Muscle strength and muscle mass in people with long term conditions and multimorbidity: A cross-sectional study of UK Biobank participants

Muscle strength and muscle mass in people with long term conditions and multimorbidity: A cross-sectional study of UK Biobank participants | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Muscle strength and muscle mass in people with long term conditions and multimorbidity: A cross-sectional study of UK Biobank participants View ORCID Profile Marion Guerrero-Wyss , Stuart Johnston , View ORCID Profile Fanny Peterman-Rocha , Irene Rodríguez-Gómez , View ORCID Profile Lyn D Ferguson , View ORCID Profile Frederick K Ho , Bhautesh D Jani , View ORCID Profile Stuart R Gray , View ORCID Profile Carlos A Celis-Morales doi: https://doi.org/10.1101/2025.08.11.25333208 Marion Guerrero-Wyss 1 School of Cardiovascular and Metabolic Health, University of Glasgow , Glasgow, UK 2 School of Health and Wellbeing, University of Glasgow , Glasgow, UK 3 Escuela de Nutrición y Dietética, Facultad de Ciencias para el Cuidado de la Salud, Universidad San Sebastián , Valdivia, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marion Guerrero-Wyss Stuart Johnston 4 NHS Greater Glasgow and Clyde , Glasgow, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fanny Peterman-Rocha 5 Centro de Investigación Biomédica, Facultad de Medicina, Universidad Diego Portales , Santiago, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Fanny Peterman-Rocha Irene Rodríguez-Gómez 6 GENUD Toledo Research Group. Faculty of Sports Sciences. Universidad de Castilla-La Mancha. Toledo , Spain 7 Department of Geriatrics. Hospital Virgen del Valle, Complejo Hospitalario Universitario de Toledo. CIBER of Frailty and Healthy Ageing (CIBERFES). Madrid , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lyn D Ferguson 1 School of Cardiovascular and Metabolic Health, University of Glasgow , Glasgow, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lyn D Ferguson Frederick K Ho 2 School of Health and Wellbeing, University of Glasgow , Glasgow, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Frederick K Ho Bhautesh D Jani 2 School of Health and Wellbeing, University of Glasgow , Glasgow, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stuart R Gray 1 School of Cardiovascular and Metabolic Health, University of Glasgow , Glasgow, UK 8 Institute of Sports Science and Innovation; Lithuanian Sports University , Lithuania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stuart R Gray Carlos A Celis-Morales 1 School of Cardiovascular and Metabolic Health, University of Glasgow , Glasgow, UK 9 Human Performance Lab, Education, Physical Activity and Health Research Unit, University Católica del Maule , Talca, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carlos A Celis-Morales Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT BACKGROUND/OBJECTIVES To compare muscle strength and muscle mass in individuals with various long-term conditions (LTCs) to those of healthy individuals, using data from the UK Biobank. SUBJECTS/METHODS This cross-sectional study of 444,420 UK Biobank participants (aged 37 – 73) examined 25 self-reported LTCs ( ≥ 1% prevalence). Multimorbidity was defined as a prevalent LTCs count ≥ 2. Muscle strength was assessed using a handgrip dynamometer, while muscle mass (fat-free mass as a percentage of body weight) was assessed using BIA, DXA, and MRI; all expressed as a percentage of total body weight. Linear regression analyses were conducted to examine how muscle strength and muscle mass varied by LTCs and multimorbidity. RESULTS The lowest muscle strength was observed in men with rheumatoid arthritis (RA), type 2 diabetes (T2D), stroke, gout, and deep venous thrombosis (-11.8% to -6.1%) lower than the reference group and in women with RA, gout, T2D, coronary heart disease (CHD), and stroke (-11.8% to -6.5%). Muscle mass was lowest in men with T2D, gout, CHD, hypertension, and high cholesterol (-4.7% to -3.1%), and in women with gout, T2D, hiatus hernia, CHD, and hypertension (-6.4% to -4.1%). Trends were consistent across measurements using DXA (n=4,719) and MRI (n=22,901). Muscle strength and muscle mass were lower in people with more LTCs, with a dose-response relationship. CONCLUSIONS This study demonstrates that muscle strength and muscle mass were lower in people with multiple LTCs and specific conditions. Emphasizing the need for targeted muscle interventions, particularly for those with T2D, RA, gout, and CHD. INTRODUCTION Maintaining adequate levels of muscle strength and muscle mass is crucial for maintaining good health, particularly in middle-aged and older adults ( 1 , 2 ). A growing body of evidence indicates that low levels of muscle strength and muscle mass are associated with an increased risk of falls, fractures, and long-term conditions (LTCs) such as cardiovascular diseases, dementia, certain cancers, and even mortality ( 1 – 5 ). Evidence shows that grip strength predicts cardiovascular and all-cause mortality