Diagnostic Value of miRNA Expression for Elderly Hypertension with Early Heart Function Injury

Diagnostic Value of miRNA Expression for Elderly Hypertension with Early Heart Function Injury | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Diagnostic Value of miRNA Expression for Elderly Hypertension with Early Heart Function Injury Yan Wang, GuangPing Fu, YingFang Liu, XinWei Yu, YiFang Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7320316/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Our objective is to investigate associations between miRNA expression profiles and echocardiographic cardiac structural parameters and to evaluate the diagnostic utility of miRNA biomarkers for identifying left ventricular diastolic dysfunction (LVDD) in older adults with essential hypertension (EH). Methods Clinical data and serum samples were collected from 10 older adults with EH (5 LVDD, 5 non-LVDD controls). Then, miRNA high-throughput sequencing (HTS) was carried out on the serum samples to define differentially expressed miRNAs (DE-miRNAs) between both groups. In the subsequent validation experiment, we collected serum samples, basic clinical data, and echocardiographic data from 237 elderly EH patients (149 Non-LVDD and 88 LVDD) from four tertiary A-level hospitals in Hebei Province. Serum DE-miRNA levels were quantified via RT-PCR, and associations with echocardiographic parameters were analyzed via Spearman's rank correlation. The diagnostic efficacy of miRNAs for LVDD was assessed using the ROC curve. Results The HTS results identified 34 DE-miRNAs between both groups (P < 0.05). Four serum-abundant miRNAs (miR-19b-3p/21-5p/15a-5p/30e-5p) were selected for diagnostic performance validation, revealing that serum circulating miR (c-miR)-19b-3p/21-5p/30e-5p were significantly differential expressed between both groups (P < 0.05). Spearman analysis manifested significant associations between miR-19b-3p and LV function parameters (LVEF, FS, e', A, E/e') and right ventricular (RV) structural parameters (P < 0.05). miR-30e-5p showed significant correlations with LV function indices (LVEF, FS, E, A, E/e'; P < 0.05). miR-15a-5p was significantly related to LV function parameters (LVEF and FS; P < 0.05). Multivariate regression analysis showcased that serum c-miR-19b-3p/21-5p/15a-5p served as independent risk factors for LVDD in elderly hypertension (HTN) patients (P < 0.05). ROC curve analysis manifested that miR-19b-3p exhibited the maximum AUC value (0.735) among the four miRNAs for diagnosing LVDD in elderly HTN patients, with diagnostic sensitivity and specificity of 88.64% and 46.98%, respectively. Combinatorial analysis showed that the combined detection of miR-19b-3p/21-5p/30e-5p significantly improved the diagnostic AUC to 0.915 (P < 0.05), with enhanced sensitivity (86.36%) and specificity (89.93%). Conclusions This study revealed that c-miR-19b-3p serum levels in elderly HTN patients demonstrated moderate diagnostic value (AUC = 0.735) for identifying LVDD. The combined use of miR-19b-3p/21-5p/30e-5p as a diagnostic panel significantly enhanced the predictive efficacy for LVDD occurrence (AUC = 0.915). These findings establish a theoretical foundation for miRNA clinical application in early diagnosis and therapeutic management of cardiac functional impairment in elderly HTN patients. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Medical research miRNA Elderly Hypertension Heart Function Injury Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Essential hypertension (EH) is the leading cause of major cardiovascular morbidities such as hypertensive heart disease (HHD), renal disease, stroke, and peripheral arterial disease [ 1 – 3 ]. A cross-sectional study in 2021 found that among populations aged 35–75 years in Northern China, above 50% have hypertension (HTN) [ 4 ]. Despite advancements in diagnostic biomarker research, HTN management remains suboptimal in China, with < 50% of patients receiving treatment and fewer than 20% achieving blood pressure control [ 5 , 6 ]. Most patients who are newly diagnosed with HTN have already developed HTN target organ damage due to poor screening habits and lack of awareness [ 7 ]. Chronic systemic arterial HTN induces a cascade of anatomical and functional modifications in the left/atrium [(LV)/(LA)] and coronary arteries, thereby developing HHD. These changes include LV hypertrophy (LVH), LV diastolic dysfunction (LVDD), atrial fibrillation, heart failure (HF), and coronary artery disease, with LVH serving as the central pathological driver. LVDD, characterized by impaired myocardial relaxation and elevated ventricular stiffness, has been established as the initial manifestation of HHD. Untreated or inadequately controlled arterial HTN may exacerbate LVDD, progressing from mild to severe stages, and ultimately result in both diastolic and systolic HF [ 8 ]. The prevalence of LVH in HTN populations was found to be 31.9% in the Chinese community [ 9 ]. Systemic HTN and its complications, such as LVDD, are often asymptomatic during the early stages, underscoring the necessity of early LVDD assessment and stringent blood pressure control to delay disease progression. Recent evidence highlights microRNAs (miRNAs) as promising biomarkers for EH. Circulatory miRNA (c-miRNA) profiles demonstrate distinct patterns in pathophysiological states, with dysregulation often occurring prior to clinical manifestation, underscoring their utility for early disease detection [ 10 , 11 ]. These miRNAs actively regulate myocardial remodeling, fibroblast apoptosis, and HF progression [ 12 – 14 ]. In our previous study, serum circulating miR (c-miR)-21 levels were elevated in older EH patients with LA dilation, correlating significantly with LA structural changes; however, its diagnostic accuracy for LA dilation was suboptimal [ 15 ]. Miao et al. found that the combined serum miRNA expression can predict right ventricular (RV) injury and malfunction in patients having chronic thromboembolic pulmonary HTN (CPTH) [ 16 ]. In this study, we focus on early heart functional injury in elderly HTN patients. Using the gene microarray approach, c-miRNA differential expression was analyzed in elderly EH patients versus those with EH complicated by LVDD. The differentially expressed miRNAs (DE-miRNAs) were further evaluated for their combined diagnostic effectiveness. We aim to provide a basis for early detection and intervention of cardiac functional impairment in elderly HTN patients. Methods Participants and clinical data collection This retrospective study enrolled 247 elderly EH patients [ 17 ] treated in the department of cardiology at four tertiary grade-A hospitals -Hebei General Hospital, Fourth Hospital of Hebei Medical University, Shijiazhuang People's Hospital, and Handan Central Hospital - between September 2022 and September 2023, including 154 EH patients without LVDD and 93 EH patients combined with LVDD. LVDD was diagnosed based on the Doppler interrogation as recommended by the American Society of Echocardiography and the European Association of Cardiovascular Imaging [(ASE)/(EACVI)] [ 18 ]. All the patients provided a detailed medical history and underwent standardized clinical assessments and laboratory tests. Patients were excluded if they met any of the following criteria: acute coronary syndrome, remote myocardial infarction, systemic inflammatory disease, renal failure, autoimmune diseases, liver diseases, moderate-to-severe aortic/mitral valve disease, prosthetic valve implantation, atrial septal defects or aneurysms, arrhythmias (e.g., atrial fibrillation, conduction abnormalities), or suboptimal echocardiographic image quality. The study was authorized by the Ethics Committee at Heibei General Hospital (2019027) and