Jia-xuan Huai1,2
Xi-qiao Zhou- 1Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- 2The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China
Diabetic encephalopathy (DE) is a serious neurological complication of diabetes and is expressed as progressive decline in cognitive function, emotional disorders, and changes in brain structure. This review brings together the relevant evidence and demonstrates that metabolic reprogramming, the adaptive reconfiguration of the core metabolic pathway in response to hyperglycemia, is a potential driver of accelerated brain aging in DE. The main pathological characteristics are: abnormal brain insulin signaling, resulting in a decrease in neuronal glucose intake and a decrease in mitochondrial oxidative phosphorylation, oxidative stress and neuroinflammation caused by high blood sugar, in which excess reactive oxygen species (ROS), impairs mitochondrial integrity and leads to activation of microglia cells. The impaired mitophagy and the macrophages remove defects and cause the accumulation and energy collapse of the dysfunctional organelles. In addition, it promotes excessive glycolytic flux, lipolysis disorder, lactic acid accumulation, and ceramide-dependent synaptic damage. We further examine shared metabolic mechanisms between DE and neurodegenerative diseases such as alzheimer’s disease (AD) and treatment strategies for pathological metabolic reprogramming including GLP-1 receptor agonists, NAD+ boosters, and AMPK activators. This analysis laid the foundation for new intervention measures against the development of DE.
Introduction
Diabetes mellitus (DM) is one of the most common chronic metabolic diseases in the world and poses a serious threat to human health (American Diabetes Association ProfessionalPractice Committee, 2024; GBD, 2021 Diabetes Collaborators, 2023). Within the scope of diabetic complications, DE is a particularly heavy neurological expression, and its adverse effects on patient health and the lack of current evidence therapy have led to increased interest in research (Chen et al., 2018). Clinically, DE is characterized by progressive cognitive impairment, as a result of memory and performance impairment, emotional disorders including anxiety and depression, and abnormal brain imaging results such as white quality high signal, decreased brain capacity and hippocampal atrophy (Ehtewish et al.), (Mogi and Horiuchi, 2011; Duarte, 2015). DE patients show distinctive brain changes including vascular lesions, demyelinating cranial nerve, and neuronal degeneration, leading to impaired cognitive performance (Mijnhout et al., 2006; Wang et al., 2020). However, the pathogenesis of DE is not fully understood. Previous studies have shown that it is associated with a variety of pathologic processes, including hyperglycaemia, alterations in brain insulin signaling, cerebrovascular disease, excessive phosphorylation of tau protein, neuroinflammation, and oxidative stress (Nagayach et al., 2024; Rösen et al., 2001). Thus, metabolic reprogramming is considered to be a potential core-driven mechanism that connects these elements. However, the molecular cascade of driven DE is only a partial description, and the generally accepted diagnostic criteria have not yet been established.
Metabolic reprogramming represents the basic cellular process in which neurons dynamically adjust the core metabolic pathways, including glucose utilization, mitochondrial respiration, and biomolecular synthesis, to meet the wavy energy demands and challenges of the microenvironment (Wu et al., 2023). Under physiological conditions, astrocytes maintain neuronal metabolic demand by aerobic glycolysis, and lactic acid is an important energy substrate for synaptic function and cognitive expression (Hui et al., 2017; Xiong et al., 2025). However, chronic hyperglycemia leads to inappropriate metabolic recombination. In a mouse model of streptoazobin-induced hyperglycemia, elevated glucose levels translate the phenotype of astrocytes into a proliferative pro-inflammatory state (Zhang et al., 2024). In a model of intellectual disability, a parallel mechanism in which SNX 27 mutations impaired glial glucose intake through glucose transporter 1 (GLUT1), reduced lactic acid production, and converted steady-state glial cells to a reactive state appeared (Lee et al., 2024). Therefore, mechanisms designed for neuroprotection instead accelerate neurodegeneration through self-reinforcing metabolic failure (Bizon et al., 2012).
This review brings together existing evidence and systematically describes the role of metabolic reprogramming in accelerating DE brain aging, including brain insulin signaling dysregulation, oxidative stress and neuroinflammation, autophagy and mitophagy dysfunction, glycolytic Flux and lipolysis Dysregulation. It focuses on abnormal pathways such as disintegration. We also explored common metabolic mechanisms between neurodegenerative diseases such as DE and AD, and developed therapeutic strategies for metabolic reprogramming such as GLP-1 receptor agonists, NAD+ boosters, and AMPK activators. Through this analysis, we provided a new theoretical basis and treatment plan for the prevention and treatment of DE.
Brain insulin signaling dysregulation
The brain is an important target for insulin action, and most brain insulin comes from circulating pancreatic insulin rather than local synthesis. Insulin integrates the functions of metabolism, neuronutrition, neuroregulation and neuroendocrine regulation in the brain and regulates processes closely related to cognitive health. These processes include synaptic plasticity, neurogenesis, and memory consolidation, especially in the hippocampus (Rask-Madsen and Kahn, 2012; Kleinridders et al., 2015). These functions are mediated by the insulin receptors (IRs), which is widely distributed in presynaptic and postsynaptic neurons and neurologlia cells, allowing insulin to modulate neurotransmitter release, receptor transport, and neuronal excitability (Cai et al., 2018; Scapin et al., 2018).
Insulin signaling in the brain has initiated a series of events that are important for neuronal survival and metabolic balance. The binding of insulin to IR activates the receptor tyrosine kinase, leading to phosphorylation of IRs proteins. This fires two pathways: the phosphatidylinositol 3-kinase (PI3K)/Akt cascade and the mitogen-activated protein kinase (MAPK) pathway (Lee et al., 2011). The PI3K/Akt pathway controls glucose intake via glucose carriers, protein making, and cell survival. Glucose enters the brain barrier via GLUT1. Neurons take glucose via GLUT3; astrocytes via GLUT1 (Peng et al., 2021). The MAPK pathway manages nerve connections and new brain cells, key for learning and memory. Insulin also changes CREB protein action, a factor vital for memory fixing (Castrén and Monteggia, 2021). Together, these paths keep brain metabolism and function steady. Their failure in T2DM breaks this balance, pushing bad metabolic reprogramming.
