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ORIGINAL ARTICLE
Year : 2022  |  Volume : 35  |  Issue : 3  |  Page : 1122-1128

Long noncoding RNA as a diagnostic tool in vascular complication of diabetes


1 Department of Internal Medicine, Shebin El-Kom Teaching Hospital, Menoufia, Egypt
2 Department of Internal Medicine, Faculty of Medicine, Menoufia University Hospital, Menoufia, Egypt
3 Department of Biochemistry, Faculty of Medicine, Menoufia University Hospital, Menoufia, Egypt
4 Department of Radiology, Faculty of Medicine, Menoufia University Hospital, Menoufia, Egypt

Date of Submission03-Mar-2022
Date of Decision03-Apr-2022
Date of Acceptance08-Apr-2022
Date of Web Publication29-Oct-2022

Correspondence Address:
Mohamed El Sayed Sakr
Shebin El-Kom, Menoufia
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/mmj.mmj_73_22

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  Abstract 


Background
At present, there is really no unique biomarker for detecting diabetes-related vascular complications. As a result, it is critical to investigate specific biomarkers for diagnosing, detecting, and catching the early stages of disease progression.
Objectives
To analyze the relationship of long noncoding RNA in patients with asymptomatic atherosclerotic diabetic.
Patients and methods
This case–control study included 312 participants recruited from Menoufia University Hospitals' Internal Medicine Outpatient Clinics and Inpatient Departments and divided into two groups: group I was the diabetic with subclinical atherosclerosis group, which included 176 patients with type 2 diabetes mellitus, with the carotid intima-media thickness assessed according to carotid intima-media thickness reference intervals, and group II included 176 apparently healthy individuals. All participants in the study underwent the following procedures: history taking; clinical examination; and laboratory examinations, including fasting and 2-h postprandial blood glucose levels, glycated hemoglobin, total lipid profile (total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol), and determination of gene expressions of long noncoding RNAs (SENCR) by real-time PCR. Carotid intima-media thickness was measured using duplex ultrasound (carotid intima-media thickness).
Results
In comparison with controls, there was a significant statistical increase in the expression levels of SENCR in the patient group's peripheral blood (P = 0.001). When the area under the curve (receiver operating characteristic) for the long noncoding SENCR gene was calculated, the area under the curve was 0.89, indicating an association between SENCR and atherosclerosis. At a cutoff point 1.68, the sensitivity was 92.5%, specificity was 92.8%, and accuracy was 92.7%.
Conclusion
Our results show an association between SENCR and atherosclerosis. Long noncoding SENCR expression levels could be used as a predictor for the detection of complications of diabetes mellitus (atherosclerosis).

Keywords: atherosclerosis, diabetes mellitus, glycated hemoglobin A, long noncoding, real-time PCR, RNA


How to cite this article:
Sakr ME, El Kafrawy NA, Zewain SK, Refaat SA, Hassanein SA. Long noncoding RNA as a diagnostic tool in vascular complication of diabetes. Menoufia Med J 2022;35:1122-8

How to cite this URL:
Sakr ME, El Kafrawy NA, Zewain SK, Refaat SA, Hassanein SA. Long noncoding RNA as a diagnostic tool in vascular complication of diabetes. Menoufia Med J [serial online] 2022 [cited 2024 Mar 28];35:1122-8. Available from: http://www.mmj.eg.net/text.asp?2022/35/3/1122/359521




  Introduction Top


Diabetes mellitus type 2 (T2DM) is a complex metabolic disease, in which hyperglycemia is caused by insulin resistance and beta-cell damage. T2DM incidence has increased rapidly in developing countries because average life expectancy has increased, obesity prevalence has increased, and lifestyle has become more westernized. Long-term complications of T2DM, on the contrary, are the leading cause of morbidity and mortality. T2DM is also a significant risk factor for cardiovascular disease (CVD) on its own. CVD is a major public health concern throughout the world[1].

