Haobiao Liu

Acrylamide is an environmental toxicant widely found in processed foods and tobacco smoke. While its neurotoxic and carcinogenic effects are well established, its impact on skeletal muscle health remains poorly understood. This study aimed to investigate the association between acrylamide exposure and muscle mass using epidemiological data and to explore potential mechanisms through bioinformatic analyses. We analyzed data from 1443 adults participating in the 2013–2016 National Health and Nutrition Examination Survey. Hemoglobin adducts of acrylamide (HbAA), glycidamide (HbGA), and their ratio (HbGA/HbAA) were used as biomarkers of exposure. Multivariable linear regression models were applied to examine associations with muscle mass index, with results reported as beta coefficients and 95% confidence intervals (CIs). Restricted cubic spline (RCS) models assessed dose–response relationships. Mediation analyses explored the roles of potential mediators. Network‐based bioinformatic analyses were performed to identify molecular targets and pathways. Higher HbAA levels were significantly associated with lower muscle mass index ( β = −0.331, 95% CI: −0.543 to −0.118, p = 0.006), while the HbGA/HbAA ratio showed a positive association ( β = 1.029, 95% CI: 0.702 to 1.356, p < 0.001). RCS further confirmed linear relationships. Mediation analyses indicated that biomarkers of inflammation and oxidative stress partially mediated these associations (mediated proportions: 5.93% to 31.39%). Bioinformatic analysis identified 346 shared genes enriched in apoptosis and inflammatory pathways. This study provides the first integrative epidemiologic and mechanistic evidence linking acrylamide exposure to reduced muscle mass. These findings highlight the potential role of inflammation and apoptosis, underscoring the importance of environmental risk factors in muscle health.

Objectives: Overweight and obesity are major factors associated with cardiometabolic disorders, yet evidence from occupational populations remains limited. This study aimed to assess the association between body mass index (BMI) and six cardiometabolic risk factors (CRFs)—hypertension, diabetes, high total cholesterol (TC), high triglycerides (TG), high low-density lipoprotein cholesterol (LDL-C), and low high-density lipoprotein cholesterol (HDL-C)—among Chinese oilfield workers. Methods: A cross-sectional analysis was conducted among 3,048 participants undergoing routine health examinations in 2022. BMI was categorized using Chinese-specific criteria. Logistic regression models and restricted cubic spline (RCS) analyses were used to evaluate associations between BMI and six CRFs. Subgroup analyses were conducted for each of the six CRFs across pre-specified effect modifiers (age, sex, chemical exposure, night-shift work, and smoking status), and sensitivity analyses were also performed. Results: Overweight and obesity were associated with higher odds of all CRFs, with obesity showing the strongest associations. In fully adjusted models, obesity was significantly associated with higher odds of hypertension (OR = 7.08), diabetes (OR = 3.25), high TC (OR = 1.81), high TG (OR = 4.80), high LDL-C (OR = 3.07), and low HDL-C (OR = 3.36) compared to normal participants. RCS analyses revealed dose–response pattern, with nonlinearity for high TG and low HDL-C (both p < 0.001). Subgroup analyses showed generally consistent associations, with stronger associations observed for hypertension among current smokers, for diabetes in older participants, and for low HDL-C in male. Sensitivity analyses supported the robustness of these findings. Conclusion: BMI was independently associated with higher odds of hypertension, diabetes, and dyslipidemia, with obesity exhibiting the strongest associations across CRFs. These findings underscore the relevance of excess adiposity in relation to early metabolic abnormalities and may help inform occupational health monitoring strategies.

