Investigating the mechanism of supraspinatus tendinopathy induced by type 2 diabetes mellitus in rats using untargeted metabolomics analysis

Kuishuai Xu; Liang Zhang; Tianrui Wang; Tengbo Yu; Xia Zhao; Ning Yu; Yingze Zhang

doi:10.1186/s12891-024-08061-1

. 2024 Nov 18;25:920. doi: 10.1186/s12891-024-08061-1

Investigating the mechanism of supraspinatus tendinopathy induced by type 2 diabetes mellitus in rats using untargeted metabolomics analysis

Kuishuai Xu ^1,^#, Liang Zhang ^2,^#, Tianrui Wang ^3,^#, Tengbo Yu ⁴, Xia Zhao ¹, Ning Yu ^2,^✉, Yingze Zhang ^1,^✉

PMCID: PMC11572000 PMID: 39558291

Abstract

Objective

To assess the mechanism of supraspinatus tendinopathy induced by type 2 diabetes mellitus (T2DM) in rats using untargeted metabolomics analysis.

Methods

The liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics approach was used to screen tendon biomarkers of supraspinatus tendinopathy in rats with T2DM. Seventy-eight Sprague-Dawley rats were divided into normal group (NG) and T2DM groups. Rats in T2DM groups were divided into 12-week (T2DM-12w), and 24-week (T2DM-24w) subgroups according to the time point of the establishment of the T2DM rat model. Histological evaluation (modified Bonar score) and biomechanical testing were used to analyze the adverse effects of type 2 diabetes on the tendon of the supraspinatus muscle in rats.Three comparable groups were set up, including T2DM-12w group vs. NG, T2DM-24w group vs. NG, and T2DM-24w group vs. T2DM-12w group. Differentially expressed metabolites (DEMs) in the supraspinatus tendons in the three groups of rats were analyzed using LC-MS, and data were analyzed using multivariate statistical methods to screen potential biomarkers. The DEMs included in the intersection of the three groups were identified as those associated with the development of diabetic supraspinatus tendinopathy, and trend analysis and pathway topology analysis were performed.

Results

With the progression of diabetes, the tendinopathy of the supracinatus muscle of diabetic rats gradually intensified, mainly manifested as inflammatory reactions, disordered collagen fibers, fat infiltration, and increased modified Bonar score. The intersection of DEMs among the three comparable groups was resulted in the identification of 10 key DEMs, in which melezitose and raffinose showed a continuous increasing trend with the prolongation of disease course. By pathway topology analysis, 10 DEMs (P < 0.01) were mainly associated with the pathways of galactose metabolism, which could be involved in the development of diabetes-induced supraspinatus tendinopathy.

Conclusion

T2DM causes tendinopathy of the supraspinatus muscle in rats. 10 key DEMs obtained by untargeted metabolomics assay suggested that the development of diabetes-induced supraspinatus tendinopathy was associated with changes in metabolic pathways, such as galactose metabolism. melezitose and raffinose hold promise as a biomarker for disease discrimination and/or disease indication in diabetic supraspinatus tendinopathy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-024-08061-1.

Keywords: Diabetes mellitus, Rotator cuff, Supraspinatus, Metabolomics, Galactose metabolism

Introduction

Tendinopathy is a condition characterized by tendon pain, swelling, and dysfunction [1]. There are several causes of tendinopathy, including chronic injury, inflammation, and metabolic disorders [2]. Previous studies have found that in diabetic patients, hyperglycemic microenvironment leads to tendon homeostasis imbalance and metabolic disorders, which may be associated with the development of tendinopathy [3]. In addition to causing harm to the microvasculature and great vessels, musculoskeletal disorders have become the primary complications in diabetic patients, of which the three most commonly affected sites of diabetes are the hand, rotator cuff, and Achilles tendon [4]. To date, numerous studies have concentrated on the adverse effects of diabetes on the rotator cuff, diabetes leads to changes in the structure of the rotator cuff tendon, and type 2 diabetes mellitus (T2DM) significantly increases the incidence of rotator cuff disease [5–8].

In a rat model, persistent hyperglycemia leads to a significant reduction in external rotation and biomechanics of the shoulder joint, which may be related to diabetes-induced inflammatory response, fat infiltration [9, 10], and the let-7b-5p/Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) pathway [11]. Bioinformatic analysis suggested that let-7b-5p may be regulated by a high-glucose microenvironment and can regulate CFTR levels. Overexpressed CFTR effectively reversed the negative effects of hyperglycemic microenvironment and let-7b-5p upregulation on the proliferation and differentiation of tendon stem cells [11]. Although these studies aimed to reveal the mechanism of diabetes and tendinopathy, no study has concentrated on metabolite changes in supraspinatus tendinopathy models in diabetic patients or typical animals, and the hidden abnormalities of metabolic pathway that lead to complex metabolic disorders in the body have not yet been fully elucidated. Therefore, it is necessary to analyze the supraspinatus tendon metabolism in combination with modern omics methods in these animal models. Metabolomics has evolved to identify tissue-intact metabolite profiles with distinct global reactivity profiles [12]. Metabolomics aims to assess the overall changes of small-molecule metabolites in body fluids or tissues after biological systems are exogenously stimulated or genetically mutated, and to study their metabolic pathways [13]. Metabolomics has been widely used in pathological studies of T2DM related complications and intrinsic metabolic diseases [12, 14]. Some scholars investigated the effects of external stimulation on disease and its mechanism through differences in metabolites [15]. Endogenous metabolites discovered and validated by metabolomics may be novel potential biomarkers for diabetes-related complications and participate in the pathogenesis of the disease. However, the application of metabolomics in diabetic tendinopathy has not been reported.

In the present study, metabolomics was applied to the animal model for the first time and a “metabolic map” of the supraspinatus tendon was established in normal and diabetic rats. The potential biomarkers and metabolic pathways underlying the development of diabetes-induced supraspinatus tendinopathy were investigated through untargeted metabolomics analysis, providing new insights into the mechanism of tendinopathy.

