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Analysis and interpretation of inflammatory fluid markers in Alzheimer’s disease: a roadmap for standardization

Journal of Neuroinflammation volume 22, Article number: 105 (2025) Cite this article

Abstract

Growing interest in the role of the immune response in Alzheimer’s Disease and related dementias (ADRD) has led to widespread use of fluid inflammatory markers in research studies. To standardize the use and interpretation of inflammatory markers in AD research, we build upon prior guidelines to develop consensus statements and recommendations to advance application and interpretation of these markers. In this roadmap paper, we propose a glossary of terms related to the immune response in the context of biomarker discovery/validation, discuss current conceptualizations of inflammatory markers in research, and recommend best practices to address key knowledge gaps. We also provide consensus principles to summarize primary conceptual, methodological, and interpretative issues facing the field: (1) a single inflammatory marker is likely insufficient to describe an entire biological cascade, and multiple markers with similar or distinct functions should be simultaneously measured in a panel; (2) association studies in humans are insufficient to infer causal relationships or mechanisms; (3) neuroinflammation displays time-dependent and disease context-dependent patterns; (4) neuroinflammatory mechanisms should not be inferred based solely on blood inflammatory marker changes; and (5) standardized reporting of CSF inflammatory marker assay validation and performance will improve incorporation of inflammatory markers into the biological AD criteria.

Introduction

Over the past decade, tremendous progress has been made in codifying the pathological changes and temporal sequencing of amyloid and tau deposition in Alzheimer’s disease (AD), most recently resulting in the AT(N) research framework [1]. Reactive and degenerative glial changes have long been noted to accompany core neurodegenerative pathologies, and genome-wide association studies (GWAS) have associated multiple immune-related genes with AD. An immune response – which may reflect activation, dysregulation, or other processes – is now also considered a core component of AD risk/pathogenesis and has been incorporated into the AT(N) framework, denoted with an (I) for “inflammatory/immune mechanisms” [1]. This immune focus additionally touches on “brain maintenance” as operationally defined by NIH, with a large corpus of data demonstrating early alterations in immune cell function and inflammatory processes that change over the course of the disease. These findings present an opportunity to identify druggable targets in AD based on the expanding repertoire of FDA-approved immune-modulating therapies.

Growing interest in the role of the immune response in AD and related disorders (ADRD) has led to the prolific use of fluid inflammatory markers in research studies. Compared to the frequent usage of these markers as evidence for inflammation, discussion on correlation with pathologic (or at least independent) measures of neuroinflammation or measurement harmonization has been more limited. Biologically, the assumption that each soluble inflammatory marker only reflects one cell type, or one immune event is overly simplistic (see Fig. 1). This is compounded by increasing recognition that proteins with largely inflammatory roles in the blood can be secreted by neurons when found in the central nervous system, and neurons can further physiologically express receptors for these “inflammatory” mediators for presumably non-inflammatory effector functions. Moreover, endeavors to develop potential marker cut points or clear immune response staging have been largely unfruitful. In order to standardize the use and interpretation of inflammatory markers in AD research, we build on prior guidelines [2,3,4] to develop consensus statements and recommendations in this Roadmap paper to advance these markers’ application and interpretation. This is not intended to be a comprehensive review of the inflammatory marker literature or a summary of pre-analytical factors that impact marker measurement, both of which have been reviewed elsewhere [5, 6]. Instead, we clarify specific challenges that have impacted the field’s progression, particularly within the context of CSF inflammatory markers, and provide recommendations for developing standardized inflammatory marker approaches.

A challenge across all scientific fields is the use of common, agreed upon nomenclature that denotes specific biological processes. The study of inflammatory markers is no exception, as each group has their own conventions and nomenclature. Considering that terminology used is varied and often not aligned with each other, we first propose a glossary of suggested terms (in English, see Table 1) that reflects our broader recommendations without being prescriptive. There are two common themes in our nomenclature recommendations. First, we follow principles laid out by prior consensus papers that terms intended to distinguish abnormal from normal immune functions (e.g., degenerative, dysregulation) in neurodegenerative disorders should be reserved for when immune cells themselves are diseased. Second, a “biomarker” should reflect an underlying biological process – normal or abnormal. While there is a trend, including in many of our own prior published works, to refer to “biomarkers” as proxies for an umbrella process, we recommend that indicators should not be labeled as “biomarkers” until they have sufficient biological validation. The purpose of this table is not to recommend elimination of a specific term, but to highlight that less specific terms are favored in human research when we cannot specifically identify or measure a marker’s context-dependent function. While terms listed in the “suggested consensus terms” column of Table 1 align with prior nomenclature consensus papers [2, 3], we acknowledge that more specific terms may be or become appropriate in some contexts when there is additional research support. We use the suggested terms throughout the manuscript as a means of highlighting the importance of the two common nomenclature themes described above.

Fig. 1
figure 1

Diversity of inflammatory markers and their cellular sources across central nervous system (CNS) compartments, including subarachnoid cerebrospinal fluid (CSF), ventricular CSF, interstitial fluid (ISF), and paravascular CSF. The schematic illustrates contributions of CNS-resident cells (neurons, astrocytes, oligodendrocytes, microglia) and peripheral immune cells (T cells, B cells, monocytes, natural killer cells, neutrophils, and red blood cells) to inflammatory responses, with currently known associated cytokine and chemokine ligands listed. This representation underscores the complexity of immune interactions in the CNS. Created in https://BioRender.com

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Table 1 Glossary of frequently used terms and suggested consensus terms related to the immune response
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In addition, given on-going discussions of incorporating “inflammatory/immune mechanisms” into the AT(N) framework, it is also worth considering how the AT(N) framework may or may not translate to inflammatory markers. Table 2 provides an overview of biomarker applications, while highlighting conceptual and practical differences in how AT(N) [1, 7] and inflammatory markers [7] are assessed and interpreted. We will follow this brief overview with in-depth discussions related to current understanding of inflammatory markers and recommend best practices to address key knowledge gaps.

Table 2 Contexts of use for inflammatory markers in AD
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Use of a single biomarker to describe a generalized neuroinflammatory state

Consensus Statement: A single inflammatory marker (e.g., IL-6) is likely insufficient to describe an entire biological cascade, and multiple markers with similar or distinct functions should be simultaneously measured in a panel.

Recommendation: Studies should assess multiple CSF inflammatory markers to capture the complexity of neuroinflammatory processes (see also Sect. 6).

Proteins and other markers can have variable temporal profiles through aging and disease. It is critically important to differentiate candidate fluid biomarkers that capture a dynamic, self-regulating network from those that appraise discrete and broadly unidirectional (i.e., monophasic) indicators of pathological burden (e.g., the AT(N) framework) [8]. While post-mortem examinations support the gradual accumulation of inflammatory cells alongside amyloid and tau neuropathology, ante-mortem studies have failed to demonstrate marked concentration differences in soluble inflammatory markers between those with and without AD. This alone is not unusual nor unexpected, given the homeostatic nature of immune cells and their effectors. However, it is unlikely that an inflammatory protein or panel would supplant soluble amyloid and tau levels as measures of core AD pathologies [9, 10].

Beyond diagnostic prediction, there have been numerous studies in the AD field using single inflammatory markers to shed light on the role of inflammation in disease onset and clinical progression. In a somewhat circuitous manner, these approaches have been validated by their inclusion in meta-analyses of individual markers to support the role of the immune responses in AD pathological cascades [11]. Ascertainment of single inflammatory markers has the advantage of being more scalable and having more pragmatic clinical utility than large-scale inflammatory panels. However, a single marker, especially one chosen based on biological theories and assay availability, is insufficient for characterization of immune function or dysfunction. For example, cerebrospinal fluid levels of interleukin-10 (IL-10) are highly correlated with levels of interleukin-6 (IL-6), even though these two proteins have been described as having opposing functions [12, 13]. If one were to measure IL-10 alone, an elevated level could be interpreted as an “anti-inflammatory” state. Conversely, an elevated IL-6 level could be interpreted as a “pro-inflammatory” state when elevated IL-10 is not simultaneously considered. Taking a step back, there are many other parallels for this concern in neurology and medicine broadly. For example, a brief ‘serial 7’ working memory test should not reflect the entire construct of “cognition.” Similarly, blood C-reactive protein (CRP) level is neither sensitive nor specific for measurement of cognitive decline [14, 15]. Thus, a single measure (e.g., IL-6, GFAP) is unlikely to adequately describe the complex immune biology of a disease state, even if it proves useful in the future for selecting and monitoring specific therapies targeting IL-6.

