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PHYSICAL FUNCTIONAL ASSESSMENT IN OLDER ADULTS

 

E. Patrizio1, R. Calvani2, E. Marzetti2,3, M. Cesari4

1. Azienda di Servizi alla Persona Istituti Milanesi Martinitt e Stelline e Pio Albergo Trivulzio, Milan, Italy; 2. Fondazione Policlinico Universitario «Agostino Gemelli» IRCCS, Rome, Italy; 3. Università Cattolica del Sacro Cuore, Institute of Internal Medicine and Geriatrics, Rome, Italy; 4. Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.
Corresponding author: Enrica Patrizio, Azienda di Servizi alla Persona Istituti Milanesi Martinitt e Stelline e Pio Albergo Trivulzio, Milan, Italy, Email: patrizio.enrica@gmail.com

J Frailty Aging 2020;in press
Published online November 24, 2020, http://dx.doi.org/10.14283/jfa.2020.61

 


Abstract

The evaluation of the physical domain represents a critical part of the assessment of the older person, both in the clinical as well as the research setting. To measure physical function, clinicians and researchers have traditionally relied on instruments focusing on the capacity of the individual to accomplish specific functional tasks (e.g., the Activities of Daily Living [ADL] or the Instrumental ADL scales). However, a growing number of physical performance and muscle strength tests has been developed in parallel over the past three decades. These measures are specifically designed to: 1) provide objective results (not surprisingly, they are frequently timed tests) taken in standardized conditions, whereas the traditional physical function scales are generally self- or proxy-reported measures; 2) be more sensitive to changes; 3) capture the real biology of the function through the assessment of standardized tasks mirroring specific functional subdomains; and 4) mirror the quality of specific mechanisms underlying more complex and multidomain functions. Among the most commonly used instruments, the usual gait speed test, the Short Physical Performance Battery, the handgrip strength, the Timed Up-and-Go test, the 6-minute walk test, and the 400-meter walk test are widely adopted by clinicians and researchers. The clinical and research importance of all these instruments has been demonstrated by their predictive capacity for negative health-related outcomes (i.e., hospitalization, falls, institutionalization, disability, mortality). Moreover, they have shown to be associated with subclinical and clinical conditions that are also not directly related to the physical domain (e.g., inflammation, oxidative stress, overall mortality). For this reason, they have been repeatedly indicated as markers of wellbeing linked to the burden of multiple chronic conditions rather than mere parameters of mobility or strength. In this work protocols of the main tests for the objective assessment of physical function in older adults are presented.

Key words: Physical function, physical performance, gait speed, muscular strength, comprehensive geriatric assessment, older adults.


 

Introduction

The aging of the global population is accompanied by an epidemiological transition from infectious and communicable diseases to a growing burden of chronic diseases. Health status in older persons is determined by the complex interaction of multiple factors (multiple chronic diseases, psychological, social, and environmental factors), that is not captured by traditional paradigms based on the concept of standalone diseases. The most common manifestation of poor health status in this population is represented by the loss of functioning, decrease in the autonomy of mobility and activities of daily living (ADLs), till the onset of disability and dependence (1, 2).
The Comprehensive Geriatric Assessment (CGA), evaluating not only the presence of diseases, but also the individual’s functions (intended both as physical and cognitive abilities), psychological factors, and social aspects, is a diagnostic and therapeutic process able to objectively define the health status of the frail older individual and support the design of tailored plans of intervention (3).
Loss of muscle strength and decline of physical performance are critical elements to consider in the detection of important age-related conditions. The definition of physical frailty usually includes measurements of handgrip strength and gait speed. Consistently, physical performance measures, as gait speed, Timed Up-and-Go (TUG) test, and the Short Physical Performance Battery (SPPB) are useful instruments for the screening of frailty in the general population (4). The European Working Group On Sarcopenia In Older People (EWGSOP2) recently published the updated diagnostic criteria for sarcopenia, that include poor muscle strength, and reduced physical performance to define the presence of sarcopenia and quantify its severity, respectively (5–7).
A recent position paper of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) working group on frailty and sarcopenia proposed a standardization of the clinical assessment of muscle function and physical performance in order to promote the diagnosis of these conditions and facilitate the design of care programs (8). Muscle strength was defined as “the amount of force a muscle can produce with a single maximal effort”. Physical performance, on the other side, is considered a rather multidimensional concept, a function of the whole body, resulting from the functioning of multiple organs and systems (e.g., musculoskeletal, cardiovascular, respiratory, central and peripheral nervous systems). Hence, poor physical performance can be considered an early marker of frailty and subclinical diseases. Muscular strength and physical performance measures are related to pathophysiological conditions (i.e., atherosclerosis, inflammation, reduction of aerobic capacities), and are proved to predict the risk of healthcare use (i.e., hospitalizations, institutionalization) and adverse events (i.e., falls, cognitive decline, disability, death) (9–12). At the same time, these measures may represent a target and marker of efficacy for preventive interventions, such as rehabilitative and physical activity programs, being sensitive to changes of the health status. Such predictive capacity is confirmed across large and different populations. Furthermore, being these tests very clinical-friendly, they have been gradually embraced in the daily routine by different disciplines and settings, from the primary care to oncology, from cardiology to surgery, in order to support the diagnostic and therapeutic process (13–16).
In the last years, a number of measures and tools have been developed for the assessment of physical function. The aim of this paper is to describe standard procedures guiding the administration of the most important measures of strength and physical performance. In particular, we here present how to administer the handgrip strength test, the gait speed test, and the 400-meters walking test (17–19). Other important and well-established instruments, as the SPPB and the TUG test are not object of the present work as detailed instructions and video tutorials are already available (https://sppbguide.com/, and https://youtu.be/BA7Y_oLElGY respectively) (20, 21).

 

Protocols

The protocols here presented are routinely part of a standard geriatric evaluation and do not require any authorization by institutional human research ethics committees. All patients can undergo the following tests. Exclusion criteria are specified within each protocol.

Handgrip strength test

To perform the test, a regularly calibrated, handheld hydraulic dynamometer is recommended (the JAMAR dynamometer is considered the gold standard). Use the same chair for every measurement. Subjects reporting current flare-up of pain in the wrist or hand or has recently undergone to surgery of the hand or wrist should not be tested on the affected side.

1 Seat the participant in a standard chair. Instruct to place forearms resting on the arms of the chair, with wrist just above the end of the arm of the chair. Tell to keep the hand in a neutral position, with the thumb pointing upwards.
2 Show the subject how to use the dynamometer. Place your hand around the handle, putting the base with the thumb on one side, and the other four fingers on the other. Tell the participant that he/she will not feel the grips moving when squeezing but the device is working and measuring his/her strength. Demonstrate the subject that the tighter the grip, the better score is registered by the instrument. Caution the participant that the dynamometer is quite heavy.
3 Place the adjustable handle of the dynamometer in the second position from the inside, unlocking the clip located on the lower post and fitting the adjustable handle on the second space between the teeth of the post.
4 Give the dynamometer to the participant and make sure that the grip bars are at the correct distance (with the fixed handle resting in the middle of the palm and the movable part in the center of the four fingers). If the handle seems to be too large or narrow to allow the patient to squeeze comfortably, remove the dynamometer and adjust, widening or tightening the handle.
5 Support the base with the palm of the hand while the subject holds the dynamometer, being careful not to limit its movement. Allow the participant to try to squeeze to familiarize with the instrument.
6 Check that the peak needle is set to zero. If it is not, rotate counter-clockwise the small caster in the middle of the gauge to move it to zero.
7 Start the test with the right hand. Invite the participant to squeeze the handle as strongly as possible. Use standard encouragement to highly motivate the participant during the test (e.g. «Squeeze, squeeze, harder!»). Ensure the participant maintains the maximum isometric effort for at least 3-5 seconds. When the needle stops rising, invite the participant to stop squeezing.
8 Read grip strength in kilograms from the outside dial and record the result to the nearest 1 kg. After each reading, reset the peak needle to zero. Repeat the measurement at the left hand. Obtain three readings in total for each hand, alternating the sides. Allow 10 seconds rest between each measurement.
Note: the peak-hold needle automatically records the maximum result. The gauge presents two dials, the inner one registers the value in lb, the outside in kg.
9 The highest reading of the 6 measurements is reported as the final result. Ask for and report about the hand dominance (i.e. right, left or ambidextrous).
10 If the participant complains about pain, discontinue the test and repeat the assessment only on the other side. If pain appears at both hands, stop the test.

Gait speed

To perform the test, the subject should wear comfortable clothes and shoes (with low heels for women). Only a single straight cane may be used during the walk. If the person can walk a short distance without it, should be encouraged to do so. If a person is unable or uses a walker, he/she should be considered as presenting mobility disability. As such, although the test can still be conducted with the use of the device, the meaning of the results for this specific geriatric outcome might be of limited value.
1 To perform the test, get a stopwatch and mark a 4-meter track along a flat floor. Ensure that the walking course is devoid of obstacles and include at least an extra meter at each end (Figure 1).
2 Encourage the person to walk without using any assisting devices. During the test watch particularly closely participants who normally use them, to prevent falls.
3 Instruct the participant to stand with both feet touching the starting line, and to start walking at usual pace over the 4-meter course, after a verbal command (“Go”). The assessor will then show how to perform the test to the participant, again stressing the request of walking at usual pace without running.
Note: The individual should not be aware where the goal line is placed in order to avoid a possible reduction of the pace when approaching to it. However, the tape on the floor might provide an implicit goal to the participant. For this reason, the participant might be instructed at walking well past the line on the floor.
4 During the walk stay to the side and slightly behind the subject, outside of the participant’s visual field, in order not to set the pace, but remaining in a good position for the safety of the person.
5 Begin timing when the first foot starts to move across the starting line, and stop when the first foot crosses the 4-meter mark. Do not start the watch when saying the verbal command, but when the participant actually begins to move. Do not stop timing if the foot lands on the line but does not completely cross it.
6 Report the time of execution of the test and calculate the gait speed. Repeat the test a second time and use the fastest time as result.
Note: If there is a problem with the stopwatch or the examiner is not sure of the timing, the test should be repeated.

Figure 1
4-meter walking test. The walking course should be unobstructed. Timing begins when the first foot starts to move across the starting line, and should be stopped when the first foot crosses the 4-meter mark

 

400-meter walking test

To perform the test, the subject should wear comfortable clothes and shoes (with low heels for women). During testing, the use of walk assistive devices, other than a single straight cane, is not allowed. If the subject does not feel safe attempting the walking course without aids (i.e. walker, quad cane, crutch), do not administer the test.
The assessor must be completely familiar with the test procedures and practice before attempting to administer the test to a participant. Procedures should be clearly demonstrated to the participants before performing the test and they should be queried to ensure that they understand the instructions. To ensure reproducibility, it is imperative that all participants are given the same instructions and that quantitative measurements associated with the tests are made in a uniform manner.
1 Identify a 20-meter long track by marking it with small traffic cones. Make sure that the walking path is not obstructed and include at least an extra meter at each end. Get a stopwatch and position two standard chairs along the walking course in order to allow the subject to rest during the test, if necessary (Figure 2).
2 Conduct the subject to the starting line and instruct to stand in a still position behind the line. It is important to clarify the goal of the test to the participant, i.e. to complete the 400-meter course.
3 Before starting the test measure the radial pulse for 30 seconds and blood pressure.
4 Instruct the subject to walk at usual pace, without overexerting, back-and-forth the 20-meter track for ten times, in order to allow the participant to plan the activity and consequently organize the walking pace according to his/her own reserves.
5 When participant indicates to feel ready to begin, proceed with the test. Instruct the individual to start to walk down the corridor at the command “Go”, and turn around the traffic cones, generating a continuous loop. Start timing when the participant takes the first step.
6 Stay by the side and just behind the participant, outside the subject’s visual field, during the walk. Be close enough to be able to support the participant if manifests difficulty or risks to fall, but not so close to dictate the pace.
7 When the 4rth lap is completed, ask the participant to report the perceived exert, and record the corresponding score of the Borg index for dyspnea.
Note: The test should be conducted at usual pace and the final goal is to complete the 400-meter course and not to reach the maximal effort. The participant should not overexert, therefore, if the participant reports “hard” or “very hard” should be invited to reduce the effort. The measurement of vital signs (radial pulse and blood pressure) before starting and at the end of the test, as well as the administration of the Borg scale after 4 laps and at the end of the test provide additional information useful to guarantee the participant’s safety and provide insights about the undergoing aerobic stress.
8 At the end of each lap (20-meter back and forth), encourage the subject with standardized phrases and count the number of completed and remaining laps.
9 Provide the participant the cane if he/she asks for it during the test, or has the evident necessity to use it to complete the walk.
10 Allow the participant to stop the walk to rest at any time, but not to lean against the wall, other surface (desk, counter, etc.), or sit. After 30 seconds, ask the participant if he/she can continue walking. If it is possible, continue the test, otherwise another 30 seconds of rest, in standing position, are allowed. If the subject is unable to continue after a 60-second rest or needs to sit down, stop the test.
Note: There is no limit to the number of rest stops as long as they can complete the walk without sitting.
11 Stop the stop-watch when the participant completes 400 meters (10 laps, first foot touching the floor beyond the finish line) or after 15 minutes, even if the participant has not covered all the distance. Record the time or, in the second case, measure the accomplished distance.
12 Immediately stop the test if participant reports chest pain or tightness, dyspnea, feeling faint, dizzy, or lower limbs pain.
13 At the end of the test, record the Borg index score, the sitting radial pulse for 30 seconds and blood pressure. Record the number, timing, and reasons for the rest stops (fatigue, chest pain, feeling faint or dizzy, shortness of breath, or other).

Figure 2
400-meter walking test. A 20-meter long track should be identified by marking it with small traffic cones. The participant has to walk at usual pace back-and-forth the 20-meter track for ten times, turning around the traffic cones in a continuous loop. The two chairs should be positioned by the side of the walking course, in order to not obstruct the track, but close enough to be rapidly reached if the subject needs to rest and sit during the test

 

Importance and predictive value of the presented tests

Low grip strength was found to be associated with slow gait speed, incident dismobility, disability, functional dependence, cognitive impairment, depression, cardiovascular diseases, hospital admission, and mortality (all-cause mortality, cardiovascular and non-cardiovascular mortality), in both sexes and independently of age and comorbidities (22–25). This relationship is confirmed across different populations and times of follow-up. Rantanen and colleagues studied the relationship between the handgrip strength with incident mobility and functional limitations in a large population (8,006 men from the Honolulu Heart Program and the Honolulu Asia Aging Study) aged at baseline 45-68 years old, with a 25 years follow-up (26). They found a strong association between the muscle strength in midlife and the risk of becoming disabled over the long-term follow-up. The strongest participants (i.e., >42.0 kg) had a significantly better risk profile when compared with those with poorest results at the handgrip, even after adjustment for a number of confounding conditions (Table 1). These results may be explained by greater physiological reserves in these subjects. Dodds and colleagues recently pooled data of grip strength from 8 different studies conducted in Great Britain on the general population. A total of 49,964 persons were considered to produce life-course nomograms of handgrip strength. The generated curves described a three period-evolution, with an increase to peak in early adulthood (i.e., 51 kg between 29-39 years old for men, and 31 kg between 26-42 years old for women), broad maintenance through to midlife, and a declining phase at older age (27). Different cut points were identified in the literature for poor handgrip strength, ranging from 16 to 21 kg for women and 26 to 30 kg for men, defining the risk of adverse events. Values adjusted for BMI or height also exists (23). The EWGSOP proposed values for poor grip strength <27 kg for men and <16 kg for women (7). Data on sensitivity to change in grip strength are still limited and inconsistent, a change of 6 kg was proposed to be significant. Some studies considered also the effect size (difference between the mean/median values of grip strength at baseline and after an intervention, divided by the standard deviation/inter-quartile range of the baseline measurement), and a value of 0.2–0.5 has been considered as indicative of low responsiveness, 0.51–0.8 of moderate responsiveness, and >0.8 of high responsiveness (17).

Table 1
Relationship between the handgrip strength with incident mobility and functional limitations. Adapted from Rantanen
et al. JAMA 1999

Results from multiple logistic regressions testing the predictive capacity of midlife grip strength for functional limitation and disability at advanced age (n=3,218); highest tertile used as reference group. Adjusted for age, weight, height, education, occupation, smoking, physical activity, and chronic conditions (i.e., arthritis, chronic obstructive pulmonary disease, coronary heart disease, stroke, diabetes, and angina).

 

Gait speed is a strong predictor of negative health outcomes, independently of the presence of common medical conditions and disease risk factors. Many studies demonstrated a strong association with incident disability (intended both as loss of ADL independency and dismobility), cognitive decline and dementia, falls and related fractures, mortality, and healthcare utilization (e.g., hospitalization and institutionalization) (11, 18, 28). Although tested in very different populations (e.g., inpatients and outpatients, independent, frail, and disabled subjects), different walking distances, and studied outcomes, the prognostic value is very consistent (Table 2). Studenski and colleagues studied the relationship between gait speed and mortality in a pooled population of 34,485 community-dwelling older people derived from 9 studies (Cardiovascular Health Study, Health, Established Populations for the Epidemiological Study of the Elderly, Aging and Body Composition study, Hispanic Established Populations for Epidemiological Study of the Elderly, InCHIANTI Study, Osteoporotic Fractures in Men, Third National Health and Nutrition Examination Survey, Predicting Elderly Performance, Study of Osteoporotic Fractures). The Authors found an overall HR for survival per each 0.1 m/s faster gait speed of 0.88 (95% CI, 0.87-0.90; P <0.001) confirmed after further adjustment for sex, BMI, smoking status, systolic blood pressure, diseases, prior hospitalization, and self-reported health (overall HR 0.90; 95% CI, 0.89-0.91; P <0.001). They also estimated the median life expectancy based on sex, age, and gait speed, providing a sort of nomograms (29). The value of 0.8 m/s for a 4-meter distance was found to identify frail patients with a high sensitivity (0.99), moderate specificity (0.64), and a high negative predictive value (0.99), and has been chosen by the EWGSOP2 as cut-off to diagnose severe sarcopenia (7, 30). At the same time, in their systematic review of the literature, Abellan van Kan and colleagues identified multiple cut-points of gait speed related to adverse outcomes, categorizing older people as slow (<0.6 m/s), intermediate (0.6-1.0 m/s), and fast (>1.0 m/s) walkers, demonstrating a continuum gradient of risk ranging from very fit to mobility impaired subjects (11). Furthermore, gait speed at usual pace in a 4-meter walk demonstrated also to be sensible to changes, with 0.05 m/s defining a minimally significant change and 0.1 m/s indicating a substantial change, with a corresponding reduction of 17.7% in absolute risk of death when increases of this value (31).

Table 2
Gait speed and ADL or mobility disability. Adapted from G. Abellan Van Kan et al.
The Journal of Nutrition, Health & Aging, 2009

* These studies analyzed the risk of disability considering gait speed variation rather than a specific cutpoint, as mentioned in the last column of the table; Health ABC study: Health Aging and Body Composition study, CHS: Cardiovascular Health Study, WHAS-I: Women’s Health and Aging Study, Hispanic EPESE: Hispanic Established Population for the Epidemiological Study of the Elderly, RR: relative risk, HR: hazard ratio, OR: odds ratio, ADL: activity of daily living.

