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F. Quiñónez-Bareiro1, J.A. Carnicero2, A. Alfaro-Acha1, C.Rosado-Artalejo1, M.C. Grau-Jimenez1,L. Rodriguez-Mañas2, F.J. García-Garcia1


1. Hospital Geriatrico Virgen del Valle, Hospital Virgen del Valle, Toledo, Spain; 2. Getafe University Hospital, Hospital Universitario de Getafe, Madrid, Spain.

Corresponding Author: Francisco Jose Garcia-Garcia, PhD, MD, Head of Geriatric Department, Hospital Virgen del Valle, Complejo Hospitalario de Toledo, Crta de Cobisa s/n,
45071, Toledo, Spain, franjogarcia@telefonica.net

J Frailty Aging 2022;in press
Published online March 24, 2022, http://dx.doi.org/10.14283/jfa.2022.25



Background: Vascular function (VF) is a general term used to describe the regulation of blood flow, arterial pressure, capillary recruitment, filtration and central venous pressure, it´s well known that age has direct effects on the VF, and this may affect the frailty status.
Objectives: To analyse the association between Frailty Trait Scale 5 (FTS 5) with VF and its changes at values below and above a nadir.
Design: Prospective population-based cohort study.
Setting and Participants: Data from 1.230 patients were taken from the first wave (2006-2009) of the Toledo Study for Healthy Aging.
Measurements: Frailty was evaluated using FTS 5, which evaluates 5 items: Body mass index, progressive Romberg, physical activity, usual gait speed and hand grip strength. VF was assessed using the ankle-brachial index (ABI) as an indirect measure of VF. Screening for cardiovascular and cerebrovascular disease was also performed by self-reporting and by searching medical records, and was used as exclusion criteria
Results: The optimal ABI cut-off point that maximized the adjusted R2 was 1.071. We observed a statistically significant association for FTS 5 score above and below the ABI cut-off points. For every tenth that the ABI decreased below the cut-off point the patient had an increase in the FTS 5 score of 0.47 points and in every tenth that increased above the cut-off point the increase in the FTS 5 score was 0.41 points. Of all FTS 5 items, the gait speed was the only item that showed a significant association with an ABI changes 0.28 and 0.21 points for every tenth below and above the cut-off point, respectively.
Conclusions: Frailty is highly associated with VF. In addition, FTS 5 and its gait speed criteria are useful to detect VF impairments, via changes in ABI.

Key words: Frailty, vascular function (VF), Ankle-brachial index (ABI), frailty trait scale (FTS 5).



