Skip to main content
Download PDF

Correlations between the long noncoding RNA MEG3 and clinical characteristics for diabetic kidney disease in type 2 diabetes mellitus

Diabetology & Metabolic Syndrome volume 16, Article number: 260 (2024) Cite this article

Abstract

Background and aims

Diabetic kidney disease (DKD) is a common complication of type 2 diabetes mellitus (T2DM) that leads to systemic inflammation. Maternally expressed gene 3 (MEG3) is a tumor suppressor that is involved in inflammation regulation. The current study investigated the association between DKD and the prevalence of the single-nucleotide polymorphisms (SNPs) of MEG3.

Methods

A total of 706 and 735 patients were included in the DKD and non-DKD groups, respectively. The five SNPs of MEG3, namely rs4081134 (G/A), rs10144253 (T/C), rs7158663 (G/A), rs3087918 (T/G), and rs11160608 (A/C), were genotyped using TaqMan allelic discrimination.

Results

Our results revealed that, in the DKD group, the distribution of the GG genotype of the MEG3 SNP rs3087918 was significantly lower than that of the wild-type genotype (AOR: 0.703, 95% CI: 0.506–0.975, P = 0.035). In addition, in the pre-ESRD DKD subgroup, the distribution of the TG + GG genotype of the MEG3 SNP rs3087918 was significantly lower than that of the wild-type genotype (AOR: 0.637, 95% CI: 0.421–0.962, P = 0.032). In addition, among men in the DKD subgroup, the distribution of the GG genotype of the MEG3 SNP rs3087918 was significantly lower than that of the wild-type genotype (AOR: 0.630, 95% CI: 0.401–0.990, P = 0.045). Glycated hemoglobin (HbA1c) level was significantly higher in all T2DM patients with the wild-type genotype of the MEG3 SNP rs3087918 (P = 0.020). In addition, HbA1c levels were significantly higher in male patients and male DKD patients with the wild-type genotype of the MEG3 SNP rs3087918 (P = 0.032 and 0.031, respectively).

Conclusion

MEG3 SNP rs3087918 is significantly less prevalent in patients with DKD, and the SNP rs3087918 of MEG3 is associated with lower HbA1c levels.

Introduction

Type 2 diabetes mellitus (T2DM) is increasingly prevalent in developed countries, with its prevalence estimated at 8.8% in 2015 [1]. Hyperglycemia and vascular inflammation play primary roles in the pathophysiology of T2DM [2]. T2DM is associated with several comorbidities; for example, patients with T2DM frequently develop cardiovascular diseases, which contribute to mortality [3, 4]. T2DM can also lead to the development of microvascular disorders, such as diabetic retinopathy [5].

Diabetic kidney disease (DKD) is a microvascular disorder of T2DM, which features proteinuria and damage to renal tissue [6]. In end-stage renal disease (ESRD; a form of DKD), glomerular filtration rate decreases considerably, and hemodialysis may be warranted to prevent mortality [7, 8]. DKD has several risk factors, such as hypertension [9], and high glucose levels in serum can lead to DKD progression [6]. As for genetic factors, the FRMD3 gene is associated with the development of DKD [10], and the single-nucleotide polymorphisms (SNPs) of the ADIPQQ gene contribute to an increased risk of DKD [11]. However, whether other genetic factors contribute to the development of DKD remains unclear.

Maternally expressed gene 3 (MEG3) is a long noncoding RNA that does not encode any protein, but this gene affects cell proliferation and the development of diseases such as cancer [12, 13]. A previous study revealed that the SNPs of MEG3 are correlated with a low incidence rate of breast cancer [14]. In addition, the SNPs of MEG3 can influence the development of hypertension [15]. However, few studies have evaluated the correlation between the SNPs of MEG3 and the development of DKD. Because the SNPs of MEG3 are associated with the development of diabetic retinopathy, which is a vascular complication of T2DM [16], a similar correlation may exist between MEG3 SNPs and other vascular complications of T2DM, such as DKD. Therefore, the present study evaluated the correlation between the genetic polymorphisms of MEG3 and the development of DKD. Subgroup analyses were also performed.

Materials and methods

Ethics

All interventions in the current study were conducted in adherence to the declaration of Helsinki in 1964 and its subsequent amendments. This study was approved by the Institutional Review Board of Chung Shan Medical University Hospital (project identification code: CS2-22190). All patients provided their written informed consent.

