DIA proteomics reveals potential biomarkers for T2DM in acute myocardial infarction patients

doi:10.21203/rs.3.rs-3603331/v1

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DIA proteomics reveals potential biomarkers for T2DM in acute myocardial infarction patients

https://doi.org/10.21203/rs.3.rs-3603331/v1

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Background： Early intervention using specific biomarkers to predict type 2 diabetes mellitus (T2DM) would be effective in reducing the global prevalence of diabetes, especially in high-risk patients with coronary heart disease who develop acute myocardial infarction (AMI). We compared the proteomic profile of AMI patients with T2DM (AD) and without T2DM (A) to obtain potential predictors and treatment factors that can be used for T2DM in AMI patients.

Methods： Twelve AMI patients and twelve AMI patients with T2DM had their serum proteomic profiles analyzed using data-independent acquisition mass spectrometry (DIA-MS). A bioinformatics analysis of the differentially expressed proteins was conducted after proteomics analysis. Receiver operating characteristic (ROC) curves and ELISA were used to evaluate the diagnostic and predictive ability of the candidate proteins.

Results： Patients belonging to two groups had similar baseline clinical data, and there were no statistically significant differences between them. Through serum proteomic profiles, a total of 416 non-redundant proteins were quantified in 2 groups, of which 14 differentially expressed proteins (DEPs) were obtained. We further focused on 6 core proteins through enrichment and protein-protein interactional (PPI) network, which included lipopolysaccharide-binding protein (LBP), apolipoprotein E (ApoE), serpin family F member 2 (SERPINF2), serpin family A member 10 (SERPINA10), insulin-like growth factor binding protein 7 (IGFBP7), hypoxia up-regulated 1 (HYOU1), these proteins involved in biological pathways related to immunity, inflammation, signaling, endocrine regulation, cholesterol metabolism, oxidative phosphorylation, complement and coagulation cascades. Then text mining complemented the association of these proteins with AMI or T2DM. Eventually, the mean area under the ROC curve of the candidate proteins was 0.77±0.03. Finally, the ELISA results validated the proteomics results.

Conclusions: Serum levels of LBP, ApoE, SERPINF2, SERPINA10, IGFBP7, and HYOU1 appear to be strongly related to T2DM episodes in AMI patients and could be used as potential biomarkers.

T2DM

AMI

biomarkers

diagnosis and prediction

proteomics

Acute myocardial infarction (AMI), which leads to ischemia and hypoxia in cardiomyocytes, fibrosis and loss of cardiac function, and ultimately heart failure, is a major contributor to death and disability globally, and the World Health Organization (WHO) has published that AMI is the cause of death in 4/5 of patients with cardiovascular diseases[1]. The prevalence of diabetes mellitus as an independent risk factor for adverse events and serious complications of AMI has been increasing annually in AMI patients over the past 20 years, leading to higher mortality rates, of which more than 90 percent are type 2 diabetes mellitus (T2DM)[2, 3].

Therefore, exploring biomarkers of T2DM in AMI patients could provide insights for early diagnosis and precision medicine. Elevated hemoglobin A1c and fasting plasma glucose have limitations as key indicators for early diagnosis of diabetes. For example, the sensitivity of HbA1c in alcoholism or anemic individuals is controversial, and FPG is also affected by diet and fasting time[4, 5]. In addition, impaired glucose tolerance or impaired fasting glucose has a long cycle and patients need multiple blood tests[6]. Previous studies have reported that hyperglycemia leads to a prothrombotic state, which exacerbates inflammation and oxidative stress, thereby adversely affecting endothelial homeostasis maintenance and microcirculatory function, which contributes to the poor prognosis of AMI[7, 8]. Nevertheless, the global protein molecular changes that underlie the pathogenesis of T2DM in AMI patients remain unclear.

Proteomics can be used to mine a genome for information on the expression, structure, function, and modification of disease-related proteins. It has been deeply applied to biomarker discovery and diagnosis in the fields of tumors, cardiovascular diseases, and immunological diseases[9-11].

In our study, we performed 4D label-free proteomics experiments to compare the proteomic profile of AMI patients with T2DM and without T2DM to obtain potential predictors and treatment factors that can be used for T2DM in AMI patients. Figure 1 presents the overall workflow of this study.

