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Simple Summary: The impact of cancer and cancer treatment on longer-term cognitive aging trajectories is currently unknown. Cancer and cancer treatment accelerate biological aging and may accelerate cognitive decline in older individuals. We compared younger and older breast cancer survivors with younger and older non-cancer controls using standard cognitive measures to estimate age- and cancer-related differences over time. We found the expected inverse association of age with cognition in both groups, lower learning and memory performance for survivors as a whole, and more prominent differences in learning and memory as well as attention, processing speed, and executive function in younger survivors, i.e., those under 75 years of age. These differences were similar to trends across the age span in deficit accumulation, with larger differences in younger survivors that may indicate a mechanism of cognitive aging more generally, and in younger survivors specifically. Background: Cancer and cancer treatments may affect aging processes, altering the trajectory of cognitive aging, but the extant studies are limited in their intervals of assessment (two–five years). We studied the cognitive performance of a cohort of survivors and controls aged from 60 to 89 years utilizing cross-sectional cognitive performance data as an indicator of potential aging trajectories and contrasted these trends with longitudinal data collected over two years. Methods: Female breast cancer survivors who had been diagnosed and treated at age 60 or older and were 5- to 15-year survivors (N = 328) and non-cancer controls (N = 158) were assessed at enrollment and at 8, 16, and 24 months with standard neuropsychological tests and comprehensive geriatric assessment. Results: A cross-sectional baseline analysis found the expected inverse association of age with cognition in both groups, with survivors performing lower overall than controls in learning and memory (LM). Younger survivors, i.e., those under 75 years of age, exhibited lower performance in both LM and attention, and processing speed and executive function (APE), compared to controls, with no differences being observed between older survivors and controls, which tracked with deficit accumulation trends. Conclusion: Cognitive differences between the survivors and controls for the LM and APE domains were prominent in younger survivors, as was deficit accumulation, suggesting a mediating effect on cognition. Deficit accumulation may represent a modifiable risk factor in cancer survivorship that may be targeted for prevention and intervention. [ABSTRACT FROM AUTHOR]

Aims: The frailty index (FI) is one way in which frailty can be quantified. While it is measured as a continuous variable, various cut-off points have been used to categorise older adults as frail or non-frail, and these have largely been validated in the acute care or community settings for older adults without cancer. This review aimed to explore which FI categories have been applied to older adults with cancer and to determine why these categories were selected by study authors. Methods: This scoping review searched Medline, EMBASE, Cochrane, CINAHL, and Web of Science databases for studies which measured and categorised an FI in adults with cancer. Of the 1994 screened, 41 were eligible for inclusion. Data including oncological setting, FI categories, and the references or rationale for categorisation were extracted and analysed. Results: The FI score used to categorise participants as frail ranged from 0.06 to 0.35, with 0.35 being the most frequently used, followed by 0.25 and 0.20. The rationale for FI categories was provided in most studies but was not always relevant. Three of the included studies using an FI > 0.35 to define frailty were frequently referenced as the rationale for subsequent studies, however, the original rationale for this categorisation was unclear. Few studies sought to determine or validate optimum FI categorises in this population. Conclusion: There is significant variability in how studies have categorised the FI in older adults with cancer. An FI ≥ 0.35 to categorise frailty was used most frequently, however an FI in this range has often represented at least moderate to severe frailty in other highly-cited studies. These findings contrast with a scoping review of highly-cited studies categorising FI in older adults without cancer, where an FI ≥ 0.25 was most common. Maintaining the FI as a continuous variable is likely to be beneficial until further validation studies determine optimum FI categories in this population. Differences in how the FI has been categorised, and indeed how older adults have been labelled as 'frail', limits our ability to synthesise results and to understand the impact of frailty in cancer care. [ABSTRACT FROM AUTHOR]

