A multi-tissue integration of immunocytes and inflammaging biomarkers predicts biological age through LASSO-optimized modeling

Dec 8, 2025Biogerontology

Combining immune cell and inflammation markers from different tissues to predict biological age using optimized modeling

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Longevity & Aging on OpenScience ↗PubMed ↗DOI ↗

Abstract

A multi-tissue immunological signature was developed as a robust predictor of biological age based on profiling 45 immunocyte subsets and 22 serum cytokines in Sprague-Dawley rats aged 1-12 months.

Classic age-related changes included thymic involution and a decrease in peripheral T-cells.
Elevated levels of IL-1α, granulocyte colony-stimulating factor (G-CSF), and TNF-α indicated a progression of chronic inflammation with age.
The model integrating cellular and cytokine data showed superior predictive performance compared to models based on single data types.
Splenic parameters contributed significantly to the aging signature, with Th-cell expansion and Tc-cell depletion being notable biomarkers.
Peripheral blood Th-cell proportion was identified as a key predictor of biological age.

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Immunosenescence, a recognized hallmark of aging, is characterized by imbalances in immunocyte populations and a state of chronic inflammation. However, the tissue-specific dynamics of these changes and their potential as predictive biomarkers for aging remain poorly characterized. In this study, we established a multi-tissue immunological signature as a robust predictor of biological age by integrating immunocyte and cytokine profiling. Using Sprague-Dawley (SD) rats from five age groups (1-12 months), we systematically quantified 45 immunocyte subsets across peripheral blood, mesenteric lymph nodes, thymus, and spleen using flow cytometry, and profiled 22 serum cytokines/chemokines via Flexible Multi-Analyte Profiling (xMAP). Firstly, classic age-dependent shifts were observed across our rat samples, including progressive thymic involution and depletion of peripheral T-cells. Cytokine levels exhibited age-related chronic inflammation progression, marked by elevated IL-1α, granulocyte colony-stimulating factor (G-CSF), and TNF-α. To integrate these multidimensional datasets into a predictive aging metric, we employed Least Absolute Shrinkage and Selection Operator (LASSO) regression, selecting 22 biomarkers through regularization (λ = 0.111). The integrated model combining cellular and cytokine data demonstrated superior performance (training R = 0.957, validation R = 0.887), outperforming single-modality models based on immunocytes or cytokines. Notably, splenic parameters dominated the aging signature, contributing seven biomarkers representing 60% of model weight-particularly Th-cell expansion and Tc-cell depletion. Peripheral blood Th-cell proportion emerged as another key predictor. Our findings position the spleen as a critical aging hub and identify peripheral/splenic Th-cell modulation as promising therapeutic targets for age-related immune dysfunction, revealing novel mechanistic insights into aging-associated immune remodeling. 2 2

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A multi-tissue integration of immunocytes and inflammaging biomarkers predicts biological age through LASSO-optimized modeling

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