A sign of T2DM is insulin resistance. Cells respond poorly to insulin’s metabolic actions. This reaches the brain as neuron insulin resistance (Muscogiur et al., 2022). In cells, neuronal insulin resistance impairs glucose uptake mechanisms, cell survival, and disrupts the steady state of metabolism. Critically, insulin’s cognitive effects may operate through non-metabolic pathways such as synaptic receptor trafficking and tau phosphorylation regulation, independent of direct glucose uptake (Hamer et al., 2019). Neuronal insulin resistance triggers metabolic reprogramming, shifting energy metabolism from active phosphorylates to glycolysis. This transfer is accompanied by mitochondrial dysfunction and lipid and amino acid metabolism. These metabolic changes accelerate neuronal and brain aging (Cunnane et al., 2020). Biomarkers of brain insulin dysfunction in DE patients reflect the severity of metabolic reprogramming and cognitive impairment. These include reduced IRS-1 phosphorylation, changes in mitochondrial morphology/function, and malfunctioning of downstream insulin signaling targets (Martínez Báez et al., 2024). These biomarkers not only tested the link between insulin resistance and DE, but also emphasized that metabolic reprogramming is a unified mechanism by which T2DM accelerates brain aging.
Oxidative stress and neuroinflammation
Oxidative stress is defined as the imbalance between the production of ROS and the antioxidant defense capacity of an organism, and is a central pathological feature of metabolic disorders, neurodegeneration and aging (Maciejczyk et al., 2019). Importantly, it is the result of metabolic reprogramming, which acts as an adaptive restructure of metabolic pathways. Sustaining accelerated brain aging and cognitive decline of DE. Antioxidative systems of the body containing catalase, superoxide dismutase, enzymes such as glutathione peroxidase, and non-enzymatic components such as reduced glutathione and uric acid, usually works to neutralize ROS and maintain cell homeostasis (Chen et al., 2025). However, in the condition of chronic diabetes, oxidative stress can overwhelm these defense mechanisms, lead to impaired regulation of metabolic pathways, and cause pathological changes in energy metabolism that perpetuate cellular dysfunction (Singh et al., 2022).
DM contributes greatly to excessive ROS production. Chronic hyperglycemia in DM accelerates ROS formation, which overcomes endogenous antioxidant defenses, through glucose self-oxidation, advanced glycemic reactions, and extreme activity of the polyol pathway (Khalid et al., 2022). In diabetic rats, weak brain antioxidant enzymes match worse memory problems (Ling et al., 2018). Specifically, ROS harm to mitochondrial DNA and parts blocks energy making (Sun et al., 2023; Watanuki et al., 2024). This forces neurons to use inefficient sugar breakdown for energy—a mark of metabolic reprogramming. This sugar shift cuts ATP output and raises lactate buildup and more ROS. It creates a bad loop that speeds neuron aging (Vargas-Soria et al., 2023; Churchward et al., 2018).
High ROS levels also push microglia cells to change. They release stuff like TNF-α, IL-1β, IL-6, IFN-γ, iNOS, MCP-1, NO, prostaglandin E2, and GRO, increasing brain swelling (Bernier et al., 2020). Brain samples from diabetic donors show microglia activation, like in AD—a zone key for memory (Valente et al., 2010). Pre-clinical studies using diabetic/obese db/db mice have shown that increases in brain levels of IL-1β, IL-6, and TNF-α are correlated with decreases in spatial recognition memory. However, genetic or pharmacological inhibition of microglial activation can mitigate both inflammation and cognitive impairment (Din et al., 2011).
Oxidative stress activates several stress-responsive signaling pathways, including p38 MAPK, NF-κB, AGE/RAGE, JNK/SAPK, and protein kinase C, which directly modulate metabolic reprogramming (Evans et al., 2002). NF-κB signalling, for example, inhibits PGC-1α, the main orchestrator of mitochondrial biological development, thereby inhibiting oxidative phosphorylation and shunting metabolism to glycolysis (Abu et al., 2023). Similarly, the p38 MAPK signaling inhibited Akt-guided glucose absorption, thereby exacerbating the imbalance of neuronal energy (Entezari et al., 2022). In total, these disorders modify proteins, lipids, and DNA oxidatively, leading to caspeze-induced apprehensis and persistent neuritis. In particular, protein-oxidation worsens synaptic plasticity and memory alignment, contributing to the cognitive decline directly observed in DE (Dong et al., 2023) (Figure 1).
Figure 1. Brain Metabolic Reprogramming as a Central Hub Linking Peripheral Metabolic Stress to Neurodegenerative Pathologies. Peripheral metabolic stress (chronic hyperglycemia/insulin resistance) triggers brain metabolic reprogramming, which in turn drives neuroinflammation and BBB breakdown. These pathologies impair synaptic plasticity, promote tau hyperphosphorylation, and reduce cognitive function.
Autophagy and mitophagy dysfunction
Autophagy is a cleanup path using lysosomes. It removes bad proteins and cell parts, key for cell balance (Dikic and Elazar, 2018; Galluzzi et al., 2017). Main control paths for autophagy include AMPK/mTOR and PI3K/Akt/mTOR chains. These cross with core metabolic reprogramming (Wang et al., 2023; Kim et al., 2002). Phage flux is usually measured by LC3 and p62. Persistent high p62° signals inhibit macrophage flow (Mizushima et al., 2010).
Mitophagy is a specialized form of autophagy that selectively target mitochondria for lysosomal degradation and play an important role in regulating inflammation, metabolic transformation, and cell reprogramming (Gustafsson and Dorn, 2019). Because neurons are specialized energy consumers, can accelerate their death even with light mitochondrial damage and force metabolic recombination, that macrophages are central tubes that connect metabolic disorders and neurodegenerative diseases A growing number of data indicate that this is the case (Fang et al., 2019).
Under physiological conditions, instantaneous energy stress triggers the AMPK/ULK1 signaling axis and activates a protective mitotic food with PINK1 stabilization and Parkin recruitment (Iorio et al., 2021). However, chronic metabolic disturbances, such as persistent high blood sugar, disrupt this time regulation. When AMPK calibration is impaired, the PINK one phosphorylation mode is no longer adaptive, resulting in Parkin mistranslation and progressive self-feeding failure (Guo et al., 2023). The resulting mitochondrial dysfunction led to a vicious cycle of self-continuation, in which defects in cell removal exacerbated the overproduction of ROS, further impair the PINK 1-mediated activation mechanism (Lazarou et al., 2015). This leads to irreparable mitochondrial rupture, which runs out of ATP reserves and accelerates the over-phosphorylation of tau proteins—a sign of synaptic degeneration (Toyama et al., 2016).
In a db/db mouse model, a simulation of T2DM due to obesity and hyperglycemia, hippocampal self-eating injury was demonstrated by a decrease in the LC3-II/I ratio and an increase in p62 accumulation (Guan et al., 2017; Li et al., 2017). T1DM has recorded a similar mitochondrial phagocytotic protective effect that reduces the accumulation of ataxia mitochondria in pancreatic beta cells, and a conservative mechanism between diabetic subtypes tends to be consistent with ataxia (Blagov et al., 2023).