Materials and methods

Experimental animals and groups

In the present study, 78 8-week-old male Sprague-Dawley rats (220–240 g, Beijing Vital River Co., Ltd., Beijing, China; License No. SCXK (Zhejiang) 2019-0001) of clean grade were randomly divided into 3 groups by the random number table: Normal (n = 26), T2DM-12w (n = 26), and T2DM-24w (n = 26). All rats were housed in the Laboratory Animal Center of Qingdao University (Qingdao, China) at ambient humidity of 55–57%, temperature of 22–24 ℃, and under a 12:12 h light-dark cycle. Rats in the non-diabetic group were fed with Chow feeding (#MD17111; MediScience Diets Co., Ltd., Yangzhou, China), rats in the diabetic group were fed with a high-fat and high-sugar diet (#MD12033; MediScience Diets Co., Ltd., Yangzhou, China) for 4 weeks, and the bedding was replaced daily. The study protocol was approved by the Ethics Committee of Experimental Animals of the Affiliated Hospital of Qingdao University (Approval No. 20220505SD8020221210126). All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

Establishment of a rat model of T2DM

Rats in the T2DM group were fasted for 12 h at the night before administration, and a T2DM model was induced by a single rapid intraperitoneal injection of STZ (40 mg/kg; Aladdin Co., Ltd., Beijing, China) on the next day [15–17]. The non-diabetic group was injected with the same amount of sodium citrate buffer (SSC, 40 mg/kg; BIOISCO Co., Ltd., Nanjing, China) according to the method described for the T2DM group. Food was not given immediately after the injection, and glucose was drunk directly 2 h later. Blood glucose level was measured in tail vein blood 72 h later. If the blood glucose level was ≥ 16.7 mmol/l for 3 consecutive days, the diabetic rat model was considered to be successfully established [18]. Fasting blood glucose level was measured before sampling in diabetic rats, and if the blood glucose level was < 16.7 mmol/L, animals were removed and supplemented in a timely manner.

Tissue sampling

All rats were euthanized by CO2 inhalation and cervical dislocation was confirmed. The left forelimb of rats was divided from the elbow joint and scapula, and excess muscle tissue was removed to obtain a complete supraspinatus tendon-humerus structure. Then, the tendon-bone samples were stored in a -80℃ freezer pending biomechanical testing. At the same time, the right shoulder joint was selected, and the muscle part of the sample was removed immediately; then, the intact supraspinatus tendon was fixed in 4% paraformaldehyde for 24 h.About three sections (parallel to the tendon course, about 5-µm-thick) were sliced from each rat by dehydration and paraffin embedding. The number of samples for biomechanics and histology in each group should be at least 6.

Histological assessment

Supraspinatus tendon was subjected to hematoxylin and eosin staining (H&E), picrosirius red staining (S8060, Solarbio, Beijing, China), and Alcian blue staining (DG0041, Leagene, Beijing, China), as described previously [19].The immunohistochemical staining was also carried out using the antibodies and described in our previous study [15]. Used antibodies included factor VIII antibody (AB275376, Abcam, UK). After staining, three fields of view were selected for densitometric value analysis at ×400 fields in the tendon region for each section. Semiquantitative analysis of the optical density was performed by Image Pro Plus 8.0 software, and the expression level of mean optical density(MOD) was the ratio of cumulative optical density value and positive area. We also used the modified Bonar score [20] to semiquantitatively assess the degree of tendon tissue lesions, with high scores representing severe tendon lesions. In brief, regions of interest within appropriately stained histologic sections were graded for cell morphology, cellularity, collagen organization, vascularity, and ground substance. The total modified Bonar score was calculated as a sum of the grades for each of these 5 categories.

Biomechanical tests

Biomechanical tests were performed on bilateral supraspinatus tendon-humerus complex in each group (6 samples). The humerus was securely fixed with polymethyl methacrylate (Thermo Fisher Scientific, Shanghai, China) at the base of the testing machine [21], and the supraspinatus tendon was flattened, fixed with sandpaper and cyanoacrylate glue (Dongxin, Shenzhen, China), and placed in customized serrated forceps. After preconditioning with 0.1 N, the traction load was gradually increased until the tendon was completely ruptured at a fixation rate of 10 mm/min [22]. The ultimate load and stiffness were recorded. In the load-displacement curve, the slope of its linear segment was used to express its stiffness value.

Preparation of metabolite sample

Bilateral supraspinatus tendons were removed from each rat and immediately placed in cold phosphate-buffered saline (PBS), the supraspinatus muscle was manually separated, only the tendon was retained, the tendon was placed into a cryotube, and all samples were quickly snap frozen in liquid nitrogen. Samples were stored at -80℃ during shipment for metabolomics analysis. Metabolomics analysis requires at least 50 mg of tissue. One animal had bilateral rotator cuff supraspinatus tendons with insufficient mass for analysis, and thus, the supraspinatus tendon tissues from two animals (four shoulders) were needed to be pooled together to construct one metabolomic sample.

Untargeted metabolomics analysis of supraspinatus tendon

The untargeted metabolomics analysis was performed by LC-Bio Technologies Co., Ltd. (Hangzhou, China). The tendon tissue samples (about 50 mg) were homogenized in 120 µL pre-cooled 50% methanol buffer using a high-throughput tissue lyser (Ningbo, China). The solutions were placed on ice for 10 min and were then centrifuged for 20 min (4 °C, 4000 g). All samples were acquired by the LC-MS system followed machine orders. A high-resolution tandem mass spectrometer Q-Exactive (Thermo Scientific) was used to detect metabolites eluted form the column. The Q-Exactive was operated in both positive and negative ion modes. In order to evaluate the stability of the LC-MS during the whole acquisition, a quality control sample (Pool of all samples) was acquired after every 10 Samples. The supernatants were collected and used for metabolomics analysis. Instrumental parameters were set using previously reported methods [23]. Metabolomics datasets were analyzed using the metaX open-source software, and univariate and multivariate analyses were performed to obtain differentially expressed metabolites(DEMs) among the three comparable groups. Collection, identification, and analysis of basic data were performed according to Yang et al.‘s study [24].

Analysis of DEMs

The P-value was adjusted by Benjamini–Hochberg approach. Features with a VIP value > 1, FC > 2 or < 0.5, and an adjusted P-value < 0.05 were selected as DE features and used for further analysis. These criteria were selected on the basis of a previous study [25]. The DEMs included in the intersection of the two comparable groups were identified by plotting a Venn diagram. Relative contents of DEMs were calculated by Z-score plot, and the trend changes of DEMs among the three diabetic stages were then investigated. All DEMs were annotated in the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.kegg.jp/) [26] and Human Metabolome Database (HMDB) (http://www.hmdb.ca/) according to Tao et al.‘s study [27], and the annotated DEMs were then mapped to the KEGG Pathway database (http://www.kegg.jp/kegg/pathway.html).

