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An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

Nature Aging volume 1, pages 598–615 (2021)Cite this article

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An Author Correction to this article was published on 26 July 2021

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Abstract

While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8–96 years, we developed a deep-learning method based on patterns of systemic age-related inflammation. The resulting inflammatory clock of aging (iAge) tracked with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The strongest contributor to iAge was the chemokine CXCL9, which was involved in cardiac aging, adverse cardiac remodeling and poor vascular function. Furthermore, aging endothelial cells in human and mice show loss of function, cellular senescence and hallmark phenotypes of arterial stiffness, all of which are reversed by silencing CXCL9. In conclusion, we identify a key role of CXCL9 in age-related chronic inflammation and derive a metric for multimorbidity that can be utilized for the early detection of age-related clinical phenotypes.

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Fig. 1: The inflammatory clock of aging tracks with multimorbidity, frailty and exceptional longevity.
Fig. 2: The inflammatory clock of aging correlates with immunosenescence.
Fig. 3: CXCL9 is a major contributor to iAge.
Fig. 4: CXCL9 is an important regulator of endothelial cell aging.
Fig. 5: Early cellular senescence and loss of angiogenesis capacity in iPSC-derived aging endothelia is reversed by silencing CXCL9.
Fig. 6: CXCL9 promotes a vascular stiffness gene expression signature in the aging endothelium and impairs endothelial function.
Fig. 7: CXCL9 regulates endothelial cell senescence and capillary network formation in vivo.

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Data availability

The cell subpopulation, immune protein and cell signaling data for the Stanford Aging and Vaccination studies are publicly available on ImmPort Bioinformatics Repository (http://www.immport.org/immport-open/public/home/home) under the following study IDs SDY311 (cytokines, phosphoflow assays and CyTOF surface phenotyping), SDY312 (cytokines, phosphoflow assays and flow cytometry surface phenotyping), SDY314 (flow cytometry surface phenotyping), SDY315 (cytokines, phosphoflow assays and CyTOF surface phenotyping) and SDY478 (cytokines and CyTOF surface phenotyping). The gene expression data utilized in this study to compute gene expression-iAge has been uploaded to the Gene Expression Omnibus under accession number GSE168753. Our study complies in full with the STROBE statement, STARD guidelines and GATHER statement.

Code availability

The code used for the identification of immunotypes and construction of the inflammatory clock has been deposited on GitHub (https://github.com/) and is available under: https://github.com/clingsz/GAE. For the immunological characterization of immunotypes, we used R programming (https://www.r-project.org/). The LASSO and Elastic Net regularized generalized linear models package (glmnet) for R programming can be found at: https://cran.r-project.org/web/packages/glmnet/index.html. Maximum likelihood estimation is a function of the STATS4 R package found at: https://www.rdocumentation.org/packages/stats4/versions/3.4.1.

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Acknowledgements

We thank the study participants for their time and dedication and the staff of the Stanford-LPCH Vaccine Program for recruiting participants and conducting the studies. Support for the conduct of these studies was from The Buck Institute for Research on Aging (to D.F.), the Ellison Foundation, National Institutes of Health (NIH) U19 AI057229, U19 AI090019 (to M.M.D.) and NIH/NCRR CTSA award number UL1 RR025744. This work was also supported by grants to C.F. from the EU Horizon 2020 Project PROPAG-AGEING (grant 634821), the EU JPND ADAGE project, the Ministry of Education and Science of the Russian Federation Agreement (agreement no. 075-15-2020-808). We gratefully acknowledge additional funding support from the NIH K01 HL135455, Stanford TRAM scholar award (N.S.), the Paul F. Glenn Foundation and the NIH Stanford Alzheimer’s Disease Research Center P50AG047366.