Another challenge in applying the one marker/one mechanism template to inflammatory marker studies is the use of a single threshold. Whereas CSF p-Tau181 and Ab42 levels undergo monophasic changes in the pre-symptomatic and symptomatic phases of AD, the same cannot be said for inflammatory proteins. For example, aging is a significant mediator for many CSF inflammatory markers, and prior studies have shown nonlinear or U-shaped trajectories for these markers in aging as well as asymptomatic AD [16]. This phenomenon is not unique even in AD, as levels of Ab42 also rise with age before falling with AD onset. This makes a single threshold-crossing impractical, especially when inflammatory markers more dynamically undergo level changes according to disease state (asymptomatic AD vs. AD dementia), disease-modifying therapies (e.g., anti-amyloid monoclonal antibodies), and non-CNS conditions/disorders (e.g., vaccination, infection) than core A/T/N markers. It is also overly reductionist to use one marker’s concentration from a single time point to dichotomize complex immune responses across the entire longitudinal disease spectrum. In other words, interpretation of inflammatory markers will need to be contextualized alongside baseline physiologic, superimposed pathological (i.e., AT(N) stratification; clinical severity), and other background (e.g., sampling time relative to seasonal allergies and vaccinations) indicators.

In place of the single marker/threshold/mechanism approach, assessing network-level change(s) in sufficient numbers as well as types of immune cells and/or inflammatory markers in a fit-for-purpose manner has greater potential of providing the window into a changing immune ecosystem which intersects or interacts with AD [17, 18]. More concrete recommendations from this group related to general vs. specialized inflammatory marker characterization will be detailed later in the current manuscript. In the meantime, while basic and translational researchers continue to refine conceptual models of brain-resident and circulating immune cells as well as their interaction with non-immune cells and extracellular pathologies, we recommend to avoid measuring or interpreting any single inflammatory marker in isolation as an overall index of AD immune biology.

Assigning origins or functions of inflammatory markers

Consensus Statement: Association studies in humans are insufficient to infer causal relationships or mechanisms.

Recommendation: Biomarker constructs and the biological role of individual markers should not be overgeneralized in application, and supporting studies (longitudinal, cellular, animal) are necessary to advance beyond statistical associations.

As the primary defense against an isolated external pathogen (e.g., influenza or vaccination), the immune system’s subacute activity waxes and wanes. In measuring inflammatory markers, often this activity is summarized as juxtaposed pro- and anti-inflammatory events. However, in conditions where there is no overall resolution of microglial or astrocytic activation such as in human AD, whether a particular inflammatory marker/event is pro- or anti-inflammatory can quickly become a matter of debate. For example, in human studies of AD and in the context of broader clinical measures, elevated levels of so-called “pro-inflammatory” markers have been associated with negative prognostic outcomes, including greater brain atrophy [19] and cognitive deficits [20, 21]. Although few CSF inflammatory markers or receptor cognates (such as IL-6, TNF, and IL-1β) [22] are typically included in such studies, levels of these markers do typically rise with age [23] and their association with all-cause dementia risk appears to support their role in AD/ADRD pathogenesis [24]. At the same time, interpreting these types of results as mechanistic support for pro-inflammatory markers in deleterious clinical outcomes has limited foundation, ignores the role of immunity in regulating many necessary neural functions, and invokes biological processes that were not directly measured.

To expand upon this concern, AD researchers often select a few CSF markers to model immune cell activation or recruitment based on experimental data in blood or in vitro, even though inflammatory markers are often measured in these other contexts to corroborate – not deduce – cellular changes. This can quickly lead to erroneous biological interpretation. For example, higher CSF levels of TH1-promoting cytokines (e.g., IFN-γ and IL-12) can be paradoxically linked to slower cognitive decline in older adults [25] despite their direct association with greater age-associated inflammatory responses. Thus, an important caveat when interpreting CSF inflammatory markers in aging studies is that the (a) origins (e.g., release of other inflammatory markers; membrane and soluble receptor binding; relative states of secreting and effector cells) and (b) multiple biological roles of inflammatory markers should be considered.

We will illustrate the importance of these caveats in assessing microglial activity. Microglia in neurodegenerative disorders have numerous roles and can initiate phagocytosis or modify synaptic processes [26, 27]. Prior data have shown exposure to effectors like IL-4/IL-13 can transition microglia in vitro from a homeostatic to a cytotoxic phenotype [28, 29]. Activation of the complement pathway via C3 can also prime microglia toward a cytotoxic state [30]. In a neurodegenerative context, measuring C3 and other complement proteins could then improve the interpretation of changing IL-13 and microglial marker levels. Conversely, in a non-pathologic state, the same handful of markers – elicited by neuronal activities or aging – can have harmful, protective, reactive, or bystander roles on neuronal/synaptic function. Applying the conceptual framework underlying measuring CSF C3 and IL-13 from neurodegenerative to healthy or non-degenerative neurological contexts may therefore conflate in vitro observations with abnormal aging [31,32,33].

Complicating matters more is the historical over-simplification of microglial activities into dichotomous good vs. bad and protective vs. harmful [34]. The parallel between this and the over-simplification of chemokine/cytokine as pro- or anti-inflammatory certainly has not escaped us. Recent data using human stem cell-derived microglia xenotransplantation models suggest that microglia display a heterogeneous range of states, including those described as homeostatic, cytokine response-1 and − 2, interferon response, disease-associated response, and antigen-presenting response [35]. These distinct microglial clusters were noted to have differential responses to both specific pathologies and AD risk genes. Moving forward, consideration of microglial subpopulations/states and genetic drivers of these states will be critical to understanding whether, when, and how microglia might impact AD across the pathological continuum.

Beyond the microenvironment and broader contexts, analytic approaches must also account for inflammatory markers’ pleiotropic functions at different concentrations. In vitro, IL-1β and IL-6 can induce and maintain long term potentiation, neural plasticity, brain homeostasis, plaque clearance via activated microglia, and tissue repair, but these effects diminish at higher concentrations [36,37,38,39,40]. How these in vitro effects – where inflammatory markers are added or measured in isolation – translate into bulk soluble levels of the same markers far removed from the brain remains unclear. As such, classic “pro-inflammatory” mediators should also be interpreted with caution due to their pleiotropic nature at concentrations which can be magnitudes apart in vitro and interstitially.

It is worth noting that parenchymal glia secretome is likely insufficiently and non-uniformly captured by bulk CSF assays. For example, one of us (WH) led a group of investigators to modestly extrapolate CSF IL-9 levels to brain parenchyma immunohistochemistry and gene expressions [41]; however, the CSF marker-neuropathology correlation cannot be assumed even for long measured CSF proteins like YKL-40, as an autopsy-based study failed to show a relationship between CSF and brain YKL-40 [42]. Development of a CSF inflammatory marker panel must thus proceed in parallel with neuropathological studies to better characterize the biological processes they represent in vivo.

In summary, pro-inflammatory markers are not inherently “bad” [43], just like anti-inflammatory markers are not necessarily “good”. Due to the context-dependent functions of these markers (perivascular space vs. interstitial space; with vs. without AD pathology; free vs. bound to soluble decoy receptors) [44, 45], a network-based (or at a minimum, not multiple regression-based) approaches are necessary to advance neuroinflammatory analysis beyond cursory log2-fold comparisons.

Interpretation of neuroinflammatory CSF marker concentrations in aging and AD as dynamic processes

Consensus Statement: Neuroinflammation displays time-dependent and disease context-dependent patterns.

Recommendation: Investigators should specify context of use, clarify timing in relation to disease state/staging and co-pathology, and incorporate independent measures of neuroinflammation when appropriate.

