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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



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.



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).



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



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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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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23. Alley DE, Shardell MD, Peters KW, et al. Grip Strength Cutpoints for the Identification of Clinically Relevant Weakness. Journals Gerontol Med Sci. 2014;69(5):559-566. doi:10.1093/gerona/glu011
24. Leong DP, Teo KK, Rangarajan S, et al. Prognostic value of grip strength: Findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet. 2015;386(9990):266-273. doi:10.1016/S0140-6736(14)62000-6
25. Giampaoli S, Ferrucci L, Cecchi F, et al. Hand-grip strength predicts incident disability in non-disabled older men. Age Ageing. 1999;28(1):283-288.
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27. Dodds RM, Syddall HE, Cooper R, et al. Grip Strength across the Life Course : Normative Data from Twelve British Studies. PLoS One. 2014:1-15. doi:10.1371/journal.pone.0113637
28. Woo J, Suzanne CHO, Yu ALM. Dependency, Mortality, and Institutionalization in Chinese Aged 70 And Older. J Am Geriatr Soc. 1999;47:1257-1260.
29. Studenski S, Perera S, Patel K, et al. Gait Speed and Survival in Older Adults. JAMA. 2011;305(1):50-58.
30. Clegg A, Rogers L, Young J. Diagnostic test accuracy of simple instruments for identifying frailty in community-dwelling older people: a systematic review. Age Ageing. 2015;44(1):148-152. doi:10.1093/ageing/afu157
31. Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc. 2006;54(5):743-749. doi:10.1111/j.1532-5415.2006.00701.x
32. Vestergaard S, Patel K V., Bandinelli S, Ferrucci L, Guralnik JM. Characteristics of 400-meter walk test performance and subsequent mortality in older adults. Rejuvenation Res. 2009;12(3):177-184. doi:10.1089/rej.2009.0853
33. Vestergaard S, Patel K V, Walkup MP, et al. Stopping to Rest during a 400-meter Walk and Incident Mobility Disability in Older Persons with Functional Limitations. J Am Geriatr Soc. 2010;57(2):260-265. doi:10.1111/j.1532-5415.2008.02097.x.Stopping
34. Peel NM, Kuys SS, Klein K. Gait speed as a measure in geriatric assessment in clinical settings: A systematic review. Journals Gerontol – Ser A Biol Sci Med Sci. 2013;68(1):39-46. doi:10.1093/gerona/gls174
35. Rolland YM, Cesari M, Miller ME, Penninx BW, Atkinson HH, Pahor M. Reliability of the 400-M usual-pace walk test as as assessment of mobility limitation in older adults. J Am Geriatr Soc. 2004;52(6):972-976. doi:10.1111/j.1532-5415.2004.52267.x



J. Guralnik1, K. Bandeen-Roche2, S.a.r. Bhasin3, S. Eremenco4, F. Landi5, J. Muscedere6, S. Perera7, J.-Y. Reginster8, L. Woodhouse9, B. Vellas10 and the ICFSR Task Force


1. University of Maryland School of Medicine, Baltimore, MD, USA; 2. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 3. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 4 – Critical Path Institute, Tucson, AZ, USA; 5. Fondazione Policlinico A. Gemelli, Catholic University, Rome, Italy; 6. Queen’s University, Kingston, Ontario, Canada; 7. University of Pittsburgh, Pittsburgh, PA, USA; 8. University of Liege, Liege, Belgium; 9. University of Alberta, Edmonton, Alberta, Canada; 10. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France.
Corresponding author: Jack Guralnik, University of Maryland School of Medicine, Baltimore, MD, USA, jguralnik@epi.umaryland.edu.

Task force members: Samuel Agus (Paris, France), Islene Araujo de Carvalho (Geneva, Switzerland), Mylène Aubertin-Leheudre (Montréal, Canada), Karen Bandeen-Roche (Baltimore, USA), Ann Belien (Hesusden-Zolder, Belgium), Shalender Bhasin (Boston, USA), Heike Bischoff-Ferrari  (Zurich, Switzerland), Andreas Busch (Vevey, Switzerland), Ryne Carney (Washington, USA), Matteo Cesari (Milano, Italy), Caroline Couleur (Epalinges, Switzerland), Alfonso Cruz Jentoft (Madrid, Spain), Susanna Del Signore (London, United Kingdom), Carla Delannoy (Vevey, Switzerland), Waly Dioh (Paris, France), Sonya Eremenco (Tucson, USA), Bill Evans (Durham, USA), Toby Ferguson (Cambridge, USA), Roger Fielding (Boston, USA), Jack Guralnik (Baltimore, USA), Ludo Haazen (Hesusden-Zolder, Belgium), Joshua Hare (Miami, USA), Aaron Hinken (Collegeville, USA), Darren Hwee (South San Francisco, USA), Lori Janesko (Uniontown, USA), Kala Kaspar (Vevey, Switzerland),  Francesco Landi (Roma, Italy), Valérie Legrand (Nanterre, France), Bradley Morgan (South San Francisco, USA), John Morley (St Louis, USA), John Muscedere (Kingston, Canada), David Neil (Collegeville, USA),  Marco Pahor (Gainesville, USA),  Marika Paul (Columbus, USA), Subashan  Perera (Pittsburgh, USA), Suzette Pereira (Columbus, USA), John Rathmacher (Ames, USA), Jean Yves Reginster (Liège, Belgium), Leocadio Rodriguez Manas (Getafe (Madrid), Spain), Yves Rolland (Toulouse, France), Michelle Rossulek (Cambridge, USA), Jorge Ruiz (Miami, USA), Lisa Tarasenko (Cambridge, USA), Effie Tozzo (Cambridge, USA), Heber Varela (Miami, USA), Bruno Vellas (Toulouse, France), Jeremy Walston (Baltimore, USA), Debra Waters (Dunedin, New Zealand), Linda Woodhouse (Edmonton, Canada)

J Frailty Aging 2019 ;in press
Published online October 10, 2019, http://dx.doi.org/10.14283/jfa.2019.33



For clinical studies of sarcopenia and frailty, clinically meaningful outcome measures are needed to monitor disease progression, evaluate efficacy of interventions, and plan clinical trials. Physical performance measures including measures of gait speed and other aspects of mobility and strength have been used in many studies, although a definition of clinically meaningful change in performance has remained unclear. The International Conference on Frailty and Sarcopenia Research Task Force (ICFSR-TF), a group of academic and industry scientists investigating frailty and sarcopenia, met in Miami Beach, Florida, USA in February 2019 to explore approaches for establishing clinical meaningfulness in a manner aligned with regulatory authorities. They concluded that clinical meaningful change is contextually dependent, and that both anchor- based and distribution-based methods of quantifying physical function are informative and should be evaluated relative to patient-reported outcomes. In addition, they identified additional research needed to enable setting criteria for clinical meaningful change in trials.

Key words: Sarcopenia, frailty, aging, disability, physical performance, clinically meaningful change, outcome measures.




Clinical research studies in older populations have shifted over the last two decades from assessing biological indicators and disease status to measuring physical function as a primary endpoint. This shift reflects the World Health Organization’s (WHO’s) framework for health and disability, the International Classification of Functioning, Disability and Health (ICF) (1), which provides a multidimensional framework for conceptualizing and understanding functioning and disability by integrating medical and biopsychosocial models. Using a patient-focused approach, the ICF promotes the use of language that frames disablement not in terms of disease but in how people live with their conditions across three domains: body function and structure, activity, and participation, i.e., movement in three-dimensional space, interacting with other people, and socialization (2).
With new interventions for sarcopenia on the horizon, the concept of clinical meaningfulness has emerged as an important concern for researchers, clinicians, and regulators. Thus, the International Conference on Frailty and Sarcopenia Research Task Force (ICFSR-TF), a group of academic and industry scientists investigating frailty and age-related muscle loss (sarcopenia), convened a group of experts on February 19, 2019 to explore approaches for establishing clinical meaningfulness and related regulatory considerations.
Clinical meaningfulness, as defined by the U.S. Food and Drug Administration (FDA), requires that an outcome assessment measure something that is clinically important and that substantively affects how the patient feels, functions, or survives. Thus, clinically meaningful outcome measures for sarcopenia and frailty typically assess physical function, quality of life, and survival. Muscle strength and muscle mass may also be considered as outcome measures but only if they correlate with improved function or predict other relevant health outcomes such as reduced mortality, morbidity, institutionalization, and disability (3-7).
Clinically meaningful measures may be used to monitor adults in clinical settings and in observational studies, to evaluate efficacy in clinical trials, and to compute sample size and power when planning a clinical trial (8). However, meaningful change in an observational study may differ from meaningful change in an intervention trial where change can occur much more rapidly in the positive direction and must have both clinical and public health relevance. Since rapid changes may be perceived as being much greater in magnitude than those that occur more gradually, objective measurement is important.


Defining a clinically meaningful change in physical performance

Meaningful change can be defined as a change that has clinical or practical importance, has an impact on an individual’s self-perceived health status or quality of life, or as a fraction of the standard deviation representing a certain level of movement across the distribution of measurements in the population. Clinically meaningful change depends on the outcome on which it is based. Physical performance measures regularly used in clinical trials include various measures of gait and balance parameters and/or the Short Physical Performance Battery (SPPB), a composite measure of walking speed, standing balance, and sit-to-stand performance (9). Gait performance measures include the 4-meter gait speed test (4MGS), the 6-minute walk distance test (6MWD), the 10-meter walk test (10MWT), the timed 400-meter walk (400MW), and the 3-meter timed “Up & Go” test (TUG) (10), (11).  Other possible measures such as gait variability, unipodal balance, and stair negotiation performance may also be used to assess mobility impairments (12, 13). Most evidence has been gathered for the 4MGS, which can be performed in a reasonably small space. For example, in a prospective cohort study of older adults, Perera and colleagues showed that a decline in gait speed of 0.1 m/s on the 4MGS  or 1 point on the SPPB over a one-year period was associated with an increased risk of subsequent mortality (14).
Clinically meaningful changes in outcomes may be expressed as changes that exceed minimally clinically important differences (MCID), clinically meaningful differences (CMD), or minimally important changes (MIC) (15). To determine the MCID and Minimally Clinically Important Improvement (MCII), either distribution-based or anchor-based measures may be used. Distribution-based methods use statistical and psychometric properties of a measure to estimate effect size and standard error of measurement (SEM=σ(1-r)1/2, where σ=standard deviation and r=reliability (16)) as functions of variability and reliability, while anchor-based methods use a change in the patient’s or provider’s perception to identify the corresponding magnitude of change in a selected measure (8).
Preliminary work by Perera and colleagues estimated what constitutes a meaningful change for three performance measures: gait speed, SPPB, and 6MWD using data from varying populations enrolled in both observational and clinical studies: 1) a basic training data set of a 3-month clinical trial of strength training intervention in people with mild-to-moderate limitations; 2) 1-year data of participants in the Predicting Elderly Performance (PEP) study dataset; and 3) 3-month data from the Stroke Rehabilitation (REHAB) randomized clinical trial of a therapeutic exercise program (8). Using both distribution- and anchor-based approaches, they concluded that small but meaningful changes are near to 0.05 m/s for gait speed, 0.5 points for SPPB, and 20m for 6MWD; and that substantial changes were near to  0.10 m/s for gait speed, 1.0 point for SPPB, and 50m for 6MWD.
They also found that meaningful changes are not affected by gender, race, or baseline performance in the Health ABC study. While men tended to have greater magnitudes for meaningful change in 400MWT and there were health and disease differences (e.g. substantial change estimate for SPPB for those with greater body mass index (BMI) when the anchor of walking ¼ mile was used, but not using other anchors), they did not show a consistent pattern and were limited by dropout bias in 400MWT (17).
In the Lifestyle Interventions and Independence for Elders Pilot (LIFE-P) study of exercise as an intervention, investigators examined the relationship between self-reported and performance measures and estimated the magnitude of meaningful change in 400MWT, 4MGS, and SPPB (18). They used both distribution-based and anchor-based methods, two magnitudes of change, and multiple indicators of self-perceived mobility. Relationships between self-reported and performance measures were consistent between treatment arms. Minimally significant changes were 20-30 seconds in the 400MWT, 0.03-0.05 m/s in the 4MGS, and 0.3-0.8 points in the SPPB. Substantial changes were 50-60 seconds in the 400MWT, 0.08 m/s in the 4MGS, and 0.4-1.5 points in the SPPB.


A validation approach to define meaningful change

A crucial first step in defining meaningful change is to clarify what is meant by the concept of meaningful change. A clinically important change in physical functioning should be large enough that a person perceives the change or that participation (e.g., daily roles) is affected. In clinical trials, a clinically important change indicates a treatment effect large enough to support market authorization of a drug. The analytical approach chosen should be driven by how meaningful change is defined for a particular study depending on its main purpose.
Defining meaningful change may be challenging for several reasons. First, meaningful change varies according to context, including baseline level of function as well as demographic and disease considerations. Second, when no gold standard exists with which to make a comparison, the measures by which meaningful changes are judged may not reflect the true state.
One method for assessing the ability of a measure to discriminate individuals by their anchor status is to determine meaningful adverse change (MAC) that achieves both good sensitivity and specificity (19). The Women’s Health and Aging Study (WHAS), an observational study on the characteristics and progression of disability in older, functionally limited women (20) provides an example of a validation framework for evaluating change over the course of one year using usual pace walking speed as the performance measure and self-reported walking difficulties as the anchor. Participants were dichotomized into those who worsened in any one of seven categories of walking difficulty and those who did not worsen in any category, and walking speed change was compared for those two groups. The difference in mean change between those two groups was estimated at -0.091 meters/sec (95% confidence interval [CI] of -0.128 to -0.054), with a mean change among those not worsening of 0.011 (95% CI of -0.014 to 0.035). A decline of 0.10 m/sec (substantial change), however, had a sensitivity of .41 and specificity of 0.73 for self-perceived worsening, and receiver operating characteristic (ROC) analysis of the ability to discriminate clinical change yielded an area under the curve (AUC) of only 0.59, suggesting that other considerations would be needed to adjudicate whether this is good enough for clinical practice in the community-dwelling context of the WHAS. Reanalyzing the data by evaluating empirical cumulative probability distributions of walking speed stratified by decline in 3 categories of walking difficulty all the way to improving in 3 categories of walking difficulty yielded overlapping curves (except when contrasting perception changes transitioning across multiple categories), indicating that either the anchor is inappropriate or a more sensitive performance measure is needed. In such a context, building performance indices combining multiple measures simultaneously may prove useful for enhancing precision.


Combining performance and patient reported outcome measures

Patient reported outcome measures (PROMs) have been advocated by regulatory agencies because they provide information about what is meaningful to patients. For example, fear of falling is one possible patient-reported measure that might correlate well with balance, strength, and other mobility-related functions. Many studies combine PROMs with performance measures since they provide complementary information (21). In a prospective cohort study, Perera and colleagues showed that performance change and self-reported change were both independently associated with 5-year survival (14).
Studies comparing self-reported versus activity-based performance measures of function such as self-paced walk, TUG, and stair tests have shown moderate correlations (22-25), suggesting that the measures provide complementary information. Moreover, these studies show that the selection of measures is condition specific. For example, in these studies the TUG was shown to be the most sensitive measure to change in patients who have undergone total hip replacement, while in patients undergoing knee arthroplasty the stair measure was more responsive to change.

Case study: Determining meaningful change in physical function in testosterone trials in older men (TOM)

The Testosterone in Older Men with Mobility Limitations (TOM) trial was designed to determine the effect of testosterone administration on physical function and lower extremity strength in older men with mobility limitations and low serum levels of testosterone. The trial was terminated early as a result of an increase in adverse cardiovascular events among participants in the treatment group (26). The trial included both a self-reported measure, the Late-Life Function and Disability Instrument (LLFDI), and several performance-based measures including handgrip strength, bilateral leg and chest press (a measure of strength and power), 12-step stair climb, the 40-meter walk test, and the SPPB. The LLFDI assesses participants’ ability to complete discrete actions or activity and their performance of socially-defined tasks (activity and participation in the ICF framework).
Both anchor-based and distribution-based methods were used to determine the MCID for physical function. To assess anchor-based responsiveness, participants were grouped according to self-reported global rating of improvement (better versus no change or worse). The distribution-based responsiveness analysis provided an estimate of effect size, minimal detectable change based on a 90% CI (MDC90), and the percentage of participants exceeding MDC90 by group.
These analyses demonstrated that loaded walk and stair climb were the most sensitive, with anchor and distribution-based measures being similar. The SPPB balance assessment was the least sensitive measure. Handgrip strength and LLFDI were not responsive to change while both the Foundation of the National Institutes of Health (FNIH) and European guidelines advocate using handgrip strength to identify participants for sarcopenia trials (27, 28).  These results suggest that this measure may be less useful to measure responsiveness to an intervention.


