Screening for Frailty Using the FRAIL Scale in Older Cancer Survivors: A Cross-sectional Comparison With the Fried Phenotype

OBJECTIVES

To examine the diagnostic performance of the FRAIL Scale for frailty screening with reference to the Fried phenotype and investigate its association with health outcomes in older cancer survivors.

DATA SOURCE

In this cross-sectional quantitative study, participants were post-treatment cancer survivors aged 65 or above. Measurements included the FRAIL Scale, Fried phenotype, Geriatric Depression Scale-15 item, Modified Barthel Inventory, and EORTC Core Quality of Life Questionnaire. Receiver operating characteristic curve analysis was performed to evaluate the diagnostic performance of the FRAIL Scale with reference to the Fried phenotype. Health outcomes associated with being frail as estimated by the FRAIL Scale and Fried phenotype were also examined using regressions.

RESULTS

Based on 293 older cancer survivors, the area under curve (AUC) of the FRAIL Scale was 0.79, and the optimal cut-off of 1 yielded a sensitivity of 92% and specificity of 41%. According to regression results, the FRAIL Scale was modified by adding an item on time since cancer treatment completion (AUC = 0.81), and using a cut-off of 2 for older cancer survivors, which yielded a sensitivity of 74% and specificity of 67%. The modified FRAIL Scale was associated with depressive symptoms, functional independence, fatigue, dyspnea, physical functioning, and role functioning.

CONCLUSIONS

The modified FRAIL Scale is proposed for use in older cancer survivors, and a cut-off of 2 should be used.

IMPLICATIONS FOR NURSING PRACTICE

The modified FRAIL Scale can serve as a brief screening tool for identifying frailty among older cancer survivors in practice.

Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study

BACKGROUND

Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer's disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults.

METHODS

The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects.

RESULTS

We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast.

CONCLUSIONS

Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.

Preserving the discreteness of deficits during coding leads to a lower frailty index in individuals living in long-term care

This study assesses two coding approaches on the frailty index (FI). Two FI were calculated using 43 variables from 29,758 older adults (84.6 ± 8 years old; 64 % female) in long-term care. Scores were coded as 0, 0.5, or 1 regardless of the number of levels (grouped), or preserved (e.g., a 4 level variable was coded as 0, 0.33, 0.67, or 1; discrete). Grouped and discrete FI were compared with each ordinal variable removed but all other ordinal variables included. This was repeated until 28 unique (14 grouped, 14 discrete) FI had been constructed each with one ordinal variable removed per FI. FI was correlated to age and mortality separated by sex. The median grouped (0.302 (0.221-0.372)) was higher relative to the discrete (0.237 (0.170-0.307)) FI. The discrete (r = 0.91, r = 0.87) and grouped (r = 0.93, r = 0.87) FI showed similar relationships to age and mortality. Removal of any ordinal variable reduced grouped FI by 0.004 or 0.016, whereas removal led to both increases (range: 0.003-0.001) and reductions (range: 0.002-0.008) for discrete FI. A grouped approach inflates FI. A discrete approach provides a more accurate measure of frailty.

How frail is frail in oncology studies? A scoping review

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.

Cognitive function is mediated by deficit accumulation in older, long-term breast cancer survivors

PURPOSE

This study aims to examine whether cognitive function in older, long-term breast cancer survivors is both a direct effect of cancer and cancer treatments and an indirect effect mediated by deficit accumulation.

PATIENTS AND METHODS

Female breast cancer survivors who had been diagnosed and treated at age 60 or older and were 5-15-year survivors (N = 220) and age- and education-matched non-cancer controls (N = 123) were assessed at enrollment and at 8-, 16-, and 24-month follow-ups with standard neuropsychological tests and the comprehensive geriatric assessment which was used to calculate the deficit accumulation frailty index (DAFI). Blood or saliva samples for APOE genotyping were collected at enrollment. Participants were purposely recruited so that approximately 50% had a history of treatment with chemotherapy or and 50% were not exposed to chemotherapy.

RESULTS

Latent variable mediation analysis revealed that cognitive performance was mediated by deficit accumulation for all three domains. The direct effect of cancer diagnosis and treatment history was significant for the Language domain (p = 0.04), a trend for the learning and memory domain (p = 0.054), and non-significant for the attention, processing speed, executive function (APE) domain. Carrying the APOE ε4 allele had a significant negative direct effect on the APE domain (p = 0.05) but no indirect effect through deficit accumulation.

CONCLUSION

Cognitive function in older, long-term breast cancer survivors appears to be primarily mediated through deficit accumulation.

