Abstract
Sepsis-associated encephalopathy (SAE) affects up to 70% of septic patients, causing significant mortality and long-term cognitive impairment. Traditional mechanisms–neuroinflammation, blood-brain barrier disruption, and oxidative stress–fail to explain SAE heterogeneity and post-septic cognitive sequelae. Metabolic reprogramming, the systematic remodeling of cellular metabolic pathways, has emerged as an integrative paradigm for understanding SAE pathogenesis. This review examines how metabolic reprogramming drives SAE through four core pathways: glycolysis/Warburg effect, tricarboxylic acid cycle dysfunction, lipid dysregulation, and amino acid disturbance. We discuss cell-type-specific adaptations in microglia, neurons, and astrocytes, and the modulatory role of gut-brain axis crosstalk. We further evaluate metabolomics-driven biomarkers for early diagnosis and emerging metabolic-targeted therapies, including metformin and dichloroacetate. Finally, we highlight single-cell metabolomics and multi-omics integration as critical frontiers toward precision medicine for SAE.
1 Introduction
Sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, constitutes the leading cause of morbidity and mortality among patients in intensive care units (ICUs) worldwide. As its most frequent neurological complication, sepsis-associated encephalopathy (SAE) carries a particularly heavy clinical burden. The diagnosis of SAE is primarily based on the presence of acute brain dysfunction in septic patients, with exclusion of direct central nervous system infection, structural lesions, or other identifiable causes (, ).
Epidemiological data reveal an alarming incidence of SAE, with reported rates among ICU septic patients varying widely, estimated between 9 and 71% (, ). This substantial variability stems primarily from heterogeneous diagnostic criteria, diverse study populations, and differences in monitoring sensitivity, yet also reflects the ubiquity of SAE from another perspective. The emergence of SAE serves as a powerful predictor of poor prognosis and is closely associated with significantly increased patient mortality. Some studies report that in-hospital mortality among septic patients with SAE may reach 20%−30%, and even higher in certain critically ill subgroups (–). However, the threat of SAE extends far beyond the patient's discharge from the ICU. Accumulating evidence indicates that sepsis survivors, particularly those who experienced SAE, face long-term cognitive dysfunction. These sequelae manifest as memory impairment, attention deficits, and executive dysfunction, severely compromising patients' quality of life, social functioning, and capacity for independent living, while increasing their future risk of dementia (–). Estimates suggest that up to 30% or more of severe sepsis survivors experience cognitive impairments persisting for months to years. This progression from acute brain dysfunction to chronic neurodegenerative changes constitutes one of the most formidable challenges in SAE research, representing a core component of “Post-Sepsis Syndrome” (PSS) (–).
Over the past decades, exploration of SAE pathophysiology has achieved considerable advances, establishing several classical theoretical frameworks: (1) Neuroinflammation: Systemic inflammatory storms penetrate the central nervous system (CNS) through multiple pathways (e.g., circulating inflammatory cytokines, activated immune cells), activating microglia and astrocytes to release abundant pro-inflammatory mediators, resulting in neuronal injury (); (2) Blood-brain barrier (BBB) disruption: Inflammatory mediators and endothelial cell damage increase BBB permeability, allowing harmful substances, plasma proteins, and peripheral immune cells to enter the brain parenchyma, compromising the immune-privileged status of the CNS (); (3) Mitochondrial damage and oxidative stress: Systemic hypoxia, hypoperfusion, and inflammatory responses during sepsis directly damage neuronal and glial mitochondria, leading to impaired energy production like ATP (adenosine triphosphate) depletion and excessive generation of reactive oxygen species (ROS), triggering oxidative stress and apoptosis (); (4) Additional mechanisms: Including excitatory amino acid toxicity, neurotransmitter imbalance, and cerebral blood flow dysregulation ().
Although these mechanisms are crucial for explaining certain pathological features of SAE, they have often been investigated as independent, parallel pathways, fostering a “fragmented” understanding. This perspective fails to address several critical questions: Why do similar levels of systemic inflammation result in markedly different neurological outcomes? How do cells, particularly immune and neural cells, make adaptive or maladaptive functional choices within harsh microenvironments? How do acute-phase inflammation and energy failure translate into long-term structural and functional remodeling? These limitations underscore the urgent need to identify an upstream core mechanism capable of integrating these diverse pathological processes and explaining their intrinsic drivers.
The concept of “metabolic reprogramming,” initially widely recognized in tumor biology, describes the systematic remodeling of core metabolic pathways (e.g., glucose, lipid, and amino acid metabolism) that cells undergo to adapt to functional demands such as proliferation and invasion (). In recent years, this concept has been successfully introduced into immunology, establishing the frontier discipline of “immunometabolism,” which reveals the tight coupling between immune cell activation, differentiation, and function with their metabolic states (, ).
In the context of sepsis, metabolic reprogramming is no longer confined to peripheral immune cells but is regarded as a universal, systemic phenomenon involving multiple organs and cell types (–). For SAE specifically, metabolic reprogramming serves as the central nexus connecting neuroinflammation, BBB disruption, mitochondrial damage, and neuronal dysfunction. It represents not merely a passive cellular response to stressors such as inflammation and hypoxia, but rather an active regulatory process determining cellular phenotype and fate (, ). For instance, pro-inflammatory (M1) polarization of microglia depends on metabolic shifts toward glycolysis (the Warburg effect), whereas the survival or death fate of neurons during energy supply disruption depends directly on their metabolic flexibility (–).
Therefore, adopting “metabolic reprogramming” as a core framework for studying SAE may enable the unification of neuroinflammation (immune cell metabolism), energy crisis (mitochondrial function), and cellular damage (neuronal/glial metabolism) within a single logical framework. This approach aims to illuminate the dynamic evolution of cellular function and metabolic states from the acute to chronic phases of disease, potentially facilitating explanation of long-term cognitive impairment formation. Key enzymes and pathways within metabolic networks may provide abundant resources for developing novel diagnostic biomarkers and therapeutic targets, though their clinical utility remains to be validated.
While recent reviews have comprehensively addressed neuroinflammation, blood-brain barrier disruption, and gut-brain axis interactions in SAE (, , ), a systematic synthesis centered specifically on metabolic reprogramming as an integrative framework remains limited. This review fills this gap by: (1) dissecting cell-type-specific metabolic adaptations across the neurovascular unit; (2) connecting metabolic biomarker discovery with therapeutic targeting strategies; and (3) critically evaluating the translational potential while acknowledging current evidence limitations.
Throughout this review, we employ an evidence-grading framework to distinguish the strength and source of supporting data: [H] indicates human clinical or postmortem studies; [A] indicates animal model studies; [I] indicates in vitro or cellular studies; and [E] indicates evidence extrapolated from related disease contexts (e.g., cancer immunometabolism, neurodegenerative diseases) that requires direct validation in SAE. This framework is essential given that some mechanistic insights discussed herein are derived from broader immunometabolism literature and may not yet be fully established in SAE specifically.
2 SAE and metabolic reprogramming
The influence of metabolic reprogramming in SAE is profound and multifaceted, manifesting not only as global remodeling of core metabolic pathways, but also as specific metabolic phenotype alterations in key brain cell types, while simultaneously being modulated by metabolic crosstalk from distal organs, particularly the intestine.
2.1 Systemic remodeling of four core metabolic pathways
The systemic stress response elicited by sepsis profoundly perturbs metabolic homeostasis within the central nervous system, triggering dramatic fluctuations in cerebral metabolic networks. Through metabolomics technologies, researchers have observed extensive alterations in metabolite profiles across brain tissue, cerebrospinal fluid, and even peripheral blood samples derived from both animal models and patients with SAE (–37). These alterations do not occur in isolation but rather center on the systemic remodeling of several core metabolic pathways.
2.1.1 Enhanced glycolysis and the emergence of the Warburg effect
Enhanced glycolysis, particularly the emergence of the Warburg effect, represents one of the most prominent features of early metabolic reprogramming in SAE (38, 39). Under physiological conditions, cerebral neurons predominantly rely on mitochondrial oxidative phosphorylation to efficiently generate ATP to meet their substantial energy demands (40). However, within the pathological microenvironment of SAE, multiple brain cell types—especially microglia and infiltrating immune cells—undergo metabolic shifts from oxidative phosphorylation toward aerobic glycolysis, manifesting the Warburg effect (). This process is driven by key signaling pathways including hypoxia-inducible factor-1α (HIF-1α) and mammalian target of rapamycin (mTOR). HIF-1α forcibly diverts glucose metabolism toward lactate production through upregulation of glucose transporters and key glycolytic enzymes, even when oxygen supply remains adequate. This metabolic conversion carries profound pathophysiological implications (41, 42). On one hand, enhanced glycolysis rapidly generates ATP to satisfy the immediate energy requirements for immune cell activation and proliferation; its metabolic intermediates provide precursors for synthesizing inflammatory mediators and reactive oxygen species, while accumulated lactate itself possesses signaling functions, collectively shaping and amplifying the pro-inflammatory microenvironment (43–46). On the other hand, for neurons with high energy dependence, the competitive glucose consumption by surrounding cells coupled with the inefficient energy output of glycolysis exacerbates cerebral energy crisis, whereas massive lactate accumulation induces local tissue acidosis, further impairing neuronal function (47–49).
The metabolic shift toward glycolysis fundamentally alters the downstream fate of pyruvate, directly impacting tricarboxylic acid (TCA) cycle dynamics [A/I]. Rather than entering mitochondria for complete oxidation, accumulating pyruvate is preferentially converted to lactate, simultaneously depriving the TCA cycle of its primary substrate and creating a metabolic bottleneck that perpetuates oxidative stress. This interconnected metabolic rewiring highlights that glycolytic enhancement and TCA cycle dysfunction are not isolated phenomena but represent coordinated adaptations to inflammatory stress.
2.1.2 Decoupling of the tricarboxylic acid cycle and oxidative phosphorylation
Building upon these observations, further investigations reveal that decoupling of the tricarboxylic acid (TCA) cycle and oxidative phosphorylation constitutes the central link of mitochondrial dysfunction in SAE. The TCA cycle serves as the hub connecting glucose, lipid, and amino acid metabolism, with its reducing equivalents essential for driving the mitochondrial electron transport chain and oxidative phosphorylation (50, 51). In SAE, the TCA cycle undergoes “breakage” and functional remodeling. On one hand, TCA cycle intermediates are extensively withdrawn to support cellular anabolism and signal transduction—for instance, citrate is exported from mitochondria for fatty acid synthesis and epigenetic regulation, while succinate accumulation stabilizes HIF-1α, further amplifying the Warburg effect (52, 53). On the other hand, inflammatory mediators and oxidative stress directly compromise mitochondrial membrane integrity and inhibit electron transport chain complex activity, resulting in uncoupling of oxidative phosphorylation from electron transport. This not only precipitates sharp declines in ATP generation but also causes electron leakage and massive reactive oxygen species production, establishing a vicious cycle of “mitochondrial damage-oxidative stress” (54, 55). Metabolomics studies have confirmed significant disturbances in TCA cycle intermediates within brain tissue of SAE model animals (56).
TCA cycle dysfunction extends beyond energy metabolism, profoundly affecting lipid homeostasis [E]. When the cycle is interrupted, citrate accumulates and is exported to the cytoplasm via the citrate shuttle, becoming a substrate for de novo lipogenesis (57, 58). This metabolic rerouting, coupled with impaired β-oxidation due to mitochondrial dysfunction, creates a lipid accumulation state that further compromises cellular membrane integrity and signaling functions. These observations suggest that energy crisis and lipid dysregulation in SAE represent interconnected facets of mitochondrial pathology rather than independent processes (59, 60).
2.1.3 Lipid metabolism dysregulation
Beyond disturbances in energy metabolic pathways, lipid metabolism dysregulation represents another critical characteristic of SAE, with impacts extending from cellular structural damage to signaling network imbalance (, 60). The brain is the most lipid-rich organ; lipids not only constitute the fundamental scaffold of cell membranes but also extensively participate in complex signal transduction (, 61). In SAE, lipid metabolism dysregulation manifests at multiple levels. First, membrane lipid degradation correlates with blood-brain barrier disruption. Phospholipase A2 and other hydrolases are activated to degrade phospholipids in neuronal and glial cell membranes, releasing arachidonic acid and other precursors that can be metabolized into potent pro-inflammatory mediators, exacerbating neuroinflammation and increasing blood-brain barrier permeability (, 61). Second, fatty acid oxidation impairment occurs. Mitochondrial dysfunction obstructs β-oxidation of long-chain fatty acids, not only reducing energy sources but potentially leading to accumulation of lipotoxic substances such as acylcarnitines—one of the significantly altered metabolic pathways confirmed in plasma metabolomics studies of SAE patients (56, 62, 63). Furthermore, signaling lipid imbalance cannot be overlooked. Levels of lysophosphatidylcholine and other signaling lipid molecules undergo substantial changes; these molecules can influence neurotransmitter release, glial cell activation, and cell survival, with their imbalance disrupting the delicate signaling networks within the brain (64–66). Integrative transcriptomic and metabolomic studies have also identified significant lipid metabolic pathway dysregulation in the hippocampus of SAE mice ().
Lipid metabolic disturbances are inextricably linked to amino acid metabolism through shared metabolic intermediates and compartmentalization [E] (67). Mitochondrial dysfunction, which impairs fatty acid oxidation, simultaneously compromises the catabolism of branched-chain amino acids that require the same organelle for their complete oxidation. Furthermore, altered membrane lipid composition affects the activity and localization of amino acid transporters, disrupting the delicate balance of neurotransmitter precursors. This metabolic convergence underscores the systemic nature of SAE-associated metabolic rewiring (68–70).
2.1.4 Amino acid metabolism disturbance
Accompanying lipid metabolism dysregulation, amino acid metabolism disturbance directly disrupts the balance of neurotransmitter systems and the supply of metabolic fuels. Amino acids play multiple roles in the brain: serving as building blocks for protein synthesis, as neurotransmitters or their precursors, and as supplementary fuels for energy metabolism (71–73). Amino acid metabolism disturbance in SAE is both universal and complex. Most critically, imbalance between excitatory and inhibitory neurotransmitters manifests primarily as elevated glutamate levels, which may induce excitotoxicity and constitute an important mechanism of neuronal death (74, 75). Simultaneously, sepsis is frequently complicated by hepatic dysfunction; coupled with increased blood-brain barrier permeability, this enables substantial entry of aromatic amino acids from blood into the brain, where they compete with endogenous amino acids for transport carriers and may be metabolized into “false neurotransmitters” that interfere with normal synaptic transmission, resulting in consciousness disturbances (76–79). Additionally, consumption of branched-chain amino acids warrants attention: peripheral tissues such as muscle degrade proteins to release branched-chain amino acids as energy sources during sepsis, leading to decreased circulating levels of these amino acids, which may affect cerebral energy supply and neurotransmitter synthesis. Multiple metabolomics studies have identified amino acid metabolism among the most significantly affected pathways in SAE (80–83).
2.2 Specific metabolic reprogramming of three key brain cell types
The aforementioned global metabolic disturbances ultimately drive SAE pathogenesis by influencing the metabolic phenotypes of specific brain cells. Microglia, neurons, and astrocytes within the brain form a tightly integrated “neurovascular unit,” each undergoing unique metabolic reprogramming and interacting to collectively determine disease trajectory (, , , 84). The metabolic reprogramming of key brain cell types is summarized in Table 1.
Table 1
| Cell type | Metabolic characteristics | Pathological significance | References |
|---|---|---|---|
| Microglia | Enhanced glycolysis (Warburg effect) driven by mTOR/HIF-1α pathway. | Polarization toward a pro-inflammatory phenotype, releasing IL-1β and mediating NLRP3 pyroptosis. | (, 82–85, 87) |
| Neurons | Highly dependent on mitochondrial oxidative phosphorylation; limited metabolic flexibility. | Energy crisis, ion pump failure, synaptic dysfunction, and cell death. | (88–92, 101–106) |
| Astrocytes | Enhanced glycolysis; potential lactate shuttle to neurons; can transition to a neurotoxic A1 phenotype. | Impaired glutamate uptake exacerbates excitotoxicity; contributes to BBB disruption. | (107–111, 113, 118) |
Specific metabolic reprogramming of key brain cell types in SAE.
2.2.1 Microglia: metabolic coupling of immune activation
As resident immune cells of the central nervous system, microglia play a central role in SAE neuroinflammation, with their activation states tightly coupled to metabolic phenotypes (85–87). Upon stimulation by pathogen-associated molecular patterns such as lipopolysaccharide, microglia rapidly switch from the oxidative phosphorylation-dependent metabolic mode characteristic of the resting state to a pro-inflammatory phenotype dominated by glycolysis. This process is driven by the mTOR/HIF-1α signaling axis; enhanced glycolysis provides necessary bioenergy and metabolic intermediates for synthesizing pro-inflammatory factors and generating reactive oxygen species (88–91). In contrast, tissue-reparative anti-inflammatory phenotypes depend on intact TCA cycle and fatty acid oxidation. Recent studies have additionally revealed that pyroptosis, an inflammatory form of programmed cell death, is activated in SAE microglia; pharmacological inhibition of pyruvate dehydrogenase kinase 4 can reverse the Warburg effect in microglia, thereby suppressing NLRP3 (NOD-, LRR- and pyrin domain-containing protein 3); SOFA (Sequential Organ Failure Assessment) inflammasome-mediated pyroptosis, reducing neuronal death, and improving cognitive function (92, 93). This provides direct evidence for targeting microglial metabolism to control neuroinflammation, demonstrating that modulating microglial metabolic states to shift from pro-inflammatory glycolytic phenotypes toward anti-inflammatory oxidative phosphorylation phenotypes represents a highly attractive therapeutic strategy.