Whereas the previous section distinguished between inflammatory proteins’ theoretical and microenvironment-specific functions, the macro- or systemic environment also impacts interpretation of inflammatory marker levels. We have thus far proposed that inflammatory proteins should be sufficiently correlated with specific biological or pathological phenomena in the brain. One new challenge in interpreting their levels is that coupled or uncoupled changes in CSF inflammatory markers can be seen across a range of neurological disorders with unknown specificity. Significant gaps in our understanding of inflammatory processes in AD/ADRD are beginning to be filled by appropriate animal models and human cellular studies (e.g., FACS, scRNASeq), although it is important to highlight that historically, much of our knowledge regarding inflammatory processes has come from patients who die from discrete insults such as traumatic brain injury and strokes (see Table 3 for non-degenerative disorders with reported inflammatory marker changes, such as isolated seizures [46], epilepsy [47], systemic lupus erythematosus [48], normal pressure hydrocephalus [49, 50], and migraine [51,52,53]. Depending upon the neurological disorder, the immune response (and the interpretation of CSF markers of inflammation) may reflect highly specific biological processes or non-specific responses to injury [54].

As noted in the previous section, in AD, microglia can phagocytose and compact amyloid, with plaque-associated microglia demonstrating different phenotypes – including transcriptional profiles of secreted markers IL-6, TNF, IL-1a, CCL2, and CCL3 – from non-plaque associated microglia [55]. One might thus surmise greater time-dependent changes in these inflammatory proteins early during or before plaque deposition, which are further modified by specific APOE alleles [56] and other AD-related genetic variants [35]. In contrast, studies in Parkinson’s disease have not shown similar transcriptional changes associated with microglial activation states [57, 58]. The number, activation status, location, and surrounding neuropathology of microglia thus all influence measured inflammatory protein levels with different biological implications.

Another good example is Triggering receptor expressed on myeloid cells 2 (TREM2). TREM2 pathogenic variants are risk factors for AD, yet – like TNF – TREM2 exists in a functional transmembrane form and the measured cleaved form (sTREM2). While function of the measured sTREM2 remains unclear [59], its levels appear to change in a non-monotonic manner across AT(N) pathological staging [18, 60], with these nonlinear changes across time evident long before AD symptom onset [61, 62].

As has now been well established, neuroinflammatory processes may also precede AD pathology. So called “inflammaging” [63, 64] is a well-known process in aging and age-related brain diseases that is tied to increased innate immune activation coupled with immunosenescence. For example, activity of the NLR family pyrin domain containing 3 (NLRP3) inflammasome in the periphery has been tied systemically to type 2 diabetes and in the brain to tau accumulation that often precedes AD [65, 66]. Inactivation of NLRP3 in mice reduces age-associated innate immune activity while lowering hyperglycemia and motor deficits [67]. Like many inflammatory processes, inflammaging is associated with increased expression of classically inflammation-related cytokines in mouse brains [68]. In other instances, the senescence-associated secretory phenotype (SASP) can activate and recruit immune cells to drive inflammation via the cardinal proinflammatory cytokines IL-6, IL-1β, and TNF [69, 70].

In addition to considering time-dependent contexts with AD staging, interpretation of CSF inflammatory markers is also affected by the presence of co-pathology. Co-pathology is commonly noted with advancing age [71], even in the absence of frank cognitive impairment [72], with individuals in their 80s and 90s typically showing evidence of multiple pathological processes [73]. Although there has been some evidence to suggest that different neurodegenerative diseases may show distinct CSF inflammatory signatures (e.g., bvFTD vs. AD [74]), disentangling the relative contribution of each pathology to CSF inflammation continues to be a challenge given the lack of in vivo biomarkers for many neuropathological processes. Although outside the scope of this Roadmap paper, many other chronic health factors may also exacerbate inflammatory pathways and result in changes in CSF inflammatory markers.

Collectively, interpretation of CSF inflammatory markers is affected by the timing of disease, the presence of co-pathology, and the presence of co-morbid health conditions. As a result, it can be challenging to link inflammatory markers with specific biological or pathological phenomena if the broader context is not fully considered. It is important to contextualize CSF inflammatory marker results in relation to these factors and understand that these markers may be influenced by different physiological and pathological variables.

Table 3 Examples of non-neurodegenerative neurological disorders with localized inflammatory response
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Relationship between blood-based and CSF inflammatory markers

Consensus Statement: Neuroinflammatory Mechanisms Should Not Be Inferred Based Solely on Blood Inflammatory Marker Changes.

Recommendation: Blood inflammatory markers provide important insights into peripheral immune system and are associated with AD-related pathological and clinical outcomes; however, in isolation, they should not be interpreted as proxies forneuroinflammation.

The contribution of neuroinflammation, as measured by CNS inflammatory markers, to neurodegenerative diseases is complex and dependent on multiple factors. Additional challenges to the interpretation of fluid markers have been introduced with blood inflammatory markers, as neuroinflammatory mechanisms have frequently been attributed to results from blood-based assays.

As context, several important key studies may be conducted with blood inflammatory markers in the context of AD, including (but not limited to) studies examining contributions of non-CNS systems to disease pathogenesis and progression, studies aimed at understanding crosstalk between peripheral and CNS compartments, and studies designed to identify therapeutic targets in the periphery. While there is clear rationale and purpose for evaluating blood-based inflammatory markers in the context of AD, it is important to avoid misinterpreting these markers as definitive indicators of neuroinflammatory mechanisms without clear evidence of CNS immune involvement. Such misinterpretation overlooks the contribution of other health conditions and peripheral processes. For example, many blood-based cytokines and interleukins reflect general or non-specific inflammation processes [75] and are produced by different tissues/organs in the periphery. Isolating the source of a blood-based inflammatory marker is thus difficult in human studies.

Moreover, although blood inflammatory markers are associated with aging and AD-related outcomes, their associations with central (e.g., CSF) inflammatory markers using paired blood-CSF samples are often poor (although higher correlations are seen with advancing age) [76]. This does not negate their potential role in AD, but it does suggest that blood inflammatory markers are insufficient proxies for neuroinflammation when interpreted in isolation.

A challenge to this assertion, however, has come with blood GFAP. Blood GFAP is a commonly used proxy marker of reactive astrogliosis in the AD field and was recently codified in the AD diagnostic criteria as the primary indicator for “I” (inflammation/immune mechanisms). Although increases in GFAP are seen in a range of conditions, including head injury and other neurological diseases, blood GFAP shows greater increases in AD compared to non-AD neurodegenerative syndromes [77]. GFAP is also less affected by age than neurofilament light chain protein (NfL), and is linked with longitudinal cognitive decline in asymptomatic and symptomatic AD [78]. The association between GFAP and AD pathology is also stronger for blood than CSF, which is unusual for a putative CNS marker and raises questions regarding the underlying biology and mechanisms by which this marker is released into the blood stream. Although several biological explanations have been offered, it has also been suggested that differences in GFAP stability between blood and CSF could be one of the factors contributing to this discrepancy [79]. Salient to our prior comments regarding the need for CNS immune validation, recent data do suggest a correlation between blood GFAP and astrocyte reactivity in post-mortem tissue [80]. In parallel, astrocytes in some brain regions, such as the hippocampus, have demonstrated higher GFAP content than other regions [81]. Dissecting the potential mechanisms by which GFAP might enter the blood stream at higher concentrations than in CSF are outside the scope of the paper but do warrant further consideration regarding interpretation.

First, it is possible that changes in blood GFAP reflect processes other than reactive astrogliosis (including other astrocyte-related processes). Peripheral contributions to circulating blood GFAP levels have not been adequately considered when interpreting this marker. In addition, external factors, such as glucocorticoid drugs and physical activity, can also elevate GFAP levels [82, 83]. Whenever a blood inflammatory protein increases, researchers should always raise the possibility of concomitant reasons for this - such as tissue damage, inflammation, and external stimuli altogether. Thus, while (a) increases in blood GFAP are likely to be maladaptive and (b) decreases in GFAP in response to an intervention might be beneficial, more research is needed to establish the extent to which these changes primarily reflect reactive astrogliosis, the mechanisms of crosstalk [84], and whether changes in blood GFAP are predictive of disease progression or remission. Collectively, several of the authors on this consensus paper have published research on the role of GFAP as a promising marker for AD-related outcomes; nonetheless, we feel that it is premature to consider GFAP a definitive indicator of neuroinflammation.

