Front. Med. Frontiers in Medicine Front. Med. 2296-858X Frontiers Media S.A. 10.3389/fmed.2026.1637499 Review Sarcopenia in type 2 diabetes mellitus: an imaging review HeLei 1 2 Writing – original draft Project administration LuoGuanghua 1 Writing – original draft Project administration Investigation JiangHongtao 2 Writing – original draft Investigation Project administration ZhangLi 1 Investigation Project administration Writing – original draft LiYusheng 1 Writing – original draft Investigation Project administration GuWenbin 1 Writing – original draft Project administration Investigation ZengQiqun 1 Investigation Writing – original draft Project administration ZhuJingjun 1 Writing – original draft Visualization LiuJincai 1 * Supervision Writing – review & editing Funding acquisition LeiHao 1 * Supervision Writing – review & editing Funding acquisition ZhaoHeng 1 * Conceptualization Project administration Funding acquisition Writing – review & editing Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China Department of Radiology, The First Affiliated Hospital, Shaoyang University, Shaoyang, Hunan, China Correspondence: Heng Zhao, Angerh9@sina.com; Hao Lei, 317755950@qq.com; Jincai Liu, liujincai6353@163.com 21 01 2026 2026 13 1637499 30 05 2025 05 01 2026 06 01 2026 Copyright © 2026 He, Luo, Jiang, Zhang, Li, Gu, Zeng, Zhu, Liu, Lei and Zhao. 2026 He, Luo, Jiang, Zhang, Li, Gu, Zeng, Zhu, Liu, Lei and Zhao https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Sarcopenia is characterized by an age-related decline in muscle mass, strength, or endurance. It is increasingly prevalent in patients with type 2 diabetes mellitus (T2DM) and is now regarded as a key complication of the disease. Additionally, it has a significant impact on patients’ prognosis. Imaging methods are crucial tools for assessing muscle mass and microchanges. Moreover, they can facilitate the early diagnosis of sarcopenia. Thus, this article reviews the pathological basis and clinical manifestations of sarcopenia in T2DM, the advantages and disadvantages of imaging assessment methods, their specific applications, imaging manifestations, and research progress.

 sarcopenia type 2 diabetes mellitus dual-energy x-ray absorptiometry computed tomography magnetic resonance imaging ultrasound The author(s) declared that financial support was received for this work and/or its publication. This work was supported by The Hengyang Science and Technology Planning Project (Grant No.202010041562 (JL)), the Natural Science Foundation of Hunan Provincial of China (Grant No.2023JJ30554 (HZ)), the Scientific Research Project of Department of Education of Hunan Province (Grant No.20A437 (HZ)), the Natural Science Foundation of Hunan Province of China (Grant No.2018JJ2357 (GL)), the Scientific Research Fund Project of Hunan Provincial Health Commission (W20243294 (HJ)), the Scientific Research Fund Project of Hunan. section-at-acceptance Geriatric Medicine Introduction

Type 2 diabetes mellitus (T2DM) is a disorder of glucose metabolism resulting from insufficient relative insulin secretion and reduced insulin sensitivity. It is characterized by hyperglycemia and is accompanied by polyuria, polydipsia, weight loss, fatigue, and other symptoms (1–3).

In 2024, the Global Leadership Initiative for Sarcopenia (GLIS) reached a consensus noting sarcopenia as a progressive and potentially reversible skeletal muscle disease characterized by loss of muscle mass and strength (4). Consequently, it is associated with a range of adverse outcomes in the geriatric population. These include reduced physical function, diminished cardiopulmonary performance, and ultimately, loss of capacity and death (5). Moreover, sarcopenia is increasingly prevalent in patients with T2DM (6). Both T2DM and sarcopenia share several common risk factors, such as older age, poor dietary intake, smoking, hormonal imbalance, unhealthy lifestyle habit, and vitamin D deficiency. Sarcopenia has been formally recognized as a diabetes-related complication (7). In T2DM patients, poor metabolic regulation, diabetes duration, and diabetes-related complications exacerbate the occurrence and progression of sarcopenia (8–10). This significantly impacts patients’ quality of life and prognosis, while also increasing the economic burden on families, society, and the healthcare system (11–14).

Dual-Energy X-Ray Absorptiometry (DXA), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and other imaging methods can reveal the imaging characteristics of sarcopenia in diabetic patients. Importantly, they can reflect the pathological process to some extent and are valuable for early disease recognition, severity assessment, treatment effect evaluation, and prognosis.

 The pathophysiological basis for sarcopenia in T2DM

The etiology of sarcopenia in diabetic patients is complex and multifactorial. This section examines several key factors that contribute to a deeper understanding of imaging signs. These factors include insulin resistance, oxidative stress, mitochondrial dysfunction, the accumulation of glycation end-products, the presence of intramuscular adipose tissue (IMAT), inflammatory responses, and the composition of the intestinal microbiota (Figure 1).

Interaction between type 2 diabetes mellitus and sarcopenia under common risk factors. IR, insulin resistance; AGEs, advanced glycation end-products; OS, oxidative stress; GM, gut microbiota; IMAT, intramuscular adipose tissue; DXA, dual-energy X-ray absorptiometry; CT, computed tomography; MRI, magnetic resonance imaging; US, ultrasound.

Flowchart illustrating factors leading to sarcopenia. T2DM is central, influenced by AGEs, OS, and inflammation, contributing to muscle function decrease and fat infiltration. Older age, poor diet, smoking, hormonal imbalance, lifestyle, and vitamin D deficiency exacerbate sarcopenia. Diagnostic tools include MRI, US, DXA, and CT. Insulin resistance (IR)

IR is a key pathogenic mechanism in T2DM. Notably, it accelerates protein breakdown and impairs new protein synthesis, reducing muscle mass and strength (15). On the one hand, IR inhibits protein synthesis by blocking the IGF-1 pathway via the rapamycin target protein mechanism (16). As muscle mass decreases, IR also promotes adipose tissue breakdown, releasing free fatty acids (FFA), this release further inhibits the IGF-1 pathway and protein synthesis (17). On the other hand, IR speeds up protein decomposition by activating the ubiquitin-proteasome system (18). Concurrently, hyperinsulinemia enhances the myostatin-activated p38-caspase signaling pathway that triggers apoptosis, increasing protein decomposition (19–21).

 Oxidative stress

Oxidative stress predominantly stems from an escalation in reactive oxygen species (ROS) (22). In individuals with T2DM, the persistent state of hyperglycemia escalates the production and aggregation of ROS by amplifying the substrate pool (23) and activating key enzymes like protein kinase C and NADPH oxidase (24). As a result, elevated ROS levels accelerate muscle protein degradation via the ubiquitin-proteasome system (25), inhibit the Akt/mTOR signaling pathway and downstream protein synthesis (26). Furthermore, heightened oxidative stress damages DNA and impairs satellite cell differentiation (27). In summary, the primary impact of T2DM oxidative stress on sarcopenia is the inhibition of muscle regeneration and an increase in muscle breakdown.

 Inflammation

Studies have demonstrated raised circulating C-reactive protein (CRP) and pro-inflammatory cytokines, such as tumor necrosis factor alpha (TNFα) and interleukin 6 (IL-6), in T2DM patients (28–30). In obesity-related T2DM, muscle tissue has upregulated pro-inflammatory pathways such as chemokine (C-C motif) ligand 2 (CCL2), signal transducer and activator of transcription 3 (STAT3), suppressor of cytokine signaling 3 (SOCS3), and nuclear factor kappa B (NF-κB) (31–33). Consequently, these are linked to increased skeletal muscle catabolism (34). As a result, the inflammatory response triggers a cascade that begins with the activation of TNF and IL-1, alongside other interleukins. This activation proceeds through signal transduction pathways involving NF-κB and Forkhead box O4(FoxO4), adversely affecting muscle mass and function (34, 35). Moreover, estrogen suppresses pro-inflammatory cytokines and up-regulates anti-inflammatory factors (36), meanwhile, indirectly regulating inflammatory responses by regulating microRNAs (37). MicroRNAs, as mediators and pathways of inflammation, may play an essential epigenetic role in the vicious cycle of osteoporosis and vascular calcification (38). However, whether it also plays a significant role in the inflammatory mechanism of T2DM-related sarcopenia deserves further study.

 Advanced glycation end-products (AGEs)

Irreversible AGEs are extensively deposited throughout the body, particularly in cartilage, muscular tissues, nerves, and the circulatory system (39, 40). In T2DM, AGE accumulation affects skeletal muscle growth and metabolism through mitochondrial dysfunction and induces cell death (41). Moreover, AGEs can activate intracellular and extracellular signaling cascades, amplifying oxidative stress and inflammatory responses (42). Thus, these effects alter intracellular and extracellular charge distribution, induce protein crosslink formation, and impair muscle contractility (42).

 Intramuscular adipose tissue (IMAT)

Heterotopic fat deposition occurs in many insulin target tissues in diabetes patients, including skeletal muscle (43). Firstly, the accumulation of ectopic lipids in skeletal muscle increases the risk of skeletal muscle insulin resistance (44). Secondly, IMAT increases the secretion of pro-inflammatory cytokines, acute-phase proteins, and biologically active lipids such as diacylglycerol and ceramide. This secretion exacerbates the low-grade inflammatory state, damages the insulin signaling pathway, and leads to skeletal muscle mitochondrial dysfunction (44–46). Additionally, mitochondrial abnormalities can cause muscle degeneration and increased ROS production (47). Thirdly, IMAT can activate transcription factors such as NF-κB, exacerbating the breakdown of muscle proteins through subsequent signaling cascades (48). Lastly, as a non-contractile tissue, excessive accumulation of IMAT can affect the elasticity and function of skeletal muscles (49).

 Gut microbiota

In patients with T2DM, excess glucose in the intestinal tract leads to a significant reduction in the imbalance of lactobacillus, clostridium, and bifidobacteria populations associated with insulin resistance (50). The imbalance of gut microbiota increases intestinal permeability (51), thereby promoting the entry of microbial metabolites, such as endotoxins (e.g., indophenol sulfate), into the bloodstream. This further encourages the transmission of inflammatory signals and skeletal muscle changes, accelerating muscle aging (52). Moreover, gut microbiota disruption raises intestinal tryptophan metabolite levels, which may increase the incidence of age-related sarcopenia by inducing inflammatory responses in the gut, nervous system, and muscle tissue (53). Healthy gut microbiota-derived phenolic conjugates upregulate the GLUT4 and PI3K pathways, enhancing glucose uptake by muscle fibres and their synthetic metabolic response, thereby increasing muscle mass and reducing skeletal muscle atrophy incidence (54). In addition, gut microbiota transplantation and rational regulation improve microbial diversity, glycaemic control, insulin sensitivity, skeletal muscle quality, and function, showing potential for sarcopenia treatment (50–52).

 Clinical features of sarcopenia in T2DM under related factors

T2DM and sarcopenia are common in the population and often coexist, with possible common factors including age, gender, body mass index, duration of diabetes, blood vessels and nerves, nutritional status and lifestyle. These common factors simultaneously influence these two diseases and exhibit different clinical features.