Recent breakthroughs have shown that enhancing mitotic diet can suppress amyloid-beta plaque accumulation, reduce excessive phosphorylation of tau protein, and prevent synaptic loss during neurodegeneration. It has been demonstrated that pharmacological activation of mitotic food by supplementation with drugs such as urolithin A or NAD+ reverses the glycolysis dependence of neurons, restores oxidative phosphorylation, and improves cognitive function in diabetic models (Fang et al., 2017; Fang et al., 2014).
Glycolytic flux and lipolysis dysregulation
Chronic hyperglycemia leads to pathological changes, characterized by “glycolysis overload and unscheduled glycolysis associated with hexalose kinases”. Unscheduled glycolysis means a state of metabolic disorder in which glycolysis fluxes are persistent and are affected by cellular energy demands. This process is characterized by too large a bundle of glucose passing through hexokinase-2 (HK2), but the downstream yeast degrading enzyme does not increase accordingly. This imbalance leads to an abnormal accumulation of yeast solution intermediates to bypass mitochondrial oxidation and promote the production of excessive lactate (Rabbani and Thornalley, 2024a). Lactic acid usually acts as the preferred oxidative fuel for neurons during acute metabolic stress, but its chronic overproduction exceeds the physiological threshold in hyperglycemia (Magistretti and Allaman, 2018). In neurons, this shift appears as a change from oxidative phosphorylation to aerobic glycolysis, similar to the Warburg effect. This change happens through increased HIF-1α activity, which raises levels of pyruvate kinase M2 (PKM2) and lactate dehydrogenase A (LDHA) (Traxler et al., 2022; Wei et al., 2023). Lactate changes from a useful energy source to a harmful substance: it increases the lactate-to-pyruvate ratio in the brain and separates glycolysis from mitochondrial oxidation (Tang, 2020). More glucose use leads to higher ATP consumption (Rabbani and Thornalley, 2024b; Wang et al., 2024). This broken lactate shuttle system prevents proper energy production, making neurons lose energy (Lee et al., 2025).
Lipid breakdown problems also speed up brain aging and cognitive loss. Research shows these problems cause free fatty acids (FFA) and lipoproteins to enter the brain, damaging the BBB. Studies found that clusters of Apolipoprotein E epsilon 4 allele (ApoEε4) turn on the CypA-MMP9 pathway (Banks and Rhea, 2021; Shen et al., 2023). This breaks down tight junction proteins and lets neurotoxic free fatty acids flood into the brain. The resulting lipid overload not only amplizes insulin resistance in neurons, but also builds a self-sustaining cycle of metabolic dysfunction (Zhao et al., 2017). The high blood sugar induced glycolysis flux directly intensified the malfunctions of lipolysis. Enhanced glycolysis generates glycerol-3-phosphate, which converts FFA esters to triglycerides and releases FFA permeated into the brain by elevated adipose triglyceride lipase (ATGL) (Pedretti et al., 2025; Byrns et al., 2024). In neurons and star-like colloidal cells, insulin resist inhibition of peroxisome proliferator-activated receptor α (PPARα) activity, inhibits fatty acid oxidation (FAO), and promotes lipid accumulation (Scheggi et al., 2022; Kumar et al., 2025).
The interaction between glycolysis and lipolysis produces a self-amplified pathological circulation in the DE. First, citric acid is transferred from the TCA cycle to the abinitio fat production mediated by acetyl-CoA carboxylase (ACC) to enhance the synthesis of palmitic acid. Palmitates then activate the mini-colloidal cell Toll-like receptor 4 (TLR4), triggering the release of pro-inflammatory cytokines (Kim et al., 2023). At the same time, lactate is released through the monocarboxylic acid transport protein, which activates neuron G protein-coupled receptor 81 (GPR81) and inhibits intracellular cAMP levels. This decrease in cAMP indirectly inhibits the activity of hormone-sensitive lipase (HSL) and captures lipids into the cell (Lee et al., 2025). Finally, the ROS oxidative low density lipoprotein (LDL) particles produced by overloading are glycosylated to form oxidized LDL (oxLDL). This in turn raised the sterol regulatory element-binding protein 1c (SREBP-1c), accelerating the synthesis of ceramides. The resulting increase in hippocampal ceramide levels directly results in synaptic damage and cognitive impairment (Goodman et al., 2024; Zhou et al., 2025) (Figure 2).
Figure 2. Putative Metabolic Reprogramming in DE: Interconnected Pathways of Glucose Utilization, Mitochondrial Oxidation, and Lipid Dysregulation. (1) Glucose Metabolism: Glucose uptake via GLUT (glucose transporter) and glycolysis, with pyruvate diverted to lactate (exported via MCT [monocarboxylate transporter]) under chronic hyperglycemia (Warburg effect); (2) Mitochondrial Dysfunction: Impaired oxidative phosphorylation (OXPHOS) due to ROS (reactive oxygen species) from the electron transport chain (ETC.), reducing ATP production; (3) Lipid Metabolism: Dysregulated fatty acid synthesis (from TCA cycle citrate) and β-oxidation, contributing to lipid accumulation.
Therapeutic strategies targeting metabolic reprogramming
Current evidence supports the targetability of therapy with pathological metabolic reprogramming to offset progression of DE (Cunnane et al., 2020; Guarente et al., 2024). GLP−1 receptor agonists (including liraglutide and semaglutide) improve neuronal insulin resistance by enhancing insulin-sensitive glucose utilization and inhibiting yeast fluke driven by hyperglycemia (Drucker et al., 2017). Clinical trials have shown that they reduce neuroinflammation and synaptic loss primarily by indirectly regulating peripheral intestinal insulin signals and steady glucose status (Wei et al., 2023; Li Z. et al., 2021). The exact involvement of HIF-1α/PKM2 signaling in neurons is not yet fully understood and further research is needed.
NAD+ boosters, exemplified by nicotinamide mononucleotide, address hallmark NAD+ depletion through the reactivation of sirtuins SIRT1 and SIRT3. This reactivation restores mitochondrial oxidative phosphorylation, enhances autophagic clearance of damaged organelles, and reduces oxidative damage in hippocampal neurons, thereby reversing cognitive deficits associated with accelerated brain aging (Fang et al., 2014; Butterfield and Halliwell, 2019; Zh et al., 2016). Emerging evidence demonstrates that supplementation with NAD+ or its biosynthetic precursors rescues high-glucose. It impaired keratinocyte proliferation and migration, expedites corneal re-epithelialization and sensory nerve regrowth in diabetic animals, and coincides with re-engagement of the SIRT1 pathway (Li Y. et al., 2021). Brain NAD+ regulation presents a tissue-specific pathway with different enzymatic controls, and these findings indicate that hyperglyceme-mediated NAD+ biosynthetic damage is a pathogen in diabetic tissue pathology.
