Neurodegenerative diseases (NDs), particularly Alzheimer’s disease (AD) and Parkinson’s disease (PD), and cardiovascular diseases (CVDs), including heart failure (HF) and coronary artery disease (CAD), are major chronic disorders associated with population aging. Their frequent coexistence in clinical practice suggests that they may not progress independently, but may instead be linked through bidirectional pathological communication within the brain-heart axis. Epidemiological evidence underscores this clinical burden: systematic reviews and meta-analyses have reported pooled relative risks for dementia of approximately 1.27 among individuals with a history of CAD and 1.60 among those with HF, while the pooled prevalence of cognitive impairment and dementia in patients with HF has been estimated at approximately 41.42% and 19.79%, respectively. This review synthesizes current evidence on the mechanistic links, disease-state manifestations, biomarkers, and potential therapeutic strategies involved in the comorbidity between NDs and CVDs from the perspective of the brain-heart axis. The brain and heart communicate through autonomic, neuroendocrine, neuroimmune, vascular, and humoral pathways. Sympathetic activation, parasympathetic withdrawal, vagal dysfunction, cerebral hypoperfusion, endothelial injury, blood-brain barrier (BBB) alterations, peripheral immune activation, and circulating inflammatory mediators may jointly contribute to cross-organ pathological signaling. Oxidative stress and inflammatory response are not specific to the brain-heart axis; rather, they may act as candidate amplifying processes within this network. Mitochondrial dysfunction and NADPH oxidase activation can increase the generation of reactive oxygen species and reactive nitrogen species, which may activate nuclear factor-κB, the NLRP3 inflammasome, and mitogen-activated protein kinase/c-Jun N-terminal kinase signaling. These pathways can promote the release of interleukin-1β, interleukin-6, tumor necrosis factor‑α, and other inflammatory mediators, which may in turn aggravate mitochondrial injury, endothelial dysfunction, immune cell activation, and further oxidant generation. Cytokines, chemokines, oxidized lipids, mitochondrial DNA, damage-associated molecular patterns, and extracellular vesicles may transmit these signals between the cardiovascular and central nervous systems, whereas autonomic reflexes provide an additional route for bidirectional amplification. Distinct disease states illustrate different manifestations of this network. The association between AD and CVDs may involve vascular risk factors, impaired amyloid-β clearance, abnormal cholesterol homeostasis, cerebral microvascular injury, neuroinflammation, and endothelial dysfunction. HF-related cognitive impairment may be linked to reduced cardiac output, recurrent cerebral hypoperfusion, hypoxia-related signaling, BBB dysfunction, circulating inflammation, and impaired autonomic feedback. In PD, cardiovascular autonomic dysfunction and abnormalities in heart rate and blood pressure regulation may interact with vascular injury, reduced cerebral perfusion, inflammation, and oxidative stress. However, the relationship between PD and CAD remains heterogeneous and may be modified by age, ethnicity, medication use, metabolic factors, and coexisting diseases. Biomarker evaluation should therefore move beyond isolated indicators. Traditional markers, including malondialdehyde, superoxide dismutase, glutathione, C-reactive protein, and interleukin-6, reflect systemic oxidative or inflammatory burden but lack sufficient specificity to distinguish primary neurodegeneration, cardiovascular injury, or a comorbid state involving both. Greater clinical value may be obtained from cross-system panels that combine oxidative stress and inflammatory markers with cardiac stress or injury biomarkers, such as N-terminal pro-B-type natriuretic peptide; glial or neuronal biomarkers, such as glial fibrillary acidic protein and neurofilament light chain; BBB- or endothelial injury-related biomarkers; and omics-derived candidates, including microRNAs, long non-coding RNAs, DNA methylation signatures, metabolites, and proteins. Multi-omics integration and artificial intelligence-assisted analysis may support risk stratification, disease monitoring, and prediction of treatment response. However, these approaches require standardized analytical platforms, validation in independent cohorts, and careful distinction between associative findings and causal mechanisms. Potential interventions include antioxidant, anti-inflammatory, mitochondria-targeted, and multi-target combination strategies, together with emerging approaches such as nanodelivery, CRISPR/Cas9- or CRISPR/dCas9-based regulation, mesenchymal stem cell-mediated mitochondrial transfer, and ergothioneine supplementation. Nevertheless, most available evidence is indirect, preclinical, or derived from other disease indications, and no single drug or technology can currently be regarded as specific to brain-heart axis comorbidity. The clinical relevance of these strategies will depend on whether they can be matched to dominant pathological phenotypes and improve both neurological and cardiovascular outcomes. Overall, comorbidity between NDs and CVDs is more appropriately understood as a heterogeneous cross-organ regulatory network than as a disorder driven by a single pathway. Future research should prioritize multicenter longitudinal cohorts, standardized clinical phenotyping, validation of cross-system biomarker panels, and mechanism-guided interventions in clearly defined patient subgroups, with integrated assessment of cognitive function, cardiovascular events, quality of life, long-term safety, and treatment response.

