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measures of health quantify the aging process of individuals. They should be interpretable, associated with future adverse outcomes, and straightforward to assemble. We use the rank-ordering of risk within a population to construct a quantile frailty index (QFI) that avoids dichotomization, is convenient and interpretable, and is associated with adverse outcomes. We show that the QFI outperforms previous frailty index (FI) measures on cross-sectional laboratory data (NHANES, CSHA, and ELSA). We construct the QFI by ranking the risk of individuals with respect to a reference population. Sex-specific reference populations narrow male-female FI differences as a function of age, and improve predictive performance. With a fixed reference population of 80-85 year olds, our QFI appears similar to earlier FI measures. With an age-matched reference population for each individual, we obtain a QFI that contains very little age information and that has similar predictive performance as other age-controlled FI measures. Adding age as an auxiliary variable leads to significantly better performance. We conclude that age should be controlled for when evaluating the predictive performance of summary measures of health. This is straight-forward to do with the QFI.

We have built a computational model of individual aging trajectories of health and survival, that contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine techniques of modern machine learning with an interpretable network approach, where health variables are coupled by an explicit interaction network within a stochastic dynamical system. Our model is scalable to large longitudinal data sets, is predictive of individual high-dimensional health trajectories and survival from baseline health states, and infers an interpretable network of directed interactions between the health variables. The network identifies plausible physiological connections between health variables and clusters of strongly connected heath variables. We use English Longitudinal Study of Aging (ELSA) data to train our model and show that it performs better than traditional linear models for health outcomes and survival. Our model can also be used to generate synthetic individuals that age realistically, to impute missing data, and to simulate future aging outcomes given an arbitrary initial health state.

The gradual accumulation of damage and dysregulation during the aging of living organisms can be quantified. Even so, the aging process is complex and has multiple interacting physiological scales -- from the molecular to cellular to whole tissues. In the face of this complexity, we can significantly advance our understanding of aging with the use of computational models that simulate realistic individual trajectories of health as well as mortality. To do so, they must be systems-level models that incorporate interactions between measurable aspects of age-associated changes. To incorporate individual variability in the aging process, models must be stochastic. To be useful they should also be predictive, and so must be fit or parameterized by data from large populations of aging individuals. In this perspective, we outline where we have been, where we are, and where we hope to go with such computational models of aging. Our focus is on data-driven systems-level models, and on their great potential in aging research.

ObjectivesTo examine the association between blood-brain barrier (BBB) integrity, brain volume and cognitive dysfunction in adult patients with systemic lupus erythematosus (SLE).MethodsA total of 65 ambulatory patients with SLE and 9 healthy controls underwent dynamic contrast-enhanced MRI scanning, for quantitative assessment of BBB permeability. Volumetric data were extracted using the VolBrain pipeline. Global cognitive function was evaluated using a screening battery consisting of tasks falling into five broad cognitive domains, and was compared between patients with normal versus extensive BBB leakage.ResultsPatients with SLE had significantly higher levels of BBB leakage compared with controls (p=0.04). Extensive BBB leakage (affecting over >9% of brain volume) was identified only in patients with SLE (16/65; 24.6%), who also had smaller right and left cerebral grey matter volumes compared with controls (p=0.04). Extensive BBB leakage was associated with lower global cognitive scores (p=0.02), and with the presence of impairment on one or more cognitive tasks (p=0.01).ConclusionOur findings provide evidence for a link between extensive BBB leakage and changes in both brain structure and cognitive function in patients with SLE. Future studies should investigate the mechanisms underlying BBB-mediated cognitive impairment, validate the diagnostic utility of BBB imaging, and determine the potential of targeting the BBB as a therapeutic strategy in patients with SLE.

We develop a computational model of human aging that generates individual health trajectories with a set of observed health attributes. Our model consists of a network of interacting health attributes that stochastically damage with age to form health deficits, leading to eventual mortality. We train and test the model for two different cross-sectional observational aging studies that include simple binarized clinical indicators of health. In both studies, we find that cohorts of simulated individuals generated from the model resemble the observed cross-sectional data in both health characteristics and mortality. We can generate large numbers of synthetic individual aging trajectories with our weighted network model. Predicted average health trajectories and survival probabilities agree well with the observed data.

Background frailty is a public health priority now that the global population is ageing at a rapid rate. A scientifically sound tool to measure frailty and generate population-based reference values is a starting point. Objective in this report, our objectives were to operationalize frailty as deficit accumulation using a standard frailty index (FI), describe levels of frailty in Canadians ≥45 years old and provide national normative data. Design this is a secondary analysis of the Canadian Longitudinal Study on Aging (CLSA) baseline data. Setting/participants about 51,338 individuals (weighted to represent 13,232,651 Canadians), aged 45–85 years, from the tracking and comprehensive cohorts of CLSA. Methods after screening all available variables in the pooled dataset, 52 items were selected to construct an FI. Descriptive statistics for the FI and normative data derived from quantile regressions were developed. Results the average age of the participants was 60.3 years (95% confidence interval [CI]: 60.2–60.5), and 51.5% were female (95% CI: 50.8–52.2). The mean FI score was 0.07 (95% CI: 0.07–0.08) with a standard deviation of 0.06. Frailty was higher among females and with increasing age, and scores >0.2 were present in 4.2% of the sample. National normative data were identified for each year of age for males and females. Conclusions the standardized frailty tool and the population-based normative frailty values can help inform discussions about frailty, setting a new bar in the field. Such information can be used by clinicians, researchers, stakeholders and the general public to understand frailty, especially its relationship with age and sex.