Another blood marker that is typically associated with neuroinflammation is soluble Triggering receptor expressed on myeloid cells 2 (sTREM2). Blood levels of sTREM2 have changed over many physiological processes. However, CSF and blood levels of sTREM2 in AD individuals have demonstrated contradictory findings [85, 86]. sTREM2 is also released by peripheral cells like leukocytes, so its use as an index of microglia activation should be cautiously interpreted. A recent preclinical study has suggested that TREM2-activating antibodies may enhance microglial function, potentially triggering a microglia-responsive state [87]. It is noteworthy that this study used both in vivo and in vitro models to measure cytokines and different microglial states to track the pathological process of interest.

Like concerns noted in section two, it is unlikely that a single blood marker will reflect an entire biological system. Taking a broader lens, we recommend caution in conflating blood inflammatory markers with CNS inflammatory markers or neuroinflammation, even in situations where blood inflammatory markers robustly predict AD pathology or clinical outcomes. For an in-depth prospectus of how to advance our understanding of the central-peripheral immune and inflammation crosstalk in AD and ADRDs the reader is referred to a prior roadmap paper [84].

Challenges in harmonizing inflammatory markers in AD/ADRD

Consensus Statement: Standard reporting of CSF inflammatory marker assay validation and performance will improve incorporation of inflammatory markers into AT(N) scheme.

Recommendation: A minimum set of CSF inflammatory markers should be measured by established laboratories for accuracy and precision using assays from manufacturers committed to transparency and continuous improvement. Preanalytical factors and assay performance should be routinely assessed and reported.

A necessary aspect of candidate biomarker discovery is its eventual application in pre-clinical and clinical settings, which often represents more of an engineering than a biological problem. For example, CSF AD biomarkers over the past 30 years were only empirically testable when there was the complete or near-complete automation of their measurements. While there are few established CSF AD biomarkers, there are hundreds for plasma or CSF inflammatory proteins. Because of these inflammatory proteins’ abundance in blood circulation, most have been extensively interrogated in non-neurological disorders. In isolation or in combination, these markers may reveal latent disease mechanisms and translate themselves into clinical diagnostics. Unfortunately, there are limited attempts at replication or head-to-head comparisons in either compartment (blood; CSF). The goal should be to encourage data democratization and open science by independently replicating markers of interest. There is a need for standardization and quality control of pre-analytical and other measurement factors, and indeed there have been considerable efforts to develop certified reference methods and materials, along with published recommendations regarding known pre-analytical factors that impact AT(N) biomarkers [5, 6]; however, it is unlikely that we will be able to ‘copy-paste’ and apply the standard AT(N) biomarker approaches for quality control to inflammatory markers for the reasons previously discussed. Reasons for this include the many folds greater and non-redundant effector proteins, greater number of assay and synthetic standard manufacturers, and lagging efforts to determine effects from pre-analytical variables.

What would this initiative entail? Reconciling concerns raised above across neurological disciplines will require large-scale efforts involving researchers, vendors, and funders in AD/ADRD, neuroinflammation, neuroinfectious diseases, and immunology. We first propose a pre-regulatory expert panel to objectively evaluate assay rigor, specificity, reproducibility, and validity for individual inflammatory markers and, more likely, targeted proteomic panels. Because of expected proprietary technology involved, this must be accompanied by a public-private partnership with sufficient flexibility between trade secrecy and data transparency. A prescriptive model outlining sample size, concentration range, and technical caveats will also likely be outpaced by emerging experimental design and assay techniques. Thus, a set of guiding principles on minimum and recommended thresholds for validation may be more appropriate to couple technical novelty with scientific rigor. For example, accuracy/precision thresholds for a new CSF cytokine assay panel could include:

  • Minimum Threshold: manufacturer-reported spike-recovery in the intended biological matrix (in this case, CSF), measured at two distinct time points;

  • Recommended Threshold: blinded assay of 15–30 samples (intended matrix) for correlation with results generated from previously validated assays requiring greater matrix volume, assay time, or operator experience;

  • High Threshold: two experienced laboratories having participated in research activities described in Minimum and Recommended thresholds, or having completed independent testing of assay accuracy and precision.

To be successful, this initiative will need the appropriate incentive structure involving assay manufacturers, investigators, funders, and publishers. With appropriate support from funders in the likeness of ADNI or MODEL-AD, it will require little guesswork to identify a core group of vendors and scientists for whom this initiative is long overdue. Vendors whose assays achieve the Minimum Threshold will be able to distinguish themselves from their peers who have only word-of-mouth reviews, and only assays with Recommended or High Thresholds should be considered in research consortiums.

We further recommend a set of CSF markers (Table 4) to be prioritized for harmonization across large national studies (e.g., ADNI) or regional studies. These include proteins which are commonly postulated to reflect microglial activation, reactive astrocytes, synaptic pruning, neuroprotection, among other biological processes. In keeping with earlier discussions on the dynamic and regulated nature of neuroinflammation, we propose the inclusion of CSF markers which model feedforward and feedback loops (e.g., TNF, sTNFR1, sTNFR2; IL-1b and sIL-1ra), CSF markers sharing cell surface receptors (IL-4, IL-13, and YKL-40; TGFb1, b2, and b3), and CSF markers which form parts of a shared pathway (C3, C5, C4, C1q).

In this list, we distinguish between soluble signaling proteins with presumed biological activity, soluble (cleaved) cell surface proteins whose function may differ or even oppose membrane-anchored versions of the same proteins, and proteins demonstrated to be secreted by inflammatory as well as non-inflammatory cells. While information included in the table is not meant to represent an exhaustive review of published evidence, we do highlight that most of these CSF proteins and their receptors have been found to be expressed by neurons especially in neurological disorders. Thus, concentration changes in these CSF proteins in the setting of neurodegeneration (including most if not all ADRD) or neuronal distress should be interpreted as evidence for neuroinflammation with caution as these markers are positioned at the intersection between inflammation and neuronal signaling.

While we acknowledge the usual logistic challenges in harmonizing these CSF measurements related to time, cost, and reproducibility, the availability of reliable targeted proteomic platforms with large numbers of targets at relatively low cost per sample (compared to traditional immunoassays) is creating ample opportunities for collaboration. Compared to similar experiments conducted across multiple experienced centers before 2010, there is an approximately 100-fold reduction in relative cost – ten times more CSF markers measured at one-tenth the cost. Success in advancing the application of CSF inflammatory markers towards true biomarker status will also hinge on funders supporting these endeavors. A coordinated effort across international researchers, high throughput proteomics service providers, and funders may thus accomplish for neuroinflammatory therapies what core AD biomarkers in ADNI have for anti-amyloid therapies.

Table 4 Recommend set of CSF markers to be prioritized for harmonization across large studies
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Conclusion

Increased use of inflammatory markers in AD research has shed light on several challenges in the field, particularly the standardization of use and interpretation of these markers in studies. In this Roadmap, we have identified core principles and suggested a glossary of terms related to inflammation and the immune response. We have further identified standardization concerns and offered both consensus statements and recommendations for how to address these gaps. We acknowledge that this is an evolving field, and thus proposed terminology, consensus statements, and recommendations will need to be updated based on new empirical work to maintain best practices over time.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

CSF:

Cerebrospinal fluid

CAM:

Central nervous system-Associated Macrophages

Mφ:

Macrophage, CCL and CXCL = families of chemokine ligands

G-CSF:

Granulocyte Colony-Stimulating Factor

GM-CSF:

Granulocyte-Macrophage Colony-Stimulating Factor

IFNγ or IFNα:

Interferon gamma or alpha

IL:

Interleukins

ISF:

Interstitial Fluid

NK:

Natural Killer cells

PGRN:

Progranulin

TNF:

Tumor Necrosis Factor

TNFR1/2:

Tumor Necrosis Factor Receptor 1 or 2

TGFβ:

Transforming Growth Factor beta

References

  1. Jack CR Jr., Andrews JS, Beach TG, et al. Revised criteria for diagnosis and staging of Alzheimer’s disease. Alzheimer’s Association Workgroup. Alzheimers Dement; 2024.

  2. Consortium AB, Jia Y-J, Wang J et al. A framework of biomarkers for brain aging: a consensus statement by the aging biomarker consortium. Life Med 2023;2.

  3. Escartin C, Galea E, Lakatos A, et al. Reactive astrocyte nomenclature, definitions, and future directions. Nat Neurosci. 2021;24:312–25.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Popp J, Oikonomidi A, Tautvydaite D, et al. Markers of neuroinflammation associated with Alzheimer’s disease pathology in older adults. Brain Behav Immun. 2017;62:203–11.