2.1 Trial design

The“Qi-blood related theories basing on the combination of syndrome and symptom trial” (QBTB-CBB trial) is a published prospective, multicentre, observational trial comprising AMI patients with Qi stagnation and blood stasis (QSBS) or qi deficiency and blood stasis (QDBS) in China (Registration number: ChiCTR1900027899). In the present analysis, we specifically focused on the identification of the protein biomarker of AMI patients with T2DM compared to non-T2DM.

2.2 Patient Recruitment

QBTB-CBB trial subjects were identified from the three hospitals in China, including Beijing Anzhen Hospital, Xiyuan Hospital, and Guangdong Province Traditional Chinese Medical Hospital. A total of 250 patients between the ages of 35 and 75 were diagnosed with acute coronary syndrome and underwent coronary angiography from December 2019 to December 2020. The exclusion criteria were (1) Renal insufficiency, male serum creatinine >2.5 mg/dl (>220umo/l) female >2.0 mg/dl (> 175 umo/l); (2) Patients with obvious liver disease or ALT and AST were three times higher than normal; (3) Severe cardiac dysfunction (EF<35%), patients with uncontrolled hypertension, combined with acute brain vascular disease or severe valvular heart disease; (4) Random blood glucose ≥ 13.7mmol/L or glycosylated hemoglobin ≥9.5%; (5) Serious mental illness; (6) A malignant tumor or a patient has a life expectancy of less than 3 years; (7) Serious hematopoietic disease patients; (8) Refusal to sign the informed consent, or to estimate the poor compliance and follow-up possibility; (9) Pregnancy or preparation of pregnant women, lactating women; (10) Have participated in or are participating in other clinical trials in the past three months.

We included patients with AMI, and serum samples were collected. All of the patients signed an informed consent form. Finally, 24 patients were included in this analysis, including 12 AMI patients, and 12 AMI patients with T2DM. This study was approved by the ethics committee of Beijing Anzhen Hospital, Capital Medical University (2023165X) and has been carried out in strict adherence to the Declaration of Helsinki.

2.3 Data collections and definitions

Patients’ demographics, medical history, laboratory test results, and angiographic evaluation results were collected. Peripheral venous blood samples were collected early in the morning after an overnight fast on admission. The Single Point Insulin Sensitivity Estimator (SPISE) and TGs/high-density lipoprotein cholesterol (HDL-C) ratio have been proven to be effective surrogate indexes for insulin sensitivity, a significant positive correlation has been reported between SPISE results and glucose clearance in weight-adjusted hyperinsulinemic euglycemic glucose clamp[12], so they are also included in the baseline clinical characteristics. The novel formula for SPISE was computed as follows: SPISE = 600 × HDL-C0.185/(TGs0.2 × body mass index (BMI)1.338) [13], with fasting HDL-C (mg/dL), fasting TGs (mg/dL), and BMI (kg/m2). The TGs/HDL-C ratio was calculated as TGs (mg/dL)/HDL-C(mg/dL).

Criteria for T2DM include: (1) Previously diagnosed T2DM under treatment of antidiabetic medication; (2) The typical symptoms of DM with a FPG ≥ 7.0 mmol/L, and/or random blood glucose (RBG)≥ 11.1 mmol/L, and/or 2-h plasma glucose level after oral glucose tolerance test (OGTT) ≥ 11.1 mmol/L[14]. Hypertension (HT) is defined as the blood pressure is greater than or equal to 140/90 mmHg three times on different days, or the antihypertensive drugs are used. The criteria for dyslipidemia is that fasting total cholesterol (TC) > 200 mg/dL, low-density lipoprotein cholesterol (LDL-C) > 130 mg/dL, TGs > 150 mg/dL, HDL-C < 40 mg/dL, or previous use of lipid-lowering drugs. AMI, including non-ST-segment elevation myocardial infarction (NSTEMI) and ST-elevation myocardial infarction (STEMI), was defined as new ST-segment changes and elevation of myocardial necrosis markers to at least twice the upper limit of the normal range. All of the patients underwent coronary angiography during this hospitalization.