AMPK activators exemplified by metformin operate through well-established mechanisms: inhibition of mitochondrial complex I activates AMPK, suppressing acetyl-CoA carboxylase-mediated lipogenesis and ceramide accumulation. It also activates PPARα to promote oxidation of fatty acid beta and relieve lipid-induced astrocytes stress (Bharath et al., 2020; Zhang et al., 2023). Clinical data have demonstrated the therapeutic effect of metformin in improving glucose control and reducing diabetic complications, but its direct impact on the specific lipolysis of human astrocytes requires testing (Dutta et al., 2023).
New evidence suggests that combinatorial targets of key metabolic nodes may enhance neural protection for age-related neurodegenerative diseases (Zhang et al., 2021). To restore brain metabolic stability and slow brain aging, a synergy of GLP-1 receptor agonists, NAD+ precursors and AMPK activators was proposed. The resulting metabolic recombination reverses the transformation of Warburg-like glycolytic shifts in senescent neurons, enhances macrophage lysosome function, and may reduce oxidative stress. It should be noted that the therapeutic potential of this combination must be balanced with clinical risk. Hypoglycemia due to combination of GLP-1RA and metformin and uncertain central nervous system interactions under tissue-specific NAD+ regulation (Xie et al., 2023; Gong et al., 2022). Targeted validation in diabetic encephalopathy models remains a necessary condition to assess the safety tradeoff of treatment efficacy (Na et al., 2025).
Conclusion
This review identifies metabolic reprogramming as an important driver of DE cognitive decline and linked the major pathologic processes: brain insulin resistance, oxidative stress, defective autophagy, and dysregulated glycolysis/lipolysis, all of which contribute to accelerated brain aging. Novel treatments for metabolic reprogramming, such as GLP-1 receptor agonists, NAD+ boosters and AMPK activators, have shown hope in preclinical studies, but the specific mechanisms in terms of DE and clinical efficacy are unknown. Importantly, direct research into DE metabolic reprogramming is not yet sufficient and there are significant differences in our understanding of its causal effects and therapeutic potential. Future research should focus on the validation of these mechanisms and conduct clinical trials to develop effective methods for treating DE related dementia.
Author contributions
J-xH: Conceptualization, Visualization, Writing – original draft, Writing – review and editing, Software. E-eC: Software, Visualization, Writing – original draft. Y-rZ: Supervision, Writing – original draft. W-lM: Conceptualization, Supervision, Writing – original draft. T-sL: Software, Supervision, Writing – review and editing. JS: Funding acquisition, Supervision, Visualization, Writing – review and editing. X-qZ: Conceptualization, Funding acquisition, Supervision, Writing – review and editing.
Funding
The authors declare that financial support was received for the research and/or publication of this article. This work was supported by the National Natural Science Foundation of China (No. 82203850 to JS).
Conflict of interest
The authors declare that the research 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 authors declare that no Generative AI was 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
Abu, S. O., Arroum, T., Morris, S., and Busch, K. B. (2023). PGC-1α is a master regulator of mitochondrial lifecycle and ROS stress response. Antioxidants (Basel) 12 (5), 1075. doi:10.3390/antiox12051075
American Diabetes Association Professional Practice Committee (2024). 2. Diagnosis and classification of diabetes: standards of care in Diabetes-2024. Diabetes Care 47 (Suppl. 1), S20–S42. doi:10.2337/dc24-S002
Banks, W. A., and Rhea, E. M. (2021). The blood-brain barrier, oxidative stress, and insulin resistance. Antioxidants (Basel) 10 (11), 1695. doi:10.3390/antiox10111695
Bernier, L. P., York, E. M., Kamyabi, A., Choi, H. B., Weilinger, N. L., and MacVicar, B. A. (2020). Microglial metabolic flexibility supports immune surveillance of the brain parenchyma. Nat. Commun. 11 (1), 1559. doi:10.1038/s41467-020-15267-z
Bharath, L. P., Agrawal, M., McCambridge, G., Nicholas, D. A., Hasturk, H., Liu, J., et al. (2020). Metformin enhances autophagy and normalizes mitochondrial function to alleviate aging-associated inflammation. Cell Metab. 32 (1), 44–55.e6. doi:10.1016/j.cmet.2020.04.015
Bizon, J. L., Foster, T. C., Alexander, G. E., and Glisky, E. L. (2012). Characterizing cognitive aging of working memory and executive function in animal models. Front. Aging Neurosci. 4, 19. doi:10.3389/fnagi.2012.00019
Blagov, A. V., Summerhill, V. I., Sukhorukov, V. N., Popov, M. A., Grechko, A. V., and Orekhov, A. N. (2023). Type 1 diabetes mellitus: inflammation, mitophagy, and mitochondrial function. Mitochondrion 72, 11–21. doi:10.1016/j.mito.2023.07.002
Butterfield, D. A., and Halliwell, B. (2019). Oxidative stress, dysfunctional glucose metabolism and alzheimer disease. Nat. Rev. Neurosci. 20 (3), 148–160. doi:10.1038/s41583-019-0132-6
Byrns, C. N., Perlegos, A. E., Miller, K. N., Jin, Z., Carranza, F. R., Manchandra, P., et al. (2024). Senescent glia link mitochondrial dysfunction and lipid accumulation. Nature 630 (8016), 475–483. doi:10.1038/s41586-024-07516-8
Cai, W., Xue, C., Sakaguchi, M., Konishi, M., Shirazian, A., Ferris, H. A., et al. (2018). Insulin regulates astrocyte gliotransmission and modulates behavior. J. Clin. Invest 128 (7), 2914–2926. doi:10.1172/JCI99366