Age

Those with both T2DM and sarcopenia tend to have a higher mean age than those without sarcopenia (55). Notably, among elderly T2DM patients over 80, nearly 40% have been identified as suffering from sarcopenia (56). Muscle mass declines with age and is negatively correlated with prediabetes prevalence (8), so older people with sarcopenia are more likely to develop diabetes.

 Gender

The influence of gender on the occurrence of sarcopenia in individuals with T2DM exhibits variability. Several investigations reported a marked increase in sarcopenia among either males (57–59) or females (60, 61). However, some studies have found that there is no significant gender difference in the incidence of T2DM-associated sarcopenia (62–73). Although there are differences in the research results on the prevalence of sarcopenia in patients with T2DM, most literature shows that the prevalence of sarcopenia in women is lower than that in men (6, 74). In addition to the role of rapid loss of muscle in males due to the decrease in testosterone (75), estrogen plays a vital role in the “anti-degradation” effect of muscle proteins (76). Importantly, it highlights the need for a nuanced understanding of gender-specific factors that may contribute to the prevalence of sarcopenia in diabetic populations, especially the role of female estrogen in this regard.

 Body mass index (BMI)

In individuals with T2DM, there is an inverse relationship between BMI and the incidence of sarcopenia (41, 63, 67, 73, 77). Patients with a high body fat percentage but a low BMI were more likely to suffer from muscle atrophy (64). Compared to Caucasians, Asians have a relatively lower BMI and a lower incidence of sarcopenic obesity (78, 79). BMI is positively correlated with muscle PDFF (80). Body composition is a vital assessment factor for sarcopenia in the context of T2DM. Therefore, it is suggested that patients with diabetes should dynamically manage their BMI and body fat rate to prevent sarcopenia.

 Diabetes duration

The relationship between the duration of diabetes and sarcopenia is controversial. Some studies show a positive correlation between sarcopenia prevalence and diabetes duration (64, 71, 81), suggesting that it may promote sarcopenia development. However, other studies find no significant link between diabetes duration and sarcopenia (69, 72, 82). Although there are inconsistent findings on this correlation, a double-blind randomized controlled trial observed that T2DM patients receiving metformin treatment walked faster (83). A cross-sectional study also observed that the proportion of sarcopenia in T2DM patients receiving metformin treatment was lower (65). Notably, this mechanism may include indirectly activating AMP-activated protein kinase (AMPK) (84), reducing hyperglycemia and insulin resistance (85), enhancing mitochondrial biogenesis, reducing reactive oxygen species (ROS), and improving muscle fiber atrophy and fibrosis (86).

 Blood vessels and nerves

Sarcopenia is closely linked to diabetic peripheral nerve and vascular complications. Research indicates that diabetic sarcopenia accelerates proliferative retinopathy progression (58) and coronary heart disease, stroke, and peripheral arterial disease(PAD) risk (68, 87). Though no significant correlation exists between sarcopenia and microvascular-associated nephropathy incidence in type 2 diabetes patients (69, 88), a retrospective observational study observed that it worsens albuminuria in those with nephropathy (66). Diabetic polyneuropathy can lead to pain and sensory abnormalities, reduce muscle strength (89), and cause muscle atrophy in the affected innervated area. Female patients are more affected than male patients (68). The combination of diabetes and sarcopenia raises the risk of diabetic foot ulcers, which are more severe, have higher Wagner grades, and are more likely to need amputation (90).

 Nutritional status and lifestyle

T2DM patients with poor nutritional status are more likely to develop sarcopenia (70, 81, 91). Patients with diabetes and sarcopenia have reduced energy intake (81) and physical activity (57, 65, 92). However, there is no significant difference between developing sarcopenia in terms of smoking and drinking (65, 92). Additionally, a cross-sectional study observed that patients can reduce the incidence of diabetes-related sarcopenia by maintaining adequate physical activity (57). Furthermore, T2DM patients with high vitamin D levels have a protective effect on sarcopenia (82).

 Imaging manifestations

Multiple imaging techniques can be used to assist in the diagnosis of sarcopenia, including DXA, ultrasound, CT, and MRI. Each of these methods has distinct advantages and limitations (Table 1). We will elaborate on the application of these imaging methods from the perspective of the comorbidity of T2DM and sarcopenia.

Imaging techniques available to detect sarcopenia.

Imaging Advantages Limitations
DXA ▪ Inexpensive▪ Fast imaging speed▪ low radiation dose ▪ Relatively inaccurate results▪ Inconsistent data from different DXA devices▪ 2Dimages▪ Inability to measure intramuscular fat content
CT ▪ Quantification of fat and muscle tissue▪ Can be used on specific muscles or the whole body▪ Can be evaluated on pre-existing CT images ▪ Expensive▪ Radiation exposure▪ Lack of normal reference ranges and diagnostic thresholds▪ Not suitable for large population screening
MRI ▪ Ability to accurately determine muscle quantity and quality▪ No radiation exposure▪ Can be used on specific muscles or the whole body ▪ Expensive▪ Long inspection time▪ Lack of normal reference ranges and diagnostic thresholds▪ Applications are limited by multiple factors
US ▪ Portable, fast, few contraindications▪ no radiation ▪ Results are influenced by body position and muscle status, operator, etc.▪ Standardization is not uniform

Additional presentation of advantages and limitations. DXA, Dual-Energy X-Ray Absorptiometry; CT, Computed Tomography; MRI, Magnetic Resonance Imaging; US, Ultrasound.

 DXA

The imaging principle of DXA is based on the differential absorption of high-energy and low-energy X-rays by different tissues (93). DXA scanning helps evaluate the fat and muscle tissue, as well as bone mineral content, of the entire body or target area (94). Due to its small radiation exposure dose, DXA has a relatively wide application (95).

DXA-derived parameters, such as appendiceal lean mass (ALM) and appendiceal lean mass index (ALMI, calculated as ALM divided by height squared), serve as a pivotal indicator of sarcopenia (96). The Asian Working Group for Sarcopenia (AWGS) has proposed ALMI thresholds of less than 5.5 kg/m2 for females and 7.0 kg/m2 for males to delineate diminished muscle mass (97). In the comparative analysis of skeletal muscle atrophy assessment based on DXA, patients with T2DM are more likely to develop skeletal muscle atrophy (98, 99), with a significantly higher incidence in elderly T2DM patients than in middle-aged ones, particularly in those aged 75 and above (100).

Bredella et al. (101) conducted a comparative analysis between DXA and CT scans. It revealed a tendency for DXA to overestimate thigh muscle mass, particularly in women with severe obesity. Consequently, this overestimation is attributed to the influence of body thickness and hydration status on DXA measurements. Moreover, the accuracy of DXA measurements may be compromised by factors related to equipment and patient positioning. Thus, it is essential to consider these factors when interpreting DXA results to ensure the reliability of body composition assessments and timely medical interventions (102).

 CT

CT imaging measures the X-ray attenuation coefficient, which varies with tissue density and thickness. It can determine the density values of individual muscles or muscle groups, in addition to estimating the area and volume of the muscle (103). There is now considerable research on aspects of sarcopenia in T2DM (Table 2).

Research results on sarcopenia in T2DM.

References Research size Inspection apparatus Research type Tissue/structure(s) MRI imaging mode Statistical analysis method Results
Han et al. (115) 420 CT Prospective cohort study Mid-thigh, umbilicus SPSS, chi-square test, Stata Consistent changes in visceral fat and thigh muscle area were associated with a higher risk of T2DM.
Kim et al. (185) 167 CT Retrospective cohort study L3 skeletal muscle SPSS 21.0, two-sample t-test, Cox regression Sarcopenia was negatively correlated with survival in diabetic patients.
Miljkovic et al. (119) 1,515 pQCT Prospective cohort study Calf Analysis of covariance (ANCOVA), logistic regression, Statistical Analysis System (SAS, version 9.1) Increased intramuscular fat is associated with the onset of T2DM.
Hildebr et al. (186) 16 HR-pQCT Cross-sectional study Tibia IBM SPSS Statistics software HR-pQCT can estimate muscle parameters and improve their accuracy.
Baum et al. (187) 9 3.0 T MR Cross-sectional study Quadriceps muscle fat 6-echo 3D spoiled gradient echo sequence SPSS (SPSS, Chicago, Ill) MRI of lipid and water based on chemical shift coding can reflect early changes in muscle strength.
Scheel et al. (188) 12 1.5 T MR Cross-sectional study Soleus muscle DTI Linear regression analysis, the software Prism DTI offers a non-invasive method for assessing changes in fiber type composition in the skeletal muscles of patients with sarcopenia.
Shen et al. (106) 328 1.5 T MR Cross-sectional study Skeletal muscle and adipose tissue in the abdominal monolayer section T1-weighted, spin-echo sequence SPSS, Two-tailed tests, Pitman’s test A single abdominal cross-sectional image can reflect the volume of skeletal muscle and adipose tissue throughout the entire body.
Marty et al. (149) 40 3.0 T MR Prospective cohort study Thighs muscles T1-mapping Matlab (MathWorks, Natick, MA, USA), Bland–Altman plots T1-mapping enabled a quantitative assessment of muscle fat infiltration, facilitating the evaluation of both acute and chronic skeletal muscle changes in sarcopenia.
Melville et al. (189) 27 3.0 T MR Prospective cohort study Quadriceps femoris T1 weighted 3D gradient echo, T2-mapping and MR spectroscopy variance (ANOVA) In patients with sarcopenia, the T2 values of muscle fat infiltration are pathologically increased
Malis et al. (164) 7 1.5 T MR Prospective cohort study Triceps Surae muscles Velocity-Encoded Phase Contrast (VE-PC) ANOVA, linear regression. The changes in SR-fiber Angle, SR plane, and shear SR, as well as their ability to predict force and force changes, may reflect functional mechanical changes in skeletal muscle associated with sarcopenia.
Andreu Simó-Servat. et al. (190) 47 Muscle US Prospective cohort study TMT T-test, Pearson’s correlation test, Youden index TMT less than 0.98 cm was 100% predictive of sarcopenia in elderly diabetic patients.
Chen et al. (181) 84 Muscle US Prospective cohort study The US-derived thickness, cross-sectional area, and SWE of the RFM The Social Sciences version 26.0 (IBM) software, T-test Muscle CSA is smaller in older T2DM patients with sarcopenia than in non-sarcopenic patients.

CT, Computed Tomography; MRI, Magnetic Resonance Imaging; US, Ultrasound; pQCT, Peripheral Quantitative Computed Tomography; HR-pQCT, High-Resolution Peripheral Quantitative Computed Tomography; DTI, Diffusion Tensor Imaging; TMT, Thigh Muscle Thickness; SWE, Shear Wave Elastography; RFM, Rectus Femoris Muscle; CSA, Cross-Sectional Area.