IMPLICATIONS FOR CANCER SURVIVORS

These findings have important clinical implications suggesting that the most effective intervention to prevent or slow cognitive aging in older cancer survivors may be through prevention or management of comorbidities and interventions that maintain functional capacity (exercise, physical therapy) and social and mental health.

Genetic and Environmental Influences on Longitudinal Frailty Trajectories From Adulthood into Old Age

BACKGROUND

Frailty is a complex, dynamic geriatric condition, but limited evidence has shown how genes and environment may contribute to its longitudinal changes. We sought to investigate sources of individual differences in the longitudinal trajectories of frailty, considering potential selection bias when including a sample of oldest-old twins.

METHODS

Data were from 2 Swedish twin cohort studies: a younger cohort comprising 1 842 adults aged 29-96 years followed up to 15 waves, and an older cohort comprising 654 adults aged ≥79 years followed up to 5 waves. Frailty was measured using the frailty index (FI). Age-based latent growth curve models were used to examine longitudinal trajectories, and extended to a biometric analysis to decompose variability into genetic and environmental etiologies.

RESULTS

A bilinear model with an inflection point at age 75 best described the data, indicating a fourfold to fivefold faster FI increase after 75 years. Twins from the older cohort had significantly higher mean FI at baseline but slower rate of increase afterward. FI level at age 75 was moderately heritable in both men (42%) and women (55%). Genetic influences were relatively stable across age for men and increasing for women, although the most salient amplification in FI variability after age 75 was due to individual-specific environmental influences for both men and women; conclusions were largely consistent when excluding the older cohort.

CONCLUSION

Increased heterogeneity of frailty in late life is mainly attributable to environmental influences, highlighting the importance of targeting environmental risk factors to mitigate frailty in older adults.

Eight-Year Changes in Multimorbidity and Frailty in Adults With Type 2 Diabetes Mellitus: Associations With Cognitive and Physical Function and Mortality

BACKGROUND

Indices of multimorbidity and deficit accumulation (ie, frailty indices) have been proposed as markers of biological aging. If true, changes in these indices over time should predict downstream changes in cognition and physical function, and mortality.

METHODS

We examined associations that 8-year changes in (i) a multimorbidity index comprised of 9 chronic diseases and (ii) a frailty index (FI) based on deficit accumulation in functional, behavioral, and clinical characteristics had with subsequent measures of cognitive and physical function over 10 years. We drew data from 3 842 participants in the Action for Health in Diabetes clinical trial. They were aged 45-76 years at baseline and at risk for accelerated biological aging due to overweight/obesity and type 2 diabetes mellitus.

RESULTS

A total of 1 501 (39%) of the cohort had 8-year increases of 1 among the 9 diseases tracked in the multimorbidity index and 868 (23%) had increases of ≥2. Those with greatest increases in multimorbidity tended to be older individuals, males, and non-Hispanic Whites. Greater FI increases tended to occur among individuals who were older, non-Hispanic White, heavier, and who had more baseline morbidities. Changes in multimorbidity and FI were moderately correlated (r = 0.26; p < .001). Increases in both multimorbidity and FI were associated with subsequently poorer composite cognitive function and 400-m walk speed and increased risk for death (all p < .001).

CONCLUSIONS

Accelerated biological aging, as captured by multimorbidity and frailty indices, predicts subsequent reduced function and mortality. Whether intensive lifestyle interventions generally targeting multimorbidity and FI reduce risks for downstream outcomes remains to be seen. Clinical Trials Registration Number: NCT00017953.

How frail is frail? A systematic scoping review and synthesis of high impact studies

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.

Sex differences in frailty: Comparisons between humans and preclinical models

Frailty can be viewed as a state of physiological decline that increases susceptibility to adverse health outcomes. This loss of physiological reserve means that even small stressors can lead to disability and death in frail individuals. Frailty can be measured with various clinical tools; the two most popular are the frailty index and the frailty phenotype. Clinical studies have used these tools to show that women are frailer than men even though they have longer lifespans. Still, factors responsible for this frailty-mortality paradox are not well understood. This review highlights evidence for male-female differences in frailty from both the clinical literature and in animal models of frailty. We review evidence for higher frailty levels in female animals as seen in many preclinical models. Mechanisms that may contribute to sex differences in frailty are highlighted. In addition, we review work that suggests frailty may play a role in susceptibility to chronic diseases of aging in a sex-specific fashion. Additional mechanistic studies in preclinical models are needed to understand factors involved in male-female differences in frailty in late life.