 

The ability to perform the 400-meter walking test in less than 15 minutes defines the presence of mobility disability. This distance, corresponding to the length of about two blocks in the United States, is considered the minimum walking distance needed to have an independent life. The limit of 15 minutes, corresponds to a gait speed of 0.4 m/s, proven to be incompatible with functional autonomy. This instrument has a strong predictive capacity for development of negative health events (disability, mortality). Although this test is mostly used as a dichotomous indicator (presence/absence of mobility disability), its predictive capacity has been established also in relation to some of the parameters characterizing specific aspects of the performance (e.g., mean gait speed, number of stops to rest). Vestergaard and colleagues studied the differences in mortality and functional impairment rates during a 3- and 6-year follow-up period in the InCHIANTI Study population, analyzing the walking time and the variability in lap time. They found these factors to be both a short- and long-term predictors of mortality, and rest stopping mostly a long-term predictor of mortality (Table 3) (32). In a second study, based on the LIFE-P study, they found that the risk of mobility disability at follow-up was higher in those taking longer to complete the baseline 400-MWT and among those who needed to rest during the test (risk adjusted for age, sex, and clinic site: OR 5.4; CI 2.7–10.9) (33).

Table 3
Risk of death according to 400-meter walk test characteristics. Adapted from Vestergaard et al. Rejuvenation research, 2009

The model is adjusted for age, sex, Mini Mental State Examination score, symptoms of depression, education, smoking, body mass index, being sedentary, number of comorbid conditions (max 10, hypertension, coronary heart disease, congestive heart failure, stroke, peripheral artery disease, diabetes, pulmonary disease, hip fracture, cancer, arthritis), and SPPB score; HR: Hazard Ratio, CI: Confidence interval

 

Discussion

The protocols presented in this paper are an attempt to standardize the methods of administration of these measures, in order to provide comparable results. Although there is not a unique way to conduct the here described assessments, given the different clinical settings and research protocols in which they can be applied, some steps are recognized as critical and able to affect results.
The absolute values and precision of grip strength measurements can be influenced by aspects of the protocol, such as hand size and dominance, posture (of the whole body and position of joints of the upper limb), provided encouragement, and the use of the maximum or the mean grip strength values (17). The observance of definite instructions in these steps, as already highlighted in the review of Roberts and colleagues, is crucial to ensure homogeneous measures and the training of the examiner assume a special importance to guarantee the reliability of the test (17). Taken with a handheld hydraulic dynamometer, the handgrip strength test demonstrated to have a good test–retest reliability (Intraclass Correlation Coefficient, ICC ≥ 0.85) and an excellent inter-rater reliability (ICC 0.95–0.98) (8, 17). The use of a handheld hydraulic dynamometer (units in Kg) is, therefore, considered the gold standard, but, for patients with upper extremity impairment or musculoskeletal deformations or diseases (as rheumatoid arthritis, osteoarthritis, or carpal tunnel syndrome), may not guarantee an accurate measure of muscle strength and may lead to underestimations, because it can cause stress on weak joints. Other available instruments are pneumatic, which measure grip pressure, mechanical, and strain dynamometer. The dynamometer should be calibrated at least once per year.
Elements of variability in the execution of gait speed test are walk distances (4, 6, or 10 meters, 8 or 15 foots), a static or dynamic start for walking, the usual or maximal gait speed, and the use of walking aids. A distance of 4 meters has been demonstrated to be feasible in different clinical settings, with a better accuracy compared to shorter walks. Moreover, the same distance is one of the components of the SPPB, allowing to deduce comparable measures from the whole battery (11). However, the test is characterized by a ceiling effect in high functioning persons with a high baseline walking speed (8). For these reasons, longer versions of the gait speed test (e.g., using 6- or 10-meter tracks) have been developed and validated in the literature for allowing a better discrimination of results in very fit individuals. Given the growing use of photocell-based systems of measurement, the method here proposed give the chance to have comparable results. Timing can also be measured differently from how we presented in the protocol, as starting and stopping the watch when the foot lands beyond the starting and finish lines. Moreover, some studies report measures of gait speed in full movement, starting the time measurement after the first two meters of walking. However, including the phase of acceleration provides information regarding subject’s abilities of coordination and movement planning, that are influenced by conditions frequently affecting older persons (i.e. Parkinson disease and other movement disorders). In the systematic review of the literature conducted by Peels and colleagues, the use of a moving start showed no significant difference in gait speed compared to a static start. They also found in a single study that subjects using a walking aid (cane) have a slower gait speed compared to those without (34).
To ensure the reproducibility of the 400-meter walking test, the training of the examiner is critical, in order to provide the same instructions and encouragement to participants, and to avoid to affect the results of the test dictating the pace during the walk. To ensure the correct execution the assessment, is also important to respect the provided timing for the stop rest and for the whole test. Moreover, the possibility to use walking aids and to warm up can influence the performance. The 400-meter walking test also demonstrated a high test-retest reliability, but it is mostly applied in research setting, requiring a higher administration time and a bigger space to be performed. On the other hand, being a dichotomous measure able to identify mobility disability, it provides an important and easy-to-understand indication of fitness of the subject, useful to address treatments or other tailored interventions (35).
The hand-grip strength test has been found to correlate with strength of other muscle groups, thus a good indicator of overall strength. The sensitivity to changes of the gait speed, together with an excellent test-retest and inter-rater reliability (ICC, 0.96-0.98) make gait speed a good marker for efficacy of intervention programs and treatments. Test-retest reliability of gait speed has been confirmed across different populations, from healthy older adults, people with comorbidities, to patients affected by stroke, cardiovascular disease, COPD. Compared to other tests (SPPB, chair stand test), gait speed has a stronger or similar predictive capacity for adverse events (ADL and mobility disability, hospitalization, health decline). Composite measures, as the SPPB, may have a better prognostic value, especially for high performance subjects (10, 11). Moreover, a walking speed less than 0.5 m/s is highly predictive of inability to perform the 400-meter walking test. Being very easy to perform, even in restricted places, and with minimal risk, it may be used as an alternative indicator of mobility disability when the performance of the 400-meters walking test is not possible (35).
In conclusion, the strong predictive capacity for adverse outcomes of muscle strength and physical performance measures as well as the reliability and the high feasibility of these tools make them suitable for supporting clinical and research decisions. In particular, the assessment of these measures may support the development of person-tailored interventions aimed at preventing/managing age-related conditions, as frailty and sarcopenia. These measures can both identify subjects at risk (who may benefit from tailored interventions), especially in primary care, but also serve as markers for monitoring the efficacy of the decisions. The dissemination of their use in clinical and research setting with a standard procedure may permit an early application and monitoring of critical aspects of the wellbeing of older persons.

 

Funding: None.
Conflcit of interest: The authors have no conflicts of interest to disclose.

 

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17. Roberts HC, Denison HJ, Martin HJ, et al. A review of the measurement of grip strength in clinical and epidemiological studies: Towards a standardised approach. Age Ageing. 2011;40(4):423-429. doi:10.1093/ageing/afr051
18. Cesari M, Kritchevsky SB, Penninx BWHJ, et al. Prognostic value of usual gait speed in well-functioning older people – Results from the health, aging and body composition study. J Am Geriatr Soc. 2005;53(10):1675-1680. doi:10.1111/j.1532-5415.2005.53501.x
19. Simonsick EM, Montgomery PS, Newman AB, Bauer DC, Harris T. Measuring fitness in healthy older adults: The health ABC long distance corridor walk. J Am Geriatr Soc. 2001;49(11):1544-1548. doi:10.1046/j.1532-5415.2001.4911247.x
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ASSOCIATION OF MUSCLE STRENGTH AND GAIT SPEED WITH CROSS-SECTIONAL MUSCLE AREA DETERMINED BY MID-THIGH COMPUTED TOMOGRAPHY – A COMPARISON WITH SKELETAL MUSCLE MASS MEASURED BY DUAL-ENERGY X-RAY ABSORPTIOMETRY

 

K. Tsukasaki1,6, Y. Matsui1,7, H. Arai2, A. Harada1, M. Tomida3, M. Takemura1, R. Otsuka3, F. Ando3,4, H. Shimokata3,5

 

1. Department of Orthopedic Surgery, National Center for Geriatrics and Gerontology, Obu, Japan; 2. National Center for Geriatrics and Gerontology, Obu, Japan; 3. Section of NILS-LSA, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Japan; 4. Faculty of Health and Medical Science, Aichi Shukutoku University, Nagakute, Japan; 5. Graduate School of Nutritional Sciences, Nagoya University of Arts and Sciences, Nisshin, Japan; 6. Hokuto Hospital, Japan; 7. Center for Frailty and Locomotive syndrome, National Center for Geriatrics and Gerontology, Obu, Japan. Corresponding author: Yasumoto Matsui, Center for Frailty and Locomotive syndrome, National Center for Geriatrics and Gerontology, 7-430. Morioka-cho, Obu, Aichi, Japan, e-mail address: matsui@ncgg.go.jp, telephone 81-522-046-2311, fax numbers:81-562-44-8518

J Frailty Aging 2020;9(2)82-89
Published online March 30, 2020, http://dx.doi.org/10.14283/jfa.2020.16

 


Abstract

Background: Muscle mass is often mentioned not to reflect muscle strength. For muscle mass assessment skeletal muscle index (SMI) is often used. We have reported that dual-energy X-ray absorptiometry (DXA)-derived SMI does not change with age in women, whereas the cross-sectional muscle area (CSMA) derived from computed tomography (CT) does. Objectives: The present study aimed to compare CT and DXA for the assessment of muscle tissue. Design & Setting: Cross-sectional study in the local residents. Participants: A total of 1818 subjects (age 40-89 years) randomly selected from community dwellers underwent CT examination of the right mid-thigh to measure the cross-sectional muscle area (CSMA). Skeletal muscle mass (SMM) was measured by DXA. The subjects performed physical function tests such as grip strength, knee extension strength, leg extension strength, and gait speed. The correlation between CT-derived CSMA and DXA-derived SMM along with their association with physical function was examined. Results: After controlling for related factors, the partial correlation coefficient of muscle cross-sectional area (CSA) with physical function was larger than that of DXA-derived SMM for gait speed in men (p=0.002) and knee extension strength in women (p=0.03). The partial correlation coefficient of quadriceps (Qc) CSA with physical function was larger than that of DXA-derived SMM for leg extension power in both sexes (p=0.01), gait speed in men (p<0.001), and knee extension strength in women (p<0.001). Conclusion: Mid-thigh CT-derived CSMA, especially Qc CSA, showed significant associations with grip strength, knee extension strength, and leg extension power, which were equal to or stronger than those of DXA-derived SMM in community-dwelling middle-aged and older Japanese people. The mid-thigh CSMA may be a predictor of mobility disability, and is considered to be useful in the diagnosis of sarcopenia.

Key words: Muscle mass, CT, DXA, muscle strength, gait speed.


 

Introduction

As the population ages, healthy life expectancy has received a considerable amount of attention. Reductions in healthy life expectancy are strongly associated with aging of the musculoskeletal system (1). Muscle weakness is an important determinant of physical function in older persons (2). In 1989, Rosenberg proposed the term ‘sarcopenia’ to describe this age-related decrease in muscle mass (3). Thereafter, sarcopenia has been defined as the loss of skeletal muscle mass and strength that occurs with advancing age (4). Sarcopenia is characterized by an age-related decline in skeletal muscle mass (SMM) plus low muscle strength and/or low physical performance. In 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) (5) and in 2011, the International Working Group on Sarcopenia (IWGS) (6) proposed an operational definition and diagnostic strategy for sarcopenia; therefore, it has become widely used throughout the world. In 2014, Chen et al. reported a consensus of the Asian Working Group for Sarcopenia (AWGS) (7). Furthermore, the EWGSOP recently revised the consensus for sarcopenia (8).
SMM measured by dual-energy X-ray absorptiometry (DXA) is used as the gold standard in the diagnosis of sarcopenia. In 1998, Baumgartner et al. reported a method for evaluating SMM from DXA measurements (9). Skeletal muscle mass index (SMI) was defined as appendicular muscle mass (AMM) /height squared. Whole-body DXA scans are available for dividing the body into bone, fat, and lean components (10). In the limbs, lean tissue mass is employed as a surrogate for muscle mass. However, whole-body DXA scans are not widely used.
On the other hand, computed tomography (CT) is used to assess a cross-sectional muscle area (CSMA). CT is the gold standard for estimating CSMA in research. Previously, we reported sex- and age-related differences in mid-thigh composition using CT in middle-aged and older Japanese individuals (11). (from their 40s to 80s) In that study with a large population, covering a wide range of generations, CSMA determined by mid-thigh CT decreased with age mainly in the quadriceps cross-sectional area (Qc CSA), especially in women. CT is considered to be a very precise imaging method. CSMA is separated into individual muscle sections, and adipose tissue is separated from the other soft tissues of the body by CT. CT is more sensitive to small changes in muscle mass than DXA (12). In previous studies, CSMA determined by mid-thigh CT has shown an association with muscle strength (13, 14) and gait speed (15, 16). However, these studies involved a small sample size (13) and did not cover a wide range of generations (13-16) as we did (1). Even in the Health, Aging and Body Composition study (15), with as many as 2979 participants, all participants were in their 70s. Furthermore, no studies evaluating the difference between CT-derived CSMA and DXA-derived SMM in relation to physical function have been performed. Therefore, we examined the association of muscle strength and gait speed with CSMA and evaluated the difference between CT-derived CSMA and DXA-derived SMM in relation to physical function in community-dwelling middle-aged and older Japanese individuals.

 

Methods

Subjects

The study participants were derived from the seventh wave survey (July 2010 to July 2012) of the National Institute for Longevity Sciences – Longitudinal Study of Aging (NILS-LSA). NILS-LSA is a study aiming to assess the normal aging process and identify factors of aging using medical, physiological, nutritional, and psychological examinations. Participants in the NILS-LSA included randomly selected age- and sex-stratified individuals from the residents in the NILS neighborhood areas of Obu City and Higashiura Town in Aichi Prefecture. The first wave examination of the NILS-LSA was conducted from November 1997 to April 2000 and comprised 2267 participants. This was a dynamic cohort study; thus, even when some participants were lost to follow-up, the same number of participants of the same age were newly chosen and recruited to ensure that the total number in each generation (40s, 50s, 60s and 70s) was kept constant age- and sex-matched random samples of the same number of dropouts were recruited, except for those aged >79 years. Male and female participants aged 40 years were also newly recruited every year. Details of the NILS-LSA have been previously reported.17 Of the 2330 participants in the seventh wave survey, 943 men and 875 women were included in the present study, all of whom underwent all of the following six examinations; mid-thigh CT, DXA, grip strength, knee extension strength, leg extension power, and gait speed. The study protocol was reviewed and approved by the ethics committee of the National Center for Geriatrics and Gerontology, and written informed consent was obtained from all subjects.

Mid-thigh CSMA measurement

Details of mid-thigh CSMA measurement have been previously reported (11) CSMA, involved whole-muscle CSA (defined as muscle CSA), and Qc CSA were measured. With the subject in a supine position, right mid-thigh CSA was measured by CT (X-Vision; Toshiba, Tokyo, Japan, and SOMATOM Sensation 64; Siemens, Munich, Germany) at the midpoint from the inguinal crease to the proximal pole of the patella (Fig. 1). A single-slice CT image was obtained with a minimal slice width of 10 mm for X-Vision and 5 mm for SOMATOM Sensation 64. CSA data were analyzed using Quick Grain version 5.2 software (Inotech, Hiroshima, Japan). The differences in CT voxels between air and skin, fat and muscle, and muscle and bone were checked by an orthopedic surgeon before calculation of CSMA by automatic tracing of the margins. CSA of subcutaneous fat, intermuscular fat, and bone CSA were removed when muscle CSA (cm2) was measured (Fig. 1). Qc CSA (cm2) was measured by manual tracing of the Qc margin by one research assistant who received training before starting to trace a large number of samples. Until she became accustomed to tracing, two other persons, both orthopedic surgeons, joined the discussion concerning how to determine where the margin should be placed. After starting the large number of tracings, when she faced difficult cases and felt uncertain, the margins were decided through discussions with the two surgeons.

Figure 1
Cross-sectional muscle area determined by mid-thigh computed tomography

CSMA was measured at the midpoint from the inguinal crease to the proximal pole of the patell; Cross-sectional areas of subcutaneous fat, intermuscular fat, and bone have been removed.

 

SMM measurement

SMM was measured by a QDR-4500 DXA (Hologic, Bedford, MA, USA). The subject lay in a supine position on the DXA table with limbs close to the body. The whole-body lean soft tissue mass was divided into the arms, legs, and trunk. AMM was determined by combining the lean soft tissue mass of the regions of arms and legs. SMI (kg/m2) was calculated by AMM / height squared. SMI and lean mass of right lower extremity / height squared were included in analysis.

Physical function measurement

Grip strength (kg) was measured using a T.K.K.4301 grip dynamometer (Takei, Niigata, Japan). In the standing position, with the arms straight by the sides, the subject gripped the dynamometer as hard as possible without pressing the instrument against the body or bending at the elbow. Two tests were performed for each hand, and the maximum value was included in the analysis. Knee extension strength (kg) was measured using a T.K.K.1281 (Takei). The subject was seated on a chair with his or her hip and knee joints flexed to 90° and advised to extend the knee joint. Three tests were performed for each leg; the maximum value was included in analysis. Leg extension power (watts) was measured using a T.K.K.4236 (Takei). The subject was fastened by a seat belt to the chair. In the starting position, the feet were placed on a footplate attached perpendicularly to a rail. The subject was advised to extend his or her legs as quickly and powerfully as possible, so that the footplate started sliding horizontally on the rail. The maximum value of eight tests was included in analysis. Gait speed (m/s) was measured using the YW-3, which is a walking analysis system using the sheet type sensors made by Yagami Co Ltd, (Nagoya, Aichi, Japan). The subject was asked to walk a distance of 10 m along a walkway, with approximately 1 m before and after the test distance for the acceleration and deceleration required to walk at the usual speed.

Other parameters

Body height (cm) and weight (kg) were assessed for all subjects, and body mass index (kg/m2) was calculated by weight/height squared. Medical history and smoking habits were obtained by self-administered questionnaires. Total energy intake per day (kcal/day) was assessed using three-day diet records.18 Foods were weighed separately on a scale before cooking, or portion sizes were estimated. Subjects also photographed meals before and after eating using disposable cameras. Trained dieticians calculated total energy intake per day based on the three-day dietary records. Alcohol intake (ethanol ml/day) over the previous year was assessed using a food frequenced questionnaire administered by trained dieticians during interviews with the subjects. Moreover, trained interviewers employed a questionnaire to analyze the frequency, intensity, and duration of exercise using a physical exertion manual,19 and physical activity (MET×min /year) during leisure time was calculated. One metabolic equivalent (MET) is defined as the amount of oxygen consumed while sitting at rest and is equal to 3.5 ml O2 per kg body weight.

Statistical analysis

Continuous variables were expressed as the mean ± standard deviation, and class variables were expressed as number (% of total men or women). Differences in continuous variables between men and women were tested by Student’s t-test, and the associations of class variables between men and women were tested by χ2-test.
To evaluate the association of SMI with CSMA, we examined scatter plot and regression analysis in both sexes. We examined the association of CSMA or DXA-derived SMM with physical function using Pearson’s partial correlation coefficient adjusted for age, alcohol intake, leisure time activity, total energy intake, smoking habits, medical history of stroke, hypertension, heart disease, hyperlipidemia, and diabetes (information on these were mostly collected through self-administered questionnaires mailed approximately one month in advance to each participant before their arrival at the clinic, and the answers were checked by trained interviewers). The difference in the partial correlation coefficient between CT-derived CSMA and DXA-derive SMM was examined in a manner described by Meng et al (20).
Statistical testing was carried out using the Statistical Analysis System release 9.3 (SAS Institute, Cary, NC, USA), and p<0.05 was considered to indicate a statistical significance.