Castrén, E., and Monteggia, L. M. (2021). Brain-Derived neurotrophic factor signaling in depression and antidepressant action. Biol. Psychiatry 90 (2), 128–136. doi:10.1016/j.biopsych.2021.05.008
Chen, R., Shi, J., Yin, Q., Li, X., Sheng, Y., Han, J., et al. (2018). Morphological and pathological characteristics of brain in diabetic encephalopathy. J. Alzheimers Dis. 65 (1), 15–28. doi:10.3233/JAD-180314
Chen, X., Xie, N., Feng, L., Huang, Y., Wu, Y., Zhu, H., et al. (2025). Oxidative stress in diabetes mellitus and its complications: from pathophysiology to therapeutic strategies. Chin. Med. J. Engl. 138 (1), 15–27. doi:10.1097/CM9.0000000000003230
Churchward, M. A., Tchir, D. R., and Todd, K. G. (2018). Microglial function during glucose deprivation: inflammatory and neuropsychiatric implications. Mol. Neurobiol. 55 (2), 1477–1487. doi:10.1007/s12035-017-0422-9
Cunnane, S. C., Trushina, E., Morland, C., Prigione, A., Casadesus, G., Andrews, Z. B., et al. (2020). Brain energy rescue: an emerging therapeutic concept for neurodegenerative disorders of ageing. Nat. Rev. Drug Discov. 19 (9), 609–633. doi:10.1038/s41573-020-0072-x
Dikic, I., and Elazar, Z. (2018). Mechanism and medical implications of mammalian autophagy. Nat. Rev. Mol. Cell Biol. 19 (6), 349–364. doi:10.1038/s41580-018-0003-4
Dinel, A. L., André, C., Aubert, A., Ferreira, G., Layé, S., and Castanon, N. (2011). Cognitive and emotional alterations are related to hippocampal inflammation in a mouse model of metabolic syndrome. PLoS One 6 (9), e24325. doi:10.1371/journal.pone.0024325
Dong, Y., Zhao, K., Qin, X., Du, G., and Gao, L. (2023). The mechanisms of perineuronal net abnormalities in contributing aging and neurological diseases. Ageing Res. Rev. 92, 102092. doi:10.1016/j.arr.2023.102092
Drucker, D. J., Habener, J. F., and Holst, J. J. (2017). Discovery, characterization, and clinical development of the glucagon-like peptides. J. Clin. Invest 127 (12), 4217–4227. doi:10.1172/JCI97233
Duarte, J. M. (2015). Metabolic alterations associated to brain dysfunction in diabetes. Aging Dis. 6 (5), 304–321. doi:10.14336/AD.2014.1104
Dutta, S., Shah, R. B., Singhal, S., Dutta, S. B., Bansal, S., Sinha, S., et al. (2023). Metformin: a review of potential mechanism and therapeutic utility beyond diabetes. Drug Des. Devel Ther. 17, 1907–1932. doi:10.2147/DDDT.S409373
Ehtewish, H., Arredouani, A., and El-Agnaf, O. (2022). Diagnostic, prognostic, and mechanistic biomarkers of diabetes mellitus-associated cognitive decline. Int. J. Mol. Sci. 23 (11), 6144. doi:10.3390/ijms23116144
Entezari, M., Hashemi, D., Taheriazam, A., Zabolian, A., Mohammadi, S., Fakhri, F., et al. (2022). AMPK signaling in diabetes mellitus, insulin resistance and diabetic complications: a pre-clinical and clinical investigation. Biomed. Pharmacother. 146, 112563. doi:10.1016/j.biopha.2021.112563
Evans, J. L., Goldfine, I. D., Maddux, B. A., and Grodsky, G. M. (2002). Oxidative stress and stress-activated signaling pathways: a unifying hypothesis of type 2 diabetes. Endocr. Rev. 23 (5), 599–622. doi:10.1210/er.2001-0039
Fang, E. F., Scheibye-Knudsen, M., Brace, L. E., Kassahun, H., SenGupta, T., Nilsen, H., et al. (2014). Defective mitophagy in XPA via PARP-1 hyperactivation and NAD(+)/SIRT1 reduction. Cell 157 (4), 882–896. doi:10.1016/j.cell.2014.03.026
Fang, E. F., Lautrup, S., Hou, Y., Demarest, T. G., Croteau, D. L., Mattson, M. P., et al. (2017). NAD+ in aging: molecular mechanisms and translational implications. Trends Mol. Med. 23 (10), 899–916. doi:10.1016/j.molmed.2017.08.001
Fang, E. F., Hou, Y., Palikaras, K., Adriaanse, B. A., Kerr, J. S., Yang, B., et al. (2019). Mitophagy inhibits amyloid-β and tau pathology and reverses cognitive deficits in models of Alzheimer's disease. Nat. Neurosci. 22 (3), 401–412. doi:10.1038/s41593-018-0332-9
Galluzzi, L., Baehrecke, E. H., Ballabio, A., Boya, P., Bravo-San Pedro, J. M., Cecconi, F., et al. (2017). Molecular definitions of autophagy and related processes. EMBO J. 36 (13), 1811–1836. doi:10.15252/embj.201796697
GBD 2021 Diabetes Collaborators (2023). Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the global Burden of Disease Study 2021. Lancet 402 (10397), 203–234. doi:10.1016/S0140-6736(23)01301-6
Gong, H., Chen, H., Xiao, P., Huang, N., Han, X., Zhang, J., et al. (2022). miR-146a impedes the anti-aging effect of AMPK via NAMPT suppression and NAD+/SIRT inactivation. Signal Transduct. Target Ther. 7 (1), 66. doi:10.1038/s41392-022-00886-3
Goodman, L. D., Ralhan, I., Li, X., Lu, S., Moulton, M. J., Park, Y. J., et al. (2024). Tau is required for glial lipid droplet formation and resistance to neuronal oxidative stress. Nat. Neurosci. 27 (10), 1918–1933. doi:10.1038/s41593-024-01740-1
Guan, Z. F., Tao, Y. H., Zhang, X. M., Guo, Q. L., Liu, Y. C., Zhang, Y., et al. (2017). G-CSF and cognitive dysfunction in elderly diabetic mice with cerebral small vessel disease: preventive intervention effects and underlying mechanisms. CNS Neurosci. Ther. 23 (6), 462–474. doi:10.1111/cns.12691
Guarente, L., Sinclair, D. A., and Kroemer, G. (2024). Human trials exploring anti-aging medicines. Cell Metab. 36 (2), 354–376. doi:10.1016/j.cmet.2023.12.007
Guo, Y., Jiang, H., Wang, M., Ma, Y., Zhang, J., and Jing, L. (2023). Metformin alleviates cerebral ischemia/reperfusion injury aggravated by hyperglycemia via regulating AMPK/ULK1/PINK1/Parkin pathway-mediated mitophagy and apoptosis. Chem. Biol. Interact. 384, 110723. doi:10.1016/j.cbi.2023.110723
