- Email: [email protected]
Non-targeted metabolomic analysis predicts the therapeutic effects of exenatide on endothelial injury in patients with type 2 diabetes
Journal Pre-proof Non-targeted metabolomic analysis predicts the therapeutic effects of exenatide on endothelial injury in patients with type 2 diabetes
Jin Yang, Yunyi Le, Tianjiao Wei, Kangli Wang, Kun Yang, Wenhua Xiao, Tianpei Hong, Rui Wei PII:
S1056-8727(20)30591-2
DOI:
https://doi.org/10.1016/j.jdiacomp.2020.107797
Reference:
JDC 107797
To appear in:
Journal of Diabetes and Its Complications
Received date:
11 July 2020
Revised date:
15 October 2020
Accepted date:
4 November 2020
Please cite this article as: J. Yang, Y. Le, T. Wei, et al., Non-targeted metabolomic analysis predicts the therapeutic effects of exenatide on endothelial injury in patients with type 2 diabetes, Journal of Diabetes and Its Complications (2020), https://doi.org/10.1016/ j.jdiacomp.2020.107797
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2020 Published by Elsevier.
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Non-targeted metabolomic analysis predicts the therapeutic effects of exenatide on endothelial injury in patients with type 2 diabetes
Jin Yang, Yunyi Le, Tianjiao Wei, Kangli Wang, Kun Yang, Wenhua Xiao, Tianpei Hong, Rui Wei
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Department of Endocrinology and Metabolism, Peking University Third Hospital,
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Beijing, China
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Tianpei Hong
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Corresponding authors at:
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Jin Yang and Yunyi Le contributed equally to this article.
Department of Endocrinology and Metabolism, Peking University Third Hospital, 49
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North Garden Road, Haidian District, Beijing 100191, China. Tel.: +86-10-82266918; Fax: +86-10-62017700. E-mail address: [email protected]. Rui Wei
Department of Endocrinology and Metabolism, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China. Tel.: +86-10-82266722. Fax: +86-10-62017700. E-mail address: [email protected].
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Journal Pre-proof ABSTRACT Aims: We aimed to investigate whether treatment with exenatide could ameliorate endothelial injury in patients with type 2 diabetes mellitus (T2DM), and to identify biomarkers for predicting amelioration of the endothelial injury induced by the treatment. Methods: Ninety-three patients with T2DM were recruited and treated with exenatide
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for 16 weeks. Enzyme-linked immunosorbent assays were performed at baseline and
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after the treatment to measure serum levels of endothelial injury markers, including
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soluble thrombomodulin (sTM). Patients were categorized as responders (n = 47) or
metabolites
at
baseline
were
measured
with
non-targeted
liquid
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of
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non-responders (n = 46) based on median changes in their sTM levels. Serum levels
multivariate analysis.
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chromatography-mass spectrometry. The results obtained were evaluated with
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Results: Treatment with exenatide for 16 weeks resulted in reduced body weight and improved levels of fasting plasma glucose, 2-hour postprandial plasma glucose, and HbA1c in patients with T2DM (all P < 0.05). Compared with baseline, serum levels of endothelial injury markers including sTM were significantly lowered after the treatment. Metabolites presented at significantly different levels in responders versus non-responders were considered as biomarkers for a therapeutic response of sTM to the
exenatide
treatment.
Among
those
identified,
4-hydroxyproline
and
12-oxo-9(Z)-dodecenoic acid were found to correlate most closely with the exenatide-induced endothelial protection response. The specificity and sensitivity of
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the multi-metabolite signature model contained higher 4-hydroxyproline and lower 12-oxo-9(Z)-dodecenoic acid were 53.3% and 92.3%, respectively, while the area under receiver operating characteristic curve was 69.2% (P < 0.001). Conclusions: Treatment with exenatide for 16 weeks ameliorates endothelial injury in patients with T2DM. Endothelial protection benefit from exenatide treatment was
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effectively predicted by the specific metabolomic combination of higher
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4-hydroxyproline and lower 12-oxo-9(Z)-dodecenoic acid.
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Keywords: exenatide; non-targeted metabolomics; endothelial injury; type 2 diabetes
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Abbreviations
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mellitus.
2hPG: 2-hour postprandial plasma glucose; AUC: Area under receiver operating
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characteristic curve; BMI: Body mass index; DBP, Diastolic blood pressure; FPG: Fasting plasma glucose; GLP-1: Glucagon-like peptide-1; HbA1c: Glycated hemoglobin A1c; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; SBP: Systolic blood pressure; sEPCR: soluble endothelial cell protein C receptor; sTM: soluble thrombomodulin; TG: Triglyceride; VIP: Variable importance in projection; vWF: von Willebrand Factor.
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Journal Pre-proof 1. Introduction Over recent decades, the prevalence of diabetes in adults is increasing worldwide. It was reported that in 2019, there were estimated 463 million adult people with diabetes, which is expected to increase to 578 million by 2030.1 Diabetes often leads to macrovascular (e.g., cardiovascular and cerebrovascular diseases) and microvascular (e.g., nephropathy and retinopathy) complications, which are the main cause of
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cardiovascular event or death, maintenance of hemodialysis, and acquired visual
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loss.2-4 The above complications not only have a significant impact on the patient’s
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quality of life, but also presents a tremendous economic burden and has become a key
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public health issue.5 Therefore, it has become necessary to identify effective
complications.
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prevention and treatment methods that reduce the stress induced by diabetic vascular
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Glucagon-like peptide-1 (GLP-1), a gut hormone produced in the intestinal L cells
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in response to nutrient ingestion, stimulates insulin secretion and inhibits glucagon release in a glucose-dependent manner.6 GLP-1 receptor agonists (GLP-1RAs) are successfully applied in clinical practice for the treatment of type 2 diabetes mellitus (T2DM). Preclinical studies have proved that GLP-1RAs have a wide range of potential pharmacological benefits for the cardiovascular system, and several large-scale trials provide increasing evidence of their beneficial cardiovascular outcomes.7-11 Meta-analysis of randomized clinical trials has reported that GLP-1RAs protect patients from macrovascular and microvascular complications.12-14 However, there is a considerable interindividual variability in the therapeutic response to GLP-1RAs for reducing vascular complications in patients with T2DM. 4
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Endothelial injury has been recognized as being critical factor in the pathogenesis of vascular complications in T2DM.15 GLP-1RAs exert potential protection through direct effects on the initiation and progression of endothelial injury.16 Identification of biomarkers to predict which patients derive the most benefit from treatment with GLP-1RAs will help precision diabetes management become common practice.17,18 It has been demonstrated that metabolomic technology may facilitate huge advances in
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the field of precision medicine.19 Many pharmacometabolomic studies have focused
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on identifying a correlation between baseline metabotypes and responses to drugs,
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such as aspirin and simvastatin.19 Such efforts have clarified the genetic and
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metabolic impacts on therapeutic outcomes.
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Differences in metabolic profiles may provide some evidence for patients with
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T2DM to optimize the GLP-1RAs treatment. In the present study, we sought to investigate whether treatment with exenatide, the first GLP-1RA approved for the
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treatment of T2DM in China in 2009, could reduce serum levels of endothelial injury markers in patients with T2DM, and to identify whether serum levels of metabolites at baseline could be used to predict therapeutic effects of exenatide on endothelial injury.
2. Methods 2.1. Study design and subjects Data from a randomized, multicenter, non-inferiority clinical trial were used in the present study. The design and detailed protocol of the trial have been published elsewhere.20 Eligibility criteria for inclusion in the present study were a diagnosis of 5
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T2DM, 2070 years of age, with inadequately controlled levels of glycated hemoglobin A1c (HbA1c) ranging from 7.010.0%, receiving monotherapy or combination therapy of metformin and insulin secretagogues. Exclusion criteria primarily included: (1) history of diabetic ketoacidosis or other serious medical condition, including diseases of the liver, kidneys, cardiovascular, nervous and
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endocrine systems, and diseases that led to the study withdrawal or affected the judgment of the study results; (2) protocol violation, loss to follow-up, withdrawal
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due to adverse events, and refusal to continue participation in the study. The
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participants were treated with branded exenatide injection (Byetta®, Lilly Pharma
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Fertigung & Distribution GmbH & Co. KG, Giessen, Germany) at an initial dose of 5
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μg twice daily for 4 weeks, followed by 10 μg twice daily for an additional 12 weeks.
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If the increase in dose was not tolerated, 5 μg per injection was maintained. A total of 120 patients in the branded exenatide treatment arm of the trial were included, and 27
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patients (1 protocol violation, 2 loss to follow-up, 10 patients’ withdrawal, 9 patients’ refusal, and 5 missing metabolomic data) were excluded from the final analyses in the present study.
2.2. Blood collection and measurement Blood samples were drawn from each patient at baseline and after 16 weeks of treatment with exenatide and centrifuged at 4000 rpm for 10 minutes for serum collection, and then stored at 80°C. The laboratory tests performed for serum chemistry analysis included blood glucose and lipid profiles. Biochemical analysis of 6
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endothelial injury marker levels in serum was performed via enzyme-linked immunosorbent assay (ELISA) for soluble thrombomodulin (sTM) (Baolai Biotechnology, Yancheng, Jiangsu, China), von Willebrand factor (vWF) (Abcam, Cambridge, MA, USA), and soluble endothelial cell protein C receptor (sEPCR)
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(Dogesce Biotechnology, Beijing, China).
