducibility, which will be critical to reaching a convergent conclusion.

Conclusion

This study was the first to explore the association between p-CS, TNF-α, and TNFR2 with cognitive performance in individuals with CN, MCI, and AD dementia. The findings revealed that TNF-α levels were slightly higher in AD than MCI, and TNFR2 levels were lowest in MCI, higher in CN, and highest in AD dementia. TNF-α levels were positively correlated with TNFR2 levels. In the MCI group, higher TNFR2 levels were linked to better cognitive function, indicating a potential neuroprotective role of TNFR2 at this stage. Further analysis showed that in MCI, both higher p-CS and TNF-α were associated with higher TNFR2 levels, which in turn correlated with better cognitive performance. This suggested a feedback loop in which p-CS may activate TNFR2, leading to an increase in TNF-α, which further amplifies TNFR2 expression, ultimately supporting cognitive function. Notably, elevated TNFR2 levels were found to mediate the relationship between TNF-α, p-CS, and cognitive function, emphasizing its potential as a a subject for further investigation in future studies. These findings highlighted the crucial role of the p-CS-TNFR2-TNF-α axis in early cognitive decline, suggesting that targeting TNFR2 may offer neuroprotective benefits. However, further in vitro, in vivo, and in silico studies are needed to fully understand the underlying mechanisms and confirm these findings.

Footnotes

Acknowledgements

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

ORCID iD

Ali Azargoonjahromi

Ethical considerations

This study was conducted using ADNI data. The ADNI study is ethically approved and operated in accordance with the Declaration of Helsinki, 1964.

Consent to participate

All participants provided written informed consent to participate in the ADNI study in accordance with the Declaration of Helsinki, and the protocol was approved by institutional review boards for data collection and research use.

Consent for publication

Not applicable.

Author contributions

Ali Azargoonjahromi: Conceptualization, Supervision, Writing – original draft, Writing – review & editing.

Hamide Nasiri: Conceptualization, Data curation, Formal analysis, Investigation, Methodology.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data used in this research was obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) and is available with permission to all researchers.

References

Hoozemans

JJM

Rozemuller

AJM

van Haastert

, et al. Neuroinflammation in Alzheimer's disease wanes with age. J Neuroinflammation 2011; 8: 171.

Onyango

Jauregui

Čarná

, et al. Neuroinflammation in Alzheimer’s disease. Biomedicines 2021; 9: 524.

Cai

Zhang

Chen

, et al. The past, present, and future of research on neuroinflammation-induced mild cognitive impairment: a bibliometric analysis. Front Aging Neurosci 2022; 14: 968444.

Ismail

Parbo

Madsen

, et al. The relationships between neuroinflammation, beta-amyloid and tau deposition in Alzheimer’s disease: a longitudinal PET study. J Neuroinflammation 2020; 17: 151.

Kouli

Spindler

Fryer

, et al. Neuroinflammation is linked to dementia risk in Parkinson’s disease. Brain 2024; 147: 923–935.

Sakharov

Golygina

. Promising markers of neuroinflammation in schizophrenia. Neurochem J 2024; 18: 1–8.

Zimmermann

Mathew

Bondareva

, et al. Chronic kidney disease leads to microglial potassium efflux and inflammasome activation in the brain. Kidney Int 2024; 106: 1101–1116.

Fang

Lau

Sun

, et al. Chronic kidney disease promotes cerebral microhemorrhage formation. J Neuroinflammation 2023; 20: 51.

Balestri

Sharma

da Silva

, et al. Modeling the neuroimmune system in Alzheimer’s and Parkinson’s diseases. J Neuroinflammation 2024; 21: 32.

10.

Porcher

Bruckmeier

Burbano

, 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: 219.

11.

Kumari

Dhapola

Sharma

, et al. The impact of cytokines in neuroinflammation-mediated stroke. Cytokine Growth Factor Rev 2024; 78: 105–119.

12.

Magaki

Mueller

Dickson

, et al. Increased production of inflammatory cytokines in mild cognitive impairment. Exp Gerontol 2007; 42: 233–240.

13.

Mazziotti

Crescenzo

Turano

, et al.

The contribution of tumor necrosis factor to multiple sclerosis: a possible role in progression independent of relapse?

J Neuroinflammation 2024; 21: 209.

14.

Wajant

Siegmund

. TNFR1 And TNFR2 in the control of the life and death balance of macrophages. Front Cell Dev Biol 2019; 7: 91.

15.

