A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes

Genome Medicine

Research

Nir Eynon¹  Andrew E. Teschendorff²  Qi Luo²  Huige Tong²  Tianyu Zhu²  Sofina Begum³  Yulu Chen³  Kevin Mendez³  Qingwen Chen³  Jessica A. Lasky-Su³  Shijie C. Zheng⁴  Kirsten Seale⁵  Sarah Voisin⁵  Joseph M. Raffaele⁶  Ryan Smith⁷  Natalia Carreras⁷  Varun B. Dwaraka⁷  Tavis L. Mendez⁷ 

[1] Australian Regenerative Medicine Institute, Monash University, 3800, Clayton, VIC, Australia;CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, 200031, Shanghai, China;Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, 02115, Boston, MA, USA;Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA;Institute for Health and Sport (iHeS), Victoria University, 3011, Footscray, VIC, Australia;PhysioAge LLC, 30 Central Park South / Suite 8A, 10019, New York, NY, USA;TruDiagnostics, 881 Corporate Dr., 40503, Lexington, KY, USA;

关键词: Immune system;    Disease risk factors;    Aging;    Sex;    Obesity;    Epigenetic clocks;    Mortality;    Covid-19;    Cancer;   

DOI  :  10.1186/s13073-023-01211-5

 received in 2023-03-08, accepted in 2023-07-10,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundChanges in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition.MethodsHere we derive a DNA methylation reference matrix for 12 immune-cell types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix, we perform a directional Stouffer and fixed effects meta-analysis comprising 23,053 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes. In a separate cohort of 4386 blood samples, we assess associations between immune-cell fractions and health outcomes.ResultsOur meta-analysis reveals many associations of cell-type fractions with age, sex, smoking and obesity, many of which we validate with single-cell RNA sequencing. We discover that naïve and regulatory T-cell subsets are higher in women compared to men, while the reverse is true for monocyte, natural killer, basophil, and eosinophil fractions. Decreased natural killer counts associated with smoking, obesity, and stress levels, while an increased count correlates with exercise and sleep. Analysis of health outcomes revealed that increased naïve CD4 + T-cell and N-cell fractions associated with a reduced risk of all-cause mortality independently of all major epidemiological risk factors and baseline co-morbidity. A machine learning predictor built only with immune-cell fractions achieved a C-index value for all-cause mortality of 0.69 (95%CI 0.67–0.72), which increased to 0.83 (0.80–0.86) upon inclusion of epidemiological risk factors and baseline co-morbidity.ConclusionsThis work contributes an extensively validated high-resolution DNAm reference matrix for blood, which is made freely available, and uses it to generate a comprehensive map of associations between immune-cell fractions and common phenotypes, including health outcomes.

【 授权许可】

CC BY   
© The Author(s) 2023

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