2.3. Non-targeted metabolomic analysis
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A DIONEX Ultimate 3000 ultra-high-performance liquid chromatography (UPLC)
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system (Dionex Corporation, Sunnyvale, CA, USA) connected to an ESI-Quadrupole
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time-of-flight tandem mass spectrometry (QTOF/MS) mass spectrometer (Thermo
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Fischer Scientific, Waltham, MA, USA) was used for metabolomic analysis. The
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methodology is described in detail elsewhere.21 For metabolite extraction, 25 μL of serum was mixed with 175 μL of extraction buffer (25% acetonitrile in 40% methanol
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and 35% water). Samples were incubated on ice for 10 minutes before centrifugation, and then centrifuged at 10 000 rpm at 4°C for 10 minutes. Next, 5 μL of supernatant was transferred to a fresh Eppendorf tube and dried under vacuum. The dried sample was then reconstituted in 200 μL buffer containing 5% methanol, 1% acetonitrile, and 94% water. Subsequently, the required amount of supernatant aliquot was transferred to a glass vial for further analysis via liquid chromatography-mass spectrometry (LC-MS) technique. Finally, TraceFinder software (Thermo Fischer Scientific) was used to record molecular features.
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2.4. Metabolomic data processing The subjects were divided into two groups (responders and non-responders), depending on median changes in serum sTM, a well-known marker of endothelial injury.22 To identify metabolites with relative abundance that differed in the baseline serum
samples
of
responders
versus
non-responders,
orthogonal
partial
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least-squares-discriminant analysis (OPLS-DA) was performed with SIMCA-P software (version 13.0, Umetrics, Umeå, Sweden). Quality control samples were
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inserted randomly in each analytical batch to ensure data quality. Variables identified
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with R software version 2.9.1 as variable importance in projection (VIP) values, the
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Wilcoxon rank-sum test, logistic regression analysis, and area under the receiver
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operating characteristic curve (AUC) were considered as predictive biomarkers. A
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VIP value > 1 in S-Plot was considered to indicate that the metabolite contributed to grouping. P values < 0.05 were considered to identify metabolites with differential
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expression between groups.
2.5 Statistical analysis
The Shapiro-Wilk test was used to test for normality. Data are presented as means ± standard deviation (SD) or median (interquartile range), as appropriate. For comparisons between groups, Student’s t-test and the Mann–Whitney U-test were applied to the data for normally distributed values and non-normally distributed values, respectively. Chi-square tests or Fisher’s exact tests were also used to compare differences among groups whenever appropriate. Pre-treatment versus post-treatment 8
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comparisons of clinical characteristics such as blood glucose profiles, lipid profiles, and endothelial injury markers were performed with SPSS version 20.0 for Windows (SPSS Japan Inc, Tokyo, Japan). P values < 0.05 were considered to indicate statistical significance.
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3. Results 3.1. Patient characteristics
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A total of 93 patients with T2DM including 55 males (59.1%) and 38 females (40.9%)
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were treated with branded exenatide (Byetta®). The median duration of diabetes was
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4.42 (2.29, 8.17) years. Various parameters that were determined at baseline and after
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treatment with exenatide for 16 weeks are listed in Table 1. At baseline, average body
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weight, body mass index (BMI), diastolic blood pressure (DBP), fasting plasma glucose (FPG), 2-hour postprandial plasma glucose (2hPG), and HbA1c were 79.3 ±
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16.2 kg, 28.1 ± 4.13 kg/m2, 77.8 ± 8.23 mmHg, 9.46 ± 1.88 mmol/L, 16.3 ± 3.60 mmol/L, and 8.23 ± 0.87%. After the treatment, these values were significantly lowered (all P < 0.05). The differences between baseline and post-treatment levels of systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) were not statistically significant (all P > 0.05).
3.2. Changes in serum levels of endothelial injury markers Serum levels of endothelial injury markers at baseline and after treatment with 9
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exenatide for 16 weeks are listed in Table 2. Compared with baseline, the levels of sTM, vWF and sEPCR were all reduced significantly after the treatment.
3.3. Clinical characteristics associated with the change in serum sTM level sTM is considered to be a marker of endothelial injury [22], and the decrease of sTM
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(ΔsTM) is a good indicator of endothelial injury amelioration. The proportion of subjects with sTM reduction was 62.4% (58/93), and 37.6% (35/93) did not show a
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decrease. In order to understand which category of patients benefited most from the
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exenatide treatment, we sought to determine which patients had the greater reduction
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in serum sTM level (defined as drug response). The subjects were divided into
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responders (ΔsTM ≤ 1.27 μg/L) and non-responders (ΔsTM > 1.27 μg/L) based on
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the median change values. As shown in Table 3, baseline body weight was significantly higher in responders than in non-responders (82.6 ± 15.3 kg versus 75.9
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± 16.5 kg, P = 0.045). None of the other clinical characteristics at baseline, including blood glucose profiles, lipid profiles, and diabetic vascular complications differed significantly between groups (all P>0.05).
3.4. Metabolic differences between responders and non-responders Plotting the metabolic patterns with an OPLS-DA model revealed significant differences between responders and non-responders at baseline in serum levels of various metabolites. The score plots revealed a significant separation between the two groups (Supplementary Figure. 1). The permutation test applied to the data revealed 10
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the validity of the OPLS-DA model. To identify the specific biomarkers associated with the differentiation of responders and non-responders, the S-plot analysis was performed based on the serum metabolite profiling data. Fifty-nine metabolites were identified, with a selection criterion was set as a VIP value > 1 in the model (Supplementary Table 1 and Supplementary Figure 2). When responders and
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non-responders were compared, a total of 15 metabolites were found to differ significantly between the two groups (P < 0.05) (Supplementary Table 2). With the
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criteria of VIP > 1 and P < 0.05, 13 metabolites were finally identified as potential
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predicting biomarkers for the effects of exenatide. Among the 13 metabolites, 6
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metabolites were significantly higher, whereas the other remaining 7 metabolites were
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lower in responders than in non-responders (Table 4). These findings suggested a
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significant difference in metabolic characteristics between the two groups at baseline.
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3.5. Multi-metabolite signature in responders The results obtained through the OPLS-DA were subjected to logistic regression analysis in order to design a multi-metabolite signature model for predicting the response of sTM to the exenatide treatment. The prediction model was applied to a training set (two thirds of subjects) and subsequently validated against a validation set (one third of subjects). Model quality was evaluated by calculating sensitivity, specificity, and AUC. To evaluate the validation set, four models were established. Model 4, which contained 4-hydroxyproline and 12-oxo-9(Z)-dodecenoic acid, was identified as the optimal model (after all variables were entered into the model, the 11
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backward stepwise elimination method was used). The specificity and sensitivity were 53.3% and 92.3%, respectively, while the AUC was 69.2% (P < 0.001). These results demonstrated that 4-hydroxyproline and 12-oxo-9(Z)-dodecenoic acid might be used as biomarkers for predicting the effect of the exenatide treatment on endothelial
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injury.
4. Discussion
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In the present study, we demonstrated that treatment with exenatide for 16 weeks
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resulted in reduced body weight and BMI as well as improved levels of FPG, 2hPG,
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and HbA1c in patients with T2DM whose HbA1c levels were inadequately controlled
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by either monotherapy or combined therapy with metformin and insulin secretagogues.
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These findings are in accordance with those reported by previous clinical studies.23-25 Moreover, we found that the exenatide treatment effectively reduced serum levels of
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endothelial injury markers including sTM, indicating that exenatide might ameliorate endothelial injury in patients with T2DM. Importantly, 4-hydroxyproline and 12-oxo-9(Z)-dodecenoic acid were identified as predictive biomarkers in evaluating the therapeutic effect of exenatide on the endothelial injury. Our group and several other groups have demonstrated that treatment with exenatide induces a significant improvement in endothelial function that can be detected by coronary flow velocity reserve and flow-mediated dilation in patients with T2DM.7,26 However, these endothelial function indicators are often difficult to detect in usual clinical practice, and whether exenatide has therapeutic effects on serum 12
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endothelial injury markers remains obscure. Our previous study showed that treatment with exenatide for 12 weeks remarkably reduced serum levels of soluble intercellular adhesion molecule-1 and soluble vascular cell adhesion molecule-1 in patients with T2DM.7 Because the sample size is small and only 18 patients were included in that study, we recruited 93 patients to evaluate the role of very easy and fast measured
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endothelial injury markers for further investigating the therapeutic effect of exenatide on endothelial injury in patients with T2DM.
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In the present study, we showed that treatment with exenatide significantly
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reduced the level of sTM, vWF, and sEPCR in serum. Since sTM is released from
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endothelial cells and is responsible for several disorders, including diabetic vascular
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complications, it has been considered to be an endothelial injury marker with potential
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powerful clinical utility.22 The therapeutic response to GLP-1RAs may vary greatly from one individual to another.27 Such a difference could originate from different
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cardiovascular risks including biological and non-biological features that are known or unknown to influence the treatment results.28 Therefore, there is a clinical need for biomarkers that can easily be measured in order to predict the therapeutic effect before initiating treatment. Therefore, we analyzed the metabolomic profiles at baseline to explore potential biomarkers for estimating the effects of exenatide in decreasing sTM level in patients with T2DM. We found that among 93 patients, 47 patients were responders, and 46 patients were non-responders, according to the median magnitude of the reduction in sTM. Moreover, there was no significant difference between responders and non-responders 13
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in blood glucose profiles or lipid profiles, indicating that neither blood glucose profiles nor lipid profiles may be important biomarkers for predicting the magnitude of a given patient’s reduction in sTM. The non-targeted metabolomic analysis of baseline serum samples was performed in the responder and non-responder groups. Our results identified a total of 13 metabolites. Logistic regression and AUC were
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performed with change in sTM as a dependent variable to find the specific biomarker combination that was most accurate. The results highlighted 4-hydroxyproline and
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12-oxo-9(Z)-dodecenoic acid as significant biomarkers. It has been reported that
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4-hydroxyproline is an abundant constituent of collagen and elastin.29 Importantly, in
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both humans and animals, the production of 4-hydroxyproline is enhanced in response
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to oxidative stress, as an adaptive mechanism for defense and survival.30 However, the
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origin and function of 12-oxo-9(Z)-dodecenoic acid is still unknown. In our study, higher levels of 4-hydroxyproline and lower 12-oxo-9(Z)-dodecenoic acid predicted
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an amelioration of endothelial injury. Therefore, testing the levels of these two biomarkers may be useful to guide treatment decision before the use of exenatide. Of note, the association between metabolomics and the therapeutic effects of GLP-1RAs on endothelial injury in patients with T2DM have not been reported previously. There are some limitations in our study. The sample size is relatively small. The generic exenatide is still in the premarket approval process. To avoid a possible bias introduced by the administration of two kinds of exenatide, only the data from the branded exenatide treatment arm were used in the present study. Although the present study indicated a therapeutic effect of exenatide in ameliorating endothelial injury, a 14
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larger number of patients would be required to confirm these findings in the future studies. Furthermore, the potentiality of the model should be further explored in prospective studies powered for clinical end points. We used only LC-MS in this study. The combined approach of using LC-MS and gas chromatography-mass spectrometry would be more sensitive, and should be studied in the future. At last, our
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results were limited to patients suffering with T2DM whose HbA1c levels were inadequately controlled on either a monotherapy or combination therapy with
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metformin and insulin secretagogue. The results presented above should be
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investigated in other T2DM populations, such as drug-naive patients with T2DM.
