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Nonuniversality of inflammaging across human populations

Nature Aging volume 5, pages 1471–1480 (2025) Cite this article

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Abstract

Inflammaging, an age-associated increase in chronic inflammation, is considered a hallmark of aging. However, there is no consensus approach to measuring inflammaging based on circulating cytokines. Here we assessed whether an inflammaging axis detected in the Italian InCHIANTI dataset comprising 19 cytokines could be generalized to a different industrialized population (Singapore Longitudinal Aging Study) or to two indigenous, nonindustrialized populations: the Tsimane from the Bolivian Amazon and the Orang Asli from Peninsular Malaysia. We assessed cytokine axis structure similarity and whether the inflammaging axis replicating the InCHIANTI result increased with age or was associated with health outcomes. The Singapore Longitudinal Aging Study was similar to InCHIANTI except for IL-6 and IL-1RA. The Tsimane and Orang Asli showed markedly different axis structures with little to no association with age and no association with age-related diseases. Inflammaging, as measured in this manner in these cohorts, thus appears to be largely a byproduct of industrialized lifestyles, with major variation across environments and populations.

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Fig. 1: Study design.
The alternative text for this image may have been generated using AI.
Fig. 2: Key factors replicate within, but not across, datasets.
The alternative text for this image may have been generated using AI.
Fig. 3: Spearman correlations among key inflammatory cytokines in each dataset.
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Fig. 4: Associations of the inflammaging factor with age and health outcomes.
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Data availability

The InCHIANTI, SLAS, THLHP and OA HeLP datasets used in this study are not publicly available due to privacy and ethical restrictions on human health data. Access can be requested from the respective data owners. Both THLHP and OA HeLP adhere to the CARE Principles for Indigenous Data Governance and the FAIR Guiding Principles, ensuring participant sovereignty and ethical data use. Requests for individual-level data require formal applications, with considerations for privacy and community benefits. Requests for SLAS data should be directed to J.P.S.Y. (yeongps@imcb.a-star.edu.sg) and R.H. (pcmrhcm@nus.edu.sg). The cohort datasets are available via https://www.nia.nih.gov/inchianti-study#access (InCHIANTI), https://tsimane.anth.ucsb.edu/data.html (THLHP) and orangaslihealth.org (OA HeLP).

Code availability

All the scripts developed for this study are available via GitHub at https://github.com/cohenaginglab/InflammagingDiversity.

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Acknowledgements

This work was financially supported by the Impetus program. J.S. acknowledges financial support from the French National Research Agency (ANR) under the Investments for the Future (Investissements d’Avenir) program, grant ANR-17-EURE-0010. J.H. received funding support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project ID 499552394 (SFB 1597/1) and grant HE9198/1-1. This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging. OA HeLP data collection was supported by the National Science Foundation (grant no. BCS-2142090). The funders were not involved in the study design, data collection and analysis, interpretation of results, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Research Center on Aging, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Quebec, Canada

    Maximilien Franck, Tamàs Fülöp & Alan A. Cohen

  2. Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA

    Kamaryn T. Tanner, Allison E. Aiello & Alan A. Cohen

  3. Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA

    Robert L. Tennyson & Amanda J. Lea

  4. Department of Sociology, University of Utah, Salt Lake City, UT, USA

    Robert L. Tennyson

  5. Population Health and Optimal Health Practices Branch, CHU de Québec Research Center, Quebec, Quebec, Canada

    Camille Daunizeau

  6. Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA

    Luigi Ferrucci

  7. Geriatric Unit, Azienda Sanitaria Firenze, Florence, Italy

    Stefania Bandinelli

  8. Center for Evolution and Medicine, School of Human Evolution and Social Change, Institute of Human Origins, Arizona State University, Tempe, AZ, USA

    Benjamin C. Trumble & Jacob E. Aronoff

  9. Economic Science Institute, Argyros College of Business and Economics, Chapman University, Orange, CA, USA

    Hillard S. Kaplan

  10. Department of Social and Behavioral Sciences, Toulouse School of Economics, Institute for Advanced Study in Toulouse, Université Toulouse Capitole, Toulouse, France

