Abstract
Objectives
Diabetic nephropathy (DN) is one of the most common and serious complications of diabetes. The purpose of this study was to explore the relationship between serum microRNA-338-3p (miR-338-3p) and miR-150-5p and bone metabolic markers in patients with DN at different stages.
Methods
A total of 153 patients diagnosed and treated in the Department of Nephrology from July 2020 to October 2021 were selected as the study objects. According to the staging criteria of diabetic nephropathy and 24-h urinary albumin quantitative level, the patients were divided into control group (35 cases), microalbuminuria group (37 cases), clinical stage albuminuria group (27 cases) and renal failure group (54 cases). Gene expressions were measured by real-time fluorescence quantitative PCR. The correlation was analyzed by Spearman. Serum miR-338-3p and miR-150-5p in the prediction of renal failure in DN was analyzed by ROC curve.
Results
The levels of urinary albumin and serum creatinine were markedly increased with the increase of DN stage (p < 0.05). Compared with the microalbuminuria group, the expression levels of serum miR-338-3p, serum miR-150-5p, 25(OH)-D, BGP and PINP were obviously decreased, but the expression of parathyroid hormone (PTH) and type I collagen (β-CTX) was largely increased in clinical proteinuria group (p < 0.05). Compared with the clinical proteinuria group, the expression levels of serum miR-338-3p, serum miR-150-5p, 25(OH)-D, BGP and PINP were largely decreased, but the expression of PTH and β-CTX was obviously increased in the renal failure group (p < 0.05). Spearman correlation results showed that serum expressions of miR-338-3p and miR-150-5p were negatively correlated with PTH and β-CTX, and positively correlated with 25(OH)-D, BGP and PINP (p < 0.05). ROC curve analysis showed that the AUC of serum miR-338-3p and miR-150-5p was 0.896 with the specificity and sensitivity of 96.66% and 73.47%, which had certain predictive value for the occurrence of renal failure in DN.
Conclusions
The expression levels of serum miR-338-3p and miR-150-5p were significantly correlated with bone metabolism markers. The combined test can provide new ideas and insights for the clinical treatment of osteoporosis in DN.
Keywords: Different staging, DN, MiR-338-3p, MiR-150-5p, bone metabolic markers, correlation
Introduction
Diabetes is a non-communicable chronic disease. According to relevant statistics, the number of patients with diabetes worldwide has exceeded 400 million, accounting for more than 8% of the total adult population [1]. Based on the different pathological types, diabetes can be divided as type 1 and type 2 diabetes. Type 2 diabetes is the main type of diabetes, accounting for more than 80% of the total diabetic cases. With the improvement of people’s living standards and the changes of living and eating habits, the incidence of diabetes has largely increased with a younger trend. Diabetes nephropathy (DN) is the most common microvascular complication of diabetes with hidden early onset and complex pathogenesis. It is believed that the occurrence and development of DN may be the result of the comprehensive action of environmental, genetic and other factors [2]. DN can gradually develop renal damage, hypertension and edema, and finally progress to an advanced stage, with severe renal failure, which is one of the main causes of death in diabetic patients. Renal failure refers to the decline of renal function caused by various acute and chronic causes, and some clinical syndromes appear, mainly manifested as the retention of metabolites and toxins, the acid-base balance disorder of water and electrolytes, and some endocrine dysfunction. Therefore, it is important to search for relevant biomarkers for early diagnosis and timely treatment.
MicroRNAs (miRNAs) are a kind of small molecule RNA, which widely exist in numerous pathophysiological processes. Many studies have found that the level of miRNAs is closely related in the occurrence and development of DN, which can be used as an important indicator to evaluate the patient’s condition [3, 4]. MiR-338-3p and miR-150-5p are both members of the miRNA family, whose expressions have been confirmed to be significantly downregulated in renal cell carcinoma, colorectal cancer, breast cancer and other diseases. Additionally, they may inhibit proliferation, invasion, migration and other biological behaviors through the 3′ untranslated region of mRNA, thereby playing an important role in the disease progress. It is shown that miR-338-3p can inhibit the expression of osteoblast differentiation markers, thereby reducing osteoblast differentiation [5]. MiR-338-3p plays an important role in osteoblast differentiation of bone marrow stromal cells and acts as a potential regulator of osteoporosis through its effects on osteoblasts. Thus, miR-338-3p maybe a potential target for the treatment of a variety of diseases, and is closely related to the occurrence and development of osteoporosis. In addition, bioinformatics predictions show that the 3′ UTR of the RUNX2 gene contains a target sequence of miR-338-3p, and the dual-luciferase reporter gene assay further confirms that RUNX2 is a direct target of miR-338-3p [6]. MiR-150-5p plays a significant role in many fields, but its mechanism of regulation of osteogenic differentiation in bone marrow mesenchymal stem cells is not still clear. Previous studies have found that the downstream target genes insulin-like growth factor (IGF-1) and insulin-like growth factor binding protein 3 (IGFBP3) closely related to miR-150-5p jointly promote osteogenic differentiation and bone matrix synthesis of bone marrow mesenchymal stem cells, and the increase of IGF-1 and IGFBP3 is tightly associated with the occurrence of osteoporosis [7].