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5. Conclusions
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The present study demonstrated that treatment with exenatide for 16 weeks ameliorated endothelial injury in patients with T2DM whose HbA1c level was
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inadequately controlled by either monotherapy or combination therapy with metformin and insulin secretagogue. Importantly, the specific metabolomic combination (higher 4-hydroxyproline and lower 12-oxo-9(Z)-dodecenoic acid) could effectively predict an amelioration of endothelial injury induced by exenatide.
Declaration of competing interest None to disclose.
Acknowledgements The authors appreciate for all the investigators from Department of Endocrinology, 15
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Beijing Hospital; Department of Endocrinology, PLA Rocket Force Characteristic Medical Center; Department of Endocrinology, Beijing Tiantan Hospital, Capital Medical University; Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University; Department of Endocrinology, The First Hospital of Shanxi Medical University; and Department of Endocrinology, People’s Hospital of Hainan
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Province, for their help with the enrollment of patients and data collection.
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Funding/support
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This research was supported by the National Key Research and Development
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Program of China (2018YFC1313900), the National Natural Science Foundation of
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China (81830022, 81970671), and the Capital’s Funds for Health Improvement and Research (2020-3-40914). The funders of this study played no role in either its design
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or conduct; collection, management, analysis or interpretation of data; preparation,
publication.
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review or approval of the manuscript; or the decision to submit the manuscript for
Author contributions
T.H. and R.W. made substantial contributions to conception, study design, and reviewing of the manuscript. J.Y. and Y.L. performed the research experiments and statistical analyses and prepared the manuscript. T.W., K.W., K.Y., and W.X. helped perform the research experiments and analyze data.
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References 1. Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9 edition. Diabetes Res Clin Pract 2019; 157:107843. https://doi.org/10.1016/j.diabres.2019.107843 2. Fu AZ, Qiu Y, Radican L, et al. Health care and productivity costs associated with diabetic patients with macrovascular comorbid conditions. Diabetes Care 2009; 32:2187-92. https://doi.org/10.2337/dc09-1128 3. Umanath K, Lewis JB. Update on diabetic nephropathy: core curriculum 2018. Am J Kidney Dis 2018; 71:884-95. https://doi.org/10.1053/j.ajkd.2017.10.026 4. Wong TY, Cheung CMG, Larsen M, et al. Diabetic retinopathy. Nat Rev Dis Primers 2016; 17:16012. https://doi.org/10.1038/nrdp.2016.12 5. Bommer C, Sagalova V, Heesemann E, et al. Global economic burden of diabetes in adults: projections from 2015 to 2030. Diabetes Care 2018; 41:963-70. https://doi.org/10.2337/dc17-1962 6. Müller TD, Finan B, Bloom SR, et al. Glucagon-like peptide 1 (GLP-1). Mol Metab 2019; 30:72-130. https://doi.org/10.1016/j.molmet.2019.09.010 7. Wei R, Ma S, Wang C, et al. Exenatide exerts direct protective effects on endothelial cells through the AMPK/Akt/eNOS pathway in a GLP-1 receptor-dependent manner. Am J Physiol Endocrinol Metab 2016; 310:E947-57. https://doi.org/10.1152/ajpendo.00400.2015 8. Pfeffer MA, Claggett B, Diaz R, et al. Lixisenatide in patients with type 2 diabetes and acute coronary syndrome. N Engl J Med 2015; 373:2247-57. https://doi.org/10.1056/NEJMoa1509225 9. Marso SP, Daniels GH, Brown-Frandsen K, et al. Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med 2016; 375:311-22. https://doi.org/10.1056/NEJMoa1603827 10. Marso SP, Bain SC, Consoli A, et al. Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med 2016; 375:1834-44. https://doi.org/10.1056/NEJMoa1607141 11. Hernandez AF, Green JB, Janmohamed S, et al. Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial. Lancet 2018; 392:1519-29. https://doi.org/10.1016/S0140-6736(18)32261-X 12. Mannucci E, Dicembrini I, Nreu B, et al. Glucagon-like peptide-1 receptor agonists and cardiovascular outcomes in patients with and without prior cardiovascular events: An updated meta-analysis and subgroup analysis of randomized controlled trials. Diabetes Obes Metab. 2020; 22:203-11. https://doi.org/10.1111/dom.13888 13. Avgerinos I, Karagiannis T, Malandris K, et al. Glucagon-like peptide-1 receptor agonists and microvascular outcomes in type 2 diabetes: A systematic review and meta-analysis. Diabetes Obes Metab 2019; 21:188-93. https://doi.org/10.1111/dom.13484 14. Kristensen SL, Rørth R, Jhund PS, et al. Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials. Lancet Diabetes Endocrinol 2019; 17
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18. 19. 20.
25.
26.
27.
28.
29.
30.
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21.
of
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ro
15.
7:776-85. https://doi.org/10.1016/S2213-8587(19)30249-9 Hadi HA, Suwaidi JA. Endothelial dysfunction in diabetes mellitus. Vasc Health Risk Manag 2007; 3:853-76. Rizzo M, Nikolic D, Patti AM, et al. GLP-1 receptor agonists and reduction of cardiometabolic risk: Potential underlying mechanisms. Biochim Biophys Acta Mol Basis Dis 2018; 1864, 2814-21. https://doi.org/10.1016/j.bbadis.2018.05.012 Heo CU, Choi CI. Current progress in pharmacogenetics of second-line antidiabetic medications: towards precision medicine for type 2 diabetes. J Clin Med 2019; 8:393. https://doi.org/10.3390/jcm8030393 Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 2016; 15:473-84. https://doi.org/10.1038/nrd.2016.32 Li B, He X, Jia W, et al. Novel applications of metabolomics in personalized medicine: a mini-review. Molecules 2017; 22:1173. https://doi.org/10.3390/molecules22071173 Yang J, Xiao W, Guo L, et al. Efficacy and safety of generic exenatide injection in Chinese patients with type 2 diabetes: a multicenter, randomized, controlled, non-inferiority trial. Acta Diabetol 2020. https://doi.org/10.1007/s00592-020-01510-y Rauschert S, Uhl O, Koletzko B, et al. Lipidomics reveals associations of phospholipids with obesity and insulin resistance in young adults. J Clin Endocrinol Metab 2016; 101:871-79. https://doi.org/10.1210/jc.2015-3525 Nawroth PP, Häring HU. Thrombomodulin and coronary heart disease. Lancet 1999; 353:1722-23. https://doi.org/10.1016/S0140-6736(99)90039-9 Buse JB, Henry RR, Han J, et al. Effects of exenatide (exendin-4) on glycemic control over 30 weeks in sulfonylurea-treated patients with type 2 diabetes. Diabetes Care 2004; 27:2628-35. https://doi.org/10.2337/diacare.27.11.2628 DeFronzo RA, Ratner RE, Han J, et al. Effects of exenatide (exendin-4) on glycemic control and weight over 30 weeks in metformin-treated patients with type 2 diabetes. Diabetes Care 2005; 28:1092-100. https://doi.org/10.2337/diacare.28.5.1092 Fehse F, Trautmann M, Holst JJ, et al. Exenatide augments first- and second-phase insulin secretion in response to intravenous glucose in subjects with type 2 diabetes. J Clin Endocrinol Metab 2005; 90:5991-7. https://doi.org/10.1210/jc.2005-1093 Hopkins ND, Cuthbertson DJ, Kemp GJ, et al. Effects of 6 months glucagon-like peptide-1 receptor agonist treatment on endothelial function in type 2 diabetes mellitus patients. Diabetes Obes Metab 2013; 15:770-3. https://doi.org/10.1111/dom.12089 Ludwig L, Darmon P, Guerci B. Computing and interpreting the number needed to treat for cardiovascular outcomes trials: perspective on GLP-1 RA and SGLT-2i therapies. Cardiovasc Diabetol 2020; 19:65. https://doi.org/10.1186/s12933-020- 01034-3. Kahkoska AR, Geybels MS, Klein KR, et al. Validation of distinct type 2 diabetes clusters and their association with diabetes complications in the DEVOTE, LEADER and SUSTAIN-6 cardiovascular outcomes trials. Diabetes Obes Metab 2020; 22:1537-47. https://doi.org/10.1111/dom.14063. Bochicchio B, Laurita A, Heinz A, et al. Investigating the role of (2S,4R)-4-hydro -xyproline in elastin model peptides. Biomacromolecules. 2013; 14:4278‐ 88. doi:10.1021/bm4011529 https://doi.org/10.1021/bm4011529 Wu G. Important roles of dietary taurine, creatine, carnosine, anserine and 18
Journal Pre-proof 4-hydroxyproline in human nutrition and health. Amino Acids 2020; 52:329-60. https://doi.org/10.1007/s00726-020-02823-6
Table 1. Clinical characteristics before and after 16 weeks of treatment with exenatide in patients with T2DM Parameters
Baseline
After treatment
Age, years
t values
P values
49.5 ± 9.52 55/38
ro
4.42 (2.29, 8.17)
Body weight, kg
79.3 ± 16.2
77.3 ± 15.7
< 0.001
BMI, kg/m2
28.1 ± 4.13
27.4 ± 3.94
4.988
< 0.001
SBP, mmHg
124.6 ± 11.0
0.103
0.918
DBP, mmHg
77.8 ± 8.23
76.0 ± 7.29
2.071
0.041
FPG, mmol/L
9.46 ± 1.88
8.04 ± 2.02
6.060
< 0.001
2hPG, mmol/L
16.3 ± 3.60
13.4 ± 4.00
6.190
< 0.001
8.23 ± 0.87
7.08 ± 1.00
10.289
< 0.001
LDL-C, mmol/L
3.07 ± 0.77
3.06 ± 0.78
0.146
0.885
HDL-C, mmol/L
1.27 ± 0.35
1.25 ± 0.34
0.705
0.483
TG, mmol/L
1.94 ± 1.05
1.89 ± 1.04
0.475
0.636
124.4 ± 12.3
lP
na
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HbA1c, %
-p
4.665
re
Diabetes duration, years
of
Gender (male/female)
Note: Data are presented as means ± SD, number or median (interquartile range), as appropriate. Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; 2hPG, 2-hour postprandial plasma glucose; HbA1c, glycated hemoglobin A1c; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride.
