Handgrip Strength Cannot Be Assumed a Proxy for Overall Muscle Strength

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY-NC-ND 4.0 https://creativecommons.org/licenses/by-nc-nd/4.0/ Handgrip Strength Cannot Be Assumed a Proxy for Overall Muscle Strength © Elsevier, 2018. Accepted version (Final draft) Yeung, Suey S.Y.; Reijnierse, Esmee M.; Trappenburg, Marijke C.; Hogrel, JeanYves; McPhee, Jamie S.; Piasecki, Mathew; Sipilä, Sarianna; Salpakoski, Anu; Butler-Browne, Gillian; Pääsuke, Mati; Gapeyeva, Helena; Narici, Marco V.; Meskers, Carel G.M.; Maier, Andrea B. Yeung, S. S., Reijnierse, E. M., Trappenburg, M. C., Hogrel, J.-Y., McPhee, J. S., Piasecki, M., Sipilä, S., Salpakoski, A., Butler-Browne, G., Pääsuke, M., Gapeyeva, H., Narici, M. V., Meskers, C. G., & Maier, A. B. (2018). Handgrip Strength Cannot Be Assumed a Proxy for Overall Muscle Strength. Journal of the American Medical Directors Association, 19(8), 703-709. https://doi.org/10.1016/j.jamda.2018.04.019 2018 Handgrip strength cannot be assumed a proxy for overall muscle strength Suey S.Y. Yeunga,b, MSc, Esmee M. Reijnierseb, PhD, Marijke C. Trappenburgc,d, M.D., PhD, Jean-Yves Hogrele, PhD, Jamie S. McPheef, PhD, Mathew Piaseckif, PhD, Sarianna Sipilag, PhD, Anu Salpakoskih, PhD, Gillian Butler-Brownee, PhD, Mati Pääsukei, PhD, Helena Gapeyevai, M.D., PhD, Marco V. Naricij, PhD, Carel G. M. Meskersa,k, M.D., PhD, Andrea B. Maiera,b, M.D., PhD aDepartment of Human Movement Sciences, MOVE Research Institute Amsterdam, Vrije Universiteit, Amsterdam, the Netherlands bDepartment of Medicine and Aged Care, Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia cDepartment of Internal Medicine, Section of Gerontology and Geriatrics, VU University Medical Center, Amsterdam, the Netherlands dDepartment of Internal Medicine, Amstelland Hospital, Amstelveen, the Netherlands eInstitute of Myology, Paris, France fSchool of Healthcare Science, John Dalton Building, Manchester Metropolitan University, Manchester, UK. gGerontology Research Centre, Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland. hDepartment of Development, The South Savo Social and Health Care Authority, Mikkeli, Finland. iInstitute of Sport Sciences and Physiotherapy, University of Tartu, Tartu, Estonia *Title Page (Containing author details) jDivision of Medical Sciences and Graduate Entry Medicine, MRC-ARUK Centre of Excellence for Musculoskeletal Ageing Research, University of Nottingham, Royal Derby Hospital Centre, Nottingham, United Kingdom. kDepartment of Rehabilitation Medicine, VU University Medical Center, Amsterdam, the Netherlands. *Correspondence to: Andrea B. Maier, Department of Medicine and Aged Care, University of Melbourne, Melbourne Health, The Royal Melbourne Hospital, City Campus, Level 6 North, 300 Grattan Street, Parkville, Victoria 3050 E: andrea.m[email protected] T: +61 3 8387 2137 F: +61 38387 222 Running title: Measurement of muscle strength Keywords: muscle strength; knee extension strength; aged; geriatric assessment Funding sources: This study was supported by the seventh framework program MYOAGE (HEALTH-2007-2.4.5-10), the UK Medical Research Council (MR/K025252/1) as part of the Lifelong Health and Wellbeing initiative, the Dutch Technology Foundation STW, and The Ministry of Education and Culture, Kela-The Social Insurance Institution of Finland, Juho Vainio Foundation. This study was also supported by the PANINI program (Horizon 2020, Marie Curie, Sklodowska, Innovative Training Network, No. 675003) 1 Abstract 1 Objectives: Dynapenia, low muscle strength, is predictive for negative health outcomes and 2 is usually expressed as handgrip strength (HGS). Whether HGS can be a proxy for overall 3 muscle strength and whether this depends on age and health status is controversial. This study 4 assessed the agreement between HGS and knee extension strength (KES) in populations 5 differing in age and health status. 6 Design: Data were retrieved from five cohorts. 7 Setting and participants: Community, geriatric outpatient clinics and a hospital. Five 8 cohorts (960 individuals, 49.8% males) encompassing healthy young and old individuals, 9 geriatric outpatients and older individuals post hip fracture were included. 