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We enrolled 211 patients from the Affiliated Hospital of Qingdao University from March 2018 to October 2020. We reviewed the brain MRI results of these patients. Patients’ images were retrospectively reviewed by two trained neurologists with more than 10 years of experience who were blind to any patient information. According to the MRI results, the patients were divided into a WMH group and a control group[32,33]. All patients, including 107 WMH patients and 104 controls, were age- and sex-matched. Cranial images from fluid-attenuated inversion recovery (FLAIR), T1-weighted, and T2-weighted sequences were obtained from all patients using a Siemens Skyra 3.0 T scanner (Siemens, Germany). WMH is defined as hyperintensities in T2WI or FLAIR images[34]. The site of WMH was divided into deep white matter hyperintensities (DWMH) and paraventricular white matter hyperintensities (PWMH)[32]. The severity of WMH was evaluated by the FLAIR sequence, which primarily uses the Fazekas scale, with scores from 0 to 3[32,35]. The severity of DWMH was graded from 0 to 3 (0: no lesion; 1: punctate foci; 2: beginning confluence; 3: large confluent areas). Similarly, PWMH was graded from 0 to 3 (0: no lesion; 1: caps or pencil-thin lining; 2: smooth halo; 3: irregular paraventricular signal extending into the deep white matter). If the DWMH and PWMH grades differed, the higher grade was taken as the value (Supplementary Figure S1, available in www.besjournal.com)[32,35].
The main exclusion criteria of this study were as follows: (I) age < 18 years; (II) incomplete brain MRI examination; (III) severe liver or kidney dysfunction; (IV) blood system diseases or tumors; (V) history of cardiac embolism; (VI) history of ischemic or hemorrhagic stroke; and (VII) WMH caused by genetic or other known etiologies[32,33,36].
Plasma exosomes from seven patients with WMH and four controls were randomly selected from each group and sequenced.
All patients in our study provided written informed consent. Furthermore, our study was approved by the Ethics Committee of the Affiliated Hospital of Qingdao University.
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Within 24 hours of admission, peripheral venous blood was collected in ethylenediaminetetra acetic acid (EDTA) tubes and centrifuged at 2,500 ×g at 4 °C for 15 min. The upper plasma layer was collected in a new 1.5 mL EP tube and stored at −80 °C[31]. Plasma samples of this study were centrifuged at 3,000 ×g for 15 min at room temperature to remove cells and cellular debris. And exosomes were mainly isolated using Total Exosome Isolation (from plasma) reagent (Invitrogen, California, USA)[37]. Furthermore, plasma samples of our study were fully centrifuged at 2,000 ×g for 20 min at room temperature to remove cells and cellular debris. Then, the supernatant was centrifuged at 10,000 ×g for 20 min to remove any remaining debris. A 0.5 volume of 1× phosphate buffer saline (PBS) and a 0.05 volume of protease K were added to the supernatant. All samples were then incubated in tubes at 37 °C for 10 min. A 0.2 volume of Exosome Precipitation Reagent was added, at which point the solution had a cloudy appearance. The samples were incubated at 2 to 8 °C for thirty min, followed by centrifugation at room temperature at 10,000 ×g for five min. Finally, exosomes were precipitated in a pellet at the bottom of the tube. The exosomes were resuspended in PBS and subsequently stored at −80 °C.
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The exosome suspension (20 µL) was placed in a carbon net and then left at room temperature for 20 min. Next, 2% phosphotungstic acid (20 µL) was dropped onto the carbon net and allowed to stand for 20 s. The image was observed using TEM (JEOL, JEM-1400, Tokyo, Japan)[38,39].
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Exosomal proteins were prepared in Radio Immunoprecipitation Assay (RIPA) buffer with Phenylmethanesulfonyl fluoride (PMSF) (MCE, USA). Total protein concentrations can be detected by the bicinchoninic acid (BCA) protein assay. Protein samples were subjected to 12% SDS-PAGE and then moved onto membranes (Millipore Co., NJ, USA). Polyvinylidene fluoride (PVDF) membranes were incubated with primary antibodies overnight at 4 °C and then with a secondary antibody (Abcam, MA, USA) at room temperature for 1 h[40]. According to the recommendations from the International Society for Extracellular Vesicles, the primary antibodies included positive markers (rabbit anti-CD63 and rabbit anti-CD9) and a negative marker (rabbit anti-GRP94) (Abcam, MA, USA)[39].