Hyperglycemia is recognized as a risk factor for CVD on its own. It has been shown to accelerate and exacerbate atherosclerosis, which is a major cause of morbidity and mortality in DM[2].

There is currently no specific biomarker for detecting diabetic complications. As a result, it is critical to investigate specific biomarkers for diagnosing, detecting, and catching the early stages of disease development[3].

Long noncoding RNA (LncRNA), a type of noncoding RNA, is made up of transcripts with a length greater than 200 nucleotides. LncRNAs are translated from either strand and are classified according to their relationship to adjacent protein-coding genes as sense, antisense, bidirectional, intergenic, or intronic. LncRNAs play a role in the pathological progression of a variety of cancers, CVDs, and nervous system diseases[4].

In diabetes, recent work has shown that LncRNAs, as a class, are frequently dysregulated during pancreatic cell differentiation and in the presence of hyperglycemia and associated with growth factors. Additionally, they can exacerbate diabetic complications by altering inflammation, fibrosis, ER stress, oxidative stress, and mitochondrial dysfunction[5].

Much research on the smooth muscle of the human vascular system cells has revealed additional LncRNAs that are involved in vascular cell function. For example, SENCR (smooth muscle and endothelial cell-enriched migration/differentiation-associated LncRNA) is a LncRNA that regulates a large number of contractile genes as well as genes involved in the regulation of MYOCD, an important transcriptional regulator[6],[7] The LncRNA SENCR plays an important role in maintaining endothelial integrity. SENCR expression in endothelial cells was robustly induced by exposure to laminar shear stress both in vitro and in vivo. Accordingly, SENCR knockdown reduced endothelial cell angiogenic capacity by decreasing proliferation and migration, although the mechanism mediating this phenotype is unclear[8].

When a patient with diabetes is compared with a healthy individual, the incidence of coronary heart disease and peripheral vascular disease increases fourfold and more than tenfold[3]. Consequently, it is critical to elucidate the precise molecular mechanisms underlying the development and acceleration of atherosclerosis in diabetic patients[3].

The aim of this work was to analyze the correlation between LncRNA in asymptomatic atherosclerotic diabetic patients.


  Patients and methods Top


This study was done at the Internal Medicine Department in collaboration with the Clinical Biochemistry and Molecular Diagnostics Department, National Liver Institute, and Radiology Department, Faculty of Medicine, Menoufia University, in the duration between February 2020 and February 2021.

This case–control study included 312 participants (males and females) ranging in age from 33 to 65 years. Patients were recruited from Menoufia University Hospitals' Internal Medicine Inpatient and Outpatient Clinics.

The study participants were divided into two groups:

Group I (diabetic with subclinical atherosclerosis): it included 176 patients with T2DM (males and females), ranging in age from 34 to 57 years. According to the carotid intima-media thickness reference intervals, the carotid intima-media thickness was measured[9].

The normal reference limits of carotid intima-media thickness for women aged 18–29, 30–39, 40–49, and 50–59 years were 0.47, 0.59, 0.67, and 0.70 mm, respectively, and for men aged 18–29, 30–39, 40–49, and 50–59 years was 0.47, 0.62, 0.72, and 0.80 mm, respectively and the presence of subintimal lucency.

Group II (control group): it consisted of 176 apparently healthy individuals (males and females) ranging in age from 33 to 65 years.

The Menoufia Faculty of Medicine's Research Ethics Committee approved the study after all participants provided written consent.

Patients with a history of diabetic ketoacidosis or hypoglycemic coma in the previous 3 months; a history of an ischemic or hemorrhagic stroke; patients with uncontrolled hypertension or congestive heart failure; patients with fever; patients with inflammatory or infectious diseases; and patients with autoimmune, rheumatic, hematological, neoplastic, or other endocrine diseases were excluded. All participants were assessed for height, weight, BMI [BMI = weight (in pounds)/height (in inches) 2 × 703][10], waist circumference, hip circumference, and waist/hip ratio[11]. They were then subjected to history taking, clinical laboratory examination, and radiological examination.

