If you don't remember your password, you can reset it by entering your email address and clicking the Reset Password button. You will then receive an email that contains a secure link for resetting your password
If the address matches a valid account an email will be sent to __email__ with instructions for resetting your password
Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, SwedenUnit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
Sarcopenia is a geriatric condition featured by a progressive loss of muscle mass and function.
•
Sarcopenia affects 10 %–16 % of the elderly worldwide.
•
Sarcopenia is associated with a high risk of a wide range of adverse health outcomes.
•
Physical inactivity, malnutrition, smoking, extreme sleep duration, and diabetes are related to sarcopenia.
•
Cohort, omics, and Mendelian randomization studies are needed to better understand the causes of sarcopenia.
Abstract
Sarcopenia is a geriatric condition featured by a progressive loss of muscle mass and function and associated with various adverse health outcomes. In this review, we aimed to summarize the epidemiological features of sarcopenia as well as consequences and risk factors of the disease. We performed a systematic review of meta-analysis on sarcopenia to collect data. The prevalence of sarcopenia varied between studies and depending on definition used. Sarcopenia was estimated to influence 10 %–16 % of the elderly worldwide. The prevalence of sarcopenia was higher among patients compared to general populations. The prevalence of sarcopenia ranged from 18 % in diabetic patients to 66 % in patients with unresectable esophageal cancer. Sarcopenia is associated with a high risk of a wide range of adverse health outcomes, including poor overall and disease-progression free survival rate, postoperative complications, and longer hospitalization in patients with different medical situations as well as falls and fracture, metabolic disorders, cognitive impairment, and mortality in general populations. Physical inactivity, malnutrition, smoking, extreme sleep duration, and diabetes were associated with an increased risk of sarcopenia. However, these associations were mainly based on non-cohort observational studies and need confirmation. High-quality cohort, omics, and Mendelian randomization studies are needed to deeply understand the etiological basis of sarcopenia.
Sarcopenia is a geriatric condition featured by a progressive loss of muscle mass and function and has been associated with several adverse health outcomes, including fracture, functional decline, and mortality [
]. Even though sarcopenia has received attention of intense research, it is poorly concluded about its epidemiological features, risk factors, and complications. This review aims to summarize the epidemiological features of sarcopenia as well as consequences and risk factors of the disease.
2. Materials and methods
To summarize available data in a comprehensive way, we performed a systematic review of meta-analysis on sarcopenia (Fig. 1). We searched “sarcopenia” and “meta” in the PubMed database and obtained 726 studies after removing publications before 2010 when most definitions of sarcopenia were published [
]. Two authors independently reviewed the 726 studies and classified included studies into two categories that are studies on risk factors and on consequences. We excluded studies on sarcopenia components instead of sarcopenia as a binary phenotype, studies on obesity sarcopenia, and studies without performed meta-analysis. We extracted information on title, PubMed ID, publication year, first author, population (general population or patients), number of studies included in meta-analysis, total sample size, prevalence of sarcopenia, the associations, and heterogeneity.
We included 130 studies in the systematic review of risk factors and consequences of sarcopenia, among which 25 and 109 studies were on risk factor and consequences, respectively. Although this review did not aim to estimate prevalence of sarcopenia in a comprehensive way, we extracted corresponding data to complement the current evidence of prevalence of sarcopenia shown in previous studies, especially among patients with different medical conditions.
3.1 Definitions and prevalence of sarcopenia
Before 2010 when the definition of sarcopenia was proposed by the European Working Group on Sarcopenia in Older People (EWGSOP) [
], which is partial and could not reflect muscle function. Nowadays, the most commonly used definition of sarcopenia is that recommended by EWGSOP, which was updated as EWGSOP2 in 2019 [
Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia.
]. These definitions have been summarized in Table 1. Sarcopenia has now been formally recognized as a muscle disease in the International Classification of Disease (ICD-10: M62 [84]) [
Table 1Commonly used definitions of sarcopenia and cut-offs of indicators after 2010.
