Immune System Problems and AI-Based Treatment Approaches in Alzheimer's Disease

Microglia, the CNS-resident immune cells, play key functions in the maintenance of neuronal homeostasis. Microglia under normal states examine the environment of the neurons, with actions of synaptic pruning, apoptotic cell clearance, and the secretion of neurotrophic factors (Mohammad et al. 2024). Microglial homeostasis is also maintained by a delicate equilibrium between context-dependent microglial states. Nevertheless, under pathological conditions like AD, microglia adopt more pro-inflammatory phenotypes, profoundly changing their functions and roles (Table 1). Due to the persistent assault of Alzheimer's-related danger signals on microglia, these cells undergo a molecular "gear-shift": they deactivate the homeostatic surveillance gene program responsible for maintaining cerebral order and activate stress-, complement-, and cytokine-dominated programs instead. This induces the highly inflammatory disease-associated microglia or microglial neurodegenerative phenotype (DAM/MGnD) state, which excels at producing pro-inflammatory signals (TNF-α, IL-1β, C1q, ROS) but increasingly falters in the essential functions of synapse remodelling and amyloid clearance (Xu et al. 2022). Microglial phenotypes vary from homeostatic/surveilling, activated (pro-inflammatory), and DAM states (Nimmerjahn et al. 2005; Mavroudis et al. 2020). Homeostatic microglia are responsible for brain health with ongoing surveillance. In response to pathological stimuli, the cells are activated, migrating into pro-inflammatory states with amplified secretion of cytokines like TNF-α, IL-1β, and IL-6, that may worsen neuronal damage (Bhusal et al. 2022). DAM, newly recognized in neurodegenerative settings, have a unique gene expression profile with upregulation of certain genes such as TREM2, APOE, CST7, and CD68, and downregulation of homeostatic genes such as CX3CR1 and P2RY12 (Keren-Shaul et al. 2017). Damaged neurons are potent microglial activators. Focal neuronal injury and network hyperexcitability release extracellular ATP that rapidly recruits and reorients microglial processes via P2Y12 signaling, producing process convergence at stressed axons and synapses. In Alzheimer's-relevant contexts, ATP and other DAMPs cooperate with plaque-associated cues to drive local chemotaxis and state transitions, linking neuronal stress to microglial reprogramming and inflammatory amplification (Davalos et al. 2005; Eyo et al. 2014; Haynes et al. 2006).

The NLRP3 (NOD-like receptor protein 3) inflammasome is a cross-cell inflammatory hub: it assembles primarily in microglia yet conditions astrocytic reactivity through IL-1 family cytokines and damage signals, thereby bridging innate immune activation across glia. The NLRP3 inflammasome is known to be a key effector of innate immunity, which is significantly involved in AD-related neuroinflammation (Terzioglu and Young-Pearse 2023). The NLRP3 inflammasome structurally consists of NLRP3 protein, apoptosis-associated speck-like protein with a caspase recruitment domain (ASC), and pro-caspase-1 (Venegas et al. 2017; Kelley et al. 2019; Ye et al. 2023). Both canonical and non-canonical activation mechanisms are followed by the inflammasome (Leung et al. 2022) (Fig. 2). The canonical pathway involves the recognition of many danger signals such as aggregated Aβ by NLRP3, leading to the recruitment of ASC, caspase-1 activation, subsequent cleavage and maturation of pro-inflammatory cytokines IL-1β and IL-18 (Heneka et al. 2018). The non-canonical pathway involves mouse caspase-11 (equivalent to human caspase-4/5), which indirectly activates the canonical inflammasome, hence augmenting inflammation (Mohammad et al. 2024). The activation of the NLRP3 inflammasome is essential for the course of the illness in AD. Activation of microglial NLRP3 triggers neuroinflammation and contributes to Aβ and tau pathology (Ising et al. 2019; Van Zeller et al. 2021).

Increased inflammasome activity is known to worsen the astrocytic activation, and contributing to an inflammatory microenvironment as well as neuronal dysfunction (Wu and Eisel 2023). Epigenetic pathways are being increasingly known to modulate inflammasome function which affects the NLRP3 expression and function in AD. DNA methylation and histone modifications can also regulate NLRP3 transcription levels, thereby modulating inflammatory reactions (Raneros et al. 2021). Additionally, recent evidence suggests that miRNAs play a crucial role in regulating NLRP3 expression as well, implying that epigenetic interventions could offer therapeutic options to manage the inflammasome-mediated neuroinflammation in AD (Heneka et al. 2018; He et al. 2025; Swanson et al. 2019). Interestingly, the expression of NLRP3 is often maintained at low levels due to the presence of conserved binding sites for many miRNAs (like miR-223, miR-7, and miR-22) inside its 3′-UTR (Table 2). When a corresponding miRNA is plentiful, it binds to the 3′-UTR, inhibits ribosomal loading, and hastens the transcript degradation, hence impeding the priming phase of the concerned inflammasome. In AD, several miRNAs are aberrantly expressed, disrupting the equilibrium and promoting persistent NLRP3 priming and the production of pyroptotic cytokines (Hu et al. 2024).