Statistical analysis

All data analyses were performed using SPSS 21.0 (IBM, Armonk, NY, USA) and GraphPad Prism 8.0 (La Jolla, CA). Univariate ANOVA analysis was conducted to statistically compare the ultimate load, stiffness, MOD value, and Banor score of the supraspinatus at each time point, and continuous data were expressed as mean ± standard deviation. Images were plotted using Prism 8.0 with mean ± standard error of the mean(SEM). Statistical significance was set at P < 0.05. Bioinformatic analysis-related widely untargeted metabolomics analysis was performed using the OmicStudio tool (https://www.omicstudio.cn/tool) (accessed on October 3, 2022).

Results

Histological evaluation

In this study, a total of 9 rats’ fasting blood glucose level did not meet the standard, and we made timely supplements. The results of HE staining revealed that the supraspinatus had altered cell morphology and number, and collagen fibers were disorganized and torn at 12 and 24 weeks after induction of type 2 diabetes compared to the control tendon. Adipose tissue infiltration, interstitial fibrous tissue hyperplasia with a little lymphocyte infiltration, and hyperplasia of thick-wall blood vessels can be seen between the tendons of the suprapinatus muscle of the T2DM-24 group rats (Fig. 1A). Picrosirius red staining showed a sparse arrangement of collagen fibers, and some bundles showed tearing and separatioAlcian blue staining showed increased mucus staining in diabetic tendon specimens compared to the controls (Fig. 1B and D). Immunohistochemical results showed that the expression of proteins involved in vascular immunostaining(VIII) was significantly increased in the T2DM groups(0.116 and 0.17 for the 12-, and 24-week groups, respectively) compared to control group (0.032; P < 0.001 and P < 0.001 when compared against the 12- and 24-week groups, respectively) (Fig. 1C and E). The mean modified Bonar score in each T2DM group(4.14 ± 0.49 and 6.17 ± 0.81 for the 12-, and 24-week groups, respectively) was significantly higher than in the control group (1.86 ± 0.71; P < 0.001 and P < 0.001 when compared against the 12- and 24-week groups, respectively). The modified Bonar score was significantly increased, suggesting that the tendinopathy of the supraspinatus muscle in rats was gradually aggravated with the progression of diabetes (Fig. 1F).

Fig. 1 — (A) Representative images of Hematoxylin and eosin(H&E) staining(5X, scale bars depict 500 μm). (B) Alcian-blue staining(40X, scale bars depict 100 μm). (C) Immunohistochemical staining of factor VIII(20X, scale bars depict 200 μm). (D) Picrosirius-red staining(20X, scale bars depict 200 μm). (E) MOD value of VIII in immunohistochemical staining. (F) Histological specimens were scored using a modified Bonar scoring scheme. (G) Failure load. (H) Stiffness.*p < 0.05 versus Control group, **p < 0.01 versus Control group, ***p < 0.001 versus Control group. The values are presented as means, with the error bars depicting the standard deviation

Biomechanical comparison

Compared with the control group, the failure load of supratinatus tendon of rats at T2DM-12w and T2DM-24w was significantly decreased (P < 0.001), and the failure load continued to decrease with the progression of diabetes. Compared with the control group, the stiffness of supratinatus tendon of rats at T2DM-12w and T2DM-24w was significantly decreased (P < 0.01). The biomechanical results further confirmed that diabetes aggravates the lesion of supraspinatus tendon (Fig. 1G-H).

Quality control and identification of metabolites

Supraspinatus tendons were collected from diabetic and non-diabetic groups for LC-MS analysis (Fig. 2). Total ion chromatogram of LC-MS data for metabolite profiles of quality control samples detected in POS mode and NEG mode. The m/z peak width and retention time peak width of metabolites detected in rat supraspinatus tendon samples in POS and NEG modes (Fig. 3). The results showed that the instrumental analysis of this experiment had satisfactory stability and repeatability, a stable detection capability, reliable experimental results, and high-quality data. A total of 8876 metabolite profiles were identified in all experimental samples, including 5758 profiles in the cationic mode and 3118 profiles in the anionic mode. Totally, 365 secondary metabolites were identified by secondary mass spectrometry, including 201 metabolites in the cation mode (Table S1) and 164 metabolites in the anion mode (Table S2).

Fig. 3 — The total ion chromatogram (A, B) and the m/z peak width and retention time peak width (C, D)

Partial least squares discriminant analysis (PLS-DA)

The PLS-DA model was further used to distinguish differences in metabolites of samples detected in the three comparable groups (Fig. 4A-C), and the results confirmed significant differences between the two groups, indicating that the PLS-DA model had a promising credibility. The permutation test showed that the PLS-DA model had no overfitting, confirming that the difference between the groups was partly significant (Fig. 4D-F).

Identification of DEMs

The DEMs were identified on the basis of multiple statistical analyses. Volcano plots were used to illustrate the distribution of DEMs among three comparable groups (Fig. 5A-C), and the red and blue circles in the volcano plots are the DEMs separated by the model. The results showed that 93 DEMs (79 upregulated and 14 downregulated DEMs) were identified in the T2DM-12w group compared with the control group (Table 1, Table S3). A total of 56 DEMs (41 upregulated and 15 downregulated DEMs) were identified in the T2DM-24w group compared with the control group (Table S4). A total of 86 DEMs (24 upregulated and 62 downregulated DEMs) were identified in the T2DM-24w group compared with the T2DM-12w group (Table S5). The results found that diabetic tendinopathy had abnormal glucose metabolism, and carbohydrate metabolites significantly increased in tendon metabolites of diabetic rats compared with controls, including Trehalose, Raffinose, Phenylphosphonic acid, Melibiose, Melezitose, Maltoheptaose, Allose, 3’-Galactosyllactose, and 1,6-Anhydro-D-beta-glucose.

Fig. 5 — (A) The Volcano plot in T2DM-12w group vs. normal group. (B) The Volcano plot in T2DM-24w group vs. normal group. (C) The Volcano plot in T2DM-24w group vs. T2DM-12w group. (D) The hierarchical clustering heat map in T2DM-12w group vs. normal group. (E) The hierarchical clustering heat map in T2DM-24w group vs. normal group. (F) The hierarchical clustering heat map in T2DM-24w group vs. T2DM-12w group

Table 1.