    Jonathan Stieglitz

  11. Department of Anthropology, University of Utah, Salt Lake City, UT, USA

    Thomas S. Kraft

  12. Department of Anthropology and Archaeology, University of Calgary, Calgary, Alberta, Canada

    Vivek V. Venkataraman

  13. Department of Anthropology, University of New Mexico, Albuquerque, NM, USA

    Ian J. Wallace

  14. Department of Parasitology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia

    Yvonne A. L. Lim

  15. Department of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia

    Kee Seong Ng

  16. Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore

    Joe Poh Sheng Yeong & Xinru Lim

  17. Department of Anatomical Pathology, Singapore General Hospital, Singapore, Republic of Singapore

    Joe Poh Sheng Yeong

  18. Department of Microbiology, Singapore General Hospital, Singapore, Republic of Singapore

    Joe Poh Sheng Yeong

  19. Duke–NUS Medical School, Singapore, Republic of Singapore

    Joe Poh Sheng Yeong

  20. Division of Functional Near Infrared Spectroscopy, Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore, Republic of Singapore

    Roger Ho

  21. Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany

    Ameneh Mehrjerd, Eleftheria G. Charalambous & Johannes Hertel

  22. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA

    Allison E. Aiello

  23. Institute of Immunology, University of Tübingen, Tübingen, Germany

    Graham Pawelec

  24. Health Sciences North Research Institute, Sudbury, Ontario, Canada

    Graham Pawelec

  25. Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod, Russia

    Claudio Franceschi

  26. German Center for Cardiovascular Diseases, Partner Site Greifswald, Greifswald, Germany

    Johannes Hertel

  27. Univ. Bordeaux, CNRS, Immuno ConcEpT, UMR 5164, Bordeaux, France

    Maël Lemoine

  28. Department of Anthropology, University of California Santa Barbara, Santa Barbara, CA, USA

    Michael Gurven

  29. Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA

    Alan A. Cohen

Authors
  1. Maximilien Franck
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  2. Kamaryn T. Tanner
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  4. Camille Daunizeau
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  6. Stefania Bandinelli
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  7. Benjamin C. Trumble
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  8. Hillard S. Kaplan
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Contributions

M.F. and A.A.C. designed the study and wrote the manuscript. M.F., K.T.T., R.L.T., C.D., A.M. and E.G.C. conducted data analysis. L.F., S.B., M.G., B.C.T., H.S.K., J.S., J.E.A., T.S.K., A.J.L., V.V.V., I.J.W., Y.A.L.L., K.S.N., J.P.S.Y., X.L. and R.H. provided access to, and help interpreting, the relevant datasets. All authors contributed to interpreting the data and editing the manuscript. A.A.C., M.G., T.F., M.L. and J.H. conceived of and obtained funding for the larger project of which this study is a part.

Corresponding authors

Correspondence to Maximilien Franck or Alan A. Cohen.

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Competing interests

A.A.C. is founder and CEO at Oken Health. The other authors declare no competing interests.

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Peer review information

Nature Aging thanks Nicole Kleinstreuer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Table 1 Associations of the InCHIANTI-derived inflammaging factor with BMI and smoking
Full size table

Extended Data Fig. 1 Axis structure of cytokines run with PCA rather than FA.

Biplots show the associations of cytokines with the first two axes run in different datasets and with different sets of cytokines. The colors of each arrow are reproduced from panel A across panels to facilitate comparison, with red indicating strong association with the first factor in panel A, purple with the second factor, and black with the origin (no association). A color pattern similar to panel A thus indicates a similar axis structure. The first column (panels A, B, E, and H) shows analyses run in InCHIANTI, but with subsets of cytokines that overlap with those available, respectively, in the full InCHIANTI set (A), SLAS (B), THLHP (E), and OA HeLP (H). The second column (panels C, F, and I) shows the axis structure of the same cytokines as in column 1 (panels B, E, and H, respectively), except run in the target datasets, not InCHIANTI. The third column (panels D, G, and J) replicates the analysis in the second column but adding in the cytokines not measured in InCHIANTI (light grey).

Extended Data Fig. 2 Properties of the inflammaging axis derived in InCHIANTI.

A. Violin plots of the distributions of the inflammaging factors scores calculated in each respective population, based on the factor loadings derived from InCHIANTI (see Methods). Note that it is not clear whether these scores are directly comparable given the different assays used for each dataset; nonetheless, high scores in THLHP is consistent with previous reporting of high levels of inflammatory and immune markers in the Tsimane. Number of biological replicates: InCHIANTI (1041), SLAS (941), THLHP (536), OA HeLP (358). The white circle is the median and the surrounding rectangle indicates the 25th-75th percentile or interquartile range (IQR). Vertical black lines (whiskers) show 1.5*IQR, and the plots extend to the maxima/minima of the smoothed kernel density estimate of the data distribution. B. Pairwise correlations among the scores of the inflammaging axis in InCHIANTI when derived from different sets of cytokines: the original set of 19, the 16 that overlap with SLAS, the eight that overlap with THLHP, and the eight that overlap with OA HeLP. All are significant at p < 0.0001.