Regulatory considerations of clinically meaningful change

Regulators prefer hard clinical endpoints to surrogate endpoints (e.g. biomarkers) when making decisions about market authorization. For example, in osteoporosis trials, a statistically significant difference in fracture rates – a hard clinical endpoint – is considered meaningful (29), whereas a surrogate endpoint such as bone mineral density would not in and of itself be considered meaningful, although it may be used to bridge studies for extension of indications.
The European Medicines Agency (EMA) guideline on clinical investigation of medicinal products used pain and function as co-primary endpoints in the treatment of osteoarthritis (30). The expert consensus committee that developed the guidelines suggested the threshold for minimal perceptible clinical improvement in pain as a 10 mm improvement on a 100 mm visual analog pain scale for drugs intended to improve symptoms or at least a 5 mm mean difference between placebo and active groups (31). These criteria were applied in a trial of chondroitin sulfate compared to placebo and the non-steroidal anti-inflammatory drug (NSAID) celecoxib, which showed that both drugs produced a statistically significant and clinically relevant improvement, yet whether the magnitude of the effect is sufficient to justify granting market approval remained an unanswered question (32).
A PROM, the SarQoL, has been developed to assess quality-of-life in sarcopenia patients (33). While it has demonstrated the ability to detect statistically significant change, the MIC has not yet been determined; thus, the clinical significance is not clear.
Whether to use continuous or dichotomous variables may also be discussed with regulators. For example, the FRActure in postmenopausal woMen with ostEoporosis (FRAME) study of the bone-forming agent romosozumab assessed percent change in BMD from baseline, demonstrating that the treatment results in a rapid increase in BMD in comparison to bone loss in the placebo group and at the same time reduces fracture risk (34). When using percent change the clinical significance of the observed absolute change must also be considered.
In addition to data on clinically meaningful change used to support marketing authorization for a treatment, payers and policy makers may require additional real-world data and cost-effectiveness studies to support reimbursement (35). For example, validation of the FRAX risk assessment tool was achieved by the Screening for Osteoporosis in Older Women for the Prevention of Fracture (SCOOP) study in the United Kingdom, which showed that screening with FRAX resulted in a reduced risk of hip fracture, i.e., that the tool is medically relevant (36). Another real-world study conducted by the French Ministry of Health – the Pharmaco-Epidemiology of GonArthroSis and coxarthrosis (PEGASus) study — assessed the ability of multiple symptomatic slow-acting drugs for osteoarthritis to reduce the consumption of NSAIDs, which are associated with substantial adverse reactions. Only glucosamine sulfate showed a significant reduction in consumption of NSAIDs.
The FDA has a somewhat different perspective on meaningful change in that they focus on within-patient anchor-based change. Distribution-based approaches may be used as supportive or supplementary information. Moreover, they require changes to be meaningful to the patient, using terms to which patients can relate. This has led them to incorporate patient preferences into their deliberations and selection of outcome measures.
The Aging in Motion (AIM) coalition has been working with FDA for several years on a project to qualify gait speed alone and the SPPB as acceptable and endorsed measures of function. However, the agency has stressed the need for both an objective measure such as SPPB and a self-report approach, which has increased the complexity of the qualification process.
PROMs present many potential challenges for sponsors.   The correlation between PROMs and objective performance measures is modest, and the FDA suggests using them together as joint outcomes. Multiple primary outcomes may increase trial sample size requirements. PROMs are also subject to differences in beliefs and behaviors, making them more difficult to compare across diverse populations. One suggested approach would be to use a goal attainment scale in which the patient sets goals as well as metrics for success.
PROMs, including QOL measures, also must be very specific to the indication. For sarcopenia, this means that PROMs should relate to how low muscle mass affects how patients feel, function, and survive. Used in combination with performance measures, they could provide a powerful way of demonstrating efficacy. While there has been a reluctance of pharmaceutical companies to embed context-specific PROMs in Phase 2 and 3 studies, doing so would produce an enormous body of data that could help establish relevant anchors to estimate MCID and validate other measures.


Moving Forward

One problem for research into how the ICF guidelines are interpreted is that structure and function are typically assessed with clinical measures applied in a controlled environment while assessment of activity and participation require capturing the patient perspective, which is heavily influenced by the environment, adaptation mechanisms, resilience, and coping. Moreover, meaningful change is context, perspective, and purpose dependent.
The Task Force identified several key areas for future research that should be considered when setting the criteria for a clinically meaningful change in a clinical trial:
•    Published estimates of MCID derived from study participants who are only mildly functionally limited may have limited value for studies that enroll participants at high risk of physical disability. In substantially impaired participants, a small improvement in a performance test may translate into a large benefit in daily life and be perceived by the participant. Future work should address MCID in subsets of the population stratified by ability, with the instruments chosen being appropriate for that level of ability.
•    The validation framework described above offers a paradigm for thinking carefully about the ideal definition of clinically meaningful change and then working backwards to identify how to measure and assess meaningful change.
•    To measure clinically meaningful changes in real-world performance, it may be appropriate to incorporate into trials continuous digital technologies such as accelerometers as well as novel analytical techniques to determine MCID, CMD, and MCII. Signal processing of accelerometer data may also identify additional features predictive of adverse or beneficial outcomes.


Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript.
Conflicts of interest:  The Task Force was partially funded by one educational grant from the Aging In Motion Coalition and registration fees from industrial participants (Biogen, Biophytis, Cytokinetics, Glaxosmithkline, Longeveron, Pfizer and Rejuvenate Biomed NV). These corporations placed no restrictions on this work. S. Eremenco, F. Landi declare there are no conflicts. Dr. Guralnik reports personal fees from Pluristem , personal fees from Viking Therapeutics, personal fees from Novartis Pharma, outside the submitted work. K. Bandeen-Roche reports grants from National Institutes of Health,  during the conduct of the study.
S.A.R.  Bhasin reports grants from AbbVie, grants from Alivegen, grants from MIB, other from FPT, other from AbbVie, outside the submitted work. J. Muscedere is Scientific Director for the Canadian Frailty Network, a non-for profit network funded by the Government of Canada. S. Perera has received Travel expenses to the International Conference on Frailty and Sarcopenia Task Force meeting in February 2019 in Miami Beach, FL paid by Alliance for Aging Research. J.Y. Reginster reports grants and personal fees from IBSA-GENEVRIER, grants and personal fees from MYLAN, grants and personal fees from RADIUS HEALTH, personal fees from PIERRE FABRE, grants from CNIEL, personal fees from DAIRY RESEARCH COUNCIL (DRC), outside the submitted work. B. Vellas reports grants from Nestle, Nutricia, Novartis outside the submitted work.
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.



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S.M.L.M.  Looijaard1, S.J. Oudbier1, E.M. Reijnierse2, G.J. Blauw3,4, C.G.M. Meskers5, A.B. Maier2,6


1. Department of Internal Medicine, Section of Gerontology and Geriatrics, VU University Medical Center, Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands;  2. Department of Medicine and Aged Care, @AgeMelbourne, The Royal Melbourne Hospital, University of Melbourne, 300 Grattan Street, Parkville, Victoria 3050, Melbourne, Australia;  3. Department of Gerontology and Geriatrics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands;  4. Department of Geriatrics, Bronovo Hospital, Bronovolaan
5, 2597 AX, The Hague, The Netherlands; 5. Department of Rehabilitation Medicine, VU University Medical Center, Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands;
6. Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands.
Corresponding author: A.B. Maier, Department of Human Movement Sciences, @Age, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands, Telephone number: 020-5982000 a.b.maier@vu.nl

J Frailty Aging 2018;in press
Published online August 1, 2018, http://dx.doi.org/10.14283/jfa.2018.19



Background: Sarcopenia is highly prevalent in the older population and is associated with several adverse health outcomes. Equipment to measure muscle mass and muscle strength to diagnose sarcopenia is often unavailable in clinical practice due to the related expenses while an easy physical performance measure to identify individuals who could potentially have sarcopenia is lacking. Objectives: This study aimed to assess the association between physical performance measures and definitions of sarcopenia in a clinically relevant population of geriatric outpatients. Design, setting and participants:  A cross-sectional study was conducted, consisting of 140 community-dwelling older adults that were referred to a geriatric outpatient clinic. No exclusion criteria were applied. Measurements: Physical performance measures included balance tests (side-by-side, semi-tandem and tandem test with eyes open and -closed), four-meter walk test, timed up and go test, chair stand test, handgrip strength and two subjective questions on mobility. Direct segmental multi-frequency bioelectrical impedance analysis was used to measure muscle mass. Five commonly used definitions of sarcopenia were applied. Diagnostic accuracy was determined by sensitivity, specificity and area under the curve.Results: Physical performance measures, i.e. side-by-side test, tandem test, chair stand test and handgrip strength, were associated with at least one definition of sarcopenia. Diagnostic accuracy of these physical performance measures was poor. Conclusions: Single physical performance measures could not identify older individuals with sarcopenia, according to five different definitions of sarcopenia.

Key words: Aged, geriatrics, physical performance, sarcopenia.



Prevalence rates of sarcopenia, defined as age-related low muscle mass and muscle strength, vary between 0% and 15% in healthy older individuals and between 2% and 34% in geriatric outpatients, depending on the applied definition (1). Sarcopenia is associated with decreased mobility, a higher risk of falls, dependency in activities of daily living, morbidity and mortality (2-4). Although there is no consensus yet on the definition of sarcopenia, the majority of definitions contain a measure of muscle mass and/or muscle strength (5-10).
Equipment to measure muscle mass and muscle strength is often unavailable in clinical practice due to the related expenses (11). Poor physical performance has been shown to be associated with sarcopenia (12-14). Recently, a prediction model was proposed to identify sarcopenia with the use of demographic parameters and physical performance measures (15). However, such a prediction model is time consuming due to its inclusion of multiple physical tests and complex calculations. Identifying individuals who could potentially have sarcopenia in clinical practice would be greatly facilitated when an easy to use physical performance measure could be applied to identify individuals with sarcopenia. This could lead to the identification of individuals who could potentially have sarcopenia, who could then be referred to diagnose sarcopenia.
This study aims to assess the association between physical performance measures and different definitions of sarcopenia in a clinically relevant population of geriatric outpatients.



Study design

This cross-sectional study consisted of 140 community-dwelling older adults who were referred to a geriatric outpatient clinic of a middle-sized teaching hospital (Bronovo, The Hague, The Netherlands) between March 2011 and January 2012. These older adults were referred because of mobility problems for a comprehensive geriatric assessment. The study originally consists of 299 older adults, but muscle mass measurements were only available in 140 older adults as these measurements were later added as part of clinical care. No exclusion criteria were applied; inclusion was based on referral. The study was approved by the Medical Ethical Committee of the Leiden University Medical Center (Leiden, the Netherlands). Informed consent was waived as this study was based on regular care.

Study population characteristics

Medical charts were used to retrieve information on age, sex and medical history. Medical history included information on the presence of: hypertension, myocardial infarction, chronic obstructive pulmonary disease, diabetes mellitus, rheumatoid- or osteoarthritis, Parkinson’s disease and malignancy. The presence of two or more of these diseases was defined as multimorbidity. Anthropometric measurements included height and body weight and were measured to the nearest 0.1 decimal. Cognitive functioning was measured by the Mini Mental State Examination (MMSE) resulting in a score ranging from 0 to 30 points, higher scores indicating better cognitive function (16).

Physical performance measures, objective

Physical performance measures included balance tests, four-meter walk test, timed up and go (TUG), chair stand test (CST) and handgrip strength (HGS).
Balance tests were performed in three different positions (side-by-side, semi-tandem and tandem) according to the protocol of the Short Physical Performance Battery (17), and were performed with eyes open and with eyes closed. Individuals were classified as unable to maintain for ten seconds (0) and able to maintain for ten seconds (1). Tandem balance test with eyes closed was excluded from the present analysis as the number of individuals who were able to maintain in the tandem position for ten seconds was less than five individuals.
Gait speed was obtained by a four-meter walk test where individuals were asked to walk at their usual pace (17). The best performance of two measurements was used and expressed in meters per second.
The TUG measures the time in seconds needed to stand up from a sitting position without using hands, walk three meters, walk around a cone, walk three meters back and return to sitting position without using hands, as fast as possible.
The CST measures the time in seconds needed to stand up five times from sitting position to a straight standing position and sit down again while keeping the arms crossed over the chest, as fast as possible (17).
HGS was measured using a hydraulic handheld dynamometer (Jamar, Sammons Preston, Inc., Bolingbrook, IL, USA). Individuals were asked to squeeze as hard as possible three times with the right and left hand side alternately. Maximal HGS of the three trials was used for analysis (18) and expressed in kilograms.
Higher gait speed and HGS implied a better physical performance while a higher TUG and CST time implied a lower physical performance. For all physical performance measures, all individuals who could not perform or finish the test or used hands to stand up from a sitting position, were given a time of 100 seconds.

Physical performance measures, subjective

In addition to the objective physical performance measures, two questions were asked: 1) Falls: “Did you fall in the past year?” (yes/no) and 2) Difficulty with walking: “Do you experience difficulty with walking?” (yes/no).

Sarcopenia definitions

Muscle mass was measured using direct segmental multi-frequency Bioelectrical Impedance Analysis (DSM-BIA; InBody 720, Biospace Co., Ltd, Seoul, Korea) (19). Five definitions of sarcopenia were used to examine the association between physical measures and sarcopenia: 1) Baumgartner et al. using appendicular lean mass (ALM) divided by height2 (5); 2) Janssen et al. using relative skeletal muscle mass (SM) (SM divided by body mass) (6); 3) European Working Group on Sarcopenia in Older Persons (EWGSOP) using an algorithm of gait speed, HGS and skeletal muscle index (SMI; SM divided by height2) (7); 4) Foundation for National Institutes of Health (FNIH) definition one using HGS and ALM divided by body mass index (BMI) (8) and 5) The International Working Group on Sarcopenia (IWGS) using gait speed and ALM divided by height2 (9).

Statistical analysis

Continuous variables were reported by mean ± standard deviation (SD) if data was normally distributed or median [interquartile range (IQR)] for skewed distributions. Associations between physical performance measures (independent variables) with definitions of sarcopenia (dependent variables) were analyzed with binary logistic regression analysis. Two models were used: the crude model and an adjusted model for sex and age. P-values of less than 0.05 were considered statistically significant.
For the statistically significant associations between physical performance measures and definitions of sarcopenia, sensitivity, specificity and the area under the curve (AUC) were calculated to determine the diagnostic accuracy. Sensitivity and specificity were defined as low <70%, moderate 70-90% and high >90%. AUC was defined as low <0.70, acceptable 0.70-0.80, excellent 0.80-0.90 and outstanding >0.90. To test diagnostic accuracy, CST and HGS were dichotomized: CST ≥13 seconds (14, 20), and HGS <20 kilograms for females and <30 kilograms for males were considered low (21). Statistical analyses were performed using Statistical Package for Social Sciences (SPSS Inc, Chicago, USA), version 22.



Table 1 shows the characteristics of the geriatric outpatients, with a mean age of 80.9 (7.1) years and 42% males. Table 2 shows the applied definitions of sarcopenia and the prevalence of sarcopenia. Prevalence of sarcopenia ranged from 3.6% to 23.6%, depending on the definition.

Table 1 Characteristics of geriatric outpatients (N=140)

Table 1
Characteristics of geriatric outpatients (N=140)

All results are given in number (percentage) unless indicated otherwise. SD: standard deviation; MMSE: Mini Mental State Examination, score 0-30; IQR: interquartile range; TUG: Timed Up and Go; CST: Chair Stand Test; HGS: Handgrip Strength; ALM: Appendicular Lean Mass; BMI: Body Mass Index; SMI: Skeletal Muscle Index; SM: Skeletal Muscle.

Table 2 Prevalence of sarcopenia in geriatric outpatients according to the applied definitions of sarcopenia

Table 2
Prevalence of sarcopenia in geriatric outpatients according to the applied definitions of sarcopenia

ALM: Appendicular Lean Mass; SM: Skeletal Muscle; EWGSOP: European Working Group on Sarcopenia in Older People; HGS: Handgrip Strength; SMI: Skeletal Muscle Index; IWGS: International Working Group on Sarcopenia; FNIH: Foundation for the National Institutes of Health; BMI: Body Mass Index


Table 3 shows the association between physical performance measures and sarcopenia according to the applied definitions. Out of all balance tests, the ability to perform the tandem stance with eyes open was most often associated with a decreased likelihood to have sarcopenia (Baumgartner et al., EWGSOP and IWGS). CST was associated with sarcopenia by use of the EWGSOP and IWGS definitions. HGS was associated with sarcopenia using the definition of Baumgartner et al. and IWGS. The other physical performance measures i.e. semi-tandem balance test with eyes open and eyes closed, gait speed, TUG and the subjective physical performance measures did not show an association with any of the definitions of sarcopenia.
Table 4 shows the diagnostic accuracy for physical performance measures significantly associated with sarcopenia. The tandem balance test with eyes open showed moderate sensitivity and low specificity and AUC for all three sarcopenia definitions. The side-by-side test with eyes closed showed low sensitivity, moderate specificity and low AUC for the EWGSOP definition. CST showed low sensitivity, specificity and AUC for the EWGSOP and IWGS definitions. HGS showed low sensitivity, moderate specificity and low AUC for the Baumgartner et al. and IWGS definitions.

Table 3 Physical performance measures and sarcopenia according to the applied definitions

Table 3
Physical performance measures and sarcopenia according to the applied definitions

OR: Odds Ratio; CI: Confidence interval; EWGSOP: European Working Group on Sarcopenia in Older People; IWGS: International Working Group on Sarcopenia; FNIH: Foundation for the National Institutes of Health; NA: Not Applicable; TUG: Timed Up and Go; CST: Chair Stand Test; HGS: Handgrip Strength. Adjusted model adjusted for sex and age. All results with a p-value < 0.05 are considered significant and are given in bold; *Balance tests were dichotomized into unable to maintain for ten seconds (0) and able to maintain for ten seconds (1).


Table 4 Diagnostic accuracy of physical performance measures according to the applied definitions of sarcopenia

Table 4
Diagnostic accuracy of physical performance measures according to the applied definitions of sarcopenia

Sensitivity and specificity are given in percentage; AUC is given with 95% confidence interval. EWGSOP: European Working Group on Sarcopenia in Older People; IWGS: International Working Group on Sarcopenia; FNIH: Foundation for the National Institutes of Health; NA: Not Applicable; AUC: Area under the curve; CST: Chair stand test; HGS: Handgrip strength. NA indicates non-significant results in logistic regression analyses for which no diagnostic accuracy was calculated. All results with a p-value < 0.05 are considered significant and are given in bold. *Balance tests were dichotomized into unable to maintain for ten seconds (0) and able to maintain for ten seconds (1).



