Associations of circulating omentin-1 levels and long noncoding RNA MALAT1 expression with coronary heart disease in type 2 diabetes mellitus

Associations of circulating omentin-1 levels and long noncoding RNA MALAT1 expression with coronary heart disease in type 2 diabetes mellitus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Associations of circulating omentin-1 levels and long noncoding RNA MALAT1 expression with coronary heart disease in type 2 diabetes mellitus Meimei Tian, Jinchao Cao, Min Li, Pingping Lou, Huijie Ma, Yan Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5044261/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 May, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Coronary heart disease (CHD) is a severe diabetic vascular complication and the main cause of mortality among diabetes patients. Early diagnosis of CHD could prevent its development. Both omentin-1 (Oment-1) and the long noncoding RNA MALAT1 (lncRNA MALAT1) can be detected in peripheral blood and exhibit protective or detrimental effects on CHD. However, whether these two factors could be predictive of CHD in T2DM patients remains unclear. Therefore, this study aimed to investigate the associations of circulating Oment-1 levels and the expression of MALAT1 with CHD in T2DM patients and to assess their predictive efficacy. A total of 137 T2DM patients were enrolled, including 68 patients without CHD (T2DM group) and 69 patients with CHD (T2DM + CHD group). Clinical parameters were collected, and plasma Oment-1 was measured by enzyme-linked immunosorbent assay (ELISA). RNA was isolated from peripheral monocytes, and the expression of MALAT1 was determined by quantitative PCR. Cardiac function was measured by echocardiography. Compared with that in T2DM patients, the plasma Oment-1 level was significantly lower, while the expression of MALAT1 was significantly greater in T2DM + CHD patients (all P values < 0.01). Bivariate correlation analysis indicated that Oment-1 was positively correlated with the left ventricular ejection fraction (LVEF) (P < 0.01). MALAT1 expression was negatively correlated with LVEF but positively correlated with age and DM duration (P < 0.05). Binary logistic regression suggested that Oment-1 and MALAT1 were significantly associated with the presence of CHD. Receiver operating characteristic (ROC) curve analysis demonstrated that both Oment-1 (AUC = 0.663, sensitivity = 75%, specificity = 49%) and MALAT1 (AUC = 0.749, sensitivity = 73%, specificity = 66%) had significant diagnostic value for CHD among T2DM patients. Notably, the combination of Oment-1 and MALAT1 exhibited better diagnostic efficiency (AUC = 0.771, sensitivity = 66.7%, specificity = 75.3%). In conclusion, decreased circulating Oment-1 levels and increased MALAT1 expression are closely associated with CHD in T2DM patients, and their combination offers superior diagnostic efficiency, suggesting Oment-1 and MALAT1 may serve as a non-invasive tool for the early CHD detection and risk stratification in high-risk T2DM patients. Further studies are warranted to explore the pathophysiological mechanisms of Omentin-1 and MALAT1 in the pathogenesis of CHD in T2DM and to validate their clinical utility as potential biomarkers in large cohort studies. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Endocrinology Health sciences/Risk factors Type 2 diabetes mellitus Coronary heart disease Omentin-1 Long noncoding RNA MALAT1 Figures Figure 1 Figure 2 Figure 3 Introduction The global prevalence of diabetes mellitus (DM) has been increasing; the number of DM patients in China is 140.9 million, and this number is estimated to reach 174.4 million by 2045[ 1 ]. Coronary heart disease (CHD) is a macrovascular disease of T2DM and the major cause of cardiovascular mortality[ 2 ]. However, the parameters used in the clinic to evaluate cardiovascular diseases are more specific to severe cardiovascular events such as myocardial infarction, which cannot provide enough information for the risk of CHD in T2DM patients. Therefore, finding predictive biomarkers would be valuable for the early diagnosis and prevention of CHD in T2DM patients. Omentin-1 (Oment-1) is a novel adipocytokine that has long been found to be protective against cardiovascular diseases[ 3 ] and T2DM-associated comorbidities[ 4 , 5 ] because of its anti-inflammatory and antioxidative effects[ 6 , 7 ]. Reduced circulating Oment-1 levels were found in CHD patients[ 8 ] and is considered a cardiovascular risk biomarker in axial spondyloarthritis patients[ 9 ] and in postmenopausal women[ 10 ]. However, few studies have evaluated the level and value of circulating Oment-1 in T2DM patients with CHD. The role of lncRNAs in cardiovascular diseases has been recognized in recent years, and some lncRNAs could be used as biomarkers for the diagnosis of CHD[ 11 ]. The lncRNA metastasis-associated lung cancer transcript 1 (MALAT1) was originally found in lung cancer but is also one of the most studied lncRNAs in cardiovascular disease[ 12 ]. Elevated MALAT1 expression in peripheral blood was found in patients with coronary slow flow[ 13 ], CHD patients[ 14 ] and CHD patients with unstable angina[ 15 ], indicating that MALAT1 has diagnostic value for CHD and related events. However, few studies have examined the role of MALAT1 in CHD among T2DM patients. Atherosclerosis and inflammation are the primary pathological alterations in CHD. Intriguingly, Oment-1 is an anti-inflammatory and anti-atherogenic adipokine[ 16 ], and circulating Omentin-1 levels are negatively correlated with atherosclerosis[ 17 ]. In contrast, MALAT1 has been shown to promote inflammation[ 18 ], and its circulating expression is positively correlated with the progression of atherosclerosis[ 14 ]. However, the correlation between omentin-1 and MALAT1 has not yet been investigated. Therefore, this study aimed to evaluate the roles of Oment-1 and MALAT1 in CHD among T2DM patients and to explore the potential roles of Oment-1, MALAT1 and their combination as specific noninvasive markers for CHD in T2DM patients. Materials and methods Patient recruitment: This study was conducted in the Department of Endocrinology, the Third Hospital of Hebei Medical University, from October 2021 to October 2023. A total of 137 T2DM patients aged 18–75 years were ultimately enrolled according to the inclusion and exclusion criteria. The inclusion criterion was as follows: T2DM diagnosed according to the WHO criteria (1999)[ 19 ]. The exclusion criteria were as follows: T2DM patients who were pregnant; presented with diabetes-related acute complications, such as diabetic ketoacidosis, hyperglycemic hyperosmolar state, and lactic acidosis; or had comorbidities, such as infectious diseases, autoimmune diseases, malignancies, heart failure, and renal and hepatic functional impairment. CHD was diagnosed by meeting one of the following criteria[ 20 ]: at least one coronary artery stenosis > 50%, confirmed by previous coronary angiography or coronary CTA; a history of myocardial infarction; stable angina pectoris; and asymptomatic myocardial ischemia. Patients were divided into two groups according to CHD history: the T2DM without CHD group (T2DM, n = 68) and the T2DM with CHD group (T2DM + CHD, n = 69). All patients with CHD received standard medical therapy, including aspirin and statins, and remained free from new episodes of myocardial infarction, angina pectoris, and heart failure. This study was approved by the Ethics Committee of the Third Hospital of Hebei Medical University (Approval Code. W2021-088-1) and performed in accordance with relevant guidelines, and written informed consent was obtained from each participant. The study was performed in accordance with the Declaration of Helsinki. Patient data and biochemical parameter collection The baseline data of the patients, including age, sex, height, weight, duration of diabetes, family history, smoking status and alcohol consumption, were recorded. The abdominal circumference, height and weight were measured, and BMI was calculated as body weight (kg)/height (m)2 (kg/m2). Blood samples were collected from the patients after 8 h of overnight fasting. The levels of triglycerides (TGs), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), very low-density lipoprotein cholesterol (VLDL-C), uric acid (UA), creatinine (Cr), alanine aminotransferase (ALT), aspartate aminotransferase (AST), C-reactive protein (CRP), and homocysteine (Hcy) were measured via photoelectric colorimetry. Hemoglobin A1c (HbA1c) was measured via high-performance liquid chromatography. Fasting C-peptide (FC-P) and 25-hydroxy vitamin D (25(OH)D) were measured via chemiluminescence immunoassay. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation[ 21 ]. Echocardiography: Transthoracic echocardiography was carried out using a color ultrasound diagnostic instrument (PHILIPS EPIQ7C). The thickness of the ventricular wall and left ventricular end-diastolic diameter (LVEDD) were measured by M-mode ultrasound. The method of LVESD measurement was the same as that of LVEDD, and the left ventricular ejection fraction (LVEF) was calculated via the following formula: LVEF = (LVEDD- LVESD)/LVEDD. Oment-1 measurement Fasting blood (5 ml) was collected from each patient, and plasma was collected after centrifugation. The plasma samples were stored at − 80°C before every measurement. The plasma level of Oment-1 was measured by enzyme-linked immunosorbent assay (ELISA) by commercial kits (CUSABIO, Wuhan; Catalog No. CSB-E09745h) according to the manufacturer’s protocol. RNA isolation and RT‒qPCR Fasting blood samples were collected, and peripheral monocytes were isolated by commercially available kits (Beijing Solabor Technology Co., Ltd.). RNA was extracted via the TRIzol method (cat. no. 15596026; Invitrogen; Thermo Fisher Scientific, Inc.), and RNA quality and concentration were assessed by UV spectroscopy at 260 and 280 nm. A HiFiScript gDNA Removal cDNA Synthesis Kit (cat. no. CW2582S; CoWin Biosciences) was used for reverse transcription. MonAmp ChemoHS qPCR mix (cat. no. rn04005M; Monad Biotech Co., Ltd.) was used to detect MALAT1 expression, and β-actin was used as a housekeeping gene. The thermocycling conditions were as follows: 95°C for 15 min; 40 cycles at 95°C for 10 sec, 56°C for 30 sec and 72°C for 30 sec. Relative gene expression was calculated via the 2 −△△CT method. The PCR primers were designed and synthesized by Sangon Biotech Co., Ltd. (Shanghai). The sequences of primers used were as follows: β-Actin: forward primer, 5'-AAGGCCAACCGCGAGAA-3'; reverse primer, 5'-ATGGGGGAGGGCATACC-3'. LncRNA MALAT1: forward primer 5'-TACCTAACCAGGCATAACA-3'; reverse primer 5'-GTAGACCAACTAAGCGAAT-3' Statistical analysis All the statistical analyses were performed using SPSS software (version 26). The normality of the continuous data was examined via the Shapiro–Wilk test. Normally distributed data are expressed as the means ± standard deviations (means ± SDs), whereas nonnormally distributed data are expressed as medians and interquartile ranges (IQRs). Independent sample t tests or Mann‒Whitney U tests were performed to assess the differences between two groups. Categorical variables are expressed as numbers and were compared using the chi-square test. Spearman's rank correlation analysis and Pearson’s correlation analysis were performed to evaluate the correlations between the study factors and the clinical and biochemical parameters. Binary logistic regression was performed to determine the contributions of the study parameters to the prediction of CHD onset. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of parameters for CHD in T2DM patients. A two-sided P value < 0.05 was considered statistically significant. Results Demographic, clinical, and biochemical characteristics of the study subjects In the present study, there were no significant differences in patients’ baseline characteristics between T2DM patients and T2DM + CHD patients, including sex, BMI, family history, smoking status, or alcohol consumption, (all P > 0.05). Compared with T2DM patients without CHD, the duration of diabetes was significantly longer in T2DM patients with CHD (P = 0.019). Compared with those in T2DM patients, SBP, HbA1c and FBG were significantly greater in T2DM + CHD patients (all P < 0.05), whereas LVEF, TC, LDL-C and eGFR were significantly lower in T2DM + CHD patients (all P 0.05) (Table 1 ). Table 1 Demographic, clinical and biochemical characteristics of the study subjects Characteristic T2DM T2DM + CHD P value Number 68 69 Age (year) 52.05 ± 12.04 65.17 ± 11.42 0.001 * Gender (female/male) 32/36 34/35 0.193 DM Duration (years) Abdominal circumferences(cm) 8.03 ± 6.80 92.76 ± 8.10 15.95 ± 6.61 92.96 ± 8.70 0.019 * 0.894 BMI(kg/m 2 ) 25.89 ± 3.27 25.31 ± 3.23 0.306 HbA1c (%) 7.76 ± 1.65 8.73 ± 1.94 0.002 * DM Family history (yes/no) 26/42 29/40 0.688 Smoking (yes/no) 29/39 20/49 0.095 Drinking alcohol (yes/no) 24/44 21/48 0.275 SBP(mmHg) 130.16 ± 16.41 138.41 ± 17.81 0.006 * DBP(mmHg) 86.57 ± 13.45 86.22 ± 10.96 0.865 FBG(mmol/L) 7.75 ± 2.62 10.14 ± 3.84 < 0.001* TC (mmol/L) 5.17 ± 1.36 4.61 ± 1.21 0.011 * TG (mmol/L) 2.62 ± 1.88 1.66 ± 1.20 0.051 LDL-C (mmol/L) 3.01 ± 0.76 2.69 ± 0.82 0.015 * HDL-C (mmol/L) 1.20 ± 0.27 1.19 ± 0.25 0.264 VLDL-C (mmol/L) 1.19 ± 1.76 0.75 ± 0.54 0.052 ALT (U/L) 31.34 ± 27.94 28.45 ± 26.76 0.538 AST (U/L) 22.90 ± 14.59 22.78 ± 11.51 0.959 Cr (µmol/L) 70.04 ± 31.87 69.92 ± 21.03 0.656 eGFR(ml/min/1.73m 2 ) 99.59 ± 22.06 88.56 ± 18.48 0.002 * UA (µmol/L) 334.07 ± 103.04 314.25 ± 90.37 0.102 CRP (mg/L) 4.83 ± 1.13 3.69 ± 1.33 0.537 FC-P (ng/mL) 2.64 ± 1.13 2.38 ± 1.18 0.239 Hcy(µmol/L) 12.38 ± 4.65 12.88 ± 4.86 0.850 25(OH)D(ng/ml) 27.74 ± 6.76 16.25 ± 5.22 0.165 LVEF(%) 56.41 ± 4.42 50.63 ± 3.35 < 0.001* BMI: body mass index; HbA1c: hemoglobin A1c; SBP: systolic pressure; DBP: diastolic blood pressure; FBG: fasting blood glucose; TC: total cholesterol; TG: triglycerides; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; VLDL-C: very low-density lipoprotein cholesterol; ALT: alanine aminotransferase; AST: aspartate aminotransferase; Cr: creatinine; eGFR: estimated glomerular filtration rate; UA: uric acid; CRP: C-reactive protein; FC-P: fasting C peptide; Hcy: homocysteine; 25 (OH) D: 25-hydroxyvitamin D; LVEF: left ventricular ejection fraction. * p < 0.05, T2DM + CHD vs. T2DM Oment-1 levels and MALAT1 expression in circulation In the present study, compared to T2DM patients, plasma Oment-1 levels were significantly decreased in T2DM + CHD patients (15.55 ± 4.55 pg/ml vs. 13.09 ± 5.35 pg/ml, P = 0.0043; Fig. 1 A), whereas MALAT1 expression in peripheral blood cells was significantly increased in T2DM + CHD patients (1.25 ± 0.42 vs. 1.69 ± 0.54, P < 0.0001; Fig. 1 B). These findings indicated that reduced plasma Oment-1 levels and increased MALAT1 expression might be involved in the pathogenesis of CHD in T2DM patients. Correlation analysis of Oment-1 and MALAT1 Bivariate correlation analysis was performed for Oment-1 levels and MALAT1 expression to determine the relevant factors. Oment-1 was positively correlated with LVEF (r = 0.223, p = 0.001) and had no correlation with other parameters, including age, BMI, HbA1c, FBG, CRP, TC, TG, HDL-C, LDL-c, or eGFR. The expression of MALAT1 was negatively correlated with LVEF (r =-0.253, p = 0.007) but positively correlated with age (r = 0.451, p = 0.002) and DM duration (r = 0.201, p = 0.019) (Table 2 ). We also investigated the correlation between Oment-1 and MALAT1 and found that the level of plasma Oment-1 was negatively correlated with MALAT1 expression (r = -0.19, p = 0.026) (Fig. 2 ). Table 2 Correlation analysis of circulating Oment-1 and MALAT1 Oment-1 MALAT1 r P r P Age(year) -0.023 0.794 0.451 0.002* DM Duration(year) -0.016 0.849 0.201 0.019* BMI -0.008 0.926 -0.085 0.322 HbA1c(%) -0.078 0.378 -0.041 0.643 FBG(mmol/L) 0.028 0.748 -0.079 0.361 CRP (mg/L) 0.169 0.132 0.063 0.574 TC(mmol/L) -0.060 0.487 0.004 0.963 TG(mmol/L) -0.056 0.520 -0.057 0.514 HDL-C(mmol/L) 0.125 0.150 -0.032 0.709 LDL-C(mmol/L) -0.014 0.876 0.050 0.563 VLDL-C(mmol/L) -0.055 0.523 -0.057 0.513 UA(µmol/L) -0.008 0.931 -0.103 0.235 eGFR(ml/min/1.73m 2 ) 0.134 0.121 -0.057 0.513 FC-P(ng/mL) -0.005 0.954 -0.167 0.062 Hcy(µmol/L) -0.091 0.334 -0.061 0.519 LVEF(%) 0.223 0.001* -0.253 0.007* The data are presented as correlation coefficients (r). Abbreviations are listed in Table 1 . *Statistically significant (p < 0.05). Association of Oment-1 and MALAT1 with the presence of CHD Binary logistic regression analysis was performed with the presence of CHD in T2DM patients as the dependent variable and the study variables (Oment-1, MALAT1) as independent predictors. The regression models and data are shown in Table 3 . In the first regression model, only the study variables were taken as predictors, and both Oment-1 and MALAT1 were significantly associated with the presence of CHD. In the second model, after adjusting for age, sex, duration of diabetes and HbA1c, Oment-1 and MALAT1 were still significantly associated with the presence of CHD. These associations were still significant even after adjusting for age, sex, duration of diabetes, HbA1c, BMI, LDL-C and eGFR, as shown in the third model (Table 3 ). Table 3 Binary logistic regression analysis of the independent factors for the presence of CHD (Oment-1 and MALAT1 as continuous variables) Model OR 95% CI P value 1 Oment-1 0.891 0.8423–0.965 0.004 * MALAT1 8.886 3.330–23.590 < 0.001 * 2 Oment-1 0.856 0.776–0.945 0.002 * MALAT1 7.104 2.363–21.361 < 0.001 * Duration (years) 1.017 0.958–1.080 0.575 Age 1.088 1.034–1.144 0.001* Gender 0.912 0.323–2.591 0.863 HbA1c 0.752 0.564–1.004 0.053 3 Oment-1 0.853 0.750–0.970 0.015 * MALAT1 4.147 1.217–14.133 0.023 * Duration (years) 1.040 0.966–1.121 0.300 Age 1.098 1.102–1.192 0.024* Gender 1.016 0.266–3.883 0.981 HbA1c 0.774 0.513–1.170 0.224 BMI 1.076 0.895–1.294 0.437 LDL-C 0.833 0.398–1.745 0.629 eGFR 1.016 0.982–1.051 0.353 LVEF 0.564 0.442–0.754 < 0.001 * Model 1: not adjusted for any variable; Model 2: adjusted for age, sex, duration of diabetes and HbA1c; Model 3: adjusted for Model 2, BMI, LDL-C and eGFR. OR: odds ratio; 95% CI: 95% confidence interval. *Statistically significant (p < 0.05). ROC curve analysis ROC curve analysis was performed to determine the diagnostic value of Oment-1 and MALAT1 for the presence of CHD in T2DM patients. On the basis of analyses of the ROC curves (Fig. 1 ), plasma Oment-1 levels with cutoff values ≤ 12.45 pg/ml were used to discriminate CHD patients from non-CHD patients with T2DM, with an AUC of 0.633 (95% CI: 0.540–0.725; p < 0.001), a sensitivity of 75%, a specificity of 49%, a PPV of 59.9%, and an NPV of 65.9%. The cutoff value of MALAT1 expression was ≥ 1.367, with an AUC was 0.749 (95% CI: 0.668–0.830; p < 0.001), a sensitivity of 73%, a specificity of 66%, a PPV of 68.6%, and an NPV of 70.7%. Additionally, the AUC of Oment-1 combined with MALAT1 was 0.771 (95% CI: 0.694–0.848; p < 0.001), the sensitivity was 66.7%, the specificity was 75.3%, the PPV was 73.3%, and the NPV was 69.1%, indicating that the combination of Oment-1 and MALAT1 had better diagnostic value for CHD in T2DM patients. (Fig. 3 ) Discussion T2DM patients are at a greater risk of developing cardiovascular disease, which is the leading cause of mortality among this population [ 22 ]. In the present study, we investigated the correlations between circulating Oment-1 levels and MALAT1 expression in T2DM patients with CHD. Data have indicated that, compared with those in T2DM patients without CHD, circulating Oment-1 levels are significantly lower, whereas MALAT1 expression is increased in CHD patients. Additionally, the plasma Oment-1 level was negatively correlated with LVEF, whereas MALAT1 expression was positively correlated with age, DM duration and LVEF. Notably, Oment-1 and MALAT1 were inversely correlated with each other. Both factors, either independently or in combination, exhibited predictive value for CHD in T2DM patients. In the present study, we found that the level of circulating Oment-1 was lower in T2DM patients with CHD and that it has predictive value for CHD. Similar results have also been reported previously in CHD patients with diabetes [ 23 ] or without diabetes[ 24 , 25 ]. The favorable effect of Oment-1 on CHD might be due to its endothelial cell protective and anti-atherosclerotic effects. Oment-1 has been found to partly ameliorate FFA-induced endothelial cell injury[ 26 ] and improve endothelial function by activating the Akt/eNOS/NO[ 27 ] and AMPK/PPARδ pathways[ 28 ], contributing to increased NO production and the inhibition of ER stress and oxidative stress. Oment-1 can modulate macrophage function and inhibit atherosclerosis formation and development[ 29 – 31 ]. Intriguingly, this was the first study to find that circulating Oment-1 levels were positively correlated with LVEF. Similar results have been reported in patients with dilated cardiomyopathy[ 32 ] and in mouse models of heart failure[ 33 ], indicating that Oment-1 levels might also be related to ventricular diastolic function. In vivo studies have shown that Oment-1 can prevent pathological cardiac remodeling following ischemia[ 34 ] and ameliorate ischemia-induced myocardial injury by activating mitophagy and maintaining dynamic mitochondrial homeostasis[ 35 ]. An in vitro study indicated that Oment-1 could protect H9C2 cells from docetaxel-induced damage by reducing endoplasmic reticulum stress[ 36 ]. Therefore, Oment-1 is a protective factors against cardiomyocytes, and its effect on diabetic cardiomyopathy is a prospective area that needs to be further investigated. In this study, we detected the expression of the lncRNA MALAT1 in peripheral blood mononuclear cells from T2DM patients and found that the expression of MALAT1 in the circulation was increased in T2DM patients with CHD. Similar results have also been reported by Sohrabifar et al.