Classification
Definition
Muscle mass
Muscle strength
ASM (kg) or ASM/height2 (kg/m2)
Grip strength (kg)
Gait speed (m/s)
EWGSOP
Low muscle mass
Low grip strength or slow gait speed
Based on BIA:
Men < 8.31–10.75 kg/m2
Women < 6.42–6.75 kg/m2
Based on DXA:
Men < 7.23–7.26 kg/m2
Women < 5.45–5.67 kg/m2
Men < 30 Women < 20
Men and women < 0.8
EWGSOP2
Low muscle mass
Low grip strength
Based on DXA:
Men < 7.0 kg/m2
Women < 5.5 kg/m2
Men < 27 Women < 16
Men and women < 0.8
AWGS
Low muscle mass
Low grip strength or slow gait speed
Based on BIA:
Men < 7.0 kg/m2
Women < 5.7 kg/m2
Based on DXA:
Men < 7.0 kg/m2
Women < 5.4 kg/m2
Men < 26 Women < 18
Men and women < 0.8
IWGS
Low muscle mass
Slow gait speed
Based on BIA:
Men < 7.23 kg/m2
Women < 5.67 kg/m2
Based on DXA:
Men < 7.23 kg/m2
Women < 5.67 kg/m2
–
Men and women < 1.0
FNIH
Low muscle mass
Low grip strength
Men < 19.75 kg Women < 15.02 kg
Men < 26 Women < 16
Men and women < 0.8
AWGS, Asian Working Group for Sarcopenia; BIA, bioelectrical impedance; DXA, dual-energy x-ray absorptiometry; EWGSOP, European Working Group on Sarcopenia in Older People; EWGSOP2, European Working Group on Sarcopenia in Older People 2; FNIH, Foundation for the National Institute of Health; IWGS, International Working Group on Sarcopenia.
Even though recent studies used comparatively consistent definitions for sarcopenia, different cut-offs and applied measurements (i.e., bioelectrical impedance (BIA) or dual-energy x-ray absorptiometry (DXA)) make it still difficult to estimate disease prevalence in a homogeneous manner, which is reflected by a wide range of prevalence of sarcopenia in the majority of meta-analyses (Supplementary Table 1). Despite this, there are several meta-analyses with comprehensively collected data on the prevalence of sarcopenia by commonly used definitions, which is informative to understand the epidemiological features of sarcopenia.
The prevalence of sarcopenia varies largely between studies and depends on definition used to define the disease (Table 2) [
], the global prevalence of sarcopenia ranged from 5 % (95 % confidence interval [CI] 1 %–10 %) for EWGSOP2 to 17 % (95 % CI 11 %–23 %) for IWGS among the elderly. However, the highest prevalence of sarcopenia was observed for EWGSOP (22 %, 95 % CI 20 %–25 %) and the lowest was for FNIH (11 %, 95 % CI 9 %–14 %) in the study by Petermann-Rocha F et al. [
]. Even though two studies were based on generally healthy populations, like community-dwelling elderlies, the estimated prevalence of sarcopenia differed, and the reasons for this heterogeneity remain unclear. In another meta-analysis of 58,404 community-dwelling participants aged 60 years and older, the overall global prevalence of sarcopenia was estimated to be 10 % and found to be slightly higher when using BIA compared to DXA to measure muscle quantity [
AWGS, Asian Working Group for Sarcopenia; EWGSOP, European Working Group on Sarcopenia in Older People; EWGSOP2, European Working Group on Sarcopenia in Older People 2; FNIH, Foundation for the National Institute of Health; IWGS, International Working Group on Sarcopenia.
The prevalence of sarcopenia was much higher in different patient groups compared to the general population (Table 3). In the included studies reporting pooled prevalence, the prevalence of sarcopenia ranged from 18 % in patients with diabetes [
CT-assessed sarcopenia is a predictive factor for both long-term and short-term outcomes in gastrointestinal oncology patients: a systematic review and meta-analysis.
