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Associations of indicators of sleep impairment and disorders with low muscle strength in middle-aged and older adults: The HypnoLaus cohort study

  • Ronaldo D. Piovezan
    Correspondence
    Corresponding author at: 28 Woodville Rd, Woodville South, SA 5011, Australia.
    Affiliations
    Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre, Adelaide Medical School, the Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia

    Aged and Extended Care Services, The Queen Elizabeth Hospital, Central Adelaide Local Health Network, Adelaide, Australia
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  • Solomon Yu
    Affiliations
    Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre, Adelaide Medical School, the Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia

    Aged and Extended Care Services, The Queen Elizabeth Hospital, Central Adelaide Local Health Network, Adelaide, Australia
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  • Camila Hirotsu
    Affiliations
    Center for Investigation and Research in Sleep (CIRS), University Hospital of Lausanne, Lausanne, Switzerland
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  • Pedro Marques-Vidal
    Affiliations
    Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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  • José Haba-Rubio
    Affiliations
    Center for Investigation and Research in Sleep (CIRS), University Hospital of Lausanne, Lausanne, Switzerland
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  • Graeme Tucker
    Affiliations
    Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre, Adelaide Medical School, the Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia
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  • Robert Adams
    Affiliations
    Flinders Health and Medical Research Institute–Sleep Health, Flinders University, Adelaide, Australia
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  • Renuka Visvanathan
    Affiliations
    Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre, Adelaide Medical School, the Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, Australia

    Aged and Extended Care Services, The Queen Elizabeth Hospital, Central Adelaide Local Health Network, Adelaide, Australia
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  • Raphaël Heinzer
    Affiliations
    Center for Investigation and Research in Sleep (CIRS), University Hospital of Lausanne, Lausanne, Switzerland
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      Highlights

      • Indicators of sleep impairment and disorders precisely detected by polysomnography were associated with low muscle strength, a marker of sarcopenia, in a population-based study that included middle-aged and older adults of both genders.
      • Objective long sleep duration was the most consistent predictor of low muscle strength across different age and gender groups.
      • A U-shaped relationship between objective sleep duration and muscle strength was found particularly in older adults, as objective short sleep duration was also associated with low muscle strength in this age group (only).
      • Severe obstructive sleep apnea was persistently associated with low muscle strength in older adults and women, suggesting muscle health deterioration may play a role in the development of age-related obstructive sleep apnea.
      • Subjectively measured indicators of sleep quality and duration, as well as daytime sleepiness, were not associated with low grip strength.

      Abstract

      Objective

      Investigate associations of objective and subjective indicators of sleep impairment and disorders with low muscle strength (LMS) in different age groups and genders using data from a population-based cohort study.

      Methods

      Polysomnographic and subjective sleep data from participants (aged 40–80 years) of the HypnoLaus study (Lausanne, Switzerland) were cross-sectionally analyzed. Indicators of sleep impairment and disorders were based on pre-defined cutoffs. LMS was defined according to the diagnosis of sarcopenia (grip strength <27 kg for men and <16 kg for women). Results obtained by multivariate logistic regression were controlled for confounders.

      Results

      1902 participants (mean [SD] age, 57.4 [10.5] years; 968 [50.9 %] female) were enrolled. Objective short (<6.2 h) and long sleep durations (>8.5 h) were associated with LMS (OR = 1.74, 95 % CI = 1.07–2.82; OR = 6.66, 95 % CI = 3.45–12.87, respectively). Increased nighttime wakefulness >90 min and severe obstructive apnea (OSA) (AHI > 30) were associated with LMS (OR = 1.60, 95 % CI = 1.01–2.56; OR = 2.36, 95 % CI = 1.29–4.31, respectively). In adults aged over 60 years, these associations persisted, and reduced sleep efficiency was associated with LMS (aOR = 1.81, 95 % CI 1.05–3.13). Objective long sleep duration was associated with LMS in both genders and severe OSA predicted LMS among women (aOR = 2.64, 95 % CI 1.11–6.24).

      Conclusions

      Markers of early sarcopenia are affected by long sleep duration from middle age onwards in both genders. Older adults are more susceptible to the effects of other indicators of inappropriate sleep duration and quality. The findings support a potential role of sarcopenia in age-related OSA. The intricate relationships between sleep and muscle health are potential targets of public health interventions and clinical research on preventive and therapeutic strategies against the increasing morbimortality observed with ageing.