2.2.2 Neurons: energy crisis in highly differentiated terminal cells
Unlike the plasticity of microglia, neurons—as highly differentiated terminal cells—require enormous energy consumption for electrical activity and maintenance of ion gradients, rendering them extremely dependent on efficient mitochondrial oxidative phosphorylation (94–98). In SAE, neurons face dual assaults from internal and external environments, plunging into profound energy crisis. Regarding external supply, systemic hypotension, hypoxemia, and cerebral microcirculatory disturbances directly reduce delivery of the two most critical energy substrates: glucose and oxygen (99–103). Regarding internal production, mitochondria become dysfunctional under attack by oxidative stress and inflammatory mediators, drastically diminishing ATP synthetic capacity (104–106). Compared with glial cells, neurons possess limited capacity to utilize alternative energy sources such as ketone bodies or fatty acids, and their glycolytic capacity is insufficient to compensate for deficits in oxidative phosphorylation. This severe energy deficit directly precipitates neuronal dysfunction and death: inability to maintain membrane potential leads to ion pump failure and cellular edema; synaptic transmission interruption causes abnormal signal processing; calcium overload activates multiple degradative enzymes, ultimately triggering apoptotic or necrotic programs (107–112). Therefore, protecting neuronal mitochondrial function and restoring energy supply constitutes the core of neuroprotective strategies in SAE. Metabolic reprogramming of key brain cell types is illustrated in Figure 1.
Figure 1
2.2.3 Astrocytes: functional reprogramming from “guardians” to “destroyers”
Within the brain's cellular network, astrocytes—as the most abundant glial cell type—play multiple critical “housekeeping” roles, from maintaining blood-brain barrier integrity and providing nutritional support to neurons, to buffering ion and neurotransmitter concentrations (113–115). In SAE, astrocytes similarly undergo complex metabolic and functional reprogramming, with their roles potentially shifting from “guardians” to “destroyers.” Under inflammatory stimulation, astrocytes become activated and undergo reactive proliferation; their metabolic mode may also enhance glycolysis, providing energy for self-proliferation while potentially supplying energy substrates to damaged neurons through “astrocyte-neuron lactate shuttle,” reflecting their potential protective effects (116–118). However, analogous to microglial polarization, activated astrocytes can be classified into neurotoxic A1 phenotypes and neuroprotective A2 phenotypes, with A1 astrocytes releasing multiple substances toxic to neurons and oligodendrocytes (119, 120). Although current research on their metabolic characteristics remains insufficient, this functional differentiation likely undergoes regulation by specific metabolic pathways (121–123). Particularly importantly, astrocytes are responsible for uptake of excess glutamate from the synaptic cleft to prevent excitotoxicity; this ATP-dependent process becomes severely impaired during SAE energy crisis, leading to glutamate accumulation in the synaptic cleft and thereby exacerbating neuronal damage (116, 124, 125). Despite the need for further in-depth research on specific metabolic reprogramming of astrocytes in SAE, their role as core components of the neurovascular unit ensures that alterations in their metabolic states will inevitably exert profound influences on SAE disease course (116, 117).
2.2.4 Peripheral immune cell infiltration and brain metabolic microenvironment remodeling
Importantly, recent metabolomic studies have identified succinate as a key metabolite linking peripheral inflammation to brain dysfunction in sepsis [A] (126). Elevated circulating succinate levels correlate with SAE severity and may serve as a metabolic signal coordinating immune cell function across the peripheral-central interface. These findings highlight that SAE metabolic disturbances are not confined to the CNS but represent systemic metabolic dysregulation manifesting in the brain (, 126).
T lymphocytes, upon antigen-independent activation during sepsis, demonstrate metabolic reprogramming toward glycolysis to support rapid proliferation and cytokine production [A]. The presence of metabolically active T cells in perivascular spaces may influence astrocytic and microglial metabolic states through paracrine signaling (, 127).
Neutrophils, characterized by their reliance on aerobic glycolysis for effector functions including NETosis, release extracellular traps enriched in oxidizing enzymes and proteases that further perturb local metabolic homeostasis [A]. Their brief but intense metabolic activity creates localized regions of hypoxia and acidification that stress neighboring neural cells (128, 129).
Circulating monocytes recruited to the brain undergo rapid metabolic adaptation upon infiltration. Recent single-cell analyses reveal that infiltrating monocyte-derived macrophages exhibit enhanced glycolytic activity compared to their circulating counterparts, competing with resident microglia for glucose substrates while amplifying local inflammatory signals [A] (130). This metabolic competition may exacerbate neuronal energy deprivation during critical disease phases.
While the preceding discussion focuses on intrinsic brain cells, accumulating evidence indicates that infiltrating peripheral immune cells significantly contribute to and reshape the brain's metabolic microenvironment during SAE [A/H] (130). Under systemic inflammatory conditions, monocytes, neutrophils, and T cells traverse the compromised blood-brain barrier, bringing their distinct metabolic profiles into the CNS parenchyma (131, 132).
2.3 Gut-brain axis metabolic dialogue: “adding fuel to the fire” from distal organs
Building upon in-depth analysis of intracerebral cellular metabolic reprogramming, recent research has revealed that metabolic disturbances in SAE do not occur in isolation within the cranium but are profoundly influenced by metabolic states of distal organs, particularly the intestine, with the gut-brain axis playing a role of “adding fuel to the fire” (133, 134). Sepsis frequently causes impaired intestinal barrier function, or “leaky gut,” accompanied by dysbiosis, producing far-reaching systemic effects that propagate to the brain through metabolic pathways. Metabolites produced by intestinal microbiota, such as short-chain fatty acids, secondary bile acids, and indole derivatives, possess important immunomodulatory and neuromodulatory functions (135–137). Sepsis-induced microbiota disturbance alters the profile of these metabolites—for instance, reduced production of anti-inflammatory and neuroprotective short-chain fatty acids, with potential increases in harmful metabolites. These altered metabolites can reach the brain through the circulatory system, directly or indirectly influencing brain cell function. They can modulate peripheral immune cell activity, affecting their migration to the brain, and can cross the compromised blood-brain barrier to directly act upon microglia and astrocytes, regulating their activation states and metabolic phenotypes (133, 138–140). A study in a sepsis mouse model explicitly revealed functional disturbance of the “gut microbiota-hippocampus-metabolite axis,” confirming the association between intestinal dysbiosis and hippocampal dysfunction and identifying related metabolite alterations (141). This suggests that when considering intervention strategies for SAE, metabolic dialogue along the gut-brain axis must be incorporated into consideration—for example, indirectly intervening in brain function through modulation of intestinal microbiota or supplementation of key microbial metabolites—opening new possibilities for future SAE prevention and treatment (133, 142).
3 Biomarker exploration driven by metabolic reprogramming
The clinical diagnosis of SAE currently remains predominantly dependent on clinical manifestations and exclusionary strategies, with a notable absence of molecular biomarkers possessing both high sensitivity and strong specificity. This limitation not only delays the implementation of early intervention but also restricts precise subject selection in clinical trials and objective assessment of therapeutic efficacy. As the terminal output products of cellular functional states and pathophysiological processes, metabolites inherently possess ideal attributes for serving as disease markers (56, 143). Therefore, systematically mining characteristic metabolites capable of reflecting core pathological alterations in SAE from the perspective of metabolic reprogramming opens novel research avenues for early identification and mechanistic stratification of this disease. Key metabolomics-driven biomarkers for SAE are summarized in Table 2.
Table 2
| Metabolic pathway | Representative metabolites | Pathological significance | References|evidence level |
|---|---|---|---|
| Fatty acid oxidation | Acylcarnitines | Mitochondrial dysfunction; impaired fatty acid oxidation. | (57, 61, 147)[A/H] |
| Phospholipid metabolism | Lysophosphatidylcholines (LysoPCs) | Membrane structural damage; signaling imbalance. | (62–64, 147)[A/H] |
| TCA cycle | Succinate | Stabilizes HIF-1α, promoting a pro-inflammatory state. | (85, 149, 150)[A/I] |
| Aromatic amino acid metabolism | 4-hydroxyphenylacetic acid | Potential marker for gut dysbiosis and neurotransmitter synthesis abnormalities. | (151, 152)[H] |
| One-carbon metabolism | Betaine, folate metabolites | Disturbances in methylation processes, affecting neuronal function. | (147)[A] |
Metabolomics-driven biomarkers for SAE.
3.1 Limitations of traditional biomarkers
Retrospective examination of prior research reveals that traditional candidate biomarkers have primarily concentrated on neuronal injury markers such as neuron-specific enolase, glial activation or injury markers such as S100B protein and glial fibrillary acidic protein, and various inflammatory cytokines such as interleukin-6 (144–146). However, these markers universally face significant limitations in clinical application and mechanistic interpretation. First, the issue of non-specificity is particularly prominent; the aforementioned markers can be elevated in multiple central nervous system injuries including traumatic brain injury and stroke, making precise targeting of SAE difficult (147–149). Second, the release of these markers typically occurs following structural neuronal injury or glial activation, and their elevated levels may miss the optimal time window for early intervention (150–152). Furthermore, the in vivo clearance of protein-based markers is highly dependent on renal function, whereas sepsis patients frequently develop acute kidney injury, which directly interferes with clinical interpretation of plasma marker concentrations (153–156). Most critically, traditional markers largely reflect the final outcomes of injury rather than revealing the dynamic pathological mechanisms driving this injury process, thus providing limited guidance for mechanism-directed intervention strategies (157, 158).
3.1.1 Critical barriers to clinical translation of metabolic biomarkers
Linking biomarkers to therapeutic decisions: For metabolic biomarkers to achieve clinical impact, they must inform therapeutic decisions beyond mere diagnosis. Future research should focus on identifying metabolite signatures that predict response to specific metabolic interventions (e.g., glycolytic inhibitors, mitochondrial protectors), enabling biomarker-guided personalized therapy rather than one-size-fits-all approaches.
Therapeutic and nutritional interference: Standard ICU interventions—including vasoactive medications, sedatives, continuous renal replacement therapy, and parenteral nutrition—substantially alter metabolite profiles. Without standardized sampling protocols accounting for these confounders, inter-study comparability and clinical utility remain limited.
Specificity for SAE vs. sepsis severity: A fundamental challenge lies in distinguishing metabolite alterations specific to brain dysfunction from those reflecting systemic sepsis severity. Many candidate biomarkers identified in SAE studies correlate equally well with SOFA (Sequential Organ Failure Assessment); scores or lactate levels, raising questions about their added diagnostic value beyond general severity assessment (159, 160).
Confounding by organ dysfunction: Sepsis commonly involves multi-organ dysfunction, particularly acute kidney injury, which profoundly affects metabolite clearance and plasma concentrations [H]. Elevated acylcarnitines or amino acid derivatives may reflect renal excretory impairment rather than specific brain metabolic pathology, complicating clinical interpretation.
Temporal dynamics and sampling windows: Metabolite profiles exhibit rapid temporal evolution during sepsis progression. The optimal timing for sample collection—whether at ICU admission, at peak illness severity, or during resolution phases—remains undefined. Single time-point measurements may miss critical metabolic transitions or capture transient fluctuations rather than sustained pathological signatures.
Point-of-care applicability: Current metabolomics platforms typically require sophisticated mass spectrometry or NMR (nuclear magnetic resonance); AMP (adenosine monophosphate) facilities with specialized personnel, limiting their deployment in emergency department settings where early SAE detection would be most valuable. Development of rapid, bedside-compatible assays for priority metabolites remains an unmet need.
3.2 Metabolomics-driven discovery of differential metabolites
With the rapid advancement of high-throughput, high-sensitivity metabolomics detection technologies—particularly the widespread application of mass spectrometry and nuclear magnetic resonance techniques—researchers can now simultaneously detect hundreds to thousands of small-molecule metabolites in blood, cerebrospinal fluid, urine, and even tissue samples, thereby capturing systemic metabolic disturbance characteristics during SAE occurrence and progression through a panoramic perspective (56, 161, 162). In recent years, multiple cutting-edge metabolomics-based studies have identified a series of differential metabolites with potential biomarker value and their associated pathways in SAE patients and animal models, providing novel data support for understanding the pathological essence of SAE (56, 163).
Notably, a prospective study conducted plasma untargeted metabolomics analysis in SAE patients, identifying multiple significantly perturbed metabolic pathways including acylcarnitine metabolism, lysophosphatidylcholine metabolism, betaine metabolism, folate biosynthesis, and cytochrome P450 drug metabolism pathways. The study further screened sixty-four potential differential metabolites, providing a rich candidate molecular library for constructing SAE diagnostic or prognostic prediction models (163). The alterations in these metabolites, respectively point to core pathological processes such as mitochondrial fatty acid oxidation impairment, cell membrane lipid degradation and signaling imbalance, and one-carbon unit metabolism disturbance, highly consistent with the aforementioned mechanistic descriptions of metabolic reprogramming. Additional independent studies have reported that significantly enriched differential metabolic pathways in SAE involve iminoglycine metabolism, aspartate and alanine metabolism, pantothenate and glutamate metabolism, coenzyme A biosynthesis, and linoleic acid, betaine, and hypoxanthine metabolism, once again confirming the central positions of amino acid metabolism disturbance, energy metabolism impairment, and lipid metabolism imbalance in SAE pathogenesis (, 56).
Particularly important is that certain metabolites are themselves embedded within specific pathophysiological pathways, with their level changes capable of directly reflecting mechanistic disease progression (164). For instance, succinate, as a key molecule connecting the tricarboxylic acid cycle with inflammatory signal transduction, its elevated levels in SAE may predict activation of mitochondrial stress states and hypoxia-inducible factor-1α-mediated inflammatory response cascades (91, 165, 166). Additionally, 4-hydroxyphenylacetic acid has been identified as a promising biomarker, with its plasma levels demonstrating significant correlation with the severity of consciousness disturbance in SAE patients, potentially reflecting abnormalities in aromatic amino acid metabolism against the background of intestinal microbiota disturbance. These findings suggest that metabolomics-driven biomarker exploration can provide not only novel diagnostic tools but, more importantly, the identified metabolites themselves carry mechanistic information regarding disease states, capable of revealing SAE heterogeneity characteristics at the molecular level—an advantage unattainable by traditional markers (167, 168). Therefore, conducting in-depth SAE biomarker research with metabolic reprogramming as the theoretical framework combined with metabolomics technology holds promise for providing critical molecular evidence for achieving early diagnosis, precise stratification, and individualized treatment of this disease.
3.3 Multi-omics integration: the inevitable path toward precision medicine
Although single-omics technologies can reveal pathological features of SAE at specific molecular levels, they capture only one facet of complex biological processes. To achieve more comprehensive and systematic resolution of the molecular essence of SAE and to genuinely advance biomarker discovery toward clinical translation, multi-omics integration analysis has become an inevitable trend in this research field (169, 170). Through systematic integration of genomic, transcriptomic, proteomic, and metabolomic data, researchers can construct complete molecular regulatory networks spanning from upstream gene regulation, through intermediate functional protein expression, to downstream metabolite output, thereby identifying biomarker combinations and potential therapeutic targets with enhanced robustness and causal relevance (171–173).
In recent years, the application of multi-omics integration strategies in SAE research has achieved significant progress. Taking the joint analysis of transcriptomics and metabolomics as an example, a study successfully mapped the molecular landscape of the disease through parallel omics detection of hippocampal tissue from SAE mice. This study identified eighty-one differentially expressed metabolites and over one thousand seven hundred differentially expressed genes, subsequently revealing intrinsic associations among neuroinflammatory activation, downregulation of multiple core metabolic pathways, and synaptic functional impairment. Such integrative analysis not only validated the systemic dysregulation of lipid, amino acid, glucose, and nucleotide metabolism in SAE but also traced upstream transcriptional regulatory events driving these metabolic alterations, providing a deeper causal perspective for understanding disease pathogenesis (174).
The joint analysis of proteomics and metabolomics offers a more direct chain of evidence for mechanistic validation of metabolic regulation. One study integrated plasma proteomic and metabolomic data from sepsis patients, identifying amino acid metabolism disturbance as a core pathological characteristic throughout the SAE disease course, and specifically highlighting the potential role of aberrant alterations in the pentose phosphate pathway in disease occurrence and progression. Proteomics can precisely identify expression-level changes in key enzymes regulating metabolic pathways, while metabolomics validates the functional consequences resulting from these changes; their complementary combination substantially enhances the reliability of research conclusions and mechanistic explanatory power (175, 176).