Physical performance measures, i.e. side-by-side test, tandem test, CST and HGS, were associated with sarcopenia using several definitions, but diagnostic accuracy was poor.
Previous studies have shown that tandem balance test, gait speed, CST and HGS are valid and reliable measures to assess physical performance (22, 23). Moreover, gait speed, CST and HGS have proven to be associated with sarcopenia according to the EWGSOP definition in community dwelling older adults (20, 24, 25). Unfortunately, the results of this study in geriatric outpatients did not show a single suitable physical performance measure to identify older individuals with sarcopenia. For a test to be suitable to identify individuals with a high risk on sarcopenia, it needs to have a high sensitivity as especially false-negatives are undesirable. Sensitivity was low to moderate, which would mean that many individuals who could potentially have sarcopenia would be missed. Moreover, specificity and AUC were low and therefore single physical performance measures have poor diagnostic accuracy to identify individuals with sarcopenia.
Sarcopenia is associated with negative health outcomes and therefore an important diagnosis in clinical practice. Nutritional and physical interventions have proven to increase muscle mass, muscle strength and physical performance (26, 27). Improving physical performance measures might reduce the risk of sarcopenia and therewith its negative health outcomes. Early recognition of sarcopenia is necessary to initiate interventions. BMI is a measurement that is often used to identify individuals who are at risk of adverse health outcomes, however, BMI does not encompass the risk of sarcopenia (28) Another proposed screening tool to identify individuals with sarcopenia is the SARC-F, a simple five-item questionnaire (subjective measures of strength, assistance in walking, rise from a chair, climb stairs, falls). This is one of the screening instruments that is increasingly being used in community-dwelling middle-aged to older adults to identify individuals who could potentially have sarcopenia (29, 30).
Multimorbidity was high in this population of geriatric outpatients and this could be an explanation for the lack of diagnostic accuracy of physical performance measures to identify older individuals with sarcopenia because physical performance could also be inflicted by disease-specific mechanisms.
To the best of our knowledge, this is the first study outlining the association between various objective physical performance measures and several commonly used definitions of sarcopenia in a clinically relevant population of geriatric outpatients. Validated physical performance measures were used. Furthermore, the population is heterogeneous and no exclusion criteria were used which makes it a good representation of the actual older population visiting outpatient clinics. A limitation of this study could be that only BIA and not Dual Energy X-ray Absorptiometry (DEXA) was used to determine muscle mass parameters of sarcopenia. However, BIA and DEXA showed high agreement in a population of community-dwelling individuals (19).
Single physical performance measures could not identify older individuals with sarcopenia, according to five different definitions. Therefore, no easy and fast method to identify individuals with sarcopenia can be recommended. Future research should focus on developing and validating screening tools so that individuals with a high probability of having sarcopenia can be identified. Individuals who are considered to be at risk of sarcopenia should be referred to diagnose sarcopenia using the diagnostic criteria that are used in the definitions of sarcopenia.


Acknowledgements: We thank M. Stijntjes and J.H. Pasma for their contribution to the study.

Funding: This study was supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organization for Scientific Research (NWO) and which is partly funded by the Ministry of Economic Affairs. Furthermore, this study was supported by the seventh framework program MYOAGE (HEALTH-2007-2.4.5-10) and 050-060-810 Netherlands Consortium for Healthy Aging (NCHA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Statement of authorship: All authors have made substantial contributions to all of the following: 1) conception and design of the study, acquisition of data or analysis and interpretation of data; 2) drafting the article or revising it critically for important intellectual content; 3) final approval of the version to be submitted.
Conflict of Interest: S.M.L.M. Looijaard: none to declare. S.J. Oudbier: none to declare. E.M. Reijnierse: none to declare. G.J. Blauw: none to declare. C.G.M. Meskers: none to declare. A.B. Maier: none to declare.
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.



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1. Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21225, USA; 2. Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
Corresponding author: Nancy Chiles Shaffer, Ph.D., National Institute on Aging, MedStar Harbor Hospital, 3001 S. Hanover Street, 5th Floor, Baltimore, MD 21225, USA, Office: 410-350-3971, Fax: 410-350-7304, Email: nancy.chiles@nih.gov

J Frailty Aging 2017;in press
Published online August 9, 2017, http://dx.doi.org/10.14283/jfa.2017.24



Background: Muscle quality is defined as the force generated by each volumetric unit of muscle tissue. No consensus exists on an optimal measure of muscle quality, impeding comparison across studies and implementation in clinical settings. It is unknown whether muscle quality measures that rely on complex and expensive tests, such as isokinetic dynamometry and computerized tomography correlate with lower extremity performance (LEP) any better than measures derived from simpler and less expensive tests, such as grip strength (Grip) and appendicular lean mass (ALM) assessed by DXA. Additionally, whether muscle quality is more strongly associated with LEP than strength has not been fully tested. Objectives: This study compares the concurrent validity of alternative measures of muscle quality and characterizes their relationship with LEP. We also whether muscle quality correlates more strongly with LEP than strength alone. Design: Cross-sectional analysis. Setting: Community. Participants: 365 men and 345 women 65 years of age and older in the Baltimore Longitudinal Study of Aging. Measures: Thigh cross-sectional area (TCSA), isokinetic and isometric knee extension strength (ID), BMI adjusted ALM (ALMBMI) from DXA, and Grip. Concurrent validity was assessed as the percent variance of different measures of LEP explained by each muscle quality measure. In addition, we compared LEP relationships between each measure of strength and its correspondent value of muscle quality. Confidence intervals for differences in percent variance were calculated by bootstrapping. Results: Grip/ALMBMI explained as much variance as ID/TCSA across all LEP measures in women and most in men. Across all LEP measures, strength explained as much variance of LEP as muscle quality. Conclusions: Grip/ALMBMI and ID/TCSA measures had similar correlations with LEP. Muscle quality did not outperform strength. Although evaluating muscle quality may be useful to assess age-related mechanisms of change in muscle strength, measures of strength alone may suffice to understand the relationship between muscle and LEP.

Key words: Muscle Quality, muscle strength, physical performance.




Muscle quality (MQ) can be conceptualized as the capacity to generate force relative to the mass/volume of contractile tissue (1-3). Declines in muscle strength with aging are not explained by declines in muscle mass; the concept of MQ was meant to describe this phenomenon (1, 2, 4-6). While strength alone quantifies the amount of force a muscle can generate, bigger muscles are not necessarily stronger. A smaller muscle may be more effective due to more contractile proteins, less fat infiltration, or other physiological properties that can alter the quality of the muscle (3, 6, 7). Therefore, MQ could be useful to comprehensively quantify physiological changes in skeletal muscle that occur with aging.
MQ has been assessed in multiple studies using different operational definitions (2, 3, 6, 8-14). In general, MQ is defined as the ratio of strength to mass, but it remains unclear which are the optimal methods to assess strength and mass.  Previous studies used the ratio of quadriceps isokinetic peak torque (Quad) via isokinetic dynamometry (ID) to thigh muscle cross-sectional area (TCSA) assessed by computed tomography (CT) to characterize the effect of age and other risk factors on MQ (8, 12, 13, 15). The feasibility of this approach on a large scale is limited because CT and ID are expensive, time consuming to ascertain and difficult to process (10, 16, 17). ID allows for measurement of contractile force at constant velocity; however, it is more expensive than a hand dynamometer (HD) (18) and can be difficult for some older adults to complete due to lower back or leg pain and/or poor strength (13). In addition, whether ID/TCSA better captures the effect of muscle impairment on mobility compared to other measures of MQ is unknown. A simpler and less expensive method to assess MQ compared to ID/TCSA is the ratio of grip strength (Grip) via HD to appendicular lean mass (ALM) assessed via dual-energy X-ray absorptiometry (DXA). HD/DXA ratio has the advantage of requiring inexpensive instruments available in many research and clinical facilities and, therefore, can be easily assessed in most research or clinical situations.
This study aimed to compare the concurrent validity of alternative measures of MQ using lower extremity performance as the reference outcome. Lower extremity performance was used because it is widely considered the gold standard measure to assess the impact of muscle on mobility in older persons.  We additionally tested the hypothesis that measures of MQ correlate with measures of lower extremity performance more strongly than strength.



Baltimore Longitudinal Study of Aging

The Baltimore Longitudinal Study of Aging (BLSA), which began in 1958, is the longest running study in the United States dedicated to studying human aging. Designed to assess normative aging, the BLSA is an observational study that continuously enrolls healthy adults aged 20 years and older. Follow-up study visits occur every 2 years for participants age 60-80, and annually for participants over 80 years of age. All enrolled participants gave informed consent. Further details on the study design have been previously reported (19).

Analytic Sample

This cross-sectional analysis included data from 365 men and 345 women aged 65 to 97 years visited between February 2011 and September 2015. All participants had measures of TCSA, ALM adjusted for body mass index (BMI) (ALMBMI), ID, and Grip.


Muscle Mass

Two measures of muscle mass were assessed, TCSA and ALM. TCSA was measured from 10mm CT scan of the mid-femur. Images were then filtered using the GEANIE BonAlyse software.  TCSA was normalized for body height by dividing by height squared (TCSAht2). ALM was acquired from DXA whole body scans from the Prodigy Scanner using the Encore Software. ALM (kg) is the sum of the lean mass of the right and left arm and the right and left leg. ALM was normalized by dividing by BMI (ALMBMI).

Muscle Strength

Multiple ID muscle strength measures were acquired from a BioDex dynamometer. First, quadriceps peak torque (Quad)(Nm), an isokinetic strength measurement, was taken as the maximum of five trials of concentric knee extension strength at an angular velocity of 30°/s and 180°/s. Additionally, hamstring peak torque (Ham)(Nm) was taken as the maximum of five trials of concentric knee flexion strength at an angular velocity of 30°/s and 180°/s. Finally, isometric knee extension (Nm) was taken as the maximum of five trials at 120° and 140°. Grip was measured via a Jamar Hydraulic hand dynamometer, which registers maximum kg of force from three trials on each hand. The average of each trial was used for this analysis, consistent with previous studies(20).

Muscle Quality

In general, MQ was assessed as a ratio between a measure of strength and a measure of muscle mass in different combinations. The difficult and expensive MQ measures are each of the ID strength measures divided by TCSAht2. The easy and relatively inexpensive MQ measure was Grip divided by ALMBMI.

Lower Extremity Physical Performance

Usual gait speed and rapid gait speed were each assessed on a 6m walking course. Participants were advised to walk at their usual pace or as fast as possible, respectively. The average gait speed is the average of two trials of participants walking at a usual pace, while rapid gait speed is the maximum of two trials where participants walked at their fastest pace.  400m walk time is derived from an endurance test, done as quickly as possible, conducted in an unobstructed corridor over a 20m long walking course.
The Short Physical Performance Battery (SPPB), an objective measure of lower-extremity function, comprises 4m usual gait speed, three standing balance tests, and time to complete five chair rises (Guralnik et al., 2000). Each of the three components of the SPPB are scored on a 0 to 4 scale, resulting in an overall SPPB score ranging from 0 to 12, with 12 indicating highest performance. The Health ABC Physical Performance Battery (HABCPPB) (Simonsick et al., 2001) is an extension of the SPPB aimed at avoiding a ceiling effect in a relatively high functioning population, including maintaining balance for longer time, a single leg stand and a narrow walk. Each component of the HABCPPB is scored on a continuous ratio scale ranging from 0 to 1, resulting in an overall HABCPPB score of 0 to 4, with 4 indicating highest performance.
Participants able to rise from a chair without assistance from chair arms completed repeated chair stands. Each chair stand pace is the ratio of number of chair stands completed to time (5s).

Analytic Strategy

Generalized linear models were used to predict average gait speed, rapid gait speed, 400m walk speed, SPPB, HABCPPB,  and 5s chair stand pace. All analyses were stratified by sex and each model was adjusted for age. Concurrent validity was assessed as the percent variance (R2) of the physical performance measures explained by each MQ measure. In addition, the amount of variance explained in each mobility outcome by different MQ measures were compared with their respective strength measure.
Bootstrapping was performed to compute confidence intervals for differences in percent variance explained by different models. The methods used for bootstrapping in SAS have been previously reported(21). A random seed was used for unrestricted random sampling of equal size as the original dataset. One thousand bootstrap samples were generated. From the bootstrap samples, generalized linear models were run with each physical performance measure as the dependent variable, and age and the MQ measures as the independent variables. The differences in R2 values from each of the ID MQ variables and Grip/ALMBMI were calculated. The bootstrap samples were used to generate a 95% confidence interval for the differences in R2 values. The same bootstrapping method was used to compare the difference in R2 values from each of the MQ measures and its respective strength variable.



Characteristics of the study sample are shown in Table 1. The average ages were 78.0 and 76.2 years for men and women, respectively. On average, participants tended to be slightly overweight and highly functional for their age (22).


Table 1 Sample Characteristics

Table 1
Sample Characteristics

*SPPB=Short Physical Performance Battery, Health ABC PPB=The Health, Aging, and Body Composition Study Physical Performance Battery.


Forest plots depicting the difference in R2 values of each comparison difficult and expensive MQ measure to Grip/ALMBMI, and the respective 95% confidence intervals are shown in Figures 1 for women and men. The vertical line at the center of each plot, zero, represents no significant difference between the percent variance of a comparison MQ measure and Grip/ALMBMI. For each horizontal line, the center symbol is the difference in R2 values (R2 of respective MQ measure minus R2 Grip/ALMBMI), and the lines are the 95% confidence interval. The numerical values for percent variance in physical performance explained by each MQ measure is shown in Supplemental Table 1 for men and women separately. In women, Grip/ALMBMI and all ID/TCSA MQ measures explained comparable percent variances of the different physical performance outcomes. In men, in most instances, Grip/ALMBMI explained as much percent variance as ID/TCSA MQ measures for different performance outcomes. Exceptions were Quad180/TCSAht2, and Ham180/TCSAht2 that explained a higher percent variance in average gait speed and Quad180/TCSAht2 that explained a higher percent variance in rapid gait speed.
Figure 2 shows forest plots depicting the differences in R2 values of each MQ measure to its respective strength measure (R2 MQ minus R2 strength), and the 95% confidence intervals for the differences. The construct of these forest plots is the same as previously described for Figure 1. The numerical percent variances of MQ versus strength for women and men are displayed in Supplemental Table 2. In women, there was no instance where MQ explained more variance than strength alone. Additionally, models fitted with MQ or strength measures yielded similar fit in term of R-square. In men, there were also no occasions where MQ produced a percent variance greater than strength alone. Strength alone, however, did produce significantly higher percent variance values compared to MQ in a few models: Ham180 when modeling rapid gait speed; Quad180, Ham30, and Ham180 when modeling rapid gait speed, and Quad180 when modeling chair stand 5s pace.


Figure 1 Difference in R2 in Muscle Quality Measures Compared to Grip/(ALM/BMI) in Women and Men

Figure 1
Difference in R2 in Muscle Quality Measures Compared to Grip/(ALM/BMI) in Women and Men

Quad30=quadricep peak torque at 30°/s, MQ=muscle quality, Quad180=quadricep peak torque at 180°/s, Ham30=hamstring peak torque at 30°/s, Ham180=hamstring peak torque at 180°/s, Isomet120=isometric knee extension at 120°/s, Isomet140=isometric knee extension at 140°/s. *All MQ measures are adjusted for Thigh cross-sectional area (kg) divided by height squared (m2). † Difference in R2 = Comparison MQ R2 – Grip/ALMBMI R2.

Figure 2 Difference in R2 in Muscle Quality Measures Compared to Strength in Women and Men

Figure 2
Difference in R2 in Muscle Quality Measures Compared to Strength in Women and Men

Grip=grip strength, MQ=muscle quality, Quad30=quadricep peak torque at 30°/s, Quad180=quadricep peak torque at 180°/s, Ham30=hamstring peak torque at 30°/s, Ham180=hamstring peak torque at 180°/s, Isomet120=isometric knee extension at 120°/s, Isomet140=isometric knee extension at 140°/s. *Grip MQ is adjusted for appendicular lean mass (kg) divided by BMI (kg/m2). All other MQ measures are adjusted for Thigh cross-sectional area (kg) divided by height squared (m2). † Difference in R2 = MQ R2 – Strength R2.



For most of the physical performance outcomes, measures of MQ based on expensive, time consuming and not widely available tests, such as CT and lower extremity dynamometry were not better correlates of measures of mobility or lower extremity performance than measures of MQ based on handgrip strength and a DEXA derived measure of lean body mass. Exceptions, found only in men, were Quad180/TCSAht2 with average gait speed and rapid gait speed, and Ham30/TCSAht2 with average gait speed. Overall, ID/CT MQ measures comparably correlated with several physical performance measures as Grip/ALMBMI, including higher order, more sensitive performance measures. Additional measures of MQ were explored in this study, specifically ID/DXA, Isometric/CT, and HD/CT measures, however none performed as well as ID/CT or HD/DXA (data not shown). MQ measures of Grip with DXA acquired upper extremity lean mass and lower extremity lean mass were also assessed but produced R2 values equivalent to Grip/ALMBMI, therefore they were not reported. These results suggest that Grip/ALMBMI is a valid if not superior substitute for more costly and burdensome measures of MQ.

