Hou BW, Ran Z, Li YT, Zhang J, Chu YQ, Gharaibeh NM, Li XM. Magnetic resonance imaging derived biomarkers for the diagnosis of type 2 diabetes with insulin resistance: A pilot study. World J Diabetes 2025; 16(9): 110183 [DOI: 10.4239/wjd.v16.i9.110183]
Corresponding Author of This Article
Xiao-Ming Li, PhD, Professor, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Road, Wuhan 430030, Hubei Province, China. lilyboston2002@tjh.tjmu.edu.cn
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Observational Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Bo-Wen Hou, Zheng Ran, Yi-Tong Li, Jing Zhang, Yong-Qiang Chu, Nadeer M Gharaibeh, Xiao-Ming Li, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
Author contributions: Hou BW and Ran Z designed the research and wrote the original manuscript; Li YT and Zhang J performed the data analysis; Chu YQ and Gharaibeh NM performed the statistics analysis and language polishing; Li XM supervised this research. Hou BW and Ran Z contribute equally to this study as co-first authorship. All authors have read and approved the final manuscript.
Supported by National Natural Science Foundation of China, No. 81930045 and No. 31630025.
Institutional review board statement: This study is conducted under the approval from the Institutional Review Board of Tongji Hospital (Approval No. TJ-IRB20220633).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xiao-Ming Li, PhD, Professor, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Road, Wuhan 430030, Hubei Province, China. lilyboston2002@tjh.tjmu.edu.cn
Received: June 3, 2025 Revised: July 1, 2025 Accepted: August 15, 2025 Published online: September 15, 2025 Processing time: 104 Days and 1.2 Hours
Abstract
BACKGROUND
Insulin resistance (IR) plays a critical role in the musculoskeletal metabolic disorders associated with type 2 diabetes mellitus (T2DM).
AIM
To develop multiparametric magnetic resonance imaging (MRI)-derived biomarkers and diagnostic models for non-invasive identification and stratification of IR.
METHODS
Parameters of paravertebral muscles and vertebra were evaluated using quantitative chemical shift-encoded MRI and diffusion tensor imaging protocols. Tripartite cohort analyses were conducted through Kruskal-Wallis H tests with post hoc Dunn-Bonferroni correction for MRI-derived metrics. Diagnostic performance for T2DM-IR was assessed after selecting the most significant features through Z-score standardization and multinomial logistic regression models.
RESULTS
This study evaluated 97 subjects (control: 39 subjects, T2DM-IR: 18 subjects, T2DM patients without IR: 40 subjects) using multiparametric MRI protocols. Significant intergroup differences were observed in the cross-sectional area (P = 0.047) and apparent diffusion coefficient (P = 0.027) of the psoas, and the cross-sectional area (P = 0.042) of the erector. More intramyocellular lipid (IMCL) in the psoas (P = 0.001) and erector (P = 0.004) were found in the T2DM-IR group. Multinomial receiver operating characteristic curve analysis demonstrated that IMCL of the erector performed better (area under the curve = 0.838, sensitivity: 0.800, specificity: 0.938) in the diagnosis of T2DM-IR.
CONCLUSION
IMCL in erector emerges as a highly discriminative metric for T2DM-IR diagnosis. Multiparametric MRI enables non-invasive quantification of early musculoskeletal metabolic injury, providing reliable biomarkers for IR identification and stratification.
Core Tip: This study reveals that insulin resistance (IR) in type 2 diabetes mellitus exacerbates myosteatosis via intramyocellular lipid (IMCL) accumulation and fat infiltration in paravertebral muscles, alongside vertebral fat fraction increases. Multiparametric magnetic resonance imaging sequences (quantitative Dixon and diffusion tensor imaging) identified key biomarkers (e.g., IMCL, fat-muscle ratios), and erector IMCL specifically showed high diagnostic accuracy for type 2 diabetes mellitus-IR. Multiparametric magnetic resonance imaging evaluation enables non-invasive quantification of early musculoskeletal metabolic injury, providing reliable imaging biomarkers for IR identification and stratification.
