Abstract
Muscle wasting syndrome, also known as sarcopenia, is an age-related geriatric condition characterized by a gradual loss of muscle mass, strength, and function. Sarcopenia can be classified into primary and secondary types. Primary sarcopenia is primarily associated with aging, while secondary sarcopenia is caused by systemic diseases such as cancer, diabetes, liver cirrhosis, musculoskeletal disorders, and disuse changes. In recent years, increasing evidence suggests that cardiovascular diseases can promote the occurrence of sarcopenia through various pathophysiological mechanisms. Additionally, sarcopenia increases the risk of adverse outcomes in patients with cardiovascular disease such as rehospitalization and mortality. Therefore, screening and diagnosing sarcopenia are particularly important for patients with cardiovascular diseases. This article provides a brief overview of the research progress on diagnostic methods for sarcopenia in patients with cardiovascular diseases.
1 Introduction
The initial definition of sarcopenia was proposed by Irwin Rosenberg in 1989, referring to the age-related decrease in muscle mass and strength (1). In 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) reached a consensus that defined it as an age-related syndrome characterized by decreased muscle mass, reduced muscle strength, and/or a decline in physical function (2). Later on, the Asian working group for sarcopenia (AWGS) published their own consensus on sarcopenia in Asia (3). Sarcopenia is associated with physical disabilities, a decreased quality of life, and an increased risk of mortality (4). In 2016, muscular dystrophy was officially recognized as a muscle disease and the ICD-10-MC diagnostic code was released (5). According to the EWGSOP standards, the prevalence of sarcopenia ranges from 8 to 36% in individuals under 60 years old and between 10 and 27% in those aged 60 and above (6). Cardiovascular diseases (CVD) include hypertension, coronary artery disease (CAD), acute myocardial infarction, arrhythmia, cardiomyopathy, valvular heart disease, congenital cardiovascular disease, and heart failure (HF). They are the leading cause of death and disability worldwide (7). Approximately 17.8 million people die from CVD each year, which accounts for one-third of global deaths (8). Myocardial infarction is present in many types of CVD (9). The prevalence of sarcopenia among patients with various CVD varies from 10.1 to 68.9% (10). Sarcopenia has emerged as a frequent complication in individuals suffering from CVD, and reduced muscle mass independently contributes to the risk of mortality associated with such conditions (11), thereby significantly impacting both the quality of life and prognosis for these patients (12, 13). Therefore, in the clinical diagnosis and treatment of CVD patients, accurately assessing and intervening in muscle wasting is becoming increasingly important.
2 The relationship between cardiovascular disease and sarcopenia
Sarcopenia is closely related to cardiovascular diseases. They interact with each other to accelerate the process of the disease. Sarcopenia and CVD share common pathogenesis, including hormonal changes, immunosenescence, impaired autophagy, oxidative stress, and mitochondrial dysfunction. The degree of muscle loss can be exacerbated by a sedentary lifestyle, prolonged bed rest, smoking and alcohol intake and obesity, and these factors are well established risk factors for cardiovascular disease.
Lack of physical activity, changes in body hormones, inadequate intake of nutrients, and imbalances in protein synthesis and utilization can easily lead to damage to mitochondrial structure and function as well as increased oxidative stress. Ultimately, this can result in the development of sarcopenia in patients with HF (14, 15). The prevalence of elderly patients with HF combined with sarcopenia is as high as 31%, and it is associated with reduced exercise capacity, poor quality of life, and adverse outcomes (16).
Hypertension and sarcopenia share similar underlying biological mechanisms, namely, low-grade chronic systemic inflammation. Hypertension is currently recognized as an inflammation-related disease. Many studies have found that proinflammatory cytokines such as CRP (C-reactive protein), IL-6 (interleukin 6), and TNF-α (tumor necrosis factor-alpha) increase abnormally in hypertensive patients (17, 18). In addition, chronic inflammation can accelerate protein breakdown and promotes sarcopenia by activation of the ubiquitin proteosome cascade (19). Accordingly, sarcopenia is common among adults with hypertension; the prevalence of sarcopenia among patients with hypertension ranges from 20.2 to 25.8%, which is significantly higher compared to the general population (20, 21).
Coronary artery disease (CAD) interacts with and influences sarcopenia. CAD promotes the occurrence of sarcopenia, as indicated by a meta-analysis that shows the prevalence of sarcopenia in CAD patients to be approximately 22.3% (10), and it is an independent risk factor for poor prognosis in CAD patients (22, 23). Sarcopenia can also contribute to the occurrence of CAD, as it results in a reduction in muscle mass and an increase in relative fat content caused by the substitution of muscle cells with adipocytes (24). On the contrary, the increase in muscle mass or muscle strength can decrease the risk of CAD (25, 26).
The incidence of sarcopenia after aortic valve replacement ranges from 21.0 to 70.2% (27). Low muscle mass is a significant predictor of increased mortality rates, prolonged hospital stays, and decreased functionality in patients after undergoing aortic valve replacement. Multiple studies have demonstrated a strong association between reduced muscle mass and higher mortality rates among post-aortic valve replacement patients (28, 29).
PAD leads to reduced blood flow in the lower limbs, which restricts the supply of energy and oxygen to the leg muscles, thereby affecting their function and quality. This may further result in sarcopenia. The incidence of sarcopenia in patients with atherosclerotic occlusive disease of the lower extremities can be as high as 35% (30). Patients with PAD who have sarcopenia experience significantly higher rates of mortality and amputation compared to those without sarcopenia (31).
3 Diagnosis methods for sarcopenia in cardiovascular diseases
The most widely used diagnostic criteria for sarcopenia currently are the consensus revised by EWGSOP in 2018 (32), which refer to low muscle mass accompanied by poor muscle strength or physical performance. Muscle mass was measured using either BIA or DXA, muscle strength was assessed through grip strength, and physical performance was evaluated by gait speed. By using DXA to measure the appendicular skeletal muscle mass (ASM) and converting it through a formula, appendicular skeletal muscle mass index (ASMI) is calculated. The cutoff values for ASMI are <7.0 kg/m2 for males and < 6.0 kg/m2 for females. The cutoff values for grip strength are <27 kg for males and < 16 kg for females, while the cutoff value for 6 m gait speed is ≤0.8 m/s for both males and females. The more suitable diagnostic criteria for sarcopenia in Asian populations proposed by AWGS (2019) include the following cutoff values: ASMI for muscle mass - DXA: males <7.0 kg/m2, females <5.4 kg/m2; BIA: males <7.0 kg/m2, females <5.7 kg/m2; grip strength - males <28 kg, females <18 kg; and usual gait speed - both males and females at a cutoff value of 1.0 m/s (3).
3.1 Physical methods
The SARC-F questionnaire, developed by Malmstrom et al. (33) is a screening tool for sarcopenia that consists of five items: walking ability, rising from a chair, stair climbing and experiences with falls. A score of ≥4 indicates a positive screening result for sarcopenia and predicts a poor prognosis. The SARC-F questionnaire demonstrated low sensitivity and high specificity in diagnosing sarcopenia among elderly individuals residing in the community (34), suggesting that this questionnaire may not be effective for early screening of sarcopenia. The greater value of this questionnaire may lie in predicting prognosis, as the SARC-F questionnaire can effectively screen for sarcopenia in patients with acute and chronic CVD and serve as a predictive factor for adverse outcomes (35, 36). Studies have suggested using a cutoff score of ≥2 on the SARC-F questionnaire to diagnose sarcopenia in CVD patients, aiming to improve its sensitivity (with sensitivities of 0.635 for males and 0.758 for females) (37).