10 Measures: HGS and KES were measured according to the protocol of each cohort. Pearson 11 correlation was performed to analyse the association between HGS and KES, stratified by 12 sex. HGS and KES were standardized into sex-specific z-scores. The agreement between 13 standardized HGS and standardized KES at population level and individual level were 14 assessed by Intraclass Correlation Coefficients (ICC) and Bland-Altman analysis. 15 Results: Pearson correlation coefficients were low in healthy young (males: 0.36 to 0.45, 16 females: 0.45) and healthy old individuals (males: 0.35 to 0.37, females: 0.44), and moderate 17 in geriatric outpatients (males and females: 0.54) and older individuals post hip fracture 18 (males: 0.44, females: 0.57) (p<0.05, except for male older individuals post hip fracture 19 (p=0.07)). ICC values were poor to moderate in all populations: i.e. healthy young 20 individuals (0.41, 0.45), healthy old individuals (0.37, 0.41, 0.44), geriatric outpatients (0.54) 21 and older individuals post hip fracture (0.54). Bland-Altman analysis showed that within the 22 same population of age and health status, agreement between HGS and KES varied on 23 individual level. 24 *Manuscript Click here to view linked References 2 Conclusion: At both population and individual level, HGS and KES showed a low to 25 moderate agreement independently of age and health status. HGS alone should not be 26 assumed a proxy for overall muscle strength. 27 3 Introduction 28 Measurement of muscle strength is an important part of the comprehensive geriatric 29 assessment (CGA)1 due to its predictive validity for decline in cognition, mobility and 30 functional status in community-dwelling older individuals.2-4 Low muscle strength, known as 31 dynapenia, was also associated with an increased risk of postoperative complications, 32 prolonged length of stay and mortality in hospitalized or postsurgical patients.5, 6 Muscle 33 strength is part of the diagnostic criteria for sarcopenia, which is defined as low muscle mass 34 and low muscle function (muscle strength and/or physical performance), depending on the 35 applied definition.7 36 In clinical practice, quantification of muscle strength in older individuals is 37 predominantly assessed by measuring handgrip strength (HGS) as the measurement is simple 38 and the device is portable and inexpensive.7 In addition to HGS, muscle strength can be 39 assessed by measurement of knee extension strength (KES) and this method is, however, 40 more technically challenging and not widely accessible.8 It has been shown that the decline of 41 muscle strength with chronological age is greater for the lower limb muscles than that of the 42 upper limb.9-11 Previous studies showed a high association between HGS and KES among 43 healthy individuals aged 18-90 years12-14 and a low association among geriatric outpatients.15 44 Furthermore, previous studies used correlation coefficients quantifying the degree to which 45 two variables are related on a population level, but not at individual level. 46 The aim of this study was to assess the agreement between HGS and KES in various 47 populations of individuals differing in age and health status at population and individual 48 level. 49 4 Methods 50 Study design 51 Data were derived from five cohorts including 960 individuals encompassing healthy young 52 and old individuals, geriatric outpatients and older individuals post hip fracture. 53 54 MyoAge cohort 55 Healthy young and old individuals were derived from the European MyoAge cohort. The 56 study rationale and design is reported in detail elsewhere.16 The MyoAge cohort included 57 healthy young (aged 18 to 30 years) and old individuals (aged 69 to 81 years) recruited from 58 five centres located in the United Kingdom (Manchester), France (Paris), The Netherlands 59 (Leiden), Estonia (Tartu) and Finland (Jyväskylä). Exclusion criteria included: inability to 60 walk for a distance of 250 meter, being institutionalised, morbidities (neurological disorders, 61 metabolic diseases, rheumatoid arthritis, recent malignancy, heart failure, coagulation 62 diseases, chronic obstructive pulmonary disease), using immunosuppressive drugs, insulin 63 and anticoagulants, fracture over the previous year, immobilisation for one week over the 64 previous three months, orthopaedic surgery during the past two years or still causing pain or 65 physical limitation. All study centres adopted the same standardized operation procedure to 66 perform the measurements of muscle strength. In the present analysis, data on HGS and KES 67 were available in 181 healthy young individuals and 320 healthy old individuals. 