 

Results

For the baseline characteristics, the proportion of men and women was similar in all age groups (Table 1). The number of subjects in the 80s group was approximately one-quarter of that in the other groups. SMI, muscle CSA, Qc CSA, grip strength, knee extension strength, leg extension power, and gait speed were significantly higher in men than in women (p<0.001). Current smoking rate and the prevalence of hypertension, heart disease, and diabetes were higher in men than in women (p<0.001, p<0.001, p=0.02, respectively).

Table 1
Characteristics of subjects by sex

The values are expressed as mean ±standard deviation and a number (% of total men or women) of samples; p-values were obtained using the t-test for continuous data and the χ²-test for categorical data; BMI, body mass index; AMM, appendicular muscle mass; SMI, skeletal muscle mass index; CSA, cross-sectional area; Qc, quadriceps

 

Figure 2 shows a positive correlation between DXA-derived SMI and CT-derived CSMA. In both sexes, SMI showed an association with muscle CSA (r=0.80, p<0.001 in men, r=0.71, p<0.001 in women) and Qc CSA (r=0.75, p<0.001 in men, r=0.63, p<0.001 in women).

Figure 2
Regression analysis showing the association between skeletal muscle mass index (SMI) and muscle cross-sectional area (muscle CSA) and quadriceps cross-sectional area (Qc CSA) in men and women

 

The association of CSMA and DXA-derived SMI with physical function is presented in Table 2. Muscle CSA showed a significant association with grip strength (r=0.33 in men, r=0.32 in women), knee extension strength (r=0.44 in men, r=0.46 in women), and leg extension power (r=0.34 in men, r=0.28 in women) in both sexes. Qc CSA showed a significant association with grip strength (r=0.34 in men, r=0.30 in women), knee extension strength (r=0.50 in men, r=0.49 in women), and leg extension power (r=0.39 in men, r=0.31 in women) in both sexes. CSMA in particular showed a strong association with knee extension strength in both sexes. Muscle CSA and Qc CSA showed a weak association with gait speed in men (r=0.10, r=0.12, respectively). SMI showed an association with grip strength (r=0.35 in men, r=0.33 in women), knee extension strength (r=0.47 in men, r=0.41 in women), and leg extension power (r=0.33 in men, r=0.24 in women) in both sexes. SMI in particular showed an association with knee extension strength in both sexes but no association with gait speed in either sex.

Table 2
The association and respective differences of CSMA or SMI with physical function

Pearson’s partial correlation coefficient was calculated controlled for age, alcohol intake, leisure time activity, total energy intake, smoking habit and medical history. The difference in the partial correlation coefficient between CSMA and SMI was examined according to Meng et al. CSMA, cross-sectional muscle area; CSA, cross-sectional area; Qc, quadriceps; SMI, skeletal muscle mass index

 

The difference in the partial correlation coefficients for CT-derived CSMA and DXA-derived SMI is also presented (Table 2). The partial correlation coefficient of muscle CSA with physical function was larger than that of DXA-derived SMI for gait speed in men (p=0.002) and knee extension strength in women (p=0.03). The partial correlation coefficient of Qc CSA with physical function was larger than that of DXA-derived SMI for leg extension power in both sexes (p=0.007), gait speed in men (p<0.001), and knee extension strength in women (p<0.001). For grip strength, almost no difference in the partial correlation coefficient was observed between CSMA and DXA-derived SMI in both sexes.
The association of CSMA or DXA-derived lean mass of right lower extremity / height squared with physical function and the difference in partial correlation coefficient between CT-derived CSMA and DXA-derived lean mass of right lower extremity / height squared are shown in Table 3. Lean mass of right lower extremity / height squared showed an association with grip strength (r=0.30 in men, r=0.30 in women), knee extension strength (r=0.46 in men, r=0.40 in women), and leg extension power (r=0.32 in men, r=0.25 in women) in both sexes. Additionally, it showed an association with knee extension strength in both sexes and a weak association with gait speed in women (r=0.08). The partial correlation coefficient of muscle CSA with physical function was larger than that of DXA-derived lean mass of right lower extremity / height squared for gait speed in men (p=0.001) and knee extension strength in women (p=0.008). The partial correlation coefficient of Qc CSA with physical function was larger than that of DXA-derived lean mass of right lower extremity / height squared for knee extension strength and leg extension power in men (p=0.04, p<0.001) and women (p<0.001, p=0.02 respectively), and gait speed in men (p<0.001), For grip strength, no difference in the partial correlation coefficient between CT-derived CSMA and DXA-derived lean mass of right lower extremity / height squared was found in either sex.

Table 3
The association and respective differnces of CSMA or lean mass of right lower extremity / height² with physical function

The difference in the partial correlation coefficient between CSMA and lean mass of right lower extremity / height² was examined according to Meng et al. CSMA, cross-sectional muscle area; CSA, cross-sectional area; Qc, quadriceps; rLM/h2, lean mass of the right lower extremity/height2

 

Discussion

The present study indicated that Qc CSA determined by mid-thigh CT showed a better association with leg extension power in both sexes, knee extension strength in women, and gait speed in men than DXA-derived SMM, whereas muscle CSA showed a better association with knee extension strength in women and gait speed in men than did DXA-derived SMM.
Previous studies have demonstrated a nice correlation between DXA-derived SMM and CT-derived CSMA for the lower limb region (21-23). Wang et al (21) reported a good (r=0.95, p<0.001) correlation between DXA-derived appendicular lean mass and multislice CT-derived muscle mass in 17 healthy men aged 35.3±12.7 years and 8 men aged 35.0±4.9 years with acquired immunodeficiency syndrome (AIDS). On the other hand, Visser et al. (22) reported a good (r=0.97, p<0.001) correlation between DXA-derived mid-thigh lean mass and single-slice mid-thigh CT-derived muscle mass in 30 healthy men aged 73.9±2.2 years and 30 healthy women aged 73.6±2.3 years. Levin et al. (23) also reported a good (r=0.86, p<0.001) correlation between DXA-derived thigh lean mass and single-slice mid-thigh CT-derived muscle mass in 18 healthy men and 25 healthy women aged 39±13 years. Our study had a larger sample size and wider age range, including middle-aged and older subjects, and showed a similar correlation between DXA-derived SMI and CT-derive CSMA.
CSMA determined by mid-thigh CT has been shown to have an association with muscle strength (13, 14). In the present study, CSMA showed an association with muscle strength and power in both sexes, especially with knee extension strength. Thus, our results confirm those of the previous studies. Less muscle mass (smaller CSMA) is associated with increased risk of mobility loss in older men and women (2). CT-derived CSMA may be a predictor of mobility disability.
In the present study, the association between Qc CSA and leg extension power by mid-thigh CT was examined. Qc CSA showed a significant association with leg extension power, and the partial correlation coefficient of Qc CSA with physical function was larger than that of DXA-derived SMM for leg extension power in both sexes. The correlation coefficients detected for grip strength were comparatively lower than those for knee extension strength for both CSA and SMI. This may be due to the «regional sarcopenia» phenomenon because sarcopenia may affect only a limited number of muscle groups in the earlier stages. Low Qc muscle mass is associated with postural instability (24). Therefore, low muscle mass of the lower extremities is particularly important, as it may lead to various physical dysfunctions (25). Mid-thigh CT separates CSMA into Qc CSA and non-Qc CSA and is a useful method for evaluating the association between Qc CSA and physical function.
Previous studies have evaluated the association of CT-derived CSMA or DXA-derived SMM with physical function. However, the intensity of the association between CT-derived CSMA and DXA-derived SMM on physical function has not been compared. In the present study, CT-derived CSMA showed an association with muscle strength and power that was equal to or stronger than that of DXA-derived SMM. The statistical significance of our results, however, may have little relevance in clinical practice because the correlation coefficients found here were rather low (most of them were far below 0.50, e.g., that between CSMA and gait speed in males was 0.10.
To examine whether there are any differences, the analyses were performed separately in the two age groups, either younger or older than 60 years old. However, the results were not different between the younger group and the older group.
We reported that the pattern of age-related decrease in DXA-derived SMI is different between men and women, although SMI did not differ according to age in women (26). DXA-derived SMM is thought to underestimate the fat component (27), and DXA-derived SMM might involve a fat component.
This study had several limitations. First, a selection bias imposed by the longitudinal design might exist in this study. NILS-LSA is a longitudinal study; therefore, the seventh wave survey participants tended to be healthier than those who dropped out of the study. Second, CT-derived CSMA was a cross-sectional area only at mid-thigh. CT-derived CSMA may not completely reflect the skeletal muscle mass in the entire body. Furthermore, concerns about radiation exposure limit the use of these whole-body imaging methods for routine measurement. With regard to the covariant adjustment for the Pearson partial correlation coefficient, we tried to adjust for body weight, but we found that the correlation coefficient became substantially lower for both CSA and SMI, although the results of the comparison analyses between CSA and SMI were the same, namely that CSA was greater than or equal to SMI. Another covariant we should discuss that we did not consider was the possible toxic effects of some drugs that some participants may have been taking.
Adipose tissue is distinguished from other soft tissues of the body, including intramuscular lipid content, by CT.5 The increasing fat infiltration into muscle with aging may be a factor for prognosis for future mobility limitations independent of muscle strength. In the present study, we could not consider the possible presence of adipose tissue infiltrates in muscles. This phenomenon may be especially relevant in sarcopenic obesity. The methodology we utilized here was unable to measure this phenomenon. Therefore, in the near future, we will conduct new analyses utilizing additional imaging software, considering the CT attenuation value. However, at present, we are not sure if the DXA measurement would be more accurate in distinguishing lean from fat tissue because DXA-measured SMI has been reported to not change according to aging in women, whose muscle should contain more lipids as they age.
Evaluation of intramuscular lipid content is important for an assessment of muscle quality. We are now examining intramuscular fat evaluation using mid-thigh CT, and we will report muscle quality assessed by mid-thigh CT examination in the future.
Another issue we have to consider with regard to the application of this new muscle mass measuring method in clinical practice is that CT, despite being an excellent tool for measuring muscle mass, costs comparatively more and results in some radiation exposure, although using only one slice as in our method results in low exposure levels. If the muscle cross-sectional area can also be measured by ultrasound, that would be a low-cost and comparatively safe examination that does not expose the patient to radiation.
In conclusion, the present study indicated that mid-thigh CT-derived CSMA, especially Qc CSA, showed significant associations with grip strength, knee extension strength and leg extension power, which were equal to or stronger than those of DXA-derived SMM in community-dwelling, middle-aged and older Japanese people.

 

Funding: The present study was supported in part by grants from The Research Fund for Longevity Sciences (25-28, 25-22) from the National Center for Geriatrics and Gerontology (NCGG), Japan.
Acknowledgements: The authors thank the participants and their colleagues involved in the NILS-LSA.
Conflict of interest: There are no declared conflict of interest.

 

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12. Chen Z, Wang Z, Lohman T et al. Dual-energy X-ray absorptiometry is a valid tool for assessing skeletal muscle mass in older women. J Nutr 2007; 137: 2775-2780.
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AN INDIVIDUALIZED LOW-INTENSITY WALKING CLINIC LEADS TO IMPROVEMENT IN FRAILTY CHARACTERISTICS IN OLDER VETERANS

 

S.E. Espinoza1,2,3, B. Orsak2,3, C.-p. Wang2,4, D. MacCarthy2,3, D. Kellogg1,2,3, B. Powers1,2,3, A. Conde2,3, M. Moris2,3, P.R. Padala5,6,7, K.P. Padala5,7

 

1. Department of Medicine, Division of Geriatrics, Gerontology & Palliative Medicine, University of Texas Health Science Center at San Antonio; 2. Sam and Ann Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio; 3. Geriatric Research, Education & Clinical Center (GRECC), South Texas Veterans Health Care System; 4. Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio; 5. GRECC, Central Arkansas Veterans Healthcare System;
6. Department of Psychiatry, University of Arkansas for Medical Sciences; 7. Department of Geriatrics, University of Arkansas for Medical Sciences.
Corresponding author: Sara Espinoza, MD, 7703 Floyd Curl Drive, Mail Code 7875, San Antonio, TX 78223, Telephone: 210-617-5197, E-mail: espinozas2@uthscsa.edu, FAX: 210-949-3060

J Frailty Aging 2019;8(4)205-209
Published online May 30, 2019, http://dx.doi.org/10.14283/jfa.2019.16

 


 

Abstract

Background: Sedentary lifestyle leads to worse health outcomes with aging, including frailty. Older adults can benefit from regular physical activity, but exercise promotion in the clinical setting is challenging. Objectives: The objective of this clinical demonstration project was to implement a Geriatric Walking Clinic for older adults and determine whether this clinical program can lead to improvements in characteristics of frailty. Design: This was a clinical demonstration project/quality improvement project. Setting: Outpatient geriatrics clinic at the South Texas Veterans Health Care System (STVHCS). Participants: Older Veterans, aged ≥60 years. Intervention: A 6-week structured walking program, delivered by a registered nurse and geriatrician. Patients received a pedometer and a comprehensive safety evaluation at an initial face-to-face visit.  They were subsequently followed with weekly phone calls and participated in a final face-to-face follow-up visit at 6 weeks. Measurements: Grip strength (handheld dynamometer), gait speed (10-ft walk), Timed Up and Go (TUG), and body mass index (BMI) were assessed at baseline and follow-up. Frailty status for gait speed was assessed using Fried criteria. Results: One hundred eighty five patients completed the program (mean age: 68.4 ±7 years, 88% male). Improvements from baseline to follow-up were observed in average steps/day, gait speed, TUG, and BMI. Improvement in gait speed (1.13 ±0.20 vs. 1.24 ± 0.23 meter/second, p<0.0001) resulted in reduced odds of meeting frailty criteria for slow gait at follow-up compared to the baseline examination (odds ratio = 0.31, 95% confidence interval: 0.13-0.72, p = 0.01). Conclusions: Our findings demonstrate that a short duration, low-intensity walking intervention improves  gait speed and TUG. This new clinical model may be useful for the promotion of physical activity, and for the prevention or amelioration of frailty characteristics in older adults.

Key words: Frailty, physical activity, gait speed.


 

Introduction

Over the past two decades, the geriatrics community has recognized frailty as a geriatric syndrome that dramatically affects physical function, quality of life, and leads to increased health care costs.  The estimated cost of frailty in the U.S. was over $18 billion in 2000 (1). Several studies have shown that frailty leads to falls, hospitalization, disability, and an increased risk of nursing home placement (2-4). Older adults are prone to adopt a sedentary lifestyle with increasing age (5), which can contribute to poor health and disability (6).  Low physical activity is one of the key frailty characteristics in the Fried frailty model;  others include unintentional weight loss, slow gait, muscle weakness, and self-reported exhaustion (2, 7). Given the enormous costs associated with frailty, both personal and economic, a clinical intervention that prevents or delays frailty that is easy to deliver in a clinical setting would have a major impact on our society.
Exercise is known to have major health benefits for older adults, including improvement in frailty-related measures, such as gait speed, and improvement in the ability to perform activities of daily living (8, 9). Even the most frail older adults can have significant improvements in strength and physical function with exercise (10). While the benefits of exercise are known, few clinical trials have focused on the Veteran population, and few studies have examined the feasibility of implementing a physical activity intervention in a clinical setting. We do know, however, that engagement and regular contact with clinical staff and physicians along with an exercise prescription can have a greater impact on future exercise success (11, 12).
To address this and to promote an active lifestyle in older Veterans at South Texas Veterans Health Care System (STVHCS), we developed an outpatient intervention to promote physical activity in older Veterans. This intervention was modeled after the Little Rock Geriatric Walking Clinic (GWC) while maintaining a specific focus of the local site which was to improve components of frailty (13). The Little Rock GWC is a patient-centric program that implements a comprehensive approach to  engage older Veterans   in a long-term program of regular physical activity primarily in the form of walking.  This clinic uses proven strategies, such as motivational counseling, follow-up phone calls from a nurse, and self-monitoring using pedometers.  Here we report short-term results of this program from the 6-week follow-up.

 

Methods

Patient Population

The Geriatric Walking Clinic is a 6-week walking program designed to encourage increased physical activity in older adults through regular walking.  Patients  from the outpatient services at STVHCS were eligible for participation if they were 60 years or older, willing to walk for exercise, and willing to accept weekly phone calls.  Patients were recruited via: 1) flyers/brochures placed in patient waiting areas within the main hospital (Audie L. Murphy Memorial VA Hospital) and Community-based Outpatient Clinics (CBOCs) within STVHCS;  2) community events and health fairs for Veterans within STVHCS; 3) an electronic kiosk placed in the Geriatric Evaluation and Management (GEM) Clinic waiting room with electronic flyers; and 4) referrals from primary care providers (PCPs) through a consult within the electronic medical record system (CPRS), which was available to all PCPs at STVHCS. This project was approved as non-research/quality improvement by the Institutional Review Board at the University of Texas Health Science Center at San Antonio, which serves the STVHCS, and consent was not required.

Patient Screening

Patients were evaluated for safety for walking exercise based on the National Institute of Aging’s (NIA) Exercise Assessment and Screening for You (EASY) criteria (14) and additional medical contraindications.  The NIA EASY is a six-item screening tool that helps in selecting older adults for safe participation in an exercise program.  The tool includes questions regarding chest pain and tightness during physical activity, dizziness, high blood pressure, pain, stiffness and swelling of joints,   falls or feeling unsteady while walking, or any other reason  the patient would be concerned about starting a physical activity program.  Specific medical contraindications were unstable angina, severe left main coronary artery disease, end stage congestive heart failure (ejection fraction <30%), severe aortic valvular disease, uncontrolled cardiac arrhythmia, uncontrolled hypertension (systolic >180 mmHg or diastolic >100 mmHg), large abdominal aortic aneurysm, severe shortness of breath, cognitive impairment that interferes with compliance, stroke within the prior six months, uncontrolled diabetes mellitus (Hemoglobin A1c [HbA1c] >10%), and pain limiting walking. All subjects were seen and evaluated by a geriatrician  who reviewed their medical history and performed an evaluation to determine appropriateness for participation in the program.

Clinical Intervention

The clinic involved an initial baseline face-to-face visit, weekly telephone follow-up calls during the period of the intervention, and a follow-up 6-week face-to-face visit. At the baseline clinic visit, patients met individually with a registered nurse (RN) (who was also a certified diabetes educator) and a geriatrician to screen for eligibility and safety for regular walking, perform baseline assessments (see Table 1), support the patient in self-setting individualized goals for the program, issue a pedometer, and train the patient on the use of the pedometer. Patients also received individualized counseling on healthy food intake along with educational materials about benefits of walking at the baseline visit.
During the first week of the program, patients were instructed to perform usual activities and not to change their routine, in order to establish a baseline average step count per day.  Weekly telephone calls were made by the RN to inquire about daily step counts; troubleshoot barriers to walking; offer suggestions, encouragement, and counseling; and help the patient establish a goal for an increase in daily step counts (targeting a 5-10% increase each week).
At approximately 6 weeks, depending on patient availability, patients were seen for a face-to-face visit for review of progress and re-assessment of physical measurements. In order to facilitate continued lifestyle change and walking for exercise, patients were encouraged and provided education on Veterans Administration (VA) and community resources available for continued exercise.  At the follow-up visit, patients were encouraged to continue to walk and monitor using their pedometers, received an additional phone call one month later, and received a certificate of completion.

