Gang Li

Chronic low back pain has a high incidence rate and poses a threat to human physical and mental health. As the disease progresses over time, patients with chronic low back pain may exhibit corresponding clinical symptoms not only in localized back pain and functional limitations but also in movement patterns, psychological states, and cognitive aspects. As research continues to advance, maladaptive changes in the brains of patients with chronic low back pain have drawn the attention of scholars. Under sustained pathological stimulation, structural and functional alterations may occur in brain regions such as the sensorimotor cortex, prefrontal cortex, and cerebellum. Recently, non-invasive brain stimulation techniques have gained widespread clinical application and hold significant therapeutic value in the treatment of chronic low back pain. This paper outlines the targets and mechanisms of non-invasive brain stimulation techniques for treating chronic low back pain. It summarizes the current clinical applications of transcranial magnetic stimulation and transcranial direct current stimulation in patients with chronic low back pain, and explores the prospects of non-invasive brain stimulation for managing this condition. The aim is to introduce novel approaches to rehabilitation therapy for chronic low back pain and provide a solid foundation for future research directions.

This study investigates the impact of adverse childhood experiences (ACEs) and depressive symptoms on incident arthritis in middle-aged and older Chinese adults, examining the mediating role of depressive symptoms in the ACEs–arthritis relationship to uncover psychological mechanisms underlying arthritis development. We used 2011–2020 data from the China Health and Retirement Longitudinal Study (CHARLS) and included 4,184 participants aged ≥ 45 years who were free of arthritis at baseline (2011). ACEs were evaluated with 11 types of childhood adversity, classified into quartiles, while depressive symptoms were measured using the CES-D scale. Multivariable logistic regression and stratified analyses assessed associations between ACEs, depressive symptoms, and incident arthritis. Restricted cubic splines and mediation analysis were applied to explore dose–response relationships and the mediating role of depression. Among 4184 participants, 969 developed incident arthritis. Individuals in the highest ACEs quartile (Q4) had a significantly higher arthritis risk (OR 1.78, 95% CI 1.43–2.21, P < 0.001). This association was stronger in females and individuals aged 45–64. Depressive symptoms also increased arthritis risk, with a nonlinear dose–response relationship. Mediation analysis showed depressive symptoms partially mediated the ACEs–arthritis relationship, accounting for 12.4% of the effect. In this national cohort, higher ACEs burden and greater depressive symptom severity were significantly associated with incident arthritis, and depressive symptoms partially mediated by the ACEs–arthritis association. Targeting childhood adversity and later-life mental health may help reduce arthritis risk in aging populations.

Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by pain, joint deformity, and disability, emerged as a primary cause of disability in the elderly. This study aims to investigate the regulatory roles of 13 RNA modification patterns in OA through integrated multi-omics analysis. Genes associated with 13 types of RNA modification patterns were selected for analysis. Pathway analysis was conducted using single-sample gene set enrichment analysis (ssGSEA), and differentially expressed genes (DEGs) were identified. DEGs were further screened using random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) to develop a diagnostic model. Internal validation of the model was performed via the bootstrap algorithm. The correlation between key genes and specific immune cell types was assessed through immune infiltration analysis. Consensus clustering and weighted gene co-expression network analysis (WGCNA) were utilized to identify key subtypes and regulatory modules. Single-cell analysis was employed to further investigate the expression of modification-related genes in different chondrocyte populations. Cell-cell communication patterns among chondrocytes were characterized using CellChat. Monocle was used to construct chondrocyte developmental trajectories, and SCENIC was applied to detect cell type-specific regulatory networks. Expression of genes associated with the 13 RNA modification patterns was analyzed in OA. ssGSEA revealed significant downregulation of N1-methyladenosine (m1A), N6-methyladenosine (m6A), and uridylation, and significant upregulation of N7-methylguanosine (m7G) and pseudouridine in OA. By integrating RF and SVM-RFE, YTHDC1, RBBP6, LSM1, EIF3D, METTL3, ELP2, and IGF2BP2 were identified as key diagnostic genes. Among them, YTHDC1, METTL3, and IGF2BP2 are associated with m6A, while LSM1 and EIF3D are associated with m7G. Internal validation using the bootstrap algorithm demonstrated the model's stable sensitivity and specificity. Further immune infiltration analysis showed that YTHDC1, METTL3, and IGF2BP2 (m6A-related) exhibit immunosuppressive effects in the OA immune microenvironment, whereas LSM1 (m7G-related) promotes immune cell activation and inflammatory responses. Consensus clustering identified two distinct subtypes; subtype 1, characterized by high LSM1 expression, exhibited higher inflammatory levels and OA positivity rates. Single-cell analysis revealed downregulation of m6A in RegC (regulator chondrocytes) and dynamic changes in YTHDC1, METTL3, and IGF2BP2 with inflammation progression. LSM1 was highly expressed in EC (effector chondrocytes) and LSM1 + ECs communicated with HomC through pathways such as FGF-FGFR. SCENIC analysis showed that YTHDC1, METTL3, and IGF2BP2 were mostly positively correlated with transcription factors, while LSM1 was mostly negatively correlated. SOX5, MEF2A, JUNB, JUND, and CEBPD were identified as specific transcription factors for RegC in OA. This study comprehensively analyzed the regulatory roles of 13 RNA modification patterns in OA and identified key modifications and regulatory genes. YTHDC1, METTL3, and IGF2BP2 (m6A-related) exhibit immunosuppressive effects in OA, while LSM1 (m7G-related) promotes inflammation, contributing to the precise diagnosis and targeted therapy for OA. The online version contains supplementary material available at 10.1007/s13205-025-04448-6.