Author information

Author notes

  1. These authors contributed equally: Nazish Sayed, Yingxiang Huang.

Authors and Affiliations

  1. Stanford 1000 Immunomes Project, Stanford University School of Medicine, Stanford, CA, USA

    Nazish Sayed, Mark M. Davis & David Furman

  2. Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA

    Nazish Sayed, François Haddad & Joseph C. Wu

  3. Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA

    Nazish Sayed

  4. Buck Artificial Intelligence Platform, the Buck Institute for Research on Aging, Novato, CA, USA

    Yingxiang Huang, Khiem Nguyen, Anissa P. Grawe & David Furman

  5. Edifice Health Inc., San Mateo, CA, USA

    Zuzana Krejciova-Rajaniemi, Vladimir Jojic & David Furman

  6. Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA

    Tianxiang Gao

  7. Department of Statistics and Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA

    Robert Tibshirani & Trevor Hastie

  8. Faculty of Medicine, Technion, Israel Institute of Technology, Haifa, Israel

    Ayelet Alpert & Shai S. Shen-Orr

  9. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Lu Cui

  10. Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium

    Tatiana Kuznetsova

  11. Human Immune Monitoring Center, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA

    Yael Rosenberg-Hasson & Holden T. Maecker

  12. Interdepartmental Centre L. Galvani (CIG), Alma Mater Studiorum, University of Bologna, Bologna, Italy

    Rita Ostan

  13. Department of Experimental Clinical and Biomedical Sciences, Mario Serio, University of Florence, Florence, Italy

    Daniela Monti

  14. Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA

    Benoit Lehallier & Tony Wyss-Coray

  15. Division of Pediatric Infectious Diseases, Stanford University School of Medicine, Stanford, CA, USA

    Cornelia L. Dekker

  16. Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA

    Cornelia L. Dekker & Mark M. Davis

  17. Paul F. Glenn Center for Aging Research, Stanford University School of Medicine, Stanford, CA, USA

    Tony Wyss-Coray

  18. Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky University, Nizhny, Russia

    Claudio Franceschi

  19. Calico Life Sciences L.L.C, South San Francisco, CA, USA

    Vladimir Jojic

  20. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    José G. Montoya

  21. Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Joseph C. Wu

  22. Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA

    Mark M. Davis

  23. Austral Institute for Applied Artificial Intelligence, Institute for Research in Translational Medicine (IIMT), Universidad Austral, CONICET, Pilar, Buenos Aires, Argentina

    David Furman

Authors
  1. Nazish Sayed
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Contributions

D.F. conceived, conceptualized and designed the study; coordinated the biological analysis of samples and contributed to the analysis of experimental data and interpretation of the results. D.F., M.M.D., C.L.D. and J.G.M. conceived the study, provided guidance and funding. J.C.W. and F.H. provided guidance for the experimental work. N.S. and L.C. conducted in vitro and in vivo mice and EC experiments. D.F., B.L., Y.H., K.N., A.A. and T.G. conducted deep-learning and statistical analyses. S.S.O., V.J., R.T. and T.H. provided guidance for the in silico analysis of experimental data. N.S., Y.R.-H., F.H. and H.T.M. carried out or supervised the human data measurements; T.K., A.G. and Z.K.-R. helped to edit the manuscript. C.F., T.W.-C., B.L., R.O., D.M. collaborated with the study in centenarians. N.S., Y.H. and D.F. wrote the manuscript. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to David Furman.

Ethics declarations

Competing interests

D.F. and M.M.D. are co-founders of Edifice Health, a company that utilizes iAge. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Aging thanks M. Luisa Iruela-Arispe and the other, anonymous, reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 1000 Immunomes Study design: systematic analysis of immune systems via ‘OMICS’ approaches.

