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Record W6943963943 · doi:10.17605/osf.io/k5v8j

Chronic inflammation and multiple dimensions of aging among women in the Philippines

2025· other· en· W6943963943 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsInflammationSystemic inflammationObesityEpidemiologyDiseasePopulationImmune systemOverweightImmunosenescence

Abstract

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Chronic systemic inflammation is a robust predictor of all-cause mortality, as well as age-related increases in cardiometabolic diseases and declines in physical and cognitive abilities [1, 2]. Concentrations of inflammatory biomarkers typically increase with age [3], and self-perpetuating cycles of tissue damage and inflammatory responses are potentially important drivers of biological aging across multiple organs and physiological systems—a process described as “inflammaging” [4, 5]. Research on inflammation and aging has been conducted almost exclusively in high income, post-epidemiologic transition populations with high levels of chronic inflammation and obesity [6, 7]. However, the global population is rapidly aging, and 80% of people over the age of 60 are forecast to live in lower- and middle-income nations by 2050 [8]. The extent to which chronic inflammation contributes to age-related morbidity and mortality in these settings is not known, with only a handful of studies providing mixed results [7, 9, 10]. Higher levels of endemic infectious diseases and relatively low but rapidly rising rates of overweight and obesity are two potentially important factors that might influence patterns of association among inflammation, aging, and chronic disease globally. C-reactive protein (CRP)—an acute phase protein and key component of innate immune defenses—is routinely assayed in blood to assess systemic inflammation in clinical and epidemiological settings [11-13]. However, transient increases in CRP in response to infection or injury can complicate efforts to capture chronic levels of inflammatory activity from samples collected at a single time point, particularly in ecological settings where infectious exposures are common [14, 15]. Recently, the methylome has been identified as a promising source of information on circulating proteins, including inflammatory biomarkers such as CRP [16]. DNA methylation (DNAm) is a reversible epigenetic process that involves the binding of methyl groups to cytosine residues, with potential effects on patterns of gene expression [17]. The application of novel bioinformatic methods to epigenome-wide DNAm data has generated several surrogate measures of chronic inflammation that predict a wide range of aging-related health outcomes [18-22]. These measures also appear to be less sensitive to acute fluctuation than CRP [18, 23]. The objective of this study is to test the hypothesis that chronic inflammation predicts trajectories of aging among a cohort of older women in the Philippines. While life expectancy has increased along with educational and economic opportunities, the Philippines remains a lower-middle income nation experiencing recent increases in the prevalence of overweight/obesity, rising burdens of diabetes, heart disease, and stroke, and a backdrop of communicable diseases that continue to cause substantial morbidity and mortality [24-26]. It is also an aging nation, with a rapidly growing population of older adults [27]. In prior analyses with this cohort we have reported concentrations of CRP that are substantially lower than age-matched adults in the US [28]. We also document weaker associations between waist circumference and CRP in the Philippines [29], suggesting that the relationships among adiposity, chronic inflammation, and aging outcomes may differ from higher income countries with greater prevalences of overweight, obesity, and chronic inflammation. In this study we measure chronic inflammation with plasma CRP, as well as a recently validated DNAm-based surrogate measure (DNAm-CRP) that performed well across a diverse set of test cohorts [23]. Correlations between DNAm-CRP and plasma CRP range from 0.26 to 0.53 in cohorts of younger and older adults; in our cohort of older Filipino women, the correlation is 0.47 [30]. We evaluate CRP and DNAm-CRP as predictors of multiple dimensions of aging, including physical capacity, cognitive function, and cardiometabolic morbidities, with assessments at baseline and seven years later. 1. Furman, D., J. Campisi, E. Verdin, P. Carrera-Bastos, S. Targ, C. Franceschi, L. Ferrucci, D.W. Gilroy, A. Fasano, and G.W. Miller, Chronic inflammation in the etiology of disease across the life span. Nature Medicine, 2019. 25(12): p. 1822-1832. 2. Proctor, M.J., D.C. McMillan, P.G. Horgan, C.D. Fletcher, D. Talwar, and D.S. Morrison, Systemic inflammation predicts all-cause mortality: a glasgow inflammation outcome study. PLoS One, 2015. 10(3): p. e0116206. 3. Goto, M., Inflammaging (inflammation+ aging): a driving force for human aging based on an evolutionarily antagonistic pleiotropy theory? Bioscience trends, 2008. 2(6). 