DN is the main cause of end-stage renal disease. It has been reported that almost half of DN patients are accompanied with microalbuminuria [8]. With the development of the disease, the DN patient may develop into bone transformation, bone sclerosis and even osteoporosis if not treated in time, which will seriously affect the patient’s life. It is generally believed that the metabolism of calcium and phosphorus in the body is mainly regulated by parathyroid hormone (PTH) and 1,25-(OH)2-D3 hormone acting on kidney and bone. As an active metabolite of vitamin D3, 1,25-(OH)2-D3 binds to its receptor after entering small intestinal epithelial cells. On the one hand, 1,25-(OH)2-D3 changes the phospholipid structural composition of brush border membrane, increases calcium permeability, and on the other hand, promotes the biosynthesis of calcium transport-related proteins [9]. PTH increases 1,25-(OH)2-D3 production by enhancing renal 1a hydroxylase activity, thereby promoting intestinal calcium absorption [10]. PTH, 25 hydroxyvitamin D (25(OH)-D), osteocalcin (BGP), type I collagen carboxy-terminal peptide β degradation product (β-CTX) and type I precollagen amino-terminal peptide (PINP) are all markers related to bone metabolism, which are closely related to bone mineral density. MiRNAs play an important role in bone formation and bone resorption. It has been shown that hsa-miR-29b inhibits alkaline phosphatase (ALP) activity and reduces bone calcium deposition in osteoblasts by targeting Col1A1 and SPARC [11]. In addition, the study has found that elevated serum miR-154-5p levels and decreased osteocalcin levels may affect osteogenesis and proteinuria in type 2 diabetes and may identify new targets for the diagnosis and treatment of DN and osteoporosis [12]. However, the correlation between miR-338-3p and miR-150-5p with bone metabolic markers is not clear.
Based on previous studies that the relationship between miR-338-3p and miR-150-5p and osteogenic differentiation was found, patients with DN were selected as the study subjects and were grouped according to the staging standard of DN and the quantitative level of 24-h urinary albumin in this study. We aimed to explore the correlation of serum miR-338-3p and miR-150-5p with bone metabolic markers in patients with different stages of DN.
1. Materials and methods
1.1. General materials
A total of 264 patients with DN diagnosed and treated in the Department of Nephrology of our hospital from July 2020 to October 2021 were randomly selected based on a convenience sampling method. 153 patients were selected according to the inclusion and exclusion criteria as the study subjects, including 94 males and 59 females, with an average age of 66.03 ± 8.24 years, an average body mass index of 22.27 ± 1.18 kg/m2, and an average course of 12.19 ± 4.28 years. According to the staging criteria of DN and the quantitative level of 24-h urinary albumin, the patients were divided into four groups: control group including 35 cases (24-h urinary albumin was normal), microalbuminuria group including 37 cases (24-h urinary albumin was between 30-300 mg/24 h), clinical proteinuria group including 27 cases (24-h urinary albumin was > 300 mg/24 h and renal function was normal), and renal failure group including 54 cases (these cases met all the following three items: serum creatinine level was increased by 0.5 mg/dl, or serum creatinine was increased by 50%, and 24-h urine albumin > 150 mg together with urea nitrogen > 21.4 mmoI/L). All patients were treated with oral antidiabetic drugs, insulin, SGLT2 inhibitors and other basic treatments, which had no effect on the study results. Inclusion criteria: (1) All subjects met the relevant diagnostic criteria for diabetes [13]. (2) All subjects met the clinical diagnostic criteria for DN [14]. These Subjects had a clear history of DM and a causal relationship with changes in urine protein and renal function, without other primary and secondary glomerular diseases and systemic diseases. On this basis, DN could be diagnosed if one of the following conditions was met. a: random urinary albumin/creatinine ratio ≥30 mg/g or urinary albumin excretion rate ≥30 mg/24 h; b: Excluding infection and other interfering factors, the urinary albumin/creatinine ratio or urinary albumin excretion rate were measured repeatedly within 3 to 6 months, and 2 out of 3 times reached or exceeded the critical value; c: Estimated glomerular filtration rate was below 60 mL·min−1· (1.73 m2)−1 for more than 3 months; d: Renal biopsy was consistent with pathological changes of DN. (3) All subjects met the diagnostic criteria for osteoporosis [15]. Based on dual-energy X-ray absorptiometry, subjects were defined as osteoporosis when the decrease in bone mineral density was exceeded or equal to 2.5 standard deviations, that is, a T-value ≤ −2.5. (4) The clinical data were complete, and all subjects signed the informed consent and actively cooperated. Exclusion criteria: (1) The subjects with gestational diabetes and other types of diabetes. (2) The subjects with other nephropathy besides DN. (3) The subjects with other endocrine diseases or autoimmune diseases. (4) The subjects with diseases history that induced osteoporosis, such as chronic liver function injury, tumor disease, etc. (5) The subjects with history of taking vitamin D, glucocorticoids, heparin and other drugs affecting bone metabolism within half a year. (6) The subjects with family history of osteoporosis. (7) The subjects with a history of the use of hydroxychloroquine. (8) Patients with a history of fractures. The process of general data selection was shown in Figure 1.