Medler

Wajant

. Tumor necrosis factor receptor-2 (TNFR2): an overview of an emerging drug target. Expert Opin Ther Targets 2019; 23: 295–307.

16.

Gough

Myles

. Tumor necrosis factor receptors: pleiotropic signaling complexes and their differential effects. Front Immunol 2020; 11: 585880.

17.

Clarke

. Anti-inflammatory TNF receptor 2 signalling unravelled. Nat Rev Rheumatol 2021; 17: 509–509.

18.

Marchetti

Klein

Schlett

, et al. Tumor necrosis factor (TNF)-mediated neuroprotection against glutamate-induced excitotoxicity is enhanced by N-methyl-D-aspartate receptor activation. Essential role of a TNF receptor 2-mediated phosphatidylinositol 3-kinase-dependent NF-kappa B pathway. J Biol Chem 2004; 279: 32869–32881.

19.

Papazian

Tsoukala

Boutou

, et al. Fundamentally different roles of neuronal TNF receptors in CNS pathology: TNFR1 and IKKβ promote microglial responses and tissue injury in demyelination while TNFR2 protects against excitotoxicity in mice. J Neuroinflammation 2021; 18: 222.

20.

Yang

Wang

Brand

, et al. Role of TNF-TNF receptor 2 signal in regulatory T cells and its therapeutic implications. Front Immunol 2018; 9: 784.

21.

Dong

Fischer

Naudé

, et al. Essential protective role of tumor necrosis factor receptor 2 in neurodegeneration. Proc Natl Acad Sci U S A 2016; 113: 12304–12309.

22.

Zhao

Cribbs

Anderson

, et al. The induction of the TNFα death domain signaling pathway in Alzheimer's disease brain. Neurochem Res 2003; 28: 307–318.

23.

Cheng

Yang

, et al. Differential activation of tumor necrosis factor receptors distinguishes between brains from Alzheimer's disease and non-demented patients. J Alzheimers Dis 2010; 19: 621–630.

24.

Jiang

Xie

, et al. Genetic deletion of TNFRII gene enhances the Alzheimer-like pathology in an APP transgenic mouse model via reduction of phosphorylated IκBα. Hum Mol Genet 2014; 23: 4906–4918.

25.

Ortí-Casañ

Wajant

Kuiperij

, et al. Activation of TNF receptor 2 improves synaptic plasticity and enhances amyloid-β clearance in an Alzheimer’s disease mouse model with humanized TNF receptor 2. J Alzheimers Dis 2023; 94: 977–991.

26.

Sun

C-Y

J-R

Wang

Y-Y

, et al. p-Cresol sulfate caused behavior disorders and neurodegeneration in mice with unilateral nephrectomy involving oxidative stress and neuroinflammation. Int J Mol Sci 2020; 21: 6687.

27.

Gryp

Vanholder

Vaneechoutte

, et al. p-Cresyl sulfate. Toxins (Basel) 2017; 9: 52.

28.

Espi

Koppe

Fouque

, et al. Chronic kidney disease-associated immune dysfunctions: impact of protein-bound uremic retention solutes on immune cells. Toxins (Basel) 2020; 12: 300.

29.

Sankowski

Księżarczyk

Raćkowska

, et al. Higher cerebrospinal fluid to plasma ratio of p-cresol sulfate and indoxyl sulfate in patients with Parkinson’s disease. Clin Chim Acta 2020; 501: 165–173.

30.

Nogueira

Freitas

Duro

, et al. Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): Normative data for the Portuguese population. Acta Med Port 2018; 31: 94–100.

31.

Mohs

. The Alzheimer's disease assessment scale. Int Psychogeriatr 1996; 8: 195–203.

32.

Hughes

Berg

Danziger

, et al. A new clinical scale for the staging of dementia. Br J Psychiatry 1982; 140: 566–572.

33.

Tzeng

R-C

Yang

Y-W

Hsu

K-C

, et al. Sum of boxes of the clinical dementia rating scale highly predicts conversion or reversion in predementia stages. Front Aging Neurosci 2022; 14: 1021792.

34.

Khan

. Chapter 2 - clinical diagnosis of Alzheimer’s disease. In: Khan

(eds) Biomarkers in Alzheimer's disease. London: Academic Press, 2016, pp.27–48.

35.

Ortí-Casañ

Zuhorn

Naudé

, et al. A TNF receptor 2 agonist ameliorates neuropathology and improves cognition in an Alzheimer’s disease mouse model. Proc Natl Acad Sci U S A 2022; 119: e2201137119.