and declines as the number of LTCs increases ( 2 ). Additionally, other studies have found that muscle strength is progressively lower with an increasing number of LTCs an individual has ( 3 ). This is important as the number of people living with two or more LTCs, termed multimorbidity, is increasing, and especially in younger individuals and deprived areas ( 6 , 7 ); and has become a growing priority for health systems and global health research efforts. Several limitations in previous studies should be noted, including small sample sizes, a lack of adjustment, and the reliance on grip strength as the sole measure of muscle strength without using other assessment methods ( 1 , 3 , 8 ). The impact of long-term conditions (LTCs) on muscle mass is not well understood, nor is there a comprehensive understanding of which specific conditions have the most detrimental impact on muscle strength and mass. Filling these gaps could inform more tailored strength training guidelines for individuals with LTCs. This study aims to compare muscle strength and muscle mass in individuals with various LTCs, to those with no LTCs; and by LTC count using data from the UK Biobank. METHODS Study design and participants From 2006 to 2010, UK Biobank recruited over 500,000 participants (5.5% response rate), aged 37 to 73 years old from the general population ( 9 ). Participants visited one out of 22 assessment centres across England, Wales, and Scotland, and completed a touch-screen questionnaire, had anthropometry, body composition (assessed by bio-impedance (BIA)) and grip strength assessed, and biological samples taken, as described in detail elsewhere ( 10 , 11 ). In 2014 the UK Biobank imaging enhancement protocol was initiated incorporating full body Magnetic Resonance Imaging (MRI) and X-ray absorptiometry (DXA) scans. The UK Biobank has amassed one of the largest datasets utilizing objective methods for quantifying strength levels as well as muscle mass using different assessment methods including DXA, MRI, and BIA. Ethical approval The UK Biobank study was approved by the Northwest Multicentre Research Ethics Committee and performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Informed consent was given by all participants at the time of recruitment. This study is part of the UK Biobank project 7155 (NHS National Research Ethics Service16/NW/0274). Outcomes assessment Muscle strength Hand grip strength was used as a proxy of muscle strength ( 3 ), and measured using a Jamar J00105 hydraulic hand dynamometer ( 12 ). Participants sat upright with their elbows on the side and bent 90 degrees so that their forearms were facing forward and resting on the armrest ( 13 ). Maximum grip strength of the right and left arms over three seconds was recorded ( 13 ). The maximum value (in kg) for either arm was used for analysis ( 12 ). Muscle mass Fat-free mass (FFM), a marker of muscle mass, was assessed by BIA, DXA and MRI and expressed as kg or liters of FFM. BIA was performed with Tanita BC418MA Body Composition Analyser (Tanita, Japan). The analyser measured body impedance with a high- frequency current (50 kHz) and 8 contact electrodes. Participants were asked to place their bare feet on the analyser platform, to keep their feet still and in good contact with the platform’s metallic electrodes on platform, and to grip the two handles firmly with palm and thumb contacting metallic electrodes and arms hanging loosely by their sides. DXA was performed with Lunar iDXA Scanner (General Electric Healthcare, Wisconsin US). The scanner was calibrated daily following standard quality control procedures to maintain consistency. Participants were asked to lie flat on their back on the scanning couch for whole body scan. All operators had standardised central training, and all scans were performed according to standard operating procedures. For the MRI assessment participants were scanned in a Siemens MAGNETOM Aera 1.5-T MRI scanner (Siemens Healthineers, Erlangen, Germany) using a 6 min dual-echo Dixon Vibe protocol, providing a water and fat separated volumetric data set covering neck to knees. Body composition analyses were performed using AMRA® Researcher (AMRA Medical AB, Linköping, Sweden ( 14 , 15 ). Fat-tissue free muscle volume was defined as the volume of all voxels with fat fraction <50% (‘viable muscle tissue’) in the thighs. This is to be differentiated from lean muscle volume (sometimes called ‘contractile muscle volume’) calculated by subtracting the fat volume from the entire muscle volume. Detailed muscle composition measurements and definitions have been previously described in previous studies ( 15 ). All muscle strength and mass measurements were presented as a percentage of body weight to account for variations in body size and to facilitate comparisons across the assessments used to estimate FFM. Long Term Conditions and multimorbidity Self-reported long-term conditions (LTCs) were identified through a nurse-led questionnaire at baseline, where participants