    Article  CAS  PubMed  Google Scholar 

  5. Delaby C, Teunissen CE, Blennow K, et al. Clinical reporting following the quantification of cerebrospinal fluid biomarkers in Alzheimer’s disease: an international overview. Alzheimers Dement. 2022;18:1868–79.

    Article  PubMed  Google Scholar 

  6. Verberk IMW, Misdorp EO, Koelewijn J, et al. Characterization of pre-analytical sample handling effects on a panel of Alzheimer’s disease-related blood-based biomarkers: results from the standardization of Alzheimer’s blood biomarkers (SABB) working group. Alzheimers Dement. 2022;18:1484–97.

    Article  CAS  PubMed  Google Scholar 

  7. Molinuevo JL, Ayton S, Batrla R, et al. Current state of Alzheimer’s fluid biomarkers. Acta Neuropathol. 2018;136:821–53.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Jack CR Jr., Bennett DA, Blennow K, et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14:535–62.

    Article  PubMed  Google Scholar 

  9. Ray S, Britschgi M, Herbert C, et al. Classification and prediction of clinical Alzheimer’s diagnosis based on plasma signaling proteins. Nat Med. 2007;13:1359–62.

    Article  CAS  PubMed  Google Scholar 

  10. Hu WT, Chen-Plotkin A, Arnold SE, et al. Biomarker discovery for Alzheimer’s disease, frontotemporal Lobar degeneration, and Parkinson’s disease. Acta Neuropathol. 2010;120:385–99.

    Article  PubMed Central  PubMed  Google Scholar 

  11. Darweesh SKL, Wolters FJ, Ikram MA, de Wolf F, Bos D, Hofman A. Inflammatory markers and the risk of dementia and Alzheimer’s disease: A meta-analysis. Alzheimers Dement. 2018;14:1450–9.

    Article  PubMed  Google Scholar 

  12. Hu WT, Ozturk T, Kollhoff A, Wharton W, Christina Howell J. Alzheimer’s disease neuroimaging I. Higher CSF sTNFR1-related proteins associate with better prognosis in very early Alzheimer’s disease. Nat Commun. 2021;12:4001.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Wennberg AMV, Hagen CE, Machulda MM, Knopman DS, Petersen RC, Mielke MM. The Cross-sectional and longitudinal associations between IL-6, IL-10, and TNFalpha and cognitive outcomes in the Mayo clinic study of aging. J Gerontol Biol Sci Med Sci. 2019;74:1289–95.

    Article  CAS  Google Scholar 

  14. Katsoulaki M, Kastrinis A, Konstantoudaki X, Tsekoura M. The role of inflammatory biomarkers as a diagnostic tool for possible Late-Life cognitive decline and dementias. Adv Exp Med Biol. 2021;1339:39–46.

    Article  CAS  PubMed  Google Scholar 

  15. Arce Renteria M, Gillett SR, McClure LA, et al. C-reactive protein and risk of cognitive decline: the REGARDS study. PLoS ONE. 2020;15:e0244612.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Yang HS, Zhang C, Carlyle BC, et al. Plasma IL-12/IFN-gamma axis predicts cognitive trajectories in cognitively unimpaired older adults. Alzheimers Dement. 2022;18:645–53. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/alz.12399.

  17. Meyer PF, Savard M, Poirier J, et al. Bi-directional association of cerebrospinal fluid immune markers with stage of Alzheimer’s disease pathogenesis. J Alzheimers Dis. 2018;63:577–90.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Ewers M, Franzmeier N, Suarez-Calvet M et al. Increased soluble TREM2 in cerebrospinal fluid is associated with reduced cognitive and clinical decline in Alzheimer’s disease. Sci Transl Med 2019;11.

  19. Sailer M, Fischl B, Salat D, et al. Focal thinning of the cerebral cortex in multiple sclerosis. Brain. 2003;126:1734–44.

    Article  PubMed  Google Scholar 

  20. McAfoose J, Baune BT. Evidence for a cytokine model of cognitive function. Neurosci Biobehav Rev. 2009;33:355–66.

    Article  CAS  PubMed  Google Scholar 

  21. Flannery BM, Bruun DA, Rowland DJ, et al. Persistent neuroinflammation and cognitive impairment in a rat model of acute diisopropylfluorophosphate intoxication. J Neuroinflammation. 2016;13:267.

    Article  PubMed Central  PubMed  Google Scholar 

  22. Furman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25:1822–32.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  23. Parker D, Sloane R, Pieper CF, et al. Age-Related adverse inflammatory and metabolic changes begin early in adulthood. J Gerontol Biol Sci Med Sci. 2019;74:283–9.

    Article  CAS  Google Scholar 

  24. Shen XN, Niu LD, Wang YJ, et al. Inflammatory markers in Alzheimer’s disease and mild cognitive impairment: a meta-analysis and systematic review of 170 studies. J Neurol Neurosurg Psychiatry. 2019;90:590–8.

    Article  PubMed  Google Scholar 

  25. Yang HS, Zhang C, Carlyle BC, et al. Plasma IL-12/IFN-gamma axis predicts cognitive trajectories in cognitively unimpaired older adults. Alzheimers Dement. 2022;18:645–53.

    Article  CAS  PubMed  Google Scholar 

  26. Van Eldik LJ, Carrillo MC, Cole PE, et al. The roles of inflammation and immune mechanisms in Alzheimer’s disease. Alzheimers Dement (N Y). 2016;2:99–109.

    Article  PubMed  Google Scholar 

  27. Villemagne VL, Burnham S, Bourgeat P, et al. Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurol. 2013;12:357–67.

    Article  CAS  PubMed  Google Scholar 

  28. Cunningham C. Microglia and neurodegeneration: the role of systemic inflammation. Glia. 2013;61:71–90.

    Article  PubMed  Google Scholar 

  29. Orihuela R, McPherson CA, Harry GJ. Microglial M1/M2 polarization and metabolic States. Br J Pharmacol. 2016;173:649–65.

    Article  CAS  PubMed  Google Scholar 

  30. Ramaglia V, Hughes TR, Donev RM, et al. C3-dependent mechanism of microglial priming relevant to multiple sclerosis. Proc Natl Acad Sci U S A. 2012;109:965–70.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Hong S, Beja-Glasser VF, Nfonoyim BM, et al. Complement and microglia mediate early synapse loss in alzheimer mouse models. Science. 2016;352:712–6.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  32. Norden DM, Godbout JP. Review: microglia of the aged brain: primed to be activated and resistant to regulation. Neuropathol Appl Neurobiol. 2013;39:19–34.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Stephan AH, Madison DV, Mateos JM, et al. A dramatic increase of C1q protein in the CNS during normal aging. J Neurosci. 2013;33:13460–74.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  34. Paolicelli RC, Sierra A, Stevens B, et al. Microglia States and nomenclature: A field at its crossroads. Neuron. 2022;110:3458–83.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Mancuso R, Fattorelli N, Martinez-Muriana A, et al. Xenografted human microglia display diverse transcriptomic States in response to Alzheimer’s disease-related amyloid-beta pathology. Nat Neurosci. 2024;27:886–900.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Bellinger FP, Madamba S, Siggins GR. Interleukin 1 beta inhibits synaptic strength and long-term potentiation in the rat CA1 hippocampus. Brain Res. 1993;628:227–34.

    Article  CAS  PubMed  Google Scholar 

  37. Griffin R, Nally R, Nolan Y, McCartney Y, Linden J, Lynch MA. The age-related Attenuation in long-term potentiation is associated with microglial activation. J Neurochem. 2006;99:1263–72.

    Article  CAS  PubMed  Google Scholar 

  38. Moore AH, Wu M, Shaftel SS, Graham KA, O’Banion MK. Sustained expression of interleukin-1beta in mouse hippocampus impairs Spatial memory. Neuroscience. 2009;164:1484–95.

    Article  CAS  PubMed  Google Scholar 

  39. Shaftel SS, Kyrkanides S, Olschowka JA, Miller JN, Johnson RE, O’Banion MK. Sustained hippocampal IL-1 beta overexpression mediates chronic neuroinflammation and ameliorates alzheimer plaque pathology. J Clin Invest. 2007;117:1595–604.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  40. Yirmiya R, Goshen I. Immune modulation of learning, memory, neural plasticity and neurogenesis. Brain Behav Immun. 2011;25:181–213.