Aims: While the frailty index (FI) is a continuous variable, an FI score of 0.25 has construct and predictive validity to categorise community-dwelling older adults as frail or non-frail. Our study aimed to explore which FI categories (FI scores and labels) were being used in high impact studies of adults across different care settings and why these categories were being chosen by study authors.Methods: For this systematic scoping review, Medline, Cochrane and EMBASE databases were searched for studies that measured and categorised an FI. Of 1314 articles screened, 303 met the eligibility criteria (community: N = 205; residential aged care: N = 24; acute care: N = 74). For each setting, the 10 studies with the highest field-weighted citation impact (FWCI) were identified and data, including FI scores and labels and justification provided, were extracted and analysed.Results: FI scores used to distinguish frail and non-frail participants varied from 0.12 to 0.45 with 0.21 and 0.25 used most frequently. Additional categories such as mildly, moderately and severely frail were defined inconsistently. The rationale for selecting particular FI scores and labels were reported in most studies, but were not always relevant.Conclusions: High impact studies vary in the way they categorise the FI and while there is some evidence in the community-dweller literature, FI categories have not been well validated in acute and residential aged care. For the time being, in those settings, the FI should be reported as a continuous variable wherever possible. It is important to continue working towards defining frailty categories as variability in FI categorisation impacts the ability to synthesise results and to translate findings into clinical practice. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the relationship between frailty and the metabolic syndrome and to evaluate how these contribute to mortality in very old people. Design: Secondary analysis of data from the Project of Longevity and Aging in Dujiangyan. Setting: Community sample from Sichuan Province, China. Participants: People aged 90+ years (n=767; baseline age=93.7±3.4 years; 68.0% women. Measurements: After a baseline health assessment, participants were followed for four years (54.0% died). A frailty index (FI) was calculated as the sum of deficits present, divided by the 35 health-related deficits considered. Relationships between the FI and the metabolic syndrome were tested; their effect on death was examined. Results: The mean FI was 0.26 ±0.11. Higher FI scores were associated with a greater risk of death, adjusted for age, sex, education, and metabolic syndrome items. The hazard ratio was 1.03 (95% confidence interval 1.02, 1.04) for each 1% percent increase of the FI. The mortality risk did not change with the metabolic syndrome (odds ratio=0.99; 0.71-1.36). Conclusions: In the oldest old, frailty was a significant risk for near-term death, regardless of the metabolic syndrome. Even using age-adjusted models, the epidemiology of late life illness may need to account for frailty routinely. [ABSTRACT FROM AUTHOR]

Background: The relationship between age-related frailty and the underlying processes that drive changes in health is currently unclear. Considered individually, most blood biomarkers show only weak relationships with frailty and ageing. Here, we examined whether a biomarker-based frailty index (FI-B) allowed examination of their collective effect in predicting mortality compared with individual biomarkers, a clinical deficits frailty index (FI-CD), and the Fried frailty phenotype. Methods: We analyzed baseline data and up to 7-year mortality in the Newcastle 85+ Study (n = 845; mean age 85.5). The FI-B combined 40 biomarkers of cellular ageing, inflammation, haematology, and immunosenescence. The Kaplan-Meier estimator was used to stratify participants into FI-B risk strata. Stability of the risk estimates for the FI-B was assessed using iterative, random subsampling of the 40 FI-B items. Predictive validity was tested using Cox proportional hazards analysis and discriminative ability by the area under receiver operating characteristic (ROC) curves. Results: The mean FI-B was 0.35 (SD, 0.08), higher than the mean FI-CD (0.22; SD, 0.12); no participant had an FI-B score <0.12. Higher values of each FI were associated with higher mortality risk. In a sex-adjusted model, each one percent increase in the FI-B increased the hazard ratio by 5.4 % (HR, 1.05; CI, 1.04-1.06). The FI-B was more powerful for mortality prediction than any individual biomarker and was robust to biomarker substitution. The ROC analysis showed moderate discriminative ability for 7-year mortality (AUC for FI-CD = 0.71 and AUC for FI-B = 0.66). No individual biomarker's AUC exceeded 0.61. The AUC for combined FI-CD/FI-B was 0.75. Conclusions: Many biological processes are implicated in ageing. The systemic effects of these processes can be elucidated using the frailty index approach, which showed here that subclinical deficits increased the risk of death. In the future, blood biomarkers may indicate the nature of the underlying causal deficits leading to age-related frailty, thereby helping to expose targets for early preventative interventions. [ABSTRACT FROM AUTHOR]