The Ishii score was first proposed by Shinya Ishii based on the two-step method recommended by EWGSOP (38). A scoring table was developed to assess the risk of sarcopenia in older adults living in the community. The final model includes three variables: age, grip strength, and calf circumference, with a threshold value for sarcopenia set at 120 for females and 105 for males in the elderly community population. However, the effectiveness of this rating in patients with CVD remains uncertain. A post-analysis provided the critical values of Ishii score for predicting sarcopenia in HF patients. For females, the value was 165 with a sensitivity of 70.9% and specificity of 68.5%. For males, the value was 141 with a sensitivity of 88.4% and specificity of 69.7% (39). The higher sensitivity suggests that this questionnaire is beneficial for early screening of sarcopenia, thereby guiding early clinical intervention to delay disease progression. In addition, in patients with HF, mid-upper arm circumference and arm muscle circumference may be more reliable than calf circumference as a variable (40), possibly due to the presence of lower limb edema in HF patients.
Barbosa-Silva et al. combined the SARC-F questionnaire with calf circumference to create SARC-CalF, which addresses the lack of muscle mass assessment in SARC-F (41). Studies have been conducted on patients with hemodialysis and type 2 diabetes (42, 43). Further research is needed to determine the effectiveness of the SARC-CalF questionnaire for CVD patients.
Research has shown that the ratio of serum creatinine (Cr) to cystatin C (CysC) can be utilized in diagnosing sarcopenia. Cr, a derivative of skeletal muscle protein phosphocreatine, is excreted by the kidneys and serves as a routine serum marker for estimating GFR. Its value reflects both muscle mass and kidney function (44). In contrast, Cys is produced by all nucleated cells with limited impact on its levels compared to Cr due to its dependence primarily on renal function (45). Considering the differences in metabolism between these two biomarkers, Cr/CysC is considered as a promising alternative marker for muscle mass (46, 47). Kashani et al. (46) initially coined the term sarcopenia index (SI) to refer to the product of Scr/CysC×100, and they validated a linear positive correlation between SI and muscle mass measured by abdominal CT scan at L4 level. Shi et al. (48) provided the optimal cutoff values for diagnosing low skeletal muscle mass based on the Cr/CysC ratio (men <1.0, women <0.8). They further developed an estimated ASM equation that incorporates age, gender, height, weight, Cr, and CysC parameters. Compared to actual ASM measured by DXA, this equation demonstrates a sensitivity and specificity of over 80% in diagnosing low muscle mass (49). In patients with HF, aortic valve replacement surgery, and hypertension, SI can also serve as an alternative indicator for muscle mass (50–52). A retrospective study established a model that combines human measurement data and SI to estimate ASMI in HF patients and obtained cutoff values (male ASMI <7.0 kg/m2, female <5.4 kg/m2) (53). The model corrects the influence of edema on simple human measurement models (including age, weight, and height), providing higher reliability and accuracy.
If the screening tool suggests probable sarcopenia, the next recommended step is assessing muscle strength as the primary parameter of sarcopenia. EWGSOP2 recommends using handgrip strength or 5-time chair stand test to indicate skeletal muscle strength.
3.2 The visualization methods
3.2.1 Computed tomography
The cross-sectional area analysis of CT is utilized for evaluating the cross-sectional area (CSA) of muscles through both axial CT scans (abdomen or mid-thigh) and peripheral CT scans (lower leg) (54). Currently, measuring the CSA of the skeletal muscle using CT images at the level of the third lumbar vertebra (L3) is considered the gold standard for assessing muscle mass. Therefore, in many studies, a decrease in muscle mass is defined by the CSA of muscles at the L3 level (55). However, in practical clinical work, since routine imaging of many patients does not include the L3 level, alternative skeletal muscle indices from other levels need to be used as substitutes. For healthy subjects, Moon et al. first reported gender-specific cutoff values for diagnosing sarcopenia in most Asians using the area of the fourth thoracic (T4) level muscle group: 100.06 cm2 for males and 66.93 cm2 for females (56). Additionally, recent studies have also suggested that levels such as the first lumbar vertebrae (L1), fourth lumbar vertebrae (L4), twelfth thoracic vertebrae (T12), and upper thigh can be used to assess sarcopenia (57–59). CT-derived quantitative body composition analysis methods and cutoff values vary for the assessment of sarcopenia in patients with different CVD. A prospective study has confirmed that low skeletal muscle mass, identified by CT at the level of the first lumbar vertebra (L1), is an independent predictor of adverse prognosis in patients with CAD. Furthermore, a specific diagnostic threshold applicable to East Asian populations has been introduced at the L1 level, with a skeletal muscle mass index (SMI) of 31.00 cm2/m2 for males and 25.00 cm2/m2 for females (60). The unilateral pectoralis muscle mass indexed to body surface area (PMI) and attenuation (approximated by mean Hounsfield units; PHUm) can quantify muscle loss in HF patients (61). Additionally, the CT-derived fatty muscle fraction (FMF) is a potential new biomarker for sarcopenia, providing additional information for risk stratification in patients undergoing transcatheter aortic valve replacement (62). The subcutaneous fat index (SFI) and SMI, measured at the L3 vertebral level, can serve as biomarkers for sarcopenia in patients undergoing endovascular aneurysm repair surgery (63). Accurate manual segmentation of different body compositions is of great significance for measuring body composition, but this task takes 10 to 30 min. Therefore, AI-based image analysis techniques, such as automated deep learning technology, have been developed for quantitative assessment of body composition (such as muscles, visceral fat, subcutaneous fat, etc.) (64–67). Compared to manual segmentation, AI-based image analysis significantly reduces the required time and offers higher effectiveness and reliability. Weston et al. developed a fully automated technique using deep convolutional neural networks for abdominal segmentation, which can achieve even higher accuracy than manual segmentation (68). Subsequently, LEE et al. utilized this technique to evaluate the level of the L3 skeletal muscle area (SMA) in patients after aortic valve replacement surgery and found that low SMA was significantly associated with poor prognosis. They also obtained gender-specific Z-score cutoff values for male and female SMAs at 41.2 cm2/m2 and 33.0 cm2/m2, respectively (69). In general, CT can directly reflect the muscle mass of specific parts of the human body and, by calculating muscle density, it can more accurately evaluate the quality and structural characteristics of muscles. Therefore, CT is considered to be the most accurate method for assessing muscle mass (70). However, due to difficulties in performing CT measurements, relatively high costs, certain radiation exposure risks, lack of normal reference ranges and diagnostic thresholds at present, it is not suitable for screening large samples of populations and thus has not been widely used in clinical practice.
3.2.2 Magnetic resonance imaging
MRI is also a measurement method that can accurately assess muscle mass. Due to the presence of varying degrees of decreased size and quantity of type II muscle fibers, as well as intramuscular and intermuscular fat infiltration in patients with sarcopenia (71), water-fat separation MRI based on Dixon imaging technology achieves high soft tissue contrast, allowing for precise measurement of muscle tissue and fat infiltration (72). Therefore, compared to BIA, MRI can accurately identify tissues such as muscles, tendons, fibers, and fats without being affected by intramuscular fat. With the advancement of technology, not only conventional MRI sequences such as T1 but also techniques like diffusion tensor imaging, ultra-short echo time imaging, T2 mapping, and diffusion-weighted imaging are gradually being applied for evaluating muscle status (73–75). The CSA of muscle measured using MRI at the L3 can effectively predict total body muscle mass (76). Kiefer et al. proposed the use of standardized manual segmentation-algorithm for quantitatively evaluating total muscle mass and fat-free muscle mass, calculating the indices of total abdominal muscle mass and fat-free abdominal muscle mass to assess muscle quality (77). Recently, studies have found that measuring the chest muscles using cardiac magnetic resonance imaging (CMR) may hold potential in assessing sarcopenia. One study discovered that unilateral chest muscle measurement under CMR demonstrated a strong predictive value for postoperative mortality among patients who underwent surgical aortic valve replacement (78). Furthermore, the utilization of bilateral SMI based on heart MRI has emerged as a novel approach to evaluate sarcopenia in HF patients (79). Consequently, opportunistic screening for sarcopenia becomes feasible during cardiac MRI examinations. However, the manual segmentation of muscles based on MRI is also time-consuming and may take several days, which limits its application and promotion in clinical work. Therefore, we need to explore new probabilistic methods such as deep learning for muscle segmentation. The previous studies have developed automatic segmentation technology for the three-dimensional structure of the quadriceps, enabling quantitative evaluation of quadriceps volume (80). Additionally, a fast whole-body MRI method has been developed to automatically quantify total skeletal muscle volume and volumes of individual muscle groups (81). However, there is currently a lack of widely available segmented MRI datasets for skeletal muscles, and the use of artificial intelligence-based MRI techniques for assessing muscle mass reduction in sarcopenia remains limited. It should be noted that MRI is expensive and time-consuming for whole-body scans, lacks normal reference ranges and diagnostic thresholds, and its application in populations is greatly restricted due to limitations on subjects with metallic implants (82).