reported any serious illnesses or disabilities diagnosed by a doctor. LTCs were classified using the International Classification of Diseases, Tenth Revision (ICD-10) ( 16 ). There were 322 LTCs coded in the UK Biobank. Of these, 25 self- reported LTCs with a prevalence >1% were included in the current study. The LTCs included were hypertension, high cholesterol, asthma, osteoarthritis, hiatal hernia, depression, type 2 diabetes (T2D), allergic rhinitis, hypothyroidism, coronary heart diseases (CHD), migraine, eczema, breast cancer (for women), prostate cancer (for men), deep venous thrombosis (DVT), anxiety, stroke, back problems, chronic obstructive pulmonary diseases (COPD), osteoporosis, gout, emphysema, rheumatoid arthritis (RA), psoriasis, diverticulitis and glaucoma, as presented in Supplementary Table 1. Participants without any of the 322 LTCs were categorized as healthy and served as the reference group (n=173,414). Multimorbidity was defined as having two or more LTCs. LTCs were examined both as individual LTCs, and collectively as a measure of multimorbidity, by counting LTCs and categorising into: 0, 1, 2, 3, 4 or ≥5 LTCs. Covariates These included age, Townsend deprivation index, alcohol intake, smoking and ethnicity. Age was calculated based on dates of birth and baseline assessment. The Townsend deprivation index was derived from the postcode of residence. Ethnicity was defined in five categories: White, Mixed, South Asian, Black and Chinese. Smoking status was categorized in never, previous and current. Alcohol intake was self-reported and combined as units per week. Statistical analyses Descriptive cohort characteristics by categories of LTCs are presented as mean and standard deviation (SD) for quantitative variables and as a proportion for categorical variables. Data were normally distributed, and Pearson Correlation was performed to compare the relationship between grip strength and muscle mass outcomes assessed with BIA, DXA and MRI. Linear regression analyses were conducted to examine the association between individuals’ LTCs and markers of muscle strength and muscle quantity. These markers included strength assessed with a hand dynamometer, and muscle mass assessed via BIA, DXA, and MRI. Results were reported as means along with their 95% confidence intervals (CIs). Similar analyses were conducted to explore the association of the number LTCs, categorized as 0, 1, 2, 3, 4, and 5+ LTCs, with muscle strength and muscle mass. All analyses were stratified by sex and adjusted for age, deprivation index, ethnicity, smoking status, and alcohol consumption. To account for potentially inflated type I errors due to multiple testing, all analyses were adjusted using Holm’s method (P adj ) ( 17 , 18 ), and the significance threshold was set at p < 0.0001. Analyses were conducted using STATA 18 (StataCorp LP). RESULTS Of the 502,350 participants recruited to the UK Biobank (53.8% women, 46.2% men), data on muscle strength and muscle mass were available for 444,420 participants measured by BIA, 4,653 by DXA, 22,091 by MRI, and 444,420 for grip strength (Supplementary Figure 1 and Supplementary Table 2). The characteristics of the cohort, stratified by the number of long-term conditions (LTCs), are summarized in Supplementary Table 3. In total, 34.8% of participants reported having no LTCs, 32.9% reported one LTC, and 32.3% reported two or more LTCs. Participants with multimorbidity tended to be older, were more likely to be women, had higher body mass index (BMI), engaged in lower levels of physical activity, had lower muscle strength and fat- free mass (FFM), and were more likely to have obesity and be former smokers compared to those without LTCs. The differences were even more pronounced for individuals with five or more LTCs (Supplementary Table 3). The correlations between the outcomes, including muscle strength and FFM are presented in Supplementary Figure 2. The correlations were strong, ranging from 0.692 to 0.924. The weakest correlation was observed between muscle strength and FFM measured by DXA (r = 0.692), while the strongest correlation was between FFM measured by MRI and DXA (r = 0.924). Figures 1-4 illustrate muscle strength and muscle mass across the 25 most prevalent LTCs. For handgrip strength, the five LTCs associated with the lowest strength in men were RA (-11.6%), T2D (-9.07 %), stroke (-6.78%), gout (-6.49 kg), and DVT (-6.09 %), compared to those without LTCs (reference group) ( Figure 1 ). In women, the five conditions with the lowest strength were RA (-11.8%), gout (-9.05%), T2D (-8.72%), CHD (-7.07%), and stroke (-6.57%), compared to the reference group ( Figure 1 ). Absolute handgrip strength values, expressed in kilograms, for each of the 25 LTCs are presented in Figure 1 . Download figure Open in new tab Figure 1. Differences in Strength Levels by Long-Term Conditions in women and men. The forest plot displays the differences in handgrip strength, expressed as a percentage of body weight, expressed as a percentage