Figure 1.
The process of general data selection.
1.2. Examination methods
(1). Urine albumin test method:
Starting with the first urination in the morning, all urine from 24 h was continuously collected, followed by a quantitative urine albumin test. Urine albumin was quantified by a fully automated biochemical instrument. Briefly, 10 mL of the specimen was taken and centrifuged at 1500 × g for 10 min to collect the supernatant. 10 μl of supernatant was added to the sample tray of the instrument by using a specific dispenser or automated dispensing system depending on the model. Then, 250 μl of buffer and 50 μl of antiserum were added to each sample well, and the reaction wavelength was set to 340 nm with the reaction temperature of 37 °C, and the assay time limit of 300 s. Close the cuvette, incubate for a certain amount of time (typically 30 min), and then perform multiple washes to remove unbound material. Add a chromogenic substrate, such as TMB, and incubate in the dark until the color change was complete. The reaction was terminated with the addition of stop solution, and the optical density (OD) value of each well was immediately measured at a wavelength of 450 nm. The urine albumin concentration in the sample was calculated according to the standard curve, and the normal range was less than 30 mg/24 h.
(2). Creatinine measurement method:
The working solution (consisting of an equal amount of picric acid and sodium hydroxide solution) was mixed and allowed to stand for 15 min. Mix 2000 μL of working solution with 200 μL of standard or serum sample. At a wavelength of 505 nm, read the initial absorbance (A1) of the mixture at a time point of 20 s. After 80 s, the absorbance (A2) of the mixture was measured again, also at a wavelength of 505 nm. The change in absorbance of the orange-yellow complex was calculated according to the equation: A = A1 − A2, which was proportional to the creatinine concentration. By comparing the standard curves, the concentration of creatinine in the sample was calculated. The standard curve was a pre-prepared creatinine solution of different concentrations and its corresponding absorbance value. Normal values: 54-106 μmoI/L for males and 44-97 μmol/L for females.
(3). Urea nitrogen detection method:
The urea nitrogen level was detected by urease method, the liquid sample was directly detected, and the turbid sample was centrifuged to take the supernatant. Preheat the spectrophotometer/microplate reader for 30 min, adjust the wavelength to 630 nm, and adjust the distilled water to zero. The 1 mg/mL urea nitrogen standard solution was diluted to 25 μg/mL with distilled water. After mixing, a water bath was at 37 °C for 10 min, the wavelength was 640 nm, the optical diameter was 1 cm, the double distilled water was adjusted to zero, and the absorbance value of each tube was measured. The normal value of fasting urea nitrogen in normal adults was 3.2 ∼ 7.1 mmol/L.
(4). Detection of the miR-338-3p and miR-150-5p expression levels:
The expression levels of miR-338-3p and miR-150-5p in sera of patients with DN at different stages were detected by real-time fluorescent quantitative PCR. The total RNA from serum in DN patients and control subjects was extracted by using the TRIzol reagent according to the operation instructions (Invitrogen, CarlsbadCA, USA). The RNA purity and concentration were examined by Nanodrop spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Then, the RNA was reverse-transcribed to cDNA using a reverse transcription system (Thermo Scientific, CA, USA). The levels of miR-338-3p and miR-150-5p were detected using real-time PCR 2 × SYBR qPCR Mix (Beijing Zhongman Biotechnology Co., Ltd., Beijing, China) on the ABI PRISM 7500 Sequence Detection System (Applied Biosystems, Foster City, CA, USA). The primer sequences were as follows: 5′ -GTGCAGGGTCCGAGGT -3′ (miR-338-3p forward), 5′-AGATCAGAAGGTGATTGTGGCT-3′ (miR-338-3p reverse); 5′-TCTCCCAACCCTTGTACC-3′ (miR-150-5p forward), 5′-GAATACCTCGGACCCTGC-3′ (miR-150-5p reverse); 5′-CTCGCTTCGGCAGCACA -3′ (U6 forward), 5′-AACGCTTCACGAATTTGCGT -3′ (U6 reverse). The amplification system contained 2 μL of cDNA, 0.4 μL forward primer, 0.4 μL reverse primer, 7.2 μL H2O2 and 10 μL SYBR. The amplification conditions were 25 °C for 10 min, 48 °C for 30 min and 95 °C for 5 min. U6 acted as experimental references.