3.2.3 Ultrasound
The ultrasound can assess muscle condition by measuring parameters such as muscle thickness, cross-sectional area, muscle volume, muscle fiber length, pennation angle, echogenicity, and muscle hardness (83). Among them, quadriceps muscle imaging has been proven to be a reliable predictor of overall skeletal muscle quality. Previous studies have confirmed the diagnostic value of ultrasound quadriceps muscle imaging for secondary sarcopenia in diseases such as chronic obstructive pulmonary disease, Parkinson’s disease, liver cirrhosis, and stroke (84–87). A study has found that the difference in cross-sectional area (ΔCSA) and shear wave elastography (ΔSWE) between the contracted and relaxed states of the rectus femoris muscle can serve as an independent predictor for sarcopenia in elderly patients with type 2 diabetes. Furthermore, a model was established that combines age, ΔCSA, and ΔSWE, which demonstrated a sensitivity and specificity of 83.3% (88). However, there is currently limited research on the ultrasound diagnosis of cardiovascular disease combined with sarcopenia. A cross-sectional study focused on elderly HF patients found that echo intensity of the quadriceps femoris and subcutaneous fat thickness in the non-contractile state were associated with muscle strength in elderly HF patients (89). Taira et al. used ultrasound to measure the anterior femoral muscle thickness of 1,075 patients with CVD, using the diagnostic criteria of AWGS as the gold standard. They found a cutoff value of 2.425 cm for males, with a sensitivity and specificity of 68.5 and 77.6%, respectively; for females, the cutoff value was 1.995 cm, with a sensitivity and specificity of 70.5 and 66.0%, respectively (90). There is still significant research potential for diagnosing CVD combined with sarcopenia using ultrasound examination. Ultrasound examination offers strong portability, relative affordability, and no radiation exposure, making it suitable for clinical or community screening and follow-up. It can also be performed at the bedside. However, obtaining ultrasound images and interpreting results rely more on the technical skills of operators.
3.2.4 Dual-energy X-ray absorptiometry
Baumgartner et al. developed a diagnostic method that utilizes dual DXA to assess the SMI (91). The DXA scan is a non-invasive, easy-to-operate, cost-effective method with relatively low radiation dose for measuring muscle mass. It accurately distinguishes between whole-body and local muscles, fat, and bones, making it widely used in clinical practice. However, DXA allows for a whole-body estimation of lean mass, which measurement is actually an estimation of all non-fat/non-bone tissues. In addition, it is worth noting that DXA measurements may be influenced by the patient’s hydration status (92). This effect is particularly evident in the measurement of lower limb skeletal muscle mass in HF patients due to fluid retention in the lower limbs (93). In contrast, CT and MRI show high accuracy in the assessment of muscle and fat CSA/volume with the segmentation of muscles on cross-sectional images. CT can measure muscle size and attenuation in specific districts. MRI allows measuring the amount of muscle and fat tissue due to its high contrast resolution and multiparametricity.
3.2.5 Bioelectrical impedance analysis
BIA is a widely used non-invasive method for measuring body composition. Its principle involves using surface electrodes to record the different electrical resistances of various tissues and then utilizing image reconstruction techniques to measure muscle mass (94). Consensus guidelines published by AWGS and EWGSOP have provided recommended cutoff values for diagnosing muscle loss based on BIA-measured ASM. However, the currently available BIA prediction models have poor accuracy, and their measurement methods are easily influenced by factors such as body water content and electrolyte imbalances. Due to the presence of varying degrees of edema in HF patients, there is a significant margin of error when assessing muscle mass using BIA (95). BIA is also influenced by obesity, often leading to an overestimation of muscle mass in obese patients (77). For individuals with sarcopenic obesity, the muscle-to-fat ratio measured by BIA may be a more appropriate biomarker for defining and diagnosing sarcopenia (96). In addition to ASM, phase angle (PA) is a parameter derived from BIA that predicts various clinical outcomes and mortality rates of diseases (97). It can be obtained by measuring the ratio of reactance (Xc) to resistance (R) (PA = arctangent Xc/R), providing information on muscle mass and function. The magnitude of the PA value mainly depends on the size of cell membrane capacitance. A low PA value indicates lower cell membrane capacitance and poorer cell membrane structure and function (98). A meta-analysis, using the European Consensus 2010/2019 and Asian Consensus 2014 diagnostic criteria for sarcopenia, determined that the cut-off range for diagnosing sarcopenia with phase angle was between 3.55°to 5.05° (99). The phase angle cut-off value for sarcopenia in elderly HF patients was found to be 5.45°, with a sensitivity of 76% and specificity of 71% (100). Although BIA is non-invasive and easy to use, it cannot be used on individuals with pacemakers due to the weak electrical current employed (101).
3.3 Molecular level
Inflammatory factors play a crucial role in the occurrence and development of sarcopenia. Inflammaging, characterized by a low-grade chronic inflammatory state caused by immune system damage that occurs with age, is the main mechanism involved. It includes immunosenescence, increased secretion of inflammatory mediators from visceral fat inflammation, dysbiosis of the microbiota, and accumulation of senescent cells. These mechanisms ultimately lead to the infiltration of neutrophils and monocytes/macrophages into adipose tissue and other tissues, resulting in excessive secretion of pro-inflammatory cytokines (102, 103). As individuals age, there is a gradual increase in the expression of pro-inflammatory cytokines. Inflammatory factors inhibit myoblast fusion, stimulate excessive production of reactive oxygen species by mitochondria, activate the ubiquitin-proteasome system, induce autophagy and apoptosis in skeletal muscle cells, accelerate skeletal muscle protein degradation. This ultimately leads to the occurrence and development of sarcopenia (104, 105). Among them, the elevation of inflammatory factors such as tumor necrosis factor-α (TNF-α), C-reactive protein (CRP), and IL-6 levels is associated with the decline in skeletal muscle strength and quality (106). In HF patients with sarcopenia, IL-6 and CRP are elevated (107, 108). TNF-α activates the transcription factor nuclear factor-κB, thereby increasing protein degradation and promoting muscle atrophy (109). Prolonged high levels of IL-6 can contribute to muscle wasting in conjunction with other mediators, while CRP is associated with insulin resistance, inhibiting muscle function and leading to decreased strength (110).