      Keywords

      1. Introduction

      Detecting early markers of sarcopenia development can diminish the risk of comorbidities, falls, disability and mortality in advanced age [
      • Kalyani R.R.
      • Corriere M.
      • Ferrucci L.
      Age-related and disease-related muscle loss: the effect of diabetes, obesity, and other diseases.
      ]. Low muscle strength (LMS) detected by grip dynamometer is a feasible and reliable indicator of poor muscle health recommended as a first step for the detection of sarcopenia [
      • Cruz-Jentoft A.J.
      • Bahat G.
      • Bauer J.
      • Boirie Y.
      • Bruyère O.
      • Cederholm T.
      • Cooper C.
      • Landi F.
      • Rolland Y.
      • Sayer A.A.
      • Schneider S.M.
      • Sieber C.C.
      • Topinkova E.
      • Vandewoude M.
      • Visser M.
      • Zamboni M.
      • Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2)
      • the Extended Group for EWGSOP2
      • Bautmans I.
      • Baeyens J.-P.
      • Cesari M.
      • Cherubini A.
      • Kanis J.
      • Maggio M.
      • Martin F.
      • Michel J.-P.
      • Pitkala K.
      • Reginster J.-Y.
      • Rizzoli R.
      • Sánchez-Rodríguez D.
      • Schols J.
      Sarcopenia: revised European consensus on definition and diagnosis.
      ]. LMS has been considered an age-related biomarker of the risk of fractures, cognitive impairment, depression, diabetes, postoperative complications, multimorbidity, hospitalizations, poor quality of life, and mortality [
      • Lee S.H.
      • Gong H.S.
      Measurement and interpretation of handgrip strength for research on sarcopenia and osteoporosis.
      ].
      Mechanisms involved in sarcopenia develop early in life and preventive measures are potentially effective across adulthood [
      • Sayer A.A.
      • Syddall H.
      • Martin H.
      • Patel H.
      • Baylis D.
      • Cooper C.
      The developmental origins of sarcopenia.
      ]. Sleep affects muscle metabolism through multiple mechanisms [
      • Piovezan R.D.
      • Abucham J.
      • dos Santos R.V.T.
      • Mello M.T.
      • Tufik S.
      • Poyares D.
      The impact of sleep on age-related sarcopenia: possible connections and clinical implications.
      ,
      • Prokopidis K.
      • Dionyssiotis Y.
      Effects of sleep deprivation on sarcopenia and obesity: a narrative review of randomized controlled and crossover trials.
      ]. Inappropriate sleep duration, sleep fragmentation and higher number of respiratory events during sleep are associated with adverse profile of factors involved in age-related muscular deterioration, such as insulin resistance, unbalanced anabolic/catabolic hormonal profiles, increased pro-inflammatory mediators, such as interleukin (IL)-6, interleukin (IL)-1, tumour necrosis factor alpha (TNF-α), and low serum vitamin D [
      • Lamon S.
      • Morabito A.
      • Arentson-Lantz E.
      • Knowles O.
      • Vincent G.E.
      • Condo D.
      • Alexander S.E.
      • Garnham A.
      • Paddon-Jones D.
      • Aisbett B.
      The effect of acute sleep deprivation on skeletal muscle protein synthesis and the hormonal environment.
      ].
      Objectively measuring sleep in large population-based studies is the most accurate method to study sleep-associated risk factors for diverse conditions. However, large population-based studies are usually restricted to subjective methods of sleep evaluation. A few epidemiological studies measured a restricted number of sleep parameters by actigraphy, which imprecisely estimate sleep duration and quality and is not indicated for the diagnosis of sleep disorders [
      • Martin J.L.
      • Hakim A.D.
      Wrist actigraphy.
      ,
      • Pana A.
      • Sourtzi P.
      • Kalokairinou A.
      • Pastroudis A.
      • Chatzopoulos S.-T.
      • Velonaki V.S.
      Association between muscle strength and sleep quality and duration among middle-aged and older adults: a systematic review.
      ]. Although polysomnography (PSG) is considered the gold standard for the assessment of sleep and the detection of obstructive sleep apnea (OSA), population-based studies including PSG are scarce.
      Previous studies targeting associations between sleep and grip strength included small or non-representative samples, were restricted to a single gender or a limited number of subjective sleep measures, which may justify inconsistent results [
      • Pana A.
      • Sourtzi P.
      • Kalokairinou A.
      • Pastroudis A.
      • Chatzopoulos S.-T.
      • Velonaki V.S.
      Association between muscle strength and sleep quality and duration among middle-aged and older adults: a systematic review.
      ]. No consensus exists in the associations of sleep duration and quality with muscle strength. Rigorous research with PSG data exploring how sleep is related to muscle strength is required. Relationships between sleep parameters and LMS may depend on whether sleep is measured by questionnaires or PSG. Additionally, we hypothesize that such associations differ according to age groups and genders, as both sleep and muscular tissue susceptibilities are affected by ageing and are gender-specific [
      • Sayer A.A.
      • Syddall H.
      • Martin H.
      • Patel H.
      • Baylis D.
      • Cooper C.
      The developmental origins of sarcopenia.
      ,
      • Moraes W.
      • Piovezan R.
      • Poyares D.
      • Bittencourt L.R.
      • Santos-Silva R.
      • Tufik S.
      Effects of aging on sleep structure throughout adulthood: a population-based study.
      ]. Age-related factors affecting slow-wave sleep, circadian rhythm and upper airway collapsibility, as well as increasing OSA risk can impact on hypothalamic-pituitary-adrenal (HPA), hypothalamic-pituitary-gonadal (HPG), somatotropic axes, and glucose metabolism, which are intrinsically implicated in muscle metabolism [
      • Piovezan R.D.
      • Abucham J.
      • dos Santos R.V.T.
      • Mello M.T.
      • Tufik S.
      • Poyares D.
      The impact of sleep on age-related sarcopenia: possible connections and clinical implications.
      ]. While in middle-age adults OSA prevalence in men is higher than in women, this gender difference tends to reduce with ageing [
      • Ayub S.
      • Won C.H.
      Obstructive sleep apnea in women.
      ]. This study therefore aimed to investigate the associations of a broad range of indicators of sleep impairment and sleep disorders with low grip strength in middle- to old-age adults from a large population-based cohort study.

