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Adverse muscle composition is a significant risk factor for all-cause mortality in NAFLD

  • Jennifer Linge
    Correspondence
    Corresponding author. dhusgatan 5 58 222 Linköping Sweden. Tel.: +46 72 399 70 29
    Affiliations
    AMRA Medical AB, Linköping, Sweden

    Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
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  • Patrik Nasr
    Affiliations
    Department of Gastroenterology and Hepatology, And Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
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  • Arun J. Sanyal
    Affiliations
    Stravitz-Sanyal Institute of Liver Disease and Metabolic Health, Department of Internal Medicine, VCU School of Medicine, Richmond, VA, USA
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  • Author Footnotes
    1 Senior authors contributed equally.
    Olof Dahlqvist Leinhard
    Footnotes
    1 Senior authors contributed equally.
    Affiliations
    AMRA Medical AB, Linköping, Sweden

    Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden

    Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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  • Author Footnotes
    1 Senior authors contributed equally.
    Mattias Ekstedt
    Footnotes
    1 Senior authors contributed equally.
    Affiliations
    Department of Gastroenterology and Hepatology, And Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden

    Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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  • Author Footnotes
    1 Senior authors contributed equally.
Open AccessPublished:December 24, 2022DOI:https://doi.org/10.1016/j.jhepr.2022.100663

      Highlights

      • Liver fat and muscle composition was measured in >40,000 UK Biobank participants
      • Neither liver fat, nor non-alcoholic fatty liver disease was linked to mortality
      • Adverse muscle composition predicted mortality in non-alcoholic fatty liver disease

      Abstract

      Background

      The aim was to investigate associations of all-cause mortality with liver fat, NAFLD, and muscle composition (MC) in the UK Biobank imaging study. Adverse MC (i.e., low muscle volume and high muscle fat) has previously been linked to poor function and comorbidity in NAFLD.

      Methods

      Magnetic resonance images of 40174 participants were analyzed for liver proton density fat fraction (PDFF), thigh fat-free muscle volume (FFMV) z-score, and muscle fat infiltration (MFI) using AMRA® Researcher. Participants with NAFLD were sex-, age-, and BMI-matched to participants without NAFLD with low alcohol consumption. Adverse MC was identified using previously published cut-offs. All-cause mortality was investigated using Cox regression in the matched dataset and NAFLD separately. Models within NAFLD were crude and subsequently adjusted for sex, age, BMI (M1), hand grip strength, physical activity, smoking, alcohol (M2), and previous cancer, coronary heart disease, type 2 diabetes (M3).

      Results

      5069 participants had NAFLD. During a mean (±SD) follow-up of 3.9 (±1.4) years, 150 out of the 10138 participants (53% men, age 64.4 [±7.6] years, BMI 29.7 [±4.4] kg/m2) died. In the matched dataset, neither NAFLD nor liver PDFF were associated with all-cause mortality (cHRs 0.86 [0.62,1.18], p=0.350; 1.01 [0.98,1.04], p=0.550), while all MC variables achieved significance. Within NAFLD, adverse MC, MFI and FFMV z-score were significant and remained so in M1 and M2 (cHRs 2.84 [1.70,4.75], p<0.001; 1.15 [1.07,1.24], p<0.001; 0.70 [0.55,0.88], p<0.001). In M3, adverse MC and FFMV z-score were attenuated (aHRs 1.72 [1.00,2.98], p=0.051; 0.77 [0.58,1.02], p=0.069) while MFI remained significant (aHR 1.13 [1.01,1.26], p=0.026).

      Conclusions

      Neither NAFLD nor liver PDFF was predictive of all-cause mortality. Adverse MC was a strong predictor of all-cause mortality within NAFLD.

      Lay summary

      Individuals with fatty liver disease and poor muscle health more often suffer from poor function and comorbidities. This study shows that they are also at a higher risk of dying. The study results indicate that measuring muscle health (the patient's muscle volume and how much fat they have in their muscles) could help in the early detection of high-risk patients and enable targeted preventative care.

      Graphical abstract

      Keywords

      Abbreviations:

      aHR ((adjusted hazard ratio)), BMI ((body mass index)), cHR ((crude hazard ratio)), FFMV ((fat-free muscle volume)), MFI ((muscle fat infiltration)), MRI ((magnetic resonance imaging)), NAFLD ((non-alcoholic fatty liver disease)), PDFF ((proton density fat fraction)), SD ((standard deviation)), T2DM ((type 2 diabetes mellitus)), UK ((United Kingdom))