[ 37 ] in Iranian patients. Elevated expression of MALAT1 in the circulation has been reported in CHD patients[ 38 , 39 ] and is associated with increased CHD severity[ 38 ] and unstable angina[ 40 ], indicating that MALAT1 could be a biomarker for CHD screening and surveillance. In the present study, we found that MALAT1 expression in the circulation of T2DM patients was positively correlated with age and diabetes duration. No similar results have been reported previously in T2DM patients. A previous study performed in patients with periodontitis and healthy subjects revealed that blood MALAT1 expression was not correlated with age [ 41 ]. The data from the present study suggest that MALAT1 might be more susceptible to the influences of aging and duration in T2DM patients. MALAT1 is known to promote inflammation and oxidative stress. Thus, the elevated MALAT1 levels in older patients with longer durations of diabetes might reflect cumulative inflammatory damage, potentially exacerbating cardiovascular complications. Additionally, this was the first study to find that MALAT1 expression was negatively correlated with LVEF in patients with T2DM. A previous study performed by Qi et al.[ 42 ] reported that, in hemodialysis patients with heart failure, lncRNA ENST00000561762 expression in peripheral blood mononuclear cells was negatively correlated with LVEF. Therefore, MALAT1 expression in peripheral blood might also play a crucial role in the diagnosis of cardiac function in T2DM patients. Some evidence has indicated that MALAT1 contributes to high glucose-induced cardiomyocyte damage by causing mitochondrial damage and oxidative stress[ 43 ]. It remains unclear whether MALAT1 expression in peripheral blood mononuclear cells reflects MALAT1 expression in heart tissue. To the best of our knowledge, this is the first study to investigate the correlation between circulating Oment-1 levels and MALAT1 expression in patients with T2DM. Our results revealed a significant negative correlation between Oment-1 and MALAT1. Given that Oment-1 has anti-inflammatory and antioxidative effects, whereas MALAT1 is associated with proinflammatory activity, our findings suggest a potential regulatory network involving adipokines (e.g., Oment-1) and lncRNAs (e.g., MALAT1) in the pathogenesis of diabetic vascular complications. However, further studies are needed to elucidate the underlying mechanisms and clinical implications. This study has several limitations. First, it is a single-center study with a relatively small sample size, which may cause patient selection bias. Second, the use of hypoglycemic or lipid-lowering drugs could influence circulating Oment-1 levels and MALAT1 expression. Additionally, since T2DM patients with CHD are older than those without CHD are, age-related factors might also affect Oment-1 and MALAT1 levels. Third, serum 25(OH)D levels could be influenced by vitamin D supplementation; therefore, its role requires further investigation. Future studies with larger sample sizes are needed to better understand the clinical implications of these findings. Conclusions Decreased circulating Oment-1 levels and elevated MALAT1 expression are significantly associated with CHD in patients with T2DM, suggesting their potential as biomarkers for the noninvasive early detection of CHD in T2DM patients. However, further validation through large-scale, multicenter studies is needed to confirm their diagnostic efficacy and determine clinically relevant cutoff values. Declarations Funding: This work was supported by the Government-funded Clinical Medicine Outstanding Talent Training Project (ZF2025150), the Natural Science Foundation of Hebei Province (H2020206478), the Central Government Guides Local Science and Technology Development Project (246Z7711G) and the Projects of Medical Science Research of the Health Commission of Hebei Province, China (20210725, 20210513, 20210372 and 20170642). Acknowledgments The authors would like to thank Xinli Jiang (Department of Ophthalmology, The Third Hospital of Hebei Medical University, Shijiazhuang, China) for technical help and discussion of the results obtained in the experiments. Data availability The data are available upon request from the corresponding author. Ethical approval This study was approved by the ethics committee of the hospital and was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study. Author contributions M.M.T., Y.K.L. and Y.L. conceived and designed the study. M.M.T., J.C.C. and M.L. performed the experiments and wrote, reviewed and revised the manuscript. M.M.T. and P.P.L. were involved in the analysis and interpretation of the data and performed the statistical analysis. Y.L., H.J.M. and Y.K.L. confirmed the authenticity of all the raw data. All the authors read and approved the final manuscript. References Sun, H., et al., IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract, 2022. 183 : p. 109119. Strain, W.D. and P.M. Paldánius, Diabetes, cardiovascular disease and the microcirculation. Cardiovasc Diabetol , 2018. 17 (1): p. 57. Bai, P., et al., Association Between Coronary Artery Disease and Plasma Omentin-1 Levels. Cureus, 2021. 13 (8): p. e17347. Biscetti, F., et al., Association between plasma omentin-1 levels in type 2 diabetic patients and peripheral artery disease. Cardiovasc Diabetol , 2019. 18 (1): p. 74. Eimal Latif, A.H., et al., Association of Plasma Omentin-1 Levels With Diabetes and Its Complications. Cureus , 2021. 13 (9): p. e18203. Binti Kamaruddin, N.A., et al., Cytoprotective Role of Omentin Against Oxidative Stress-Induced Vascular Endothelial Cells Injury . Molecules , 2020. 25 (11). Zhao, A., et al., Omentin-1: a newly discovered warrior against metabolic related disease s. Expert Opin Ther Targets , 2022. 26 (3): p. 275-289. Du, Y., et al., Association between omentin-1 expression in human epicardial adipose tissue and coronary atherosclerosis. Cardiovasc Diabetol , 2016. 15 : p. 90. Genre, F., et al., Omentin: a biomarker of cardiovascular risk in individuals with axial spondyloarthritis. Sci Rep , 2020. 10 (1): p. 9636. Christodoulatos, G.S., et al., Circulating Omentin-1 as a Biomarker at the Intersection of Postmenopausal Breast Cancer Occurrence and Cardiometabolic Risk: An Observational Cross-Sectional Study. Biomolecule s, 2021. 11 (11). Zhang, Y., et al., MicroRNAs or Long Noncoding RNAs in Diagnosis and Prognosis of Coronary Artery Disease . Aging Dis , 2019. 10 (2): p. 353-366. Yan Y, Song D, Song X, Song C. The role of lncRNA MALAT1 in cardiovascular disease. IUBMB Life , 2020 Mar; 72 (3): p. 334-342. Zhao, C., et al., The lncRNA MALAT1 participates in regulating coronary slow flow endothelial dysfunction through the miR-181b-5p-MEF2A-ET-1 axis . Vascul Pharmacol , 2021. 138 : p. 106841. Qiu, S. and J. Sun, lncRNA-MALAT1 expression in patients with coronary atherosclerosis and its predictive value for in-stent restenosis . Exp Ther Med , 2020. 20 (6): p. 129. Barbalata T, Niculescu LS, Stancu CS, Pinet F, Sima AV. Elevated Levels of Circulating lncRNAs LIPCAR and MALAT1 Predict an Unfavorable Outcome in Acute Coronary Syndrome Patients. Int J Mol Sc i. 2023 Jul 28;24(15). Sena, C.M., Omentin: A Key Player in Glucose Homeostasis, Atheroprotection, and Anti-Inflammatory Potential for Cardiovascular Health in Obesity and Diabetes. Biomedicine s, 2024. 12 (2). Kadoglou NPE, Kassimis G, Patsourakos N, Kanonidis I, Valsami G. Omentin-1 and vaspin serum levels in patients with pre-clinical carotid atherosclerosis and the effect of statin therapy on them. Cytokine , 2021. 138 : p. 155364. Shi Z, Zheng Z, Lin X, Ma H. Long Noncoding RNA MALAT1 Regulates the Progression of Atherosclerosis by miR-330-5p/NF-κB Signal Pathway . J Cardiovasc Pharmaco l, 2021. 78 (2): p. 235-246. Grimaldi, A. and A. Heurtier, [Diagnostic criteria for type 2 diabetes] . Rev Pra t, 1999. 49 (1): p. 16-21. Knuuti, J., et al., 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes . Eur Heart J , 2020. 41 (3): p. 407-477. Kidney Disease: Improving Global Outcomes (KDIGO) Glomerular Diseases Work Group.KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney Int , 2021. 100 (4s): p. S1-s276. Einarson TR, Acs A, Ludwig C, Panton UH. Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007-2017 . Cardiovasc Diabetol , 2018. 17 (1): p. 83. Ali, S., et al., Evaluation of serum adipokines (omentin-1 and visfatin) in coronary artery disease at a North Indian hospita l. Endocr Regul , 2023. 57 (1): p. 262-268. Onur, I., et al., Serum omentin 1 level is associated with coronary artery disease and its severity in postmenopausal women . Angiology , 2014. 65 (10): p. 896-900. Shibata, R., et al., Circulating omentin is associated with coronary artery disease in men. Atherosclerosis , 2011. 219 (2): p. 811-4. Chen Y, Liu F, Han F, Lv L, Tang CE, Luo F. Omentin-1 Ameliorated Free Fatty Acid-Induced Impairment in Proliferation, Migration, and Inflammatory States of HUVECs . Cardiol Res Prac t, 2020. 2020 : p. 3054379. Dong Q, Xing W, Li K, Zhou X, Wang S, Zhang H. Tetrahydroxystilbene glycoside improves endothelial dysfunction and hypertension in obese rats: The role of omentin-1 . Biochem Pharmacol , 2021. 186 : p. 114489. Liu, F., et al., Omentin-1 protects against high glucose-induced endothelial dysfunction via the AMPK/PPARδ signaling pathway . Biochem Pharmaco l, 2020. 174 : p. 113830. Hiramatsu-Ito, M., et al., Omentin attenuates atherosclerotic lesion formation in apolipoprotein E-deficient mice. Cardiovasc Res , 2016. 110 (1): p. 107-17. Lin, X., et al., Omentin-1 Modulates Macrophage Function via Integrin Receptors αvβ3 and αvβ5 and Reverses Plaque Vulnerability in Animal Models of Atherosclerosis . Front Cardiovasc Med , 2021. 8 : p. 757926. Tan, Y.L., et al., Tanshinone IIA Promotes Macrophage Cholesterol Efflux and Attenuates Atherosclerosis of apoE-/- Mice by Omentin-1/ABCA1 Pathway . Curr Pharm Biotechnol , 2019. 20 (5): p. 422-432. Huang, Y., et al., Circulating Omentin-1 Levels Are Decreased in Dilated Cardiomyopathy Patients with Overt Heart Failure . Dis Markers , 2016. 2016 : p. 6762825. Li, F., et al., YiQiFuMai powder injection ameliorates chronic heart failure through cross-talk between adipose tissue and cardiomyocytes via up-regulation of circulating adipokine omentin. Biomed Pharmacother , 2019. 119 : p. 109418. Ito, M., et al., Omentin Modulates Chronic Cardiac Remodeling After Myocardial Infarctio n. Circ Rep , 2023. 5 (2): p. 46-54. Hu, J., et al., Omentin1 ameliorates myocardial ischemia-induced heart failure via SIRT3/FOXO3a-dependent mitochondrial dynamical homeostasis and mitophagy . J Transl Med , 2022. 20 (1): p. 447. Lage R, Cebro-Márquez M, Rodríguez-Mañero M, González-Juanatey JR, Moscoso I. Omentin protects H9c2 cells against docetaxel cardiotoxicity . PLoS One , 2019. 