19
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Table 2. Serum levels of endothelial injury markers before and after 16 weeks of exenatide treatment in patients with T2DM Note: Data are presented as median (interquartile range).
Baseline
After treatment
Z values
sTM, μg/L
60.5 (25.9, 86.6)
53.0 (27.3, 83.7)
1.971
0.049
vWF, mIU/mL
1176.6 (988.4, 1553.7)
939.7 (685.2, 1395.7)
4.278
< 0.001
sEPCR, ng/mL
100.1 (47.4, 143.5)
76.9 (28.2, 125.7)
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Parameters
0.006
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2.772
P values
Abbreviations: sTM, soluble thrombomodulin; vWF, von Willebrand factor; sEPCR, soluble
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na
lP
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-p
endothelial cell protein C receptor.
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Table 3. Clinical characteristics of patients stratified by the median magnitude of the reduction in sTM Responders (n=47)
Parameters Baseline
Non-responders (n=46)
After treatment
Baseline
Age, years
48.5 ±10.1
50.5 ± 8.91
Male, n (%)
29 (62)
26 (57)
Diabetes duration, years
4.58 (2.25, 8.25)
Body weight, kg 2
82.6 ± 15.3
80.1 ± 14.5
75.9 ±16.5
b
27.4 ± 3.95
BMI, kg/m
28.8 ± 4.23
27.8 ± 3.89
SBP, mmHg
126.2 ±10.2
124.2 ± 12.0
DBP, mmHg
78.7 ± 7.32
76.7 ± 7.73
FPG, mmol/L 2hPG, mmol/L
9.58 ± 1.98 16.4 ± 3.36
r P
76.7 ± 9.04
7.86 ± 1.68
b
13.1 ± 3.36
b
rn
b
0.311
0.611
0.676
0.081
0.936
74.5 ± 16.4
2.030
0.045
26.9 ± 3.98
1.586
0.116
124.7 ± 12.8
1.468
0.146
75.2 ± 6.82
f o
1.177
0.242
8.23 ± 2.32
b
0.628
0.532
13.6 ± 4.59
b
0.284
0.777
8.26 ± 0.90
7.11 ± 0.97
b
0.323
0.747
9.33 ± 1.77
l a
1.019
o r p
e
122.9 ± 11.6
P values a
After treatment
4.17 (2.33, 8.14) b
t or Z or χ2 values
16.2 ± 3.86
HbA1c, %
8.20 ± 0.85
7.05 ± 1.01
LDL-C, mmol/L
3.09 ± 0.73
3.03 ± 0.76
3.12 ±0.97
3.08 ± 0.81
0.157
0.876
HDL-C, mmol/L
1.23 ± 0.31
1.21 ± 0.32
1.32 ± 0.40
1.29 ± 0.35
1.252
0.214
TG, mmol/L
1.97 ± 1.07
1.93 ± 1.11
1.93 ± 1.05
1.85 ± 0.98
0.195
0.846
1 (2.17)
1 (2.17)
0.000
1.000
26.4 (18.7, 81.5)
30.9 (25.6, 82.5)
6.071
<0.001
1165.0 (986.5, 1631.2)
1035.6 (802.8, 1521.3)
0.450
0.653
57.3 (22.9, 132.0) 1.990
0.047
u o
J
Diabetic vascular complications, n (%) 1 (2.13)
1 (2.13) b
sTM, μg/L
86.4 (50.2, 89.1)
82.3 (27.6, 84.1)
vWF, mIU/mL
1200.5 (988.6, 1539.6)
859.0 (641.4, 1311.4) b
sEPCR, ng/mL
118.1 (62.5, 152.7) 85.2 (41.45, 124.3) b 70.7 (40.9, 126.1)
Note: Data are presented as means ± SD, number (%) or median (interquartile range), as appropriate. The group of responders represents patients with ΔsTM ≤ 1.27 μg/L, and the group of non-responders represents patients with ΔsTM > 1.27 μg/L. a P values represent the differences between two baseline values. b P <0.05 versus baseline in the same group. 21
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Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; 2hPG, 2-hour postprandial plasma glucose; HbA1c, glycated hemoglobin A1c; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; sTM, soluble thrombomodulin; vWF, von Willebrand factor; sEPCR, soluble endothelial cell protein C receptor.
f o
l a
o r p
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e
n r u
o J
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Journal Pre-proof Table 4. Metabolites present at different levels in responders versus non-responders at baseline Number
Metabolite
P values
VIP values
1
Pidolic acid
0.003699
1.789875
2
4-Hydroxyproline
0.001416
2.334635
3
Ethyl malonic acid
0.049943
1.586994
4
5-Amino-2-oxopentanoic acid
0.001416
2.334635
5
Stearidonic acid
6
SM(d18:0/22:3(10Z,13Z,16Z))
of
Higher in responders
ro
0.008768
2.117529 1.794133
0.019766
1.646536
0.024935
1.193592
-p
0.026822
re
Lower in responders L-Lysine
2
L-Serine
3
12-Oxo-9(Z)-dodecenoic acid
0.028244
1.045632
4
LysoPC(18:1(11Z))
0.049574
1.012197
PC(o-18:1(9Z)/18:2(9Z,12Z))
0.039718
1.779808
Abscisic alcohol
0.029104
1.301562
Dehydrovomifoliol
0.00241
2.061183
6 7
na
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5
lP
1
Note: VIP values were obtained from OPLS-DA with a threshold of 1.0. P values were calculated from the Wilcoxon rank-sum test. Abbreviation: VIP, variable importance in projection.
23
Journal Pre-proof Table 5. AUC parameters for discriminating larger changes in sTM from smaller changes in sTM Model Groups
AUC (95% CI)
P values
Sensitivity (%)
Specificity (%)
Model 1
0.600 (0.373-0.827)
0.131
46.7
100
Model 2
0.652 (0.495-0.845)
< 0.001
66.7
78.9
Model 3
0.626 (0.418-0.834)
< 0.001
71.4
66.7
Model 4
0.692 (0.480-0.904)
< 0.001
53.3
92.3
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na
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-p
ro
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Note: Model 1 contains all metabolites in Table 4. Model 2 contains pidolic acid, 4-hydroxyproline, stearidonic acid, and dehydrovomifoliol, all with P < 0.01 and VIP > 1.5. Model 3 contains pidolic acid, L-lysine, 4-hydroxyproline, ethyl malonic acid, stearidonic acid, PC(o-18:1(9Z)/18:2(9Z,12Z)), SM(d18:0/22:3(10Z,13Z,16Z)), and dehydrovomifoliol, all with P < 0.01 and VIP > 1.5. Model 4 contains 4-hydroxyproline and 12-Oxo-9(Z)-dodecenoic acid, both with P < 0.01 and VIP > 1.5.
24
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Figure 1. AUC analysis of metabolites for discriminating responders from non-responders. Note: Model 1 contains all metabolites in Table 4. Model 2 contains pidolic acid, 4-hydroxyproline, stearidonic acid, and dehydrovomifoliol. Model 3 contains pidolic acid, L-lysine, 4-hydroxyproline, ethyl malonic acid, stearidonic acid, PC(o-18:1(9Z)/18:2(9Z,12Z)), SM(d18:0/22:3(10Z,13Z,16Z)), and dehydrovomifoliol. Model 4 contains 4-hydroxyproline and 12-Oxo-9(Z)-dodecenoic acid. The group of responders represents patients with ΔsTM ≤ 1.27 μg/L, and the group of non-responders represents patients with ΔsTM > 1.27 μg/L.
25
Journal Pre-proof Author Statement
Jin Yang: Methodology, Investigation, Writing - original draft. Yunyi le: Methodology, Investigation, Writing - original draft. TianJiao Wei: Investigation, Data analysis. Kangli Wang: Investigation, Data analysis. Kun Yang: Investigation, Data analysis. Wenhua Xiao: Investigation, Data analysis. Tianpei Hong: Conceptualization, Supervision, Writing- Reviewing and Editing. Rui Wei:
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na
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ro
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Conceptualization, Data curation, Writing- Reviewing and Editing.