2.4 Sample preparation

Samples were prepared using the filter-aided sample preparation (FASP) method, where 50 ul of serum from each case were placed in a 10 kD ultrafiltration filter (Millipore, USA), washed with urea (8M, 400 µL) and concentrated to 50 ul. Protein reduction was performed using 1,4-dithiothreitol (DTT; Sigma-Aldrich, cat. no. 43815) at a final concentration of 20 mmol/L, and alkylation with iodoacetamide (IAA, Sigma-Aldrich, cat. no. I1149) at a final concentration of 40 mmol/L (dark setting). The samples were subsequently washed twice with urea (8M, 400 µL) and NH₄CO₃ (50 mM, 400 µL), respectively. After neutralization of IAA, 100 µL of trypsin solution (containing 20 µg of lyophilized sequencing grade modified trypsin dissolved in 50 mM NH4HCO3; Promega, USA) was added, enzymatic cleavage was terminated by 1 µL of 10% formic acid (Sigma-Aldrich, USA). Centrifugation was performed to collect the lower layer for mass spectrometry analysis, and all centrifugation steps in the FASP protocol were performed at 14,000g for 20 min.

2.5 Identification of proteins by Orbitrap

LC/MS analysis utilized an Eclipse Tribrid Orbitrap (Thermo Fisher Scientific) coupled with an Ultimate 3000 nano-ultra performance liquid chromatography (UPLC) system (Thermo Fisher Scientific). The pre-column and column were homemade (pre-column: 100 µm i.d. filled with 2 cm length of 3 µm C18 packing; column: 150 µm i.d. filled with 25 cm length of 1.9 µm C18 packing). Solvent A was 0.1% (v/v) formic acid and Solvent B was aqueous 80% (v/v) acetonitrile in 0.1% (v/v) formic acid. The total duration of the assay was 90 min, and the liquid chromatography gradient grade was increased from 8% B-phase to 50% B-phase over 60 min at an elution flow rate of 300 nl/min. The sample volume was 2 µL.

The mass spectrometry measurements were conducted in data-independent acquisition (DIA) mode. The spray voltage was 2.2 kV and the temperature of the ion transfer tube was 320°C. The primary mass spectrum scanning range was set at 350-1150 m/z and the resolution was 60,000, and the secondary spectrum was scanned at m/z 200-1400 with a resolution of 30,000, the collision energy of HCD was 32% with 54-ms maximum injection time. A total of 30 acquisition windows of varying widths were set up for the secondary spectrum. We have uploaded the proteomic raw data into the iProX[15], the accession number is PXD046147.

2.6 Quantitative data pre-processing

DIA data were analyzed in Spectronaut software version 13.9 (Biognosys) in direct DIA mode. Homo sapiens reference protein sequences were retrieved from UniProt (Proteome ID: UP000005640). The search parameters were: Peptide identification was performed with an initial precursor mass deviation of up to 10 ppm and a fragment mass deviation of 0.02 Da, a maximum of two missed cleavage sites was allowed, the FDR was estimated using the mProphet scoring algorithm with 1% FDR control at the peptide-spectrum match, peptide, and protein levels. The fixed modification was cysteine alkylation and the variable modification was methionine oxidation and N-terminal acetylation. Afterward, missing values were filled with means (missing threshold 0.8), and imlog2 transformations to correct the skewed distribution, normalization, and p-value calculation.

2.7 Bioinformatics analysis and data visualization

Differentially expressed proteins (DEPs; p < 0.05 and fold-change ≥1.2 or p < 0.05 and fold-change <0.83) were identified for further analysis, details of DEPs were listed in Supplementary Table S2. Bioinformatics analysis of DEPs was used to search for relevant biological pathways, and protein interaction information, construct relevance networks, and screen biomarkers with high diagnostic potential. GO term enrichment analysis was performed using the Gene ontology (http://geneontology.org/) and Uniprot (https://www.uniprot.org/) database, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was done using DAVID (https://david.ncifcrf.gov/summary.jsp) and KOBAS.3.0 (http://kobas.cbi.pku.edu.cn/anno_iden.php) website. we also used Reactome pathway enrichment analysis(https://reactome.org/). Gene-set Enrichment Analysis (GSEA) was performed by OmicShare GSEA tool (https://www.omicsshare.com/tools/). Furthermore, the protein-protein interactional (PPI) network was constructed by STRING (version 12.0, https://string-db.org/) and Cytoscape 3.6.1. GraphPad Prism (version 8.0.2) was used to plot ROC curves, an AUC greater than 0.70 was considered an acceptable level of discrimination. Subcellular localization was predicted using Wolfpsort, a subcellular localization prediction software.

2.8 Validation of protein concentration by ELISA

Levels of ApoE, LBP, HYOU1, LUM, SERPINA10, SERPINF2 were determined using Human LBP ELISA Kit (11748); Human ApoE ELISA Kit (11725); Human SERPINF2 / A2AP ELISA Kit (19751); Human SERPINA10 ELISA Kit (17839); Human IGFBP7 ELISA Kit (11650); Human HYOU1 ELISA Kit (17167); (Huangshi Aens Biological Technology, Huangshi, China). All protocols were conducted according to manufacturer instructions.