Citation: Hou BW, Ran Z, Li YT, Zhang J, Chu YQ, Gharaibeh NM, Li XM. Magnetic resonance imaging derived biomarkers for the diagnosis of type 2 diabetes with insulin resistance: A pilot study. World J Diabetes 2025; 16(9): 110183
Diabetes mellitus is a chronic disorder characterized by hyperglycemia, which is a major cause of several complications, leading to reduction of life quality[1,2]. Insulin resistance (IR) is a vital pathogenic component of type 2 diabetes mellitus (T2DM)[3], characterized by impaired responsiveness of insulin-targeting tissues to physiological insulin level[4]. It has been reported that IR in T2DM is strongly associated with the deposition of ectopic fat and acceleration of muscle loss[5-7]. Ectopic fat refers to the abnormal accumulation of lipids in non-adipose tissues - such as the liver, skeletal muscle, and heart, which are physiologically with minimal amounts of fat storage[8]. Ectopic lipid infiltration correlates with IR through dual mechanisms: Lipotoxic intermediates impair insulin receptor signaling, and adipose-like secretory profiles (e.g., elevated resistin, reduced adiponectin) promote systemic metabolic inflammation[8-12]. Ectopic lipid in skeletal muscle can be classified as intramyocellular lipid (IMCL) and extramyocellular lipid (EMCL)[13]. IMCL accumulation is a much better predictor of T2DM compared to visceral adipose tissue, which highlights that skeletal muscle lipid depositions impair insulin signaling and induce IR[14,15]. EMCL is an indicator for assessing muscle metabolism and muscle quality, which has been shown to be associated with amyotrophy[16,17].
The hyperinsulinemic-euglycemic clamp technique is widely considered as the “gold standard” for quantifying insulin sensitivity. However, its application is limited by its complexity, invasiveness, and time-consuming nature[18,19]. Magnetic resonance imaging (MRI) is considered a reliable and non-invasive technique for evaluating muscle quality parameters like muscular atrophy and fat infiltration[20]. The chemical shift-encoded MRI (Dixon) technique based on chemical shifting imaging is used widely in fat deposit evaluation[21]. The quantitative Dixon (Q-Dixon) technique overcomes the limitation of T1 bias and the T2* effect seen in the traditional Dixon technique, enabling precise fat fraction (FF) quantification with high spatial resolution and rapid acquisition[22]. Another advantage of Q-Dixon is its simultaneous generation of R2* maps, which facilitates vertebral bone health assessment. Diffusion MRI plays a crucial role in evaluating muscle injury or degeneration. The fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values derived from diffusion tensor imaging (DTI) sequences reflect the rate and restriction of water diffusion within tissues[23]. These metrics serve as valuable tools for assessing microstructure and can help differentiate healthy skeletal muscle from pathological conditions.
This study performed a comparative assessment of muscular architecture, ectopic lipid distribution, and bone microstructure among T2DM patients stratified by IR status and healthy controls, employing multiparametric protocols of Q-Dixon and diffusion MRI protocols. The study further aimed to develop MRI-derived biomarkers and diagnostic models for IR, establishing a non-invasive imaging approach for early-stage IR stratification in diabetic populations.
MATERIALS AND METHODS
Participants
This study was conducted according to the Declaration of Helsinki, with approval from the Institutional Review Board of Tongji Hospital (Approval No. TJ-IRB20220633). Anonymized clinical data and MRI images of participants were retrospectively analyzed in the picture archiving and communication system from September 2022 to June 2023. Inclusion criteria were as follows: (1) Subjects diagnosed with T2DM according to World Health Organization criteria[24]; (2) Subjects diagnosed with IR based on Homeostatic Model Assessment of IR (HOMA-IR) value; (3) Adequate quality of lumbar MRI images; (4) Laboratory examinations: Random insulin and blood glucose, fasting insulin and blood glucose (tested after fasting for at least 8 hours), glycated hemoglobin, c-peptide, and lipid profile (total cholesterol, triglyceride, and high-density lipoprotein cholesterol); and (5) Related risk factors (hypertension, smoking, and alcohol history). Exclusion criteria were: Age < 18 years, pregnancy, history of tumor or chemotherapy/radiotherapy, congenital neuromuscular disease, prior history of lumbar surgery, poor quality of MRI images, and incomplete medical history. The control group comprised healthy volunteers and individuals identified as healthy through retrospective picture archiving and communication system screening and had normoglycemic status. The recruitment flowchart is illustrated in Figure 1.