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The exosome samples of the study were resuspended in 1 mL PBS. The size and concentration of exosomes were determined using a Zeta View PMX 110 (Particle Metrix, Meerbusch, Germany)[41]. Particle characteristics were evaluated using NTA software (ZetaView 8.02.28)[39].
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In our study, total RNA was isolated from exosomes using the miRNeasy Serum/Plasma Advanced kit (Qiagen, cat. 217204, Germany) according to the manufacturer’s protocol[30]. A total of 20 ng RNA per sample was used as the input material to prepare the RNA sample. The ribosomal RNA was cleaned up by an Epicenter Ribo-zero™ rRNA Removal Kit (Epicenter, USA), and then the rRNA-free residues were removed by ethanol precipitation. Furthermore, the sequencing libraries were developed using rRNA-depleted RNA by the NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina® (NEB, USA), as recommended by the manufacturer. The RNA concentration, purity, and integrity were evaluated using the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA)[42]. According to the manufacturer’s instructions, and the indexed coded samples were clustered on a cBot clustering system using TruSeq PE Cluster Kit V3-CBOT-HS (Illumina). After clustering, the libraries were sequenced on an Illumina HiSeq 2500 platform, and then the 125 bp paired-end reads were obtained[43].
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In our study, differentially expressed genes (DEGs) were compared using an unpaired t-test. The cutoff value was P ≤ 0.05, and a log2 fold change ≥ 1.5. Additionally, volcano plots and heatmaps were prepared using GraphPad Prism 8 and TBtools. The biological functions of DE exo-lncRNAs were analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment[44].
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A PPI network based on the STRING database (https://cn.string-db.org/) was used to analyze DE mRNAs. Then, the submodules were identified with a cutoff score > 2.0 using the MCODE plugin from the PPI network. Furthermore, the PPI network analysis was visualized using Cytoscape 3.8.2 (NIGMS, USA)[45].
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The extraction method of exosomal RNAs was the same as described above. RNA was converted into cDNA using a Goldenstar RT6 cDNA synthesis kit v. 2 (TSINGKE, Beijing, China). The cDNA templates were amplified by real-time PCR using 2×T5 Fast qPCR Mix (SYBR Green I) (TSINGKE, Beijing, China). The specific primers are listed in Table 1. PCR was performed in 20 μL reaction volumes, which included 2 μL of cDNA, 10 μL of 2×T5 Fast qPCR Mix, 0.8 μL of forward primer (10 μmol/L), 0.8 μL of reverse primer (10 μmol/L), and 6.4 μL of double distilled water. All experiments were performed in duplicate. ACTB was used as the internal control, and the 2−ΔΔCt method was used to evaluate the expression of relative genes.
Table 1. Primer sequences in the study
Gene Primer sequences 5'–3' Exo-lnc_011776 F: ATTCCCGCAAGTTTGACTCCTTCC R: TCTGGTCCTTGTTCCCGTCTGG Exo-lnc_011797 F: AGGTGGAACGGGTTTAGGGCTAG R: CACTTGGACGAACCAGAGTGTAGC Exo-lnc_004326 F: CTCTGTCAAACCCTGAGCCAACC R: GAGCCGTCTCCACCTCCCATAG Exo-lnc_013222 F: GAAGTCGTGCCTTTCCTGTCCTG R: GTGAGTCCACCTCGCTGAAGATAC Exo-WNK1 F: CGAGTGAGCAGCCAACAGACAG R: TGTGCTTGGACAGTAGAAGGTATATGC Exo-RPL18 F: CCGCCATAACAAGGACCGAAAGG R: TGGTTGAATGTGGAGTTGGTTCTTCTG ACTB F: TGCTGTCACCTTCACCGTTCCA R: GCGGACTATGACTTAGTTGCGTTACA -
The correlation between lncRNAs and mRNAs among samples was analyzed using the Pearson correlation coefficient. Coexpression refers to a correlation coefficient > 0.95. lncRNA-mRNA networks were constructed using Cytoscape 3.8.2.
