BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

BACKGROUND:Epidemiological studies indicate that individuals with neurodegenerative diseases exhibit a comparatively lower risk of developing the majority of cancers.Although the precise mechanisms underlying this inverse correlation remain unclear,it is noteworthy that aberrant glucose metabolism,a pathological factor common to both conditions,may significantly contribute to this association.OBJECTIVE:To review the potential relationship between cancers and neurodegenerative diseases in glucose metabolism.METHODS:PubMed was searched for relevant literature using the search terms of"cancer,neurodegenerative diseases,Alzheimer's disease,Parkinson's disease,metabolic reprogramming,glucose metabolism,aerobic glycolysis,neuroprotection,aging,"and 136 articles were finally included for analysis.RESULTS AND CONCLUSION:Cancer and neurodegenerative diseases exhibit a profound pathological correlation at the level of glucose metabolism imbalance associated with aging.Cancer cells promote uncontrolled proliferation,invasion,and metastasis through the persistent activation of aerobic glycolysis,whereas neurodegenerative diseases are characterized by a reduction in aerobic glycolysis.Restoring aerobic glycolysis may confer neuroprotective effects and delay disease progression.The key nodes of glucose metabolism demonstrate a bidirectional regulatory pattern:metabolic regulators,which are significantly upregulated or aberrantly activated in cancer,are inhibited or functionally inactivated in neurodegenerative diseases.Mitochondria play a crucial role in mediating the aging process through the regulation of reactive oxygen species homeostasis and mitochondrial autophagy.They establish regulatory networks that connect cancer and neurodegenerative diseases,and maintaining their functional homeostasis is of paramount importance for disease prevention and treatment.

ObjectiveTo investigate the effect of Toxoplasma gondii infection on copper metabolism in the kidneys of mice. MethodsA total of 80 7-8-week-old C57BL/6 female mice were randomly divided into four groups of 20 mice in each group after one week of adaptation， including Control group， Cu group， TgCtwh6 group and Cu+TgCtwh6 group. Mice that were not infected and fed with normal diet and water were used as the Control group； Mice fed with 1 g/kg of copper chloride processing diet and 0.1% copper chloride water for 60 consecutive days were used as Cu group； Mice infected with 25-30 TgCtwh6 cysts （one of the predominant genotype Chinese 1 in China） fed with normal diet and water were used as the TgCtwh6 group； mice infected with 25-30 TgCtwh6 cysts and fed with a processed diet containing 1 g/kg of copper chloride and water with 0.1% copper chloride for 60 consecutive days were used as the Cu+TgCtwh6 group. ICP-MS was used to determine the changes in copper content in kidney tissues. Hematoxylin-eosin （HE） staining was used to observe the pathological changes of mouse kidney tissue. The number of apoptotic cells was observed by PI staining. Western blot was used to detect the protein expression levels of glutathione peroxidase 4 （GPX4） and superoxide dismutase （SOD1， SOD2）. RT-qPCR was used to detect the mRNA expression of cuproptosis-related genes. ResultsPathological manifestations such as inflammatory cell infiltration in the Cu group and TgCtwh6 group were seen under the microscope， and the inflammatory infiltrating cells of the renal interstitial were reduced in the Cu+TgCtwh6 group， and the pathological manifestations

Objective: To develop a dual-branch deep learning framework for accurate multi-label classification of fundus diseases, addressing the key limitations of insufficient complementary feature extraction and inadequate cross-modal feature fusion in existing automated diagnostic methods. Methods: The fundus multi-label classification dataset with 12 disease categories (FMLC-12) dataset was constructed by integrating complementary samples from Ocular Disease Intelligent Recognition (ODIR) and Retinal Fundus Multi-Disease Image Dataset (RFMiD), yielding 6 936 fundus images across 12 retinal pathology categories, and the framework was validated on both FMLC-12 and ODIR. Inspired by the holistic multi-regional assessment principle of the Five Wheels theory in traditional Chinese medicine (TCM) ophthalmology, the dual-branch multi-label network (DBMNet) was developed as a novel framework integrating complementary visual feature extraction with pathological correlation modeling. The architecture employed a TransNeXt backbone within a dual-branch design: one branch processed red-green-blue (RGB) images to capture color-dependent features, such as vascular patterns and lesion morphology, while the other processed grayscale-converted images to enhance subtle textural details and contrast variations. A feature interaction module (FIM) effectively integrated the multi-scale features from both branches. Comprehensive ablation studies were conducted to evaluate the contributions of the dual-branch architecture and the FIM. The performance of DBMNet was compared against four state-of-the-art methods, including EfficientNet Ensemble, transfer learning-based convolutional neural network (CNN), BFENet, and EyeDeep-Net, using mean average precision (mAP), F1-score, and Cohen's kappa coefficient. Results: The dual-branch architecture improved mAP by 15.44 percentage points over the single-branch TransNeXt baseline, increasing from 34.41% to 44.24%, and the addition of FIM further boosted mAP to 49.85%. On FMLC-12, DBMNet achieved an mAP of 49.85%, a Cohen’s kappa coefficient of 62.14%, and an F1-score of 70.21%. Compared with BFENet (mAP: 45.42%, kappa: 46.64%, F1-score: 71.34%), DBMNet outperformed it by 4.43 percentage points in mAP and 15.50 percentage points in kappa, while BFENet achieved a marginally higher F1-score. On ODIR, DBMNet achieved an F1-score of 85.50%, comparable to state-of-the-art methods. Conclusion DBMNet effectively integrates RGB and grayscale visual modalities through a dual-branch architecture, significantly improving multi-label fundus disease classification. The framework not only addresses the issue of insufficient feature fusion in existing methods but also demonstrates outstanding performance in balancing detection across both common and rare diseases, providing a promising and clinically applicable pathway for standardized, intelligent fundus disease classification.

