Abnormal fasting blood glucose enhances the risk of long-term exposure to air pollution on dyslipidemia: A cross-sectional study

Association between Air Pollution and Lipid Profiles

Dyslipidemia is a critical factor in the development of atherosclerosis and consequent cardiovascular disease. Numerous pieces of evidence demonstrate the association between air pollution and abnormal blood lipids. Although the results of epidemiological studies on the link between air pollution and blood lipids are unsettled due to different research methods and conditions, most of them corroborate the harmful effects of air pollution on blood lipids. Mechanism studies have revealed that air pollution may affect blood lipids via oxidative stress, inflammation, insulin resistance, mitochondrial dysfunction, and hypothalamic hormone and epigenetic changes. Moreover, there is a risk of metabolic diseases associated with air pollution, including fatty liver disease, diabetes mellitus, and obesity, which are often accompanied by dyslipidemia. Therefore, it is biologically plausible that air pollution affects blood lipids. The overall evidence supports that air pollution has a deleterious effect on blood lipid health. However, further research into susceptibility, indoor air pollution, and gaseous pollutants is required, and the issue of assessing the effects of mixtures of air pollutants remains an obstacle for the future.

The association between exposure to volatile organic compounds and serum lipids in the US adult population

BACKGROUND AND AIM

Epidemiological evidence on the relationship between exposure to volatile organic compounds (VOCs), both single and mixed, and serum lipid levels is limited, and their relationship remains unclear. Our study aimed to investigate the associations of exposure to VOCs with serum lipid levels in the US adult population.

METHODS AND RESULTS

The study examined the association of 16 VOC levels (2-methylhippuric acid, 3- and 4-methylhippuric acid, N-acetyl-S-(2-carbamoylethyl)-L-cysteine, N-acetyl-S-(N-methylcarbamoyl)-L-cysteine, 2-aminothiazoline-4-carboxylic acid, N-acetyl-S-(benzyl)-L-cysteine, N-acetyl-S-(n-propyl)-L-cysteine, N-acetyl-S-(2-carboxyethyl)-L-cysteine, N-acetyl-S-(2-cyanoethyl)-L-cysteine, N-acetyl-S-(3,4-dihydroxybutyl)-L-cysteine, N-acetyl-S-(2-hydroxypropyl)-L-cysteine. N-Acetyl-S-(3-hydroxypropyl)-L-cysteine, mandelic acid, N-acetyl-S-(4-hydroxy-2-butenyl)-L-cysteine, phenylglyoxylic acid and N-acetyl-S-(3-hydroxypropyl-1-methyl)-L-cysteine) with total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL) and high-density lipoprotein cholesterol (HDL) using data from the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2015, and a total of 1410 adults were enrolled. The association was evaluated by Bayesian kernel machine regression (BKMR), multiple linear regression and weighted quantile sum (WQS) regression. In BKMR analysis, exposure to VOCs is positively correlated with levels of TC, TG, and LDL-C. However, statistical significance was observed only for the impact on TG. Our linear regression analysis and WQS regression generally support the BKMR results. Several VOCs were positively associated with serum lipid profiles (e.g., the ln-transformed level of mandelic acid (MA) displayed an increase in estimated changes of 7.01 (95% CIs: 2.78, 11.24) mg/dL for TC level), even after the effective number of tests for multiple testing (P < 0.05).

CONCLUSIONS

Exposure to VOCs was associated with serum lipids, and more studies are needed to confirm these findings.

Associations of long-term exposure to air pollution with prevalence of pulmonary nodules: A cross-sectional study in Shijiazhuang, China

A complete understanding of the associations of ambient air pollution with prevalence of pulmonary nodule is lacking. We aimed to investigate the associations of ambient air pollutants with prevalence of pulmonary nodule. A total of 9991 health examination participants was enrolled and 3166 was elected in the final in Shijiazhuang between April 1st, 2018, and December 31st, 2018. 107 participants were diagnosed in pulmonary nodule while 3059 participants were diagnosed in non-pulmonary (named control). The individual exposure of participants was evaluation by Empirical Bayesian Kriging model according to their residential or work addresses. The pulmonary nodules were found and diagnosed by health examination through chest x-ray detection. Our results suggested that there were positive associations between prevalence of pulmonary nodules and PM2.5 (OR = 1.06, 95% CI: 1.02, 1.11) as well as O3 (OR = 1.49, 95% CI: 1.35, 1.66) levels. The platelet count (PLT) acted as the mediator of pulmonary nodules related with the PM2.5 exposure, while the neutrophil-to-lymphocyte ratio (NLR) as well as platelet-to-lymphocyte ratio (PLR) were the mediators of pulmonary nodules related with the O3 exposure. This study suggests that long-term exposure to PM2.5 and O3 may significantly associated with prevalence of pulmonary nodules, and the above associations are mediated by PLT, NLR and PLR.