Homocysteine (Hcy) is a sulfur-containing amino acid that plays an important role in the remethylation and transsulfuration pathways in the human body (111). Elevated blood Hcy levels can lead to oxidative stress, protein aggregation and dysfunction, cell apoptosis, inflammation, mitochondrial dysfunction, resulting in reduced muscle fiber regeneration and decreased energy production. These effects partially contribute to the occurrence and development of sarcopenia (112, 113). Recent studies have shown a significant association between Hcy and decreased muscle strength, including among patients with PAD (114, 115).
miRNA-1-3p is primarily produced by skeletal muscles and regulates the proliferation and differentiation of muscle cells (116). miR-1-3p is also a biomarker associated with the pathogenesis of HF (117), which leads to skeletal muscle damage and related cell death, resulting in passive release of miR-1-3p into the systemic circulation. The study found a significant correlation between the expression of miRNA-1-3p and the activation of the Akt/mTOR pathway (118). The levels of miRNA-1-3p in HF patients with sarcopenia were significantly higher than those without sarcopenia, and there was a strong correlation between miRNA-1-3p expression and both ASMI as well as grip strength. The cutoff value for predicting muscle wasting using miR-1-3p was 1.01, with a sensitivity of 75.0% and specificity of 62.5%. These findings suggest that this small molecule can serve as a predictive marker for sarcopenia in HF patients.
Research has found that the expression of HIF-1α and pax7 is significantly reduced in sarcopenia (119). HIF-1α, a major regulator of oxygen-dependent expression of several target genes involved in oxygen transport, metabolic adaptation, angiogenesis, as well as various cellular functions such as cell cycle regulation and apoptosis (120), shows significant reduction.Pax7 serves as the primary stem cell marker for satellite cells, which are regenerative cells in skeletal muscle. These cells proliferate in response to physiological stimulation, injury, and degenerative diseases, resulting in a significant increase in myogenic cell proliferation. Subsequently, these myogenic cells differentiate into muscle fibers to facilitate skeletal muscle regeneration (121). This indicates that HIF-1α and Pax7 can be utilized for diagnosing sarcopenia.
Wnt signaling is involved in muscle development, muscle regeneration, and stem cell renewal during processes of muscle atrophy and muscle wasting. Upregulation of Wnt signaling during aging can inhibit myogenesis and promote sarcopenia (122). A randomized controlled study on HF patients found a significant correlation between hand grip strength and three biomarkers of Wnt signaling: dickkopf-3 (Dkk-3), sterol regulatory element-binding protein-1 (SREBP1), and dickkopf-1 (Dkk-1) (123). This suggests that they have significant potential as plasma biomarkers for assessing sarcopenia in HF patients.
Some messenger RNAs, such as HERC5, S100A11, and FLNA, have also been shown to serve as potential biomarkers for sarcopenia (124). Serum meteorin-like protein (Metrnl), a novel myokine with protective effects against CVD, has been found to be associated with sarcopenia in elderly patients with HF (125). The phylum Synergistetes has also been identified as a potential biomarker for sarcopenia in HF patients (126). In elderly patients with CVD, the triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-C) is negatively correlated with relative grip strength (127), suggesting that this ratio may be used to evaluate sarcopenia in CVD patients. However, these serum markers are not specific for diagnosing sarcopenia.
4 Summary and future prospects
Nowadays, the attention to sarcopenia is increasing year by year, and different countries have different diagnostic methods and thresholds for different populations with sarcopenia. Currently, comprehensive diagnosis mainly relies on assessing muscle mass, muscle strength, and physical function. Finding a simple and reliable alternative diagnostic indicator remains an urgent problem for researchers in the field of sarcopenia. Physical methods are simple and feasible, but they have low sensitivity and are not conducive to early screening. Among them, the estimation equation demonstrates high sensitivity and specificity, making it a promising new method for assessing muscle mass. In the future, visualization will become a trend. CT and MRI are often used in clinical examinations, so there may be an opportunity to apply CT and MRI imaging for screening CVD in the clinical diagnosis of sarcopenia. However, there is currently a lack of standardized diagnostic protocols, and manual segmentation is time-consuming. Therefore, research on AI-based fully automated segmentation methods may be the focus. In addition, three-dimensional imaging techniques based on CT and MRI can directly assess the volume of skeletal muscles, which may more accurately represent muscle mass than CSA of muscle. Ultrasound examination can be used as a dynamic monitoring method in clinical practice; however, there is currently a lack of evaluation for other muscle groups besides the quadriceps femoris. Apart from two-dimensional ultrasound imaging techniques, other ultrasound technologies such as shear wave elastography are also worth further research. Currently, there is a lack of specificity in serum biomarkers for diagnosing sarcopenia, and more high-quality studies are needed to explore and identify a specific serum biomarker as a diagnostic indicator.
Statements
Author contributions
XH: Writing – original draft, Writing – review & editing. GZ: Writing – original draft, Writing – review & editing. QL: Writing – original draft, Writing – review & editing. ZZ: Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Acknowledgments
The authors wish to thank all hands and minds involved in this review.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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References
1.
RosenbergIH. Sarcopenia: origins and clinical relevance. J Nutr. (1997) 127:990S–1S. doi: 10.1093/jn/127.5.990S
2.
Cruz-JentoftAJBaeyensJPBauerJMBoirieYCederholmTLandiFet al. Sarcopenia: European consensus on definition and diagnosis: report of the European working group on sarcopenia in older people. Age Ageing. (2010) 39:412–23. doi: 10.1093/ageing/afq034
3.
ChenL-KWooJAssantachaiPAuyeungT-WChouM-YIijimaKet al. Asian working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. (2020) 21:300–7.e2. doi: 10.1016/j.jamda.2019.12.012
4.
XuJWanCSKtorisKReijnierseEMMaierAB. Sarcopenia is associated with mortality in adults: a systematic review and Meta-analysis. Gerontology. (2022) 68:361–76. doi: 10.1159/000517099
5.
Sanchez-RodriguezDMarcoECruz-JentoftAJ. Defining sarcopenia: some caveats and challenges. Curr Opin Clin Nutr Metab Care. (2020) 23:127–32. doi: 10.1097/MCO.0000000000000621
6.
Petermann-RochaFBalntziVGraySRLaraJHoFKPellJPet al. Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. (2022) 13:86–99. doi: 10.1002/jcsm.12783
7.
Writing Group MembersMozaffarianDBenjaminEJGoASArnettDKBlahaMJet al. Heart disease and stroke Statistics-2016 update: a report from the American Heart Association. Circulation. (2016) 133:e38–e360. doi: 10.1161/CIR.0000000000000350
8.
Collaborators GBDCoD. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the global burden of disease study 2016. Lancet. (2017) 390:1151–210. doi: 10.1016/S0140-6736(17)32152-9
9.
WuYWangWLiuTZhangD. Association of Grip Strength with Risk of all-cause mortality, cardiovascular diseases, and Cancer in community-dwelling populations: a Meta-analysis of prospective cohort studies. J Am Med Dir Assoc. (2017) 18:551.e17–35. doi: 10.1016/j.jamda.2017.03.011
10.
ZuoXLiXTangKZhaoRWuMWangYet al. Sarcopenia and cardiovascular diseases: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. (2023) 14:1183–98. doi: 10.1002/jcsm.13221
11.
KimDLeeJParkROhCMMoonS. Association of low muscle mass and obesity with increased all-cause and cardiovascular disease mortality in US adults. J Cachexia Sarcopenia Muscle. (2024) 15:240–54. doi: 10.1002/jcsm.13397
12.
LimHSonKLimH. Association between skeletal muscle mass-to-visceral fat ratio and dietary and Cardiometabolic health risk factors among Korean women with obesity. Nutrients. (2023) 15:574. doi: 10.3390/nu15071574
13.
CaiXLiuMXuXZhangSHuangRWangPet al. Cardiovascular effects of weight loss in old adults with overweight/obesity according to change in skeletal muscle mass. J Cachexia Sarcopenia Muscle. (2024) 15:342–51. doi: 10.1002/jcsm.13409
14.
PickeringRJ. Oxidative stress and inflammation in cardiovascular diseases. Antioxidants (Basel). (2021) 10:171. doi: 10.3390/antiox10020171
15.
FonsecaGSantosMRDSouzaFRCostaMHaehlingSVTakayamaLet al. Sympatho-vagal imbalance is associated with sarcopenia in male patients with heart failure. Arq Bras Cardiol. (2019) 112:739–46. doi: 10.5935/abc.20190061
16.