of body weight, along with their 95% confidence intervals, in individuals with LTCs compared to those without (reference group). Muscle strength means are presented both as a percentage of body weight (*) and in liters (Ψ absolute values). The analyses were adjusted for age, smoking status, alcohol intake, deprivation index, and ethnicity. Abbreviations: Kg, kilograms; CI, confidence intervals; COPD, chronic obstructive pulmonary disease; CHD, coronary heart disease; DVT, deep vein thrombosis; RA, rheumatoid arthritis. Muscle mass, estimated by BIA, was lower in men with T2D (-4.71%), gout (-4.25%), CHD (-3.50%), hypertension (-3.24%), and high cholesterol (-3.15%), in comparison to the reference group ( Figure 2 ). In women the lowest muscle mass was seen in gout (-6.42%), T2D (-5.89%), hiatus hernia (-4.68%), CHD (-4.35%), and hypertension (-4.14%), compared to the reference group ( Figure 2 ). Muscle mass, measured by DXA, showed slightly different patterns: for men, the top five LTCs with the lowest muscle mass were type 2 diabetes (- 6.08%), RA (-5.65%), hiatus hernia (-3.80%), gout (-3.63%), and COPD (-3.55%), lower than the reference group ( Figure 3 ). In women, hiatus hernia (-5.74%), T2D (-5.10%), COPD (-4.22%), CHD (-4.16%), and emphysema (-3.77%) had the lowest muscle mass, compared to the reference group ( Figure 3 ). Thigh muscle mass, measured by MRI, revealed similar trends, with the lowest levels in men seen in T2D (-1.88%), gout (-1.11%), CHD (-0.99%), diverticulitis (-0.92%), and hypertension (-0.92%), compared to the reference group ( Figure 4 ). In women, T2D (-1.51%), COPD (-1.15%), hiatus hernia (-1.09%), gout (-1.04%), and emphysema (-0.98%) were associated with the lowest levels, in comparison to the reference group ( Figure 4 ). Absolute muscle mass values, expressed in liters or Kg, for each of the 25 LTCs are presented in Figure 2 - 4 . Download figure Open in new tab Figure 2. Differences in BIA Fat Free Mass Levels by Long-Term Conditions in women and men. The forest plot illustrates the differences in Fat-Free Mass (FFM) measured by BIA, expressed as a percentage of body weight, along with their 95% confidence intervals, in individuals with LTCs compared to those without (reference group). FFM means are presented both as a percentage of body weight (*) and in liters (Ψ absolute values). The analyses were adjusted for age, smoking status, alcohol intake, deprivation index, and ethnicity. Abbreviations: Kg, kilograms; CI, confidence intervals; COPD, chronic obstructive pulmonary disease; CHD, coronary heart disease; DVT, deep vein thrombosis; RA, rheumatoid arthritis. Download figure Open in new tab Figure 3. Differences in DXA Fat Free Mass Levels by Long-Term Conditions in women and men. The forest plot illustrates the differences in Fat-Free Mass (FFM) measured by DXA, expressed as a percentage of body weight, along with their 95% confidence intervals, in individuals with LTCs compared to those without (reference group). FFM means are presented both as a percentage of body weight (*) and in liters (Ψ absolute values). The analyses were adjusted for age, smoking status, alcohol intake, deprivation index, and ethnicity. Abbreviations: Kg, kilograms; CI, confidence intervals; COPD, chronic obstructive pulmonary disease; CHD, coronary heart disease; DVT, deep vein thrombosis; RA, rheumatoid arthritis. Download figure Open in new tab Figure 4. Differences in MRI Fat Free Mass Levels by Long-Term Conditions in women and men. The forest plot illustrates the differences in Fat-Free Mass (FFM) measured by MRI, expressed as a percentage of body weight, along with their 95% confidence intervals, in individuals with LTCs compared to those without (reference group). FFM means are presented both as a percentage of body weight (*) and in liters (Ψ absolute values). The analyses were adjusted for age, smoking status, alcohol intake, deprivation index, and ethnicity. Abbreviations: Kg, kilograms; CI, confidence intervals; COPD, chronic obstructive pulmonary disease; CHD, coronary heart disease; DVT, deep vein thrombosis; RA, rheumatoid arthritis. Figure 5 presents the associations between LTC count and muscle strength and muscle mass. Both muscle strength and muscle mass showed an inverse dose-response relationship with LTC count. For each additional LTC, muscle strength was lower on average by 1.81% in men and 1.71% in women. Strength levels were 10.6% lower in men and 9.96% lower in women with 5 or more LTCs compared to those without LTCs. FFM, assessed by BIA, was 0.94% lower in men and 1.03% lower in women per additional LTC. In individuals with 5 or more LTCs, FFM levels were 5.14% lower in men and 5.66% lower in women compared to those without LTCs. Comparable trends were observed for FFM, assessed by DXA and MRI, as shown in Figure 5 . Levels of muscle strength and muscle mass expressed in absolute terms (kg or liters) by number of LTCs are presented in Supplementary Figure 3. Download figure Open in new tab Figure 5. Levels of