Assessments

Several assessments were measured at either the baseline, follow-up, or both, as shown in Table 1. At baseline, components of frailty measured included unintentional weight loss, usual levels of physical activity (15), self-reported exhaustion, Timed Get Up and Go (TUG), gait speed, and grip strength. The Fried frailty phenotype criteria (3) were used to determine whether patients  met standardized criteria for the physical characteristics of frailty, including weakness (grip strength) and slowness (10-foot walk). The cut-points used were standardized in the San Antonio Longitudinal Study of Aging, which is ethnically similar to our clinic population (7) and are provided in Appendix Table 1.  The TUG and gait speed measured lower extremity strength at baseline and follow-up (16). Follow-up measurements of exhaustion and physical activity were not conducted. Therefore, change in meeting frailty classification could not be assessed for exhaustion or physical activity. Although weight loss with the intervention was assessed, unintentional weight loss per se would not be expected to change within the time frame of the intervention. Therefore, formal assessment of change in frailty criteria from baseline to follow-up was conducted for gait speed and grip strength only. A brief evaluation of cognitive function was assessed at the baseline clinic visit using either the CLOX (17) or the Mini Cog (18) assessments to exclude participants with severe cognitive impairment that may hinder their participation.

Table 1 Baseline and follow-up assessments in the Geriatrics Walking Clinic

Table 1
Baseline and follow-up assessments in the Geriatrics Walking Clinic

a. To exclude patients with significant cognitive impairment from participating in the clinic; b. To establish the diagnosis of frailty at baseline

 

Statistical Analysis

Summary statistics were used to characterize the patient population.  Change in steps taken per day, gait speed, TUG, BMI, and grip strength from baseline to follow-up at 6 weeks was examined using paired t-tests.  To account for correlation of repeated measures, the generalized estimating equation (GEE) technique assuming a binomial distribution with the logit link was used to determine the changes in frailty classification (not frail vs. frail) in slowness and weakness based on walking speed and grip strength from baseline to the end of the program. Both the unadjusted GEE analysis (or asymptotic unconditional McNemar test)[19] and the adjusted results are reported.  The covariates included in the adjusted model are age, education, diabetes, and baseline physical activity.

 

Results

Patient characteristics are shown in Table 2. Mean age (±SD) was 68.4 ±6.8 years, 88% were male, and the majority were Hispanic (42.7%).  The majority of the patients were obese (61.8%) and had diabetes (55.7%). Follow-up information is provided only for those who completed the follow-up assessments (N = 157); 28 individuals examined at baseline did not complete the follow-up exam (15%).  There were no major side effects or injuries that occurred as a result of the intervention. As shown in Table 3, from baseline to follow-up we observed a significant increase in the number of steps taken per day and significant improvements in gait speed (improved by 0.096 ±0.017 meter/second),  TUG (improved by 0.8 second), and BMI (improved by 0.3 kg/m2). The average number of steps per day after the intervention increased by 1,426 (p <0.001) steps in the unadjusted analysis, and by 1,523 steps (p <0.001) in the adjusted analysis. No significant change was observed in grip strength from baseline to follow-up. Thirteen patients met the frailty criterion of low gait speed at baseline. Nearly 85% (N=11) of them improved their gait speed sufficiently so as to not meet the frailty criteria at the 6-week follow-up visit. Improvement in gait speed (1.13 ±0.20 vs. 1.24 ±0.23 meters/second, p <0.0001) resulted in  reduced number of patients meeting criteria for frailty at the follow-up (10.3% met criteria at baseline vs. 3.5% met criteria following the intervention). This resulted in reduced odds of meeting frailty criteria for slow gait at follow-up compared to the baseline examination with an unadjusted odds ratio (OR) of 0.31, 95% confidence interval (CI): 0.13-0.72, p =0.0062. After adjustment for covariates, the adjusted OR was 0.26, 95% CI: 0.10-0.69, p =0.0063.    This suggests a 74% reduced odds of meeting frailty criteria for gait speed.  There was limited, non-significant improvement in grip strength at the follow-up compared to the baseline (31.7 ±9.8 kg vs. 31.2 ±9.9 kg, p =0.1765). This resulted in 37.2% meeting frailty criteria for grip strength post-intervention compared to 38.5% meeting frailty criteria at baseline. The odds of meeting frailty criteria for grip strength at follow-up compared to the baseline examination was not significant in either the unadjusted model (OR = 0.83, p =0.147) or the adjusted model (OR = 0.81, p =0.149).

Table 2 Patient characteristics (N = 185)

Table 2
Patient characteristics (N = 185)

Table 3 Change in characteristics from baseline to follow-up at 6 weeks (N =157)

Table 3
Change in characteristics from baseline to follow-up at 6 weeks (N =157)

 

Discussion

Our results demonstrate the feasibility of a low-impact activity promotion clinic. We also demonstrate that a clinic that promotes walking as physical activity in older adults leads to improvements in gait speed, TUG, and BMI even at a short follow-up of six weeks. The improvement in gait was associated with reduction in the proportion of individuals who met gait speed criteria for frailty from baseline to follow-up. The improvement in gait speed is also clinically significant, and it is expected that these changes will translate, on the long term, into clinically relevant improved outcomes (20). Prior studies have demonstrated the potential usefulness and effectiveness of exercise for the promotion of healthy aging, prevention of frailty (21-24, 9, 25), and for amelioration of frailty in those who were already frail (8, 26). However, the results of the prior studies were mixed which may be partially related to differing follow-up periods as well as methods used for assessing frailty across studies.  The Lifestyle Interventions and Independence for Elders (LIFE) study examined the effectiveness of a physical activity intervention in sedentary older adults who were at increased risk for future disability. Initial exploratory findings suggested that the intervention, which included aerobic, strength, flexibility, and balance training, led to improvement in frailty (measured by Fried criteria) (3) over one year follow-up, primarily due to an increase in physical activity in the intervention group (21). Later analysis from this study demonstrated that there was no difference in frailty (measured using an abbreviated frailty criteria) (27) from baseline to follow-up (22).  In spite of these conflicting results, the general consensus remains that physical activity is the main intervention for frailty (9).
Our study has some weaknesses, including the fairly small number of patients evaluated, a short follow-up period, and that this was not a randomized controlled trial.  We also emphasize that even though individuals improved in frailty on one of the frailty characteristics (walking speed), this may not necessarily reflect a global change in frailty classification (i.e., non-frail, pre-frail, or frail).  Further, because the patients referred to the Geriatric Walking Clinic were either self-referred or were referred by their treating PCP, we can only observe the effect of this clinical intervention in motivated individuals, many of them who also have support from their doctor. Further, individuals with medical conditions that would limit safety with exercise were excluded. Therefore, this sample includes relatively healthy older adults. Here, we present the results of a quality improvement program; therefore, a strength of our evaluation is that we have demonstrated the effect of promoting walking for exercise in a “real world” clinical setting. Robust representation of minority participants is a significant strength of our program. Similarly, high rates of diabetes could be seen as a strength as patients with diabetes have higher sedentary behavior.
Prior work has demonstrated that exercise is beneficial for older adults. A meta-analysis which conducted a combined analysis of 1,068 participants in eight clinical trials demonstrated that exercise led to improvement in gait speed, balance, and disability in activities of daily living (8). Current exercise recommendations for older adults include 150 minutes of moderate-intensity aerobic activity (such as brisk walking) each week and muscle strengthening exercises on two or more days per week (28). Practically speaking, however, the majority of older adults are highly sedentary, and these recommendations may be an ambitious goal and difficult to achieve in the clinical setting with other competing needs. In fact, among adults aged 65 to 74 years, only 34% of men and 17% of women expend more than 2,000 kcal per week in exercise (29). Physical inactivity and health consequences cost over 11% of US healthcare expenditures, about $117 billion in 2014 alone (30).  Further, although the exercise recommendations above are a reasonable starting point, several exercise programs with varied approaches, frequency and duration have led to improvements in muscle strength, gait speed, balance, and falls reduction in older adults (31).  Therefore, the optimal exercise program for older adults with varying needs is not always clear and likely depends upon a patient’s comorbid medical conditions as well as desired results.  Current evidence does support that exercise which is delivered in a prescribed manner concludes in improved results (11, 12).  Our program adds to the existing literature by showing the feasibility and outcomes of a low impact walking prescription wherein the goals were negotiated with the patient, and ongoing motivational support was provided with weekly phone calls. Improvements in gait speed may have a lasting benefit on individual patients’ longevity (32). Best et al., in a recent paper, showed that early change in walking speed was predictive of improvement in chronic physical activity among a group of over 2800 community dwelling older adults, further emphasizing the lasting health effects of the increased gait speed found in this study (33).
In summary, our Geriatric Walking Clinic encourages walking for exercise in a “start low and go slow” manner (a familiar tenet in the practice of geriatric medicine) with frequent monitoring and encouragement. Our findings demonstrate that a low-intensity walking intervention leads to improvements in gait speed, one of the frailty criteria for slowness even at a short follow-up. This new clinical model may be useful for the promotion of physical activity and for the prevention or amelioration of frailty in older adults.

 

Funding: Funded by VA T21 Non-Institutional Long Term Care Initiative grant “Activity Promotion Consultation Clinic to Engage High-risk Older Veterans in Regular Walking” (VISN 17-G598-4), VA ORH grant “Geriatrics Walking Clinic: Rural Expansion” (VA ORH S2-P00738), and the San Antonio Older Americans Independence Center (NIA/NIH P30 AG044271).
Acknowledgements: Supported by funding from the Veterans Administration (VA) Geriatrics and Extended Care (GEC) T21 Non-Institutional Long Term Care Initiative, the VA Office of Rural Health (ORH), the San Antonio Geriatrics Research, Education and Clinical Center at the South Texas Veterans Health Care System, and the San Antonio Older Americans Independence Center at the University of Texas Health Science Center at San Antonio.
Conflict of Interest: There are no conflicts of interest
Ethical standard: This article details results from a clinical demonstration project and does not contain any study involving human subjects.
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

MATERIAL ONLINE

 

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28.     Prevention CfDCa. Physical activity basics: Physical activity is essential to healthy aging. 2018. https://www.cdc.gov/physicalactivity/basics/older_adults/index.htm. Accessed August 9, 2018.
29.     Crespo CJ, Keteyian SJ, Heath GW, Sempos CT. Leisure-time physical activity among US adults: results from the Third National Health and Nutrition Examination Survey. Archives of internal medicine. 1996;156(1):93-8.
30.     Carlson SA, Fulton JE, Pratt M, Yang Z, Adams EK. Inadequate physical activity and health care expenditures in the United States. Progress in cardiovascular diseases. 2015;57(4):315-23.
31.     de Labra C, Guimaraes-Pinheiro C, Maseda A, Lorenzo T, Millán-Calenti JC. Effects of physical exercise interventions in frail older adults: a systematic review of randomized controlled trials. BMC geriatrics. 2015;15(1):154.
32.     Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M et al. Gait speed and survival in older adults. Jama. 2011;305(1):50-8.
33.     Best JR, Liu-Ambrose T, Metti AL, Rosso AL, Satterfield S, Studenski S et al. Longitudinal associations between walking speed and amount of self-reported time spent walking over a 9-year period in older women and men. The Journals of Gerontology: Series A. 2017;73(9):1265-71.

DESIGNING DRUG TRIALS FOR FRAILTY: ICFSR TASK FORCE 2018

 

M. Pahor1, S.B. Kritchevsky2, D.L. Waters3, D.T. Villareal4, J. Morley5, J.M. Hare6, B. Vellas7,8,9 and the ICFSR Task Force

 

1. University of Florida Institute on Aging, Gainesville, FL, USA; 2. Sticht Center for Healthy Aging and Alzheimer’s Prevention.  Wake Forest School of Medicine.  Winston-Salem, NC USA; 3. University of Otago, Dunedin School of Medicine, Dunedin, New Zealand; 4. Baylor College of Medicine and Michael E DeBakey VA Medical Center, Houston, TX, USA; 5. Division of Geriatrics, St. Louis, University Medical School, St. Louis, MO, USA; 6. Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL, USA; 7. UMR1027 Inserm, F-31073, Toulouse, France; 8. University of Toulouse III, F-31073, France; 9. Gérontopôle Toulouse, Toulouse University Hospital, F-31000, Toulouse, France;
Corresponding author: Marco Pahor, University of Florida Institute on Aging, Gainesville, FL, USA, mpahor@ufl.edu

Task Force members: Hidenori Arai (Obu City, Japan); Mylène Aubertin-Leheudre (Montréal, Canada); Jürgen Bauer (Heidelberg, Germany); Ryne Carney (Washington, USA); Brian Clark (Athens, USA); Alfonso Cruz Jentoft (Madrid, Spain); Carla Delannoy (Vevey, Switzerland); Susanna Del Signore (Paris, France); Elsa Dent (Adelaide, Australia); Waly Dioh (Paris, France); Roger Fielding (Boston, USA); Bertrand Fougère (St Louis, USA); Juerg Gasser (Basel, Switzerland); Geoff Green (Miami, USA); Jack Guralnik (Baltimore, USA); Hare Joshua (Miami, USA); Aaron Hinken (King of Prussia, USA); Evgueni Ivanov (Basel, Switzerland); Naotoshi Kanemitsu (Tokyo, Japan); Kala Kaspar (Vevey, Switzerland); Tatiana Klompenhouwer (Utrecht, The Netherlands); Stephen Kritchevsky (Winston-Salem, USA); Francesco Landi (Roma, Italy); Valérie Legrand (Nanterre, France); Yvette Luiking (Utrecht, The Netherlands); Ram Miller (Cambridge, USA); Bradley Morgan (South San Francisco, USA); John Morley (St Louis, USA); Vikkie Mustad (Columbus, USA); David Neil (King of Prussia, USA); Suzanne Page (Miami, USA); Marco Pahor (Gainesville, USA); Dimitris Papanicolaou (East Hanover, USA); Suzette Pereira (Columbus, USA); Claire Regard (Vevey, Switzerland); Daniel Rooks (Cambridge, USA); Jorge Ruiz (Miami, USA); Cornel Sieber (Nürnberg, Germany); Sitra Tauscher Wisniewski (Northbrook, USA); Brooke Travnicek (Clearwater, USA);  Vellas Bruno (Toulouse, France); Dennis Villareal (Houston, USA); Debra Waters (Dunedin,New Zealand); Lixin Zhang Auberson (Basel, Switzerland)

J Frailty Aging 2018;7(3):150-154
Published online July 9, 2018, http://dx.doi.org/10.14283/jfa.2018.20


Abstract

To reduce disability and dependence in older adults, frailty may represent an appropriate target for intervention. While preventing frailty through lifestyle interventions may be the optimal public health approach for many population groups, pharmacological approaches will likely be needed for individuals who meet frailty criteria or who have comorbid conditions that contribute to and complicate the frailty syndrome, and for those who are not compliant with lifestyle interventions. Barriers to successful development of drug treatments for frailty include variability in how the frailty syndrome is defined, lack of agreement on the best diagnostic tools and outcome measures, and the paucity of sensitive, reliable, and validated biomarkers. The International Conference on Frailty and Sarcopenia Research Task Force met in Miami, Florida, on February 28, 2018, to consider the status of treatments under development for frailty and discuss potential strategies for advancing the field. They concluded that at the present time, there may be a more productive regulatory pathway for adjuvant treatments or trials targeting specific functional outcomes such as gait speed. They also expressed optimism that several studies currently underway may provide the insight needed to advance drug development for frailty.

Key words: Sarcopenia, frailty, gait speed, short physical performance battery, clinical trials.


 

Introduction

The frailty syndrome has emerged as a public health priority worldwide (1) and a major contributor to late-life disability, loss of independence (2), poor health outcomes, and increased costs (3). Consensus definitions of frailty conceptualize the condition as progressive functional decline and increased vulnerability to stress resulting from decreased physiological reserve and resilience (1, 4, 5). To reduce disability and dependence in older adults, frailty may thus represent an appropriate target for intervention (6). Complex genetic, physiologic, and psychosocial processes contribute to the development of frailty (7). As a result, both lifestyle and pharmacological approaches will likely be required to successfully prevent and treat frailty. Recognizing the need to accelerate development of treatments for frailty, the International Conference on Frailty and Sarcopenia Research (ICFSR) Task Force met in February 2018 to address the challenges faced in designing clinical trials to assess the efficacy of these interventions in older populations.

 

Non-pharmacologic approaches to preventing frailty

Evidence supports the use of exercise (8), nutritional support (9, 10), and reduction of polypharmacy as efficacious approaches to the treatment of frailty (11, 12). Poor nutrition is a major risk factor for frailty (13), and nutritional approaches such as the Mediterranean diet (14) or supplementation with specific nutrients such as Vitamin D (15) or the leucine metabolite beta-hydroxy-beta-methylbutyrate (HMB) (16) have also been shown to improve physical performance in older adults. Polypharmacy, or the use of more than five drugs simultaneously, has also been associated with increased risk of frailty (17), and the combination of frailty and polypharmacy is associated with poorer outcomes, including increased mortality (18, 19). Reducing overprescription of drugs via medication optimization may thus represent a beneficial strategy for combatting frailty (20).
One of the most important studies in recent years has been the Lifestyle Interventions and Independence for Elders (LIFE) study, which showed that among a group of sedentary adults at risk for mobility disability, a physical activity (PA) intervention, despite being relatively modest in terms of intensity and duration, was superior to health education alone in preventing major and persistent mobility disability as measured using the 400-meter walk test (21). The study was notable for its sample size and strong study design. Subgroup analysis showed older and those with lower Short Physical Performance Battery (SPPB) scores benefited most from the intervention.  In the LIFE pilot study, PA was also shown to be more effective than health education in reducing the frailty burden, especially in those with frailty or comorbidity at baseline (22). Further analysis of the LIFE data indicated that results differed depending on the outcome measure used: while the PA intervention was associated with improvement in frailty measured with the Fried criteria (23), it did not alter other measures of frailty (24).