Drivers of Frailty from Adulthood into Old Age: Results from a 27-Year Longitudinal Population-Based Study in Sweden

BACKGROUND

Frailty is a strong predictor of adverse outcomes. However, longitudinal drivers of frailty are not well understood. This study aimed at investigating the longitudinal trajectories of a frailty index (FI) from adulthood to late life and identifying the factors associated with the level and rate of change in FI.

METHODS

An age-based latent growth curve analysis was performed in the Swedish Adoption/Twin Study of Aging (N = 1,842; aged 29-102 years) using data from up to 15 measurement waves across 27 years. A 42-item FI was used to measure frailty at each wave.

RESULTS

A bilinear, two-slope model with a turning point at age 65 best described the age-related change in FI, showing that the increase in frailty was more than twice as fast after age 65. Underweight, obesity, female sex, overweight, being separated from one's co-twin during childhood, smoking, poor social support, and low physical activity were associated with a higher FI at age 65, with underweight having the largest effect size. When tested as time-varying covariates, underweight and higher social support were associated with a steeper increase in FI before age 65, whereas overweight and obesity were associated with less steep increase in FI after age 65.

CONCLUSIONS

Factors associated with the level and rate of change in frailty are largely actionable and could provide targets for intervention. As deviations from normal weight showed the strongest associations with frailty, future public health programs could benefit from monitoring of individuals with abnormal BMI, especially those who are underweight.

Natural Regression of Frailty Among Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis

BACKGROUND AND OBJECTIVES

Frailty is a dynamic process with potential transitions over time. However, there is limited understanding of the patterns of frailty improvement. We conducted a systematic review and meta-analysis to estimate the natural rate of frailty regression among community-dwelling older adults aged at least 60 years.

RESEARCH DESIGN AND METHODS

Systematic searches for studies reporting frailty improvement were performed in 5 databases (Medline, Embase, CINAHL plus, Web of Science, and PsycINFO) from inception until January 2019.

RESULTS

Twenty-five studies from 26 countries were included. Among a baseline population of more than 50,000 individuals, the pooled prevalence of pre-frailty and frailty was 50.5% (95% confidence interval [CI] 47.8-53.3) and 12.8% (95% CI 9.1-17.0), respectively. During a median follow-up of 3.0 (range 1-10.0) years, 23.3% of surviving pre-frail individuals regressed to a robust state and 35.2% of surviving frail individuals reversed to a pre-frail or robust state. The pooled remission rates among people with pre-frailty and frailty were 80.4 (95% CI 61.7-104.6) and 135.3 (95% CI 98.1-186.5) per 1,000 person-years, respectively. Frailty and pre-frailty improvement rates varied by sex, diagnostic criteria, study region, and follow-up duration. The remission rates were significantly reduced when accounting for progressions to death. The heterogeneity of included studies was high which reflected considerable differences in methodological approach.

DISCUSSION AND IMPLICATIONS

Although frailty is highly prevalent in older people, natural remission is possible and common. Improved understanding of the factors that confer increased likelihood of frailty regression may support the design of interventions to reduce the burden of frailty.

Operationalization of a frailty index among older adults in the InCHIANTI study: predictive ability for all-cause and cardiovascular disease mortality

Background

The frailty index (FI) is a sensitive instrument to measure the degree of frailty in older adults, and is increasingly used in cohort studies on aging.

Aims

To operationalize an FI among older adults in the “Invecchiare in Chianti” (InCHIANTI) study, and to validate its predictive capacity for mortality.

Methods

Longitudinal data were used from 1129 InCHIANTI participants aged ≥ 65 years. A 42-item FI was operationalized following a standard procedure using baseline data (1998/2000). Associations of the FI with 3- and 6-year all-cause and cardiovascular disease (CVD) mortality were studied using Cox regression. Predictive accuracy was estimated by the area under the ROC curve (AUC), for a continuous FI score and for different cut-points.

Results

The median FI was 0.13 (IQR 0.08–0.21). Scores were higher in women, and at advanced age. The FI was associated with 3- and 6-year all-cause and CVD mortality (HR range per 0.01 FI increase = 1.03–1.07, all p < 0.001). The continuous FI score predicted the mortality outcomes with moderate-to-good accuracy (AUC range 0.72–0.83). When applying FI cut-offs between 0.15 and 0.35, the accuracy of this FI for predicting mortality was moderate (AUC range 0.61–0.76). Overall, the predictive accuracy of the FI was higher in women than in men.

Conclusions

The FI operationalized in the InCHIANTI study is a good instrument to grade the risk of all-cause mortality and CVD mortality. More measurement properties, such as the responsiveness of this FI when used as outcome measure, should be investigated in future research.