followed the Helsinki Declaration and Good Clinical Practice guidelines, defined by the International Conference on Harmonisation. Patients signed informed consent prior to enrollment. A dedicated data and safety monitoring committee oversaw protocol compliance, adverse event tracking, and efficacy evaluation. Study registration was completed on ResMan [approval number ChiCTR1900026699]. Small RNA Library Preparation and High-Throughput Sequencing (HTS) Peripheral blood (3–4 mL) was collected from each participant into procoagulant tubes (Hebei Xinle Sci & Tech Co., Ltd., Hebei, China), followed by centrifugation (2000 r/min, 10 min) and transferring the resulting supernatant to a clean EP tube and subjected to a second centrifugation (13,000 r/min,10 min) to remove cellular debris and intact chromatin. The final supernatant was aliquoted into new EP tubes and maintained at − 80°C until use. The serum from 10 patients with EH (5 LVDD, 5 non-LVDD controls) at Hebei Provincial People's Hospital was selected for HTS analysis. Per protocols, total RNA was extracted utilizing a commercial kit, RNA quantity, and purity were determined through a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA), and RNA integrity was determined via an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA). For library construction, total RNA was denatured at 70°C for 2 min and subsequently incubated with 3' RNA adapters and T4 RNA Ligase 2, truncated K227Q (NEB, M0351L) at room temperature. Ligation was carried out at 16°C for over 8 h. Unligated 3' adapters were removed via RTP digestion at 37°C for 30 min. Thereafter, 5' RNA adapters were subjected to ligation to the products using T4 RNA Ligase 1 (NEB, M0204L) at 37°C for 60 min. Reverse transcription was executed via SuperScript II Reverse Transcriptase (Thermo Fisher Scientific, 18064014) at 50°C for an hour, then enzyme inactivation at 80°C for 10 min. The cDNA was amplified via PCR through Phusion® High-Fidelity DNA Polymerase (NEB, M0530L) as follows: initial denaturation at 98°C for 30 s, annealing at 60°C for 30 s, extension at 72°C for 15 s, for 10–16 cycles, followed by a final extension at 72°C for 5 min. Amplified products were size-selected and purified using PAGE. Sequencing was conducted on an Illumina HiSeq 2500 platform via single-end 50 bp reads following the protocol. Raw sequencing reads were processed employing ACGT101-miR (v4.2) to eliminate adapter dimers, low-quality sequences, repetitive elements, and RNA families (rRNA, tRNA, snRNA, snoRNA). Unique sequences of 18–26 nucleotides were aligned to miRBase 22.1 precursor sequences using BLAST, allowing for length variation at both ends and one internal mismatch. Known miRNAs were identified based on their mapping to annotated mature miRNA regions of specific species. Novel miRNA candidates were defined as sequences derived from the opposite arm of known precursors (either 5p or 3p). The other sequences were mapped to precursor miRNAs from other species in miRBase 22.1 and further aligned to the reference genome of the target species to determine genomic origin. Unmapped sequences underwent de novo miRNA prediction using RNAfold ( http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi ), analyzing flanking 80 nt regions for potential hairpin structures. Predicted hairpins were filtered based on established criteria: (1) ≤ 12 nucleotides in any stem bulge; (2) ≥ 16 base pairs in the stem; (3) free energy ≤ − 15 kcal/mol; (4) total hairpin length ≥ 50 nt; (5) loop length ≤ 20 nt; (6) ≤ 8 nucleotides in any mature region bulge; (7) ≤ 4 biased mismatches in mature region bulges; (8) ≤ 2 biased bulges in the mature region; (9) ≤ 7 total errors in the mature region; (10) ≥ 12 base pairs in the mature region; (11) ≥ 80% of the mature sequence located within the stem. RT-qPCR Validation of Serum miRNAs DE-miRNA was assessed using normalized HTS counts, and a Student's t-test was used with P < 0.05 to identify DE-miRNAs between the non-LVDD and LVDD groups. miRNAs were chosen on the basis of significant differential expression (P < 0.05), a minimum absolute fold change ≥ 1.5, and high sequencing read counts. For RNA-seq data validation, total miRNA extraction from serum samples was performed using the miRNeasy Serum/Plasma Advanced Kit, and quantitative RT-PCR was performed with Specific primers for selected miRNAs (all from Qiagen, Germany) following the protocols. The sample exhibiting the lowest Ct value for the target miRNAs served as a reference control, calculating each sample's results using the 2 −∆∆Ct method. For normalization, miR-191-5p was deployed as an endogenous control due to its consistent expression in human serum samples [ 19 , 20 ], and Cel-miR-39 was employed as an exogenous control [ 21 , 22 ]. Biochemical Assays and Echocardiographic Examination Fresh blood samples were analyzed using Sysmex XN-3000 automatic blood analyzer and Beckman AU5800 clinical chemistry autoanalyzer to measure the levels of Hemoglobin (Hb), serum creatinine (Scr), blood urea nitrogen (BUN), uric acid (UA), Cystatin C (CysC), fasting blood glucose (FBG), total cholesterol (TC), triglyceride (TG), high/low-density lipoprotein cholesterol [(LDL)(HDL)], Albumin (Alb), Alanine/Aspartate transaminase [(ALT)/(AST)]. The estimated glomerular filtration rate (eGFR) was calculated with the CKD-EPI Creatinine-CysC Eq. (2021). All echocardiographic assessments were conducted by a board-certified cardiologist blinded to the participant's clinical data employing a Philips Epiq7C cardiac ultrasound system (Philips Medical Systems, Andover, MA, USA). All patients were in sinus rhythm during the examination, and all measurements were derived from three consecutive cardiac cycles. The averages of the three Left atrial diameter (LAD), interventricular septum (IVS) in diastole, LV posterior wall thickness (LVPWT), right ventricle (RV) diameter (RVD), Pulmonary artery systolic pressure (PASP), Fraction Shortening% (FS%), Main Pulmonary Artery (MPA), and LV ejection fraction (LVEF) measurements were recorded. Diagnosis of LVDD was based on current guidelines for LV diastolic function evaluation by echocardiography [ 18 ]. Statistical Analysis Data analysis was performed using SPSS (v26.0; SPSS Inc.; Chicago, IL, USA.). Before analysis, the distribution normality for all continuous variables was ascertained with the Kolmogorov–Smirnov test. Normally distributed data were expressed as mean ± standard deviation (SD) while presenting non-normally distributed data as median with interquartile range (25th − 75th percentiles). To compare normally distributed continuous variables between both groups, an independent samples t-test was deployed, whereas the Mann–Whitney U test was used for non-normally distributed variables. Categorical variables were summarized as frequencies or percentages and compared using the chi-square (χ²) test. Spearman's rank correlation coefficient was calculated to evaluate the association between miRNA expression levels and echocardiographic parameters. Multivariate logistic regression (MLR) models were utilized to evaluate the associations of miRNA levels with LVDD in elderly EH patients. Potential multicollinearity among continuous predictor variables was ascertained using the variance inflation factor (VIF); a VIF > 10 was considered indicative of significant collinearity. Model calibration was evaluated with the Hosmer–Lemeshow goodness-of-fit test, with P < 0.05 reflecting poor calibration. The MLR models were adjusted for (1) variables showing P < 0.05 (Table 2 ) and (2) clinically relevant factors previously associated with LVDD. Finally, ROC curves were constructed for individual and combined biomarkers to evaluate their diagnostic performance in predicting