Patient selection

This prospective case–control study was conducted at Chung Shan Medical University Hospital, which is a tertiary hospital in central Taiwan. Patients with T2DM who visited the outpatient department of the hospital were included in this study, and patients who received hemodialysis or peritoneal dialysis were excluded. A total number of 1441 patients were included. The patients were further divided into DKD and non-DKD groups. A patient was defined as having DKD if they met any one of the following criteria: (1) the presence of T2DM, (2) an estimated glomerular filtration rate of < 60 mL/min/1.73 m2 in a biochemical examination, (3) a urinary albumin/creatinine ratio of > 30 mg g− 1 in a urinary analysis, and (4) a urinary albumin excretion rate of > 30 mg per day in a urinary analysis. Finally, a total of 706 and 735 patients were included in the DKD and non-DKD groups, respectively.

Medical condition and sample collection

The medical records of all the patients were reviewed to obtain the following information: age, gender, duration of diabetes, glycated hemoglobin (HbA1c), systolic blood pressure, diastolic blood pressure, serum creatinine, glomerular filtration rate, triglycerides (TGs), total cholesterol, HDL cholesterol, and LDL cholesterol. The patients’ most recent laboratory findings were included in the analysis. Samples were collected as follows. Venous blood was extracted from the patients and then transferred to a tube containing ethylenediaminetetraacetic acid. Thereafter, the venous blood sample was centrifuged and then stored in a refrigerator at − 80 °C. If the venous blood sample degraded before DNA analysis, the sample of the patient was excluded from the analysis.

DNA analysis of MEG3 SNPs by the real-time PCR

The MEG3 SNPs that including in the current study are: rs4081134 (G/A), rs10144253 (T/C), rs7158663 (G/A), rs3087918 (T/G) and rs11160608 (A/C). We selected these MEG3 SNPs because their minor alleles frequencies were higher than 5% and these SNPs were proved to associate with cancer development or diabetic complication [13, 14, 16, 17]. The DNA extraction and examination in the current study referenced to the intervantions in our earlier publication [16]. Firstly, we took the genome/DNA from leukocytes via the QIAamp DNA kits (Qiagen, Valencia, Valencia, CA, USA). We used the kit according to the manufacture’s instruction of DNA/genome isolation. The isolated DNA was put into the refrigerator with − 20 Celsius degree. Then five MEG3 SNPs which including rs4081134 (G/A), rs10144253 (T/C), rs7158663 (G/A), rs3087918 (T/G) and rs11160608 (A/C) were sequenced via the ABI StepOne Real-Time PCR System (Applied Biosystems, Foster City, California). Finally, the exact expressions of MEG3 SNPs were determined by the SDS version 3.0 software (Applied Biosystems).

Statistical analysis

The SAS version 9.4 (SAS Institute Inc, NC, USA) was consumed for the statistical analyses in the current study. The descriptive analysis was consumed for the demography and laboratory data between the two groups, and the independent T test and Chi-square test were consumed to compare these clinical characteristics between the two groups depending on the property of the characteristics. Then the multiple logistic regression model was consumed to compare the distribution of each MEG3 SNP between the two groups after controlling for age, the duration of diabetes, HbA1c, serum creatinine levels, glomerular filtration rate, systolic blood pressure, TG, HDL cholesterol and LDL cholesterol levels. In the next step, the whole DKD population was divided into the early DKD subgroup and the pre-ESRD DKD subgroup. The early DKD was defined as the chronic kidney disease of stage 1to stage 3a, while the pre-ESRD DKD was defined as the chronic kidney disease of stage 3b to stage 5. Then the same multiple logistic regression model was consumed to compared the distribution of each MEG3 SNP of the two DKD subgroups to the non-DKD group. In addition, the male population in both the DKD and non-DKD groups were extracted and the distribution of each MEG3 SNP were compared by same multiple logistic regression model. Finally, the whole study population were divided according to MEG3 rs3087918 genotypes, then the HbA1c levels in the T2DM patients with different clinical characteristics were compared via the usage of independent T test. A P value less than 0.05 was set as significant difference in the current study.