Electronic supplementary material

The online version of this article (10.1007/s40520-020-01478-3) contains supplementary material, which is available to authorized users.

Counting deficits or diseases? The agreement between frailty and multimorbidity in subjects with cognitive disturbances

In the present study, we explored the relationship between multimorbidity and frailty in a population of older individuals with cognitive disturbances attending a memory clinic. All subjects consecutively attending the Memory Clinic of the Department of Human Neuroscience, "Sapienza" University of Rome, between January 2017 and April 2018 for a first neurological evaluation were considered for the present analysis. Multimorbidity was defined as the simultaneous presence of two or more diseases in the same individual. A Frailty Index was computed by considering 44 age-related, multidimensional health deficits. Overall, 185 subjects were recruited in the study. A condition of multimorbidity was detected in 87.6% of the sample, whereas only the 44.6% of the study population was considered as frail. A poor agreement was observed between multimorbidity and frailty. The present findings confirm that counting diseases or health deficits may provide discordant information concerning the risk profile of older subjects.

A Murine Frailty Index Based on Clinical and Laboratory Measurements: Links Between Frailty and Pro-inflammatory Cytokines Differ in a Sex-Specific Manner

A frailty index (FI) based on clinical deficit accumulation (FI-Clinical) quantifies frailty in aging mice. We aimed to develop a laboratory test-based murine FI tool (FI-Lab) and to investigate the effects of age and sex on FI-Lab scores, FI-Clinical scores, and the combination (FI-Combined), as well as to explore links between frailty and inflammation. Studies used older (17 and 23 months) C57BL/6 mice of both sexes. We developed an FI-Lab (blood pressure, blood chemistry, echocardiography) based on deviation from reference values in younger adults (12 months), which showed similar characteristics to a human FI-Lab tool. Interestingly, while FI-Clinical scores were higher in females, the opposite was true for FI-Lab scores and there was no sex difference in FI-Combined scores. All three FI tools revealed a positive correlation between pro-inflammatory cytokine levels and frailty in aging mice that differed between the sexes. Elevated levels of the pro-inflammatory cytokines interleukin (IL)-6, IL-9, and interferon-γ were associated with higher FI scores in aging females, while levels of IL-12p40 rose as FI scores increased in older males. Thus, an FI tool based on common laboratory tests can quantify frailty in mice; the positive correlation between inflammation and frailty scores in naturally aging mice differs between the sexes.

Frailty biomarkers in humans and rodents: Current approaches and future advances

Even though they would have great benefit across research and clinical fields, currently there are no accepted biomarkers of frailty. Cross-sectional studies in humans have identified promising candidates including inflammatory markers such as IL-6, immune markers such as WBC count, clinical markers such as albumin, endocrine markers such as vitamin D, oxidative stress markers such as isoprostanes, proteins such as BDNF and epigenetic markers such as DNA methylation, but there are limitations to the current state of the research. Future approaches to the identification of frailty biomarkers should include longitudinal studies, studies using animal models of frailty, studies incorporating novel biomarkers combined into composite panels, and studies investigating sex differences and potential overlap between markers of biological age and frailty.

Being born in the aftermath of World War II increases the risk for health deficit accumulation in older age: results from the KORA-Age study

Morbidity trends may result from cohort experiences in critical developmental age. Our objective was to compare the health status of 65-71 year-olds who were in critical developmental age before (1937-June 1945), during (June 1945-June 1948) and after (June 1948-1950) the early reconstruction and food crisis (ERFC) period in Germany following World War II. Data originate from the KORA (Cooperative Health Research in the Region of Augsburg)-Age study in Southern Germany. We used the 2008 baseline sample born 1937-1943 and the 2015 enrichment sample born 1944-1950. Health status was assessed as the number of accumulated health deficits using a Frailty Index (FI). Cohorts were defined based on co-occurrence of critical developmental age (gestation and the first 2 years of life) and the ERFC period. Cohort, age and sex effects on older-age health status were analyzed using generalized linear models. We included 590 (53% male) pre-war and war (PWW), 475 (51% male) ERFC and 171 post-currency reform (PCR) cohort participants (46% male). Adjusted for covariates, FI levels were significantly higher for the ERFC (Ratio 1.14, CL [1.06, 1.23]) but not for the PCR (Ratio 1.06, CL [0.94, 1.20]) as compared to the PWW cohort. Being in critical developmental age during the ERFC period increased FI levels in adults aged 65-71 years. Covariates did not explain these effects, suggesting a direct detrimental effect from being in critical developmental age during the ERFC period on older-age health. This expansion of morbidity in Germany was not detected in the PCR cohort.