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In this study, quantitative variables are expressed as the mean ± standard deviation (SD) and categorical variables are expressed as percentages. They were analyzed using chi-square tests, with t-tests used to compare two groups. Nonnormally distributed data were compared between two groups using the Mann-Whitney U test. One-way analysis of variance (ANOVA) was used to compare three or more groups. Binary logistic regression analysis was used to evaluate the relationship between exo-lncRNAs and WMH. A receiver operating characteristic (ROC) curve analysis was used to compare the potential diagnostic value of the exosomal lncRNAs between the control and WMH groups. The diagnostic value of differentially expressed exo-lncRNAs was determined by the area under the curve (AUC). A P value < 0.05 was considered statistically significant, and all statistical analyses were conducted with SPSS12.0 (SPSS Inc., Chicago, USA) and GraphPad Prism 8 (GraphPad, California, USA) software.
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There were 107 WMH patients and 104 controls in the study. There were no obvious differences in age, sex, hypertension, diabetes, TC, HDL, and LDL between the WMH and control groups. The smoking consumption and the levels of TG in WMH patients was higher than that in control subjects (Table 2).
Table 2. Basic clinical data of two groups of patients
Variables WMH Control P value Case number 107 104 Age (years) 61.09 ± 12.59 63.65 ± 10.70 0.111 Gender, male, n (%) 50 (46.72) 59 (56.73) 0.146 Hypertension, n (%) 0.109 Yes 76 (71.03) 63 (60.58) No 31 (28.97) 41 (39.42) Diabetes, n (%) 0.098 Yes 31 (28.97) 20 (19.23) No 76 (73.07) 84 (80.77) Smoke, n (%) 0.037* Yes 34 (31.78) 20 (19.23) No 73 (68.22) 84 (80.77) TG (mmol/L) 1.63 ± 0.96 1.37 ± 0.78 0.031* TC (mmol/L) 4.45 ± 1.00 4.28 ± 1.08 0.252 HDL (mmol/L) 1.28 ± 0.43 1.20 ± 0.28 0.099 LDL (mmol/L) 2.55 ± 0.76 2.62 ± 0.84 0.586 Note. *P < 0.05 is considered statistically significant. -
The extracted exosomes were observed by TEM as cup-shaped vesicles with an average diameter of 40–160 nm (Figure 1A)[13,23]. According to previous literature, we performed western blot analysis on exosomes extracted from WMH and controls[31]. Two positive exosome markers (CD63 and CD9) were identified, while the negative marker, GRP94, was absent from exosomes (Figure 1B). The size range of the exosomes was determined using NTA (Figure 1C). The results confirmed the characteristics of plasma exosomes[39].
Figure 1. Identification and characterization of exosomes. (A) TEM image shows that the exosomes are cup-shaped vesicles with round membranes. White arrow indicates typical exosomes. Scale bar = 100 nm; (B) Western blot analysis of exosome protein markers (CD9, CD63, GRP94). Column 1: plasma without exosomes of controls; Column 2: plasma without exosomes of WMH; Column 3: control exosomes; Column 4: WMH exosomes; (C) the size of exosomes ranged from 40 to 160 nm, as revealed by NTA. CK, control.
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To analyze the expression of exosomal lncRNAs in WMH patients, we screened four controls and seven WMH patients for RNA sequencing. A total of 899 DE exo-lncRNAs were identified between the WMH and control samples, including 107 upregulated and 792 downregulated exo-lncRNAs. The volcano plot and heatmap better display the sequencing results, as shown in Figure 2.
Figure 2. Workflow and differential expression of exo-lncRNAs. (A) Volcano plot of exo-lncRNAs. (B) Heatmap of exo-lncRNA, where red indicates upregulated RNAs and green indicates downregulated RNAs. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, Protein-protein Interaction; ROC, receiver operating characteristic; CK, control.
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The potential and important biological functions of DE exo-lncRNAs were predicted using GO and KEGG enrichment analyses. The GO analysis was characterized by biological process (BP), cellular component (CC), and molecular function (MF) terms, which included, among others, ubiquitin-specific protease activity (GO: 0004843) and transferase activity (GO: 0016772) (Figure 3A–B). The KEGG analysis showed that the upregulated exo-lncRNAs were primarily related to autophagy regulation (hsa04140) and ribosomes (hsa03010) (Figure 3C), whereas downregulated exo-lncRNAs were related mainly to the neurotrophin signaling pathway (hsa04722) (Figure 3D). These functional analyses suggest that exosomal lncRNAs are involved in regulating the WMH process.
