Experience of Time and Subjective Age When Facing a Limited Lifetime: The Case of Older Adults with Advanced Cancer

Laryionava, Katsiaryna; Schönstein, Anton; Heußner, Pia; Hiddemann, Wolfgang; Winkler, Eva C.; Wahl, Hans‐Werner

doi:10.1177/08982643211063162

Cited by 4 publications

(4 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Number of physical health conditions was a count of the conditions reported. Although cancer in full remission is not a current physical health condition, we kept it in the count as past history of cancer may influence VoA, our study outcome ( 44 ).…”

Section: Methodsmentioning

confidence: 99%

Physical and mental health conditions account for variability in awareness of age-related changes

et al. 2023

View full text Add to dashboard Cite

BackgroundThe concept of Awareness of Age-Related Changes captures people’s perceptions of the positive (AARC-gains) and negative (AARC-losses) age-related changes they experience in several life domains, including their health. We investigated the cross-sectional associations of number and type of physical and mental health conditions with AARC-gains and AARC-losses.MethodsThe sample comprised 3,786 middle-aged and older adults (mean age = 67.04 years; SD = 6.88) participating to the UK PROTECT study. We used hierarchical regression models to analyze whether after having included sociodemographic variables (model 1), number of physical (model 2) and of mental (model 3) health conditions explained a significant additional amount of variance in AARC-gains and AARC-losses, and whether the association between number of conditions and AARC depended on participants’ age. We used multiple regression models to analyze the associations of types of physical and mental health conditions with AARC-gains and AARC-losses.ResultsA higher number of physical health conditions was associated with higher AARC-gains and higher AARC-losses, but the association did not depend on participant age. After controlling for the number of physical health conditions, a higher number of mental health conditions was associated with higher AARC-losses but not with AARC-gains, and the association was stronger among older participants. Small effects were found between greater AARC-gains and current cancer and between greater AARC-losses and diagnoses of mild cognitive impairment, Parkinson’s disease, arthritic condition, cancer in full remission, osteoporosis, depression, anxiety disorders, and personality disorder. The remaining health conditions were either negligibly or non-statistically related to AARC-losses.ConclusionMiddle-aged and older adults having more physical health conditions and more mental health conditions may be at higher risk of negative views on their own aging. However, specific physical health conditions, such as arthritis, and certain mental health conditions, such as depression, may make adults particularly vulnerable to negative age-related perceptions.

show abstract

Section: Methodsmentioning

confidence: 99%

Physical and mental health conditions account for variability in awareness of age-related changes

et al. 2023

View full text Add to dashboard Cite

show abstract

“…31 Moreover, higher SA was shown to be correlated with worse qualify of life in cancer survivors. 30,31 In our prior work, we have shown that higher SA is associated with adverse biomarker profiles, 29 and other studies have reported that SA is associated with negative health outcomes including higher mortality in the general population. 28,32,33 Although measures of BA, such as PhenoAge, KDM-BA, ECs, and SA, are correlated with each other, they likely estimate different aspects of the underlying age-related physiological dysfunction.…”

Section: Introductionmentioning

confidence: 99%

“…24 In addition to biomarker-based aging measures, subjective age (SA), which estimates how old people feel, i.e., an individual’s self-perception of their own age, 28-30 was 2% higher than CA in people diagnosed with cancer and 8% younger than CA among cancer-free controls. 31 Moreover, higher SA was shown to be correlated with worse qualify of life in cancer survivors. 30,31 In our prior work, we have shown that higher SA is associated with adverse biomarker profiles, 29 and other studies have reported that SA is associated with negative health outcomes including higher mortality in the general population.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aging measures and cancer: Findings from the Health and Retirement Study

Wang,

Prizment,

Moshele

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Compared to cancer-free persons, cancer survivors of the same chronological age (CA) have increased physiological dysfunction, i.e., higher biological age (BA), which may lead to higher morbidity and mortality. We estimated BA using eight aging metrics: BA computed by Klemera Doubal method (KDM-BA), phenotypic age (PhenoAge), five epigenetic clocks (ECs, Horvath, Hannum, Levine, GrimAge, and pace of aging (POA)), and subjective age (SA). We tested if aging constructs were associated with total cancer prevalence and all-cause mortality in cancer survivors and controls, i.e., cancer-free persons, in the Health and Retirement Study (HRS), a large population-based study. Methods In 2016, data on BA-KDM, PhenoAge, and SA were available for 946 cancer survivors and 4,555 controls; data for the five ECs were available for 582 cancer survivors and 2,805 controls. Weighted logistic regression was used to estimate the association between each aging construct and cancer prevalence (odds ratio, OR, 95%CI). Weighted Cox proportional hazards regression was used to estimate the associations between each aging construct and cancer incidence as well as all-cause mortality (hazard ratio, HR, 95%CI). To study all BA metrics (except for POA) independent of CA, we estimated age acceleration as residuals of BA regressed on CA. Results Age acceleration for each aging construct and POA were higher in cancer survivors than controls. In a multivariable-adjusted model, five aging constructs (age acceleration for Hannum, Horvath, Levine, GrimAge, and SA) were associated with cancer prevalence. Among all cancer survivors, age acceleration for PhenoAge and four ECs (Hannum, Horvath, Levine, and GrimAge), was associated with higher all-cause mortality over 4 years of follow-up. PhenoAge, Hannum, and GrimAge were also associated with all-cause mortality in controls. The highest HR was observed for GrimAge acceleration in cancer survivors: 2.03 (95% CI, 1.58-2.60). In contrast, acceleration for KDM-BA and POA was significantly associated with mortality in controls but not in cancer survivors. When all eight aging constructs were included in the same model, two of them (Levine and GrimAge) were significantly associated with mortality among cancers survivors. None of the aging constructs were associated with cancer incidence. Conclusion Variations in the associations between aging constructs and mortality in cancer survivors and controls suggests that aging constructs may capture different aspects of aging and that cancer survivors may be experiencing age-related physiologic dysfunctions differently than controls. Future work should evaluate how these aging constructs predict mortality for specific cancer types.

show abstract