Rodent models of frailty and their application in preclinical research

In clinical medicine, the concept of frailty is viewed as a state of high vulnerability to adverse health outcomes in people of the same age. Frailty is an important challenge because the loss of physiological reserve means that even minor stressors can lead to disability and death in those who are frail. Even so, the biology of frailty is not well understood. Rodent models of frailty are stimulating research into the biology of frailty. These pre-clinical models are based on "reverse-translation". Investigators have adapted either the "frailty phenotype" approach or the "frailty index" approach, originally developed in humans, for use in animals. This review briefly describes rodent models of frailty, discusses how these models have been used to explore mechanisms of frailty and how they have been employed to assess the impact of frailty on various experimental outcomes. The review also highlights studies that have used rodent models to investigate interventions to attenuate frailty, including drug treatment, dietary modifications and exercise. The ability to model frailty in animals is an exciting development that promises to accelerate the translation of laboratory discoveries into new clinical interventions, and situates frailty research in the larger context of geroscience.

Validation of the JEN frailty index in the National Long-Term Care Survey community population: identifying functionally impaired older adults from claims data

Background

Use of a claims-based index to identify persons with physical function impairment and at risk for long-term institutionalization would facilitate population health and comparative effectiveness research. The JEN Frailty Index [JFI] is comprised of diagnosis domains representing impairments and multimorbid clusters with high long-term institutionalization [LTI] risk. We test the index’s discrimination of activities-of-daily-living [ADL] dependency and 1-year LTI and mortality in a nationally representative sample of over 12,000 Medicare beneficiaries, and compare long-term community survival stratified by ADL and JFI.

Methods

2004 U.S. National Long-Term Care Survey data were linked to Medicare, Minimum Data Set, Veterans Health Administration files and vital statistics. ADL dependencies, JFI score, age and sex were measured at baseline survey. ADL and JFI groups were cross-tabulated generating likelihood ratios and classification statistics. Logistic regression compared discrimination (areas under receiver operating characteristic curves), multivariable calibration and accuracy of the JFI and, separately, ADLs, in predicting 1-year outcomes. Hall-Wellner bands facilitated contrasts of JFI- and ADL-stratified 5-year community survival.

Results

Likelihood ratios rose evenly across JFI risk categories. Areas under the curves of functional dependency at ≥3 and ≥ 2 for JFI, age and sex models were 0.807 [95% c.i.: 0.795, 0.819] and 0.812 [0.801, 0.822], respectively. The area under the LTI curve for JFI and age (0.781 [0.747, 0.815]) discriminated less well than the ADL-based model (0.829 [0.799, 0.860]). Community survival separated by JFI strata was comparable to ADL strata.

Conclusions

The JEN Frailty Index with demographic covariates is a valid claims-based measure of concurrent activities-of-daily-living impairments and future long-term institutionalization risk in older populations lacking functional information.

Electronic supplementary material

The online version of this article (10.1186/s12913-018-3689-2) contains supplementary material, which is available to authorized users.

Fifteen years of progress in understanding frailty and health in aging

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.

Development and validation of a frailty index in the Longitudinal Aging Study Amsterdam

Background

Frailty is a state of increased vulnerability to adverse outcomes. The frailty index (FI), defined by the deficit accumulation approach, is a sensitive instrument to measure levels of frailty, and therefore important for longitudinal studies of aging.

Aims

To develop an FI in the Longitudinal Aging Study Amsterdam (LASA), and to examine the predictive validity of this FI for 19-year mortality.

Methods

LASA is an ongoing study among Dutch older adults, based on a nationally representative sample. A 32-item FI (LASA–FI) was developed at the second LASA measurement wave (1995–1996) among 2218 people aged 57–88 years. An FI score between 0 and 1 was calculated for each individual. The LASA–FI included health deficits from the physical, mental and cognitive domain and can be constructed for most LASA measurement waves. Associations with 19-year mortality were assessed using Kaplan–Meier curves and Cox proportional hazards models.

Results

The mean LASA–FI score was 0.19 (SD = 0.12), with a 99% upper limit of 0.53. Scores were higher in women than men (women = 0.20, SD = 0.13 vs. men = 0.17, SD = 0.11, p < 0.001). The average age-related increase in the log-transformed LASA–FI score was 3.5% per year. In a model adjusted for age and sex, the FI score was significantly associated with 19-year all-cause mortality (HR per 0.01 = 1.03, 95% CI 1.03–1.04, p < 0.001).