2.9 Statistical analysis

Continuous variables were presented as mean ± standard deviation. Comparisons between the two groups were analyzed by Student’s t-test or Mann–Whitney U-test. A two‐sided probability value of p < 0.05 was considered statistically significant. P-values were less than or equal to 0.05.

3.1 Baseline characteristics of patients

The baseline characteristics of the study population are shown in Table 1. There are no statistical differences among age, male, BMI, medical history, Gensini score, medication used before admission, laboratory value, and insulin sensitivity surrogate index. The age, BNP, and SPISE index levels had a higher tendency in group AD than those in group A (p > 0.05).

Table 1 Baseline clinical characteristics

	AMI with T2DM (n=12)	AMI without T2DM (n=12)	p value
Age, years	61.42±4.29	58.75±8.71	0.352
Male gender	8(40.0%)	12(60.0%)	0.093
BMI, kg/m2	27.79±8.17	32.12±5.98	0.152
Medical history
Current/ex-Smoker, n (%)	4(33.3%)	8(66.7%)	0.220
Hypertension, n (%)	9(69.2%)	4(30.8%)	1.000
Dyslipidemia, n (%)	1(33.3%)	2(66.7%)	1.000
Gensini Score	34.25±19.35	38.75±9.97	0.482
Medication used before admission
Antiplatelet agent, n (%)	0(0.0%)	1(100%)	1.000
ACEI/ARB, n (%)	2(66.7%)	1(33.3%)	1.000
Beta-blocker, n (%)	2(66.7%)	1(33.3%)	1.000
Statins, n (%)	0	0
CCB, n (%)	4(80.0%)	1(20.0%)	0.317
Diuretic, n (%)	1(100.0%)	0(0.0%)	1.000
Laboratory value
BNP, pg/mL	959.70±762.12	516.51±603.63	0.129
Hemoglobin, g/L	142.17±20.28	140.17±11.57	0.769
TC, mg/dL	173.94±43.99	167.15±43.69	0.708
TGs, mg/dL	125.15±41.88	128.54±63.84	0.879
LDL-C, mg/dL	109.33±40.39	106.11±34.89	0.837
HDL-C, mg/dL	38.64±10.12	38.61±10.52	0.994
TNF-α, pg/mL	5.76±1.19	5.78±1.10	0.970
ALT, U/L	20.06±7.97	26.66±12.66	0.141
AST, U/L	22.72±11.57	33.70±20.61	0.122
Creatinine, umol/L	67.50±16.30	78.15±16.08	0.121
BUN, mmol/L	13.43±3.71	10.37±4.85	0.097
Insulin sensitivity surrogate index
TGs/HDL-C ratio	3.57±1.72	3.70±2.23	0.877
SPISE index	6.01±2.44	4.69±1.39	0.116

Data are presented as mean ± SD, median (IQR), or number (%)

BMI, body mass index; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker; CCB: calcium channel blockers; BNP, B‐type natriuretic peptide; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; TGs, triglycerides; HDL-C, high-density lipoprotein cholesterol; TNF-α: tumor necrosis factor; ALT: glutamic-pyruvic transaminase ; AST: glutamic oxalacetic transaminase; BUN: blood urea nitrogen; SPISE index, Single Point Insulin Sensitivity Estimator.

3.2 Proteomics

3.2.1 Data quality control of quantitative proteomics

To further investigate the biomarkers associated with T2DM in AMI patients, the overall protein levels of the two groups were assessed using DIA. A total of 416 non-redundant proteins were quantified, but there were missing values in the raw data, Figure 2A shows the completeness of the data, the number of proteins in group A and AD were (385.2±9.79) and (379.4±10.85) respectively, and the data completeness was higher in group A (p＞0.05). T2DM may elevate the complexity of the protein composition of serum from AMI patients. Raw quantitative data for proteins are provided in Supplementary Table S1.

The distribution of the coefficient of variation (CV) shows the repeatability of the quantitative data. The data was log-transformed to fit a normal distribution before calculation. Figure 2B provides a detailed analysis of the CV distribution in the 2 groups, with median CV values of 0.053 and 0.060 and mean CV values of 0.083 and 0.080 for groups A and AD, respectively. The data of both groups have a majority distribution in the lower CV interval that showed excellent intragroup agreement.