 CT in estimating muscle cross-sectional area and volume

Initially, scholars used the cross-sectional area to represent the volume of muscle. Since CT was first used to measure arm skeletal muscle cross-sectional area in 1979 (104), typical anatomical sites, such as the thigh, proximal femur, and trunk, have been used to measure the cross-sectional area of skeletal muscles (105). Shen et al. (106) introduced a technique to calculate total body skeletal muscle and adipose tissue content from a single conventional abdominal CT scan in 2004. This approach (106) selects the third lumbar vertebrae (L3) as the reference plane, outlining a Region of Interest (ROI) to measure muscle cross-sectional area (CSA), and applying specific threshold criteria (−29 to +150 Hounsfield Units) to segment muscle tissue, the cross-sectional area of muscles obtained based on this calculation method correlate well with total body muscle mass (107, 108), this method eliminates the need for additional examinations when evaluating sarcopenia for patients who already require abdominal CT scans. Derstine et al. (109) have demonstrated that although L3 is the ideal location for skeletal muscle measurements, measurements taken at other levels of T10-L5 can also achieve similar results; thus, the diagnosis of sarcopenia can also be made using existing CT images of the chest and pelvis. Lower skeletal muscle index (SMI) values (CSA/height2) are more indicative of sarcopenia (110, 111). A study summarized that the most common diagnostic threshold for diagnosing sarcopenia using SMI derived from abdominal wall muscle tissue was 52–55 cm2/m2 for males and 39–41 cm2/m2 for females. This standard has clinical significance for the early identification of high-risk populations, assessment of disease progression, and guidance for personalized treatment (112–114). Han et al. used CT studies and found that synchronized changes in visceral fat and thigh muscle area are associated with an increased risk of T2DM (115, 116). Perhaps for T2DM, the measurement scope is not limited to the trunk; limb measurements may also be highly beneficial.

With advancements in algorithms, some scholars have begun to evaluate muscles by their actual volume. However, due to the radiation dose, whole-muscle scanning is not commonly used and is mainly reserved for limb muscles. A South Korean team developed an automatic muscle segmentation software based on the UNETR (U-net Transformer) architecture to quantify thigh muscle volume. This method not only improves computational efficiency, but also enhances the accuracy of muscle volume calculation (117). The study’s results show AI’s strong development potential in this field.

 CT assessment of muscle density (MD)

Although cross-sectional area and volume can reflect muscle atrophy, they cannot reflect changes in muscle composition. However, MD can reflect these. MD can be reflected by CT values, which are directly related to the IMAT and can reflect muscle mass to a certain extent (118). The IMAT proportion of T2DM patients is higher than that of normal people (119), and IMAT is related to IR (120). Peripheral quantitative computed tomography (pQCT) and high-resolution peripheral quantitative computed tomography (HR-pQCT) techniques can mitigate the impact of unstable CT results on measurements, thereby yielding more accurate results for skeletal muscle CSA and muscle density. A cross-sectional study of animals and humans evaluated the MD and IMAT produced by intramuscular adipose tissue using HR-pQCT and found that it has good predictive value for the progression of myosteatosis or sarcopenia (121).

By utilizing X-ray beams of varying energy levels to differentiate the penetration and absorption differences of human tissues, Dual-energy CT (DECT) can not only distinguish different components such as skeletal muscle, adipose tissue, and connective tissue. Additionally, it can quantitatively analyze fat infiltration (122). Therefore, it can simultaneously measure multiple parameters, such as muscle cross-sectional area, muscle density, and fat content, which reflect the state of muscles from different perspectives. DECT - determined fat fraction (FF) values strongly correlate with those from MR (123). A study has shown that intermuscular and intramuscular fat increase with age and that intermuscular fat contributes to the increased incidence of T2DM (119). Furthermore, higher muscle fat infiltration correlates with increased insulin resistance (124).

 MRI

MRI is an imaging method based on differences in pulse signals released by the relaxation phenomenon of hydrogen nuclei in tissue components following external magnetic field radiofrequency pulses (103). Its multiple sequences and parameters enable it to reflect muscle inflammation, muscle fiber structure, muscle fat infiltration, muscle volume, and ultimately changes in muscle function, thereby reflecting the pathological process of sarcopenia to a certain extent. Many studies have achieved satisfactory results using various imaging sequences (Table 2).

MRI manifestations of inflammation in sarcopenia

Inflammation typically appears earlier in sarcopenia and progresses in conjunction with its progression. Inflammation can manifest as fibrosis, fat replacement, abnormally significant enhancement, and muscle atrophy, but edema is the most common manifestation. Inflammatory edema prolongs T1 and T2 relaxation times of muscle tissue, among which T2 imaging technology is the preferred method for evaluating muscle injury (125). Moreover, combining T1 and T2 values can improve the accuracy of identifying the early stages of muscle edema/inflammation (126). Additionally, the apparent diffusion coefficient (ADC) value of inflamed muscles increases compared to that of normal muscles (93). However, the MRI manifestations of inflammation in T2DM patients with sarcopenia require further research.

 MRI manifestations of muscle fiber changes in sarcopenia

Sarcopenia can lead to changes in the type and structure of muscle fibers. Diffusion magnetic resonance imaging (DMRI) can study the microstructure of skeletal muscle and quantify it to a certain extent (127). Fractional anisotropy (FA) values can help distinguish and identify different fiber types. DMRI fiber tracking technology can track the main diffusion characteristic vectors within muscle volume, thereby estimating fiber angle, curvature, muscle fiber length, and cross-sectional area (128). The proportion of type I fibers in the muscles of patients with sarcopenia increases (129). A cross-sectional study observed that DMRI can help elucidate the changes in muscles in the early stages of sarcopenia (130).

T1ρ(T1rho) is a spin–lattice relaxation time in a rotating frame that probes slow molecular motion in muscle tissue macromolecules (131, 132). T1ρ mapping reflects myofibrillar properties and is regarded as a novel quantitative index for assessing myocardial diffuse fibrosis (133). Notably, a prospective cross-sectional observational study shows that it may be an alternative non-contrast method for early T2DM myocardial diffuse fibrosis and is more sensitive than natural T1 (134). Its role in diabetes-associated sarcopenia requires further investigation.

 MRI in assessing muscle fat infiltration

MRI reflects the severity of muscle fat degeneration through the degree of muscle fat infiltration. Importantly, it is considered the gold standard for non-invasive quantitative tissue fat content (135). Muscle fat infiltration can monitor the disease progression, guide the rational selection of treatment options, and accurately evaluate the treatment effect (136, 137). Using this approach, a study has shown that the reduction of intramuscular lipids (IMCL) contributes to the improvement of IR (138).

Dixon and 1H-magnetic resonance spectroscopy (MRS) is the preferred sequence for measuring fat infiltration. Dixon technology’s imaging principle is based on the different precession frequencies of fat and water particles in a magnetic field (139, 140). The two or three-point Dixon sequence quantifies the proton density fat fraction (PDFF) with high reliability (141, 142). Magnetic resonance spectroscopy (MRS) is used to distinguish small molecular metabolites according to their chemical shift characteristics under the action of an external magnetic field (143). In contrast, 1H-MRS does not require specialized hardware and is considered the gold standard for non-invasive fat infiltration quantification (144, 145). In patients with T2DM, the muscle fat fraction (FF) is higher than in individuals with normal blood glucose levels (146). A cross-sectional observational study showed a positive correlation between decreased thigh muscle strength and muscle fat infiltration in T2DM patients (147).

T1 mapping quantifies the PDFF in terms of the modulation of T1 values by fat pools. In the case of fat replacement, the amount of fat fraction was positively correlated with a decrease in longitudinal T1 relaxation time (148), T1 mapping can also be used to distinguish between healthy and undernourished muscle (149). In contrast, T2 mapping is based on the T2 relaxation time of human tissue (150, 151), which is significantly elevated in the case of muscle fat infiltration (135). Although there are no diagnostic criteria for muscle fat infiltration in the literature, quantitative T2 values may be influenced by edema changes (152). Regardless, they still serve as a reliable quantitative assessment tool. Diffusion tensor imaging (DTI) evolved from DWI principles and is based on differences in the degree of diffusion of water molecules in various directions (135). In the case of fat infiltration, the muscle fibers appear more clearly on DTI. Furthermore, a prospective cross-sectional observational study has demonstrated a high correlation between fractional anisotropy (FA) and Mercuri grading, which is used clinically to assess the degree of muscle fat infiltration (153). Thus, patients with T2DM have higher apparent diffusion coefficient and lower FA values (154).

 MRI in estimating muscle volume

Several sites are used to measure muscle cross-sectional area, including the mid-thigh, the L2 to L5 vertebral levels, the superior mesenteric artery level, and the C3 muscle level. Common indicators for assessing muscle atrophy are CSA, total abdominal muscle area (TAMA), total psoas muscle area (TPA), volume, and the thickness of the muscle (155–158). In exceptional circumstances, temporal muscle thickness can be used to evaluate muscle atrophy, which has been shown to correlate with CSA of the psoas major muscle (159). T2DM can also reduce muscle weight, volume, and cross-sectional area (137), as well as plantar tissue and skin thickness (160).

 MRI signs reflecting muscle function

Sarcopenia can lead to changes in skeletal muscle function. Magnetic resonance elastography (MRE) is a technique that captures the dynamic biomechanical properties of skeletal muscle throughout phases of contraction and relaxation, enabling a precise quantitative assessment of skeletal muscle tissue biomechanical properties and reflecting skeletal muscle function (161). Moreover, MRE is reliable for quantitatively measuring muscle stiffness (162, 163). Strain rate tensor imaging is another innovative approach that quantifies muscle contractility and elasticity through strain rate (SR) and strain rate fiber angle (SR-fiber angle). Notably, it shows promise for evaluating muscle mechanical changes in sarcopenia (164, 165). Moreover, 31P-containing metabolite concentrations, measured by magnetic resonance spectroscopy (MRS), are strongly correlated with muscle fat infiltration and subsequent decline in muscle strength (166). The signal changes of Blood-Oxygen-Level-Dependent (BOLD) MRI are also closely related to the level of blood oxygenation. On the one hand, BOLDMRI can reflect the status of muscle microcirculation (167), and on the other hand, it can indirectly reflect the functional status of muscle by dynamically monitoring changes in muscle oxygen metabolism under various physiological conditions (168). Therefore, its performance and clinical application in diabetes sarcopenia warrant further study (169).

 Ultrasound (US)

US has become a valuable and reliable tool for assessing muscle volume and mass. It is affordable, allows for real-time dynamic imaging of target tissues during the procedure, and is safe, non-invasive, portable, free from ionizing radiation, and offers high-resolution imaging (170, 171).

US evaluation of muscle volume

Muscle thickness, pennation angle, fascicle length, and muscle cross-sectional area are standard parameters for US assessment of muscle volume. A study proposed the formula for estimating muscle volume: MV = 0.3 × MT + 30.5 × LL (172), where MV = muscle volume, MT = muscle thickness and LL = limb length. Gastrocnemius muscle thickness and muscle fascicle length are highly sensitive and highly accurate for negative results in the detection of sarcopenia (173). A cross-sectional observational study has shown that T2DM patients have significant reductions in plantar tissue and intrinsic foot muscle thickness (174), particularly in the extensor digitorum brevis and first and second intermetatarsal muscles (175).