26
Journal Pre-proof Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
27
Journal Pre-proof Highlights:
Treatment with exenatide for 16 weeks ameliorated endothelial injury in patients with T2DM
Endothelial protection benefit from exenatide treatment was effectively predicted by some specific metabolomic combination Higher 4-hydroxyproline and lower 12-oxo-9(Z)-dodecenoic acid was identified
na
lP
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as the most optimal predictive biomarkers
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28
Jin Yang, Yunyi Le, Tianjiao Wei, Kangli Wang, Kun Yang, Wenhua Xiao, Tianpei Hong, Rui Wei PII:
S1056-8727(20)30591-2
DOI:
https://doi.org/10.1016/j.jdiacomp.2020.107797
Reference:
JDC 107797
To appear in:
Journal of Diabetes and Its Complications
Received date:
11 July 2020
Revised date:
15 October 2020
Accepted date:
4 November 2020
Please cite this article as: J. Yang, Y. Le, T. Wei, et al., Non-targeted metabolomic analysis predicts the therapeutic effects of exenatide on endothelial injury in patients with type 2 diabetes, Journal of Diabetes and Its Complications (2020), https://doi.org/10.1016/ j.jdiacomp.2020.107797
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© 2020 Published by Elsevier.
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Non-targeted metabolomic analysis predicts the therapeutic effects of exenatide on endothelial injury in patients with type 2 diabetes
Jin Yang, Yunyi Le, Tianjiao Wei, Kangli Wang, Kun Yang, Wenhua Xiao, Tianpei Hong, Rui Wei
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Department of Endocrinology and Metabolism, Peking University Third Hospital,
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Beijing, China
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Tianpei Hong
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Corresponding authors at:
re
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Jin Yang and Yunyi Le contributed equally to this article.
Department of Endocrinology and Metabolism, Peking University Third Hospital, 49
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North Garden Road, Haidian District, Beijing 100191, China. Tel.: +86-10-82266918; Fax: +86-10-62017700. E-mail address: [email protected]. Rui Wei
Department of Endocrinology and Metabolism, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China. Tel.: +86-10-82266722. Fax: +86-10-62017700. E-mail address: [email protected].
1
Journal Pre-proof ABSTRACT Aims: We aimed to investigate whether treatment with exenatide could ameliorate endothelial injury in patients with type 2 diabetes mellitus (T2DM), and to identify biomarkers for predicting amelioration of the endothelial injury induced by the treatment. Methods: Ninety-three patients with T2DM were recruited and treated with exenatide
of
for 16 weeks. Enzyme-linked immunosorbent assays were performed at baseline and
ro
after the treatment to measure serum levels of endothelial injury markers, including
-p
soluble thrombomodulin (sTM). Patients were categorized as responders (n = 47) or
metabolites
at
baseline
were
measured
with
non-targeted
liquid
lP
of
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non-responders (n = 46) based on median changes in their sTM levels. Serum levels
multivariate analysis.
na
chromatography-mass spectrometry. The results obtained were evaluated with
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Results: Treatment with exenatide for 16 weeks resulted in reduced body weight and improved levels of fasting plasma glucose, 2-hour postprandial plasma glucose, and HbA1c in patients with T2DM (all P < 0.05). Compared with baseline, serum levels of endothelial injury markers including sTM were significantly lowered after the treatment. Metabolites presented at significantly different levels in responders versus non-responders were considered as biomarkers for a therapeutic response of sTM to the
exenatide
treatment.
Among
those
identified,
4-hydroxyproline
and
12-oxo-9(Z)-dodecenoic acid were found to correlate most closely with the exenatide-induced endothelial protection response. The specificity and sensitivity of
2
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the multi-metabolite signature model contained higher 4-hydroxyproline and lower 12-oxo-9(Z)-dodecenoic acid were 53.3% and 92.3%, respectively, while the area under receiver operating characteristic curve was 69.2% (P < 0.001). Conclusions: Treatment with exenatide for 16 weeks ameliorates endothelial injury in patients with T2DM. Endothelial protection benefit from exenatide treatment was
of
effectively predicted by the specific metabolomic combination of higher
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4-hydroxyproline and lower 12-oxo-9(Z)-dodecenoic acid.
-p
Keywords: exenatide; non-targeted metabolomics; endothelial injury; type 2 diabetes
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Abbreviations
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mellitus.
2hPG: 2-hour postprandial plasma glucose; AUC: Area under receiver operating
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characteristic curve; BMI: Body mass index; DBP, Diastolic blood pressure; FPG: Fasting plasma glucose; GLP-1: Glucagon-like peptide-1; HbA1c: Glycated hemoglobin A1c; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; SBP: Systolic blood pressure; sEPCR: soluble endothelial cell protein C receptor; sTM: soluble thrombomodulin; TG: Triglyceride; VIP: Variable importance in projection; vWF: von Willebrand Factor.
3
Journal Pre-proof 1. Introduction Over recent decades, the prevalence of diabetes in adults is increasing worldwide. It was reported that in 2019, there were estimated 463 million adult people with diabetes, which is expected to increase to 578 million by 2030.1 Diabetes often leads to macrovascular (e.g., cardiovascular and cerebrovascular diseases) and microvascular (e.g., nephropathy and retinopathy) complications, which are the main cause of
of
cardiovascular event or death, maintenance of hemodialysis, and acquired visual
ro
loss.2-4 The above complications not only have a significant impact on the patient’s
-p
quality of life, but also presents a tremendous economic burden and has become a key
re
public health issue.5 Therefore, it has become necessary to identify effective
complications.
lP
prevention and treatment methods that reduce the stress induced by diabetic vascular
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Glucagon-like peptide-1 (GLP-1), a gut hormone produced in the intestinal L cells
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in response to nutrient ingestion, stimulates insulin secretion and inhibits glucagon release in a glucose-dependent manner.6 GLP-1 receptor agonists (GLP-1RAs) are successfully applied in clinical practice for the treatment of type 2 diabetes mellitus (T2DM). Preclinical studies have proved that GLP-1RAs have a wide range of potential pharmacological benefits for the cardiovascular system, and several large-scale trials provide increasing evidence of their beneficial cardiovascular outcomes.7-11 Meta-analysis of randomized clinical trials has reported that GLP-1RAs protect patients from macrovascular and microvascular complications.12-14 However, there is a considerable interindividual variability in the therapeutic response to GLP-1RAs for reducing vascular complications in patients with T2DM. 4
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Endothelial injury has been recognized as being critical factor in the pathogenesis of vascular complications in T2DM.15 GLP-1RAs exert potential protection through direct effects on the initiation and progression of endothelial injury.16 Identification of biomarkers to predict which patients derive the most benefit from treatment with GLP-1RAs will help precision diabetes management become common practice.17,18 It has been demonstrated that metabolomic technology may facilitate huge advances in
of
the field of precision medicine.19 Many pharmacometabolomic studies have focused
ro
on identifying a correlation between baseline metabotypes and responses to drugs,
-p
such as aspirin and simvastatin.19 Such efforts have clarified the genetic and
re
metabolic impacts on therapeutic outcomes.
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Differences in metabolic profiles may provide some evidence for patients with
na
T2DM to optimize the GLP-1RAs treatment. In the present study, we sought to investigate whether treatment with exenatide, the first GLP-1RA approved for the
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treatment of T2DM in China in 2009, could reduce serum levels of endothelial injury markers in patients with T2DM, and to identify whether serum levels of metabolites at baseline could be used to predict therapeutic effects of exenatide on endothelial injury.
2. Methods 2.1. Study design and subjects Data from a randomized, multicenter, non-inferiority clinical trial were used in the present study. The design and detailed protocol of the trial have been published elsewhere.20 Eligibility criteria for inclusion in the present study were a diagnosis of 5
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T2DM, 2070 years of age, with inadequately controlled levels of glycated hemoglobin A1c (HbA1c) ranging from 7.010.0%, receiving monotherapy or combination therapy of metformin and insulin secretagogues. Exclusion criteria primarily included: (1) history of diabetic ketoacidosis or other serious medical condition, including diseases of the liver, kidneys, cardiovascular, nervous and
of
endocrine systems, and diseases that led to the study withdrawal or affected the judgment of the study results; (2) protocol violation, loss to follow-up, withdrawal
ro
due to adverse events, and refusal to continue participation in the study. The
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participants were treated with branded exenatide injection (Byetta®, Lilly Pharma
re
Fertigung & Distribution GmbH & Co. KG, Giessen, Germany) at an initial dose of 5
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μg twice daily for 4 weeks, followed by 10 μg twice daily for an additional 12 weeks.
na
If the increase in dose was not tolerated, 5 μg per injection was maintained. A total of 120 patients in the branded exenatide treatment arm of the trial were included, and 27
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patients (1 protocol violation, 2 loss to follow-up, 10 patients’ withdrawal, 9 patients’ refusal, and 5 missing metabolomic data) were excluded from the final analyses in the present study.
2.2. Blood collection and measurement Blood samples were drawn from each patient at baseline and after 16 weeks of treatment with exenatide and centrifuged at 4000 rpm for 10 minutes for serum collection, and then stored at 80°C. The laboratory tests performed for serum chemistry analysis included blood glucose and lipid profiles. Biochemical analysis of 6
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endothelial injury marker levels in serum was performed via enzyme-linked immunosorbent assay (ELISA) for soluble thrombomodulin (sTM) (Baolai Biotechnology, Yancheng, Jiangsu, China), von Willebrand factor (vWF) (Abcam, Cambridge, MA, USA), and soluble endothelial cell protein C receptor (sEPCR)
of
(Dogesce Biotechnology, Beijing, China).