Results

Basic characteristics between DKD and non-DKD groups

The clinical characteristics of the two groups are presented in Table 1. The DKD group (63.91 ± 11.81 years) had a significantly higher mean age than the non-DKD group (58.64 ± 11.99 years, P < 0.001). The duration of diabetes was significantly longer in the DKD group than in the non-DKD group (P < 0.001). The DKD group had significantly higher HbA1c levels, systolic blood pressure, serum creatinine, glomerular filtration rate, TG, HDL cholesterol, and LDL cholesterol than the non-DKD group (all P < 0.05) (Table 1).

Table 1 Clinical and laboratory characteristics of patients with diabetic kidney disease and diabetic patients
Full size table

MEG3 genotypic frequency between DKD and non-DKD groups

Table 2 presents the variant genotypic frequencies and the wild-type genotypic frequencies of MEG3 for the two groups for comparison. In the DKD group, the GG genotype of MEG3 SNP rs3087918 was less prevalent than the wild-type genotype (AOR: 0.703, 95% CI: 0.506–0.975, P = 0.035). The other MEG3 SNPs did not significantly differ in frequency from wild-type MEG3 in both groups (all P > 0.05) (Table 2). The distribution of all MEG3 SNPs in the non-DKD groups and early DKD group was similar to that of wild-type MEG3 (all P > 0.05) (Table 3). However, in the pre-ESRD subgroup, the distribution of the TG + GG genotype of the MEG3 SNP rs3087918 was significantly lower than that of the wild-type genotype (AOR: 0.637, 95% CI: 0.421–0.962, P = 0.032) (Table 4). In addition, among men in the DKD subgroup, the distribution of the GG genotype of the MEG3 SNP rs3087918 was significantly lower than that of the wild-type genotype (AOR: 0.630, 95% CI: 0.401–0.990, P = 0.045) (Table 5).

Table 2 Odds ratio and 95% confidence interval of diabetic kidney disease associated with MEG3 genotypic frequencies
Full size table
Table 3 Odds ratio and 95% confidence interval of early diabetic kidney disease associated with MEG3 genotypic frequencies
Full size table
Table 4 Odds ratio and 95% confidence interval of pre-end stage renal disease associated with MEG3 genotypic frequencies
Full size table
Table 5 Odds ratio and 95% confidence interval of diabetic retinopathy associated with MEG3 genotypic frequencies in male group
Full size table

HbA1c levels in patients with different MEG3 SNP rs3087918 genotypes and clinical characteristics

Concerning the correlation of HbA1c, MEG3 SNP rs3087918 genotypes and clinical features, HbA1c levels were significantly higher in all T2DM patients with the TT genotype of the MEG3 SNP rs3087918 (P = 0.020), in all male patients (P = 0.032), and in male DKD patients with the TT genotype of the MEG3 SNP rs3087918 (P = 0.031). HbA1c expression did not significantly differ between genotypes of the MEG3 SNP rs3087918 among all female patients, the non-DKD group, the DKD group, and female patients with DKD (all P > 0.05) (Table 6).

Table 6 HbA1c level in diabetic patients according to MEG3 rs3087918 genotypes
Full size table

Discussion

In this study, the prevalence of the GG genotype of the MEG3 SNP rs3087918 was significantly lower than that of the wild-type genotype in DKD patients and male DKD patients. Moreover, in the pre-ESRD DKD subgroup, the prevalence of the TG + GG genotype of the MEG3 SNP rs3087918 was significantly lower than that of the wild-type genotype. Moreover, HbA1c levels were significantly higher in T2DM patients, all male patients, and male patients with DKD with the TT genotype of the SNP rs3087918.