      Introduction

      The prevalence of non-alcoholic fatty liver disease (NAFLD) is high and rising in parallel with the obesity and type 2 diabetes mellitus (T2DM) epidemic [
      • Kechagias S.
      • Nasr P.
      • Blondahl J.
      • Ekstedt M.
      Established and emerging factors affecting the progression of nonalcoholic fatty liver disease.
      ]. Although NAFLD is highly prevalent, it remains challenging to foresee who will progress towards more advanced liver disease with the risk of liver-related events (i.e., decompensation and/or hepatocellular carcinoma) [
      • Ekstedt M.
      • Nasr P.
      • Kechagias S.
      Natural History of NAFLD/NASH.
      ]. A reason for this unpredictability is the high heterogeneity and wide range of clinical phenotypes observed within NAFLD. Most individuals with NAFLD do not progress to advanced liver disease, and there is a recent rise in discussions on whether NAFLD should be redefined to include criteria related to obesity and metabolic syndrome (e.g., metabolic [dysfunction] associated fatty liver disease [MAFLD]) [
      • Eslam M.
      • Sanyal A.J.
      • George J.
      International Consensus Panel. International Consensus Panel. MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease.
      ]. Identifying clinically meaningful sub-phenotypes within NAFLD could improve preventative care, aid in the development of effective pharmacological treatments, and save health care costs by separating high- and low-risk patients in the early stages of liver disease.
      Sarcopenia (a muscle disease characterized by progressive loss of muscle mass and function) is intensified in individuals with metabolic disorders and is highly prevalent in end-stage diseases [
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      Harnessing Muscle–Liver Crosstalk to Treat Nonalcoholic Steatohepatitis.
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      • et al.
      Definition and classification of cancer cachexia: an international consensus.
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      Etiology and Management of Muscle Wasting in Chronic Liver Disease.
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      • Stenvinkel P.
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      Wasting in chronic kidney disease.
      ]. Although sarcopenia and frailty are clear concerns in later stages of liver disease, the importance of muscle health is commonly overlooked in earlier disease states and more prevalent conditions like obesity, T2DM, or NAFLD [
      • Montano-Loza A.J.
      • Angulo P.
      • Meza-Junco J.
      • Prado C.M.M.
      • Sawyer M.B.
      • Beaumont C.
      • et al.
      Sarcopenic obesity and myosteatosis are associated with higher mortality in patients with cirrhosis.
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      • Sonnenday C.
      • Tapper E.B.
      • Tandon P.
      • Duarte-Rojo A.
      • et al.
      A North American expert opinion statement on sarcopenia in liver transplantation.
      ]. It is still unclear if sarcopenia accelerates the progression of disease or the other way around, but research has shown that poor muscle health is associated with higher mortality in patients with cirrhosis and may affect the outcome of liver transplantation [
      • Montano-Loza A.J.
      • Angulo P.
      • Meza-Junco J.
      • Prado C.M.M.
      • Sawyer M.B.
      • Beaumont C.
      • et al.
      Sarcopenic obesity and myosteatosis are associated with higher mortality in patients with cirrhosis.
      ,
      • Carey E.J.
      • Lai J.C.
      • Sonnenday C.
      • Tapper E.B.
      • Tandon P.
      • Duarte-Rojo A.
      • et al.
      A North American expert opinion statement on sarcopenia in liver transplantation.
      ,
      • Ebadi M.
      • Bhanji R.A.
      • Mazurak V.C.
      • Montano-Loza A.J.
      Sarcopenia in cirrhosis: from pathogenesis to interventions.
      ,
      • Englesbe M.J.
      • Patel S.P.
      • He K.
      • Lynch R.J.
      • Schaubel D.E.
      • Harbaugh C.
      • et al.
      Sarcopenia and mortality after liver transplantation.
      ]. Although sarcopenia is recognized as a highly debilitating condition with personal, social, and economic burdens when untreated, there is no consensus on how to diagnose and quantitatively assess the disease [
      • Mijnarends D.M.
      • Luiking Y.C.
      • Halfens R.J.G.
      • Evers E.L.A.
      • Lenaerts E.L.A.
      • Verlaan S.
      • et al.
      Muscle, health and costs: a glance at their relationship.
      ,
      • Cawthon P.M.
      • Lui L.Y.
      • Taylor B.C.
      • McCulloch C.E.
      • Cauley J.A.
      • Lapidus J.
      • et al.
      Clinical definitions of sarcopenia and risk of hospitalization in community-dwelling older men: the osteoporotic fractures in men study.
      ,
      • Cruz-Jentoft A.J.
      • Bahat G.
      • Bauer J.
      • Boirie Y.
      • Bruyére O.
      • Cederholm C.T.
      • et al.
      Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), and the Extended Group for EWGSOP2. Sarcopenia: revised European consensus on definition and diagnosis.
      ,
      • Studenski S.A.
      • Peters K.W.
      • Alley D.E.
      • Cawthon P.M.
      • McLean R.R.
      • Harris T.B.
      • et al.
      The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates.
      ]. This complicates the implementation of sarcopenia assessment to support treatment decisions in late stages of liver disease and hinders further understanding of whether early knowledge of a patient's muscle status could help guide treatment plans.
      Based on a rapid and standardized magnetic resonance imaging (MRI) protocol, muscle composition can be quantified with high accuracy and precision [
      • Linge J.
      • Borga M.
      • West J.
      • Tuthill T.
      • Miller M.R.
      • Dumitriu A.
      • et al.
      Body composition profiling in the UK Biobank imaging study.
      ,
      • Karlsson A.
      • Rosander J.
      • Romu T.
      • Tallberg J.
      • Grönqvist A.
      • Borga M.
      • Dahlqvist Leinhard O.
      Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI.
      ,
      • West J.
      • Romu T.
      • Thorell S.
      • Lindblom H.
      • Berin E.
      • Spetz Holm A.C.
      • et al.
      Precision of MRI-based body composition measurements of postmenopausal women.
      ,
      • Borga M.
      • Ahlgren A.
      • Romu T.
      • Widholm P.
      • Dahlqvist Leinhard O.
      • West J.
      Reproducibility and repeatability of MRI-based body composition analysis.
      ]. Measuring both thigh fat-free muscle volume (FFMV) and muscle fat infiltration (MFI) allows for detection of a condition called 'adverse muscle composition’. Adverse muscle composition is prevalent within NAFLD and has been linked to increased comorbidity (coronary heart disease and T2DM) and poor function within NAFLD, and to all-cause mortality within an adult general population [
      • Linge J.
      • Ekstedt M.
      • Dahlqvist Leinhard O.
      Adverse muscle composition is linked to poor functional performance and metabolic comorbidities in NAFLD.
      ,
      • Linge J.
      • Petersson M.
      • Forsgren M.F.
      • Sanyal A.J.
      • Dahlqvist Leinhard O.
      Adverse muscle composition predicts all-cause mortality in the UK Biobank imaging study.
      ].
      This work aimed to investigate the associations of NAFLD, liver fat, and muscle composition with all-cause mortality in the UK Biobank imaging study.

      Participants and methods

      Participants included in this study were stratified from the first 40,174 individuals scanned in the UK Biobank imaging study. UK Biobank is a long-term population study following 500,000 volunteers aged 40-69 years at recruitment in 2006-2010 []. As a sub-study, 100,000 participants are being re-called for a detailed imaging assessment, including repeat of the baseline assessment.

      Inclusion

      The inclusion of participants and analyses are summarized in Figure 1. For muscle composition assessment, participants were required to have known sex, age, weight, height, and a complete description of muscle composition (FFMV and MFI of at least one leg) (N=374 with missing data). For investigations of NAFLD, participants were also required to have non-missing values for MRI liver proton density fat fraction (PDFF), known alcohol consumption (N=703 with missing data), and low alcohol consumption (<14/21 units/week [females/males] [
      • Leoni S.
      • Tovoli F.
      • Napoli L.
      • Serio I.
      • Ferri S.
      • Bolondi L.
      Current guidelines for the management of non-alcoholic fatty liver disease: a systematic review with comparative analysis.
      ]) (N=17,112 excluded), resulting in 21,988 participants.
      Figure thumbnail gr1
      Figure 1Inclusion of study participants and summary of analyses. BMI, body mass index; MFI, muscle fat infiltration; MRI, magnetic resonance imaging; NAFLD, non-alcoholic fatty liver disease; PDFF, proton density fat fraction.
      Based on the subset of participants with low alcohol consumption, participants with NAFLD (N=5,069) were matched 1:1 with controls using sex, age, and body mass index (BMI), creating a matched dataset of 10,138.