Discussion/conclusions

The key characteristics of the LASA–FI were in line with findings from previous FI studies in population-based samples of older people. The LASA–FI score was associated with mortality and may serve as an internal and external reference value.

A frailty index from common clinical and laboratory tests predicts increased risk of death across the life course

A frailty index (FI) based entirely on common clinical and laboratory tests might offer scientific advantages in understanding ageing and pragmatic advantages in screening. Our main objective was to compare an FI based on common laboratory tests with an FI based on self-reported data; we additionally investigated if the combination of subclinical deficits with clinical ones increased the ability of the FI to predict mortality. In this secondary analysis of the 2003–2004 and 2005–2006 National Health and Nutrition Examination Survey data, 8888 individuals aged 20+ were evaluated. Three FIs were constructed: a 36-item FI using self-reported questionnaire data (FI-Self-report); a 32-item FI using data from laboratory test values plus pulse and blood pressure measures (FI-Lab); and a 68-item FI that combined all items from each index (FI-Combined). The mean FI-Lab score was 0.15 ± 0.09, the FI-Self-report was 0.11 ± 0.11 and FI-Combined was 0.13 ± 0.08. Each index showed some typical FI characteristics (skewed distribution with long right tail, non-linear increase with age). Even so, there were fewer people with low frailty levels and a slower increase with age for the FI-Lab compared to the FI-Self-report. Higher frailty level was associated with higher risk of death, although it was strongest at older ages. Both FI-Lab and FI-Self-report remained significant in a combined model predicting death. The FI-Lab was feasible and valid, demonstrating that even subclinical deficit accumulation increased mortality risk. This suggests that deficit accumulation, from the subcellular to the clinically visible is a useful construct that may advance our understanding of the ageing process.

A Comparison of Two Mouse Frailty Assessment Tools

The mouse clinical frailty index and the mouse frailty phenotype assessment are two recently developed tools used to assess frailty in mice. The objectives of this study were to investigate whether the same mice are identified as frail with both tools and to examine the association of each of the assessment tools with age and frailty-related outcomes. Frailty was measured using both tools in old (~24 months; n = 36) C57BL/6 male mice. After 2 weeks, blood pressure and heart rate were measured and serum samples were collected for analysis of alanine aminotransferase, creatinine, and albumin levels. The mouse frailty phenotype assessment identified no mice as frail but modification of the assessment tool identified six mice as frail. The mouse clinical frailty index identified 16 mice as frail and the agreement between the two scales was 50.0%. Increasing clinical frailty index scores were correlated with low serum alanine aminotransferase, as well as decreased heart rate, and reduced heart rate variance. We conclude that, consistent with equivalent frailty assessment scales in humans, both tools have value but do not necessarily identify the same mice as frail.

Development of a Rat Clinical Frailty Index

Rats are a commonly used model for aging studies, and a frailty assessment tool for rats would be of considerable value. There has been a recent focus on the development of preclinical models of frailty in mice. A mouse clinical frailty index (FI) was developed based on clinical frailty assessment tools. This FI measures the accumulation of clinically evident health-related deficits in mice. This paper aimed to develop a rat clinical FI. Male Fischer 344 rats were aged from 6 to 9 months (n = 12), and from 13 to 21 months (n = 41). A FI comprised of 27 health-related deficits was developed from a review of the literature and consultation with a veterinarian. Deficits were scored 0 if absent, 0.5 if mild, or 1 if severe. A FI score was determined for each rat every 3–4 months, and for the older group mortality was assessed up to 21 months. Mean FI scores significantly increased at each time point for the older rats. A high FI score measured at both 17 months of age and terminally was also associated with decreased probability of survival as assessed with Kaplan–Meier curves. The rat clinical FI has significant value for use in aging and interventional studies.

Changes in the Lethality of Frailty Over 30 Years: Evidence From Two Cohorts of 70-Year-Olds in Gothenburg Sweden

Background

With aging, health deficits accumulate: people with few deficits for their age are fit, and those with more are frail. Despite recent reports of improved health in old age, how deficit accumulation is changing is not clear. Our objectives were to evaluate changes over 30 years in the degree of deficit accumulation and in the relationship between frailty and mortality in older adults.

Methods

We analyzed data from two population based, prospective longitudinal cohorts, assembled in 1971-1972 and 2000-2001, respectively. Residents of Gothenburg Sweden, systematically drawn from the Swedish population registry. The 1901-1902 cohort (N = 973) had a response rate of 84.8%; the 1930 cohort (N = 500) had a response rate of 65.1%. A frailty index using 36 deficits was calculated using data from physical examinations, assessments of physical activity, daily, sensory and social function, and laboratory tests. We evaluated mortality over 12.5 years in relation to the frailty index.