 

Challenges for drug trials

While preventing frailty through lifestyle interventions may be the optimal public health approach for many population groups (25), pharmacological approaches will likely be needed for individuals who meet frailty criteria or who have comorbid conditions that contribute to and complicate the frailty syndrome (26). In order to conduct clinical trials for drugs targeting frailty, however, several challenges must first be addressed. First, to ensure more consistent diagnosis and more reliable selection of appropriate trial participants, alignment is needed between those who view frailty as vulnerability or risk (23, 27) and those who see it as a multidimensional continuum (28). While these two perspectives open the door to different approaches, the use of two different definitions also leads to variability in the selection of trial parameters such as inclusion criteria and outcome measures.
In selecting a target population at risk of frailty, many factors need to be considered, including age, low physical activity, impaired physical function, impaired cognition, disability (activities of daily living [ADLs] and instrumental ADLs [IADLs]), comorbidities, involuntary weight loss, lack of social support, incontinence, depression, exhaustion and fatigue, history of hospitalization, polypharmacy, sensory deficits, low-grade inflammation, pressure sores risk, and of course, clinical judgement. In the LIFE study, the physical activity intervention was most effective in the most frail group (24), indicating that this high risk group is an excellent target for interventions to reduce major mobility disability. Many frailty assessment instruments have been proposed for different purposes (e.g., to assess risk of adverse outcomes, to assess risk factors for clinical studies, or for clinical decision making.). Frailty has only infrequently been used as an outcome in interventional studies. A systematic study of frailty instruments identified 67, of which nine were most frequently cited (29).  Among the nine highly-cited instruments all assess physical function but only six include assessment of disability, three assess physical activity, four assess cognition, five assess comorbidity, two assess weight loss, and five assess other factors such as social, sensory, or demographic (29).
Selecting primary and secondary outcomes represents another challenge for investigators designing trials. As mentioned earlier, the selection of outcome measure can determine whether a study succeeds or fails (24). Nonetheless, both the LIFE and Sarcopenia and Physical fRailty IN older people: multi-componenT Treatment strategies” (SPRINTT) studies have demonstrated that it is feasible to assess outcomes in intervention trials for frailty (30).
A related trial, called the ENabling Reduction of low-Grade Inflammation in Seniors (ENRGISE) pilot study is now underway to examine whether improvements in mobility can also be achieved by reducing the level of inflammatory markers with a nutritional supplement (fish oil) and the angiotensin receptor blocker losartan (31). Primary outcome measures in this study include changes in interleukin-6 (IL-6) levels and changes in the 400-meter walk test. Secondary outcome measures include the SPPB, frailty according to the Fried/CHS criteria (23), other measures of muscle strength and power, and a patient-reported measure of disability. This study represents a transition to show if the outcomes demonstrated in non-pharmacological trials can be reproduced in studies with drugs and nutraceuticals. Supporting this approach, a cross-sectional analysis of data from the Women’s Health and Aging Studies (WHAS) demonstrated correlation between markers of inflammation and the prevalence of frailty in community-dwelling older women (32).
In selecting outcome measures for clinical studies, investigators must balance the desire to better understand mechanistic pathways and responses to treatment with participant burden and controlling the overall cost of the study.
Selecting drugs or other interventions to be tested in frailty trials represents another challenge. The choice of treatments should be based on a solid understanding of pathophysiology of frailty, which is complex and not fully understood. It may be necessary to treat frailty using a  multimodal approach tailored for individual patients.

 

Progress in testing drugs for frailty – human mesenchymal stem cells

Mesenchymal stem cells (MSCs) derived from bone marrow have been shown to have potent anti-inflammatory, anti-fibrotic, neoangiogenic, and pro-regenerative properties that may have therapeutic potential for many diseases of aging, including frailty (33). Hare and colleagues have shown that hMSCs can be delivered safely, circulate throughout the body, localize to areas of inflammation, and retain effectiveness in older individuals (33, 34). In collaboration with Longeveron, they launched a clinical trial program in 2014 called the AllogeneiC Human Mesenchymal Stem Cells in Patients with Aging, FRAilTy via intravenoUS Delivery (CRATUS) Project (NCT02065245) to establish the safety of allogeneic human MSCs in individuals with frailty, determine the efficacy parameters in various domains of functional capacity and quality of life, and evaluate the usefulness of biomarkers to assess clinical responses in individuals with aging frailty
In the initial Phase I non-blinded study, 15 participants aged 60-95 who met frailty criteria established by the Canadian Study on Health and Aging were given escalating doses by intravenous infusions of 20, 100, or 200 million allo-hMSCs (35).  A second, Phase I/II study enrolled 30 participants randomized to receive placebo or either 100 million or 200 million allo-hMSCs. The primary outcome measure for safety was the incidence of treatment-related serious adverse effects such as death, pulmonary embolism, stroke, worsening dyspnea resulting in hospitalization, or clinically significant laboratory tests abnormalities.  Secondary efficacy endpoints included reduced rate of decline as measured by the 4-meter gait speed test and the 6-minute walk test (6MWT); weight loss; decreased handgrip strength assessed by dynamometer and SPPB; exhaustion assessed using the multidimensional fatigue inventory questionnaire; difference in quality of life assessment; death from any cause; exercise change in ejection fraction; and a panel of inflammatory biomarkers (36).

Table 1 Effect of Mesenchymal Stem Cells on Phenotypes of Frailty

Table 1
Effect of Mesenchymal Stem Cells on Phenotypes of Frailty

TNF-α – Tumor Necrosis Factor alpha; IL-1β – interleukin-1 beta; IL-10 – interleukin 10; Note: MSCs home to sites of injury and enhance repair of damaged tissues (heart, joints, muscle, blood vessels) and exert their regenerative effects via paracrine signaling, mitochondrial transfer, direct cellular contact, and exosome excretion.

 

The infusions were well tolerated, with no treatment-related serious adverse events. Blood tests at baseline and at 6 and 12 months after the infusions showed a dose-related reduction in markers of inflammation, notably marked and sustained declines in TNF-α, as well as a decreased number of “exhausted” B cells, suggesting improved immunosenescence.  The results in terms of frailty measures are shown in Table 1 (37).
A Phase IIb dose-ranging multicenter clinical trial (n=120) is now underway with a more narrowly defined target population, i.e., a clinical frailty scale score of 5-6 and 6MWT between 200 and 400 meters, as well as a Tumor Necrosis Factor alpha (TNFα) level ≥ 2.5 pg/ml. The primary outcome in this trial will be a change in the 6MWT. Secondary outcomes will include change in TNFα level and score on the PROMIS Physical Function Patient Reported Outcome assessment. Exploratory endpoints will include other physical performance measures, a frailty score, upper and lower extremity function patient-report outcome (PRO) scores, falls efficacy scale score, spirometry, neuroinflammatory biomarkers, Performance Oriented Mobility Assessment, and clinical outcomes. The study is powered to show a difference in the 6MWT between 3 different treatment groups (dose-response) and placebo groups at 6 months.  Thirty (30) subjects per treatment arm will provide 80% power to demonstrate an effect size of 0.75, defined as the treatment difference of each dose vs. placebo in change from baseline in 6MWT divided by the common standard deviation, at α=0.05.

 

Biomarkers in drug trials

Biomarkers are essential for treatment development yet have received little attention since relatively few studies have been conducted to treat frailty. In the context of clinical trials, biomarkers can provide mechanistic insight or serve as intermediate or surrogate endpoints. An ideal trial biomarker for frailty should 1) be associated with frailty independent of age and comorbidities, predict what frailty predicts (i.e., disability), and be on the causal pathway to the target outcome; 2) be sensitive to change in response to interventions that affect the risk or severity of frailty: rapidly responding biomarkers allow for shorter trials and 3) be insensitive to common treatments used in older populations, not overly burdensome, and show low within-subject variation.
Composite biomarkers that combine several measurements into a single summary scale have shown promise in epidemiological studies but may include measures not targeted in a clinical trial, or measures that are not sensitive enough to detect change during the period of the trial. For example, Sanders and colleagues developed a modified physiologic index score combining measures of systolic blood pressure, forced vital capacity (FVC), the Digit-Symbol Substitution Test (DSST), serum cystatin C, and serum fasting glucose (38). This index was associated with incident disability and death, but it might not be useful in the clinical trial context. DSST and FVC may lack sufficient sensitivity to detect change over a 6- or 12-month study. Also, many older people have undiagnosed hypertension or glucose abnormalities that when detected by the study could lead to treatment which would add noise to study results. The interpretation of some physiologic measures varies over the life course, as low blood pressure is associated with lower mortality at younger ages, but higher mortality in the oldest old (39) complicating the interpretation of an index, which included blood pressure.
In 2013, López-Otin and colleagues described nine biological hallmarks of aging and proposed that health can be improved by directly targeting those hallmarks (40). Three hallmarks – inflammation, mitochondrial energetics, and senescence – contribute to frailty as demonstrated by their association with the five dimensions of the Fried/CHS frailty scale — weakness, slowness, low energy, weight loss, and inactivity. Many putative biomarkers of inflammation have been identified. In a study exploring the association of these biomarkers with physical function, Hsu and colleagues found that eight different biomarkers coalesced into independent TNF-α and C-reactive protein (CRP)-related factors. Both were associated with poor function but differed in their association with body composition (41).
Biomarkers will likely find most use for risk prediction and mechanistic insight and there are many candidate biomarkers. Repositories of tissue and serum from well-characterized people in the context of interventions that did or did not work will be essential to identify additional biomarkers and correlate them with phenotypes. The strongest candidates identified thus far are related to inflammation. IL-6 and TNF-α soluble receptor (either 1 or 2), and possibly TNF-α itself.  Other multi-dimensional biomarker panels including T- and B-cell subsets are presently being evaluated in the CRATUS trial cohort.   Both are associated with weakness and muscle loss, yet it is as yet unclear whether these associations are specific to or a companion to inflammation and whether they are in the causal pathway of frailty. Trials targeting inflammation may provide answers to this question.

 

Conclusions

A regulatory pathway for frailty interventions would require a better understanding of the biological pathways that contribute to frailty and a clearer definition of frailty as an outcome. It was suggested that the next step for the ICFSR Task Force might be to convene a consensus conference to define frailty. Alternatively, lacking a consensus definition, it may be more productive to develop adjuvant treatments rather than targeting frailty itself.
Some Task Force members suggested that frailty may be too heterogeneous to be used as an intervention target, and that functional measures such as gait speed, chair rise, or stair climb performance are more reasonable outcomes to target. Given the heterogeneity of individuals with frailty, it would also be helpful to define subgroups that can be tested with different interventions to see how they respond. For example, frailty may be associated with obesity, malnutrition, etc., and more research is needed to understand how these other factors may lead to frailty. Biomarker profiles could enable individualized approaches to treatment but will only become possible with multi-marker strategies and complex statistical methodology.
The Fried criteria are the most widely used to define frailty and to classify individuals as frail or prefrail (23). However, it may be necessary to define different stages of frailty itself, e.g. mild, moderate and severe. In addition, the Fried criteria focus only on physical frailty, yet there are also social, cognitive (42), and psychological forms of frailty. The Rockwood approach captures additional elements to define frailty but may be somewhat onerous for clinicians and patients to administer (28).
Intermediate endpoints and biomarkers are also needed for efficient clinical trials. Much more research is needed on biomarkers before the field can select and reach consensus on the most useful biomarkers. There is a regulatory pathway for qualification of biomarkers, but this will require a great deal more data than are currently available.  While individual biomarkers may provide some mechanistic insight that enables the design of a successful trial, eventually it may be desirable to profile patients based on multiple factors including function, inflammatory markers, etc., in order to select the most appropriate treatment. However, at present there is insufficient knowledge about why some individuals respond to a treatment and others do not. Outcomes in the ENRGISE pilot study may provide some clarity on the relationship of inflammatory markers to treatment response, which may be especially important since most diseases of aging are linked to inflammation. ENRGISE is asking a simple question – is inflammation a bystander to frailty or is it in a pathway that can be modified through intervention. Only when that question has been answered will it make sense to move to additional questions, including what mechanisms and pathways are involved and what are the subsets of responders.
The SPRINTT investigators have proposed inability to complete the 400-meter walk as the primary endpoint, although European Medicines Association (EMA) approval of this endpoint is still pending. EMA would like to see additional data on the clinical relevance of failing the 400-meter walk test. Gait speed has been suggested as a more clinically relevant indicator.

 

Conflicts of interest: Marco Pahor: ENRGISE is funded by the National Institutes of Health grant number U01AG050499. Abbott provided a grant for study drug, but the company has no other involvement with the study.  Joshua M. Hare: holds a patent for cardiac cell-based therapy and holds equity in Vestion Inc. He maintains a professional relationship with Vestion Inc. as a consultant and member of the Board of Directors and Scientific Advisory Board. Vestion Inc. did not play a role in the design, conduct, or funding of the study. Dr. Hare is the Chief Scientific Officer, a compensated consultant and advisory board member for Longeveron and holds equity in Longeveron. Dr. Hare is also the co-inventor of intellectual property licensed to Longeveron. Longeveron did not play a role in the design, conduct, or funding of the study.
Acknowledgements: The authors thank Lisa Bain for assistance in preparing this manuscript.  

 

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40.    Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194-217.
41.    Hsu FC, Kritchevsky SB, Liu Y, Kanaya A, Newman AB, Perry SE, et al. Association between inflammatory components and physical function in the health, aging, and body composition study: a principal component analysis approach. J Gerontol A Biol Sci Med Sci. 2009;64(5):581-9.
42.    Kelaiditi E, Cesari M, Canevelli M, van Kan GA, Ousset PJ, Gillette-Guyonnet S, et al. Cognitive frailty: rational and definition from an (I.A.N.A./I.A.G.G.) international consensus group. J Nutr Health Aging. 2013;17(9):726-34.

 

DESIGNING DRUG TRIALS FOR FRAILTY: ICFSR TASK FORCE 2018

 

M. PAHOR1, S.B. KRITCHEVSKY2, D.L. WATERS3, D.T. VILLAREAL4, J. MORLEY5, J.M. HARE6, B. VELLAS7,8,9 AND THE ICFSR TASK FORCE

 

1. University of Florida Institute on Aging, Gainesville, FL, USA; 2. Sticht Center for Healthy Aging and Alzheimer’s Prevention.  Wake Forest School of Medicine.  Winston-Salem, NC USA; 3. University of Otago, Dunedin School of Medicine, Dunedin, New Zealand; 4. Baylor College of Medicine and Michael E DeBakey VA Medical Center, Houston, TX, USA; 5. Division of Geriatrics, St. Louis, University Medical School, St. Louis, MO, USA; 6. Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL, USA; 7. UMR1027 Inserm, F-31073, Toulouse, France; 8. University of Toulouse III, F-31073, France; 9. Gérontopôle Toulouse, Toulouse University Hospital, F-31000, Toulouse, France;
Corresponding author: Marco Pahor, University of Florida Institute on Aging, Gainesville, FL, USA, mpahor@ufl.edu
Task Force members: Hidenori Arai (Obu City, Japan); Mylène Aubertin (Montréal, Canada); Jürgen Bauer (Heidelberg, Germany); Ryne Carney (Washington, USA); Brian Clark (Athens, USA); Alfonso Cruz Jentoft (Madrid, Spain); Carla Delannoy (Vevey, Switzerland); Susanna Del Signore (Paris, France); Elsa Dent (Adelaide, Australia); Waly Dioh (Paris, France); Roger Fielding (Boston, USA); Bertrand Fougère (St Louis, USA); Juerg Gasser (Basel, Switzerland); Jack Guralnik (Baltimore, USA); Hare Joshua (Miami, USA); Aaron Hinken (King of Prussia, USA); Evgueni Ivanov (Basel, Switzerland); Naotoshi Kanemitsu (Tokyo, Japan); Kala Kaspar (Vevey, Switzerland); Tatiana Klompenhouwer (Utrecht, The Netherlands); Stephen Kritchevsky (Winston-Salem, USA); Francesco Landi (Roma, Italy); Valérie Legrand (Nanterre, France); Yvette Luiking (Utrecht, The Netherlands); Ram Miller (Cambridge, USA); Bradley Morgan (South San Francisco, USA); John Morley (St Louis, USA); Vikkie Mustad (Columbus, USA); David Neil (King of Prussia, USA); Suzanne Page (Miami, USA); Marco Pahor (Gainesville, USA); Dimitris Papanicolaou (East Hanover, USA); Suzette Pereira (Columbus, USA); Claire Regard (Vevey, Switzerland); Daniel Rooks (Cambridge, USA); Jorge Ruiz (Miami, USA); Cornel Sieber (Nürnberg, Germany); Sitra Tauscher Wisniewski (Northbrook, USA); Brooke Travnicek (Clearwater, USA);  Vellas Bruno (Toulouse, France); Dennis Villareal (Houston, USA); Debra Waters (Dunedin,New Zealand); Lixin Zhang Auberson (Basel, Switzerland)

J Frailty Aging 2018;7(3):150-154
Published online July 9, 2018, http://dx.doi.org/10.14283/jfa.2018.20

 


Abstract

To reduce disability and dependence in older adults, frailty may represent an appropriate target for intervention. While preventing frailty through lifestyle interventions may be the optimal public health approach for many population groups, pharmacological approaches will likely be needed for individuals who meet frailty criteria or who have comorbid conditions that contribute to and complicate the frailty syndrome, and for those who are not compliant with lifestyle interventions. Barriers to successful development of drug treatments for frailty include variability in how the frailty syndrome is defined, lack of agreement on the best diagnostic tools and outcome measures, and the paucity of sensitive, reliable, and validated biomarkers. The International Conference on Frailty and Sarcopenia Research Task Force met in Miami, Florida, on February 28, 2018, to consider the status of treatments under development for frailty and discuss potential strategies for advancing the field. They concluded that at the present time, there may be a more productive regulatory pathway for adjuvant treatments or trials targeting specific functional outcomes such as gait speed. They also expressed optimism that several studies currently underway may provide the insight needed to advance drug development for frailty.

Key words: Sarcopenia, frailty, gait speed, short physical performance battery, clinical trials.


 

Introduction

The frailty syndrome has emerged as a public health priority worldwide (1) and a major contributor to late-life disability, loss of independence (2), poor health outcomes, and increased costs (3). Consensus definitions of frailty conceptualize the condition as progressive functional decline and increased vulnerability to stress resulting from decreased physiological reserve and resilience (1, 4, 5). To reduce disability and dependence in older adults, frailty may thus represent an appropriate target for intervention (6). Complex genetic, physiologic, and psychosocial processes contribute to the development of frailty (7). As a result, both lifestyle and pharmacological approaches will likely be required to successfully prevent and treat frailty. Recognizing the need to accelerate development of treatments for frailty, the International Conference on Frailty and Sarcopenia Research (ICFSR) Task Force met in February 2018 to address the challenges faced in designing clinical trials to assess the efficacy of these interventions in older populations.

Non-pharmacologic approaches to preventing frailty

Evidence supports the use of exercise (8), nutritional support (9, 10), and reduction of polypharmacy as efficacious approaches to the treatment of frailty (11, 12). Poor nutrition is a major risk factor for frailty (13), and nutritional approaches such as the Mediterranean diet (14) or supplementation with specific nutrients such as Vitamin D (15) or the leucine metabolite beta-hydroxy-beta-methylbutyrate (HMB) (16) have also been shown to improve physical performance in older adults. Polypharmacy, or the use of more than five drugs simultaneously, has also been associated with increased risk of frailty (17), and the combination of frailty and polypharmacy is associated with poorer outcomes, including increased mortality (18, 19). Reducing overprescription of drugs via medication optimization may thus represent a beneficial strategy for combatting frailty (20).
One of the most important studies in recent years has been the Lifestyle Interventions and Independence for Elders (LIFE) study, which showed that among a group of sedentary adults at risk for mobility disability, a physical activity (PA) intervention, despite being relatively modest in terms of intensity and duration, was superior to health education alone in preventing major and persistent mobility disability as measured using the 400-meter walk test (21). The study was notable for its sample size and strong study design. Subgroup analysis showed older and those with lower Short Physical Performance Battery (SPPB) scores benefited most from the intervention.  In the LIFE pilot study, PA was also shown to be more effective than health education in reducing the frailty burden, especially in those with frailty or comorbidity at baseline (22). Further analysis of the LIFE data indicated that results differed depending on the outcome measure used: while the PA intervention was associated with improvement in frailty measured with the Fried criteria (23), it did not alter other measures of frailty (24).