LVDD. Seeking to assess predictive accuracy, we calculated AUC, sensitivity, specificity, and Youden index. Comparisons between ROC curves were conducted using DeLong's test. A two-tailed P < 0.05 was deemed statistical significance. Table 2 Demographic and Clinical characteristics of hypertension patients in the study group. Data are expressed as median (quartile range) or mean ± SD. SBP: apolipoprotein A1; DBP: apolipoprotein B; LDL-C: low density lipoprotein cholesterol; HDL-C: high density lipoprote in cholesterol; eGFR: estimated glomerular filtration rate; ALT:Alaninetransaminase; AST:Aspartate Transaminase; Hb:Hemoglobin; CKD:Chronic kidney disease; ARB:Angiotensin II Receptor Blocker‌; ACEI:Angiotensin-Converting Enzyme Inhibitors; ARNI:Angiotensin Receptor Neprilysin Inhibitor; CCB:calcium channel blocker; MRA:mineralocorticoid receptor antagonist. P < 0.05 has statistically significant. Demographic and Clinical Data LVDD(n = 88) Non-LVDD(n = 149) P Age (years) 70.00[67,75] 71[68,76] 0.199 Gender (male) 48 74 0.468 SBP (mmHg) 145.00[131,160.25] 144.00[130.5,157.0] 0.678 DBP (mmHg) 84.00[76.25,92.00] 81.00[73.5,91.5] 0.504 Fasting blood glucose (mg/dL) 5.00[4.52,5.39] 5.45[4.81,6.53] < 0.001 Body mass index (kg/m2 ) 26.16[23.47,28.23] 25.63[23.47,27.55] 0.187 Total cholesterol (mmol/L) 4.45 ± 1.08 4.13 ± 1.06 0.026 LDL-C (mmol/L) 2.84[2.24,3.40] 2.22[1.77,2.93] < 0.001 HDL-C (mmol/L) 1.12[0.97,1.32] 1.03[0.87,1.17] < 0.001 Triglycerides (mmol/L) 1.17[0.85,1.61] 1.27[0.93,1.74] 0.191 Serum creatinine (mg/dl) 0.82[0.69,0.94] 0.74[0.63,0.87] 0.019 CysC(mg/L) 2.06[1.59,2.72] 1.82[1.49,2.25] 0.012 Uric acid(umol/L) 313.70[260.87,369.50] 307.00[246.50,378.55] 0.777 Blood urea nitrogen(mmol/L) 5.20[4.12,6.37] 5.20[4.30,6.72] 0.409 eGFR (mL/min/1.73 m2 ) 50.43[38.07,60.84] 54.71[42.96,65.57] 0.02 Albumin(g/L) 38.26 ± 3.36 38.99 ± 4.13 0.164 ALT(U/L) 16.35[12.62,25.1] 17.00[12.25,23.55] 0.939 AST(U/L) 22.75[18.42,27.80] 19.80[16.95,23.00] 0.001 Hb(g/L) 132[122.25,139] 130[118,140] 0.225 miR-15a-5p 0.73[0.29,1.44] 0.89[0.36,2.24] 0.151 miR-21-5p 0.67[0.32,1.51] 1.24[0.39,2.37] 0.017 miR-19b-3p 0.32[0.16,0.94] 0.12[0.06,0.28] < 0.001 miR-30e-5p 0.22[0.09,0.45] 0.16[0.06,0.37] 0.039 Coronary Heart Disease 37 35 0.003 Diabetes 18 11 0.003 CKD 64 93 0.105 cancer 8 5 0.061 Smoking 7 7 0.304 alcohol 10 5 0.014 Drug treatment ARB 33 32 0.008 ACEI 5 7 0.739 ARNI 5 5 0.389 CCB 49 63 0.046 loopdiuretics 10 5 0.014 MRA 5 7 0.739 Thiazides 6 5 0.221 α-receptor antagonist 0 1 0.441 β-receptor antagonist 24 23 0.027 Statins 48 53 0.004 Anti-platelet 43 51 0.026 Nitrates 15 14 0.083 Results Patient Demographics The outcomes showcased no significant disparities in age or HTN Classification groups (Table 1 ) as well as in the age, gender, SBP, DBP, BMI, or proportions of patients with CKD, cancer, Smoking, and drug treatment with ACEI, ARNI, MRA, Thiazides, α-receptor antagonist, Nitrates between the EH and EH-LVDD groups (all P > 0.05; Table 2 ). Table 1 The characteristics of High-throughput sequencing patients. Hypertension patients ID No.1 No.2 No.3 No.4 No.5 Age (years) 82 82 70 78 82 Gender Male Male Male Male Male Coronary Heart Disease No No Yes No No Diabetes No Yes No No No Hyperlipidemia No No No No No Renal dysfunction No No No No No Hypertension Classification 3 3 3 3 3 Hypertension patients with LVDD ID No.1 No.2 No.3 No.4 No.5 Age (years) 69 74 84 80 74 Gender Male Male Female Female Female Coronary Heart Disease Yes No Yes No No Diabetes No No No No No Hyperlipidemia Yes No No No No Renal dysfunction No No No No No Hypertension Classification 3 3 3 3 3 However, there were more patients with concomitant coronary heart disease (37/88 to 35/149, P = 0.003), diabetes (18/88 to 11/149, P = 0.003), and alcohol history (10/88 to 5/149, P = 0.014) in the EH-LVDD group, unlike the EH group. For the drug treatment, the EH-LVDD group had more patients with ARB (33/88 to 32/149, P = 0.008), loop diuretics (10/88 to 5/149, P = 0.014), and β-receptor antagonist (24/88 to 23/149, P = 0.027) than the EH patients, while fewer patients with CCB (49/88 to 63/149, P = 0.046), Statins (48/88 to 53/149, P = 0.004) and anti-platelet drugs (43/88 to 51/149, P = 0.026) in the EH-LVDD group. C-miRNAs Identified between Non-LVDD and LVDD patients and related KEGG Pathways The HTS analysis between Non-LVDD and LVDD patients found 32 significant DE-miRNAs (P < 0.05). Under the selection criteria, the two groups had high expression levels and an absolute Fold Change ≥ 1.5. Four miRNAs were selected for further identification in the serum of 237 EH patients from four hospitals Figure 2 shows the four selected c-miRNA expressions, indicating higher miR-19b-3p/30e-5p level (P < 0.001, p = 0.039) in the LVDD group. Meanwhile, miR-15a-5p/21-5p level (p = 0.151, p = 0.017) was lower in the LVDD group. To identify gene networks potentially regulated by the selected miRNAs, we performed in silico target prediction analysis using TargetScan (v5.0) and miRanda (v3.3a) algorithms to predict putative miRNA target sites. Only overlapping targets predicted by both algorithms were retained for further functional analysis. The KEGG Pathway of these miRNA targets was also annotated. In these validated miRNAs, Target prediction defined key elements that contributed to cardiac injury: LPAR1, LPA3-modulated lysophosphatidic acid signaling enhances postnatal heart regeneration in mice, GNB5 related to Ras signaling, LPAR1 in actin cytoskeleton, PDE3A, and ATP2B1 in cGMP-PKG signaling regulation (Fig. 1 ) . Expression of Serum Biomarkers and Clinical Factors in the Patients Unlike the Non-LVDD group, the levels of TC [4.45 ± 1.08 vs. 4.13 ± 1.06, P = 0.026], LDL (2.84 [2.24, 3.40] vs. 2.22 [1.77, 2.93], P < 0.001), HDL (1.12 [0.97, 1.32] vs. 1.03 [0.87, 1.17], P < 0.001), Scr (0.82 [0.69, 0.94] vs. 0.74 [0.63, 0.87], P = 0.019), CysC (2.06 [1.59, 2.72] vs. 1.82 [1.49, 2.25], P = 0.012) and AST (22.75 [18.42, 27.80] vs. 19.80 [16.95, 23.00], P = 0.001) were escalated in the LVDD group, and GFR levels (50.43 [38.07, 60.84] vs. 54.71 [42.96, 65.57], P = 0.020) were elevated in the Non-LVDD group. However, no significant disparities were noted between both groups in TG, UA, BUN, Alb, ALT, and Hb levels ( P > 0.05; Table 2 ). For the Echocardiographic examination, IVS in diastole (10 [ 10 , 11 ] vs. 9.81 [9.00,10.65], P < 0.001), MPA (23 [ 21 , 25 ] vs. 22.00 [ 20 , 24 ], P = 0.003), A (1.00 ± 0.22 vs. 0.93 ± 0.20, P = 0.016) and E/e (13.06 [10.81, 15.12] vs. 11.50 [9.49, 12.99], P < 0.001) were escalated in the LVDD group. While, LAD (36.50 [ 34 , 39 ] vs. 38.00 [ 35 , 41 ], P = 0.017), RV (25.5 [21.0, 31.21] vs. 29.90 [ 27 , 32 ], P < 0.001), LVEF% (58 [55, 62] vs. 64.00 [60, 68], P < 0.001), FS% (29.9 [28.6, 35] vs. 35.00 [30.40, 37.00], P < 0.001) and e (5.44 [4.59, 6.89] vs. 6.16 [5.12, 7.37], P = 0.008) were diminished in the LVDD group. No significant differences were observed in the LVPWT, E, E/A, PASP, and Vmax-t between both groups (P > 0.05; Table 3 ). Table 3 Echocardiographic parameters of hypertension patients in the study group. Data are expressed as median (quartile range) or mean ± SD. IVS:interventricular septum; LVPWT: ;RV: right ventricle diameter; LVEF:left ventricular ejection fraction;FS:Fractional Shortening; MPA:Main Pulmonary Artery; PASP:Pulmonary artery systolic pressure. P < 0.05 has statistically significant. Echocardiographic parameters LVDD(n = 88) Non-LVDD(n = 149) P Left atrial diameter(mm) 36.50[ 34 , 39 ] 38.00[ 35 , 41 ] 0.017 IVS in diastole(mm) 10[ 10 , 11 ] 9.81[9.00,10.65] < 0.001 LVPWT(mm) 10[ 9 , 10 ] 9.75[9.00,10.21] 0.094 RV(mm) 25.5[21.0,31.21] 29.90[ 27 , 32 ] < 0.001 LVEF% 58[55,62] 64.00[60,68] < 0.001 FS% 29.9[28.6,35] 35.00[30.40,37.00] < 0.001 MPA(mm) 23[ 21 , 25 ] 