ChenRXuJWangYJiangBXuXLanYet al. Prevalence of sarcopenia and its association with clinical outcomes in heart failure: an updated meta-analysis and systematic review. Clin Cardiol. (2023) 46:260–8. doi: 10.1002/clc.23970
17.
FrancoCSciattiEFaveroGBonominiFVizzardiERezzaniR. Essential hypertension and oxidative stress: novel future perspectives. Int J Mol Sci. (2022) 23:489. doi: 10.3390/ijms232214489
18.
TsounisDBourasGGiannopoulosGPapadimitriouCAlexopoulosDDeftereosS. Inflammation markers in essential hypertension. Med Chem. (2014) 10:672–81. doi: 10.2174/1573406410666140318111328
19.
NishikawaHFukunishiSAsaiAYokohamaKNishiguchiSHiguchiK. Pathophysiology and mechanisms of primary sarcopenia (review). Int J Mol Med. (2021) 48:4989. doi: 10.3892/ijmm.2021.4989
20.
KaraMKaraOCeranYKaymakBKayaTCCitirBNet al. SARcopenia assessment in hypertension: the SARAH study. Am J Phys Med Rehabil. (2023) 102:130–6. doi: 10.1097/PHM.0000000000002045
21.
XingEWanC. Prevalence of and factors associated with sarcopenia among elderly individuals with hypertension. J Int Med Res. (2022) 50:3000605221110490. doi: 10.1177/03000605221110490
22.
LiuXWangYWangZLiLYangHLiuJet al. Association between sarcopenia-related traits and cardiovascular diseases: a bi-directional Mendelian randomization study. Front Endocrinol (Lausanne). (2023) 14:1237971. doi: 10.3389/fendo.2023.1237971
23.
ParkSLeeSKimYLeeYKangMWKimKet al. Relation of poor handgrip strength or slow walking pace to risk of myocardial infarction and fatality. Am J Cardiol. (2022) 162:58–65. doi: 10.1016/j.amjcard.2021.08.061
24.
KimTNChoiKM. The implications of sarcopenia and sarcopenic obesity on cardiometabolic disease. J Cell Biochem. (2015) 116:1171–8. doi: 10.1002/jcb.25077
25.
HeJHuangMLiNZhaLYuanJ. Genetic association and potential mediators between sarcopenia and coronary heart disease: a bidirectional two-sample, two-step Mendelian randomization study. Nutrients. (2023) 15:3013. doi: 10.3390/nu15133013
26.
LiuHMZhangQShenWDLiBYLvWQXiaoHMet al. Sarcopenia-related traits and coronary artery disease: a bi-directionalMendelian randomization study. Aging (Albany NY). (2020) 12:3340–53. doi: 10.18632/aging.102815
27.
BertschiDKissCMSchoenenbergerAWStuckAEKressigRW. Sarcopenia in patients undergoing Transcatheter aortic valve implantation (Tavi): a systematic review of the literature. J Nutr Health Aging. (2021) 25:64–70. doi: 10.1007/s12603-020-1448-7
28.
SteinEJNeillCNairSTerryJGCarrJJFearonWFet al. Associations of sarcopenia and body composition measures with mortality after Transcatheter aortic valve replacement. Circ Cardiovasc Interv. (2024) 17:e013298. doi: 10.1161/CIRCINTERVENTIONS.123.013298
29.
HeidariBAl-HijjiMAMoynaghMRTakahashiNWelleGEleidMet al. Transcatheter aortic valve replacement outcomes in patients with sarcopaenia. EuroIntervention. (2019) 15:671–7. doi: 10.4244/EIJ-D-19-00110
30.
PizzimentiMMeyerACharlesALGianniniMChakfeNLejayAet al. Sarcopenia and peripheral arterial disease: a systematic review. J Cachexia Sarcopenia Muscle. (2020) 11:866–86. doi: 10.1002/jcsm.12587
31.
MatsubaraYMatsumotoTInoueKMatsudaDYoshigaRYoshiyaKet al. Sarcopenia is a risk factor for cardiovascular events experienced by patients with critical limb ischemia. J Vasc Surg. (2017) 65:1390–7. doi: 10.1016/j.jvs.2016.09.030
32.
Cruz-JentoftAJBahatGBauerJBoirieYBruyereOCederholmTet al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. (2019) 48:16–31. doi: 10.1093/ageing/afy169
33.
MalmstromTKMorleyJE. SARC-F: a simple questionnaire to rapidly diagnose sarcopenia. J Am Med Dir Assoc. (2013) 14:531–2. doi: 10.1016/j.jamda.2013.05.018
34.
BahatGYilmazOKilicCOrenMMKaranMA. Performance of SARC-F in regard to sarcopenia definitions, muscle mass and functional measures. J Nutr Health Aging. (2018) 22:898–903. doi: 10.1007/s12603-018-1067-8
35.
NodaTKamiyaKHamazakiNNozakiKIchikawaTYamashitaMet al. SARC-F predicts poor motor function, quality of life, and prognosis in older patients with cardiovascular disease and cognitive impairment. Exp Gerontol. (2023) 171:112021. doi: 10.1016/j.exger.2022.112021
36.
FayhAPTGuedesFFOCaladoGCFQueirozSAAnselmoMSousaIM. SARC-F is a predictor of longer LOS and hospital readmission in hospitalized patients after a cardiovascular event. Nutrients. (2022) 14:3154. doi: 10.3390/nu14153154
37.
NodaTKamiyaKHamazakiNYamashitaMMikiTNozakiKet al. Screening for sarcopenia with SARC-F in older patients hospitalized with cardiovascular disease. Eur J Cardiovasc Nurs. (2024) 23:675–84. doi: 10.1093/eurjcn/zvae017
38.
IshiiSTanakaTShibasakiKOuchiYKikutaniTHigashiguchiTet al. Development of a simple screening test for sarcopenia in older adults. Geriatr Gerontol Int. (2014) 14:93–101. doi: 10.1111/ggi.12197
39.
MaedaDMatsueYKagiyamaNFujimotoYSunayamaTDotareTet al. Predictive value of the Ishii score for sarcopenia and the prognosis of older patients hospitalized with heart failure. Geriatr Gerontol Int. (2024) 24:147–53. doi: 10.1111/ggi.14736
40.
UchidaSKamiyaKHamazakiNNozakiKIchikawaTYamashitaMet al. Prognostic utility of skeletal muscle mass metrics in patients with heart failure. Can J Cardiol. (2023) 39:1630–7. doi: 10.1016/j.cjca.2023.08.006
41.
Barbosa-SilvaTGMenezesAMBielemannRMMalmstromTKGonzalezMCGrupo de Estudos em Composição Corporal e Nutrição. Enhancing SARC-F: Improving sarcopenia screening in the clinical practice. J Am Med Dir Assoc. (2016) 17:1136–41. doi: 10.1016/j.jamda.2016.08.004
42.
PDMRibeiroHSAlmeidaLSBaiaoVMInda-FilhoAAvesaniCMet al. SARC-F and SARC-CalF are associated with sarcopenia traits in hemodialysis patients. Nutr Clin Pract. (2022) 37:1356–65. doi: 10.1002/ncp.10819
43.
XuZZhangPChenYJiangJZhouZZhuH. Comparing SARC-CalF with SARC-F for screening sarcopenia in adults with type 2 diabetes mellitus. Front Nutr. (2022) 9:803924. doi: 10.3389/fnut.2022.803924
44.
LeveyASPerroneRDMadiasNE. Serum creatinine and renal function. Annu Rev Med. (1988) 39:465–90. doi: 10.1146/annurev.me.39.020188.002341
45.