Figure 1 Flowchart of subject recruitment.
T2DM: Type 2 diabetes mellitus; IR: Insulin resistance; MRI: Magnetic resonance imaging; HOMA: Homeostatic Model Assessment; PACS: Picture archiving and communication system.
Multiparametric MRI and measurement
All the subjects in this study underwent lumbar MRI scans using a Siemens Skyra 3.0-T scanner with the following sequences: Sagittal T1-weighted imaging, T2-weighted imaging (T2WI), fat-suppressed-T2WI, axial fat-suppressed-T2WI, DTI, and Q-Dixon. The detailed parameters of the protocols are provided in Table 1. Quantitative assessments of muscle and lipid deposition were performed at the mid-L4 vertebral level with MRI images. Paravertebral muscles assessed in this study including the psoas, erector, and multifidus. Quantitation of ectopic lipid deposition was performed using Q-Dixon with a fat-water separation algorithm, measuring total fat content, IMCL and EMCL through FF mapping. Specifically, EMCL represents fat localized to the intermuscular connective tissue matrix between muscle bundles, while IMCL represents lipid droplets within skeletal muscle fibers. Parameters of the muscles, fat content and the ratio between muscle and fat content, including total fat content/muscle (FMR), IMCL/muscle (IMCL/M) and EMCL/muscle, were measured (Figure 2). Parameters were normalized for bilateral symmetry (mean of left and right measurements) and height-adjusted indexing (parameters/height2) using standing height (m2) in accordance with International Society for Clinical Densitometry guidelines for body composition standardization[25].
Figure 2 Illustrations of the measurements.
Blue squares represent the regions of interest in L4 and L5 vertebrae, and purple regions represent the paravertebral muscles. A: Sagittal T1-weighted imaging; B: R2* maps; C: T2* maps; D: Fat fraction; E: Diffusion tensor imaging.
Table 1 Parameters of magnetic resonance imaging study protocols.
The vertebral bone composition was quantitatively assessed by the Q-Dixon sequence, which was co-registered with T1-weighted images to provide an anatomical reference. The region of interest was specifically delineated to encompass the entire L4-L5 vertebral body, while meticulously excluding the cortical margins, endplates, and basivertebral veins based on their distinct anatomical features and low-signal characteristics. Relaxation rates (R2*, T2*) as well as FF measurements were obtained via multiparametric quantitative mapping.
Statistical analysis
Comparative analyses of the different groups [T2DM-IR patients, T2DM patients without IR (T2DM-nonIR), and controls] were conducted through Kruskal-Wallis H tests with post hoc Dunn-Bonferroni correction for MRI-derived metrics. Multinomial logistic regression models were used to select the features of highest importance after Z-score standardization. The feature coefficients (β values) for all outcome categories were extracted, and the absolute values were aggregated to calculate the total marginal effects. The diagnostic performance of the five top-ranked features was assessed through multinomial receiver operating characteristic (ROC) curve analysis. P < 0.05 was set statistically significant. All analyses were performed using SPSS and R (version 4.4.1, https://www.r-project.org/).
RESULTS
Basic characteristics of subjects
The study cohort comprised 97 participants stratified into three groups: 39 healthy controls, 40 T2DM-nonIR patients, and 18 T2DM-IR patients. T2DM patients demonstrated significantly elevated metabolic parameters vs controls, including older age (P = 0.031), increased waist circumference (P = 0.021), and higher blood glucose (P = 0.011). Subjects in the T2DM-IR group were older, and had a prolonged disease duration and higher HOMA-IR, compared to the subjects in the T2DM-nonIR group. There was no statistical difference in cardiovascular history, dyslipidemia markers, or lifestyles (Table 2).
Table 2 Basic characteristics of subjects, mean ± SD/n (total).