Metabolomics changes of supraspinatus tendon in type 2 diabetic rats compared with normal group (NG)

Comparison	All	Upregulated	Downregulated	NEG	POS
T2DM−12w vs. NG	93	79	14	42	51
T2DM−24w vs. NG	56	41	15	24	32
T2DM−24w vs. T2DM−12w	86	24	62	46	40

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Hierarchical cluster analysis was used to further characterize the specific and unique expression patterns of these DEMs in the supraspinatus tendons of diabetic and nondiabetic rats (Fig. 5D-F), indicating an overall profile of all metabolites detected and visualized. To clarify the functions of DEMs, KEGG analyzed the enriched pathways of different DEMs in the three comparable groups (Table S6). The pathways enriched in 93 DEMs between the T2DM-12w group and the NG mainly included protein digestion and transporters, ABC metabolism, aminoacyl-tRNA biosynthesis, central carbon metabolism in cancer, biosynthesis of amino acids, etc. (Fig. 6A). The pathways enriched in DEMs between the T2DM-24w group and the NG mainly included ABC transporters, protein metabolism and absorption, central carbon metabolism in cancer, purine metabolism, aminoacyl-tRNA, etc. (Fig. 6B). The pathways enriched in DEMs between the T2DM-24w group and the T2DM-12w group mainly included choline metabolism in cancer, glycerophospholipid metabolism, and arginine biosynthesis, etc. (Fig. 6C).

Fig. 6 — The KEGG pathways that distinguished metabolites in different comparable groups. (A) T2DM-12w group vs. normal group; (B) T2DM-24w group vs. normal group; (B) T2DM-24w group vs. T2DM-12w group; The color of the point represents the P-value. The smaller the value is, the higher the reliability of the test is and the greater the statistical significance is. The size of the point represents the number of differentially expressed metabolites in the corresponding path. The larger the point, the more differentially expressed metabolites in the pathway

Critical metabolites associated with the development of tendinopathy

In order to obtain metabolites that differed among the three groups, the intersection of DEMs among the three comparable groups consisted of 10 key metabolites (Table 2) and the representative metabolites was directly expressed by a Venn diagram (Fig. 7A). Among them, lipid and lipid molecules accounted for the largest proportion, 50%, indicating that the occurrence of diabetic tendinopathy is closely related to lipid metabolism. Annotation analysis of KEGG pathways for 10 key metabolites revealed 6 pathways, including: galactose metabolism、fructose and mannose metabolism、ABC transporters、glycerophospholipid metabolism、choline metabolism in cancer and microbial metabolism in diverse environments (Fig. 7B, Table S7). Using P < 0.01 as a screening criterion, 10 key metabolites associated with the development of tendinopathy were mainly enriched in one pathway, including 2 metabolites (Melibiose and Raffinose) in annotated galactose metabolism. Among the 10 metabolites closely related to diabetic tendinopathy, the expressions of Melibiose and Raffinose gradually increased with the progression of diabetes, and are expected to be biomarkers for the disease identification and/or disease indication of diabetic supraspinatus tendinopathy (Fig. 7C-H).

Table 2.

List of basic information of 10 key metabolites

Compound name	m/z ^a	rt^b (min)	Mode	KEGG ID	Trend
Compound name	m/z ^a	rt^b (min)	Mode	KEGG ID	12w vs. N	24w vs. N	24w vs. 12w
Allose	179.0560238	0.856716667	NEG	C01487	Up	Up	Down
Melibiose	387.1148344	0.8992	NEG	C05402	Up	Up	Down
LysoPC 16:0	496.3401806	5.000783333	POS	C04230	Up	Down	Down
LysoPE 19:0	494.3265068	6.358516667	NEG	C04438	Down	Up	Up
LysoPC 18:1	566.3488982	5.22505	NEG	C04230	Up	Down	Down
1-Oleoyl-sn-glycero-3-phosphocholine	522.3532059	5.225333333	POS	C04230	Up	Down	Down
LysoPC 20:4	588.3331801	4.506383333	NEG	C04230	Up	Down	Down
Raffinose	505.17316	0.895708333	POS	C00492	Up	Up	Up
Melezitose	522.1994295	0.89565	POS	C08243	Up	Up	Up
Gerberinol	365.1038214	0.881616667	POS	-	Down	Up	Up

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^a mass to charge ratio of the features; ^b retention time of the features

Fig. 7 — (A) The Venn diagram compares the number of different metabolites among the three comparable groups, including the paired comparison groups of T2DM-12w group vs. normal group, T2DM-24w group vs. normal group, and T2DM-24w group vs. T2DM-12w group; (B) Scatter plot of 23 differentially expressed metabolites with the most significant enrichment. P-value is plotted in a color map.(C-H) The expression of 10 metabolites closely related to the occurrence of diabetic tendinopathy

Discussion

In this study, we extended the modeling time of diabetic rats to 24 weeks, which is more meaningful for studying the pathogenesis of diabetic tendinopathy. The development of tendinopathy is closely related to the abnormal accumulation of metabolites [28, 29], as a unique metabolite of diabetes, the most studied of which are advanced glycation end products [30–33]. The metabolomics was therefore applied to the animal model, and it is noteworthy that this is the first study to apply metabolomics to investigate changes in supraspinatus tendon metabolism caused by diabetes until the development of tendinopathy. Using metabolomics analysis, the three important findings were summarized as follows: first, “metabolic profiles” of supraspinatus tendons in normal and diabetic rats were established. In addition, metabolomics at two time points after induction of diabetes was compared with controls, and DEMs suggested that hyperglycemia was resulted in glucose, fat, and amino acid metabolism disorders in the supraspinatus tendon. Secondly, 10 key metabolites were found to be closely related to the development of tendinopathy, of which 10 key metabolites were mainly concentrated in 6 metabolic pathways, the most important being galactose metabolism (P < 0.001). These 6 metabolic pathways may be involved in the development of diabetic tendinopathy. Finally, with melibiose and raffinose continuing to rise throughout the course of diabetes, which is expected to be a biomarker with disease discrimination and/or disease indication in diabetic tendinopathy.

