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The secretome of senescent monocytes predicts age-related clinical outcomes in humans

Nature Aging volume 5, pages 1266–1279 (2025)Cite this article

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Abstract

Cellular senescence increases with age and contributes to age-related declines and pathologies. We identified circulating biomarkers of senescence and related them to clinical traits in humans to facilitate future noninvasive assessment of individual senescence burden, and efficacy testing of novel senotherapeutics. Using a nanoparticle-based proteomic workflow, we profiled the senescence-associated secretory phenotype (SASP) in THP-1 monocytes and examined these proteins in 1,060 plasma samples from the Baltimore Longitudinal Study of Aging. Machine-learning models trained on THP-1 monocyte SASP associated SASP signatures with several age-related phenotypes in a test cohort, including body fat composition, blood lipids, inflammatory markers and mobility-related traits, among others. Notably, a subset of SASP-based predictions, including a high-impact SASP panel, were validated in InCHIANTI, an independent aging cohort. These results demonstrate the clinical relevance of the circulating SASP and identify potential senescence biomarkers that could inform future clinical studies.

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Fig. 1: Workflow for identification of SASP signatures from the aging plasma proteome.
Fig. 2: Establishing an IR-induced model of senescence in THP-1 monocytes.
Fig. 3: Monocyte SASP is Associated with Age in the BLSA.
Fig. 4: LASSO Modeling Using SASP of Clinical Traits in the BLSA.
Fig. 5: Modeling of the fat content in BLSA using SASP candidates.
Fig. 6: SASP-based associations show robust replication in the InCHIANTI aging study.
Fig. 7: A high-impact SASP panel robustly predicts multiple clinical traits.

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Data availability

All raw MS data files and associated quantitative and statistical reports, metadata and supplementary data are available on MassIVE (dataset identifier: MSV000095315). FTP download link: ftp://massive.ucsd.edu/v08/MSV000095315/. The aggregated phenotype data have been provided as source data as well as supplementary tables. The non-aggregated clinical and SomaScan data are private because the participants did not consent to unrestricted data sharing at the time of the study conducted for BLSA. To comply with patient consent and data-sharing agreements, researchers are welcome and encouraged to request use of more detailed BLSA data for scientific projects by developing a pre-analysis plan that can be submitted for approval (https://blsa.nia.nih.gov/how-apply).

Code availability

The R scripts for the core LASSO analysis described60,61 are available at https://github.com/geroproteomics/EN_Repeat/ and https://github.com/geroproteomics/EN_Test.

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Acknowledgements

This work was supported by the National Institute on Aging (NIA) Intramural Research Program, NIH. N.B. was supported by a SenNet NIH Common Fund Grant (NIA U54 AG079779, PI Elisseeff) and a Hevolution GRO grant (HF-GRO-23-1199068-44). We gratefully acknowledge L. Brick and NIDA/NIA Visual Media for assistance in figure preparation and G. Howard for editing of the manuscript.

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Author notes

  1. These authors contributed equally: Bradley Olinger, Reema Banarjee.

Authors and Affiliations

  1. Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD, USA

    Bradley Olinger, Reema Banarjee, Amit Dey, Toshiko Tanaka, Anjana Ram, Thedoe Nyunt, Mansi Shrivastava, Linna Cui, Julian Candia, Eleanor M. Simonsick, Luigi Ferrucci & Nathan Basisty

  2. Department of Biology, Johns Hopkins University, Baltimore, MD, USA

    Bradley Olinger

  3. Laboratory of Genetics and Genomics, National Institute on Aging, NIH, Baltimore, MD, USA

    Dimitrios Tsitsipatis & Myriam Gorospe

  4. Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD, USA

    Gulzar N. Daya, Zhongsheng Peng & Keenan A. Walker

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Contributions

R.B., D.T., A.R., T.N., M.S. and L.C. performed the experiments. B.O. performed the computational analysis. A.D. was involved in MS data analysis. R.B., B.O. and N.B. prepared the manuscript. T.T., G.D., Z.P. and J.C. helped in performing clinical associations. E.S., K.W. and L.F. provided the clinical data. M.G. and N.B. provided the facilities and guidance for the study.

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Correspondence to Nathan Basisty.

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Nature Aging thanks Sundeep Khosla, Yannick van Sleen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Optimization of IR-induced senescence in THP-1 monocytes.

a, Line plot indicating the cell number after exposure to different doses of IR indicates inhibition of cell proliferation up to 7 days after exposure to different doses of IR. b–e, Bar plots showing increased expression of known senescence markers over time after exposure to different doses of IR. f, Bar plots showing elevated SPiDER β-gal confirming induction of senescence in IR-treated THP-1 cells. Statistical analysis was performed using two-tailed Students’ t-test (**P < 0.01). g, Representative fluorescence microscopy images from Edu incorporation assay after exposure to different doses of IR indicating inhibition of cell proliferation on Day 7 after exposure to different doses of IR. Data are represented as mean +/− standard deviation for n number of replicates in all graphs.

Source data

Extended Data Fig. 2 Validation of Cell Identity after IR Treatment.

a, Classical SA Beta-gal staining performed 7 days after IR treatment showed increased number of beta galactosidase positive cells as indicated by the formation of blue color in senescent cells as compared to the proliferating controls. b, Brightfield microscopy images to assess any change in morphology after senescence induction shows conservation of suspension cell characteristic of monocytes 7 days after IR treatment. Flow cytometry analysis performed to confirm cell identity after IR treatment shows no increase the monocyte differentiation markers c, CD14 and d, CD11b indicating conservation of monocyte cell lineage after IR treatment.