    Article  CAS  PubMed  Google Scholar 

  41. Wharton W, Kollhoff AL, Gangishetti U, et al. Interleukin 9 alterations linked to alzheimer disease in African Americans. Ann Neurol. 2019;86:407–18.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  42. Hok AHYS, Hoozemans JJM, Hu WT, et al. YKL-40 changes are not detected in post-mortem brain of patients with Alzheimer’s disease and frontotemporal Lobar degeneration. Alzheimers Res Ther. 2022;14:100.

    Article  Google Scholar 

  43. Weitz TM, Town T. Microglia in Alzheimer’s disease: it’s all about context. Int J Alzheimers Dis. 2012;2012:314185.

    PubMed Central  PubMed  Google Scholar 

  44. Ophir G, Amariglio N, Jacob-Hirsch J, Elkon R, Rechavi G, Michaelson DM. Apolipoprotein E4 enhances brain inflammation by modulation of the NF-kappaB signaling cascade. Neurobiol Dis. 2005;20:709–18.

    Article  CAS  PubMed  Google Scholar 

  45. Town T, Nikolic V, Tan J. The microglial activation continuum: from innate to adaptive responses. J Neuroinflammation. 2005;2:24.

    Article  PubMed Central  PubMed  Google Scholar 

  46. Korczowska-Lacka I, Hurla M, Banaszek N, et al. Selected biomarkers of oxidative stress and energy metabolism disorders in neurological diseases. Mol Neurobiol. 2023;60:4132–49.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  47. Naffah-Mazzacoratti MG, Amado D, Cukiert A, et al. Monoamines and their metabolites in cerebrospinal fluid and Temporal cortex of epileptic patients. Epilepsy Res. 1996;25:133–7.

    Article  CAS  PubMed  Google Scholar 

  48. Joseph FG, Lammie GA, Scolding NJ. CNS lupus: a study of 41 patients. Neurology. 2007;69:644–54.

    Article  PubMed  Google Scholar 

  49. de Oliveira FF, Cardoso TA, Sampaio-Barros PD, Damasceno BP. Normal pressure hydrocephalus in the spectrum of neurological complications of systemic lupus erythematosus. Neurol Sci. 2013;34:1009–13.

    Article  PubMed  Google Scholar 

  50. Klassen BT, Ahlskog JE. Normal pressure hydrocephalus: how often does the diagnosis hold. water? Neurol. 2011;77:1119–25.

    Google Scholar 

  51. Acarsoy C, Ruiter R, Bos D, Ikram MK. No association between blood-based markers of immune system and migraine status: a population-based cohort study. BMC Neurol. 2023;23:445.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  52. Gerring ZF, McRae AF, Montgomery GW, Nyholt DR. Genome-wide DNA methylation profiling in whole blood reveals epigenetic signatures associated with migraine. BMC Genomics. 2018;19:69.

    Article  PubMed Central  PubMed  Google Scholar 

  53. Price BR, Johnson LA, Norris CM. Reactive astrocytes: the nexus of pathological and clinical hallmarks of Alzheimer’s disease. Ageing Res Rev. 2021;68:101335.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  54. Liddelow SA, Guttenplan KA, Clarke LE, et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature. 2017;541:481–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  55. Huang Y, Happonen KE, Burrola PG, et al. Microglia use TAM receptors to detect and engulf amyloid beta plaques. Nat Immunol. 2021;22:586–94.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  56. Yin Z, Rosenzweig N, Kleemann KL, et al. APOE4 impairs the microglial response in Alzheimer’s disease by inducing TGFbeta-mediated checkpoints. Nat Immunol. 2023;24:1839–53.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  57. Adams L, Song MK, Yuen S, Tanaka Y, Kim YS. A single-nuclei paired multiomic analysis of the human midbrain reveals age- and Parkinson’s disease-associated glial changes. Nat Aging. 2024;4:364–78.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  58. Martirosyan A, Ansari R, Pestana F, et al. Unravelling cell type-specific responses to Parkinson’s disease at single cell resolution. Mol Neurodegener. 2024;19:7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  59. Wu K, Byers DE, Jin X, et al. TREM-2 promotes macrophage survival and lung disease after respiratory viral infection. J Exp Med. 2015;212:681–97.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  60. Suarez-Calvet M, Morenas-Rodriguez E, Kleinberger G, et al. Early increase of CSF sTREM2 in Alzheimer’s disease is associated with Tau related-neurodegeneration but not with amyloid-beta pathology. Mol Neurodegener. 2019;14:1.

    Article  PubMed Central  PubMed  Google Scholar 

  61. Li Y, Yen D, Hendrix RD, et al. Timing of biomarker changes in sporadic Alzheimer’s disease in estimated years from symptom onset. Ann Neurol. 2024;95:951–65.

    Article  CAS  PubMed  Google Scholar 

  62. Suarez-Calvet M, Araque Caballero MA, Kleinberger G, et al. Early changes in CSF sTREM2 in dominantly inherited Alzheimer’s disease occur after amyloid deposition and neuronal injury. Sci Transl Med. 2016;8:369ra178.

    Article  PubMed Central  PubMed  Google Scholar 

  63. Franceschi C, Garagnani P, Vitale G, Capri M, Salvioli S. Inflammaging and ‘Garb-aging’. Trends Endocrinol Metab. 2017;28:199–212.

    Article  CAS  PubMed  Google Scholar 

  64. Giunta S. Exploring the complex relations between inflammation and aging (inflamm-aging): anti-inflamm-aging remodelling of inflamm- aging, from robustness to frailty. Inflamm Res. 2008;57:558–63.

    Article  CAS  PubMed  Google Scholar 

  65. Dixit VD. Nlrp3 inflammasome activation in type 2 diabetes: is it clinically relevant? Diabetes. 2013;62:22–4.

    Article  CAS  PubMed  Google Scholar 

  66. Ising C, Venegas C, Zhang S, et al. NLRP3 inflammasome activation drives Tau pathology. Nature. 2019;575:669–73.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  67. Youm YH, Grant RW, McCabe LR, et al. Canonical Nlrp3 inflammasome links systemic low-grade inflammation to functional decline in aging. Cell Metab. 2013;18:519–32.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  68. Sierra A, Gottfried-Blackmore AC, McEwen BS, Bulloch K. Microglia derived from aging mice exhibit an altered inflammatory profile. Glia. 2007;55:412–24.

    Article  PubMed  Google Scholar 

  69. Schafer MJ, Zhang X, Kumar A et al. The senescence-associated secretome as an indicator of age and medical risk. JCI Insight. 2020;5.

  70. Bektas A, Schurman SH, Franceschi C, Ferrucci L. A public health perspective of aging: do hyper-inflammatory syndromes such as COVID-19, SARS, ARDS, cytokine storm syndrome, and post-ICU syndrome accelerate short- and long-term inflammaging? Immun Ageing. 2020;17:23.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  71. Robinson JL, Xie SX, Baer DR, et al. Pathological combinations in neurodegenerative disease are heterogeneous and disease-associated. Brain. 2023;146:2557–69.

    Article  PubMed Central  PubMed  Google Scholar 

  72. Wennberg AM, Whitwell JL, Tosakulwong N, et al. The influence of Tau, amyloid, alpha-synuclein, TDP-43, and vascular pathology in clinically normal elderly individuals. Neurobiol Aging. 2019;77:26–36.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  73. Jack CR Jr., Wiste HJ, Weigand SD, et al. Age-specific and sex-specific prevalence of cerebral beta-amyloidosis, tauopathy, and neurodegeneration in cognitively unimpaired individuals aged 50–95 years: a cross-sectional study. Lancet Neurol. 2017;16:435–44.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  74. Bostrom G, Freyhult E, Virhammar J, et al. Different inflammatory signatures in Alzheimer’s disease and frontotemporal dementia cerebrospinal fluid. J Alzheimers Dis. 2021;81:629–40.

