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The association between eosinophil count, serum lipids and metabolic syndrome in Taiwanese

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Title: The association between eosinophil count, serum lipids and metabolic syndrome in Taiwanese
Author: Wei-Yu Lin MD and Jong-Dar Chen MD
Publisher: American Journal of the Medical Sciences
Publication: 2023
Language: English
Pages: 5
Format: PDF
File size: 225 KB
DOI: 10.1016/j.amjms.2022.07.015
ID: CBR89

Categories: Researches, Medical Researches Tags: American Journal of the Medical Sciences, clinical investigation, serum lipids and metabolic syndrome in Taiwanese, The association between eosinophil count

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Introduction

Eosinophil plays a role in inflammation, and its count increases in conditions such as allergies, parasitic infection, and some inflammatory diseases^, ; thus increased eosinophil count is related to certain diseases. Studies have demonstrated an association of metabolic syndrome (MetS) with inflammation. Some studies have discussed the association of eosinophil with MetS or its components. Amini et al found that high eosinophil count is associated with an increased risk of MetS ; however, the association has been demonstrated to be inconsistent in other studies. Meng et al and Kim et al have found no association between eosinophil and MetS components. ^, Huang et al demonstrated an association between triglycerides and all leukocyte subtypes, except eosinophil. These controversial findings may be attributed to small study populations and inconsistent definitions of MetS in most previous studies. ^, Previous studies have demonstrated the association of dyslipidemia, a MetS component, with eosinophil count, ^, ^, but rare studies investigated the association between eosinophil count and ApoA-I and ApoB, which were the superior indicators for coronary artery disease. ^,

We performed this large population-based study in Taiwan to investigate the association between elevated eosinophil count, MetS, and serum lipids, including ApoA-I and ApoB.

Methods

In this cross-sectional study, we enrolled 10,357 individuals (6919 men and 3438 women) who underwent health checkups at Shin Kong Wu Ho-Su Memorial Hospital in Taiwan between January 2006 and December 2016. Ethics approval for the study protocol and data analysis were obtained from Shin Kong Wu Ho-Su Memorial Hospital’s Institutional Review Board.

Anthropometry data, including body weight, height, body mass index (BMI), waist circumference, and blood pressure, were obtained during physical examination. The measurements of body height and weight were made to the nearest 0.1 cm and 0.1 kg, respectively, with waist circumference measured to the nearest 0.5 cm.

Blood samples were obtained from all individuals after at least 8 h of fasting. Serum levels of fasting glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), ApoA-I, and ApoB were measured using an automated Hitachi 7600 clinical analyser (Hitachi Ltd., Tokyo, Japan). A hemogram, including complete blood count and differential count, was performed using a Sysmex automated haematology analyser (XN-9000; Sysmex Corporation, Kobe, Japan). Individuals with incomplete anthropometry or laboratory data were excluded from initial enrollment.

According to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) guideline criteria [modified by the International Diabetes Federation (IDF) specifically for the Chinese population], MetS is defined as the presence of 3 or more of the following conditions: (1) waist circumference ≥ 90 cm in men and ≥ 80 cm in women; (2) blood pressure ≥ 130/85 mmHg; (3) fasting glucose ≥ 100 mg/dL(5.6 mmol/L); (4) HDL-C < 40 mg/dL(1.03 mmol/L) in men or < 50 mg/dL(1.29 mmol/L) in women; and (5) triglycerides ≥ 150 mg/dL(1.69 mmol/L).

All continuous variables were expressed in terms of mean and standard deviation. Categorial variables were expressed as numbers and percentages. Student’s t test and analysis of variance were used for continuous variables; for categorical variables, a chi-square test was used. Pearson’s correlation and linear regression were used to determine the association of eosinophil count with waist circumference, blood pressure, fasting glucose, and serum lipids. Participants were classified into 4 groups according to eosinophil count quartiles, with the lowest quartile referred to as the reference group. Multivariate logistic regression analysis was used to determine the odds ratio of MetS and abnormal serum lipid levels in each quartile. A P value < 0.05 was considered significant.