strength and Fat-free mass by number of LTCs in men and women. The data present adjusted means and their 95% confidence intervals (CI) for handgrip strength and Fat-Free Mass (FFM) levels, assessed via BIA, DXA, and MRI, all reported as a percentage of body weight. Differences between individuals with no LTCs, 2 LTCs, and 5 or more LTCs are displayed, with means shown above the plotted squares and the deltas presented below (Δ). Trend values indicate the mean change in the outcome per 1 LTC increment. The analyses were adjusted for age, ethnicity, deprivation, smoking status, and alcohol intake. Kg, kilograms; CI, confidence intervals. DISCUSSION This study demonstrates that there is significantly lower muscle strength and mass, regardless of measurement method, in people with the 25 most prevalent LTCs compared with people without any LTCs. Our results also show an inverse dose-response relationship between LTC count and both muscle strength and mass. Specifically, individuals with five or more LTCs had about 10% lower strength and 5-6% lower muscle mass compared to those without any LTCs. Of all the included LTCs, people with RA had the lowest muscle strength, and people with T2D had the lowest muscle mass. These findings highlight the importance of directing the limited resource, to the clinical population where it could have the most benefits, and the greatest impact, aimed at increasing muscle strength and mass, particularly for individuals with T2D, RA, or those with multimorbidity. While this study does not establish causality, the strong association between LTCs and lower muscle strength and muscle mass in both sexes may stem from common mechanisms, particularly inflammation and low physical activity. Cardiovascular diseases (e.g., stroke, hypertension, CHD), metabolic disorders (e.g., T2D), and inflammatory arthritides (e.g., RA and gout) trigger inflammatory pathways that impair muscle protein synthesis, leading to muscle atrophy and weakness ( 19 , 20 ). Indeed, elevated cytokines, such as IL-6 and TNF-α, are linked to muscle loss in both healthy and diseased individuals ( 21 , 22 ) and chronic inflammation contributes to skeletal muscle mass loss ( 23 ) and muscle degradation ( 24 ). Physical activity helps mitigate these effects by promoting anabolism, reducing catabolism, and decreasing inflammation, which may explain the lower muscle strength and mass in LTC populations with lower levels of physical activity ( 12 , 25 ). In our study, musculoskeletal diseases, particularly rheumatoid arthritis, were associated with the lowest levels of handgrip strength, likely due to inflammation, joint damage, and resulting physical disability. Similar findings have been reported in individuals with RA, highlighting significant declines in muscle mass and strength, even among patients with early-stage RA ( 26 ). In another study, RA patients had 10% less appendicular lean mass, and a 27% higher body fat percentage compared to healthy controls, with muscle function measures being 24- 34% poorer in RA patients ( 27 ). Additionally, muscle tissue does not fully recover, even during periods of clinical remission, suggesting that rheumatoid arthritis may lead to persistent, long-term muscle damage ( 26 ). These conditions lead to disuse atrophy and contractile dysfunction ( 28 ). RA, in particular, is characterized by progressive muscle loss, with sarcopenia masked by increased adiposity, known as ’rheumatoid cachexia.’ Recent studies highlight a higher prevalence of sarcopenic obesity in rheumatoid arthritis compared to non-RA controls, which is linked to functional decline and increased disability over time ( 29 , 30 ). This underscores the importance of muscle-strengthening interventions, such as resistance exercise, in this population, especially considering the high multimorbidity prevalence ( 31 ). In our study, RA was most strongly associated with low grip strength and reduced muscle mass, placing this population at significantly higher risk. Low levels of strength and muscle mass are linked to an increased risk of both all-cause and cause-specific mortality ( 32 ), exponentially elevating the risk for these patients. These findings underscore the importance of implementing appropriate strategies to improve muscle health in individuals with RA. Metabolic and cardiovascular conditions, including T2D, stroke, and CHD, are also strongly associated with lower muscle mass and strength ( 8 , 33 ). Notably, higher levels of handgrip strength have been linked to significantly reduced event rates of cardiovascular disease (CVD) ( 34 ). In fact, handgrip strength has been recognized as a predictor of CVD incidence ( 8 , 34 ). Other conditions, such as hiatal hernia, though less frequently discussed, also contribute to reduced muscle mass due to physical inactivity and nutritional challenges ( 35 , 36 ). In older adults, the combination of sarcopenia and hiatal hernia can exacerbate muscle loss ( 37 ). Additionally, obesity is a major risk factor for hiatal