4. Franceschi, C. and J. Campisi, Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, 2014. 69(Suppl_1): p. S4-S9. 5. Zenkov, N., P. Kozhin, A. Chechushkov, N. Kandalintseva, G. Martinovich, and E. Menshchikova, Oxidative stress in aging. Advances in Gerontology, 2020. 33(1): p. 10-22. 6. McDade, T.W., J.M. Meyer, S.M. Koning, and K.M. Harris, Body mass and the epidemic of chronic inflammation in early mid-adulthood. Social Science & Medicine, 2021. 281: p. 114059. 7. McDade, T.W., Three common assumptions about inflammation, aging, and health that are probably wrong. Proceedings of the National Academy of Sciences, 2023. 120(51): p. e2317232120. 8. Organization, W.H., World report on ageing and health. 2015: World Health Organization. 9. Gurven, M., H. Kaplan, J. Winking, D. Eid Rodriguez, S. Vasunilashorn, J.K. Kim, C. Finch, and E. Crimmins, Inflammation and infection do not promote arterial aging and cardiovascular disease risk factors among lean horticulturalists. PLoS One, 2009. 4(8): p. e6590. 10. Koopman, J.J., D. van Bodegom, J.W. Jukema, and R.G. Westendorp, Risk of cardiovascular disease in a traditional African population with a high infectious load: a population-based study. 2012. 11. Collaboration, E.R.F., C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis. The Lancet, 2010. 375(9709): p. 132-140. 12. Pearson, T.A., G.A. Mensah, R.W. Alexander, J.L. Anderson, R.O. Cannon, M. Criqui, Y.Y. Fadl, S.P. Fortmann, Y. Hong, G.L. Myers, N. Rifai, S.C. Smith, K. Taubert, R.P. Tracy, and F. Vinicor, Markers of inflammation and cardiovascular disease: Application to clinical and public health practice. Circulation, 2003. 107: p. 499-511. 13. McDade, T.W., J.M. Meyer, S.M. Koning, and K.M. Harris, Body mass and the epidemic of chronic inflammation in early mid-adulthood. Social Science and Medicine, 2021. 281: p. 114059. 14. Bogaty, P., G.R. Dagenais, L. Joseph, L. Boyer, A. Leblanc, P. Belisle, and J.M. Brophy, Time variability of C-reactive protein: implications for clinical risk stratification. PLoS One, 2013. 8(4): p. e60759. 15. McDade, T.W., P.S. Tallman, F.C. Madimenos, M.A. Liebert, T.J. Cepon, L.S. Sugiyama, and J.J. Snodgrass, Analysis of variability of high sensitivity C-reactive protein in lowland Ecuador reveals no evidence of chronic low-grade inflammation. American Journal of Human Biology, 2012. 24: p. 675-81. 16. Gadd, D.A., R.F. Hillary, D.L. McCartney, S.B. Zaghlool, A.J. Stevenson, Y. Cheng, C. Fawns-Ritchie, C. Nangle, A. Campbell, and R. Flaig, Epigenetic scores for the circulating proteome as tools for disease prediction. Elife, 2022. 11: p. e71802. 17. Aristizabal, M.J., I. Anreiter, T. Halldorsdottir, C.L. Odgers, T.W. McDade, A. Goldenberg, S. Mostafavi, M.S. Kobor, E.B. Binder, and M.B. Sokolowski, Biological embedding of experience: a primer on epigenetics. Proceedings of the National Academy of Sciences, 2020. 117(38): p. 23261-23269. 18. Verschoor, C.P., C. Vlasschaert, M.J. Rauh, and G. Paré, A DNA methylation based measure outperforms circulating CRP as a marker of chronic inflammation and partly reflects the monocytic response to long‐term inflammatory exposure: A Canadian Longitudinal Study on Aging analysis. Aging Cell, 2023. 22(7): p. e13863. 19. Conole, E.L., A.J. Stevenson, S. Muñoz Maniega, S.E. Harris, C. Green, M.d.C. Valdés Hernández, M.A. Harris, M.E. Bastin, J.M. Wardlaw, and I.J. Deary, DNA methylation and protein markers of chronic inflammation and their associations with brain and cognitive aging. Neurology, 2021. 97(23): p. e2340-e2352. 20. Ligthart, S., C. Marzi, S. Aslibekyan, M.M. Mendelson, K.N. Conneely, T. Tanaka, E. Colicino, L.L. Waite, R. Joehanes, and W. Guan, DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases. Genome biology, 2016. 17: p. 1-15. 21. Wielscher, M., P.R. Mandaviya, B. Kuehnel, R. Joehanes, R. Mustafa, O. Robinson, Y. Zhang, B. Bodinier, E. Walton, and P.P. Mishra, DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases. Nature Communications, 2022. 13(1): p. 2408. 22. Meier, H.C., C. Mitchell, T. Karadimas, and J.D. Faul, Systemic inflammation and biological aging in the Health and Retirement Study. GeroScience, 2023. 45(6): p. 3257-3265. 23. Hillary, R.F., H.K. Ng, D.L. McCartney, H.R. Elliott, R.M. Walker, A. Campbell, F. Huang, K. Direk, P. Welsh, and N. Sattar, Blood-based epigenome-wide analyses of chronic low-grade inflammation across diverse population cohorts. Cell Genomics, 2024. 4(5). 24. Adair, L.S., S. Gultiano, and C. Suchindran, 20-year trends in Filipino women's weight reflect substantial secular and age effects. The Journal of nutrition, 2011. 141(4): p. 667-673. 25. Adair, L.S., B.M. Popkin, J.S. Akin, D.K. Guilkey, S. Gultiano, J. Borja, L. Perez, C.W. Kuzawa, T. McDade, and M.J. Hindin, Cohort profile: the Cebu Longitudinal Health and Nutrition Survey. Int J Epidemiol, 2011. 40(3): p. 619-25. 26. Authority, P.S. Registered deaths in the Philippines. 2023 April 2, 2025]; Available from: https://psa.gov.ph/content/regist

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.308
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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