(5). Detection of the bone metabolism index:
3 mL of fasting blood was collected from the patient and centrifuged at the speed of 3000 r/min to collect the supernatant. The level of PTH was detected by magnetic particle chemiluminescence. The level of serum 25 (OH)-D was detected by chemiluminescence immunoassay. The levels of BGP, β-CTX and PINP were detected by electrochemiluminescence. Among them, the magnetic particle detector (model: BEF-1) was purchased from Tianjin Tianhe Analytical Instrument Co., Ltd. (Tianjin, China), the PTH detection kit was purchased from Shanghai Enzyme-linked Biotechnology Co., Ltd. (Shanghai, China), the chemiluminescence immunoassay analyzer (model: BKI4200) purchased from Huanxi Medical Equipment Co., Ltd. (Guizhou, China), 25(OH)-D detection kit was purchased from Guangzhou Fikang Biotechnology Co., Ltd. (Guangdong, China), the electrochemiluminescence analyzer (model: LK5100) was purchased from Tianjin Lanlik Chemical Electronic High-tech Co., Ltd. (Tianjin, China) and BGP, β-CTX, and PINP detection kits were purchased from Shanghai Ruifan Biotechnology Co., Ltd. (Shanghai, China).
1.3. Statistical analysis
SPSS 21.0 data software was used to analyze different indicators and data. The measurement data were all in accordance with normal distribution, and were expressed as mean ± standard deviation (SD). Comparison between two groups was conducted using t-test, and among multiple groups was conducted using single-factor multiple-sample mean test. The relationship between the expression of miR-338-3p and miR-150-5p with bone metabolic markers was analyzed by Spearman correlation test. ROC curve was used to analyze the serum miR-338-3p and miR-150-5p in predicting renal failure in DN. p < 0.05 was indicated as significantly different.
For parametric tests, power calculations for sample size mainly relied on the following parameters: population mean, population standard deviation, effect size, significance level (α), and power (1-β). Effect size (Cohen’s d): it indicated the degree of difference between the two means, which was used to measure the size of the experimental effect. Cohen’s d was calculated as follows: Cohen’s d = (X1−X2)/S, where X1 and X2 were the sample mean of the two populations, respectively, and S was the common standard deviation of the two populations.
2. Results
2.1. Comparison of urinary albumin and creatinine levels in patients with different stages of DN
Compared with the control group, the levels of urinary albumin and serum creatinine in the microalbuminuria group were significantly higher (p < 0.05). Compared with the microalbuminuria group, the levels of urinary albumin and serum creatinine in the clinical proteinuria group were significantly higher (p < 0.05). Compared with the clinical proteinuria group, the levels of urinary albumin and serum creatinine in the renal failure group were significantly higher (P < 0.05) (Table 1). The more serious the disease was, the higher the levels of urinary albumin and serum creatinine was.
Table 1.
Comparison of urinary albumin and creatinine levels in patients with different stages of DN.
| Groups | Cases | Urinary albumin (mg/24h) | Creatinine (μmol/L) |
|---|---|---|---|
| The control group | 35 | 15.21 ± 4.20 | 168.23 ± 15.42 |
| The microalbuminuria group | 37 | 221.05 ± 35.62* | 197.42 ± 20.66* |
| The clinical proteinuria group | 27 | 426.21 ± 45.97*# | 238.05 ± 69.40*# |
| The renal failure group | 54 | 556.33 ± 57.26*#△ | 597.77 ± 68.11*#△ |
| F | 1250.41 | 711.68 | |
| P | <0.001 | <0.001 |
Notes: *p < 0.05 vs. control group, #p < 0.05 vs. microalbuminuria group, △p < 0.05 vs. clinical proteinuria group.
2.2. Expression levels of serum miR-338-3p and miR-150-5p in patients with DN at different stages
The serum expression levels of miR-338-3p and miR-150-5p were sharply decreased in the microalbuminuria group than these in the control group (p < 0.05). The serum expression levels of miR-338-3p and miR-150-5p were obviously decreased in the clinical proteinuria group than these in the microalbuminuria group (p < 0.05). The serum expression levels of miR-338-3p and miR-150-5p were largely decreased in the renal failure group than these in the clinical proteinuria group (p < 0.05). The more severe the condition was, the lower the serum expressions of miR-338-3p and miR-150-5p were (Figure 2).
