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
Download tableSomalogic Serum Protein Levels
Download tableCBC & TBNK Measurements
Download tableTranscripional Module (TM) Membership
Download tableProtein Module (PM) Membership
Download table
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
Download tableTranscriptomic Surrogate Signature
Download tableProtein Surrogate Signature
Download tableEstimate 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.
Sample datasets:
gene matrix with EntrezGene symbols
,
protein measurements with UniProt ID
Step 1: Specify row ID type
Step 2: Upload data