To our knowledge, this is the only study to compare multiple measures of MQ as correlated with multiple physical performance measures. A previous comparison of grip strength, knee extension strength, and lower extremity muscle power found no statistical difference between measures in ability to identify those with gait speed less than 0.8 m/s (11).
When comparing MQ to muscle strength, all muscle strength measures explained as much, if not more, variance than their respective MQ measures. When assessing physical function, strength alone may be the appropriate measure of muscular function. Other studies have also reported stronger associations between muscle strength vs MQ and physical performance measures. A comparison of multiple measures of CT acquired body composition and ID acquired muscle strength in the Age, Gene/Environment Susceptibility-Reykjavik (AGES-Reykjavik) Study found muscle strength to have the strongest associations with decline in gait speed over 5 years in men and women when compared to muscle mass, MQ, muscle attenuation, and intermuscular adipose tissue (14). Similarly, a comparison of muscle mass, muscle strength, and MQ in older men found muscle strength to have the strongest magnitude of association with functional limitation and physical disability and thus was the best clinical indicator of age-related muscle change(9). The current findings add to these previous studies by additionally assessing a MQ measure with muscle mass from CT and isokinetic quadricep strength. These results suggest that strength may suffice as a measure of muscle in a clinical setting.
A review assessing the optimal MQ measure suggested that muscle power, or the rate at which force is developed, should be used with muscle mass and muscle strength(1). They also concluded that energetic metabolism may add insight into the mechanisms that support MQ(1). It is possible that these additions may produce a MQ measure that correlates with physical performance better than strength alone.
MQ may provide a differential diagnosis of poor physical performance. Poor physical performance in the presence of normal MQ may be due to issues such as arthritis and pain, whereas poor physical performance with deficient MQ may be due to fat infiltration, decreased innervation, or decreased metabolism. Additionally, there are several factors that impact physical performance beyond strength and MQ, as evidenced by the modest R2 values obtained in these analyses.
The BLSA allowed for the comparison of multiple MQ measures, including HD/DXA and ID/CT with respect to their association with several different physical performance outcomes of varying difficulty. A limitation of the BLSA is that it comprises a population that is healthy upon enrollment, therefore the results may not be generalizable to the broader older adult population. However, given that the muscular and physical function of many of the BLSA participants may be higher than that of the overall older adult population, it is possible that these results would be stronger in the general population. Even with the health of the BLSA population, 29% of those 65 and older who completed the handgrip assessment did not complete the BioDex, primarily due to pain or safety concerns. This proportion would probably be greater in the general population, highlighting the need for an easy and inexpensive clinical assessment of muscle for older adults of varying health statuses. An additional limitation is the lack of assessment of adiposity, particularly intramuscular fat, on MQ. Adiposity is associated with poorer MQ (14, 23); therefore, future research should assess whether the impact of adiposity on MQ differs by MQ measure.
From these results, it appears that Grip/ALMBMI is as good a MQ measure as those obtained using much more expensive and labor intensive machinery, such as Quad30/TCSAht2. Further assessment will be needed to determine if these finding are maintained longitudinally. Grip/ALMBMI may not be as sensitive to detecting change in physical performance; however, it could serve as an easily obtained initial screening tool for clinical application to identify older adults in need of more precise/extensive assessment of MQ. Furthermore, strength measures alone appear sufficient for assessing muscle function as it relates to physical performance.


Funding: This research was supported by the Intramural Research Program of the National Institutes of Health, National Institute on Aging.
Conflict of interest: None



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1. Faculté de Chirurgie Dentaire, Université Toulouse 3 – Paul Sabatier, Toulouse, France; 2. Pôle Gériatrie, CHU de Toulouse, Gérontopôle, Toulouse, France.
Corresponding author: Dr. Marie-Hélène Lacoste-Ferré, DDS, PhD, Pôle Gériatrie, CHU de Toulouse, Gérontopôle, Toulouse, France , Faculté de Chirurgie Dentaire, Université Toulouse 3 – Paul Sabatier, Toulouse, France, lacoste.mh@chu-toulouse.fr


J Frailty Aging 2017;6(3):154-160
Published online March 15, 2017, http://dx.doi.org/10.14283/jfa.2017.9



Introduction: The relationships between oral health conditions and frailty have rarely been explored. A systematic review of frailty components and oral health concluded that differences in study population endpoint criteria and study design cannot establish a relationship between frailty and oral health. Objective: This study aims to describe the distribution of the OHAT (Oral Health Assessment Tool) score in a population of frail subjects and to assess associated parameters (age, socio-economic status, living conditions, education level, nutritional habits, cognitive functioning, autonomy). Design: Cross-sectional observational study among patients referred to the Geriatric Frailty Clinic.  Measurements: 1314 patients participated in different standardized tests to evaluate their health status, cognitive and affective functioning, adaptation to usual daily activities, nutritional status, and oral health status. Results: The risk of oral health deterioration was higher with the appearance of frailty: the OHAT increased significantly with the Fried Frailty Score (p<0.001). Physical performance and oral health were correlated (p<0.001). The OHAT score and the MNA (Mini Nutritional Assessment) score were significantly correlated: oral status seemed better for malnourished subjects (p<0.001). Dementia significantly increased the risk of an unhealthy oral status (p<0.001). There was no significant correlation between oral status and depression, just a trend. Conclusion: This pilot study establishes a relationship between the OHAT and Fried Frailty Criteria in a population of frail elderly. It must be expanded to follow the distribution of the different items composing the OHAT score (items assessing lips; tongue; gums and tissues; saliva; natural teeth; dentures; oral cleanliness; and dental pain) with different parameters (age, socio-economic status, living conditions, educational level, medical history, drug treatment, nutritional habits, cognitive functioning, disabilities and handicaps).

Key words: Frailty phenotype, physical performance, malnutrition, prevention.




After the age of 60 years, because of changes in physical and cognitive health and psychosocial living conditions, the population of elderly adults is heterogeneous, and can be divided into 3 groups: robust older adults, frail older adults, and dependent older adults (1, 3).
Robust older adults age successfully and often have few comorbidities. They have a high level of functioning and can take care of themselves, their health, hygiene and particularly their oral care. They are not different from younger adults in regards to oral care.
Dependence refers to the partial or total inability to perform basic activities of daily life without help. Many studies have shown that the dependent population usually exhibits poor oral health. In the geriatric nursing home, oral health is often neglected, and oral care is considered difficult because of behavior disorders, lack of cooperation, and loss of autonomy, which impair good access to dental health care (4-6).
Frailty is a clinical geriatric syndrome characterized by a decrease in energy reserve, strength and performance, resulting in a progressive decline in multiple physiological systems leading to a state of greater vulnerability. This is defined as a pre-disability state which, different from disability, is still amenable to intervention and is reversible (2-3, 7-8).
The relationship between oral health conditions and frailty has rarely been explored (9). Moreover, it is also necessary to associate general diseases (such as diabetes, rheumatoid arthritis, stroke, or cardiovascular disease), with frailty and oral health. Comorbidities have an undeniable impact on frailty. General health and oral health are interrelated and have a complex and multifaceted relationship, especially in elderly adults.
The elderly have a wide variety of oral health problems, including loss of teeth, edentulism, periodontitis, coronal and root caries, oral mucosal lesions, utilization of nonfunctional dental prostheses, xerostomia and chewing problems, among others (6). Oral health is instrumental to older people’s health, life satisfaction, quality of life, and self-perception (10-11). Oral infections may have biological consequences that later manifest as health problems (12). Oral status can also contribute to changes in diet, weight and physical functioning (13-14). Hence, poor oral health can affect the individual’s overall well-being.
This study aims to describe the distribution of the OHAT (Oral Health Assessment Tool) score in a population of frail subjects, and to assess its correlation with associated parameters (age, socio-economic status, living conditions, education level, nutritional habits, cognitive functioning, autonomy).



Participants and study design

Cross-sectional observational study of patients referred to the Geriatric Frailty Clinic. The Geriatric Center of Toulouse (i.e., the Gerontopole of the Toulouse University Hospital, in association with the university department of General Medicine and the Midi-Pyrenees Regional Health Authority) designed and developed the innovative “Geriatric Frailty Clinic (GFC) for assessment of frailty and prevention of disability” in 2011. Patients participate in different standardized tests to evaluate their health status, cognitive and affective functioning, adaptation to usual daily activities, nutritional status, and oral health status. All collected data are systematically computerized in a database that can be used for research purposes and statistical analyses. 1314 patients have already been evaluated in the clinic and are included in the study, without any exclusion criteria (15).

Data collected

Tables 1 and 2 recapitulate the data collected and the tests performed in the study.

Table 1 Baseline characteristics of the GFC population (n= 1,314)

Table 1
Baseline characteristics of the GFC population (n= 1,314)



Table 2 Oral Evaluation OHAT [0-4 = Healthy, 4-8 = Mild healthy requiring oral attention, 8-12 = Unhealthy, requiring care, 12-16 = Unhealthy, requiring compulsory care]

Table 2
Oral Evaluation OHAT [0-4 = Healthy, 4-8 = Mild healthy requiring oral attention, 8-12 = Unhealthy, requiring care, 12-16 = Unhealthy, requiring compulsory care]


Nutritional evaluation

– Mini Nutritional Assessment (MNA) (16)
The Mini Nutritional Assessment (score /30) is a validated nutrition screening and assessment tool that can identify geriatric patients aged 65 and above who are malnourished or at risk of malnutrition.

– Body Mass Index (BMI) (17)
Body Mass Index (BMI) classifications were developed based on associations between BMI, chronic disease, and mortality risk in healthy populations. The formula of BMI is kg/m2 where kg is the person’s weight in kilograms and m2 is his height in meters squared.

Functional evaluation

– Frailty Status (2)
Five criteria defined by Fried characterize frailty: weight loss, exhaustion, low physical activity, slowness, weakness. The sum score of these five criteria classifies the elderly as not frail (score 0), pre-frail (score 1-2) and frail (score 3-5).

– Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) (18, 19)
Activities of Daily Living scale is the most appropriate instrument to assess functional status. The ADL ranks adequacy of performance in the six functions of bathing, dressing, toileting, transferring, continence, and feeding. Scores range from 0 to 6, with a score of 1 point if totally independent, 0.5 if partially dependent and 0 if totally dependent in each of the 6 assessed activities.
Instrumental activities of daily living are the activities that people engage in when they are up, dressed and put together. These tasks support an independent life style. Many people can still live independently even though they need help with one or two of these IADLs. They include: cooking, driving, using the telephone or computer, shopping, keeping track of finances and managing medications.
– Short Physical Performance Battery (SPPB) (20)
The Short Physical Performance Battery (Score /12) is a simple test to measure lower extremity function using tasks that mimic daily activities. The SPPB examines 3 areas of lower extremity function: static balance, gait speed, and getting in and out of a chair. These areas represent essential tasks important for independent living.

Cognitive and affective evaluation

– Mini Mental State Examination (MMSE) (21)
The Mini Mental State Examination (Score /30) is a tool that can be used to systematically and thoroughly assess mental status. It is a 30 question test that assesses five areas of cognitive function: orientation, registration, attention and calculation, recall, and language.

– Geriatric Depression Scale (GDS) (22)
The 15 item GDS is a self or hetero-rating scale. Patients are asked to respond based on how they have felt in the last week. The scale uses a yes-no format to be clearer for older patients.

Oral evaluation

– Oral Health Assessment Tool (OHAT) (23, 24)
The Oral Health Assessment Tool (OHAT) provides a global indication of oral health based on observation, conducted whatever the general health of the patient (cognitive state), by a physician, dentist or nurse. It is a recent version of the BOHSE (Brief Oral Health Status Examination), validated by Chalmers et al. in 2005.
The OHAT assesses 8 areas: lips, tongue, gums and tissues, saliva, natural teeth, dentures, oral cleanliness and dental pain. A score of 0 (healthy), 1 (oral changes), or 2 (unhealthy) is given to each of the assessment categories, and a score of the eight categories is summed to give a total oral health score.

Statistical aspects

Data from the 1314 patients already seen at the Geriatric Frailty Clinic were analyzed on SAS® (SAS Institute Inc., Cary, NC, USA) statistical software.
A description of the study sample was first provided, which included: distribution of categorical variables (number and percentage), mean, median, standard deviation, interquartile range, minimal and maximal values of continuous variables.
The global OHAT score was analyzed as a continuous variable. Each of its components was analyzed as a categorical variable (including three categories: 0, 1, 2).
The usual tests were used to assess univariate associations between OHAT scores and other clinical variables (age, socio-economic status, living conditions, education level, medical history, drug treatments, nutritional habits, cognitive functioning, disabilities and handicaps). The Chi square test (or Fisher’s exact test when conditions required for the Chi square test were not fulfilled) was used to compare categorical variables. Student’s t-test (or the Kruskal Wallis test) was used to compare the distribution of a continuous variable between categories of a categorical variable. Pearson’s (or Spearman’s) correlation coefficient was estimated to assess the strength of the link between continuous variables.

Ethical considerations

The study protocol was approved by the local Research Ethics Committee (Comité d’Ethique de la Recherche des Hôpitaux de Toulouse). It did not require either a verbal or a written consent from participants, as this study collected and analyzed only data and information from usual care during hospitalization (without any further assessment, experimentation, procedures, or follow-up). Patient records and information were anonymized and de-identified prior to analysis.



Patient Characteristics (Tables 1-2)

The description of the main characteristics of the 1314 patients recruited is reported in Tables 2, 3 and 4. Participants had a median age of 82.5 (SD 6.3), range 76.2-88.8 years. Most patients were women (65.1%). Most participants (n=1254, 95.1%) had gone to school, but few of those had attended higher education (n=232, 18.5%). Most participants were widowed (45.1%), single (6.4%) or divorced (9.7%). The living environment was individual (61.3%). There were 46.9% participants living alone at home.

Table 3 Bivariate analysis of OHAT according to socio-demographic characteristics (OHAT = continuous variable)

Table 3
Bivariate analysis of OHAT according to socio-demographic characteristics (OHAT = continuous variable)


According to the Fried definition of frailty, 552 subjects were pre-frail (42.6%) and 606 subjects were frail (46.8%).
Concerning the functional evaluation, physical performances were good for 397 subjects (30.7%). Concerning autonomy, the mean ADL score was 5.4 ±0.9 and the mean IADL score was 5.3 ±2.4.
For the BMI, the mean score was 26.9 ±8.4 and only 27 participants were underweight (2.1%). Regarding the nutritional evaluation, 793 (60.6%) participants presented a good nutritional status, 456 (36.8%) participants presented a risk of malnutrition and 60 (4.6%) participants were malnourished. Concerning the cognitive evaluation, the mean MMSE score was 24.6 ±5.1. Moderate to severe dementia was observed in 215 (16.8%) participants, with a MMSE score < 19. For the affective evaluation, the mean GDS score was 4.7 ±3.0. Moderate to severe depression was detected in 394 subjects (30%). Concerning the oral evaluation, the mean OHAT score was 1.98 ±2.1. Most subjects (n=1029, 79.7%) had an OHAT score < 4; they presented good oral health.

Relationships between oral health and socio-demographic characteristics (Table 3)

Statistical analysis showed no significant difference between women and men or according to education level. The OHAT decreased with age (p<0.001). The difference was significant according to marital status and living conditions; participants who were married and participants who lived with their spouse had better oral health.


Table 4 Bivariate analysis of OHAT according to frailty characteristics

Table 4
Bivariate analysis of OHAT according to frailty characteristics


Relationships between oral health and the functional, nutritional, cognitive and affective evaluation (Table 4)
The risk of oral health deterioration was higher with the appearance of frailty: the OHAT increased significantly with Fried Frailty Score (p<0.001). Physical performance and oral health were correlated (p<0.001). The OHAT score and the MNA score were significantly correlated: oral status seemed better for malnourished subjects (p<0.001). Dementia significantly increased the risk of unhealthy oral status (p<0.001). There was no significant correlation between oral status and depression, just a trend.



Published studies on the relationship between oral health and frailty have not provided strong conclusions because oral health and frailty criteria measurements and the study designs were different (9). Target populations were not homogenous. Only 75% of the participants in a Mexican study had an oral examination (25). In a Japanese study, the distribution of frail, pre-frail and robust people did not agree with the current distribution (26). Most studies used handgrip strength as the frailty criteria (9). Concerning the oral examination, there is no possible comparison because of variations in criteria such as the number of teeth or number of functional teeth, comfort or pain, use of prosthesis or need for prosthesis, periodontal pocket or periodontitis (25-28). Moriya et al. used a validated index: the General Oral Health Assessment Index (GOHAI) combined with handgrip strength (29). But the GOHAI is a self-questionnaire, only usable by patients without cognitive impairment.
Our pilot study provides a complete screening of frailty and a global oral examination for persons aged 65 years and older considered frail by their physician. Frailty criteria are based on international validated scales (2). The oral health evaluation is based on the OHAT, which is a global oral health index conducted by a GFC geriatrician or nurse. The OHAT can be carried out whatever the cognitive, psychic, or physical health condition of the patient. It involves locating damage affecting the whole mouth. It is not a diagnostic tool, because it does not specify the number of teeth or the severity of the pathologies (caries, severe periodontitis). It does not assess oral function (number of functional units, chewing ability). Furthermore, the OHAT orients care needs by identifying degraded areas (23-24). It can explain the unexpected results with age and nutritional status. In fact, an edentulous patient without prosthesis can constitute a bias; he presents no dental or gingival diseases because he has no teeth, daily oral hygiene is easier, and only the item “prosthesis” can be scored as 2. However, because he is edentulous, he cannot chew correctly and consequently will select food. Thus, the OHAT score for an edentulous patient without prosthesis can be lower than that of dentate patients with 4 or 5 caries and gingival disease because of poor oral hygiene. A mouth with dental or periodontal disease may maintain acceptable function but will remain damaged. Also, the oldest patients are frequently edentulous, without complete dentures. Watanabe et al. described the associations between frailty, age and oral function (number of functional teeth, number of teeth present, occlusal force, masseter muscle thickness, and oral diadochokinesis) (26).Even when they had as many functional teeth as the pre-frail and the robust, the frail oldest had a decrease of occlusal force, and their performance was decreased in the phonetic test (oral diadochokinesis).
Although the MNA is commonly used by geriatricians, weight loss indicates nutritional status for most of the studies concerned with oral health and frailty. However, their conclusions are contradictory. Weight loss and oral health are still not correlated, particularly the edentulous and loss of teeth categories. Patients with poor oral health have difficulty in maintaining an appropriate diet; they adapt and select foods, and they eat more sweet foods, which do not result in weight loss (9).
The results of this pilot study show that oral health deteriorates significantly with the advance of frailty. It is prefigured in many studies: very poor oral health is observed with dependence, because of the low perception of oral health, neglected dental check-up visits and poor daily oral hygiene (6). Access to dental care is often decried for elderly people. Cost, availability and accessibility constitute commonly recognized barriers to the use of dental services (5, 11). The decrease in physical performance could also be another barrier to dental care. Castrejon-Perez et al. underline the fact that the low utilization of dental services could be considered a possible risk marker for frailty syndrome (25). The oral health of patients living with a spouse is better than that of patients living alone; accompaniment to the dental office can be facilitated. The worst OHAT score was among patients who live with family; it seems that family reunification does not help to support satisfactory oral health, because of the break with dental maintenance.
Oral health deteriorates with progressive cognitive disease. Patients with severe cognitive impairment have the worst oral disease. Many studies have described the same results (30). It is necessary to insist on the importance of dental maintenance from the first signs of dementia. Patients with Alzheimer’s disease progressively lose the ability to care for themselves. This is true for oral care, especially for dental and prosthetic hygiene.