Frailty is defined as a biological syndrome of decreased reserve and resistance to stressors. This syndrome is an aggregate expression of risk resulting from changes in different organs and systems due to both age and disease, including associated accumulation of sub-threshold decrements affecting multiple physiologic systems (1-4) like the cardiovascular system, which is involved not only in the presence of symptomatic disease, but also in subclinical stages, (5, 6) probably because cardiovascular disease and frailty share common points in their pathogenesis (7-11).
It is known that the vascular ageing process begins to develop in the middle stages of life when clinical manifestations are not yet evident. In addition, advancing age has direct effects on vascular structure and function (12) .Vascular function (VF) is a general term used to describe the regulation of blood flow, arterial pressure, capillary recruitment and filtration and central venous pressure (13), their dysfunction can lead to deleterious health effects.
Non-invasive tests like an ankle-brachial index (ABI) can be useful as an indirect measure of VF to diagnose pathology when it is impaired (14, 15). Cross-sectional studies have demonstrated the association between subclinical atherosclerosis, assessed using ABI, with functional limitations, stating that endurance and gait speed are closely related to lower limb vascular perfusion and balance (16-17). A longitudinal study showed that subclinical atherosclerosis is an independent predictor of functional limitation in one year follow-up in older people with a high functional level, assessed with the test timed up and go (18).
Along last decades, two classic tools have been used to assess frailty: the frailty phenotype proposed by Fried et al (4-19) and the frailty index proposed by Rockwood and Mitnitski (20-21). Although both scales have shown their usefulness and the frailty phenotype is considered the gold standard, they have some disadvantages. The frailty phenotype classifies patients into only 3 categories (robust, prefrail and frail) and the items are scored as positive or negative. Which make it not possible to measure the continuous evolution between robust and frail. With respect to the Frailty Index, it is composed of many items that are not reversible, such as suffering a myocardial infarction. It also includes items that measure disability in basic and instrumental activities of daily living (ADL) which are difficult to reverse.
A new operational tool has recently emerged to overcome some of these disadvantages. The Frailty Tait Scale 12 (FTS 12) (22), which adds other dimensions to the previous classical tools to assess frailty as cognitive, nutritional, and vascular domains, turning this scale into a multidimensional tool, thus increasing its validity, representing an additional tool for the diagnosis of frailty.
Considering that predictive capacity of the three scales is similar, a short form of the FTS 12 was developed the FTS 5, where the item reduction and scoring were done using an algorithm that maximizes the Area Under the Curve (AUC) sum improving its classification power and performance for predicting adverse events (23). This tool shows some advantages for its use in multiple settings (clinical, research, public health, etc.). First, FTS 5 provides a frailty assessment that is valid and useful in different populations and region, in addition, this scale is short, sensitive to change, with higher performance in identifying individuals with higher risk of adverse events, especially in pre-frail individuals (23).
VF performs an important role in the etiology of frailty, it is well known that low ABI values were associated with arterial stenosis/occlusion and high ABI values were associated with arterial stiffness (24), causing a decrease in tissue perfusion that leads to changes in organs and systems, increasing the risk of frailty. Therefore, the aim of the current study was to assess the association between FTS 5 and VF and analyse how this association changes at values below and above a nadir.



Design and sample

The Toledo Study for Healthy Aging (TSHA) is a population-based, prospective, cohort study, with waves performed every 3-4 years, created to evaluate the characteristics and consequences of frailty in individuals ≥65 years old living in the province of Toledo (Spain), which is described elsewhere (25). The TSHA is composed by two cohorts. The first one, called the historic cohort, is formed by the survivors of a previous study (the Toledo Study) among persons aged 77 and older (26). The second cohort, called the new cohort, was formed by individuals 65-76 years of age, who had just joined the study. Basically, the study is carried out in different phases: first, a team of trained psychologists carried out an interview in the subject´s home and collected socio-demographic data; self-reported comorbidity; alcohol, tobacco, and drug intake; extensive self-reported functional assessment, physical activity, screening for cardiorespiratory symptoms, screening for cerebrovascular accident (CVA) and intermittent claudication, among others. Second, a trained nurse carried out a physical battery task and collected weight, height, hip and waist circumference, ABI and blood pressure at rest were measured according to measurement standards. Physical performance measurements were also taken: chair test, gait speed, progressive romberg test, grip strength in the dominant hand using a hydraulic jamar dynamometer. Third, a nurse obtained blood and urine samples after 12 hours of fasting. The study complies with Spanish law on biomedical research and obtained approval by our centre’s Clinical Research Ethics Committee. (ref: 22/2005) Before data acquisition, participants gave written informed consent.
For this analysis, we used data from the 2.488 patients included in the first wave (2006-2009) of the TSHA (25). Of these, 774 patients did not participate in the nursing assessment/interview and 204 patients did not complete the FTS 5. Also, we used as exclusion criteria, previous history of: Myocardial Infarction, Angina pectoris, Intermittent claudication: 132 patients; CVA, Transient Ischemic Attack: 10 patients; and an active screening of CVA: 84 patients, so the final number of patients studied was 1.230. (Figure 1)

Figure 1. Flow chart

ABI: Ankle-brachial index; FTS: Frailty Trait Scale; MI: Myocardial Infarction; CVA: cerebrovascular accident; TIA: Transient Ischemic Attack