Multiparametric MRI quantification of muscle and vertebrae
Morphological and functional MRI parameters of paravertebral muscles (psoas, erector, multifidus) were evaluated. There was muscle loss in the psoas (P = 0.047) and erector (P = 0.042), and microstructural damage (FA: P = 0.040, ADC: P = 0.027) of the muscle fibers in the psoas. The ectopic lipid distribution of the paravertebral muscles was assessed. More fat was distributed in the muscles (psoas: P = 0.003, erector: P = 0.005, multifidus: P = 0.040) of patients with T2DM-IR. Subjects with T2DM-IR exhibited more IMCL in the psoas (P = 0.001) and erector (P = 0.004), a higher IMCL/M in the psoas (P = 0.005) and erector (P = 0.040), and a higher FMR in the psoas (P = 0.046). The details are displayed in Table 3. Quantification of L4-L5 vertebrae fat infiltration and microstructure was performed. There was a statistical difference in the FF of L5 vertebra (P = 0.043) among the groups, with a larger FF found in the T2DM-IR group. Illustrations of the measurements and feature distribution of vertebral parameters are shown in Figures 2 and 3, respectively.
Multinomial logistic regression was utilized to identify the most significant MRI biomarkers and elucidate their relative contributions to the diagnosis of T2DM-IR. The results revealed that the top-ranked biomarkers were as follows: IMCL/M (18.5%) and FMR (9.5%) of the psoas; and IMCL (12.9%), IMCL/M (10.7%), and total fat content (9.7%) of the erector, among others. Collectively, these five top-ranked biomarkers accounted for a cumulative 61.3% of the most significant biomarkers. The importance ranking of these features is visually depicted in Figure 4. Multinomial ROC analysis based on the five top-ranked features demonstrated that IMCL of the erector (area under the curve = 0.838, sensitivity: 0.800, specificity: 0.938) and total fat content of the erector (area under the curve = 0.812, sensitivity: 0.800, specificity: 0.813) exhibited superior performance in the diagnosis of T2DM-IR. Detailed results are displayed in Table 4. Furthermore, multinomial ROC analysis was employed to evaluate models incorporating both clinical and MRI biomarkers (Table 5).
Table 5 Receiver operating characteristic curve parameters of the diagnosis of type 2 diabetes mellitus-insulin resistance with magnetic resonance imaging biomarkers and clinical factors.
T2DM has emerged as a global health concern, and projections warning that by 2050, affected individuals could surpass 1.31 billion worldwide[26,27]. Given its elevated rates of occurrence, complications, and fatalities, T2DM has emerged as a significant public medical challenge globally, associated with excessive healthcare expenditure[28]. T2DM-IR is a key pathophysiologic factor contributing to the metabolic syndrome and atherosclerotic cardiovascular disease caused by progressive β-cell failure[29,30]. Morphological and functional MRI parameters of paravertebral muscles, ectopic lipid depositions, and vertebral bone were analyzed and compared between control and T2DM subjects. Subjects in the T2DM-IR group exhibited older age and longer disease duration than T2DM-nonIR subjects, which is consistent with previous studies[31]. This may be due to the gradual decline in metabolic function and other physiological changes associated with age, which can lead to a higher risk of cardiovascular disease and thus increased health risks for patients.
DTI is a favorable and non-invasive technique to assess human muscle injury because of the highly anisotropic nature of muscle tissue[32]. Compared with healthy subjects, patients with muscle tear injuries showed an increase in the ADC and a decrease in FA[33]. Lower FA of the psoas and a higher ADC value were found in our T2DM-IR group, which suggested microstructure damage to the psoas fibers. Wang et al[34] demonstrated that the muscles of mice in their T2DM-IR group exhibited a decline in both quality and mass. In healthy individuals, insulin drives muscle protein anabolism by stimulating protein synthesis while concurrently inhibiting proteolysis to support metabolic homeostasis[35]. In subjects with T2DM-IR, the capacity of insulin to stimulate muscle protein synthesis might be significantly impaired, leading to microstructural damage to muscle fibers and a prolonged recovery period following injuries.