Studies have uncovered some predisposing factors that lead to DKD development [6, 9]. The extent of hyperglycemia is a crucial factor in the development and progression of DKD [6]. Moreover, T2DM patients with blood pressure < 150 mmHg exhibited a 37% reduced risk of DKD [9]. Systemic inflammation is another predisposing factor for DKD development [18]; the inflammation marker of nuclear factor kappa-light-chain-enhancer of activated B cells is involved in DKD development [19]. Tumor necrosis factor also contributes to inflammation and DKD development in T2DM patients [20]. In addition to biochemical markers, genetic factors can alter the incidence of DKD in the T2DM population. The CARS gene is correlated with a higher likelihood of DKD development [10]. Moreover, the LRG1 gene can promote the development of DKD through angiogenesis [21]. In a study investigating the correlation between genetic polymorphisms and DKD development, the AFF3 SNP rs7583877 was associated with a higher risk of DKD [10]. In addition, the ICAM1 SNP rs5498 was more prevalent in patients with DKD [22]. Moreover, the AA genotype of the AKR1B1 SNP rs759853 was more strongly correlated with DKD risk than the wild-type genotype [23]. These genes demonstrated similar features of inflammation regulation, which is correlated with the biochemical risk factor for DKD [20]. The MEG3 gene can regulate cell proliferation and differentiation in several diseases, including inflammatory diseases or bone neoplasm [24]. A study on non-small-cell lung cancer revealed that tumor growth can be suppressed by MEG3 expression [12]. MEG3 expression can reduce liver cancer development by targeting miRNAs [25]. The MEG3 SNP can increase the incidence of ischemic stroke [26]. The MEG3 SNP rs7158663 variant demonstrated a higher distribution frequency in those without diabetic retinopathy [16]. Given that the MEG3 gene can regulate inflammation and that the SNPs of MEG3 can alter the incidence of cardiovascular diseases and diabetic retinopathy [15, 26, 27], we conjecture that the SNPs of MEG3 affect the development of DKD. This conjecture is made according to the findings of the present study.

In this study, in the DKD group, the GG genotype of the MEG3 SNP rs3087918 was significantly less prevalent than that of the wild-type genotype. In a previous study investigating the prevalence of MEG3 SNPs, breast cancer status was associated with a significantly lower prevalence of the TT genotype of the MEG3 SNP rs3087918 [14]. Diabetic retinopathy status was also associated with a significantly lower prevalence of the GA genotype of the MEG3 SNP rs7158663 [16]. Nevertheless, the correlations between kidney disease and MEG3 polymorphisms have remained unclear. To the best of our knowledge, our study is the first to highlight the relationship between DKD and the prevalence of the MEG3 SNP rs3087918. Furthermore, in the present study, all the DKD patients were diagnosed by the same physician; thus, DKD was likely to be diagnosed uniformly across patients. In addition, we adjusted for the effects of age, gender, duration of diabetes, hypertensive status, and dyslipidemia in the multiple logistic regression model, and all of them are known risk factors for DKD [7, 9, 28]. Consequently, the logistic regression revealed that MEG3 SNP rs3087918 prevalence may be an independent factor for DKD development. A previous study demonstrated that MEG3 participates in inflammation regulation and that MEG3 overexpression could reduce inflammation by inhibiting the secretion of interleukin and tumor necrosis factor [27]. Furthermore, according to an earlier study, MEG3 SNPs can alter inflammation status [29]. Elevated inflammation is a critical factor influencing the development of DKD [30]. Thus, reduced inflammation due to MEG3 SNPs is more likely to be present in the non-DKD population. In addition, MEG3 can reduce the risks of vascular endothelial injury and cardiovascular disease [27]. This finding indicates the effects of MEG3 SNP rs3087918 on vasculature, which is supported by vascular defects found in DKD patients in the aforementioned study. Altogether, the results reveal the lower prevalence of the GG genotype of the MEG3 SNP rs3087918 in patients with DKD. However, the exact mechanism underlying this phenomenon warrants further investigation.

In the subgroup analysis by DKD severity in the present study, pre-ESRD DKD status was associated with a lower prevalence of the TG + GG genotype of the MEG3 SNP rs3087918. This is the first study to uncover this relationship. Similar to the lower prevalence of the TG + GG genotype of the MEG3 SNP rs3087918 in the pre-ESRD DKD population, the prevalence of the TG genotype of the MEG3 SNP rs3087918 and that of the GG genotype of the MEG3 SNP rs3087918 were also marginally lower than that of the wild-type genotype. Consequently, we propose that the variant rs3087918 of MEG3 may reduce the risk of DKD. Thus, severe DKD status was observed to be significantly associated with a lower total prevalence of the MEG3 SNP rs3087918 in the present study. However, the prevalence of the MEG3 SNP rs3087918 was similar to that of its wild-type counterpart in the early-DKD population. The possible explanation is that the MEG3 SNP rs3087918 may regulate the pathway that against DKD development; thus, T2DM patients with such variation may develop milder DKD, but DKD development cannot be fully prevented because other factors such as hyperglycemia can contribute to DKD development. The GG genotype of the MEG3 SNP rs3087918 was less prevalent than the wild-type genotype among all men in this study, as well as among all patients enrolled in this study. A study demonstrated that men are mainly susceptible to DKD development [9]. The present study’s finding of the lower prevalence of the GG genotype of the MEG3 SNP rs3087918 in male patients with DKD implies that patients carrying MEG3 SNPs are highly susceptible to developing DKD.