Astrocytes are abundant CNS glia that regulate neurotransmission, provide metabolic support, help maintain the blood–brain barrier, and shape immune signaling. Reactive astrocytes comprise diverse, context-dependent states defined by transcriptomic, morphological, and functional changes. Current consensus discourages binning into A1/A2 and recommends describing stimuli, features, and context explicitly (Escartin et al. 2021; Paolicelli et al. 2022; Santos et al. 2025). Microglia-derived cytokines, particularly IL-1α, TNF, and C1q, can induce inflammatory/reactive astrocyte programs marked by complement C3 upregulation and synapse-modulating outputs (Liddelow et al. 2017). Cytokines such as IL-1β and TNF-α further augment astrocytic reactivity and can exacerbate neuroinflammatory cascades. In parallel, metabolic coupling and lipid handling between microglia and astrocytes constrain or amplify inflammation and influence synaptic support (Mi et al. 2023). The interactions between astrocytes and microglia constitute an important aspect of neuroimmune dynamics in the pathophysiology of AD (Deng et al. 2024). Importantly, activated microglia is known to produce certain cytokines that stimulate astrocytic responses, and hence resulting into a cascade of chronic inflammation processes. Whereas, microglial interleukin-1β (IL-1β) and tumour necrosis factor-alpha (TNF-α) significantly augment astrocytic reactivity, and this adversely affect the microglial function and further intensify various inflammatory processes (Wu and Eisel 2023). As per the current understanding, cytokine and chemokine signalling significantly influence different glial connections and neuroinflammation in the aetiology of AD. Reactive astrocyte phenotypes, from early stressed to neurotoxic, neuroprotective and peri-plaque states, are compared in Fig. 3.

A healthy blood-brain barrier (BBB) is crucial for its responsibility for preserving CNS homeostasis to a large level (Segarra et al. 2021). This is done by keeping the entry of peripheral immune cells and molecules tightly regulated from peripheral circulation. In AD, the integrity of BBB has been observed to be disrupted, allowing the undesirable infiltration of these peripheral immune cells into the brain parenchyma (Bhusal et al. 2022; Xingi et al. 2023). In addition to that, dysfunctional endothelium, diminished expression of tight junction proteins, and inflammatory mediators all contribute to the disruption of the BBB permeability. This breakdown of the barrier enables immune cells like CD4 + and CD8 + T lymphocytes, regulatory T cells (also known as Tregs), B cells, natural killer (NK) cells, and peripheral macrophages to invade the CNS (Castellani and Schwartz 2020; Gate et al. 2020). CD4 + T cells normally perpetuate inflammation by secreting cytokines, while CD8 + cytotoxic T cells have the potential to cause neuronal damage. CD4 + cells are not intrinsically destructive; the ratio of pro-inflammatory Th1/Th17 to counter-regulatory Th2/Treg signals determines whether they perpetuate or resolve glial activation (Xie et al. 2023). CD4 + T cells dictate the inflammatory tone through their cytokine profile, while CD8 + T cells convert that tone into direct neuronal damage when the "do-not-kill" MHC-I silence is removed (Zhang et al. 2023). Targeting chemokines, cytokines and checkpoint pathways that bias these T-cell responses is true potential to tone down inflammasome-mediated neurodegeneration without crippling systemic immunity (Dias-Carvalho et al. 2023). Tregs, however, can potentially hold back overinflammation, though they are themselves dysregulated in the context of AD pathology. B cells penetrating the CNS can enhance neuroinflammation through the production of autoantibodies or cytokines, whereas NK cells escalate inflammatory responses and neuronal damage via cytotoxic mechanisms (Lin et al. 2024).