In studies involving patients with different medical conditions, mortality, survival, and postoperative complications were the primarily studied and observed outcomes (Supplementary Table 1). Overall, baseline sarcopenia or preoperative sarcopenia was associated with an increased risk of short- and long-term mortality, poor overall and progression-free survival rate, overall and severe complications, postoperative infection, and prolonged hospitalization in most included studies (Supplementary Table 1). However, the risks varied between different patient groups concerning mortality and survival rate (Fig. 2) and other consequences. The all-cause mortality of sarcopenia was the highest among patients with emergency laparotomy (odds ratio [OR] = 3.50, 95 % CI 2.54–4.81) [
The association between sarcopenia and bladder cancer-specific mortality and all-cause mortality after radical cystectomy: a systematic review and meta-analysis.
] (Fig. 2). Likewise, the risk of poor overall survival was observed to be highest among patients with lung cancer (OR = 3.07, 95 % CI 2.45–3.85) and to be lowest among patients with esophageal cancer (HR = 1.12, 95 % CI 1.04–1.20) (Fig. 2). Of note, even though the comparison of the magnitude of the associations might reflect seriousness of sarcopenia in the risk of death and poor survival among different patient groups, it should be interpreted with caution for following reasons. First, there were moderate to high heterogeneity between studies in these combined associations. Second, certain associations with large CI due to small sample sizes were imprecise. Third, some studies were mainly based on a retrospective design where measurement error of sarcopenia might bias the results. Last but not least, the associations might be largely influenced by used definitions of sarcopenia and possibly by different measurements of muscle mass, which confined the comparability of these associations. Sarcopenia was additionally associated with an increased risk of disease progression in patients with liver diseases [
Fig. 2Mortality and survival associated with sarcopenia in patients with different medical conditions. HR, hazard ratio; OR, odds ratio; RR, relative risk.
The focus of consequences of sarcopenia differed between studies in patients and general populations. With exception for an increased risk of mortality, sarcopenia was further associated with a high risk of cognitive impairment, osteoporosis, falls, fracture, functional decline, hospitalization, metabolic syndrome, diabetes, nonalcoholic liver disease, liver fibrosis, hypertension, depression, and dysphagia among general populations (Table 4). Even though most these associations were based on meta-analyses of cohort studies, the causality remained uncertain due to residual confounding and measurement errors. In addition, the associations may differ using different definitions of sarcopenia, which may also partly explain the high heterogeneity in certain studies. However, falls appeared to be robustly associated with sarcopenia regardless of definition used for sarcopenia [
There are comparatively fewer studies exploring the risk factors for sarcopenia (Supplementary Table 2). Overall, evidence of these studies was low with a few prospective cohort studies. Thus, the associations reported in previous meta-analysis of risk factors for sarcopenia (Table 5) should be interpreted with caution due to the possibility of reverse causality and confounding affecting the results.
Table 5Risk factors for sarcopenia.
PMID
First author
Risk factor
No. of studies
N
Heterogeneity
36443946
Liu C
Obesity OR = 0.66 (0.48–0.91) Obesity OR = 3.08 (1.65–5.74) after adjusting for muscle mass
34
–
High
27170042
Steffl M
Alcohol consumption OR = 0.77 (0.67–0.88)
13
13,155
Moderate
36014771
Hong SH
Alcohol consumption OR = 1.00 (0.83–1.20)
19
422,870
Moderate
28553092
Steffl M
Physical inactive OR = 2.22 (1.82–2.70)
25
40,007
Moderate
30409494
Shen Y
Malnutrition OR = 1.74 (1.36–2.24)
16
3585
Moderate
35096921
Zhang Y
Omega-3 PUFAs highest vs. lowest OR = 0.41 (0.26–0.65) Omega-6 PUFAs highest vs. lowest OR = 0.64 (0.33–1.24)
6
6648
Moderate
31832982
Pourmotabbed A
<6 v.s. 6–8 h OR = 1.71 (1.11–2.64) >8 v.s. 6–8 h OR = 1.52 (1.23–1.88)
4
17,551
Moderate
34959843
Gao Q