The Stanford 1000 Immunomes Project consist of 1001 ambulatory subjects age 8 to 96 (34% males, 66% females) recruited during the years 2007 to 2016 for a longitudinal study of aging and vaccination, and for an independent study of chronic fatigue syndrome from which only healthy controls were included. For all samples of the Stanford 1KIP, deep immune phenotyping was conducted at the Stanford Human Immune Monitoring Center, where peripheral blood specimens were isolated and analyzed using standard procedures. Peripheral blood samples were obtained by venipuncture and peripheral blood mononuclear cells or whole blood samples were used for determination of cellular phenotypes and frequencies (N = 935) and for investigation of in vitro cellular responses to a variety of cytokine stimulations (N = 818); serum samples were obtained and used for protein content determination (including a total of 50 cytokines, chemokines and growth factors) (N = 1001). Clinical characterization was assessed via clinical questionnaire in a total of 902 subjects who completed the full set of 53 clinical items. From a total of 97 healthy young and older adults, comprehensive cardiovascular phenotyping was also conducted

Extended Data Fig. 2

Age distribution of the Stanford 1KIP cohort.

Source data

Extended Data Fig. 3 Estimation of the GAE code length and accuracy of age prediction.

We used 5-fold cross-validation to identify the best code length, among lengths from 1 to 10. We selected the length of code k, whose performance was not statistically significantly worse than that of longer codes (paired t-test p-value > 0.05). Within each fold we performed nested 3-fold cross-validation to select hyper-parameters (depth, weight decay and guidance-ratio). In our experiment, the best code length is 5 (a) as adding one more code (6) does not significantly improve the total loss (p = 0.18). After obtaining the best code length as 5, we used the 5-fold-cross-validation to select the best hyper-parameter setting (depth = 2, guidance-ratio = 0.2, L2 = 0.001) on all GAE with code length 5. Finally, we trained the GAE on the whole dataset with the selected best hyper-parameter setting and obtained the predictive function as the inflammatory clock predictor. GAE was compared to other machine learning methods such as autoencoder, neural networks, PCA, and RAW in (b). For the neural network, 2 fully connected layers with 5 nodes in each layer and tanh activation function were used. For PCA and RAW, we used elastic net to predict age. The GAE method outperforms linear methods for protein data reconstruction and prediction of chronological age (b). In (c), we found that the predictive performance of gradient boosting decision tree (GBDT) has similar performance as PCA. We conclude that GAE is superior to traditional machine learning methods.

Source data

Extended Data Fig. 4 Elimination of batch effect for serum immune protein data.

. Immune protein data from serum samples were subjected to normalization and batch correction procedures (See Methods) to ensure data from different sources can be combined and used as a whole. a, Spearman correlation between immune protein features and batch ID shows a strong dependency of data source on top 4 components (raw data, green line), which reaches a steady state after component 5. Data normalization and batch correction removes batch effect as indicated by lower mean absolute Spearman correlation between all features and batch id (blue line), which indicates impossibility to distinguish sample source from corrected data. b, Upper panel: immune protein expression heatmap of uncorrected data, Lower panel: immune protein expression heatmap of corrected data. The two batches come from two study cohorts, the Chronic Fatigue Syndrome Study (CFS) and Aging and vaccination study cohort (Flu).

Source data

Extended Data Fig. 5 iAge predictive of multi-mordity.

To select for predictors of comorbidity without bias, based on available data for all 902 subjects while controlling for the age effect, age-adjusted cross-validation was performed (a). By applying differential penalty values for each regressor, age variable is ‘forced in’, while imposing a stringent penalty (the lasso penalty) to all other features, so that selected variables do not correlate with age. A Mean Absolute Error (MAE) for the prediction of comorbidity of 0.41 is observed (b). Eighteen features are selected including inflammatory clock, high cholesterol and BMI (c) and immune parameters such as total CD8 (+) T cells, plasmablasts and transitional B cells (negative predictors) and IgD+CD27- and IgD-CD27- B cells, effector CD8 (+) T cells, total lymphocytes and monocytes, and central memory T cells (positive predictors) (d)

Source data

Extended Data Fig. 6 Univariate Regression between Age and CXCL9.