22.00[ 20 , 24 ] 0.003 E(m/s) 0.72 ± 0.20 0.69 ± 0.18 0.29 A(m/s) 1.00 ± 0.221 0.93 ± 0.20 0.016 E/A 6.80[5.68,8.71] 7.31[5.93,8.94] 0.569 e(cm/s) 5.44[4.59,6.89] 6.16[5.12,7.37] 0.008 E/e 13.06[10.81,15.12] 11.50[9.49,12.99] < 0.001 PASP(mmHg) 34.14[31.52,39.15] 34.77[31.00,37.75] 0.535 Correlations of C-miRNAs and Echocardiographic Parameters Figure 3 shows that the levels of RV (r = − 0.200, P = 0.002), LVEF (r = − 0.240, P < 0.001), FS(r = − 0.181, P = 0.005), A (r = 0.138, P = 0.034), e (r =- 0.135, P = 0.038), and E/e (r = 0.198, P = 0.002) were significantly related to miR-19b-3p expression. For the miR-30e-5p, Fig. 4 shows the significantly related to LVEF (r = − 0.137, P = 0.035), E (r = 0.176, P = 0.007), E/e (r = 0.170, P = 0.009), FS (r = − 0.181, P = 0.005) and A (r = 0.141, P = 0.030). FS (r = -0.148, P = 0.024), and LVEF (r = -0.182, P = 0.005) was significantly related to miR-15a-5p. MLR Analysis Among the variables that could distinguish the two groups (Tables 3 ), the level of miR-19b-3p (OR = 1.565, 95% CI: 1.098–2.231), miR-21-5p (OR = 0.713, 95% CI: 0.611–0.831), and miR-15a-5p (OR = 0.762, 95% CI: 0.656–0.885) was significantly related to LVDD in the unadjusted model. Furthermore, we adjusted for CHD, DM, alcohol, ARB, CCB, loop diuretics, β-receptor antagonists, Statins, anti-platelet, eGFR, and AST, showing significant disparities between both groups ( P < 0.05 , Table 2 ). In the MLR analysis, miR-19b-3p (OR = 4.359, 95% CI: 2.154–8.822), miR-21-5p (OR = 0.664, 95% CI: 0.516–0.854), and miR-15a-5p (OR = 0.728, 95% CI: 0.579–0.915) level still showed a significant odds ratio in the adjusted model (Table 4 ). Furthermore, multicollinearity was assessed among CHD, DM, alcohol, ARB, CCB, loop diuretics, β-receptor antagonists, Statins, Anti-platelet, eGFR, AST, and miRNAs by the VIF. No VIF values exceeded 10, indicating no significant multicollinearity among the included variables. The Hosmer–Lemeshow tests manifested significant goodness of fit for the adjusted model (P = 0.214). Table 4 Multivariate analysis using logistic regression: predictive factors for the risk of LVDD in elderly essential hypertension. Multivariate analysis was adjusted for CHD,DM, alcohol, ARB, CCB, loopdiuretics, β-receptor antagonist, Statins, Anti-platelet, eGFR, AST, which showed significant differences between the two groups (P < 0.05, Tables 2 ). Factors Univariate analysis Multivariate analysis OR(95%CI) P-value OR(95%CI) P-value miR-19b-3p 1.565(1.098,2.231) 0.013 4.359(2.154,8.822) \(\:<\) 0.001 miR-30e-5p 1.549(0.948,2.532) 0.080 1.396(0.860,2.268) 0.177 miR-21-5p 0.713(0.611,0.831) \(\:<\) 0.001 0.664(0.516,0.854) 0.001 miR-15a-5p 0.762(0.656,0.885) \(\:<\) 0.001 0.728(0.579,0.915) 0.007 The Value of the C-miRNAs Level for LVDD Diagnosis in Elderly Patients with EH Figure 5 presents the effect of individual and combined miRNAs on LVDD diagnosis. Among these individual miRNAs, we observed that miR-19b-3p presented the best AUC of 0.735 (95% CI: 0.669-0.800), a Youden index of 0.356, a sensitivity of 88.64%, and a specificity of 46.98% (Table 5 ). Furthermore, the combined analysis of miR-19b-3p/21-5p/30e-5p demonstrated superior diagnostic performance, yielding an AUC of 0.915 (95% CI: 0.872–0.947), better than that of miRNA alone (P < 0.001). Table 5 AUC for individual/combination miRNAs. Receiver operating characteristic (ROC) curve analysis was used to calculate the area under the curve (AUC) of individual and combined miRNAs for diagnosing LVDD in elderly hypertension patients. Variables The under area of ROC curve (95% CI) sensitivity (%) specifcity(%) Youden index miR−19b−3p 0.735(0.669−0.800) 88.640 46.980 0.356 miR−21−5p 0.587(0.514–0.661) 70.450 51.010 0.214 miR−30e−5p 0.580(0.506–0.655) 82.950 33.560 0.165 miR−15a−5p 0.556(0.481–0.631) 71.590 42.070 0.136 miR−19b−3p + miR−21−5p + miR−30e−5p 0.915(0.872–0.947) 86.360 89.930 0.763 miR−19b−3p + miR−21−5p + miR−15a−5p 0.898(0.854–0.943) 80.680 89.660 0.703 miR−19b−3p + miR−21−5p + miR−15a−5p + miR−30e−5p 0.914(0.871–0.947) 86.360 88.280 0.746 Discussion This study focused on early heart functional injury in elderly HTN patients. Using the HTS approach; we analyzed c-miRNA differential expressions in elderly HTN patients versus those with HTN complicated by LVDD. The DE-miRNAs were further quantitatively evaluated for the diagnostic value in LVDD with HTN. Our results elucidated that LVDD patients displayed significantly repressed miR-21-5p/15a-5p and overexpressed miR-30e-5p/19b-3p compared to EH patients. In addition, these four miRNA expressions were related to some echocardiographic parameters, suggesting their potential utility as diagnostic biomarkers for LVDD. Moreover, ROC curve analysis showcased that miR-19b-3p expression could moderately predict LVDD in EH, providing evidence for the c-miRNAs to diagnose elderly HTN patients with LV injury early. Combining miRNA biomarkers (miR-19b-3p/21-5p/30e-5p) revealed robust predictive accuracy for LV dysfunction and injury. miRNAs have emerged as promising biomarkers for EH, with c-miRNA profiles exhibiting distinct deregulation patterns in pathophysiological states. Notably, c-miRNA dysregulation often precedes clinical disease manifestation, highlighting their utility in early detection [ 23 , 24 ]. Accumulating evidence underscores miRNAs' involvement in EH pathogenesis [ 24 – 27 ]. Karolina et al. [ 28 ] demonstrated that MetS traits exhibit miRNA-level interconnectivity, with certain miRNAs uniquely related to individual components. Specifically, miR-130a/195/92a were linked with HT but not other MetS features. Subsequent research validated miR-92a's association with HT. Two Japanese prospective targeted investigations studies [ 29 , 30 ] found that serum miR-27a/126/133/221/222 were dysregulated before HT diagnosis, implicating pathways related to vascular dysfunction, inflammation, and oxidative stress in preclinical stages. Chronic uncontrolled HTN induces cardiac remodeling via sustained afterload elevation, manifesting as LV or left atrial structural and functional alterations. Early LVDD, characterized by impaired LV relaxation, frequently arises from inadequately managed HT or its comorbidities, including obesity, type 2 diabetes, and dyslipidemia [ 31 ]. Being an independent predictor of major adverse cardiovascular events in HTN populations, LVDD necessitates rigorous diagnostic criteria for effective risk stratification. Standardized Doppler echocardiography, per ASE/EACVI guidelines, remains the cornerstone for LVDD assessment. Our prior investigation demonstrated elevated c-miR-21 levels in elderly patients with EH and LA dilation, with significant correlations between miR-21 expression and LA structural changes. These findings implicated miR-21 in the pathophysiological mechanisms and therapeutic monitoring of HTN-related early cardiac injury in EH populations [ 15 ]. Herein, we further carried out HTS to investigate c-miRNA differential expressions in elderly patients with HTN versus those with HTN complicated by LVDD. Enrichment analyses found that the differential expression of miRNAs-related genes mainly involves vascular structural cell proliferation and migration, inflammatory cell function activation, and tissue remodeling (Figs. 1 C–D). Throughout a median follow-up of 19.1 months (IQR: 17.7–20.7), the highest miR-19b-3p quartile patients exhibited the poorest survival outcomes (Log-rank P < 0.001). These findings support c-miR-19b-3p as a prognostic biomarker for AHF and suggest its association with ventricular hypertrophy in AHF patients [ 32 ]. Xue et al. reported that reductions in c-miR-19 levels throughout dietary weight-loss interventions correlated with decreased ASCVD risk, particularly in individuals without sleep