Identifying exposome drivers of age-associated low-grade inflammation

1. OVERVIEW

Chronic inflammation is implicated in the development and progression of major chronic noncommunicable diseases, including metabolic, cardiovascular, and neurodegenerative diseases, as well as many cancers, and modulating this process can alter clinical risk.¹ Understanding what drives chronic inflammation is therefore a major challenge for medicine. The pattern of age‑related inflammation typical of industrialized populations is weak or absent in some non‑industrialized groups, with little or no increase with age and no association with chronic disease. This project treats industrialized age-related inflammation as exposure-driven and asks which environmental and lifestyle determinants generate it and how they interact. Analyses will be conducted in the UK Biobank, leveraging its ≈50,000‑participant plasma proteomics resource linked to rich lifestyle and environmental variables. We estimate completion within five months.

2. INTRODUCTION

In age-related chronic diseases and in aging in general levels of inflammation-associated markers are elevated. This phenomenon, known as inflammaging, is considered an intrinsic aspect of aging, often framed as a one‑dimensional process that rises with age and can at best be slowed—as reflected by lower‑than‑expected inflammatory age (iAge) in some centenarians.²

However, we recently found that inflammaging, as traditionally defined and measured, is absent in some non-industrialized populations.^3,4 In InCHIANTI (Italy) and SLAS (Singapore Longitudinal Ageing Study), an inflammaging metric built through factor analysis performed on 19 cytokines correlated positively with age and chronic diseases. In contrast, in the THLHP (Tsimane Health and Life History Project in Bolivia) and OA HeLP (Orang Asli Health and Lifeways Project in Malaysia) cohorts, scores on the same metric showed no such association (Fig. 1).

Fig. 1. Inflammaging across populations. A, Inflammaging factor versus age: increases in InCHIANTI and SLAS; minimal change in THLHP and OA HeLP. B–D, Odds ratios per unit factor score for chronic conditions in paired cohorts: InCHIANTI–SLAS (B), InCHIANTI–THLHP (C), InCHIANTI–OA HeLP (D). Reprinted from ref. ⁴ (Fig. 4).

These results call into question the universality of inflammaging as traditionally conceptualized and suggest that, in those populations whose lifestyle and environment differ radically from those of industrialized societies, inflammaging may manifest differently. In fact, what we call “inflammaging” may not be an inevitable aspect of biological aging, but rather the consequence of aging in an environment mismatched to our evolutionary history, as in industrialized societies. Accordingly, we have argued that inflammaging is best understood as a reaction norm.⁵ Industrialized inflammaging may be driven by specific features of the industrialized exposome (life‑course environmental and lifestyle exposures), including physical activity,^6,7 pollution,^8,9 diet and gut microbiota.^10–13 In this view, individual inflammaging trajectories are shaped by the exposome; industrialized inflammaging represents one common, exposure‑driven trajectory/pattern in industrialized populations rather than a universal signature (Fig. 2). Using this conceptual and methodological framework, the present project aims to investigate the determinants of the individualization of inflammaging. We want to understand which exposures shape individual inflammatory aging trajectories and which contribute most to the mismatched industrialized inflammaging.

Fig. 2. Inflammaging as a norm of reaction. Waddington-inspired landscape illustrating how different exposomes can shape divergent inflammatory aging trajectories. The horizontal axis represents a gradient of increasing mismatch between evolutionary expectations (i.e., evolved human biology) and environmental exposures. Valleys represent basins of attraction (stable trajectories) of the inflammatory system; the exposome reshapes the landscape, thereby changing the age‑trajectory of inflammaging. Reprinted from ref. ⁵ (Fig. 8). Abbreviation: CNCD, chronic noncommunicable disease.

Research Questions

Does the inflammaging factor from “Nonuniversality of Inflammaging”³ replicate in the UK Biobank, remain stable with added markers, rise with age, and predict chronic disease?

How much do key exposome determinants contribute to industrialized inflammaging in the UK Biobank, and do interactions between these determinants modify their effects?

Team

The project is led by investigators with complementary strengths spanning fieldwork in immunosenescence/inflammaging biology (Franck, Fülöp), multivariate immune modeling and complex-systems theory (Cohen, Hertel), high-dimensional modeling (Hertel), epidemiology/biostatistics (Hertel, Aronoff, Daunizeau) and dietary score development (Daunizeau).

Impact statement

Noncommunicable diseases linked to chronic inflammation collectively represent the leading global causes of disability and death;¹ in 2021 they caused ≈43 million deaths (≈75% of non‑pandemic‑related deaths), including ≈18 million before age  70.¹⁴ Thus, by identifying modifiable determinants of industrialized inflammaging, this project targets upstream exposures that account for a large share of morbidity and premature mortality.

3. METHODS

Foundation cohorts

Prior analyses were conducted in InCHIANTI, SLAS, THLHP, and OA HeLP. These datasets have established the industrialized versus non‑industrialized contrast and provide the initial inflammaging constructs that motivate the present project; the proposed analyses will be conducted in the UK Biobank.