Future-oriented research paradigms are increasingly incorporating machine learning and artificial intelligence algorithms into the processing and interpretation of multi-omics data. By integrating clinical phenotypic data with multi-dimensional omics information, researchers aim to construct complex predictive models capable of early prediction of SAE risk, precise assessment of disease severity, and even prediction of long-term cognitive prognosis. This data-driven integrative analytical strategy not only facilitates precise stratification of SAE patients but also provides scientific foundations for the formulation of personalized treatment protocols (, 116, 177–179).
In summary, biomarker research driven by the perspective of metabolic reprogramming is undergoing a profound transformation from discovery of individual differential metabolites toward construction of biomarker combinations and predictive models guided by multi-omics integration. This evolution in research paradigms not only holds promise for reshaping clinical diagnostic and therapeutic pathways for SAE but also lays a solid scientific foundation for the ultimate realization of precision medicine in this field.
4 Therapeutic strategies targeting metabolic reprogramming
Given the central driving role of metabolic reprogramming in the pathological progression of SAE, direct intervention in specific metabolic pathways and correction of systemic metabolic disturbances have emerged as highly promising new therapeutic directions in this field. This strategy not only requires us to re-examine and optimize existing clinical supportive treatments from a novel metabolic perspective but also encompasses the development and application of a series of new modulators targeting metabolic nodes (, , 169). Specific therapeutic strategies are summarized in Table 3.
Table 3
| Intervention type | Representative agent/strategy | Mechanism of action | References|evidence level|major limitations |
|---|---|---|---|
| Metabolic modulator | Metformin | Activates AMPK, inhibits mTOR/HIF-1α axis, reducing neuroinflammation. | (176–184)[A]|lactic acidosis risk; BBB penetration unclear |
| Antioxidant and mitochondrial protector | Melatonin | Activates SIRT1/NRF2, inhibits NLRP3, protects mitochondrial function. | (187–193)[A] vs. [H-]|ICU trials negative; inconsistent PK |
| Selective antioxidant | Hydrogen gas | Scavenges hydroxyl radicals, stabilizes BBB, modulates mitochondrial function. | (196–204)[A]|no human SAE data; delivery standardization needed |
| Metabolic redirector | Dichloroacetate (DCA) | Inhibits PDK4, activates PDH, reverses the Warburg effect, inhibits pyroptosis. | (87, 207, 208)[A]|neurotoxicity concerns; limited efficacy data |
| Supportive care | Precision nutrition | Dynamically adjusts carbohydrate, lipid, and amino acid ratios to alleviate metabolic burden. | (166, 169, 170)[E]|optimal composition undefined; delivery challenges |
Therapeutic strategies targeting metabolic reprogramming in SAE.
4.1 Re-evaluating standard supportive care from a metabolic perspective
Reflecting on metabolic reprogramming, many standardized supportive treatments for septic patients in intensive care units inherently possess profound metabolic intervention significance. Taking glycemic control as an example, sepsis frequently induces stress hyperglycemia, which not only exacerbates tissue damage through oxidative stress pathways but more importantly provides excessive glycolytic substrates for the Warburg effect in immune cells, thereby “fueling” the inflammatory response (42, 180–183). Therefore, strict glycemic management exerts anti-inflammatory effects largely by restricting fuel supply for pro-inflammatory metabolism (184). Nutritional support similarly faces complex metabolic trade-offs: overfeeding may aggravate the already overwhelmed mitochondrial burden, whereas undernutrition intensifies energy crisis and muscle catabolism (185, 186). Future precision nutrition strategies require dynamic adjustment of carbohydrate, lipid, and specific amino acid proportions based on patients' metabolic phenotypes, aiming to provide appropriate energy substrates for the damaged brain while avoiding exacerbation of pathological metabolic reprogramming. Furthermore, aggressive fluid resuscitation and vasoactive agent administration fundamentally target restoration of macroscopic and microscopic tissue perfusion, ensuring effective delivery of critical metabolic substrates such as oxygen and glucose, thereby alleviating cellular energy crisis at its source (187–189). Thus, integrating the theoretical framework of metabolic reprogramming into conventional treatments deepens understanding of their intrinsic mechanisms and provides theoretical guidance for achieving personalized management based on individual metabolic characteristics.
4.2 Novel metabolic modulators in preclinical models
Building upon in-depth understanding of existing treatments, a series of drugs capable of precisely regulating cellular metabolism have demonstrated encouraging therapeutic potential in preclinical animal models of SAE in recent years (39, 190, 191).
4.2.1 Metformin
Metformin, as a first-line therapeutic agent for type 2 diabetes mellitus, has activation of adenosine monophosphate-activated protein kinase (AMPK) as one of its core mechanistic actions. AMPK serves as the cellular “energy sensor,” activated when ATP levels decline, restoring energy homeostasis through inhibiting anabolism and promoting catabolism (192–195). In the SAE context, AMPK activation effectively suppresses glycolysis driven by the mTOR/HIF-1α axis, thereby attenuating pro-inflammatory responses in microglia (196, 197). Numerous animal experiments demonstrate that metformin can alleviate sepsis-induced neuroinflammation, neuronal apoptosis, and blood-brain barrier injury while significantly improving cognitive function; its protective effects also involve multiple mechanisms including antioxidant activity and epigenetic modification (198–200). Although some retrospective clinical studies suggest metformin may improve prognosis in septic patients, its use during the acute critical phase requires vigilance regarding lactic acidosis risk, and confirmation from large-scale prospective clinical trials remains lacking (201, 202).
Clinical translation status: Currently not recommended for acute SAE treatment outside clinical trials.
Major translational limitations: (1) The risk of lactic acidosis in critically ill patients with hemodynamic instability or renal dysfunction contraindicates metformin use during acute sepsis; (2) BBB penetration of metformin under inflammatory conditions is incompletely characterized; (3) Optimal dosing and timing relative to sepsis onset remain undefined.
Evidence assessment: Current support for metformin in SAE derives predominantly from animal models [A], with limited clinical data specifically examining neurological outcomes in septic patients. A retrospective cohort study suggested improved mortality in diabetic septic patients receiving metformin, though neurocognitive endpoints were not assessed [H].
4.2.2 Melatonin
Melatonin, as a potent endogenous antioxidant, exhibits diverse neuroprotective mechanisms. From a metabolic perspective, melatonin not only directly scavenges free radicals and activates antioxidant pathways such as SIRT1/NRF2 (sirtuin 1/nuclear factor erythroid 2-related factor 2); but also attenuates inflammatory responses through NLRP3 inflammasome inhibition; more critically, it protects mitochondrial function and reduces energy failure resulting from oxidative stress (203–206). In various SAE animal models, melatonin has been proven effective in reducing cerebral edema, cellular apoptosis, and cognitive impairment (207–209). Given its extremely high safety profile, small-scale clinical trials have already demonstrated its potential in treating multi-organ injury in sepsis, though optimal dosing and administration timing for SAE treatment await determination through large-scale clinical studies (210, 211).
Clinical translation status: Evidence insufficient to support routine clinical use; may warrant further investigation in specifically selected SAE subpopulations.
Major translational limitations: (1) Inconsistent pharmacokinetics in critically ill patients; (2) Optimal dosing (high-dose vs. physiological replacement) remains debated; (3) Timing of administration relative to injury onset may be critical but is undefined; (4) Conflicting clinical trial results question the generalizability of animal findings to human ICU populations.
Evidence assessment: Robust neuroprotective effects are demonstrated in multiple SAE animal models [A]. However, recent randomized controlled trials in critically ill patients have yielded disappointing results. The MENDS2 (Maximizing the Efficacy of Sedation and Reducing Neurological Dysfunction and Mortality in Septic Patients with Acute Respiratory Failure) trial and other studies examining melatonin or melatonin receptor agonists for delirium prevention in ICU settings showed no significant benefit in reducing acute neurological dysfunction [H]. These negative findings highlight the translational gap between preclinical promise and clinical efficacy.
4.2.3 Hydrogen gas
Hydrogen gas, as a selective antioxidant, possesses the unique advantage of effectively neutralizing the most toxic hydroxyl radicals and peroxynitrite anions while minimally affecting reactive oxygen species with normal physiological signaling functions (212). Its protective mechanisms additionally include activation of the Nrf2 antioxidant pathway, NLRP3 inflammasome inhibition, blood-brain barrier stabilization, and mitochondrial function modulation (213–218). Animal experiments consistently demonstrate that hydrogen inhalation or consumption of hydrogen-rich water significantly improves survival rates and neurological outcomes in septic animals (219, 220). The biological safety of hydrogen has been confirmed in clinical trials for other diseases; as a simple and inexpensive therapeutic modality, its clinical translation potential in SAE is substantial, though targeted clinical research data remain currently unavailable (221, 222).
Clinical translation status: Preclinical stage; clinical trials in SAE or general sepsis populations needed before therapeutic consideration.
Major translational limitations: (1) Delivery methods (inhalation vs. hydrogen-rich water) require standardization; (2) Optimal concentration, duration, and timing of administration undefined; (3) Safety profile in critically ill, ventilated patients requires formal evaluation; (4) Regulatory pathways for hydrogen as a therapeutic agent remain unclear in most jurisdictions.
Evidence assessment: All current evidence for hydrogen therapy in SAE derives from animal models [A]; no human SAE-specific studies have been published. Promising findings in rodent CLP and LPS models demonstrate improved survival and reduced brain injury biomarkers.
4.2.4 Dichloroacetate
The mechanism of dichloroacetate (DCA) exemplifies precise linkage from metabolic correction to cellular protection. DCA is an inhibitor of pyruvate dehydrogenase kinase (PDK), capable of activating pyruvate dehydrogenase (PDH)—the key rate-limiting enzyme converting the glycolytic product pyruvate into acetyl-CoA for entry into the tricarboxylic acid cycle—through PDK inhibition. Consequently, DCA forcibly redirects cellular metabolism from glycolysis toward oxidative phosphorylation (223, 224). A pioneering study discovered that DCA specifically inhibits pyroptosis in microglia in SAE models, with the mechanism precisely involving reversal of the Warburg effect through PDK4 isoform inhibition, thereby blocking NLRP3 inflammasome activation. This research precisely linked metabolic modulation to specific cell death programs, providing robust theoretical justification for DCA application (93). However, DCA was previously used for treating inherited lactic acidosis, and its neurotoxicity with long-term administration represents the primary obstacle to clinical application; nevertheless, short-term use in acute diseases such as SAE may offer a more favorable risk-benefit ratio, though rigorous clinical trials remain necessary to evaluate its safety and efficacy (225, 226).
Clinical translation status: Early preclinical; significant safety and efficacy data required before clinical consideration.
Major translational limitations: (1) Historical concerns regarding peripheral neuropathy with chronic DCA administration, though short-term use in acute illness may present acceptable risk-benefit profile; (2) BBB penetration characteristics in sepsis-related BBB disruption unknown; (3) Potential for lactic acidosis in patients with thiamine deficiency; (4) No pharmaceutical-grade formulation readily available for clinical use.
Evidence assessment: The evidence base for DCA in SAE consists of a single mechanistic study demonstrating microglial pyroptosis inhibition through PDK4 blockade [A]. No broader efficacy studies or dose-response analyses in SAE models have been published.
4.3 Challenges and future directions in clinical translation
Despite abundant achievements in preclinical research, successful translation of metabolic modulators into clinical therapies for SAE faces severe challenges. First is the issue of patient heterogeneity: the etiology, severity, and pathophysiological processes of sepsis are highly heterogeneous, and uniform intervention protocols may prove ineffective or even harmful for certain patients; future approaches must employ precise stratification based on biomarkers (227–231). Second is the determination of therapeutic time windows: metabolic reprogramming is a dynamic process—early enhancement of glycolysis may facilitate pathogen clearance, whereas sustained Warburg effects in later stages are deleterious; identifying optimal intervention timing is therefore critical (232, 233). Additionally, target specificity and off-target effects present substantial difficulties: many metabolic enzymes play divergent roles in different cell types, and systemic administration may produce unintended effects; developing drug delivery systems targeting specific cells represents an important future direction (234–236). Simultaneously, drugs targeting the central nervous system must effectively penetrate the blood-brain barrier, and the physicochemical properties of many candidate drugs limit their cerebral bioavailability (237–242). Finally, preclinical studies predominantly employ young, healthy inbred animals, which differ substantially from the complex, multi-comorbid ICU patients encountered clinically—this disparity constitutes a primary reason for drug failures in clinical trials (243–245). Future drug development should focus on developing novel metabolic modulators with higher cell-type and pathway specificity, utilizing advanced delivery systems such as nanotechnology to achieve targeted therapy, and conducting adaptive clinical trials based on precision biomarker stratification to bridge the translational gap from basic research to clinical application.
5 Research technologies and future perspectives
The continuous advancement of metabolic reprogramming research in SAE relies indispensably on the refined application of advanced research tools and prospective strategic planning. From optimized selection of animal models to breakthroughs in single-cell resolution multi-omics technologies, and further to systematic design of translational medicine pathways, each determinant critically influences the efficiency and feasibility of translating basic research discoveries into clinical practice (246–248). These technologies are summarized in Table 4.
Table 4
| Technology/direction | Applications and significance | Challenges and future outlook | References |
|---|---|---|---|
| Animal models | CLP model for polymicrobial sepsis; LPS model for mechanistic screening. | High inter-model variability; metabolic profiles influenced by anesthesia, strain, and circadian rhythms; requires multi-model validation. | (233–254) |
| Multi-omics integration | Constructs regulatory networks by integrating genomics, transcriptomics, proteomics, and metabolomics. | Data standardization and algorithmic complexity; requires interdisciplinary collaboration. | (255–266) |
| Single-cell metabolomics | Resolves cellular heterogeneity and reveals metabolic interactions within the microenvironment. | Technical bottlenecks: low metabolite content, need for high sensitivity, and loss of spatial information. | (267–284) |
| Translational pathways | Biomarker validation; drug repurposing; novel drug development; combination therapies. | Requires large-scale validation, precise patient stratification, and targeted drug delivery systems. | (285–298) |
Research technologies and future perspectives in SAE.
5.1 Advantages and limitations of commonly used animal models
The advantages and limitations of commonly used animal models constitute the cornerstone of metabolic research. The cecal ligation and puncture (CLP) model is widely recognized as the “gold standard” for sepsis research (249–251). Through surgical intestinal perforation, this model induces intraperitoneal polymicrobial infection and peritonitis, effectively simulating the pathophysiological processes of human sepsis, including hyperdynamic circulatory states, dynamic evolution of inflammatory responses, and multiple organ dysfunction. The model demonstrates relatively high reproducibility; researchers can precisely control infection severity by adjusting ligation length, needle gauge, and puncture frequency (251). However, the technical complexity of CLP procedures demands high surgical expertise, and variability in standardization across different laboratories may introduce result heterogeneity, while surgical trauma itself introduces additional inflammatory variables (252–255). In contrast, the lipopolysaccharide (LPS) model, administered via intraperitoneal or intravenous injection of the principal component of Gram-negative bacterial outer membranes, rapidly induces intense, predictable systemic inflammatory responses with extremely simple operation, controllable dosage, and excellent repeatability, rendering it suitable for high-throughput drug screening and preliminary mechanistic exploration (256–258). Nevertheless, the primary limitation of the LPS model lies in its simulation of pure, single-pathogen-molecule-driven “sterile inflammation,” lacking authentic infectious foci and pathogen proliferation processes; its inflammatory kinetics diverge substantially from clinical sepsis, and high-dose administration may even induce hypodynamic shock states inconsistent with clinical presentations (259–263). When conducting metabolic research, regardless of the model employed, investigators must exercise extreme caution in controlling the significant influences of anesthetics, surgical stress, animal strain, age, sex, and circadian rhythms on metabolic profiles, while recognizing the limited translational rate of animal model data to clinical applications; critical findings require rigorous validation in human specimens (264–269). The optimal strategy involves combining multiple models, utilizing respective advantages for cross-validation of key conclusions to enhance research reliability (270).
5.2 Necessity and challenges of multi-omics integration analysis
The necessity and challenges of multi-omics integration analysis are becoming increasingly prominent. Single-omics technologies are now insufficient to reveal the complex pathological processes of SAE; future research must systematically integrate genomic, epigenomic, transcriptomic, proteomic, and metabolomic data (271–274). The core value of such integrative analysis lies in its capacity to construct complete pathological regulatory networks spanning from upstream genetic mutations or epigenetic modifications, through alterations in transcription and translation, to final metabolic functional outputs, thereby identifying core driver nodes that decisively influence disease states within complex molecular networks as the most efficient therapeutic targets (275–277). More importantly, through clustering analysis of patient multi-omics data, SAE subtypes based on distinct molecular characteristics may be discovered, laying foundations for stratified treatment and precision medicine (274, 278–280). However, multi-omics integration faces numerous challenges including data standardization, dimensional disparities between different omics datasets, complexity of bioinformatic analysis algorithms, and massive data storage and computational requirements, urgently necessitating close interdisciplinary collaboration (279–282).