disturbances or with elevated physical activity [ 33 ]. The miR-19b-3p target prediction LPAR1 gene in mice could mediate lysophosphatidic acid signaling and promote postnatal heart regeneration [ 34 ]. Our research also indicated that RV, LVEF, A, e, and E/e were significantly correlated with miR-19b-3p level. MiR-19b-3p was significantly related to LVDD in the univariate (ULR) and MLR analysis, and it showed a moderate diagnostic value in LVDD with AUC 0.735 (95% CI: 0.669–0.800). MiRNA-21 is among the most extensively investigated miRNAs in HT, with a current meta-analysis (546 cases, 436 controls) demonstrating overexpressed miR-21 in HT patients [ 35 ]. The modulation of cytochrome by miR-21 helped to reduce ROS production and protect the mitochondria from oxidative stress. A recombinant adeno-associated virus-mediated miR-21 overexpression in spontaneously hypertensive rats reduced systolic blood pressure by nearly 15 mm Hg after one month [ 36 ]. miR-21-5p expression was elevated in doxorubicin-treated cardiomyocytes and murine myocardial tissue, with cardiac overexpression demonstrating protective effects against doxorubicin-provoked injury [ 37 ]. Here, we identified GNA12 as a miR-21-5p target, potentially activated by mechanical stretch and oxidative stress to function as a stress-response mediator, offering cardioprotection via mitochondrial apoptotic pathway inhibition [ 38 ]. Moreover, miR-21-5p was significantly impaired in LVDD patients, confirming it as an independent correlate of LVDD, as evidenced by ULR and MLR (Table 4 ). However, miR-21-5p exhibited only marginal diagnostic utility for LVDD (AUC 0.587; 95% CI: 0.514–0.661). MiR-30e-5p, belonging to the miR-30 family, is dysregulated in EH. Bioinformatic analysis revealed its significant downregulation in LV tissue of SD rats post-MI compared to non-infarcted controls, with experimental studies demonstrating its anti-inflammatory and cardioprotective role via PTEN suppression [ 39 ]. ADAM9, a validated miR-30e-5p target, conversed the protective effects of miR-30e-5p overexpression in angiotensin II-treated cardiomyocytes [ 40 ]. Herein, miR-30e-5p may target the predicted Atp2b1 gene. Wang et al. proved that alternative splicing of the Atp2b1 gene in cGMP/PKG/Ca2 + signaling could decrease the risk of cardiomyopathy and HF in diabetes patients [ 41 ]. Figures 4 A–D show that LVEF, E, A, E/e, and FS were significantly correlated with miR-30e-5p expression. MiR-30e-5p showed moderate diagnostic value in LVDD with AUC 0.580 (95% CI: 0.506–0.655). MiR-15a-5p was overexpressed in patients with adverse cardiac events in arrhythmogenic RV cardiomyopathy [ 42 ]. In a study evaluating whether c-miRNAs could be potential biomarkers for diffuse myocardial fibrosis in cases having hypertrophic cardiomyopathy, miR-15a-5p presented a moderate diagnostic value for this condition [ 43 ]. Fen et al. demonstrated that exosomes derived from Ang-II-treated cardiomyocytes transfer miR-15a-5p to cardiac fibroblasts, where it targets Dyrk2, leading to NFAT dephosphorylation, enhancing cell viability and overexpressing α-smooth muscle actin, collagen type I/III α1, thereby promoting myocardial fibrosis [ 44 ]. Our results elucidated GNB5 as a putative miR-15a-5p target gene, with experimental evidence suggesting its role in promoting myofibroblast transition and pathological remodeling, where sustained activation contributes to HF progression [ 45 ]. Despite the absence of significant intergroup differences in miR-15a-5p expression between non-LVDD and LVDD cohorts, our findings revealed significant correlations between miR-15a-5p levels, LVEF, and FS. MiR-15a-5p was significantly associated with LVDD in the ULR and MLR analyses, and it showed a moderate diagnostic value in LVDD with an AUC of 0.556 (95% CI: 0.481–0.631). Combining multiple miRNAs may offer a strategic advantage over single-miRNA approaches for improving risk stratification and long-term outcomes in cardiovascular disease. Beyond their diagnostic and prognostic applications, miRNA-based therapies hold promise for enabling integration with emerging platforms and computational tools to advance precision medicine [ 16 , 43 ]. In patients with CPTH complicated by RV dysfunction, correlation analyses between dysregulated miRNAs and echocardiographic parameters highlighted the diagnostic potential of miR-20a-5p/93-5p/17-5p/3202. Notably, a tripartite miRNA panel derived from these four biomarkers demonstrated robust predictive performance, underscoring the added value of combinatorial miRNA strategies in clinical decision-making [ 16 ]. Lmitations Although this study was conducted across multiple centers, it was limited to a single geographic region (northern Hebei Province), restricting the finding generalizability to broader populations. Future multi-region prospective cohort studies should validate these results in ethnically and geographically diverse cohorts. Our current analysis primarily focused on EH populations, with findings warranting further investigation in patients stratified by varying stages of LVDD severity. Subsequent studies will expand inclusion criteria to encompass LVDD subtypes to comprehensively assess the diagnostic utility and clinical relevance of these miRNA biomarkers. Conclusions In summary, our ROC curve analysis demonstrated that miRNA expression profiles possess predictive capacity for LVDD in patients with EH. Notably, miR-19b-3p emerged as the most specific and robust single biomarker for LVDD development, outperforming other assessed miRNAs. Additionally, miRNAs exhibit critical roles in intercellular communication through direct paracrine signaling or by modulating downstream mediators of cell-to-cell signaling [ 46 , 47 ]. Importantly, integrating miR-19b-3p/21-5p/30e-5p significantly enhanced diagnostic accuracy for LVDD, as evidenced by improved AUC values compared to individual biomarkers. These findings underscore the additive value of combinatorial miRNA panels in augmenting risk stratification and diagnostic precision beyond conventional echocardiographic assessment, providing a mechanistic and translational foundation for miRNA-based clinical decision-making in EH-related LVDD. Abbreviations AUC Area Under the Curve DE Differentially Expressed EH Essential Hypertensive FC Fold-change FS Fractional Shortening HTN Hypertension LVDD Left Ventricular Diastolic Dysfunction LVEF Left Ventricular Ejection Fraction ROC Receiver Operating Characteristic RV Right Ventricle Declarations The study was authorized by the Ethics Committee at Heibei General Hospital (2019027) and followed the Helsinki Declaration and Good Clinical Practice guidelines, defined by the International Conference on Harmonisation. Patients signed informed consent prior to enrollment. Study registration was completed on ResMan [approval number ChiCTR1900026699]. Data availability The dataset analysed during the current study are available in GEO (https://www.ncbi.nlm.nih/) with accession numbers: GSE305634. All data generated for analysis are available from the corresponding author upon reasonable request. Acknowledgements We are grateful to Hebei General Hospital, Fourth Hospital of Hebei Medical University, Shijiazhuang People's Hospital, and Handan Central Hospital for their assistance in data collection for this study. Funding This work was supported by these grants including: S&T Program of Hebei(Grant No. 19277787D,199776249D); Natural Science Foundation of Hebei (Grant No. H2023307018),Medical Science Research Project of Hebei (Grant No. 20220061). Consent for publication Not applicable. Competing interests The authors declare no conficts of interest related to this work. 