      2. Methods

      2.1 Participants

      Participants from the HypnoLaus cohort study, conducted between September 1, 2009, and June 30, 2013, were considered eligible [
      • Heinzer R.
      • Vat S.
      • Marques-Vidal P.
      • Marti-Soler H.
      • Andries D.
      • Tobback N.
      • Mooser V.
      • Preisig M.
      • Malhotra A.
      • Waeber G.
      • Vollenweider P.
      • Tafti M.
      • Haba-Rubio J.
      Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study.
      ]. HypnoLaus is a population-based study that recruited participants from the 5-year follow-up of the Colaus/Psycolaus study, which included a baseline sample of 6733 people aged 35–75 years, randomly selected from the population register of Lausanne, Switzerland [
      • Firmann M.
      • Mayor V.
      • Vidal P.M.
      • Bochud M.
      • Pécoud A.
      • Hayoz D.
      • Paccaud F.
      • Preisig M.
      • Song K.S.
      • Yuan X.
      • Danoff T.M.
      • Stirnadel H.A.
      • Waterworth D.
      • Mooser V.
      • Waeber G.
      • Vollenweider P.
      The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome.
      ]. The initial 3043 consecutive participants of the first follow-up of the CoLaus/PsyCoLaus study (aged 40–80 years) were invited for a full home PSG and 2168 (71 %) accepted. Among the 60 (3 %) with technical problems, 54 participants agreed and completed a second PSG.

      2.2 Muscle strength

      Grip strength was assessed using the Baseline® Hydraulic Hand Dynamometer (Fabrication Enterprises Inc., Elmsford, NY, USA) with the subject seated, shoulders adducted and neutrally rotated, elbow flexed at 90°, forearm in neutral position and wrist between 0 and 30° of dorsiflexion. Standard verbal encouragement (‘squeeze the handle as hard as possible’) was used in the same tone of voice during the measurements. Obtained maximal isometric grip strength was recorded with a 15-second interval between three measurements of each grip, and the highest value was retained.
      Low muscle strength (LMS) was defined according to the revised European consensus on diagnosis of sarcopenia, which recommends grip strength to identify LMS as the first criteria for the screening of sarcopenia [
      • Cruz-Jentoft A.J.
      • Bahat G.
      • Bauer J.
      • Boirie Y.
      • Bruyère O.
      • Cederholm T.
      • Cooper C.
      • Landi F.
      • Rolland Y.
      • Sayer A.A.
      • Schneider S.M.
      • Sieber C.C.
      • Topinkova E.
      • Vandewoude M.
      • Visser M.
      • Zamboni M.
      • Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2)
      • the Extended Group for EWGSOP2
      • Bautmans I.
      • Baeyens J.-P.
      • Cesari M.
      • Cherubini A.
      • Kanis J.
      • Maggio M.
      • Martin F.
      • Michel J.-P.
      • Pitkala K.
      • Reginster J.-Y.
      • Rizzoli R.
      • Sánchez-Rodríguez D.
      • Schols J.
      Sarcopenia: revised European consensus on definition and diagnosis.
      ]. Sarcopenia cut-off points for LMS by handgrip dynamometer were <27 kg for men and <16 kg for women.

      2.3 Objective sleep characteristics

      An overnight and complete home-based PSG study performed by a certified technician was obtained for each participant using a digital recorder (Titanium, Embla Flaga, Reykjavik, Iceland) from 1700 h to 2000 h and following the 2007 American Academy of Sleep Medicine (AASM) recommended setup specifications [
      • Iber C.
      • Ancoli-Israel S.
      • Chesson A.
      • Quan S.
      The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Technical Specifications.
      ]. Electroencephalography, electrooculography, electromyography of tibialis anterior muscle, electrocardiography (ECG, derivation D2 modified), and airflow detection by nasal pressure sensor. Respiratory effort was assessed by inductance plethysmography belts. Peripheral oxygen saturation and pulse rate were also evaluated [
      • Heinzer R.
      • Vat S.
      • Marques-Vidal P.
      • Marti-Soler H.
      • Andries D.
      • Tobback N.
      • Mooser V.
      • Preisig M.
      • Malhotra A.
      • Waeber G.
      • Vollenweider P.
      • Tafti M.
      • Haba-Rubio J.
      Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study.
      ].
      PSG measures evaluated the following objective sleep characteristics: sleep quantity (TST [hours]), sleep quality (sleep onset latency [SOL, time from the start of the test to the first 30-second epoch scored as sleep in minutes], sleep efficiency [SE, % total time in bed spent in sleep], arousal index [AI, events/h], and wake time after sleep onset [WASO, min]), sleep architecture (slow-wave sleep [SWS] and rapid-eye-movement [REM] sleep [% TST]), movement disorders (periodic limb movement index [PLMI, events/h of sleep]), and respiratory parameters: apnea/hypopnea index (AHI) [number of apneas/hypopneas per hour of sleep] and measures of nocturnal hypoxia (TST with oxygen saturation <90 % [SpO2 < 90], defined as percentage of TST spent under a 90 % oxygen saturation threshold, and hypoxia burden). Nocturnal hypoxia burden is a recently described measure of OSA severity capturing the total amount of respiratory event-related hypoxemia over TST. The area under the desaturation curve associated with respiratory events quantifies OSA-related hypoxia severity. Hypoxic burden is defined as the total area under the respiratory event-related desaturation curve [
      • Azarbarzin A.
      • Sands S.A.
      • Stone K.L.
      • Taranto-Montemurro L.
      • Messineo L.
      • Terrill P.I.
      • Ancoli-Israel S.
      • Ensrud K.
      • Purcell S.
      • White D.P.
      • Redline S.
      • Wellman A.
      The hypoxic burden of sleep apnoea predicts cardiovascular disease-related mortality: the osteoporotic fractures in men study and the sleep heart health study.
      ].