Homo- or heterodimeric compounds that affect dimeric protein function through interaction between monomeric moieties and protein subunits can serve as valuable sources of potent and selective drug candidates. Here, we screened an in-house dimeric natural product collection, and panepocyclinol A (PecA) emerged as a selective and potent STAT3 inhibitor with profound anti-tumor efficacy. Through cross-linking C712/C718 residues in separate STAT3 monomers with two distinct Michael receptors, PecA inhibits STAT3 DNA binding affinity and transcription activity. Molecular dynamics simulation reveals the key conformation changes of STAT3 dimers upon the di-covalent binding with PecA that abolishes its DNA interactions. Furthermore, PecA exhibits high efficacy against anaplastic large T cell lymphoma in vitro and in vivo, especially those with constitutively activated STAT3 or STAT3^Y640F. In summary, our study describes a distinct and effective di-covalent modification for the dimeric compound PecA to disrupt STAT3 function.

Accurate timing of myelination is crucial for the proper functioning of the central nervous system. Here, we identified a de novo heterozygous mutation in TMEM63A (c.1894G>A; p. Ala632Thr) in a 7-year-old boy exhibiting hypomyelination. A Ca²⁺ influx assay suggested that this is a loss-of-function mutation. To explore how TMEM63A deficiency causes hypomyelination, we generated Tmem63a knockout mice. Genetic deletion of TMEM63A resulted in hypomyelination at postnatal day 14 (P14) arising from impaired differentiation of oligodendrocyte precursor cells (OPCs). Notably, the myelin dysplasia was transient, returning to normal levels by P28. Primary cultures of Tmem63a^-/- OPCs presented delayed differentiation. Lentivirus-based expression of TMEM63A but not TMEM63A_A632T rescued the differentiation of Tmem63a^-/- OPCs in vitro and myelination in Tmem63a^-/- mice. These data thus support the conclusion that the mutation in TMEM63A is the pathogenesis of the hypomyelination in the patient. Our study further demonstrated that TMEM63A-mediated Ca²⁺ influx plays critical roles in the early development of myelin and oligodendrocyte differentiation.
Animals ; Cell Differentiation/physiology* ; Oligodendroglia/metabolism* ; Mice, Knockout ; Mice ; Male ; Myelin Sheath/metabolism* ; Humans ; Child ; Cells, Cultured ; Oligodendrocyte Precursor Cells/metabolism*

Patients suffering from nerve injury often experience exacerbated pain responses and complain of memory deficits. The dorsal hippocampus (dHPC), a well-defined region responsible for learning and memory, displays maladaptive plasticity upon injury, which is assumed to underlie pain hypersensitivity and cognitive deficits. However, much attention has thus far been paid to intracellular mechanisms of plasticity rather than extracellular alterations that might trigger and facilitate intracellular changes. Emerging evidence has shown that nerve injury alters the microarchitecture of the extracellular matrix (ECM) and decreases ECM rigidity in the dHPC. Despite this, it remains elusive which element of the ECM in the dHPC is affected and how it contributes to neuropathic pain and comorbid cognitive deficits. Laminin, a key element of the ECM, consists of α-, β-, and γ-chains and has been implicated in several pathophysiological processes. Here, we showed that peripheral nerve injury downregulates laminin β1 (LAMB1) in the dHPC. Silencing of hippocampal LAMB1 exacerbates pain sensitivity and induces cognitive dysfunction. Further mechanistic analysis revealed that loss of hippocampal LAMB1 causes dysregulated Src/NR2A signaling cascades via interaction with integrin β1, leading to decreased Ca²⁺ levels in pyramidal neurons, which in turn orchestrates structural and functional plasticity and eventually results in exaggerated pain responses and cognitive deficits. In this study, we shed new light on the functional capability of hippocampal ECM LAMB1 in the modulation of neuropathic pain and comorbid cognitive deficits, and reveal a mechanism that conveys extracellular alterations to intracellular plasticity. Moreover, we identified hippocampal LAMB1/integrin β1 signaling as a potential therapeutic target for the treatment of neuropathic pain and related memory loss.
Animals ; Laminin/genetics* ; Hippocampus/metabolism* ; Neuralgia/metabolism* ; Cognitive Dysfunction/etiology* ; Male ; Peripheral Nerve Injuries/metabolism* ; Extracellular Matrix/metabolism* ; Integrin beta1/metabolism* ; Pyramidal Cells/metabolism* ; Signal Transduction