Figure 3. Functional enrichment of differentially expressed exo-lncRNAs. (A, B) Pathways of GO analysis, including biological processes, cell components, and molecular functions. (C, D) Pathways by KEGG analysis shown on a bubble graph. The range of P values corresponds to the dot color, and the dot size indicates the number of genes.
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We further studied the biological functions of exosomal lncRNAs, such as the target genes of lncRNAs. We sequenced exosomal mRNA to analyze the differential expression (DE) of exo-mRNA between the control and WMH groups. Finally, 523 DE exo-mRNA transcripts were detected in this study, among which 20 were upregulated, and 503 were downregulated. The volcano plot and heatmap are shown in Figure 4. Data analysis was performed using the STRING database to generate a PPI network for DE mRNAs. The network of 397 nodes and 1,532 edges was analyzed and visualized using Cytoscape (Figure 5A). The Cytoscape plugin MCODE was used primarily to identify two modules with a score > 10 (Figure 5B–C).
Figure 4. Expression profile of exo-mRNAs. (A) Volcano plot of exo-mRNAs. (B) Heatmap of exo-mRNAs. Red indicates upregulated RNAs, and green indicates downregulated RNAs. CK, control.
Figure 5. Protein-protein interaction networks and submodules of DE mRNAs. (A) PPI network of DE mRNAs. (B) Molecular 1 (score = 16) contains 16 nodes and 120 edges; (C) Molecular 2 (score = 11.048) contains 22 nodes and 116 edges. Upregulated RNAs are denoted in red, and downregulated RNAs are denoted in green.
To further analyze the possible target genes of exosomal lncRNAs, we constructed coexpression lncRNA-mRNA networks. We selected four DE exo-lncRNAs (exo-lnc_011797, exo-lnc_011776, exo-lnc_004326, and exo-lnc_013222) based primarily on the DE and functional enrichment analysis of these RNAs. The coexpression lncRNA-mRNA networks showed that the upregulated exo-lncRNAs (exo-lnc_011797, exo-lnc_004326) were correlated with the upregulated mRNAs (WNK1, RPL18, USP45, UBE21, LCTL, PTPN13) (Supplementary Figures S2–S5, available in www.besjournal.com). As is well known, lncRNAs can indirectly upregulate target gene expressions by weakening the inhibition of target genes by miRNAs. Therefore, exo-lnc_011797 and exo-lnc_004326 may affect the occurrence or development of WMH by regulating the expression of with-no-lysine (K) 1 (WNK1), (ribosomal protein L18) RPL18, (ubiquitin specific processing protease 45) USP45, (ubiquitin-conjugating enzyme E2I) UBE21, (Lactase-like protein) LCTL, and Recombinant Protein Tyrosine Phosphatase, Non-Receptor Type 13 (PTPN13).
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To verify the sequencing results, we confirmed the expression levels of the previously selected four DE exo-lncRNAs in the validation set by qRT-PCR. Meanwhile, two coexpressed mRNAs with large DEs were selected for verification. The two mRNAs were protein kinase with-no-lysine (K) 1 (WNK1) and ribosomal protein L18 (RPL18). The results showed that exo-lnc_011797, lnc_011776, exo-lnc_004326, exo-WNK1, and exo-RPL18 exhibited higher expressions in WMH patients than in controls (Figure 6). In contrast, exo-lnc_013222 was significantly downregulated in WMH (Figure 6). These study results were more consistent with the sequencing results.
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To better evaluate and prove the diagnostic performance of candidate exosomal lncRNAs for WMH, we first used binary logistic analysis to assess the association of the exo-lncRNAs and WMH. After adjusting for confounding variables, we found that exo-lnc_011797 and exo-lnc_004326 were independent risk factors for WMH, while exo-lnc_013222 was an independent protective factor for WMH (Table 3). We then performed a ROC curve analysis for exo-lnc_011797, exo-lnc_00432, and exo-lnc_013222. The ROC curve analysis showed that the AUCs of exo-lnc_011797 and exo-lnc_004326 were 0.815 and 0.7592, respectively (Figure 7). These results indicated that exo-lnc_011797 and exo-lnc_004326 exhibited a greater diagnostic efficacy for WMH. However, exo-lnc_013222 had lesser diagnostic efficacy for WMH (AUC = 0.5989).