Venous thromboembolism (VTE) remains a significant global health burden, particularly in older adults. While fondaparinux sodium, enoxaparin sodium, and dalteparin sodium are commonly used anticoagulants, their safety profiles require further evaluation. This study analyzes their adverse drug events (ADEs) using data from the FDA Adverse Event Reporting System (FAERS). A retrospective pharmacovigilance study was conducted using FAERS data from Q1 2004 to Q2 2024. Reports identifying fondaparinux sodium, enoxaparin sodium, or dalteparin sodium as the primary suspect drug were extracted. ADEs were classified using MedDRA 23.0 at the System Organ Class (SOC) and Preferred Term (PT) levels. Disproportionality analysis was performed with Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). FAERS contained 470 reports for fondaparinux sodium, 1,375 for enoxaparin sodium, and 344 for dalteparin sodium. Most cases involved patients aged ≥ 60, with a female predominance. Hospitalization was the most frequent outcome. Fondaparinux showed the strongest signals for intra-abdominal haematoma (ROR = 374.14, PRR = 371.14), muscle haemorrhage (ROR = 354.91, PRR = 347.04), and retroperitoneal haematoma (ROR = 214.97, PRR = 213.25). Enoxaparin demonstrated notable signals for heparin-induced thrombocytopenia (HIT) (ROR = 149.42, PRR = 147.53) and retroperitoneal haemorrhage (ROR = 287.68, PRR = 284.03). Dalteparin showed notable signals for HIT (ROR = 127.88, PRR = 126.49) and retroperitoneal haemorrhage (ROR = 103.23, PRR = 102.75). Distinct ADE profiles were identified among the three anticoagulants, underscoring the need for individualized risk assessment. These findings highlight the importance of close monitoring, particularly in high-risk patients, to optimize anticoagulation safety.