In conclusion, general health and oral health are interrelated and have a complex and multifactorial relationship, especially in frail elderly people. This pilot study demonstrates the relationship between the OHAT and the Fried Frailty Criteria in a population of frail elderly. It should be expanded to follow the distribution of the different items composing the OHAT score (items assessing lips; tongue; gums and tissues; saliva; natural teeth; dentures; oral cleanliness; and dental pain) with different parameters (age, socio-economic status, living conditions, education level, medical history, drug treatment, nutritional habits, cognitive functioning, disabilities and handicaps). Thus, it will allow the following possibilities:
– Identification of oral health as a possible criterion of frailty
– Screening of the frail population at the first sign of oral degradation and development of monitoring adapted to this targeted population
– Education of the elderly on geriatric oral health principles: oral hygiene, curative care


Conflicts of interest: None reported by authors.



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1. Chair of Geriatrics, University of Verona, Italy; 2. Department of Medicine and Sciences of Aging, and Unit of Biogerontology and Cytokines, University of Chieti, Italy.
Corresponding author: A.P. Rossi, MD, PhD, Cattedra di Geriatria, Università di Verona, Ospedale Maggiore, Piazzale Stefani 1, 37126 VERONA, Italy, Tel:+39-45-8122537;FAX:+39-45-8122043, E-mail:andrea.rossi@hotmail.it


J Frailty Aging 2017;6(2):65-71
Published online February 22, 2017, http://dx.doi.org/10.14283/jfa.2017.4


Objectives: investigate the presence of a correlation between systemic inflammatory profile of community-dwelling individuals and the loss of muscular mass and performance in old age over a 4.5y follow-up, focusing on the role of anti-inflammatory cytokines in muscular changes in elderly. Design: Longitudinal clinical study. Setting: Subjects were randomly selected from lists of 11 general practitioners in the city of Verona, Italy. Participants: The study included 120 subjects, 92 women and 28 men aged 72.27±2.06 years and with BMI of 26.52±4.07 kg/m2 at baseline. Measurements: Six minutes walking test (6MWT), appendicular and leg fat free mass (FFM) as measured with Dual Energy X-ray absorptiometry, were obtained at baseline and after 4.5 years (4.5y) of mean follow-up. Height, weight, body mass index (BMI), and circulating levels of TNFα, IL-4, IL-10, and IL-13 were evaluated at baseline. Results: A significant reduction of appendicular FFM, leg FFM and 6MWT performance (all p<0.001) was observed after 4.5 y follow-up. In a stepwise regression model, considering appendicular FFM decline as dependent variable, lnIL-4, BMI, baseline appendicular FFM, lnTNFα and lnIL-13 were significant predictors of appendicular FFM decline explaining 30.8% of the variance. While building a stepwise multiple regression considering leg FFM as a dependent variable, lnIL-4, BMI and leg FFM were significant predictors of leg FFM decline and explained 27.4% of variance. When considering 6MWT decline as a dependent variable, baseline 6MWT, lnIL-13 and lnTNFα were significant predictors of 6MWT decline to explain 22.9% of variance. Conclusions: Our study suggest that higher serum levels of anti-inflammatory markers, and in particular IL-4 and IL-13, may play a protective role on FFM and performance maintenance in elderly subjects.

Key words: Muscle mass, anti-inflammatory cytokines, physical performance.



Lately, the importance of physical function assessment in advanced age is considered as both central component of clinical evaluation and a specific outcome for interventions (1). Functional and structural muscle changes linked to the aging process are determined by physical function impairment and chronic low-grade inflammation status. This later condition, also known as “inflammaging”, explains several aspects of the aging phenotype and is correlated with morbidity and mortality levels in older age (2, 3). Moreover, “inflammaging” results from an imbalance between pro- and anti-inflammatory networks. Aging has been frequently associated with an increased level of pro-inflammatory cytokines (4), in particular interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα) (5). Several epidemiological studies proved a strong statistical correlation between the level of pro-inflammatory cytokines, the rate of muscle mass loss (6, 7) and impaired physical performance in elderly individuals (8-11). Nevertheless, results of experimental studies are in some way contradictory, as some support the direct link between inflammatory markers and muscle homeostasis (12), while others suggest that, under particular conditions some pro-inflammatory cytokines might induce protein synthesis (13). On the other hand, fewer papers concentrated on the role of anti-inflammatory cytokines on muscle mass decline. In particular, it had been shown that baseline interleukin-10 (IL-10) expression was reduced in aged mice skeletal muscle (14) and that the absence of IL-10 was associated with a greater IL-6 response to lipopolysaccharide in skeletal muscle (15). IL-10 is a pleiotropic cytokine that has been shown to suppress the synthesis of pro-inflammatory cytokines, such as interleukin-1 (IL-1), IL-6, TNFα (16). Moreover, IL-10 might control the switch of muscle macrophages from M1 to M2 phenotype in injured muscle, and this transition was necessary for normal muscle regeneration (17). Nevertheless, only a few studies have investigated the relation between IL-10 systemic levels and muscle mass and strength in elderly subjects, and none clearly found a significant relationship (18). Even fewer studies addressed the role of anti-inflammatory cytokines, such as interleukin-4 (IL-4) and interleukin-13 (IL-13), in the skeletal muscle homeostasis of elderly individuals. Nevertheless, it has been demonstrated that during mammalian muscle growth IL-4 acts as a myoblast recruitment factor to form mature myotubes (19). IL-4 was found to be secreted by myotubes and determined muscle cell fusion during muscle growth, while muscle cells lacking IL-4 or the IL-4α receptor were reduced in size and myonuclear number. Moreover, it is well known that IL-4 and IL-13 signaling functionally overlap, because they share the IL-4α chain as a common receptor component. Hence, IL-4 and IL-13 could potentially be seen as growth factors acting on muscle differentiation stages (20). All these preliminary results point to the role of anti-inflammatory cytokines in muscle homeostasis, although there is no data for elderly individuals who show a shift to an anti-inflammatory cytokine pattern (21, 22).
As the inflammatory imbalance in elderly was correlated with physical performance, understanding the role on anti-inflammatory cytokines in muscle function decline in old age, could be able to predict several health-related events apparently extraneous to physical function (e.g., incident cognitive impairment, hospitalization, surgical complications, institutionalization, and mortality) (23).
The aim of our study was to investigate the presence of a correlation between systemic inflammatory profile of community-dwelling individuals and the loss of muscular mass and performance in old age over a 4.5y follow-up.


Materials and methods


Body composition was evaluated at baseline and at 4.5y of follow-up in a cohort of community-dwelling elderly men and women (24). Subjects were randomly selected from lists of 11 general practitioners in the city of Verona, Italy. Subjects were considered eligible if they were able to walk at least 1/2 mile without difficulty and if they had no cognitive impairment (Mini-mental Status Examination score >24). None of the subjects were engaged in regular physical exercise more than once a week during the study. Exclusion criteria included renal insufficiency, disabling osteoarthritis, heart failure (NYHA≥2), previous cancer and serious lung disease. Individuals who had lost >5% of their body weight in the year preceding the study were also excluded. At baseline, 177 women and 97 men, aged between 68 to 78 years, were eligible and gave their consent to participate in the study. During the follow-up, 11 subjects died, 17 subjects could not be revisited due to the onset of illnesses such as cancer, cerebrovascular disease or other serious chronic disease, 78 were lost at follow-up and 48 subjects were missing for serum sample at follow-up. The present analyses were performed on a final sample of 120 participants.
Women excluded from the present analysis were 1 year older (p=0.002) than those included. No significant differences were observed for any of the variables considered between men who were excluded and those included in this study. A total of 92 women and 28 men underwent Dual Energy X-ray absorptiometry (DXA) determinations, at baseline and after 4.5y of follow-up and were thus included in the analysis. All of the subjects gave their consent to participate in the study. The study was approved by the Ethics Committee of the University of Verona.


With the subject barefoot and wearing light indoor clothing, body weight was measured to the nearest 0.1 kg (Salus scale, Milan, Italy), and height to the nearest 0.5 cm using a stadiometer (Salus stadiometer, Milan, Italy). BMI was calculated as body weight adjusted by stature (kg/m2).


Appendicular and leg fat free mass (FFM) were determined using DXA. Baseline evaluations were performed using Hologic-QDR-2000 fan beam densitometer with software version 7.2. Due to technical reasons the 4.5y body composition evaluation was performed using Hologic QDR 4500 fan beam densitometer with software version 8.21. The characteristics and physical concepts of DXA measurements have been described elsewhere (25).

DXA 4500 calibration

As previous studies have shown that Hologic-QDR-4500 overestimates FFM compared with Hologic-QDR-2000 [26], a sub-sample of 13 subjects was evaluated on the same day with both Hologic-QDR-2000 and 4500 to obtain regression equations in order to adjust for differences in FFM measurement as reported elsewhere (27).

Six minutes walking test (6MWT)

Each subject performed a 6MWT in which the number of meters walked was determined for each subject [28]. Prior the walking test, subjects were instructed to walk as fast as they comfortably could, trying to cover the maximum distance possible without overexerting or pushing themselves beyond what they thought was safe. The 6MWT was conducted indoors, on a flat course and with colored marks each meter, in order to easier calculate the walked distance.

Health status

The presence of acute and chronic conditions was determined using standardized questionnaires already in use in the Italian Longitudinal Study on Aging (29). Subjects underwent careful clinical investigation at the beginning of the study, and again at the 4.5y follow-up. Information regarding the onset of new diseases was obtained for each subject through their general practitioners. Level of physical activity was assessed by SF-36 questionnaire, and it was considered low if <70 points (26). The SF-36 is a self administered questionnaire that measures the following eight subscales: physical functioning, social functioning, role limitations due to a physical problem, role limitations due to an emotional problem, mental health, vitality, bodily pain, and general health perception. [30] Chronic conditions assessed included: cardiovascular disease, lung disease (emphysema, chronic bronchitis and asthma), degenerative joint disease and hypertension.

Cytokines measurement

Serum cytokines were measured by a multiplex assay (Search Light Human T-helper1/T-helper2 Array, Pierce-Endogen, Rockford, IL), a chemiluminescence 9-plex kit allowing to simultaneously detect multiple cytokines using a cooled CCD camera. Samples were diluted 1:5 and the assay was performed according to the manufacturer’s instructions. The minimum detectable concentrations were: 0.8pg/ml for TNFα, 0.4pg/ml for IL-4, 0.2pg/ml for IL-10 and 0.7pg/ml for IL-13.  The Blood tests results were categorized according to quartiles of distribution: (a) TNFα (<1.25, 1.25-4.49, 4.50-7.28, and >7.28 pg/ml); (b) IL-4 (<1.5, 1.5-3.5, 3.5-6.99, and >7 pg/ml); (c) IL-10 (<2.00, 2.00-3.50, 3.51-10.57, and >10.57 pg/ml); (d) IL-13 (<0.74, 0.74-2.53, 2.54-11.38, and >11.38 pg/ml).

Statistical analysis

Results are shown as means with standard deviation. Preliminarily, the Kolmogorov-Smirnov test was performed on all anthropometric and biochemical variables (separately in males and in females) to assess for evidence of non-parametric in the data. Thus, non-parametric distributed variables were natural log transformed. Repeated measures of ANOVA were performed on the original variables. Differences in mean changes in fat free mass body composition according to quartiles of TNFα, IL-10 and IL-13 over the 4.5y period were tested by repeated measures of ANCOVA, considering baseline as covariate, while changes between baseline and 4.5y follow-up were tested using paired t-tests.
Univariate correlation analyses were used to test associations between changes in appendicular FFM, legs FFM, 6MWT performance and most closely related variables. A stepwise linear regression analysis was employed, considering changes in appendicular FFM, leg FFM and meters walked at the 6MWT from baseline to subsequent assessment at the outcome and sex, age, BMI, physical activity level, co-morbidity, lnTNFα, lnIL-4, lnIL-10, lnIL-13 as independent variables. A significance level of 0.05 was used throughout the study. All statistical analyses were performed using SPSS (version 21.0 for Windows) [26].



Table 1 shows the main characteristics of the studied population (mean ± SD) at baseline. A total of 120 subjects with baseline mean age of 72.27 ± 2.06 years and baseline mean BMI values of 26.52 ± 4.07 kg/m² were studied. Our study group showed a significant reduction of weight, appendicular FFM, legs FFM and 6MWT performance after 4.5y follow-up (all p<0.001, Table 2).

Table 1 Baseline Characteristics of the Study Population (n=120)

Table 1
Baseline Characteristics of the Study Population (n=120)

BMI: Body Mass Index; FFM: fat free mass; 6MWT: 6 minute walking test performance; TNFα: tumor tumor necrosis factor alpha; IL: interleukin; SD: standard deviation; COPD: Chronic obstructive pulmonary disease.


Table 2 Changes in weight, total FFM, appendicular FFM, legs FFM and 6MWT performance

Table 2
Changes in weight, total FFM, appendicular FFM, legs FFM and 6MWT performance

Data are presented as mean ± SDs. FFM: fat free mass; 6MWT: 6 minute walking test.


Appendicular FFM, legs FFM and 6 minutes walking test changes at 4.5y were directly related with lnIL-4, lnIL-10 and lnIL-13 levels, while inversely related with lnTNFα (Table 3).


Table 3 Correlations between appendicular FFM, legs FFM, 6 minutes walking test performance 4.5 year change and age, BMI, physical activity level and cytokines

Table 3
Correlations between appendicular FFM, legs FFM, 6 minutes walking test performance 4.5 year change and age, BMI, physical activity level and cytokines

BMI: Body Mass Index; FFM: fat free mass; TNFα: tumor necrosis factor alpha; IL: interleukin; 6MWT: 6-minute walking test. * p < 0.001; † p < 0.01; ‡ p < 0.05.


In Table 4 age, BMI, physical activity level, the changes in appendicular FFM, leg FFM and 6MWT performance during 4.5y of follow-up are compared across quartiles of TNFα, IL-4 and IL-13. Thus, participants in the two highest quartiles of TNFα showed a higher loss of appendicular FFM and leg FFM and a greater decline in meters walked at the 6MWT. Moreover, subjects in the highest quartile of IL-4 and IL-13 preserved appendicular and leg FFM loss and experienced a lower decline in 6MWT performance.
Table 5a shows a stepwise regression considering appendicular FFM decline as a dependent variable, and sex, age, BMI, physical activity level, baseline FFM, basal level of lnIL-10, lnIL-13, lnIL-4 and lnTNFα as independent variables. LnIL-4, BMI, baseline appendicular FFM, lnTNFα and lnIL-13 were significant predictors of appendicular FFM decline and explained 30.8% of the variance. More interestingly, while building a different stepwise multiple regression with the same independent variables, but considering leg FFM as a dependent variable (Table 5b), we observed that lnIL-4 entered first, explaining 19.8% of variance, followed by BMI and baseline leg FFM, explaining 24.8% and 27.4% of variance, respectively. Moreover it was evaluated a regression model that considers 6MWT decline as a dependent variable, and sex, age, BMI, physical activity level, co-morbidity, baseline walking test performance, basal level of lnIL-10, lnIL-13, lnIL-4 and lnTNFα as independent variables (Table 5c). Baseline walking test performance, lnIL-13 and lnTNFα were significant predictors of 6MWT decline to explain 22.9% of variance.

Table 4 Age, BMI, physical activity level (SF-36) and 4.5-year changes in appendicular FFM, legs FFM and distance at 6MWT across TNFα, IL-4 and IL-13 quartiles

Table 4
Age, BMI, physical activity level (SF-36) and 4.5-year changes in appendicular FFM, legs FFM and distance at 6MWT across TNFα, IL-4 and IL-13 quartiles

Abbreviations: 6MWT: 6-minutes-walking-test, FFM: fat free mass; TNFα: Tumor Necrosis Factor alpha; IL: interleukin; P values versus lowest quartile: * p <0.001; † p <0.01; ‡ p <0.05;
TNFα quartiles (pg/ml): <1.25, 1.25-4.49, 4.50-7.28, >7.28; IL-4 quartiles (pg/ml): <1.5, 1.5-3.5, 3.5-7.0, >7.0; IL-13 quartiles (pg/ml): <0.74, 0.74-2.53, 2.54-11.38, >11.38.