Challenges for drug trials

While preventing frailty through lifestyle interventions may be the optimal public health approach for many population groups (25), pharmacological approaches will likely be needed for individuals who meet frailty criteria or who have comorbid conditions that contribute to and complicate the frailty syndrome (26). In order to conduct clinical trials for drugs targeting frailty, however, several challenges must first be addressed. First, to ensure more consistent diagnosis and more reliable selection of appropriate trial participants, alignment is needed between those who view frailty as vulnerability or risk (23, 27) and those who see it as a multidimensional continuum (28). While these two perspectives open the door to different approaches, the use of two different definitions also leads to variability in the selection of trial parameters such as inclusion criteria and outcome measures.
In selecting a target population at risk of frailty, many factors need to be considered, including age, low physical activity, impaired physical function, impaired cognition, disability (activities of daily living [ADLs] and instrumental ADLs [IADLs]), comorbidities, involuntary weight loss, lack of social support, incontinence, depression, exhaustion and fatigue, history of hospitalization, polypharmacy, sensory deficits, low-grade inflammation, pressure sores risk, and of course, clinical judgement. In the LIFE study, the physical activity intervention was most effective in the most frail group (24), indicating that this high risk group is an excellent target for interventions to reduce major mobility disability. Many frailty assessment instruments have been proposed for different purposes (e.g., to assess risk of adverse outcomes, to assess risk factors for clinical studies, or for clinical decision making.). Frailty has only infrequently been used as an outcome in interventional studies. A systematic study of frailty instruments identified 67, of which nine were most frequently cited (29).  Among the nine highly-cited instruments all assess physical function but only six include assessment of disability, three assess physical activity, four assess cognition, five assess comorbidity, two assess weight loss, and five assess other factors such as social, sensory, or demographic (29).
Selecting primary and secondary outcomes represents another challenge for investigators designing trials. As mentioned earlier, the selection of outcome measure can determine whether a study succeeds or fails (24). Nonetheless, both the LIFE and Sarcopenia and Physical fRailty IN older people: multi-componenT Treatment strategies” (SPRINTT) studies have demonstrated that it is feasible to assess outcomes in intervention trials for frailty (30).
A related trial, called the ENabling Reduction of low-Grade Inflammation in Seniors (ENRGISE) pilot study is now underway to examine whether improvements in mobility can also be achieved by reducing the level of inflammatory markers with a nutritional supplement (fish oil) and the angiotensin receptor blocker losartan (31). Primary outcome measures in this study include changes in interleukin-6 (IL-6) levels and changes in the 400-meter walk test. Secondary outcome measures include the SPPB, frailty according to the Fried/CHS criteria (23), other measures of muscle strength and power, and a patient-reported measure of disability. This study represents a transition to show if the outcomes demonstrated in non-pharmacological trials can be reproduced in studies with drugs and nutraceuticals. Supporting this approach, a cross-sectional analysis of data from the Women’s Health and Aging Studies (WHAS) demonstrated correlation between markers of inflammation and the prevalence of frailty in community-dwelling older women (32).
In selecting outcome measures for clinical studies, investigators must balance the desire to better understand mechanistic pathways and responses to treatment with participant burden and controlling the overall cost of the study.
Selecting drugs or other interventions to be tested in frailty trials represents another challenge. The choice of treatments should be based on a solid understanding of pathophysiology of frailty, which is complex and not fully understood. It may be necessary to treat frailty using a  multimodal approach tailored for individual patients.

Progress in testing drugs for frailty – human mesenchymal stem cells

Mesenchymal stem cells (MSCs) derived from bone marrow have been shown to have potent anti-inflammatory, anti-fibrotic, neoangiogenic, and pro-regenerative properties that may have therapeutic potential for many diseases of aging, including frailty (33). Hare and colleagues have shown that hMSCs can be delivered safely, circulate throughout the body, localize to areas of inflammation, and retain effectiveness in older individuals (33, 34). In collaboration with Longeveron, they launched a clinical trial program in 2014 called the AllogeneiC Human Mesenchymal Stem Cells in Patients with Aging, FRAilTy via intravenoUS Delivery (CRATUS) Project (NCT02065245) to establish the safety of allogeneic human MSCs in individuals with frailty, determine the efficacy parameters in various domains of functional capacity and quality of life, and evaluate the usefulness of biomarkers to assess clinical responses in individuals with aging frailty
In the initial Phase I non-blinded study, 15 participants aged 60-95 who met frailty criteria established by the Canadian Study on Health and Aging were given escalating doses by intravenous infusions of 20, 100, or 200 million allo-hMSCs (35).  A second, Phase I/II study enrolled 30 participants randomized to receive placebo or either 100 million or 200 million allo-hMSCs. The primary outcome measure for safety was the incidence of treatment-related serious adverse effects such as death, pulmonary embolism, stroke, worsening dyspnea resulting in hospitalization, or clinically significant laboratory tests abnormalities.  Secondary efficacy endpoints included reduced rate of decline as measured by the 4-meter gait speed test and the 6-minute walk test (6MWT); weight loss; decreased handgrip strength assessed by dynamometer and SPPB; exhaustion assessed using the multidimensional fatigue inventory questionnaire; difference in quality of life assessment; death from any cause; exercise change in ejection fraction; and a panel of inflammatory biomarkers (36).

Table 1 Effect of Mesenchymal Stem Cells on Phenotypes of Frailty

Table 1
Effect of Mesenchymal Stem Cells on Phenotypes of Frailty

TNF-α – Tumor Necrosis Factor alpha; IL-1β – interleukin-1 beta; IL-10 – interleukin 10; Note: MSCs home to sites of injury and enhance repair of damaged tissues (heart, joints, muscle, blood vessels) and exert their regenerative effects via paracrine signaling, mitochondrial transfer, direct cellular contact, and exosome excretion.

 

The infusions were well tolerated, with no treatment-related serious adverse events. Blood tests at baseline and at 6 and 12 months after the infusions showed a dose-related reduction in markers of inflammation, notably marked and sustained declines in TNF-α, as well as a decreased number of “exhausted” B cells, suggesting improved immunosenescence.  The results in terms of frailty measures are shown in Table 1 (37).
A Phase IIb dose-ranging multicenter clinical trial (n=120) is now underway with a more narrowly defined target population, i.e., a clinical frailty scale score of 5-6 and 6MWT between 200 and 400 meters, as well as a Tumor Necrosis Factor alpha (TNFα) level ≥ 2.5 pg/ml. The primary outcome in this trial will be a change in the 6MWT. Secondary outcomes will include change in TNFα level and score on the PROMIS Physical Function Patient Reported Outcome assessment. Exploratory endpoints will include other physical performance measures, a frailty score, upper and lower extremity function patient-report outcome (PRO) scores, falls efficacy scale score, spirometry, neuroinflammatory biomarkers, Performance Oriented Mobility Assessment, and clinical outcomes. The study is powered to show a difference in the 6MWT between 3 different treatment groups (dose-response) and placebo groups at 6 months.  Thirty (30) subjects per treatment arm will provide 80% power to demonstrate an effect size of 0.75, defined as the treatment difference of each dose vs. placebo in change from baseline in 6MWT divided by the common standard deviation, at α=0.05.

Biomarkers in drug trials

Biomarkers are essential for treatment development yet have received little attention since relatively few studies have been conducted to treat frailty. In the context of clinical trials, biomarkers can provide mechanistic insight or serve as intermediate or surrogate endpoints. An ideal trial biomarker for frailty should 1) be associated with frailty independent of age and comorbidities, predict what frailty predicts (i.e., disability), and be on the causal pathway to the target outcome; 2) be sensitive to change in response to interventions that affect the risk or severity of frailty: rapidly responding biomarkers allow for shorter trials and 3) be insensitive to common treatments used in older populations, not overly burdensome, and show low within-subject variation.
Composite biomarkers that combine several measurements into a single summary scale have shown promise in epidemiological studies but may include measures not targeted in a clinical trial, or measures that are not sensitive enough to detect change during the period of the trial. For example, Sanders and colleagues developed a modified physiologic index score combining measures of systolic blood pressure, forced vital capacity (FVC), the Digit-Symbol Substitution Test (DSST), serum cystatin C, and serum fasting glucose (38). This index was associated with incident disability and death, but it might not be useful in the clinical trial context. DSST and FVC may lack sufficient sensitivity to detect change over a 6- or 12-month study. Also, many older people have undiagnosed hypertension or glucose abnormalities that when detected by the study could lead to treatment which would add noise to study results. The interpretation of some physiologic measures varies over the life course, as low blood pressure is associated with lower mortality at younger ages, but higher mortality in the oldest old (39)complicating the interpretation of an index, which included blood pressure.
In 2013, López-Otin and colleagues described nine biological hallmarks of aging and proposed that health can be improved by directly targeting those hallmarks (40). Three hallmarks – inflammation, mitochondrial energetics, and senescence – contribute to frailty as demonstrated by their association with the five dimensions of the Fried/CHS frailty scale — weakness, slowness, low energy, weight loss, and inactivity. Many putative biomarkers of inflammation have been identified. In a study exploring the association of these biomarkers with physical function, Hsu and colleagues found that eight different biomarkers coalesced into independent TNF-α and C-reactive protein (CRP)-related factors. Both were associated with poor function but differed in their association with body composition (41).
Biomarkers will likely find most use for risk prediction and mechanistic insight and there are many candidate biomarkers. Repositories of tissue and serum from well-characterized people in the context of interventions that did or did not work will be essential to identify additional biomarkers and correlate them with phenotypes. The strongest candidates identified thus far are related to inflammation. IL-6 and TNF-α soluble receptor (either 1 or 2), and possibly TNF-α itself.  Other multi-dimensional biomarker panels including T- and B-cell subsets are presently being evaluated in the CRATUS trial cohort.   Both are associated with weakness and muscle loss, yet it is as yet unclear whether these associations are specific to or a companion to inflammation and whether they are in the causal pathway of frailty. Trials targeting inflammation may provide answers to this question.

 

Conclusions

A regulatory pathway for frailty interventions would require a better understanding of the biological pathways that contribute to frailty and a clearer definition of frailty as an outcome. It was suggested that the next step for the ICFSR Task Force might be to convene a consensus conference to define frailty. Alternatively, lacking a consensus definition, it may be more productive to develop adjuvant treatments rather than targeting frailty itself.
Some Task Force members suggested that frailty may be too heterogeneous to be used as an intervention target, and that functional measures such as gait speed, chair rise, or stair climb performance are more reasonable outcomes to target. Given the heterogeneity of individuals with frailty, it would also be helpful to define subgroups that can be tested with different interventions to see how they respond. For example, frailty may be associated with obesity, malnutrition, etc., and more research is needed to understand how these other factors may lead to frailty. Biomarker profiles could enable individualized approaches to treatment but will only become possible with multi-marker strategies and complex statistical methodology.
The Fried criteria are the most widely used to define frailty and to classify individuals as frail or prefrail (23). However, it may be necessary to define different stages of frailty itself, e.g. mild, moderate and severe. In addition, the Fried criteria focus only on physical frailty, yet there are also social, cognitive (42), and psychological forms of frailty. The Rockwood approach captures additional elements to define frailty but may be somewhat onerous for clinicians and patients to administer (28).
Intermediate endpoints and biomarkers are also needed for efficient clinical trials. Much more research is needed on biomarkers before the field can select and reach consensus on the most useful biomarkers. There is a regulatory pathway for qualification of biomarkers, but this will require a great deal more data than are currently available.  While individual biomarkers may provide some mechanistic insight that enables the design of a successful trial, eventually it may be desirable to profile patients based on multiple factors including function, inflammatory markers, etc., in order to select the most appropriate treatment. However, at present there is insufficient knowledge about why some individuals respond to a treatment and others do not. Outcomes in the ENRGISE pilot study may provide some clarity on the relationship of inflammatory markers to treatment response, which may be especially important since most diseases of aging are linked to inflammation. ENRGISE is asking a simple question – is inflammation a bystander to frailty or is it in a pathway that can be modified through intervention. Only when that question has been answered will it make sense to move to additional questions, including what mechanisms and pathways are involved and what are the subsets of responders.
The SPRINTT investigators have proposed inability to complete the 400-meter walk as the primary endpoint, although European Medicines Association (EMA) approval of this endpoint is still pending. EMA would like to see additional data on the clinical relevance of failing the 400-meter walk test. Gait speed has been suggested as a more clinically relevant indicator.

 

Conflicts of interest: Marco Pahor: ENRGISE is funded by the National Institutes of Health grant number U01AG050499. Abbott provided a grant for study drug, but the company has no other involvement with the study.  Joshua M. Hare: holds a patent for cardiac cell-based therapy and holds equity in Vestion Inc. He maintains a professional relationship with Vestion Inc. as a consultant and member of the Board of Directors and Scientific Advisory Board. Vestion Inc. did not play a role in the design, conduct, or funding of the study. Dr. Hare is the Chief Scientific Officer, a compensated consultant and advisory board member for Longeveron and holds equity in Longeveron. Dr. Hare is also the co-inventor of intellectual property licensed to Longeveron. Longeveron did not play a role in the design, conduct, or funding of the study.
Acknowledgements: The authors thank Lisa Bain for assistance in preparing this manuscript.

 

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42.    Kelaiditi E, Cesari M, Canevelli M, van Kan GA, Ousset PJ, Gillette-Guyonnet S, et al. Cognitive frailty: rational and definition from an (I.A.N.A./I.A.G.G.) international consensus group. J Nutr Health Aging. 2013;17(9):726-34.

RISK FACTORS FOR LOW GAIT SPEED: A NESTED CASE-CONTROL SECONDARY ANALYSIS OF THE MEXICAN HEALTH AND AGING STUDY

M.U. PÉREZ-ZEPEDA1, J.G. GONZÁLEZ-CHAVERO2, R. SALINAS-MARTINEZ2, L.M. GUTIÉRREZ-ROBLEDO1

1. Clinical and Epidemiologic Research Department at Instituto Nacional de Geriatría, Mexico City, México; 2. Centro Regional para el Estudio del Adulto Mayor, Departamento de Medicina Interna, Hospital Universitario “Doctor José Eleuterio González”, Facultad de Medicina, Universidad Autónoma de Nuevo León, Monterrey, Nuevo León, México.

Corresponding author: Dr. Mario Ulises Pérez-Zepeda, Epidemiological and Clinical and Epidemiological Research Department, Instituto Nacional de Geriatría, Mexico City, México; Periférico Sur 2767, colonia san Jerónimo Lídice, delegación Magdalena Contreras, 11800, México D.F.; Phone: (52) 5555738686; luis.gutierrez@salud.gob.mx

J Frailty Aging 2015;4(3):139-143
Published online June 23, 2015, http://dx.doi.org/10.14283/jfa.2015.63


Abstract

Background: Physical performance tests play a major role in the geriatric assessment. In particular, gait speed has shown to be useful for predicting adverse outcomes. However, risk factors for slow gait speed (slowness) are not clearly described. Objectives: To determine risk factors associated with slowness in Mexican older adults. Design: A two-step process was adopted for exploring the antecedent risk factors of slow gait speed. First, the cut-off values for gait speed were determined in a representative sample of Mexican older adults. Then, antecedent risk factors of slow gait speed (defined using the identified cut-points) were explored in a nested cohort case-control study. Setting, participants: One representative sample of a cross-sectional survey for the first step and the Mexican Health and Aging Study (a cohort characterized by a 10-year follow-up). Measurements: A 4-meter usual gait speed test was conducted. Lowest gender and height-stratified groups were considered as defining slow gait speed. Sociodemographic characteristics, comorbidities, psychological and health-care related variables were explored to find those associated with the subsequent development of slow gait speed. Unadjusted and adjusted logistic regression models were performed. Results: In the final model, age, diabetes, hypertension, and history of fractures were associated with the development of slow gait speed. Conclusions: Early identification of subjects at risk of developing slow gait speed may halt the path to disability due to the robust association of this physical performance test with functional decline.

Key words: physical performance, gait speed, frailty, sarcopenia.


Introduction

Physical performance tests have proven to be of important prognostic value in older adults (1, 2). Although they are not yet fully adopted in the clinical setting, physical performance tests (such as gait speed, balance assessment, handgrip strength) are still considered of major importance. The utility of clinical tests is mirrored by their capacity to substantially modify the management of patients (3, 4). In this context, gait speed has the potential to become a widely used tool, especially as part of the standard geriatric assessment (5). It is a simple and reliable measurement of physical function, quick to assess, inexpensive (6), with high inter-rater and test–retest reliability (7), and informative throughout multiple clinical and research settings (8).

It has been demonstrated that a slow gait speed (or slowness) predicts adverse outcomes, including mortality, institutionalization, disability, and falls (6, 9-11). Moreover, due to its ability of reflecting the global health of the individual, it has been considered in the design of instruments classifying conditions such as frailty and sarcopenia (12-14). It is noteworthy that some authors have even indicated gait speed as a “vital sign” (15), although it might be perhaps more accurate a functional sign.

A recent meta-analysis including more than 23,000 subjects, showed that gait speed declines with aging, although it remains fairly constant from 20 to 70 years of age (men: 1.34-1.43m/s and women: 1.24-1.39m/s). Notwithstanding some authors consider that an accelerated decline begins when subjects reach 80 years (16). Despite the fact that gait speed diminishes with aging, some subjects have an accelerated process that anticipates functional decline and, consequently, dependency (17). The causes of functional decline in older adults have been attributed to a number of factors closely related to gait speed, including sarcopenia (18), muscle weakness, deconditioning, joint pain, poor balance, and incipient cognitive impairment (19, 20). Therefore, gait speed may indeed represent a biomarker in the pathway from a completely functional subject to a disabled one. Gait speed is closely related to adverse outcomes of the body (21, 22) and has the potential to serve as a core indicator of health and function in ageing and disease, especially in the domain of mobility (23). 

Walking requires energy and movement control. It demands appropriate functioning of multiple organs and systems (including the cardiovascular, respiratory, nervous, and musculoskeletal ones). A slow gait speed may reflect both higher energy expenditure for accomplishing the task and larger impairment of underlying systems (5, 24, 25). However, from an epidemiologic viewpoint, there is a lack of knowledge about the early risk factors that may predict the onset of slow gait speed in older persons. For example, there are reports associating comorbidities, insulin resistance, atherosclerosis, inflammation with slow gait speed. However, besides of adopting a cross-sectional design, these studies explore specific physiological processes (26-29).

Different cut-off values have been proposed in order to discriminate subjects at risk of negative events. However, in order to determine the range of abnormality for a clinical measure (including gait speed), it is important to define specific cut-off values tailored to the studied population (5, 30-31).

Finally, gait speed has shown to be a robust predictor of mortality, however, the question of what leads an older adult to be slow is not thoroughly answered (9, 32). The aims of this study are to determine the risk factors associated with slow gait speed in Mexican older adults after having previously established the most accurate ranges of normality in this type of population.

Methods

This study was performed following two consecutive steps and taking advantage of two different datasets: a cross-sectional survey from a representative sample of the whole Mexican population, and a longitudinal cohort with follow-up of ten years. The first step consistent in the determination of Mexican-specific cut-off values for defining slow gait speed. Then, a nested case-control design was implemented in the cohort study for estimating those risk factors present early in life that were associated with slow gait speed (cases) ten years later.

Data from the Mexican Nutrition and Health Survey (ENSANUT; latest version 2012) were used to determine the cut-off values for gait speed. Complete methods and scope of this survey are available elsewhere (33). In brief, a representative nation-wide sample of older adults was assessed for the latest edition of this study. From a total of 7,164 subjects 60-year and older, 6,998 (97.54%) had complete measurements. Subjects with missing data (mainly for height and weight) were not included in the analyses, but did not significantly differ from the analytical sample for age or gender.

