Medicine最新文献

Human blood metabolites have been closely linked to ankylosing spondylitis (AS) in observational studies, yet direct causal evidence remains limited. This study aims to use Mendelian randomization (MR) to pinpoint causal metabolites associated with AS and to predict potential side effects of metabolite interventions. Genetic instruments for exposure were sourced from a genome-wide association study of 1400 blood metabolites, while genome-wide association study data for AS outcomes were derived from the FinnGen cohort. The primary MR analysis was conducted using the inverse variance weighted method. Supplemental analyses were conducted using weighted median, MR-Egger, simple mode, and weighted mode methods, while sensitivity analyses were performed to evaluate heterogeneity and pleiotropy. A replication analysis using an additional the UK Biobank cohort was also performed to determine metabolites associated with AS. The Steiger test and linkage disequilibrium score regression were used to further strengthen causal inference. Lastly, a phenome-wide Mendelian randomization analysis was performed to investigate the potential on-target side effects of metabolite interventions. After comprehensive analyses, 3 metabolites (the 2'-deoxyuridine levels, the hate to mannose ratio, and the Uridine to 2'-deoxyuridine ratio) were identified as being genetically associated with AS. The phenome-wide Mendelian randomization analysis revealed that the hate to mannose ratio might have deleterious effects on 4 other diseases, while no significant associations were found for the 2'-deoxyuridine levels or the uridine to 2'-deoxyuridine ratio with other diseases. This systematic MR analysis unveiled the potential role of the 2'-deoxyuridine levels, hate to mannose ratio and uridine to 2'-deoxyuridine ratio as the causal mediator in the development of AS. Considering the advantages and disadvantages, 2'-deoxyuridine appears as the most promising prospective therapeutic target for the prevention of AS.

Cataract is a common ocular complication of diabetes mellitus (DM), characterized by chronic inflammation and oxidative stress. Sequential bilateral cataract surgery provides a unique model to explore intraocular immune modulation under different systemic metabolic conditions. This study aimed to evaluate the relationship between preoperative glycated hemoglobin (HbA1c) levels and inter-eye variations in aqueous humor inflammatory cytokines among patients with type 2 diabetes mellitus. A retrospective analysis was performed on 88 patients with type 2 diabetes mellitus who underwent bilateral phacoemulsification cataract surgery with intraocular lens implantation between January 2023 and January 2025. Aqueous humor samples were collected at the beginning of each surgery, and the concentrations of inflammatory cytokines (interleukin-6 [IL-6], interleukin-8 [IL-8], tumor necrosis factor-alpha, monocyte chemoattractant protein-1, and interleukin-10) were measured using a multiplex immunoassay. Patients were stratified into 3 groups according to HbA1c levels (<7.0%, 7.0-8.9%, and ≥9.0%). The interocular differences in cytokine concentrations (Δcytokines) were compared among groups, and multivariate regression analysis was performed to evaluate the associations between HbA1c, surgical interval, and Δcytokines. IL-6 and IL-8 levels were significantly higher in second-eye surgeries compared with first-eye surgeries (P < .001). HbA1c was positively correlated with ΔIL-6 (r = 0.48, P < .001) and ΔIL-8 (r = 0.42, P = .002), independent of age, sex, and diabetic retinopathy severity. Patients with HbA1c ≥ 9.0% had nearly twofold higher ΔIL-6 than those with HbA1c < 7.0%. Longer surgical intervals (≥28 days) further amplified these inflammatory differences (interaction P = .031). Poor glycemic control and extended surgical intervals synergistically enhance intraocular inflammation during sequential cataract surgery in diabetic patients. Optimizing HbA1c and minimizing inter-surgery intervals may mitigate postoperative inflammatory risk and improve surgical outcomes.

Asthma is a chronic inflammatory airway disease imposing a substantial global health burden. NLRP3 is an immune sensor involved in infection and cellular stress responses. Recent studies suggest that NLRP3 may be involved in the pathogenesis of asthma. We hypothesized that genetic variation in NLRP3 may contribute to asthma susceptibility. However, the causal relationship between NLRP3 and asthma still remains unclear. In this study, bioinformatics analysis using asthma data and R software was performed to identify NLRP3-related genes. We performed weighted gene co-expression network analysis to identify co-expressed genes, resulting in 12 candidate genes. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses were used to identify the functions of these candidate genes, revealing their involvement in cellular metabolism. Mendelian randomization analysis of the 12 candidate genes identified 2 biomarkers: NLRP3 and TNFAIP8L2 (TIPE2). We validated their diagnostic value for asthma using the GSE182503 dataset, with area under the curve values of 0.83 and 0.66 for NLRP3 and TIPE2, respectively. This project discusses how NLRP3 promotes asthma pathogenesis, whereas TIPE2 may alleviate it, and explores the potential interplay between them. NLRP3 and TIPE2 may serve as diagnostic biomarkers for asthma: NLRP3 may promote, whereas TIPE2 may alleviate asthma development. Both genes represent potential diagnostic biomarkers and therapeutic targets that warrant further functional investigation.