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16:41:19","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149410,"visible":true,"origin":"","legend":"","description":"","filename":"8002bf95a1e14a91917da9063c4476c31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7320316/v1/075b3a5b36cf0e2d4bb109c5.xml"},{"id":98427226,"identity":"74b8ff66-3318-41d9-8b1b-51c20305324c","added_by":"auto","created_at":"2025-12-17 16:40:00","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161127,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7320316/v1/ed660fea935a795024445af8.html"},{"id":98425877,"identity":"6bfc010c-4439-4593-b946-1aef115d6a67","added_by":"auto","created_at":"2025-12-17 16:35:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":687914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression analysis of serum miRNAs in elderly HTN patients with and without LVDD, as determined by RNA-seq.\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003e \u003cstrong\u003eHierarchical clustering heatmap:\u003c/strong\u003e miRNA expression profiles. Hyper 1–5: five elderly HTN patients without LVDD; Hyper-LVDD 1–5: five elderly HTN patients with LVDD. \u003cstrong\u003eB.\u003c/strong\u003e \u003cstrong\u003eVolcano plot\u003c/strong\u003e: Significantly DE-miRNAs (P \u0026lt; 0.05). Each dot represents an individual miRNA; downregulated miRNAs are shown in blue (log₂ FC \u0026lt; 1.5), upregulated miRNAs in red (log₂ FC \u0026gt; 1.5), and non-significant changes in gray. \u003cstrong\u003eC. \u003c/strong\u003eKEGG pathways over-represented in the DE-miRNAs. \u003cstrong\u003eD. \u003c/strong\u003emiRNA-gene-KEGG pathway interaction network of the four miRNAs (miR-19b-3p/21-5p/15a-5p/30e-5p).Used with permission from KEGG[48–50].\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7320316/v1/7f9cedd8fdaab1f26099a419.jpg"},{"id":98428367,"identity":"9da99ff9-557a-4f96-af0b-1679f7d61b83","added_by":"auto","created_at":"2025-12-17 16:41:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":419492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003emiRNA expressions differed between the two groups.\u003c/strong\u003e \u003cstrong\u003eA–D\u003c/strong\u003e miR-15a-5p/21-5p/19b-3p/30e-5p. *P \u0026lt; 0.05; **P \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7320316/v1/a9a38dfe58f9b607b84c333e.jpg"},{"id":98427558,"identity":"0a26d2dc-3c2e-4193-924c-74760950bb79","added_by":"auto","created_at":"2025-12-17 16:40:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":418406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between miR-19b-3p and Echocardiographic parameters.\u003c/strong\u003e miR-19b-3p levels correlated significantly with RV \u003cstrong\u003e(A)\u003c/strong\u003e, LVEF \u003cstrong\u003e(B)\u003c/strong\u003e, FS \u003cstrong\u003e(C)\u003c/strong\u003e, A \u003cstrong\u003e(D)\u003c/strong\u003e, e \u003cstrong\u003e(E)\u003c/strong\u003e, and E/e \u003cstrong\u003e(F)\u003c/strong\u003ein 237 elderly EH patients.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7320316/v1/de0c8918210f2986e139a07b.jpg"},{"id":98427916,"identity":"38fff1cc-966f-4014-9e6d-26fcf8476476","added_by":"auto","created_at":"2025-12-17 16:41:21","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":503390,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between miR-30e-5p/15a-5p and Echocardiographic parameters.\u003c/strong\u003e miR-30e-5p levels correlated significantly with LVEF \u003cstrong\u003e(A)\u003c/strong\u003e, E \u003cstrong\u003e(B)\u003c/strong\u003e, E/e \u003cstrong\u003e(C)\u003c/strong\u003e, FS \u003cstrong\u003e(D)\u003c/strong\u003e, and A \u003cstrong\u003e(E)\u003c/strong\u003ein 237 elderly EH patients. miR-15a-5p levels correlated significantly with FS \u003cstrong\u003e(F)\u003c/strong\u003eand LVEF \u003cstrong\u003e(G)\u003c/strong\u003e in 237 elderly EH patients.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7320316/v1/de62bc091a083503b58ccd89.jpg"},{"id":98427410,"identity":"a541d00e-3aaa-40ef-a680-f4edc443786a","added_by":"auto","created_at":"2025-12-17 16:40:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":328890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC analysis of the individual/combination miRNAs for diagnosing the possibility of LVDD in elderly EH patients.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7320316/v1/684739ebc43f531dbc65af5e.jpg"},{"id":106961602,"identity":"7a84b625-901a-4a8d-9bc4-cbbc781d4034","added_by":"auto","created_at":"2026-04-15 09:26:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3906258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7320316/v1/4f8c4b92-8b83-49e9-a9bf-3e9534ca624d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic Value of miRNA Expression for Elderly Hypertension with Early Heart Function Injury","fulltext":[{"header":"Background","content":"\u003cp\u003eEssential hypertension (EH) is the leading cause of major cardiovascular morbidities such as hypertensive heart disease (HHD), renal disease, stroke, and peripheral arterial disease [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A cross-sectional study in 2021 found that among populations aged 35\u0026ndash;75 years in Northern China, above 50% have hypertension (HTN) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite advancements in diagnostic biomarker research, HTN management remains suboptimal in China, with \u0026lt;\u0026thinsp;50% of patients receiving treatment and fewer than 20% achieving blood pressure control [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Most patients who are newly diagnosed with HTN have already developed HTN target organ damage due to poor screening habits and lack of awareness [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChronic systemic arterial HTN induces a cascade of anatomical and functional modifications in the left/atrium [(LV)/(LA)] and coronary arteries, thereby developing HHD. These changes include LV hypertrophy (LVH), LV diastolic dysfunction (LVDD), atrial fibrillation, heart failure (HF), and coronary artery disease, with LVH serving as the central pathological driver. LVDD, characterized by impaired myocardial relaxation and elevated ventricular stiffness, has been established as the initial manifestation of HHD. Untreated or inadequately controlled arterial HTN may exacerbate LVDD, progressing from mild to severe stages, and ultimately result in both diastolic and systolic HF [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The prevalence of LVH in HTN populations was found to be 31.9% in the Chinese community [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Systemic HTN and its complications, such as LVDD, are often asymptomatic during the early stages, underscoring the necessity of early LVDD assessment and stringent blood pressure control to delay disease progression.