– Frailty Measurement: Frailty was evaluated using the 5-items Frailty Trait Scale (FTS 5). FTS 5 Evaluates 5 items: Body mass index (BMI), progressive Romberg, physical activity measured by the Physical Activity Scale for the Elderly (PASE), usual gait speed and hand grip strength. Each item scores from 0 (the best) to 10 (the worst), and the final score is obtained from the addition of the scores of each item and ranges between 0 and 50. Those subjects with a score of >25 was considered frail (23).
– Indirect measurement of vascular function: It was carried out by measuring the ankle-brachial index (ABI), using eco-Doppler (HADECO mini-Doppler ES-100X) according to performance standards (27).
– Cardiovascular and cerebrovascular disease: By both self-reporting as well as searching in the medical records of the participants, looking for the antecedents of myocardial infarction, angina pectoris and intermittent claudication for cardiovascular disease and stroke and cerebrovascular symptomatology for cerebrovascular disease.
– Waist-hip ratio (WHR): It is calculated by dividing the waist circumference by the hip circumference.
– Tobacco: We considered patients as smoker those individuals who answered affirmatively to the question: “Have you smoked daily (every day for at least 1 year)?”.

Statistical analysis

Descriptive data were presented as mean (SD) and N (%). Differences between categories were tested by Mann-Whitney, ANOVA and Chi-square test.
The relationship between ABI and different health events is J-shaped (27), so we hypothesize that the relationship between FTS 5 and the VF measured by ABI could also have this shape. To study this possibility, we selected the ABI cut off point that maximized the sum of goodness of fit statistic (adjusted R2) of the multivariate regression model for both sub-samples (below or above the threshold). We also analysed the relationship between each item of FTS 5 and ABI using multivariate linear regression models and the relationship between frailty status and ABI using multivariate logistic regression models. We used age, gender, WHR and active smoking as potential confounders.
All analyses were performed with the Statistical Package R for windows (Vienna, Austria) (http://www.r-project.org), version 3.5.3. Statistical significance was set a P-value <0.05.



We have analysed a total of 1.230 patients, the mean (SD) of age was 74 years (5), 712 (58%) were women. The mean (SD) of ABI score and FTS 5 were 1.05 (0.17) and 19.65 pts (7.42), respectively. Regarding cardiovascular risk factors: 28.9% were smokers, 47.6% had hypertension, 17.3% diabetes mellitus, 35.4% dyslipidaemia, while the mean WHR in men was 0.97 cm and in women 0.90 cm. (Table 1)

Table 1. Study sample characteristics

*: Mean values and Standart Desviation (SD); +: N (%)


The algorithm used in our study estimated that the optimal ABI cut-off point was 1.071. When we compared patients with ABI below the threshold and those with ABI above the threshold, we observed a statistically significant association between ABI and FTS 5 score (20.02 vs 19.15 points; p=0.032) and age (74.8 vs 73.6 years; p=0.003). (Table 1)
Table 2 shows that for every tenth that the ABI decreased below the cut-off point the patient showed an increase in the FTS 5 score of 0.47 pts (B=0.473, p=0.048) and for every tenth that increases above the cut-off point the increase in the FTS 5 score will be 0.41 pts. (B=0.413, p=0.044).

Table 2. Change in FTS 5 score and FTS 5 items and risk to be frail based on the distance (tenths*) of ABI score and cut off point

ASE: Physical Activity Scale for the Elderly; BMI: Body mass index; *tenths=0.1


When we analysed the individual contribution of each item of the FTS 5 in the overall score, we observed that the effect on gait speed was the only one showing a significant relationship with a change in ABI. For every tenth that the ABI decreased below the cut-off point, the patient had an increase in the FTS 5- gait speed score of 0.28 pts (B=0.281, p=0.004), while for every tenth that the ABI increased above the cut-off point the patient had an increase of 0.22 pts (B=0.219, p=0.011) in the FTS 5-gait speed score. Furthermore, the contribution of gait speed to total FTS 5 score change were 59% and 53% for ABI values below and above the threshold (Table 2).
Similar results were obtained in the association between FTS 5 and ABI after the inclusion of high blood pressure (HBP), diabetes mellitus (DM), dyslipidaemia. (Data not shown).
However, when we assessed the association between the items of FTS 5 and ABI stratified by gender, some differences were observed in the findings in male and females. In men the BMI was related with changes ABI above the cut-off point (B= –0.144, p=0.025, contribution = – 63%), and gait speed and PASE below the ABI cut-off point (B=0.232, p=0.131, contribution 61%, and B = 0.190, p=0.187, contribution 50% for gait speed and PASE respectively) (Table 3). In women the gait speed is related to values both below and above the ABI cut-off point (B=0.324, p=0.011, contribution 59%, and B = 0.269, p=0.038, contribution 43%, for above and below the threshold respectively (Table 4). The change in the score of each item of the FTS 5 according to the change in the ABI values shown in table 2, 3 and 4 were plotted in supplementary material (figures 1, 2 and 3).