      2.4 Subjective sleep characteristics

      Sleep complaints and habits were investigated using the Pittsburgh sleep quality index (PSQI) and the Epworth sleepiness scale. The PSQI is a questionnaire measuring sleep quality and quantity over a one-month period. Subjective sleep duration was categorized as short (<6 h), normal (6 h to 8 h), and long (>8 h). Short and long sleep durations and global PSQI scores >5 (poor self-reported sleep quality) were considered subjective indicators of sleep impairment [
      • Buysse D.J.
      • Reynolds C.F.
      • Monk T.H.
      • Berman S.R.
      • Kupfer D.J.
      The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research.
      ], as well as excessive daytime sleepiness (EDS) indicated by the Epworth sleepiness scale (EES) with sum score ≥11 [
      • Johns M.W.
      A new method for measuring daytime sleepiness: the Epworth sleepiness scale.
      ].

      2.5 Covariates

      Questionnaires assessed demographic, medical and treatment history, smoking, physical activity and alcohol consumption. Education level was classified as low (mandatory education or apprenticeship), intermediate (high school) and high (university diploma). Diabetes was defined as fasting plasmatic glucose (FPG) > 7 mmol/L or antidiabetic drug treatment. Height (stadiometer to the nearest 0.1 cm) and weight (electronic scale to the nearest 0.1 kg) were used to calculate body mass index (BMI, kg/m2). Cognition was assessed in participants ≥60 years by the Mini-Mental State Examination [
      • Folstein M.F.
      • Folstein S.E.
      • McHugh P.R.
      Mini-mental state.
      ]. Depressive symptoms evaluated by the 20-item CES-D questionnaire defined depression status by scores ≥17 for men and ≥23 for women [
      • Fuhrer R.
      • Rouillon F.
      La version française de l’échelle CES-D (Center for Epidemiologic Studies-Depression Scale). Description et traduction de l’échelle d’autoévaluation.
      ].

      2.6 Statistical analyses

      Characteristics of participants were described using means and standard deviations (or medians and IQRs when appropriate) or counts and percentages. Univariate analysis by one-way ANOVA or Wilcoxon rank sum test and Fisher's exact tests were applied to compare participants' characteristics according to muscle strength status (normal or reduced). First, PSG parameters were analyzed as continuous variables (Appendix Table A1) and quadratic associations between continuous sleep variables and the outcome were verified through univariate logistic regression models with quadratic terms (Appendix Table A2). A U-shaped association of TST with LMS was found (Appendix Fig. A1). To set non-arbitrary cut-offs for the definition of TST categories, ROC analysis was performed to maximize the accuracy for detecting LMS by using TST as a predictor, both above and below the median TST. Objective sleep duration was categorized as short (<6.2 h), normal (≥6.2 and ≤8.5 h) or long (>8.5 h).
      Other PSG parameters were subsequently analyzed in separate multivariate models as continuous variables (Appendix Table A3). To define indicators of sleep impairment and disorders, PSG parameters were categorized according to pre-defined cutoffs, quartiles or quintiles: SOL: ≤30 min vs. >30 min, SE: <80 % vs. ≥80 %, AI: ≤20 and >20 events/h and WASO: <90 min vs. ≥90 min [
      • Dam T.-T.L.
      • Ewing S.
      • Ancoli-Israel S.
      • Ensrud K.
      • Redline S.
      • Stone K.
      For the osteoporotic fractures in men research group, association between sleep and physical function in older men: the osteoporotic fractures in men Sleep study: SLEEP AND PHYSICAL FUNCTION IN OLDER MEN.
      ]. SWS and REM were categorized into quartiles (highest as reference). Periodic limb movement disorder (PLMD) was defined as PLMI >15 events/h [
      • Scofield H.
      • Roth T.
      • Drake C.
      Periodic limb movements during sleep: population prevalence, clinical correlates, and racial differences.
      ]. AHI ≥ 30 defined the diagnosis of severe OSA. TST with SpO2 < 90 was categorized according to the presence of nocturnal hypoxemia >10 % TST with SpO2 < 90. The hypoxic burden was categorized into quartiles (lowest as reference).
      To evaluate the associations of objective and subjective indicators of sleep impairment and disorders (predictors) with LMS (outcome), multivariate logistic regression models were separately performed for each one of predictors with a P ≤ 0.2 in univariate analysis. According to biological plausibility, potential confounders in the associations between LMS and sleep were considered as: sociodemographic factors (age [years], gender [male/ female], education level [low, intermediate, and high], marital status [married vs. others]), BMI, lifestyle factors (smoking status [never vs. former or current], alcohol drinking [<7, 7–14, and >14 drinks per week], physical activity [twice to three times a week vs. never or once a week]), cognition, and comorbidity-associated factors (diabetes and depression). Multivariate adjusted models for total sample, age groups (<60 and ≥60 years]) and genders were performed. Multicollinearity among the covariates was evaluated. The Hosmer-Lemeshow test was applied. Models' capacity to accurately classify the individuals according to the muscle strength status was also evaluated. The study had 80 % power to detect an OR increase of 1.30 (or decrease of 0.77). All the analyses were conducted using STATA software, version 14.2 (Stata Corp., College Station, Texas, USA).

      2.7 Ethical statement

      The ethics committee of the University of Lausanne approved the study, which complies with the Declaration of Helsinki. Written informed consent was obtained from all participants.