5.3 Breakthrough potential of single-cell resolution metabolomics
The breakthrough potential of single-cell resolution metabolomics is becoming a highly anticipated frontier direction within the field. Traditional tissue metabolomics analysis provides averaged metabolic states across millions of cells, inevitably masking critical metabolic heterogeneity between different cell types and even among different subpopulations within the same cell type (283–286). Considering that the brain is a complex organ composed of multiple highly specialized cell types with fundamentally distinct metabolic reprogramming patterns in SAE—microglia, neurons, and astrocytes—achieving single-cell resolution metabolic analysis holds paramount significance for understanding SAE pathophysiology (, 287–289). Although single-cell transcriptomics has been extensively applied in sepsis research and revealed immune cell heterogeneity, single-cell metabolomics remains in its infancy, facing technical bottlenecks including extremely low metabolite content within single cells, extraordinarily high detection sensitivity requirements, and loss of spatial information (290–293). Currently, no published studies have directly applied single-cell metabolomics in the SAE field. With continuous development of technologies including mass spectrometry imaging, microfluidics, and novel probes, single-cell metabolomics is expected to achieve breakthroughs within the coming years (294, 295). Once realized, it will enable precise delineation of specific metabolic atlases for each brain cell type in SAE, discovery of rare but potentially critical cell subpopulations in disease progression, revelation of “metabolic communication” and nutritional support networks between cells through metabolites, and assessment of drug effects on metabolic states of different cell types at the single-cell level (296–298). Single-cell multi-omics, particularly the combination of single-cell transcriptomics with single-cell metabolomics, will provide unprecedented high-resolution perspectives for deconstructing SAE complexity (299, 300).
5.4 Four feasible pathways for translational medicine
Four feasible pathways for translational medicine illuminate the direction for ultimate realization of SAE research value. Translating basic research discoveries into clinical practice benefits represents the ultimate objective of SAE research. Based on current research progress, future translational medicine exploration can proceed systematically along four pathways (301, 302). First is clinical validation and application of biomarkers: systematically validating metabolic reprogramming-driven candidate biomarkers discovered in animal models and small cohorts within large multicenter prospective clinical cohorts, developing clinical detection systems applicable for early diagnosis, risk stratification, and prognostic prediction (303–306). Second is drug repurposing strategies: prioritizing randomized controlled clinical trials of metabolic modulators already in clinical use with established safety profiles for SAE, substantially shortening development timelines and reducing costs (307–309). Third is development of novel targeted drugs: designing and developing novel small-molecule inhibitors or activators with high specificity and favorable blood-brain barrier penetrability targeting metabolic enzymes or pathways validated as core driver nodes in basic research (310–313). Fourth is exploration of combination therapeutic strategies: considering the multidimensional nature of SAE pathological mechanisms, future treatment will likely involve not single-target intervention but comprehensive combination regimens, such as combining metabolism-targeting drugs with anti-inflammatory agents or neuroprotectants to achieve synergistic therapeutic effects (86, 191, 198, 314). These four pathways complement one another, collectively constituting a systematic translational framework from laboratory to bedside.
6 Conclusion
This review systematically demonstrates that metabolic reprogramming offers an integrative framework for understanding SAE pathophysiology. The four core metabolic pathways—glycolysis/Warburg effect, TCA cycle dysfunction, lipid dysregulation, and amino acid disturbance—interact to drive neuroinflammatory activation, energy crisis, and cellular damage across microglia, neurons, and astrocytes. The gut-brain axis further modulates these processes through microbiota-derived metabolites.
However, significant evidence gaps must be acknowledged. Most mechanistic insights derive from animal models and require validation in human SAE. Causal relationships between specific metabolic alterations and neurological outcomes remain incompletely established. The translational barriers facing metabolic biomarkers and targeted therapies—including assay standardization, therapeutic time windows, and patient heterogeneity—are substantial and have impeded clinical progress.
Future priorities should include: (1) longitudinal multi-omics studies capturing metabolic evolution from acute illness through long-term cognitive outcomes; (2) single-cell and spatial metabolomics to resolve cellular heterogeneity; (3) biomarker-guided therapeutic trials testing whether metabolic modulation improves patient-centered outcomes; and (4) rigorous validation of preclinical findings in clinically relevant human cohorts. Addressing these challenges will determine whether metabolic reprogramming can transition from a compelling research framework to a clinically actionable paradigm for SAE prevention and treatment.
However, despite substantial progress achieved through existing experimental findings, we must soberly recognize the deficiencies and limitations present in current research. First, causal relationship verification remains insufficient; the majority of studies, particularly human investigations, are essentially correlational in nature. While we observe that alterations in specific metabolites are closely associated with SAE occurrence and progression, whether such alterations represent driving factors, pathological consequences, or merely accompanying phenomena remains to be rigorously validated through more stringent experimental approaches including gene editing, isotope tracing, and metabolic flux analysis. Second, serious neglect of cellular heterogeneity constrains our in-depth understanding of SAE pathological essence; most existing data derive from bulk tissue analysis, which completely masks critical metabolic differences between different cell types and even among different subpopulations within the same cell type, while our knowledge regarding specific metabolic reprogramming patterns and functional significance of non-immune cells such as astrocytes, oligodendrocytes, and vascular endothelial cells in SAE remains remarkably limited. Third, substantial challenges in clinical translation cannot be overlooked; from young healthy inbred animal models to elderly patients with multiple comorbidities, from standardized experimental conditions to complex and variable clinical scenarios, and from short-term observational indicators to long-term cognitive prognosis, differences at every stage may constitute fatal barriers to translating basic research findings into clinical practice.
Based on profound reflection upon these limitations, future research should seek breakthroughs in the following strategic directions to bring revolutionary advances to SAE diagnosis and treatment. First, embrace single-cell and spatial resolution technologies: vigorously develop and apply cutting-edge methodologies in single-cell metabolomics and spatial metabolomics to precisely delineate, with highest resolution, the metabolic states, metabolic dynamics, spatial distribution patterns, and intercellular interactions of different cell types within SAE lesions, thereby truly deconstructing cellular heterogeneity and molecular network complexity of the disease. Second, conduct longitudinal, multi-omics integrated clinical studies: design large-scale prospective clinical cohorts with continuous collection of multidimensional biological samples from patients at various critical time points throughout disease evolution—from intensive care unit admission through acute phase outcomes to long-term follow-up—for longitudinal integrated analysis of genomics, transcriptomics, proteomics, and metabolomics, thereby capturing dynamic evolution patterns of metabolic reprogramming and discovering functional biomarkers capable of early SAE risk warning, precise disease severity assessment, and prediction of long-term cognitive prognosis. Third, deepen systematic research on the gut-brain-immune metabolic axis: comprehensively utilize metagenomics, metabolomics, and immunological approaches to systematically resolve how intestinal microbiota and their metabolites shape SAE metabolic microenvironments and neurological functional outcomes through modulation of systemic immune states and direct action on the central nervous system, and explore novel “outside-in” intervention strategies based on probiotics, prebiotics, and fecal microbiota transplantation for intestinal microbiota modulation. Fourth, develop precision-targeted therapeutic regimens: relying upon biomarker-driven patient stratification systems combined with single-cell resolution technological approaches, develop novel drugs and intelligent delivery systems capable of specifically targeting key cell types or specific metabolic pathways, achieving precise metabolic modulation of activated microglia, energy-crisis neurons, or functionally transformed astrocytes, ultimately advancing toward a new era of personalized, precision treatment for SAE.
In conclusion, positioning metabolic reprogramming at the core of SAE research not only provides unprecedented theoretical depth and breadth of vision for understanding this complex syndrome but also ignites new hope for overcoming the severe clinical challenges it presents. While the road ahead is undoubtedly fraught with difficulties and obstacles, with the continuous emergence of novel technologies and deepening interdisciplinary collaboration, we have ample reason to believe that a new paradigm for SAE diagnosis and treatment—centered on metabolic modulation, characterized by precision diagnosis, and oriented toward individualized therapy—is gradually taking shape and maturing.
Statements
Author contributions
MH: Conceptualization, Data curation, Investigation, Methodology, Resources, Writing – original draft. WQ: Conceptualization, Data curation, Writing – original draft. DL: Investigation, Software, Writing – original draft. YZe: Investigation, Methodology, Resources, Software, Writing – original draft. YZh: Investigation, Software, Writing – original draft. SZ: Investigation, Methodology, Validation, Writing – original draft. YL: Data curation, Investigation, Software, Writing – original draft. ZL: Investigation, Resources, Visualization, Writing – original draft. XD: Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1.
SingerMDeutschmanCSSeymourCWShankar-HariMAnnaneDBauerMet al. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA (2016) 315:801–10. doi: 10.1001/jama.2016.0287
2.
FerliniLMaenhoutCCrippaIAQuispe-CornejoAACreteurJTacconeFSet al. The association between the presence and burden of periodic discharges and outcome in septic patients: an observational prospective study. Crit Care (2023) 27:179. doi: 10.1186/s13054-023-04475-w
3.
MolnárLFülesdiBNémethNMolnárC. Sepsis-associated encephalopathy: a review of literature. Neurol India (2018) 66:352–61. doi: 10.4103/0028-3886.227299
4.
SonnevilleRBenghanemSJeantinLde MontmollinEDomanMGaudemerAet al. The spectrum of sepsis-associated encephalopathy: a clinical perspective. Crit Care (2023) 27:386. doi: 10.1186/s13054-023-04655-8
5.
ZhangLWangXAiYGuoQHuangLLiuZet al. Epidemiological features and risk factors of sepsis-associated encephalopathy in intensive care unit patients: 2008-2011. Chin Med J. (2012) 125:828–31.
6.
LuXQinMWallineJHGaoYYuSGeZet al. A retrospective cohort study. Shock (2023) 59:583–90. doi: 10.1097/SHK.0000000000002092
7.
ZhaoQXiaoJLiuXLiuH. The nomogram to predict the occurrence of sepsis-associated encephalopathy in elderly patients in the intensive care units: a retrospective cohort study. Front Neurol. (2023) 14:1084868. doi: 10.3389/fneur.2023.1084868
8.
YangYLiangSGengJWangQWangPCaoYet al. Development of a nomogram to predict 30-day mortality of patients with sepsis-associated encephalopathy: a retrospective cohort study. J Intensive Care (2020) 8:45. doi: 10.1186/s40560-020-00459-y
9.
ChenJShiXDiaoMJinGZhuYHuWet al. A retrospective study of sepsis-associated encephalopathy: epidemiology, clinical features and adverse outcomes. BMC Emerg Med. (2020) 20:77. doi: 10.1186/s12873-020-00374-3
10.
LeiSLiXZhaoHFengZChunLXieYet al. Risk of dementia or cognitive impairment in sepsis survivals: a systematic review and meta-analysis. Front Aging Neurosci. (2022) 14:839472. doi: 10.3389/fnagi.2022.839472
11.
EvansLRhodesAAlhazzaniWAntonelliMCoopersmithCMFrenchCet al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Crit Care Med. (2021) 49:e1063–143. doi: 10.1097/CCM.0000000000005337
12.
Bircak-KuchtovaBChungH-YWickelJEhlerJGeisC. Neurofilament light chains to assess sepsis-associated encephalopathy: are we on the track toward clinical implementation?Crit Care (2023) 27:214. doi: 10.1186/s13054-023-04497-4
13.
ZhangQ-HShengZ-YYaoY-M. Septic encephalopathy: when cytokines interact with acetylcholine in the brain. Mil Med Res. (2014) 1:20. doi: 10.1186/2054-9369-1-20
14.
HuangYChenRJiangLLiSXueY. Basic research and clinical progress of sepsis-associated encephalopathy. J Intensive Med. (2021) 1:90–5. doi: 10.1016/j.jointm.2021.08.002
15.
SpodenMHartogCSSchlattmannPFreytagAOstermannMWedekindLet al. Occurrence and risk factors for new dependency on chronic care, respiratory support, dialysis and mortality in the first year after sepsis. Front Med. (2022) 9:878337. doi: 10.3389/fmed.2022.878337
16.
IwashynaTJElyEWSmithDMLangaKM. Long-term cognitive impairment and functional disability among survivors of severe sepsis. JAMA (2010) 304:1787–94. doi: 10.1001/jama.2010.1553
17.
MostelZPerlAMarckMMehdiSFLowellBBathijaSet al. Post-sepsis syndrome - an evolving entity that afflicts survivors of sepsis. Mol Med. (2019) 26:6. doi: 10.1186/s10020-019-0132-z
18.
van der SlikkeECAnAYHancockREWBoumaHR. Exploring the pathophysiology of post-sepsis syndrome to identify therapeutic opportunities. EBioMedicine (2020) 61:103044. doi: 10.1016/j.ebiom.2020.103044
19.
GaoSJiangYChenZZhaoXGuJWuHet al. Metabolic reprogramming of microglia in sepsis-associated encephalopathy: insights from neuroinflammation. Curr Neuropharmacol. (2023) 21:1992–2005. doi: 10.2174/1570159X21666221216162606
20.
DumbuyaJSLiSLiangLZengQ. Paediatric sepsis-associated encephalopathy (SAE): a comprehensive review. Mol Med. (2023) 29:27. doi: 10.1186/s10020-023-00621-w
21.
CatarinaAVBranchiniGBettoniLDe OliveiraJRNunesFB. Sepsis-associated encephalopathy: from pathophysiology to progress in experimental studies. Mol Neurobiol. (2021) 58:2770–9. doi: 10.1007/s12035-021-02303-2
22.
TangCJinYWangH. The biological alterations of synapse/synapse formation in sepsis-associated encephalopathy. Front Synaptic Neurosci. (2022) 14:1054605. doi: 10.3389/fnsyn.2022.1054605
23.
FaubertBSolmonsonADeBerardinisRJ. Metabolic reprogramming and cancer progression. Science (2020) 368:eaaw5473. doi: 10.1126/science.aaw5473
24.
ChouW-CRampanelliELiXTingJP-Y. Impact of intracellular innate immune receptors on immunometabolism. Cell Mol Immunol. (2022) 19:337–51. doi: 10.1038/s41423-021-00780-y
25.
MakowskiLChaibMRathmellJC. Immunometabolism: from basic mechanisms to translation. Immunol Rev. (2020) 295:5–14. doi: 10.1111/imr.12858
26.
HuDSheeja PrabhakaranHZhangY-YLuoGHeWLiouY-C. Mitochondrial dysfunction in sepsis: mechanisms and therapeutic perspectives. Crit Care (2024) 28:292. doi: 10.1186/s13054-024-05069-w
27.
Van WyngeneLVandewalleJLibertC. Reprogramming of basic metabolic pathways in microbial sepsis: therapeutic targets at last?EMBO Mol Med. (2018) 10:e8712. doi: 10.15252/emmm.201708712
28.
LiuJZhouGWangXLiuD. Metabolic reprogramming consequences of sepsis: adaptations and contradictions. Cell Mol Life Sci. (2022) 79:456. doi: 10.1007/s00018-022-04490-0
29.
XinYTianMDengSLiJYangMGaoJet al. The key drivers of brain injury by systemic inflammatory responses after sepsis: microglia and neuroinflammation. Mol Neurobiol. (2023) 60:1369–90. doi: 10.1007/s12035-022-03148-z
30.
ValletPGCharpiotA. Cerebral hippocampic ischemia, metabolic disorders and neuronal death. Encephale (1994) 20:131–7.
31.
ChausseBMalornyNLewenAPoschetGBerndtNKannO. Metabolic flexibility ensures proper neuronal network function in moderate neuroinflammation. Sci Rep. (2024) 14:14405. doi: 10.1038/s41598-024-64872-1
32.
TianQTangH-LTangY-YZhangPKangXZouWet al. Hydrogen sulfide attenuates the cognitive dysfunction in Parkinson's disease rats via promoting hippocampal microglia M2 polarization by enhancement of hippocampal Warburg effect. Oxid Med Cell Longev. (2022) 2022:2792348. doi: 10.1155/2022/2792348
33.
WangXWenXYuanSZhangJ. Gut-brain axis in the pathogenesis of sepsis-associated encephalopathy. Neurobiol Dis. (2024) 195:106499. doi: 10.1016/j.nbd.2024.106499
34.
GaoQHernandesMS. Sepsis-associated encephalopathy and blood-brain barrier dysfunction. Inflammation (2021) 44:2143–50. doi: 10.1007/s10753-021-01501-3
35.
XuKLiHZhangBLeMHuangQFuRet al. Integrated transcriptomics and metabolomics analysis of the hippocampus reveals altered neuroinflammation, downregulated metabolism and synapse in sepsis-associated encephalopathy. Front Pharmacol. (2022) 13:1004745. doi: 10.3389/fphar.2022.1004745
36.
SemmlerAHermannSMormannFWeberpalsMPaxianSAOkullaTet al. Sepsis causes neuroinflammation and concomitant decrease of cerebral metabolism. J Neuroinflammation (2008) 5:38. doi: 10.1186/1742-2094-5-38
37.
Cesare MarincolaFManninaL. Special issue on “NMR-based metabolomics and its applications volume 2.” Metabolites (2020) 10:45. doi: 10.3390/metabo10020045
38.