FergusonTWKomendaPTangriN. Cystatin C as a biomarker for estimating glomerular filtration rate. Curr Opin Nephrol Hypertens. (2015) 24:295–300. doi: 10.1097/MNH.0000000000000115
46.
KashaniKBFrazeeENKukralovaLSarvottamKHerasevichVYoungPMet al. Evaluating muscle mass by using markers of kidney function: development of the sarcopenia index. Crit Care Med. (2017) 45:e23–9. doi: 10.1097/CCM.0000000000002013
47.
KashaniKSarvottamKPereiraNLBarretoEFKennedyCC. The sarcopenia index: a novel measure of muscle mass in lung transplant candidates. Clin Transpl. (2018) 32:e13182. doi: 10.1111/ctr.13182
48.
ShiSJiangYChenWChenKLiaoYHuangK. Diagnostic and prognostic value of the creatinine/cystatin C ratio for low muscle mass evaluation among US adults. Front Nutr. (2022) 9:897774. doi: 10.3389/fnut.2022.897774
49.
ShiSChenWJiangYChenKLiaoYHuangK. A more accurate method to estimate muscle mass: a new estimation equation. J Cachexia Sarcopenia Muscle. (2023) 14:1753–61. doi: 10.1002/jcsm.13254
50.
OmoteSWatanabeAHiramatsuTSaitoEYokogawaMOkamotoRet al. A foot-care program to facilitate self-care by the elderly: a non-randomized intervention study. BMC Res Notes. (2017) 10:586. doi: 10.1186/s13104-017-2898-9
51.
RomeoFJChiabrandoJGSeropianIMRaleighJVde ChazalHMGarmendiaCMet al. Sarcopenia index as a predictor of clinical outcomes in older patients undergoing transcatheter aortic valve replacement. Catheter Cardiovasc Interv. (2021) 98:E889–96. doi: 10.1002/ccd.29799
52.
LiaoLShiSDingBZhangRTuJZhaoYet al. The relationship between serum creatinine/cystatin C ratio and mortality in hypertensive patients. Nutr Metab Cardiovasc Dis. (2024) 34:369–76. doi: 10.1016/j.numecd.2023.09.004
53.
SunayamaTFujimotoYMatsueYDotareTDaichiMYatsuSet al. Prognostic value of estimating appendicular muscle mass in heart failure using creatinine/cystatin C. Nutr Metab Cardiovasc Dis. (2023) 33:1733–9. doi: 10.1016/j.numecd.2023.05.031
54.
AminiBBoyleSPBoutinRDLenchikL. Approaches to assessment of muscle mass and Myosteatosis on computed tomography: a systematic review. J Gerontol A Biol Sci Med Sci. (2019) 74:1671–8. doi: 10.1093/gerona/glz034
55.
ParkJGilJRShinYWonSEHuhJYouMWet al. Reliable and robust method for abdominal muscle mass quantification using CT/MRI: an explorative study in healthy subjects. PLoS One. (2019) 14:e0222042. doi: 10.1371/journal.pone.0222042
56.
MoonSWLeeSHWooALeemAYLeeSHChungKSet al. Reference values of skeletal muscle area for diagnosis of sarcopenia using chest computed tomography in Asian general population. J Cachexia Sarcopenia Muscle. (2022) 13:955–65. doi: 10.1002/jcsm.12946
57.
MizunoTMatsuiYTomidaMSuzukiYNishitaYTangeCet al. Differences in the mass and quality of the quadriceps with age and sex and their relationships with knee extension strength. J Cachexia Sarcopenia Muscle. (2021) 12:900–12. doi: 10.1002/jcsm.12715
58.
PickhardtPJPerezAAGarrettJWGraffyPMZeaRSummersRM. Fully automated deep learning tool for sarcopenia assessment on CT: L1 versus L3 vertebral level muscle measurements for opportunistic prediction of adverse clinical outcomes. AJR Am J Roentgenol. (2022) 218:124–31. doi: 10.2214/AJR.21.26486
59.
CziganyZKrampWLurjeIMillerHBednarschJLangSAet al. The role of recipient myosteatosis in graft and patient survival after deceased donor liver transplantation. J Cachexia Sarcopenia Muscle. (2021) 12:358–67. doi: 10.1002/jcsm.12669
60.
ParkEJParkSYKangJChuWKangDO. Quantitative association between computed-tomography-based L1 skeletal muscle indices and major adverse clinical events following percutaneous coronary intervention. J Clin Med. (2023) 12:483. doi: 10.3390/jcm12237483
61.
KlajdaMTrachtenbergBAraujoREstepJDMasottiMTeigenLet al. Pre-operative sarcopenia is predictive of recurrent gastrointestinal bleeding on left ventricular assist device support: a multicenter analysis. J Heart Lung Transplant. (2022) 41:757–62. doi: 10.1016/j.healun.2022.01.004
62.
LuetkensJAFaronAGeisslerHLAl-KassouBShamekhiJStundlAet al. Opportunistic computed tomography imaging for the assessment of fatty muscle fraction predicts outcome in patients undergoing Transcatheter aortic valve replacement. Circulation. (2020) 141:234–6. doi: 10.1161/CIRCULATIONAHA.119.042927
63.
BradleyNAWalterADolanRWilsonASiddiquiTRoxburghCSDet al. Evaluation of the prognostic value of computed tomography-derived body composition in patients undergoing endovascular aneurysm repair. J Cachexia Sarcopenia Muscle. (2023) 14:1836–47. doi: 10.1002/jcsm.13262
64.
KullbergJHedstromABrandbergJStrandRJohanssonLBergstromGet al. Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies. Sci Rep. (2017) 7:10425. doi: 10.1038/s41598-017-08925-8
65.
LeeHTroschelFMTajmirSFuchsGMarioJFintelmannFJet al. Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis. J Digit Imaging. (2017) 30:487–98. doi: 10.1007/s10278-017-9988-z
66.
PopuriKCobzasDEsfandiariNBaracosVJagersandM. Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging. (2016) 35:512–20. doi: 10.1109/TMI.2015.2479252
67.
GraingerATKrishnarajAQuinonesMHTustisonNJEpsteinSFullerDet al. Deep learning-based quantification of abdominal subcutaneous and visceral fat volume on CT images. Acad Radiol. (2021) 28:1481–7. doi: 10.1016/j.acra.2020.07.010
68.
WestonADKorfiatisPKlineTLPhilbrickKAKostandyPSakinisTet al. Automated abdominal segmentation of CT scans for body composition analysis using deep learning. Radiology. (2019) 290:669–79. doi: 10.1148/radiol.2018181432
69.
LeeSAJangIYParkSYKimKWParkDWKimHJet al. Benefit of sarcopenia screening in older patients undergoing surgical aortic valve replacement. Ann Thorac Surg. (2022) 113:2018–26. doi: 10.1016/j.athoracsur.2021.06.067
70.
GoodpasterBHKelleyDEThaeteFLHeJRossR. Skeletal muscle attenuation determined by computed tomography is associated with skeletal muscle lipid content. J Appl Physiol 1985. (2000) 89:104–10. doi: 10.1152/jappl.2000.89.1.104
71.
CseteME. Basic science of frailty-biological mechanisms of age-related sarcopenia. Anesth Analg. (2021) 132:293–304. doi: 10.1213/ANE.0000000000005096
72.
DixonWT. Simple proton spectroscopic imaging. Radiology. (1984) 153:189–94. doi: 10.1148/radiology.153.1.6089263
73.
GiraudoCMotykaSWeberMFeiweierTTrattnigSBognerW. Diffusion tensor imaging of healthy skeletal muscles: a comparison between 7 T and 3 T. Investig Radiol. (2019) 54:48–54. doi: 10.1097/RLI.0000000000000508
74.
XiongXYeZTangHWeiYNieLWeiXet al. MRI of temporomandibular joint disorders: recent advances and future directions. J Magn Reson Imaging. (2021) 54:1039–52. doi: 10.1002/jmri.27338
75.