Table 5 Different linear regression analysis enter method considering 4.5-year changes of a) appendicular FFM, b) legs FFM, and c) 6MWT as the dependent variable, with sex, age, BMI, baseline FFM, physical activity level, comorbidity, lnTNFα, lnIL-4, lnIL-10, and lnIL-13 as independent variables

Table 5
Different linear regression analysis enter method considering 4.5-year changes of a) appendicular FFM, b) legs FFM, and c) 6MWT as the dependent variable, with sex, age, BMI, baseline FFM, physical activity level, comorbidity, lnTNFα, lnIL-4, lnIL-10, and lnIL-13 as independent variables

BMI: Body Mass Index; FFM: fat free mass; aFFM: appendicular FFM; TNFα: tumor necrosis factor alpha; IL: interleukin; 6MWT: 6-minute walk test



Our 4.5y longitudinal study in a sample of healthy elderly subjects, initially selected at the high-end of the functional spectrum, showed a significant decline in appendicular and leg FFM, as well as, a significant decline in physical performance as assessed with 6MWT. More importantly, we found that high levels of anti-inflammatory cytokines played a protective role in appendicular FFM preservation. It is well known that aging is associated with loss of skeletal muscle mass and function with concomitant fibrosis and extracellular matrix deposition, functional impairment and disability (29). In our study, we found that subjects with high serum levels of the anti-inflammatory markers IL-4, IL-10, and IL-13 presented a significantly lower decrease in appendicular FFM and performance loss. On the other hand, subjects in the highest quartiles of TNFα had a steeper decline in appendicular muscle mass and performance, as evaluated with the 6MWT. We also demonstrated an inverse relationship between TNFα and IL-10 levels.
In our study population, we found that IL-4 independently predicts loss of appendicular FFM, even after adjustment for potential confounders. Interestingly, recent data pleads for an additional role of IL-4 in skeletal muscle growth and repair. In fact, IL-4 was proven to act as a myoblast recruitment factor involved in mature myotubes formation, during mammalian muscle growth (18). More importantly, Horsley found that IL-4 is secreted by myotubes and is able to recruit further cell fusion during muscle growth. Additionally, it has been proven that IL-4 activates M2 macrophages, in particular the M2a sub-type, which increases in advanced stages of tissue-repair and in fibrotic muscle in mice and men (30, 31). This might be one explanation for the results of our study, as the subjects in the higher quartile of IL-4 preserved appendicular FFM at 4.5y follow-up.
Our data support the hypothesis that high circulating IL-10 and lower TNFα levels negatively influence physical performance. Slower and inadequate repair and adaptation mechanisms at muscle level may result from chronic inflammation and stress, leading to a perpetual cycle of disuse and muscle atrophy, which characterizes sarcopenia in the elderly (32). Only a few studies showed that IL-10 and TNFα are both involved in the development of skeletal muscle progenitors, exerting opposite roles. It is broadly known that IL-10 governs the level of inflammation, as it greatly inhibits TNFα and IFNγ, which activate cytotoxic M1 macrophages, driving pro-inflammatory response (33). Moreover, IL-10 seems to have a local effect on muscle cells homeostasis, as C2C12 myoblast, treated in vitro with IL-10 prior to pro-inflammatory stimuli, determine the induction of signaling pathways that normally in-activate inflammation (34). Additionally, IL-10 promotes the shift toward the anti-inflammatory M2c macrophages phenotype, which is closely involved in muscle regeneration after muscular injury. M2c macrophages reduce iNOS expression and increase myoblast proliferation (35). This pleiotropic effect of IL-10 at different levels might partially explain the fact that, when considered in a multiple regression model with other anti-inflammatory markers, an independent effect of IL-10 on FFM and performance preservation was not observed. However, our study is in line with the results of Cesari, which did not find any correlation between IL-10 plasmatic levels and performance status in a population of elderly subjects (17).
Jiang showed that IL-13 plays an autocrine role on skeletal muscle metabolism involving the microRNA let-7 expression with an indirect influence also on cellular protein levels (19), but only a few studies have correlated IL-13 systemic levels with low muscle mass or physical function (21). Recently, in a population of community-dwelling elderly subjects, serum concentration of IL-13 and IL-10 were significantly lower in slow walkers compared with subjects with normal walking speed, while in a regression model slow-walkers were characterized by higher circulating levels of TNFα, IL-8 and myeloperoxidase (18).
Moreover, it seems that IL-13 exerts a regulatory role on myogenesis, controlling the MyoD expression in human rhabdomyosarcoma cell lines (37). This is supported by the results of our study that shows an independent role of IL-13 in appendicular FFM preservation and with a decline in physical performance as evaluated with the 6MWT. This results are in relation with the fact that IL-13 effects might be synergic or additional to those determined by IL-4, as both cytokines share the same receptor system in the muscle cell, IL-4Rα. This latter signaling pathway was proven to be a crucial factor in myotube maturation through the NFATc2-dependent recruitment of IL-4Rα+ myoblast (37).

Some limitations of our study should be recognized

First, our study population was small and limited to healthy elderly men and women who initially were well functioning and in good health condition, as confirmed by the fact that the majority of them were weight stable during the study and thus cannot be considered representative of a normal aging population. Second, our study evaluated only serum anti-inflammatory cytokine levels but further histological and molecular studies are needed in order to establish their potential role in skeletal muscle homeostasis of elderly individuals.
Finally, only a baseline cytokine evaluation was available for this analysis and therefore a direct cause-effect relationship could not be established. Future studies should investigate the relationship between anti-inflammatory cytokines levels and body composition changes associated with aging.



In conclusion, high serum levels of anti-inflammatory markers, and in particular IL-4 and IL-13 seem to play a protective role on FFM and performance maintenance, independently of age, sex, BMI and TNFα levels. This new evidence of IL-4 and IL-13 action should be tested on a wider population and these studies may help improve strategies for sarcopenia prevention and clinical intervention on muscle mass loss with aging.


Acknowledgements: The authors’ responsibilities were as follows—AR, SB, FF, RP, CC: analysis and interpretation of data and preparation of manuscript; EZ, FF and MZ: study concept and design and preparation of manuscript; MZ, GM, EZ: consulted on study design, recruited subjects, and edited the manuscript and RP, SB, EZ, GM edited the manuscript.  EZ, GM, RP, MZ: acquisition of subjects, collection of data, and review of the manuscript, RP and MD cytokines measurements. A native English speaker (Prof. Mark Newman) corrected the English of the final version of the manuscript. This work was supported by grants from MIUR COFIN 2003 n2003069951_002 and MIUR project 2009KENS9K-002.
Conflict of interest: None of the authors have any conflict of interest to disclose.
Ehical standards: The experiments comply with the current laws of the country.



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1. Institute of Movement and Sport Gerontology, German Sport University Cologne; 2. Department of Molecular and Cellular Sport Medicine, Institute of Cardiovascular Research and Sport Medicine, German Sport University Cologne.

Corresponding author: Prof. Dr. Klara Brixius, German Sport University Cologne, Department of Molecular and Cellular Sport Medicine, Am Sportpark Muengersdorf 6, 50933 Cologne, Germany, Email: brixius@dshs-koeln.de, Phone: +49 221 4982 5220

J Frailty Aging 2015;4(4):216-222
Published online June 11, 2015, http://dx.doi.org/10.14283/jfa.2015.67


 Background: At present, it is unclear whether older, obese persons with or without sarcopenia respond differently to training. Furthermore, there are no differentiated recommendations for resistance training for this special target group. Objectives:  The objectives are to investigate the changes in the physical parameters of older, obese men caused by training and to reappraise the modalities of resistance training for older persons. Design: Pre-test-post-test design. Participants: The participants were 33 physically inactive and obese older men (≥ 65 years, BMI ≥ 30 kg/m2), with-out severe diseases. Subjects were divided into two groups: NSAR (no or presarcopenia, n= 15) or SAR (sarcopenia, n= 18). Intervention: The intervention consisted of progressive resistance training, twice a week for 16 weeks with finally 80-85% of maximum strength and three sets with 8-12 repetitions. The training contained six exercises for the major muscle groups. Measurements: Sarcopenia was assessed using the Short Physical Performance Battery (SPPB), hand-grip strength, skeletal muscle mass index (SMI), and gait speed over a 6-meter walkway. Furthermore, the maximum dynamic strength (1 RM) was assessed.  Results: At baseline, the NSAR group had significantly better values in SMI, SPPB score, hand-grip strength, and 1 RM. After training, the results in both groups displayed an increase in 1 RM at the lower limbs (NSAR 18%, SAR 38%) and the upper limbs (NSAR 12%, SAR 14%). Also, the SPPB score (NSAR 11%, SAR 15%) and the 6-m-gait speed (NSAR 5%, SAR 10%) increased. The SAR group was able to increase their right hand-grip strength by 12%, whereas the NSAR group maintained their initial high strength values. SMI did not change in both groups. Conclusions: Both groups show improvements after resistance training with slightly more benefits for  men with sarcopenia. Results of this study can be used to define specific training regimens for N(SAR) subjects.

Key words: Sarcopenic obesity, muscle strength, physical performance, resistance training.



Age-related loss of skeletal muscle mass and strength (i.e. sarcopenia) can have an impact on functioning. However, the coexistence of obesity and sarcopenia (i.e. sarcopenic obesity) can even have more profound consequences for the affected individuals, as the negative consequences of both aspects add up. These negative consequences include a decreased physical performance, an increased number of falls, a reduced quality of life, a decreased cardiovascular fitness as well as changes in metabolism, e.g. an increased insulin resistance (1-3). In the United States as well as in Europe, the number of obese older people is rapidly increasing because of an increase in the older population and the percentage of older people who are obese (4-5). The prevalence of sarcopenia varies between 5-13% in persons aged 60 to 70 years and between 11-50% in people over 80 years of age, for example, which depends on the respective population (6).

To counteract the negative aspects of sarcopenic obesity, endurance training used to be performed by obese, older persons, often combined with a diet intervention (7-9). Only in recent years, components of strength training have been added (7). In general, interventions have been carried out as a combined training according to the recommendations of the American College of Sport Medicine (ACSM, 10-12). The results of these studies show that a sole diet leads to a significant reduction in weight, but at the same time causes a reduction in muscle mass. With exercise based training, the muscle mass may be increased, but on the other hand, there is no reduction of the fat mass. Only a combined intervention of exercise and diet can reduce the fat mass while maintaining or increasing muscle mass (8-9). In addition, the exercise part of these interventions has positive effects on physical performance (depending on the training design, there is an improvement of strength or endurance parameters) and functional skills that have a crucial importance for the accomplishment of everyday activities, e.g. increasing gait speed (10-12).

Previous study designs had their focus mainly on the different effects of different interventions, e.g. exercise vs. diet or exercise vs. control group. This means that subjects had to fulfill the same conditions, so that they were randomized and then divided into different groups (10-13).

The key messages of these studies refer to the respective effects of the different interventions in terms of physical performance capacity and body composition in comparable groups of subjects. The training recommendations of the ACSM used therein refer to older, healthy people or people with type 2 diabetes mellitus. In this area, specific recommendations for the design of differentiated training in older, obese people are still lacking. However, due to the increasing prevalence of serious adverse effects (in particular the declining physical activity), it is even more important for the individuals concerned to fill this gap.

The statement that the negative consequences of the simultaneous presence of sarcopenia and obesity add up has not yet been considered with regard to the effects of resistance training. This brings up the question to what extent maximum strength, body composition and functional parameters change differently in obese adults with sarcopenia in comparison with obese adults without sarcopenia due to resistance training. The answer to this question can provide insights into the influence of lower baseline values of muscle mass and function in terms on the response to the training programme.

For the investigation of this aspect, the baseline values (i.e. before training) of the maximum strength of the upper and lower extremities, body composition (especially regarding muscle mass) as well as functional and every-daylife testing are crucial. Based on the baseline values, possible differences and similarities of adults with and without sarcopenia should be shown. The hypothesis is that the group with sarcopenia has a higher increase due to the training because of their lower baseline performance, particularly in the maximum strength and the physical performance measurements



Volunteers were recruited by advertisement. In this first part of the current study, only men could participate. They were included for the study if they were 65 years and older, had a BMI of 30 kg/m2 or more and had not been physically active in the past 12 months. The exclusion criteria were any serious inflammatory, neurological or cardiovascular diseases. Finally, 36 men who met the above criteria participated at the beginning.

The participants were divided according to muscle function and proportion of muscle mass into a high-performance and a low-performance group. The proceeding and the precise conditions for each group will be explained in the following section.

This study was approved by the Ethics Committee of the German Sports University Cologne. Each participant provided written informed consent.


Using a questionnaire, the current and previous diseases and also the physical activity within the last 12 months were recorded. This was followed by the determination of sarcopenia based on the test battery of Cruz-Jentoft and colleagues (14). They determined three components in order to evaluate the presence of sarcopenia and the stage of sarcopenia:

• Muscle mass

• Muscle strength

• Physical performance

The existence of low muscle mass and low maximum strength or reduced physical performance is called sarcopenia. If all three components are reduced, Cruz Jentoft et al. (14) call it severe sarcopenia. Presarcopenia is characterised by only a low muscle mass. Based on the data collected by Cruz-Jentoft et al. (table 1), the exact cut-off points for each criterion are shown.

Table 1 Overview of the methods used and the cut-off points for the sarcopenia test battery (by Cruz-Jentoft et al. (14))

BMI: body mass index; SPPB: Short Physical Performance Battery

Muscle mass

The measurement of muscle mass was performed in the supine position using bioelectrical impedance analysis (EgoFit BIA series 4, monofrequency, Germany). Before the measurement, the participants had to adhere to different specifications according to Heyward (15). This method has the advantage that the measurement can be carried out regardless of location, and the results of the analysis of the body composition are available immediately. The important parameter is the skeletal muscle mass, which was determined by the equation of Janssen et al. (16). This value was converted to percent, so that the socalled skeletal muscle mass index (SMI) resulted (17).

Muscle strength 

The measurement of the isometric handgrip strength was performed using JAMAR hand dynamometer (Sammons Preston Rolyan, Bolingbrook, USA) and was carried out alternately with the right and left hand. There were at least two attempts on each side. If there was a difference of ≥ 10 % between the two attempts, a third attempt was performed. For the statistical analysis the best attempt overall was taken.

Physical performance 

To determine this parameter, the Short Physical Performance Battery (SPPB; 18-19) was performed according to the guidelines given by Cruz-Jentoft et al. (14). It consists of the following three parts: balance (consecutively side-by-side, semi-tandem, and tandem stands, each for 10 seconds), walking a 4-meter distance at normal gait speed and rise from a chair and return to the seated position five times. A maximum of four points will be awarded in each category. This results in a total score. As an additional item, walking a 6-meter distance was also tested in this category because of the existence of separate cut-off points.

Maximum muscle strength 

If there were no objections by physician, a maximum-strength measurement was performed on the leg- and chest-press machine (ERGO-FIT 4000, Pirmasens, Germany). Here, the subjects were first familiarized with the test procedure and the measuring apparatus. The determination of dynamic maximum strength (one-repetition-maximum, 1 RM) was performed following the protocol of Baechle, Earle and Wathen (20). After three warm-up attempts, up to five maximum attempts were carried out to determine the 1 RM. The highest weight moved was used for the evaluation. This weight also determines the starting weight for the subsequent strength training.

All test procedures performed at baseline measurements were repeated at the end of training intervention after 16 weeks.

Group division 

According to the scheme of Cruz-Jentoft and colleagues, the participants were divided into non- and presarcopenic (NSAR) or sarcopenic (SAR, stage 1 and 2) subjects. It should be noted that there are two possibilities or two assessments in the field of physical performance to be considered as sarcopenic. Firstly, the total score of the Short Physical Performance Battery, and secondly the gait speed over a 6-meter walking distance.

If the participants met the inclusion criteria and if they achieved an adequate result in the sarcopenia test battery, a sports-medical examination was carried out, in particular to exclude cardiac contraindications.


The participants took part in a progressive resistance training, which was carried out on machines (Cybex EAGLE, SANIMED Nordicline). The training lasted 16 weeks with two sessions of 60 minutes per week. During the first three weeks, the participants trained at 60% of 1RM and carried out two sets of 12-15 repetitions each muscle group. During weeks 4 to 16, the participants increased the intensity gradually to 80-85% of 1 RM, and carried out three sets of 8-12 repetitions. First, there was a warming up for ten minutes on a bicycle ergometer. Then the training followed, which contained seven exercises for the major muscle groups (knee extensors, biceps and chest muscles, hip adductors and abductors, abdominal muscles, back muscles). For cooling down, the participants did about five minutes of bicycle-ergometer work.

Statistical analysis

The statistical analysis was performed using the IBM SPSS statistics software (version 22; IBM, Ehningen, Germany). Normal distribution was analyzed using the Kolmogorow-Smirnow-Test. The significance level was set at α = 5% at analysis of variance. Baseline characteristics between groups were compared using the t-test for unpaired samples for continuous variables. A two-factorial analysis of variance with repeated measures on two main factors (time and group) was conducted. The homogeneity of group variances was ensured for all variables.

Using this method, pre- and post-training differences between the groups were to be determined. The t-test for paired samples was performed to determine whether there were statistically significant within-group changes.

The SPPB has a score at the ordinal scale level. Therefore, for all calculations nonparametric tests (Wilcoxon signed-rank test and Mann-Whitney U test) were used.


33 test subjects completed the intervention. Three men dropped out due to health problems.

Overall, the training participation of the 33 men was 86 % on average.

The basic conditions of the participants were similar, i.e. there were no significant differences of the anthropometric data (table 2).

Table 2 Description of the participants at baseline

Means ± standard deviation are shown. BMI: body mass index; NSAR: group with no or presarcopenia; SAR: group with sarcopenia

When testing dynamic maximum strength, the preset protocol could not be completed in all subjects, for example due to pain in the shoulder or knee joints. Therefore, for these subjects there is no maximum strength value available.

Table 3 indicates baseline performance as well as performance after the intervention. The two-factorial analysis of variance with repeated measures showed no statistical significant interaction between group and time. Thus, for the following analysis the results of the different t-tests are shown.

Table 3 Comparison of the means between and within the groups before and after training

Means ± standard deviation are shown. NSAR: group with no or presarcopenia; SAR: group with sarcopenia; SMI: skeletal muscle mass index; 1 RM: one-repetition-maximum; SPPB: Short Physical Performance Battery; *p-value within group NSAR (t-test for paired sample); †p-value within group SAR (t-test for paired sample); §p-value between groups post-training (t-test for unpaired samples) #Wilcoxon signed-rank test for differences within groups and Mann-Whitney U test for differences between groups were used


Baseline performance 

Before the start of the intervention, the two groups differed significantly in several tests, which confirmed the planned group difference. The NSAR had significantly better values in the skeletal muscle mass index, the gait speed over six meters, the hand-grip strength, and the dynamic maximum strength at the chest press machine compared to the SAR.

There were no group differences of the dynamic maximum strength test for the lower extremities.