14 (2): p. e0212782. Sohrabifar N, Ghaderian SMH, Alipour Parsa S, Ghaedi H, Jafari H. Variation in the expression level of MALAT1, MIAT and XIST lncRNAs in coronary artery disease patients with and without type 2 diabetes mellitus . Arch Physiol Biochem , 2022. 128 (5): p. 1308-1315. Lv F, Liu L, Feng Q, Yang X. Long non-coding RNA MALAT1 and its target microRNA-125b associate with disease risk, severity, and major adverse cardiovascular event of coronary heart disease. J Clin Lab Anal , 2021. 35 (4): p. e23593. Zhu Y, Yang T, Duan J, Mu N, Zhang T. MALAT1/miR-15b-5p/MAPK1 mediates endothelial progenitor cells autophagy and affects coronary atherosclerotic heart disease via mTOR signaling pathway. Aging (Albany NY) , 2019. 11 (4): p. 1089-1109. Liu S, Hou J, Gu X, Weng R, Zhong Z. Characterization of LncRNA expression profile and identification of functional LncRNAs associated with unstable angina . J Clin Lab Anal , 2021. 35 (11): p. e24036. Gholami, L., et al., The lncRNA ANRIL is down-regulated in peripheral blood of patients with periodontitis . Noncoding RNA Res , 2020. 5 (2): p. 60-66. Qi, X., et al., The expression profile analysis and functional prediction of lncRNAs in peripheral blood mononuclear cells in maintenance hemodialysis patients developing heart failure. Sci Rep , 2024. 14 (1): p. 29577. Wang, T., et al., MALAT1/miR-185-5p mediated high glucose-induced oxidative stress, mitochondrial injury and cardiomyocyte apoptosis via the RhoA/ROCK pathway. J Cell Mol Med , 2023. 27 (17): p. 2495-2506. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 05 May, 2025 Reviews received at journal 27 Apr, 2025 Reviews received at journal 17 Apr, 2025 Reviewers agreed at journal 12 Apr, 2025 Reviews received at journal 11 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviewers invited by journal 11 Apr, 2025 Submission checks completed at journal 10 Apr, 2025 First submitted to journal 01 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5044261","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":441936409,"identity":"a6e06061-e1ea-414e-ba61-5aaa4561fa21","order_by":0,"name":"Meimei Tian","email":"","orcid":"","institution":"The Third Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meimei","middleName":"","lastName":"Tian","suffix":""},{"id":441936411,"identity":"1bac0136-df31-42b1-a650-aaf05c2fb9bc","order_by":1,"name":"Jinchao Cao","email":"","orcid":"","institution":"The Third Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinchao","middleName":"","lastName":"Cao","suffix":""},{"id":441936412,"identity":"fd578e9e-2f08-4823-a75b-1d5fba6362bc","order_by":2,"name":"Min Li","email":"","orcid":"","institution":"The Third Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Li","suffix":""},{"id":441936414,"identity":"99ca3123-b8be-44fb-8e9d-4337f6bce73b","order_by":3,"name":"Pingping Lou","email":"","orcid":"","institution":"The Third Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pingping","middleName":"","lastName":"Lou","suffix":""},{"id":441936417,"identity":"13c2593a-b6c2-447a-8ca7-b4e40a4a9068","order_by":4,"name":"Huijie Ma","email":"","orcid":"","institution":"Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huijie","middleName":"","lastName":"Ma","suffix":""},{"id":441936419,"identity":"d368453e-55ad-406b-8a89-9a6ede685132","order_by":5,"name":"Yan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACPmYwZZPAIAFmMBPWwgZRk0aKFgh1mBQt7DxmEj93nM/jl+5Ok2CosE5sYD97gIDDeMwke8/cLpacc3abBMOZ9MQGnrwEglokeNtuJ264kbtNgrHtcGKDBI8BYVv+tp1L3A/W8o9ILdK8bQcSN0iAtDQQpYWt2Fq2LTlxxp2zmy0SjqUbt/Hk4NfCz3944823bXaJ/bN7N974UGMt289+Br8WIGCRgDMTGOAxhRcwfyBC0SgYBaNgFIxkAABYcz6fvXMKPgAAAABJRU5ErkJggg==","orcid":"","institution":"The Third Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Liu","suffix":""},{"id":441936421,"identity":"4bfdc90b-ec3a-446d-842a-a5bc6f6afbcf","order_by":6,"name":"Yukun Li","email":"","orcid":"","institution":"The Third Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yukun","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-09-06 12:27:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5044261/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5044261/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-01153-5","type":"published","date":"2025-05-11T15:57:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80586856,"identity":"3efa1ea6-3021-40e0-b69e-498310e0b66e","added_by":"auto","created_at":"2025-04-15 01:13:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40223,"visible":true,"origin":"","legend":"\u003cp\u003eOment-1 levels and MALAT1 expression in the circulation of T2DM and T2DM+CHD patients. Compared with that inT2DM patients, the level of Oment-1 was significantly decreased (A), whereas the expression of MALAT1 (B) was significantly increased in the circulation of T2DM+CHD patients.\u003c/p\u003e\n\u003cp\u003e* p \u0026lt; 0.05 for T2DM+CHD vs\u003cem\u003e. \u003c/em\u003eT2DM\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5044261/v1/637912073ee90fb48f480551.png"},{"id":80586857,"identity":"0e048da6-224f-442b-b8a5-acc4a3eea1ca","added_by":"auto","created_at":"2025-04-15 01:13:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30158,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of Oment-1 levels with MALAT1 expression in patients with T2DM. The Oment-1 level was negatively correlated with MALAT1 expression (r = -0.19, p=0.026).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5044261/v1/07991b07fc19d2fbe9a1d20e.png"},{"id":80587541,"identity":"35a47ce5-3673-4994-833a-0e71da7b0349","added_by":"auto","created_at":"2025-04-15 01:21:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112387,"visible":true,"origin":"","legend":"\u003cp\u003eOment-1, lncMALAT1 and their combination to predict T2DM with CHD. ROC curves indicated that both Oment-1 and MALAT1 had significant predictive value for T2DM patients with CHD, and the predictive value was even greater when these two factors were combined.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5044261/v1/6127651e9a3279f0bbe0d98d.png"},{"id":82538079,"identity":"9504881f-092f-43f7-9b7b-0a4d2bb8bc74","added_by":"auto","created_at":"2025-05-12 16:10:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1053571,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5044261/v1/46e76d59-0af7-4ac7-af65-78c334675a87.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations of circulating omentin-1 levels and long noncoding RNA MALAT1 expression with coronary heart disease in type 2 diabetes mellitus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global prevalence of diabetes mellitus (DM) has been increasing; the number of DM patients in China is 140.9\u0026nbsp;million, and this number is estimated to reach 174.4\u0026nbsp;million by 2045[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Coronary heart disease (CHD) is a macrovascular disease of T2DM and the major cause of cardiovascular mortality[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the parameters used in the clinic to evaluate cardiovascular diseases are more specific to severe cardiovascular events such as myocardial infarction, which cannot provide enough information for the risk of CHD in T2DM patients. Therefore, finding predictive biomarkers would be valuable for the early diagnosis and prevention of CHD in T2DM patients.\u003c/p\u003e \u003cp\u003eOmentin-1 (Oment-1) is a novel adipocytokine that has long been found to be protective against cardiovascular diseases[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and T2DM-associated comorbidities[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] because of its anti-inflammatory and antioxidative effects[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Reduced circulating Oment-1 levels were found in CHD patients[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and is considered a cardiovascular risk biomarker in axial spondyloarthritis patients[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and in postmenopausal women[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, few studies have evaluated the level and value of circulating Oment-1 in T2DM patients with CHD.\u003c/p\u003e \u003cp\u003eThe role of lncRNAs in cardiovascular diseases has been recognized in recent years, and some lncRNAs could be used as biomarkers for the diagnosis of CHD[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The lncRNA metastasis-associated lung cancer transcript 1 (MALAT1) was originally found in lung cancer but is also one of the most studied lncRNAs in cardiovascular disease[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Elevated MALAT1 expression in peripheral blood was found in patients with coronary slow flow[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], CHD patients[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and CHD patients with unstable angina[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], indicating that MALAT1 has diagnostic value for CHD and related events. However, few studies have examined the role of MALAT1 in CHD among T2DM patients.\u003c/p\u003e \u003cp\u003eAtherosclerosis and inflammation are the primary pathological alterations in CHD. Intriguingly, Oment-1 is an anti-inflammatory and anti-atherogenic adipokine[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and circulating Omentin-1 levels are negatively correlated with atherosclerosis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In contrast, MALAT1 has been shown to promote inflammation[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and its circulating expression is positively correlated with the progression of atherosclerosis[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the correlation between omentin-1 and MALAT1 has not yet been investigated.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to evaluate the roles of Oment-1 and MALAT1 in CHD among T2DM patients and to explore the potential roles of Oment-1, MALAT1 and their combination as specific noninvasive markers for CHD in T2DM patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient recruitment:\u003c/h2\u003e \u003cp\u003eThis study was conducted in the Department of Endocrinology, the Third Hospital of Hebei Medical University, from October 2021 to October 2023. A total of 137 T2DM patients aged 18\u0026ndash;75 years were ultimately enrolled according to the inclusion and exclusion criteria. The inclusion criterion was as follows: T2DM diagnosed according to the WHO criteria (1999)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The exclusion criteria were as follows: T2DM patients who were pregnant; presented with diabetes-related acute complications, such as diabetic ketoacidosis, hyperglycemic hyperosmolar state, and lactic acidosis; or had comorbidities, such as infectious diseases, autoimmune diseases, malignancies, heart failure, and renal and hepatic functional impairment.