hernia, and individuals with multimorbidity generally have higher BMI and lower muscle mass compared to healthy participants ( 38 ). Although muscle mass measurements assessed by BIA, DXA, and MRI show moderate to strong correlations, notable differences exist among these muscle mass phenotypes. The discrepancies in fat-free mass (FFM) estimates obtained by DXA, bioimpedance, and MRI in individuals with long-term conditions (LTCs) could be attributed to variations in measurement techniques, the influence of LTCs on body composition, and the scope of each method. Fat-free or lean tissue, as assessed by DXA and bioimpedance, encompasses not only skeletal muscle but also organs, bone, bodily fluids, and water content. In contrast, MRI provides a more precise quantification of true muscle tissue, particularly in the thigh region. Additionally, MRI offers valuable insights into muscle quality by assessing the extent of intramuscular fat infiltration, often revealing more pronounced muscle loss in specific areas ( 15 , 39 ). Additionally, LTCs may alter tissue composition, affecting the accuracy of these methods in estimating true muscle mass. These factors together may explain the discrepancies in FFM measurements across different techniques in this population. This study highlights the critical importance of prioritizing muscle health in individuals with LTCs. It reveals significantly lower muscle strength and mass among those with the 25 most prevalent LTCs compared to healthy individuals, with a dose-response relationship showing further declines as the number of LTCs increases. Notably, individuals with five or more LTCs exhibited about 10% lower muscle strength and 5-6% lower muscle mass, placing them at a heightened risk for adverse health outcomes, including increased all-cause and cause- specific mortality. Conditions such as RA and T2D were associated with the most severe declines in muscle strength and mass, likely due to inflammation, inactivity, and altered body composition. Interventions like resistance exercise and physical activity could mitigate these effects, improve functional outcomes, and reduce cardiovascular disease risk, given the predictive value of muscle strength for cardiovascular events ( 32 ). The findings advocate for targeted healthcare resources to benefit individuals with high multimorbidity or severe muscle impairment. In conclusion, the study’s findings highlight a clear inverse relationship between the LTC count and both muscle strength and FFM regardless of measurements. Participants with multimorbidity, particularly those with five or more LTCs, exhibited significantly weaker muscle strength and lower FFM compared to those without LTCs. The most affected groups were individuals with type 2 diabetes, rheumatoid arthritis, gout, and coronary heart disease. Overall, the results emphasize the need for targeted interventions to maintain muscle mass and strength in individuals with LTCs, especially in those with multimorbidity. DATA AVAILABILITY The data that supports the findings of this study are available upon reasonable request from the corresponding author (CC-M). AUTHOR CONTRIBUTIONS Marion Guerrero-Wyss (MGW), Frederick K Ho (FH), Bhautesh D Jani (BJ), Stuart R Gray (SG), and Carlos A Celis-Morales (CCM) designed the study. MGW and CCM analysed the data. All authors wrote the paper and have read and agreed to the published version of the paper. FUNDING Open access funding provided by University of Glasgow. COMPETING INTERESTS The authors declared no competing interests. ETHICAL APPROVAL The study was approved by the Northwest Multicentre Research Ethics Committee. This study is part of the UK Biobank project 7155 (NHS National Research Ethics Service16/NW/0274). The study protocol is available online ( http://www.ukbiobank.ac.uk/ ). ADDITIONAL INFORMATION Correspondence and requests for materials should be addressed to Carlos Celis-Morales. ACKNOWLEDGEMENTS We are grateful to the UK Biobank participants. This research has been conducted using the UK Biobank resource under application number 71932. IRG is supported by a Sara Borrell contract from Instituto de Salud Carlos III (CD23/00236). References 1. ↵ García-Hermoso A , Cavero-Redondo I , Ramírez-Vélez R , Ruiz JR , Ortega FB , Lee DC , et al. Muscular Strength as a Predictor of All-Cause Mortality in an Apparently Healthy Population: A Systematic Review and Meta-Analysis of Data From Approximately 2 Million Men and Women . Arch Phys Med Rehabil . 2018 ; 99 ( 10 ): 2100 – 13 .e5. OpenUrl CrossRef PubMed 2. ↵ Pavasini R , Serenelli M , Celis-Morales CA , Gray SR , Izawa KP , Watanabe S , et al. Grip strength predicts cardiac adverse events in patients with cardiac disorders: an individual patient pooled meta- analysis . Heart . 2019 ; 105 ( 11 ): 834 – 41 . OpenUrl Abstract / FREE Full Text 3. ↵ Cheung CL , Nguyen US , Au E , Tan KC , Kung AW . Association of handgrip strength with chronic diseases and multimorbidity: a cross-sectional study . Age (Dordr ). 