68 69 Manchester Metropolitan University (MMU) cohort 70 This cohort encompasses healthy young and old males aged 18 to 40 years or 60 to 90 years 71 and were recruited as part of a study investigating the nature and extent of motor unit changes 72 in the vastus lateralis of individuals.17 Young individuals were recruited from the university 73 and local communities around Manchester, United Kingdom (UK). Older individuals were 74 5 recruited from the local community. Exclusion criteria were: recent history of leg bone 75 fracture, diagnosis with any form of cancer or a stroke within the past two years, 76 immobilization for more than five days within the past four weeks, diagnosis of any 77 neuromuscular disease or dementia at any time, not living independently, body mass index 78 (BMI) <18 or >35 kg/m2. In the present analysis, data on HGS and KES were available in 42 79 young and 108 old individuals. 80 81 DHEAge cohort 82 This cohort examining oral Dehydroepiandrosterone in older individuals (DHEAge) included 83 healthy females and males aged 60 to 80 years.18 Individuals attended geriatric consultations 84 in a geriatric outpatient clinic for various symptoms related to aging such as fatigue, memory 85 complaints, pain and anxiety. Data was collected before the administration of DHEA. 86 Exclusion criteria included diseases such as dementia, major depressive state, cardiovascular 87 disorder, respiratory deficiency, Parkinson disease, and endocrine disorder, and antecedent of 88 hormone-dependent cancer. In the present analysis, data on HGS and KES were available in 89 68 females. 90 91 Geriatric outpatients 92 This inception cohort included community-dwelling older individuals referred due to 93 mobility problems to a geriatric outpatient clinic in a middle-sized teaching hospital 94 (Bronovo Hospital, The Hague, The Netherlands).19 The CGA included questionnaires and 95 measurements of physical and cognitive function was performed by trained nurses or medical 96 staff. In the present analysis, data on HGS and KES were available in 163 outpatients. 97 6 ProMo cohort 98 This cohort includes community-dwelling older individuals aged 60 years and older with a 99 hip fracture operated at the Central Finland Central Hospital, Finland.20 Individuals were 100 asked to participate in a randomized controlled trial investigating the effects of a 101 rehabilitation program aiming to restore mobility and functional capacity. Baseline 102 measurements were performed after discharged home from hospital; on average 65±21 days 103 after hip fracture operation. Exclusion criteria included being institutionalised or confined to 104 bed at the time of the fracture, Mini Mental State Examination of <18 points, alcoholism, 105 severe cardiovascular, pulmonary or progressive disease, para-or tetraplegic or severe 106 depression. In the present analysis, data on HGS and KES were available in 78 individuals. 107 108 Characteristics of the different cohorts 109 Demographics of individuals were assessed by questionnaires in the MyoAge, ProMo and 110 MMU cohort and by medical charts in the DHEAge cohort and geriatric outpatients. In all 111 cohorts, body weight was measured to the nearest 0.1 kg and height to the nearest 1 mm (to 112 the nearest centimeter for DHEAge cohort). Body composition was assessed by dual-energy 113 X-ray absorptiometry (DXA, MyoAge, DHEAge and MMU cohorts), or by direct segmental 114 multi-frequency bioelectrical impedance analysis (DSM-BIA, geriatric outpatients and 115 ProMo cohort). Fat mass percentage and lean mass percentage were calculated as total fat 116 mass and total lean mass as percentage of total body mass respectively. Appendicular lean 117 mass percentage was calculated as the sum of lean mass in all four limbs as percentage of 118 total body mass. Gait speed was assessed by the six-minute (MyoAge cohort), four-meter 119 (MMU cohort and geriatric outpatients) and ten-meter walking test (ProMo cohort). Gait 120 speed was expressed in meters per second. It was not performed in the DHEAge cohort. 