 US assessment of muscle fat infiltration

Muscle echo intensity can reflect the degree of fat infiltration degree (176). It is significantly higher in elderly sarcopenia patients than in young people (177). A cross-sectional observational study quantified rotator cuff muscle fat infiltration via US Backscatter Coefficient (BSC) values, which rose with the Goutallier grade (178). Thus, we hypothesize that US also has the potential to evaluate sarcopenia in T2DM.

 Shear wave elastography (SWE) for muscle assessment

SWE is a quantitative technique that determines the absolute elasticity of soft tissues. Notably, it evaluates skeletal muscle stiffness and reflects early changes associated with muscle function (179, 180). A cross-sectional observational study has demonstrated that patients with T2DM and sarcopenia have smaller muscle CSA and increased stiffness values; therefore, SWE is beneficial in identifying sarcopenia in patients with T2DM (181).

 Prospectives

Research on imaging methods in patients with diabetes and sarcopenia is currently limited, with most studies featuring small sample sizes. This limitation may affect the universality and reliability of the research findings. Thus, future studies with larger samples are needed to further explore and validate the application value of imaging methods in this patient population.

Different studies employ various diagnostic techniques, standards, and clinical applications for sarcopenia, making it challenging to compare and integrate research findings. Currently, there is a lack of unified methods for detecting and measuring sarcopenia, which hinders effective comparison and comprehensive analysis across different studies. Developing a feasible solution to address or unify these internal differences is necessary.

Artificial intelligence (AI) algorithms and models have achieved initial results in the detection and evaluation of sarcopenia (182–184),. However, they still lack the application of sarcopenia in the context of T2DM. This gap highlights a promising direction for future research. The combined use of AI algorithms and models during imaging examinations is expected to enhance their role in the study of sarcopenia significantly. Radiomics, a vital research trend, involves extracting and quantifying the characteristic manifestations of sarcopenia in imaging to form a robust database and conduct personalized analysis. Thus, this approach may improve and integrate existing imaging diagnostic criteria, enhance the sensitivity of early identification, and strengthen the accuracy of efficacy and prognosis evaluation.

 Conclusion

Sarcopenia, a disease characterized by age-related decline in muscle mass, strength, or endurance, is increasingly prevalent in patients with T2DM and is closely related to their prognosis. Imaging methods, including DXA, CT, MRI, and US, play a crucial role in evaluating muscle mass, microstructural changes, and function. They are of great value for early diagnosis, understanding of disease pathological processes, assessment of disease severity, evaluation of treatment effectiveness, and prognosis. Thus assisting in the development of personalized treatment plans. By fully leveraging the advantages of each imaging examination, clinicians can gain a more comprehensive understanding of the sarcopenia status in patients with T2DM, which is expected to significantly improve the lifespan and quality of life for these patients.

 Author contributions

LH: Writing – original draft, Project administration. GL: Writing – original draft, Project administration, Investigation. HJ: Writing – original draft, Investigation, Project administration. LZ: Investigation, Project administration, Writing – original draft. YL: Writing – original draft, Investigation, Project administration. WG: Writing – original draft, Project administration, Investigation. QZ: Investigation, Writing – original draft, Project administration. JZ: Writing – original draft, Visualization. JL: Supervision, Writing – review & editing, Funding acquisition. HL: Supervision, Writing – review & editing, Funding acquisition. HZ: Conceptualization, Project administration, Funding acquisition, Writing – review & editing.

 Acknowledgments

The author would like to thank all those who contributed to this review.

 Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 References YedjouCG SimsJN NjikiS ChitohAM JosephM CherkosAS . Health and racial disparities in diabetes mellitus prevalence, management, policies, and outcomes in the United States. J Community Med Public Health. (2024) 8:460. doi: 10.29011/2577-2228.100460, 39697180 ForouhiNG WarehamNJ. Epidemiology of diabetes. Medicine. (2014) 42:698–702. doi: 10.1016/j.mpmed.2014.09.007, 25568613 JosephJ. Beyond the triad: uncommon initial presentations in newly diagnosed type 2 diabetes mellitus. Cureus. (2025) 17:e89525. doi: 10.7759/cureus.89525, 40787191 BeaudartC AlcazarJ AprahamianI BatsisJA YamadaY PradoCM . Health outcomes of sarcopenia: a consensus report by the outcome working group of the global leadership initiative in sarcopenia (GLIS). Aging Clin Exp Res. (2025) 37:100. doi: 10.1007/s40520-025-02995-9, 40120052 HannaJS. Sarcopenia and critical illness: a deadly combination in the elderly. JPEN J Parenter Enteral Nutr. (2015) 39:273–81. doi: 10.1177/0148607114567710, 25591973 YogeshM PatelM GandhiR PatelA KidechaKN. Sarcopenia in type 2 diabetes mellitus among Asian populations: prevalence and risk factors based on AWGS- 2019: a systematic review and meta-analysis. BMC Endocr Disord. (2025) 25:101. doi: 10.1186/s12902-025-01935-y, 40247215 de Luis RománD GómezJC García-AlmeidaJM ValloFG RoloGG GómezJJL . Diabetic sarcopenia. A proposed muscle screening protocol in people with diabetes. Rev Endocr Metab Disord. (2024) 25:651–61. doi: 10.1007/s11154-023-09871-9, 38315411 QiaoY-S ChaiY-H GongH-J ZhuldyzZ StehouwerCDA ZhouJ-B . The association between diabetes mellitus and risk of sarcopenia: accumulated evidences from observational studies. Front Endocrinol. (2021) 12:782391. doi: 10.3389/fendo.2021.782391, 35002965 IdaS KanekoR ImatakaK MurataK. Association between sarcopenia and renal function in patients with diabetes: A systematic review and Meta-analysis. J Diabetes Res. (2019) 2019:1365189. doi: 10.1155/2019/1365189, 31828155 WannarongT SukpornchairakP NaweeraW GeigerCD UngprasertP. Association between diabetic peripheral neuropathy and sarcopenia: A systematic review and meta-analysis. Geriatr Gerontol Int. (2022) 22:785–9. doi: 10.1111/ggi.14462, 36053982 VogeleD OttoS SollmannN HaggenmüllerB WolfD BeerM . Sarcopenia – definition, radiological diagnosis, clinical significance. Rofo Fortschr Geb Rontgenstr Bildgeb Verfahr. (2023) 195:393–405. doi: 10.1055/a-1990-0201, 36630983 TakahashiF HashimotoY KajiA SakaiR OkamuraT KitagawaN . Sarcopenia is associated with a risk of mortality in people with type 2 diabetes mellitus. Front Endocrinol. (2021) 12:783363. doi: 10.3389/fendo.2021.783363, 34858351 XuJ WanCS KtorisK ReijnierseEM MaierAB. Sarcopenia is associated with mortality in adults: a systematic review and meta-analysis. Gerontology. (2022) 68:361–76. doi: 10.1159/000517099, 34315158 Cruz-JentoftAJ SayerAA. Sarcopenia. Lancet. (2019) 393:2636–46. doi: 10.1016/S0140-6736(19)31138-9 IzzoA MassiminoE RiccardiG Della PepaG A narrative review on sarcopenia in type 2 diabetes mellitus: prevalence and associated factors - PubMed. Available online at: https://pubmed.ncbi.nlm.nih.gov/33435310/ (Accessed May 28, 2025). YoshidaT DelafontaineP. Mechanisms of IGF-1-mediated regulation of skeletal muscle hypertrophy and atrophy. Cells. (2020) 9:1970. doi: 10.3390/cells9091970, 32858949 KalyaniRR CorriereM FerrucciL. Age-related and disease-related muscle loss: the effect of diabetes, obesity, and other diseases. Lancet Diabetes Endocrinol. (2014) 2:819–29. doi: 10.1016/S2213-8587(14)70034-8, 24731660 BassilMS GougeonR. Muscle protein anabolism in type 2 diabetes. Curr Opin Clin Nutr Metab Care. (2013) 16:83–8. doi: 10.1097/MCO.0b013e32835a88ee, 23196814 BatailleS ChauveauP FouqueD AparicioM KoppeL. Myostatin and muscle atrophy during chronic kidney disease. Nephrol Dial Transplant. (2021) 36:1986–93. doi: 10.1093/ndt/gfaa129, 32974666 DuJ WangX MierelesC BaileyJL DebigareR ZhengB . Activation of caspase-3 is an initial step triggering accelerated muscle proteolysis in catabolic conditions. J Clin Invest. (2004) 113:115–23. doi: 10.1172/JCI18330, 14702115 BaczekJ SilkiewiczM WojszelZB. Myostatin as a biomarker of muscle wasting and other pathologies-state of the art and knowledge gaps. Nutrients. (2020) 12:2401. doi: 10.3390/nu12082401, 32796600 OmuraT ArakiA. Skeletal muscle as a treatment target for older adults with diabetes mellitus: the importance of a multimodal intervention based on functional category. Geriatr Gerontol Int. (2022) 22:110–20. doi: 10.1111/ggi.14339, 34986525 GoldbergT CaiW PeppaM DardaineV BaligaBS UribarriJ . Advanced glycoxidation end products in commonly consumed foods. J Am Diet Assoc. (2004) 104:1287–91. doi: 10.1016/j.jada.2004.05.214 YanSF RamasamyR SchmidtAM. Mechanisms of disease: advanced glycation end-products and their receptor in inflammation and diabetes complications. Nat Clin Pract Endocrinol Metab. (2008) 4:285–93. doi: 10.1038/ncpendmet0786, 18332897 BowenTS SchulerG AdamsV. Skeletal muscle wasting in cachexia and sarcopenia: molecular pathophysiology and impact of exercise training. J Cachexia Sarcopenia Muscle. (2015) 6:197–207. doi: 10.1002/jcsm.12043 ZhangL KimballSR JeffersonLS ShenbergerJS. Hydrogen peroxide impairs insulin-stimulated assembly of mTORC1. Free Radic Biol Med. (2009) 46:1500–9. doi: 10.1016/j.freeradbiomed.2009.03.001, 19281842 ScicchitanoBM PelosiL SicaG MusaròA. The physiopathologic role of oxidative stress in skeletal muscle. Mech Ageing Dev. (2018) 170:37–44. doi: 10.1016/j.mad.2017.08.009 PickupJC MattockMB ChusneyGD BurtD. NIDDM as a disease of the innate immune system: association of acute-phase reactants and interleukin-6 with metabolic syndrome X. Diabetologia. (1997) 40:1286–92. doi: 10.1007/s001250050822, 9389420 SprangerJ KrokeA MöhligM HoffmannK BergmannMM RistowM . Inflammatory cytokines and the risk to develop type 2 diabetes: results of the prospective population-based European prospective investigation into Cancer and nutrition (EPIC)-Potsdam study. Diabetes. (2003) 52:812–7. doi: 10.2337/diabetes.52.3.812, 12606524 HerderC BaumertJ ThorandB KoenigW de JagerW MeisingerC . Chemokines as risk factors for type 2 diabetes: results from the MONICA/KORA Augsburg study, 1984-2002. Diabetologia. (2006) 49:921–9. doi: 10.1007/s00125-006-0190-y MashiliF ChibalinAV KrookA ZierathJR. Constitutive STAT3 phosphorylation contributes to skeletal muscle insulin resistance in type 2 diabetes. Diabetes. (2013) 62:457–65. doi: 10.2337/db12-0337 RieussetJ BouzakriK ChevillotteE RicardN JacquetD BastardJ-P . Suppressor of cytokine signaling 3 expression and insulin resistance in skeletal muscle of obese and type 2 diabetic patients. Diabetes. (2004) 53:2232–41. doi: 10.2337/diabetes.53.9.2232, 15331532 SriwijitkamolA Christ-RobertsC BerriaR EaganP PratipanawatrT DeFronzoRA . Reduced skeletal muscle inhibitor of kappaB beta content is associated with insulin resistance in subjects with type 2 diabetes: reversal by exercise training - PubMed. (2006) Available online at: https://pubmed.ncbi.nlm.nih.gov/16505240/ (Accessed May 28, 2025). MaJF SanchezBJ HallDT TremblayA-MK Di MarcoS GallouziI-E. STAT3 promotes IFNγ/TNFα-induced muscle wasting in an NF-κB-dependent and IL-6-independent manner. EMBO Mol Med. (2017) 9:622–37. doi: 10.15252/emmm.201607052, 28264935 RongY-D BianA-L HuH-Y MaY ZhouX-Z. Study on relationship between elderly sarcopenia and inflammatory cytokine IL-6, anti-inflammatory cytokine IL-10. BMC Geriatr. (2018) 18:308. doi: 10.1186/s12877-018-1007-9, 30541467 HardingAT HeatonNS. The impact of estrogens and their receptors on immunity and inflammation during infection. Cancer. (2022) 14:909. doi: 10.3390/cancers14040909, 35205657 GotelliE CampitielloR HysaE SoldanoS CasabellaA PizzorniC . The epigenetic effects of glucocorticoids, sex hormones and vitamin D as steroidal hormones in rheumatic musculoskeletal diseases. Clin Exp Rheumatol. (2024) 42:2131–40. doi: 10.55563/clinexprheumatol/t03g31, 39212127 WangQ PengF YangJ ChenX PengZ ZhangM . MicroRNAs regulate the vicious cycle of vascular calcification-osteoporosis in postmenopausal women. Mol Biol Rep. (2024) 51:622. doi: 10.1007/s11033-024-09550-1, 38709309 MoonS-S. Low skeletal muscle mass is associated with insulin resistance, diabetes, and metabolic syndrome in the Korean population: the Korea National Health and nutrition examination survey (KNHANES) 2009-2010. Endocr J. (2014) 6:61–70. doi: 10.1507/endocrj.ej13-0244, 24088600 CleasbyME JamiesonPM AthertonPJ. Insulin resistance and sarcopenia: mechanistic links between common co-morbidities. J Endocrinol. (2016) 229:R67–81. doi: 10.1530/JOE-15-0533, 26931135 MesinovicJ ZenginA De CourtenB EbelingPR ScottD. Sarcopenia and type 2 diabetes mellitus: a bidirectional relationship. Diabetes Metab Syndr Obes Targets Ther. (2019) 12:1057–72. doi: 10.2147/DMSO.S186600, 31372016 SuzukiA YabuA NakamuraH. Advanced glycation end products in musculoskeletal system and disorders. Methods San Diego Calif. (2022) 203:179–86. doi: 10.1016/j.ymeth.2020.09.012 KahnSE HullRL UtzschneiderKM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. (2006) 444:840–6. doi: 10.1038/nature05482 MeexRCR BlaakEE van LoonLJC. Lipotoxicity plays a key role in the development of both insulin resistance and muscle atrophy in patients with type 2 diabetes. Obes Rev Off J Int Assoc Study Obes. (2019) 20:1205–17. doi: 10.1111/obr.12862, 31240819 Lopez-PedrosaJM Camprubi-RoblesM Guzman-RoloG Lopez-GonzalezA Garcia-AlmeidaJM Sanz-ParisA . The vicious cycle of type 2 diabetes mellitus and skeletal muscle atrophy: clinical, biochemical, and nutritional bases. Nutrients. (2024) 16:172. doi: 10.3390/nu16010172, 38202001 SokolowskaE Blachnio-ZabielskaA. The role of ceramides in insulin resistance. Front Endocrinol. (2019) 10:577. doi: 10.3389/fendo.2019.00577, 31496996 Jimenez-GutierrezGE Martínez-GómezLE Martínez-ArmentaC PinedaC Martínez-NavaGA Lopez-ReyesA. Molecular mechanisms of inflammation in sarcopenia: diagnosis and therapeutic update. Cells. (2022) 11:2359. doi: 10.3390/cells11152359, 35954203 KahnD MaciasE ZariniS GarfieldA Zemski BerryK GersztenR . Quantifying the inflammatory secretome of human intermuscular adipose tissue. Physiol Rep. (2022) 10:e15424. doi: 10.14814/phy2.15424, 35980018 CsapoR MalisV SinhaU DuJ SinhaS. Age-associated differences in triceps surae muscle composition and strength - an MRI-based cross-sectional comparison of contractile, adipose and connective tissue. BMC Musculoskelet