\u003c/p\u003e\u003cp\u003eRecent evidence highlights microRNAs (miRNAs) as promising biomarkers for EH. Circulatory miRNA (c-miRNA) profiles demonstrate distinct patterns in pathophysiological states, with dysregulation often occurring prior to clinical manifestation, underscoring their utility for early disease detection [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These miRNAs actively regulate myocardial remodeling, fibroblast apoptosis, and HF progression [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In our previous study, serum circulating miR (c-miR)-21 levels were elevated in older EH patients with LA dilation, correlating significantly with LA structural changes; however, its diagnostic accuracy for LA dilation was suboptimal [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Miao et al. found that the combined serum miRNA expression can predict right ventricular (RV) injury and malfunction in patients having chronic thromboembolic pulmonary HTN (CPTH) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we focus on early heart functional injury in elderly HTN patients. Using the gene microarray approach, c-miRNA differential expression was analyzed in elderly EH patients versus those with EH complicated by LVDD. The differentially expressed miRNAs (DE-miRNAs) were further evaluated for their combined diagnostic effectiveness. We aim to provide a basis for early detection and intervention of cardiac functional impairment in elderly HTN patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and clinical data collection\u003c/h2\u003e\u003cp\u003eThis retrospective study enrolled 247 elderly EH patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] treated in the department of cardiology at four tertiary grade-A hospitals -Hebei General Hospital, Fourth Hospital of Hebei Medical University, Shijiazhuang People's Hospital, and Handan Central Hospital - between September 2022 and September 2023, including 154 EH patients without LVDD and 93 EH patients combined with LVDD. LVDD was diagnosed based on the Doppler interrogation as recommended by the American Society of Echocardiography and the European Association of Cardiovascular Imaging [(ASE)/(EACVI)] [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. All the patients provided a detailed medical history and underwent standardized clinical assessments and laboratory tests. Patients were excluded if they met any of the following criteria: acute coronary syndrome, remote myocardial infarction, systemic inflammatory disease, renal failure, autoimmune diseases, liver diseases, moderate-to-severe aortic/mitral valve disease, prosthetic valve implantation, atrial septal defects or aneurysms, arrhythmias (e.g., atrial fibrillation, conduction abnormalities), or suboptimal echocardiographic image quality. The study was authorized by the Ethics Committee at Heibei General Hospital (2019027) and followed the Helsinki Declaration and Good Clinical Practice guidelines, defined by the International Conference on Harmonisation. Patients signed informed consent prior to enrollment. A dedicated data and safety monitoring committee oversaw protocol compliance, adverse event tracking, and efficacy evaluation. Study registration was completed on ResMan [approval number ChiCTR1900026699].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSmall RNA Library Preparation and High-Throughput Sequencing (HTS)\u003c/h3\u003e\n\u003cp\u003ePeripheral blood (3\u0026ndash;4 mL) was collected from each participant into procoagulant tubes (Hebei Xinle Sci \u0026amp; Tech Co., Ltd., Hebei, China), followed by centrifugation (2000 r/min, 10 min) and transferring the resulting supernatant to a clean EP tube and subjected to a second centrifugation (13,000 r/min,10 min) to remove cellular debris and intact chromatin. The final supernatant was aliquoted into new EP tubes and maintained at \u0026minus;\u0026thinsp;80\u0026deg;C until use.\u003c/p\u003e\u003cp\u003eThe serum from 10 patients with EH (5 LVDD, 5 non-LVDD controls) at Hebei Provincial People's Hospital was selected for HTS analysis.\u003c/p\u003e\u003cp\u003ePer protocols, total RNA was extracted utilizing a commercial kit, RNA quantity, and purity were determined through a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA), and RNA integrity was determined via an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA). For library construction, total RNA was denatured at 70\u0026deg;C for 2 min and subsequently incubated with 3' RNA adapters and T4 RNA Ligase 2, truncated K227Q (NEB, M0351L) at room temperature. Ligation was carried out at 16\u0026deg;C for over 8 h. Unligated 3' adapters were removed via RTP digestion at 37\u0026deg;C for 30 min. Thereafter, 5' RNA adapters were subjected to ligation to the products using T4 RNA Ligase 1 (NEB, M0204L) at 37\u0026deg;C for 60 min. Reverse transcription was executed via SuperScript II Reverse Transcriptase (Thermo Fisher Scientific, 18064014) at 50\u0026deg;C for an hour, then enzyme inactivation at 80\u0026deg;C for 10 min. The cDNA was amplified via PCR through Phusion\u0026reg; High-Fidelity DNA Polymerase (NEB, M0530L) as follows: initial denaturation at 98\u0026deg;C for 30 s, annealing at 60\u0026deg;C for 30 s, extension at 72\u0026deg;C for 15 s, for 10\u0026ndash;16 cycles, followed by a final extension at 72\u0026deg;C for 5 min. Amplified products were size-selected and purified using PAGE. Sequencing was conducted on an Illumina HiSeq 2500 platform via single-end 50 bp reads following the protocol.\u003c/p\u003e\u003cp\u003eRaw sequencing reads were processed employing ACGT101-miR (v4.2) to eliminate adapter dimers, low-quality sequences, repetitive elements, and RNA families (rRNA, tRNA, snRNA, snoRNA). Unique sequences of 18\u0026ndash;26 nucleotides were aligned to miRBase 22.1 precursor sequences using BLAST, allowing for length variation at both ends and one internal mismatch. Known miRNAs were identified based on their mapping to annotated mature miRNA regions of specific species. Novel miRNA candidates were defined as sequences derived from the opposite arm of known precursors (either 5p or 3p). The other sequences were mapped to precursor miRNAs from other species in miRBase 22.1 and further aligned to the reference genome of the target species to determine genomic origin. Unmapped sequences underwent de novo miRNA prediction using RNAfold (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi\u003c/span\u003e\u003cspan address=\"http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ), analyzing flanking 80 nt regions for potential hairpin structures. Predicted hairpins were filtered based on established criteria: (1)\u0026thinsp;\u0026le;\u0026thinsp;12 nucleotides in any stem bulge; (2)\u0026thinsp;\u0026ge;\u0026thinsp;16 base pairs in the stem; (3) free energy \u0026le; \u0026minus;\u0026thinsp;15 kcal/mol; (4) total hairpin length\u0026thinsp;\u0026ge;\u0026thinsp;50 nt; (5) loop length\u0026thinsp;\u0026le;\u0026thinsp;20 nt; (6)\u0026thinsp;\u0026le;\u0026thinsp;8 nucleotides in any mature region bulge; (7)\u0026thinsp;\u0026le;\u0026thinsp;4 biased mismatches in mature region bulges; (8)\u0026thinsp;\u0026le;\u0026thinsp;2 biased bulges in the mature region; (9)\u0026thinsp;\u0026le;\u0026thinsp;7 total errors in the mature region; (10)\u0026thinsp;\u0026ge;\u0026thinsp;12 base pairs in the mature region; (11)\u0026thinsp;\u0026ge;\u0026thinsp;80% of the mature sequence located within the stem.\u003c/p\u003e\n\u003ch3\u003eRT-qPCR Validation of Serum miRNAs\u003c/h3\u003e\n\u003cp\u003eDE-miRNA was assessed using normalized HTS counts, and a Student's t-test was used with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to identify DE-miRNAs between the non-LVDD and LVDD groups. miRNAs were chosen on the basis of significant differential expression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a minimum absolute fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.5, and high sequencing read counts.\u003c/p\u003e\u003cp\u003eFor RNA-seq data validation, total miRNA extraction from serum samples was performed using the miRNeasy Serum/Plasma Advanced Kit, and quantitative RT-PCR was performed with Specific primers for selected miRNAs (all from Qiagen, Germany) following the protocols. The sample exhibiting the lowest Ct value for the target miRNAs served as a reference control, calculating each sample's results using the 2\u003csup\u003e\u0026minus;∆∆Ct\u003c/sup\u003e method. For normalization, miR-191-5p was deployed as an endogenous control due to its consistent expression in human serum samples [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and Cel-miR-39 was employed as an exogenous control [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eBiochemical Assays and Echocardiographic Examination\u003c/h3\u003e\n\u003cp\u003eFresh blood samples were analyzed using Sysmex XN-3000 automatic blood analyzer and Beckman AU5800 clinical chemistry autoanalyzer to measure the levels of Hemoglobin (Hb), serum creatinine (Scr), blood urea nitrogen (BUN), uric acid (UA), Cystatin C (CysC), fasting blood glucose (FBG), total cholesterol (TC), triglyceride (TG), high/low-density lipoprotein cholesterol [(LDL)(HDL)], Albumin (Alb), Alanine/Aspartate transaminase [(ALT)/(AST)]. The estimated glomerular filtration rate (eGFR) was calculated with the CKD-EPI Creatinine-CysC Eq.