Extended Data Fig. 3 Sex-specific biplots of factor scores in each dataset.

Biplots show the associations of cytokines with the first two factors run in different datasets and by sex with the full set of cytokines available in that dataset. The colors of each arrow are reproduced from Fig. 2a across panels to facilitate comparison, with red indicating strong association with the first factor in Fig. 2a, purple with the second factor, and black with the origin (no association). Similar color patterns between female subjects (A, C, E, and G) and male subjects (B, D, F, and H) thus indicates a similar axis structure. Note that the structure is nearly identical for InCHIANTI (A, B) and SLAS (C, D), quite similar for THLHP (E, F), and somewhat more distinct for OA HeLP (G, H), where the sample size is smaller and structure is estimated with greater error.

Extended Data Fig. 4 Replication of the SLAS factor structure across populations.

Biplots show the associations of cytokines with the first two factors run in different datasets and with different sets of cytokines. The colors of each arrow are reproduced from panel A across panels to facilitate comparison, with red indicating strong association with the first factor in panel A, purple with the second factor, and black with the origin (no association). A color pattern similar to panel A thus indicates a similar axis structure. The first column (panels A, B, E, and H) shows analyses run in SLAS, but with subsets of cytokines that overlap with those available, respectively, in the full SLAS set (A), InCHIANTI (B), THLHP (E), and OA HeLP (H). The second column (panels C, F, and I) shows the axis structure of the same cytokines as in column 1 (panels B, E, and H, respectively), except run in the target datasets, not SLAS. The third column (panels D, G, and J) replicates the analysis in the second column but adding in the cytokines not measured in SLAS (light grey). Correlation coefficients between columns 1 and 2 show the Spearman correlations of the loadings between the indicated panels.

Extended Data Fig. 5 Replication of the THLHP factor structure across populations.

Biplots show the associations of cytokines with the first two factors run in different datasets and with different sets of cytokines. The colors of each arrow are reproduced from panel A across panels to facilitate comparison, with red indicating strong association with the first factor in panel A, purple with the second factor, and black with the origin (no association). A color pattern similar to panel A thus indicates a similar axis structure. The first column (panels A, B, E, and H) shows analyses run in THLHP, but with subsets of cytokines that overlap with those available, respectively, in the full THLHP set (A), InCHIANTI (B), SLAS (E), and OA HeLP (H). The second column (panels C, F, and I) shows the axis structure of the same cytokines as in column 1 (panels B, E, and H, respectively), except run in the target datasets, not THLHP. The third column (panels D, G, and J) replicates the analysis in the second column but adding in the cytokines not measured in THLHP (light grey). Correlation coefficients between columns 1 and 2 show the Spearman correlations of the loadings between the indicated panels.

Extended Data Fig. 6 Replication of the OA HeLP factor structure across populations.

Biplots show the associations of cytokines with the first two factors run in different datasets and with different sets of cytokines. The colors of each arrow are reproduced from panel A across panels to facilitate comparison, with red indicating strong association with the first factor in panel A, purple with the second factor, and black with the origin (no association). A color pattern similar to panel A thus indicates a similar axis structure. The first column (panels A, B, E, and H) shows analyses run in OA HeLP, but with subsets of cytokines that overlap with those available, respectively, in the full OA HeLP set (A), InCHIANTI (B), SLAS (E), and THLHP (H). The second column (panels C, F, and I) shows the axis structure of the same cytokines as in column 1 (panels B, E, and H, respectively), except run in the target datasets, not OA HeLP. The third column (panels D, G, and J) replicates the analysis in the second column but adding in the cytokines not measured in OA HeLP (light grey). Correlation coefficients between columns 1 and 2 show the Spearman correlations of the loadings between the indicated panels.

Extended Data Fig. 7 Associations of the first SLAS factor with age and health outcomes.