\u003c/p\u003e \u003cp\u003eCHD was diagnosed by meeting one of the following criteria[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]: at least one coronary artery stenosis\u0026thinsp;\u0026gt;\u0026thinsp;50%, confirmed by previous coronary angiography or coronary CTA; a history of myocardial infarction; stable angina pectoris; and asymptomatic myocardial ischemia.\u003c/p\u003e \u003cp\u003ePatients were divided into two groups according to CHD history: the T2DM without CHD group (T2DM, n\u0026thinsp;=\u0026thinsp;68) and the T2DM with CHD group (T2DM\u0026thinsp;+\u0026thinsp;CHD, n\u0026thinsp;=\u0026thinsp;69). All patients with CHD received standard medical therapy, including aspirin and statins, and remained free from new episodes of myocardial infarction, angina pectoris, and heart failure.\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee of the Third Hospital of Hebei Medical University (Approval Code. W2021-088-1) and performed in accordance with relevant guidelines, and written informed consent was obtained from each participant. The study was performed in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient data and biochemical parameter collection\u003c/h3\u003e\n\u003cp\u003eThe baseline data of the patients, including age, sex, height, weight, duration of diabetes, family history, smoking status and alcohol consumption, were recorded. The abdominal circumference, height and weight were measured, and BMI was calculated as body weight (kg)/height (m)2 (kg/m2).\u003c/p\u003e \u003cp\u003eBlood samples were collected from the patients after 8 h of overnight fasting. The levels of triglycerides (TGs), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), very low-density lipoprotein cholesterol (VLDL-C), uric acid (UA), creatinine (Cr), alanine aminotransferase (ALT), aspartate aminotransferase (AST), C-reactive protein (CRP), and homocysteine (Hcy) were measured via photoelectric colorimetry. Hemoglobin A1c (HbA1c) was measured via high-performance liquid chromatography. Fasting C-peptide (FC-P) and 25-hydroxy vitamin D (25(OH)D) were measured via chemiluminescence immunoassay. The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eEchocardiography:\u003c/h3\u003e\n\u003cp\u003eTransthoracic echocardiography was carried out using a color ultrasound diagnostic instrument (PHILIPS EPIQ7C). The thickness of the ventricular wall and left ventricular end-diastolic diameter (LVEDD) were measured by M-mode ultrasound. The method of LVESD measurement was the same as that of LVEDD, and the left ventricular ejection fraction (LVEF) was calculated via the following formula: LVEF = (LVEDD- LVESD)/LVEDD.\u003c/p\u003e\n\u003ch3\u003eOment-1 measurement\u003c/h3\u003e\n\u003cp\u003eFasting blood (5 ml) was collected from each patient, and plasma was collected after centrifugation. The plasma samples were stored at \u0026minus;\u0026thinsp;80\u0026deg;C before every measurement. The plasma level of Oment-1 was measured by enzyme-linked immunosorbent assay (ELISA) by commercial kits (CUSABIO, Wuhan; Catalog No. CSB-E09745h) according to the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e\n\u003ch3\u003eRNA isolation and RT‒qPCR\u003c/h3\u003e\n\u003cp\u003eFasting blood samples were collected, and peripheral monocytes were isolated by commercially available kits (Beijing Solabor Technology Co., Ltd.). RNA was extracted via the TRIzol method (cat. no. 15596026; Invitrogen; Thermo Fisher Scientific, Inc.), and RNA quality and concentration were assessed by UV spectroscopy at 260 and 280 nm. A HiFiScript gDNA Removal cDNA Synthesis Kit (cat. no. CW2582S; CoWin Biosciences) was used for reverse transcription. MonAmp ChemoHS qPCR mix (cat. no. rn04005M; Monad Biotech Co., Ltd.) was used to detect MALAT1 expression, and β-actin was used as a housekeeping gene. The thermocycling conditions were as follows: 95\u0026deg;C for 15 min; 40 cycles at 95\u0026deg;C for 10 sec, 56\u0026deg;C for 30 sec and 72\u0026deg;C for 30 sec. Relative gene expression was calculated via the 2\u003csup\u003e\u0026minus;△△CT\u003c/sup\u003e method.\u003c/p\u003e \u003cp\u003eThe PCR primers were designed and synthesized by Sangon Biotech Co., Ltd. (Shanghai). The sequences of primers used were as follows:\u003c/p\u003e \u003cp\u003eβ-Actin: forward primer, 5'-AAGGCCAACCGCGAGAA-3'; reverse primer, 5'-ATGGGGGAGGGCATACC-3'. LncRNA MALAT1: forward primer 5'-TACCTAACCAGGCATAACA-3'; reverse primer 5'-GTAGACCAACTAAGCGAAT-3'\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll the statistical analyses were performed using SPSS software (version 26). The normality of the continuous data was examined via the Shapiro\u0026ndash;Wilk test. Normally distributed data are expressed as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs), whereas nonnormally distributed data are expressed as medians and interquartile ranges (IQRs). Independent sample t tests or Mann‒Whitney U tests were performed to assess the differences between two groups. Categorical variables are expressed as numbers and were compared using the chi-square test. Spearman's rank correlation analysis and Pearson\u0026rsquo;s correlation analysis were performed to evaluate the correlations between the study factors and the clinical and biochemical parameters. Binary logistic regression was performed to determine the contributions of the study parameters to the prediction of CHD onset. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of parameters for CHD in T2DM patients. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDemographic, clinical, and biochemical characteristics of the study subjects\u003c/h2\u003e \u003cp\u003eIn the present study, there were no significant differences in patients\u0026rsquo; baseline characteristics between T2DM patients and T2DM\u0026thinsp;+\u0026thinsp;CHD patients, including sex, BMI, family history, smoking status, or alcohol consumption, (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Compared with T2DM patients without CHD, the duration of diabetes was significantly longer in T2DM patients with CHD (P\u0026thinsp;=\u0026thinsp;0.019).\u003c/p\u003e \u003cp\u003eCompared with those in T2DM patients, SBP, HbA1c and FBG were significantly greater in T2DM\u0026thinsp;+\u0026thinsp;CHD patients (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas LVEF, TC, LDL-C and eGFR were significantly lower in T2DM\u0026thinsp;+\u0026thinsp;CHD patients (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no differences in DBP or other biochemical parameters, including TG, HDL-C, VLDL-C, ALT, AST, Cr, UA, CRP, FC-P, Hcy and 25(OH)d, between the two groups (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic, clinical and biochemical characteristics of the study subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2DM\u0026thinsp;+\u0026thinsp;CHD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.05\u0026thinsp;\u0026plusmn;\u0026thinsp;12.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.17\u0026thinsp;\u0026plusmn;\u0026thinsp;11.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (female/male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32/36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34/35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM Duration (years)\u003c/p\u003e \u003cp\u003eAbdominal circumferences(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.03\u0026thinsp;\u0026plusmn;\u0026thinsp;6.80\u003c/p\u003e \u003cp\u003e92.76\u0026thinsp;\u0026plusmn;\u0026thinsp;8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.95\u0026thinsp;\u0026plusmn;\u0026thinsp;6.61\u003c/p\u003e \u003cp\u003e92.96\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM Family history (yes/no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26/42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29/40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (yes/no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29/39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking alcohol (yes/no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24/44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21/48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130.16\u0026thinsp;\u0026plusmn;\u0026thinsp;16.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.41\u0026thinsp;\u0026plusmn;\u0026thinsp;17.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.57\u0026thinsp;\u0026plusmn;\u0026thinsp;13.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.22\u0026thinsp;\u0026plusmn;\u0026thinsp;10.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.34\u0026thinsp;\u0026plusmn;\u0026thinsp;27.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.45\u0026thinsp;\u0026plusmn;\u0026thinsp;26.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.90\u0026thinsp;\u0026plusmn;\u0026thinsp;14.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.78\u0026thinsp;\u0026plusmn;\u0026thinsp;11.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.04\u0026thinsp;\u0026plusmn;\u0026thinsp;31.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.92\u0026thinsp;\u0026plusmn;\u0026thinsp;21.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR(ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.59\u0026thinsp;\u0026plusmn;\u0026thinsp;22.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.56\u0026thinsp;\u0026plusmn;\u0026thinsp;18.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334.07\u0026thinsp;\u0026plusmn;\u0026thinsp;103.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314.25\u0026thinsp;\u0026plusmn;\u0026thinsp;90.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC-P (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHcy(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25(OH)D(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.74\u0026thinsp;\u0026plusmn;\u0026thinsp;6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBMI: body mass index; HbA1c: hemoglobin A1c; SBP: systolic pressure; DBP: diastolic blood pressure; FBG: fasting blood glucose; TC: total cholesterol; TG: triglycerides; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; VLDL-C: very low-density lipoprotein cholesterol; ALT: alanine aminotransferase; AST: aspartate aminotransferase; Cr: creatinine; eGFR: estimated glomerular filtration rate; UA: uric acid; CRP: C-reactive protein; FC-P: fasting C peptide; Hcy: homocysteine; 25 (OH) D: 25-hydroxyvitamin D; LVEF: left ventricular ejection fraction.\u003c/p\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, T2DM\u0026thinsp;+\u0026thinsp;CHD vs. T2DM\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOment-1 levels and MALAT1 expression in circulation\u003c/h2\u003e \u003cp\u003eIn the present study, compared to T2DM patients, plasma Oment-1 levels were significantly decreased in T2DM\u0026thinsp;+\u0026thinsp;CHD patients (15.