Figure 2.
Serum expression levels of miR-338-3p and miR-150-5p in patients with DN at different stages. ***p < 0.001. Note: Control group: the control group; MA group: the microalbuminuria group; DN2 group: the clinical proteinuria group; ARF group: the renal failure group.
2.3. Changes of bone metabolism indexes in patients with DN at different stages
Compared with the control group, the serum expression levels of PTH and β-CTX were significantly increased by 36.67% and 14.19% (p < 0.05), and the expression levels of 25(OH)-D, BGP and PINP were significantly reduced by 77.74%, 82.09% and 73.18% (p < 0.05) in the microalbuminuria group. Compared with the microalbuminuria group, the serum expression levels of PTH and β-CTX were significantly increased by 31.75% and 10.05% (p < 0.05), and the expression levels of 25(OH)-D, BGP and PINP were significantly reduced by 74.57%, 79.80% and 81.45% (p < 0.05) in the clinical proteinuria group. Compared with the clinical proteinuria group, the serum expression levels of PTH and β-CTX were significantly increased by 30.52% and 14.98% (p < 0.05), and the expression levels of 25(OH)-D, BGP and PINP were significantly reduced by 58.62%, 73.16% and 80.89% (p < 0.05) in the renal failure group. The more severe the disease, the serum levels of PTH and β-CTX were gradually increased and the levels of 25(OH)-D, BGP, AND PINP were gradually decreased (Table 2).
Table 2.
Changes of bone metabolism indexes in patients with DN at different stages ( ).
| Groups | Cases | PTH (pg/mL) | 25(OH)-D (ng/mL) | BGP (ng/mL) | β-CTX (pg/mL) | PINP (ng/mL) |
|---|---|---|---|---|---|---|
| The control group | 35 | 33.79 ± 10.86 | 14.51 ± 3.17 | 18.03 ± 5.28 | 359.74 ± 78.85 | 44.86 ± 10.83 |
| The microalbuminuria group | 37 | 46.18 ± 12.03* | 11.28 ± 2.72* | 14.80 ± 3.90* | 410.78 ± 69.86* | 32.83 ± 7.06* |
| The clinical proteinuria group | 27 | 60.84 ± 11.53*# | 8.41 ± 2.13*# | 11.81 ± 2.83*# | 452.07 ± 84.29*# | 26.74 ± 6.38*# |
| The renal failure group | 54 | 79.41 ± 13.52*#△ | 4.93 ± 2.36*#△ | 8.64 ± 1.74*#△ | 520.50 ± 88.74*#△ | 21.63 ± 4.73*#△ |
| F | 112.450 | 104.450 | 55.050 | 30.470 | 74.550 | |
| P | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
Note: *p < 0.05 vs. control group, #p < 0.05 vs. microalbuminuria group, △p < 0.05 vs. clinical proteinuria group.
2.4. Correlation between serum levels of miR-338-3p and miR-150-5p and bone metabolism markers
Spearman correlation test showed that serum level of miR-338-3p was significantly negatively correlated with PTH and β-CTX (r = -0.762, −0.519, p < 0.001), and significantly positively with 25(OH)-D, BGP and PINP (r = 0.783, 0.673, 0.707, p < 0.001). Serum level of miR-150-5p expression was significantly negatively correlated with PTH and β-CTX (r = -0.801, −0.587, p < 0.001), and significantly positively with 25(OH)-D, BGP and PINP (r = 0.799, 0.703, 0.723, p < 0.001). Serum miR-338-3p and miR-150-5p expression levels were significantly correlated with bone metabolism markers (Table 3, Figures 3 and 4).
Table 3.
Correlation between serum levels of miR-338-3p and miR-150-5p and bone metabolism markers.
| Indicators | miR-338-3p | miR-150-5p | ||
|---|---|---|---|---|
| r | P | r | P | |
| PTH | −0.762 | <0.001 | −0.801 | <0.001 |
| 25(OH)-D | 0.783 | <0.001 | 0.799 | <0.001 |
| BGP | 0.673 | <0.001 | 0.703 | <0.001 |
| β-CTX | −0.519 | <0.001 | −0.587 | <0.001 |
| PINP | 0.707 | <0.001 | 0.723 | <0.001 |
Figure 3.