121 13 the range of 95% LOA being wider in healthy old compared to healthy young. This is 246 consistent with a cross-sectional study in healthy young and healthy old men with the same 247 level of daily physical activity which revealed that lower limb muscles strength was 248 significantly lower in older men than in young men while upper limbs muscles strength was 249 similar between the age groups.29 Differences may be further accelerated by using 250 compensation strategies, i.e. extensive use of arm muscles when rising from a chair.30 251 It was expected that the agreement of HGS and KES would be lower as a function of 252 health status. However, ICC values showed higher agreement and Bland-Altman analysis 253 showed a smaller range of 95% LOA in geriatric outpatients and older individuals post hip 254 fracture compared to healthy old. Apart from higher population variance which results in 255 higher ICC values, HGS weakness may increasingly link to KES weakness in lower health 256 status; physiological “floor” effects may further contribute as both HGS and KES may 257 approach their low limits.31 The result might also be explained by the potentially higher 258 variance in physical activity among healthy old compared to geriatric outpatients and older 259 individuals post hip fracture. 260 Our findings suggested that measure of a single muscle group should not be regarded 261 as a proxy for overall muscle strength. Even within the same population of age and health 262 status, Bland-Altman analysis showed that the agreement between HGS and KES were lower 263 in some individuals compared to the others. Therefore, it may pose a challenge in using one 264 single muscle group strength measurement as a surrogate of overall muscle strength on an 265 individual basis or in clinical practices.32 Some feasibility issues such as the availability of 266 standardized protocol and the need for special equipment pose a challenge in measuring KES 267 in clinical practice. However, instrumented KES measurement such as hand-held 268 dynamometry33 and isokinetic dynamometry34 should be used instead of manual muscle 269 testing because of its subjectivity and the lack of sensitivity.35 270 14 Our findings showed a low agreement between HGS and KES, however, whether 271 HGS, KES or both are associated with clinical outcomes was not investigated. A population-272 based cohort study (n=1755) showed that lower KES in females was associated with 273 increased mortality and hospitalization while lower HGS in males was associated with 274 increased risk of mortality alone.32 Another study in community-dwelling older females 275 showed that a faster rate of decline in HGS but not KES was predicted of mortality.36 These 276 results suggest that there were sex-specific differences in the association between HGS and 277 KES, mortality and hospitalization. Another point to be noted is that the reliability and 278 accuracy of measuring HGS and especially KES is not known in our study. Therefore, it 279 remains questionable of whether it is worthwhile to measure both HGS and KES. 280 A strength of this study is the inclusion of different cohorts representing different age 281 and health status, thereby making the results generalizable to the wider population differing 282 in age and health status. However, HGS and KES was measured using different types of 283 devices and protocols in the cohorts, resulting in the use of different units (Newton 284 meters/Newton or kilograms), which made it necessary to use z-scores in ICC and Bland-285 Altman analysis. It is recommended that in future studies the measurement of HGS and KES 286 should be conducted according to the same standardized operation procedure to ensure 287 reproducibility and consistency across different studies. 