      MRI measurements

      The participants were scanned in a Siemens MAGNETOM Aera 1.5-T MRI scanner (Siemens Healthineers, Erlangen, Germany) using a 6-10 min dual-echo Dixon Vibe protocol, providing a water and fat separated volumetric neck-to-knee dataset. Body composition analyses were performed using AMRA® Researcher (AMRA Medical AB, Linköping, Sweden) [
      • Linge J.
      • Borga M.
      • West J.
      • Tuthill T.
      • Miller M.R.
      • Dumitriu A.
      • et al.
      Body composition profiling in the UK Biobank imaging study.
      ,
      • Karlsson A.
      • Rosander J.
      • Romu T.
      • Tallberg J.
      • Grönqvist A.
      • Borga M.
      • Dahlqvist Leinhard O.
      Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI.
      ,
      • West J.
      • Romu T.
      • Thorell S.
      • Lindblom H.
      • Berin E.
      • Spetz Holm A.C.
      • et al.
      Precision of MRI-based body composition measurements of postmenopausal women.
      ,
      • Borga M.
      • Ahlgren A.
      • Romu T.
      • Widholm P.
      • Dahlqvist Leinhard O.
      • West J.
      Reproducibility and repeatability of MRI-based body composition analysis.
      ]. Supplemental material provides further details. The MRI measurements are publicly available through UK Biobank (Category 149, Abdominal composition):
      • Liver proton density fat fraction (PDFF): The average PDFF of 9 regions of interest, placed while avoiding any inhomogeneities, major vessels, and bile ducts [
        • Borga M.
        • Ahlgren A.
        • Romu T.
        • Widholm P.
        • Dahlqvist Leinhard O.
        • West J.
        Reproducibility and repeatability of MRI-based body composition analysis.
        ]. Supplementary material provides further details.
      • Fat-free muscle volume (FFMV, referred to as ”muscle volume”): The volume of all voxels with fat fraction <50% (also known as ”viable muscle tissue”) in the thighs [
        • Karlsson A.
        • Rosander J.
        • Romu T.
        • Tallberg J.
        • Grönqvist A.
        • Borga M.
        • Dahlqvist Leinhard O.
        Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI.
        ,
        • West J.
        • Romu T.
        • Thorell S.
        • Lindblom H.
        • Berin E.
        • Spetz Holm A.C.
        • et al.
        Precision of MRI-based body composition measurements of postmenopausal women.
        ]. If data were missing for one leg, the total thigh muscle volume was estimated through multiplication by two of the leg with available data.
      • Fat-free muscle volume z-score (referred to as "muscle volume z-score"): For each participant, a matched virtual control group (VCG) including at least 150 individuals with same sex and similar BMI was stratified from the study participants with complete muscle composition data. Based on each VCG, a personalized muscle volume z-score was calculated, measuring how many standard deviations each participant was from the mean thigh FFMV/height2 of their VCG [
        • Linge J.
        • Heymsfield S.B.
        • Dahlqvist Leinhard O.
        On the definition of sarcopenia in the presence of aging and obesity—initial results from UK Biobank.
        ]. This variable is sex-, weight-, and height-invariant and has been associated with poor function and increased hospitalization [
        • Linge J.
        • Ekstedt M.
        • Dahlqvist Leinhard O.
        Adverse muscle composition is linked to poor functional performance and metabolic comorbidities in NAFLD.
        ,
        • Linge J.
        • Heymsfield S.B.
        • Dahlqvist Leinhard O.
        On the definition of sarcopenia in the presence of aging and obesity—initial results from UK Biobank.
        ].
      • Muscle fat infiltration (MFI): The mean fat fraction in the ”viable muscle tissue” (i.e., FFMV) of the right and left anterior thighs [
        • Karlsson A.
        • Rosander J.
        • Romu T.
        • Tallberg J.
        • Grönqvist A.
        • Borga M.
        • Dahlqvist Leinhard O.
        Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI.
        ,
        • West J.
        • Romu T.
        • Thorell S.
        • Lindblom H.
        • Berin E.
        • Spetz Holm A.C.
        • et al.
        Precision of MRI-based body composition measurements of postmenopausal women.
        ]. If data were missing for one leg, the mean MFI was estimated by the MFI of the other leg. A sex-adjusted MFI was calculated by subtracting the sex-specific population median from each participant's MFI value.

      Definitions

      NAFLD: Defined by MRI liver PDFF >5% and lack of excess alcohol consumption (<14/21 units/week [females/males]) [
      • Leoni S.
      • Tovoli F.
      • Napoli L.
      • Serio I.
      • Ferri S.
      • Bolondi L.
      Current guidelines for the management of non-alcoholic fatty liver disease: a systematic review with comparative analysis.
      ,
      • Szczepaniak L.S.
      • Nurenberg P.
      • Leonard D.
      • Browning J.D.
      • Reingold J.S.
      • Grundy S.
      • et al.
      Magnetic resonance spectroscopy to measure hepatic triglyceride content: prevalence of hepatic steatosis in the general population.
      ]. NAFLD stratification was made directly from the UK Biobank imaging participants (community volunteers, not selected owing to abnormal liver function tests). No exclusions were made based on rarer forms of liver disease or medications.
      High MFI: As the magnitude of MFI differs between female and male participants [
      • Linge J.
      • Borga M.
      • West J.
      • Tuthill T.
      • Miller M.R.
      • Dumitriu A.
      • et al.
      Body composition profiling in the UK Biobank imaging study.
      ,
      • Linge J.
      • Petersson M.
      • Forsgren M.F.
      • Sanyal A.J.
      • Dahlqvist Leinhard O.
      Adverse muscle composition predicts all-cause mortality in the UK Biobank imaging study.
      ], ”high MFI” was defined as >75th percentile of the whole cohort (N=40,177) for female/male participants separately (>8.82/7.69 %) [
      • Linge J.
      • Ekstedt M.
      • Dahlqvist Leinhard O.
      Adverse muscle composition is linked to poor functional performance and metabolic comorbidities in NAFLD.
      ,
      • Linge J.
      • Petersson M.
      • Forsgren M.F.
      • Sanyal A.J.
      • Dahlqvist Leinhard O.
      Adverse muscle composition predicts all-cause mortality in the UK Biobank imaging study.
      ].
      Low muscle volume z-score: As the muscle volume z-score is sex invariant [
      • Linge J.
      • Heymsfield S.B.
      • Dahlqvist Leinhard O.
      On the definition of sarcopenia in the presence of aging and obesity—initial results from UK Biobank.
      ], “low muscle volume z-score” was defined as <25th percentile of the whole cohort (N=40,174) for both female and male participants (<−0.68 SD) [
      • Linge J.
      • Ekstedt M.
      • Dahlqvist Leinhard O.
      Adverse muscle composition is linked to poor functional performance and metabolic comorbidities in NAFLD.
      ,
      • Linge J.
      • Petersson M.
      • Forsgren M.F.
      • Sanyal A.J.
      • Dahlqvist Leinhard O.
      Adverse muscle composition predicts all-cause mortality in the UK Biobank imaging study.
      ].
      Adverse muscle composition: Defined by low muscle volume z-score and high MFI according to previously published definition [
      • Linge J.
      • Ekstedt M.
      • Dahlqvist Leinhard O.
      Adverse muscle composition is linked to poor functional performance and metabolic comorbidities in NAFLD.
      ,
      • Linge J.
      • Petersson M.
      • Forsgren M.F.
      • Sanyal A.J.
      • Dahlqvist Leinhard O.
      Adverse muscle composition predicts all-cause mortality in the UK Biobank imaging study.
      ].