CameronDReiterDAAdelniaFUbaida-MohienCBergeronCMChoiSet al. Age-related changes in human skeletal muscle microstructure and architecture assessed by diffusion-tensor magnetic resonance imaging and their association with muscle strength. Aging Cell. (2023) 22:e13851. doi: 10.1111/acel.13851
76.
SchweitzerLGeislerCPourhassanMBraunWGluerCCBosy-WestphalAet al. What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults?Am J Clin Nutr. (2015) 102:58–65. doi: 10.3945/ajcn.115.111203
77.
KieferLSFabianJRospleszczSLorbeerRMachannJKrausMSet al. Population-based cohort imaging: skeletal muscle mass by magnetic resonance imaging in correlation to bioelectrical-impedance analysis. J Cachexia Sarcopenia Muscle. (2022) 13:976–86. doi: 10.1002/jcsm.12913
78.
MirzaiSAleixoGFPMazumderSBerglundFPatilMLayounHet al. Sarcopenia evaluation on cardiac magnetic resonance imaging in older adults for outcomes prediction following surgical aortic valve replacement. Int J Cardiol. (2023) 391:131216. doi: 10.1016/j.ijcard.2023.131216
79.
ShiKZhangGFuHLiXMYuSQShiRet al. Reduced thoracic skeletal muscle size is associated with adverse outcomes in diabetes patients with heart failure and reduced ejection fraction: quantitative analysis of sarcopenia by using cardiac MRI. Cardiovasc Diabetol. (2024) 23:28. doi: 10.1186/s12933-023-02109-7
80.
Le TroterAFoureAGuyeMConfort-GounySMatteiJPGondinJet al. Volume measurements of individual muscles in human quadriceps femoris using atlas-based segmentation approaches. MAGMA. (2016) 29:245–57. doi: 10.1007/s10334-016-0535-6
81.
KarlssonARosanderJRomuTTallbergJGronqvistABorgaMet al. Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI. J Magn Reson Imaging. (2015) 41:1558–69. doi: 10.1002/jmri.24726
82.
HemkeRBucklessCTorrianiM. Quantitative imaging of body composition. Semin Musculoskelet Radiol. (2020) 24:375–85. doi: 10.1055/s-0040-1708824
83.
PerkisasSBastijnsSBaudrySBauerJBeaudartCBeckweeDet al. Application of ultrasound for muscle assessment in sarcopenia: 2020 SARCUS update. Eur Geriatr Med. (2021) 12:45–59. doi: 10.1007/s41999-020-00433-9
84.
DengMZhouXLiYYinYLiangCZhangQet al. Ultrasonic Elastography of the rectus Femoris, a potential tool to predict sarcopenia in patients with chronic obstructive pulmonary disease. Front Physiol. (2021) 12:783421. doi: 10.3389/fphys.2021.783421
85.
DingCWSongXFuXYZhangYCMaoPShengYJet al. Shear wave elastography characteristics of upper limb muscle in rigidity-dominant Parkinson's disease. Neurol Sci. (2021) 42:4155–62. doi: 10.1007/s10072-021-05088-3
86.
DhariwalSRoyATanejaSBansalAGorsiUSinghSet al. Assessment of sarcopenia using muscle ultrasound in patients with cirrhosis and Sarcopenic obesity (AMUSE STUDY). J Clin Gastroenterol. (2023) 57:841–7. doi: 10.1097/MCG.0000000000001745
87.
ParkSKimYKimSAHwangIKimDE. Utility of ultrasound as a promising diagnostic tool for stroke-related sarcopenia: a retrospective pilot study. Medicine (Baltimore). (2022) 101:e30245. doi: 10.1097/MD.0000000000030244
88.
ChenZTJinFSGuoLHLiXLWangQZhaoHet al. Value of conventional ultrasound and shear wave elastography in the assessment of muscle mass and function in elderly people with type 2 diabetes. Eur Radiol. (2023) 33:4007–15. doi: 10.1007/s00330-022-09382-2
89.
Fuentes-AbolafioIJRicciMBernal-LopezMRGomez-HuelgasRCuesta-VargasAIPerez-BelmonteLM. Biomarkers and the quadriceps femoris muscle architecture assessed by ultrasound in older adults with heart failure with preserved ejection fraction: a cross-sectional study. Aging Clin Exp Res. (2022) 34:2493–504. doi: 10.1007/s40520-022-02189-7
90.
FukudaTYokomachiJYamaguchiSYagiHShibasakiIUgataYet al. Can we diagnose sarcopenia using anterior femoral muscle thickness in patients with cardiovascular disease?J Rehabil Med Clin Commun. (2024) 7:12378. doi: 10.2340/jrmcc.v7.12378
91.
BaumgartnerRNKoehlerKMGallagherDRomeroLHeymsfieldSBRossRRet al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol. (1998) 147:755–63. doi: 10.1093/oxfordjournals.aje.a009520
92.
HeymsfieldSBAdamekMGonzalezMCJiaGThomasDM. Assessing skeletal muscle mass: historical overview and state of the art. J Cachexia Sarcopenia Muscle. (2014) 5:9–18. doi: 10.1007/s13539-014-0130-5
93.
KonishiMAkiyamaEMatsuzawaYSatoRKikuchiSNakahashiHet al. Prognostic impact of upper and lower extremity muscle mass in heart failure. ESC Heart Fail. (2023) 10:732–7. doi: 10.1002/ehf2.14195
94.
WardLC. Bioelectrical impedance analysis for body composition assessment: reflections on accuracy, clinical utility, and standardisation. Eur J Clin Nutr. (2019) 73:194–9. doi: 10.1038/s41430-018-0335-3
95.
SaitoHMatsueYMaedaDKasaiTKagiyamaNEndoYet al. Prognostic values of muscle mass assessed by dual-energy X-ray absorptiometry and bioelectrical impedance analysis in older patients with heart failure. Geriatr Gerontol Int. (2022) 22:610–5. doi: 10.1111/ggi.14424
96.
YuPCHsuCCLeeWJLiangCKChouMYLinMHet al. Muscle-to-fat ratio identifies functional impairments and cardiometabolic risk and predicts outcomes: biomarkers of sarcopenic obesity. J Cachexia Sarcopenia Muscle. (2022) 13:368–76. doi: 10.1002/jcsm.12877
97.
de AlmeidaCPennaPMPereiraSSRosaCOBFranceschiniS. Relationship between phase angle and objective and subjective indicators of nutritional status in Cancer patients: a systematic review. Nutr Cancer. (2021) 73:2201–10. doi: 10.1080/01635581.2020.1850815
98.
LukaskiHCKyleUGKondrupJ. Assessment of adult malnutrition and prognosis with bioelectrical impedance analysis: phase angle and impedance ratio. Curr Opin Clin Nutr Metab Care. (2017) 20:330–9. doi: 10.1097/MCO.0000000000000387
99.
Di VincenzoOMarraMDi GregorioAPasanisiFScalfiL. Bioelectrical impedance analysis (BIA) -derived phase angle in sarcopenia: a systematic review. Clin Nutr. (2021) 40:3052–61. doi: 10.1016/j.clnu.2020.10.048
100.
BiegerPSangaliTDRibeiroÉCTSchweigert PerryIDSouzaGC. Association of phase angle values and sarcopenia in older patients with heart failure. Nutr Clin Pract. (2023) 38:672–85. doi: 10.1002/ncp.10956
101.
StringerHJWilsonD. The role of ultrasound as a diagnostic tool for sarcopenia. J Frailty Aging. (2018) 7:258–61. doi: 10.14283/jfa.2018.24
102.
ChambersESAkbarAN. Can blocking inflammation enhance immunity during aging?J Allergy Clin Immunol. (2020) 145:1323–31. doi: 10.1016/j.jaci.2020.03.016
103.