Metabolomics is the discipline of quantitative analysis of all metabolites in organisms and searching for the relationship between metabolites and physiological and pathological changes [34]. The present study found that diabetic tendinopathy had abnormal glucose metabolism, and carbohydrate metabolites significantly increased in tendon metabolites of diabetic rats compared with controls, including Trehalose, Raffinose, Phenylphosphonic acid, Melibiose, Melezitose, Maltoheptaose, Allose, 3’-Galactosyllactose, and 1,6-Anhydro-D-beta-glucose. Using P < 0.01 as a screening criterion, 10 key metabolites associated with the development of tendinopathy were mainly enriched in one pathway, including 2 metabolites (Melibiose and Raffinose) in annotated galactose metabolism. In particular, melibiose and raffinose were consistently elevated throughout the course of diabetes. Elevated glucose level is a metabolic feature of diabetes, which is attributed to the increased glucose production and glycogenolysis from non-carbohydrate sources [35]. Carina et al. [36] performed oral glucose tolerance test (OGTT) on healthy individuals, prediabetics, and diabetic patients, and demonstrated that plasma levels of maltose, trehalose, fructose, and mannose increased in prediabetics and diabetic patients compared with healthy individuals, which was the same as the findings of the present study. We therefore have reason to believe that excessive accumulation of sugars is one of the important causes of diabetic tendinopathy.

Diabetes is mainly associated with lipid metabolism disorders [37]. In the present study, alterations in lipid metabolism were clearly delineated in the supraspinatus tendon. Among the 10 key metabolites, lipid and lipid molecules accounted for the largest proportion (50%), indicating that the occurrence of diabetic tendinopathy is closely related to lipid metabolism. The most important lipid metabolite is lysophosphatidylcholine (LPC). LPC, a lipid biomolecular formed by lysophosphatidylcholine cleavage, has harmful effects on a variety of cells through the G protein-coupled receptor signaling pathway, including enhancing inflammation, inducing apoptosis, and insulin resistance [38]. The level of LPC in the plasma is associated with diabetes and diabetes-related complications. An increased concentration of LPC is independently associated with diabetes [39].

In addition, LPC can activate homocysteineinduced insulin resistance [40]. For diabetes-related complications, the high level of LPC is correlated with diabetic retinopathy and neurodegeneration [41]. However, other researchers have found that LPC can decrease the level of blood glucose. researchers have also found that LPC, especially 16:0 LPC, stimulates insulin secretion by combining the GPR40, GPR55 and GPR119 receptors and activating Ca (2+) signalling [42]. Levels of LysoPC 16:0, LysoPE 19:0, LysoPC 18:1 and LysoPC 20:4 were significantly elevated at 12 weeks of diabetes modeling. However, in the 24-week diabetic group, all four metabolites decreased significantly. Metabolites can change with the progression of tendon injury and become new sensitive biomarkers. Therefore, targeting LPC and lipid metabolism might be a potential therapeutic method for diabetic tendinopathy. However, the mechanism by which LPC regulates blood glucose and the development of diabetes is complex, and the mechanism of metabolite changes needs to be further studied in more detail.

Although this was the first study to report metabolomic profiles and potential biomarkers in the supraspinatus tendon of diabetic rats, there were some experimental shortcomings. First, there was a noticeable heterogeneity between biological individuals, some experimental results contradicted the findings of previous studies, and further research is required to verify the findings of the present study. In addition, the small sample size of this study, the collected rat supraspinatus tendon samples were regional, and the animal model was affected by factors, such as exercise volume and diet, hindering making a conclusive statement on metabolic tendon disorders in diabetic patients. Despite the above-mentioned shortcomings, the experimental design was scientific and innovative, and this study provided novel ideas and theoretical basis for further exploration of the pathogenesis of diabetic supraspinatus tendinopathy.

Conclusions

The results of untargeted metabolomics provided new insights into the mechanism of diabetic supraspinatus tendinopathy by screening 10 key DEMs and changes in metabolic pathways. Melibiose and Raffinose are expected to be biomarkers with disease discrimination and/or disease indication in diabetic supraspinatus tendinopathy, while the underlying mechanisms need to be more profoundly studied.

Electronic supplementary material

Below is the link to the electronic supplementary material.

12891_2024_8061_MOESM1_ESM.xlsx^{(32.3KB, xlsx)}

Supplementary Material 1: Additional file 1: Table S1. A total of 201 metabolites were identified from 5758 metabolite features extracted from the raw data acquired in positive-ionization modes by untargeted metabolomics analysis

12891_2024_8061_MOESM2_ESM.xlsx^{(41.8KB, xlsx)}

Supplementary Material 2: Additional file 2: Table S2. A total of 164 metabolites were identified from 3118 metabolite features extracted from the raw data acquired in negative-ionization modes

12891_2024_8061_MOESM3_ESM.xlsx^{(10.4KB, xlsx)}

Supplementary Material 3: Additional file 3: Table S3. A total of 93 differentially expressed metabolites (79 upregulated and 14 downregulated) were identified in the T2DM-12w group compared with the normal group

12891_2024_8061_MOESM4_ESM.xlsx^{(26.2KB, xlsx)}

Supplementary Material 4: Additional file 4: Table S4. A total of 56 differentially expressed metabolites (41 upregulated and 15 downregulated) were identified in the T2DM-24w group compared with the normal group

12891_2024_8061_MOESM5_ESM.xlsx^{(44.3KB, xlsx)}

Supplementary Material 5: Additional file 5: Table S5. A total of 86 differentially expressed metabolites (24 upregulated and 62 downregulated) were identified in the T2DM-24w group compared with the T2DM-12w group

12891_2024_8061_MOESM6_ESM.xlsx^{(70.7KB, xlsx)}

Supplementary Material 6: Additional file 6: Table S6. KEGG pathways, in which differentially expressed metabolites from the three comparison groups are involved. Dots are colored to represent P-values. The smaller the value, the greater the reliability of the test and the greater the statistical significance. The size of the dots represents the number of differentially expressed metabolites in the corresponding path. The larger the point, the more differentially expressed metabolites in the pathway (the same as Table S7)

12891_2024_8061_MOESM7_ESM.xlsx^{(102.3KB, xlsx)}

Supplementary Material 7: Additional file 7: Table S7. KEGG pathways, in which 10 key metabolites are involved

Acknowledgements

This work was supported by National Natural Science Foundation of China.