      3. Results

      3.1 General characteristics

      Among the 2162 participants with PSG recordings, 1902 without missing covariates were included in this study (median age 56.3 years, 761 [40.1 %] older adults, 968 [50.9 %] women) (Fig. 1). Table 1 shows sample distributions according to muscle strength classification (95 [5.0 %] were classified as LMS). Univariate analysis showed no differences in participants with LMS according to education, marital status, BMI, smoking status, alcohol intake, physical activity, diabetes, cognition, and depression. Older age and a higher proportion of women were observed among participants with LMS. Differences in the proportion of LMS between excluded (with missing data for covariates) and included individuals were not detected for covariates (data not shown).
      Table 1Relationships between participants characteristics and muscle strength classification in the Hypnolaus study.
      Overall sample (n = 1902)Normal (n = 1807)Reduced (n = 95)P-value
      P < 0.05.
      Age, years - median (IQR)56.3 (17.6)55.8 (17.4)66.4 (12.5)0.0001
      Female gender - n (%)968 (50.9)904 (50.0)64 (67.4)0.001
      Education - n (%)0.05
       Low524 (27.6)889 (49.2)57 (60.0)
       Middle946 (49.7)499 (27.6)25 (26.3)
       High432 (22.7)419 (23.2)13 (13.7)
      Marital status (married) - n (%)1121 (58.9)1073 (59.4)48 (50.5)0.11
      BMI (kg/m2) - mean (SD)26.1 (4.3)26.13 (4.3)25.92 (4.3)0.65
      Smoking status (never)
      Versus past/current smoking.
      784 (41.2)1071 (59.3)48 (50.5)0.07
      Weekly alcohol consumption - n (%)0.96
       <7 drinks1182 (62.2)1124 (62.2)58 (61.1)
       7–14 drinks487 (25.6)462 (25.6)25 (26.3)
       >14 drinks233 (12.3)221 (12.2)12 (12.6)
      Physical activity (2–3 times a week) - n (%)
      Versus never or once a week.
      1111 (58.4)1061 (58.7)50 (52.6)0.20
      Diabetes - n (%)188 (9.9)173 (9.6)15 (15.8)0.05
      Depression - n (%)281 (14.8)261 (14.4)20 (21.1)0.10
      Cognition score – mean (SD)
      Assessed by the mini-mental state exam in participants >60 years (total sample = 739).
      29.3 (1.6)29.3 (1.5)28.9 (1.8)0.16
      Note. BMI: body mass index.
      a Versus past/current smoking.
      b Versus never or once a week.
      c Assessed by the mini-mental state exam in participants >60 years (total sample = 739).
      low asterisk P < 0.05.

      3.2 Sleep characteristics

      Univariate associations between indicators of sleep impairment and LMS are displayed in Table 2. A higher proportion of participants with LMS had objective short or long TST, increased AI and WASO, reduced SE, severe OSA, nocturnal hypoxemia, hypoxia burden, and subjective poor sleep quality. Prolonged SOL, SWS and REM sleep stages, subjective sleep duration, and EDS were not associated with LMS.
      Table 2Univariate associations between indicators of sleep impairment and muscle strength classification.
      Overall sample (n = 1902)Normal (n = 1807)Reduced (n = 95)P-value
      P < .05 (sleep indicators and disorders with P < 0.2 in the univariate analysis were included in separate multivariate models).
      PSG indicators of sleep impairment
      Total sleep time (TST)
      Categorized according to ROC analysis to maximize the accuracy for detecting LMS.
      - n (%)
      <0.001
      Short sleep duration (<6.2 h)621 (32.7)583 (32.3)38 (40.0)
      Normal sleep duration (6.2–8.5 h)1186 (62.4)1146 (63.4)40 (42.1)
      Long sleep duration (>8.5 h)95 (5.0)78 (4.3)17 (17.9)
      Prolonged sleep latency (>30 min) - n (%)303 (15.9)285 (15.8)18 (19.0)0.39
      Increased arousal index (>20 events/h) - n (%)859 (45.2)404 (35.4)455 (59.8)0.04
      Increased nighttime wakefulness (≥90 min) - n (%)553 (29.1)507 (28.1)46 (48.4)<0.001
      Reduced sleep efficiency (<80 %) - n (%)494 (26.0)455 (25.2)39 (41.1)0.001
      Slow wave sleep (1st quartile) - n (%)456 (24.0)425 (23.5)31 (32.6)0.23
      Reduced REM sleep (1st quartile) - n (%)467 (24.6)436 (24.1)31 (32.6)0.11
      Periodic limb movement disorder (PLMI > 15 events/h) - n (%)545 (28.7)205 (18.0)340 (44.7)0.05
      Severe OSA (AHI ≥ 30 events/h)
      n = 1785.
      - n (%)
      194 (10.9)174 (10.2)20 (23.8)<0.001
      Nocturnal hypoxemia (>10 % TST with SpO2 < 90 %) - n (%)216 (11.4)198 (11.0)18 (19.0)0.03
      Hypoxic burden
      Nocturnal hypoxic burden is defined as the total area under the respiratory event-related desaturation curve.
      (4st quartile) – n (%)
      441 (25.0)406 (24.2)35 (39.8)0.005
      Subjective indicators of sleep impairment
      Subjective sleep duration
      n = 1888.
      - n (%)
      0.96
       <6 h193 (9.4)183 (9.4)10 (9.5)
       6–8 h1686 (82.1)1599 (82.0)87 (82.9)
       >8 h175 (8.5)167 (8.6)8 (7.6)
      Poor subjective sleep quality (PSQI score > 5)
      n = 1724.
      - n (%)
      689 (37.4)640 (36.6)49 (54.9)0.001
      Excessive daytime sleepiness (ESS score > 10)
      n = 1842.
      - n (%)
      261 (13.2)249 (13.2)12 (12.2)0.88
      Notes. PLMI: periodic limb movement index; AHI: apnea/hypopnea index (events/h); PSG: polysomnography; SpO2 < 90: percentage of total sleep time spent under a 90 % oxygen saturation threshold; TST: total sleep time.
      a Categorized according to ROC analysis to maximize the accuracy for detecting LMS.
      b n = 1785.
      c Nocturnal hypoxic burden is defined as the total area under the respiratory event-related desaturation curve.
      d n = 1888.
      e n = 1724.
      f n = 1842.
      low asterisk P < .05 (sleep indicators and disorders with P < 0.2 in the univariate analysis were included in separate multivariate models).