Gustafsson, Å. B., and Dorn, G. W. (2019). Evolving and expanding the roles of mitophagy as a homeostatic and pathogenic process. Physiol. Rev. 99 (1), 853–892. doi:10.1152/physrev.00005.2018
Hamer, J. A., Testani, D., Mansur, R. B., Lee, Y., Subramaniapillai, M., and McIntyre, R. S. (2019). Brain insulin resistance: a treatment target for cognitive impairment and anhedonia in depression. Exp. Neurol. 315, 1–8. doi:10.1016/j.expneurol.2019.01.016
Hui, S., Ghergurovich, J. M., Morscher, R. J., Jang, C., Teng, X., Lu, W., et al. (2017). Glucose feeds the TCA cycle via circulating lactate. Nature 551 (7678), 115–118. doi:10.1038/nature24057
Iorio, R., Celenza, G., and Petricca, S. (2021). Mitophagy: molecular mechanisms, new concepts on parkin activation and the emerging role of AMPK/ULK1 axis. Cells 11 (1), 30. doi:10.3390/cells11010030
Khalid, M., Petroianu, G., and Adem, A. (2022). Advanced Glycation end products and diabetes mellitus: mechanisms and Perspectives. Biomolecules 12 (4), 542. doi:10.3390/biom12040542
Kim, D. H., Sarbassov, D. D., Ali, S. M., King, J. E., Latek, R. R., Erdjument-Bromage, H., et al. (2002). mTOR interacts with raptor to form a nutrient-sensitive complex that signals to the cell growth machinery. Cell 110 (2), 163–175. doi:10.1016/s0092-8674(02)00808-5
Kim, B., Kang, Y. T., Mendelson, F. E., Hayes, J. M., Savelieff, M. G., Nagrath, S., et al. (2023). Palmitate and glucose increase amyloid precursor protein in extracellular vesicles: missing link between metabolic syndrome and Alzheimer's disease. J. Extracell. Vesicles 12 (11), e12340. doi:10.1002/jev2.12340
Kleinridders, A., Cai, W., Cappellucci, L., Ghazarian, A., Collins, W. R., Vienberg, S. G., et al. (2015). Insulin resistance in brain alters dopamine turnover and causes behavioral disorders. Proc. Natl. Acad. Sci. U. S. A. 112 (11), 3463–3468. doi:10.1073/pnas.1500877112
Kumar, M., Wu, Y., Knapp, J., Pontius, C. L., Park, D., Witte, R. E., et al. (2025). Triglycerides are an important fuel reserve for synapse function in the brain. Nat. Metab. 7 (7), 1392–1403. doi:10.1038/s42255-025-01321-x
Lazarou, M., Sliter, D. A., Kane, L. A., Sarraf, S. A., Wang, C., Burman, J. L., et al. (2015). The ubiquitin kinase PINK1 recruits autophagy receptors to induce mitophagy. Nature 524 (7565), 309–314. doi:10.1038/nature14893
Lee, C. C., Huang, C. C., and Hsu, K. S. (2011). Insulin promotes dendritic spine and synapse formation by the PI3K/Akt/mTOR and Rac1 signaling pathways. Neuropharmacology 61 (4), 867–879. doi:10.1016/j.neuropharm.2011.06.003
Lee, K. S., Yoon, S. H., Hwang, I., Ma, J. H., Yang, E., Kim, R. H., et al. (2024). Hyperglycemia enhances brain susceptibility to lipopolysaccharide-induced neuroinflammation via astrocyte reprogramming. J. Neuroinflammation 21 (1), 137. doi:10.1186/s12974-024-03136-1
Lee, W. D., Weilandt, D. R., Liang, L., MacArthur, M. R., Jaiswal, N., Ong, O., et al. (2025). Lactate homeostasis is maintained through regulation of glycolysis and lipolysis. Cell Metab. 37 (3), 758–771.e8. doi:10.1016/j.cmet.2024.12.009
Li, Y., Zhang, Y., Wang, L., Wang, P., Xue, Y., Li, X., et al. (2017). Autophagy impairment mediated by S-nitrosation of ATG4B leads to neurotoxicity in response to hyperglycemia. Autophagy 13 (7), 1145–1160. doi:10.1080/15548627.2017.1320467
Li, Z., Chen, X., Vong, J. S. L., Zhao, L., Huang, J., Yan, L. Y. C., et al. (2021a). Systemic GLP-1R agonist treatment reverses mouse glial and neurovascular cell transcriptomic aging signatures in a genome-wide manner. Commun. Biol. 4 (1), 656. doi:10.1038/s42003-021-02208-9
Li, Y., Li, J., Zhao, C., Yang, L., Qi, X., Wang, X., et al. (2021b). Hyperglycemia-reduced NAD+ biosynthesis impairs corneal epithelial wound healing in diabetic mice. Metabolism 114, 154402. doi:10.1016/j.metabol.2020.154402
Ling, H., Zhu, Z., Yang, J., He, J., Yang, S., Wu, D., et al. (2018). Dihydromyricetin improves type 2 diabetes-induced cognitive impairment via suppressing oxidative stress and enhancing brain-derived neurotrophic factor-mediated neuroprotection in mice. Acta Biochim. Biophys. Sin. (Shanghai) 50 (3), 298–306. doi:10.1093/abbs/gmy003
Maciejczyk, M., Żebrowska, E., and Chabowski, A. (2019). Insulin resistance and oxidative stress in the brain: what's new? Int. J. Mol. Sci. 20 (4), 874. doi:10.3390/ijms20040874
Magistretti, P. J., and Allaman, I. (2018). Lactate in the brain: from metabolic end-product to signalling molecule. Nat. Rev. Neurosci. 19 (4), 235–249. doi:10.1038/nrn.2018.19
Martínez Báez, A., Ayala, G., Pedroza-Saavedra, A., González-Sánchez, H. M., and Chihu Amparan, L. (2024). Phosphorylation codes in IRS-1 and IRS-2 are associated with the Activation/Inhibition of Insulin Canonical Signaling pathways. Curr. Issues Mol. Biol. 46 (1), 634–649. doi:10.3390/cimb46010041
Mijnhout, G. S., Scheltens, P., Diamant, M., Biessels, G. J., Wessels, A. M., Simsek, S., et al. (2006). Diabetic encephalopathy: a concept in need of a definition. Diabetologia 49 (6), 1447–1448. doi:10.1007/s00125-006-0221-8
Mizushima, N., Yoshimori, T., and Levine, B. (2010). Methods in mammalian autophagy research. Cell 140 (3), 313–326. doi:10.1016/j.cell.2010.01.028
Mogi, M., and Horiuchi, M. (2011). Neurovascular coupling in cognitive impairment associated with diabetes mellitus. Circ. J. 75 (5), 1042–1048. doi:10.1253/circj.cj-11-0121
Muscogiuri, G., Barrea, L., Caprio, M., Ceriani, F., Chavez, A. O., El Ghoch, M., et al. (2022). Nutritional guidelines for the management of insulin resistance. Crit. Rev. Food Sci. Nutr. 62 (25), 6947–6960. doi:10.1080/10408398.2021.1908223