36.

Ortí-Casañ

Wajant

Kuiperij

, et al. Activation of TNF receptor 2 rescues cognitive impairments and improves neuropathology in an Alzheimer’s disease mouse model with humanized TNF receptor 2. J Alzheimers Dis 2023; 94: 977–991.

37.

Stellwagen

Malenka

. Synaptic scaling mediated by glial TNF-α. Nature 2006; 440: 1054–1059.

38.

Plantone

Pardini

Righi

, et al. The role of TNF-α in Alzheimer’s disease: a narrative review. Cells 2023; 13: 54.

39.

Weiss

Grell

Hessabi

, et al. Enhancement of TNF receptor p60-mediated cytotoxicity by TNF receptor p80: requirement of the TNF receptor-associated factor-2 binding site. J Immunol 1997; 158: 2398–2404.

40.

Maier

Fischer

Agresti

, et al. TNF Receptor 2 protects oligodendrocyte progenitor cells against oxidative stress. Biochem Biophys Res Commun 2013; 440: 336–341.

41.

Rao

Hsu

Chao

. Upregulation of NF-κB-dependent gene expression mediated by the p75 tumor necrosis factor receptor. J Interferon Cytokine Res 1995; 15: 171–177.

42.

Medvedev

Sundan

Espevik

. Involvement of the tumor necrosis factor receptor p75 in mediating cytotoxicity and gene regulating activities. Eur J Immunol 1994; 24: 2842–2849.

43.

Dong

Fischer

Naudé

, et al. Essential protective role of tumor necrosis factor receptor 2 in neurodegeneration. Proc Natl Acad Sci U S A 2016; 113: 12304–12309.

44.

Ortí-Casañ

Wajant

Kuiperij

, et al. Activation of TNF receptor 2 improves synaptic plasticity and enhances amyloid-β clearance in an Alzheimer's disease mouse model with humanized TNF receptor 2. J Alzheimers Dis 2023; 94: 977–991.

45.

Ortí-Casañ

Zuhorn

Naudé

PJW

, et al. A TNF receptor 2 agonist ameliorates neuropathology and improves cognition in an Alzheimer’s disease mouse model. Proc Natl Acad Sci U S A 2022; 119: e2201137119.

46.

de Lange

. The effect of targeting TNFR2 and EPAC2 on humanized transgenic Alzheimer’s disease mouse models . Thesis. University of Groningen, Groningen, Netherlands, 2022.

47.

Azargoonjahromi

. Immunotherapy in Alzheimer’s disease: focusing on the efficacy of gantenerumab on amyloid-β clearance and cognitive decline. J Pharm Pharmacol 2024; 76: 1115–1131.

48.

Subedi

Lee

Madiha

, et al. Phytochemicals against TNF α-mediated neuroinflammatory diseases. Int J Mol Sci 2020; 21: 764.

49.

Medler

Kucka

Wajant

. Tumor necrosis factor receptor 2 (TNFR2): an emerging target in cancer therapy. Cancers (Basel) 2022; 14: 2603.

50.

Fischer

Maier

Siegemund

, et al. A TNF receptor 2 selective agonist rescues human neurons from oxidative stress-induced cell death. PLoS One 2011; 6: e27621.

51.

Dolga

Nijholt

Ostroveanu

, et al. Lovastatin induces neuroprotection through tumor necrosis factor receptor 2 signaling pathways. J Alzheimers Dis 2008; 13: 111–122.

52.

Pan

Yao

Wang

, et al. The role of PI3 K signaling pathway in Alzheimer’s disease. Front Aging Neurosci 2024; 16: 1459025.

53.

Ortí-Casañ

Naudé

PJW

, et al. Targeting TNFR2 as a novel therapeutic strategy for Alzheimer’s disease. Front Neurosci 2019; 13: 49.

54.

Desu

Thougaard

Carney

, et al. TNFR2 Signaling in oligodendrocyte precursor cells suppresses their immune-inflammatory function and detrimental microglia activation in CNS demyelinating disease. Brain Behav Immun 2025; 123: 81–98.

55.

Desu

Illiano

Choi

, et al. TNFR2 Signaling regulates the immunomodulatory function of oligodendrocyte precursor cells. Cells 2021; 10: 1785.

56.

Raffaele

Thougaard

Laursen

CCH

, et al. Microglial TNFR2 signaling regulates the inflammatory response after CNS injury in a sex-specific fashion. Brain Behav Immun 2024; 116: 269–285.

57.

