Exploring the association and causal effect between white blood cells and psoriasis using large-scale population data

Guowei Zhou; Xiangmei Ren; Zhenwei Tang; Wang Li; Wenqiong Chen; Yi He; Benliang Wei; Hailun Zhang; Fangyu Ma; Xiang Chen; Guanxiong Zhang; Minxue Shen; Hong Liu

doi:10.3389/fimmu.2023.1043380

Exploring the association and causal effect between white blood cells and psoriasis using large-scale population data

Front Immunol. 2023 Feb 14:14:1043380. doi: 10.3389/fimmu.2023.1043380. eCollection 2023.

Authors

Guowei Zhou^{1

2

3}, Xiangmei Ren^{1

2

3}, Zhenwei Tang^{1

2

3}, Wang Li^{1

2

3}, Wenqiong Chen^{1

2

3}, Yi He^{1

2

3}, Benliang Wei¹, Hailun Zhang⁴, Fangyu Ma⁵, Xiang Chen^{1

2

3

6}, Guanxiong Zhang^{1

2

3}, Minxue Shen^{1

7}, Hong Liu^{1

2

3

6}

Affiliations

¹ Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
² Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Changsha, Hunan, China.
³ Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, Hunan, China.
⁴ Department of Research and Development, Beijing GAP Biotechnology Co., Ltd, Beijing, China.
⁵ Department of Health Management Center, Xiangya Hospital, Central South University, Changsha, Hunan, China.
⁶ National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China.
⁷ Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, Hunan, China.

Abstract

Introduction: Psoriasis is a chronic inflammatory disease of the skin. A few studies have shown that psoriasis is an immune-mediated disease in which multiple immune cells play crucial roles. However, the association between circulating immune cells and psoriasis remains elusive.

Methods: To explore the role of circulating immune cells in psoriasis, 361,322 individuals from the UK Biobank (UKB) and 3,971 patients with psoriasis from China were included to investigate the association between white blood cells and psoriasis via an observational study. Genome-wide association studies (GWAS) and Mendelian randomization (MR) were used to evaluate the causal relationship between circulating leukocytes and psoriasis.

Results: The risk of psoriasis increased with high levels of monocytes, neutrophils, and eosinophils (relative risks and 95% confidence intervals, respectively: 1.430 (1.291-1.584) for monocytes, 1.527 (1.379-1.692) for neutrophils, and 1.417 (1.294-1.551) for eosinophils). Upon further MR analysis, eosinophils showed a definite causal relationship with psoriasis (odds ratio of inverse-variance weighted: 1.386, 95% confidence intervals: 1.092-1.759) and a positive correlation with the psoriasis area and severity index (PASI) score (P = 6.6 × 10^-5). The roles of the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio (LMR) in psoriasis were also assessed. More than 20,000 genetic variations associated with NLR, PLR, and LMR were discovered in a GWAS analysis using the UKB data. Following adjustment for covariates in the observational study, NLR and PLR were shown to be risk factors for psoriasis, whereas LMR was a protective factor. MR results indicated that there was no causal relationship between these three indicators and psoriasis; however, NLR, PLR, and LMR correlated with the PASI score (NLR: rho = 0.244, P = 2.1 × 10^-21; PLR: rho = 0.113, P = 1.4 × 10^-5; LMR: rho = -0.242, P = 3.5×10^-21).

Discussion: Our findings revealed an important association between circulating leukocytes and psoriasis, which is instructive for the clinical practice of psoriasis treatment.

Keywords: Mendelian randomization; lymphocyte-monocyte ratio; neutrophil-lymphocyte ratio; platelet-lymphocyte ratio; psoriasis; white blood cells.

Publication types

Observational Study
Research Support, Non-U.S. Gov't

MeSH terms

Causality
Genome-Wide Association Study*
Humans
Leukocytes
Monocytes
Psoriasis* / genetics

Grants and funding

This work was supported by the National Key Research and Development Program of China (no. 2019YFE0120800 and no. 2019YFA0111600 to HL), the National Natural Science Foundation of China (no. 31800979 to HL, no. 82102472 to XZ, and no.62102455 to GXZ), the Natural Science Foundation of China for outstanding Young Scholars (no. 82022060 to HL), Science Foundation for Youths of Hunan Province (no. 2020JJ5875 to XZ), the Natural Science Foundation of Hunan Province for outstanding Young Scholars (no. 2019JJ30040 to HL), the Natural Science Foundation of Hunan Province of China (no. 2018SK2082 to HL), the Scientific Research Project of Hunan Health and Family Planning Commission (no. B20180855 to HL), China Postdoctoral Science Foundation (no. 2020M682587 to GXZ), Hunan Outstanding Postdoctoral Innovative Talents Program (no.2021RC2035 to GXZ), the Fundamental Research Funds for the Central Universities of Central South University (no. 2022ZZTS0861 to GWZ), and the Project of Intelligent Management Software for Multimodal Medical Big Data for New Generation Information Technology, Ministry of Industry and Information Technology of People’s Republic of China (TC210804V).