Spearman correlation test for the correlation between miR-338-3p and bone metabolic markers. A: the correlation between miR-338-3p and PTH, B: the correlation between miR-338-3p and 25(OH)-D, C: the correlation between miR-338-3p and BGP, D: the correlation between miR-338-3p and β-CTX, E: the correlation between miR-338-3p and PINP.
Figure 4.
Spearman correlation test for the correlation between miR-150-5p and bone metabolic markers. A: the correlation between miR-150-5p and PTH, B: the correlation between miR-150-5p and 25(OH)-D, C: the correlation between miR-150-5p and BGP, D: the correlation between miR-150-5p and β-CTX, E: the correlation between miR-150-5p and PINP.
2.5. Downstream gene expression of serum miR-338-3p and miR-150-5p in patients with DN at different stages
Compared with the control group, the mRNA levels of IGF-1 and IGFBP3 were increased significantly by 63.48% and 80.61% (p < 0.05), and the mRNA level of RUNX2 was decreased by 75.53% (p < 0.05) in the microalbuminuria group. Compared with the microalbuminuria group, the mRNA levels of IGF-1 and IGFBP3 were increased significantly by 30.32% and 44.63% (p < 0.05), and the mRNA level of RUNX2 was decreased by 58.88% (p < 0.05) in the clinical proteinuria group. Compared with the clinical proteinuria group, the mRNA levels of IGF-1 and IGFBP3 were increased significantly by 58.37% and 21.88% (p < 0.05), and the mRNA level of RUNX2 was decreased by 40.74% (p < 0.05) in the renal failure group. The more severe the disease, the mRNA levels of IGF-1 and IGFBP3 in patients were gradually increased, and the mRNA level of RUNX2 was gradually decreased (Table 4).
Table 4.
Downstream gene expression of serum miR-338-3p and miR-150-5p in patients with DN at different stages ( ).
| Group | Case | RUNX2 mRNA | IGF-1 mRNA | IGFBP3 mRNA |
|---|---|---|---|---|
| The control group | 35 | 4.25 ± 1.33 | 1.15 ± 0.56 | 0.98 ± 0.12 |
| The microalbuminuria group | 37 | 3.21 ± 1.05* | 1.88 ± 0.42* | 1.77 ± 0.23* |
| The clinical proteinuria group | 27 | 1.89 ± 0.75*# | 2.45 ± 0.69*# | 2.56 ± 0.49*# |
| The renal failure group | 54 | 0.77 ± 0.26*#△ | 3.88 ± 1.23*#△ | 3.12 ± 1.02*#△ |
| F | 123.800 | 81.980 | 84.210 | |
| P | <0.001 | <0.001 | <0.001 |
p < 0.05 vs. control group, #p < 0.05 vs. microalbuminuria group, △p < 0.05 vs. clinical proteinuria group.
2.6. The value of serum miR-338-3p and miR-150-5p in predicting renal failure in DN
ROC curve analysis showed that the AUC of serum miR-338-3p and miR-150-5p was 0.896 with the specificity and sensitivity of 96.66% and 73.47%, which had certain predictive value for the occurrence of renal failure in DN (Table 5 and Figure 5).
Table 5.
The value of serum miR-338-3p and miR-150-5p in predicting renal failure in DN.
| Indicators | AUC | Specificity | Sensitivity | Youden index | Cutoff value | p value | 95% CI |
|---|---|---|---|---|---|---|---|
| miR-338-3p | 0.826 | 80.00 | 87.76 | 0.678 | 5.42 | <0.05 | 0.754–0.898 |
| miR-150-5p | 0.822 | 86.67 | 83.67 | 0.703 | 4.75 | <0.05 | 0.745–0.900 |
| Combined detection | 0.896 | 96.66 | 73.47 | 0.701 | —— | <0.05 | 0.841–0.952 |
ROC: receiver operating characteristic curve; AUC: area under the curve; CI: credibility interval.
Figure 5.
The value of serum miR-338-3p and miR-150-5p in predicting renal failure in DN.