Metabolic reprogramming is key to microglial activation and polarization. At homeostatic states, microglia favor oxidative phosphorylation as the mode of energy generation. Activated microglia, however, transition towards glycolysis, a metabolic reprogramming that facilitates the generation of ATP quickly and facilitates inflammatory response (Huang et al. 2024). This reprogramming is facilitated by enhanced glycolytic enzyme expression and downregulation of mitochondrial respiration (Huang et al. 2024). Lipid metabolism also has important effects on microglial function. Activated microglia contain lipid droplets, which reflect dysregulated cholesterol metabolism and defective lipid clearance. Such accumulation is associated with inflammatory amplification and dysfunction in AD (Kang and Rivest 2012; Dou et al. 2024). Additionally, dysregulation of cholesterol homeostasis, partly controlled by APOE, also promotes microglial inflammatory conditions and resultant AD pathology (Wu and Eisel 2023).

Interactions between CNS-resident cells (like astrocytes, microglia) and invading peripheral immune cells establish intricate neuroimmune feedback loops. Activated microglia and astrocytes release cytokines like IL-1β, TNF-α, and IL-6, which also attract peripheral immune cells into the CNS, strengthening inflammation (Bhusal et al. 2022; Kim and Lee 2024). Invading peripheral immune cells additionally release other pro-inflammatory factors also, perpetuating a chronic neuroinflammation (Fig. 4). Cross-talk between the peripheral macrophages and microglia entails a signal exchange that impacts their activation status, as a result. Astrocytes also influence the peripheral immune cell activity through a secretion of chemokines (e.g., CCL2), which supports the leukocyte recruitment into our CNS (Fisher and Liddelow 2024). One must understand that continuous activation of these pathways leads to prolonged inflammatory states that play a critical role in the AD pathogenesis (Kodi et al. 2024).

Activation of microglia has a significant impact on AD pathology through inflammatory cascades. Therapies involve the modulation of microglial activation using inhibitors against pro-inflammatory signaling pathways, e.g., p38 MAPK inhibitors, and selective inhibition of receptors such as TREM2 (Li et al. 2023; Liu et al. 2019). The TREM2 agonists increase microglial phagocytosis and decrease inflammation, implying a therapeutic potential (Mohammad et al. 2024). Astrocytic protective mechanisms are also a potential therapeutic target. Glucagon-like peptide-1 (GLP-1) receptor agonists, such as liraglutide and semaglutide, have neuroprotective actions through the reduction of astrocytic inflammation and the promotion of neurogenesis and synaptic plasticity (Kopp et al. 2022). Similarly, the αB-crystallin, which is a molecular chaperone upregulated in reactive astrocytes, has shown neuroprotective actions through the inhibition of inflammatory pathways and maintenance of protein homeostasis (Bhusal et al. 2022). Nevertheless, NLRP3 inflammasome inhibitors including the MCC950, OLT1177 (dapansutrile), CY-09, and Tranilast, have been of interest due to their strong anti-inflammatory properties (shown in the Table 3). Summarily, MCC950 specifically inhibits NLRP3 formation (Coll et al. 2015), inhibiting IL-1β and IL-18 production, and has shown strong efficacy in preclinical models. OLT1177, which is an orally available NLRP3 inhibitor, is known to reduce the systemic inflammation and improve cognitive outcomes in various animal models. Studies show that CY-09 inhibits ATPase activity, which is essential for NLRP3 activation, whereas Tranilast is known to inhibit the inflammasome oligomerisation. So we can easily understand that these molecules offer us many high quality potential treatment options (Wu and Eisel 2023).

Targeting microglial immunometabolism offers us new and innovative therapeutic possibilities. Therapeutic modulation of microglial metabolic reprogramming from oxidative phosphorylation to glycolysis during activation can be mediated. This mediation is done by the AMPK and HIF-1α pathways (Baik et al. 2019). Activating AMPK, for instance, is known to enhance mitochondrial biogenesis, reduce dependency on glycolysis, and countering the inflammatory responses. Similarly, HIF-1α inhibitors can recapture glycolytic reprogramming, thereby reducing inflammatory cytokine synthesis and neuronal damage (Wu and Eisel 2023). Lipid metabolism pathways that are related to TREM2 and APOE show promising therapeutic targets. Research show augmentation of TREM2-regulated lipid metabolism has the ability to restore microglial lipid homeostasis (Wei et al. 2024). And thereby reducing lipid accumulation and inflammation. Treatments aimed at APOE, such as APOE modulators and lipid-lowering drugs, show promise in correcting cholesterol deregulation and decreasing the risk of Alzheimer's disease, especially among APOE ε4 carriers (Williams et al. 2020). Pharmacologic repurification of metabolic drugs, such as GLP-1 receptor agonists (liraglutide, semaglutide) and beta-blockers (propranolol), is promising treatment patterns for Alzheimer's disease. GLP-1 receptor agonists, originally approved in type 2 diabetes, promote neuronal well-being by enhancing anti-inflammatory activities, optimizing glucose metabolism, and promoting synaptic plasticity and neurogenesis (Femminella et al. 2019; Batista et al. 2018). Clinical trials indicate these agonists could slow cognitive impairment in Alzheimer's patients by limiting microglial and astrocytic inflammation, adjusting mitochondrial activity, and enhancing neuronal insulin signaling (Femminella et al. 2019). Propranolol, classically employed for treating cardiovascular diseases, is beneficial in Alzheimer's models by reducing stress-induced sympathetic nervous system activation, lowering peripheral and central inflammatory cytokines, and limiting oxidative neuronal injury, thereby maintaining cognitive functions (Miliotou et al. 2025). Repurposed drugs are valid adjunctive therapies that act on multiple pathogenic mechanisms of Alzheimer's disease.