      Data collection UK Biobank

      Mortality data were obtained through UK Biobank's linkage to national death registries. Height was recorded using a Seca stadiometer and weight with a Tanita BC418ma. Previous diagnoses of cancer, coronary heart disease, and T2DM were based on electronic health care records (accessed September 2021, available from April 1992 to September 2021) and/or self-reported information collected via interviews with trained nurses. Hand grip strength was measured using a Jamar J00105 hydraulic hand dynamometer (protocol in supplementary sections 1-3, supplementary sections 1-3, supplementary sections 1-3). The data recorded for the dominant hand were used. If information on handedness was missing or a participant reported using both hands, the mean of the right and left hand was used. Low hand grip strength was defined using the sex-specific cut-offs recommended by the European Working Group on Sarcopenia in Older People (EWGSOP2) (16/27 kg for female participants/male participants) [
      • Cruz-Jentoft A.J.
      • Bahat G.
      • Bauer J.
      • Boirie Y.
      • Bruyére O.
      • Cederholm C.T.
      • et al.
      Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), and the Extended Group for EWGSOP2. Sarcopenia: revised European consensus on definition and diagnosis.
      ]. Information on walking pace, number of falls last year, stairs climbed, smoking, alcohol consumption, and physical activity were acquired through touchscreen questionnaires. The Townsend deprivation index was calculated by UK Biobank immediately before each participant joined. Supplemental material holds further details on variable definitions.

      Statistical analysis

      Population characteristics

      Population characteristics were described using mean and standard deviation (SD) for continuous variables and percentages for binary/categorical variables. Statistical testing for differences between groups was linear/logistic regression or Pearson's chi-squared test unadjusted and adjusted for sex, age, and BMI. Due to the skewed distribution of liver PDFF, its distribution was described using the median and interquartile range.

      Associations of NAFLD and muscle composition with all-cause mortality

      Investigations were performed using Kaplan-Meier survival curves and Cox regression with years after imaging as the timescale. Both categorical representations (NAFLD=yes/no, 'high MFI’=yes/no, 'low muscle volume’=yes/no and 'adverse muscle composition’=yes/no) and continuous representations (liver PDFF [%], MFI [%], and muscle volume z-score [SD]) were used as predictors. As sensitivity analyses, models were also implemented using the whole cohort (N=40,174), the whole cohort excluding participants with a previous cancer diagnosis, in men and women separately, and for younger and older participants (using the median age as cut-point), crude and adjusted for sex, age, and BMI.

      Associations between muscle composition and all-cause mortality within NAFLD

      Investigations were performed using Kaplan-Meier survival curves and Cox regression with adverse muscle composition as well as continuous representations of MFI (%) and muscle volume z-score (SD) as predictors. An unadjusted model (M0) was implemented, followed by models subsequently adjusted for sex, age, and BMI (M1), hand grip strength, physical activity, alcohol consumption and smoking status (M2), and diagnosis of cancer, coronary heart disease, and T2DM before imaging (M3).

      Adverse muscle composition and functional performance within NAFLD

      The associations of muscle composition (i.e., adverse muscle composition, low muscle volume z-score and high MFI) and functional performance (i.e., hand grip strength, walking pace, falls and stair climbing) with all-cause mortality within NAFLD were investigated using separate crude and combined Cox regressions, including each muscle composition variable with all measures of functional performance as predictors in the same model.

      Adverse muscle composition and liver-related outcomes within NAFLD

      Liver-related events were identified through electronic health care records (accessed September 2021, available from April 1992 to September 2021) and grouped according to Hagström, et al. [
      • Hagström H.
      • Adams L.A.
      • Allen A.M.
      • Byrne C.D.
      • Chang Y.
      • Grønbaek H.
      • et al.
      Administrative Coding in Electronic Health Care Record-Based Research of NAFLD: An Expert Panel Consensus Statement.
      ]. For this specific investigation, participants with NAFLD and other liver diseases at/before imaging, alcohol/drug use disorder at/before imaging, and recorded liver-related events at/before imaging were additionally excluded. Details can be found in Supplementary Table S1. Logistic regressions of a composite variable including all new liver-related events were performed for sex-adjusted MFI and muscle volume z-score.
      This research was conducted using the UK Biobank resource, project ID 6569. The study was approved by the North West Multicenter Research Ethics Committee, UK. Written informed consent was obtained before study entry.

      Results

      Population characteristics

      When adjusted for sex, age, and BMI, NAFLD participants had lower muscle volume z-score but similar MFI and prevalence of low functional performance compared to their matched controls. The prevalence of T2DM was higher within NAFLD compared to matched controls (14.7% vs. 6.5%), while the prevalence of coronary heart disease was lower (6.5% vs. 7.7%). Further details on population characteristics are presented in Table 1.
      Table 1Population characteristics. Values are mean (standard deviation) for continuous variables and percentages for binary/categorical. p-values are from linear/logistic regression or chi-squared test unadjusted and adjusted for sex, age, BMI. Liver PDFF is given in median (interquartile range). BMI, body mass index, FFMV, fat-free muscle volume, MFI, muscle fat infiltration, NAFLD, non-alcoholic fatty liver disease, PDFF, proton density fat fraction.
      NAFLDMatched controlsp-valuep-value (adjusted)
      N50695069--
      Sex (female/male)46.9%/53.1%46.9%/53.1%1.000-
      Age (years)64.33 (7.47)64.54 (7.69)0.152-
      BMI (kg/m2)30.26 (4.83)29.19 (3.75)<0.001-
      Waist circumference (cm)98.40 (11.92)94.48 (10.53)<0.001<0.001
      Liver PDFF (%)8.42 (6.24-12.30)2.70 (1.99-3.62)<0.001<0.001
      Muscle composition
      Muscle volume z-score (SD)-0.06 (0.97)-0.03 (1.01)0.1030.022
      FFMV, total thigh (L)10.75 (2.56)10.69 (2.62)0.2430.003
      FFMV, left anterior thigh (L)1.79 (0.49)1.79 (0.51)0.744<0.001
      FFMV, right anterior thigh (L)1.80 (0.50)1.80 (0.51)0.680<0.001
      FFMV, left posterior thigh (L)3.54 (0.80)3.52 (0.82)0.1290.013
      FFMV, right posterior thigh (L)3.59 (0.82)3.56 (0.83)0.0870.022
      MFI, mean anterior thigh (%)8.03 (2.16)7.82 (2.07)<0.0010.938
      MFI, sex-adjusted (%)1.02 (2.03)0.81 (1.93)<0.0010.938
      MFI, left anterior thigh (%)8.16 (2.18)7.95 (2.11)<0.0010.786
      MFI, right anterior thigh (%)7.91 (2.19)7.71 (2.11)<0.0010.758
      MFI, left posterior thigh (%)11.94 (2.63)11.61 (2.53)<0.0010.044
      MFI, right posterior thigh (%)11.74 (2.66)11.41 (2.54)<0.0010.069
      Adverse muscle composition (yes/no)15.2%/84.8%14.7%/85.3%0.4860.487
      Low muscle volume (yes/no)26.2%/73.8%26.2%/73.8%1.0000.751
      High MFI (yes/no)38.8%/61.2%35.2%/64.8%<0.0010.566
      Functional performance
      Hand grip strength (kg)31.31 (10.97)31.78 (10.95)0.032<0.001
      Low hand grip strength (yes/no/missing)8.9%/88.2%/2.9%8.0%/89.2%/2.7%0.1020.093
      Slow walking pace (yes/no/missing)9.3%/90.4%/0.3%7.8%/92.0%/0.3%0.0060.576
      >1 fall last year (yes/no/missing)5.5%/94.3%/0.2%5.8%/94.1%/0.1%0.4960.163
      No stair climbing (yes/no/missing)10.9%/88.6%/0.5%10.4%/89.1%/0.6%0.3730.702
      Comorbidity
      Cancer (yes/no/missing)11.0%/88.5%/0.4%10.5%/89.4%/0.1%0.3110.227
      Type 2 diabetes (yes/no/missing)14.7%/84.4%/0.9%6.5%/92.9%/0.6%<0.001<0.001
      Coronary heart disease (yes/no/missing)6.5%/93.1%/0.4%7.7%/91.9%/0.4%0.0300.010
      Lifestyle factors
      Smoking (no/previous/current/missing)65.7%/31.1%/2.8%/0.4%64.9%/31.2%/3.5%/0.3%0.0920.056
      Alcohol consumption (g/day)6.83 (6.91)7.43 (7.09)<0.001<0.001
      Physical activity IPAQ (moderate/low/high/missing)45.5%/28.2%/26.1%/0.2%43.5%/23.8%/32.6%/0.2%<0.0010.024
      Townsend deprivation index-1.61 (2.87)-1.71 (2.77)0.0740.581
      During a mean follow-up of 3.9 (SD ±1.4), representing 39,450 person-years at risk, 150 out of the 10,138 participants died. Specific causes of death are visualized in Figure 2. The most common causes of death were from neoplasms (51%) and diseases of the circulatory system (27%). The numbers of death were similar between NAFLD and matched controls across ICD-10 code chapters, and the breakdown in ICD-10 code blocks also showed similar distributions. A complete breakdown can be found in Supplementary Tables S2–S3.
      Figure thumbnail gr2
      Figure 2Breakdown of primary causes of death within NAFLD and sex-, age-, and BMI-matched controls. Middle panel shows distribution of counts between NAFLD and matched controls in ICD-10 chapters with more than 10 deaths recorded. Pie charts show breakdown of ICD-10 blocks within each ICD-10 chapter for NAFLD (left) and matched controls (right) separately.