2.3. Non-targeted metabolomic analysis
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A DIONEX Ultimate 3000 ultra-high-performance liquid chromatography (UPLC)
-p
system (Dionex Corporation, Sunnyvale, CA, USA) connected to an ESI-Quadrupole
re
time-of-flight tandem mass spectrometry (QTOF/MS) mass spectrometer (Thermo
lP
Fischer Scientific, Waltham, MA, USA) was used for metabolomic analysis. The
na
methodology is described in detail elsewhere.21 For metabolite extraction, 25 μL of serum was mixed with 175 μL of extraction buffer (25% acetonitrile in 40% methanol
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and 35% water). Samples were incubated on ice for 10 minutes before centrifugation, and then centrifuged at 10 000 rpm at 4°C for 10 minutes. Next, 5 μL of supernatant was transferred to a fresh Eppendorf tube and dried under vacuum. The dried sample was then reconstituted in 200 μL buffer containing 5% methanol, 1% acetonitrile, and 94% water. Subsequently, the required amount of supernatant aliquot was transferred to a glass vial for further analysis via liquid chromatography-mass spectrometry (LC-MS) technique. Finally, TraceFinder software (Thermo Fischer Scientific) was used to record molecular features.
7
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2.4. Metabolomic data processing The subjects were divided into two groups (responders and non-responders), depending on median changes in serum sTM, a well-known marker of endothelial injury.22 To identify metabolites with relative abundance that differed in the baseline serum
samples
of
responders
versus
non-responders,
orthogonal
partial
of
least-squares-discriminant analysis (OPLS-DA) was performed with SIMCA-P software (version 13.0, Umetrics, Umeå, Sweden). Quality control samples were
ro
inserted randomly in each analytical batch to ensure data quality. Variables identified
-p
with R software version 2.9.1 as variable importance in projection (VIP) values, the
re
Wilcoxon rank-sum test, logistic regression analysis, and area under the receiver
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operating characteristic curve (AUC) were considered as predictive biomarkers. A
na
VIP value > 1 in S-Plot was considered to indicate that the metabolite contributed to grouping. P values < 0.05 were considered to identify metabolites with differential
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expression between groups.
2.5 Statistical analysis
The Shapiro-Wilk test was used to test for normality. Data are presented as means ± standard deviation (SD) or median (interquartile range), as appropriate. For comparisons between groups, Student’s t-test and the Mann–Whitney U-test were applied to the data for normally distributed values and non-normally distributed values, respectively. Chi-square tests or Fisher’s exact tests were also used to compare differences among groups whenever appropriate. Pre-treatment versus post-treatment 8
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comparisons of clinical characteristics such as blood glucose profiles, lipid profiles, and endothelial injury markers were performed with SPSS version 20.0 for Windows (SPSS Japan Inc, Tokyo, Japan). P values < 0.05 were considered to indicate statistical significance.
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3. Results 3.1. Patient characteristics
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A total of 93 patients with T2DM including 55 males (59.1%) and 38 females (40.9%)
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were treated with branded exenatide (Byetta®). The median duration of diabetes was
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4.42 (2.29, 8.17) years. Various parameters that were determined at baseline and after
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treatment with exenatide for 16 weeks are listed in Table 1. At baseline, average body
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weight, body mass index (BMI), diastolic blood pressure (DBP), fasting plasma glucose (FPG), 2-hour postprandial plasma glucose (2hPG), and HbA1c were 79.3 ±
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16.2 kg, 28.1 ± 4.13 kg/m2, 77.8 ± 8.23 mmHg, 9.46 ± 1.88 mmol/L, 16.3 ± 3.60 mmol/L, and 8.23 ± 0.87%. After the treatment, these values were significantly lowered (all P < 0.05). The differences between baseline and post-treatment levels of systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) were not statistically significant (all P > 0.05).
3.2. Changes in serum levels of endothelial injury markers Serum levels of endothelial injury markers at baseline and after treatment with 9
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exenatide for 16 weeks are listed in Table 2. Compared with baseline, the levels of sTM, vWF and sEPCR were all reduced significantly after the treatment.
3.3. Clinical characteristics associated with the change in serum sTM level sTM is considered to be a marker of endothelial injury [22], and the decrease of sTM
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(ΔsTM) is a good indicator of endothelial injury amelioration. The proportion of subjects with sTM reduction was 62.4% (58/93), and 37.6% (35/93) did not show a
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decrease. In order to understand which category of patients benefited most from the
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exenatide treatment, we sought to determine which patients had the greater reduction
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in serum sTM level (defined as drug response). The subjects were divided into
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responders (ΔsTM ≤ 1.27 μg/L) and non-responders (ΔsTM > 1.27 μg/L) based on
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the median change values. As shown in Table 3, baseline body weight was significantly higher in responders than in non-responders (82.6 ± 15.3 kg versus 75.9
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± 16.5 kg, P = 0.045). None of the other clinical characteristics at baseline, including blood glucose profiles, lipid profiles, and diabetic vascular complications differed significantly between groups (all P>0.05).
3.4. Metabolic differences between responders and non-responders Plotting the metabolic patterns with an OPLS-DA model revealed significant differences between responders and non-responders at baseline in serum levels of various metabolites. The score plots revealed a significant separation between the two groups (Supplementary Figure. 1). The permutation test applied to the data revealed 10
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the validity of the OPLS-DA model. To identify the specific biomarkers associated with the differentiation of responders and non-responders, the S-plot analysis was performed based on the serum metabolite profiling data. Fifty-nine metabolites were identified, with a selection criterion was set as a VIP value > 1 in the model (Supplementary Table 1 and Supplementary Figure 2). When responders and
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non-responders were compared, a total of 15 metabolites were found to differ significantly between the two groups (P < 0.05) (Supplementary Table 2). With the
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criteria of VIP > 1 and P < 0.05, 13 metabolites were finally identified as potential
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predicting biomarkers for the effects of exenatide. Among the 13 metabolites, 6
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metabolites were significantly higher, whereas the other remaining 7 metabolites were
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lower in responders than in non-responders (Table 4). These findings suggested a
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significant difference in metabolic characteristics between the two groups at baseline.
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3.5. Multi-metabolite signature in responders The results obtained through the OPLS-DA were subjected to logistic regression analysis in order to design a multi-metabolite signature model for predicting the response of sTM to the exenatide treatment. The prediction model was applied to a training set (two thirds of subjects) and subsequently validated against a validation set (one third of subjects). Model quality was evaluated by calculating sensitivity, specificity, and AUC. To evaluate the validation set, four models were established. Model 4, which contained 4-hydroxyproline and 12-oxo-9(Z)-dodecenoic acid, was identified as the optimal model (after all variables were entered into the model, the 11
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backward stepwise elimination method was used). The specificity and sensitivity were 53.3% and 92.3%, respectively, while the AUC was 69.2% (P < 0.001). These results demonstrated that 4-hydroxyproline and 12-oxo-9(Z)-dodecenoic acid might be used as biomarkers for predicting the effect of the exenatide treatment on endothelial
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injury.
4. Discussion
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In the present study, we demonstrated that treatment with exenatide for 16 weeks
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resulted in reduced body weight and BMI as well as improved levels of FPG, 2hPG,
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and HbA1c in patients with T2DM whose HbA1c levels were inadequately controlled
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by either monotherapy or combined therapy with metformin and insulin secretagogues.
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These findings are in accordance with those reported by previous clinical studies.23-25 Moreover, we found that the exenatide treatment effectively reduced serum levels of
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endothelial injury markers including sTM, indicating that exenatide might ameliorate endothelial injury in patients with T2DM. Importantly, 4-hydroxyproline and 12-oxo-9(Z)-dodecenoic acid were identified as predictive biomarkers in evaluating the therapeutic effect of exenatide on the endothelial injury. Our group and several other groups have demonstrated that treatment with exenatide induces a significant improvement in endothelial function that can be detected by coronary flow velocity reserve and flow-mediated dilation in patients with T2DM.7,26 However, these endothelial function indicators are often difficult to detect in usual clinical practice, and whether exenatide has therapeutic effects on serum 12
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endothelial injury markers remains obscure. Our previous study showed that treatment with exenatide for 12 weeks remarkably reduced serum levels of soluble intercellular adhesion molecule-1 and soluble vascular cell adhesion molecule-1 in patients with T2DM.7 Because the sample size is small and only 18 patients were included in that study, we recruited 93 patients to evaluate the role of very easy and fast measured
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endothelial injury markers for further investigating the therapeutic effect of exenatide on endothelial injury in patients with T2DM.
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In the present study, we showed that treatment with exenatide significantly
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reduced the level of sTM, vWF, and sEPCR in serum. Since sTM is released from
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endothelial cells and is responsible for several disorders, including diabetic vascular
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complications, it has been considered to be an endothelial injury marker with potential
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powerful clinical utility.22 The therapeutic response to GLP-1RAs may vary greatly from one individual to another.27 Such a difference could originate from different
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cardiovascular risks including biological and non-biological features that are known or unknown to influence the treatment results.28 Therefore, there is a clinical need for biomarkers that can easily be measured in order to predict the therapeutic effect before initiating treatment. Therefore, we analyzed the metabolomic profiles at baseline to explore potential biomarkers for estimating the effects of exenatide in decreasing sTM level in patients with T2DM. We found that among 93 patients, 47 patients were responders, and 46 patients were non-responders, according to the median magnitude of the reduction in sTM. Moreover, there was no significant difference between responders and non-responders 13
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in blood glucose profiles or lipid profiles, indicating that neither blood glucose profiles nor lipid profiles may be important biomarkers for predicting the magnitude of a given patient’s reduction in sTM. The non-targeted metabolomic analysis of baseline serum samples was performed in the responder and non-responder groups. Our results identified a total of 13 metabolites. Logistic regression and AUC were
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performed with change in sTM as a dependent variable to find the specific biomarker combination that was most accurate. The results highlighted 4-hydroxyproline and
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12-oxo-9(Z)-dodecenoic acid as significant biomarkers. It has been reported that
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4-hydroxyproline is an abundant constituent of collagen and elastin.29 Importantly, in
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both humans and animals, the production of 4-hydroxyproline is enhanced in response
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to oxidative stress, as an adaptive mechanism for defense and survival.30 However, the
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origin and function of 12-oxo-9(Z)-dodecenoic acid is still unknown. In our study, higher levels of 4-hydroxyproline and lower 12-oxo-9(Z)-dodecenoic acid predicted
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an amelioration of endothelial injury. Therefore, testing the levels of these two biomarkers may be useful to guide treatment decision before the use of exenatide. Of note, the association between metabolomics and the therapeutic effects of GLP-1RAs on endothelial injury in patients with T2DM have not been reported previously. There are some limitations in our study. The sample size is relatively small. The generic exenatide is still in the premarket approval process. To avoid a possible bias introduced by the administration of two kinds of exenatide, only the data from the branded exenatide treatment arm were used in the present study. Although the present study indicated a therapeutic effect of exenatide in ameliorating endothelial injury, a 14
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larger number of patients would be required to confirm these findings in the future studies. Furthermore, the potentiality of the model should be further explored in prospective studies powered for clinical end points. We used only LC-MS in this study. The combined approach of using LC-MS and gas chromatography-mass spectrometry would be more sensitive, and should be studied in the future. At last, our
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results were limited to patients suffering with T2DM whose HbA1c levels were inadequately controlled on either a monotherapy or combination therapy with
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metformin and insulin secretagogue. The results presented above should be
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investigated in other T2DM populations, such as drug-naive patients with T2DM.