Study cohort

Analyses will be conducted in the UK Biobank to replicate the previously observed inflammaging pattern and extend it using the UK Biobank’s richer exposure data. The UK Biobank provides plasma proteomics measuring 2,923 unique proteins (including 77 cytokines) and extensive exposure variables: diet (33 items), physical activity and sedentary behavior (27 items plus nine derived energy‑expenditure variables), sleep (7), alcohol (21), smoking (33), residential air pollution (17 items), and socioeconomic position (e.g., education, income, occupation, Townsend deprivation index), as well as seven‑day wrist‑worn accelerometry in ≈100,000 participants; a longitudinal subsample (≈1,000) has repeated proteomics.

Planned Analyses

1. Extending the “Nonuniversality of Inflammaging”³ analysis to the UK Biobank: We will re‑derive the inflammaging factor in the UK Biobank and evaluate (i) replication of the pattern (structure and score concordance), (ii) stability to marker‑set expansion/removal, (iii) association with age, and (iv) predictive validity for chronic disease.

2. Key exposome determinants: We will first compute unadjusted associations between each exposome determinant and industrialized‑inflammaging metrics. We will then fit confounder‑adjusted models. Next, we will assess effect modification by adding a prespecified set of pairwise exposure×exposure interaction terms to the adjusted model—i.e., whether two exposures together shift the inflammaging score more (synergy) or less (antagonism) than expected from their separate effects.

3. Longitudinal proteomics (≈1,000 participants): We will estimate within‑person change in the inflammaging score; test whether within‑person slopes differ by age, sex, socioeconomic status, and key exposures.

Expected results

● Estimation of the contributions of key exposome determinants to industrialized inflammaging.

● Internal and temporal validation within the UK Biobank of the identified inflammaging pattern.

Overall expected outcome: consolidation of the reaction‑norm interpretation of inflammaging and progress toward context‑tailored biomarker panels and strategies.

Ethics and Data Management

This project analyzes existing cohort data that were previously approved by each cohort’s ethics committees. The lead analyst is affiliated with a university as a postdoctoral researcher, has signed all required data use and confidentiality agreements for these datasets, and has already worked with them under those approvals.

We commit to open science: analysis code will be shared in a public repository and results will be posted as a preprint.

4. BUDGET

USD 8,000 to cover the postdoctoral researcher’s salary for approximately four months and USD 4,000 for the UK Biobank data-access fee (total USD 12,000).

5. REFERENCES

1. Furman, D. et al. Chronic inflammation in the etiology of disease across the life span. Nat. Med. 25, 1822–1832 (2019).

2. Sayed, N. et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nat. Aging 1, 598–615 (2021).

3. Aronoff, J. E. et al. Inflammaging is minimal among forager-horticulturalists in the Bolivian Amazon. Proc. R. Soc. B Biol. Sci. 292, 20251111 (2025).

4. Franck, M. et al. Nonuniversality of inflammaging across human populations. Nat. Aging 5, 1471–1480 (2025).

5. Franck, M. et al. Inflamm-aging as a diverse and context-dependent process: from species and population differences to individual trajectories. Ageing Res. Rev. 102880 (2025) doi:10.1016/j.arr.2025.102880.

6. Nicklas, B. J. et al. Diet-induced weight loss, exercise, and chronic inflammation in older, obese adults: a randomized controlled clinical trial. Am. J. Clin. Nutr. 79, 544–551 (2004).

7. Cronin, O., Keohane, D. M., Molloy, M. G. & Shanahan, F. The effect of exercise interventions on inflammatory biomarkers in healthy, physically inactive subjects: a systematic review. QJM Int. J. Med (2017) doi:10.1093/qjmed/hcx091.

8. Glencross, D. A., Ho, T.-R., Camiña, N., Hawrylowicz, C. M. & Pfeffer, P. E. Air pollution and its effects on the immune system. Free Radic. Biol. Med. 151, 56–68 (2020).

9. Yuan, J. et al. Long-term Persistent Organic Pollutants Exposure Induced Telomere Dysfunction and Senescence-Associated Secretary Phenotype. J. Gerontol. Ser. A 73, 1027–1035 (2018).

10. Franceschi, C., Garagnani, P., Parini, P., Giuliani, C. & Santoro, A. Inflammaging: a new immune–metabolic viewpoint for age-related diseases. Nat. Rev. Endocrinol. 14, 576–590 (2018).

11. Hornero-Ramirez, H. et al. Multifunctional dietary approach reduces intestinal inflammation in relation with changes in gut microbiota composition in subjects at cardiometabolic risk: the SINFONI project. Gut Microbes 17, 2438823 (2025).

12. Daunizeau, C. et al. The gut microbiota of Indigenous populations in the context of dietary westernization: a systematic review and meta-analysis. Front. Nutr. 12, 1652598 (2025).

13. Temba, G. S. et al. Immune and metabolic effects of African heritage diets versus Western diets in men: a randomized controlled trial. Nat. Med. 31, 1698–1711 (2025).

14. World Health Organization. Noncommunicable diseases. (2025). 2025 Sep 25. URL: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases

Identifying exposome drivers of age-associated low-grade inflammation

Identifying exposome drivers of age-associated low-grade inflammation

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Request for Proposals - Aging and Longevity

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