      Associations of NAFLD and muscle composition with all-cause mortality

      The rate of all-cause mortality was similar comparing NAFLD and matched controls (Figure 3; number at risk in Fig. S1). Accordingly, the Cox regression showed that neither NAFLD nor liver PDFF were predictive of all-cause mortality (crude [cHR] 0.86; 95% CI 0.62-1.18, p=0.350 and cHR 1.01; 95% CI 0.98-1.04, p=0.550, respectively) (Figure 3). However, a lower muscle volume z-score was significantly associated with all-cause mortality in both categorical (low compared to normal muscle volume) and continuous representation, as was a higher MFI. Adverse muscle composition showed the strongest association (cHR: 3.05 [2.16,4.30], p<0.001).
      Figure thumbnail gr3
      Figure 3Left: Kaplan-Meier survival curves for all-cause mortality comparing non-alcoholic fatty liver disease (NAFLD) [blue] and sex-, age- and BMI-matched controls [grey]. Right: Unadjusted hazard ratios from Cox regression in the matched cohort (NAFLD and matched controls [N=10,138]) for categorical [yes/no] variables: NAFLD, low muscle volume z-score, high muscle fat infiltration and adverse muscle, and for continuous variables: liver proton density fat fraction (PDFF) [%], muscle volume z-score [SD], and muscle fat infiltration [%].
      Sensitivity analysis showed similar results: NAFLD was non-significant in all stratified groups (whole cohort, whole cohort without cancer, women only, men only, younger participants, and older participants) both in crude and sex-, age-, and BMI-adjusted models. Liver PDFF was significant in crude modelling for the whole cohort excluding cancer, and in younger participants (cHR 1.02; 95% CI 1.00-1.05, p=0.024 and 1.05; 95% CI 1.02-1.08, p=0.002, respectively), but not in adjusted modelling (adjusted HR [aHR] 1.01; 95% CI 0.98-1.03, p=0.559 and 1.03; 95% CI 0.99-1.07, p=0.140, respectively). All muscle composition variables showed significant associations both in crude and adjusted models except for muscle volume z-score in the adjusted model for younger participants (aHR 0.84; 95% CI 0.70-1.02, p=0.073). Supplementary Tables S4–5 includes full reporting of modelling results.

      Associations between muscle composition and all-cause mortality within NAFLD

      In participants with NAFLD, adverse muscle composition, high MFI, and low muscle volume z-scores were significantly associated with all-cause mortality (Figure 4, Table 2). After adjustment for sex, age, BMI, low hand grip strength, physical activity, smoking status, and alcohol, all muscle composition variables remained significant. When additionally adjusting for relevant comorbidities (previous cancer, coronary heart disease, and T2DM), adverse muscle composition and muscle volume z-score were attenuated to non-significance (p=0.051 and 0.069, respectively), while MFI remained significantly associated with all-cause mortality (p=0.026). Supplementary Tables S6–S8 include full reporting of modeling results.
      Figure thumbnail gr4
      Figure 4Kaplan-Meier survival curves comparing NAFLD participants with/without adverse muscle composition, low muscle volume z-score, high muscle fat infiltration.
      Table 2Cox proportional-hazard ratios (HRs) of all-cause mortality within NAFLD for adverse muscle composition [yes/no], muscle fat infiltration (MFI) [%], and muscle volume z-score [SD] including crude HRs and subsequent adjustments for sex, age, BMI (M1); low hand grip strength, smoking status, alcohol (M2); previous cancer, coronary heart disease, type 2 diabetes diagnosis (M3).
      CrudeM1M2M3
      HRp-valueHRp-valueHRp-valueHRp-value
      Adverse muscle composition2.84 (1.70, 4.75)<0.0011.83 (1.08, 3.12)0.02511.82 (1.06, 3.14)0.03021.72 (1.00, 2.98)0.0514
      Muscle fat infiltration1.15 (1.07, 1.24)<0.0011.13 (1.03, 1.24)0.00971.14 (1.02, 1.26)0.01711.13 (1.01, 1.26)0.0263
      Muscle volume z-score0.70 (0.55, 0.88)0.00280.74 (0.56, 0.96)0.02480.75 (0.57, 0.99)0.04420.77 (0.58, 1.02)0.0688