Sample collection

Five milliliters of fasting venous blood was collected from each participant and divided into three samples: 3 ml was collected in a plain tube and centrifuged, and the resulting sera were used for biochemical studies. Two milliliters of blood was collected in an EDTA-contained sterile tube for glycated hemoglobin (HbA1c) estimation (Sysmex XT-1800i Shanghai, China, Japan). The remaining 2 ml was added to another EDTA-contained sterile tube and centrifuged at 4000 rpm for 20 min. The freshly obtained plasma was immediately separated for total RNA extraction. The Beckman Coulter (Synchron Cx9 ALX) Clinical Auto analyzer was used to measure fasting blood glucose (BG) (normal range, ≤100 mg/dl), 2-h postprandial blood sugar (normal range ≤140 mg/dl), and lipid profile [total cholesterol (normal range, ≤200 mg/dl), triglycerides (normal range, ≤180 mg/dl), low-density lipoprotein cholesterol (LDL-C) (normal range, ≤100 mg/dl), and high-density lipoprotein cholesterol (HDL-C) (normal range, ≥30 mg/dl)] in the blood (Beckman Instruments, Fullerton, California, USA). Urine samples were collected in a sterile plastic container for creatinine and albumin measurements (Beckman Instruments, Synchron CX9 ALX) for albumin-to- creatinine ratio.

The obtained fresh plasma was separated immediately for total RNA extraction using miRNeasy kit (Qiagen, Zweigniederlassung Österreich QIAGEN Straße 1 D-40724 Hilden Germany, USA). RNA yield and purity were assessed using the instrument of NanoDrop (Thermo Scientific, 168 3RD AVE WALTHAM, MA 02451 United states). RNA extract was kept at −80°C. SensiFAST cDNA synthesis kit from Germany was used for the step of reverse transcriptase and complementary DNA (cDNA) production. Each reaction was done on ice in a volume of 20 μl, containing 1 μl of RT enzyme, 4 μl of RT buffer, 10 μl of extracted RNA, and 5 μl of nuclease-free H2O. Incubation was carried out using 2720 thermocyclers (Applied Biosystems, 850 Lincoln Centre Drive, Foster City, CA, 94404, United States, Singapore, Singapore) for one cycle as follows: 12 min at 42°C, 6 min at 94°C for reverse transcriptase enzyme inactivation, and finally, for 4 min at 4°C. cDNA produced was stored at −20°C till the PCR step. Real-time PCR was carried out using SensiFASTTM SYBR Lo-ROX Kit, USA. A total volume of 20 μl was reached, containing 10 μl of SYBR green Master Mix, 1 μl of nuclease-free H2O, 6 μl of cDNA, and 1.5 μl of every primer (forward and reverse). The primer sequence was confirmed by the National Center for Biotechnology Information (NCBI). Primer sequence for SENCR was as follows: forward 5′-TCAGAAGAGGCCTTCAGAGC-3′, reverse 5′-CTCGAGAAGATGCGGATAGG-3′, and primer sequence for reference gene, glyceraldehyde 3-phosphate dehydrogenase, as follows: forward 5-GGGAAACTGTGGCGTGAT-3 and reverse, 5-GAGTGGGTGTCGCTGTTGA-3. The PCR condition for SENCR gene amplification included three phases: initial activation step at 94°C for 5 min followed by 55 cycles at 94°C for 30 s, 62°C for 40 s, 74°C for 1 min, and the final extension step at 72°C for 10 min. Lastly, recognition of fluorescence and analysis of data was performed using 7500 ABI PRISM (Applied Biosystems, USA) v. 2.0.1.