YaoPWuLYaoHShenWHuP. Acute hyperglycemia exacerbates neuroinflammation and cognitive impairment in sepsis-associated encephalopathy by mediating the ChREBP/HIF-1α pathway. Eur J Med Res. (2024) 29:546. doi: 10.1186/s40001-024-02129-3
39.
GuoJKongZYangSDaJChuLHanGet al. Therapeutic effects of orexin-A in sepsis-associated encephalopathy in mice. J Neuroinflammation (2024) 21:131. doi: 10.1186/s12974-024-03111-w
40.
GePDawsonVLDawsonTM. PINK1 and Parkin mitochondrial quality control: a source of regional vulnerability in Parkinson's disease. Mol Neurodegener. (2020) 15:20. doi: 10.1186/s13024-020-00367-7
41.
DangCPLeelahavanichkulA. Over-expression of miR-223 induces M2 macrophage through glycolysis alteration and attenuates LPS-induced sepsis mouse model, the cell-based therapy in sepsis. PLoS ONE (2020) 15:e0236038. doi: 10.1371/journal.pone.0236038
42.
SrivastavaAMannamP. Warburg revisited: lessons for innate immunity and sepsis. Front Physiol. (2015) 6:70. doi: 10.3389/fphys.2015.00070
43.
ColegioORChuN-QSzaboALChuTRhebergenAMJairamVet al. Functional polarization of tumour-associated macrophages by tumour-derived lactic acid. Nature (2014) 513:559–63. doi: 10.1038/nature13490
44.
ManoharanIPrasadPDThangarajuMManicassamyS. Lactate-dependent regulation of immune responses by dendritic cells and macrophages. Front Immunol. (2021) 12:691134. doi: 10.3389/fimmu.2021.691134
45.
ChangC-HCurtisJDMaggiLBFaubertBVillarinoAVO'SullivanDet al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell (2013) 153:1239–51. doi: 10.1016/j.cell.2013.05.016
46.
MichalekRDGerrietsVAJacobsSRMacintyreANMacIverNJMasonEFet al. Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets. J Immunol. (2011) 186:3299–303. doi: 10.4049/jimmunol.1003613
47.
MonteiroARBarbosaDJRemiãoFSilvaR. Co-culture models: key players in in vitro neurotoxicity, neurodegeneration and BBB modeling studies. Biomedicines (2024) 12:626. doi: 10.3390/biomedicines12030626
48.
GizaCCHovdaDA. The neurometabolic cascade of concussion. J Athl Train. (2001) 36:228–35.
49.
Bouzier-SoreA-KVoisinPBouchaudVBezanconEFranconiJ-MPellerinL. Competition between glucose and lactate as oxidative energy substrates in both neurons and astrocytes: a comparative NMR study. Eur J Neurosci. (2006) 24:1687–94. doi: 10.1111/j.1460-9568.2006.05056.x
50.
Martínez-ReyesIChandelNS. Mitochondrial TCA cycle metabolites control physiology and disease. Nat Commun. (2020) 11:102. doi: 10.1038/s41467-019-13668-3
51.
TaiY-HEngelsDLocatelliGEmmanouilidisIFecherCTheodorouDet al. Targeting the TCA cycle can ameliorate widespread axonal energy deficiency in neuroinflammatory lesions. Nat Metab. (2023) 5:1364–81. doi: 10.1038/s42255-023-00838-3
52.
ChenJZhuLCuiZZhangYJiaRZhouDet al. Spermidine restricts neonatal inflammation via metabolic shaping of polymorphonuclear myeloid-derived suppressor cells. J Clin Invest. (2025) 135:e183559. doi: 10.1172/JCI183559
53.
LinehanWMSrinivasanRSchmidtLS. The genetic basis of kidney cancer: a metabolic disease. Nat Rev Urol. (2010) 7:277–85. doi: 10.1038/nrurol.2010.47
54.
KohoutováMDejmekJTumaZKuncováJ. Variability of mitochondrial respiration in relation to sepsis-induced multiple organ dysfunction. Physiol Res. (2018) 67:S577–92. doi: 10.33549/physiolres.934050
55.
SingerM. Critical illness and flat batteries. Crit Care (2017) 21:309. doi: 10.1186/s13054-017-1913-9
56.
ZhuJZhangMHanTWuHXiaoZLinSet al. Exploring the biomarkers of sepsis-associated encephalopathy (SAE): metabolomics evidence from gas chromatography-mass spectrometry. Biomed Res Int. (2019) 2019:2612849. doi: 10.1155/2019/2612849
57.
LuukkonenPKPorthanKAhlholmNRosqvistFDufourSZhangX-Met al. The PNPLA3 I148M variant increases ketogenesis and decreases hepatic de novo lipogenesis and mitochondrial function in humans. Cell Metab. (2023) 35:1887–96.e5. doi: 10.1016/j.cmet.2023.10.008
58.
YenilmezBKellyMZhangG-FWetoskaNIlkayevaORMinKet al. Paradoxical activation of transcription factor SREBP1c and de novo lipogenesis by hepatocyte-selective ATP-citrate lyase depletion in obese mice. J Biol Chem. (2022) 298:102401. doi: 10.1016/j.jbc.2022.102401
59.
Flores-LeonMOuteiroTF. More than meets the eye in Parkinson's disease and other synucleinopathies: from proteinopathy to lipidopathy. Acta Neuropathol. (2023) 146:369–85. doi: 10.1007/s00401-023-02601-0
60.
LiLLixiaDGanGLiJYangLWuYet al. Astrocytic HILPDA promotes lipid droplets generation to drive cognitive dysfunction in mice with sepsis-associated encephalopathy. CNS Neurosci Ther. (2024) 30:e14758. doi: 10.1111/cns.14758
61.
KuperbergSJWadgaonkarR. Sepsis-associated encephalopathy: the blood-brain barrier and the sphingolipid rheostat. Front Immunol. (2017) 8:597. doi: 10.3389/fimmu.2017.00597
62.
DavisATCradySKStrongSAAlbrechtRMScholtenDJ. Increased acylcarnitine clearance and excretion in septic rats. Biomed Biochim Acta. (1991) 50:81–6.
63.
VankoningslooSPiensMLecocqCGilsonADe PauwARenardPet al. Mitochondrial dysfunction induces triglyceride accumulation in 3T3-L1 cells: role of fatty acid beta-oxidation and glucose. J Lipid Res. (2005) 46:1133–49. doi: 10.1194/jlr.M400464-JLR200
64.
KondoNSakuraiYTakataTKanoKKumeKMaedaMet al. Persistent elevation of lysophosphatidylcholine promotes radiation brain necrosis with microglial recruitment by P2RX4 activation. Sci Rep. (2022) 12:8718. doi: 10.1038/s41598-022-12293-3
65.
IkeuchiYNishizakiTMatsuokaT. Lysophosphatidylcholine inhibits NMDA-induced currents by a mechanism independent of phospholipase A2-mediated protein kinase C activation in hippocampal glial cells. Biochem Biophys Res Commun. (1995) 217:811–6. doi: 10.1006/bbrc.1995.2844
66.
SheikhAMNagaiARyuJKMcLarnonJGKimSUMasudaJ. Lysophosphatidylcholine induces glial cell activation: role of rho kinase. Glia (2009) 57:898–907. doi: 10.1002/glia.20815
67.
ShiFLiuQYueDZhangYWeiXWangYet al. Exploring the effects of the dietary fiber compound mediated by a longevity dietary pattern on antioxidation, characteristic bacterial genera, and metabolites based on fecal metabolomics. Nutr Metab. (2024) 21:18. doi: 10.1186/s12986-024-00787-y
68.
MarshNMMacEwenMJSCheaJKenersonHLKwongAALockeTMet al. Mitochondrial calcium signaling regulates branched-chain amino acid catabolism in fibrolamellar carcinoma. Sci Adv. (2025) 11:eadu9512. doi: 10.1126/sciadv.adu9512
69.
AquilaniRCotta RamusinoMMaestriRIadarolaPBoselliMPeriniGet al. Several dementia subtypes and mild cognitive impairment share brain reduction of neurotransmitter precursor amino acids, impaired energy metabolism, and lipid hyperoxidation. Front Aging Neurosci. (2023) 15:1237469. doi: 10.3389/fnagi.2023.1237469
70.
DehuryBMishraSPandaSSinghMKSimhaNLPatiS. Structural dynamics of neutral amino acid transporter SLC6A19 in simple and complex lipid bilayers. J Cell Biochem. (2025) 126:e30693. doi: 10.1002/jcb.30693
71.
HutsonSMLiethELaNoueKF. Function of leucine in excitatory neurotransmitter metabolism in the central nervous system. J Nutr. (2001) 131:846S−50S. doi: 10.1093/jn/131.3.846S
72.
YudkoffMDaikhinYNissimIHorynOLuhovyyBLuhovyyBet al. Brain amino acid requirements and toxicity: the example of leucine. J Nutr. (2005) 135:1531S−8S. doi: 10.1093/jn/135.6.1531S
73.
TraceyTJSteynFJWolvetangEJNgoST. Neuronal lipid metabolism: multiple pathways driving functional outcomes in health and disease. Front Mol Neurosci. (2018) 11:10. doi: 10.3389/fnmol.2018.00010
74.
FreundHRMuggia-SullamMLaFranceRHolroydeJFischerJE. Regional brain amino acid and neurotransmitter derangements during abdominal sepsis and septic encephalopathy in the rat. The effect of amino acid infusions. Arch Surg. (1986) 121:209–16. doi: 10.1001/archsurg.1986.01400020095011
75.
XieZXuMXieJLiuTXuXGaoWet al. Inhibition of ferroptosis attenuates glutamate excitotoxicity and nuclear autophagy in a CLP septic mouse model. Shock (2022) 57:694–702. doi: 10.1097/SHK.0000000000001893
76.
WinderTRMinukGYSargeantEJSelandTP. gamma-aminobutyric acid (GABA) and sepsis-related encephalopathy. Can J Neurol Sci. (1988) 15:23–5. doi: 10.1017/S0317167100027128
77.
BergRMGMøllerKBaileyDM. Neuro-oxidative-nitrosative stress in sepsis. J Cereb Blood Flow Metab. (2011) 31:1532–44. doi: 10.1038/jcbfm.2011.48
78.
ShulyatnikovaTVerkhratskyA. Astroglia in sepsis associated encephalopathy. Neurochem Res. (2020) 45:83–99. doi: 10.1007/s11064-019-02743-2
79.
VerkhratskyAHoMSVardjanNZorecRParpuraV. General pathophysiology of astroglia. Adv Exp Med Biol. (2019) 1175:149–79. doi: 10.1007/978-981-13-9913-8_7
80.
LiJJiaQYangLWuYPengYDuLet al. Sepsis-associated encephalopathy: mechanisms, diagnosis, and treatments update. Int J Biol Sci. (2025) 21:3214–28. doi: 10.7150/ijbs.102234
81.
HussainHVutipongsatornKJiménezBAntcliffeDB. Patient stratification in sepsis: using metabolomics to detect clinical phenotypes, sub-phenotypes and therapeutic response. Metabolites (2022) 12:376. doi: 10.3390/metabo12050376
82.
DrumlWHeinzelGKleinbergerG. Amino acid kinetics in patients with sepsis. Am J Clin Nutr. (2001) 73:908–13. doi: 10.1093/ajcn/73.5.908
83.
García-MartínezCLloveraMLópez-SorianoFJdel SantoBArgilésJM. Lipopolysaccharide (LPS) increases the in vivo oxidation of branched-chain amino acids in the rat: a cytokine-mediated effect. Mol Cell Biochem. (1995) 148:9–15. doi: 10.1007/BF00929497
84.
YangKChenJWangTZhangY. Pathogenesis of sepsis-associated encephalopathy: more than blood-brain barrier dysfunction. Mol Biol Rep. (2022) 49:10091–9. doi: 10.1007/s11033-022-07592-x
85.
QinJMaZChenXShuS. Microglia activation in central nervous system disorders: a review of recent mechanistic investigations and development efforts. Front Neurol. (2023) 14:1103416. doi: 10.3389/fneur.2023.1103416
86.
YanXYangKXiaoQHouRPanXZhuX. Central role of microglia in sepsis-associated encephalopathy: from mechanism to therapy. Front Immunol. (2022) 13:929316. doi: 10.3389/fimmu.2022.929316
87.
Gimeno-BayónJLópez-LópezARodríguezMJMahyN. Glucose pathways adaptation supports acquisition of activated microglia phenotype. J Neurosci Res. (2014) 92:723–31. doi: 10.1002/jnr.23356
88.
NairSSobotkaKSJoshiPGressensPFleissBThorntonCet al. Lipopolysaccharide-induced alteration of mitochondrial morphology induces a metabolic shift in microglia modulating the inflammatory response in vitro and in vivo. Glia (2019) 67:1047–61. doi: 10.1002/glia.23587
89.
HuYMaiWChenLCaoKZhangBZhangZet al. mTOR-mediated metabolic reprogramming shapes distinct microglia functions in response to lipopolysaccharide and ATP. Glia (2020) 68:1031–45. doi: 10.1002/glia.23760
90.
BielaninJPSunD. Significance of microglial energy metabolism in maintaining brain homeostasis. Transl Stroke Res. (2023) 14:435–7. doi: 10.1007/s12975-022-01069-6
91.
TannahillGMCurtisAMAdamikJPalsson-McDermottEMMcGettrickAFGoelGet al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature (2013) 496:238–42. doi: 10.1038/nature11986
92.
QinCYangSChenMDongM-HZhouL-QChuY-Het al. Modulation of microglial metabolism facilitates regeneration in demyelination. iScience (2023) 26:106588. doi: 10.1016/j.isci.2023.106588
93.
HuangXZhengYWangNZhaoMLiuJLinWet al. Dichloroacetate prevents sepsis associated encephalopathy by inhibiting microglia pyroptosis through PDK4/NLRP3. Inflammation (2025) 48:1159–75. doi: 10.1007/s10753-024-02105-3
94.
Galván-PeñaSO'NeillLAJ. Metabolic reprograming in macrophage polarization. Front Immunol. (2014) 5:420. doi: 10.3389/fimmu.2014.00420
95.
OliveiraJMA. Techniques to investigate neuronal mitochondrial function and its pharmacological modulation. Curr Drug Targets (2011) 12:762–73. doi: 10.2174/138945011795528895
96.
CyrinoLARDelwing-de LimaDUllmannOMMaiaTP. Concepts of neuroinflammation and their relationship with impaired mitochondrial functions in bipolar disorder. Front Behav Neurosci. (2021) 15:609487. doi: 10.3389/fnbeh.2021.609487
97.
PekkurnazGWangX. Mitochondrial heterogeneity and homeostasis through the lens of a neuron. Nat Metab. (2022) 4:802–12. doi: 10.1038/s42255-022-00594-w
98.
ChenYGuoSTangYMouCHuXShaoFet al. Mitochondrial fusion and fission in neuronal death induced by cerebral ischemia-reperfusion and its clinical application: a mini-review. Med Sci Monit. (2020) 26:e928651. doi: 10.12659/MSM.928651
99.
SchmidtJMKoS-BHelbokRKurtzPStuartRMPresciuttiMet al. Cerebral perfusion pressure thresholds for brain tissue hypoxia and metabolic crisis after poor-grade subarachnoid hemorrhage. Stroke (2011) 42:1351–6. doi: 10.1161/STROKEAHA.110.596874
100.
ChoiDW. Glutamate neurotoxicity and diseases of the nervous system. Neuron (1988) 1:623–34. doi: 10.1016/0896-6273(88)90162-6
101.
TreggiariMM. Participants in the international multi-disciplinary consensus conference on the critical care management of subarachnoid hemorrhage. Hemodynamic management of subarachnoid hemorrhage. Neurocrit Care (2011) 15:329–35. doi: 10.1007/s12028-011-9589-5
102.
BakerMBastinMTCookAMFraserJHesselE. Hypoxemia associated with nimodipine in a patient with an aneurysmal subarachnoid hemorrhage. Am J Health Syst Pharm. (2015) 72:39–43. doi: 10.2146/ajhp140196
103.
Skjøth-RasmussenJSchulzMKristensenSRBjerreP. Delayed neurological deficits detected by an ischemic pattern in the extracellular cerebral metabolites in patients with aneurysmal subarachnoid hemorrhage. J Neurosurg. (2004) 100:8–15. doi: 10.3171/jns.2004.100.1.0008
104.
XuXPangYFanX. Mitochondria in oxidative stress, inflammation and aging: from mechanisms to therapeutic advances. Signal Transduct Target Ther. (2025) 10:190. doi: 10.1038/s41392-025-02253-4
105.
FriesGRSaldanaVAFinnsteinJReinT. Molecular pathways of major depressive disorder converge on the synapse. Mol Psychiatry (2023) 28:284–97. doi: 10.1038/s41380-022-01806-1
106.
XieBChenQDaiZJiangCChenX. Progesterone (P4) ameliorates cigarette smoke-induced chronic obstructive pulmonary disease (COPD). Mol Med. (2024) 30:123. doi: 10.1186/s10020-024-00883-y
107.