Table 3. Logistic analysis of risk factors associated with WMH
LncRNA expression level Unadjusted Adjusted# OR (95% CI) P OR (95% CI) P Exo-lnc_011797 1.043 (1.013–1.074) 0.004* 1.033 (1.000–1.066) 0.048* Exo-lnc_011776 1.046 (0.994–1.101) 0.082 1.051 (0.991–1.051) 0.098 Exo-lnc_004326 1.075 (1.020–1.132) 0.007* 1.061 (1.061–1.008) 0.022* Exo-lnc_013222 0.494 (0.358–0.680) 0.000* 0.406 (0.277–0.596) 0.000* Note. *P < 0.05 indicates statistical significance. #OR was adjusted for age, sex, hypertension, diabetes, smoking, TG, TC, HDL, and LDL. -
All 100 WMH patients underwent cerebral MRI. The Fazekas scale was used to assess the severity of WMH on FLAIR sequences. All patients had WMH; 35 patients had a score of 1, 34 patients had a score of 2, and 31 patients had a score of 3. Then we excluded patients with the same DWMH and PWMH Fazekas scale, and the site of WMH was divided into DWMH (n = 31) and PWMH (n = 25). We tested the expression levels of exosomal lncRNAs in different subgroups by qRT-PCR and analyzed the signaling role of exo-lncRNAs in WMH severity and localization. Our analysis revealed that the expression of exo-lnc_011797 was positively correlated with the severity of WMH (P = 0.0087) (Figure 8). Additionally, the expression of exo-lnc_011797 was significantly higher in PWMH patients than in DWMH patients (Figure 9). Consequently, lnc_011797 may have predictive value for the severity and localization of WMH.
doi: 10.3967/bes2023.149
Circulating Exosomal LncRNAs as Novel Diagnostic Predictors of Severity and Sites of White Matter Hyperintensities
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Abstract:
Objective Exosomal long noncoding RNAs (lncRNAs) are the key to diagnosing and treating various diseases. This study aimed to investigate the diagnostic value of plasma exosomal lncRNAs in white matter hyperintensities (WMH). Methods We used high-throughput sequencing to determine the differential expression (DE) profiles of lncRNAs in plasma exosomes from WMH patients and controls. The sequencing results were verified in a validation cohort using qRT-PCR. The diagnostic potential of candidate exosomal lncRNAs was proven by binary logistic analysis and receiver operating characteristic (ROC) curves. The diagnostic value of DE exo-lncRNAs was determined by the area under the curve (AUC). The WMH group was then divided into subgroups according to the Fazekas scale and white matter lesion site, and the correlation of DE exo-lncRNAs in the subgroup was evaluated. Results In our results, four DE exo-lncRNAs were identified, and ROC curve analysis revealed that exo-lnc_011797 and exo-lnc_004326 exhibited diagnostic efficacy for WMH. Furthermore, WMH subgroup analysis showed exo-lnc_011797 expression was significantly increased in Fazekas 3 patients and was significantly elevated in patients with paraventricular matter hyperintensities. Conclusion Plasma exosomal lncRNAs have potential diagnostic value in WMH. Moreover, exo-lnc_011797 is considered to be a predictor of the severity and location of WMH. -
Key words:
- Exosome /
- Long noncoding RNA (lncRNA) /
- White matter hyperintensities (WMH) /
- RNA sequencing /
- Diagnostic performance
The authors have stated explicitly that there are no conflicts of interest in connection with this article.
&These authors contributed equally to this work.
注释:1) AUTHOR CONTRIBUTIONS: 2) COMPETING INTERESTS: -
Figure 1. Identification and characterization of exosomes. (A) TEM image shows that the exosomes are cup-shaped vesicles with round membranes. White arrow indicates typical exosomes. Scale bar = 100 nm; (B) Western blot analysis of exosome protein markers (CD9, CD63, GRP94). Column 1: plasma without exosomes of controls; Column 2: plasma without exosomes of WMH; Column 3: control exosomes; Column 4: WMH exosomes; (C) the size of exosomes ranged from 40 to 160 nm, as revealed by NTA. CK, control.