Results

Mean frailty levels were the same (x¯ = 0.20, p = .37) in the 1901-1902 cohort as in the 1930 cohort. Although the frailty index was linked to the risk of death in both cohorts, the hazards ratio decreased from 1.67 per 0.1 increment in the frailty index for the first cohort to 1.32 for the second cohort (interaction term p = .005).

Discussion

Although frailty was as common at age 70 as before, its lethality appears to be less. Just why this is so should be explored further.

The association between frailty, the metabolic syndrome, and mortality over the lifespan

Frailty and the metabolic syndrome are each associated with poor outcomes, but in very old people (90+ years) only frailty was associated with an increased mortality risk. We investigated the relationship between frailty, metabolic syndrome, and mortality risk, in younger (20-65 years) and older (65+ years) people. This is a secondary analysis of the US National Health and Nutrition Examination Survey (NHANES) datasets for 2003-2004 and 2005-2006, linked with mortality data up to 2011. The metabolic syndrome was defined using the International Diabetes Federation criteria. Frailty was operationalized using a 41-item frailty index (FI). Compared to the younger group (n = 6403), older adults (n = 2152) had both a higher FI (0.10 ± 0.00 vs. 0.22 ± 0.00, p < 0.001) and a greater prevalence of the metabolic syndrome (24.1 vs. 45.5%, p < 0.001). The metabolic syndrome and FI were correlated in younger people (r = 0.25, p < 0.001) but not in older people (r = 0.08, p < 0.1). In bivariate analyses, the FI predicted mortality risk in both age groups whereas the metabolic syndrome did so only in the younger group. In Cox models, adjusted for age, sex, race, education, and each other, the FI was associated with increased mortality risk at both ages (younger HR 1.05 (1.04-1.06); older HR 1.04 (1.03-1.04) whereas the metabolic syndrome did not contribute to mortality risk. The FI better predicted mortality than did the metabolic syndrome, regardless of age.

The rate of aging: the rate of deficit accumulation does not change over the adult life span

People age at different rates. We have proposed that rates of aging can be quantified by the rate at which individuals accumulate health deficits. Earlier estimates, using cross-sectional analyses suggested that deficits accumulated exponentially, at an annual rate of 3.5%. Here, we estimate the rate of deficit accumulation using longitudinal data from the Canadian National Population Health Survey. By analyzing age-specific trajectories of deficit accumulation in people aged 20 years and over (n = 13,668) followed biannually for 16 years, we found that the longitudinal average annual rate of deficit accumulation was 4.5% (±0.75%). This estimate was notably stable during the adult life span. The corresponding average doubling time in the number of deficits was 15.4 (95% CI 14.82-16.03) years, roughly 30% less than we had reported from the cross-sectional analysis. Earlier work also established that the average number of deficits accumulated by individuals (N), equals the product of the intensity of environmental stresses (λ) causing damage to the organism, by the average recovery time (W). At the individual level, changes in deficit accumulation can be attributed to both changes in environmental stresses and changes in recovery time. By contrast, at the population level, changes in the number of deficits are proportional to the changes in recovery time. In consequence, we propose here that the average recovery time, W doubles approximately every 15.4 years, independently of age. Such changes quantify the increase of vulnerability to stressors as people age that gives rise to increasing risk of frailty, disability and death. That deficit accumulation will, on average, double twice between ages 50 and 80 highlights the importance of health in middle age on late life outcomes.

Reliability of a Frailty Index Based on the Clinical Assessment of Health Deficits in Male C57BL/6J Mice

We investigated the reliability of a newly developed clinical frailty index (FI) that measures frailty based on deficit accumulation in aging mice. FI scores were measured by two different raters independently in a large cohort (n = 233) of 343-430 day-old male C57BL/6J mice. Inter-rater reliability was evaluated with correlation coefficients, the kappa statistic, and intra-class correlation coefficients (ICC) in three separate groups of mice (n = 45, 50, and 138 mice/group) sequentially over 3 months. After each group was evaluated, descriptions of techniques used to identify health deficits were amended. Mice had comparable overall FI scores regardless of rater (0.213±0.002 vs 0.212±0.002; p = .802), although discordant measures declined as techniques were refined. Correlation coefficients (r (2) values) between raters improved throughout the study and mean kappa values increased (mean ± SEM; 0.621±0.018, 0.764±0.017, and 0.836±0.009 for groups 1, 2, and 3; p < .05). Values for intra-class correlation coefficient also improved from .51 (95% confidence interval = 0.11-.73) to .74 (0.54-0.85) and .77 (0.67-.83). FI scores increased over 3 months (p < .05), but did not differ between raters. These results show a high overall inter-rater reliability when the clinical FI tool is used to assess frailty in a large cohort of mice.