IoannouMSJacksonJSheuS-HChangC-LWeigelAVLiuHet al. Neuron-astrocyte metabolic coupling protects against activity-induced fatty acid toxicity. Cell (2019) 177:1522–35.e14. doi: 10.1016/j.cell.2019.04.001
108.
McMullenEHertensteinHStrassburgerKDehardeLBrankatschkMSchirmeierS. Glycolytically impaired drosophila glial cells fuel neural metabolism via β-oxidation. Nat Commun. (2023) 14:2996. doi: 10.1038/s41467-023-38813-x
109.
LeFoll C. Hypothalamic fatty acids and ketone bodies sensing and role of FAT/CD36 in the regulation of food intake. Front Physiol. (2019) 10:1036. doi: 10.3389/fphys.2019.01036
110.
WachtelEVHendricks-MuñozKD. Current management of the infant who presents with neonatal encephalopathy. Curr Probl Pediatr Adolesc Health Care (2011) 41:132–53. doi: 10.1016/j.cppeds.2010.12.002
111.
WoodruffTMThundyilJTangS-CSobeyCGTaylorSMArumugamTV. Pathophysiology, treatment, and animal and cellular models of human ischemic stroke. Mol Neurodegener. (2011) 6:11. doi: 10.1186/1750-1326-6-11
112.
LiptonP. Ischemic cell death in brain neurons. Physiol Rev. (1999) 79:1431–568. doi: 10.1152/physrev.1999.79.4.1431
113.
FungTCOlsonCAHsiaoEY. Interactions between the microbiota, immune and nervous systems in health and disease. Nat Neurosci. (2017) 20:145–55. doi: 10.1038/nn.4476
114.
VerkhratskyAParpuraV. Recent advances in (patho)physiology of astroglia. Acta Pharmacol Sin. (2010) 31:1044–54. doi: 10.1038/aps.2010.108
115.
MederosSGonzález-AriasCPereaG. Astrocyte-neuron networks: a multilane highway of signaling for homeostatic brain function. Front Synaptic Neurosci. (2018) 10:45. doi: 10.3389/fnsyn.2018.00045
116.
CuiJLiuJCuiYXieK. Astrocytes in sepsis-associated encephalopathy: pivotal roles in dual pathogenesis and therapeutic perspectives. Shock (2025) doi: 10.1097/SHK.0000000000002709
117.
GuoQGobboDZhaoNZhangHAwukuN-OLiuQet al. Adenosine triggers early astrocyte reactivity that provokes microglial responses and drives the pathogenesis of sepsis-associated encephalopathy in mice. Nat Commun. (2024) 15:6340. doi: 10.1038/s41467-024-50466-y
118.
PellerinLMagistrettiPJ. Glutamate uptake into astrocytes stimulates aerobic glycolysis: a mechanism coupling neuronal activity to glucose utilization. Proc Natl Acad Sci U S A. (1994) 91:10625–9. doi: 10.1073/pnas.91.22.10625
119.
LiddelowSAGuttenplanKAClarkeLEBennettFCBohlenCJSchirmerLet al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature (2017) 541:481–7. doi: 10.1038/nature21029
120.
MiyazakiIAsanumaM. Neuron-astrocyte interactions in Parkinson's disease. Cells (2020) 9:2623. doi: 10.3390/cells9122623
121.
SrivastavaRKSapraLMishraPK. Osteometabolism: metabolic alterations in bone pathologies. Cells (2022) 11:3943. doi: 10.3390/cells11233943
122.
WangRDillonCPShiLZMilastaSCarterRFinkelsteinDet al. The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity (2011) 35:871–82. doi: 10.1016/j.immuni.2011.09.021
123.
BerodLFriedrichCNandanAFreitagJHagemannSHarmrolfsKet al. De novo fatty acid synthesis controls the fate between regulatory T and T helper 17 cells. Nat Med. (2014) 20:1327–33. doi: 10.1038/nm.3704
124.
PhilipsTRothsteinJD. Glial cells in amyotrophic lateral sclerosis. Exp Neurol. (2014) 262 Pt B:111–20. doi: 10.1016/j.expneurol.2014.05.015
125.
TakahashiS. Neuroprotective function of high glycolytic activity in astrocytes: common roles in stroke and neurodegenerative diseases. Int J Mol Sci. (2021) 22:6568. doi: 10.3390/ijms22126568
126.
HuHFengYZhouYPengSLiDWuSet al. Widely targeted metabolomics and machine learning identify succinate as a key metabolite in sepsis-associated encephalopathy. iScience (2026) 29:114520. doi: 10.1016/j.isci.2025.114520
127.
DuHDaiXZhangTZhangZXuXLiuYet al. Multi-omics and clinical validation identify key glycolysis- and immune-related genes in sepsis. Int J Gen Med. (2025) 18:5085–103. doi: 10.2147/IJGM.S539158
128.
YeYWangYXuQLiuJYangZWurenTet al. In vitrostudy: HIF-1α-dependent glycolysis enhances NETosis in hypoxic conditions. Front Immunol. (2025) 16:1583587. doi: 10.3389/fimmu.2025.1583587
129.
PoliVZanoniI. Neutrophil intrinsic and extrinsic regulation of NETosis in health and disease. Trends Microbiol. (2023) 31:280–93. doi: 10.1016/j.tim.2022.10.002
130.
WangJZhongZLuoHHanQWuKJiangAet al. Modulation of brain immune microenvironment and cellular dynamics in systemic inflammation. Theranostics (2025) 15:5153–71. doi: 10.7150/thno.107061
131.
ZhangJChenSHuXHuangLLohPYuanXet al. The role of the peripheral system dysfunction in the pathogenesis of sepsis-associated encephalopathy. Front Microbiol. (2024) 15:1337994. doi: 10.3389/fmicb.2024.1337994
132.
ZhongYGuanJMaYXuMChengYXuLet al. Role of imaging modalities and N-acetylcysteine treatment in sepsis-associated encephalopathy. ACS Chem Neurosci. (2023) 14:2172–82. doi: 10.1021/acschemneuro.3c00180
133.
LiZZhangFSunMLiuJZhaoLLiuSet al. The modulatory effects of gut microbes and metabolites on blood-brain barrier integrity and brain function in sepsis-associated encephalopathy. PeerJ (2023) 11:e15122. doi: 10.7717/peerj.15122
134.
GareauMG. The microbiota-gut-brain axis in sepsis-associated encephalopathy. mSystems (2022) 7:e0053322. doi: 10.1128/msystems.00533-22
135.
ChancharoenthanaWKamolratanakulSSchultzMJLeelahavanichkulA. The leaky gut and the gut microbiome in sepsis - targets in research and treatment. Clin Sci. (2023) 137:645–62. doi: 10.1042/CS20220777
136.
FangHWangYDengJZhangHWuQHeLet al. Sepsis-induced gut dysbiosis mediates the susceptibility to sepsis-associated encephalopathy in mice. mSystems (2022) 7:e0139921. doi: 10.1128/msystems.01399-21
137.
MomenYSMishraJKumarN. Brain-gut and microbiota-gut-brain communication in type-2 diabetes linked Alzheimer's disease. Nutrients (2024) 16:2558. doi: 10.3390/nu16152558
138.
LouXXueJShaoRYangYNingDMoCet al. Fecal microbiota transplantation and short-chain fatty acids reduce sepsis mortality by remodeling antibiotic-induced gut microbiota disturbances. Front Immunol. (2022) 13:1063543. doi: 10.3389/fimmu.2022.1063543
139.
NanZ. Microbiome and metabolic immune mechanisms in sepsis-associated encephalopathy. J Intensive Care Med. (2025) 8850666251385540. doi: 10.1177/08850666251385540. [Epub ahead of print].
140.
GiridharanVVGenerosoJSLenceLCandiottoGStreckEPetronilhoFet al. A crosstalk between gut and brain in sepsis-induced cognitive decline. J Neuroinflammation (2022) 19:114. doi: 10.1186/s12974-022-02472-4
141.
SongFLiQCuiJWangJXiaoSYuBet al. Exploring the gut microbiota-hippocampus-metabolites axis dysregulation in sepsis mice. Front Microbiol. (2024) 15:1302907. doi: 10.3389/fmicb.2024.1302907
142.
ZhangHXuJWuQFangHShaoXOuyangXet al. Gut microbiota mediates the susceptibility of mice to sepsis-associated encephalopathy by butyric acid. J Inflamm Res. (2022) 15:2103–19. doi: 10.2147/JIR.S350566
143.
YuechenZShaosongXZhouxingZFuliGWeiH. A summary of the current diagnostic methods for, and exploration of the value of microRNAs as biomarkers in, sepsis-associated encephalopathy. Front Neurosci. (2023) 17:1125888. doi: 10.3389/fnins.2023.1125888
144.
ThelinEPJeppssonEFrostellASvenssonMMondelloSBellanderB-Met al. Utility of neuron-specific enolase in traumatic brain injury; relations to S100B levels, outcome, and extracranial injury severity. Crit Care (2016) 20:285. doi: 10.1186/s13054-016-1450-y
145.
HondaMTsurutaRKanekoTKasaokaSYagiTTodaniMet al. Serum glial fibrillary acidic protein is a highly specific biomarker for traumatic brain injury in humans compared with S-100B and neuron-specific enolase. J Trauma (2010) 69:104–9. doi: 10.1097/TA.0b013e3181bbd485
146.
OoiSZYSpencerRJHodgsonMMehtaSPhillipsNLPreestGet al. Interleukin-6 as a prognostic biomarker of clinical outcomes after traumatic brain injury: a systematic review. Neurosurg Rev. (2022) 45:3035–54. doi: 10.1007/s10143-022-01827-y
147.
QiCLiuYHuaTYangMLiuY. Biomarkers of sepsis associated encephalopathy: a bibliometric and visualized analysis. Front Neurol. (2025) 16:1605351. doi: 10.3389/fneur.2025.1605351
148.
MrozekSDumurgierJCiterioGMebazaaAGeeraertsT. Biomarkers and acute brain injuries: interest and limits. Crit Care (2014) 18:220. doi: 10.1186/cc13841
149.
YokoboriSHoseinKBurksSSharmaIGajavelliSBullockR. Biomarkers for the clinical differential diagnosis in traumatic brain injury–a systematic review. CNS Neurosci Ther. (2013) 19:556–65. doi: 10.1111/cns.12127
150.
PelinkaLEKroepflALeixneringMBuchingerWRaabeARedlHet al. GFAP versus S100B in serum after traumatic brain injury: relationship to brain damage and outcome. J Neurotrauma (2004) 21:1553–61. doi: 10.1089/neu.2004.21.1553
151.
MondelloSPapaLBukiABullockMRCzeiterETortellaFCet al. Neuronal and glial markers are differently associated with computed tomography findings and outcome in patients with severe traumatic brain injury: a case control study. Crit Care (2011) 15:R156. doi: 10.1186/cc10286
152.
HerrmannMCurioNJostSGrubichCEbertADForkMLet al. Release of biochemical markers of damage to neuronal and glial brain tissue is associated with short and long term neuropsychological outcome after traumatic brain injury. J Neurol Neurosurg Psychiatry (2001) 70:95–100. doi: 10.1136/jnnp.70.1.95
153.
AgorastosASommerAHeinigAWiedemannKDemiralayC. Vasopressin surrogate marker copeptin as a potential novel endocrine biomarker for antidepressant treatment response in major depression: a pilot study. Front Psychiatry (2020) 11:453. doi: 10.3389/fpsyt.2020.00453
154.
JudsonIMaughanTBealePPrimroseJHoskinPHanwellJet al. Effects of impaired renal function on the pharmacokinetics of raltitrexed (Tomudex ZD1694). Br J Cancer (1998) 78:1188–93. doi: 10.1038/bjc.1998.652
155.
PengJTangRYuQWangDQiD. No sex differences in the incidence, risk factors and clinical impact of acute kidney injury in critically ill patients with sepsis. Front Immunol. (2022) 13:895018. doi: 10.3389/fimmu.2022.895018
156.
UchinoSKellumJABellomoRDoigGSMorimatsuHMorgeraSet al. Acute renal failure in critically ill patients: a multinational, multicenter study. JAMA (2005) 294:813–8. doi: 10.1001/jama.294.7.813
157.
Shankar-HariMPhillipsGSLevyMLSeymourCWLiuVXDeutschmanCSet al. Developing a new definition and assessing new clinical criteria for septic shock: for the third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA (2016) 315:775–87. doi: 10.1001/jama.2016.0289
158.
MengXZhengZYangLYangCLiXHaoY. Dynamic mechanisms and targeted interventions in cerebral ischemia-reperfusion injury: pathological cascade from ischemia to reperfusion and promising therapeutic strategies. Front Neurosci. (2025) 19:1649533. doi: 10.3389/fnins.2025.1649533
159.
PiazzaORussoECotenaSEspositoGTufanoR. Elevated S100B levels do not correlate with the severity of encephalopathy during sepsis. Br J Anaesth. (2007) 99:518–21. doi: 10.1093/bja/aem201
160.
WuLFengQAiM-LDengS-YLiuZ-YHuangLet al. The dynamic change of serum S100B levels from day 1 to day 3 is more associated with sepsis-associated encephalopathy. Sci Rep. (2020) 10:7718. doi: 10.1038/s41598-020-64200-3
161.
SaoiMLiAMcGloryCStokesTvon AllmenMTPhillipsSMet al. Metabolic perturbations from step reduction in older persons at risk for sarcopenia: plasma biomarkers of abrupt changes in physical activity. Metabolites (2019) 9:134. doi: 10.3390/metabo9070134
162.
PengZZhangWZhangXMaoJZhangQZhaoWet al. Recent advances in analysis of capsaicin and its effects on metabolic pathways by mass spectrometry. Front Nutr. (2023) 10:1227517. doi: 10.3389/fnut.2023.1227517
163.
KessenikhEDBykovaKMMurashkoEADubrovskiiYADorofeykovVVSavvinaIA. Metabolomic profiling of patients with sepsis-associated encephalopathy. Biomed Khim. (2025) 71:441–53. doi: 10.18097/PBMCR1599
164.
LinCTianQGuoSXieDCaiYWangZet al. Metabolomics for clinical biomarker discovery and therapeutic target identification. Molecules (2024) 29:2198. doi: 10.3390/molecules29102198
165.
BeloborodovaNPautovaASergeevAFedotchevaN. Serum levels of mitochondrial and microbial metabolites reflect mitochondrial dysfunction in different stages of sepsis. Metabolites (2019) 9:196. doi: 10.3390/metabo9100196
166.
HuangHLiGHeYChenJYanJZhangQet al. Cellular succinate metabolism and signaling in inflammation: implications for therapeutic intervention. Front Immunol. (2024) 15:1404441. doi: 10.3389/fimmu.2024.1404441
167.
SunSWangDDongDXuLXieMWangYet al. Altered intestinal microbiome and metabolome correspond to the clinical outcome of sepsis. Crit Care (2023) 27:127. doi: 10.1186/s13054-023-04412-x
168.
DemichevaEDordiukVPolanco EspinoFUsheninKAboushanabSShevyrinVet al. Advances in mass spectrometry-based blood metabolomics profiling for non-cancer diseases: a comprehensive review. Metabolites (2024) 14:54. doi: 10.3390/metabo14010054
169.
ZhangZQiuXZengXLiuXLuJXuCet al. Integrated multi omics and machine learning reveal mitochondrial immunometabolic networks in sepsis associated encephalopathy. Sci Rep. (2025) 15:33572. doi: 10.1038/s41598-025-18650-2
170.
KongYTaiYChenBZhangMJiHFengRet al. A multi-omics study of magnesium sulfate to improve prognosis in sepsis-related encephalopathy: integrating clinical data-driven network pharmacology. Front Cell Infect Microbiol. (2025) 15:1607586. doi: 10.3389/fcimb.2025.1607586
171.
IdekerTThorssonVRanishJAChristmasRBuhlerJEngJKet al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science (2001) 292:929–34. doi: 10.1126/science.292.5518.929
172.
WanichthanarakKFahrmannJFGrapovD. Genomic, proteomic, and metabolomic data integration strategies. Biomark Insights (2015) 10:1–6. doi: 10.4137/BMI.S29511
173.
BebekGKoyutürkMPriceNDChanceMR. Network biology methods integrating biological data for translational science. Brief Bioinform. (2012) 13:446–59. doi: 10.1093/bib/bbr075
174.
MisraBBLangefeldCOlivierMCoxLA. Integrated omics: tools, advances and future approaches. J Mol Endocrinol. (2019) 62:R21–45. doi: 10.1530/JME-18-0055
175.
BalasubramanianDOhneckEAChapmanJWeissAKimMKReyes-RoblesTet al. Staphylococcus aureus coordinates leukocidin expression and pathogenesis by sensing metabolic fluxes via RpiRc. MBio (2016) 7:e00818–16. doi: 10.1128/mBio.00818-16
176.