    Article  PubMed Central  PubMed  Google Scholar 

  75. Kany S, Vollrath JT, Relja B. Cytokines in inflammatory disease. Int J Mol Sci 2019;20.

  76. Gigase FAJ, Smith E, Collins B, et al. The association between inflammatory markers in blood and cerebrospinal fluid: a systematic review and meta-analysis. Mol Psychiatry. 2023;28:1502–15.

    Article  PubMed Central  PubMed  Google Scholar 

  77. Benedet AL, Mila-Aloma M, Vrillon A, et al. Differences between plasma and cerebrospinal fluid glial fibrillary acidic protein levels across the alzheimer disease continuum. JAMA Neurol. 2021;78:1471–83.

    Article  PubMed  Google Scholar 

  78. Hansson O, Edelmayer RM, Boxer AL, et al. The Alzheimer’s association appropriate use recommendations for blood biomarkers in Alzheimer’s disease. Alzheimers Dement. 2022;18:2669–86.

    Article  CAS  PubMed  Google Scholar 

  79. Simren J, Weninger H, Brum WS, et al. Differences between blood and cerebrospinal fluid glial fibrillary acidic protein levels: the effect of sample stability. Alzheimers Dement. 2022;18:1988–92.

    Article  CAS  PubMed  Google Scholar 

  80. Sanchez-Juan P, Valeriano-Lorenzo E, Ruiz-Gonzalez A, et al. Serum GFAP levels correlate with astrocyte reactivity, post-mortem brain atrophy and neurofibrillary tangles. Brain. 2024;147:1667–79.

    Article  PubMed Central  PubMed  Google Scholar 

  81. Griemsmann S, Hoft SP, Bedner P, et al. Characterization of Panglial gap junction networks in the thalamus, neocortex, and hippocampus reveals a unique population of glial cells. Cereb Cortex. 2015;25:3420–33.

    Article  PubMed  Google Scholar 

  82. O’Callaghan JP, Brinton RE, McEwen BS. Glucocorticoids regulate the synthesis of glial fibrillary acidic protein in intact and adrenalectomized rats but do not affect its expression following brain injury. J Neurochem. 1991;57:860–9.

    Article  PubMed  Google Scholar 

  83. Rodriguez JJ, Terzieva S, Olabarria M, Lanza RG, Verkhratsky A. Enriched environment and physical activity reverse astrogliodegeneration in the hippocampus of AD Transgenic mice. Cell Death Dis. 2013;4:e678.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  84. Bettcher BM, Tansey MG, Dorothee G, Heneka MT. Peripheral and central immune system crosstalk in alzheimer disease - a research prospectus. Nat Rev Neurol. 2021;17:689–701.

    Article  PubMed Central  PubMed  Google Scholar 

  85. Park SH, Lee EH, Kim HJ, et al. The relationship of soluble TREM2 to other biomarkers of sporadic Alzheimer’s disease. Sci Rep. 2021;11:13050.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  86. Rodriguez-Vieitez E, Ashton NJ. Plasma sTREM2: a potential marker of cerebrovascular injury in neurodegenerative disorders. Brain. 2021;144:3283–5.

    Article  PubMed Central  PubMed  Google Scholar 

  87. van Lengerich B, Zhan L, Xia D, et al. A TREM2-activating antibody with a blood-brain barrier transport vehicle enhances microglial metabolism in Alzheimer’s disease models. Nat Neurosci. 2023;26:416–29.

    PubMed Central  PubMed  Google Scholar 

  88. Janelsins MC, Mastrangelo MA, Park KM, et al. Chronic neuron-specific tumor necrosis factor-alpha expression enhances the local inflammatory environment ultimately leading to neuronal death in 3xTg-AD mice. Am J Pathol. 2008;173:1768–82.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  89. Papazian I, Tsoukala E, Boutou A, et al. Fundamentally different roles of neuronal TNF receptors in CNS pathology: TNFR1 and IKKbeta promote microglial responses and tissue injury in demyelination while TNFR2 protects against excitotoxicity in mice. J Neuroinflammation. 2021;18:222.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  90. Madsen PM, Motti D, Karmally S, et al. Oligodendroglial TNFR2 mediates membrane TNF-Dependent repair in experimental autoimmune encephalomyelitis by promoting oligodendrocyte differentiation and remyelination. J Neurosci. 2016;36:5128–43.

    Article  PubMed Central  PubMed  Google Scholar 

  91. Filiano AJ, Xu Y, Tustison NJ, et al. Unexpected role of interferon-gamma in regulating neuronal connectivity and social behaviour. Nature. 2016;535:425–9.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  92. Monteiro S, Roque S, Marques F, Correia-Neves M, Cerqueira JJ. Brain interference: revisiting the role of IFNgamma in the central nervous system. Prog Neurobiol. 2017;156:149–63.

    Article  CAS  PubMed  Google Scholar 

  93. Mizuno T, Zhang G, Takeuchi H, et al. Interferon-gamma directly induces neurotoxicity through a neuron specific, calcium-permeable complex of IFN-gamma receptor and AMPA GluR1 receptor. FASEB J. 2008;22:1797–806.

    Article  CAS  PubMed  Google Scholar 

  94. Clausen BH, Lambertsen KL, Dagnaes-Hansen F, et al. Cell therapy centered on IL-1Ra is neuroprotective in experimental stroke. Acta Neuropathol. 2016;131:775–91.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  95. Liu X, Nemeth DP, McKim DB, et al. Cell-Type-Specific Interleukin 1 receptor 1 signaling in the brain regulates distinct neuroimmune activities. Immunity. 2019;50:764–6.

    Article  CAS  PubMed  Google Scholar 

  96. Giulian D, Baker TJ, Shih LC, Lachman LB. Interleukin 1 of the central nervous system is produced by ameboid microglia. J Exp Med. 1986;164:594–604.

    Article  CAS  PubMed  Google Scholar 

  97. Lechan RM, Toni R, Clark BD, et al. Immunoreactive interleukin-1 beta localization in the rat forebrain. Brain Res. 1990;514:135–40.

    Article  CAS  PubMed  Google Scholar 

  98. Suzuki S, Tanaka K, Nogawa S, et al. Temporal profile and cellular localization of interleukin-6 protein after focal cerebral ischemia in rats. J Cereb Blood Flow Metab. 1999;19:1256–62.

    Article  CAS  PubMed  Google Scholar 

  99. Vollenweider F, Herrmann M, Otten U, Nitsch C. Interleukin-6 receptor expression and localization after transient global ischemia in gerbil hippocampus. Neurosci Lett. 2003;341:49–52.

    Article  CAS  PubMed  Google Scholar 

  100. Ashutosh, Kou W, Cotter R, et al. CXCL8 protects human neurons from amyloid-beta-induced neurotoxicity: relevance to Alzheimer’s disease. Biochem Biophys Res Commun. 2011;412:565–71.

    Article  PubMed  Google Scholar 

  101. Danik M, Puma C, Quirion R, Williams S. Widely expressed transcripts for chemokine receptor CXCR1 in identified glutamatergic, gamma-aminobutyric acidergic, and cholinergic neurons and astrocytes of the rat brain: a single-cell reverse transcription-multiplex polymerase chain reaction study. J Neurosci Res. 2003;74:286–95.

    Article  CAS  PubMed  Google Scholar 

  102. Fontaine RH, Cases O, Lelievre V, et al. IL-9/IL-9 receptor signaling selectively protects cortical neurons against developmental apoptosis. Cell Death Differ. 2008;15:1542–52.

    Article  CAS  PubMed  Google Scholar 

  103. Andreadou M, Ingelfinger F, De Feo D, et al. IL-12 sensing in neurons induces neuroprotective CNS tissue adaptation and attenuates neuroinflammation in mice. Nat Neurosci. 2023;26:1701–12.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  104. Zhao X, Wang H, Sun G, Zhang J, Edwards NJ, Aronowski J. Neuronal Interleukin-4 as a modulator of microglial pathways and ischemic brain damage. J Neurosci. 2015;35:11281–91.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  105. Derecki NC, Cardani AN, Yang CH, et al. Regulation of learning and memory by meningeal immunity: a key role for IL-4. J Exp Med. 2010;207:1067–80.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  106. Hanuscheck N, Thalman C, Domingues M et al. Interleukin-4 receptor signaling modulates neuronal network activity. J Exp Med 2022;219.