Frailty in the Honolulu-Asia Aging Study: deficit accumulation in a male cohort followed to 90% mortality

BACKGROUND

A frailty index (FI) based on the accumulation of deficits typically has a submaximal limit at about 0.70. The objectives of this study were to examine how population characteristics of the FI change in the Honolulu-Asia Aging Study cohort, which has been followed to near-complete mortality. In particular, we were interested to see if the limit was exceeded.

METHODS

Secondary analysis of six waves of the Honolulu-Asia Aging Study. Men (n = 3,801) aged 71-93 years at baseline (1991) were followed until death (N = 3,455; 90.9%) or July 2012. FIs were calculated across six waves and the distribution at each wave was evaluated. Kaplan-Meier analyses and Cox proportional hazard models were performed to examine the relationship of frailty with mortality.

RESULTS

At each wave, frailty was nonlinearly associated with age, with acceleration in later years. The distributions of the FIs were skewed with long right tails. Despite the increasing mortality in each successive wave, the 99% submaximal limit never exceeded 0.65. The risk of death increased with increasing values of the FI (eg, the hazard rate increased by 1.44 [95% CI = 1.39-1.49] with each increment in the baseline FI grouping). Depending on the wave, the median survival of people with FI more than 0.5 ranged 0.84-2.04 years.

CONCLUSIONS

Even in a study population followed to almost complete mortality, the limit to deficit accumulation did not exceed 0.65, confirming a quantifiable, maximum number of health deficits that older men can tolerate.

Sex differences in the limit to deficit accumulation in late middle-aged and older Chinese people: results from the Beijing Longitudinal Study of Aging

Background.

On average, as people age, they accumulate more health deficits and have an increased risk of death. The deficit accumulation–based frailty index (FI) can quantify health and its outcomes in aging. Previous studies have suggested that women show higher FI values than men and that the highest FI score (the “limit to frailty”) occurs at a value of FI ~ 0.7. Even so, gender differences in the limit to frailty have not been reported.

Methods.

Data for this analysis were obtained from the Beijing Longitudinal Study of Aging that involved 3,257 community-dwelling Chinese people, aged 55+ years at baseline. The main outcome measure was 5-year mortality. An FI consisting of 35 health-related variables was constructed. The absolute and 99% FI limits were calculated for different age groups and analyzed by sex.

Results.

The mean level of the FI increased with age and was lower in men than in women (F = 67.87, p < .001). The 99% FI limit leveled off slightly earlier with a relatively lower value in men (60 years; 0.44 ± 0.02) compared with that in women (65 years; 0.52 ± 0.04). The highest absolute FI value was 0.61 in men and 0.69 in women. In both groups, people with an FI greater than or equal to the 99% limit showed close to 100% mortality by 5 years.

Conclusion.

Compared with men, women appeared to better tolerate deficits in health, yielding both relatively lower mortality and higher limit values to the FI. Even so, the FI did not exceed 0.7 in any individual.

A clinical frailty index in aging mice: comparisons with frailty index data in humans

We previously quantified frailty in aged mice with frailty index (FI) that used specialized equipment to measure health parameters. Here we developed a simplified, noninvasive method to quantify frailty through clinical assessment of C57BL/6J mice (5-28 months) and compared the relationship between FI scores and age in mice and humans. FIs calculated with the original performance-based eight-item FI increased from 0.06 ± 0.01 at 5 months to 0.36 ± 0.06 at 19 months and 0.38 ± 0.04 at 28 months (n = 14). By contrast, the increase was graded with a 31-item clinical FI (0.02 ± 0.005 at 5 months; 0.12 ± 0.008 at 19 months; 0.33 ± 0.02 at 28 months; n = 14). FI scores calculated from 70 self-report items from the first wave of the Survey of Health, Ageing and Retirement in Europe were plotted as function of age (n = 30,025 people). The exponential relationship between FI scores and age (normalized to 90% mortality) was similar in mice and humans for the clinical FI but not the eight-item FI. This noninvasive FI based on clinical measures can be used in longitudinal studies to quantify frailty in mice. Unlike the performance-based eight-item mouse FI, the clinical FI exhibits key features of the FI established for use in humans.