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Indoor fine particulate matter (PM2.5) poses a considerable hazard to the aging process, particularly in vulnerable populations such as schizophrenia patients who frequently spend extended periods in indoor environments. Currently, the evidence on which PM2.5 components contribute to accelerated aging remains unclear. To address these issues, we conducted a prospective, repeated-measurement study on 104 schizophrenia patients. Our findings indicated that exposure to PM2.5 components was significantly associated with accelerated biological aging in schizophrenia patients. Notably, the most prominent effects were observed for thallium (1.303, 95 % CI: 0.481-2.125), chromium (1.029, 95 % CI: 0.303-1.756), lead (1.021, 95 % CI: 0.296-1.746), antimony (0.915, 95 % CI: 0.233-1.597), selenium (0.854, 95 % CI: 0.209-1.499), and manganese (0.833, 95 % CI: 0.186-1.480). Multivariate analysis revealed that PM2.5 components predominantly induced alterations in serum glycerophospholipid metabolites, accelerating the aging process. This intricate connection was closely linked to the gut microbiota, particularly to species such as Dorea and Blautia. Mediation analysis showed that the Blautia-PC (16:0/0:0) pathway mediated the largest proportion (30.69 %) of the effect of manganese exposure on accelerating immune biological aging in schizophrenia patients, as measured using the Klemera-Doubal method. These results underscore the need to address pollution sources that harm health, and provide new evidence for improving regional air quality.
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BACKGROUND: The effect of fine particulate matter (PM2.5) components on prediabetes and diabetes is of concern, but the evidence is limited and the specific role of different green space types remains unclear. This study aims to investigate the relationship of PM2.5 and its components with prediabetes and diabetes as well as the potential health benefits of different types and combinations of green spaces. METHODS: A multicenter cross-sectional study was conducted in eastern China by using a multi-stage random sampling method. Health screening and questionnaires for 98,091 participants were performed during 2017-2020. PM2.5 and its five components were estimated by the inverse distance weighted method, and green space was reflected by the Normalized Difference Vegetation Index (NDVI), percentages of tree or grass cover. Multivariate logistic regression and quantile g-computing were used to explore the associations of PM2.5 and five components with prediabetes and diabetes and to elucidate the potential moderating role of green space and corresponding type combinations in these associations. RESULTS: Each interquartile range (IQR) increment of PM2.5 was associated with both prediabetes (odds ratio [OR]: 1.15, 95%CI [confidence interval]: 1.10-1.20) and diabetes (OR: 1.18, 95% CI: 1.11-1.25), respectively. All five components of PM2.5 were related to prediabetes and diabetes. The ORs of PM2.5 on diabetes were 1.49 (1.35-1.63) in the low tree group and 0.90 (0.82-0.98) in the high tree group, respectively. In the high tree-high grass group, the harmful impacts of PM2.5 and five components were significantly lower than in the other groups. CONCLUSION: Our study suggested that PM2.5 and its components were associated with the increased risk of prediabetes and diabetes, which could be diminished by green space. Furthermore, the coexistence of high levels of tree and grass cover provided greater benefits. These findings had critical implications for diabetes prevention and green space-based planning for healthy city.
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Contaminantes Atmosféricos , Contaminación del Aire , Diabetes Mellitus , Estado Prediabético , Humanos , Estado Prediabético/etiología , Estado Prediabético/inducido químicamente , Estudios Transversales , Parques Recreativos , Contaminantes Atmosféricos/toxicidad , Contaminantes Atmosféricos/análisis , Exposición a Riesgos Ambientales , Diabetes Mellitus/etiología , Diabetes Mellitus/inducido químicamente , Material Particulado/análisis , China/epidemiologíaRESUMEN
BACKGROUND: Tumor microenvironment plays pivotal roles in carcinogenesis, cancer development and metastasis. Composition of cancer immune cell subsets can be inferred by deconvolution of gene expression profile accurately. Compositions of the cell types in cancer microenvironment including cancer infiltrating immune and stromal cells have been reported to be associated with the cancer outcomes markers for cancer prognosis. However, rare studies have been reported on their association with the response to preoperative radiotherapy for rectal cancer. METHODS: In this paper, we deconvoluted the immune/stromal cell composition from the gene expression profiles. We compared the composition of immune/stromal cell types in the RT responsive versus nonresponsive for rectal cancer. We also compared the peripheral blood immune cell subset composition in the stable diseases versus progressive diseases of rectal cancer patients with fluorescence-activated cell sorting from our institution. RESULTS: Compared with the non-responsive group, the responsive group showed higher proportions of CD4+ T cell (0.1378 ± 0.0368 vs. 0.1071 ± 0.0373, p = 0.0215), adipocytes, T cells CD4 memory resting, and lower proportions of CD8+ T cell (0.1798 ± 0.0217 vs. 0.2104 ± 0.0415, p = 0.0239), macrophages M2, and preadipocytes in their cancer tissue. The responsive patients showed a higher ratio of CD4+/CD8+ T cell proportions (mean 0.7869 vs. 0.5564, p = 0.0210). Consistently, the peripheral blood dataset showed higher proportion of CD4+ T cells and higher ratio of CD4+/CD8+ T cells, and lower proportion of CD8+ T cells for favorable prognosis. We validated these results with a pooled dataset of GSE3493 and GSE35452, and more peripheral blood data, respectively. Finally, we imported these eight cell features including eosinophils and macrophage M1 to Support Vector Machines and could predict the pre-radiotherapy responsive versus non-responsive with an accuracy of 76%, ROC AUC 0.77, 95% confidential interval of 0.632-0.857, better than the gene signatures. CONCLUSIONS: Our results showed that the proportions of tumor-infiltrating subsets and peripheral blood immune cell subsets can be important immune cell markers and treatment targets for outcomes of radiotherapy for rectal cancer.
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Linfocitos T CD8-positivos , Neoplasias del Recto , Humanos , Pronóstico , Neoplasias del Recto/radioterapia , Microambiente TumoralRESUMEN
BACKGROUND: Genetic polymorphisms in the 15q25 region have been associated with the risk of lung cancer (LC). However, studies have yielded conflicting results. METHODS: Searches were conducted in databases, including PubMed, EMbase, Web of Science, CNKI, and Wanfang, for case-control studies up to August 1, 2019. After retrieving eligible studies and data extraction, we calculated pooled odds ratios with 95% confidence intervals. In the meta-analysis, we included 32 publications with a total of 52,795 patients with LC and 97,493 control cases to evaluate the polymorphisms in the CHRNA5/A3/B4 gene cluster in the 15q25 region. RESULTS: Data of the meta-analysis showed a significantly increased risk of LC in the presence of genetic polymorphisms (rs1051730, rs16969968, rs8034191). In the smoking subgroup, the CHRNA3 rs1051730 polymorphism was found to contribute to LC risk using following 5 models: the allelic model, the homozygous model, the heterozygous model, the dominant model, and the recessive model. Thus, the rs1051730 polymorphism may modify LC susceptibility under the condition of smoking. Stratification studies for CHRNA5-rs8034191 showed that Caucasian groups with the wild-type genotype (C/C) may be susceptible to LC in all 5 models. No significant relationship between CHRNA3 rs6495309 or rs3743073 and LC susceptibility was found. However, Asians with the rs3743037 B-allele showed an obviously higher risk of LC susceptibility than the Caucasian population, observed via allelic, heterozygous, and dominant models. CONCLUSIONS: The 3 polymorphisms of rs1051730, rs16969968 and rs8034191 in the CHRNA5/A3/B4 gene cluster in the 15q25 region were associated with LC risk, which might be influenced by ethnicity and smoking status.