Na, D., Zhang, Z., Meng, M., Li, M., Gao, J., Kong, J., et al. (2025). Energy metabolism and brain aging: strategies to delay neuronal degeneration. Cell Mol. Neurobiol. 45 (1), 38. doi:10.1007/s10571-025-01555-z
Nagayach, A., Bhaskar, R., Ghosh, S., Singh, K. K., Han, S. S., and Sinha, J. K. (2024). Advancing the understanding of diabetic encephalopathy through unravelling pathogenesis and exploring future treatment perspectives. Ageing Res. Rev. 100, 102450. doi:10.1016/j.arr.2024.102450
Pedretti, S., Palermo, F., Braghin, M., Imperato, G., Tomaiuolo, P., Celikag, M., et al. (2025). D-lactate and glycerol as potential biomarkers of sorafenib activity in hepatocellular carcinoma. Signal Transduct. Target Ther. 10 (1), 200. doi:10.1038/s41392-025-02282-z
Peng, W., Tan, C., Mo, L., Jiang, J., Zhou, W., Du, J., et al. (2021). Glucose transporter 3 in neuronal glucose metabolism: health and diseases. Metabolism 123, 154869. doi:10.1016/j.metabol.2021.154869
Rabbani, N., and Thornalley, P. J. (2024a). Hexokinase-linked glycolytic overload and unscheduled glycolysis in hyperglycemia-induced pathogenesis of insulin resistance, beta-cell glucotoxicity, and diabetic vascular complications. Front. Endocrinol. (Lausanne) 14, 1268308. doi:10.3389/fendo.2023.1268308
Rabbani, N., and Thornalley, P. J. (2024b). Unraveling the impaired incretin effect in obesity and type 2 diabetes: key role of hyperglycemia-induced unscheduled glycolysis and glycolytic overload. Diabetes Res. Clin. Pract. 217, 111905. doi:10.1016/j.diabres.2024.111905
Rask-Madsen, C., and Kahn, C. R. (2012). Tissue-specific insulin signaling, metabolic syndrome, and cardiovascular disease. Arterioscler. Thromb. Vasc. Biol. 32 (9), 2052–2059. doi:10.1161/ATVBAHA.111.241919
Rösen, P., Nawroth, P. P., King, G., Möller, W., Tritschler, H. J., and Packer, L. (2001). The role of oxidative stress in the onset and progression of diabetes and its complications: a summary of a Congress Series sponsored by UNESCO-MCBN, the American Diabetes Association and the German Diabetes Society. Diabetes Metab. Res. Rev. 17 (3), 189–212. doi:10.1002/dmrr.196
Scapin, G., Dandey, V. P., Zhang, Z., Prosise, W., Hruza, A., Kelly, T., et al. (2018). Structure of the insulin receptor-insulin complex by single-particle cryo-EM analysis. Nature 556 (7699), 122–125. doi:10.1038/nature26153
Scheggi, S., Pinna, G., Braccagni, G., De Montis, M. G., and Gambarana, C. (2022). PPARα signaling: a candidate target in psychiatric disorder management. Biomolecules 12 (5), 723. doi:10.3390/biom12050723
Shen, Z., Li, Z. Y., Yu, M. T., Tan, K. L., and Chen, S. (2023). Metabolic perspective of astrocyte dysfunction in Alzheimer's disease and type 2 diabetes brains. Biomed. Pharmacother. 158, 114206. doi:10.1016/j.biopha.2022.114206
Singh, A., Kukreti, R., Saso, L., and Kukreti, S. (2022). Mechanistic Insight into oxidative stress-triggered signaling pathways and type 2 diabetes. Molecules 27 (3), 950. doi:10.3390/molecules27030950
Sun, D., Chen, S., Li, S., Wang, N., Zhang, S., Xu, L., et al. (2023). Enhancement of glycolysis-dependent DNA repair regulated by FOXO1 knockdown via PFKFB3 attenuates hyperglycemia-induced endothelial oxidative stress injury. Redox Biol. 59, 102589. doi:10.1016/j.redox.2022.102589
Tang, B. L. (2020). Glucose, glycolysis, and neurodegenerative diseases. J. Cell Physiol. 235 (11), 7653–7662. doi:10.1002/jcp.29682
Toyama, E. Q., Herzig, S., Courchet, J., Lewis, T. L., Losón, O. C., Hellberg, K., et al. (2016). Metabolism. AMP-activated protein kinase mediates mitochondrial fission in response to energy stress. Science 351 (6270), 275–281. doi:10.1126/science.aab4138
Traxler, L., Herdy, J. R., Stefanoni, D., Eichhorner, S., Pelucchi, S., Szücs, A., et al. (2022). Warburg-like metabolic transformation underlies neuronal degeneration in sporadic Alzheimer's disease. Cell Metab. 34 (9), 1248–1263.e6. doi:10.1016/j.cmet.2022.07.014
Valente, T., Gella, A., Fernàndez-Busquets, X., Unzeta, M., and Durany, N. (2010). Immunohistochemical analysis of human brain suggests pathological synergism of Alzheimer's disease and diabetes mellitus. Neurobiol. Dis. 37 (1), 67–76. doi:10.1016/j.nbd.2009.09.008
Vargas-Soria, M., García-Alloza, M., and Corraliza-Gómez, M. (2023). Effects of diabetes on microglial physiology: a systematic review of in vitro, preclinical and clinical studies. J. Neuroinflammation 20 (1), 57. doi:10.1186/s12974-023-02740-x
Wang, C., Li, J., Zhao, S., and Huang, L. (2020). Diabetic encephalopathy causes the imbalance of neural activities between hippocampal glutamatergic neurons and GABAergic neurons in mice. Brain Res. 1742, 146863. doi:10.1016/j.brainres.2020.146863
Wang, S., Long, H., Hou, L., Feng, B., Ma, Z., Wu, Y., et al. (2023). The mitophagy pathway and its implications in human diseases. Signal Transduct. Target Ther. 8 (1), 304. doi:10.1038/s41392-023-01503-7
Wang, H., Vant, J. W., Zhang, A., Sanchez, R. G., Wu, Y., Micou, M. L., et al. (2024). Organization of a functional glycolytic metabolon on mitochondria for metabolic efficiency. Nat. Metab. 6 (9), 1712–1735. doi:10.1038/s42255-024-01121-9
Watanuki, S., Kobayashi, H., Sugiura, Y., Yamamoto, M., Karigane, D., Shiroshita, K., et al. (2024). SDHAF1 confers metabolic resilience to aging hematopoietic stem cells by promoting mitochondrial ATP production. Cell Stem Cell 31 (8), 1145–1161.e15. doi:10.1016/j.stem.2024.04.023