Background: Platelet indices (PIs) - including platelet count (PC), plateletcrit (PCT), mean platelet volume, and platelet distribution width (PDW) - are routinely assessed in clinical practice. Although observational studies have reported associations between PIs and lung cancer outcomes, the dose-response relationship and causality remain unestablished. This study aims to determine prognostic thresholds of PIs and elucidate their causal roles in lung cancer.

Methods: We systematically reviewed PubMed, Medline, and Web of Science (through December 2024) for studies on PIs and lung cancer prognosis. Hazard ratios (HRs) were pooled via random-effects models. Dose-response thresholds were identified using restricted cubic splines and generalized least squares. Two-sample Mendelian randomization (MR) analyses with inverse variance weighting assessed causality, complemented by sensitivity analyses (MR-Egger, weighted median).

Results: In the meta-analysis of 62 studies (N = 38,562 patients), elevated PC (HR = 1.016, 95% CI: 1.009 to 1.024) and PCT (HR = 1.704, 95% CI: 1.040-2.790) independently predicted poorer overall survival. A nonlinear dose-response relationship revealed that each 109/L increase in PC conferred a 4.2% higher risk of adverse prognosis in non-small cell lung cancer (HR = 1.042, 95% CI: 1.029 to 1.056), with a critical threshold at 177.5 × 109/mL. MR analyses demonstrated population-specific causality: a 1-SD increase in PC elevated lung cancer risk by 33% in East Asians (OR = 1.33, P < .001), while in Europeans, equivalent increments in PC and PCT increased small cell lung cancer (SCLC) risk by 17% (OR = 1.17, P = .01) and 19% (OR = 1.19, P < .001), respectively. Additionally, higher PDW was causally linked to a 6% increased lung cancer risk (OR = 1.06, P = .02).

Conclusion: This first study integrating dose-response meta-analysis and MR evidence identifies PC and PCT as robust prognostic biomarkers for lung cancer, with population-specific causal effects. The identified PC threshold (177.5 × 109/mL) provides a clinically actionable tool for NSCLC risk stratification, highlighting the translational potential of routine PIs monitoring in oncology practice.

This study aimed to investigate the potential causal roles of specific circulating microRNAs (miRNAs) and immune cell subsets in the pathogenesis of hypertrophic scars and keloids using a 2-step Mendelian randomization framework. We employed a 2-sample Mendelian randomization approach to evaluate the causal relationships between miRNAs, immune cell genotypes, and scar phenotypes. The analysis integrated miRNA expression quantitative trait loci, immune cell genome-wide association studies, and scar datasets. A 2-step mediation analysis was conducted to assess the indirect effects of miRNAs on scars through immune cell genotypes, using inverse variance weighted methods and complementary sensitivity analyses to ensure robustness. Our analysis identified significant associations between specific miRNAs and scar phenotypes. Notably, miR-6887-5p exhibited a total effect on keloid formation risk (β = 0.324, 95% confidence interval [CI]: 0.073-0.576) and a direct effect (β = 0.283, 95% CI: 0.027, 0.538), with a marginally significant mediation effect through B-cell activating factor receptor on CD20- CD38- B cells (β = 0.042, 95% CI: -0.001, 0.084, P = .047). For hypertrophic scars, miR-345-5p demonstrated a significant total effect (β = -0.501, 95% CI: -0.903, -0.099) and direct effect (β = -0.469, 95% CI: -0.872, -0.066), with a significant mediation effect through CD28+ CD45RA- CD8dim T cell percentage (β = -0.032, 95% CI: -0.062, -0.002, P = .034). miR-4801 showed a significant total effect (β = -0.246, 95% CI: -0.429, -0.064) and direct effect (β = -0.218, 95% CI: -0.402, -0.033), with a marginally significant mediation effect through T cell absolute count (β = -0.028, 95% CI: -0.057, -0.000, P = .043). These findings highlight the interplay between miRNAs and immune cell subsets in scar pathogenesis. This study provides preliminary evidence for the causal roles of specific miRNAs and immune cell subsets in scar formation, emphasizing the potential of miRNA-immune cell axes as therapeutic targets. While the identified associations offer important insights into the molecular mechanisms of scar heterogeneity, further validation through mechanistic studies and clinical trials is necessary to translate these genetic insights into clinical interventions.

This study aims to investigate the effects of popular 6000/10,000 daily step recommendations of physical activity on bone mass in young female nurses. A sample of 69 female nurses aged 22 to 35 from a teaching hospital in Chongqing, China, were invited to participate in the study. Daily walking steps were recorded by a smartphone-based application for 30 consecutive days. The participants were divided into 3 groups based on their adherence to daily step recommendations: low (average daily steps < 6000), moderate (6000 ≤ average daily steps ≤ 10,000 steps), and high (average daily steps > 10,000 steps) steps. Bone mineral density (BMD) at multiple sites (spine, hip, and heel) was measured with dual-emission X-ray scanner. Analysis of variance was used to compare BMDs in different level of daily steps, multivariable regression analysis was used to adjust the confounders. Nurses with adherence to higher step recommendation had higher means of BMDs at all bone sites, but only BMDs at spine (L1-L4, all P < .05) were significant. Multivariate regression analysis showed the nurses with moderate to high steps had significantly higher BMDs at the spine (P ≤ .001 for L1-L4) and hip (Ward triangle P = .047, trochanteric region P = .025, and intertrochanteric region P = .019) than those with low steps after controlling for potential confounders. However, there was no significant relationship between daily step recommendation adherence and BMD at the femoral neck (P = .092) or the heel (calcaneus P = .367). Adhere to the recommendation of 6000/10,000 steps per day without considering types of physical activity increases the bone mass in bones of spine and hip in young females.The 6000/10,000 steps daily can be recommended as a brief goal of physical activity for the prevention of osteoporosis.Cohort studies with big sample size, and diverse population are warranted to further confirm the effect of step goals on bone health.

