A Unified Metric of Human Immune Health

Rachel Sparks, Nicholas Rachmaninoff, William W. Lau, Dylan C. Hirsch, Neha Bansal, ..., John S. Tsang. Nature Medicine, 2024.

Abstract

Immunological health has been challenging to characterize but could be defined as the absence of immune pathology. While shared features of some immune diseases and the concept of immunologic resilience based on age-independent adaptation to antigenic stimulation have been developed, general metrics of immune health and its utility for assessing clinically healthy individuals remain ill defined. Here we integrated transcriptomics, serum protein, peripheral immune cell frequency and clinical data from 228 patients with 22 monogenic conditions impacting key immunological pathways together with 42 age- and sex-matched healthy controls. Despite the high penetrance of monogenic lesions, differences between individuals in diverse immune parameters tended to dominate over those attributable to disease conditions or medication use. Unsupervised or supervised machine learning independently identified a score that distinguished healthy participants from patients with monogenic diseases, thus suggesting a quantitative immune health metric (IHM). In ten independent datasets, the IHM discriminated healthy from polygenic autoimmune and inflammatory disease states, marked aging in clinically healthy individuals, tracked disease activities and treatment responses in both immunological and nonimmunological diseases, and predicted age-dependent antibody responses to immunizations with different vaccines. This discriminatory power goes beyond that of the classical inflammatory biomarkers C-reactive protein and interleukin-6. Thus, deviations from health in diverse conditions, including aging, have shared systemic immune consequences, and we provide a web platform for calculating the IHM for other datasets, which could empower precision medicine.

Subject Demographics

Number of samples

Due to privacy consideration and patient consent restrictions, only aggregated mean expression profiles and clinical measurements by condition are shown here. Conditions with only one primary subject/sample are excluded. Subject-level data can be requested through dbGaP using the study accession number phs002732 .

Microarray Gene Expressions

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Somalogic Serum Protein Levels

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CBC & TBNK Measurements

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Transcripional Module (TM) Membership

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Protein Module (PM) Membership

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Differentially expressed (FDR < 0.05) markers for each condition compared to healthy controls are listed below. Click here to download all of the markers as a .csv file.

CBC & TBNK Markers

Transcripional Module (TM) Markers

Protein Module (PM) Markers

Module Gene Markers

Module Protein Markers

IHM Components

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Transcriptomic Surrogate Signature

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Protein Surrogate Signature

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Estimate the IHM surrogate signature scores for your own samples by uploading their expression profiles in .csv or .tsv format (maximum size: 10 MB), with genes or proteins listed in the rows and samples in the columns. Optionally, sample grouping can be specified by adding a row with 'Group' in the gene/protein label column (see sample protein dataset as reference). Please bear in mind that the IHM signatures were developed using gene expression data from whole blood and serum proteins. Exercise caution when interpreting the results, particularly if your data originate from other tissue types.
Step 1: Specify row ID type
Step 2: Upload data