  107. Li S, Olde Heuvel F, Rehman R, et al. Interleukin-13 and its receptor are synaptic proteins involved in plasticity and neuroprotection. Nat Commun. 2023;14:200.

    Article  PubMed Central  PubMed  Google Scholar 

  108. Flanders KC, Ren RF, Lippa CF. Transforming growth factor-betas in neurodegenerative disease. Prog Neurobiol. 1998;54:71–85.

    Article  CAS  PubMed  Google Scholar 

  109. Logan TT, Villapol S, Symes AJ. TGF-beta superfamily gene expression and induction of the Runx1 transcription factor in adult neurogenic regions after brain injury. PLoS ONE. 2013;8:e59250.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  110. Di Filippo M, Mancini A, Bellingacci L, et al. Interleukin-17 affects synaptic plasticity and cognition in an experimental model of multiple sclerosis. Cell Rep. 2021;37:110094.

    Article  PubMed  Google Scholar 

  111. Alboni S, Cervia D, Sugama S, Conti B. Interleukin 18 in the CNS. J Neuroinflammation. 2010;7:9.

    Article  PubMed Central  PubMed  Google Scholar 

  112. Xia MQ, Bacskai BJ, Knowles RB, Qin SX, Hyman BT. Expression of the chemokine receptor CXCR3 on neurons and the elevated expression of its ligand IP-10 in reactive astrocytes: in vitro ERK1/2 activation and role in Alzheimer’s disease. J Neuroimmunol. 2000;108:227–35.

    Article  CAS  PubMed  Google Scholar 

  113. Banisadr G, Queraud-Lesaux F, Boutterin MC, et al. Distribution, cellular localization and functional role of CCR2 chemokine receptors in adult rat brain. J Neurochem. 2002;81:257–69.

    Article  CAS  PubMed  Google Scholar 

  114. Sorce S, Myburgh R, Krause KH. The chemokine receptor CCR5 in the central nervous system. Prog Neurobiol. 2011;93:297–311.

    Article  CAS  PubMed  Google Scholar 

  115. Gomez-Pinilla F, Knauer DJ, Nieto-Sampedro M. Epidermal growth factor receptor immunoreactivity in rat brain. Development and cellular localization. Brain Res. 1988;438:385–90.

    Article  CAS  PubMed  Google Scholar 

  116. Yamada M, Ikeuchi T, Hatanaka H. The neurotrophic action and signalling of epidermal growth factor. Prog Neurobiol. 1997;51:19–37.

    Article  CAS  PubMed  Google Scholar 

  117. Stevens B, Allen NJ, Vazquez LE, et al. The classical complement cascade mediates CNS synapse elimination. Cell. 2007;131:1164–78.

    Article  CAS  PubMed  Google Scholar 

  118. Davoust N, Jones J, Stahel PF, Ames RS, Barnum SR. Receptor for the C3a anaphylatoxin is expressed by neurons and glial cells. Glia. 1999;26:201–11.

    Article  CAS  PubMed  Google Scholar 

  119. O’Barr SA, Caguioa J, Gruol D, et al. Neuronal expression of a functional receptor for the C5a complement activation fragment. J Immunol. 2001;166:4154–62.

    Article  PubMed  Google Scholar 

  120. Daskoulidou N, Shaw B, Torvell M, et al. Complement receptor 1 is expressed on brain cells and in the human brain. Glia. 2023;71:1522–35.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Keenan Walker and Henrik Zetterberg for reading and commenting upon earlier versions of the manuscript. This manuscript was facilitated by the Alzheimer’s Association International Society to Advance Alzheimer’s Research and Treatment (ISTAART), through the Immunity and Neurodegeneration Professional Interest Area (PIA). The views and opinions expressed in this manuscript represent those of the authors and do not necessarily reflect those of the PIA membership, ISTAART or the Alzheimer’s Association.

Funding

BMB was supported by a grant from the National Institute on Aging (NIA; R01 AG058772). AW was supported by a grant from NIH (R00 AG047282; R01 MH126979; R21 EB034184). AR was supported by a grant from the Alzheimer’s Association (AARFD-24-1307665). WTH has received research support from NIH (R01 AG054046, RF1 AG054991, R01 AG066203, RF1 AG079251). FFdO was supported by a grant from FAPESP - The State of São Paulo Research Foundation, grant #2015/10109-5.

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Authors and Affiliations

  1. Department of Neurology, University of Colorado Anschutz Medical Campus, 12469 East 17th Place, Room 217- Campus Box F429, Aurora, CO, 80045, USA

    Brianne M. Bettcher

  2. Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil

    Fabricio Ferreira de Oliveira

  3. Department of Neurology, Rutgers-Robert Wood Johnson Medical School and Center for Healthy Aging Research, Rutgers Institute for Health, Health Care Policy, and Aging Research, Rutgers Health, New Brunswick, USA

    Auriel A. Willette & William T. Hu

  4. Department of Clinical Neuroscience, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden

    Malgorzata M. Michalowska

  5. Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden

    Luiza Santos Machado

  6. Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California – San Francisco, San Francisco, USA

    Binita Rajbanshi

  7. Department of Morphological Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Wyllians V. Borelli

  8. Department of Neurology, Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, USA

    Malú Gámez Tansey

  9. Department of Psychiatry, University of Pittsburgh, Pittsburgh, USA

    Andréia Rocha

  10. Department of Radiology, Stanford University, Stanford, USA

    Vidyani Suryadevara

Authors
  1. Brianne M. Bettcher
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  2. Fabricio Ferreira de Oliveira
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  3. Auriel A. Willette
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  4. Malgorzata M. Michalowska
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  5. Luiza Santos Machado
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  6. Binita Rajbanshi
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  7. Wyllians V. Borelli
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  8. Malú Gámez Tansey
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  9. Andréia Rocha
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  10. Vidyani Suryadevara
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  11. William T. Hu
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Contributions

This is a review and roadmap paper in which all authors made substantial contributions to the conception and design of the work; drafted the work and revised it; and have approved the submitted version. BMB and WTH oversaw and finalized the manuscript.

Corresponding author

Correspondence to Brianne M. Bettcher.

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Competing interests

MGT is co-inventor of Xencor’s XPro1595, a soluble TNF-specific biologic under clinical development by INmune Bio for neurological indications. MGT is a consultant/advisor INmune Bio, Merck, Cellestial Therapeutics, Forward Therapeutics, Jaya, NysnoBio Longevity Biotech, Longeveron, iMetabolic Pharm, NovoNordisk, IMMvention, Ventus. MGT has served as an advisor/member on the Medical and Scientific Advisory Group of the Alzheimer’s Association, Weston Family Foundation of Canada, Parkinson’s Foundation, Parkinson’s UK, World Parkinson Coalition, MJ Fox Foundation for Parkinson’s Research, and the Alzheimer’s Disease Cooperative Study Intervention Selection Committee (ADCS-ISC), and the International Linked Clinical Trials (iLCT) of the Cure Parkinson’s Trust. MGT has served on grant review panels for NIH, MF Fox Foundation, Weston Family Foundation, Alzheimer’s Association, Bright Focus Foundation, Alzheimer’s Drug Discovery Foundation (ADDF), Parkinson’s UK. MGT has served as Editor-in-Chief for Nature Partner Journal Parkinson’s Disease, Associate Editor for Science Advances, Alzheimer’s & Dementia: Translational Research and Clinical Interventions (AE), and Journal of Neuroinflammation. WTH has received research support from NIH, Robert Wood Johnson Foundation, Fujirebio Diagnostics Inc, Atlanta Family Foundation, and TMCity Foundation; has consulted for Apellis Pharmaceuticals, Beckman Coulter Diagnostics, Biogen, Fujirebio Diagnostics Inc; has patents on CSF-based diagnosis of FTLD-TDP, prognosis of MCI due to AD, and prognosis of spinal muscular atrophy on gene therapy; and has copyright on Mandarin-based cognitive assessments (licensed to Linus Health).

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Bettcher, B.M., de Oliveira, F.F., Willette, A.A. et al. Analysis and interpretation of inflammatory fluid markers in Alzheimer’s disease: a roadmap for standardization. J Neuroinflammation 22, 105 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12974-025-03432-4

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12974-025-03432-4

Keywords

Journal of Neuroinflammation

ISSN: 1742-2094