Veroni

Gabriele

Canini

, et al. Activation of TNF receptor 2 in microglia promotes induction of anti-inflammatory pathways. Mol Cell Neurosci 2010; 45: 234–244.

58.

Adamu

Gao

, et al. The role of neuroinflammation in neurodegenerative diseases: current understanding and future therapeutic targets. Front Aging Neurosci 2024; 16: 1347987.

59.

Dong

Fischer

Naudé

PJW

, et al. Essential protective role of tumor necrosis factor receptor 2 in neurodegeneration. Proc Natl Acad Sci U S A 2016; 113: 12304–12309.

60.

Marchetti

Klein

Schlett

, et al. Tumor necrosis factor (TNF)-mediated neuroprotection against glutamate-induced excitotoxicity is enhanced by N-methyl-D-aspartate receptor activation: essential role of a TNF receptor 2-mediated phosphatidylinositol 3-kinase-dependent NF-κB pathway. J Biol Chem 2004; 279: 32869–32881.

61.

Dolga

Granic

Blank

, et al. TNF-Α-mediates neuroprotection against glutamate-induced excitotoxicity via NF-κB-dependent up-regulation of KCa2. 2 channels. J Neurochem 2008; 107: 1158–1167.

62.

Kuno

Yoshida

Nitta

, et al. The role of TNF-alpha and its receptors in the production of NGF and GDNF by astrocytes. Brain Res 2006; 1116: 12–18.

63.

Camara

Corrigan

Jaehne

, et al. TNF-α and its receptors modulate complex behaviours and neurotrophins in transgenic mice. Psychoneuroendocrinology 2013; 38: 3102–3114.

64.

Leal

Bramham

Duarte

. BDNF And hippocampal synaptic plasticity. Vitam Horm 2017; 104: 153–195.

65.

Nagappan

. BDNF And synaptic plasticity, cognitive function, and dysfunction. Handb Exp Pharmacol 2014; 220: 223–250.

66.

Azargoonjahromi

Abutalebian

Hoseinpour

. The role of resveratrol in neurogenesis: a systematic review. Nutr Rev 2025; 83: e257–e272.

67.

Chan

Watkins

Cosgaya

, et al. NGF Controls axonal receptivity to myelination by Schwann cells or oligodendrocytes. Neuron 2004; 43: 183–191.

68.

Eftimiadi

Soligo

Manni

, et al. Topical delivery of nerve growth factor for treatment of ocular and brain disorders. Neural Regen Res 2021; 16: 1740–1750.

69.

Sun

Wang

, et al. p-Cresol sulfate caused behavior disorders and neurodegeneration in mice with unilateral nephrectomy involving oxidative stress and neuroinflammation. Int J Mol Sci 2020; 21: 6687.

70.

Sun

Hsu

. p-Cresol sulfate and indoxyl sulfate induce similar cellular inflammatory gene expressions in cultured proximal renal tubular cells. Nephrol Dial Transplant 2013; 28: 70–78.

71.

Poveda

Sanchez-Niño

Glorieux

, et al. p-Cresyl sulphate has pro-inflammatory and cytotoxic actions on human proximal tubular epithelial cells. Nephrol Dial Transplant 2014; 29: 56–64.

72.

Faucher

van der Made

De Lange

, et al.

Blood-brain barrier perturbations by uremic toxins: key contributors in chronic kidney disease-induced neurological disorders?

Eur J Pharm Sci 2023; 187: 106462.

73.

Meijers

Zadora

Lowenstein

. A historical perspective on uremia and uremic toxins. Toxins (Basel) 2024; 16: 227.

74.

Zimmermann

Mathew

Bondareva

, et al. Chronic kidney disease leads to microglial potassium efflux and inflammasome activation in the brain. Kidney Int 2024; 106: 1101–1116.

75.

Ali

Ziada

Al Husseni

, et al. Motor and behavioral changes in rats with adenine-induced chronic renal failure: influence of acacia gum treatment. Exp Biol Med (Maywood) 2011; 236: 107–112.

76.

Jing

Jabbari

Vaziri

. Uremia induces upregulation of cerebral tissue oxidative/inflammatory cascade, down-regulation of Nrf2 pathway and disruption of blood brain barrier. Am J Transl Res 2018; 10: 2137–2147.

77.

Bobot

Thomas

Moyon

, et al. Uremic toxic blood-brain barrier disruption mediated by AhR activation leads to cognitive impairment during experimental renal dysfunction. J Am Soc Nephrol 2020; 31: 1509–1521.