frailty is proposed as a summative measure of health status and marker of individual vulnerability. We aimed to investigate the discriminative capacity of a frailty index (FI) derived from interRAI Comprehensive Geriatric Assessment for Acute Care (AC) in relation to multiple adverse inpatient outcomes.in this prospective cohort study, an FI was derived for 1,418 patients ≥70 years across 11 hospitals in Australia. The interRAI-AC was administered at admission and discharge by trained nurses, who also screened patients daily for geriatric syndromes.in adjusted logistic regression models an increase of 0.1 in FI was significantly associated with increased likelihood of length of stay28 days (odds ratio [OR]: 1.29 [1.10-1.52]), new discharge to residential aged care (OR: 1.31 [1.10-1.57]), in-hospital falls (OR: 1.29 [1.10-1.50]), delirium (OR: 2.34 [2.08-2.63]), pressure ulcer incidence (OR: 1.51 [1.23-1.87]) and inpatient mortality (OR: 2.01 [1.66-2.42]). For each of these adverse outcomes, the cut-point at which optimal sensitivity and specificity occurred was for an FI0.40. Specificity was higher than sensitivity with positive predictive values of 7-52% and negative predictive values of 88-98%. FI-AC was not significantly associated with readmissions to hospital.the interRAI-AC can be used to derive a single score that predicts multiple adverse outcomes in older inpatients. A score of ≤0.40 can well discriminate patients who are unlikely to die or experience a geriatric syndrome. Whether the FI-AC can result in management decisions that improve outcomes requires further study.

Introduction The clinical meaningfulness of Alzheimer's Disease Assessment Scale–Cognitive subscale (ADAS-Cog) subscale change is disputed. We compared 2- to 4-point ADAS-Cog changes with changes in Goal Attainment Scaling (GAS) and everyday function across initial ADAS-Cog scores and treatment responses. Methods This exploratory analysis evaluated mild-moderate Alzheimer's disease patients treated with donepezil (12 months) or galantamine (8 months). Clinical meaningfulness was defined as concomitant ADAS-Cog and GAS changes of ±3 points and/or functional improvement. Results Patients with ≥3-point ADAS-Cog improvement significantly improved on GAS but not on standard tests of everyday function. ADAS-Cog "no change" (≤±3 points) was seen with mean GAS improvement. Initial ADAS-Cog improvement made endpoint improvement (ADAS-Cog 3 points and GAS 1 point) more likely (odds ratio = 6.9; 95% confidence interval=2.5–19.5). In contrast, initial deterioration made endpoint improvement unlikely (0.33; 0.14–0.64). Discussion ADAS-Cog improvement and no change were each associated with GAS improvement. Initial ADAS-Cog worsening was unlikely to result in later improvement. Clinical trial registration number ISRCTN26167328.

Frailty indices (FI) based on continuous valued health data, such as obtained from blood and urine tests, have been shown to be predictive of adverse health outcomes. However, creating FI from such biomarker data requires a binarization treatment that is difficult to standardize across studies. In this work, we explore a “quantile” methodology for the generic treatment of biomarker data that allows us to construct an FI without preexisting medical knowledge (i.e. risk thresholds) of the included biomarkers. We show that our quantile approach performs as well as, or even slightly better than, established methods for the National Health and Nutrition Examination Survey (NHANES) and the Canadian Study of Health and Aging (CSHA) data sets. Furthermore, we show that our approach is robust to cohort effects within studies as compared to other data-based methods. The success of our binarization approaches provides insight into the robustness of the FI as a health measure, the upper limits of the FI observed in various data sets, and highlights general difficulties in obtaining absolute scales for comparing FI between studies.

Our goal is to address human health as a whole, with an ultimate goal of understanding how health changes with age. Here we discuss the need to integrate multidimensional information about health (and not only human) that is available in many biological and clinical databases. Reviewing several means of such integration (frailty index, dysregulation score, and some indices of biological age) we pay particular attention to the frailty index, which allows such an integration in a simple but an effective way. Most importantly, it makes it possible to apply mathematical modeling techniques and computer simulations to gain insight into the complexity of human health. By applying such modeling methodology (complex dynamical networks) we can take advantage of one of the most essential characteristics of biological systems—the interdependence of multiple biological and clinical characteristics of such systems. That also explains the effectiveness in many practical applications of using the frailty index based on the multiple clinical and biological traits instead of selecting only those that survive iterative p value testing.

We thank Dr Pamuk et al 1 for their interest in our work,2 and for providing insights regarding mechanisms that may help explain our findings. Their correspondence highlights the importance of further understanding the processes underlying our main finding: extensive blood-brain barrier (BBB) leakage affects ~25% of patients with systemic lupus erythematosus (SLE) and is linked to smaller grey matter volume and cognitive impairment. We agree that the mechanisms resulting in BBB leakage in SLE remain poorly understood, and that …