      Adverse muscle composition and functional performance within NAFLD

      Adverse muscle composition showed the strongest association with all-cause mortality (cHR: 2.84; 95% CI 0.35-4.75, p<0.001) also when compared to measures of functional performance (low hand grip strength, slow walking pace, >1 fall last year, and no stair climbing) (Table 3). The only functional tests that reached significance in the crude modeling were low hand grip strength (p=0.035) and slow walking pace (p=0.035). Interestingly, when muscle composition was combined with the functional tests, muscle composition remained significant while the associations with the functional tests were attenuated below significance.
      Table 3Cox proportional-hazard ratios (HRs) of all-cause mortality within NAFLD for categorical muscle composition variables (adverse muscle composition, high muscle fat infiltration, low muscle volume z-score) and measures of functional performance including crude HRs and results from multivariable modelling (MV1-MV3) including each respective muscle composition variable and all measures of functional performance.
      CrudeMV1MV2MV3
      HRp-valueHRp-valueHRp-valueHRp-value
      Adverse muscle composition [N=724 (15%)]2.84 (1.70, 4.75)<0.0012.72 (1.60, 4.63)<0.001
      High muscle fat infiltration [N=1,910 (39%)]2.10 (1.30, 3.38)0.0021.83 (1.11, 3.00)0.017
      Low muscle volume z-score [N=1,289 (26%)]2.02 (1.24, 3.29)0.0051.95 (1.18, 3.22)0.009
      Low hand grip strength [N=437 (9%)]2.06 (1.05, 4.04)0.0351.67 (0.83, 3.33)0.1481.79 (0.90, 3.56)0.0971.69 (0.84, 3.38)0.141
      Slow walking pace [N=454 (9%)]2.00 (1.05, 3.82)0.0351.55 (0.78, 3.09)0.2081.54 (0.77, 3.06)0.221.68 (0.85, 3.33)0.136
      >1 fall last year [N=266 (5%)]1.57 (0.68, 3.62)0.2941.22 (0.51, 2.90)0.6601.28 (0.54, 3.05)0.5761.26 (0.52, 3.02)0.607
      No stair climbing [N=540 (11%)]1.13 (0.54, 2.36)0.7450.97 (0.46, 2.06)0.9421.01 (0.48, 2.12)0.9871.02 (0.48, 2.14)0.969

      Adverse muscle composition and liver related outcomes within NAFLD

      After excluding NAFLD participants with other liver diseases at/before baseline or alcohol/drug use disorder at/before baseline, N=4,923 NAFLD participants remained. Of the remaining participants, N=8 had at least one liver-related event at/before imaging and were excluded. Of the N=4,915 remaining NAFLD participants, 13 (0.26%) had at least one liver-related event post imaging (Supplementary Table S1). Participants with a liver-related event post imaging, showed a higher sex-adjusted MFI (1.01 [2.02] vs. 2.14 [2.58] %, p=0.039). No other significant associations related to muscle composition were observed.

      Discussion

      In this study, we investigated the relationship of NAFLD and muscle composition with all-cause mortality in the UK Biobank imaging study. The main findings include (1) NAFLD (nor liver fat) was associated with all-cause mortality, and (2) adverse muscle composition was predictive of all-cause mortality within NAFLD.

      NAFLD, liver fat, and all-cause mortality

      Several studies have shown strong associations between fibrosis stage and increased mortality within NAFLD [
      • Dulai P.S.
      • Singh S.
      • Patel J.
      • Soni M.
      • Prokop L.J.
      • Younossi Z.
      • et al.
      Increased risk of mortality by fibrosis stage in non-alcoholic fatty liver disease: Systematic review and meta-analysis.
      ,
      • Ekstedt M.
      • Hagström H.
      • Nasr P.
      • Fredrikson M.
      • Stål P.
      • Kechagias S.
      • Hultcrantz R.
      Fibrosis stage is the strongest predictor for disease-specific mortality in NAFLD after up to 33 years of follow-up.
      ,
      • Angulo P.
      • Kleiner D.E.
      • Dam-Larsen S.
      • Adams L.A.
      • Bjornsson E.S.
      • Charatcharoenwitthaya P.
      • et al.
      Liver Fibrosis, but No Other Histologic Features, Is Associated With Long-term Outcomes of Patients With Nonalcoholic Fatty Liver Disease.
      ]. Similarly, a recent study on >10,000 individuals with biopsy-proven NAFLD showed increased mortality risk by histological stage [
      • Simon T.G.
      • Roelstraete B.
      • Khalili H.
      • Hagström H.
      • Ludvigsson J.F.
      Mortality in biopsy-confirmed non-alcoholic fatty liver disease: results from a nationwide cohort.
      ]. The study also concluded that simple steatosis significantly increased the risk of death. However, the design of biopsy-based studies is not optimal for investigating if simple steatosis alone drives an increased risk of death as they commonly include controls where there is no indication to biopsy, or there is no matching on (or adjustment for differences in) overall adiposity or BMI. Although NAFLD was not confirmed by biopsy, but through MRI (liver PDFF) in our study, the results indicate an opposite conclusion. The link between NAFLD (as identified by elevated alanine aminotransferases) and all-cause mortality has also been investigated in the NHANES III study: An early study showed a significant association with all-cause mortality (aHR 1.038; 95% CI 1.036-1.041) [
      • Ong J.P.
      • Pitts A.
      • Younossi Z.B.
      Increased overall mortality and liver-related mortality in non-alcoholic fatty liver disease.
      ]. This was however challenged by another study, on the same dataset, published at about the same time [
      • Ruhl C.E.
      • Everhart J.E.
      Elevated serum alanine aminotransferase and gamma-glutamyltransferase and mortality in the United States population.
      ]. In that study, no significant association was observed (aHR 1.20; 95% CI 0.88-1.60) and authors claim the main difference between their studies was statistical approach [
      • Ruhl C.E.
      • Everhart J.E.
      Elevated serum alanine aminotransferase and gamma-glutamyltransferase and mortality in the United States population.
      ,
      • Ruhl C.E.
      • Everhart J.E.
      Non-alcoholic fatty liver disease (NAFLD) and mortality.
      ]. A more recent study using the NHANES III data also showed a non-significant association with all-cause mortality [
      • Huang Q.
      • Zou X.
      • Wen X.
      • Zhou X.
      • Ji L.
      NAFLD or MAFLD: Which Has Closer Association With All-Cause and Cause-Specific Mortality?-Results From NHANES III.
      ]. In fact, there was a trend towards a negative association with all-cause mortality, and a significant negative association with cardiovascular mortality with full adjustment. In the presence of visceral obesity, a low liver fat has also recently been shown to increase the risk for cardiovascular disease in both the Dallas Heart Study and UK Biobank [
      • Tejani S.
      • McCoy C.
      • Ayers C.R.
      • Powell-Wiley T.M.
      • Després J.P.
      • Linge J.
      • et al.
      Cardiometabolic Health Outcomes Associated With Discordant Visceral and Liver Fat Phenotypes: Insights From the Dallas Heart Study and UK Biobank.
      ]. These are important facts to consider as an increasing number of pharmacological therapies are aiming to attenuate histopathological surrogates associated with hepatic fibrosis deposition and progression, including the presence of steatosis. Clearly, there are differences in results when investigating the association between NAFLD and all-cause mortality. Several studies show an increased mortality [
      • Ekstedt M.
      • Hagström H.
      • Nasr P.
      • Fredrikson M.
      • Stål P.
      • Kechagias S.
      • Hultcrantz R.
      Fibrosis stage is the strongest predictor for disease-specific mortality in NAFLD after up to 33 years of follow-up.
      ,
      • Simon T.G.
      • Roelstraete B.
      • Khalili H.
      • Hagström H.
      • Ludvigsson J.F.
      Mortality in biopsy-confirmed non-alcoholic fatty liver disease: results from a nationwide cohort.
      ,
      • Ong J.P.
      • Pitts A.
      • Younossi Z.B.
      Increased overall mortality and liver-related mortality in non-alcoholic fatty liver disease.
      ] while others do not [
      • Ruhl C.E.
      • Everhart J.E.
      Elevated serum alanine aminotransferase and gamma-glutamyltransferase and mortality in the United States population.
      ,
      • Huang Q.
      • Zou X.
      • Wen X.
      • Zhou X.
      • Ji L.
      NAFLD or MAFLD: Which Has Closer Association With All-Cause and Cause-Specific Mortality?-Results From NHANES III.
      ,
      • Hagström H.
      • Nasr P.
      • Ekstedt M.
      • Hammar U.
      • Stål P.
      • Hultcrantz R.
      • Kechagias S.
      Fibrosis stage but not NASH predicts mortality and time to development of severe liver disease in biopsy-proven NAFLD.
      ]. The differences probably lie in selection bias within the NAFLD population studied. In our study, NAFLD stratification was made directly from the UK Biobank imaging participants (community volunteers, not selected owing to abnormal liver function tests), hence, although unknown, the low prevalence of liver disease in our cohort indicates it mostly constitutes of participants with NAFLD without presence of advanced fibrosis.