3.2.2 Identification of differentially regulated proteins

In total, 14 differentially expressed proteins (DEPs) were screened according to the preset parameters, of which 6 proteins were up-regulated and 8 were down-regulated (Figure 2C). Volcano plots were further drawn based on significance levels and fold change values, red dots represent up-regulated proteins, blue dots represent down-regulated proteins, and grey dots represent non-differentially expressed genes. Hierarchical clustering analysis of expression patterns showed that 14 DEPs were induced or inhibited by T2DM stimulation in AMI patients, which could be significantly stratified in the 2 groups (Figure 2D). Furthermore, subcellular localization of these DEPs revealed their distribution in the extracellular (64.3%), plasma membrane (21.4%), mitochondrion (7.1%), and endoplasmic reticulum (7.1%) (Figure 2E).

3.2.3 Functional classification of differentially regulated proteins

According to the Go ontology (GO) enrichment, 14 DEPs covered a wide range of biological processes (BP), molecular functions (MF), and cellular components (CC). BP included regulation of body fluid levels, with SERPINF2, ApoE, and SERPINA10 participating in the entry; No results were obtained in MF; CC focused on endocytic vesicle lumen, platelet alpha granule, blood microparticle, endoplasmic reticulum lumen, collagen-containing extracellular matrix, extracellular vesicle (Table 2).

Table 2 GO enrichment analysis of DEPs

GO entries	GO categories	Fold enrichment	p value
Regulation of body fluid levels (GO:0050878)	BP	20.95	2.64^E-06
-	MF	-	-
Endocytic vesicle lumen (GO:0071682)	CC	> 100	1.17^E-04
Platelet alpha granule (GO:0031091)	CC	48.48	3.23^E-05
Blood microparticle (GO:0072562)	CC	30.85	1.20^E-04
Endoplasmic reticulum lumen (GO:0005788)	CC	28.01	3.69^E-08
Collagen-containing extracellular matrix (GO:0062023)	CC	13.94	1.53^E-04
Extracellular vesicle (GO:1903561)	CC	6.23	1.66^E-06

BP: biological process; MF: molecular function; CC: cellular component.

3.2.4 Enrichment analysis of DEPs

To explain the biological significance represented by the DEPs between the two groups, DAVID and KOBAS were used to analyze KEGG pathways, as shown in Figure 3A, fourteen DEPs were enriched for four functional clusters, the horizontal axis represents significance and the vertical axis represents the degree of enrichment, with the larger the bubble the higher the degree of enrichment. Biological differences between the two groups included the NF-kappa B signaling pathway, toll-like receptor signaling pathway, Notch signaling pathway, Th1 and Th2 cell differentiation, thyroid hormone signaling pathway, cholesterol metabolism, oxidative phosphorylation, complement and coagulation cascades, and so on. hormone signaling pathway, cholesterol metabolism, oxidative phosphorylation, complement and coagulation cascades, and so on.

In addition, Figure 3C shows the GSEA analyses of the top-ranked Reactome and KEGG according to enrichment sore, which provides a more comprehensive interpretation of the biological processes involved in the pathogenesis of T2DM in AMI patients. It contains the PI3K pathway, complement cascade, GPCR signaling pathway, regulation of IGF transport and uptake by IGFBPs, phosphodiesterase D signaling pathway, NF-kappaB signaling pathway, and a large number of phosphorylation pathways.

Table 3 Results of Reactome pathways

Reactome pathways	Fold enrichment	Raw p-value	Protein/gene details
Post-translational protein phosphorylation (R-HSA-8957275)	68.72	8.35E-09	CALU F5 ApoE SERPINA10 IGFBP7
Regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs) (R-HSA-381426)	59.3	1.70E-08	CALU F5 ApoE SERPINA10 IGFBP7
Binding and Uptake of Ligands by Scavenger Receptors (R-HSA-2173782)	43.25	4.49E-05	HYOU1 IGLV3-25 ApoE
Platelet degranulation (R-HSA-114608)	34.74	8.49E-05	CALU SERPINF2 F5
Response to elevated platelet cytosolic Ca2+ (R-HSA-76005)	33.42	9.50E-05	CALU SERPINF2 F5

3.2.5 Protein-protein interaction (PPI) analysis of DEPs

We further constructed the PPI network using the STRING database and identified hub genes. A confidence level of 0.15 was set to obtain more interactions. Figure 3B shows that the input 14 DEPs matched 13 nodes, among which the top-ranked node proteins included LBP, ApoE, SERPINF2, SERPINA10, IGFBP7, HYOU1, LUM, and most of the proteins were up-regulated. These proteins share nodes of biological pathways through a complex network, informing subsequent laboratory studies and targeted biomarker validation projects.