Figure 2. Workflow and differential expression of exo-lncRNAs. (A) Volcano plot of exo-lncRNAs. (B) Heatmap of exo-lncRNA, where red indicates upregulated RNAs and green indicates downregulated RNAs. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, Protein-protein Interaction; ROC, receiver operating characteristic; CK, control.
Figure 3. Functional enrichment of differentially expressed exo-lncRNAs. (A, B) Pathways of GO analysis, including biological processes, cell components, and molecular functions. (C, D) Pathways by KEGG analysis shown on a bubble graph. The range of P values corresponds to the dot color, and the dot size indicates the number of genes.
Figure 5. Protein-protein interaction networks and submodules of DE mRNAs. (A) PPI network of DE mRNAs. (B) Molecular 1 (score = 16) contains 16 nodes and 120 edges; (C) Molecular 2 (score = 11.048) contains 22 nodes and 116 edges. Upregulated RNAs are denoted in red, and downregulated RNAs are denoted in green.
Table 1. Primer sequences in the study
Gene Primer sequences 5'–3' Exo-lnc_011776 F: ATTCCCGCAAGTTTGACTCCTTCC R: TCTGGTCCTTGTTCCCGTCTGG Exo-lnc_011797 F: AGGTGGAACGGGTTTAGGGCTAG R: CACTTGGACGAACCAGAGTGTAGC Exo-lnc_004326 F: CTCTGTCAAACCCTGAGCCAACC R: GAGCCGTCTCCACCTCCCATAG Exo-lnc_013222 F: GAAGTCGTGCCTTTCCTGTCCTG R: GTGAGTCCACCTCGCTGAAGATAC Exo-WNK1 F: CGAGTGAGCAGCCAACAGACAG R: TGTGCTTGGACAGTAGAAGGTATATGC Exo-RPL18 F: CCGCCATAACAAGGACCGAAAGG R: TGGTTGAATGTGGAGTTGGTTCTTCTG ACTB F: TGCTGTCACCTTCACCGTTCCA R: GCGGACTATGACTTAGTTGCGTTACA Table 2. Basic clinical data of two groups of patients
Variables WMH Control P value Case number 107 104 Age (years) 61.09 ± 12.59 63.65 ± 10.70 0.111 Gender, male, n (%) 50 (46.72) 59 (56.73) 0.146 Hypertension, n (%) 0.109 Yes 76 (71.03) 63 (60.58) No 31 (28.97) 41 (39.42) Diabetes, n (%) 0.098 Yes 31 (28.97) 20 (19.23) No 76 (73.07) 84 (80.77) Smoke, n (%) 0.037* Yes 34 (31.78) 20 (19.23) No 73 (68.22) 84 (80.77) TG (mmol/L) 1.63 ± 0.96 1.37 ± 0.78 0.031* TC (mmol/L) 4.45 ± 1.00 4.28 ± 1.08 0.252 HDL (mmol/L) 1.28 ± 0.43 1.20 ± 0.28 0.099 LDL (mmol/L) 2.55 ± 0.76 2.62 ± 0.84 0.586 Note. *P < 0.05 is considered statistically significant. Table 3. Logistic analysis of risk factors associated with WMH
LncRNA expression level Unadjusted Adjusted# OR (95% CI) P OR (95% CI) P Exo-lnc_011797 1.043 (1.013–1.074) 0.004* 1.033 (1.000–1.066) 0.048* Exo-lnc_011776 1.046 (0.994–1.101) 0.082 1.051 (0.991–1.051) 0.098 Exo-lnc_004326 1.075 (1.020–1.132) 0.007* 1.061 (1.061–1.008) 0.022* Exo-lnc_013222 0.494 (0.358–0.680) 0.000* 0.406 (0.277–0.596) 0.000* Note. *P < 0.05 indicates statistical significance. #OR was adjusted for age, sex, hypertension, diabetes, smoking, TG, TC, HDL, and LDL. -
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