      Muscle composition, liver disease and all-cause mortality

      Recent literature highlights an increased awareness of the importance of assessing muscle health in earlier stages of liver disease [
      • Chakravarthy M.V.
      • Siddiqui M.S.
      • Forsgren M.F.
      • Sanyal A.J.
      Harnessing Muscle–Liver Crosstalk to Treat Nonalcoholic Steatohepatitis.
      ,
      • Nachit M.
      • Leclercq I.A.
      Emerging awareness on the importance of skeletal muscle in liver diseases: time to dig deeper into mechanisms.
      ]. Further research within NAFLD (or early metabolic/liver disease) investigating the link between muscle health and disease progression and related outcomes is important in evaluating muscle measurements as potential prognostic biomarkers. Multiple studies have reported an association between sarcopenia and NAFLD [
      • De Fré C.H.
      • De Fré M.A.
      • Kwanten W.J.
      • Op de Beeck B.J.
      • Van Gaal L.F.
      • Francque S.M.
      Sarcopenia in patients with non-alcoholic fatty liver disease: is it a clinically significant entity?.
      ,
      • Lee Y.H.
      • Kim S.U.
      • Song K.
      • Park J.Y.
      • Kim D.Y.
      • Ahn S.H.
      • et al.
      Sarcopenia is associated with significant liver fibrosis independently of obesity and insulin resistance in nonalcoholic fatty liver disease: Nationwide surveys (KNHANES 2008-2011).
      ,
      • Yu R.
      • Shi Q.
      • Liu L.
      • Chen L.
      Relationship of sarcopenia with steatohepatitis and advanced liver fibrosis in non-alcoholic fatty liver disease: a meta-analysis.
      ]. However, it has also been shown that common ways of adjusting muscle mass for body size (i.e., through division by height2, weight or BMI) do not achieve proper normalization, and that different adjustments result in completely different conclusions about the association between NAFLD and sarcopenia [
      • Linge J.
      • Heymsfield S.B.
      • Dahlqvist Leinhard O.
      On the definition of sarcopenia in the presence of aging and obesity—initial results from UK Biobank.
      ,
      • Peng T.C.
      • Wu L.W.
      • Chen W.L.
      • Liaw F.Y.
      • Chang Y.W.
      • Kao T.W.
      Nonalcoholic fatty liver disease and sarcopenia in a Western population (NHANES III): The importance of sarcopenia definition.
      ]. The use of muscle volume z-score has been shown to effectively remove the association to body size (height, weight, and BMI) [
      • Linge J.
      • Heymsfield S.B.
      • Dahlqvist Leinhard O.
      On the definition of sarcopenia in the presence of aging and obesity—initial results from UK Biobank.
      ]. This is especially important to consider for patient populations, like NAFLD, where obesity is more prevalent. In line with our results, a significant association between low muscle mass and all-cause mortality was observed in a recent study investigating the relationship between physical inactivity, NAFLD and sarcopenia [
      • Golabi P.
      • Berber L.
      • Paik J.M.
      • Deshpande R.
      • de Avila L.
      • Younossi Z.M.
      Contribution of sarcopenia and physical inactivity to mortality in people with non-alcoholic fatty liver disease.
      ]. Our study showed that muscle composition was predictive of all-cause mortality within NAFLD independent of functional performance (measured by hand grip strength) and physical activity level. It is also important to note that MFI was a strong predictor across all models. A combined assessment of both muscle volume and fat infiltration has been shown to effectively stratify the most vulnerable individuals within a wide range of individuals — from general population to NAFLD, chronic kidney disease, critically ill patients, and patients with pancreatic cancer [
      • Linge J.
      • Ekstedt M.
      • Dahlqvist Leinhard O.
      Adverse muscle composition is linked to poor functional performance and metabolic comorbidities in NAFLD.
      ,
      • Linge J.
      • Petersson M.
      • Forsgren M.F.
      • Sanyal A.J.
      • Dahlqvist Leinhard O.
      Adverse muscle composition predicts all-cause mortality in the UK Biobank imaging study.
      ,
      • Loosen S.H.
      • Schulze-Hagen M.
      • Püngel T.
      • Bündgens L.
      • Wirtz T.
      • Kather J.N.
      • et al.
      Skeletal Muscle Composition Predicts Outcome in Critically Ill Patients.
      ,
      • Stretch C.
      • Aubin J.M.
      • Mickiewicz B.
      • Leugner D.
      • Al-Manasra T.
      • Tobola E.
      • et al.
      Sarcopenia and myosteatosis are accompanied by distinct biological profiles in patients with pancreatic and periampullary adenocarcinomas.
      ], and recent literature has started to indicate the potential of myosteatosis as a prognostic biomarker for NASH and fibrosis progression [
      • Nachit M.
      • Kwanten W.J.
      • Thissen J.P.
      • Op de Beeck B.
      • Van Gaal L.
      • Vonghia L.
      • et al.
      Muscle fat content is strongly associated with NASH: A longitudinal study in patients with morbid obesity.
      ,
      • Hsieh Y.C.
      • Joo S.K.
      • Koo B.K.
      • Lin H.C.
      • Lee D.H.
      • Chang M.S.
      • et al.
      Innovative Target Exploration of NAFLD (ITEN) Consortium. Myosteatosis, but not Sarcopenia, Predisposes NAFLD Subjects to Early Steatohepatitis and Fibrosis Progression.
      ]. Although too few NAFLD participants in our study had a liver-related event post imaging to draw any conclusions, the significant association with MFI is in line with previous research. Further research, with longer observation time or in a more high risk population, is needed to confirm the potential relationship between MFI and liver disease progression.
      The lack of consensus around diagnosis, and how to detect and track sarcopenia, hinders further understanding of how assessment of muscle health could guide preventative care in early liver disease. A wide range of tests for muscle strength and functional performance are used as well as several techniques to measure (and normalize) muscle quantity [
      • Cruz-Jentoft A.J.
      • Bahat G.
      • Bauer J.
      • Boirie Y.
      • Bruyére O.
      • Cederholm C.T.
      • et al.
      Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), and the Extended Group for EWGSOP2. Sarcopenia: revised European consensus on definition and diagnosis.
      ,
      • Studenski S.A.
      • Peters K.W.
      • Alley D.E.
      • Cawthon P.M.
      • McLean R.R.
      • Harris T.B.
      • et al.
      The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates.
      ,
      • Chen L.K.
      • Woo J.
      • Assantachai P.
      • Auyeung T.W.
      • Chou M.Y.
      • Iijima K.
      • et al.
      Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment.
      ]. Due to the modest performance of muscle mass compared to strength or function in predicting adverse outcomes, the sarcopenia field has moved from definitions based on muscle mass alone towards a more pronounced focus on muscle strength and/or functional performance [
      • Cruz-Jentoft A.J.
      • Bahat G.
      • Bauer J.
      • Boirie Y.
      • Bruyére O.
      • Cederholm C.T.
      • et al.
      Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), and the Extended Group for EWGSOP2. Sarcopenia: revised European consensus on definition and diagnosis.
      ,
      • Newman A.B.
      • Kupelian V.
      • Visser M.
      • Simonsick E.M.
      • Goodpaster B.H.
      • Kritchevsky S.B.
      • et al.
      Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort.
      ]. However, use of measures for muscle strength and/or function alone is not sufficient to diagnose sarcopenia as low muscle strength/function can be caused by, or associated with, a variety of factors besides sarcopenia. This study shows that assessing muscle composition could be relevant in the early stages of liver disease and that measures of muscle strength, function and frailty do not attenuate the association between muscle composition and all-cause mortality. Quantitative muscle biomarkers with high precision and a strong link to functional performance and outcomes are important for tracking of sarcopenia during development of treatments, and allows for rapid detection of disease progression and shortening of trial durations.