Radiological examination: neck ultrasound examination

All ultrasound examinations were carried out using logic P7 (General Electric, Boston, MA 02210, United States) ultrasound equipment with a 7.5-MHz linear transducer. For better visualization of the subintimal lucency, a gray-scale analysis of the carotid system was performed using harmonic frequencies. The intima-media thickness was measured in millimeters from the echogenic line of the intima to the echogenic line of the media.

Statistical analysis

All data were analyzed statistically using the Statistical Package for the Social Sciences (SPSS, IBM Corp. (2020). (Version 27.0) [Computer software]). The mean and SD of quantitative data were calculated, whereas qualitative data were expressed as frequency and percentages. The χ2 test was used to compare qualitative variables, whereas the Mann–Whitney and one-way analysis of variance tests were used to compare quantitative continuous data. A scatter plot was created for each scale to compare the accuracy of the models under consideration.

The area under the curve was calculated for better assessment of the cut-off point using receiver operating characteristic analysis. Statistical significance was defined as a P value less than 0.05. To determine the shape of the relationship between continuous variables and outcome, univariate analysis with nonlinear correlation (cubic spline functions) was used.


  Results Top


Patients' age ranged from 31 to 58 years, with mean age of 47.53 ± 10.3 years (37.23–57.8 years). There was no significant difference between the two groups regarding age (P = 0.09). The majority of included patients were females [166 (53.2%) vs. 146 (46.8%) males], with no significant difference between the two groups (P = 0.12). The mean duration of DM since the first diagnosis in the case group was 7.85 ± 5.2 years (2.65–13 years). Regarding comorbidities (hypertension and obesity), 73 (23.3%) patients were diagnosed with hypertension and 53 (16.9%) patients were diagnosed with obesity (BMI above 30), and 25 (8%) patients with hypertension and obesity. Obesity and hypertension were significantly higher in the case group (30 patients and 64 patients, respectively) than the control group (0 patients and 0 patients, respectively), with P value of 0.03 and 0.001, respectively. All patients selected were assessed for height, weight, BMI, waist circumference, hip circumference, and waist/hip ratio and correlated with long noncoding RNA. Mean height of selected patients was 164.3 ± 7.5 cm (156–171 cm), with P value of 0.07. On the contrary, weight was higher in the case group (94.6 ± 8.1 kg) than the control group (79.7 ± 5.2 kg), with P value of 0.001. BMI was also higher in the case group (32.19 ± 3.2) than the control group (26.6 ± 4.6), with P value of 0.001. Waist circumference and hip circumference were higher in the case group (94.4 ± 7.7 and 95.4 ± 7.3 cm, respectively) than the control group (78.2 ± 9.9 and 78.8 ± 8.8 cm, respectively), with P value of 0.001 and 0.001, respectively. Moreover, there was a difference in waist/hip ratio between the two groups (P = 0.001) [Table 1].
Table 1: The comparison of sociodemographic and anthropometric data between two groups

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All patients included were assessed for vitals and BG levels. Systolic and diastolic blood pressures, fasting BG levels, 2-h postprandial BG levels, and HbA1c were assessed and correlated with long noncoding RNA. There was difference regarding the mean systolic blood pressure between in the case group (137.7 ± 17.4) and the control group (105.8 ± 13) (P = 0.001). On the contrary, there was no difference in diastolic blood pressure between the two groups (P = 0.803). Logically, fasting BG levels, 2-h postprandial BG levels, and HbA1c were higher in the case group (150 ± 24.1, 280.4 ± 44.9, and 8.66 ± 1.4, respectively) than the control group (79.5 ± 8.7, 114.59 ± 12.8, and 5.3 ± 0.31, respectively), with P values of 0.001, 0.001, and 0.0002, respectively. All patients underwent lipid profile (triacyl-glycerides, LDL, HDL, and cholesterol), liver function tests (alanine transaminase and aspartate transaminase), kidney function tests (albumin/creatinine ratio), and complete blood picture (HB and platelets). Lipid profile, including triglycerides, LDL, and cholesterol, was significantly higher in the case group (172.35, 158.7, and 238, respectively) than the control group (122.5, 109, and 167, respectively), with P values of 0.001, 0.003, and 0.001, respectively. HDL was lower in the case group (43.6 ± 5.6) than the control group (47.6 ± 5.3), with P value of 0.001. We found no difference between the two groups in the liver function test (P = 0.23). Moreover, no significant difference between the two groups regarding hemoglobin and platelets, with P value of 0.24 and 0.68, respectively. However, the albumin/creatinine ratio was higher in the case group (48.68 ± 4.06) than the control group (22.8 ± 2.7), with P value of 0.001 [Table 2].
Table 2: The comparison between vital data and blood glucose levels between two groups