SrivastavaACreekDJ. Discovery and validation of clinical biomarkers of cancer: a review combining metabolomics and proteomics. Proteomics (2019) 19:e1700448. doi: 10.1002/pmic.201700448
177.
TakahashiSTakahashiMTanakaSTakayanagiSTakamiHYamazawaEet al. A new era of neuro-oncology research pioneered by multi-omics analysis and machine learning. Biomolecules (2021) 11:565. doi: 10.3390/biom11040565
178.
NarayananRDeGroatWMendheDAbdelhalimHAhmedZ. IntelliGenes: interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine. Biol Methods Protoc. (2024) 9:bpae040. doi: 10.1093/biomethods/bpae040
179.
NamYKimJJungS-HWoernerJSuhEHLeeD-Get al. Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine. Annu Rev Biomed Data Sci. (2024) 7:225–50. doi: 10.1146/annurev-biodatasci-102523-103801
180.
van den BergheGWoutersPWeekersFVerwaestCBruyninckxFSchetzMet al. Intensive insulin therapy in critically ill patients. N Engl J Med. (2001) 345:1359–67. doi: 10.1056/NEJMoa011300
181.
NICE-SUGAR StudyInvestigatorsFinferSChittockDRSuSY-SBlairDFosterDet al. Intensive versus conventional glucose control in critically ill patients. N Engl J Med. (2009) 360:1283–97. doi: 10.1056/NEJMoa0810625
182.
EnglertJARogersAJ. Metabolism, metabolomics, and nutritional support of patients with sepsis. Clin Chest Med. (2016) 37:321–31. doi: 10.1016/j.ccm.2016.01.011
183.
ChengSLiYSunXLiuZGuoLWuJet al. The impact of glucose metabolism on inflammatory processes in sepsis-induced acute lung injury. Front Immunol. (2024) 15:1508985. doi: 10.3389/fimmu.2024.1508985
184.
HansenTKThielSWoutersPJChristiansenJSVan den BergheG. Intensive insulin therapy exerts antiinflammatory effects in critically ill patients and counteracts the adverse effect of low mannose-binding lectin levels. J Clin Endocrinol Metab. (2003) 88:1082–8. doi: 10.1210/jc.2002-021478
185.
Samocha-BonetDCampbellLVMoriTACroftKDGreenfieldJRTurnerNet al. Overfeeding reduces insulin sensitivity and increases oxidative stress, without altering markers of mitochondrial content and function in humans. PLoS ONE (2012) 7:e36320. doi: 10.1371/journal.pone.0036320
186.
GentonLPichardC. Protein catabolism and requirements in severe illness. Int J Vitam Nutr Res. (2011) 81:143–52. doi: 10.1024/0300-9831/a000058
187.
BöhrerHSchmidtHBachA. Elevation of systemic oxygen delivery in the treatment of critically ill patients. N Engl J Med. (1994) 331:1161. doi: 10.1056/NEJM199410273311715
188.
CecconiMDe BackerDAntonelliMBealeRBakkerJHoferCet al. Consensus on circulatory shock and hemodynamic monitoring. Task force of the European society of intensive care medicine. Intensive Care Med. (2014) 40:1795–815. doi: 10.1007/s00134-014-3525-z
189.
DyerWBTungJ-PLi BassiGWildiKJungJ-SColomboSMet al. An ovine model of hemorrhagic shock and resuscitation, to assess recovery of tissue oxygen delivery and oxygen debt, and inform patient blood management. Shock (2021) 56:1080–91. doi: 10.1097/SHK.0000000000001805
190.
KrzyzaniakKKrionRSzymczykAStepniewskaESieminskiM. Exploring neuroprotective agents for sepsis-associated encephalopathy: a comprehensive review. Int J Mol Sci. (2023) 24:10780. doi: 10.3390/ijms241310780
191.
SongGLiangHSongHDingXWangDZhangXet al. Metformin improves the prognosis of adult mice with sepsis-associated encephalopathy better than that of aged mice. J Immunol Res. (2022) 2022:3218452. doi: 10.1155/2022/3218452
192.
ZhouGMyersRLiYChenYShenXFenyk-MelodyJet al. Role of AMP-activated protein kinase in mechanism of metformin action. J Clin Invest. (2001) 108:1167–74. doi: 10.1172/JCI13505
193.
ForetzMGuigasBViolletB. Understanding the glucoregulatory mechanisms of metformin in type 2 diabetes mellitus. Nat Rev Endocrinol. (2019) 15:569–89. doi: 10.1038/s41574-019-0242-2
194.
GoodmanMLiuZZhuPLiJ. AMPK activators as a drug for diabetes, cancer and cardiovascular disease. Pharm Regul Aff. (2014) 3:118. doi: 10.4172/2167-7689.1000118
195.
DamanhouriZAAlkreathyHMAlharbiFAAbualhamailHAhmadMS. A review of the impact of pharmacogenetics and metabolomics on the efficacy of metformin in type 2 diabetes. Int J Med Sci. (2023) 20:142–50. doi: 10.7150/ijms.77206
196.
HuangQWangYChenSLiangF. Glycometabolic reprogramming of microglia in neurodegenerative diseases: insights from neuroinflammation. Aging Dis. (2024) 15:1155–75. doi: 10.14336/AD.2023.0807
197.
ChenC-CLinJ-TChengY-FKuoC-YHuangC-FKaoS-Het al. Amelioration of LPS-induced inflammation response in microglia by AMPK activation. Biomed Res Int. (2014) 2014:692061. doi: 10.1155/2014/692061
198.
TangGYangHChenJShiMGeLGeXet al. Metformin ameliorates sepsis-induced brain injury by inhibiting apoptosis, oxidative stress and neuroinflammation via the PI3K/Akt signaling pathway. Oncotarget (2017) 8:97977–89. doi: 10.18632/oncotarget.20105
199.
QinZZhouCXiaoXGuoC. Metformin attenuates sepsis-induced neuronal injury and cognitive impairment. BMC Neurosci. (2021) 22:78. doi: 10.1186/s12868-021-00683-8
200.
ZhongTMenYLuLGengTZhouJMitsuhashiAet al. Metformin alters DNA methylation genome-wide via the H19/SAHH axis. Oncogene (2017) 36:2345–54. doi: 10.1038/onc.2016.391
201.
LiangHDingXLiLWangTKanQWangLet al. Association of preadmission metformin use and mortality in patients with sepsis and diabetes mellitus: a systematic review and meta-analysis of cohort studies. Crit Care (2019) 23:50. doi: 10.1186/s13054-019-2346-4
202.
Doenyas-BarakKBeberashviliIMarcusREfratiS. Lactic acidosis and severe septic shock in metformin users: a cohort study. Crit Care (2016) 20:10. doi: 10.1186/s13054-015-1180-6
203.
KumarJHaldarCVermaR. Melatonin ameliorates LPS-induced testicular nitro-oxidative stress (iNOS/TNFα) and inflammation (NF-kB/COX-2) via modulation of SIRT-1. Reprod Sci. (2021) 28:3417–30. doi: 10.1007/s43032-021-00597-0
204.
ShahSAKhanMJoM-HJoMGAminFUKimMO. Melatonin stimulates the SIRT1/Nrf2 signaling pathway counteracting lipopolysaccharide (LPS)-induced oxidative stress to rescue postnatal rat brain. CNS Neurosci Ther. (2017) 23:33–44. doi: 10.1111/cns.12588
205.
WangHWangHHuangHQuZMaDDangXet al. Melatonin attenuates spinal cord injury in mice by activating the Nrf2/ARE signaling pathway to inhibit the NLRP3 inflammasome. Cells (2022) 11:2809. doi: 10.3390/cells11182809
206.
YangYJiangSDongYFanCZhaoLYangXet al. Melatonin prevents cell death and mitochondrial dysfunction via a SIRT1-dependent mechanism during ischemic-stroke in mice. J Pineal Res. (2015) 58:61–70. doi: 10.1111/jpi.12193
207.
YangSTangWHeYWenLSunBLiS. Long non-coding RNA and microRNA-675/let-7a mediates the protective effect of melatonin against early brain injury after subarachnoid hemorrhage via targeting TP53 and neural growth factor. Cell Death Dis. (2018) 9:99. doi: 10.1038/s41419-017-0155-8
208.
ZengLZhuYHuXQinHTangJHuZet al. Efficacy of melatonin in animal models of intracerebral hemorrhage: a systematic review and meta-analysis. Aging (2021) 13:3010–30. doi: 10.18632/aging.202457
209.
JiM-HXiaD-GZhuL-YZhuXZhouX-YXiaJ-Yet al. Short- and long-term protective effects of melatonin in a mouse model of sepsis-associated encephalopathy. Inflammation (2018) 41:515–29. doi: 10.1007/s10753-017-0708-0
210.
SieminskiMSzaruta-RafleszKSzypenbejlJKrzyzaniakK. Potential neuroprotective role of melatonin in sepsis-associated encephalopathy due to its scavenging and anti-oxidative properties. Antioxidants (2023) 12:1786. doi: 10.3390/antiox12091786
211.
HuWDengCMaZWangDFanCLiTet al. Utilizing melatonin to combat bacterial infections and septic injury. Br J Pharmacol. (2017) 174:754–68. doi: 10.1111/bph.13751
212.
OhsawaIIshikawaMTakahashiKWatanabeMNishimakiKYamagataKet al. Hydrogen acts as a therapeutic antioxidant by selectively reducing cytotoxic oxygen radicals. Nat Med. (2007) 13:688–94. doi: 10.1038/nm1577
213.
OhtaS. Molecular hydrogen as a novel antioxidant: overview of the advantages of hydrogen for medical applications. Methods Enzymol. (2015) 555:289–317. doi: 10.1016/bs.mie.2014.11.038
214.
WuCZouPFengSZhuLLiFLiuTC-Yet al. Molecular hydrogen: an emerging therapeutic medical gas for brain disorders. Mol Neurobiol. (2023) 60:1749–65. doi: 10.1007/s12035-022-03175-w
215.
NieCZouRPanSARGaoYYangHet al. Hydrogen gas inhalation ameliorates cardiac remodelling and fibrosis by regulating NLRP3 inflammasome in myocardial infarction rats. J Cell Mol Med. (2021) 25:8997–9010. doi: 10.1111/jcmm.16863
216.
HiranoS-IIchikawaYSatoBYamamotoHTakefujiYSatohF. Potential therapeutic applications of hydrogen in chronic inflammatory diseases: possible inhibiting role on mitochondrial stress. Int J Mol Sci. (2021) 22:2549. doi: 10.3390/ijms22052549
217.
YuYFengJLianNYangMXieKWangGet al. Hydrogen gas alleviates blood-brain barrier impairment and cognitive dysfunction of septic mice in an Nrf2-dependent pathway. Int Immunopharmacol. (2020) 85:106585. doi: 10.1016/j.intimp.2020.106585
218.
ZhaiMHuHZhengYWuBSunW. PGC1α: an emerging therapeutic target for chemotherapy-induced peripheral neuropathy. Ther Adv Neurol Disord. (2023) 16:17562864231163361. doi: 10.1177/17562864231163361
219.
ZhaoNSunRCuiYSongYMaWLiYet al. High concentration hydrogen mitigates sepsis-induced acute lung injury in mice by alleviating mitochondrial fission and dysfunction. J Pers Med. (2023) 13:244. doi: 10.3390/jpm13020244
220.
JiangYBianYLianNWangYXieKQinCet al. iTRAQ-based quantitative proteomic analysis of intestines in murine polymicrobial sepsis with hydrogen gas treatment. Drug Des Devel Ther. (2020) 14:4885–900. doi: 10.2147/DDDT.S271191
221.
ColeARSperottoFDiNardoJACarlisleSRivkinMJSleeperLAet al. Safety of prolonged inhalation of hydrogen gas in air in healthy adults. Crit Care Explor. (2021) 3:e543. doi: 10.1097/CCE.0000000000000543
222.
YuanTZhaoJ-NBaoN-R. Hydrogen applications: advances in the field of medical therapy. Med Gas Res. (2023) 13:99–107. doi: 10.4103/2045-9912.344978
223.
WhitehouseSCooperRHRandlePJ. Mechanism of activation of pyruvate dehydrogenase by dichloroacetate and other halogenated carboxylic acids. Biochem J. (1974) 141:761–74. doi: 10.1042/bj1410761
224.
MichelakisEDWebsterLMackeyJR. Dichloroacetate (DCA) as a potential metabolic-targeting therapy for cancer. Br J Cancer (2008) 99:989–94. doi: 10.1038/sj.bjc.6604554
225.
AbdelmalakMLewARamezaniRShroadsALCoatsBSLangaeeTet al. Long-term safety of dichloroacetate in congenital lactic acidosis. Mol Genet Metab. (2013) 109:139–43. doi: 10.1016/j.ymgme.2013.03.019
226.
StacpoolePWGilbertLRNeibergerRECarneyPRValensteinETheriaqueDWet al. Evaluation of long-term treatment of children with congenital lactic acidosis with dichloroacetate. Pediatrics (2008) 121:e1223–8. doi: 10.1542/peds.2007-2062
227.
GottsJEMatthayMA. Sepsis: pathophysiology and clinical management. BMJ (2016) 353:i1585. doi: 10.1136/bmj.i1585
228.
SciclunaBPBaillieJK. The search for efficacious new therapies in sepsis needs to embrace heterogeneity. Am J Respir Crit Care Med. (2019) 199:936–8. doi: 10.1164/rccm.201811-2148ED
229.
GarveyM. Hospital acquired sepsis, disease prevalence, and recent advances in sepsis mitigation. Pathogens (2024) 13:461. doi: 10.3390/pathogens13060461
230.
JainASingamAMudigantiVNKS. Recent advances in immunomodulatory therapy in sepsis: a comprehensive review. Cureus (2024) 16:e57309. doi: 10.7759/cureus.57309
231.
BozzaFABozzaPTCastro Faria NetoHC. Beyond sepsis pathophysiology with cytokines: what is their value as biomarkers for disease severity?Mem Inst Oswaldo Cruz. (2005) 100 Suppl 1:217–21. doi: 10.1590/S0074-02762005000900037
232.
KellyBO'NeillLAJ. Metabolic reprogramming in macrophages and dendritic cells in innate immunity. Cell Res. (2015) 25:771–84. doi: 10.1038/cr.2015.68
233.
SmallwoodHSDuanSMorfouaceMRezinciucSShulkinBLShelatAet al. Targeting metabolic reprogramming by influenza infection for therapeutic intervention. Cell Rep. (2017) 19:1640–53. doi: 10.1016/j.celrep.2017.04.039
234.
MuroS. Challenges in design and characterization of ligand-targeted drug delivery systems. J Control Release (2012) 164:125–37. doi: 10.1016/j.jconrel.2012.05.052
235.
EzikeTCOkpalaUSOnojaULNwikeCPEzeakoECOkparaOJet al. Advances in drug delivery systems, challenges and future directions. Heliyon (2023) 9:e17488. doi: 10.1016/j.heliyon.2023.e17488
236.
YuHYangZLiFXuLSunY. Cell-mediated targeting drugs delivery systems. Drug Deliv. (2020) 27:1425–37. doi: 10.1080/10717544.2020.1831103
237.
DyabinaASRadchenkoEVPalyulinVAZefirovNS. Prediction of blood-brain barrier permeability of organic compounds. Dokl Biochem Biophys. (2016) 470:371–4. doi: 10.1134/S1607672916050173
238.
TrippierPC. Selecting good “drug-like” properties to optimize small molecule blood-brain barrier penetration. Curr Med Chem. (2016) 23:1392–407. doi: 10.2174/0929867323666160405112353
239.
LagorceDDouguetDMitevaMAVilloutreixBO. Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors. Sci Rep. (2017) 7:46277. doi: 10.1038/srep46277
240.
GuptaMFengJBhisettiG. Experimental and computational methods to assess central nervous system penetration of small molecules. Molecules (2024) 29:1264. doi: 10.3390/molecules29061264
241.
RizkMLZouLSavicRMDooleyKE. Importance of drug pharmacokinetics at the site of action. Clin Transl Sci. (2017) 10:133–42. doi: 10.1111/cts.12448
242.
LuC-TZhaoY-ZWongHLCaiJPengLTianX-Q. Current approaches to enhance CNS delivery of drugs across the brain barriers. Int J Nanomedicine (2014) 9:2241–57. doi: 10.2147/IJN.S61288
243.
KampmeierTGErtmerCRehbergS. Translational research in sepsis - an ultimate challenge?Exp Transl Stroke Med. (2011) 3:14. doi: 10.1186/2040-7378-3-14
244.
GuillonAPreauSAboabJAzabouEJungBSilvaSet al. Preclinical septic shock research: why we need an animal ICU. Ann Intensive Care (2019) 9:66. doi: 10.1186/s13613-019-0543-6
245.
FrançoisBClavelMVignonPLaterreP-F. Perspective on optimizing clinical trials in critical care: how to puzzle out recurrent failures. J Intensive Care (2016) 4:67. doi: 10.1186/s40560-016-0191-y
246.