      Strengths and limitations

      The main strengths of this study are the sample size, allowing analysis of a large number of participants fulfilling the NAFLD criteria with sex-, age-, and BMI-matched controls, and the detailed characterization of muscle composition through volumetric MRI. It is, however, important to point out that the NAFLD population studied is likely not the same as the NAFLD population seen in clinical care. The application of the NAFLD criteria to the population volunteering for the UK Biobank imaging study is like screening for NAFLD in this group – they were not biopsied or imaged due to a referral in clinical care. However, measuring liver PDFF in a large population study like UK Biobank enables investigation of the independent risk of elevated liver fat content avoiding confounders associated with the strata of patients in clinical care with an indication to biopsy or image. In addition, the participants were not followed for an extensive number of years and although previous diseases (cancer, coronary heart disease and T2DM) were included in the analysis, it is unknown whether these diseases preceded muscle composition changes or the other way around. It is important to further explore interactions between metabolic diseases and muscle composition in the pursuit of validating muscle measurements as potential prognostic biomarkers for liver disease. Additionally, as the serum biomarker panel is not yet released for the UK Biobank imaging visit, such data was not included in this study. The biomarker panel data from the baseline assessment (collected years 2006-2010) for adverse muscle composition in NAFLD has been published before [
      • Linge J.
      • Ekstedt M.
      • Dahlqvist Leinhard O.
      Adverse muscle composition is linked to poor functional performance and metabolic comorbidities in NAFLD.
      ]. Those data showed that adverse muscle composition was associated with a higher Fibrosis-4 and HbA1c when adjusted for sex, age, liver PDFF, and BMI. Lastly, although hand grip strength was measured according to gold-standard methods, the other variables describing functional performance were self-reported, and the categories for low function (slow walking pace, no stair climbing and >1 fall last year) stratified relatively few participants. Further research is needed to understand how adverse muscle composition assessed by MRI can be introduced into clinical care and if the workflow could benefit from screening with functional tests.

      Conclusion

      Neither NAFLD nor liver fat content was predictive of all-cause mortality in the UK Biobank imaging study. Adverse muscle composition (the combination of low muscle volume z-score with high muscle fat infiltration) was a strong predictor of all-cause mortality within NAFLD, independent of functional performance. This research further supports the potential of muscle measurements as prognostic biomarkers for liver disease progression.

      Financial support statement

      Funding for image analysis was gratefully received from Pfizer Inc. ALF-grants Region Östergötland.

      Conflict of interest statement

      JL is an employee of AMRA Medical AB and reports other from BioMarin and Eli Lilly. AJS reports grants from Intercept, during the conduct of the study; other from Sanyal Bio, Genfit, Indalo, Tiziana, Durect, Exhalenz, Galmed, second genome, Cymabay, Prosciento, Labcorp, Medimmune, Astra Zeneca, Albireo; grants from Merck, Bristol Myers, Boehringer Ingelhiem, Immuron, Malinkrodt, Cumberland, Sequana; grants and personal fees from Novartis, Gilead, Conatus, Echosens; personal fees from Pfizer, Lilly, Novo Nordisk, Sanofi, Tern, Hemoshear, Glympse, Birdrock, Blade, Teva, Artham, Salix, NASH pharmaceuticals, outside the submitted work. ODL is an employee of and shareholder in AMRA Medical AB and reports other from Eli Lilly. ME is a member of the advisory board at AMRA Medical AB.

      Author contributions

      Study concept and design: JL, ODL, ME; analysis and interpretation of data: JL, PN, ODL, ME; drafting of the manuscript: JL; critical revision of the manuscript for important intellectual content: all authors; statistical analysis: JL.

      Clinical trial number

      Not applicable.

      Data availability statement

      This research was conducted using the UK Biobank resource, project ID 6569. UK Biobank data are available through the procedure described at http://www.ukbiobank.ac.uk/using-the-resource/.

      Uncited References

      • Volpi E.
      • Nazemi R.
      • Fujita S.
      Muscle tissue changes with aging.
      .

      Appendix A. Supplementary data

      The following is the supplementary data to this article:

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