Chronic inflammation and multiple dimensions of aging among women in the Philippines
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».