Age in years OR = 1.12 (1.10–1.13) Female OR = 1.10 (0.80–1.51) Underweight OR = 3.78 (2.55–5.60) Overweight/obesity OR = 0.27 (0.17–0.44) Smoking OR = 1.20 (1.10–1.21) Alcohol consumption OR = 0.92 (0.84–1.01) Physical inactivity OR = 1.73 (1.48–2.01) Malnutrition OR = 2.99 (2.40–3.72) Long sleep duration OR = 2.30 (1.37–3.86) Short sleep duration OR = 3.32 (1.86–5.93) Diabetes OR = 1.40 (1.18–1.66) Cognitive impairment OR = 1.62 (1.05–2.51) Heart diseases OR = 1.14 (1.00–1.30) Respiratory diseases OR = 1.22 (1.09–1.36) Osteopenia OR = 2.73 (1.63–4.57) Osteoarthritis OR = 1.33 (1.23–1.44) Disability for activities of daily living OR = 1.49 (1.15–1.92) Depression OR = 1.46 (1.17–1.83) Falls OR = 1.28 (1.14–1.44) Anorexia OR = 1.50 (1.14–1.96) Anemia OR = 1.39 (1.06–1.82)
68
98,502
Moderate to high
34652699
Veronese N
Diabetes OR = 1.64 (1.20–2.22)
17
54,676
Moderate
32772138
Anagnostis P
Type 2 diabetes OR = 1.55 (1.25–1.91)
15
6526
Moderate
35002965
Qiao YS
Diabetes OR = 2.09 (1.62–2.70) Diabetic complications OR = 2.09 (1.62–2.70)
7
6783
Moderate Low
34095184
Chung SM
Diabetes OR = 1.64 (1.20–2.22)
6
7022
Moderate
36053982
Wannarong T
Diabetic peripheral neuropathy OR = 1.62 (1.30–2.02)
These studies were not based on general populations (Ai Y study in patients with type 2 diabetes; Feng L study in patients with diabetes; and Zhang JZ study in patients with kidney transplantation).
Age OR = 4.73 (4.30–5.19) Higher HbA1c OR = 1.16 (1.05–2.47) Osteoporosis OR = 1.16 (1.05–2.47)
These studies were not based on general populations (Ai Y study in patients with type 2 diabetes; Feng L study in patients with diabetes; and Zhang JZ study in patients with kidney transplantation).
Age OR = 1.10 (1.07–1.14) Glycated hemoglobin OR = 1.16 (1.09–1.24) Visceral fat area OR = 1.03 (1.02–1.05) Duration of diabetes OR = 1.06 (1.00–1.11) High-sensitivity C-reactive protein OR = 1.33 (1.12–1.58) Exercise OR = 0.37 (0.18–0.76) Metformin use OR = 0.39 (0.19–0.79)
These studies were not based on general populations (Ai Y study in patients with type 2 diabetes; Feng L study in patients with diabetes; and Zhang JZ study in patients with kidney transplantation).
Age OR = 1.08 (1.05–1.10) Female OR = 0.31 (0.16–0.61) Lower body mass index OR = 0.57 (0.39–0.84)
a These studies were not based on general populations (Ai Y study in patients with type 2 diabetes; Feng L study in patients with diabetes; and Zhang JZ study in patients with kidney transplantation).
]. This association was partly in line with a positive association between visceral fat area (a more precise indicator of fat accumulation) and the risk of sarcopenia [
], which indicates that purely excessive fat is not a protective factor for sarcopenia. Instead, sarcopenic obesity affecting 11 % of global older adults has been associated with various adverse outcomes [
Among lifestyle factors, physical activity and nutritional status determined by dietary intake or nutrient supplementation appear to be associated with the risk of sarcopenia [
]. To detail corresponding prevention and therapeutic strategies, studies on comparative effects of individual and combinations of different types of physical activities and dietary patterns are warranted. Alcohol consumption was not associated with the risk of sarcopenia [
] and these diseases may also be the consequences of sarcopenia as shown above. The bidirectional associations imply mutual influences between muscle and bone systems and between muscle and endocrine systems. Other comorbidities, like heart diseases [
] were also positively associated with the risk of sarcopenia. However, whether certain associations, like that for heart diseases and cognitive impairment, are causal or linked by confounders, such as ageing, needs to be investigated. Regarding the link between sarcopenia and metabolic diseases, like diabetes and cardiovascular disease, some hypotheses concerning chronic inflammation [
], have been proposed to explain these associations. However, given that sarcopenia and metabolic diseases often coexist among populations and possibly have mutual influences, it is difficult to determine which is the cause of the link. Even though some studies found that a prior diagnosis of sarcopenia was associated with an increased risk of cardiovascular disease [
Association between sarcopenia and cardiovascular disease among middle-aged and older adults: findings from the China health and retirement longitudinal study.