Background Older adults are at an increased risk of death, but not all people of the same age have the same risk. Many methods identify frail people (that is, those at increased risk) but these often require time-consuming interactions with health care providers. We evaluated whether standard laboratory tests on their own, or added to a clinical frailty index (FI), could improve identification of older adults at increased risk of death. Methods This is a secondary analysis of a prospective cohort study, where community dwelling and institutionalized participants in the Canadian Study of Health and Aging who also volunteered for blood collection (n = 1,013) were followed for up to six years. A standard FI (FI-CSHA) was constructed from data obtained during the clinical evaluation and a second, novel FI was constructed from laboratory data plus systolic and diastolic blood pressure measurements (FI-LAB). A combined FI included all items from each index. Predictive validity was tested using Cox proportional hazards analysis and discriminative ability by the area under receiver operating characteristic (ROC) curves. Results Of 1,013 participants, 51.3% had died by six years. The mean baseline value of the FI-LAB was 0.27 (standard deviation 0.11; range 0.05 to 0.63), the FI-CSHA was 0.25 (0.11; 0.02 to 0.72), and the combined FI was 0.26 (0.09; 0.06 to 0.59). In an age- and sex-adjusted model, with each increment in the FI-LAB, the hazard ratios increased by 2.8% (95% confidence interval 1.02 to 1.04). The hazard ratios for the FI-CSHA and the combined FI were 1.02 (1.01 to 1.03) and 1.04 (1.03 to 1.05), respectively. The FI-LAB and FI-CSHA remained independently associated with death in the face of the other. The areas under the ROC curves were 0.72 for FI-LAB, 0.73 for FI-CSHA and 0.74 for the combined FI. Conclusions An FI based on routine laboratory data can identify older adults at increased risk of death. Additional evaluation of this approach in clinical settings is warranted. [ABSTRACT FROM AUTHOR]

All the current frailty measures count deficits. They differ chiefly in which items, and how many, they consider. These differences are related: if a measure considers only a few items, to define broad risks those items need to integrate across several systems (e.g. mobility or function). If many items are included, the cumulative effect of small deficits can be considered. Even so, it is not clear just how small deficits can be. To better understand how the scale of deficit accumulation might impact frailty measurement, we consider how age-related, subcellular deficits might become macroscopically visible and so give rise to frailty. Cellular deficits occur when subcellular damage has neither been repaired nor cleared. With greater cellular deficit accumulation, detection becomes more likely. Deficit detection can be done by either subclinical (e.g. laboratory, imaging, electrodiagnostic) or clinical methods. Not all clinically evident deficits need cross a disease threshold. The extent to which cellular deficit accumulation compromises organ function can reflect not just what is happening in that organ system, but deficit accumulation in other organ systems too. In general, frailty arises in relation to the number of organ systems in which deficits accumulate. This understanding of how subcellular deficits might scale has implications for understanding frailty as a vulnerability state. Considering the cumulative effects of many small deficits appears to allow important aspects of the behaviour of systems close to failure to be observed. It also suggests the potential to detect frailty with less reliance on clinical observation than current methods employ. [ABSTRACT FROM PUBLISHER]

The notion of frailty has evolved for more than 15 years. Although there is no consensus definition, frailty reflects a state of increased vulnerability to adverse health outcomes for individuals of the same chronological age. Two commonly used clinical tools, the frailty index and the frailty phenotype, both measure health-related deficits. The frailty index is a ratio of the number of deficits that an individual has accumulated divided by all deficits measured, whereas the phenotype specifies frailty as represented by poor performance in three of five criteria (i.e., weight loss, exhaustion, weakness, slowness, lack of activity). From human studies, animal models of both approaches have been developed and are beginning to shed light on mechanisms underlying frailty, the influence of frailty on disease expression, and new interventions to attenuate frailty. Currently, back-translation to humans is occurring. As we start to understand subcellular mechanisms involved in damage and repair as well as their response to treatment, we will begin to understand the molecular basis of aging and, thus, of frailty. [ABSTRACT FROM AUTHOR]