Both groups differ significantly in their total SPPB score. Here, one subtest of the SPPB, the repeated chair stands, is considered separately. The two groups did not differ, however, in the chair stands when time is considered.  

Performance after training 

In both groups no changes were observed in the SMI after training.

The SAR could increase their hand-grip strength (by 12%). The NSAR kept their handgrip strength constant at a high level. In both groups there was a statistically significant improvement of the dynamic maximum strength performance on the leg-press machine (NSAR by 18%, SAR by 38%) and of the dynamic maximum strength on the chest-press machine (NSAR by 12%, SAR by 14%).

The gait speed over a 6-meter course of both groups improved statistically significantly (NSAR by 5% and SAR by 10%).

Both groups increased their value in the total SPPB score significantly. The improvements of SAR were slightly larger, so that the difference between the two groups after training was no longer significant.


Both groups of obese individuals, regardless of whether they are sarcopenic or not, benefit from a progressive resistance training. The improvements could be recognized in two domains: maximum strength of the upper and lower extremities as well as aspects of physical performance. This means that the training did not only caused positive changes to the muscular level; there is also a possible transfer to certain skills of everyday life, e.g. gait speed and getting up from a chair. No statistically significant changes could be noticed after training relating to muscle mass in both groups.

The division into groups of obese men with and without sarcopenia was done in accordance with the recommendations of Cruz-Jentoft and colleagues (14). The system developed by this working group initially applies to all people aged 65 and over. However, they point out that comorbidities and individual circumstances must be taken into account. Before starting our study, it was unclear whether this system can be applied to obese older people to differ sarcopenic from non-sarcopenic people. According to the predetermined scheme, there were differences in baseline characteristics, such as SMI, hand-grip strength and gait speed, between the groups. This is a confirmation that the predetermined scheme is also applicable to older, obese individuals and can thus be used to measure group differences. However, it should be noted that 95% of the participants were not identified by using gait speed but by using the other categories, muscle mass and muscle strength. We decided to adopt the definition by Cruz-Jentoft et al. because of its addition of muscle function and because of the possibility to use the recommended tests and cut-off points in a clinical setting. The initial concepts of a simple decrease in muscle mass have been modified (30).

Detailed information about the design of resistance training with obese older men with or without sarcopenia is still lacking in the literature. This applies in particular with regard to the question to what extent strength training leads to different effects in the case of a different status in muscle mass and function. The intervention in the present study followed up on the recommendations for healthy older people as well as on the recommendations for diabetics. It was a big challenge to implement these guidelines for this particular target group. Given the very high participation rate and the good increases in maximum-strength values in both groups, the intervention should be seen as a success. With a loss of only three participants out of 36 (8%) the drop-out rate was very low. The high compliance is also reflected in the participation rate of the remaining 33 people. For the four-month strength training it was 86% on average, which is a good value and comparable to other studies (10-11).

All maximal strength measurements could not be conducted with the entire group. As far as the measurement of dynamic maximum strength is concerned, the final values are missing for some subjects. Due to health problems, it was not possible to perform the entire protocol with all subjects. Before the fifth attempt the measurement had to be terminated, for example because of shoulder or knee pain, so that only a submaximal value was available. As in the other subjects, too, this value was used as a clue to the initial weight during resistance training. These submaximal values were not considered for the evaluation of maximum strength because here the limitation is not due to maximum muscle performance.

Muscle mass

No statistically significant changes could be noticed after training. This result applies to both groups. This suggests that there was no hypertrophy of the muscles. In other studies (9-13), an increase in muscle mass was observed. However, these studies had a longer intervention period (6 or 12 months). Hence, the question arises to what extent an increase in muscle mass in overweight, partially sarcopenic older people can be expected in such a short intervention period. Perhaps the changes in muscle strength take place to a greater extent on the neurophysiological level, e.g. in the form of an improved inter- and intramuscular coordination. But this can only be speculated about since no explicit measurements were conducted.

It should be pointed out that previous studies which reported intervention effects on SMI used dual energy X-ray absorptiometry (DXA) or Magnetic Resonance Imaging (MRI) for the measurements of body composition, so that the results cannot really be compared to our results based on BIA. The BIA has established itself as an inexpensive and mobile alternative to DXA or MRI. However, Janssen et al. (16) and Boneva-Asiova et al. (21) among others found in their studies that for measurements with the BIA, fat mass is slightly overestimated or the muscle mass is underestimated and therefore the reliability of measurements decreases if there is a BMI of > 35 kg/m2 (21). However, this was found especially in obese women. It is important to consider whether another BIA device can be used alternatively or other complementary measurements such as girth measurements can be added as additional parameters for hypertrophy. 

Muscle strength

The relatively large increase in the 1 RM in our subjects can be observed in the older adults in other studies too (10, 12). One explanation could be the low base line strength in our subjects. It can be seen that the 1 RM on the leg-press machine increased to a higher degree in the sarcopenic group. This could be explained with the lower starting level of this group at the beginning and a very good compliance. In the post-test, this group achieved the level of NSAR. This result suggests that particularly the leg muscles benefited from this training. In this test, no statistically significant group differences could be seen, which could be due to the large standard deviation in each group.

In contrast, the rates of increase of maximum strength on the chest-press machine after training turned out to be considerably lower. This trend was also observed with this exercise during training. However, it should be noted that after the training SAR could increase to the initial level of NSAR. This result differs from the other studies (22-23). The causes of this difference are still unclear, which might possibly be due to the different machine producers (measurement vs. training). Thus, the participants had to complete two different paths of movement at measurement and training.  

Physical performance

The domain of the functional skills is an important field concerning the transfer to everyday-life activities. There was a significant increase in gait speed in both groups. This improvement could be seen over both walking distances (4m and 6m). This could be related to the remarkable increase in leg strength or to a presumably better neuromuscular control. Generally, however, it should be noted that both groups (at least on average) exhibited a relatively high gait speed, even before the start of training. They were significantly faster than the gait speed of the “Grim Reaper” which is defined in the literature at 0.82 m/s (24).

The total SPPB score changed only slightly when the values before and after training are compared. This applies to both groups to an equal extent. This could be due to the fact that even in the pre-test there were ceiling effects concerning the balance test and the gait speed over four meters. For the next studies with this target group, it may be possible to use the modified SPPB, which was designed for older people with a better performance. However, this would have the disadvantage that it would no longer be possible to use the cut-off points of Cruz-Jentoft and colleagues.

One interesting feature is the isolated consideration of the repeated chair stands. Here, no group differences before and after training could be shown. However, both groups showed statistically significant improvements in this test after training. These results suggest that this phenomenon is also related to the improvement in maximum strength of the lower extremities. This is a particularly important result since getting up from a chair is a fundamental factor for an independent life at older age (25).

The clinical relevance of our results is supported by Perera and colleagues (26) who analyzed data in common physical performance measures in older adults. Based on their results they estimate that substantial changes are near 0.1 m/s for gait speed, and 1.0 point for SPPB. In both of our groups (SAR and NSAR) the pre-post changes are similar or larger to these values.


The ACSM recommends a progressive and gradual increase of the weights (27-28). This applies first to healthy older people. It is the extent to which these recommendations can be adapted to this particular target group due to the initial physical situation and the health problems that in the target group can occur more often than in healthy older people. However, current guidelines do not specifically address obesity (7). A main message of Hills et al. is to adapt the method of increasing the weights to this target group, so that the compliance can be improved. Westcott and Baechle (29) recommended for healthy older people to increase the weights by 5% or less (depending on the muscle group), if they were able to lift the current weight with the highest number of repetitions in two consecutive sessions. This was a little too progressive for this sample. Instead, it proved to be worthwhile to increase the weight after approximately every fourth unit. However, individual limitations had to be taken into account, such as joint pain which led to a slower increase. In addition, the slow introduction of strength training is important. The first three weeks at the beginning of strength training were crucial not only for getting accustomed to the exercises and resistance training machines, but also for the participants’ body awareness. Thus, it was also more likely that the partici-pants were able to give an adequate assessment of their effort level and current performance limit.

The representativeness of the sample for larger populations is difficult to determine. Hence, more studies in similar target populations are needed.

In conclusion, implementing intervention guidelines was possible, adherence of the participants was high and positive effects after training were achieved. The results lead to the conclusion that – regardless of the proportion of muscle or fat mass and regardless of muscle function – older obese men benefit from intensive resistance training very much. This is particularly true of the maximum strength, the gait speed and getting up from a chair. These are important aspects with regard to the longest possible independent lifestyle and successful fall prevention.

Moreover, the additional administration of nutrition supplements, such as protein, could lead to further insights to what extent this could support an increase in muscle mass. In future studies, women should also be examined to verify a transfer of the results to the other sex.

The group of obese, sarcopenic older people will increase dramatically in the next years. Therefore, informed and evaluated preventive and therapeutic interventions are needed to prevent or at least slow down the serious consequences of a possible metabolic syndrome.

Funding: This study was funded by the German Sport University Cologne.  The sponsors had no role in the design and conduct of the study, in the collection, analysis, interpretation of data, and in the preparation of the manuscript.

Acknowledgement: We thank all men who agreed to participate in the study and all assistants who supported the measurements and the intervention.

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

Ethical standards: This study was approved by the Ethics Committee of the German Sports University Cologne. 


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1. Gérontopôle, Centre Hospitalier Universitaire de Toulouse, Toulouse, France; 2. INSERM UMR1027, Université de Toulouse III Paul Sabatier, Toulouse, France; 3. Jean Mayer USDA, Human Nutrition Research Center, Boston, MA, USA; 4. Musculoskeletal Area, Eli Lilly & Co, Paris, France; 5. Department of Geriatrics, Neurosciences, and Orthopedics, Catholic University of the Sacred Heart, Roma, Italy; 6. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 7. Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA; 8. Health and Disability Research Institute, Boston University School of Public Health, Boston, MA, USA; 9. Department of Aging and Geriatric Research, University of Florida-Institute on Aging, Gainesville, FL, USA; 10. Servicio de Geriatria, Hospital Universitario de Getafe, Getafe, Spain; 11. Global Translational Medicine, Novartis Institutes for Biomedical Research, Basel, Switzerland; 12. Foundation for Diabetes Research in Older People, Luton, United Kingdom; 13. National Institute on Aging, Baltimore, MD, USA; 14. Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA

Corresponding author: Matteo Cesari, MD, PhD. Gérontopôle, Centre Hospitalier Universitaire de Toulouse; 37 Allées Jules Guesde, 31000 Toulouse, France. Phone: +33 (0)5 61145628; Fax: +33 (0)5 61145640; email: macesari@gmail.com

J Frailty Aging 2015;4(3):114-120
Published online June 25, 2015, http://dx.doi.org/10.14283/jfa.2015.64

Task Force members: Christian Asbrand (Frankfurt am Main, Germany), Olivier Benichou (Paris, France), Cynthia A Bens (Washington, DC, USA), Roberto Bernabei (Roma, Italy), Shalender Bhasin (Boston, MA, USA), Denis Breuillé (Lausanne, Switzerland), Andrea Buschiazzo (Martinez, Provincia de Buenos Aires, Argentina), Francesca Cerreta (London, United Kingdom), Matteo Cesari (Toulouse, France), Alfonso Cruz-Jentoft (Madrid, Spain), Susanna Del Signore (Paris, France), Stephen Donahue (Tarrytown, NY, USA), Roger Fielding (Boston, MA, USA), Francesco Landi (Roma, Italy), Juerg Gasser (Basel, Switzerland), Joerg Goldhahn (Basel, Switzerland), Jack M Guralnik (Baltimore, MD, USA), Michaela Hoehne (Lutry, Switzerland), Lori Janesko (Uniontown, PA, USA), Alan Jette (Boston, MA, USA), Makoto Kashiwa (Tokyo, Japan), Francesco Landi (Rome, Italy), Valerie Legrand (Nanterre, France), Fady Malik (San Francisco, CA, USA), Cecilia Marta (Bridgewater, NJ, USA), John Morley (St. Louis, MO, USA), Vikkie Mustad (Columbus, OH, USA), Marco Pahor (Gainesville, FL, USA), Suzette Pereira (Columbus, OH, USA), Leocadio Rodriguez-Manas (Getafe, Spain), Yves Rolland (Toulouse, France), Ronenn Roubenoff (Basel, Switzerland), Fariba Roughead (Minnetonka, MN, USA), Jonathan Sadeh (Bridgewater, NJ, USA), Antoni Salva (Barcelona, Spain), Brad Shumel (Tarrytown, NY, USA), Alan J Sinclair (Luton, United Kingdom), Stephanie Studenski (Baltimore, MD, USA), Richard Swanson (Tarrytown, NY), Min Tian (Columbus OH, USA), Thomas Travison, Boston, MA, USA), Estelle Trifilieff (Basel, Switzerland),  Bruno Vellas (Toulouse, France), Sjors Verlaan (Utrecht, The Netherlands), Sander Wijers (Utrecht, The Netherlands).


Sarcopenia and frailty often co-exist and both have physical function impairment as a core component. Yet despite the urgency of the problem, the development of pharmaceutical therapies for sarcopenia and frailty has lagged, in part because of the lack of consensus definitions for the two conditions. A task force of clinical and basic researchers, leaders from the pharmaceutical and nutritional industries, and representatives from non-profit organizations was established in 2012 with the aim of addressing specific issues affecting research and clinical activities on frailty and sarcopenia. The task force came together on April 22, 2015 in Boston, Massachusetts, prior to the International Conference on Frailty and Sarcopenia Research (ICFSR). The theme of this meeting was to discuss challenges related to drugs designed to target the biology of frailty and sarcopenia as well as more general questions about designing efficient drug trials for these conditions. The present article reports the results of the task force’s deliberations based on available evidence and preliminary results of ongoing activities. Overall, the lack of a consensus definition for sarcopenia and frailty was felt as still present and severely limiting advancements in the field. However, agreement appears to be emerging that low mass alone provides insufficient clinical relevance if not combined with muscle weakness and/or functional impairment. In the next future, it will be important to build consensus on clinically meaningful functional outcomes and test/validate them in long-term observational studies. 

Key words: Clinical trial, methodology, prevention, disability, physical performance, skeletal muscle.


Sarcopenia, the age-related loss of muscle mass and strength, represents an increasing public health risk as the world’s population ages at a rapid pace (1). Between 2000, and 2050, the United Nations predicts a doubling of the number of people over age 60 (2), and a recent analysis of prevalence studies concluded that sarcopenia prevalence ranges from 1-29% in community dwelling populations and 14-33% in long-term care populations (3).  Muscle weakness and impaired function that result from sarcopenia are also major components of the geriatric syndrome of frailty (4), thus sarcopenia and frailty are frequently studied in parallel, and indeed they both have physical function impairment as a core condition (5). Yet despite the urgency of the problem, developing treatments for sarcopenia and frailty has lagged, in part because of the lack of consensus definitions for the two conditions.

A task force of clinical and basic researchers, leaders from the pharmaceutical and nutritional industries, and representatives from non-profit organizations came together on April 22, 2015 in Boston, Massachusetts, prior to the International Conference on Frailty and Sarcopenia Research (ICSFR) to address issues that have slowed the development of new treatments. The increase in size of the Task Force since it was established in 2012 reflects the increasing recognition of the need for more effective ways of treating sarcopenia and frailty, as well as increased attention on the part of industry. This, the third meeting of the Task Force, discussed specific challenges related to drugs designed to target the underlying biology of sarcopenia as well as more general questions about designing efficient drug trials for these conditions.

Targeting the underlying biology of sarcopenia 

Research over the past fifteen years has revealed multiple complex and intersecting pathways involved in the regulation of muscle protein balance as well as many possible approaches to reverse muscle loss. Potential regulators include androgens, which act through androgen receptor/Wnt/beta-catenin signaling pathway; insulin and insulin growth factor 1 (IGF-1), which regulate protein synthesis and degradation through the PI3K/AKT pathway; myostatin, a powerful inhibitot of muscle growth, as well as other members of the transforming growth factor-β (TGF- β) superfamily, which act through SMAD signaling; and inflammatory modulators  including pro-inflammatory cytokines such as tumor necrosis factor-α and interleukin-1 (6). Compounds currently in development for the treatment of muscle wasting and sarcopenia are providing further insight into underlying mechanisms. Examples of two classes of these compounds follow.

Myostatin antagonists 

Bimagrumab is a monoclonal antibody that binds to type II activin receptors (ActRII), blocking the binding of myostatin, GDF11, and activin A. Binding of these ligands normally initiates a signaling cascade that results in decreased muscle growth 7. A single dose of bimagrumab increases muscle mass in healthy young men similar to that achieved with 12 week of high-intensity resistance training (8, 9), and in sedentary middle-aged adults, equivalent to that achieved with 9 months of jogging 12-20 miles per week (10).  It has also been shown to work in elderly people; and, in a single leg casted model in healthy young men, bimagrumab improved recovery from atrophy.

Novartis received breakthrough therapy approval for bimagrumab for the treatment of sporadic inclusion body myositis (sIBM) in 2013. In people with this rare muscle disease, a single dose of the drug resulted in an increase in 6-minute walking distance (6MWD) of 52 meters over placebo (11), providing the first evidence of a possible clinical benefit. Now the drug is being tested in people in their 70s with low lean muscle mass. Initial results suggest that a single dose of the drug is well tolerated and associated with an increase in appendicular lean mass (aLM) and handgrip strength (12). Interestingly, gait speed was improved only in those with poor results at the baseline 6MWD test, suggesting that frail people may respond best.

Selective Androgen Receptor Modulators (SARMs) 

Selective androgen receptor modulators (SARMs), a class of androgen receptor ligands that display tissue-selective activation of androgenic signaling, may have potential as function promoting therapies for a variety of conditions, including functional limitations associated with ageing and chronic diseases, osteoporosis, anemia, and hypogonadism, and for male contraception. Osteoporosis represents another component of the frailty syndrome and evidence suggests links between osteoporosis and sarcopenia (13).  Indeed, SARMs also have been shown to have favorable effects on bone mass and quality.