      3.3 Multivariate associations between indicators of sleep impairment and LMS

      For the total sample, objectively measured short sleep duration (<6.2 h) and long sleep duration (>8.5 h) were associated with LMS (aOR = 1.74, 95 % CI = 1.07–2.82 and aOR = 6.66 95 % CI = 3.45–12.87, respectively). Increased nighttime wakefulness and severe OSA also predicted LMS (aOR = 1.60, 95 % CI 1.02–2.36 and aOR = 2.36, 95 % CI 1.29–4.31, respectively). Other objective indicators of impaired sleep quality and structure, and of movement disorders and sleep hypoxia (nocturnal hypoxemia and hypoxic burden) were not associated with LMS. None of the subjective indicators of sleep impairment predicted LMS in the total sample (Table 3).
      Table 3Multivariate adjusted odds ratios
      Results are presented as adjusted odds ratios and (95 % confidence intervals) for low grip strength (according to sarcopenia cut-off points <27 kg for men and <16 kg for women).
      of the associations between indicators of sleep impairment and low muscle strength.
      Sleep indicators with P ≤ 0.2 in the univariate analysis were included in separate multivariate models.
      Total sample (n = 1902)Age groups
      Middle-aged adults: >40 and <60 years; older adults: 60–80 years.
      Gender groups
      Middle-aged adults (n = 1141)Older adults (n = 761)Women (n = 968)Men (n = 934)
      PSG indicators
      Short sleep duration (<6.2 h)
      Normal sleep duration (6.2–8.5 h) as reference.
      1.74 (1.07–2.82)0.88 (0.28–2.80)2.03 (1.14–3.59)1.81 (0.98–3.36)1.68 (0.75–3.73)
      Long sleep duration (>8.5 h)
      Normal sleep duration (6.2–8.5 h) as reference.
      6.66 (3.45–12.87)7.39 (2.68–20.36)4.90 (1.87–12.86)8.03 (3.67–17.58)5.96 (1.44–24.74)
      Increased arousal index (>20 events/h)1.29 (0.81–2.03)1.01 (0.41–2.46)1.50 (0.86–2.64)1.25 (0.72 2.18)1.17 (0.52–2.62)
      Increased nighttime wakefulness (≥90 min)1.60 (1.01–2.56)0.46 (0.12–2.03)2.18 (1.24–3.82)1.51 (0.85–2.67)2.02 (0.87–4.66)
      Reduced sleep efficiency (<80 %)1.34 (0.85–2.15)0.26 (0.03–2.02)1.81 (1.05–3.13)1.27 (0.71–2.30)1.63 (0.71–3.73)
      Slow wave sleep (SWS, %TST) - (1st vs. 4th quartile)1.33 (0.70–2.50)1.90 (0.48–7.56)1.23 (0.57–2.63)1.15 (0.55–2.41)2.84 (0.60–13.42)
      Rapid eye movement sleep (REM, %TST) - (1st vs. 4th quartile)1.11 (0.58–2.12)1.26 (0.28–5.68)1.25 (0.59–2.68)1.17 (0.53–2.59)1.03 (0.34–3.14)
      Periodic limb movement disorder
      PLMI>15 events/h.
      1.02 (0.64–3.36)1.36 (0.51–3.61)0.96 (0.56–1.64)1.02 (0.57–1.81)1.01 (0.46–2.22)
      Severe obstructive sleep apnea (AHI ≥ 30 events/h)2.36 (1.29–4.31)0.91 (0.18–4.57)3.08 (1.54–6.18)2.64 (1.11–6.24)2.06 (0.87–4.91)
      Nocturnal hypoxemia (>10%TST SpO2 < 90 %)1.58 (0.77–3.24)1.28 (0.37–4.45)1.81 (0.74–4.42)2.12 (0.81–5.53)1.02 (0.34–3.05)
      Nocturnal hypoxic burden (4th vs. 1st quintile)1.31 (0.68–2.52)1.24 (0.32–4.81)1.35 (0.60–3.04)1.24 (0.55–2.78)1.24 (0.38–4.07)
      Subjective indicators
      Poor sleep quality (PSQI score > 5)1.61 (0.99–2.62)1.73 (0.69–4.37)1.71 (0.93–3.15)1.29 (0.71–2.34)2.69 (1.17–6.10)
      Notes: PSG: polysomnography; TST: total sleep time during PSG; AHI: apnea-hypopnea index; PSQI: Pittsburg Sleep Quality Index.
      Potential confounders were considered a priori and based on biological plausibility: age, gender, education, marital status, smoking status, alcohol consumption, physical activity, BMI, diabetes, cognition (>60 years) and depression.
      Results are presented as adjusted odds ratios and (95 % confidence intervals) for low grip strength (according to sarcopenia cut-off points <27 kg for men and <16 kg for women), by each one of sleep indicators and disorders with P < 0.2 in previous univariate analysis.
      Bold values indicates statistically significant at p<0.05.
      a Results are presented as adjusted odds ratios and (95 % confidence intervals) for low grip strength (according to sarcopenia cut-off points <27 kg for men and <16 kg for women).
      b Sleep indicators with P ≤ 0.2 in the univariate analysis were included in separate multivariate models.
      c Middle-aged adults: >40 and <60 years; older adults: 60–80 years.
      d Normal sleep duration (6.2–8.5 h) as reference.
      e PLMI>15 events/h.
      Among middle-aged adults, objective long, but no short sleep duration predicted LMS (aOR = 7.39, 95 % CI 2.68–20.36). Differently, in older adults, objective short sleep duration was associated with LMS (aOR = 2.03, 95 % CI 1.14–3.59), as well as objective long sleep duration (aOR = 4.90, 95 % CI 1.87–12.86). Among older adults, reduced sleep efficiency was associated with LMS (aOR = 1.81, 95 % CI 1.05–3.13), as well as increased nighttime wakefulness and severe OSA (aOR = 2.18, 95 % CI = 1.24–3.82 and aOR = 3.08 95 % CI = 1.54–6.18, respectively). Age-stratified analysis did not demonstrate associations of subjective altered sleep quantity and poor sleep quality with LMS.
      In both genders, objective long, but not short sleep duration predicted LMS (aOR = 8.03, 95 % CI 3.67–17.58 for women; aOR = 5.96, 95 % CI 1.44–24.74 for men). In gender-stratified analysis, objective indicators of impaired sleep quality, including increased nighttime wakefulness, were not associated with LMS. Severe OSA predicted LMS in women (aOR = 2.64, 95 % CI 1.11–6.24). An association between subjective poor sleep quality and LMS was found in men (aORs = 2.69, 95 % CI (1.17–6.10). Fig. 2 displays estimates of the findings for the total sample and Appendix Fig. A2 for age groups and genders.
      Fig. 2
      Fig. 2Multivariate aORs for LMS by sleep characteristics in the total sample.
      The test for the lack of predictive capacity by the Hosmer-Lemeshow test for goodness of fit was not significant for all the models. The accuracy in the capacity to classify the individuals according to the muscle strength status surpassed 90 % in all predictive models.