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5. Conclusions
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The present study demonstrated that treatment with exenatide for 16 weeks ameliorated endothelial injury in patients with T2DM whose HbA1c level was
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inadequately controlled by either monotherapy or combination therapy with metformin and insulin secretagogue. Importantly, the specific metabolomic combination (higher 4-hydroxyproline and lower 12-oxo-9(Z)-dodecenoic acid) could effectively predict an amelioration of endothelial injury induced by exenatide.
Declaration of competing interest None to disclose.
Acknowledgements The authors appreciate for all the investigators from Department of Endocrinology, 15
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Beijing Hospital; Department of Endocrinology, PLA Rocket Force Characteristic Medical Center; Department of Endocrinology, Beijing Tiantan Hospital, Capital Medical University; Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University; Department of Endocrinology, The First Hospital of Shanxi Medical University; and Department of Endocrinology, People’s Hospital of Hainan
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Province, for their help with the enrollment of patients and data collection.
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Funding/support
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This research was supported by the National Key Research and Development
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Program of China (2018YFC1313900), the National Natural Science Foundation of
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China (81830022, 81970671), and the Capital’s Funds for Health Improvement and Research (2020-3-40914). The funders of this study played no role in either its design
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or conduct; collection, management, analysis or interpretation of data; preparation,
publication.
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review or approval of the manuscript; or the decision to submit the manuscript for
Author contributions
T.H. and R.W. made substantial contributions to conception, study design, and reviewing of the manuscript. J.Y. and Y.L. performed the research experiments and statistical analyses and prepared the manuscript. T.W., K.W., K.Y., and W.X. helped perform the research experiments and analyze data.
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References 1. Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9 edition. Diabetes Res Clin Pract 2019; 157:107843. https://doi.org/10.1016/j.diabres.2019.107843 2. Fu AZ, Qiu Y, Radican L, et al. Health care and productivity costs associated with diabetic patients with macrovascular comorbid conditions. Diabetes Care 2009; 32:2187-92. https://doi.org/10.2337/dc09-1128 3. Umanath K, Lewis JB. Update on diabetic nephropathy: core curriculum 2018. Am J Kidney Dis 2018; 71:884-95. https://doi.org/10.1053/j.ajkd.2017.10.026 4. Wong TY, Cheung CMG, Larsen M, et al. Diabetic retinopathy. Nat Rev Dis Primers 2016; 17:16012. https://doi.org/10.1038/nrdp.2016.12 5. Bommer C, Sagalova V, Heesemann E, et al. Global economic burden of diabetes in adults: projections from 2015 to 2030. Diabetes Care 2018; 41:963-70. https://doi.org/10.2337/dc17-1962 6. Müller TD, Finan B, Bloom SR, et al. Glucagon-like peptide 1 (GLP-1). Mol Metab 2019; 30:72-130. https://doi.org/10.1016/j.molmet.2019.09.010 7. Wei R, Ma S, Wang C, et al. Exenatide exerts direct protective effects on endothelial cells through the AMPK/Akt/eNOS pathway in a GLP-1 receptor-dependent manner. Am J Physiol Endocrinol Metab 2016; 310:E947-57. https://doi.org/10.1152/ajpendo.00400.2015 8. Pfeffer MA, Claggett B, Diaz R, et al. Lixisenatide in patients with type 2 diabetes and acute coronary syndrome. N Engl J Med 2015; 373:2247-57. https://doi.org/10.1056/NEJMoa1509225 9. Marso SP, Daniels GH, Brown-Frandsen K, et al. Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med 2016; 375:311-22. https://doi.org/10.1056/NEJMoa1603827 10. Marso SP, Bain SC, Consoli A, et al. Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med 2016; 375:1834-44. https://doi.org/10.1056/NEJMoa1607141 11. Hernandez AF, Green JB, Janmohamed S, et al. Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial. Lancet 2018; 392:1519-29. https://doi.org/10.1016/S0140-6736(18)32261-X 12. Mannucci E, Dicembrini I, Nreu B, et al. Glucagon-like peptide-1 receptor agonists and cardiovascular outcomes in patients with and without prior cardiovascular events: An updated meta-analysis and subgroup analysis of randomized controlled trials. Diabetes Obes Metab. 2020; 22:203-11. https://doi.org/10.1111/dom.13888 13. Avgerinos I, Karagiannis T, Malandris K, et al. Glucagon-like peptide-1 receptor agonists and microvascular outcomes in type 2 diabetes: A systematic review and meta-analysis. Diabetes Obes Metab 2019; 21:188-93. https://doi.org/10.1111/dom.13484 14. Kristensen SL, Rørth R, Jhund PS, et al. Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials. Lancet Diabetes Endocrinol 2019; 17
Journal Pre-proof
17.
18. 19. 20.
25.
26.
27.
28.
29.
30.
lP
24.
na
23.
Jo ur
22.
re
-p
21.
of
16.
ro
15.
7:776-85. https://doi.org/10.1016/S2213-8587(19)30249-9 Hadi HA, Suwaidi JA. Endothelial dysfunction in diabetes mellitus. Vasc Health Risk Manag 2007; 3:853-76. Rizzo M, Nikolic D, Patti AM, et al. GLP-1 receptor agonists and reduction of cardiometabolic risk: Potential underlying mechanisms. Biochim Biophys Acta Mol Basis Dis 2018; 1864, 2814-21. https://doi.org/10.1016/j.bbadis.2018.05.012 Heo CU, Choi CI. Current progress in pharmacogenetics of second-line antidiabetic medications: towards precision medicine for type 2 diabetes. J Clin Med 2019; 8:393. https://doi.org/10.3390/jcm8030393 Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 2016; 15:473-84. https://doi.org/10.1038/nrd.2016.32 Li B, He X, Jia W, et al. Novel applications of metabolomics in personalized medicine: a mini-review. Molecules 2017; 22:1173. https://doi.org/10.3390/molecules22071173 Yang J, Xiao W, Guo L, et al. Efficacy and safety of generic exenatide injection in Chinese patients with type 2 diabetes: a multicenter, randomized, controlled, non-inferiority trial. Acta Diabetol 2020. https://doi.org/10.1007/s00592-020-01510-y Rauschert S, Uhl O, Koletzko B, et al. Lipidomics reveals associations of phospholipids with obesity and insulin resistance in young adults. J Clin Endocrinol Metab 2016; 101:871-79. https://doi.org/10.1210/jc.2015-3525 Nawroth PP, Häring HU. Thrombomodulin and coronary heart disease. Lancet 1999; 353:1722-23. https://doi.org/10.1016/S0140-6736(99)90039-9 Buse JB, Henry RR, Han J, et al. Effects of exenatide (exendin-4) on glycemic control over 30 weeks in sulfonylurea-treated patients with type 2 diabetes. Diabetes Care 2004; 27:2628-35. https://doi.org/10.2337/diacare.27.11.2628 DeFronzo RA, Ratner RE, Han J, et al. Effects of exenatide (exendin-4) on glycemic control and weight over 30 weeks in metformin-treated patients with type 2 diabetes. Diabetes Care 2005; 28:1092-100. https://doi.org/10.2337/diacare.28.5.1092 Fehse F, Trautmann M, Holst JJ, et al. Exenatide augments first- and second-phase insulin secretion in response to intravenous glucose in subjects with type 2 diabetes. J Clin Endocrinol Metab 2005; 90:5991-7. https://doi.org/10.1210/jc.2005-1093 Hopkins ND, Cuthbertson DJ, Kemp GJ, et al. Effects of 6 months glucagon-like peptide-1 receptor agonist treatment on endothelial function in type 2 diabetes mellitus patients. Diabetes Obes Metab 2013; 15:770-3. https://doi.org/10.1111/dom.12089 Ludwig L, Darmon P, Guerci B. Computing and interpreting the number needed to treat for cardiovascular outcomes trials: perspective on GLP-1 RA and SGLT-2i therapies. Cardiovasc Diabetol 2020; 19:65. https://doi.org/10.1186/s12933-020- 01034-3. Kahkoska AR, Geybels MS, Klein KR, et al. Validation of distinct type 2 diabetes clusters and their association with diabetes complications in the DEVOTE, LEADER and SUSTAIN-6 cardiovascular outcomes trials. Diabetes Obes Metab 2020; 22:1537-47. https://doi.org/10.1111/dom.14063. Bochicchio B, Laurita A, Heinz A, et al. Investigating the role of (2S,4R)-4-hydro -xyproline in elastin model peptides. Biomacromolecules. 2013; 14:4278‐ 88. doi:10.1021/bm4011529 https://doi.org/10.1021/bm4011529 Wu G. Important roles of dietary taurine, creatine, carnosine, anserine and 18
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Table 1. Clinical characteristics before and after 16 weeks of treatment with exenatide in patients with T2DM Parameters
Baseline
After treatment
Age, years
t values
P values
49.5 ± 9.52 55/38
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4.42 (2.29, 8.17)
Body weight, kg
79.3 ± 16.2
77.3 ± 15.7
< 0.001
BMI, kg/m2
28.1 ± 4.13
27.4 ± 3.94
4.988
< 0.001
SBP, mmHg
124.6 ± 11.0
0.103
0.918
DBP, mmHg
77.8 ± 8.23
76.0 ± 7.29
2.071
0.041
FPG, mmol/L
9.46 ± 1.88
8.04 ± 2.02
6.060
< 0.001
2hPG, mmol/L
16.3 ± 3.60
13.4 ± 4.00
6.190
< 0.001
8.23 ± 0.87
7.08 ± 1.00
10.289
< 0.001
LDL-C, mmol/L
3.07 ± 0.77
3.06 ± 0.78
0.146
0.885
HDL-C, mmol/L
1.27 ± 0.35
1.25 ± 0.34
0.705
0.483
TG, mmol/L
1.94 ± 1.05
1.89 ± 1.04
0.475
0.636
124.4 ± 12.3
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HbA1c, %
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4.665
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Diabetes duration, years
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Gender (male/female)
Note: Data are presented as means ± SD, number or median (interquartile range), as appropriate. Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; 2hPG, 2-hour postprandial plasma glucose; HbA1c, glycated hemoglobin A1c; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride.
