Introduction: Chronological age is a particularly well-known indicator of variability in systemic inflammation. Other pertinent aspects of age (or "age proxies") - subjective or epigenetic age - may offer nuanced information about age and inflammation associations. Using the Midlife in the United States Study, we explored how chronological, subjective, and epigenetic age were associated with inflammation. Further, we tested whether chronological age remained a unique predictor of inflammation after accounting for the variance of subjective and epigenetic age. Using an intersectionality framework, we also tested whether associations differed by race and gender.
Method: 1,307 (85.39% White, 52.99% men) participants reported on their chronological and subjective age and provided blood from which epigenetic DNA and inflammatory biomarkers (IL-6, IL-8, fibrinogen, TNF-α, and E-selectin) were determined.
Results: Linear regressions showed that being chronologically older was related to higher levels of inflammation. Being biologically older (higher epigenetic age or pace of aging) was also related to higher levels of all but IL-8. Subjective age was related to inflammatory biomarkers but only for people who identified their racial identity as White. Gender differences emerged, primarily with biological and chronological age. With all age indicators in one model, chronological age remained a unique indicator of inflammation in the sample, as similar to or a better predictor than biological age.
Conclusion: The current study provides a better scientific understanding of the relative association of chronological age versus subjective and epigenetic age on inflammation with evidence suggesting that chronological age provides novel information above and beyond other proxies of age.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.bbi.2025.03.018 | DOI Listing |
Sci Adv
March 2025
College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Brain age gap (BAG), the deviation between estimated brain age and chronological age, is a promising marker of brain health. However, the genetic architecture and reliable targets for brain aging remains poorly understood. In this study, we estimate magnetic resonance imaging (MRI)-based brain age using deep learning models trained on the UK Biobank and validated with three external datasets.
View Article and Find Full Text PDFAm J Clin Dermatol
March 2025
Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Photoaging is the consequence of chronic exposure to solar irradiation, encompassing ultraviolet (UV), visible, and infrared wavelengths. Over time, this exposure causes cumulative damage, leading to both aesthetic changes and structural degradation of the skin. These effects manifest as rhytids, dyschromia, textural changes, elastosis, volume loss, telangiectasias, and hyperkeratosis, collectively contributing to a prematurely aged appearance that exceeds the skin's chronological age.
View Article and Find Full Text PDFBMC Public Health
March 2025
Flinders University, College of Nursing and Health Sciences, Caring Futures Institute, Adelaide, SA, Australia.
Frailty and pre-frailty are major public health concerns. While frailty is typically associated with older adults, evidence suggests that pre-frailty commonly starts in middle-age. This study examined associations between behavioural and psychological correlates of pre-frailty and frailty in adults from 40 years to help identify at-risk individuals and inform interventions.
View Article and Find Full Text PDFJ Psychiatry Neurosci
March 2025
From the Department of Psychiatry, Dalhousie University, Halifax, N.S. (Selitser, Dietze, McWhinney, Hajek) and the Charles University, Third Faculty of Medicine, Prague, Czech Republic (Hajek).
Background: Cardiometabolic risk factors - including diabetes, hypertension, and obesity - have long been linked with adverse health outcomes such as strokes, but more subtle brain changes in regional brain volumes and cortical thickness associated with these risk factors are less understood. Computer models can now be used to estimate brain age based on structural magnetic resonance imaging data, and subtle brain changes related to cardiometabolic risk factors may manifest as an older-appearing brain in prediction models; thus, we sought to investigate the relationship between cardiometabolic risk factors and machine learning-predicted brain age.
Methods: We performed a systematic search of PubMed and Scopus.
Brain Behav Immun
March 2025
Center for Healthy Aging, Penn State University, United States; Human Development and Family Studies, Penn State University, United States; Population Research Institute, Penn State University, United States.
Introduction: Chronological age is a particularly well-known indicator of variability in systemic inflammation. Other pertinent aspects of age (or "age proxies") - subjective or epigenetic age - may offer nuanced information about age and inflammation associations. Using the Midlife in the United States Study, we explored how chronological, subjective, and epigenetic age were associated with inflammation.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!