Table 3. Change in FTS 5 score and FTS 5 items and risk to be frail based on the distance (tenths*) of ABI score and cut off point stratified by gender Males

PASE: Physical Activity Scale for the Elderly; BMI: Body mass index. *tenths=0.1

Table 4. Change in FTS 5 score and FTS 5 items and risk to be frail based on the distance (tenths*) of ABI score and cut off point stratified by gender Females

PASE: Physical Activity Scale for the Elderly; BMI: Body mass index; *tenths=0.1



In this study, using data from the TSHA, we have observed a clear dose-effect relationship between VF (ABI) and frailty (FTS 5). This relationship was different for ABI values above and below a threshold. This association was also observed for each of the FTS5 domains. After stratifying by gender, males and females showed different results suggesting that ABI influenced FTS5 and its domains differently for each gender and for values above and below the threshold. FTS 5 has the advantage that it is a continuous scale that measures progressive decline, for example, gait speed starts to be scored from 1.22m/s downwards. This makes the FTS 5 more sensitive to change over time due to lifestyle changes and as have been shown in Garcia-Garcia et al (23), changes in one point in FTS5 score has clinical relevance, which reinforces the usefulness of FTS 5 as a multi-domain tool.
The algorithm used in our study established that the optimal ABI cut-off point was 1.071. The results showed that the further we moved away from this cut-off in both directions, the frailty score measured by FTS 5 increased, reflecting a J-shaped association, like other known health events (28). In contrast to other published studies where frailty was associated with ABI, in which ABI were split in intervals and compared with reference interval values (29), our study suggests that the risk of frailty may start even with ABI values considered to be within normal ranges, with a dose-response relationship both below and above the suggested cut-off point.
In our study the established ABI cut off point (1.071) is within the normal range established by the ESC 2017 Guide, (30) which is from 1.00 to 1.40. We observed that changes in FTS 5 score were higher as ABI moved away from the cut-off point. This association was strong for the values below the threshold than for the above ones, this may be because the cut-off value was closer to the lower limit from which the ABI is considered altered, while for values higher than the cut-off point there is a greater range within normal range until reaching the value of 1.40 established by the Guidelines from which it is considered altered. The ABI-ESC 2017 guideline classification determines overall cardiovascular risk, ABI values lower than 0.9 were associated with arterial stenosis/occlusion and ABI values greater than 1.4 were associated with arterial stiffness, but as demonstrated in our study the behavior reflected for the ABI/frailty risk was different, showing a J-shaped behavior.
In the analysis for each FTS 5 item, the unique item that showed a statistically significant association with ABI was gait speed, this association would be justified by a reduction of tissue perfusion in the lower extremities, conditioned by vascular stenosis, or vascular stiffness. Gait speed is closely related to the vascular perfusion of the extremities and balance (14), but considering that the ABI is a measure of systemic atherosclerosis, and gait speed the resultant of many organs’ dysfunctions, there are other factors that may influence this association, such as central nervous system involvement (31).
The rest of the items that make up FTS 5 did not show statistically significant associations. This may have been due to two main reasons, lack of statistical power or selection bias. The contribution of some of these non-significant items to the overall score of the scale was greater than 50% in some cases. The maximum contribution of a single domain, like gait speed, to the total FTS5 score is 40%. In fact, in the worst gait speed performance situation, this item will provide only 10 points of the 25 points needed to classify an individual as frail. In addition, we have shown that there is an association between the ABI and being frail according to the FTS 5. With all these pieces of information in mind, we can conclude that the association of the FTS5 with the ABI cannot be due only to its gait speed component.
The prevalence of ABI changes as an indirect manifestation of VF is very age-dependent but not gender-dependent (32). The prevalence, both symptomatic and asymptomatic, is higher in men than in women, especially in the younger population, as the difference narrows to almost equal in older ages (30). In our study there was a difference in terms of gender from the general population, our data showed that women had a greater association between ABI changes and frailty than men, which could be influenced by a greater FTS 5 score in women than in men (22).
The binomial frailty/VF has been the focus of studies for years. Both entities share a common inflammatory substrate, in which there is a chronic inflammatory state of low degree characteristic of old age, triggered by oxidative stress and by the production of cytokines from different body systems, which becomes more evident in frailty patients (33-34). Furthermore, the high presence of cytokines such as IL-6, IL-1α, TNF-α and IFN-a, has been associated with the presence of cardiovascular disease, worse physical performance and frailty (35).
VF performs an important role in the etiology of frailty, although the exact point at which the decline begins is not yet well established. One of the first evidences of the association of VF and frailty was published by Newman and others in 2001, in which, among several indirect measures of vascular involvement, the presence of ABI less than 0.8 was associated with frailty versus robustness (OR 3.17, p<0.001), (8) in contrast to this previous study, our analysis showed an association not only for low ABI values but also for high values, with an increased risk of being frail as we moved away from the suggested cut-off point in both directions.
The results lead us to two unanswered questions that could converge into a single entity: first, is VF the one that predisposes to the worsening in frailty status? and second, is frailty a risk factor or a clinical manifestation of impairment VF?; it is likely that frailty can be a cause or consequence of vascular disease.
Finally, this study has some strengths and limitations, that deserve to be commented. The strengths of this work are [1] we showed a dose-response relationship between VF and frailty for [2] low and high values [3] including the normal range that can enable us to identify that risk early. The weakness are [1] we performed a cross-sectional analysis, [2] as we commented in the discussion, the relationship between ABI and several FTS5-domains may not be direct and to address this issue we need to estimate this association over time, nor does it allow us to know whether frailty conditions the ABI or vice versa, in addition, an exhaustive characterisation of the subjects with an imaging test was not contemplated, which may mean that subjects with asymptomatic cardiovascular and cerebrovascular pathologies could have been included in the sample and (3) Participants in the study were younger and with less comorbidity, what limits the generalization of our conclusions to the whole older population.