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.55 pg/ml vs. 13.09\u0026thinsp;\u0026plusmn;\u0026thinsp;5.35 pg/ml, P\u0026thinsp;=\u0026thinsp;0.0043; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), whereas MALAT1 expression in peripheral blood cells was significantly increased in T2DM\u0026thinsp;+\u0026thinsp;CHD patients (1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 vs. 1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These findings indicated that reduced plasma Oment-1 levels and increased MALAT1 expression might be involved in the pathogenesis of CHD in T2DM patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of Oment-1 and MALAT1\u003c/h2\u003e \u003cp\u003eBivariate correlation analysis was performed for Oment-1 levels and MALAT1 expression to determine the relevant factors. Oment-1 was positively correlated with LVEF (r\u0026thinsp;=\u0026thinsp;0.223, p\u0026thinsp;=\u0026thinsp;0.001) and had no correlation with other parameters, including age, BMI, HbA1c, FBG, CRP, TC, TG, HDL-C, LDL-c, or eGFR. The expression of MALAT1 was negatively correlated with LVEF (r =-0.253, p\u0026thinsp;=\u0026thinsp;0.007) but positively correlated with age (r\u0026thinsp;=\u0026thinsp;0.451, p\u0026thinsp;=\u0026thinsp;0.002) and DM duration (r\u0026thinsp;=\u0026thinsp;0.201, p\u0026thinsp;=\u0026thinsp;0.019) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also investigated the correlation between Oment-1 and MALAT1 and found that the level of plasma Oment-1 was negatively correlated with MALAT1 expression (r = -0.19, p\u0026thinsp;=\u0026thinsp;0.026) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis of circulating Oment-1 and MALAT1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOment-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMALAT1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM Duration(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR(ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC-P(ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHcy(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data are presented as correlation coefficients (r).\u003c/p\u003e \u003cp\u003eAbbreviations are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e*Statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of Oment-1 and MALAT1 with the presence of CHD\u003c/h2\u003e \u003cp\u003eBinary logistic regression analysis was performed with the presence of CHD in T2DM patients as the dependent variable and the study variables (Oment-1, MALAT1) as independent predictors. The regression models and data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the first regression model, only the study variables were taken as predictors, and both Oment-1 and MALAT1 were significantly associated with the presence of CHD. In the second model, after adjusting for age, sex, duration of diabetes and HbA1c, Oment-1 and MALAT1 were still significantly associated with the presence of CHD. These associations were still significant even after adjusting for age, sex, duration of diabetes, HbA1c, BMI, LDL-C and eGFR, as shown in the third model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary logistic regression analysis of the independent factors for the presence of CHD (Oment-1 and MALAT1 as continuous variables)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOment-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8423\u0026ndash;0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMALAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.330\u0026ndash;23.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOment-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.776\u0026ndash;0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMALAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.363\u0026ndash;21.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.958\u0026ndash;1.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.034\u0026ndash;1.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.323\u0026ndash;2.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.564\u0026ndash;1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOment-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.750\u0026ndash;0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMALAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.217\u0026ndash;14.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.966\u0026ndash;1.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.102\u0026ndash;1.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.266\u0026ndash;3.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.513\u0026ndash;1.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.895\u0026ndash;1.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.398\u0026ndash;1.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.982\u0026ndash;1.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.442\u0026ndash;0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 1: not adjusted for any variable; Model 2: adjusted for age, sex, duration of diabetes and HbA1c; Model 3: adjusted for Model 2, BMI, LDL-C and eGFR. OR: odds ratio; 95% CI: 95% confidence interval.\u003c/p\u003e \u003cp\u003e*Statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eROC curve analysis\u003c/h2\u003e \u003cp\u003eROC curve analysis was performed to determine the diagnostic value of Oment-1 and MALAT1 for the presence of CHD in T2DM patients. On the basis of analyses of the ROC curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), plasma Oment-1 levels with cutoff values\u0026thinsp;\u0026le;\u0026thinsp;12.45 pg/ml were used to discriminate CHD patients from non-CHD patients with T2DM, with an AUC of 0.633 (95% CI: 0.540\u0026ndash;0.725; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a sensitivity of 75%, a specificity of 49%, a PPV of 59.9%, and an NPV of 65.9%. The cutoff value of MALAT1 expression was \u0026ge;\u0026thinsp;1.367, with an AUC was 0.749 (95% CI: 0.668\u0026ndash;0.830; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a sensitivity of 73%, a specificity of 66%, a PPV of 68.6%, and an NPV of 70.7%. Additionally, the AUC of Oment-1 combined with MALAT1 was 0.771 (95% CI: 0.694\u0026ndash;0.848; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the sensitivity was 66.7%, the specificity was 75.3%, the PPV was 73.3%, and the NPV was 69.1%, indicating that the combination of Oment-1 and MALAT1 had better diagnostic value for CHD in T2DM patients. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eT2DM patients are at a greater risk of developing cardiovascular disease, which is the leading cause of mortality among this population [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the present study, we investigated the correlations between circulating Oment-1 levels and MALAT1 expression in T2DM patients with CHD. Data have indicated that, compared with those in T2DM patients without CHD, circulating Oment-1 levels are significantly lower, whereas MALAT1 expression is increased in CHD patients. Additionally, the plasma Oment-1 level was negatively correlated with LVEF, whereas MALAT1 expression was positively correlated with age, DM duration and LVEF. Notably, Oment-1 and MALAT1 were inversely correlated with each other. Both factors, either independently or in combination, exhibited predictive value for CHD in T2DM patients.\u003c/p\u003e \u003cp\u003eIn the present study, we found that the level of circulating Oment-1 was lower in T2DM patients with CHD and that it has predictive value for CHD. Similar results have also been reported previously in CHD patients with diabetes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] or without diabetes[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The favorable effect of Oment-1 on CHD might be due to its endothelial cell protective and anti-atherosclerotic effects. Oment-1 has been found to partly ameliorate FFA-induced endothelial cell injury[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and improve endothelial function by activating the Akt/eNOS/NO[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and AMPK/PPARδ pathways[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], contributing to increased NO production and the inhibition of ER stress and oxidative stress. Oment-1 can modulate macrophage function and inhibit atherosclerosis formation and development[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIntriguingly, this was the first study to find that circulating Oment-1 levels were positively correlated with LVEF. Similar results have been reported in patients with dilated cardiomyopathy[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and in mouse models of heart failure[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], indicating that Oment-1 levels might also be related to ventricular diastolic function. In vivo studies have shown that Oment-1 can prevent pathological cardiac remodeling following ischemia[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and ameliorate ischemia-induced myocardial injury by activating mitophagy and maintaining dynamic mitochondrial homeostasis[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. An in vitro study indicated that Oment-1 could protect H9C2 cells from docetaxel-induced damage by reducing endoplasmic reticulum stress[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, Oment-1 is a protective factors against cardiomyocytes, and its effect on diabetic cardiomyopathy is a prospective area that needs to be further investigated.\u003c/p\u003e \u003cp\u003eIn this study, we detected the expression of the lncRNA MALAT1 in peripheral blood mononuclear cells from T2DM patients and found that the expression of MALAT1 in the circulation was increased in T2DM patients with CHD. Similar results have also been reported by Sohrabifar et al.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] in Iranian patients. Elevated expression of MALAT1 in the circulation has been reported in CHD patients[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and is associated with increased CHD severity[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and unstable angina[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], indicating that MALAT1 could be a biomarker for CHD screening and surveillance.\u003c/p\u003e \u003cp\u003eIn the present study, we found that MALAT1 expression in the circulation of T2DM patients was positively correlated with age and diabetes duration. No similar results have been reported previously in T2DM patients. A previous study performed in patients with periodontitis and healthy subjects revealed that blood MALAT1 expression was not correlated with age [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The data from the present study suggest that MALAT1 might be more susceptible to the influences of aging and duration in T2DM patients. MALAT1 is known to promote inflammation and oxidative stress. Thus, the elevated MALAT1 levels in older patients with longer durations of diabetes might reflect cumulative inflammatory damage, potentially exacerbating cardiovascular complications.