In the present study, higher serum levels of HbA1c were noted in patients with T2DM, in all men, and in male patients with DKD who had the wild-type genotype of the MEG3 SNP rs3087918. Moreover, the relationship between the genotypes of the MEG3 SNP rs3087918 and HbA1c levels was marginally significant in the patients with DKD. These findings correspond to our previous results for the prevalence of the MEG3 SNP rs3087918. Moreover, HbA1c level was more strongly correlated with the prevalence rates of the MEG3 SNP rs3087918 variants among men (than among women or all patients) in this study. This result implies that the correlation between male DKD patients and the lower prevalence rates of MEG3 SNP rs3087918 variants in our earlier analysis is robust and not an artifact of chance. Drawing on these results, we propose that the MEG3 SNP rs3087918 variants are associated with lower HbA1c levels, and that HbA1c is an important risk factor for DKD. Thus, MEG3 SNP rs3087918 variants were significantly less prevalent in the patients with (vs. without) DKD in this study. Consequently, the MEG3 SNP rs3087918 variants may influence the risk of DKD in T2DM patients through HbA1c. Given that high HbA1c levels are attributed to cardiovascular diseases [31, 32], the MEG3 SNP rs3087918 may also influence the risk of T2DM-related cardiovascular diseases, which warrants further study.

The current study has some limitations. First, the DKD and non-DKD groups significantly differed in their basic characteristics. This may affect the integrity of our results despite the adjustment of potential confounders in the multiple logistic regression model. Second, the imbalance in group size between the non-DKD group and the pre-ESRD DKD subgroup may have led to bias. Moreover, with the case–control design of the current study, the mechanism underlying the correlation between MEG3 SNP rs3087918 variants and DKD progression remained unclear. Finally, all the patients in the current study were Han Taiwanese, and the findings may not be generalizable outside this population.

Conclusion

In conclusion, DKD status is associated with a lower prevalence of the MEG3 SNP rs3087918, especially in male patients. Furthermore, the MEG3 SNP rs3087918 is correlated with lower HbA1c levels in the T2DM population. Consequently, regular renal function tests should be performed in T2DM patients with the wild-type genotype of the MEG3 SNP rs3087918. Further large-scale prospective studies with adequate patient numbers should be conducted to investigate whether the genotype of the MEG3 SNP rs3087918 affects DKD progression and prognosis.

Data availability

All data produced and/or analyzed during this study is accessible from the corresponding author upon reasonable request.

References

  1. Koye DN, Magliano DJ, Nelson RG, Pavkov ME. The global epidemiology of diabetes and kidney disease. Adv Chronic Kidney Dis. 2018;25:121–32.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Henning RJ. Type-2 diabetes mellitus and cardiovascular disease. Future Cardiol. 2018;14:491–509.

    Article  CAS  PubMed  Google Scholar 

  3. Cloete L. Diabetes mellitus: an overview of the types, symptoms, complications and management. Nurs Stand. 2022;37:61–6.

    Article  PubMed  Google Scholar 

  4. Glovaci D, Fan W, Wong ND. Epidemiology of diabetes Mellitus and Cardiovascular Disease. Curr Cardiol Rep. 2019;21:21.

    Article  PubMed  Google Scholar 

  5. Kaul K, Tarr JM, Ahmad SI, Kohner EM, Chibber R. Introduction to diabetes mellitus. Adv Exp Med Biol. 2012;771:1–11.

    PubMed  Google Scholar 

  6. Bonner R, Albajrami O, Hudspeth J, Upadhyay A. Diabet Kidney Disease Prim Care. 2020;47:645–59.

    Google Scholar 

  7. Gupta S, Dominguez M, Golestaneh L. Diabetic kidney disease: an update. Med Clin North Am. 2023;107:689–705.

    Article  PubMed  Google Scholar 

  8. Reutens AT. Epidemiology of diabetic kidney disease. Med Clin North Am. 2013;97:1–18.

    Article  PubMed  Google Scholar 

  9. Alicic RZ, Rooney MT, Tuttle KR. Diabetic kidney disease: challenges, Progress, and possibilities. Clin J Am Soc Nephrol. 2017;12:2032–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Reidy K, Kang HM, Hostetter T, Susztak K. Molecular mechanisms of diabetic kidney disease. J Clin Invest. 2014;124:2333–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Han Q, Geng W, Zhang D, Cai G, Zhu H. ADIPOQ rs2241766 Gene Polymorphism and Predisposition to Diabetic Kidney Disease. J Diabetes Res. 2020; 2020: 5158497.