3.2.6 ROC curve

To assess the discriminative ability of 14 DEPs, we constructed ROC and calculated AUC, of which 6 met statistical differences (Figure 4A). These proteins also had top-ranked importance in the PPI network based on literature information and public databases. All the AUC values showed favorable diagnostic performance with a mean AUC of 0.77±0.03.

3.2.7 Text-mined and validation by ELISA analysis

When candidate biomarkers were text-mined using the method of Pletscher-Frankild et al[16], relevant clinical associations were generated, with the majority of candidate proteins associated with heart disease and diabetes (Table 4). LBP, ApoE, SERPINF2, SERPINA10, IGFBP7, and HYOU1 were selected for validation based on AUC values, PPI network, and enrichment results. ELISA results showed higher concentrations of LBP, ApoE, SERPINF2, SERPINA10, and IGFBP7 in AD patients, and low concentrations of HYOU1 in AD patients, all changes were consistent with statistical differences. Figure 4B shows a concordance of ELISA results and MS data for candidate biomarkers.

Table 4 Correlation and confidence level of target proteins mined from the literature with diabetes

protein	disease	Z-score	confidience
ApoE	Type 2 diabetes mellitus	6.9	4
LBP	Type 2 diabetes mellitus	4.6	3
SERPINF2	Diabetes mellitus	4	2
IGFBP7	Diabetes mellitus	4	2
HYOU1	Type 2 diabetes mellitus	3.6	2
SERPINA10	Diabetes mellitus	1.8	1

The Z-score and confidence level corresponding to each protein were determined using the text-mining database. In statistics, the Z-score denotes how many standard deviations an element or datum is from the mean whereas the confidence level refers to the likelihood of all possible samples that can be expected to include the true population parameter.

Patients with coronary artery disease combined with T2DM have more severe and complex coronary artery disease compared to non-diabetic patients. T2DM is an independent risk factor of AMI and leads to poor prognosis and increased mortality, therefore mining its pathogenesis can help early intervention, improve prognosis, and increase therapeutic strategies. Proteins, as the terminal substance of genes, provide molecular mapping of disease, especially in recent years, advances in molecular biology techniques have accelerated the discovery of pathological biomarkers. Proteomics is a powerful tool for clinical research and translation to unearth disease mechanisms and discover potential therapeutic targets. We collected 24 patients with AMI, 12 of whom had comorbid T2DM, and yielded quantitative information on a total of 416 proteins by DIA proteomics, 14 differentially expressed proteins (DEPs) involved in biological pathways related to immunity, inflammation, signaling, endocrine regulation, cholesterol metabolism, oxidative phosphorylation, complement, and coagulation cascades. Bioinformatics analysis, ROC curves, text mining, and ELISA were performed to assess the diagnostic and predictive capabilities of candidate proteins, lipopolysaccharide-binding protein (LBP), apolipoprotein E (ApoE), serpin family F member 2 (SERPINF2), serpin family A member 10 (SERPINA10), insulin-like growth factor binding protein 7 (IGFBP7), hypoxia up-regulated 1 (HYOU1) were screened as distinguish biomarkers.

LBP, as one of the most important ligands for lipopolysaccharide (LPS), delivers LPS to LPS receptors on monocytes/macrophages or neutrophils and activates pro-inflammatory cytokines[17]. After binding to LPS, LBP causes LPS to present as a cluster of differentiation (CD) 14, which interacts with toll-like receptors to activate the immune[18]. Toll-like receptor signaling pathway in Figure 3A indicates that the immune system is further activated in AD patients. Some studies have reported an association between high levels of LBP and dyslipidemia, obesity, and insulin resistance (IR)[19-21]. Many cross-sectional studies in different races have shown that increased levels of LBP or polymorphisms in the LBP gene are associated with an increased risk of T2DM[20, 22, 23].

