| Title: | Single-cell analyses of global cohorts outline determinants of immune aging and link the ratio of GZMK+ to GZMB+ Tem cells to health trajectories |
| Journal: | Immunity |
| Published: | 9 Oct 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42854692/ |
| DOI: | https://doi.org/10.1016/j.immuni.2026.09.012 |
Publication 21565
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Abstract
Human immune aging is heterogeneous, with immune responses becoming increasingly variable over the lifespan. Here, we integrated seven large-scale public single-cell peripheral blood mononuclear cell (PBMC) datasets and a newly generated cohort, encompassing 2,609 ostensibly healthy individuals across diverse ages, ancestries, and biological sexes. We identified both conserved and cohort-associated age-associated remodeling across 59 immune populations, together with sex- and ancestry-dependent immune differences. CD8+ T cells emerged as major drivers of aging heterogeneity, with the GZMK+/GZMB+ effector memory ratio distinguishing healthy and pathology-associated aging trajectories. This ratio reflected immune remodeling driven by chronic viral exposure, including CMV, and disease-associated immune dysregulation, with the dominant driver varying across populations. Proteomic profiling across four cohorts revealed a Tem GZMB+ plasma signature associated with inflammaging, poor self-reported health, all-cause mortality, and increased risk of immune-related, metabolic, and cardiovascular diseases. Together, these findings define clinically relevant immune determinants for monitoring and guiding healthy aging interventions.</p>
27 Authors
- Marina Terekhova
- Maksim Kleverov
- Thomas Francis
- Dmitrii Usoltsev
- Pavla Bohacova
- Laura Arthur
- Petr Tsurinov
- Denis Kleverov
- Luke Trinity
- Kamila Husarcikova
- Ivan Molotkov
- Titas Grabauskas
- Amir Fathi
- Eloise Milbourn
- Esme Newton
- Leah Siegel
- Adnaan Ghanchi
- Urika Tse
- Ross D Pollock
- Niharika A Duggal
- Janet M Lord
- Norman R Lazarus
- Ali H Ellebedy
- Duygu Ucar
- Stephen D R Harridge
- Mykyta Artomov
- Maxim N Artyomov
1 Application
| Application ID | Title |
|---|---|
| 88907 | A model for health risk prediction based on inherited DNA variation and clinical data |
Enabling scientific discoveries that improve human health