Abbreviations

T2DM: Type 2 diabetes mellitus
LC-MS: Liquid chromatography-mass spectrometry
NG: Normal group
DEMs: Differentially expressed metabolites
UPLC-Q-TOF-MS: Ultra-high performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry
STZ: Streptozotocin
PBS: Phosphate-buffered saline
KEGG: Kyoto Encyclopedia of Genes and Genomes
HMDB: Human Metabolome Database
TIC: Total ion current
POS: Positive
NEG: Negative
m/z-rt: Metabolite charge ratio-retention time
PCA: Principal Component Analysis
PLS-DA: Partial Least Squares Discriminant Analysis
FXR: Farnesoid X receptor
ER: Endoplasmic reticulum
BCAA: Branched-chain amino acid
ATP: Adenosine triphosphate
UPR: Unfolded protein response
OGTT: Oral glucose tolerance test
AGEs: Advanced glycation end products
ECM: Extracellular matrix

Author contributions

KSX and TBY contributed to the study concept. KSX contributed to the data analysis, manuscript writing, and revision. KSX and LZ contributed to sample collection. TRW and LZ contributed to data cleaning and quality control. KX contributed to the review of statistical analysis. YZZ and NY contributed to the paper review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by the National Natural Science Foundation of China (Grant No. 31872310 to T.Y.).

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Ethics approval and consent to participate

This study was carried out in accordance with NIH guidelines for the care and use of laboratory animals (8th edition, NIH). The study protocol was approved by the Ethics Committee of Experimental Animals of the Affiliated Hospital of Qingdao University (Approval No. 20220505SD8020221210126). All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. The methods are reported in accordance with ARRIVE guidelines (https://arriveguidelines.org) for the reporting of animal experiments.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Kuishuai Xu, Liang Zhang and Tianrui Wang contributed equally to this work.

Contributor Information

Ning Yu, Email: yn11321@163.com.

Yingze Zhang, Email: dzyzhangyingze@163.com.

References

1.Millar NL, Silbernagel KG, Thorborg K, Kirwan PD, Galatz LM, Abrams GD, et al. Tendinopathy Nat Rev Dis Primers. 2021;7(1):1. 10.1038/s41572-020-00234-1. [DOI] [PubMed] [Google Scholar]
2.Leong HT, Fu SC, He X, Oh JH, Yamamoto N, Hang S. Risk factors for rotator cuff tendinopathy: a systematic review and meta-analysis. J Rehabil Med. 2019;51(9):627–37. 10.2340/16501977-2598. [DOI] [PubMed] [Google Scholar]
3.Xu K, Zhang L, Yu N, et al. Effects of advanced glycation end products (AGEs) on the differentiation potential of primary stem cells: a systematic review. Stem Cell Res Ther. 2023;14(1):74. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Giha HA, Sater MS, Alamin OAO. Diabetes mellitus tendino-myopathy: epidemiology, clinical features, diagnosis and management of an overlooked diabetic complication. Acta Diabetol. 2022;59(7):871–83. 10.1007/s00592-022-01860-9. [DOI] [PubMed] [Google Scholar]
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6.Huang SW, Wang WT, Chou LC, Liou TH, Chen YW, Lin HW. Diabetes mellitus increases the risk of rotator cuff tear repair surgery: A population-based cohort study. J Diabetes Complications. 2016 Nov-Dec;30(8):1473–1477. 10.1016/j.jdiacomp.2016.07.015. Epub 2016 Jul 25. [DOI] [PubMed]
7.Ranger TA, Wong AM, Cook JL, Gaida JE. Is there an association between tendinopathy and diabetes mellitus? A systematic review with meta-analysis. Br J Sports Med. 2016 Aug;50(16):982–9. 10.1136/bjsports-2015-094735. Epub 2015 Nov 23. [DOI] [PubMed]
8.Mavrikakis ME, Drimis S, Kontoyannis DA, Rasidakis A, Moulopoulou ES, Kontoyannis S. Calcific shoulder periarthritis (tendinitis) in adult onset diabetes mellitus: a controlled study. Ann Rheum Dis. 1989;48(3):211–4. 10.1136/ard.48.3.211. [DOI] [PMC free article] [PubMed] [Google Scholar]
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10.Kim DH, Min SG, Kim HM, Kang HR, Choi JH, Lee HJ et al. Comparison of the characteristics of Rotator Cuff tissue in a Diabetic Rat Model. Orthop 2022 May-Jun;45(3):e154–61. 10.3928/01477447-20220128-08. Epub 2022 Feb 3. [DOI] [PubMed]
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14.Xu J, Cai M, Wang Z, Chen Q, Han X, Tian J et al. Phenylacetylglutamine as a novel biomarker of type 2 diabetes with distal symmetric polyneuropathy by metabolomics. J Endocrinol Invest. 2022 Oct 25.10.1007/s40618-022-01929-w. Epub ahead of print. [DOI] [PMC free article] [PubMed]
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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12891_2024_8061_MOESM1_ESM.xlsx^{(32.3KB, xlsx)}

12891_2024_8061_MOESM2_ESM.xlsx^{(41.8KB, xlsx)}

Supplementary Material 2: Additional file 2: Table S2. A total of 164 metabolites were identified from 3118 metabolite features extracted from the raw data acquired in negative-ionization modes

12891_2024_8061_MOESM3_ESM.xlsx^{(10.4KB, xlsx)}

12891_2024_8061_MOESM4_ESM.xlsx^{(26.2KB, xlsx)}

12891_2024_8061_MOESM5_ESM.xlsx^{(44.3KB, xlsx)}

12891_2024_8061_MOESM6_ESM.xlsx^{(70.7KB, xlsx)}

12891_2024_8061_MOESM7_ESM.xlsx^{(102.3KB, xlsx)}

Supplementary Material 7: Additional file 7: Table S7. KEGG pathways, in which 10 key metabolites are involved

Data Availability Statement

Data is provided within the manuscript or supplementary information files.

