In elderly populations, frailty is associated with higher mortality risk. Although many frailty scores (FS) have been proposed, no single score is considered the gold standard. We aimed to evaluate the agreement between a wide range of FS in the English Longitudinal Study of Ageing (ELSA). Through a literature search, we identified 35 FS that could be calculated in ELSA wave 2 (2004–2005). We examined agreement between each frailty score and the mean of 35 FS, using a modified Bland-Altman model and Cohen's kappa (κ). Missing data were imputed. Data from 5,377 participants (ages ≥60 years) were analyzed (44.7% men, 55.3% women). FS showed widely differing degrees of agreement with the mean of all scores and between each pair of scores. Frailty classification also showed a very wide range of agreement (Cohen's κ = 0.10–0.83). Agreement was highest among “accumulation of deficits”-type FS, while accuracy was highest for multidimensional FS. There is marked heterogeneity in the degree to which various FS estimate frailty and in the identification of particular individuals as frail. Different FS are based on different concepts of frailty, and most pairs cannot be assumed to be interchangeable. Research results based on different FS cannot be compared or pooled.

OBJECTIVE Frailty is a dynamic state of vulnerability in the elderly. We examined whether individuals with overt diabetes or higher levels of HbA_1c or fasting plasma glucose (FG) experience different frailty trajectories with aging. RESEARCH DESIGN AND METHODS Diabetes, HbA_1c, and FG were assessed at baseline, and frailty status was evaluated with a 36-item frailty index every 2 years during a 10-year follow-up among participants from the English Longitudinal Study of Ageing (ELSA). Mixed-effects models with age as time scale were used to assess whether age trajectories of frailty differed as a function of diabetes, HbA_1c, and FG. RESULTS Among 5,377 participants (median age [interquartile range] 70 [65, 77] years, 45% men), 35% were frail at baseline. In a model adjusted for sex, participants with baseline diabetes had an increased frailty index over aging compared with those without diabetes. Similar findings were observed with higher levels of HbA_1c, while FG was not associated with frailty. In a model additionally adjusted for income, social class, smoking, alcohol, and hemoglobin, only diabetes was associated with an increased frailty index. Among nonfrail participants at baseline, both diabetes and HbA_1c level were associated with a higher increased frailty index over time. CONCLUSIONS People with diabetes or higher HbA_1c levels at baseline had a higher frailty level throughout later life. Nonfrail participants with diabetes or higher HbA_1c also experienced more rapid deterioration of frailty level with aging. This observation could reflect a role of diabetes complications in frailty trajectories or earlier shared determinants that contribute to diabetes and frailty risk in later life.

Frail elderly people experience elevated mortality. However, no consensus exists on the definition of frailty, and many frailty scores have been developed. The main aim of this study was to compare the association between 35 frailty scores and incident cardiovascular disease (CVD), incident cancer, and all-cause mortality. Also, we aimed to assess whether frailty scores added predictive value to basic and adjusted models for these outcomes. Through a structured literature search, we identified 35 frailty scores that could be calculated at wave 2 of the English Longitudinal Study of Ageing (ELSA), an observational cohort study. We analysed data from 5,294 participants, 44.9% men, aged 60 years and over. We studied the association between each of the scores and the incidence of CVD, cancer, and all-cause mortality during a 7-year follow-up using Cox proportional hazard models at progressive levels of adjustment. We also examined the added predictive performance of each score on top of basic models using Harrell’s C statistic. Using age of the participant as a timescale, in sex-adjusted models, hazard ratios (HRs) (95% confidence intervals) for all-cause mortality ranged from 2.4 (95% CI: 1.7–3.3) to 26.2 (95% CI: 15.4–44.5). In further adjusted models including smoking status and alcohol consumption, HR ranged from 2.3 (95% CI: 1.6–3.1) to 20.2 (95% CI: 11.8–34.5). In fully adjusted models including lifestyle and comorbidity, HR ranged from 0.9 (95% CI: 0.5–1.7) to 8.4 (95% CI: 4.9–14.4). HRs for CVD and cancer incidence in sex-adjusted models ranged from 1.2 (95% CI: 0.5–3.2) to 16.5 (95% CI: 7.8–35.0) and from 0.7 (95% CI: 0.4–1.2) to 2.4 (95% CI: 1.0–5.7), respectively. In sex- and age-adjusted models, all frailty scores showed significant added predictive performance for all-cause mortality, increasing the C statistic by up to 3%. None of the scores significantly improved basic prediction models for CVD or cancer. A source of bias could be the differences in mortality follow-up time compared to CVD/cancer, because the existence of informative censoring cannot be excluded. There is high variability in the strength of the association between frailty scores and 7-year all-cause mortality, incident CVD, and cancer. With regard to all-cause mortality, some scores give a modest improvement to the predictive ability. Our results show that certain scores clearly outperform others with regard to three important health outcomes in later life. Finally, we think that despite their limitations, the use of frailty scores to identify the elderly population at risk is still a useful measure, and the choice of a frailty score should balance feasibility with performance.

Early life adversity (ELA) has been associated with an increased risk for diseases in which the immune system plays a critical role. The ELA immune phenotype is characterized by inflammation, impaired cellular immunity, and immunosenescence. However, data on cell-specific immune effects are largely absent. Additionally, stress systems and health behaviors are altered in ELA, which may contribute to the generation of the ELA immune phenotype. The present investigation tested cell-specific immune differences in relationship to the ELA immune phenotype, altered stress parameters, and health behaviors in individuals with ELA (n = 42) and those without a history of ELA (control, n = 73). Relative number and activation status (CD25, CD69, HLA-DR, CD11a, CD11b) of monocytes, NK cells, B cells, T cells, and their main subsets were assessed by flow cytometry. ELA was associated with significantly reduced numbers of CD69⁺CD8⁺ T cells (p = 0.022), increased numbers of HLA-DR⁺ CD4 and HLA-DR⁺ CD8 T cells (p < 0.001), as well as increased numbers of CD25⁺CD8⁺ T cells (p = 0.036). ELA also showed a trend toward higher numbers of CCR4⁺CXCR3⁻CCR6⁺ CD4 T cells. Taken together, our data suggest an elevated state of immune activation in ELA, in which particularly T cells are affected. Although several aspects of the ELA immune phenotype were related to increased activation markers, neither stress nor health-risk behaviors explained the observed group differences. Thus, the state of immune activation in ELA does not seem to be secondary to alterations in the stress system or health-risk behaviors, but rather a primary effect of early life programming on immune cells.

Rationale. The value of microRNAs (miRNAs) as biomarkers has been addressed in various clinical contexts. Initial studies suggested that miRNAs, such as the brain-enriched miR-124-3p, might improve outcome prediction after out-of-hospital cardiac arrest. The aim of this study is to determine the prognostic value of miR-122-5p in a large cohort of comatose survivors of out-of-hospital cardiac arrest.