The assessment of the timed usual gait speed was conducted as it is nowadays considered reference standard. The 4-meter walk, starting from a standing still position, was performed at usual pace with or without the use of auxiliary aids (cane, walker, etc.) (32). The best result of two trials was used to in the present analyses to estimate gait speed (in meters/second). Training and standardization was performed by one of the authors (MUPZ), using online available material for Short Physical Performance Battery (http://www.grc.nia.nih.gov/branches/leps/sppb/). Test-retest and inter-rater reliability both resulted in a correlation above 0.9. Subjects unable to perform the test were scored as the worst gender-specific speed registered in the sample (0.072 m/s for women; 0.076 m/s for men). Gait speed cut-points were defined in gender- and height-stratified groups (mean height for men 1.62 m, mean height for women 1.48 m). Height was measured with stadiometers in two trials and the mean of the two measures was used (or imputed from knee height if missing). Gait speed quintiles according to height and gender-specific groups were calculated. Subjects in the lowest quintile of each of the four groups were classified has having slow gait speed. These cut-points were then used in the next step to determine cases.

In the second step, a nested case-control approach was used to determine risk factors (present 10 years before) associated with slow gait speed. An analysis of the first and third waves of the Mexican Health and Aging Study (MHAS) was conducted. Full design, sampling and methods have been previously published (34). In brief, the MHAS consists of three waves: 2001, 2003 and 2012. The original sample consisted of 15,402 older adults followed-up to a maximum of eleven years. The last wave included a sub-sample of subjects that had anthropometric measurements such as: height, weight, gait speed and handgrip strength. These sub-sample was drawn from four geographical zones (called Estados in Mexico), each one representing a condition of interest (high diabetes, high migration, highly urban, highly rural). This sub-sample consisted of 2,089 subjects, including MHAS participants in the ongoing follow-up and a newly recruited sample; this latter (n=1,027) was however excluded from the present analyses due to missing baseline information (lack of assessment at the first wave). The final sample, after additional exclusion of subjects with incomplete data, was constituted of 960 older adults (90.65%). This group was divided in two categories, slow gait speed and normal gait speed, according to the cut-off values generated in step one. A set of variables from the first wave (conducted 10 years earlier) was chosen to be tested as risk factors. These variables included sociodemographic characteristics (age, gender, education, marital status), comorbidities, self-rated health, smoking status, alcohol consumption, physical activity level in the previous two years, number of visits to a physician in the past year, locus of control, self-reported comorbidities (diabetes, hypertension, ischemic heart disease, stroke, cancer, arthritis, chronic pain, history of fracture, falls, urinary incontinence, depressive symptoms, weight loss, and any difficulty in activities of daily living [ADL] or instrumental ADL [IADL]).

Depressive symptoms were considered as present if five or more (out of nine) were detected by an ad hoc screening questionnaire (35). Locus of control reflects personal beliefs that someone has regarding the potential to influence important life events. The total score based on seven questions ranges from 0 to 7, with 0 as the lowest locus of control and 7 as the highest (36). Weight loss was considered as present if the subject unintentionally lost 5 kg or more in the last two years. Finally, comorbidities were self-reported and their coding followed the question: “Has a doctor or medical personnel ever diagnosed you with….?”.

Statistical analysis

For the first step, descriptive analysis of age, gender and gait speed were performed, defining quintile values from stratified groups for gender and mean height. In the second step (nested case-control), descriptive analysis were performed, with means for continuous variables and frequencies for categorical. Bivariate analyses were conducted for the whole sample with t-test and chi square, to compare variables from the first wave between cases (slow walkers) and controls (normal walkers). Finally, a random sample of control subjects –same number as cases– was depicted from the whole sample in order to fit a logistic regression model between and obtain unadjusted and adjusted (model including all variables) odds ratios. Statistical significance was set at a p value <0.05. Link test was performed in order to assess goodness of fit of the model. Interactions between falls and history of fractures, and falls and urinary incontinence were checked. All analyses were ran with the statistical software STATA® version 13.1 (StataCorp, Texas, U.S.A).

Ethics

The MHAS study was approved by the Institutional Review Boards or Ethics Committees of the University of Texas Medical Branch in the United States, the Instituto Nacional de Estadistica y Geografia (INEGI), and the Instituto Nacional de Salud Pública (INSP) in Mexico.

The data for the ENSANUT analysis were obtained from the surveys public repository hosted at the National Institute of Public Health (NIPH) (See webpage at: http://ensanut.insp.mx/). The Institutional Board (Ethics Committee) at the NIPH in Mexico reviewed and approved the survey. All interviewees provided informed consent prior to participating.

Results

The ENSANUT study sample (first step) included 6,998 subjects, with a mean age of 70.7 years (standard deviation [SD] 8.07) and 54.76% of women (n=3,923). Overall, mean gait speed was 0.81 m/s (SD 0.41 m/s). Cut-off values ranged from 0.5 to 0.66 m/s (Table 1).

Table 1 Cut-off values of gait speed, according to the lowest gender- and height-specific quintiles

* Mean heights: 1.62 m for men, 1.48 m for women; m/s=meters per second

The sample from the MHAS (second step) consisted of 960 subjects with a mean age of 57.9 (SD 6.8); 58.1% (n=558) were women and 13.2% (n=127) presented slow gait speed. Mean number of years in school was significantly different between groups (5.3 vs 3.3, p=0.003). A total of 681 individuals were married (70.9%) and 622 (64.8%) subjects rated their own health as good (both variables statistically different between cases and controls). Significant differences were also reported for alcohol consumption, physical activity level, number of visits to a physician, and locus of control. Regarding proportions of comorbidities between cases and controls, diabetes, hypertension, arthritis, chronic pain, history of fractures, falls, urinary incontinence and depressive symptoms were statistically different between older adults with slow gait speed and those with normal gait speed. Having any difficulty in performing ADL or IADL had a prevalence of 51.8% (n=432) in controls and 78.7% (n=100) in cases (p<0.0001).

At the unadjusted logistic regression, most of the variables were statistically significant between the two groups. The adjusted model (simultaneously including all the variables) presented an overall R-squared of 0.325 (p<0.001). Age (OR 1.14, 95% confidence intervals [CI] 1.09-1.2, p<0.001), diabetes mellitus (OR 3.33, 95% CI 1.32-8.39, p=0.01), hypertension (OR 2.41, 95% CI 1.16-4.98, p=0.02) and history of fracture (OR 3.23, 95% CI 1.01-2.53, p=0.04) were statistically associated with slow gait speed (Table 2).

Table 2 Multiple unadjusted and adjusted (all the variables simultaneously included) logistic regression predicting slow gait speed

OR=odds ratio, CI=confidence interval, ADL=activities of daily living, IADL=instrumental activities of daily living

Discussion

To our knowledge, this is the first study reporting risk factors associated with slow gait speed in Mexican older adults. Moreover, we have provided normative values for gait speed in the Mexican population on the basis of gender-and height-specific percentiles derived in a representative sample of the overall population.

Compared to data from a recent meta-analysis (16), the cut-points of gait speed used as defined slowness in our study are below from those previously reported. Although such differences are not particularly striking, when it comes to generating categories for clinical parameters (i.e., defining ranges of normality), accuracy acquires a main role (4). The way cut-off values can be obtained is not only one; several methodologies can be applied. In the present study, we chose to follow the same approach already used for the operationalization of slowness in the frailty phenotype. Another option could have been to perform the analysis with gait speed as a continuous variable. However, such approach could have been resulted of not immediate clinical use.

As expected, age has a major influence on gait speed. The fact that the two are strongly associated is well known (16). An accelerated decline of gait speed can be expected in subjects presenting “pathologic slow gait speed” or “lower than expected gait speed”; the physical decline process follows a continuous pattern and end in disability and dependence (32). It has been proposed that aging depends on stochastic factors and exposure to stressors experienced during the life course. Individuals with a greater number of noxious stimuli would become more subject to adverse outcomes. In this context, gait speed would be no exception; thus, as the negative burden of deficits increases, the physical performance declines (37).

The burden of diabetes and hypertension seems to play a major role in the onset of slow gait speed. A relevant issue in the Mexican population where diabetes and hypertension prevalence are estimated to be 22.5% and 32%, respectively (33).  It should be underlined how these two diseases are major risk factors driving older adults to disability. It is also noteworthy that, due to the design of this study, a substantial number of subjects would have had diabetes and/or hypertension in the first wave but then died in the ten-year lapse between each wave. In other words, the associations we reported might even be underestimated in their strength. In Mexico according to the National Institute of Statistics and Geography (INEGI, for its acronym in Spanish, 2012 census) diabetes mellitus is the second leading cause of death in those over 65-years old. Nevertheless, those who survived, have features –in this case slow gait speed– compatible with the profile of subjects that would have a rapid functional decline (1-3). These results should raise alert about the importance of intervening against diabetes and/or hypertension at old age. In this scenario, gait speed may well serve as screening tool and 1) help in the identification of unknown/undetected clinical conditions, and 2) support the follow-up of elders by providing referent and objective measures of the overall health status.

A slow gait speed has shown to be a good predictor of falls and (probably as a consequence) fractures (38). In this report, the association seems to also proceed from history of fractures to slow gait speed. One of the possible explanations of this association might be the presence of fear of falling, a known risk factor for falls consequent to a first event. In addition, abnormal anatomy, pain and sequelae of surgical interventions could negatively impact on walking.

Although some variables used as risk factors for slow gait speed are time-dependent, they still provide an ample characterization of the situation of the subject in a particular time and complements with time-independent variables. Moreover, the only variables remaining significant in the fully adjusted model were time-independent. This gives rise to the issue of how different features of older adults change along time, demonstrating the need of obtaining such this kind of information for understanding the different trajectories of age-related declines.

Conflict of interest: Authors report no conflict of interest.

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GAIT SPEED AS A PREDICTOR OF RESPIRATORY MUSCLE FUNCTION, STRENGTH, AND FRAILTY SYNDROME IN COMMUNITY-DWELLING ELDERLY PEOPLE

 

A.N. PARENTONI1, V.A. MENDONÇA1, K.D. DOS SANTOS2, L.F. SÁ2, F.O. FERREIRA3, D.A. GOMES PEREIRA4, L.P. LUSTOSA4

 

1. Department of Physical Therapy, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Minas Gerais, Brazil; 2. Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Minas Gerais, Brazil; 3. Department of Basic Sciences, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, Minas Gerais, Brazil; 4. Department of Physical Therapy, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

Corresponding author: Adriana Netto Parentoni, Rodovia MGT 367, Km 583, No. 5000, Alto da Jacuba, ZIP: 39100000, Campus JK, Diamantina, Minas Gerais, Brazil,  Phone: +55-38-35321239, E-mail: adrianaparentoni@gmail.com

J Frailty Aging 2015;4(2):64-68
Published online March 30, 2015, http://dx.doi.org/10.14283/jfa.2015.41


Abstract

Background: Gait speed is considered a predictor of adverse health outcomes and functional decline in the elderly. This decline is also identified in respiratory muscles. Objective: To assess the impact of gait speed in maximal inspiratory pressure, maximal expiratory pressure, handgrip strength, and the different types of frailty syndrome in community-dwelling elderly people. Design: Cross-sectional study. Participants: Women (aged ≥ 65 years) were classified into different frailty phenotypes (n = 106). Measurements: Gait speed (10 m), handgrip strength (Jamar dynamometer), and maximum inspiratory and expiratory pressures (GerAr manovacuometer, MV-150/300 model) were measured. Linear regression analyses were conducted to determine the influence of gait speed and age on handgrip strength, maximal inspiratory pressure, and maximal expiratory pressure. Logistic regression was performed to assess the influence of gait speed and frailty age (α = 0.05). Results: A total of 106 elderly women participated in the study (73.96 ± 6.91 years). Thirty-two subjects were not frail, 42 were pre-frail, and 32 were frail. Gait speed and age significantly predicted handgrip strength and frailty (p < 0.05). In the multivariate model, gait speed had the greatest contribution, while age lost statistical significance. Regarding maximal inspiratory and maximal expiratory pressures, gait speed and age were significant explanatory variables (p < 0.05). In the multivariate model, gait speed lost statistical significance to predict maximal inspiratory pressure. Conclusion: Gait speed was confirmed to be a predictor of some health outcomes, including respiratory muscle function. The results suggest that interventions to increase gait speed may contribute to improve respiratory function and muscle strength, and decrease the risk of frailty among elderly people.

 

Key words: Frailty, respiratory muscle strength, gait speed, handgrip strength, elderly people.


Introduction 

Human gait depends on the general health, motor control, muscle performance, musculoskeletal and sensory conditions, and perceptual function of the individual. Moreover, resistance, and usual levels of physical activity and endurance, as well as cognition, motivation, and mental health are necessary. In addition, environmental characteristics (1) should also be considered. For these reasons, gait speed (GS) has been used as one of the best clinical predictive indicator of adverse health outcomes (1, 2), especially in the elderly. However, in clinical and research settings, measurement methods greatly vary, compromising the interpretation of results (2). Meanwhile, the literature unanimously points out the convenience and low cost of performing the GS test, which does not require sophisticated instruments, thus facilitating its implementation and application (1-5). Studenski et al. (4) found that GS is a strong predictor of survival. The authors demonstrated that a 0.1-m/s increase of the usual GS is associated with longer survival. Furthermore, they suggested cutoff values for the clinical interpretation of GS and specific treatments of choice for the elderly (4).

Likewise, a generalized loss of muscle strength is known to occur as part of the aging process. This decrease in overall muscle strength has been associated with various phenomena such as falls, functional loss, and mortality (6). Therefore, another predictive measure of adverse health outcomes that is used in the field of gerontology is handgrip strength (HGS).

In this regard, several studies demonstrated the association between HGS and overall muscle strength, suggesting that HGS might represent overall muscle strength (6). Recently, Bohannon (3) evaluated HGS and GS as functional predictors in older adults. The author concluded that although both measures are good predictors of functional disorders in elderly, GS proved to be more sensitive. Despite this result, the author (3) suggested that both measurements should complement rather than substitute each other.

Fried et al. (7) also describes frailty syndrome according to GS and HGS and proposed a phenotype with five items, classifying elderly people as non-frail, pre-frail and frail. Two of these items are GS measurements (7) and low muscle strength obtained by assessing HGS.

Normal lung function depends, among other factors, on overall muscle strength. Thus, a decrease in respiratory muscle strength (RMS) possibly leads to lung function impairment. Similarly, a decrease in the amount of energy supplied in an appropriate manner may also contribute to lower overall muscle strength [8]. In this case, we can hypothesize that functionality measurements such as GS and HGS might predict respiratory function. In a recent study, Parentoni et al. (9) demonstrated a moderate correlation between HGS and RMS, suggesting that HGS measurements could be used to identify the overall loss of muscle strength, including RMS. However, the literature on this subject is still scarce. It points to associations in populations affected with specific diseases (10); however, it does not clearly clarify this assumption in individuals without lung disease.

According to the above-mentioned, the objective of this study was to determine whether GS, controlled for age, predicts maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP), HGS, and frailty in community-dwelling elderly people.

Methods

This is a cross-sectional study, which is part of a research project to evaluate community-dwelling elderly people in four centers for family health (FH) in the city of Diamantina, Minas Gerais, Brazil. The project was approved under No. 056/11 by the Research Ethics Committee of the Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, and all participants signed an informed consent form. In the study, a convenience sample was used, which was composed of female volunteers who were recruited in the four FHS centers.

Samples

Women aged ≥ 65 years, without distinction of race and/or social class, were invited to participate in the study. We excluded those who obtained values incompatible with their educational level (11) in the Mini-Mental State Examination (MMSE), those who were unable to walk without mechanical or human assistance, those with neurological diseases, those hospitalized for less than 3 months earlier, those with a history of fractures within the last 6 months, those with acute musculoskeletal disorders that could have interfered with the HGS and GS assessments, those with uncompensated respiratory or cardiovascular diseases, those unable to perform the maneuvers required to measure the RMS, and those using the drug digoxin for its positive influence on RMS (12).

Procedures

Evaluations were always performed in the afternoon. Anthropometric measurements were initially performed (body weight and height to calculate body mass index), followed by classification into three subgroups according to the frailty criteria suggested by Fried et al. (7), namely non-frail (NF), pre-frail (PF), and frail (F).

Then, RMS was assessed by measuring MIP and MEP. The measurement was performed using an MV-150/300 manovacuometer (GerAr Comércio e Equipamentos Ltda, Brazil). Volunteers were placed in a sitting position, with their feet supported and nose occluded with a nose clip. The maneuvers were repeated up to a maximum of five times, collecting three acceptable maneuvers and maximum respiratory efforts held for at least 2 seconds (13). Measurements performed without air leaks, with a ≤10% variation from the highest value obtained, were considered acceptable. MIP and MEP were randomly measured, and the highest result was selected for analysis (13, 14). The interval between MIP and MEP collection was defined according to the normalization of O2 saturation and the return of systemic blood pressure and heart rate to basal levels. This methodology has been described in detail in a study previously published (9).

Statistical analysis

This study was conducted as part of a project to assess and identify the profile of elderly people at risk of frailty who were attending the public health system. To this end, considering the need to evaluate the different frailty subgroups, a sample calculation for the comparison of quantitative variables between groups was performed using a confidence interval of 95%, which indicated the need for a subgroup of 31 elderly people per frailty subgroup.

The distribution of data was verified by the Kolmogorov-Smirnov test. Linear regression analyses were conducted to determine the influence of GS and age on HGS, MIP, and MEP. The influences of GS and age on frailty were evaluated by using logistic regression analysis. To conduct the logistic regression, we considered frailty as a dependent variable, grouping pre-frail and frail versus non-frail. An alpha of 5% was considered statistically significant.

Results

The study included 106 elderly women aged between 65 and 91 years. The mean ± SD age was 73.96 ± 6.91 years. After classifying the subjects according to frailty phenotype, we divided them into three groups, namely NF (n = 32), PF (n = 42), and F (n = 32). Data on the comparison between the frailty subgroups and the correlation between HGS and RMS were presented in a work previously published (9). The characterization of the sample is shown in Table 1. The mean GS of the total sample was 0.72 ± 0.25 m/s. The mean GS of the non-frail (n = 32) and pre-frail + frail groups (n = 74) were 0.95 ± 0.17 m/s and 0.62 ± 0.21 m/s, respectively.

Table 1 Characteristics of the population studied in terms of age, weight, height, and body mass index in the different frailty syndrome categories

SD, standard deviation; BMI, body mass index; NF, non-frail; PF, pre-frail; F, frail.

In the univariate model, GS and age significantly predicted HGS. In the multiple regression model, age lost its statistical significance and GS remained a significant predictor of HGS (Table 2).

Table 2 Linear regression used to evaluate gait speed and age as explanatory variables for handgrip strength

Models 1 and 2, univariate regression; model 3, multivariate regression; GS, gait speed

Similarly, GS and age significantly predicted frailty in the univariate model. However, in the multivariate model, GS remained as the variable with the greatest contribution, while age lost statistical significance (Table 3).

Table 3 Logistic regression used to evaluate gait speed and age as explanatory variables of frailty

Models 1 and 2, univariate regression; model 3, multivariate regression; GS, gait speed

The explanatory variables GS and age significantly predicted MIP in the univariate model. In the multivariate model, GS lost its statistical significance (Table 4). Regarding MEP, GS and age also significantly contributed in the univariate model, which was still the case in the multivariate model (Table 5).