3. Discussion
DN is one of the most common microvascular complications of diabetes with complex pathogenesis, which is also the main cause of chronic renal failure. With the improvement of people’s living standards, DN has attracted more and more attention. The early development of the disease is relatively hidden. The early urine protein value and glomerular filtration rate of the patients are still within the normal and controllable range, and only a small number of patients has high blood pressure. Thus, it is easily ignored by patients, which causes the delay of the optimal treatment time. Osteoporosis is a metabolic bone disease, the onset of which is found to be closely related to the age and endocrine, especially diabetes [16, 17]. With the aggravation of the condition, the metabolism of calcium and phosphorus in the body and thyroid function appear obvious abnormalities in patients with DN. Disturbances in calcium and phosphorus metabolism can lead to decreased bone mineral density and changes in bone microstructure. Long-term abnormal bone, and calcium and phosphorus metabolism not only cause bone pain and bone deformity, but also lead to calcification of blood vessels and heart valves, and high incidence of cardiovascular events. If not treated in time, the end-stage renal disease will seriously affect the patient’s life [18]. In this study, bone metabolism markers were not used for osteoporosis diagnosis, but only to assess the level of bone metabolism, that is, the active degree of bone formation and bone resorption, in patients with DN. Although bone density testing is the “gold standard” for diagnosing osteoporosis, changes in bone metabolic markers after drug intervention are far more sensitive than changes in bone density in the evaluation and monitoring of drug efficacy, so it is more clinically instructive. In this study, the expression levels of serum miR-338-3p and miR-150-5p in patients with DN were significantly correlated with bone metabolism markers, suggesting that these two miRNAs might be involved in the occurrence and development of DN.
According to studies at home and abroad [19, 20], the occurrence and development of DN may be closely associate with the accumulation of extracellular matrix, autophagy, oxidative stress, inflammatory reaction and the production of glycosylation end products. Various studies have shown that miRNAs play an important role in regulating key signaling pathways and in the pathogenesis of DN [21–23]. After mice in the experimental group were treated with advanced glycation end products and rat serum albumin via the tail vein, or mice in the control group were received with phosphate buffer, the expression levels of miR-21-5p, miR-92b-3p, miR-150-5p and other miRNAs in the experimental group were abnormal, and the miRNAs could lead to the abnormal development of the kidney of DN rats by regulating the targeted genes [24]. MiR-338-3p was also reported to inhibit age-related osteoporosis by modulating the proprotein invertase subtilisin 5, but its specific mechanism of action in osteoporosis was not fully understood [25]. In this study, the levels of miR-338-3p and miR-150-5p were obviously decreased with the increased level of 24-h urinary albumin. Therefore, we speculated that miR-338-3p and miR-150-5p were closely related in the occurrence and development of DN. The analysis may be due to the fact that miR-338-3p is considered to be a miRNA with anti-inflammatory and antifibrotic effects, the decrease in its expression level in patients with kidney disease may be related to the inflammatory response and fibrotic progression of the kidney, and the low expression of miR-338-3p may not be able to effectively inhibit the expression of inflammation-related genes and fibrosis-related genes, resulting in aggravation of renal injury and increased urinary albumin excretion [26]. miR-150-5p has been found to be associated with inflammation and cell proliferation. In patients with DN, high expression of miR-150-5p may disrupt the glomerular filtration barrier and increase urinary albumin leakage by regulating related target genes and promoting inflammatory response and glomerular endothelial cell proliferation [27]. However, the study has shown that with the decline of renal function, bone metabolism markers (PINP and BGP) are significantly increased, and the serum levels of PINP and BGP in patients with chronic kidney disease are significantly higher than those in healthy controls [28]. The results were different from the results of this study. The analysis may be due to differences in pathophysiology and clinical manifestations at different stages of DN. In studies that include patients with DN, the disease stage of the participants may differ from other studies, which may lead to divergent results, as there are significant differences in treatment response and prognosis at different stages of DN.
Osteoporosis is a metabolic bone disease, and DN is an important cause of osteoporosis. Bone metabolic markers are markers reflecting bone formation and bone resorption. Many studies have shown that bone turnover markers in diabetes patients, and bone formation and bone resorption markers are strongly reduced, indicating that diabetes patients have significantly impaired bone metabolism [29, 30]. This study found that the level of bone metabolic markers was obviously abnormal. This may be because in the early stage of the development of DN, intestinal calcium absorption is reduced, blood calcium is reduced, glomerular filtration rate is reduced, and even calcium and phosphorus metabolism are imbalanced, which induces or exacerbates secondary abnormalities of parathyroid function, stimulates the proliferation and differentiation of osteoclasts, and further promotes the development of osteoporosis. Previous studies have found that miRNAs can regulate the growth, differentiation and functional activity of bone tissue cells and participate in the progress of osteogenesis [31]. Changes in the levels of various miRNAs actively participate in the occurrence and development of osteoporosis and fracture. MiR-338-3p is a regulatory molecule for bone tissue homeostasis, which can regulate osteoclast production by targeting the IKKβ gene, osteogenic differentiation of mouse bone marrow stromal stem cells by targeting Runx2 and Fgfr2, and osteoporosis by targeting MafB to inhibit osteoclast differentiation [32]. Normally, miRNAs work by pairing and binding downstream target genes to inhibit the post-transcriptional translation of target genes, thus, it is thought that the inhibitory effect of miR-150-5p on osteogenesis may work by acting on downstream genes that promote osteogenesis. Based on the previous bioinformatics predictions, among the many downstream target genes of miR-150-5p, the target mRNAs with significant expression differences, namely IGF-1 and IGFBP3, were locked on to the most closely differentiated osteogenic genes [33]. The results showed that with the increase of 24-h urine albumin level, miR-338-3p and miR-150-5p-related target genes showed significant changes, suggesting that the mechanism of action of miR-338-3p and miR-150-5p in the development of DN might be related to downstream target genes rather than their role. A study investigating the expression profile ofmiR-338-3p in bone marrow mesenchymal stem cells (BMSC) differentiation found that the miR-338-3p expression level was decreased with osteoblast differentiation [34]. Therefore, miR-338-3p is potentially involved in inhibiting osteoclast formation and accelerating age-related osteoporosis. In addition, another study reported that miR-338-3p overexpression in osteoclast precursor cells limited osteoclast formation [35]. Similarly, a study on the function of miR-338-3p in osteoclast differentiation and activation showed that miR-338-3p was significantly downregulated during this process [36]. Relevant data showed miR-150-5p regulated the osteogenic differentiation of BMSC in the elderly by targeting and inhibiting the downstream target genes IGF-1 and IGFBP3, resulting in the occurrence of osteoporosis in the elderly [37].