Background: The rapid growth of internet health care (IH) offers older adults convenient medical services like remote consultations and health monitoring. However, its adoption among this group remains low, highlighting a significant digital divide. Understanding the behavioral patterns and determinants of IH use in the older population is crucial for optimizing digital health design and improving service accessibility. Objective: This study aimed to analyze the multidimensional influencing factors of Chinese older adults’ use of IH services based on the integrated framework of the technology acceptance model and social ecological model, and explore their behavioral patterns and key driving factors. Methods: A cross-sectional study design was adopted to conduct a multistage stratified cluster random sampling survey in 3 cities in Shandong Province from May 2024 to July 2024, with a total of 1828 older adults aged 60 to 75 years included. The study uses latent category analysis to classify the use of IH service behaviors and employs multiple logistic regression, decision tree models, and structural equation modeling to analyze influencing factors and mediating pathways. Results: Five distinct user groups were identified: nonusers (n=911), registration-dominant users (n=286), low-activity users (n=320), moderate comprehensive users (n=288), and full-service users (n=23). Multinomial logistic regression with nonusers as the reference group identified key determinants: individuals with below primary education had 96% lower odds of membership (odds ratios [OR] 0.039, 95% CI 0.012‐0.084) compared to the reference group with junior college education or above in moderate comprehensive users, while male participants had higher odds of being full-service (OR 1.980, 95% CI 1.126‐3.514) or moderate comprehensive (OR 1.310, 95% CI 1.012‐1.705) users. Older age was consistently associated with lower adoption across all classes. Full-service users exhibited exceptionally high social support (OR 4.502, 95% CI 3.601‐5.627), while moderate comprehensive users showed the highest technology acceptance (OR 2.803, 95% CI 2.355‐3.342). The decision tree model (area under the curve of 0.94) found the optimal path: sufficient social support (≥2), good health status (>5), and high technical acceptance (≥30) yield the highest use probability (92%→96%). Mediation analysis indicated that social support influences usage willingness through both direct and indirect pathways. The direct effect was 0.712 (95% CI 0.552‐0.972; P <.001). Among indirect pathways, technology availability and practicality accounted for the largest proportion of mediation (19.7%, 95% CI 16.8%‐22.6%), followed by technology acceptance (13.7%, 95% CI 11.1%‐16.3%) and social influence (8.9%, 95% CI 6.9%‐10.9%). Conclusions: Optimizing age-friendly design, strengthening social support networks, and improving technological usability are keys to increasing the adoption of IH services among the older population. Future policies should develop targeted intervention strategies for different user groups to narrow the digital health divide.

Trace elements, though required only in minute quantities, are indispensable for the proper functioning of biological systems

Osteoarthritis (OA) is a prevalent and disabling joint disorder characterized by progressive cartilage degradation, subchondral bone changes, synovial inflammation, and chronic pain. While traditionally attributed to mechanical and age-related factors, increasing attention has been directed toward the role of nutritional components in disease modulation. This article critically examines the emerging role of three essential trace elements, zinc, copper, and selenium, in the pathophysiology of OA. These micronutrients are fundamental to antioxidant defense, immune modulation, and extracellular matrix (ECM) integrity. Altered systemic or local levels of zinc, copper, and selenium have been associated with oxidative stress, inflammation, and dysregulated cartilage metabolism in OA. Drawing on clinical studies, in vivo and in vitro experimental models, and population-based data, we synthesize evidence on trace element status in OA patients, mechanistic pathways, and therapeutic potential, including emerging nanomedicine strategies that enhance the targeted delivery and therapeutic efficacy of trace elements in joint tissues. This review highlights the need for integrated trace element profiling in OA research and clinical care and supports the exploration of targeted nutritional interventions in OA prevention and management.

Both T-2 toxin and selenium (Se) cause immune impairment in the spleen, but the mechanism of their occurrence is not clear. In this experiment, the Se content of the spleen was tested by atomic fluorescence spectrometry after a 12-week intervention in Sprague–Dawley (SD) rats, divided into normal, normal + T-2 toxin (10 ng/g), normal + T-2 toxin (100 ng/g), low Se, low Se + T-2 toxin (10 ng/g), and low Se + T-2 toxin (100 ng/g) groups. The pathological changes and fibrosis of spleen tissue were observed using hematoxylin–eosin (HE) staining and Masson staining, respectively. Mitogen-activated protein kinase p38 (p38 MAPK), phosphorylated protein-38 (P-p38 MAPK), nuclear factor kappa-B (NF-κB), and interleukin-6 (IL-6) expression levels in spleen tissues were analyzed by Western blotting (WB) and immunohistochemistry (IHC) staining. This study found that both Se deficiency and T-2 toxin induced inflammatory injury and fibrotic changes in rat spleen, but low selenium and low selenium combined with T-2 toxin intervention showed more intense splenic injury. Se deficiency combined with T-2 toxin intervention aggravated spleen injury, and the mechanism of occurrence involved an increase in the inflammatory injury in the spleen by elevating the expression levels of p38 MAPK, NF-κB, and IL-6 in rat spleen.

Osteoarthritis (OA) is a prevalent and disabling joint disorder characterized by progressive cartilage degradation, subchondral bone changes, synovial inflammation, and chronic pain. While traditionally attributed to mechanical and age-related factors, increasing attention has been directed toward the role of nutritional components in disease modulation. This review critically examines the emerging role of three essential trace elements, zinc, copper, and selenium, in the pathophysiology of OA. These micronutrients are fundamental to antioxidant defense, immune modulation, and extracellular matrix (ECM) integrity. Altered systemic or local levels of zinc, copper, and selenium have been associated with oxidative stress, inflammation, and dysregulated cartilage metabolism in OA. Drawing on clinical studies, in vivo, and in vitro experimental models, and population-based data, we synthesize evidence on trace element status in OA patients, mechanistic pathways, and therapeutic potential, including emerging nanomedicine strategies. This review highlights the need for integrated trace element profiling in OA research and clinical care and supports the exploration of targeted nutritional interventions in OA prevention and management.