Tuberous sclerosis complex (TSC) is an autosomal dominant genetic disorder primarily caused by pathogenic variants in the TSC1 or TSC2 genes. It is characterized by multisystem hamartomatous lesions and neuropsychiatric manifestations. Here we report a young male patient with TSC involving multiple organs, caused by a heterozygous frameshift mutation in TSC2 (c.2071delC, p.Arg691Alafs^*7). The patient initially presented with classic facial angiofibromas and hypomelanotic macules, and subsequently developed psychiatric and behavioral disturbances with auditory hallucinations. Neuroimaging revealed an intracranial mass lesion, which was confirmed as a subependymal giant cell astrocytoma on postoperative histopathology following surgery performed at an outside institution. After admission, the patient underwent a comprehensive diagnostic workup, and multidisciplinary treatment evaluation revealed extensive multisystem involve-ment, including the nervous system, skin, kidneys, lungs, heart, eyes, pancreas, liver and bones. Combined with relevant literature, this article discusses the molecular mechanisms, clinical phenotypes, and comprehensive management of TSC, in order to provide a reference for the standardized and individualized management of this disease.

The Golgi body, a core organelle in eukaryotic cells, plays a critical role in protein modification, sorting, vesicular transport, and serves as a key site for lipid synthesis and glycosylation. Glucose and lipid metabolism are central processes for cellular energy maintenance and biosynthesis, and are closely linked to Golgi function. Recent studies have revealed the extensive involvement of the Golgi body in regulating glucose and lipid metabolism, where maintaining its structural and functional homeostasis is crucial for normal physiological activity. Under various stress conditions such as acidosis, hypoxia, and nutrient deficiency, the Golgi body undergoes structural and functional disruption, leading to Golgi stress. This in turn activates specific signaling pathways, such as those mediated by the cAMP-responsive element binding protein 3 (CREB3) and proteoglycans, to alleviate Golgi stress and enhance Golgi function. Golgi stress contributes to glucose and lipid metabolic disorders by affecting the activity of insulin receptors, glucose transporters, and lipid metabolism-related enzymes. For example, Golgi stress triggers the cleavage and release of the active fragment of CREB3, which enters the nucleus and upregulates the transcription of ADP-ribosylation factor 4 (ARF4) and key gluconeogenic enzymes, including phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G6Pase). ARF4 promotes vesicle retrograde transport between the Golgi and endoplasmic reticulum, maintains secretory capacity, and enhances hepatic glucose output. This pathway is particularly active under high-fat or lipotoxic stress, leading to fasting hyperglycemia. When damaged Golgi components accumulate beyond a tolerable threshold, the cell initiates an autophagic response, selectively encapsulating the damaged Golgi into autophagosomes, which then fuse with lysosomes to form autolysosomes, leading to Golgiphagy. This process results in the degradation and clearance of damaged Golgi, thereby regulating Golgi quantity, quality, and function. Golgiphagy also plays a significant role in regulating glucose and lipid metabolism. For instance, under high-glucose conditions, autophagic flux may be suppressed, impairing the timely clearance and renewal of damaged Golgi, compromising its normal function, and further exacerbating glucose metabolism disorders. Additionally, Golgiphagy may participate in lipid degradation and influence lipid synthesis and transport. Research indicates that Golgi stress and Golgiphagy play important roles in glucose and lipid metabolism-related diseases. For example, the leucine zipper protein (LZIP) under Golgi stress conditions can promote hepatic steatosis. In mouse primary cells and human tissues, LZIP induces the expression of apolipoprotein A-IV (APOA4), which increases peripheral free fatty acid uptake, resulting in lipid accumulation in the liver and contributing to the development of fatty liver disease. This review systematically outlines the structure and function of the Golgi apparatus, the molecular regulatory mechanisms of Golgi stress and Golgiphagy, and their synergistic roles. It further elaborates on how Golgi stress and Golgiphagy participate in the regulation of glucose and lipid metabolism, discusses their clinical significance in related diseases such as diabetes, fatty liver disease, and obesity, and highlights potential novel therapeutic strategies from the perspective of Golgi-targeted medicine. Finally, this article addresses the challenges and future directions in Golgi-targeted interventions, aiming to advance the clinical translation of such strategies and foster breakthroughs in the treatment of glucose and lipid metabolism-related disorders.

