Effects of PM2.5 and PM10 on congenital hypothyroidism in Qingdao, China, 2014-2017: a quantitative analysis

Distributed lag effects and vulnerable groups of PM and active pulmonary TB in Qingdao, China

There has been a gap regarding current knowledge of the effect of PM on pulmonary TB, such as the exposure-time-response between them. This study aimed to explore the distributed lag effects of particulate matter (PM) on active pulmonary tuberculosis (TB) and identify the vulnerable groups. A generalized additive mixed model combined with a distributed lag non-linear model was applied to quantify the association between PM and active pulmonary TB with adjustment for potential confounders. Relative risk (RR) and cumulative RR with 95% confidence interval (CI) were calculated to quantify the exposure-time-response. A total of 16,486 cases of active pulmonary TB were notified. Results suggested that a unit 10 µg/m3 increase of daily PM2.5 concentration was positively associated with active pulmonary TB morbidity at 36-115 lag day and RR reached maximum at 66 lag day (1.0076; 95%CI, 1.0031-1.0122), and the cumulative RR was 2.1940 (95%CI, 1.2292-3.9161). For PM10, this association was significantly positive at 73-117 lag day, and RR reached maximum at 100 lag day (1.0036; 95%CI, 1.0003-1.0067), and the cumulative RR was not significant. This study provides evidence that PM significantly associate with active pulmonary TB. Vulnerability to PM2.5 was identified in male, female, 0-18 ages, 19-64 ages, workers, and students. Our findings have significant implications for developing local strategies to prevent and reduce health impact in PM polluted areas.

Causal relationship between particulate matter 2.5 and hypothyroidism: A two-sample Mendelian randomization study

Background

Epidemiological surveys have found that particulate matter 2.5 (PM_2.5) plays an important role in hypothyroidism. However, due to the methodological limitations of traditional observational studies, it is difficult to make causal inferences. In the present study, we assessed the causal association between PM_2.5 concentrations and risk of hypothyroidism using two-sample Mendelian randomization (TSMR).

Methods

We performed TSMR by using aggregated data from genome-wide association studies (GWAS) on the IEU Open GWAS database. We identified seven single nucleotide polymorphisms (SNPs) associated with PM_2.5 concentrations as instrumental variables (IVs). We used inverse-variance weighting (IVW) as the main analytical method, and we selected MR-Egger, weighted median, simple model, and weighted model methods for quality control.

Results

MR analysis showed that PM_2.5 has a positive effect on the risk of hypothyroidism: An increase of 1 standard deviation (SD) in PM_2.5 concentrations increases the risk of hypothyroidism by ~10.0% (odds ratio 1.10, 95% confidence interval 1.06-1.13, P = 2.93E-08, by IVW analysis); there was no heterogeneity or pleiotropy in the results.

Conclusion

In conclusion, increased PM_2.5 concentrations are associated with an increased risk of hypothyroidism. This study provides evidence of a causal relationship between PM_2.5 and the risk of hypothyroidism, so air pollution control may have important implications for the prevention of hypothyroidism.

Alterations in the gut microbiota and its metabolic profile of PM2.5 exposure-induced thyroid dysfunction rats

Fine particulate matter (PM_2.5) has drawn more and more interest due to its adverse effects on health. Thyroid has been demonstrated to be the key organ impacted by PM_2.5. However, the mechanisms for PM_2.5 exposure-induced thyrotoxicity remain unclear. To explore the mechanisms, a rat thyroid injury model was established by exposing rats to PM_2.5 via passive pulmonary inhalation. Thyroid hormones and thyroid function proteins were detected. The thyroid function affected by PM_2.5 exposure was investigated via metabolomics analysis using liquid chromatography-mass spectrometry and 16S rRNA gene sequencing. Results showed that PM_2.5 exposure induced remarkable alterations in gut microbiome evenness, richness, and composition. Metabolomics profiling revealed that the urine metabolites levels were changed by PM_2.5 exposure. The altered gut microbiota and urine metabolites showed significant correlations with thyroid function indicators (total T3, total T4 and thyrotropin hormone, etc.). These metabolites were involved in metabolic pathways including thyroid hormone synthesis, metabolisms of tryptophan, d-Glutamine and D-glutamate, histidine, glutathione, etc. The altered gut microbiota showed significant correlations with urine metabolites (glutathione, citric acid, D-Glutamic acid, kynurenic acid and 5-Aminopentanoic acid, etc.). For example, the taurocholic acid levels positively correlated with the relative abundance of several genera including Elusimicrobium (r = 0.9741, p = 0.000000), Muribaculum (r = 0.9886, p = 0.000000), Candidatus_Obscuribacter (r = 0.8423, p = 0.000585), Eubacterium (r = 0.9237, p = 0.000017), and Parabacteroides (r = 0.8813, p = 0.000150), while it negatively correlated with the relative abundance of Prevotella (r = -0.8070, p = 0.001509). PM_2.5 exposure-induced thyrotoxicity led to remarkable alterations both in gut microbiome composition and some metabolites involved in metabolic pathways. The altered intestinal flora and metabolites can in turn influence thyroid function in rats. These findings may provide novel insights regarding perturbations of the gut-thyroid axis as a new mechanism for PM_2.5 exposure-induced thyrotoxicity.