Emerging treatment approaches for AD treatment, including the cell-based and exosomal therapies hold a great promise. Cell-based therapy using MSCs has been reported with encouraging results based on their inherent immunomodulatory and regenerative capabilities, and they were able to mitigate neuroinflammation and aid in neuronal repair (Duncan and Valenzuela 2017). MSC-derived EVs (like exosomes), is one of the cell-free therapeutic alternatives that can directly target therapeutic molecules such as microRNAs, proteins, and also the anti-inflammatory mediators into our CNS (Liu et al. 2022). These exosomes are capable of crossing the BBB and regulate microglial activation, astrocytic responses, and synaptic function. That is how they help in reducing AD pathology and cognitive impairment observed in preclinical models (Elia et al. 2019). Recent research work demonstrates the effectiveness of MSC-derived exosomes containing miR-146a and miR-124 in inhibiting neuroinflammatory pathways, enhancing synaptic plasticity, and improving cognitive performance in AD animal models (Elia et al. 2019). Additionally, targeting the peripheral immune system is becoming an attractive area of AD therapy. Peripheral immune modulation, like, the administration of inhibitors of programmed death-1 (PD-1) immune checkpoint, has been shown to control the infiltration and activity of immune cells into the CNS. Thus, decreasing neuroinflammation and cognitive dysfunction in model systems (Baruch et al. 2016). Also, regulation of peripheral immune cell migration and cytokine release, e.g., with the use of chemokine receptor antagonists and/or cytokine-neutralizing antibodies, would also further maximize therapeutic efficacy. This would be done by restricting harmful immune cell migration and inflammation within the CNS (Lin et al. 2024). Such approaches imply a widening therapeutic horizon for AD, coupling peripheral immune interventions with regenerative therapies for better clinical effectiveness.

One of the core AI/ML approaches is predictive modeling (Bjerre et al. 2024). This approach helps in the identification of potential therapeutic targets in the intricate neuroimmune interactions. Also, the machine learning techniques like random forests, support vector machines (SVMs), and deep neural networks (DNNs) are especially suited for dealing with large-scale data (Ali et al. 2023). Therefore, this helps in uncovering the dormant or latent patterns in various neuroimmune pathways and predict possible drug targets. These predictive technologies use really large biological datasets from reported experimental and clinical research, providing a data-driven, strong method of discovering new interventional areas (Lin et al. 2024). Moreover, high-throughput screening (HTS) with the addition of AI/ML technologies greatly accelerates drug discovery (Wallach et al. 2024). Classical HTS techniques can be time-consuming and also labor-intensive. However, with the addition of AI/ML, researchers can now screen large chemical libraries against multiple neuroimmune targets in parallel. This is saving a lot of time and resources. AI-driven screening platforms leverage the upgraded algorithms to quickly evaluate molecular interactions, toxicity, efficacy, and likely off-target effects. This is enhancing the accuracy and speed of finding promising therapeutic leads significantly. For example, the recent AI-powered HTS investigations have effectively identified modulators that target microglial activation and inflammatory mediators in AD pathology (Maniam and Maniam 2024; Qiu and Cheng 2024; Mani et al. 2025). AI-augmented biomarker discovery represents a further area of importance in neuroimmune research. Biomarkers are critical for the early diagnosis, monitoring of disease, and assessment of therapeutic responses. Machine learning algorithms such as logistic regression, random forest classifiers, and convolutional neural networks (CNNs) offer advanced analytical tools to analyze complex clinical, imaging, genetic, and proteomic datasets (Chakraborty et al. 2024). These latest algorithms are capable of identifying subtle patterns and correlations that are mostly missed by our conventional analytical techniques. This progress in technology is presently leading to biomarkers with improved diagnostic accuracy, specificity, and high predictive value. For instance, ML-based analysis of fluid biomarkers and neuroimaging has successfully distinguished AD from other neurodegenerative diseases (Blanco et al. 2023; Cao et al. 2025). Moreover, with latest upgradations AI/ML has the capability of predicting disease progression rates accurately, and identified subgroups of patients that are likely to respond to particular therapies (Dixon et al. 2024; Jiang et al. 2025). Therefore, it is also considered as a big leap in precision medicine. Nevertheless, AI-assisted discovery of biomarkers enables personalization of therapy at various levels of treatment. Through stratification of patient populations according to specific biomarker profiles, clinicians are able to personalize treatments to aim for specific patient profiles to maximize therapeutic benefit while reducing side effects. This approach greatly facilitates the promotion of precision medicine in the treatment of AD as well as in neuroimmunology. Such use of AI and ML technology in neuroimmunology, apart from rebirthing the present, may also expedite scientific research as well as therapy development for various other disorders. These approaches further enhance our ability to comprehend complex interactions between the immune system and neuronal function and enable novel targets, efficacious therapeutics, and specific diagnostic biomarkers to be identified. Ongoing integration and innovation of AI/ML technologies into neuroimmune research have immense potential to revolutionize our ability to deal with and eventually control the effects of Alzheimer's disease.