The Golgi body, a core organelle in eukaryotic cells, plays a critical role in protein modification, sorting, vesicular transport, and serves as a key site for lipid synthesis and glycosylation. Glucose and lipid metabolism are central processes for cellular energy maintenance and biosynthesis, and are closely linked to Golgi function. Recent studies have revealed the extensive involvement of the Golgi body in regulating glucose and lipid metabolism, where maintaining its structural and functional homeostasis is crucial for normal physiological activity. Under various stress conditions such as acidosis, hypoxia, and nutrient deficiency, the Golgi body undergoes structural and functional disruption, leading to Golgi stress. This in turn activates specific signaling pathways, such as those mediated by the cAMP-responsive element binding protein 3 (CREB3) and proteoglycans, to alleviate Golgi stress and enhance Golgi function. Golgi stress contributes to glucose and lipid metabolic disorders by affecting the activity of insulin receptors, glucose transporters, and lipid metabolism-related enzymes. For example, Golgi stress triggers the cleavage and release of the active fragment of CREB3, which enters the nucleus and upregulates the transcription of ADP-ribosylation factor 4 (ARF4) and key gluconeogenic enzymes, including phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G6Pase). ARF4 promotes vesicle retrograde transport between the Golgi and endoplasmic reticulum, maintains secretory capacity, and enhances hepatic glucose output. This pathway is particularly active under high-fat or lipotoxic stress, leading to fasting hyperglycemia. When damaged Golgi components accumulate beyond a tolerable threshold, the cell initiates an autophagic response, selectively encapsulating the damaged Golgi into autophagosomes, which then fuse with lysosomes to form autolysosomes, leading to Golgiphagy. This process results in the degradation and clearance of damaged Golgi, thereby regulating Golgi quantity, quality, and function. Golgiphagy also plays a significant role in regulating glucose and lipid metabolism. For instance, under high-glucose conditions, autophagic flux may be suppressed, impairing the timely clearance and renewal of damaged Golgi, compromising its normal function, and further exacerbating glucose metabolism disorders. Additionally, Golgiphagy may participate in lipid degradation and influence lipid synthesis and transport. Research indicates that Golgi stress and Golgiphagy play important roles in glucose and lipid metabolism-related diseases. For example, the leucine zipper protein (LZIP) under Golgi stress conditions can promote hepatic steatosis. In mouse primary cells and human tissues, LZIP induces the expression of apolipoprotein A-IV (APOA4), which increases peripheral free fatty acid uptake, resulting in lipid accumulation in the liver and contributing to the development of fatty liver disease. This review systematically outlines the structure and function of the Golgi apparatus, the molecular regulatory mechanisms of Golgi stress and Golgiphagy, and their synergistic roles. It further elaborates on how Golgi stress and Golgiphagy participate in the regulation of glucose and lipid metabolism, discusses their clinical significance in related diseases such as diabetes, fatty liver disease, and obesity, and highlights potential novel therapeutic strategies from the perspective of Golgi-targeted medicine. Finally, this article addresses the challenges and future directions in Golgi-targeted interventions, aiming to advance the clinical translation of such strategies and foster breakthroughs in the treatment of glucose and lipid metabolism-related disorders.