This study demonstrates an association between frailty and VF as indirectly measured by ABI, and this association is independent of other cardiovascular risk factors such as gender, WHR, smoking, high blood pressure, diabetes mellitus and dyslipidaemia. In this association FTS 5 and its gait speed criteria can be useful for screening ABI changes as a manifestation of VF impairment, but estimating the association over time, between ABI changes and the development of frailty is one of the challenges to achieve, to design preventive measures that lead to healthy aging.


Acknowledgments: We would like to thank the support received by grants from the European Union (the Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES) and FEDER funds), from the Instituto de Salud Carlos III, from FISCAM (Junta de Comunidades de Castilla-La Mancha, Spain), and from FP7-Health-2012-Innovation (European Union).

Funding: This work was supported by grants [CB16/10/00456 and CB16/10/00464] from the European Union (the Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES) and FEDER funds), [PI15/01305] from the Instituto de Salud Carlos III (Ministerio de Ciencia e Innovación, Spain), [DEP2015-69386-R and RD12/0043] from the Instituto de Salud Carlos III (Ministerio de Economía y Competitividad, Spain), [PI2010/020] from FISCAM (Junta de Comunidades de Castilla-La Mancha, Spain), and [FP7- 305483-2] (“Frailomic Iniciative”) from FP7-Health-2012-Innovation (European Union).

Conflicts of interest: The authors declare no conflicts of interest.

Ethical Standards: This work was performed according to the ethical standards laid down in the 1964 Declaration of Helsinki and later amendments; complies with Spanish law on biomedical research and obtained approval by our centre’s Clinical Research Ethics Committee.





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