Body mass index affects the association between plasma lipids and peripheral eosinophils in a general chinese population: a cross-sectional survey

BACKGROUND

Lipid metabolism affects type 2 immunity; however, the association between plasma lipids and eosinophilic inflammation in humans is uncertain. This study analysed the relationship between plasma lipids and peripheral eosinophils and whether patterns differ with different body mass indexes (BMI).

METHODS

A cross-sectional survey including 62,441 healthy participants recruited from a regular health screening programme was conducted. Participants were divided into normal weight, overweight and obese subgroups according to BMI.

RESULTS

Multiple linear regression analysis revealed that elevated logarithmic-transformed eosinophil counts (log(EOS)) significantly correlated with high total cholesterol(TC), triglyceride(TG), low-density lipoprotein-cholesterol (LDL-C), and low high-density lipoprotein-cholesterol (HDL-C)levels in the overall population, as well as in men and women, while certain associations between peripheral blood eosinophil percentage and serum lipids varied by gender. These correlations existed across almost all BMI subgroups, and standardised β values decreased sequentially with increasing BMI. HDL-C had the most significant effect on eosinophils in obese women. Two-factor analysis of variance showed log(EOS) increased with higher BMI and hyperlipidemia whether in male or female and a synergistic effect exists of lipid levels (TG and LDL-C) and BMI in men.

CONCLUSIONS

Blood eosinophil counts were correlated with blood lipid levels and modified by body mass index status. The effects of lipid levels and body mass index on blood eosinophil counts were synergistic. Therefore, lipid metabolism may be involved in systemic eosinophil inflammation.

The causal association between peripheral blood eosinophils and nasal polyps: a Mendelian randomization study

OBJECTIVE

Observational studies suggested that peripheral blood eosinophils were associated with the risk of nasal polyps. However, these studies did not confirm the causality. This study aims to apply Mendelian randomization (MR) method to comprehensively assess the potential causal association between peripheral blood eosinophils and nasal polyps.

METHODS

Genetic instrumental variables were extracted from the largest available genome-wide association study (GWAS) of European participants, which were used to investigate the relationship between peripheral blood eosinophils and nasal polyps. The inverse variance weighted method, the MR Egger method, and the weighted median method were applied for this analysis. MR-Egger intercept tests, leave-one-out analyses, and funnel plots were performed for the sensitivity analysis.

RESULTS

With the inverse variance weighted method, the MR analysis suggested that there was a significant difference between peripheral blood eosinophils and the risk of nasal polyps (ukb-a-97, OR 1.004, 95% CI 1.003-1.005, p < 0.001; ukb-a-541, OR 1.005, 95% CI 1.004-1.006, p < 0.001; ukb-b-7211, OR 1.004, 95% CI 1.003-1.005, p < 0.001; ukb-b-8425, OR 1.004, 95% CI 1.003-1.005, p < 0.001; finn-b-J10_NASALPOLYP, OR 3.089, 95% CI 2.537-3.761, p < 0.001). Consistent results were also proved by using the weighted median method and the MR Egger method.

CONCLUSIONS

Our findings reveal the causal effect of peripheral blood eosinophils on the increased risk of nasal polyps.

Oral corticosteroid dose changes and impact on peripheral blood eosinophil counts in patients with severe eosinophilic asthma: a post hoc analysis

BACKGROUND

An inverse relationship between oral corticosteroid (OCS) dose and peripheral blood eosinophil (PBE) count is widely recognized in patients with severe eosinophilic asthma; however, there are limited data available to quantify this relationship. This post hoc analysis of the SIRIUS study (NCT01691508) examined the impact of weekly incremental OCS dose reductions on PBE counts during the 3-8-week optimization phase of the study.

METHODS

SIRIUS was a randomized, double-blind study involving patients with severe asthma (≥12 years old), which included an initial OCS dose optimization phase prior to randomization. Regression analysis assuming a linear relationship between change in OCS dose and change in log (PBE count) during the optimization phase was used to estimate the changes in PBE count following specific decreases in OCS dose.

RESULTS

All 135 patients from the SIRIUS intent-to-treat population were included in this analysis. During the optimization period, 44% (60/135) of patients reduced their OCS dose, with an increase in geometric mean PBE count of 110 cells/μL (200 to 310 cells/μL; geometric mean ratio from beginning to end of the optimization phase: 1.52) recorded in these patients. The model estimated that reduction of daily OCS dose by 5 mg/day led to a 41% increase in PBE count (mean ratio to beginning of optimization phase: 1.41 [95% confidence interval (CI); 1.22, 1.63]).

CONCLUSION

These data confirmed and quantified the inverse association between OCS dose and PBE count. These insights will help to inform clinicians when tapering OCS doses in patients with severe eosinophilic asthma.