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A test of correlation of SENCR with age, duration of DM (in case of a group), height, weight, waist circumference, hip circumference, waist/hip ratio, and BMI was conducted. We found a significant correlation of SENCR with age, waist circumference, hip circumference, and BMI (R = 0.441, 0.381, 0.409, and 0.273, respectively), with P value of 0.001, 0.001, 0.001, and 0.001, respectively. There was no correlation with duration of DM and waist/hip ratio. We found that SENCR has a significant positive correlation with systolic blood pressure, fasting glucose, 2-h postprandial glucose, HbA1c, cholesterol, LDL, and hemoglobin in addition to a negative correlation with HDL, with P value of 0.001 [Table 3].
Table 3: Correlation between SENCR and sociodemographic data and anthropometric measures

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Long noncoding RNA genes (SENCR) were measured by real-time PCR for all included patients. The mean value of the long noncoding gene (SENCR) was significantly higher in the case group (1.68) than the control group (0.79), with P value of 0.0001. For a better assessment of the cutoff point of SENCR for diagnosis of vascular complication of DM, we calculate receiver operating characteristic for SENCR. Interestingly, we found that the area under the curve for SENCR shows an accuracy of 92.7% at a cutoff point of 1.68 [Figure 1].
Figure 1: ROC curve shows vascular changes in DM. DM, diabetes mellitus; ROC, receiver operating characteristic.

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  Discussion Top


LncRNA is critical for modulating a variety of cellular responses, system development, and pathogenesis[12].

Progression of atherosclerosis can trigger plaque rupture or erosion, leading to ischemic events like myocardial infarction and ischemic stroke. Despite major advances in the clinical management of atherosclerosis and its risk factors, it persists as the most common cause of death in both the United States and throughout the world[13].

MicroRNAs and LncRNAs are regulatory noncoding RNAs that have had a significant effect on research in a variety of fields, including CVD, diabetes, and cancer[14],[15].

In the current study, the mean age of included patients was 47.53 ± 12.3 years and in the control group was 44.6 ± 13.4 years. There were insignificant differences between the two groups regarding age, which agreed with Gomes CP,[16] as the mean age in the diabetic group was 57.5 ± 5.4 years whereas in the control group was 57.7 ± 6.7 years, with no significant difference in between. Regarding disease duration, the mean duration of DM since the first diagnosis in our studied diabetic group was 7.85 ± 5.2 years, whereas in the work done by Suwal et al.[3], the mean duration of DM was 4.65 ± 3.86 years.

Obesity could be determined using the BMI, and abdominal obesity can be evaluated by measuring the waist circumference. In this study, BMI and waist circumference of the case group were considerably greater than those of the control group, with a significant difference (P = 0.001).

In the present study, the mean waist circumference was 94.4 ± 7.7 in the patient group, whereas in the control group was 78.2 ± 9.9, with a significant difference in between, which is in line with the results in the study done by de Gonzalo-Calvo[16], who found that the mean waist circumference was 106.4 ± 9.8 in the patient group, whereas in the control group was 98.8 ± 8.9, with a significant difference in between the two groups.

In this study, BMI was significantly higher in the cases group (32.19 ± 3.6) than the control group (26.6 ± 4.6), which disagrees with Suwal et al.[3], as BMI has no significant difference between the diabetic group (25.25 ± 4.54) and control group (24.02 ± 5.89).