A number of steroidal and non-steroidal SARMs have undergone phase I, II and III trials. For instance, the non-steroidal SARM LGD-4033 has demonstrated preferential tissue selectivity for muscle compared to prostate. Moreover, a 21-day ascending dose study of LGD-4033 in healthy young men showed that the drug was well tolerated, had a favorable pharmacokinetic profile, and increased lean body mass and leg press strength (14).

Another SARM, MK-773, has undergone phase II studies in both men and women with sarcopenia. In one study of women age 65 or older with sarcopenia and frailty, treatment with MK-0773 produced statistically significant increases in LBM compared to placebo, but no significant improvement in strength or function. Both the treatment group and placebo group also received Vitamin D and protein supplementation (15). Other SARMs have also shown benefits in the treatment of muscle wasting associated with cancer, a condition known as cachexia (16, 17).

While SARMS appear to be safe and efficacious in increasing LBM and possibly strength and function, their effects on muscle mass and function at the doses that have been studied have been modest in comparison to the effects from treatment with supraphysiologic doses of testosterone (18).  It is possible that longer studies are needed to demonstrate functional improvements, but it is also likely that more potent and selective SARMs are needed, particularly compounds that are agonists on muscle and antagonists on prostate. The effects of androgens are augmented by functional exercise training (19), and it is possible that translation of muscle mass and strength gains induced by androgens into functional improvements may require functional exercise training. SARMs have generally well tolerated in short term trials. Larger trials of longer duration are needed to demonstrate the long-term safety and efficacy of SARMs in improving physical function and health outcomes.

Designing efficient drug trials for sarcopenia and frailty 

Despite the fact that both sarcopenia and frailty are highly prevalent in older populations, a high degree of heterogeneity and the absence of consensus diagnostic criteria makes the design and implementation of treatment trials extremely challenging. The Task Force addressed many of these issues at its first meeting in 2012 (20). Since then, there has been some progress to define what does and does not represent sarcopenia. In 2014, Anker et al proposed “muscle wasting disease” as a new disease classification which brings together the concepts of sarcopenia, frailty, muscle wasting, and cachexia (21). This framework distinguishes acute from chronic conditions; classifies according to etiology (e.g., due to aging or an underlying medical condition), and then classifies by disease severity and progression.

Defining the target population

Patients with frailty and sarcopenia usually present with multiple chronic diseases that contribute to physical, cognitive, and functional disability. In clinical trials, this large variability increases the uncertainty in possible drug effects by inflating the confidence intervals. However, attempting to control variability by using homogeneous but less representative study populations reduces the generalizability of the results of a trial.

Recent trials have taken two general approaches to targeting patients with sarcopenia: either assessing the degree of sarcopenia and selecting those who are more severely affected or those in the middle of the spectrum; or selecting patients with specific conditions that predispose them to sarcopenia, for example, hip fracture (22). In the Lifestyle Interventions and Independence for Elders (LIFE) study, sedentary older adults were stratified according to their score on the Short Physical Performance Battery (SPPB), excluding those with scores over 9, and oversampling those with scores of 7 or below (23). There was an overall benefit of the exercise intervention. Subgroup analysis showed that those with scores of 8 or 9 had little effect from the physical activity intervention, while a strong beneficial effect was seen in those with poorer function at baseline (scores of 7 or below). At the same time, those with lower SPPB scores are also in poorer health, resulting also in a higher rate of hospitalizations.

In terms of targeting specific conditions, the Aging in Motion (AIM) coalition (aginginmotion.org), which was established by the Alliance for Aging Research in 2011, has been working to obtain regulatory qualification for functional outcomes for clinical trials in specific conditions, including hip fracture, elective total hip arthroplasty, and hospital immobilization (Intensive Care Unit-Acquired Weakness, or ICUAW). The Task Force heard a report on one such study, a trial of LY2495655, a monoclonal antibody that targets myostatin in older individuals who were frequent fallers.  Other studies have targeted patients based on age, inactivity, or presence of frailty; and patients with COPD, diabetes, heart failure, and stroke. A goal of the AIM effort is to puch for regulatory recognition of functional outcomes by including those who are functionally limited.

Whether there is a generalizable way to target sarcopenia across these conditions remains a question. The International Working Group on Sarcopenia proposed targeting patients based on an assessment of physical functioning or weakness; considering patients who are non-ambulatory or who cannot rise from a chair unassisted; or an assessment of habitual gait speed, possibly in combination with a quantitative measurement of body composition of DXA (24). The Foundation for the National Institutes of Health (FNIH) Sarcopenia Project proposed a clinical strategy to identify subjects with muscle weakness and low muscle mass (Figure 1) (25). They went on to amass clinical data from over 26,000 individuals in nine studies to conduct a cross-sectional analysis and define normal/abnormal cut-points based on a logic that a clinician would use for differential diagnosis. For example, if a subject complains he or she has functional problems in getting out of a chair, the clinician might first test to see if muscle weakness is present and, if so, whether reduced lean body mass coexists.

Figure 1 Clinical paradigm for targeting subjects with sarcopenia (reproduced after authorization) (25)

Interestingly, gender seems to be extremely relevant at modifying the relationship between different body composition parameters and physical function (26). Muscle quality, i.e., the capacity of muscle to generate force, also appears to be important, pointing to the possible need to develop criteria for muscle quality as force per unit of mass. Further studies are also needed to clarify the relationship between mass, strength, and function in diverse populations.  One problem with existing data is that most of it has been obtained in high-income countries. Different screening criteria and measures may be needed in developing countries that may have limited access to imaging and other technologies.   

Outcome measures

Physical function as a primary outcome 

Measures of physical function, particularly walking measures, have typically been used in clinical trials of sarcopenia, since walking appears to be the best predictor of disability, hospitalization, mortality, and health care expenditure (27). Several measures of walking ability have been used, including the 400 meter walk (400-MWT), 6MWD, usual gait speed test, and the SPPB which also includes a gait speed subtest. The difficulty or incapability to walk a quarter of a mile or 400 meters is also the standard measure used by the U.S. Census Bureau to assess disability. For the 400-MWT, subjects are permitted to stop but not sit or receive assistance during the walk, although a cane is allowed; and must complete the course within 15 minutes. Ability to complete the test and the time required to do so have been shown to discriminate the risk for mortality, cardiovascular disease, mobility limitation, and disability in community dwelling older adults (28).

An advantage of the 400-MWT is that there are no safety exclusions; an individual may “fail” if he or she is unable to complete the test, but failure is the outcome, rather than missing data. It also has high test-retest reliability (29). This test can therefore be used as a primary outcome measure in an intervention trial, as it was in the LIFE study. The LIFE study randomized over 1,600 sedentary individuals between the ages of 70 and 89 years to a structured moderate intensity physical activity program or a health education program for an average period of 2.6 years (23). Importantly, the use of the 400-MWT as the primary outcome enabled adjudication of the outcome even if participants were unable to come to the lab for assessment. For the LIFE study, adjudication was based on the following outcomes:

– unable to complete 400 meter walk

– unable to walk 4 m or unable to complete 4 m walk test in 10 seconds or less, i.e., gait speed less than 0.4 m/sec

– self reported inability to walk across a room without assistance

– proxy report of inability to walk across a room without assistance

– medical record documentation of inability to walk across a room (bedbound, wheelchair bound, etc.)

Using this adjudication framework, the LIFE study was able to substantially increase the amount of available information for determining the absence/presence of the studied outcomes, even among individuals who were too sick or frail to come to the clinic. In a trial without such methods, such patients would be lost to follow up, resulting in a loss of power and potentially introducing bias into the interpretation of results.

The 6MWT has been used as an outcome measures in a number of other trials, for example the Testosterone Trials (T-TRIAL) (30, 31). Like the 400-MWT, the 6MWT is strongly predictive of mortality (32). Other performance measures, such as the SPPB also have high prognostic value. In the LIFE pilot study, the SPPB was shown to be not only a risk factor for future health outcomes, but modifiable as well (33). Each of the different assessment tools measures different characteristics, for example performance vs. endurance; and each defines a meaningful change differently (34).

The key questions addressed at the Task Force meeting were, which measure is more efficient and what sample size is needed to demonstrate efficacy using different measures. As discussed by Espeland et al. (35), the categorization of continuous outcomes usually reduces statistical power. However, a categorical variable may still more efficiently represent an underlying continuous commonality than continuous parameters that are less directly related. Thus, for example, in the LIFE-P, analyses showed that using the 400-MWT with a 20% effect size requires about 1,669 people for 80% power, compared to 5,178 subjects using the 4-meter walk test and 4,673 using the SPPB.

Physical measures such as DXA measures of aLM and leg extension strength may also be used as either primary or secondary outcome measures, but it is unlikely that a drug indication would ever be approved for these endpoints alone since they are not directly linked to how the subject feels of functions.

Other outcome measures

Multiple secondary outcomes are also typically used in clinical trials, including additional physical performance and functional measures; changes in body composition or size; changes in nutritional status; functional changes such as a reduction in the incidence of falls, fractures, or disability; cognitive and mood changes; quality of life and health care utilization measures; and mortality. Patient-reported outcomes (PROs) have also increasingly been used as secondary outcomes to provide clinically meaningful data.

Secondary outcomes may be selected to study events that may not be widely recognized as relevant or essential to the condition. They also include those leading to a better understanding of the clinical and functional reaction of the organism to the pharmacological/non-pharmacological intervention, including adverse events. Availability of resources and time, and burden to the subject also play roles in the selection of secondary outcomes. Events/conditions that are interesting to be evaluated but may lack sufficient power to be used as primary or secondary may be referred as tertiary or exploratory outcomes.

The biological background of the disease and the phase of the study, also drive the selection of secondary outcomes.  For example, secondary outcomes in early phase studies may be more focused on the kinetic and dynamic characteristics of the tested pharmacologic intervention, while in later phase studies, secondary outcomes may rather elucidate the interaction between the drug and organism as well as the clinical relevance.

Secondary outcomes may also be found embedded in the primary outcome. For example, a single measure of mobility such as the 400-MWT may provide information relevant to functional ability, such as the speed of completing the test, the number of stops during the conduct of the test, the speed variability, the average gait speed, etc. (36). As well, the SPPB can be deconstructed into its specific subtasks in order to obtain additional information beyond that provided by the overall score.

PROs for sarcopenia have started being incorporated into many studies of treatments for both sarcopenia and frailty. For example, in the bimagrumab studies described earlier, PROs have been included although the data have not yet been analyzed. Also in the FNIH Sarcopenia Project, a set of outcome measures have been proposed, including performance measures, PROs, health care utilization, serious injuries (e.g. fractures), and mortality. The investigators will then evaluate correlations of these measures with measures of lean mass, strength, and muscle quality. The exploration of a wide variety of secondary outcomes in this data set may provide important data regard the optimal design of future trials.

Many PROs have been proposed for measuring the frailty phenotype, yet the multidimensionality of the syndrome, as well as a serious and disturbing lack of consensus on the definition of frailty creates obvious challenges for scientific measurement. For example, Fried and colleagues have centered the definition of frailty around the cumulative effect of five criteria (largely focused on the physical aspect), while others adopted a more comprehensive approach including loss in psychological or social domains (37).

Even the five components of the frailty phenotype as defined by Fried and colleagues (38) are not included in many of the PRO measures that have been developed. Most of the measures discussed assess various physical components, while others address the psychological, cognitive, social, demographic, and health care utilization dimensions of frailty. The Irish Longitudinal Study on Aging, for example, included two multidimensional measures — the Self-Reported Frailty Index (SRFI) and the Test-Based Frailty Index (TBFI). The investigators found that prevalence estimates varied from 11% with the SRFI to 17% with the TBFI. Interestingly, women had a higher prevalence of frailty using the SRFI compared to a lower prevalence with the TBFI, suggesting that frailty PROs may mis-estimate the prevalence of frailty in community dwelling elders and obscure gender differences.

For trials of conditions such as sarcopenia and frailty in which definitions remain unclear, flexibility is essential in selecting secondary outcomes that will enable applying results across different settings and cross-checking them according to the current different definitions and possible developments in such a dynamic field of research. Outcome measures must be selected not only based on the type of drug being tested, but also based on a clear definition of the main outcome, baseline differences in the target population, the number of sites, and the experience and training of personnel at the sites. Moreover, trialists must take into account the impact of the intervention on the studied outcome measure, which might significantly affect the duration of the trial.


There is a need for biomarkers of frailty and sarcopenia aimed at improving diagnostic performance, monitor the progression of the condition(s), predict outcomes, assess treatment response, and optimize the clinical decision making process. Biomarkers should also help us better understand the relationships between aging, frailty, sarcopenia, and disability.

A complex network of biological processes influence the development of sarcopenia and frailty, including physiologic changes in metabolism, muscle strength and power, hormones, inflammatory process, and insulin resistance, among others. For example, results from the Cardiovascular Healthy Study identified increases in components of the inflammation and coagulation systems in frail compared to non-frail community-dwelling adults (39).

FRAILOMIC (www.frailomic.org) is an initiative undertaken by a consortium of university and hospital-based research centers and the World Health Organization (WHO) to analyze multiple classical and non-classical blood-and urine-based laboratory biomarkers on samples collected from approximately 75,000 older individuals. In combination with clinical biomarkers collected from the same cohort, FRAILOMIC will identify through data mining combinations of no more than 5 biomarkers that can be used clinically for diagnosis and prognosis of disability as well as predicting the risk of frailty. Cohorts will be followed prospectively for at least 2.5 years in order to assess progression of frailty, and gender will be included in the analysis. Secondary objectives of the project include 1) assessing interactions between –omic based biomarkers and nutrition and physical exercise on the natural history of frailty, 2) testing whether the identified assessment are useful in special populations, such as people with diabetes, obesity, and cardiovascular disease; and 3) test the validity of existing frailty criteria.           

Novel designs

From a statistical and study design perspective, heterogeneity in the population of individuals with sarcopenia and frailty, as well as in possible interventions, treatment effectiveness and efficacy, and clinical meaningfulness across subpopulations, combine to make clinical trial design particularly challenging. Adaptive trials have been used in other disease areas such as oncology to deal with heterogeneity, since they enable modification of multiple design elements to increase the efficiency of a trial. For sarcopenia studies, adaptive approaches may use machine learning and simulation to tailor trials for individuals with specific risk profiles at the time of randomization, such as weakness or slow gait speed, particularly when there is a specific threshold that is predictive of a downstream outcome such as disability. Eligible participants can be stratified according to their risk profiles to various interventions.

Given the complexity of such designs, Berry et al have proposed the use of platform trial designs, an extension of adaptive trial design that enables the evaluation of multiple treatments in multiple subpopulations simultaneously (40).  The innovation comes from the ability to think of multiple types of trials within a single platform that handles multicomponent interventions and directly targets the effects of specific combination, enabling rapid identification of winning combinations and culling of non-efficacious arms. However, while tailoring and adapting may offer some benefits for trials of sarcopenia and frailty, unclear definitions of the conditions and at-risk populations remain obstacles to carefully consider.

Overcoming barriers to clinical trial participation

Despite the growing recognition among clinicians and scientists about the importance of frailty and sarcopenia, a lack of operational definitions for these conditions has limited their inclusion on national health policy agendas. Indeed, there have been no major studies examining the health economics of interventions for these conditions in well-defined populations. Added to this, although the vast majority of health care is delivered through primary care settings, most clinical research is carried out in specialist-oriented and hospital-based rather than primary care networks, and such research may have limited relevance to primary care processes and pathways.

A non-for-profit institute called the Foundation for Diabetes Research in Older People at Diabetes Frail (www.diabetesfrail.org) was established to address this discrepancy by focusing on primary care, where a wider number and more representative group of patients should be available for studies. The institute aims to convince primary care teams to collaborate in research studies and facilitate their involvement by creating infrastructure within primary care settings and care homes, and building the costs of primary care research into grant applications.


The lack of consensus definitions again arose during discussions at this Task Force meeting, as well as at previous meetings. At this point, while consensus on a definition of sarcopenia has still not been reached, agreement appears to be emerging that low mass alone provides insufficient clinical relevance if not combined with muscle weakness and/or functional impairment.

Pharmacologic and non-pharmacologic interventions for frailty and sarcopenia are nevertheless in development. The Task Force agreed on the need to build consensus on clinically meaningful functional outcomes in a more systematic manner, as well as on the need for long-term observational studies to test and validate these outcomes. In order to accomplish this, participants called for stakeholders to come together in a collaborative framework.

Conflicts of interest: Olivier Benichou is employee at Ely Lilli & Co. Roberto Bernabei is principal investigator of an Innovative Medicines Initiative (IMI)-funded project (including partners from the European Federation of Pharmaceutical Industries and Aassociations [EFPIA]). Shalender Bhasin has received research grants from NIA, NINR, Regeneron, Lilly, and Abbvie, which are administered by the Brigham and Women’s Hospital; he has served as a consultant for Abbvie, Regeneron, Sanofi, Lilly, and Viking. Matteo Cesari has received honoraria for presentations at scientific meetings and/or research fundings from Nestlé, Pfizer, Novartis and serves as workpackage leader in an IMI-funded project (including partners from EFPIA). Ronenn Roubenoff is employee at Novartis. The Gérontopôle (Chair Bruno Vellas) has received grant support from the PHRC, ANR, European Comission as well as: Abbvie, Affiris, Avid, BMS,  Eisai,  Elan, Envivo, Exhonit, Genentech, GSK, Ipsen, Lilly, Lundbeck, Médivation, MSD, Nutricia, Otsuka, Pharnext, Pfizer, Pierre-Fabre, Régénéron, Roche, Sanofi, Servier, TauRx Therapeutics, Wyeth. Bruno Vellas has served as consultant/advisor to Biogen, GSK,  Lilly, Lundbeck, Medivation, MSD, Nestlé, Nutricia,  Pfizer, Roche, Sanofi, Servier, TauRx Therapeutics, Novartis. The other authors have no conflict of interest to disclose.


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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


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.


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.


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).


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.


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


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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