Wei, Y., Miao, Q., Zhang, Q., Mao, S., Li, M., Xu, X., et al. (2023). Aerobic glycolysis is the predominant means of glucose metabolism in neuronal somata, which protects against oxidative damage. Nat. Neurosci. 26 (12), 2081–2089. doi:10.1038/s41593-023-01476-4
Wu, H., Huang, H., and Zhao, Y. (2023). Interplay between metabolic reprogramming and post-translational modifications: from glycolysis to lactylation. Front. Immunol. 14, 1211221. doi:10.3389/fimmu.2023.1211221
Xie, Z., Hu, J., Gu, H., Li, M., and Chen, J. (2023). Comparison of the efficacy and safety of 10 glucagon-like peptide-1 receptor agonists as add-on to metformin in patients with type 2 diabetes: a systematic review. Front. Endocrinol. (Lausanne) 14, 1244432. doi:10.3389/fendo.2023.1244432
Xiong, J., Ge, X., Pan, D., Zhu, Y., Zhou, Y., Gao, Y., et al. (2025). Metabolic reprogramming in astrocytes prevents neuronal death through a UCHL1/PFKFB3/H4K8la positive feedback loop. Cell Death Differ. 32 (7), 1214–1230. doi:10.1038/s41418-025-01467-x
Zhang, H., Ryu, D., Wu, Y., Gariani, K., Wang, X., Luan, P., et al. (2016). NAD+ repletion improves mitochondrial and stem cell function and enhances life span in mice. Science 352 (6292), 1436–1443. doi:10.1126/science.aaf2693
Zhang, S., Lachance, B. B., Mattson, M. P., and Jia, X. (2021). Glucose metabolic crosstalk and regulation in brain function and diseases. Prog. Neurobiol. 204, 102089. doi:10.1016/j.pneurobio.2021.102089
Zhang, J., Zhang, W., Yang, L., Zhao, W., Liu, Z., Wang, E., et al. (2023). Phytochemical gallic acid alleviates nonalcoholic fatty liver disease via AMPK-ACC-PPARa axis through dual regulation of lipid metabolism and mitochondrial function. Phytomedicine 109, 154589. doi:10.1016/j.phymed.2022.154589
Zhang, H., Zheng, Q., Guo, T., Zhang, S., Zheng, S., Wang, R., et al. (2024). Metabolic reprogramming in astrocytes results in neuronal dysfunction in intellectual disability. Mol. Psychiatry 29 (6), 1569–1582. doi:10.1038/s41380-022-01521-x
Zhao, N., Liu, C. C., Van Ingelgom, A. J., Martens, Y. A., Linares, C., Knight, J. A., et al. (2017). Apolipoprotein E4 impairs neuronal insulin signaling by trapping insulin receptor in the endosomes. Neuron 96 (1), 115–129.e5. doi:10.1016/j.neuron.2017.09.003
Zhou, S., Liu, Y., Zhang, N., Sun, L., Ji, C., Cui, T., et al. (2025). Glycolytic enzyme PFKFB4 governs lipolysis by promoting de novo lipogenesis to drive the progression of hepatocellular carcinoma. Cancer Lett. 626, 217774. doi:10.1016/j.canlet.2025.217774
Glossary
ACC Acetyl-CoA carboxylase
AD Alzheimer’s disease
AGE/RAGE Advanced Glycation Endproducts/Receptor for AGEs
Akt Protein kinase B (PKB)
AMPK AMP-activated protein kinase
Apaf-1 Apoptotic protease-activating factor 1
ApoEε4 Apolipoprotein E epsilon 4 allele
ATGL Adipose triglyceride lipase
ATP Adenosine triphosphate
BBB Blood-brain barrier
CaMKK2 Calcium/calmodulin-dependent protein kinase kinase 2
CREB cAMP-response element-binding protein
CypA-MMP9 Cyclophilin A- Matrix metallopeptidase 9
DE Diabetic encephalopathy
DM Diabetes mellitus
FAO Fatty acid oxidation
FFA Free fatty acid
GLP-1 Glucagon-like peptide-1
GLP-1RA GLP-1 receptor agonist
GLUT4 Glucose transporter type 4
GPR81 G protein-coupled receptor 81/Hydroxycarboxylic acid receptor 1
GRO Growth-regulated oncogene (chemokine)
HK2 Hexokinase 2
HIF-1α Hypoxia-inducible factor 1-alpha
HSL Hormone-sensitive lipase
IFN-γ Interferon gamma
IL-1β Interleukin-1 beta
IL-6 Interleukin-6
iNOS Inducible nitric oxide synthase
IR Insulin receptor
IRS Insulin receptor substrate
JNK/SAPK c-Jun N-terminal kinase/Stress-activated protein kinase
LDHA Lactate dehydrogenase A
LDL Low-density lipoprotein
LPS Lipopolysaccharide
MAP1LC3/LC3 Microtubule-associated protein 1A/1B-light chain 3
MAPK Mitogen-activated protein kinase
MCP-1 Monocyte chemoattractant protein-1 (CCL2)
MMP Matrix metallopeptidase
mTOR Mechanistic target of rapamycin
NAD+ Nicotinamide adenine dinucleotide (oxidized form)
NF-κB Nuclear factor kappa-light-chain-enhancer of activated B cells
NO Nitric oxide
Nrf2 Nuclear factor erythroid 2-related factor 2
oxLDL Oxidized low-density lipoprotein
p62/SQSTM1 Sequestosome 1
PGC-1α Peroxisome proliferator-activated receptor gamma coactivator 1-alpha
PI3K Phosphatidylinositol 3-kinase
PKM2 Pyruvate kinase isoform M2
PPARα Peroxisome proliferator-activated receptor alpha
RAGE Receptor for Advanced Glycation Endproducts
ROS Reactive oxygen species
SDHAF1 Succinate dehydrogenase complex assembly factor 1
SIRT Sirtuin (Silent mating type information regulation 2 homolog)
SREBP-1c Sterol regulatory element-binding protein 1c
T1DM Type 1 diabetes mellitus
T2DM Type 2 diabetes mellitus
TCA cycle Tricarboxylic acid cycle (Krebs cycle)
TLR4 Toll-like receptor 4
TNF-α Tumor necrosis factor-alpha
TXNIP Thioredoxin-interacting protein
Keywords: diabetic encephalopathy, metabolic reprogramming, brain insulin resistance, oxidative stress, mitophagy, glycolytic flux, GLP-1 receptor agonists, NAD⁺ boosters
Citation: Huai J-x, Chang E-e, Zhu Y-r, Ma W-l, Lv T-s, Sun J and Zhou X-q (2025) Diabetic encephalopathy: metabolic reprogramming as a potential driver of accelerated brain aging and cognitive decline. Front. Cell Dev. Biol. 13:1701406. doi: 10.3389/fcell.2025.1701406
Received: 08 September 2025; Accepted: 03 November 2025;
Published: 17 November 2025.
Edited by:
Qiong Lu, Central South University, ChinaReviewed by:
Xu Zhao, Southern Medical University, ChinaCopyright © 2025 Huai, Chang, Zhu, Ma, Lv, Sun and Zhou. 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: Xi-qiao Zhou, emhvdXhpcWlhb0BuanVjbS5lZHUuY24=; Jing Sun, c3VuamluZ0BuanVjbS5lZHUuY24=