  12. Xu J, Wang X, Zhu C, Wang K. A review of current evidence about lncRNA MEG3: a tumor suppressor in multiple cancers. Front Cell Dev Biol. 2022;10:997633.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Ghafouri-Fard S, Taheri M. Maternally expressed gene 3 (MEG3): a tumor suppressor long non coding RNA. Biomed Pharmacother. 2019;118:109129.

    Article  CAS  PubMed  Google Scholar 

  14. Zheng Y, Wang M, Wang S, Xu P, Deng Y, Lin S, Li N, Liu K, Zhu Y, Zhai Z, Wu Y, Dai Z, Zhu G. LncRNA MEG3 rs3087918 was associated with a decreased breast cancer risk in a Chinese population: a case-control study. BMC Cancer. 2020;20:659.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Luo F, Wu Y, Ding Q, Yuan Y, Jia W. Rs884225 polymorphism is associated with primary hypertension by compromising interaction between epithelial growth factor receptor (EGFR) and miR-214. J Cell Mol Med. 2021;25:3714–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Chien HW, Wang K, Chao SC, Lee CY, Lin HY, Yang SF. The genetic variants of long noncoding RNA MEG3 and its Association to the clinical features of Diabetic Retinopathy. Curr Eye Res. 2024;49:980–7.

    Article  CAS  PubMed  Google Scholar 

  17. Hou Y, Zhang B, Miao L, Ji Y, Yu Y, Zhu L, Ma H, Yuan H. Association of long non-coding RNA MEG3 polymorphisms with oral squamous cell carcinoma risk. Oral Dis. 2019;25:1318–24.

    Article  PubMed  Google Scholar 

  18. Guo W, Song Y, Sun Y, Du H, Cai Y, You Q, Fu H, Shao L. Systemic immune-inflammation index is associated with diabetic kidney disease in type 2 diabetes mellitus patients: evidence from NHANES 2011–2018. Front Endocrinol (Lausanne). 2022;13:1071465.

    Article  PubMed  Google Scholar 

  19. Ziyadeh FN, Wolf G. Pathogenesis of the podocytopathy and proteinuria in diabetic glomerulopathy. Curr Diabetes Rev. 2008;4:39–45.

    Article  CAS  PubMed  Google Scholar 

  20. Lin YC, Chang YH, Yang SY, Wu KD, Chu TS. Update of pathophysiology and management of diabetic kidney disease. J Formos Med Assoc. 2018;117:662–75.

    Article  CAS  PubMed  Google Scholar 

  21. Hong Q, Zhang L, Fu J, Verghese DA, Chauhan K, Nadkarni GN, Li Z, Ju W, Kretzler M, Cai GY, Chen XM, D’Agati VD, Coca SG, Schlondorff D, He JC, Lee K. LRG1 promotes Diabetic kidney Disease Progression by enhancing TGF-β-Induced Angiogenesis. J Am Soc Nephrol. 2019;30:546–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhang X, Seman NA, Falhammar H, Brismar K, Gu HF. Genetic and Biological Effects of ICAM-1 E469K Polymorphism in Diabetic Kidney Disease. J Diabetes Res. 2020; 2020: 8305460.

  23. Dieter C, Lemos NE, de Faria Corrêa NR, Pellenz FM, Canani LH, Crispim D, Bauer AC. The A allele of the rs759853 single nucleotide polymorphism in the AKR1B1 gene confers risk for diabetic kidney disease in patients with type 2 diabetes from a Brazilian population. Arch Endocrinol Metab. 2022;66:12–8.

    PubMed  PubMed Central  Google Scholar 

  24. Sun H, Peng G, Wu H, Liu M, Mao G, Ning X, Yang H, Deng J. Long non-coding RNA MEG3 is involved in osteogenic differentiation and bone diseases (review). Biomed Rep. 2020;13:15–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Al-Rugeebah A, Alanazi M, Parine NR. MEG3: an oncogenic long non-coding RNA in different cancers. Pathol Oncol Res. 2019;25:859–74.