This study showed that serum miR-338-3p and miR-150-5p were correlated with abnormal bone metabolism in patients with DN, and the AUC of serum miR-338-3p and miR-150-5p was 0.896, which had a certain predictive value for the occurrence of renal failure in DN. These findings provided a new idea for the early diagnosis and treatment of abnormal bone metabolism in DN, and could help to reveal the intrinsic relationship between DN and abnormal bone metabolism, and provide a new theoretical basis for clinical diagnosis and treatment. This study for DN early diagnosis and treatment of bone metabolic abnormalities provides a new way of thinking. The expression levels of serum miR-338-3p and miR-150-5p were significantly correlated with clinical indicators such as bone metabolism markers, and the detection of the expression of these miRNAs in peripheral blood is expected to be a new biomarker for the diagnosis of osteoporosis and improve the accuracy and sensitivity of diagnosis. In addition, serum expression levels of miR-338-3p and miR-150-5p are associated with the progression of DN, and the regulatory strategies of these two miRNAs may become a new way to treat abnormal bone metabolism in DN, which shows that miRNAs have great potential roles in the diagnosis, prognosis assessment, therapeutic target identification and early detection of osteoporosis. Moreover, miRNA has certain advantages in clinical detection. Firstly, miRNA has a high degree of stability in vivo and is not easy to be degraded by RNase, so it is highly feasible to detect in clinical samples. Secondly, miRNA is abundant in biological samples such as blood and urine, which is convenient for collection and detection, and provides convenience for clinical application.
However, there are still some limitations. Due to the limited resources and research funds, associated bone metabolic biomarkers such as calcium, phosphate, and bone-specific alkaline phosphatase are not reported, and some factors affecting bone metabolism indexes, fat factors and bone mineral density have not been included. Therefore, it may lead to limited clinical application in clinical practice. Due to various reasons, the thyroid function of the subjects is failed to be detected in the present study, which may result in the interference due to relevant indicators. Besides, due to the small sample size, the data of patients with renal function in the failure group were not further analyzed by staging, and the study did not include patients with DN without osteoporosis, so the value of miRNA detection in diagnosing osteoporosis could not be performed, and the sample size should be expanded to include patients without osteoporosis in DN to further verify the value of miRNA detection in diagnosing osteoporosis. In the following studies, relevant contents will be further improved and explored.
4. Conclusion
Serum levels of miR-338-3p and miR-150-5p and bone metabolic markers are different in patients with DN at different stages, and serum expression levels of miR-338-3p and miR-150-5p are significantly correlated with bone metabolic markers. The combined test can provide new ideas and insights for the clinical treatment of osteoporosis in DN, and has important implications for finding potential therapeutic targets for osteoporosis in DN.
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.
Funding Statement
The author(s) reported there is no funding associated with the work featured in this article.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Ethics approval and consent to participate
This study conformed to the requirements of the Ethics Committee of Hubei NO.3 People’s Hospital of Jianghan University (HBSDSRMYY 2020-B005-02).
Author contributions
Approval of final manuscript: All authors. Jinlan Liu, Yi Zhang: Conceptualization, Methodology, Software, Data curation. Jinlan Liu: Writing-Original draft preparation, Visualization, Investigation, Supervision, Yi Zhang, Lixing Dai: Validation, Writing-Reviewing and Editing,
Data availability statement
The data analyzed and used during the current study are available from the corresponding author on reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data analyzed and used during the current study are available from the corresponding author on reasonable request.