\u003c/p\u003e \u003cp\u003eAdditionally, this was the first study to find that MALAT1 expression was negatively correlated with LVEF in patients with T2DM. A previous study performed by Qi et al.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] reported that, in hemodialysis patients with heart failure, lncRNA ENST00000561762 expression in peripheral blood mononuclear cells was negatively correlated with LVEF. Therefore, MALAT1 expression in peripheral blood might also play a crucial role in the diagnosis of cardiac function in T2DM patients. Some evidence has indicated that MALAT1 contributes to high glucose-induced cardiomyocyte damage by causing mitochondrial damage and oxidative stress[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It remains unclear whether MALAT1 expression in peripheral blood mononuclear cells reflects MALAT1 expression in heart tissue.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to investigate the correlation between circulating Oment-1 levels and MALAT1 expression in patients with T2DM. Our results revealed a significant negative correlation between Oment-1 and MALAT1. Given that Oment-1 has anti-inflammatory and antioxidative effects, whereas MALAT1 is associated with proinflammatory activity, our findings suggest a potential regulatory network involving adipokines (e.g., Oment-1) and lncRNAs (e.g., MALAT1) in the pathogenesis of diabetic vascular complications. However, further studies are needed to elucidate the underlying mechanisms and clinical implications.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, it is a single-center study with a relatively small sample size, which may cause patient selection bias. Second, the use of hypoglycemic or lipid-lowering drugs could influence circulating Oment-1 levels and MALAT1 expression. Additionally, since T2DM patients with CHD are older than those without CHD are, age-related factors might also affect Oment-1 and MALAT1 levels. Third, serum 25(OH)D levels could be influenced by vitamin D supplementation; therefore, its role requires further investigation. Future studies with larger sample sizes are needed to better understand the clinical implications of these findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eDecreased circulating Oment-1 levels and elevated MALAT1 expression are significantly associated with CHD in patients with T2DM, suggesting their potential as biomarkers for the noninvasive early detection of CHD in T2DM patients. However, further validation through large-scale, multicenter studies is needed to confirm their diagnostic efficacy and determine clinically relevant cutoff values.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Government-funded Clinical Medicine Outstanding Talent Training Project (ZF2025150), the Natural Science Foundation of Hebei Province (H2020206478), the Central Government Guides Local Science and Technology Development Project (246Z7711G) and the Projects of Medical Science Research of the Health Commission of Hebei Province, China (20210725, 20210513, 20210372 and 20170642).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Xinli Jiang (Department of Ophthalmology, The Third Hospital of Hebei Medical University, Shijiazhuang, China) for technical help and discussion of the results obtained in the experiments.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committee of the hospital and was conducted in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Author contributions\u003c/p\u003e\n\u003cp\u003eM.M.T., Y.K.L. and Y.L. conceived and designed the study. M.M.T., J.C.C. and M.L. performed the experiments and wrote, reviewed and revised the manuscript. M.M.T. and P.P.L. were involved in the analysis and interpretation of the data and performed the statistical analysis. Y.L., H.J.M. and Y.K.L. confirmed the authenticity of all the raw data. All the authors read and approved the final manuscript.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun, H., et al., IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. \u003cem\u003eDiabetes Res Clin Pract,\u003c/em\u003e 2022. \u003cstrong\u003e183\u003c/strong\u003e: p. 109119.\u003c/li\u003e\n\u003cli\u003eStrain, W.D. and P.M. Pald\u0026aacute;nius, Diabetes, cardiovascular disease and the microcirculation. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e, 2018. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 57.\u003c/li\u003e\n\u003cli\u003eBai, P., et al., Association Between Coronary Artery Disease and Plasma Omentin-1 Levels. \u003cem\u003eCureus,\u003c/em\u003e 2021. \u003cstrong\u003e13\u003c/strong\u003e(8): p. e17347.\u003c/li\u003e\n\u003cli\u003eBiscetti, F., et al., Association between plasma omentin-1 levels in type 2 diabetic patients and peripheral artery disease. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e, 2019. \u003cstrong\u003e18\u003c/strong\u003e(1): p. 74.\u003c/li\u003e\n\u003cli\u003eEimal Latif, A.H., et al., Association of Plasma Omentin-1 Levels With Diabetes and Its Complications.\u003cem\u003e Cureus\u003c/em\u003e, 2021. \u003cstrong\u003e13\u003c/strong\u003e(9): p. e18203.\u003c/li\u003e\n\u003cli\u003eBinti Kamaruddin, N.A., et al., Cytoprotective Role of Omentin Against Oxidative Stress-Induced Vascular Endothelial Cells Injury\u003cem\u003e.\u003c/em\u003e \u003cem\u003eMolecules\u003c/em\u003e, 2020. \u003cstrong\u003e25\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eZhao, A., et al., Omentin-1: a newly discovered warrior against metabolic related disease\u003cem\u003es.\u003c/em\u003e \u003cem\u003eExpert Opin Ther Targets\u003c/em\u003e, 2022. \u003cstrong\u003e26\u003c/strong\u003e(3): p. 275-289.\u003c/li\u003e\n\u003cli\u003eDu, Y., et al., Association between omentin-1 expression in human epicardial adipose tissue and coronary atherosclerosis.\u003cem\u003e Cardiovasc Diabetol\u003c/em\u003e, 2016. \u003cstrong\u003e15\u003c/strong\u003e: p. 90.\u003c/li\u003e\n\u003cli\u003eGenre, F., et al., Omentin: a biomarker of cardiovascular risk in individuals with axial spondyloarthritis. \u003cem\u003eSci Rep\u003c/em\u003e, 2020. \u003cstrong\u003e10\u003c/strong\u003e(1): p. 9636.\u003c/li\u003e\n\u003cli\u003eChristodoulatos, G.S., et al., Circulating Omentin-1 as a Biomarker at the Intersection of Postmenopausal Breast Cancer Occurrence and Cardiometabolic Risk: An Observational Cross-Sectional Study. \u003cem\u003eBiomolecule\u003c/em\u003es, 2021. \u003cstrong\u003e11\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eZhang, Y., et al., MicroRNAs or Long Noncoding RNAs in Diagnosis and Prognosis of Coronary Artery Disease\u003cem\u003e.\u003c/em\u003e \u003cem\u003eAging Dis\u003c/em\u003e, 2019. \u003cstrong\u003e10\u003c/strong\u003e(2): p. 353-366.\u003c/li\u003e\n\u003cli\u003eYan Y, Song D, Song X, Song C. The role of lncRNA MALAT1 in cardiovascular disease. \u003cem\u003eIUBMB Life\u003c/em\u003e, 2020 Mar; \u003cstrong\u003e72\u003c/strong\u003e(3): p. 334-342.\u003c/li\u003e\n\u003cli\u003eZhao, C., et al., The lncRNA MALAT1 participates in regulating coronary slow flow endothelial dysfunction through the miR-181b-5p-MEF2A-ET-1 axis\u003cem\u003e.\u003c/em\u003e \u003cem\u003eVascul Pharmacol\u003c/em\u003e, 2021. \u003cstrong\u003e138\u003c/strong\u003e: p. 106841.\u003c/li\u003e\n\u003cli\u003eQiu, S. and J. Sun, lncRNA-MALAT1 expression in patients with coronary atherosclerosis and its predictive value for in-stent restenosis\u003cem\u003e.\u003c/em\u003e \u003cem\u003eExp Ther Med\u003c/em\u003e, 2020. \u003cstrong\u003e20\u003c/strong\u003e(6): p. 129.\u003c/li\u003e\n\u003cli\u003eBarbalata T, Niculescu LS, Stancu CS, Pinet F, Sima AV. Elevated Levels of Circulating lncRNAs LIPCAR and MALAT1 Predict an Unfavorable Outcome in Acute Coronary Syndrome Patients. \u003cem\u003eInt J Mol Sc\u003c/em\u003ei. 2023 Jul 28;24(15).\u003c/li\u003e\n\u003cli\u003eSena, C.M., Omentin: A Key Player in Glucose Homeostasis, Atheroprotection, and Anti-Inflammatory Potential for Cardiovascular Health in Obesity and Diabetes. \u003cem\u003eBiomedicine\u003c/em\u003es, 2024. \u003cstrong\u003e12\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003eKadoglou NPE, Kassimis G, Patsourakos N, Kanonidis I, Valsami G. Omentin-1 and vaspin serum levels in patients with pre-clinical carotid atherosclerosis and the effect of statin therapy on them. \u003cem\u003eCytokine\u003c/em\u003e, 2021. \u003cstrong\u003e138\u003c/strong\u003e: p. 155364.\u003c/li\u003e\n\u003cli\u003eShi Z, Zheng Z, Lin X, Ma H. Long Noncoding RNA MALAT1 Regulates the Progression of Atherosclerosis by miR-330-5p/NF-\u0026kappa;B Signal Pathway\u003cem\u003e.\u003c/em\u003e \u003cem\u003eJ Cardiovasc Pharmaco\u003c/em\u003el, 2021. \u003cstrong\u003e78\u003c/strong\u003e(2): p. 235-246.\u003c/li\u003e\n\u003cli\u003eGrimaldi, A. and A. Heurtier, [Diagnostic criteria for type 2 diabetes]\u003cem\u003e. Rev Pra\u003c/em\u003et, 1999. \u003cstrong\u003e49\u003c/strong\u003e(1): p. 16-21.\u003c/li\u003e\n\u003cli\u003eKnuuti, J., et al., 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes\u003cem\u003e.\u003c/em\u003e \u003cem\u003eEur Heart J\u003c/em\u003e, 2020. \u003cstrong\u003e41\u003c/strong\u003e(3): p. 407-477.\u003c/li\u003e\n\u003cli\u003eKidney Disease: Improving Global Outcomes (KDIGO) Glomerular Diseases Work Group.KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. \u003cem\u003eKidney Int\u003c/em\u003e, 2021. \u003cstrong\u003e100\u003c/strong\u003e(4s): p. S1-s276.\u003c/li\u003e\n\u003cli\u003eEinarson TR, Acs A, Ludwig C, Panton UH. Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007-2017\u003cem\u003e.\u003c/em\u003e \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e, 2018. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 83.\u003c/li\u003e\n\u003cli\u003eAli, S., et al., Evaluation of serum adipokines (omentin-1 and visfatin) in coronary artery disease at a North Indian hospita\u003cem\u003el.\u003c/em\u003e \u003cem\u003eEndocr Regul\u003c/em\u003e, 2023. \u003cstrong\u003e57\u003c/strong\u003e(1): p. 262-268.\u003c/li\u003e\n\u003cli\u003eOnur, I., et al., Serum omentin 1 level is associated with coronary artery disease and its severity in postmenopausal women\u003cem\u003e.\u003c/em\u003e \u003cem\u003eAngiology\u003c/em\u003e, 2014. \u003cstrong\u003e65\u003c/strong\u003e(10): p. 896-900.\u003c/li\u003e\n\u003cli\u003eShibata, R., et al., Circulating omentin is associated with coronary artery disease in men. \u003cem\u003eAtherosclerosis\u003c/em\u003e, 2011. \u003cstrong\u003e219\u003c/strong\u003e(2): p. 811-4.\u003c/li\u003e\n\u003cli\u003eChen Y, Liu F, Han F, Lv L, Tang CE, Luo F. Omentin-1 Ameliorated Free Fatty Acid-Induced Impairment in Proliferation, Migration, and Inflammatory States of HUVECs\u003cem\u003e.