QinMGaoYGuoSLuXZhaoQGeZet al. Establishment and evaluation of animal models of sepsis-associated encephalopathy. World J Emerg Med. (2023) 14:349–53. doi: 10.5847/wjem.j.1920-8642.2023.088
247.
HuJChenZWangJXuASunJXiaoWet al. Identification and evaluation of lipocalin-2 in sepsis-associated encephalopathy via machine learning approaches. J Inflamm Res. (2025) 18:3843–58. doi: 10.2147/JIR.S504390
248.
Vella BonannoPErmischMGodmanBMartinAPVan Den BerghJBezmelnitsynaLet al. Adaptive pathways: possible next steps for payers in preparation for their potential implementation. Front Pharmacol. (2017) 8:497. doi: 10.3389/fphar.2017.00497
249.
SinghRGholipourmalekabadiMShafikhaniSH. Animal models for type 1 and type 2 diabetes: advantages and limitations. Front Endocrinol. (2024) 15:1359685. doi: 10.3389/fendo.2024.1359685
250.
KleinertMClemmensenCHofmannSMMooreMCRennerSWoodsSCet al. Animal models of obesity and diabetes mellitus. Nat Rev Endocrinol. (2018) 14:140–62. doi: 10.1038/nrendo.2017.161
251.
SeemannSZohlesFLuppA. Comprehensive comparison of three different animal models for systemic inflammation. J Biomed Sci. (2017) 24:60. doi: 10.1186/s12929-017-0370-8
252.
BerguerRAlarconAFengSGuttC. Laparoscopic cecal ligation and puncture in the rat. Surgical technique and preliminary results. Surg Endosc. (1997) 11:1206–8. doi: 10.1007/s004649900570
253.
DejagerLPinheiroIDejonckheereELibertC. Cecal ligation and puncture: the gold standard model for polymicrobial sepsis?Trends Microbiol. (2011) 19:198–208. doi: 10.1016/j.tim.2011.01.001
254.
MishraSKChoudhuryS. Experimental protocol for cecal ligation and puncture model of polymicrobial sepsis and assessment of vascular functions in mice. Methods Mol Biol. (2018) 1717:161–87. doi: 10.1007/978-1-4939-7526-6_14
255.
Roberto Rodrigues BicalhoPMagna RibeiroFHenrique Ferreira MarçalPGomes de AlvarengaDde SilvaFS. Does helium pneumoperitoneum reduce the hyperinflammatory response in septic animals during laparoscopy?Surg Res Pract. (2020) 2020:5738236. doi: 10.1155/2020/5738236
256.
SheenKMyungSLeeD-MYuSChoiYKimTet al. RNA-Seq of an LPS-induced inflammation model reveals transcriptional profile patterns of inflammatory processes. Life (2024) 14:558. doi: 10.3390/life14050558
257.
BrooksDBarrLCWiscombeSMcAuleyDFSimpsonAJRostronAJ. Human lipopolysaccharide models provide mechanistic and therapeutic insights into systemic and pulmonary inflammation. Eur Respir J. (2020) 56:1901298. doi: 10.1183/13993003.01298-2019
258.
YangL-LWangG-QYangL-MHuangZ-BZhangW-QYuL-Z. Endotoxin molecule lipopolysaccharide-induced zebrafish inflammation model: a novel screening method for anti-inflammatory drugs. Molecules (2014) 19:2390–409. doi: 10.3390/molecules19022390
259.
NwaforDCBrichacekALMohammadASGriffithJLucke-WoldBPBenkovicSAet al. Targeting the blood-brain barrier to prevent sepsis-associated cognitive impairment. J Cent Nerv Syst Dis. (2019) 11:1179573519840652. doi: 10.1177/1179573519840652
260.
Hahmeyer ML daSda Silva-SantosJE. Rho-proteins and downstream pathways as potential targets in sepsis and septic shock: what have we learned from basic research. Cells (2021) 10:1844. doi: 10.3390/cells10081844
261.
ParkJWLeeSJKimJEKangMJBaeSJChoiYJet al. Comparison of response to LPS-induced sepsis in three DBA/2 stocks derived from different sources. Lab Anim Res. (2021) 37:2. doi: 10.1186/s42826-020-00079-5
262.
HammerMEchtenachterBWeighardtHJozefowskiKRose-JohnSMännelDNet al. Increased inflammation and lethality of Dusp1-/- mice in polymicrobial peritonitis models. Immunology (2010) 131:395–404. doi: 10.1111/j.1365-2567.2010.03313.x
263.
de AzevedoLCPParkMNoritomiDTMacielATBrunialtiMKSalomãoR. Characterization of an animal model of severe sepsis associated with respiratory dysfunction. Clinics (2007) 62:491–8. doi: 10.1590/S1807-59322007000400017
264.
OvermyerKAThonusinCQiNRBurantCFEvansCR. Impact of anesthesia and euthanasia on metabolomics of mammalian tissues: studies in a C57BL/6J mouse model. PLoS ONE (2015) 10:e0117232. doi: 10.1371/journal.pone.0117232
265.
BresillaDHabischHPritišanacIZarseKParichatikanondWRistowMet al. The sex-specific metabolic signature of C57BL/6NRj mice during aging. Sci Rep. (2022) 12:21050. doi: 10.1038/s41598-022-25396-8
266.
FernandesJDunigan-RussellKZhongHLinVSilverbergMMooreSBet al. Transcriptomic-metabolomic profiling in mouse lung tissues reveals sex- and strain-based differences. Metabolites (2022) 12:932. doi: 10.3390/metabo12100932
267.
ArbleDMRamseyKMBassJTurekFW. Circadian disruption and metabolic disease: findings from animal models. Best Pract Res Clin Endocrinol Metab. (2010) 24:785–800. doi: 10.1016/j.beem.2010.08.003
268.
ChighineAStoccheroMDe-GiorgioFNioiMd'AlojaELocciE. Translating metabolomic evidence gathered from an animal model to a real human scenario: the post-mortem interval issue. Metabolomics (2025) 21:125. doi: 10.1007/s11306-025-02321-4
269.
TangJJiangRGaoHXiaJMaYHanZet al. Development and multi-center validation of machine learning models based on targeted metabolomics for rheumatoid arthritis. J Transl Med. (2025) 23:1257. doi: 10.1186/s12967-025-07265-w
270.
XuYWilsonIDGoodacreR. Combining clinical chemistry with metabolomics for metabolic phenotyping at population levels. Metabolomics (2025) 21:126. doi: 10.1007/s11306-025-02331-2
271.
SubramanianIVermaSKumarSJereAAnamikaK. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights (2020) 14:1177932219899051. doi: 10.1177/1177932219899051
272.
CominettiODayonL. Unravelling disease complexity: integrative analysis of multi-omic data in clinical research. Expert Rev Proteomics (2025) 22:149–62. doi: 10.1080/14789450.2025.2491357
273.
LuoYZhaoCChenF. Multiomics research: principles and challenges in integrated analysis. Biodes Res. (2024) 6:0059. doi: 10.34133/bdr.0059
274.
WuC-RZhuH-LSunY-TShenS-HShiP-LCuiY-Het al. Clinical manifestations of anxiety and depression in sepsis-associated encephalopathy and multi-omics identification of cluster of differentiation 38 as an early biomarker. World J Psychiatry (2025) 15:105889. doi: 10.5498/wjp.v15.i6.105889
275.
Di NanniNBersanelliMCupaioliFAMilanesiLMezzelaniAMoscaE. Network-based integrative analysis of genomics, epigenomics and transcriptomics in autism spectrum disorders. Int J Mol Sci. (2019) 20:3363. doi: 10.3390/ijms20133363
276.
BarabásiA-LGulbahceNLoscalzoJ. Network medicine: a network-based approach to human disease. Nat Rev Genet. (2011) 12:56–68. doi: 10.1038/nrg2918
277.
ZouJZhengM-WLiGSuZ-G. Advanced systems biology methods in drug discovery and translational biomedicine. Biomed Res Int. (2013) 2013:742835. doi: 10.1155/2013/742835
278.
ShenROlshenABLadanyiM. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics (2009) 25:2906–12. doi: 10.1093/bioinformatics/btp543
279.
RappoportNShamirR. Multi-omic and multi-view clustering algorithms: review and cancer benchmark. Nucleic Acids Res. (2018) 46:10546–62. doi: 10.1093/nar/gky889
280.
KimSOesterreichSKimSParkYTsengGC. Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization. Biostatistics (2017) 18:165–79. doi: 10.1093/biostatistics/kxw039
281.
ChengTXuYLiuZWangYZhangZHuangW. Multi-omics analysis reveals neutrophil heterogeneity and key molecular drivers in sepsis-associated acute kidney injury. Front Immunol. (2025) 16:1637692. doi: 10.3389/fimmu.2025.1637692
282.
ChalisePKoestlerDCBimaliMYuQFridleyBL. Integrative clustering methods for high-dimensional molecular data. Transl Cancer Res. (2014) 3:202–16. doi: 10.3978/j.issn.2218-676X.2014.06.03
283.
AliADavidsonSFraenkelEGilmoreIHankemeierTKirwanJAet al. Single cell metabolism: current and future trends. Metabolomics (2022) 18:77. doi: 10.1007/s11306-022-01934-3
284.
SeydelC. Single-cell metabolomics hits its stride. Nat Methods (2021) 18:1452–6. doi: 10.1038/s41592-021-01333-x
285.
ZenobiR. Single-cell metabolomics: analytical and biological perspectives. Science (2013) 342:1243259. doi: 10.1126/science.1243259
286.
EversTMJHochaneMTansSJHeerenRMASemrauSNemesPet al. Deciphering metabolic heterogeneity by single-cell analysis. Anal Chem. (2019) 91:13314–23. doi: 10.1021/acs.analchem.9b02410
287.
ZhuDWangPChenXWangKWuYZhangMet al. Astrocyte-derived interleukin 11 modulates astrocyte-microglia crosstalk via nuclear factor-κB signaling pathway in sepsis-associated encephalopathy. Research (2025) 8:0598. doi: 10.34133/research.0598
288.
FangJLianYXieKCaiSWenP. Epigenetic modulation of neuronal apoptosis and cognitive functions in sepsis-associated encephalopathy. Neurol Sci. (2014) 35:283–8. doi: 10.1007/s10072-013-1508-4
289.
JingGGongHWangHZuoJWuDLiuHet al. OTUD1 exacerbates sepsis-associated encephalopathy by promoting HK2 mitochondrial release to drive microglia pyroptosis. J Neuroinflammation (2025) 22:154. doi: 10.1186/s12974-025-03480-w
290.
DuanWChenQLiWZhouHDengXZhangY. Decoding monocyte heterogeneity in sepsis: a single-cell apoptotic signature for immune stratification and guiding precision therapy. Front Pharmacol. (2025) 16:1675887. doi: 10.3389/fphar.2025.1675887
291.
LiuHLiangQ. Single-cell multi-omics-based immune temporal network resolution in sepsis: unravelling molecular mechanisms and precise therapeutic targets. Front Immunol. (2025) 16:1616794. doi: 10.3389/fimmu.2025.1616794
292.
WeversDRamautarRClarkCHankemeierTAliA. Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics. Electrophoresis (2023) 44:2000–24. doi: 10.1002/elps.202300105
293.
de SouzaLPBorghiMFernieA. Plant single-cell metabolomics-challenges and perspectives. Int J Mol Sci. (2020) 21:8987. doi: 10.3390/ijms21238987
294.
BhusalDWije MunigeSPengZYangZ. Exploring single-probe single-cell mass spectrometry: current trends and future directions. Anal Chem. (2025) 97:4750–62. doi: 10.1021/acs.analchem.4c06824
295.
PetrovaBGulerAT. Recent developments in single-cell metabolomics by mass spectrometry–a perspective. J Proteome Res. (2025) 24:1493–518. doi: 10.1021/acs.jproteome.4c00646
296.
ChenXHuangYHuangLHuangZHaoZ-ZXuLet al. A brain cell atlas integrating single-cell transcriptomes across human brain regions. Nat Med. (2024) 30:2679–91. doi: 10.1038/s41591-024-03150-z
297.
ZhaoWJohnstonKGRenHXuXNieQ. Inferring neuron-neuron communications from single-cell transcriptomics through NeuronChat. Nat Commun. (2023) 14:1128. doi: 10.1038/s41467-023-36800-w
298.
HouYYaoHLinJ-M. Recent advancements in single-cell metabolic analysis for pharmacological research. J Pharm Anal. (2023) 13:1102–16. doi: 10.1016/j.jpha.2023.08.014
299.
WuXYangXDaiYZhaoZZhuJGuoHet al. Single-cell sequencing to multi-omics: technologies and applications. Biomark Res. (2024) 12:110. doi: 10.1186/s40364-024-00643-4
300.
MaoXXiaDXuMGaoYTongLLuCet al. Single-cell simultaneous metabolome and transcriptome profiling revealing metabolite-gene correlation network. Adv Sci. (2025) 12:e2411276. doi: 10.1002/advs.202411276
301.
JuffermansNP. ICM experimental is growing, from bench via bedside to big data-and back! Intensive Care Med Exp. (2023) 11:24. doi: 10.1186/s40635-023-00507-5
302.
WoolfSH. The meaning of translational research and why it matters. JAMA (2008) 299:211–3. doi: 10.1001/jama.2007.26
303.
MannaSKTanakaNKrauszKWHaznadarMXueXMatsubaraTet al. Biomarkers of coordinate metabolic reprogramming in colorectal tumors in mice and humans. Gastroenterology (2014) 146:1313–24. doi: 10.1053/j.gastro.2014.01.017
304.
LuoPYinPHuaRTanYLiZQiuGet al. A large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma. Hepatology (2018) 67:662–75. doi: 10.1002/hep.29561
305.
SchmidtDRPatelRKirschDGLewisCAVander HeidenMGLocasaleJW. Metabolomics in cancer research and emerging applications in clinical oncology. CA Cancer J Clin. (2021) 71:333–58. doi: 10.3322/caac.21670
306.
PepeMSEtzioniRFengZPotterJDThompsonMLThornquistMet al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst. (2001) 93:1054–61. doi: 10.1093/jnci/93.14.1054
307.
AshburnTTThorKB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. (2004) 3:673–83. doi: 10.1038/nrd1468
308.
Ioakeim-SkoufaITobajas-RamosNMendittoEAza-Pascual-SalcedoMGimeno-MiguelAOrlandoVet al. Drug repurposing in oncology: a systematic review of randomized controlled clinical trials. Cancers (2023) 15:2972. doi: 10.3390/cancers15112972
309.
ZongNWenAMoonSFuSWangLZhaoYet al. Computational drug repurposing based on electronic health records: a scoping review. NPJ Digit Med. (2022) 5:77. doi: 10.1038/s41746-022-00617-6
310.
XiongBWangYChenYXingSLiaoQChenYet al. Strategies for structural modification of small molecules to improve blood-brain barrier penetration: a recent perspective. J Med Chem. (2021) 64:13152–73. doi: 10.1021/acs.jmedchem.1c00910
311.
CaoHZhuGSunLChenGMaXLuoXet al. Discovery of new small molecule inhibitors targeting isocitrate dehydrogenase 1 (IDH1) with blood-brain barrier penetration. Eur J Med Chem. (2019) 183:111694. doi: 10.1016/j.ejmech.2019.111694
312.
KieliszekAMMobilioDBassey-ArchibongBIJohnsonJWPiotrowskiMLde AraujoEDet al. De novo GTP synthesis is a metabolic vulnerability for the interception of brain metastases. Cell Rep Med. (2024) 5:101755. doi: 10.1016/j.xcrm.2024.101755
313.
ZhangYXuLZhangYPanJWangP-QTianSet al. Discovery of novel MIF inhibitors that attenuate microglial inflammatory activation by structures-based virtual screening and in vitro bioassays. Acta Pharmacol Sin. (2022) 43:1508–20. doi: 10.1038/s41401-021-00753-x
314.
RamsayRRPopovic-NikolicMRNikolicKUliassiEBolognesiML. A perspective on multi-target drug discovery and design for complex diseases. Clin Transl Med. (2018) 7:3. doi: 10.1186/s40169-017-0181-2
Summary
Keywords
biomarkers, immunometabolism, metabolic reprogramming, neuroinflammation, precision medicine, sepsis-associated encephalopathy
Citation
Huang M, Qian W, Lin D, Zeng Y, Zhou Y, Zhang S, Luo Y, Li Z and Du X (2026) Metabolic reprogramming in sepsis-associated encephalopathy: emerging mechanisms, candidate biomarkers, and future therapeutic directions. Front. Med. 13:1821690. doi: 10.3389/fmed.2026.1821690
Received
02 March 2026
Revised
16 April 2026
Accepted
27 April 2026
Published
14 May 2026
Volume
13 - 2026
Edited by
Weihu Ma, Ningbo University, China
Reviewed by
JunHao Wang, Baylor College of Medicine, United States
Nora Wolff, Sheikh Zayed Institute for Pediatric Surgical Innovation, United States
Updates
Copyright
© 2026 Huang, Qian, Lin, Zeng, Zhou, Zhang, Luo, Li and Du.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xiaodong Du, 18980601189@163.com
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.