More IMCL and a higher IMCL/M were found in the psoas and erector in the T2DM-IR group in this study. The correlation between IMCL and IR has aroused increasing interest in the research community. Schön et al[36] reported that IMCL changes over time with the progression of diabetes, while its dynamics and association with complications remain unclear. IMCL accumulation was significantly associated not only with T2DM[37], but also with impaired insulin-sensing ability, as seen in obesity[38]. Interestingly, the first-degree relatives of T2DM subjects had more IMCL and showed impaired insulin-stimulated glucose uptake[39]. Paradoxes were demonstrated in youths and athletes: Despite higher ICML levels, the skeletal muscles of trained endurance athletes showed significantly greater insulin sensitivity[40]. Petersen et al[41] reported that IMCL accumulation is reversible with weight loss in young subjects, which suggests that elevated IMCL levels do not necessarily correlate with IR in all contexts, especially in youths.
The current study focused on the L4-L5 vertebral levels based on their established clinical relevance as critical anatomical sites for assessing spinal degeneration and abdominal myosteatosis. Katergari et al[42] found that the reference measurement site was at the L4-L5 Level for detecting early musculoskeletal deterioration. Similar to previous studies, our study found a relationship between bone marrow fat amplification and T2DM-IR. Zhu et al[43] reported that elevated HOMA-IR levels were correlated with increased bone marrow FF in newly diagnosed T2DM patients, independent of body composition. However, conflicting results were observed by An et al[44], who found no statistically significant difference in bone marrow fat content between diabetic and non-diabetic individuals. These discrepancies highlight the need for further investigation to elucidate the precise role of bone marrow fat in IR pathogenesis.
A variety of studies have been conducted to predict the onset of T2DM. For example, Yu et al[45] found that intramuscular adipose tissue had high sensitivity for discriminating between T2DM and control subjects, but few studies have used MRI-related metrics for model predictions of IR. Considering that individuals with diabetes often show muscle atrophy and increased fatty infiltration, our study utilized muscle- and fat-related indicators to anticipate the likelihood of IR among diabetic patients. Notably, IMCL of the erector muscle exhibited the most promising results in detecting IR in diabetic subjects in our investigation.
Magnetic resonance spectroscopy is widely acknowledged as the reference standard for muscle fat infiltration assessment. However, its clinical application is constrained by a small region of interest and the inability to characterize the whole-body lipid distribution[46]. Quantitative computed tomography (CT) is a reliable method for evaluating body composition, despite the presence of ionizing radiation[47,48]. Q-Dixon addresses the inherent limitations of quantitative CT, which is dependent on the CT value threshold and vulnerable to noise interference, while offering superior soft-tissue resolution. Dual-energy X-ray absorptiometry can be used to quantify total fat, lean soft tissue, and trunk and visceral fat[49]. In contrast with the two-dimensional projection of dual-energy X-ray absorptiometry, the three-dimensional tomography of Q-Dixon can accurately differentiate between muscle and fat tissue, effectively excluding interference by spinal degeneration. Q-Dixon has become the primary method for quantitative muscle fat assessment owing to its non-invasiveness, high accuracy, and multidimensional output with voxel-wise precision. It is especially crucial for pediatric populations, where avoiding ionizing radiation and ensuring continuous monitoring of neuromuscular disorders are of the utmost importance.
This study has several limitations. First, this study is cross-sectional with relatively small sample size. A longitudinal cohort and follow-up studies with an adequate sample size are needed in the future. Second, this study was conducted in a single center, which calls for multicenter studies to validate and generalize our findings. The potential influence of antidiabetic therapies on skeletal muscle and lipid metabolism was not systematically evaluated in this study. Future research should consider stratification by medication type to elucidate their modulatory roles. Finally, although MRI is a non-invasive tool widely used for soft tissue quantitation, there was no gold standard, like biopsy, for assessing muscle lipid content in this study.
CONCLUSION
The findings of this study demonstrated that IMCL of the erector is a highly discriminative metric for T2DM-IR diagnosis. Multiparametric evaluation enables non-invasive quantification of early musculoskeletal metabolic injury, providing reliable imaging biomarkers for IR stratification and monitoring diabetic complications. This method may become a diagnostic approach. But it needs to undergo many validations before it can be definitely regarded as one of the diagnostic methods.
ACKNOWLEDGEMENTS
The authors thank all of the patients, nurses, doctors and technicians for their efforts in data and sample collection.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
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