Osteoarthritis (OA), a highly prevalent degenerative joint disease worldwide, is defined by articular cartilage degradation, abnormal bone remodeling, and persistent chronic inflammation. It severely compromises patients’ quality of life, and currently, there is no radical cure. Abnormal mechanical stress is widely regarded as a core driver of OA pathogenesis, and the exploration of mechanical signal perception and transduction mechanisms has become crucial for deciphering OA’s pathophysiological processes. Piezo1, a key mechanosensitive cation channel belonging to the Piezo protein family, has recently gained significant attention due to its pivotal role in mediating cellular responses to mechanical stimuli in joint tissues. This review systematically examines Piezo1’s expression patterns, regulatory mechanisms, and pathological functions in OA, with a particular focus on its dual roles in modulating chondrocyte homeostasis and bone metabolism disorders, while also delving into the underlying molecular signaling pathways and potential therapeutic implications. Piezo1, consisting of approximately 2 500 amino acids and forming a unique trimeric propeller-like structure, is widely expressed in chondrocytes, osteocytes, mesenchymal stem cells, and synovial cells. It exhibits permeability to cations such as Ca²⁺, K⁺, and Na⁺, and directly responds to membrane tension changes induced by mechanical stimuli like fluid shear stress and mechanical overload. In OA patients and animal models, Piezo1 expression is significantly upregulated, especially in cartilage regions subjected to abnormal mechanical stress (e.g., human temporomandibular joint cartilage). This overexpression is closely associated with aggravated cartilage degeneration, increased chondrocyte apoptosis, accelerated cellular senescence, and intensified inflammatory responses. Mechanical overload and pro-inflammatory cytokines (e.g., IL-1β) are key inducers of Piezo1 upregulation: IL-1β activates the PI3K/AKT/mTOR signaling pathway to enhance Piezo1 expression, forming a pathogenic positive feedback loop that inhibits chondrocyte autophagy, promotes apoptosis, and further accelerates joint degeneration. Mechanistically, Piezo1 mediates OA progression through multiple interconnected pathways. When activated by mechanical stress, Piezo1 triggers excessive Ca²⁺ influx, leading to endoplasmic reticulum stress (ERS) and mitochondrial dysfunction, which directly induce chondrocyte apoptosis. This process involves the activation of downstream signaling cascades such as cGAS-STING and YAP-MMP13/ADAMTS5. YAP, a transcriptional regulator, upregulates the expression of matrix metalloproteinase 13 (MMP13) and aggrecanase (ADAMTS5), thereby accelerating cartilage matrix degradation. Additionally, Piezo1-driven Ca²⁺ overload promotes the accumulation of reactive oxygen species (ROS) and upregulates senescence markers (p16 and p21), accelerating chondrocyte senescence via the p38MAPK and NF-κB pathways. Senescent chondrocytes secrete senescence-associated secretory phenotype (SASP) factors (e.g., IL-6, IL-1β), further amplifying joint inflammation. In terms of bone metabolism, Piezo1 maintains joint homeostasis by promoting the differentiation of fibrocartilage stem cells into chondrocytes and balancing bone formation and resorption through regulating the FoxC1/YAP axis and RANKL/OPG ratio. Therapeutically, targeting Piezo1 shows promising potential. Preclinical studies have demonstrated that Piezo1 inhibitors (e.g., GsMTx4) can reduce joint damage and alleviate pain in OA mice. Simultaneously, siRNA-mediated co-silencing of Piezo1 and TRPV4 (another mechanosensitive channel) decreases intracellular Ca²⁺ concentration, inhibits chondrocyte apoptosis, and promotes cartilage repair. Conditional knockout of Piezo1 using Gdf5-Cre transgenic mice alleviates cartilage degeneration in post-traumatic OA models by downregulating MMP13 and ADAMTS5 expression. Despite existing challenges, such as off-target effects of inhibitors, inefficient local drug delivery, and interindividual genetic variability, strategies like developing selective Piezo1 antagonists, optimizing targeted nanocarriers, and combining Piezo1-targeted therapy with physical therapy provide viable avenues for clinical translation. The authors propose that Piezo1 serves as a critical therapeutic target for OA, and future research should focus on deciphering its context-dependent regulatory networks, developing tissue-specific intervention strategies, and validating their efficacy and safety in clinical trials to address the unmet medical needs of OA patients.

BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

BACKGROUND:Epidemiological studies indicate that individuals with neurodegenerative diseases exhibit a comparatively lower risk of developing the majority of cancers.Although the precise mechanisms underlying this inverse correlation remain unclear,it is noteworthy that aberrant glucose metabolism,a pathological factor common to both conditions,may significantly contribute to this association.OBJECTIVE:To review the potential relationship between cancers and neurodegenerative diseases in glucose metabolism.METHODS:PubMed was searched for relevant literature using the search terms of"cancer,neurodegenerative diseases,Alzheimer's disease,Parkinson's disease,metabolic reprogramming,glucose metabolism,aerobic glycolysis,neuroprotection,aging,"and 136 articles were finally included for analysis.RESULTS AND CONCLUSION:Cancer and neurodegenerative diseases exhibit a profound pathological correlation at the level of glucose metabolism imbalance associated with aging.Cancer cells promote uncontrolled proliferation,invasion,and metastasis through the persistent activation of aerobic glycolysis,whereas neurodegenerative diseases are characterized by a reduction in aerobic glycolysis.Restoring aerobic glycolysis may confer neuroprotective effects and delay disease progression.The key nodes of glucose metabolism demonstrate a bidirectional regulatory pattern:metabolic regulators,which are significantly upregulated or aberrantly activated in cancer,are inhibited or functionally inactivated in neurodegenerative diseases.Mitochondria play a crucial role in mediating the aging process through the regulation of reactive oxygen species homeostasis and mitochondrial autophagy.They establish regulatory networks that connect cancer and neurodegenerative diseases,and maintaining their functional homeostasis is of paramount importance for disease prevention and treatment.