      4. Discussion

      In a large population-based study with middle- to old-age adults from 40 to 80 years broadly investigating associations between adverse sleep characteristics and disorders obtained by multiple measurement sources including PSG and low grip strength, objectively measured short duration below 6.2 h and long sleep duration over 8.5 h, increased nighttime wakefulness (WASO>90 min), and severe OSA (AHI >30) were associated with reduced muscle strength. In older participants, these associations persisted and reduced sleep efficiency (<80 %), another marker of impaired objective sleep quality, also predicted LMS. This seems to be the first population-based study including older adults from both genders targeting risk factors for LMS by investigating PSG-derived and subjective sleep indicators. After controlling for potential confounders, subjective sleep duration and quality and daytime sleepiness were not consistently associated with LMS.
      Previous research aiming to evaluate connections between LMS and sleep in ageing populations found inconsistent results. A recent systematic review revealed an absence of high-quality population-based studies including both genders and PSG [
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      ]. A possible U-shaped association between reported sleep duration and the risk of sarcopenia has been suggested by studies using diverse ageing-related outcomes [
      • Ohara T.
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      ,
      • Wang D.
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      ]. Our findings added to the hypothesis that a U-shaped relationship between objective sleep duration and muscle strength exists particularly in older adults [
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      ]. Previous conclusions based on sleep data provided by self-reported information were imprecise [
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      ]. In older individuals with comorbidities, sleep disorders, and low levels of physical activity, PSG is considered the gold standard to measure sleep parameters [
      • Van Den Berg J.F.
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      • Vos H.
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      ,
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      ]. Additionally, our findings suggest that older, but not middle-aged adults were specifically vulnerable to the effects of short sleep duration, objectively-measured indicators of poor sleep quality and severe OSA, whose prevalence and underdiagnosis largely increase with ageing [
      • Benjafield A.V.
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      ].
      Sleep duration and LMS have been similarly associated with diverse outcomes related to ageing, such as cardiovascular disease, cognitive decline, depression, falls, frailty, lung cancer, diabetes, and mortality [
      • Lee S.H.
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      ,
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      ]. Our findings supporting the relationship between abnormal nighttime sleep duration and LMS are possibly explained by several mechanisms. Inflammatory, hormonal, and metabolic factors have a potential role in how inappropriate sleep time affects muscle function. Both short and long sleep durations are linked to higher levels of TNF-α, IL-6 and C-reactive protein (CRP) [
      • Prather A.A.
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      ]. Abnormal sleep duration is related to altered insulin sensitivity [
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      ], glucose intolerance [
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      Effects of poor and short sleep on glucose metabolism and obesity risk.
      ], and impaired glycaemic control [
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      • Gang C.
      Association of napping and night-time sleep with impaired glucose regulation, insulin resistance and glycated haemoglobin in Chinese middle-aged adults with no diabetes: a cross-sectional study.
      ]. Additionally, experimentally induced acute sleep debt or sleep prolongation alter growth hormone (GH)/ insulin-like growth factor 1 (IGF-1) axis and sleep debt negatively impacts the circadian cortisol profile, all involved in muscular catabolism [
      • Leproult R.
      • Cauter E.Van
      Role of sleep and sleep loss in hormonal release and metabolism.
      ]. Recent findings also suggest short sleep duration can affect muscle anabolism by reducing testosterone levels [
      • Andersen M.L.
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      The association of testosterone, sleep, and sexual function in men and women.
      ].
      Potential relationships between OSA and muscle strength have been rarely investigated. Our results including both genders showed an association between severe OSA and LMS, particularly persistent among older individuals and women. A recently published population-based study including middle- to old-aged male participants and PSG-derived OSA indices showed lower grip strength was associated with some OSA parameters of hypoxia [
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      ]. Similarly restricted by including only older men, previous findings from a cross-sectional study showed that not severe OSA but hypoxia was associated with low grip strength [
      • Dam T.-T.L.
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      For the osteoporotic fractures in men research group, association between sleep and physical function in older men: the osteoporotic fractures in men Sleep study: SLEEP AND PHYSICAL FUNCTION IN OLDER MEN.
      ]. By applying a broader range of robust nocturnal hypoxia parameters, we could not find associations between hypoxia and LMS in different age groups and genders. However, our results suggest that gender-specific factors can be involved in the association of severe OSA defined by the number of respiratory events with LMS. Gender differences in how mechanisms related to respiratory events and hypoxia during sleep affect muscle health still require further investigation.
      Age-associated muscular deterioration can be impacted by OSA pathophysiology by diverse pathways [
      • Piovezan R.D.
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      ]. After controlling for potential confounders that could be associated with OSA in the Hypnolaus cohort, such as BMI and physical activity, the relationship between severe OSA and LMS was particularly preserved in participants >60 years, suggesting differences in the relationship between muscle health and OSA across adulthood. The cross-sectional nature of this study and the absence of prediction effect by markers of hypoxia suggest a possible inverse association of reduced muscle strength with increased respiratory events during sleep in older individuals. In fact, the impact of poor muscle health on the collapsibility of the upper airway has been considered the most important mechanism for the increasing risk of OSA in older adults [
      • Eikermann M.
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      ,
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      ]. The persistence of this association among females, who like older adults have a lower muscle mass and strength, additionally suggest that LMS plays a role in the development of OSA. Future research exploring the effects of interventions aiming to reverse sarcopenia on OSA indices is supported by recent meta-analyses showing that general and airway-focused physical exercise programs are therapeutic alternatives for OSA particularly in aged individuals [
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      ].
      Several indicators of sleep quality and structure were included in this study. Restricted to older participants, prolonged nighttime wakefulness and reduced sleep efficiency were associated with LMS. Using actigraphy-based data, a previous cohort study with older women found similar results [
      • Spira A.P.
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      ]. Remarkably, an association between subjective poor sleep quality and LMS was only suggested by the gender-stratified analysis among males, but not in total sample and age-stratified analyses, confirming previous findings [
      • Spira A.P.
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      ]. Remarkably, a predictive capacity of subjective poor sleep quality was observed in sensitivity analysis after exclusion of confounders, particularly depression (data not shown), which was not evaluated by some previous studies although subjective aspects of sleep are thoroughly impacted by mental health [
      • Thase M.E.
      Depression and sleep: pathophysiology and treatment, dialogues.
      ].
      This cross-sectional study has limitations precluding conclusions toward a causality for its findings. Although the wide range of potential confounders reduced the risk of imprecise results, residual confounding may have persisted. Furthermore, our findings cannot be generalized to very old age populations and different ethnicities [
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      ,
      • Shaffer N.Chiles
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      The roles of body composition and specific strength in the relationship between race and physical performance in older adults.
      ]. Although several comparisons were performed, results confirmed predefined hypotheses based on biological plausibility, reducing the risk of randomness effect in the analysis [
      • Rothman K.J.
      No adjustments are needed for multiple comparisons.
      ]. Even though a single PSG measurement may not reflect sleep patterns of participants, the high accuracy of PSG recordings performed in home environment respected habitual bedtimes and other nighttime lifestyle-related factors.