Metachromatic leukodystrophy (MLD) is an inherited disease caused by a deficiency of the enzyme arylsulfatase A (ARSA). Lentivirus-modified autologous hematopoietic stem cell gene therapy (HSCGT) has recently been approved for clinical use in pre and early symptomatic children with MLD to increase ARSA activity. Unfortunately, this advanced therapy is not available for most patients with MLD who have progressed to more advanced symptomatic stages at diagnosis. Patients with late-onset juvenile MLD typically present with a slower neurological progression of symptoms and represent a significant burden to the economy and healthcare system, whereas those with early onset infantile MLD die within a few years of symptom onset. We conducted a pilot study to determine the safety and benefit of HSCGT in patients with postsymptomatic juvenile MLD and report preliminary results. The safety profile of HSCGT was favorable in this long-term follow-up over 9 years. The most common adverse events (AEs) within 2 months of HSCGT were related to busulfan conditioning, and all AEs resolved. No HSCGT-related AEs and no evidence of distorted hematopoietic differentiation during long-term follow-up for up to 9.6 years. Importantly, to date, patients have maintained remarkably improved ARSA activity with a stable disease state, including increased Functional Independence Measure (FIM) score and decreased magnetic resonance imaging (MRI) lesion score. This long-term follow-up pilot study suggests that HSCGT is safe and provides clinical benefit to patients with postsymptomatic juvenile MLD.
Humans ; Leukodystrophy, Metachromatic/genetics* ; Pilot Projects ; Genetic Therapy/methods* ; Hematopoietic Stem Cell Transplantation ; Male ; Follow-Up Studies ; Female ; Lentivirus/genetics* ; Child ; Child, Preschool ; Hematopoietic Stem Cells/metabolism* ; Cerebroside-Sulfatase/metabolism* ; Adolescent

OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method. METHODS: WES was performed to identify potential single nucleotide polymorphism (SNP) or mutation sites in a DR pedigree comprising 10 members. A prediction model was established and validated in a cohort of 420 type 2 diabetic patients based on both genetic and demographic features. The contribution of each feature was assessed using Shapley Additive explanation analysis. The efficacies of the models with and without SNP were compared. RESULTS: WES revealed that seven SNPs/mutations ( rs116911833 in TRIM7, 1997T>C in LRBA, 1643T>C in PRMT10, rs117858678 in C9orf152, rs201922794 in CLDN25, rs146694895 in SH3GLB2, and rs201407189 in FANCC) were associated with DR. Notably, the model including rs146694895 and rs201407189 achieved better performance in predicting DR (accuracy: 80.2%; sensitivity: 83.3%; specificity: 76.7%; area under the receiver operating characteristic curve [AUC]: 80.0%) than the model without these SNPs (accuracy: 79.4%; sensitivity: 80.3%; specificity: 78.3%; AUC: 79.3%). CONCLUSION Novel SNP sites associated with DR were identified in the DR pedigree. Inclusion of rs146694895 and rs201407189 significantly enhanced the performance of the ML-based DR prediction model.
Diabetic Retinopathy/diagnosis* ; Humans ; Machine Learning ; Male ; Female ; Polymorphism, Single Nucleotide ; Middle Aged ; Exome Sequencing ; Aged ; Adult ; Pedigree ; Diabetes Mellitus, Type 2/complications* ; Genetic Predisposition to Disease ; Mutation

OBJECTIVE: This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications (PCCs) for older patients. METHODS: This secondary analysis of data from a randomized controlled trial involved 1,720 elderly participants in five tertiary hospitals in Beijing, China. Participants aged 60-90 years undergoing major non-cardiac surgery under general anesthesia. The primary outcome metric of the study was the occurrence of PCCs, according to the European Society of Cardiology and the European Society of Anaesthesiology diagnostic criteria. The analysis metrics contained 67 candidate variables, including baseline characteristics, laboratory tests, and scale assessments. RESULTS: Our feature selection process identified key variables that significantly impact patient outcomes, including the duration of ICU stay, surgery, and anesthesia; APACHE-II score; intraoperative average heart rate and blood loss; cumulative opioid use during surgery; patient age; VAS-Move-Median score on the 1st to 3rd day; Charlson comorbidity score; volumes of intraoperative plasma, crystalloid, and colloid fluids; cumulative red blood cell transfusion during surgery; and endotracheal intubation duration. Notably, our Random Forest model demonstrated exceptional performance with an accuracy of 0.9872. CONCLUSION We have developed and validated an algorithm for predicting PCCs in elderly patients by identifying key risk factors.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Cardiovascular Diseases/etiology* ; Machine Learning ; Postoperative Complications/etiology* ; Risk Factors ; Randomized Controlled Trials as Topic ; Secondary Data Analysis