Significant correlation between age and CXCL9 using univariate regression analysis. We used linear regression where CXCL9 were regressed onto age. Correlation coefficient (R2) and p-value of F-test of overall significance are reported.

Source data

Extended Data Fig. 7 Luminex data for cardiovascular validation cohort.

In a validation study, 97 healthy adults (aged 25–90) well matched for cardiovascular risk factors, were selected from a total of 151 recruited subjects. Immune protein analysis was conducted in samples from these subjects. CXCL9, HGF, CXCL1, and LIF were found to change in the same direction in both the Stanford 1KIP and the validation cohort.

Source data

Extended Data Fig. 8 Human blood endothelial progenitor cells and mice endothelial cells.

a, Representative images of human blood progenitor endothelial cells from young (left) and old (right) individuals. b, Representative images of capillary-like networks show impaired tube formation by human BECs of old individuals compared to young. To further confirm the potential contribution of CXCL9 in cardiovascular aging, we assessed its expression in young (3–4 month) and old mice (2 yr.) endothelial cells (c). ECs isolated from old mice showed higher levels of CXCL9 (P value = 0.023) (d), while at the same time showed impaired EC function as evident by decreased tube formation (P value = 0.042) (a, f). Figure S8: All data represented as mean ± SEM, n = 3, *P < 0.05. Statistical analyses were performed using Student’s t-test (paired). Scale bar: 50 μm.

Extended Data Fig. 9 Expression of CXCR3 RNA in different tissue types.

CXCR3 was not expressed in iPSC induced cardiomyocytes (iPSC-CM), Fibroblast, or iPSC. However, it is highly expressed in iPSC induced endothelial cells and Human Umbilical Vein Endothelial Cells (HUVEC). All data represented as mean ± SEM.

Source data

Extended Data Fig. 10 Validation of the effects of CXCL9 on endothelial function.

Representative images of capillary-like networks from scramble- and CXCL9-KD hiPSC-ECs show that CXCL9-KD hiPSC-ECs retain their capacity to form tubes even at later passages when compared to scramble that showed impaired tube formation towards later passages of hiPSC-ECs. Scale bar: 50 μm. Experiment was repeated 3 times.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, and Tables 1–3.

Reporting Summary

Source data

Source Data Fig. 1

Age, number of comorbidities, frailty source data.

Source Data Fig. 2

CyTOF, cytokine source data.

Source Data Fig. 3

Cytokine data,CXCL9, arterial stiffness, PWV raw data.

Source Data Fig. 4

CXCL9 expression in blood EC (relative fold change). Tube formation in blood EC (number of tubes). NO production (μmol l−1). LDL uptake (mg protein h−1). Tube formation in iPSC-EC (number of tubes). LDL uptake (mg protein h−1). NO production (μmol l−1).

Source Data Fig. 5

iPSC-KO and Scramble gene expression data. Count data of specific pathways. Enrichment scores of pathways.

Source Data Fig. 6

iPSC-KO and Scramble gene expression data. Percentage relaxation of aortas to acetylcholine.

Source Data Fig. 7

Cell count for proliferation assay. SA-b-Gal activity. count for CD31+ capillaries.

Source Data Extended Data Fig. 2

Demographics source data.

Source Data Extended Data Fig. 3

Raw data used in predictive models.

Source Data Extended Data Fig. 4

Batch corrected before and after cytokine expression.

Source Data Extended Data Fig. 5

CyTOF source data.

Source Data Extended Data Fig. 6

Age and CXCL9 source data.

Source Data Extended Data Fig. 7

Raw data of cytokine expression in validation dataset.

Source Data CXCL9 expression in mouse EC (relative fold change).

Tube formation in mouse ECs (number of tubes).

Source Data Extended Data Fig. 9

Expression of CXCR3 RNA source data.

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Sayed, N., Huang, Y., Nguyen, K. et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nat Aging 1, 598–615 (2021). https://doi.org/10.1038/s43587-021-00082-y

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