288 One limitation of this study is that the reliability and accuracy of HGS and KES is 289 unknown. It is difficult to know whether individuals truly gave a maximal voluntary effort in 290 each trial. Different conditions of individuals including pain in joints and osteoarthritis were 291 not registered and could have influenced the muscle strength. In addition, HGS and especially 292 KES measurement are not gold standard to quantify muscle strength. 293 15 Conclusion 294 A low to moderate agreement between HGS and KES was found as a function of age and 295 health status at population level. Within the same population of age and health status, 296 agreement between HGS and KES also varied on individual level. The use of one muscle 297 group strength measure seems not justified as an indicator of overall limb muscle strength .298 16 Acknowledgements 299 We thank Marjon Stijntjes, Jantsje Pasma, Astrid Bijlsma, Yoann Barnouin, Thomas Maden-300 Wilkinson, Alex Ireland and Thomas Maden-Wilkinson for their contribution to collect the 301 data. 302 Authors’ contributions 303 Study concept and design: EMR, MCT, CGMM, ABM. Acquisition of data: JH, JSM, MP, 304 SS, AS, GB, MP, HG, MVN, CGMM, ABM. Data analysis: SSYY. Contributed to data 305 analysis: EMR, MCT, CGMM, ABM. Interpretation of data: SSYY, EMR, MCT, CGMM. 306 Drafting of the manuscript: SSYY. Critically revision of the manuscript for important 307 intellectual content: EMR, MCT, JH, JSM, MP, SS, AS, GB, MP, HG, MVN, CGMM, ABM. 308 All authors read and approved the final manuscript. 309 Sponsor’s role 310 None of the funders had a role in the study design, methods, data collection, data analysis, 311 interpretation of data or preparation of this manuscript. 312 Conflict of interest 313 The authors have no conflict of interest. 314 Funding sources 315 This study was supported by the seventh framework program MYOAGE (HEALTH-2007-316 2.4.5-10), the UK Medical Research Council (MR/K025252/1) as part of the Lifelong Health 317 and Wellbeing initiative, the Dutch Technology Foundation STW, and The Ministry of 318 Education and Culture, Kela-The Social Insurance Institution of Finland, Juho Vainio 319 Foundation. 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Heterogeneity in rate of decline in grip, hip, 410 and knee strength and the risk of all-cause mortality: the Women's Health and Aging Study 411 II. J Am Geriatr Soc. 2010; 58(11):2076-2084. 412 21 Figure 1. Bland-Altman plots of z-scores of HGS and z-scores KES 413 Results are stratified by cohort and age: MyoAge cohort (A: healthy young, B: healthy old), 414 MMU cohort (C: healthy young, D: healthy old), DHEAge cohort (E), geriatric outpatients 415 (F), ProMo cohort (G) and the pooled analysis (H). The solid line represents the mean 416 difference in HGS and KES, while the dashed lines represent the upper and lower 95% limits 417 of agreement (mean difference + 1.96 SD). Grey dots represent males and black dots 418 represent females. 419 Supplementary Figure 1. Scatterplot illustrating the correlation between handgrip strength 420 (HGS) and knee extension strength (KES). Results are stratified by cohort and age: MyoAge 421 cohort (A: healthy young, B: healthy old), MMU cohort (C: healthy young, D: healthy old), 422 DHEAge cohort (E), geriatric outpatients (F), ProMo cohort (G) and the pooled analysis (H). 423 Grey lines represent regression line for females and black lines represent regression line for 424 males. Grey dots represent males and black dots represent females. 425 MyoAge cohort MMU cohort DHEAge cohort Geriatric outpatients ProMo cohort Young N=181 Old N=320 Young N=42 Old N=108 N=68 N=163 N=78 Sociodemographics Age, years 23.4 (2.9) 74.4 (3.2) 26.2 (4.4) 72.8 (6.7) 65.7 (2.7) 81.7 (7.2) 79.8 (7.0) Male, n (%) 85 (47.0) 161 (50.3) 42 (100) 108 (100) 0 (0) 64 (39.3) 18 (23.1) Independent livinga, n (%) 181 (100) 320 (100) 42 (100) 108 (100) 68 (100) 138 (86.3) 78 (100) Lifestyle factors Excessive alcohol useb, n (%) 22 (12.2) 28 (8.8) 1 (2.4) 15 (14.0) 0 (0) 7 (4.3) 0 (0) Current smoking, n (%) 23 (12.7) 14 (4.4) 0 (0) 4 (3.7) 0 (0) 21 (15.4) 7 (9.0) Health characteristics Multimorbidityc, n (%) 0 (0) 56 (17.5) 0 (0) 13 (12.3) 0 (0) 60 (38.2) 68 (87.2) Polypharmacyd, n (%) 0 (0) 23 (7.2) 0 (0) 29 (27.3) 0 (0) 98 (61.6) 61 (78.2) Table 1. Characteristics of different cohorts, stratified by age Table 1