2013 ; 35 ( 3 ): 929 – 41 . OpenUrl PubMed 4. Lee J . Associations Between Handgrip Strength and Disease-Specific Mortality Including Cancer, Cardiovascular, and Respiratory Diseases in Older Adults: A Meta-Analysis . J Aging Phys Act . 2020 ; 28 ( 2 ): 320 – 31 . OpenUrl PubMed 5. ↵ López-Bueno R , Andersen LL , Koyanagi A , Núñez-Cortés R , Calatayud J , Casaña J , et al. Thresholds of handgrip strength for all-cause, cancer, and cardiovascular mortality: A systematic review with dose-response meta-analysis . Ageing Res Rev . 2022 ; 82 : 101778 . 6. ↵ Barnett K , Mercer SW , Norbury M , Watt G , Wyke S , Guthrie B . Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study . Lancet . 2012 ; 380 (9836): 37 -43. OpenUrl CrossRef PubMed Web of Science 7. ↵ Cassell A , Edwards D , Harshfield A , Rhodes K , Brimicombe J , Payne R , et al. The epidemiology of multimorbidity in primary care: a retrospective cohort study . Br J Gen Pract . 2018 ; 68 ( 669 ): e245 – e51 . OpenUrl Abstract / FREE Full Text 8. ↵ Liu W , Chen R , Song C , Wang C , Chen G , Hao J , et al. A Prospective Study of Grip Strength Trajectories and Incident Cardiovascular Disease . Front Cardiovasc Med . 2021 ; 8 : 705831 . 9. ↵ Collins R . What makes UK Biobank special? Lancet . 2012 ; 379 (9822): 1173 -4. OpenUrl CrossRef PubMed Web of Science 10. ↵ Palmer LJ . UK Biobank: bank on it . Lancet . 2007 ; 369 (9578): 1980 -2. OpenUrl CrossRef PubMed Web of Science 11. ↵ Sudlow C , Gallacher J , Allen N , Beral V , Burton P , Danesh J , et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age . PLoS medicine . 2015 ; 12 ( 3 ). 12. ↵ Celis-Morales CA , Lyall DM , Anderson J , Iliodromiti S , Fan Y , Ntuk UE , et al. The association between physical activity and risk of mortality is modulated by grip strength and cardiorespiratory fitness: evidence from 498 135 UK-Biobank participants . Eur Heart J . 2017 ; 38 ( 2 ): 116 – 22 . OpenUrl CrossRef PubMed 13. ↵ Celis-Morales CA , Petermann F , Hui L , Lyall DM , Iliodromiti S , McLaren J , et al. Associations Between Diabetes and Both Cardiovascular Disease and All-Cause Mortality Are Modified by Grip Strength: Evidence From UK Biobank, a Prospective Population-Based Cohort Study . Diabetes Care . 2017 ; 40 ( 12 ): 1710 – 8 . OpenUrl Abstract / FREE Full Text 14. ↵ Freer CL , George ES , Tan SY , Abbott G , Scott D , Daly RM . Prevalence of Sarcopenia and Its Defining Components in Non-alcoholic Fatty Liver Disease Varies According to the Method of Assessment and Adjustment: Findings from the UK Biobank . Calcif Tissue Int . 2024 ; 114 ( 6 ): 592 – 602 . OpenUrl PubMed 15. ↵ Linge J , Borga M , West J , Tuthill T , Miller MR , Dumitriu A , et al. Body Composition Profiling in the UK Biobank Imaging Study . Obesity (Silver Spring ). 2018 ; 26 ( 11 ): 1785 – 95 . OpenUrl CrossRef PubMed 16. ↵ (WHO) WHO . Centers for Disease Control and Prevention (CDC) . International classification of diseases, tenth revision, clinical modification (ICD - 10 -CM). 2018. 17. ↵ Menyhart O , Weltz B , Győrffy B . MultipleTesting.com: A tool for life science researchers for multiple hypothesis testing correction . PLoS One . 2021 ; 16 ( 6 ): e0245824 . OpenUrl CrossRef PubMed 18. ↵ Zurich E. Adjust P-value for multiple comparisons ETH Zurich [Available from: https://stat.ethz.ch/R-manual/R-devel/library/stats/html/p.adjust.html . 19. ↵ Soysal P , Arik F , Smith L , Jackson SE , Isik AT. Inflammation , Frailty and Cardiovascular Disease . Adv Exp Med Biol . 2020 ;1216: 55 – 64 . 20. ↵ Taams LS . Inflammation and immune resolution . Clin Exp Immunol . 2018 ; 193 ( 1 ): 1 – 2 . OpenUrl PubMed 21. ↵ Schols AM , Buurman WA , Staal van den Brekel AJ, Dentener MA, Wouters EF. Evidence for a relation between metabolic derangements and increased levels of inflammatory mediators in a subgroup of patients with chronic obstructive pulmonary disease . Thorax . 1996 ; 51 ( 8 ): 819 – 24 . OpenUrl Abstract / FREE Full Text 22. ↵ Visser M , Pahor M , Taaffe DR , Goodpaster BH , Simonsick EM , Newman AB , et al. Relationship of interleukin-6 and tumor necrosis factor-alpha with muscle mass and muscle strength in elderly men and women: the Health ABC Study . J Gerontol A Biol Sci Med Sci. 2002 ;57(5):M326-32. 22. ↵ Schaap LA , Pluijm SM , Deeg DJ , Harris TB , Kritchevsky SB , Newman AB , et al. Higher inflammatory marker levels in older persons: associations with 5-year change in muscle mass and muscle strength . J Gerontol A Biol Sci Med Sci . 2009 ; 64 ( 11 ): 1183 – 9 . OpenUrl CrossRef PubMed Web of Science 23. ↵ Roubenoff R , Roubenoff RA , Cannon JG , Kehayias JJ , Zhuang H , Dawson-Hughes B , et al. Rheumatoid cachexia: cytokine-driven hypermetabolism accompanying reduced body cell mass in chronic inflammation . J Clin Invest . 1994 ; 93 ( 6 ): 2379 – 86 . OpenUrl CrossRef PubMed Web of Science 24. ↵ Tay J , Goss AM , Locher JL , Ard JD , Gower BA . Physical Function and Strength in Relation to Inflammation in Older Adults with Obesity and Increased Cardiometabolic Risk . J Nutr Health Aging . 