Browsing by Subject "Biological aging"

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    Applying a Life Course Biological Age Framework to Improving the Care of Individuals with Adult Cancers: Review and Research Recommendations
    (American Medical Association, 2021) Mandelblatt, Jeanne S.; Ahles, Tim A.; Lippman, Marc E.; Isaacs, Claudine; Adams-Campbell, Lucile; Saykin, Andrew J.; Cohen, Harvey J.; Carroll, Judith; Radiology and Imaging Sciences, School of Medicine
    Importance: The practice of oncology will increasingly involve the care of a growing population of individuals with midlife and late-life cancers. Managing cancer in these individuals is complex, based on differences in biological age at diagnosis. Biological age is a measure of accumulated life course damage to biological systems, loss of reserve, and vulnerability to functional deterioration and death. Biological age is important because it affects the ability to manage the rigors of cancer therapy, survivors' function, and cancer progression. However, biological age is not always clinically apparent. This review presents a conceptual framework of life course biological aging, summarizes candidate measures, and describes a research agenda to facilitate clinical translation to oncology practice. Observations: Midlife and late-life cancers are chronic diseases that may arise from cumulative patterns of biological aging occurring over the life course. Before diagnosis, each new patient was on a distinct course of biological aging related to past exposures, life experiences, genetics, and noncancer chronic disease. Cancer and its treatments may also be associated with biological aging. Several measures of biological age, including p16INK4a, epigenetic age, telomere length, and inflammatory and body composition markers, have been used in oncology research. One or more of these measures may be useful in cancer care, either alone or in combination with clinical history and geriatric assessments. However, further research will be needed before biological age assessment can be recommended in routine practice, including determination of situations in which knowledge about biological age would change treatment, ascertaining whether treatment effects on biological aging are short-lived or persistent, and testing interventions to modify biological age, decrease treatment toxic effects, and maintain functional abilities. Conclusions and relevance: Understanding differences in biological aging could ultimately allow clinicians to better personalize treatment and supportive care, develop tailored survivorship care plans, and prescribe preventive or ameliorative therapies and behaviors informed by aging mechanisms.
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    Epigenetic Aging in Older Breast Cancer Survivors and Non-Cancer Controls: Preliminary Findings from the Thinking and Living with Cancer (TLC) Study
    (Wiley, 2023) Rentscher, Kelly E.; Bethea, Traci N.; Zhai, Wanting; Small, Brent J.; Zhou, Xingtao; Ahles, Tim A.; Ahn, Jaeil; Breen, Elizabeth C.; Cohen, Harvey Jay; Extermann, Martine; Graham, Deena M. A.; Jim, Heather S. L.; McDonald, Brenna C.; Nakamura, Zev M.; Patel, Sunita K.; Root, James C.; Saykin, Andrew J.; Van Dyk, Kathleen; Mandelblatt, Jeanne S.; Carroll, Judith E.; Radiology and Imaging Sciences, School of Medicine
    Background: Cancer and its treatments may accelerate aging in survivors; however, research has not examined epigenetic markers of aging in longer term breast cancer survivors. This study examined whether older breast cancer survivors showed greater epigenetic aging than noncancer controls and whether epigenetic aging related to functional outcomes. Methods: Nonmetastatic breast cancer survivors (n = 89) enrolled prior to systemic therapy and frequency-matched controls (n = 101) ages 62 to 84 years provided two blood samples to derive epigenetic aging measures (Horvath, Extrinsic Epigenetic Age [EEA], PhenoAge, GrimAge, Dunedin Pace of Aging) and completed cognitive (Functional Assessment of Cancer Therapy-Cognitive Function) and physical (Medical Outcomes Study Short Form-12) function assessments at approximately 24 to 36 and 60 months after enrollment. Mixed-effects models tested survivor-control differences in epigenetic aging, adjusting for age and comorbidities; models for functional outcomes also adjusted for racial group, site, and cognitive reserve. Results: Survivors were 1.04 to 2.22 years biologically older than controls on Horvath, EEA, GrimAge, and DunedinPACE measures (p = .001-.04) at approximately 24 to 36 months after enrollment. Survivors exposed to chemotherapy were 1.97 to 2.71 years older (p = .001-.04), and among this group, an older EEA related to worse self-reported cognition (p = .047) relative to controls. An older epigenetic age related to worse physical function in all women (p < .001-.01). Survivors and controls showed similar epigenetic aging over time, but Black survivors showed accelerated aging over time relative to non-Hispanic White survivors. Conclusion: Older breast cancer survivors, particularly those exposed to chemotherapy, showed greater epigenetic aging than controls that may relate to worse outcomes. If replicated, measurement of biological aging could complement geriatric assessments to guide cancer care for older women.
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    Integrative analysis of DNA methylation and gene expression identifies genes associated with biological aging in Alzheimer's disease
    (Wiley, 2022-09-20) Kim, Bo-Hyun; Vasanthakumar, Aparna; Li, Qingqin S.; Nudelman, Kelly N.H.; Risacher, Shannon L.; Davis, Justin W.; Idler, Kenneth; Lee, Jong-Min; Seo, Sang Won; Waring, Jeffrey F.; Saykin, Andrew J.; Nho, Kwangsik; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Radiology and Imaging Sciences, School of Medicine
    Introduction: The acceleration of biological aging is a risk factor for Alzheimer's disease (AD). Here, we performed weighted gene co-expression network analysis (WGCNA) to identify modules and dysregulated genes involved in biological aging in AD. Methods: We performed WGCNA to identify modules associated with biological clocks and hub genes of the module with the highest module significance. In addition, we performed differential expression analysis and association analysis with AD biomarkers. Results: WGCNA identified five modules associated with biological clocks, with the module designated as "purple" showing the strongest association. Functional enrichment analysis revealed that the purple module was related to cell migration and death. Ten genes were identified as hub genes in purple modules, of which CX3CR1 was downregulated in AD and low levels of CX3CR1 expression were associated with AD biomarkers. Conclusion: Network analysis identified genes associated with biological clocks, which suggests the genetic architecture underlying biological aging in AD. Highlights: Examine links between Alzheimer's disease (AD) peripheral transcriptome and biological aging changes. Weighted gene co-expression network analysis (WGCNA) found five modules related to biological aging. Among the hub genes of the module, CX3CR1 was downregulated in AD. The CX3CR1 expression level was associated with cognitive performance and brain atrophy.