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The complex relationship between life course traumas and cardiovascular disease (CVD) and the underpinning pathways are poorly understood. We aimed to (1) examine the associations of three separate assessments including childhood, adulthood (after 16 years of age), and lifetime traumas (childhood or adulthood) with CVD; (2) examine the associations between diverse life course traumatic profiles and CVD; and (3) examine the extent to which PhenoAge, a well-developed phenotypic aging measure, mediated these associations. Using data from 104,939 participants from the UK Biobank, we demonstrate that subgroups of childhood, adulthood, and lifetime traumas were associated with CVD. Furthermore, life course traumatic profiles were significantly associated with CVD. For instance, compared with the subgroup experiencing nonsevere traumas across life course, those who experienced nonsevere childhood and severe adulthood traumas, severe childhood and nonsevere adulthood traumas, or severe traumas across life course had significantly higher odds of CVD (odds ratios: 1.07–1.33). Formal mediation analyses suggested that phenotypic aging partially mediated the above associations. These findings suggest a potential pathway from life course traumas to CVD through phenotypic aging, and underscore the importance of policy programs targeting traumas over the life course in ameliorating inequalities in cardiovascular health.

For many years, it was believed that the aging process was inevitable and that age-related diseases could not be prevented or reversed. The geroscience hypothesis, however, posits that aging is, in fact, malleable and, by targeting the hallmarks of biological aging, it is indeed possible to alleviate age-related diseases and dysfunction and extend longevity. This field of geroscience thus aims to prevent the development of multiple disorders with age, thereby extending healthspan, with the reduction of morbidity toward the end of life. Experts in the field have made remarkable advancements in understanding the mechanisms underlying biological aging and identified ways to target aging pathways using both novel agents and repurposed therapies. While geroscience researchers currently face significant barriers in bringing therapies through clinical development, proof-of-concept studies, as well as early-stage clinical trials, are underway to assess the feasibility of drug evaluation and lay a regulatory foundation for future FDA approvals in the future.

BACKGROUND The authors measured epigenetic age acceleration (EAA) during and after cancer treatment and its association with inflammation and fatigue, which is a debilitating symptom in patients with cancer. METHODS Patients who had head and neck cancer without distant metastases were assessed before, immediately after, and at 6 months and 12 months postradiotherapy. Blood DNA methylation was assessed using a proprietary bead chip (the Illumina MethylationEPIC BeadChip). EAA was calculated using the Levine epigenetic clock (DNAmPhenoAge), adjusted for chronological age. Fatigue was assessed using the Multidimensional Fatigue Inventory-20. Inflammatory markers were measured using standard techniques. RESULTS Most patients (N = 133) were men, White, had advanced disease, and received concurrent chemoradiation. EAA changes over time were significant, with the largest increase (4.9 years) observed immediately after radiotherapy (P < .001). Increased EAA was associated with elevated fatigue (P = .003) over time, and patients who had severe fatigue experienced 3.1 years higher EAA than those who had low fatigue (P < .001), which was more prominent (5.6 years; P = .018) for patients who had human papillomavirus-unrelated disease at 12 months posttreatment. EAA was also positively associated with inflammatory markers, including C-reactive protein (CRP) and interleukin-6 (IL-6), over time (P < .001), and patients who had high CRP and IL-6 levels exhibited increases of 4.6 and 5.9 years, respectively, in EAA compared with those who had low CRP and IL-6 levels (P < .001). CRP and IL-6 mediated the association between EAA and fatigue (CRP: 95% CI, 0.060-0.279; IL-6: 95% CI, 0.024-0.220). CONCLUSIONS Patients with head and neck cancer experienced increased EAA, especially immediately after treatment completion. EAA was associated with greater fatigue and inflammation, including 1 year after treatment. Inflammation may be a target to reduce the impact of age acceleration on poor functional outcomes.

Objectives Concepts such as Allostatic Load, Framingham Risk Score, and Biological Age were developed to combine information from multiple measures into a single latent variable that can be used to quantify a person's biological state. Given these varying approaches, the goal of this article is to compare how well these three measures predict subsequent all-cause and disease-specific mortality within a large nationally representative U.S. sample. Methods Our study population consisted of 9,942 adults, ages 30 and above from National Health and Nutrition Examination Survey III. Receiver Operating Characteristic curves and Cox Proportional Hazard models for the whole sample and for stratified age groups were used to compare how well Allostatic Load, Framingham Risk Score, and Biological Age predict ten-year all-cause and disease-specific mortality in the sample, for whom there were 1,076 deaths over 96,420 person years of exposure. Results Overall, Biological Age predicted 10-year mortality more accurately than other measures for the full age range, as well as for participants ages 50 to 69 and 70+. Additionally, out of the three measures, Biological Age had the strongest association with all-cause and cancer mortality, while the Framingham Risk Score had the strongest association with CVD mortality. Conclusions Methods for quantifying biological risk provide important approaches to improving our understanding of the causes and consequences of changes in physiological function and dysregulation. Biological Age offers an alternative and, in some cases a more accurate summary approach to the traditionally used methods, such as Allostatic Load and Framingham Risk Score. Am. J. Hum. Biol. 26:768–776, 2014. © 2014 Wiley Periodicals, Inc.

Background and Objective Age is highly related to oral health status. The higher prevalence of oral disease within subgroups of the population may reflect a tendency towards “early aging” and dysregulation of multiple physiological systems. This study examines whether the association between periodontal disease and demographic factors is mediated by physiological measures of health. Material and Methods Logistic regression was used to examine whether biomarkers and demographic factors, such as socio-economic status (SES) and race/ethnicity, were associated with periodontal disease, and then whether the strength of these relationships could be attributed to associations between demographic variables and physiological measures of systemic health. Results Periodontal disease was associated with measures of SES and race/ethnicity. Furthermore, 1-unit increases in cytomegalovirus (CMV), optical density, C-reactive protein (CRP) and glycated hemoglobin (HbA1c) were associated with a 25% [odds ratio (OR) = 1.25; 95% confidence interval (CI): 1.14–1.36], 13% (OR = 1.13; 95% CI = 1.03–1.24) and 19% (OR = 1.19; 95% CI = 1.12–1.27) increased likelihood of periodontal disease, respectively. However, when biomarkers and socio-demographic variables were both included in the model, their associations with periodontal disease were significantly reduced or eliminated. Conclusions The risk of periodontal disease is higher among black and/or low-income individuals; however, these associations appear to be partly due to the greater probability of elevated levels of CRP, CMV or HbA1c among these groups.