Disord. (2014) 15:209. doi: 10.1186/1471-2474-15-209, 24939372 SlouhaE RezazadahA FarahbodK GertsA ClunesLA KolliasTF. Type-2 diabetes mellitus and the gut microbiota: systematic review. Cureus. (2023) 15:e49740. doi: 10.7759/cureus.49740, 38161953 PiccaA FanelliF CalvaniR MulèG PesceV SistoA . Gut Dysbiosis and muscle aging: searching for novel targets against sarcopenia. Mediat Inflamm. (2018) 2018:7026198. doi: 10.1155/2018/7026198, 29686533 GrosickiGJ FieldingRA LustgartenMS. Gut microbiota contribute to age-related changes in skeletal muscle size, composition, and function: biological basis for a gut-muscle axis. Calcif Tissue Int. (2018) 102:433–42. doi: 10.1007/s00223-017-0345-5, 29058056 XuY MaoT WangY QiX ZhaoW ChenH . Effect of gut microbiota-mediated tryptophan metabolism on Inflammaging in frailty and sarcopenia. J Gerontol A Biol Sci Med Sci. (2024) 79:glae044. doi: 10.1093/gerona/glae044, 38366346 HoughtonMJ KerimiA MoulyV TumovaS WilliamsonG. Gut microbiome catabolites as novel modulators of muscle cell glucose metabolism. FASEB J Off Publ Fed Am Soc Exp Biol. (2019) 33:1887–98. doi: 10.1096/fj.201801209R, 30183376 IzzoA MassiminoE RiccardiG Della PepaG. A narrative review on sarcopenia in type 2 diabetes mellitus: prevalence and associated factors. Nutrients. (2021) 13:183. doi: 10.3390/nu13010183, 33435310 MurataY KadoyaY YamadaS SankeT. Sarcopenia in elderly patients with type 2 diabetes mellitus: prevalence and related clinical factors. Diabetol Int. (2017) 9:136–42. doi: 10.1007/s13340-017-0339-6, 30603361 de FreitasMM de OliveiraVLP GrassiT ValdugaK MillerMEP SchuchmannRA . Difference in sarcopenia prevalence and associated factors according to 2010 and 2018 European consensus (EWGSOP) in elderly patients with type 2 diabetes mellitus. Exp Gerontol. (2020) 132:110835. doi: 10.1016/j.exger.2020.110835, 31954325 FukudaT BouchiR TakeuchiT TsujimotoK MinamiI YoshimotoT . Sarcopenic obesity assessed using dual energy X-ray absorptiometry (DXA) can predict cardiovascular disease in patients with type 2 diabetes: a retrospective observational study. Cardiovasc Diabetol. (2018) 17:55. doi: 10.1186/s12933-018-0700-5, 29636045 SazlinaS-G LeePY ChanYM A HamidMS TanNC. The prevalence and factors associated with sarcopenia among community living elderly with type 2 diabetes mellitus in primary care clinics in Malaysia. PLoS One. (2020) 15:e0233299. doi: 10.1371/journal.pone.0233299, 32433712 ChenF XuS WangY ChenF CaoL LiuT . Risk factors for sarcopenia in the elderly with type 2 diabetes mellitus and the effect of metformin. J Diabetes Res. (2020) 2020:3950404. doi: 10.1155/2020/3950404, 33083494 IdaS NakaiM ItoS IshiharaY ImatakaK UchidaA . Association between sarcopenia and mild cognitive impairment using the Japanese version of the SARC-F in elderly patients with diabetes. J Am Med Dir Assoc. (2017) 18:809.e9–809.e13. doi: 10.1016/j.jamda.2017.06.012, 28739493 HashimotoY KajiA SakaiR HamaguchiM OkadaH UshigomeE . Sarcopenia is associated with blood pressure variability in older patients with type 2 diabetes: a cross-sectional study of the KAMOGAWA-DM cohort study. Geriatr Gerontol Int. (2018) 18:1345–9. doi: 10.1111/ggi.13487, 30039599 KajiA HashimotoY KobayashiY SakaiR OkamuraT MikiA . Sarcopenia is associated with tongue pressure in older patients with type 2 diabetes: A cross-sectional study of the KAMOGAWA-DM cohort study. Geriatr Gerontol Int. (2019) 19:153–8. doi: 10.1111/ggi.13577, 30585390 FukuokaY NaritaT FujitaH MoriiT SatoT SassaMH . Importance of physical evaluation using skeletal muscle mass index and body fat percentage to prevent sarcopenia in elderly Japanese diabetes patients. J Diabetes Investig. (2019) 10:322–30. doi: 10.1111/jdi.12908, 30098231 CuiM GangX WangG XiaoX LiZ JiangZ . A cross-sectional study: associations between sarcopenia and clinical characteristics of patients with type 2 diabetes. Medicine. (2020) 99:e18708. doi: 10.1097/MD.0000000000018708, 31914078 BouchiR FukudaT TakeuchiT MinamiI YoshimotoT OgawaY. Sarcopenia is associated with incident albuminuria in patients with type 2 diabetes: A retrospective observational study. J Diabetes Investig. (2017) 8:783–7. doi: 10.1111/jdi.12636, 28130832 SungMJ LimTS JeonMY LeeHW KimBK KimDY . Sarcopenia is independently associated with the degree of liver fibrosis in patients with type 2 diabetes mellitus. Gut Liver. (2020) 14:626–35. doi: 10.5009/gnl19126, 32135582 IdaS KanekoR NagataH NoguchiY ArakiY NakaiM . Association between sarcopenia and sleep disorder in older patients with diabetes. Geriatr Gerontol Int. (2019) 19:399–403. doi: 10.1111/ggi.13627, 30773802 SugimotoK TabaraY IkegamiH TakataY KamideK IkezoeT . Hyperglycemia in non-obese patients with type 2 diabetes is associated with low muscle mass: the multicenter study for clarifying evidence for sarcopenia in patients with diabetes mellitus. J Diabetes Investig. (2019) 10:1471–9. doi: 10.1111/jdi.13070, 31074209 LiX XuF HuL FangH AnY. Revisiting: “prevalence of and factors associated with sarcopenia among multi-ethnic ambulatory older Asians with type 2 diabetes mellitus in a primary care setting.”. BMC Geriatr. (2020) 20:415. doi: 10.1186/s12877-020-01727-0, 33081718 MoriH KurodaA IshizuM OhishiM TakashiY OtsukaY . Association of accumulated advanced glycation end-products with a high prevalence of sarcopenia and dynapenia in patients with type 2 diabetes. J Diabetes Investig. (2019) 10:1332–40. doi: 10.1111/jdi.13014, 30677242 IdaS MurataK NakadachiD IshiharaY ImatakaK UchidaA . Association between dynapenia and decline in higher-level functional capacity in older men with diabetes. Geriatr Gerontol Int. (2018) 18:1393–7. doi: 10.1111/ggi.13498, 30094914 OkamuraT HashimotoY MikiA KajiA SakaiR IwaiK . High brain natriuretic peptide is associated with sarcopenia in patients with type 2 diabetes: a cross-sectional study of KAMOGAWA-DM cohort study. Endocr J. (2019) 66:369–77. doi: 10.1507/endocrj.EJ19-0024, 31019148 WeiW LvF LiuS CaoH LinR ChenH . Lipoprotein(a) is associated with sarcopenia in patients with type 2 diabetes: A cross-sectional study. Diabetes Metab Syndr Obes. (2024) 17:4511–24. doi: 10.2147/DMSO.S489605, 39619221 NguyenHT NguyenAH LePTM. Sex differences in frailty of geriatric outpatients with type 2 diabetes mellitus: a multicentre cross-sectional study. Sci Rep. (2022) 12:16122. doi: 10.1038/s41598-022-20678-7, 36167775 AiY XuR LiuL. The prevalence and risk factors of sarcopenia in patients with type 2 diabetes mellitus: a systematic review and meta-analysis. Diabetol Metab Syndr. (2021) 13:93. doi: 10.1186/s13098-021-00707-7, 34479652 KimH SuzukiT KimM KojimaN YoshidaY HiranoH . Incidence and predictors of sarcopenia onset in community-dwelling elderly Japanese women: 4-year follow-up study. J Am Med Dir Assoc. (2015) 16:85.e1–8. doi: 10.1016/j.jamda.2014.10.006 DeurenbergP Deurenberg-YapM GuricciS. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes Rev Off J Int Assoc Study Obes. (2002) 3:141–6. doi: 10.1046/j.1467-789x.2002.00065.x, 12164465 RohE ChoiKM. Health consequences of Sarcopenic obesity: A narrative review. Front Endocrinol. (2020) 11:332. doi: 10.3389/fendo.2020.00332, 32508753 BurianE SyväriJ DieckmeyerM HolzapfelC DrabschT SollmannN . Age- and BMI-related variations of fat distribution in sacral and lumbar bone marrow and their association with local muscle fat content. Sci Rep. (2020) 10:9686. doi: 10.1038/s41598-020-66649-8, 32546722 OkamuraT MikiA HashimotoY KajiA SakaiR OsakaT . Shortage of energy intake rather than protein intake is associated with sarcopenia in elderly patients with type 2 diabetes: a cross-sectional study of the KAMOGAWA-DM cohort. J Diabetes. (2019) 11:477–83. doi: 10.1111/1753-0407.12874, 30407749 AnagnostisP GkekasNK AchillaC PananastasiouG TaouxidouP MitsiouM . Type 2 diabetes mellitus is associated with increased risk of sarcopenia: a systematic review and meta-analysis. Calcif Tissue Int. (2020) 107:453–63. doi: 10.1007/s00223-020-00742-y, 32772138 LaksmiPW SetiatiS TaminTZ SoewondoP RochmahW NafrialdiN . Effect of metformin on handgrip strength, gait speed, myostatin serum level, and health-related quality of life: a double blind randomized controlled trial among non-diabetic pre-frail elderly patients. Acta Med Indones. (2017) 49:118–27. 28790226 ShangR MiaoJ. Mechanisms and effects of metformin on skeletal muscle disorders. Front Neurol. (2023) 14:1275266. doi: 10.3389/fneur.2023.1275266, 37928155 GoreDC WolfSE SanfordA HerndonDN WolfeRR. Influence of metformin on glucose intolerance and muscle catabolism following severe burn injury. Ann Surg. (2005) 241:334–42. doi: 10.1097/01.sla.0000152013.23032.d1, 15650645 HuY LuS XueC HuZ WangY ZhangW . Exploring the protective effect of metformin against sarcopenia: insights from cohort studies and genetics. J Transl Med. (2025) 23:356. doi: 10.1186/s12967-025-06357-x, 40119457 PurnamasariD TetrasiwiEN KartikoGJ AstrellaC HusamK LaksmiPW. Sarcopenia and chronic complications of type 2 diabetes mellitus. Rev Diabet Stud RDS. (2022) 18:157–65. doi: 10.1900/RDS.2022.18.157, 36309772 ÇelikerM SelçukMY OltS. Sarcopenia in diabetic nephropathy: a cross-sectional study. Rom J Intern Med. (2018) 56:102–8. doi: 10.2478/rjim-2018-0003, 29389667 KalyaniRR MetterEJ EganJ GoldenSH FerrucciL. Hyperglycemia predicts persistently lower muscle strength with aging. Diabetes Care. (2015) 38:82–90. doi: 10.2337/dc14-1166, 25392294 ChengQ HuJ YangP CaoX DengX YangQ . Sarcopenia is independently associated with diabetic foot disease. Sci Rep. (2017) 7:8372. doi: 10.1038/s41598-017-08972-1, 28827604 OkamuraT HashimotoY MikiA KajiA SakaiR IwaiK . Reduced dietary omega-3 fatty acids intake is associated with sarcopenia in elderly patients with type 2 diabetes: a cross-sectional study of KAMOGAWA-DM cohort study. J Clin Biochem Nutr. (2020) 66:233–7. doi: 10.3164/jcbn.19-85, 32523250 YangR ZhangY ShenX YanS. Sarcopenia associated with renal function in the patients with type 2 diabetes. Diabetes Res Clin Pract. (2016) 118:121–9. doi: 10.1016/j.diabres.2016.06.023, 27368064 GiraudoC CavaliereA LupiA GuglielmiG QuaiaE. Established paths and new avenues: a review of the main radiological techniques for investigating sarcopenia. Quant Imaging Med Surg. (2020) 10:1602–13. doi: 10.21037/qims.2019.12.15, 32742955 BazzocchiA PontiF AlbisinniU BattistaG GuglielmiG. DXA: technical aspects and application. Eur J Radiol. (2016) 85:1481–92. doi: 10.1016/j.ejrad.2016.04.004, 27157852 Cruz-JentoftAJ BaeyensJP BauerJM BoirieY CederholmT LandiF . 