      5. Conclusion

      Different objectively measured characteristics of sleep impairment and sleep disorder may increase the risk of sarcopenia in older populations. Muscle strength is potentially affected by long sleep duration earlier, since middle-age, and is potentially more susceptible to effects of other indicators of inappropriate sleep duration and quality in advanced age. An age-related relationship between muscle strength and OSA found in this study supports increasing evidence about the potential role of sarcopenia on the pathophysiology of OSA in older adults. Upcoming public health strategies and clinical research targeting associations between specific markers of adverse sleep and muscle health can result in new evidence-based strategies aiming to reduce the increased morbimortality observed with ageing.

      Contributors

      Ronaldo D Piovezan participated in the study planning, data analysis, data interpretation, manuscript writing, and manuscript review.
      Solomon Yu participated in data interpretation, manuscript writing, and manuscript review.
      Camila Hirotsu participated in the study planning, data interpretation, manuscript writing, and manuscript review.
      Pedro Marques-Vidal participated in the study planning, data interpretation, manuscript writing, and manuscript review.
      José Haba-Rubio participated of data interpretation, manuscript writing, and manuscript review.
      Graeme Tucker participated in the study planning, data interpretation, manuscript writing, and manuscript review.
      Robert Adams participated in data interpretation, manuscript writing, and manuscript review.
      Renuka Visvanathan participated in data interpretation, manuscript writing, and manuscript review.
      Raphaël Heinzer participated in the study planning, data interpretation, manuscript writing, and manuscript review.
      All the authors read and approved the final version of the manuscript.

      Funding

      The HypnoLaus and the CoLaus/PsyCoLaus study were and are supported by research grants from GlaxoSmithKline , the Faculty of Biology and Medicine of Lausanne , the Swiss National Science Foundation (grants 3200B0-105993 , 3200B0-118308 , 33CSCO-122661 , 33CS30-139468 and 33CS30-148401 ), Leenaards Foundation , and Vaud Pulmonary League (Ligue Pulmonaire Vaudoise).
      The funding sources did not participate in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.

      Ethical approval

      The ethics committee of the University of Lausanne approved the study, which complies with the Declaration of Helsinki. Written informed consent was obtained from all participants.

      Provenance and peer review

      This article was not commissioned and was externally peer reviewed.

      Research data (data sharing and collaboration)

      There are no linked research data sets for this paper. Data will be made available on request.

      Declaration of competing interest

      The authors declare that they have no competing interest.

      Appendix A. Supplementary data

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