[CR1] 1.Millar NL, Silbernagel KG, Thorborg K, Kirwan PD, Galatz LM, Abrams GD, et al. Tendinopathy Nat Rev Dis Primers. 2021;7(1):1. 10.1038/s41572-020-00234-1. [DOI] [PubMed] [Google Scholar]

[CR2] 2.Leong HT, Fu SC, He X, Oh JH, Yamamoto N, Hang S. Risk factors for rotator cuff tendinopathy: a systematic review and meta-analysis. J Rehabil Med. 2019;51(9):627–37. 10.2340/16501977-2598. [DOI] [PubMed] [Google Scholar]

[CR3] 3.Xu K, Zhang L, Yu N, et al. Effects of advanced glycation end products (AGEs) on the differentiation potential of primary stem cells: a systematic review. Stem Cell Res Ther. 2023;14(1):74. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR4] 4.Giha HA, Sater MS, Alamin OAO. Diabetes mellitus tendino-myopathy: epidemiology, clinical features, diagnosis and management of an overlooked diabetic complication. Acta Diabetol. 2022;59(7):871–83. 10.1007/s00592-022-01860-9. [DOI] [PubMed] [Google Scholar]

[CR5] 5.Thomas SJ, McDougall C, Brown ID, Jaberoo MC, Stearns A, Ashraf R, et al. Prevalence of symptoms and signs of shoulder problems in people with diabetes mellitus. J Shoulder Elb Surg. 2007 Nov-Dec;16(6):748–51. 10.1016/j.jse.2007.02.133. [DOI] [PubMed]

[CR6] 6.Huang SW, Wang WT, Chou LC, Liou TH, Chen YW, Lin HW. Diabetes mellitus increases the risk of rotator cuff tear repair surgery: A population-based cohort study. J Diabetes Complications. 2016 Nov-Dec;30(8):1473–1477. 10.1016/j.jdiacomp.2016.07.015. Epub 2016 Jul 25. [DOI] [PubMed]

[CR7] 7.Ranger TA, Wong AM, Cook JL, Gaida JE. Is there an association between tendinopathy and diabetes mellitus? A systematic review with meta-analysis. Br J Sports Med. 2016 Aug;50(16):982–9. 10.1136/bjsports-2015-094735. Epub 2015 Nov 23. [DOI] [PubMed]

[CR8] 8.Mavrikakis ME, Drimis S, Kontoyannis DA, Rasidakis A, Moulopoulou ES, Kontoyannis S. Calcific shoulder periarthritis (tendinitis) in adult onset diabetes mellitus: a controlled study. Ann Rheum Dis. 1989;48(3):211–4. 10.1136/ard.48.3.211. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR9] 9.Thomas SJ, Sarver JJ, Yannascoli SM, Tucker JJ, Kelly JD 4th, Ahima RS et al. Effect of isolated hyperglycemia on native mechanical and biologic shoulder joint properties in a rat model. J Orthop Res. 2014;32(11):1464-70. 10.1002/jor.22695. Epub 2014 Jul 28. [DOI] [PMC free article] [PubMed]

[CR10] 10.Kim DH, Min SG, Kim HM, Kang HR, Choi JH, Lee HJ et al. Comparison of the characteristics of Rotator Cuff tissue in a Diabetic Rat Model. Orthop 2022 May-Jun;45(3):e154–61. 10.3928/01477447-20220128-08. Epub 2022 Feb 3. [DOI] [PubMed]

[CR11] 11.Cao T, Hong J, Qi F, Zheng B, Chen G, Yu B, et al. A Hyperglycemic Microenvironment inhibits Tendon-to-bone healing through the let-7b-5p/CFTR pathway. Comput Math Methods Med. 2022;2022:8268067. 10.1155/2022/8268067. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]

[CR12] 12.Monteiro MS, Carvalho M, Bastos ML, Guedes de Pinho P. Metabolomics analysis for biomarker discovery: advances and challenges. Curr Med Chem. 2013;20(2):257–71. 10.2174/092986713804806621. [DOI] [PubMed] [Google Scholar]

[CR13] 13.Zhang AH, Qiu S, Xu HY, Sun H, Wang XJ. Metabolomics in diabetes. Clin Chim Acta. 2014;429:106–10. Epub 2013 Dec 7. [DOI] [PubMed] [Google Scholar]

[CR14] 14.Xu J, Cai M, Wang Z, Chen Q, Han X, Tian J et al. Phenylacetylglutamine as a novel biomarker of type 2 diabetes with distal symmetric polyneuropathy by metabolomics. J Endocrinol Invest. 2022 Oct 25.10.1007/s40618-022-01929-w. Epub ahead of print. [DOI] [PMC free article] [PubMed]

[CR15] 15.Wu C, Fei J, Xu Q, Tao Y, Zhou Z, Wang Y, et al. Interaction between plasma metabolomics and intestinal microbiome in db/db mouse, an animal model for study of type 2 diabetes and Diabetic kidney disease. Metabolites. 2022;12(9):775. 10.3390/metabo12090775. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR16] 16.Xu K, Zhang L, Ren Z, Wang T, Zhang Y, Zhao X, et al. Evaluating the role of type 2 diabetes mellitus in rotator cuff tendinopathy: development and analysis of a novel rat model. Front Endocrinol (Lausanne). 2022;13:1042878. 10.3389/fendo.2022.1042878. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR17] 17.Vaidya R, Lake SP, Zellers JA. Effect of diabetes on Tendon structure and function: not limited to Collagen Crosslinking. J Diabetes Sci Technol 2022 Jun 2:19322968221100842. 10.1177/19322968221100842. Epub ahead of print. [DOI] [PMC free article] [PubMed]

[CR18] 18.Xu K, Zhang L, Wang T, Ren Z, Yu T, Zhang Y, et al. Untargeted metabolomics reveals dynamic changes in metabolic profiles of rat supraspinatus tendon at three different time points after diabetes induction. Front Endocrinol (Lausanne). 2023;14:1292103. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR19] 19.Xu K, Zhang L, Wang T, Yu T, Zhao X, Zhang Y. Transcriptome sequencing and bioinformatics analysis of gastrocnemius muscle in type 2 diabetes mellitus rats. BMC Musculoskelet Disord. 2024;25(1):457. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR20] 20.Eliasberg CD, Wada S, Carballo CB, et al. Identification of Inflammatory mediators in Tendinopathy using a murine subacromial impingement Model[J]. J Orthop Res. 2019;37(12):2575–82. [DOI] [PubMed] [Google Scholar]

[CR21] 21.Liu Y, Deng XH, Carballo CB, Cong T, Piacentini A, Jordan Hall A, Ying L, Rodeo SA. Evaluating the role of subacromial impingement in rotator cuff tendinopathy: development and analysis of a novel rat model. J Shoulder Elb Surg. 2022;31(9):1898–908. 10.1016/j.jse.2022.02.041. [DOI] [PubMed] [Google Scholar]