A. Associations between the SLAS factors derived in Extended Data Figs. 4A, B, E, and H with age, when applied in the respective datasets. The shaded region is a 95% confidence interval for the best-fit lines shown. *: p < 0.05; **: p < 0.01; ***: p < 0.001 based on a 2-sided Wald test. B. Comparison of prediction of health outcomes in InCHIANTI and SLAS using the first SLAS factor derived in Extended Data Fig. 4B. C. Comparison of prediction of health outcomes in SLAS and THLHP using the first SLAS factor derived in Extended Data Fig. 4E. Not all outcomes are available in THLHP. D. Comparison of prediction of health outcomes in SLAS and OA HeLP using the first SLAS factor derived in Extended Data Fig. 4H. Not all outcomes are available in OA HeLP. For panels B–D, the point indicates the estimated odds ratio and the line the 95% confidence interval; the vertical line indicates no effect. *: p < 0.05; **: p < 0.01; ***: p < 0.001 based on a 2-sided Wald test. Odds ratios are scaled per unit factor score. Because factor score ranges are 6+ within populations (cf. Extended Data Fig. 2A), an odds ratio of 1.5 translates into at least 1.56 = 11.4 across the range of values in the populations. Note that different ORs for SLAS in panels B, C, and D are due to different versions of the factor using overlapping biomarker sets with the other datasets. Number of biological replicates: InCHIANTI (1041), SLAS (941), THLHP (536), OA HeLP (358). Supplemental Table S1 details availability of health outcomes by dataset.

Extended Data Fig. 8 Associations of the first THLHP factor with age and health outcomes.

A. Associations between the THLHP factors derived in Extended Data Fig. 5A, B, E, and H with age, when applied in the respective datasets. The shaded region is a 95% confidence interval for the best-fit lines shown. *: p < 0.05; **: p < 0.01; ***: p < 0.001 based on a 2-sided Wald test. B. Comparison of prediction of health outcomes in InCHIANTI and THLHP using the first THLHP factor derived in Extended Data Fig. 5B. C. Comparison of prediction of health outcomes in SLAS and THLHP using the first THLHP factor derived in Extended Data Fig. 5E. Not all outcomes are available in THLHP. D. Comparison of prediction of health outcomes in THLHP and OA HeLP using the first THLHP factor derived in Extended Data Fig. 5H. Not all outcomes are available in OA HeLP. For panels B–D, the point indicates the estimated odds ratio and the line the 95% confidence interval; the vertical line indicates no effect. *: p < 0.05 based on a 2-sided Wald test. Odds ratios are scaled per unit factor score. Because factor score ranges are 6+ within populations (cf. Extended Data Fig. 2A), an odds ratio of 1.5 translates into at least 1.56 = 11.4 across the range of values in the populations. Note that different ORs for THLHP in panels B, C, and D are due to different versions of the factor using overlapping biomarker sets with the other datasets. Number of biological replicates: InCHIANTI (1041), SLAS (941), THLHP (536), OA HeLP (358). Supplemental Table S1 details availability of health outcomes by dataset.

Extended Data Fig. 9 Associations of the first OA HeLP factor with age and health outcomes.

A. Associations between the OA HeLP factors derived in Extended Data Fig. 6A, B, E, and H with age, when applied in the respective datasets. The shaded region is a 95% confidence interval for the best-fit lines shown. *: p < 0.05; **: p < 0.01; ***: p < 0.001 based on a 2-sided Wald test. B. Comparison of prediction of health outcomes in InCHIANTI and OA HeLP using the first OA HeLP factor derived in Extended Data Fig. 6B. C. Comparison of prediction of health outcomes in OA HeLP and SLAS using the first OA HeLP factor derived in Extended Data Fig. 6E. Not all outcomes are available in THLHP. D. Comparison of prediction of health outcomes in THLHP and OA HeLP using the first SLAS factor derived in Extended Data Fig. 6H. Not all outcomes are available in OA HeLP. For panels B–D, the point indicates the estimated odds ratio and the line the 95% confidence interval; the vertical line indicates no effect. *: p < 0.05; **: p < 0.01; ***: p < 0.001 based on a 2-sided Wald test. Odds ratios are scaled per unit factor score. Because factor score ranges are 6+ within populations (cf. Extended Data Fig. 2A), an odds ratio of 1.5 translates into at least 1.56 = 11.4 across the range of values in the populations. Note that different ORs for OA HeLP in panels B, C, and D are due to different versions of the factor using overlapping biomarker sets with the other datasets. Number of biological replicates: InCHIANTI (1041), SLAS (941), THLHP (536), OA HeLP (358). Supplemental Table S1 details availability of health outcomes by dataset.

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Franck, M., Tanner, K.T., Tennyson, R.L. et al. Nonuniversality of inflammaging across human populations. Nat Aging 5, 1471–1480 (2025). https://doi.org/10.1038/s43587-025-00888-0

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