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Table 2. Serum levels of endothelial injury markers before and after 16 weeks of exenatide treatment in patients with T2DM Note: Data are presented as median (interquartile range).
Baseline
After treatment
Z values
sTM, μg/L
60.5 (25.9, 86.6)
53.0 (27.3, 83.7)
1.971
0.049
vWF, mIU/mL
1176.6 (988.4, 1553.7)
939.7 (685.2, 1395.7)
4.278
< 0.001
sEPCR, ng/mL
100.1 (47.4, 143.5)
76.9 (28.2, 125.7)
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0.006
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2.772
P values
Abbreviations: sTM, soluble thrombomodulin; vWF, von Willebrand factor; sEPCR, soluble
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endothelial cell protein C receptor.
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Table 3. Clinical characteristics of patients stratified by the median magnitude of the reduction in sTM Responders (n=47)
Parameters Baseline
Non-responders (n=46)
After treatment
Baseline
Age, years
48.5 ±10.1
50.5 ± 8.91
Male, n (%)
29 (62)
26 (57)
Diabetes duration, years
4.58 (2.25, 8.25)
Body weight, kg 2
82.6 ± 15.3
80.1 ± 14.5
75.9 ±16.5
b
27.4 ± 3.95
BMI, kg/m
28.8 ± 4.23
27.8 ± 3.89
SBP, mmHg
126.2 ±10.2
124.2 ± 12.0
DBP, mmHg
78.7 ± 7.32
76.7 ± 7.73
FPG, mmol/L 2hPG, mmol/L
9.58 ± 1.98 16.4 ± 3.36
r P
76.7 ± 9.04
7.86 ± 1.68
b
13.1 ± 3.36
b
rn
b
0.311
0.611
0.676
0.081
0.936
74.5 ± 16.4
2.030
0.045
26.9 ± 3.98
1.586
0.116
124.7 ± 12.8
1.468
0.146
75.2 ± 6.82
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1.177
0.242
8.23 ± 2.32
b
0.628
0.532
13.6 ± 4.59
b
0.284
0.777
8.26 ± 0.90
7.11 ± 0.97
b
0.323
0.747
9.33 ± 1.77
l a
1.019
o r p
e
122.9 ± 11.6
P values a
After treatment
4.17 (2.33, 8.14) b
t or Z or χ2 values
16.2 ± 3.86
HbA1c, %
8.20 ± 0.85
7.05 ± 1.01
LDL-C, mmol/L
3.09 ± 0.73
3.03 ± 0.76
3.12 ±0.97
3.08 ± 0.81
0.157
0.876
HDL-C, mmol/L
1.23 ± 0.31
1.21 ± 0.32
1.32 ± 0.40
1.29 ± 0.35
1.252
0.214
TG, mmol/L
1.97 ± 1.07
1.93 ± 1.11
1.93 ± 1.05
1.85 ± 0.98
0.195
0.846
1 (2.17)
1 (2.17)
0.000
1.000
26.4 (18.7, 81.5)
30.9 (25.6, 82.5)
6.071
<0.001
1165.0 (986.5, 1631.2)
1035.6 (802.8, 1521.3)
0.450
0.653
57.3 (22.9, 132.0) 1.990
0.047
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J
Diabetic vascular complications, n (%) 1 (2.13)
1 (2.13) b
sTM, μg/L
86.4 (50.2, 89.1)
82.3 (27.6, 84.1)
vWF, mIU/mL
1200.5 (988.6, 1539.6)
859.0 (641.4, 1311.4) b
sEPCR, ng/mL
118.1 (62.5, 152.7) 85.2 (41.45, 124.3) b 70.7 (40.9, 126.1)
Note: Data are presented as means ± SD, number (%) or median (interquartile range), as appropriate. The group of responders represents patients with ΔsTM ≤ 1.27 μg/L, and the group of non-responders represents patients with ΔsTM > 1.27 μg/L. a P values represent the differences between two baseline values. b P <0.05 versus baseline in the same group. 21
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Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; 2hPG, 2-hour postprandial plasma glucose; HbA1c, glycated hemoglobin A1c; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; sTM, soluble thrombomodulin; vWF, von Willebrand factor; sEPCR, soluble endothelial cell protein C receptor.
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Metabolite
P values
VIP values
1
Pidolic acid
0.003699
1.789875
2
4-Hydroxyproline
0.001416
2.334635
3
Ethyl malonic acid
0.049943
1.586994
4
5-Amino-2-oxopentanoic acid
0.001416
2.334635
5
Stearidonic acid
6
SM(d18:0/22:3(10Z,13Z,16Z))
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Higher in responders
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0.008768
2.117529 1.794133
0.019766
1.646536
0.024935
1.193592
-p
0.026822
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Lower in responders L-Lysine
2
L-Serine
3
12-Oxo-9(Z)-dodecenoic acid
0.028244
1.045632
4
LysoPC(18:1(11Z))
0.049574
1.012197
PC(o-18:1(9Z)/18:2(9Z,12Z))
0.039718
1.779808
Abscisic alcohol
0.029104
1.301562
Dehydrovomifoliol
0.00241
2.061183
6 7
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5
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1
Note: VIP values were obtained from OPLS-DA with a threshold of 1.0. P values were calculated from the Wilcoxon rank-sum test. Abbreviation: VIP, variable importance in projection.
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AUC (95% CI)
P values
Sensitivity (%)
Specificity (%)
Model 1
0.600 (0.373-0.827)
0.131
46.7
100
Model 2
0.652 (0.495-0.845)
< 0.001
66.7
78.9
Model 3
0.626 (0.418-0.834)
< 0.001
71.4
66.7
Model 4
0.692 (0.480-0.904)
< 0.001
53.3
92.3
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Note: Model 1 contains all metabolites in Table 4. Model 2 contains pidolic acid, 4-hydroxyproline, stearidonic acid, and dehydrovomifoliol, all with P < 0.01 and VIP > 1.5. Model 3 contains pidolic acid, L-lysine, 4-hydroxyproline, ethyl malonic acid, stearidonic acid, PC(o-18:1(9Z)/18:2(9Z,12Z)), SM(d18:0/22:3(10Z,13Z,16Z)), and dehydrovomifoliol, all with P < 0.01 and VIP > 1.5. Model 4 contains 4-hydroxyproline and 12-Oxo-9(Z)-dodecenoic acid, both with P < 0.01 and VIP > 1.5.
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Figure 1. AUC analysis of metabolites for discriminating responders from non-responders. Note: Model 1 contains all metabolites in Table 4. Model 2 contains pidolic acid, 4-hydroxyproline, stearidonic acid, and dehydrovomifoliol. Model 3 contains pidolic acid, L-lysine, 4-hydroxyproline, ethyl malonic acid, stearidonic acid, PC(o-18:1(9Z)/18:2(9Z,12Z)), SM(d18:0/22:3(10Z,13Z,16Z)), and dehydrovomifoliol. Model 4 contains 4-hydroxyproline and 12-Oxo-9(Z)-dodecenoic acid. The group of responders represents patients with ΔsTM ≤ 1.27 μg/L, and the group of non-responders represents patients with ΔsTM > 1.27 μg/L.
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Journal Pre-proof Author Statement
Jin Yang: Methodology, Investigation, Writing - original draft. Yunyi le: Methodology, Investigation, Writing - original draft. TianJiao Wei: Investigation, Data analysis. Kangli Wang: Investigation, Data analysis. Kun Yang: Investigation, Data analysis. Wenhua Xiao: Investigation, Data analysis. Tianpei Hong: Conceptualization, Supervision, Writing- Reviewing and Editing. Rui Wei:
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Conceptualization, Data curation, Writing- Reviewing and Editing.
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Journal Pre-proof Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
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Journal Pre-proof Highlights:
Treatment with exenatide for 16 weeks ameliorated endothelial injury in patients with T2DM
Endothelial protection benefit from exenatide treatment was effectively predicted by some specific metabolomic combination Higher 4-hydroxyproline and lower 12-oxo-9(Z)-dodecenoic acid was identified
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as the most optimal predictive biomarkers
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