Intravenous therapy often triggers phlebitis as an adverse effect, and there are limitations in modern medical treatments. The aim of this study was to investigate the mechanism of action of Qufushengji hydrogel, a novel topical preparation of traditional Chinese medicine, in the treatment of phlebitis. Based on network pharmacology and molecular docking technology, we screened the active ingredients (criteria: relative molecular weight molecular weight ≤500 Da, drug-likeness DL ≥0.18) from 4 Chinese medicines (e.g., Panax ginseng, Resina Draconis) contained in the hydrogel and combined them with the GeneCards database to obtain phlebitis-related targets. We predicted transdermal permeability by the Potts-Guy model, constructed protein-protein interaction networks and drug-component-target-disease networks, and conducted gene ontology/KEGG pathway enrichment analysis and molecular docking validation. A total of 90 active ingredients and 221 potential targets were screened, and 21 key targets were obtained after taking the intersection with phlebitis-related targets. Abietic acid, dehydroabietic acid, and oleanolic acid were identified as the core active ingredients, and IL-10, TNF, IL-6, VCAM1, and ICAM1 were identified as the core targets. Enrichment analysis indicated that Qufushengji hydrogel may intervene in immune-inflammatory responses, metabolic complications, and pathogen infection-related mechanisms by modulating AGE-RAGE, TNF, and other pathways. This study reveals the potential mechanism of Qufushengji hydrogel in treating phlebitis through the "multi-component-multi-target-multi-pathway" model, which provides a theoretical basis for the treatment of phlebitis by traditional Chinese medicine.

The difference between hematocrit (HCT) and plasma albumin (ALB), denoted as HCT-ALB, has been proposed as a new predictor of sepsis and infectious diseases, among others. The role of this index in predicting all-cause mortality in patients with acute pancreatitis (AP), however, has not been evaluated. The aim of this study was therefore to elucidate the correlation between HCT-ALB and 90-day all-cause mortality in non-elderly patients with AP. This retrospective cohort study utilized data from the MIMIC-IV (v1.0) database and included non-elderly adult patients diagnosed with AP who were admitted to the intensive care unit during the study. The primary outcome was the ability of HCT-ALB to predict mortality within 90 days of admission. A total of 254 patients met the inclusion criteria, and the mortality rate at 90 days after admission was 11.8%. A significant difference in HCT-ALB was observed between survivor and nonsurvivor groups, and the area under the receiver operating characteristic curve for predicting 90-day mortality was 0.692, with an optimal threshold value of 9.05 as calculated by the Youden Index. When modeled for prediction using HCT-ALB, age, and blood urea nitrogen, the predictive model showed good predictive efficacy. In conclusion, HCT-ALB serves as an independent predictor of all-cause mortality within 90 days of admission in non-elderly patients with AP. A predictive model that integrates HCT-ALB with age and serum urea nitrogen shows strong predictive efficacy.

The relationship between neutrophil percentage to albumin (NPAR) index and mortality in patients with diabetic kidney disease (DKD) remains unclear. This study aimed to investigate this association by designing a nationally representative longitudinal cohort. We conducted a longitudinal cohort analysis of 1778 adults with DKD from the National Health and Nutrition Examination Survey (2009-2018) with linkage to mortality data through December 31, 2019. Multivariate Cox proportional hazards models were conducted to explore the correlation between the NPAR index and all-cause and cardiovascular disease (CVD) mortality. A restricted cubic spline analysis was performed to explore the potential nonlinear relationship. Additionally, subgroup and sensitivity analysis were conducted to ensure the robustness of the findings. Over a median follow-up of 71 months (84,604 person-years), 462 all-cause and 146 CVD deaths occurred. Compared to the lowest quartile, participants in the highest NPAR quartile had significantly increased risks of all-cause (hazard ratio 1.96, 95% confidence interval 1.49-2.58, P < .001) and CVD mortality (hazard ratio 3.62, 95% confidence interval 2.04-6.41, P < .001). Restricted cubic spline analysis revealed a J-shaped relationship between NPAR and all-cause mortality (P for nonlinearity < .001), whereas NPAR showed a linear positive association with CVD mortality (P for nonlinearity = .4771). These findings remained consistent across subgroup and sensitivity analysis. Higher NPAR is independently associated with increased risks of all-cause and cardiovascular mortality in adults with DKD, supporting its potential role as a prognostic biomarker.