] between sarcopenic patients and non-sarcopenic individuals. These associations need to be confirmed in prospective cohort studies or other studies that can minimize reverse causation and strengthen causality. In addition, gut microbiota may play a role in the development of sarcopenia [
]. Thus, whether probiotics, prebiotics, and bacterial products have preventive and therapeutic potentials deserves exploration.
4. Limitations
Several limitations of this study need discussion. First, this is a review of published meta-analyses. Thus, some novel risk factors and rare consequences of sarcopenia may have been missed due to a few original studies on these topics. Second, even though this review identified many factors and morbidities associated with sarcopenia, the listed associations need to be carefully considered, particularly associations with high heterogeneity between studies or from low-quality studies. Third, this review was mainly based on evidence from observational studies, which cannot provide information on causality of the observed associations.
5. Future directions
5.1 Omics for sarcopenia
There are genome-wide association analyses on components of sarcopenia, such as muscle mass (fat-free mass) [
]. A large-scale international genetic consortium collecting unified data on sarcopenia is warranted. Similarly, more studies are needed on epigenetics, transcriptomics, proteomics, metabolomics, and microbiome on sarcopenia. Such studies could deepen the understanding of the etiological basis of sarcopenia from genetic and molecular perspectives as well as facilitate prevention strategy formulation and drug development for the disease. In addition, potential gene-environmental interactions in sarcopenia are of interest to explore.
5.2 High-quality cohort studies and Mendelian randomization analysis
High-quality prospective cohort studies are lacking in this field, especially concerning the exploration of the risk factors for sarcopenia. Except for focusing on clinical patients who are vulnerable to sarcopenia, cohort studies with accurate measurements of muscle quantity and function in generally healthy population are needed to provide evidence to formulate primary prevention strategies. In addition, Mendelian randomization analysis is a widely used epidemiological tool that can strengthen causal inference by using genetic variants as unbiased instrumental variables for the potential risk factor [
]. The causality of observed associations for sarcopenia should be examined using Mendelian randomization analysis.
6. Conclusion
This review summarized evidence on epidemiological features of sarcopenia (Fig. 3) . Even though the prevalence of sarcopenia varies according to definition used, it is a prevalent disease among the elderly and patients with varying medical conditions. Sarcopenia is associated with a high risk of a wide range of adverse health outcomes, including poor survival rate, postoperative complications, and longer hospitalization in patients as well as falls and facture, metabolic disorders, cognitive impairment, and mortality in general populations. Physical inactivity, malnutrition, smoking, extreme sleep duration, and diabetes and several other comorbidities were associated with an increased risk of sarcopenia. However, these associations were mainly based on non-cohort observational studies and require confirmation. High-quality cohort, omics, and Mendelian randomization studies are needed to understand the etiological basis of sarcopenia with the aims of preventing and better managing the disease.
Fig. 3Summary of risk factors and consequences of sarcopenia.
Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia.
CT-assessed sarcopenia is a predictive factor for both long-term and short-term outcomes in gastrointestinal oncology patients: a systematic review and meta-analysis.
The association between sarcopenia and bladder cancer-specific mortality and all-cause mortality after radical cystectomy: a systematic review and meta-analysis.
Association between sarcopenia and cardiovascular disease among middle-aged and older adults: findings from the China health and retirement longitudinal study.
00136-1&id=ga1.jpg){kind=link}
00136-1&id=gr1.jpg){kind=link}
00136-1&id=gr2.jpg){kind=link}
00136-1&id=gr3.jpg){kind=link}