There was a significant difference in systolic and diastolic blood pressures between the patient and control groups. Logically, we found a risk of atherosclerosis in the case group than the control group regarding systolic blood pressure, whereas no significant difference regarding diastolic blood pressure, which was similar to that mentioned by de Gonzalo-Calvo[16].

Regarding HbA1c in patients with long-term uncontrolled diabetes, we found that HbA1c was significantly higher in the case group (8.66 ± 1.4) than the control group (5.3 ± 0.31), with a highly significant difference. This coincides with Suwal et al.[3], as HbA1c was 5.26 ± 0.48 in the control group and 10.42 ± 2.81 in DM group, with a highly significant difference in between.

In our study, a mean value of LncRNA genes expression (SENCR) was significantly higher in the diabetic group (1.68) than the control group (0.79), with a highly significant difference. Various reports indicate that the smooth muscle and EC-enriched migration/differentiation-associated long non-coding RNA (SENCR) was downregulated in diabetic mice's VSMCs and increased VSMC proliferation and migration via induction of FOXO1 and a SENCR target, the short transient receptor potential channel (TRPC6)[17],[18].

Additionally, SENCR is a well-established circulating biomarker for left ventricular dysfunction in type 2 diabetes[16].

Apart from its role in the separation of vascular smooth muscle cells and endothelial cells, SENCR has been proposed to influence coronary artery disease. This LncRNA was found to be downregulated in vascular smooth muscle cells from a T2DM mouse model, thereby promoting cell expansion and migration. Additionally, overexpression of SENCR protected mouse VSMCs from the effects of high glucose weight, and its decreased articulation has been associated with premature coronary artery disease in humans[6],[19],[20].

SENCR knockdown reduced endothelial cell angiogenic capacity by decreasing proliferation and migration, although the mechanism mediating this phenotype is unclear. SENCR expression was also decreased among patients with coronary artery disease[21].

There is a significant positive correlation in the current study of SENCR with age, cholesterol, and LDL and a negative correlation with HDL. This is in line with other studies[18],[22], which stated that SENCR expression levels were correlated with age and blood lipid levels. Previously published research has established SENCR's role in smooth muscle cell differentiation and the regulation of early EC commitment[17],[23]. SENCR's mechanism of action in these or other cell types, however, is unknown. The interaction of SENCR with a noncanonical RNA-binding domain of cytoskeletal-associated protein 4 (CKAP4), a relatively unstudied cytosolic protein in the vessel wall, is demonstrated. Taken together, these findings suggest a role for SENCR in maintaining vascular EC membrane homeostasis[20].

The LncRNA profiles of patients with type 2 diabetes and diabetic nephropathy were analyzed by Dev K, Sharma SB et al.[24] (DN). A total of 245 LncRNAs were found to be upregulated and 680 downregulated in the serum of diabetic patients compared with healthy individuals, whereas 45 and 813 LncRNAs were found to be upregulated and downregulated in the serum of patients with DN compared with diabetic patients.

The LncRNA smooth muscle and endothelial cell-enriched migration/differentiation-associated long noncoding RNA (SENCR) inhibits smooth muscle cell migration[25]. Owing to their unique characteristics, small dense LDL particles are more atherogenic, which includes a lower affinity for LDL receptors, increased penetration into the subendothelial layer, a longer half-life, and decreased resistance to oxidative stress[1]. Glycated apolipoprotein B may be used as a proxy for subclinical atherosclerosis[2].

Increased lipoprotein (a) levels are associated with poor CVD outcomes and are a predictor of recurrent CVD events in patients with DM[3].


  Conclusion Top


Our results show an association between SENCR and early vascular changes in DM. SENCR could be used as a predictor for detection of vascular complications of DM. LDL, cholesterol, HbA1c, and BMI are considered independent risk factors for developing atherosclerosis.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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