Impacts of lipid-related metabolites, adiposity, and genetic background on blood eosinophil counts: the Nagahama study

Abstract

Blood eosinophil count is a useful measure in asthma or COPD management. Recent epidemiological studies revealed that body mass index (BMI) is positively associated with eosinophil counts. However, few studies focused on the role of adiposity and fatty acid-related metabolites on eosinophil counts, including the effect of genetic polymorphism. In this community-based study involving 8265 participants (30–74 year old) from Nagahama city, we investigated the relationship between eosinophil counts and serum levels of fatty acid-related metabolites. The role of MDC1, a gene that is related to eosinophil counts in our previous study and encodes a protein that is thought to be involved in the repair of deoxyribonucleic acid damage, was also examined taking into account its interaction with adiposity. Serum levels of linoleic acid (LA) and β-hydroxybutyric acid (BHB) were negatively associated with eosinophil counts after adjustment with various confounders; however, there were positive interactions between serum LA and BMI and between serum BHB and BMI/body fat percentages in terms of eosinophil counts. In never-smokers, there was positive interaction for eosinophil counts between the CC genotype of MDC1 rs4713354 and BMI/body fat percentages. In conclusion, both serum LA and BHB have negative impacts on eosinophil counts, while adiposity shows robust positive effects on eosinophil counts, partly via genetic background in never-smokers.

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

“…This study confirmed that in the general population, in men or in women, peripheral blood eosinophil counts are significantly positively correlated with serum LDL-C, TG, and TC levels and negatively correlated with HDL-C levels. These results are consistent with those of a prospective Lifeline’s cohort study in the Netherlands [ 2 ], a follow-up investigation of a community population in Japan [ 12 ]and studies in special population with type 2 diabetes [ 16 ] or atopic asthma [ 17 ], different from the data of a multi-ethnic cohort in the US [ 13 ] and a Caucasian population in the UK [ 14 ].This may be due to the differences in genetic background.…”

“…Further conflicting results have been reported. A prospective lifeline cohort study in the Netherlands [ 2 ] and follow-up programme in a Japanese community-based population [ 12 ] reported that high eosinophil counts were related to low HDL-C levels and high LDL-C, TG, and total cholesterol (TC) levels. In contrast, data from a US multi-ethnic cohort showed no significant association between the level of each lipid and eosinophil count [ 13 ], and results from a white British cohort showed a negative association of TC and LDL-C with eosinophil count [ 14 ].…”

Sputum microbiota and inflammatory subtypes in asthma, COPD, and its overlap

“… 29 , 30 Previous research including ours displays that butyrate induces eosinophil apoptosis 29 and suppresses the proliferation of type-2 innate lymphoid cells 30 ; β-hydroxybutyric acid, a structural analog of butyrate, is negatively associated with blood eosinophil counts. 31 Although because these bacteria are obligate anaerobes, additional studies are required to determine whether they generate butyrate in the lower airways; increased phylum Bacteroidetes or class Bacteroidia may determine the eosinophil-low phenotype and decrease the possibility of ACO. Finally, it is important to note that the current findings were for stable conditions.…”

Association between circulating fatty acid metabolites and asthma risk: a two-sample bidirectional Mendelian randomization study

“…Finally, potential confounders which could simultaneously affect the incidence of asthma and fatty acid metabolite concentrations, including BMI, smoking status, alcohol intake [ 44 ], and the eosinophil counts as a risk factor of asthma which could be affected by fatty acid metabolites [ 45 ], were statistically adjusted using regression-based multivariable MR approach to obtain direct effects of metabolites on asthma. Statistical significance was defined as P < 0.05.…”