[CR22] 22.Sun Y, Kwak JM, Kholinne E, Tan J, Koh KH, Jeon IH. Nonabsorbable suture knot on the Tendon affects Rotator Cuff Healing: a comparative study of the knots on Tendon and Bone in a rat model of Rotator Cuff tear. Am J Sports Med. 2019;47(12):2809–15. 10.1177/0363546519867928. [DOI] [PubMed] [Google Scholar]

[CR23] 23.Yonemitsu R, Tokunaga T, Shukunami C, Ideo K, Arimura H, Karasugi T, Nakamura E, Ide J, Hiraki Y, Mizuta H. Fibroblast growth factor 2 enhances Tendon-to-bone Healing in a rat rotator cuff repair of chronic tears. Am J Sports Med. 2019;47(7):1701–12. 10.1177/0363546519836959. [DOI] [PubMed] [Google Scholar]

[CR24] 24.Han Q, Li H, Jia M, Wang L, Zhao Y, Zhang M, et al. Age-related changes in metabolites in young donor livers and old recipient sera after liver transplantation from young to old rats. Aging Cell. 2021;20(7):e13425. 10.1111/acel.13425. Epub 2021 Jun 22. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR25] 25.Yang L, Yan Y, Zhao B, Xu H, Su X, Dong C. Study on the regulation of exogenous hormones on the absorption of elements and the Accumulation of secondary metabolites in the Medicinal Plant Artemisia argyi leaves. Metabolites. 2022;12(10):984. 10.3390/metabo12100984. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR26] 26.Wang H, Ding J, Ding S, Chang Y. Metabolomic changes and polyunsaturated fatty acid biosynthesis during gonadal growth and development in the sea urchin Strongylocentrotus Intermedius. Comp Biochem Physiol Part D Genomics Proteom. 2019;32:100611. 10.1016/j.cbd.2019.100611. Epub 2019 Jul 26. [DOI] [PubMed] [Google Scholar]

[CR27] 27.Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008;36(Database issue):D480-4. 10.1093/nar/gkm882. Epub 2007 Dec 12. [DOI] [PMC free article] [PubMed]

[CR28] 28.Tao Y-F, Qiang J, He J, Zhu H-J, Bao J-W, Xu P. Untargeted LC-MS metabolomics approach reveals metabolic changes in genetically improved farmed tilapia (Oreochromis niloticus) with fatty liver induced by a high-fat diet. Aquac Res. 2020;52(2):724–35. [Google Scholar]

[CR29] 29.Vaidya R, Lake SP, Zellers JA. Effect of diabetes on Tendon structure and function: not limited to Collagen Crosslinking. J Diabetes Sci Technol. 2023;17(1):89–98. 10.1177/19322968221100842. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR30] 30.Vlassara H, Palace MR. Diabetes and advanced glycation endproducts. J Intern Med. 2002;251(2):87–101. 10.1046/j.1365-2796.2002.00932.x. [DOI] [PubMed] [Google Scholar]

[CR31] 31.Vlassara H, Uribarri J. Advanced glycation end products (AGE) and diabetes: cause, effect, or both? Curr Diab Rep. 2014;14(1):453. 10.1007/s11892-013-0453-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR32] 32.Brownlee M. Biochemistry and molecular cell biology of diabetic complications. Nature. 2001;414(6865):813–20. 10.1038/414813a. [DOI] [PubMed] [Google Scholar]

[CR33] 33.Lee JM, Veres SP. Advanced glycation end-product cross-linking inhibits biomechanical plasticity and characteristic failure morphology of native tendon. J Appl Physiol (1985). 2019;126(4):832–41. 10.1152/japplphysiol.00430.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR34] 34.Shi L, Lu PP, Dai GC, Li YJ, Rui YF. Advanced glycation end productions and tendon stem/progenitor cells in pathogenesis of diabetic tendinopathy. World J Stem Cells. 2021;13(9):1338–48. 10.4252/wjsc.v13.i9.1338. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR35] 35.Sun JL, Zhao LL, Wu H, Liu Q, Liao L, Luo J, et al. Acute hypoxia changes the mode of glucose and lipid utilization in the liver of the largemouth bass (Micropterus salmoides). Sci Total Environ. 2020;713:135157. 10.1016/j.scitotenv.2019.135157. Epub 2019 Nov 23. [DOI] [PubMed] [Google Scholar]

[CR36] 36.Cai K, Liu YP, Wang D. Prevalence of diabetic retinopathy in patients with newly diagnosed type 2 diabetes: a systematic review and meta-analysis. Diabetes Metab Res Rev 2022 Oct 26:e3586. 10.1002/dmrr.3586. Epub ahead of print. [DOI] [PubMed]

[CR37] 37.Mack CI, Ferrario PG, Weinert CH, Egert B, Hoefle AS, Lee YM, et al. Exploring the diversity of Sugar compounds in Healthy, Prediabetic, and Diabetic volunteers. Mol Nutr Food Res. 2020;64(9):e1901190. 10.1002/mnfr.201901190. Epub 2020 Apr 7. [DOI] [PubMed] [Google Scholar]

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PERMALINK

Investigating the mechanism of supraspinatus tendinopathy induced by type 2 diabetes mellitus in rats using untargeted metabolomics analysis

Kuishuai Xu

Liang Zhang

Tianrui Wang

Tengbo Yu

Xia Zhao

Ning Yu

Yingze Zhang

Abstract

Objective

Methods

Results

Conclusion

Supplementary Information

Introduction

Materials and methods

Experimental animals and groups

Establishment of a rat model of T2DM

Tissue sampling

Histological assessment

Biomechanical tests

Preparation of metabolite sample

Untargeted metabolomics analysis of supraspinatus tendon

Analysis of DEMs

Statistical analysis

Results

Histological evaluation

Fig. 1.

Biomechanical comparison

Quality control and identification of metabolites

Fig. 2.

Fig. 3.

Partial least squares discriminant analysis (PLS-DA)

Fig. 4.

Identification of DEMs

Fig. 5.

Table 1.

Fig. 6.

Critical metabolites associated with the development of tendinopathy

Table 2.

Fig. 7.

Discussion

Conclusions

Electronic supplementary material

Acknowledgements

Abbreviations

Author contributions

Funding

Data availability

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

Contributor Information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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