The Physiological Effects of Air Pollution: Particulate Matter, Physiology and Disease

Nine out of 10 people breathe air that does not meet World Health Organization pollution limits. Air pollutants include gasses and particulate matter and collectively are responsible for ~8 million annual deaths. Particulate matter is the most dangerous form of air pollution, causing inflammatory and oxidative tissue damage. A deeper understanding of the physiological effects of particulate matter is needed for effective disease prevention and treatment. This review will summarize the impact of particulate matter on physiological systems, and where possible will refer to apposite epidemiological and toxicological studies. By discussing a broad cross-section of available data, we hope this review appeals to a wide readership and provides some insight on the impacts of particulate matter on human health.

The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis

Background

We conducted a distributed lag non-linear time series analysis to quantify the association between air pollution and scarlet fever in Qingdao city during 2014–2018.

Methods

A distributed lag non-linear model (DLNM) combined with a generalized additive mixed model (GAMM) was applied to quantify the distributed lag effects of air pollutions on scarlet fever, with daily incidence of scarlet fever as the dependent variable and air pollutions as the independent variable adjusted for potential confounders.

Results

A total of 6316 cases of scarlet fever were notified, and there were 376 days occurring air pollution during the study period. Scarlet fever was significantly associated with air pollutions at a lag of 7 days with different relative risk (RR) of air pollution degrees [1.172, 95% confidence interval (CI): 1.038–1.323 in mild air pollution; 1.374, 95% CI 1.078–1.749 in moderate air pollution; 1.610, 95% CI 1.163–2.314 in severe air pollution; 1.887, 95% CI 1.163–3.061 in most severe air pollution].

Conclusions

Our findings show that air pollution is positively associated with scarlet fever in Qingdao, and the risk of scarlet fever could be increased along with the degrees of air pollution. It contributes to developing strategies to prevent and reduce health impact from scarlet fever and other non-vaccine-preventable respiratory infectious diseases in air polluted areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06674-8.

Geographic variations in the incidence of congenital hypothyroidism in China: a retrospective study based on 92 million newborns screened in 2013-2018

Background:

Although congenital hypothyroidism (CH) has been widely studied in Western countries, CH incidence at different administrative levels in China during the past decade remains unknown. This study aimed to update the incidence and revealed the spatial pattern of CH incidence in the mainland of China, which could be helpful in the planning and implementation of preventative measures.

Methods:

The data used in our study were derived from 245 newborns screening centers that cover 30 provinces of the Chinese Newborn Screening Information System. Spatial auto-correlation was analyzed by Global Moran I and Getis-Ord Gi statistics at the provincial level. Kriging interpolation methods were applied to estimate a further detailed spatial distribution of CH incidence at city level throughout the mainland of China, and Kulldorff space scanning statistical methods were used to identify the spatial clusters of CH cases at the city level.

Results:

A total of 91,921,334 neonates were screened from 2013 to 2018 and 42,861 cases of primary CH were identified, yielding an incidence of 4.66 per 10,000 newborns screened (95% confidence interval [CI]: 4.62–4.71). Neonates in central (risk ratio [RR] = 0.84, 95% CI: 0.82–0.85) and western districts (RR = 0.71, 95% CI: 0.69–0.73) had lower probability of CH cases compared with the eastern region. The CH incidence indicated a moderate positive global spatial autocorrelation (Global Moran I value = 0.394, P < 0.05), and the CH cases were significantly clustered in spatial distribution. A most likely city-cluster (log-likelihood ratio [LLR] = 588.82, RR = 2.36, P < 0.01) and 25 secondary city-clusters of high incidence were scanned. The incidence of each province and each city in the mainland of China was estimated by kriging interpolation, revealing the most affected province and city to be Zhejiang Province and Hangzhou city, respectively.

Conclusion:

This study offers an insight into the space clustering of CH incidence at provincial and city scales. Future work on environmental factors need to focus on the effects of CH occurrence.