ApoE also showed higher concentrations in group AD (Figure 4B). Plasma ApoE concentration-dependent induction of LBP member phospholipid transfer protein (PLTP) was activated in T2DM[24-26]. At the same time, the systemic inflammation that accompanies T2DM further activates PLTP, which has been shown to co-localize with ApoE-only containing particles under inflammatory conditions[24, 27]. Recent studies have also revealed that ApoE polymorphisms are associated with T2DM, for instance, the ApoEε4 allele is an independent risk for T2DM[28]. Different ApoE isoforms have different affinities and binding stabilities for PLTP, but all of them have been documented to activate PLTP[29]. Therefore, further ApoE-related genetic studies to add information beyond the conventional cardiovascular risk factors in T2DM and to identify risk genotypes will help in the early prediction and identification of at-risk patients.

We identified group AD has higher levels of α-antiplasmin encoded by SERPINF2, which is similar to published research[30]. Patients with T2DM have a prothrombotic fibrin clot phenotype characterized by the formation of a dense fibrin network with reduced permeability and solubility, and increased binding of antifibrinolytic proteins to the clot leads to impaired fibrinolysis in T2DM, encompassing complement C3 and α2-antifibrinolytic[31, 32]. Plasma coagulation test results in young diabetic patients (unspecified type) complicated by myocardial infarction showed increased α2-antiplasmin[33]. It has also been reported that impaired fibrinolysis in diabetic patients may be the result of post-translational modification of fibrinogen molecules as they are exposed to the dysmetabolic environment associated with diabetes[30]. Prolonged exposure of proteins to hyperglycemia produces advanced glycation end products, which further activate the binding and uptake of ligands by scavenger receptors (fold enrichment=43.25) (Table 3)[34, 35]. IR and hyperglycemia induce sustained cardiovascular production of ROS, as a by-product of oxidative phosphorylation, which can promote post-translational protein phosphorylation (fold enrichment=68.72), we also enriched a large number of phosphorylation modifications in the Reactome module of GSEA. Phosphorylation is deeply involved in the regulation of insulin secretion, but the mechanism of phosphorylation in pancreatic β-cell dysfunction is not clear, so it is of significant value to reveal the mechanism of regulating insulin secretion from the perspective of phosphorylation modifications.

We found higher levels of ZPI (SERPINA10) in AD patients but did not obtain quantitative information on its ligand protein Z (PZ), although the potential of PZ to indicate abnormalities in glucose metabolism in prediabetes has recently been reported, and in-depth characterization using proteomics could help to reveal the role of ZPI in T2DM[36]. DM is accompanied by an imbalance in the fibrinolytic system. Anticoagulant proteins play an important role in coagulation and fibrinolysis, and the mechanisms of protein C, protein S, and antithrombin have been largely clarified. PZ is a newly discovered vitamin K-dependent glycoprotein. On the one hand, PZ promotes the binding of thrombin to phospholipids to induce coagulation with the participation of Ca²⁺; on the other hand, PZ binds to ZPI on the phospholipid surface to rapidly inhibit the activity of activate factor X indirectly anticoagulant, and the ability of ZPI to inhibit factor X decreases by 99.99% if it loses the involvement of PZ[37-39]. Another evidence of an impaired fibrinolytic system is platelet degranulation (fold enrichment=34.74) and response to elevated platelet cytosolic Ca²⁺ (fold enrichment=33.42) (Table 3), which is reflected in thrombosis, platelet degranulation, and activation of calcium ion signaling[40].

The pathogenesis of diabetic cardiomyopathy involves IR in cardiac, oxidative stress, mitochondrial dysfunction, and accumulation of glycosylation products[35]. We found that IGFBP7 has a higher level in group AD, and regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins (IGFBPs) (fold enrichment=59.3) was significantly enriched (Table 3). IGFBP7 is considered a biomarker of diabetes combined with diastolic dysfunction accompanied by myocardial cell fibrosis, cardiac hypertrophy, and vascular remodeling[41]. Guo et al. found that IGFBP7 was associated with fiber formation and increased collagen accumulation leading to diastolic stiffness in diabetes[42]. IGFBP7 is known to undergo significant changes in diabetic cardiomyopathy[43]. High serum concentrations of IGFBP7 may contribute to T2DM lesions in a cohort of AMI patients. In addition, the GO term showed that DEPs were involved in the regulation of body fluid levels, the potential underlying cause might be IR elevates blood glucose and osmotic pressure of the extracellular fluid, which leads to cellular dehydration, and therefore thirst and frequent urination are more common in diabetics.