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, 20392703 PetakS BarbuCG YuEW FieldingR MulliganK SabowitzB . The official positions of the International Society for Clinical Densitometry: body composition analysis reporting. J Clin Densitom Off J Int Soc Clin Densitom. (2013) 16:508–19. doi: 10.1016/j.jocd.2013.08.018, 24183640 ChenL-K WooJ AssantachaiP AuyeungT-W ChouM-Y IijimaK . Asian working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. (2020) 21:300–307.e2. doi: 10.1016/j.jamda.2019.12.012, 32033882 AnbalaganVP VenkataramanV PradeepaR DeepaM AnjanaRM MohanV. The prevalence of presarcopenia in Asian Indian individuals with and without type 2 diabetes. Diabetes Technol Ther. (2013) 15:768–75. doi: 10.1089/dia.2013.0068, 23789632 AnoopS MisraA BhattSP GulatiS MahajanH PrabakaranG. High plasma glucagon levels correlate with waist-to-hip ratio, Suprailiac skinfold thickness, and deep subcutaneous abdominal and intraperitoneal adipose tissue depots in nonobese Asian Indian males with type 2 diabetes in North India. J Diabetes Res. (2017) 2017:2376016. doi: 10.1155/2017/2376016, 28634585 KooBK RohE YangYS MoonMK. Difference between old and young adults in contribution of β-cell function and sarcopenia in developing diabetes mellitus. J Diabetes Investig. (2016) 7:233–40. doi: 10.1111/jdi.12392, 27042276 BredellaMA GhomiRH ThomasBJ TorrianiM BrickDJ GerweckAV . Comparison of DXA and CT in the assessment of body composition in premenopausal women with obesity and anorexia nervosa. Obesity. (2010) 18:2227–33. doi: 10.1038/oby.2010.5, 20111013 WangD ZhangG YuY ZhangZ. Imaging of sarcopenia in type 2 diabetes mellitus. Clin Interv Aging. (2024) 19:141–51. doi: 10.2147/CIA.S443572, 38292460 SergiG TrevisanC VeroneseN LucatoP ManzatoE. Imaging of sarcopenia. Eur J Radiol. (2016) 85:1519–24. doi: 10.1016/j.ejrad.2016.04.009, 27117135 HeymsfieldSB OlafsonRP KutnerMH NixonDW. A radiographic method of quantifying protein-calorie undernutrition. Am J Clin Nutr. (1979) 32:693–702. doi: 10.1093/ajcn/32.3.693, 105625 EngelkeK MuseykoO WangL LaredoJ-D. Quantitative analysis of skeletal muscle by computed tomography imaging—state of the art. J Orthop Transl. (2018) 15:91–103. doi: 10.1016/j.jot.2018.10.004, 30533385 ShenW PunyanityaM WangZ GallagherD St-OngeM-P AlbuJ . Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol. (2004) 97:2333–8. doi: 10.1152/japplphysiol.00744.2004, 15310748 Cespedes FelicianoEM AvrutinE CaanBJ BoroianA MourtzakisM. Screening for low muscularity in colorectal cancer patients: a valid, clinic-friendly approach that predicts mortality. J Cachexia Sarcopenia Muscle. (2018) 9:898–908. doi: 10.1002/jcsm.12317, 30066490 Blauwhoff-BuskermolenS VersteegKS MAEv d S den BraverNR BerkhofJ LangiusJAE . Loss of muscle mass during chemotherapy is predictive for poor survival of patients with metastatic colorectal cancer. J Clin Oncol Off J Am Soc Clin Oncol. (2016) 34:1339–44. doi: 10.1200/JCO.2015.63.6043, 26903572 DerstineBA HolcombeSA RossBE WangNC SuGL WangSC. Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci Rep. (2018) 8:11369. doi: 10.1038/s41598-018-29825-5, 30054580 van VugtJLA LevolgerS de BruinRWF van RosmalenJ MetselaarHJ IJzermansJNM. Systematic review and meta-analysis of the impact of computed tomography-assessed skeletal muscle mass on outcome in patients awaiting or undergoing liver transplantation. Am J Transplant. (2016) 16:2277–92. doi: 10.1111/ajt.13732 van der WerfA LangiusJ van der SchuerenM NurmohamedSA van der PantK Blauwhoff-BuskermolenS . Percentiles for skeletal muscle index, area and radiation attenuation based on computed tomography imaging in a healthy Caucasian population. Eur J Clin Nutr. (2018) 72:288–96. doi: 10.1038/s41430-017-0034-5, 29242526 TagliaficoAS BignottiB TorriL RossiF. Sarcopenia: how to measure, when and why. Radiol Med. (2022) 127:228–37. doi: 10.1007/s11547-022-01450-3, 35041137 GuDH KimMY SeoYS KimSG LeeHA KimTH . Clinical usefulness of psoas muscle thickness for the diagnosis of sarcopenia in patients with liver cirrhosis. Clin Mol Hepatol. (2018) 24:319–30. doi: 10.3350/cmh.2017.0077, 29706058 YokoiK TanakaT KojoK MiuraH YamanashiT SatoT . Skeletal muscle changes assessed by preoperative computed tomography images can predict the long-term prognosis of stage III colorectal Cancer. Ann Gastroenterol Surg. (2021) 6:386–95. doi: 10.1002/ags3.12532, 35634179 HanSJ KimS-K FujimotoWY KahnSE LeonettiDL BoykoEJ. Effects of combination of change in visceral fat and thigh muscle mass on the development of type 2 diabetes. Diabetes Res Clin Pract. (2017) 134:131–8. doi: 10.1016/j.diabres.2017.10.003, 29032053 HanSJ BoykoEJ KimS-K FujimotoWY KahnSE LeonettiDL. Association of Thigh Muscle Mass with insulin resistance and incident type 2 diabetes mellitus in Japanese Americans. Diabetes Metab J. (2018) 42:488–95. doi: 10.4093/dmj.2018.0022, 30302961 KimHS KimH KimS ChaY KimJ-T KimJ-W . Precise individual muscle segmentation in whole thigh CT scans for sarcopenia assessment using U-net transformer. Sci Rep. (2024) 14:3301. doi: 10.1038/s41598-024-53707-8, 38331977 AubreyJ EsfandiariN BaracosVE ButeauFA FrenetteJ PutmanCT . Measurement of skeletal muscle radiation attenuation and basis of its biological variation. Acta Physiol. (2014) 210:489–97. doi: 10.1111/apha.12224, 24393306 MiljkovicI KuipersA CvejkusR BunkerC PatrickA GordonC . Myosteatosis increases with aging and is associated with incident diabetes in African ancestry men. Obesity. (2016) 24:476–82. doi: 10.1002/oby.21328, 26694517 GoodpasterBH BergmanBC BrennanAM SparksLM. Intermuscular adipose tissue in metabolic disease. Nat Rev Endocrinol. (2023) 19:285–98. doi: 10.1038/s41574-022-00784-2, 36564490 ChowSK-H van MourikM HungVW-Y ZhangN LiMM-C WongRM-Y . HR-pQCT for the evaluation of muscle quality and intramuscular fat infiltration in ageing skeletal muscle. J Pers Med. (2022) 12:1016. doi: 10.3390/jpm12061016, 35743800 JohnsonTRC KraussB SedlmairM GrasruckM BruderH MorhardD . Material differentiation by dual energy CT: initial experience. Eur Radiol. (2007) 17:1510–7. doi: 10.1007/s00330-006-0517-6, 17151859 MolwitzI LeidererM McDonoughR FischerR OzgaA-K OzdenC . Skeletal muscle fat quantification by dual-energy computed tomography in comparison with 3T MR imaging. Eur Radiol. (2021) 31:7529–39. doi: 10.1007/s00330-021-07820-1, 33770247 KimMJ ChoYK JungHN KimEH LeeMJ JungCH . Association between insulin resistance and Myosteatosis measured by abdominal computed tomography. J Clin Endocrinol Metab. (2023) 108:3100–10. doi: 10.1210/clinem/dgad382, 37401630 FuC XiaY MengF LiF LiuQ ZhaoH . MRI quantitative analysis of eccentric exercise-induced skeletal muscle injury in rats. Acad Radiol. (2020) 27:e72–9. doi: 10.1016/j.acra.2019.05.011 PengF TangD QingW ChenW LiS GuoY . Utilization of multi-parametric quantitative magnetic resonance imaging in the early diagnosis of Duchenne muscular dystrophy. J Magn Reson Imaging. (2024) 60:1402–13. doi: 10.1002/jmri.29182, 38095338 BerryDB GordonJA AdairV FrankLR WardSR. From voxels to physiology: A review of diffusion magnetic resonance imaging applications in skeletal muscle. J Magn Reson Imaging. (2025) 61:595–615. doi: 10.1002/jmri.29489, 39031753 DamonBM DingZ AndersonAW FreyerAS GoreJC. Validation of diffusion tensor MRI-based muscle fiber tracking. Magn Reson Med. (2002) 48:97–104. doi: 10.1002/mrm.10198, 12111936 TanganelliF MeinkeP HofmeisterF JarmuschS BaberL MehaffeyS . Type-2 muscle fiber atrophy is associated with sarcopenia in elderly men with hip fracture. Exp Gerontol. (2021) 144:111171. doi: 10.1016/j.exger.2020.111171, 33248151 CameronD ReiterDA AdelniaF Ubaida-MohienC BergeronCM ChoiS . 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, 37162031 BustinA WitscheyWRT van HeeswijkRB CochetH StuberM. Magnetic resonance myocardial T1ρ mapping. J Cardiovasc Magn Reson. (2023) 25:34. doi: 10.1186/s12968-023-00940-1, 37331930 WattsR AndrewsT HipkoS GonyeaJV FilippiCG. In vivo whole-brain T1-rho mapping across adulthood: normative values and age dependence. J Magn Reson Imaging. (2014) 40:376–82. doi: 10.1002/jmri.24358 YinQ AbendscheinD MuccigrossoD O’ConnorR GoldsteinT ChenR . A non-contrast CMR index for assessing myocardial fibrosis. Magn Reson Imaging. (2017) 42:69–73. doi: 10.1016/j.mri.2017.04.012, 28461132 ShuH XuH PanZ LiuY DengW ZhaoR . Early detection of myocardial involvement by non-contrast T1ρ mapping of cardiac magnetic resonance in type 2 diabetes mellitus. Front Endocrinol. (2024) 15:1335899. doi: 10.3389/fendo.2024.1335899, 38510696 ChiancaV VincenzoB CuocoloR ZappiaM GuarinoS Di PiettoF . MRI quantitative evaluation of muscle fatty infiltration. Magnetochemistry. (2023) 9:111. doi: 10.3390/magnetochemistry9040111 MortellaroS TriggianiS MascarettiF GalloniM GarroneO CarrafielloG . Quantitative and qualitative radiological assessment of sarcopenia and Cachexia in Cancer patients: A systematic review. J Pers Med. (2024) 14:243. doi: 10.3390/jpm14030243, 38540985 KemmochiY OhtaT MotohashiY KaneshigeA KatsumiS KakimotoK . Pathophysiological analyses of skeletal muscle in obese type 2 diabetes SDT fatty rats. J Toxicol Pathol. (2018) 31:113–23. doi: 10.1293/tox.2017-0064, 29750000 JazetIM SchaartG GastaldelliA FerranniniE HesselinkMK SchrauwenP . Loss of 50% of excess weight using a very low energy diet improves insulin-stimulated glucose disposal and skeletal muscle insulin signalling in obese insulin-treated type 2 diabetic patients. Diabetologia. (2008) 51:309–19. doi: 10.1007/s00125-007-0862-2, 18080107 HuberFA Del GrandeF RizzoS GuglielmiG GuggenbergerR. MRI in the assessment of adipose tissues and muscle composition: how to use it. Quant Imaging Med Surg. (2020) 10:1636–49. doi: 10.21037/qims.2020.02.06, 32742957 ChiancaV CuocoloR AlbanoD. Editorial for “quantification of bone marrow fat fraction and iron by MRI for distinguishing aplastic anemia and myelodysplastic syndromes.”. J Magn Reson Imaging. (2021) 54:1761–2. doi: 10.1002/jmri.27778 LeeS LucasRM LansdownDA NardoL LaiA LinkTM . Magnetic resonance rotator cuff fat fraction and its relationship with tendon tear severity and subject characteristics. J Shoulder Elb Surg. (2015) 24:1442–51. doi: 10.1016/j.jse.2015.01.013, 25819731 DuijnisveldBJ HenselerJF ReijnierseM FioccoM KanHE NelissenRGHH. Quantitative Dixon MRI sequences to relate muscle atrophy and fatty degeneration with range of motion and muscle force in brachial plexus injury. Magn Reson Imaging. (2017) 36:98–104. doi: 10.1016/j.mri.2016.10.020, 27989913 GujarSK MaheshwariS Björkman-BurtscherI SundgrenPC. Magnetic resonance spectroscopy. J Neuro Ophthalmol Off J North Am Neuro-Ophthalmol Soc. (2005) 25:217–26. doi: 10.1097/01.wno.0000177307.21081.81 FischerMA NanzD ShimakawaA SchirmerT GuggenbergerR ChhabraA . Quantification of muscle fat in patients with low back pain: comparison of multi-echo MR imaging with single-voxel MR spectroscopy. Radiology. (2013) 266:555–63. doi: 10.1148/radiol.12120399, 23143025 ReederSB CruiteI HamiltonG SirlinCB. Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy. J Magn Reson Imaging. (2011) 34:729–49. doi: 10.1002/jmri.22775, 22025886 KieferLS FabianJ RospleszczS LorbeerR MachannJ KrausMS . Distribution patterns of intramyocellular and extramyocellular fat by magnetic resonance imaging in subjects with diabetes, prediabetes and normoglycaemic controls. Diabetes Obes Metab. (2021) 23:1868–78. doi: 10.1111/dom.14413, 33914415 HuangY YanJ ZhuH ZhangZ JiangY ZhangX . Low thigh muscle strength in relation to myosteatosis in patients with type 2 diabetes mellitus. Sci Rep. (2023) 13:1957. doi: 10.1038/s41598-022-24002-1, 36732561 MurphyWA TottyWG CarrollJE. MRI of normal and pathologic skeletal muscle. AJR Am J Roentgenol. (1986) 146:565–74. doi: 10.2214/ajr.146.3.565, 3484872 MartyB CoppaB CarlierPG. Monitoring skeletal muscle chronic fatty degenerations with fast T1-mapping. Eur Radiol. (2018) 28:4662–8. doi: 10.1007/s00330-018-5433-z, 29713767 AlbanoD ChiancaV CuocoloR BignoneR CicciaF SconfienzaLM . T2-mapping of the sacroiliac joints at 1.5 tesla: a feasibility and reproducibility study. Skeletal Radiol. (2018) 47:1691–6. doi: 10.1007/s00256-018-2951-3, 29679101 ChiancaV AlbanoD CuocoloR MessinaC GittoS BrunettiA . T2 mapping of the trapeziometacarpal joint and triangular fibrocartilage complex: a feasibility and reproducibility study at 1.5 T. Radiol Med. (2020) 125:306–12. doi: 10.1007/s11547-019-01123-8, 31863359 de MelloR MaY JiY DuJ ChangEY. Quantitative MRI Musculoskeletal Techniques: An Update. AJR Am J Roentgenol. (2019) 213:524–33. doi: 10.2214/AJR.19.21143, 30995086 PonrartanaS Ramos-PlattL WrenTAL HuHH PerkinsTG ChiaJM . Effectiveness of diffusion tensor imaging in assessing disease severity in Duchenne muscular dystrophy: preliminary study. Pediatr Radiol. (2015) 45:582–9. doi: 10.1007/s00247-014-3187-6, 25246097 StougeA KhanKS KristensenAG TankisiH SchlaffkeL FroelingM . MRI of skeletal muscles in participants with type 2 diabetes with or without diabetic polyneuropathy. Radiology. (2020) 297:608–19. doi: 10.1148/radiol.2020192647, 33048033 SeaboltLA WelchEB SilverHJ. Imaging methods for analyzing body composition in human obesity and cardiometabolic disease. Ann N Y Acad Sci. (2015) 1353:41–59. doi: 10.1111/nyas.12842, 26250623 LeeC-M KangBK KimM. Radiologic definition of sarcopenia in chronic liver disease. Life. (2021) 11:86. doi: 10.3390/life11020086, 33504046 LemosT GallagherD. Current body composition measurement techniques. Curr Opin Endocrinol Diabetes Obes. (2017) 24:310–4. doi: 10.1097/MED.0000000000000360, 28696961 ZwartAT BeckerJ-N LamersMJ DierckxRAJO de BockGH HalmosGB . Skeletal muscle mass and sarcopenia can be determined with 1.5-T and 3-T neck MRI scans, in the event that no neck CT scan is performed. Eur Radiol. (2021) 31:4053–62. doi: 10.1007/s00330-020-07440-1, 33219847 HuqS KhalafallahAM Ruiz-CardozoMA BotrosD OliveiraLAP DuxH . A novel radiographic marker of sarcopenia with prognostic value in glioblastoma. Clin Neurol Neurosurg. (2021) 207:106782. doi: 10.1016/j.clineuro.2021.106782, 34186275 ThukralN KaurJ MalikM. A systematic review on foot muscle atrophy in patients with diabetes mellitus. Int J Diabetes Dev Ctries. (2023) 43:331–7. doi: 10.1007/s13410-022-01118-8 StrijkersGJ AraujoECA AzzabouN BendahanD BlamireA BurakiewiczJ . Exploration of new contrasts, targets, and MR imaging and spectroscopy techniques for neuromuscular disease - A workshop report of working group 3 of the biomedicine and molecular biosciences COST action BM1304 MYO-MRI. J Neuromuscul Dis. (2019) 6:1–30. doi: 10.3233/JND-180333, 30714967 HsiehT-J ChouM-C ChenY-C ChouY-C LinC-H ChenCK-H. Reliability of gradient-echo magnetic resonance elastography of lumbar muscles: phantom and clinical studies. Diagnostics. (2022) 12:1385. doi: 10.3390/diagnostics12061385, 35741195 ChakouchMK CharleuxF BensamounSF. Quantifying the elastic property of nine thigh muscles using magnetic resonance elastography. PLoS One. (2015) 10:e0142958. doi: 10.1371/journal.pone.0138873 MalisV SinhaU CsapoR NariciM SinhaS. Relationship of changes in strain rate indices estimated from velocity-encoded MR imaging to loss of muscle force following disuse atrophy. Magn Reson Med. (2018) 79:912–22. doi: 10.1002/mrm.26759, 28560822 SinhaU MalisV CsapoR NariciM SinhaS. Magnetic resonance imaging based muscle strain rate mapping during eccentric contraction to study effects of unloading induced by unilateral limb suspension. Eur J Transl Myol. (2020) 30:8935. doi: 10.4081/ejtm.2019.8935, 32499902 JanssenBH VoetNBM NabuursCI KanHE de RooyJWJ GeurtsAC . Distinct disease phases in muscles of facioscapulohumeral dystrophy patients identified by MR detected fat infiltration. PLoS One. (2014) 9:e85416. doi: 10.1371/journal.pone.0085416, 24454861 LopezC TaivassaloT BerruMG SaavedraA RasmussenHC BatraA . Postcontractile blood oxygenation level-dependent (BOLD) response in Duchenne muscular dystrophy. J Appl Physiol. (2021) 131:83–94. doi: 10.1152/japplphysiol.00634.2020, 34013753 LedermannH-P SchulteA-C HeideckerH-G AschwandenM JägerKA SchefflerK . Blood oxygenation level-dependent magnetic resonance imaging of the skeletal muscle in patients with peripheral arterial occlusive disease. Circulation. (2006) 113:2929–35. doi: 10.1161/CIRCULATIONAHA.105.605717, 16785340 JacobiB BongartzG PartoviS SchulteA-C AschwandenM LumsdenAB . Skeletal muscle BOLD MRI: from underlying physiological concepts to its usefulness in clinical conditions. J Magn Reson Imaging. (2012) 35:1253–65. doi: 10.1002/jmri.23536, 22588992 PillenS van AlfenN. Skeletal muscle ultrasound. Neurol Res. (2011) 33:1016–24. doi: 10.1179/1743132811Y.0000000010, 22196753 TicinesiA MeschiT NariciMV LauretaniF MaggioM. Muscle ultrasound and sarcopenia in older individuals: A clinical perspective. J Am Med Dir Assoc. (2017) 18:290–300. doi: 10.1016/j.jamda.2016.11.013, 28202349 MerriganJJ WhiteJB HuYE StoneJD OliverJM JonesMT. Differences in elbow extensor muscle characteristics between resistance-trained men and women. Eur J Appl Physiol. (2018) 118:2359–66. doi: 10.1007/s00421-018-3962-4, 30097710 MinettoMA CaresioC MenapaceT HajdarevicA MarchiniA MolinariF . Ultrasound-based detection of low muscle mass for diagnosis of sarcopenia in older adults. PM R. (2016) 8:453–62. doi: 10.1016/j.pmrj.2015.09.014, 26431809 KumarCGS RajagopalKV HandeHM MaiyaAG MayyaSS. Intrinsic foot muscle and plantar tissue changes in type 2 diabetes mellitus. J Diabetes. (2015) 7:850–7. doi: 10.1111/1753-0407.12254, 25496489 HuangH WuS. Application of high-resolution ultrasound on diagnosing diabetic peripheral neuropathy. Diabetes Metab Syndr Obes Targets Ther. (2021) 14:139–52. doi: 10.2147/DMSO.S292991, 33469331 ZaidmanCM HollandMR AndersonCC PestronkA. Calibrated quantitative ultrasound imaging of skeletal muscle using backscatter analysis. Muscle Nerve. (2008) 38:893–8. doi: 10.1002/mus.21052, 18563722 StrasserEM DraskovitsT PraschakM QuittanM GrafA. Association between ultrasound measurements of muscle thickness, pennation angle, echogenicity and skeletal muscle strength in the elderly. Age Dordr Neth. (2013) 35:2377–88. doi: 10.1007/s11357-013-9517-z Toto-BrocchiM WuY JerbanS HanA AndreM ShahSB . Quantitative ultrasound assessment of fatty infiltration of the rotator cuff muscles using backscatter coefficient. Eur Radiol Exp. (2024) 8:119. doi: 10.1186/s41747-024-00522-5, 39436589 AlfuraihAM TanAL O’ConnorP EmeryP WakefieldRJ. The effect of ageing on shear wave elastography muscle stiffness in adults. Aging Clin Exp Res. (2019) 31:1755–63. doi: 10.1007/s40520-019-01139-0, 30762201 SaitoA WakasaM KimotoM IshikawaT TsugaruyaM KumeY . Age-related changes in muscle elasticity and thickness of the lower extremities are associated with physical functions among community-dwelling older women. Geriatr Gerontol Int. (2019) 19:61–5. doi: 10.1111/ggi.13567, 30556237 ChenZ-T JinF-S GuoL-H LiX-L WangQ ZhaoH . 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, 36648552 Turimov MustapoevichD KimW. Machine learning applications in sarcopenia detection and management: A comprehensive survey. Healthcare. (2023) 11:2483. doi: 10.3390/healthcare11182483, 37761680 MatsushitaY YokoyamaT NoguchiT NakagawaT. Assessment of skeletal muscle using deep learning on low-dose CT images. Glob Health Med. (2023) 5:278–84. doi: 10.35772/ghm.2023.01050, 37908512 OnishiS KuwaharaT TajikaM TanakaT YamadaK ShimizuM . Artificial intelligence for body composition assessment focusing on sarcopenia. Sci Rep. (2025) 15:1324. doi: 10.1038/s41598-024-83401-8, 39779762 KimYK LeeHS RyuJJ In LeeH SeoSG. Sarcopenia increases the risk for mortality in patients who undergo amputation for diabetic foot. J Foot Ankle Res. (2018) 11:32. doi: 10.1186/s13047-018-0274-1, 29946364 HildebrandKN SidhuK GabelL BeslerBA BurtLA BoydSK. The assessment of skeletal muscle and cortical bone by second-generation HR-pQCT at the tibial midshaft. J Clin Densitom Off J Int Soc Clin Densitom. (2021) 24:465–73. doi: 10.1016/j.jocd.2020.11.001 BaumT InhuberS DieckmeyerM CordesC RuschkeS KluppE . Association of Quadriceps Muscle fat with Isometric Strength Measurements in healthy males using chemical shift encoding-based water-fat magnetic resonance imaging. J Comput Assist Tomogr. (2016) 40:447–51. doi: 10.1097/RCT.0000000000000374, 26953765 ScheelM von RothP WinklerT ArampatzisA ProkschaT HammB . Fiber type characterization in skeletal muscle by diffusion tensor imaging. NMR Biomed. (2013) 26:1220–4. doi: 10.1002/nbm.2938, 23553895 MelvilleDM MohlerJ FainM MuchnaAE KrupinskiE SharmaP . Multi-parametric MR imaging of quadriceps musculature in the setting of clinical frailty syndrome. Skeletal Radiol. (2016) 45:583–9. doi: 10.1007/s00256-015-2313-3, 26743776 Simó-ServatA GuevaraE PereaV AlonsoN QuirósC Puig-JovéC . Role of muscle ultrasound for the study of frailty in elderly patients with diabetes: A pilot study. Biology. (2023) 12:884. doi: 10.3390/biology12060884, 37372168

Edited by: Gang Ye, Sichuan Agricultural University, China

Reviewed by: Chun-Hung Ko, Chi-Mei Medical Center, Taiwan

Andres Zuniga Vera, Hospital Universitario de la Plana, Spain