Associations between exposure to various air pollutants and risk of metabolic syndrome: a systematic review and meta-analysis.

BACKGROUND: Metabolic Syndrome (MetS) is a widely observed metabolic disorder that is increasingly prevalent worldwide, leading to substantial societal consequences. Previous studies have conducted two separate meta-analyses to investigate the relationship between MetS and air pollutants. However, these studies yielded conflicting results, necessitating a thorough systematic review and meta-analysis to reassess the link between different air pollutants and the risk of developing MetS. METHODS: We conducted a comprehensive search of relevant literature in databases including PubMed, Embase, Cochrane Library, and Web of Science up to October 9, 2023. The search was specifically restricted to publications in the English language. Following the screening of studies investigating the correlation between air pollution and MetS, we utilized random-effects models to calculate pooled effect sizes along with their respective 95% confidence intervals (CIs). We would like to highlight that this study has been registered with PROSPERO, and it can be identified by the registration number CRD42023484421. RESULTS: The study included twenty-four eligible studies. The results revealed that an increase of 10 µg/m3 in annual concentrations of PM1, PM2.5, PM10, NO2, SO2, and O3 was associated with a 29% increase in metabolic syndrome (MetS) risk for PM1 (OR = 1.29 [CI 1.07-1.54]), an 8% increase for PM2.5 (OR = 1.08 [CI 1.06-1.10]), a 17% increase for PM10 (OR = 1.17 [CI 1.08-1.27]), a 24% increase for NO2 (OR = 1.24 [CI 1.01-1.51]), a 19% increase for SO2 (OR = 1.19 [CI 1.04-1.36]), and a 10% increase for O3 (OR = 1.10 [CI 1.07-1.13]). CONCLUSION: The findings of this study demonstrate a significant association between exposure to fine particulate matter (PM1, PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and the incidence of metabolic syndrome (MetS). Moreover, the results suggest that air pollution exposure could potentially contribute to the development of MetS in humans.

Predicting COVID-19 Re-Positive Cases in Malnourished Older Adults: A Clinical Model Development and Validation.

Purpose: Building and validating a clinical prediction model for novel coronavirus (COVID-19) re-positive cases in malnourished older adults. Patients and Methods: Malnourished older adults from January to May 2023 were retrospectively collected from the Department of Geriatrics of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine. They were divided into a "non-re-positive" group and a "re-positive" group based on the number of COVID-19 infections, and into a training set and a validation set at a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify predictive factors for COVID-19 re-positivity in malnourished older adults, and a nomogram was constructed. Independent influencing factors were screened by multivariate logistic regression. The model's goodness-of-fit, discrimination, calibration, and clinical impact were assessed by Hosmer-Lemeshow test, area under the curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CIC), respectively. Results: We included 347 cases, 243 in the training set, and 104 in the validation set. We screened 10 variables as factors influencing the outcome. By multivariate logistic regression analysis, preliminary identified protective factors, risk factors, and independent influencing factors that affect the re-positive outcome. We constructed a clinical prediction model for COVID-19 re-positivity in malnourished older adults. The Hosmer-Lemeshow test yielded χ2 =5.916, P =0.657; the AUC was 0.881; when the threshold probability was >8%, using this model to predict whether malnourished older adults were re-positive for COVID-19 was more beneficial than implementing intervention programs for all patients; when the threshold was >80%, the positive estimated value was closer to the actual number of cases. Conclusion: This model can help identify the risk of COVID-19 re-positivity in malnourished older adults early, facilitate early clinical decision-making and intervention, and have important implications for improving patient outcomes. We also expect more large-scale, multicenter studies to further validate, refine, and update this model.