LivshitsGKalinkovichA. Inflammaging as a common ground for the development and maintenance of sarcopenia, obesity, cardiomyopathy and dysbiosis. Ageing Res Rev. (2019) 56:100980. doi: 10.1016/j.arr.2019.100980
104.
YeoDKangCZhangTJiLL. Avenanthramides attenuate inflammation and atrophy in muscle cells. J Sport Health Sci. (2019) 8:189–95. doi: 10.1016/j.jshs.2018.08.002
105.
SharmaBDaburR. Role of pro-inflammatory cytokines in regulation of skeletal muscle metabolism: a systematic review. Curr Med Chem. (2020) 27:2161–88. doi: 10.2174/0929867326666181129095309
106.
TuttleCSLThangLANMaierAB. Markers of inflammation and their association with muscle strength and mass: a systematic review and meta-analysis. Ageing Res Rev. (2020) 64:101185. doi: 10.1016/j.arr.2020.101185
107.
Herrera-MartinezADMunoz JimenezCLopez AguileraJCrespinMCManzano GarciaGGalvez MorenoMAet al. Mediterranean diet, vitamin D, and Hypercaloric, Hyperproteic Oral supplements for treating sarcopenia in patients with heart failure-a randomized clinical trial. Nutrients. (2023) 16:110. doi: 10.3390/nu16010110
108.
SangaliTDSouzaGCRibeiroECTPerryIDS. Sarcopenia: inflammatory and humoral markers in older heart failure patients. Arq Bras Cardiol. (2023) 120:e20220369. doi: 10.36660/abc.20220369
109.
EbadiMBhanjiRAMazurakVCMontano-LozaAJ. Sarcopenia in cirrhosis: from pathogenesis to interventions. J Gastroenterol. (2019) 54:845–59. doi: 10.1007/s00535-019-01605-6
110.
Shokri-MashhadiNMoradiSHeidariZSaadatS. Association of circulating C-reactive protein and high-sensitivity C-reactive protein with components of sarcopenia: a systematic review and meta-analysis of observational studies. Exp Gerontol. (2021) 150:111330. doi: 10.1016/j.exger.2021.111330
111.
BlomHJSmuldersY. Overview of homocysteine and folate metabolism. With special references to cardiovascular disease and neural tube defects. J Inherit Metab Dis. (2011) 34:75–81. doi: 10.1007/s10545-010-9177-4
112.
VidoniMLPettee GabrielKLuoSTSimonsickEMDayRS. Relationship between homocysteine and muscle strength decline: the Baltimore longitudinal study of aging. J Gerontol A Biol Sci Med Sci. (2018) 73:546–51. doi: 10.1093/gerona/glx161
113.
VeerankiSWinchesterLJTyagiSC. Hyperhomocysteinemia associated skeletal muscle weakness involves mitochondrial dysfunction and epigenetic modifications. Biochim Biophys Acta. (2015) 1852:732–41. doi: 10.1016/j.bbadis.2015.01.008
114.
KositsawatJVogrinSFrenchCGebauerMCandowDGDuqueGet al. Relationship between plasma homocysteine and bone density, lean mass, muscle strength and physical function in 1480 middle-aged and older adults: data from NHANES. Calcif Tissue Int. (2023) 112:45–54. doi: 10.1007/s00223-022-01037-0
115.
McDermottMMFerrucciLGuralnikJMTianLGreenDLiuKet al. Elevated levels of inflammation, d-dimer, and homocysteine are associated with adverse calf muscle characteristics and reduced calf strength in peripheral arterial disease. J Am Coll Cardiol. (2007) 50:897–905. doi: 10.1016/j.jacc.2007.05.017
116.
TeodoriLCostaACampanellaLAlbertiniMC. Skeletal muscle atrophy in simulated microgravity might be triggered by immune-related microRNAs. Front Physiol. (2018) 9:1926. doi: 10.3389/fphys.2018.01926
117.
MushtaqueRSHameedSMushtaqueRIdreesMSirajF. Role of cardio-specific Micro-ribonucleic acids and correlation with cardiac biomarkers in acute coronary syndrome: a comprehensive systematic review. Cureus. (2019) 11:e5878. doi: 10.7759/cureus.5878
118.
XuRCuiSChenLChenXCMaLLYangHNet al. Circulating miRNA-1-3p as biomarker of accelerated sarcopenia in patients diagnosed with chronic heart failure. Rev Investig Clin. (2022) 74:276–68. doi: 10.24875/RIC.22000151
119.
CirilloFMangiaviniLLa RoccaPPiccoliMGhiroldiARotaPet al. Human Sarcopenic myoblasts can be rescued by pharmacological reactivation of HIF-1alpha. Int J Mol Sci. (2022) 23:7114. doi: 10.3390/ijms23137114
120.
WangRZhangZXuZWangNYangDLiuZZet al. Gastrin mediates cardioprotection through angiogenesis after myocardial infarction by activating the HIF-1alpha/VEGF signalling pathway. Sci Rep. (2021) 11:15836. doi: 10.1038/s41598-021-95110-7
121.
Sousa-VictorPGarcia-PratLMunoz-CanovesP. Control of satellite cell function in muscle regeneration and its disruption in ageing. Nat Rev Mol Cell Biol. (2022) 23:204–26. doi: 10.1038/s41580-021-00421-2
122.
ArthurSTCooleyID. The effect of physiological stimuli on sarcopenia; impact of notch and Wnt signaling on impaired aged skeletal muscle repair. Int J Biol Sci. (2012) 8:731–60. doi: 10.7150/ijbs.4262
123.
KarimAMuhammadTShahIKhanJQaisarR. A multistrain probiotic reduces sarcopenia by modulating Wnt signaling biomarkers in patients with chronic heart failure. J Cardiol. (2022) 80:449–55. doi: 10.1016/j.jjcc.2022.06.006
124.
FurutaniMSuganumaMAkiyamaSMitsumoriRTakemuraMMatsuiYet al. RNA-sequencing analysis identification of potential biomarkers for diagnosis of sarcopenia. J Gerontol A Biol Sci Med Sci. (2023) 78:1991–8. doi: 10.1093/gerona/glad150
125.
CaiJWangQMLiJWXuFBuYLWangMet al. Serum Meteorin-like is associated with weight loss in the elderly patients with chronic heart failure. J Cachexia Sarcopenia Muscle. (2022) 13:409–17. doi: 10.1002/jcsm.12865
126.
PengJGongHLyuXLiuYLiSTanSet al. Characteristics of the fecal microbiome and metabolome in older patients with heart failure and sarcopenia. Front Cell Infect Microbiol. (2023) 13:1127041. doi: 10.3389/fcimb.2023.1127041
127.
HuangYLiaoJLiuY. Triglyceride to high-density lipoprotein cholesterol ratio was negatively associated with relative grip strength in older adults: a cross-sectional study of the NHANES database. Front Public Health. (2023) 11:1222636. doi: 10.3389/fpubh.2023.1222636
Summary
Keywords
cardiology, diagnosis, sarcopenia, skeletal muscle mass, cardiovascular diseases
Citation
Han X, Zhang GS, Li QR and Zhang Z (2024) Current approach to the diagnosis of sarcopenia in cardiovascular diseases. Front. Nutr. 11:1422663. doi: 10.3389/fnut.2024.1422663
Received
24 April 2024
Accepted
01 November 2024
Published
15 November 2024
Volume
11 - 2024
Edited by
Gabriela Salim de Castro, University of São Paulo, Brazil
Reviewed by
Tatsuya Igawa, International University of Health and Welfare, Japan
Yoshimasa Aso, Dokkyo Medical University, Japan
Updates
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© 2024 Han, Zhang, Li and Zhang.
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*Correspondence: Zhen Zhang, zhangzhen@cmu.edu.cn
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