Source data

Extended Data Fig. 3 NanoParticle Analysis.

a, Peptide expression level is compared between quantification methods of 3 proliferating and 3 senescent monocyte samples, using NanoParticles or Neat (control). Only human peptides present in at least 4 of 6 samples, showing differential expression (Pval < 0.05, t-tests) in neat samples were included (n = 428). b, Protein expression level is compared between quantification methods of 3 proliferating and 3 senescent monocyte samples, using NanoParticles or Neat (control). Only human proteins present in at least 4 of 6 samples, and showing differential expression (Pval < 0.05, t-tests) in neat samples were included (n = 132) For all figures, human mapped peptides and proteins were used. c, CV (SD / Mean log10 intensities) was calculated for all proteins present in at least 2 of 3 Senescent samples and 2 of 3 Proliferating samples in Both Neat and NP MaxRep rollup (N = 919). d, The number of identified peptides in any sample in either NP or Neat treatments, classified as either human, bovine, or mixed. e, The distribution of the peptide log10 intensities sorted by species, quantified using either the standard (Neat) or Proteograph (NP) workflows. f, The sum of the log10 intensities sorted by species, quantified using either the standard (Neat) or Proteograph (NP) workflows.

Source data

Extended Data Fig. 4 LSPs Show Age-Independent Predictive Potential.

a, Pearson linear models were constructed using covariates only (age, sex, race, and eGFR), LSPs only, or LSPs and covariates. Correlation coefficients for all models are shown by trait. b, This analysis is repeated but including fat percent as a covariate. Pearson linear models were constructed using covariates only (age, sex, race, and eGFR, and fat percent), LSPs only, or LSPs and covariates. Correlation coefficients for all models are shown by trait.

Source data

Extended Data Fig. 5 Senescence Signatures Predict Frailty.

a, LASSO modeling was used with age, sex, race, and eGFR as covariates in one model, and with CRP and IL-6 added as two additional covariates in a second model, for feature selection of proteins implicated in either BMI or walking pace. Selected features were used for trait prediction (90% train, 10% test). b, Senescence signatures were selected for a 44-component frailty index. Ten rounds of cross-validated (90% train, 10% test) trait prediction using a linear model with LSPs only was performed. Total sample sizes per trait are indicated in Table 1. c, Proteins chosen via machine learning and positively associated with frailty are shown by their association with the frailty index using linear modeling, with covariates age, sex, race, and eGFR.

Source data

Extended Data Fig. 6 LASSO-Selected Proteins by Cohort.

a, 220 monocyte SASP were detected in both the BLSA (7k SomaScan) and Inchianti (1.3k SomaScan). LASSO modeling was used for feature selection in both Inchianti and BLSA. The number of LSPs selected via LASSO for each train in the BLSA are shown, including 220 SASP and 2 covariates (age and sex). b, LSPs selected in InCHIANTI, including 220 SASP and 2 covariates (age and sex). c, Linear models were constructed using only proteins selected in both studies for each trait. Spearman’s correlation of predicted values of linear models trained on the BLSA and observed values in InCHIANTI. d, Spearman’s correlation of predicted values of linear models trained on InCHIANTI and observed values in the BLSA.

Source data

Extended Data Fig. 7 Permutation Tests and LASSO Optimized Lambda Values.

a, Principal-Component Analysis was used to condense the high-impact panel into a composite senescence burden score in the BLSA. Principal Component 1 was used to represent an eigengene for the high-impact panel. With the InCHIANTI cohort ranked from low to moderate to high senescence burden, linear trait trends reveal that positive traits HDL and Walking Pace show a negative trend, while negative traits BMI and CRP show a positive trend. b, Permutation tests comparing the predictive potential is shown for LSPs compared with randomly selected proteins. Linear models for each trait were created either using LSPs or randomly selected proteins of the same size. Models were trained on 80% of the data and used to predict the clinical traits for the remaining 20%. Randomly selected proteins models were trained and tested 100,000 times per trait and compared with the accuracy of the LSP-only model. Red dotted lines show where the Spearman’s correlation of the LSP-only model lies in relation to the bell curve for the randomly selected protein models.

Source data

Supplementary information

Reporting Summary (download PDF )

Supplementary Table 1 (download XLSX )

Replicate data from quantification of proteins from MS-based secretomics of senescent and nonsenescent monocytes.

Supplementary Table 2 (download XLSX )

log2 fold changes, P values, FDR of senescence/proliferating proteins in monocytes; age associations (Rho), P values and FDR for circulating plasma proteins in BLSA.

Supplementary Table 3 (download XLSX )

Phenotype table for InCHIANTI study.

Supplementary Table 4 (download XLSX )

ANOVA testing of nested linear models comparing covariate-only model (age, sex, race, eGFR) with ENSPs + covariates model.

Supplementary Table 5 (download XLSX )

Permutation testing comparing ENSP-only linear model predictive accuracy compared to 100,000 iterations of randomly selected proteins.

Supplementary Table 6 (download XLSX )

qPCR primers.

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Olinger, B., Banarjee, R., Dey, A. et al. The secretome of senescent monocytes predicts age-related clinical outcomes in humans. Nat Aging 5, 1266–1279 (2025). https://doi.org/10.1038/s43587-025-00877-3

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