Single-cell RNA sequencing (or scRNA-seq) and spatial transcriptomics allow us now an unprecedented resolution in the profiling of neuroimmune cellular heterogeneity. The AI/ML algorithms help in easing the analysis of these high-dimensional data, enabling identification of distinct cell subtypes, gene regulatory networks, and disease-specific cell states. Additionally, methods such as t-distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) are helping in visualizing and interpreting complex single-cell datasets (Lin et al. 2024; Roca et al. 2023). Immuno-metabolomics and the lipidomics, profile immune-related metabolic pathways and changes in lipid metabolism, respectively. These give essential insights into microglial and astrocytic functions. Combining these datasets with AI-assisted analysis allows one to have an integrated understanding of metabolic dysregulation in the neuroinflammation process. Machine learning algorithms like partial least squares discriminant analysis (or PLS-DA) and hierarchical clustering optimize the detection of metabolic biomarkers and therapeutic targets in different neuroimmune pathways (Wu and Eisel 2023; Worley and Powers 2016). Also, the AI/ML-based multi-omics data integration to detect new therapeutic pathways is related to applying AI/ML-based integration of multi-omics data. This includes genomics, transcriptomics, proteomics, and metabolomics and helps to identify interactive networks and regulation pathways underlying the neuroimmune interactions. Network-based analysis, Bayesian networks, and graph neural networks systematically recognize new biological pathways to offer possible innovative therapeutic targets in AD (Qiu and Cheng 2024).

While both ML and AI offer substantial potential to develop neuroimmune studies in AD, some substantial hurdles need to be overcome for this to become a full reality. Top of these are heterogeneity of the data, scarce availability of good-quality, standard datasets, and challenges to interpreting advanced AI algorithms (Fig. 5). Variation in data collection protocols, heterogeneous analysis approaches, and varying standards make study integration and interpretations meaningful only across studies challenging. All these difficulties must be overcome through the implementation of standard data acquisition protocols, comprehensive repositories, and data quality and interoperability guidelines across research teams and platforms (Lin et al. 2024; Sedlakova et al. 2023). One of the critical challenges is also the interpretability and transparency of advanced AI models, typically so-called "black box" models. Deep learning architectures, by their intrinsic complexity and untransparent nature, can be discouraged from being applied in clinical environments and research arenas, where reasons behind predictions have to be understandable. The technology that furnishes interpretable and transparent outcomes from AI models, known as Explainable AI (XAI), offers one such solution (Shaughnessy et al. 2024). The XAI techniques like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) can be used to explain the decision-making of the AI models (Hassan et al. 2025), making them easier to adopt, use and verify in neuroimmune research at multiple levels. These are the latest techniques which are in development and improvement mode and hold a lot of potential in neuroimmune research.

Nevertheless, new computational approaches like transfer learning and federated learning provide further enhancements in AI model generalizability and usability (Berkani et al. 2025). Transfer learning enables using pre-trained models to efficiently process smaller, disease-specific datasets prevalent in neuroimmune research. Federated learning, which supports collaborative analysis without violating data privacy, can support the creation of robust AI models by aggregating data from diverse sources. Moreover, cloud-based analytical platforms and automated data processing pipelines enable in smooth integration, analysis, and also the sharing of intricate multi-omics datasets. This helps in greatly promoting collaboration, reproducibility, and accessibility across the global research community (Lin et al. 2024).