SEARCHMETA-ANALYSISepidemiology, immunology

meta-analysis·epidemiology, immunology, public health, biomedical research, meta-analysis·PMC10388560

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

Genome Medicine · 18 authors, 7 centres

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TL;DR

The study developed and validated a DNA methylation reference matrix for 12 immune-cell types in human blood.

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The study developed and validated a DNA methylation reference matrix for 12 immune-cell types in human blood. This matrix was applied to a meta-analysis of 23,053 blood samples from 22 cohorts to map associations between immune-cell fractions and common phenotypes. A machine learning model using only immune-cell fractions predicted mortality with a C-index of 0.69, which improved to 0.83 when clinical variables were included. The study's implications are limited to the identification of associations in a large, observational dataset.

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meta-analysis

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epidemiology

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ORIGINAL PAPER

PMC10388560

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

Genome Medicine

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ABSTRACT

Background Changes 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. Methods Here 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. Results Our 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. Conclusions This 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. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-023-01211-5.

CITATION EXPORT

Qi Luo, Varun B. Dwaraka, Qingwen Chen et al.. A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes. Genome Medicine. (2023). https://doi.org/10.1186/s13073-023-01211-5

AUTHORS

18 · 7 CENTRES

Qi Luo, Varun B. Dwaraka, Qingwen Chen, Huige Tong, Tianyu Zhu, Kirsten Seale, Joseph M. Raffaele, Shijie C. Zheng, Tavis L. Mendez, Yulu Chen, Natalia Carreras, Sofina Begum, Kevin Mendez, Sarah Voisin, Nir Eynon, Jessica A. Lasky-Su, Ryan Smith, Andrew E. Teschendorff

1grid.507675.6CAS 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, Shanghai, 200031 China
2TruDiagnostics, 881 Corporate Dr., Lexington, KY 40503 USA
3grid.38142.3c000000041936754XChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115 USA
4grid.1019.90000 0001 0396 9544Institute for Health and Sport (iHeS), Victoria University, Footscray, VIC 3011 Australia
5PhysioAge LLC, 30 Central Park South / Suite 8A, New York, NY 10019 USA
6grid.21107.350000 0001 2171 9311Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
7grid.484852.70000 0004 0528 0478Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800 Australia