Metabolic pathways altered by air pollutant exposure in association with lipid profiles in young adults

Mounting evidence suggests that air pollution influences lipid metabolism and dyslipidemia. However, the metabolic mechanisms linking air pollutant exposure and altered lipid metabolism is not established. In year 2014-2018, we conducted a cross-sectional study on 136 young adults in southern California, and assessed lipid profiles (triglycerides, total cholesterol, high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol, very-low-density lipoprotein (VLDL)-cholesterol), and untargeted serum metabolomics using liquid chromatography-high-resolution mass spectrometry, and one-month and one-year averaged exposures to NO2, O3, PM2.5 and PM10 air pollutants at residential addresses. A metabolome-wide association analysis was conducted to identify metabolomic features associated with each air pollutant. Mummichog pathway enrichment analysis was used to assess altered metabolic pathways. Principal component analysis (PCA) was further conducted to summarize 35 metabolites with confirmed chemical identity. Lastly, linear regression models were used to analyze the associations of metabolomic PC scores with each air pollutant exposure and lipid profile outcome. In total, 9309 metabolomic features were extracted, with 3275 features significantly associated with exposure to one-month or one-year averaged NO2, O3, PM2.5 and PM10 (p < 0.05). Metabolic pathways associated with air pollutants included fatty acid, steroid hormone biosynthesis, tryptophan, and tyrosine metabolism. PCA of 35 metabolites identified three main PCs which together explained 44.4% of the variance, representing free fatty acids and oxidative byproducts, amino acids and organic acids. Linear regression indicated that the free fatty acids and oxidative byproducts-related PC score was associated with air pollutant exposure and outcomes of total cholesterol and LDL-cholesterol (p < 0.05). This study suggests that exposure to NO2, O3, PM2.5 and PM10 contributes to increased level of circulating free fatty acids, likely through increased adipose lipolysis, stress hormone and response to oxidative stress pathways. These alterations were associated with dysregulation of lipid profiles and potentially could contribute to dyslipidemia and other cardiometabolic disorders.

Long-term effect of submicronic particulate matter (PM1) and intermodal particulate matter (PM1-2.5) on incident dyslipidemia in China: A nationwide 5-year cohort study

BACKGROUND

There is insufficient evidence of associations between incident dyslipidemia with PM₁ (submicronic particulate matter) and PM_1-2.5 (intermodal particulate matter) in the middle-aged and elderly. We aimed to determine the long-term effects of PM₁ and PM_1-2.5 on incident dyslipidemia respectively.

METHODS

We studied 6976 individuals aged ≥45 from the China Health and Retirement Longitudinal Study from 2013 to 2018. The concentrations of particular matter (PM) for every individual's address were evaluated using a satellite-based spatiotemporal model. Dyslipidemia was evaluated by self-reported. The generalized linear mixed model was applied to quantify the correlations between PM and incident dyslipidemia.

RESULTS

After a 5-year follow-up, 333 (4.77%) participants developed dyslipidemia. Per 10 μg/m³ uptick in four-year average concentrations of PMs (PM₁ and PM_1-2.5) corresponded to 1.11 [95% confidence interval (CI): 1.01-1.23)] and 1.23 (95% CI: 1.06-1.43) fold risks of incident dyslipidemia. Nonlinear exposure-response curves were observed between PM and incident dyslipidemia. The effect size of PM₁ on incident dyslipidemia was slightly higher in males [1.14 (95% CI: 0.98-1.32) vs. 1.04 (95% CI: 0.89-1.21)], the elderly [1.23 (95% CI: 1.04-1.45) vs. 1.03 (95% CI: 0.91-1.17)], people with less than primary school education [1.12 (95% CI: 0.94-1.33) vs. 1.08 (95% CI: 0.94-1.23)], and solid cooking fuel users [1.17 (95% CI: 1.00-1.36) vs. 1.06 (95% CI: 0.93-1.21)], however, the difference was not statistically significant (Z = -0.82, P = 0.413; Z = -1.66, P = 0.097; Z = 0.32, P = 0.752; Z = -0.89, P = 0.372).

CONCLUSIONS

Long-term exposure to PM₁ and PM_1-2.5 were linked with an increased morbidity of dyslipidemia in the middle-aged and elderly population. Males, the elderly, and solid cooking fuel users had higher risk. Further studies would be warranted to establish an accurate reference value of PM to mitigate growing dyslipidemia.

Association of Short-Term Exposure to PM2.5 with Blood Lipids and the Modification Effects of Insulin Resistance: A Panel Study in Wuhan

Results of previous studies about the acute effects of fine particulate matter (PM2.5) on blood lipids were inconsistent. This study aimed to quantify the short-term effects of PM2.5 on blood lipids and estimate the modifying role of insulin resistance, reflected by the homeostasis model assessment of insulin resistance (HOMA-IR). From September 2019 to January 2020, the study recruited 70 healthy adults from Wuhan University for a total of eight repeated data collections. At each visit, three consecutive days were monitored for personal exposure to PM2.5, and then a physical examination was carried out on the fourth day. The linear mixed-effect models were operated to investigate the impact of PM2.5 over diverse exposure windows on blood lipids. With the median of the HOMA-IR 1.820 as the cut-off point, participants were assigned to two groups for the interaction analyses. We found the overall mean level (standard deviation, SD) of PM2.5 was 38.34 (18.33) μg/m3. Additionally, with a 10 μg/m3 rise in PM2.5, the corresponding largest responses in triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), as well as high-density lipoprotein cholesterol (HDL-C), were −0.91% (95% confidence interval (CI): −1.63%, −0.18%), −0.33% (95% CI: −0.64%, −0.01%,), −0.94% (95% CI: −1.53%, −0.35%), and 0.67% (95% CI: 0.32%, 1.02%), respectively. The interaction analyses revealed that a significantly greater reduction in the four lipids corresponded to PM2.5 exposure when in the group with the lower HOMA-IR (<1.820). In conclusion, short-term PM2.5 exposure over specific time windows among healthy adults was associated with reduced TG, TC, as well as LDL-C levels, and elevated HDL-C. Additionally, the association of PM2.5−lipids may be modulated by insulin resistance.