Asthma start, development, and exacerbation have all been linked in numerous studies to exposure to a variety of metal elements. However, there is still a dearth of epidemiological data linking heavy metal exposure to death in asthmatics. The investigation included 2432 eligible adults with asthma. The study examined the possible correlation between blood heavy metal levels and all-cause mortality. This was done by utilizing Cox proportional hazards models, restricted cubic spline (RCS), threshold effect models, and CoxBoost models. Subgroup analyses were conducted to investigate the associations between blood metal levels and all-cause mortality among distinct asthmatic populations. An inverse association was found between blood selenium and all-cause mortality in asthmatics, while blood manganese showed a positive association with all-cause mortality. However, there were no significant connections found between blood lead, cadmium, mercury, and all-cause mortality via multivariate Cox proportional hazard models. In model 3, after accounting for all factors, all-cause mortality dropped by 10% for every additional 10 units of blood selenium (μg/L) and increased by 6% for every additional unit of blood manganese (μg/L). The RCS and threshold effect model found a U-shaped correlation between blood selenium, blood manganese, and all-cause mortality. The lowest all-cause mortality among asthmatics was observed when blood selenium and manganese were 188.66 μg/L and 8.47 μg/L, respectively. Our investigation found a U-shaped correlation between blood selenium levels, blood manganese levels, and all-cause mortality in asthmatic populations. Optimizing dietary selenium intake and effectively managing manganese exposure could potentially improve the prognosis of asthma.
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http://dx.doi.org/10.1038/s41598-024-70250-8 | DOI Listing |
Eur Heart J Digit Health
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Aims: Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
January 2025
Department of Neurosurgery, Huzhou Central Hospital, Huzhou, China.
Background: Abnormal thyroid hormone levels may occur in critical illness, which may have an interactive relationship with inflammatory reaction. At present, the relationship between triiodothyronine (T3)/thyroxine (T4) ratio and inflammatory indicators and all-cause mortality of stroke survivors is still unclear.
Methods: We obtained the relevant data of the respondents from 2007 to 2012 through the National Health and Nutrition Examination Survey (NHANES) database for statistical analysis.
Front Med (Lausanne)
January 2025
VA Connecticut Healthcare System, West Haven, CT, United States.
[This corrects the article DOI: 10.3389/fmed.2023.
View Article and Find Full Text PDFOptimal dosing of VTE prophylaxis for specific patient populations remains an area of concern as insufficient evidence exists regarding dosing for underweight patients. The purpose of this study is to compare the incidence of major bleeding events in underweight patients given different prophylactic doses of enoxaparin. This is a retrospective analysis performed at multiple hospitals within a single health care system.
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