OBJECTIVE: We aimed to investigate the patterns of fasting blood glucose (FBG) trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers. METHODS: The study cohort included 3,728 workers who met the selection criteria for the Tanggang Occupational Cohort (TGOC) between 2017 and 2022. A group-based trajectory model was used to identify the FBG trajectories. Environmental risk scores (ERS) were constructed using regression coefficients from the occupational hazard model as weights. Univariate and multivariate logistic regression analyses were performed to explore the effects of occupational hazard factors using the ERS on FBG trajectories. RESULTS: FBG trajectories were categorized into three groups. An association was observed between high temperature, noise exposure, and FBG trajectory ( P < 0.05). Using the first quartile group of ERS1 as a reference, the fourth quartile group of ERS1 had an increased risk of medium and high FBG by 1.90 and 2.21 times, respectively (odds ratio [ OR] = 1.90, 95% confidence interval [ CI]: 1.17-3.10; OR = 2.21, 95% CI: 1.09-4.45). CONCLUSION An association was observed between occupational hazards based on ERS and FBG trajectories. The risk of FBG trajectory levels increase with an increase in ERS.
Humans ; Male ; Adult ; Blood Glucose/analysis* ; China ; Prospective Studies ; Occupational Exposure/adverse effects* ; Risk Factors ; Middle Aged ; Steel ; Fasting/blood* ; Metal Workers ; East Asian People

BACKGROUND: Quantitative flow ratio (QFR) holds significant value in guiding drug-coated balloon (DCB) treatment and enhancing outcomes. However, the predictive capability of post-angioplasty QFR for long-term clinical events in patients with de novo lesions who receive DCB treatment remains uncertain. The aim of this study was to explore the potential significance of post-angioplasty QFR measurements in predicting clinical outcomes in patients underwent DCB treatment for de novo lesions. METHODS: Patients who underwent DCB-only intervention for de novo lesions were enrolled. QFR was conducted after DCB treatment. The patients were then categorized based on post-angioplasty QFR. The primary endpoint was major adverse cardiac events (MACE), encompassing all-cause death, cardiovascular death, nonfatal myocardial infarction, stroke, and target vessel revascularization. RESULTS: A total of 553 patients with 561 lesions were included. The median follow-up period was 505 days, during which 66 (11.8%) MACEs occurred. Based on post-procedural QFR grouping, there were 259 cases in the high QFR group (QFR > 0.93) and 302 cases in the low QFR group (QFR ≤ 0.93). Kaplan-Meier analysis revealed a significantly higher cumulative incidence of MACE in the low QFR group (log-rank P = 0.004). The multivariate Cox proportional hazards model demonstrated a significant inverse correlation between QFR and the occurrence of MACEs (HR = 0.522, 95%CI: 0.289-0.942, P = 0.031). Landmark analysis indicated that high QFR had a significant reducing effect on the cumulative incidence of MACEs within 1 year (log-rank P = 0.016) and 1-5 years (log-rank P = 0.026). CONCLUSIONS In patients who underwent DCB-only treatment for de novo lesions, higher post-procedural QFR values (> 0.93) were identified as an independent protective factor against adverse prognosis.

BACKGROUND: The atherogenic index of plasma (AIP) has been shown to be positively correlated with cardiovascular disease in previous studies. However, it is unclear whether elderly people with long-term high AIP levels are more likely to develop coronary heart disease (CHD). Therefore, the aim of this study was to investigate the relationship between AIP trajectory and CHD incidence in elderly people. METHODS: 19,194 participants aged ≥ 60 years who had three AIP measurements between 2018 and 2020 were included in this study. AIP was defined as log₁₀ (triglyceride/high-density lipoprotein cholesterol). The group-based trajectory model was used to identify different trajectory patterns of AIP from 2018 to 2020. Cox proportional hazards models were used to estimate the hazard ratio (HR) with 95% CI of CHD events between different trajectory groups from 2020 to 2023. RESULTS: Three different trajectory patterns were identified through group-based trajectory model: the low-level group (n = 7410, mean AIP: -0.25 to -0.17), the medium-level group (n = 9981, mean AIP: 0.02-0.08), and the high-level group (n = 1803, mean AIP: 0.38-0.42). During a mean follow-up of 2.65 years, a total of 1391 participants developed CHD. After adjusting for potential confounders, compared with the participants in the low-level group, the HR with 95% CI of the medium-level group and the high-level group were estimated to be 1.24 (1.10-1.40) and 1.43 (1.19-1.73), respectively. These findings remained consistent in subgroup analyses and sensitivity analyses. CONCLUSIONS There was a significant correlation between persistent high AIP level and increased CHD risk in the elderly. This suggests that monitoring the long-term changes in AIP is helpful to identify individuals at high CHD risk in elderly people.