Objective: To investigate the correlation between the atherogenic index of plasma (AIP), body mass index (BMI), and sarcopenia in the older adults in China, and to analyze the predictive ability of AIP and BMI for sarcopenia. Methods: This study utilized data from the 2011–2015 CHARLS database (China Health and Retirement Longitudinal Study, CHARLS), focusing on participants aged 60 years and older. The cross-sectional analysis included 7,744 samples, with 2,398 in the sarcopenia group and 5,346 in the non-sarcopenia group. In the retrospective cohort study, 1,441 participants without sarcopenia at baseline were selected and followed for the development of sarcopenia. Multivariable logistic regression was employed to analyze the association between AIP, BMI, and sarcopenia risk. A restricted cubic spline regression model was used to evaluate the dose-response association, and ROC curve analysis was performed to assess the predictive ability of individual and combined indicators (AIP and BMI). Additionally, subgroup analysis was conducted to explore the association between AIP, BMI, and sarcopenia risk across different demographic groups. Results: The cross-sectional analysis demonstrated that sarcopenia was significantly associated with various factors, including age, marital status, education level, residence, smoking, BMI, uric acid (UA), glycosylated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), AIP, as well as hypertension, diabetes, dyslipidemia, and heart disease (p < 0.05). Logistic regression analysis, adjusted for potential confounders, revealed that the low AIP group was significantly associated with an increased risk of sarcopenia (OR = 1.22, 95% CI 1.03–1.44, p = 0.02), while no significant difference was observed in the high AIP group (OR = 0.83, 95% CI 0.69–1.01, p = 0.07). In the retrospective cohort study, the low AIP group showed a positive association with sarcopenia risk (OR = 1.79, 95% CI 1.18–2.72, p = 0.01), and a similar trend was observed in the high AIP group (OR = 1.69, 95% CI 1.03–2.77, p = 0.04). BMI was inversely associated with sarcopenia incidence, consistent with the cross-sectional findings. Both AIP and BMI showed a nonlinear dose-response relationship with sarcopenia risk, with AIP approximating a U-shaped curve and BMI approximating an L-shaped curve. Subgroup analysis indicated that, in the 65–69 age group, low AIP levels were significantly associated with an increased risk of sarcopenia. In participants aged 70 and above, as well as in females, both low and high AIP levels were significantly associated with higher incidence risk. ROC curve analysis showed that the combined use of AIP and BMI for predicting sarcopenia had an Area Under the Curve (AUC) of 0.8913, which was moderately better than the use of AIP (0.6499) or BMI (0.8888) alone. Conclusion: The changes in AIP and BMI are associated with the risk of sarcopenia, and both provide some predictive value for sarcopenia. Taken together, the combined prediction using AIP and BMI appears to be somewhat more effective than using either indicator alone in assessing the risk of sarcopenia.

Falls represent the second leading cause of injury-related mortality among older adults globally. The occurrence of falls is the consequence of the interaction of numerous complex risk factors. The objective of this study was to develop a validated fall risk prediction model for the Chinese older individuals. The study used data from the China Health and Retirement Longitudinal Study (CHARLS), a dataset representative of the Chinese population. Thirty-eight indicators including biological factors, behavioral factors and health status were analyzed in this study. The study cohort was randomly divided into the training set (70%) and the validation set (30%). Variables were screened using LASSO regression analysis, the best predictive model based on 10-fold cross-validation, logistic regression model was applied to explore the correlates of fall risk in the older individuals, a nomogram was constructed to develop the predictive model, calibration curves were applied to assess the accuracy of the nomogram model, and predictive performance was assessed by area under the receiver operating characteristic curve and decision curve analysis. A total of 4,913 cases from the 2015 CHARLS database for people aged 60 years and older were ultimately included, and a total of 1,082 (22.02%) of the older individuals had experienced a fall within two years. Multivariate logistic regression analysis showed that Sleeping time, Hearing, Grip strength, ADL score, Cognition, Depression, Health, KD, and Pain DRUG were predictors of fall risk in the older individuals. These factors were used to construct nomogram models that showed good agreement and accuracy. The AUC value for the predictive model was 0.644 (95% CI = 0.621–0.666), with a specificity of 0.695 and a sensitivity of 0.522. For the internal validation set, the AUC value was 0.644 (95% CI = 0.611–0.678), with a specificity of 0.629 and a sensitivity of 0.577. The Hosmer-Lemeshow test value of the model for the training set is p = 0.9368 and for the validation set is p = 0.8545 (both > 0.05). The calibration curves show a more significant agreement between the nomogram model and the actual observations. The ROC and DCA indicate a better predictive performance of the nomogram. The comprehensive nomogram constructed in this study is a promising and convenient tool for assessing the risk of falls in the Chinese older individuals and to help older adults understand the risk level of falls, avoid and eliminate modifiable risk factors, and reduce the incidence of falls. Not applicable.