2019 ; 23 ( 10 ): 949 – 57 . OpenUrl PubMed 25. ↵ Farrow M , Biglands J , Tanner S , Hensor EMA , Buch MH , Emery P , et al. Muscle deterioration due to rheumatoid arthritis: assessment by quantitative MRI and strength testing . Rheumatology (Oxford ). 2021 ; 60 ( 3 ): 1216 – 25 . OpenUrl PubMed 26. ↵ Lemmey AB , Wilkinson TJ , Clayton RJ , Sheikh F , Whale J , Jones HS , et al. Tight control of disease activity fails to improve body composition or physical function in rheumatoid arthritis patients . Rheumatology (Oxford ). 2016 ; 55 ( 10 ): 1736 – 45 . OpenUrl CrossRef PubMed 27. ↵ Yamada T , Steinz MM , Kenne E , Lanner JT . Muscle Weakness in Rheumatoid Arthritis: The Role of Ca(2+) and Free Radical Signaling . EBioMedicine . 2017 ; 23 : 12 – 9 . OpenUrl PubMed 28. ↵ Baker JF , Giles JT , Weber D , George MD , Leonard MB , Zemel BS , et al. Sarcopenic obesity in rheumatoid arthritis: prevalence and impact on physical functioning . Rheumatology (Oxford ). 2022 ; 61 ( 6 ): 2285 – 94 . OpenUrl CrossRef PubMed 29. ↵ Siebert S , Lyall DM , Mackay DF , Porter D , McInnes IB , Sattar N , et al. Characteristics of rheumatoid arthritis and its association with major comorbid conditions: cross-sectional study of 502 649 UK Biobank participants . RMD Open . 2016 ; 2 ( 1 ): e000267 . OpenUrl Abstract / FREE Full Text 30. ↵ Katz J , Bartels CM . Multimorbidity in Rheumatoid Arthritis: Literature Review and Future Directions . Curr Rheumatol Rep . 2024 ; 26 ( 1 ): 24 – 35 . OpenUrl CrossRef PubMed 31. ↵ Celis-Morales CA , Welsh P , Lyall DM , Steell L , Petermann F , Anderson J , et al. Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: prospective cohort study of half a million UK Biobank participants . Bmj . 2018 ; 361 : k1651 . 32. ↵ Boonpor J , Parra-Soto S , Petermann-Rocha F , Ferrari G , Welsh P , Pell JP , et al. Associations between grip strength and incident type 2 diabetes: findings from the UK Biobank prospective cohort study . BMJ Open Diabetes Res Care . 2021 ; 9 ( 1 ). 33. ↵ Welsh CE , Celis-Morales CA , Ho FK , Brown R , Mackay DF , Lyall DM , et al. Grip Strength and Walking Pace and Cardiovascular Disease Risk Prediction in 406,834 UK Biobank Participants . Mayo Clin Proc . 2020 ; 95 ( 5 ): 879 – 88 . OpenUrl CrossRef PubMed 34. ↵ Murao T , Sakurai K , Mihara S , Marubayashi T , Murakami Y , Sasaki Y . Lifestyle change influences on GERD in Japan: a study of participants in a health examination program . Dig Dis Sci . 2011 ; 56 ( 10 ): 2857 – 64 . OpenUrl CrossRef PubMed 35. ↵ Zelicha H , Bell DS , Chen D , Chen Y , Livingston EH . Obesity and abdominal hernia in ambulatory patients, 2018-2023 . Hernia . 2024 ;28(4):1317-24. 36. ↵ Rinaldi JM , Geletzke AK , Phillips BE , Miller J , Dykes TM , Soybel DI . Sarcopenia and sarcopenic obesity in patients with complex abdominal wall hernias . Am J Surg . 2016 ; 212 ( 5 ): 903 – 11 . OpenUrl PubMed 37. ↵ Lee YY , McColl KE . Disruption of the gastroesophageal junction by central obesity and waist belt: role of raised intra-abdominal pressure . Dis Esophagus . 2015 ; 28 ( 4 ): 318 – 25 . OpenUrl PubMed 38. ↵ Linge J , Heymsfield SB , Dahlqvist Leinhard O . On the Definition of Sarcopenia in the Presence of Aging and Obesity-Initial Results from UK Biobank . J Gerontol A Biol Sci Med Sci . 2020 ; 75 ( 7 ): 1309 – 16 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted August 13, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Muscle strength and muscle mass in people with long term conditions and multimorbidity: A cross-sectional study of UK Biobank participants Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Muscle strength and muscle mass in people with long term conditions and multimorbidity: A cross-sectional study of UK Biobank participants Marion Guerrero-Wyss , Stuart Johnston , Fanny Peterman-Rocha , Irene Rodríguez-Gómez , Lyn D Ferguson , Frederick K Ho , Bhautesh D Jani , Stuart R Gray , Carlos A Celis-Morales medRxiv 2025.08.11.25333208; doi: https://doi.org/10.1101/2025.08.11.25333208 Share This Article: Copy Citation Tools Muscle strength and muscle mass in people with long term conditions and multimorbidity: A cross-sectional study of UK Biobank participants Marion Guerrero-Wyss , Stuart Johnston , Fanny Peterman-Rocha , Irene Rodríguez-Gómez , Lyn D Ferguson , Frederick K Ho , Bhautesh D Jani , Stuart R Gray , Carlos A Celis-Morales medRxiv 2025.08.11.25333208; doi: https://doi.org/10.1101/2025.08.11.25333208 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Epidemiology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4436) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6600) Geriatric Medicine (668) Health Economics (997) Health Informatics (4538) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (542) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3333) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9232) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00e53c7183cad07',t:'MTc3OTY0Njk2MQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();