\u003c/em\u003e \u003cem\u003eCardiol Res Prac\u003c/em\u003et, 2020. \u003cstrong\u003e2020\u003c/strong\u003e: p. 3054379.\u003c/li\u003e\n\u003cli\u003eDong Q, Xing W, Li K, Zhou X, Wang S, Zhang H. Tetrahydroxystilbene glycoside improves endothelial dysfunction and hypertension in obese rats: The role of omentin-1\u003cem\u003e.\u003c/em\u003e \u003cem\u003eBiochem Pharmacol\u003c/em\u003e, 2021. \u003cstrong\u003e186\u003c/strong\u003e: p. 114489.\u003c/li\u003e\n\u003cli\u003eLiu, F., et al., Omentin-1 protects against high glucose-induced endothelial dysfunction via the AMPK/PPAR\u0026delta; signaling pathway\u003cem\u003e.\u003c/em\u003e \u003cem\u003eBiochem Pharmaco\u003c/em\u003el, 2020. \u003cstrong\u003e174\u003c/strong\u003e: p. 113830.\u003c/li\u003e\n\u003cli\u003eHiramatsu-Ito, M., et al., Omentin attenuates atherosclerotic lesion formation in apolipoprotein E-deficient mice. \u003cem\u003eCardiovasc Res\u003c/em\u003e, 2016. \u003cstrong\u003e110\u003c/strong\u003e(1): p. 107-17.\u003c/li\u003e\n\u003cli\u003eLin, X., et al., Omentin-1 Modulates Macrophage Function via Integrin Receptors \u0026alpha;v\u0026beta;3 and \u0026alpha;v\u0026beta;5 and Reverses Plaque Vulnerability in Animal Models of Atherosclerosis\u003cem\u003e.\u003c/em\u003e \u003cem\u003eFront Cardiovasc Med\u003c/em\u003e, 2021. \u003cstrong\u003e8\u003c/strong\u003e: p. 757926.\u003c/li\u003e\n\u003cli\u003eTan, Y.L., et al., Tanshinone IIA Promotes Macrophage Cholesterol Efflux and Attenuates Atherosclerosis of apoE-/- Mice by Omentin-1/ABCA1 Pathway\u003cem\u003e.\u003c/em\u003e \u003cem\u003eCurr Pharm Biotechnol\u003c/em\u003e, 2019. \u003cstrong\u003e20\u003c/strong\u003e(5): p. 422-432.\u003c/li\u003e\n\u003cli\u003eHuang, Y., et al., Circulating Omentin-1 Levels Are Decreased in Dilated Cardiomyopathy Patients with Overt Heart Failure\u003cem\u003e.\u003c/em\u003e \u003cem\u003eDis Markers\u003c/em\u003e, 2016. \u003cstrong\u003e2016\u003c/strong\u003e: p. 6762825.\u003c/li\u003e\n\u003cli\u003eLi, F., et al., YiQiFuMai powder injection ameliorates chronic heart failure through cross-talk between adipose tissue and cardiomyocytes via up-regulation of circulating adipokine omentin. \u003cem\u003eBiomed Pharmacother\u003c/em\u003e, 2019. \u003cstrong\u003e119\u003c/strong\u003e: p. 109418.\u003c/li\u003e\n\u003cli\u003eIto, M., et al., Omentin Modulates Chronic Cardiac Remodeling After Myocardial Infarctio\u003cem\u003en.\u003c/em\u003e \u003cem\u003eCirc Rep\u003c/em\u003e, 2023. \u003cstrong\u003e5\u003c/strong\u003e(2): p. 46-54.\u003c/li\u003e\n\u003cli\u003eHu, J., et al., Omentin1 ameliorates myocardial ischemia-induced heart failure via SIRT3/FOXO3a-dependent mitochondrial dynamical homeostasis and mitophagy\u003cem\u003e.\u003c/em\u003e \u003cem\u003eJ Transl Med\u003c/em\u003e, 2022. \u003cstrong\u003e20\u003c/strong\u003e(1): p. 447.\u003c/li\u003e\n\u003cli\u003eLage R, Cebro-M\u0026aacute;rquez M, Rodr\u0026iacute;guez-Ma\u0026ntilde;ero M, Gonz\u0026aacute;lez-Juanatey JR, Moscoso I. Omentin protects H9c2 cells against docetaxel cardiotoxicity\u003cem\u003e.\u003c/em\u003e \u003cem\u003ePLoS One\u003c/em\u003e, 2019. \u003cstrong\u003e14\u003c/strong\u003e(2): p. e0212782.\u003c/li\u003e\n\u003cli\u003eSohrabifar N, Ghaderian SMH, Alipour Parsa S, Ghaedi H, Jafari H. Variation in the expression level of MALAT1, MIAT and XIST lncRNAs in coronary artery disease patients with and without type 2 diabetes mellitus\u003cem\u003e.\u003c/em\u003e \u003cem\u003eArch Physiol Biochem\u003c/em\u003e, 2022. \u003cstrong\u003e128\u003c/strong\u003e(5): p. 1308-1315.\u003c/li\u003e\n\u003cli\u003eLv F, Liu L, Feng Q, Yang X. Long non-coding RNA MALAT1 and its target microRNA-125b associate with disease risk, severity, and major adverse cardiovascular event of coronary heart disease.\u003cem\u003e J Clin Lab Anal\u003c/em\u003e, 2021. \u003cstrong\u003e35\u003c/strong\u003e(4): p. e23593.\u003c/li\u003e\n\u003cli\u003eZhu Y, Yang T, Duan J, Mu N, Zhang T. MALAT1/miR-15b-5p/MAPK1 mediates endothelial progenitor cells autophagy and affects coronary atherosclerotic heart disease via mTOR signaling pathway. \u003cem\u003eAging (Albany NY)\u003c/em\u003e, 2019. \u003cstrong\u003e11\u003c/strong\u003e(4): p. 1089-1109.\u003c/li\u003e\n\u003cli\u003eLiu S, Hou J, Gu X, Weng R, Zhong Z. Characterization of LncRNA expression profile and identification of functional LncRNAs associated with unstable angina\u003cem\u003e.\u003c/em\u003e \u003cem\u003eJ Clin Lab Anal\u003c/em\u003e, 2021. \u003cstrong\u003e35\u003c/strong\u003e(11): p. e24036.\u003c/li\u003e\n\u003cli\u003eGholami, L., et al., The lncRNA ANRIL is down-regulated in peripheral blood of patients with periodontitis\u003cem\u003e.\u003c/em\u003e \u003cem\u003eNoncoding RNA Res\u003c/em\u003e, 2020. \u003cstrong\u003e5\u003c/strong\u003e(2): p. 60-66.\u003c/li\u003e\n\u003cli\u003eQi, X., et al., The expression profile analysis and functional prediction of lncRNAs in peripheral blood mononuclear cells in maintenance hemodialysis patients developing heart failure.\u003cem\u003e Sci Rep\u003c/em\u003e, 2024. \u003cstrong\u003e14\u003c/strong\u003e(1): p. 29577.\u003c/li\u003e\n\u003cli\u003eWang, T., et al., MALAT1/miR-185-5p mediated high glucose-induced oxidative stress, mitochondrial injury and cardiomyocyte apoptosis via the RhoA/ROCK pathway. \u003cem\u003eJ Cell Mol\u003c/em\u003e \u003cem\u003eMed\u003c/em\u003e, 2023. \u003cstrong\u003e27\u003c/strong\u003e(17): p. 2495-2506.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes mellitus, Coronary heart disease, Omentin-1, Long noncoding RNA MALAT1","lastPublishedDoi":"10.21203/rs.3.rs-5044261/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5044261/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoronary heart disease (CHD) is a severe diabetic vascular complication and the main cause of mortality among diabetes patients. Early diagnosis of CHD could prevent its development. Both omentin-1 (Oment-1) and the long noncoding RNA MALAT1 (lncRNA MALAT1) can be detected in peripheral blood and exhibit protective or detrimental effects on CHD. However, whether these two factors could be predictive of CHD in T2DM patients remains unclear. Therefore, this study aimed to investigate the associations of circulating Oment-1 levels and the expression of MALAT1 with CHD in T2DM patients and to assess their predictive efficacy.\u003c/p\u003e \u003cp\u003eA total of 137 T2DM patients were enrolled, including 68 patients without CHD (T2DM group) and 69 patients with CHD (T2DM\u0026thinsp;+\u0026thinsp;CHD group). Clinical parameters were collected, and plasma Oment-1 was measured by enzyme-linked immunosorbent assay (ELISA). RNA was isolated from peripheral monocytes, and the expression of MALAT1 was determined by quantitative PCR. Cardiac function was measured by echocardiography.\u003c/p\u003e \u003cp\u003eCompared with that in T2DM patients, the plasma Oment-1 level was significantly lower, while the expression of MALAT1 was significantly greater in T2DM\u0026thinsp;+\u0026thinsp;CHD patients (all P values\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Bivariate correlation analysis indicated that Oment-1 was positively correlated with the left ventricular ejection fraction (LVEF) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). MALAT1 expression was negatively correlated with LVEF but positively correlated with age and DM duration (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Binary logistic regression suggested that Oment-1 and MALAT1 were significantly associated with the presence of CHD. Receiver operating characteristic (ROC) curve analysis demonstrated that both Oment-1 (AUC\u0026thinsp;=\u0026thinsp;0.663, sensitivity\u0026thinsp;=\u0026thinsp;75%, specificity\u0026thinsp;=\u0026thinsp;49%) and MALAT1 (AUC\u0026thinsp;=\u0026thinsp;0.749, sensitivity\u0026thinsp;=\u0026thinsp;73%, specificity\u0026thinsp;=\u0026thinsp;66%) had significant diagnostic value for CHD among T2DM patients. Notably, the combination of Oment-1 and MALAT1 exhibited better diagnostic efficiency (AUC\u0026thinsp;=\u0026thinsp;0.771, sensitivity\u0026thinsp;=\u0026thinsp;66.7%, specificity\u0026thinsp;=\u0026thinsp;75.3%).\u003c/p\u003e \u003cp\u003eIn conclusion, decreased circulating Oment-1 levels and increased MALAT1 expression are closely associated with CHD in T2DM patients, and their combination offers superior diagnostic efficiency, suggesting Oment-1 and MALAT1 may serve as a non-invasive tool for the early CHD detection and risk stratification in high-risk T2DM patients. Further studies are warranted to explore the pathophysiological mechanisms of Omentin-1 and MALAT1 in the pathogenesis of CHD in T2DM and to validate their clinical utility as potential biomarkers in large cohort studies.\u003c/p\u003e","manuscriptTitle":"Associations of circulating omentin-1 levels and long noncoding RNA MALAT1 expression with coronary heart disease in type 2 diabetes mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 01:13:48","doi":"10.21203/rs.3.rs-5044261/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-05-05T05:15:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-27T05:03:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-17T18:53:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33654236482615906602411495391945346912","date":"2025-04-12T05:32:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T15:13:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144031644440905244955260195452995537432","date":"2025-04-11T14:00:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224790168006593504689659900700242372764","date":"2025-04-11T12:15:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-11T08:18:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-10T17:04:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-01T07:49:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c8243710-6c32-4b2e-b166-42b4f8301844","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47058845,"name":"Health sciences/Biomarkers"},{"id":47058846,"name":"Health sciences/Cardiology"},{"id":47058847,"name":"Health sciences/Diseases"},{"id":47058848,"name":"Health sciences/Endocrinology"},{"id":47058849,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-05-12T16:08:49+00:00","versionOfRecord":{"articleIdentity":"rs-5044261","link":"https://doi.org/10.1038/s41598-025-01153-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-11 15:57:42","publishedOnDateReadable":"May 11th, 2025"},"versionCreatedAt":"2025-04-15 01:13:48","video":"","vorDoi":"10.1038/s41598-025-01153-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-01153-5","workflowStages":[]},"version":"v1","identity":"rs-5044261","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5044261","identity":"rs-5044261","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}