In this study, since DEPs are derived from serum, the distribution of subcellular localization is arranged from more to less according to extracellular (64.3%), plasma membrane (21.4%), mitochondrion (7.1%), and endoplasmic reticulum (ER) (7.1%), more than 85% of the proteins were localized to the extracellular and plasma membranes. These proteins are synthesized in cytoplasm and ER and then released to the extracellular environment through exocytosis, and studies at the cellular and subcellular organelle level are expected to yield more information on intracellular proteins. The CC term of the GO also contains endocytic vesicle lumen, ER lumen, extracellular vesicles, and extracellular matrix, which seems to suggest the process of ER stress. ER stress is activated in diabetics with cardiometabolic abnormalities while expressing a large number of molecular chaperones to maintain the dynamic balance of cells, including the heat shock protein 70 family member HYOU1[44, 45]. Higher concentrations of HYOU1 in group AD may be associated with the development of T2DM in AMI patients.

Our current study showed that LBP, ApoE, SERPINF2, SERPINA10, IGFBP7, and HYOU1 were involved in the pathogenesis of T2DM in AMI patients. To ensure consistency, we did not enroll T2DM patients with poor glycemic control. In the future, larger longitudinal and scope studies should be conducted in similar populations to assess the association of these biomarkers with T2DM and AMI. In addition, as this was a single-center retrospective study, it is difficult to exclude influence from some unmeasured and residual confounding factors.

We used DIA proteomics to explore T2DM-related biomarkers in AMI and identified six biomarkers, including LBP, ApoE, SERPINF2, SERPINA10, IGFBP7, and HYOU1. Overall, this study provides a set of biomarkers associated with the development of T2DM in patients with AMI, providing new insights for early diagnosis and precision medicine, reducing the global prevalence of T2DM, especially in the population of patients with high-risk coronary artery disease who have developed AMI.

T2DM Type 2 diabetes mellitus

AMI Acute myocardial infarction

DIA-MS Data-independent acquisition mass spectrometry

ROC Receiver operating characteristic

DEPs Differentially expressed proteins

PPI Protein-protein interactional

LBP Lipopolysaccharide-binding protein

ApoE Apolipoprotein E

SERPINF2 Serpin family F member 2

SERPINA10 Serpin family A member 10

IGFBP7 Insulin-like growth factor binding protein 7

HYOU1 Hypoxia up-regulated 1

SPISE Point Insulin Sensitivity Estimator

BMI Body mass index

FASP Filter-aided sample preparation

DIA Data-independent acquisition

KEGG Kyoto Encyclopedia of Genes and Genomes

GSEA Gene-set Enrichment Analysis

PPI Protein-protein interactional

CV Coefficient of variation

LPS Lipopolysaccharide

IR Insulin resistance

Acknowledgments

Not applicable.

Funding

This study was funded by the National Natural Science Foundation of China (Grant No. 82030124), and the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (Grant No. ZYYCXTD-C-202007).

Author information

Authors and Affiliations

Beijing Key Laboratory of Pharmacology of Chinese Materia Region, Institute of Basic Medical Sciences, Xiyuan Hospital, China Academy of Chinese Medical Sciences, National clinical research center of cardiovascular disease of traditional Chinese Medicine, Beijing, 100000, China.

Zhibo Wang, Ju Qin Peng, Ying Li, Lan Miao, Junguo Ren, Jianxun Liu

Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China

Jinghua Liu

Contributions

Zhibo Wang, Ju Qin Peng: Investigation, Methodology, Writing—Original Draft. Ying Li: Investigation, Methodology, Writing-Review and Editing. Lan Miao: Investigation. Junguo Ren, Jianxun Liu: Resources, Funding acquisition. Jinghua Liu: Conceptualization, Methodology.

Corresponding author

Correspondence to Jianxun Liu and Jinghua Liu.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the ethics committee of Beijing Anzhen Hospital, Capital Medical University (2023165X) and has been carried out in strict adherence to the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

None.

Supplementary Information

Supplementary file1:Table S1.

Raw quantitative data.

Supplementary file2: Table S2.

Details of DEPs.

Data availability

The analyzed data are available for non-commercial use from the corresponding author upon request. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD046147.

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No competing interests reported.

supplementS1detailsofDEPs.xlsx
Supplementary file1:Table S1. Raw quantitative data.
supplementS2DIAdataoriginal.xls
Supplementary file2: Table S2. Details of DEPs.

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DIA proteomics reveals potential biomarkers for T2DM in acute myocardial infarction patients

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