    Article  CAS  PubMed  Google Scholar 

  26. Han X, Zheng Z, Wang C, Wang L. Association between MEG3/miR-181b polymorphisms and risk of ischemic stroke. Lipids Health Dis. 2018;17:292.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Li Z, Gao J, Sun D, Jiao Q, Ma J, Cui W, Lou Y, Xu F, Li S, Li H. LncRNA MEG3: potential stock for precision treatment of cardiovascular diseases. Front Pharmacol. 2022;13:1045501.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Rayego-Mateos S, Rodrigues-Diez RR, Fernandez-Fernandez B, Mora-Fernández C, Marchant V, Donate-Correa J, Navarro-González JF, Ortiz A, Ruiz-Ortega M. Targeting inflammation to treat diabetic kidney disease: the road to 2030. Kidney Int. 2023;103:282–96.

    Article  CAS  PubMed  Google Scholar 

  29. Zhong C, Yao Q, Han J, Yang J, Jiang F, Zhang Q, Zhou H, Hu Y, Wang W, Zhang Y, Sun Y. SNP rs322931 (C > T) in miR-181b and rs7158663 (G > A) in MEG3 aggravate the inflammatory response of anal abscess in patients with Crohn’s disease. Aging. 2022;14:3313–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Pérez-Morales RE, Del Pino MD, Valdivielso JM, Ortiz A, Mora-Fernández C. Navarro-González JF. Inflammation in Diabetic kidney disease. Nephron. 2019;143:12–6.

    Article  PubMed  Google Scholar 

  31. Caussy C, Aubin A, Loomba R. The relationship between type 2 diabetes, NAFLD, and Cardiovascular Risk. Curr Diab Rep. 2021;21:15.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Sun B, Gao Y, He F, Liu Z, Zhou J, Wang X, Zhang W. Association of visit-to-visit HbA1c variability with cardiovascular diseases in type 2 diabetes within or outside the target range of HbA1c. Front Public Health. 2022;10:1052485.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful to the Human Biobank of Chung Shan Medical University Hospital, Taichung, Taiwan for sample preparation.

Funding

None.

Author information

Authors and Affiliations

  1. Division of Cardiology, Department of Internal Medicine, Yunlin Branch, Changhua Christian Hospital, Yunlin, Taiwan

    Ke-Hsin Ting

  2. Department of Nursing, Hungkuang University, Taichung, Taiwan

    Ke-Hsin Ting

  3. Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan

    Ke-Hsin Ting

  4. Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan

    Ke-Hsin Ting, Po-Yu Tsai, Chia-Yi Lee & Shun-Fa Yang

  5. School of Medicine, Chung Shan Medical University, Taichung, Taiwan

    Po-Jen Yang

  6. Department of Family and Community Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan

    Po-Jen Yang

  7. Division of Nephrology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan

    Po-Yu Tsai

  8. Department of Ophthalmology, Nobel Eye Institute, Taipei, Taiwan

    Chia-Yi Lee

  9. Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan

    Shun-Fa Yang

Authors
  1. Ke-Hsin Ting
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Po-Jen Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Po-Yu Tsai
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Chia-Yi Lee
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Shun-Fa Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

KHT participation the data interpretation, writing and revision of the manuscript. PJY participation the data interpretation, writing the manuscript. PYT carried out statistics analysis and interpretation of the data. CYL carried out statistics analysis and writing the manuscript. SFY participation the data interpretation, statistics analysis, writing and revision of the manuscript. The authors declare that there is no conflict of interest with the current publication, and all authors have approved the final version of the manuscript.

Corresponding author

Correspondence to Shun-Fa Yang.

Ethics declarations

Ethics approval and consent to participate

All interventions in the current study were conducted in adherence to the declaration of Helsinki in 1964 and its subsequent amendments. This study was approved by the Institutional Review Board of Chung Shan Medical University Hospital (project identification code: CS2-22190). All patients provided their written informed consent.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ting, KH., Yang, PJ., Tsai, PY. et al. Correlations between the long noncoding RNA MEG3 and clinical characteristics for diabetic kidney disease in type 2 diabetes mellitus. Diabetol Metab Syndr 16, 260 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-024-01502-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-024-01502-w

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

Contact us