Table 4 Linear regression used to evaluate gait speed and age as explanatory variables of maximal inspiratory pressure

Models 1 and 2, univariate regression; model 3, multivariate regression; GS, gait speed

 

Table 5 Linear regression used to evaluate gait speed and age as explanatory variables of maximal expiratory pressure

Models 1 and 2, univariate regression; model 3, multivariate regression; GS, gait speed

 

Discussion 

GS and HGS, as measures of capacity and functional performance, have been indicated as predictors of various health outcomes. However, little information is available about the predictive capacity of these measures in relation to respiratory function in elderly people. Therefore, this study examined whether GS, controlled for age, predicts respiratory muscle function (obtained by MIP and MEP measurements), HGS, and frailty of community-dwelling elderly people. The results confirmed GS and age as predictors of respiratory muscle function, frailty, and HGS.

Buchman et al. (8), through a cohort study, evaluated whether lung function was associated with muscle strength in the lower limbs and the development of mobility deficit. The authors demonstrated that low levels of lung function and muscle strength were independently associated with the appearance of mobility deficit in elderly individuals. They argued that any change in lung function could trigger a low-energy supply cycle, with the consequent decrease in muscle function and increased difficulty to perform tasks (8). The results reported hereby support this assumption, thus assuming that a poor pulmonary condition reflects lower levels of energy, less availability for muscle activation, and consequently, lower GS.

In this context, Studenski indicated that GS below 0.67 m/s could indicate a limitation of the energy required to perform daily activities (15). In addition, Schimada et al. (16), using a sample of 10.351 Japanese, compared GS, HGS, Timed Up-and-Go, and Sit-to-Stand tests to identify which of these clinical tests were more indicated to ascertain the need for personal care of the elderly. The authors concluded that GS was the best test to determine the need for care in these individuals and that a GS below 1 m/s was the measurement that most strongly correlated with the need for care (16). In the present study, the mean GS of the total population was 0.72 m/s, while when analyzed separately, the mean of the pre-frail + frail group was 0.62 m/s. These values are very similar to those recommended by Studenski (15) and may be indicative of increased vulnerability to adverse health outcomes, including respiratory conditions.

Likewise, Ilgin et al. (10) demonstrated that GS had a low to moderate correlation with age, clinical symptoms, pulmonary function, and quality of life in a specific population with chronic obstructive pulmonary disease. The authors also observed that those with the worst clinical condition caused by the disease (increased severity) had lower GS and suggested that GS could be used as an indicator of functional capacity, especially in the most severe cases of lung disease (10). Given that frailty syndrome is a clinical condition characterized by increased vulnerability resulting from a decline in the reserve and function of multiple physiological systems (7, 17), and that frailty is often associated with a higher number of comorbidities, the results of this study support the findings of Ilgin et al. (10).

Schwenk et al. (17) conducted a systematic review to summarize the available information on the use of gait assessment to test elderly for frailty syndrome. The authors confirmed that gait characteristics in frail and pre-frail individuals are still poorly examined, with great variation in the terminology, definitions, methodology, and tools used in assessment. Moreover, the authors demonstrated that a wide gait variation may reflect a reduction of the appropriate responses of physiological systems, which are commonly observed in frail syndrome, as pre-frail elderly people present an increased alteration of temporal gait parameters, while frail older individuals show greater variability of the spatial parameters (17). This study only evaluated GS and not its spatiotemporal parameters, which might have been its limitation. However, the use of tests that are easy to apply in clinical practice renders these results relevant, thus emphasizing the importance of using the GS test in the daily evaluations of the elderly.

Regarding HGS, recent studies suggest that this measure is valid and sensitive enough to early detect individuals at risk of sarcopenia. Dam et al. (18) compared a number of existing operational criteria for the detection of sarcopenia in elderly people. Among other factors, they considered HGS (using 26 Kgf for men and 16 Kgf for women as cutoff points) and GS (cutoff at 0.8 m/s) for the classification of individuals as sarcopenic (18). Likewise, the Sarcopenia European Consensus (2010) suggested the use of an algorithm for the identification of sarcopenia, which includes HGS and GS measurements (19). Similarly, Ishii et al. (20), after evaluating 1.971 independent community-dwelling elderly, suggested, among others, the use of HGS to identify sarcopenia (20). These measurements are also used in the context of frailty, in which Fried et al. (7) proposed a phenotype for its identification, including HGS and GS measurements. Therefore, the results obtained in this study are consistent with the literature, confirming that these measurements can predict adverse health outcomes, including respiratory muscle function.

Some limitations of this study should be noted. First, the population analyzed included only elderly women who were attending the centers for FH. They were periodically monitored at these health centers and therefore had the habit of travelling to the site, thus demonstrating greater functionality and better health status, which somehow limits the possibility to generalize our results.

GS and HGS values are known to differ significantly between sexes; therefore, highlighting that these results were obtained from a population exclusively composed of women is important, and different interactions between men and women are considered, either jointly or independently.

As this study aimed to evaluate GS, HGS, and RMS in community-dwelling elderly people, those with various diseases were excluded. Further studies that use the same instruments with other populations and taking various diseases into consideration should therefore be considered in the future.

GS was confirmed as a predictor of respiratory muscle function, frailty, and HGS. With these results, we can infer that interventions that can increase GS might contribute to improve respiratory function and muscle strength, and decrease the risk of frailty among elderly people.

Conflicts of interest: None

References

1. Fritz S, Lusardi M. White paper: “Gait speed: the sixth vital sign.” J Geriatr Phys Ther 2009;32(2):46-49.

2. Peel NM, Kuys SS, Klein K. Gait speed as a measure in geriatric assessment in clinical settings: a systematic review. J Gerontol A Biol Sci Med Sci 2012;68(1):39-46.

3. Bohannon RW. Grip strength and gait speed of older women receiving physical therapy in a home-care setting. J Frailty Aging 2014;3(1):15-17.

4. Studenski S, Perera S, Patel K, et al. Gait speed and survival in older adults. JAMA 2011;305(1):50-58.

5. Cesari M, Kritchevsky SB, Penninx BWHJ, et al. Prognostic value of usual gait speed in well-functioning older people: results from the Health, Aging and Body Composition Study. J Am Geriatr Soc 2005;53:1675-1680.

6. Felicio DC, Pereira DS, Assumpção AM, et al. Poor correlation between handgrip strength and isokinetic performance of knee flexor and extensor muscles in community-dwelling elderly women. Geriatr Gerontol Int 2014;14:185-189.

7. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56(3):146-156.

8. Buchman AS, Boyle PA, Leurgans SE, Evans DA, Bennett DA. Pulmonary function, muscle strength, and incident mobility disability in elders. Proc Am Thorac Soc 2009;6:581-587.

9. Parentoni AN, Lustosa LP, Santos KD, et al. Comparação da força muscular respiratória entre os subgrupos de fragilidade em idosas da comunidade. Fisioter Pesq 2013;20(4):361-366.

10. Ilgin D, Ozalevli S, Kilinc O, et al. Gait speed as a functional capacity indicator in patients with chronic obstructive pulmonary disease. Ann Thorac Med 2011;6(3):141-146.

11. Bertolucci P, Brucki S, Campacci S. The Mini-Mental State Examination in a general population: impact of educational status. Arq Neuropsiquiatr 1994;52:1-7.

12. Siafakas NM, Stathopoulou M, Tzanakis N, et al. Effect of digoxin on global respiratory muscle strength after cholecystectomy: a double blind study. Thorax 2000;55(6):497-501.

13. Souza RB. Pressões respiratórias estáticas máximas. J Pneumol 2002;28:155-165.

14. Miranda AS, Novaes RD, Ferreira AE, et al. Assessment of respiratory muscle strength, peak expiratory flow and pain after open cholecystectomy. Acta Gastroenterol Latinoam 2009;39(1):38-46.

15. Studenski S. An evidence-based comparison of operational criteria for presence of sacopenia. J Gerontol A Biol Sci Med Sci 2014;69(5):584-590.

16. Schimada H, Suzuki T, Suzukawa M, et al. Performance-based assessments and demand for personal care in older Japanese people: a cross-sectional study. BMJ Open 2013;3(4). pii: e002424. doi: 10.1136/bmjopen-2012-002424.

17. Schwenk M, Howe C, Saleh A, et al. Frailty and technology: a systematic review of gait analysis in those with frailty. Gerontology 2014;60:79-89.

18. Dam TT, Peters KW, Fragala M, et al. An evidence-based comparison of operational criteria for the presence of sarcopenia. J Gerontol A Biol Sci Med Sci 2014;69(5):584-590.

19. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, et al. Sarcopenia: European consensus on definition and diagnosis. Age Ageing 2010;39(4):412-423.

20. Ishii S, Tanaka T, Shibasaki K, et al. Development of a simple screening test for sarcopenia in older adults. Geriatr Gerontol Int 2014;14(Suppl. 1):93-101.

 

GAIT SPEED AND HANDGRIP STRENGTH AS PREDICTORS OF INCIDENT DISABILITY IN MEXICAN OLDER ADULTS

T. LOPEZ-TEROS, L.M. GUTIERREZ-ROBLEDO, M.U. PEREZ-ZEPEDA

Clinical and Epidemiologic Research Department at Instituto Nacional de Geriatría, Mexico City, México

Corresponding author: Mario Ulises Pérez Zepeda. Periférico Sur 2767, colonia San Jerónimo Lídice, delegación Magdalena Contreras, C.P. 10200, México Distrito Federal, Mexico. e-mail: ulises.perez@salud.gob.mx

J Frailty Aging 2014;3(2):109-112
Published online December 8, 2014, http://dx.doi.org/10.14283/jfa.2014.10


Abstract

Physical performance tests are associated with different adverse outcomes in older people. The objective of this study was to test the association between handgrip strength and gait speed with incident disability in community-dwelling, well-functioning, Mexican older adults (age ≥70 years). Incident disability was defined as the onset of any difficulty in basic or instrumental activities of daily living. Of a total of 133 participants, 52.6% (n=70) experienced incident disability during one year of follow-up. Significant associations of handgrip strength (odds ratio [OR] 0.96, 95% confidence interval [95%CI] 0.93-0.99) and gait speed (OR 0.27, 95%CI 0.07-0.99) with incident disability were reported. The inclusion of covariates in the models reduced the statistical significance of the associations without substantially modifying the magnitude of them. Handgrip strength and gait speed are independently associated with incident disability in Mexican older adults.

Key words: Physical performance tests, disability, gait speed, handgrip strength.


Introduction

Physical performance tests have shown to be predictors of numerous adverse outcomes in elderly, such as mortality and disability (1, 2). Moreover, these instruments, in particular the gait speed (GS) and handgrip strength (HG) tests, are easy to perform, reproducible, and readily available for a different clinical settings (3). GS as well as HG are strong predictors of incident events of disability even among well-functioning older adults (4, 5).

Unfortunately, there is still scarce information about these associations in Hispanic older adults (6). Social, cultural, and clinical aspects may affect the immediate translation of findings across different populations, ethnicities, and geographic settings. The objective of this study was to test whether GS and HG are able to predict incident disability in a group of community-dwelling, initially well-functioning, Mexican older adults.

 

Methods

Data sample and study design

Data are from the Mexican Study of Nutritional and Psychosocial Markers of Frailty among Community-Dwelling Elderly, a cohort study whose rationale and methodologies have been previously described (7). In brief, 1,124 participants aged 70 years and older were initially evaluated in 2008 by trained interviewers in order to gather data about socio-demographic characteristics and general health status. Participants were randomly drawn from one of several districts (Coyoacan) of Mexico City (Mexico). The assessment conducted after 12 months of follow-up mirrored the same procedures and assessment instruments used at the baseline evaluation. The study was reviewed and approved by the Institutional Ethical Committee of the Instituto Nacional de Geriatría. All the subjects provided written informed consent.

In the present analyses, we only considered participants with no disability at the baseline and with valid data at both the baseline and one-year follow-up visit.

Measurements

The GS test (in m/sec) was performed as part of the Short Physical Performance Battery (8) over a 4-meter long track at usual pace and starting from an initial still position. HG (in kg) was measured at the dominant side by a hand dynamometer (Takei Ltd., Tokyo, Japan). The best effort of three repetitions was used for the present analyses. Both variables were considered as continous variables.

Physical function was measured using a questionnaire composed by a total of 17 tasks, that is 7 Activities of Daily living (ADL) and 10 Instrumental ADL (IADL). The outcome of interest for the present study (i.e., incident disability) was considered present if the participant developed one or more difficulty (or incapacity) at performing any of the 17 activities contained in the instrument.

Socio-demographic characteristics (age, gender, education, marital status), medical conditions (number of comorbidities, number of medications, history of falls, pain, weight loss), lifestyle factors (current smoking, current alcohol consumption), body mass index (BMI), and mental status (depressive symptoms [15-item Geriatric Depression Scale] and cognitive status [Mini Mental Status Examination]) were considered in the present analyses as potential confounders.

Statistical Analysis

Continuous variables are presented as means (± standard deviations, [SD]), while categorical variables as percentages. Differences of main characteristics according to the onset of disability were tested by independent t-test and chi-squared test for continuous and categorical variables, respectively. Unadjusted and adjusted logistic regression analyses were used to test the association of incident disability (dependent variable) with GS and HG (independent variables). Two models of adjustment were performed. In a first approach (Model 1), separate logistic regression models were run adjusting the studied relationships (i.e., incident disability-GS, incident disability-HG) for those variables showing significant differences at the univariate analysis. A second exploratory approach (Model 2) was then used to simultaneously test GS and HG together with the other covariates in the prediction of incident disability

Statistical analyses were performed using STATA/SE 13.0 (STATA Corp LP, College Station, TX).

 

Results

A total of 133 non-disabled older adults were assessed at the baseline and followed-up for 12 months. None of the participants was lost to follow-up.

Main characteristics of the study sample are reported in Table 1. Participants had a mean age of 75.5 (SD 4.7) years at baseline. Women were slightly predominant (53.4% of the total sample). The incidence of disability after one year of follow-up was 52.6% (n=70). Subjects with incident disability were significantly older (76.2 [SD 5.5] years vs 74.7 [SD 3.5] years), and presented poorer HG (12.2 [SD 10.0] kg vs 17.2 [12.5] kg) and slower GS (0.83 [0.30] m/sec vs 0.93 [0.23] m/sec) compared with participants not developing disability (p values ≤0.05). Married participants had also a significantly Lower incidence of incident disability was also reported in married participants (44.8%) and those presenting history of falls (20.0%). No other significant difference was reported between the two groups (see table 1).

Table 2 shows the unadjusted and adjusted odds ratios (ORs) and 95% confidence intervals (95%CIs) obtained from unadjusted and adjusted logistic regression models testing the association between incident disability with HG and GS. The risk of developing disability in the sample after one year of follow-up was 4% lower (OR 0.96, 95%CI 0.93-0.99; p=0.01) for each additional kilogram of HG at the baseline. After adjustment for potential confounders (i.e., age, marital status, falls), the association showed a statistical significance (OR 0.97, 95% CI 0.93-0.99; p=0.01). For what concerns the association between incident disability and GS, each unit (m/sec) increase of GS at the baseline was associated with a 73% lower risk of disability after one year of follow- up. This result lost statistical significance after adjustment for age, gender, marital status, and falls (OR 0.34, 95%CI 0.08- 1.34, p=0.12), although the strength of the relationship was not substantially modified.

In the second model of adjustment (the two physical performance tests are simultaneously entered in the regression analysis together with the other covariates), HG was still significantly associated (OR 0.95, 95% CI 0.92-0.99, p=0.03) with incident disability. GS again showed a borderline significance without substantial modification in the direction and strength of the studied relationship (OR 0.28, 95% CI 0.07- 1.16, p=0.08).

Table 1: Main characteristics of the study sample according to the study outcome

SD=standard deviation, BMI=body mass index, kg/m2=kilograms per squared meters, m/s=meters per second, GDS=Geriatric Depression Scale, MMSE=Mini-Mental Status Examination 

 

Table 2: Logistic regression models testing the capacity of handgrip strength and/or gait speed to predict one-year incident disability

OR: Odds ratio, CI: Confidence Interval; Model 1: adjusted for age, gender, marital status and falls. Handgrip strength and gait speed are tested in separate models as independent variables of interest; Model 2: adjusted for covariate included in Model 1 + handgrip strength and gait speed (simultaneously included in the same model)

 

Discussion

Consistently with results obtained in different populations/ethnicities/geographical settings, physical performance tests (i.e., HG and GS) have been found to also be strongly associated with incident disability in Mexican older adults (9). In settings with scarce (both economic and human) resources for taking care of older people (e.g., primary care), an easy to use and readily available tool (or set of tools) supporting the screening of individuals in the need of a full geriatric assessment would be of great utility (10). The present results are similar to those obtained in different care settings (e.g., hospital (11)), supporting the hypothesis that physical performance measures indeed represent “global” markers of risk.

Physical performance tests are reproducible, accurate, and available for a wide range of settings. Nevertheless, research in specific subgroups of older adults is still necessary to guarantee the validity of these tests, mainly because populations have different backgrounds, lifestyles, living arrangements, and genetics (12). When similar results are found in different groups, the possibility of standardization of the new biomarker as a “functional sign” becomes legitimate and its integration into clinical practice allowed (13, 14).

There are a number of limitations in our study. A lack of statistical power may explain the loss of significance of GS in the second model of adjustment. Nevertheless, the consistency of results does not discard the capacity of this test from the prediction of incident disability. The incident disability definition we adopted was based on a questionnaire assessing a wide spectrum of activities of daily living. Different results might have been obtained if the outcome was differently defined. However, our definition meets the recommendations of a recent report by the World Health Organization defining the onset of functional limitations in daily activities as the result of the negative interaction between the older person’s health status and an adverse environment (15). Finally, the length of the follow-up was limited. This led us defining our dicothomous outcome on the basis of small variations at the functional scale (in order to maximize the number of events).

Physical performance tests in well-functioning Mexican older persons are associated with incident disability, thus representing useful tools for the daily clinicalpractice.

 

Funding: This study was funded by the Mexican Council of Science and Technology (CONACyT-SALUD-2006-C01- 45075). The funding agency had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest: Authors declare no conflict of interest on this paper.

 

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2. Sallinen J, Stenholm S, Rantanen T, Heliövaara M, Sainio P, Koskinen S. Hand-grip strength cut points to screen older persons at risk for mobility limitation. J Am Geriatr Soc 2010;58(9):1721-1726.

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4. Cesari M, Kritchevsky SB, Penninx BW, et al. Prognostic value of usual gait speed in well-functioning older people – Results from the Health, Aging and Body Composition Study. J Am Geriatr Soc 2005;53(10):1675- 1680.

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9. Cesari M. Role of gait speed in the assessment of older patients. JAMA 2011;305(1):93-94.

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11. Garcia-Pena C, Garcia-Fabela LC, Gutierrez-Robledo LM, Garcia- Gonzalez JJ, Arango-Lopera VE, Perez-Zepeda MU. Handgrip strength predicts functional decline at discharge in hospitalized male elderly: a hospital cohort study. PLoS One 2013;8(7):e69849.

12. Guralnik JM. Assessment of physical performance and disability in older persons. Muscle Nerve 1997;5 Suppl:S14-16.

13. Cesari M, Kritchevsky SB, Newman AB, et al. Added value of physical performance measures in predicting adverse health-related events: results from the Health, Aging And Body Composition Study. J Am Geriatr Soc 2009;57(2):251-259.

14. Cesari M, Onder G, Russo A, et al. Comorbidity and physical function: results from the aging and longevity study in the Sirente geographic area (ilSIRENTE study). Gerontology 2006;52(1):24-32.

15. World Health Organization. World report on disability. Geneva, Switzerland. 2011.