\u0026nbsp;(2021).\u003c/p\u003e\u003cp\u003eAll echocardiographic assessments were conducted by a board-certified cardiologist blinded to the participant's clinical data employing a Philips Epiq7C cardiac ultrasound system (Philips Medical Systems, Andover, MA, USA). All patients were in sinus rhythm during the examination, and all measurements were derived from three consecutive cardiac cycles. The averages of the three Left atrial diameter (LAD), interventricular septum (IVS) in diastole, LV posterior wall thickness (LVPWT), right ventricle (RV) diameter (RVD), Pulmonary artery systolic pressure (PASP), Fraction Shortening% (FS%), Main Pulmonary Artery (MPA), and LV ejection fraction (LVEF) measurements were recorded. Diagnosis of LVDD was based on current guidelines for LV diastolic function evaluation by echocardiography [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData analysis was performed using SPSS (v26.0; SPSS Inc.; Chicago, IL, USA.). Before analysis, the distribution normality for all continuous variables was ascertained with the Kolmogorov\u0026ndash;Smirnov test. Normally distributed data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) while presenting non-normally distributed data as median with interquartile range (25th \u0026minus;\u0026thinsp;75th percentiles). To compare normally distributed continuous variables between both groups, an independent samples t-test was deployed, whereas the Mann\u0026ndash;Whitney U test was used for non-normally distributed variables. Categorical variables were summarized as frequencies or percentages and compared using the chi-square (χ\u0026sup2;) test. Spearman's rank correlation coefficient was calculated to evaluate the association between miRNA expression levels and echocardiographic parameters.\u003c/p\u003e\u003cp\u003eMultivariate logistic regression (MLR) models were utilized to evaluate the associations of miRNA levels with LVDD in elderly EH patients. Potential multicollinearity among continuous predictor variables was ascertained using the variance inflation factor (VIF); a VIF\u0026thinsp;\u0026gt;\u0026thinsp;10 was considered indicative of significant collinearity. Model calibration was evaluated with the Hosmer\u0026ndash;Lemeshow goodness-of-fit test, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 reflecting poor calibration. The MLR models were adjusted for (1) variables showing P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and (2) clinically relevant factors previously associated with LVDD. Finally, ROC curves were constructed for individual and combined biomarkers to evaluate their diagnostic performance in predicting LVDD. Seeking to assess predictive accuracy, we calculated AUC, sensitivity, specificity, and Youden index. Comparisons between ROC curves were conducted using DeLong's test. A two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistical significance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eDemographic and Clinical characteristics of hypertension patients in the study group.\u003c/b\u003e Data are expressed as median (quartile range) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. SBP: apolipoprotein A1; DBP: apolipoprotein B; LDL-C: low density lipoprotein cholesterol; HDL-C: high density lipoprote in cholesterol; eGFR: estimated glomerular filtration rate; ALT:Alaninetransaminase; AST:Aspartate Transaminase; Hb:Hemoglobin; CKD:Chronic kidney disease; ARB:Angiotensin II Receptor Blocker\u0026zwnj;; ACEI:Angiotensin-Converting Enzyme Inhibitors; ARNI:Angiotensin Receptor Neprilysin Inhibitor; CCB:calcium channel blocker; MRA:mineralocorticoid receptor antagonist. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 has statistically significant.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic and Clinical Data\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLVDD(n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-LVDD(n\u0026thinsp;=\u0026thinsp;149)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.00[67,75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71[68,76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145.00[131,160.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144.00[130.5,157.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.00[76.25,92.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.00[73.5,91.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFasting blood glucose (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.00[4.52,5.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.45[4.81,6.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody mass index (kg/m2 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.16[23.47,28.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.63[23.47,27.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.84[2.24,3.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.22[1.77,2.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12[0.97,1.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03[0.87,1.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17[0.85,1.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27[0.93,1.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum creatinine (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.82[0.69,0.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.74[0.63,0.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCysC(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.06[1.59,2.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.82[1.49,2.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e313.70[260.87,369.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e307.00[246.50,378.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood urea nitrogen(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.20[4.12,6.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.20[4.30,6.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR (mL/min/1.73 m2 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.43[38.07,60.84]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.71[42.96,65.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.26\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.99\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.35[12.62,25.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.00[12.25,23.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.75[18.42,27.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.80[16.95,23.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132[122.25,139]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130[118,140]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR-15a-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.73[0.29,1.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89[0.36,2.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR-21-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.67[0.32,1.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.24[0.39,2.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR-19b-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.32[0.16,0.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12[0.06,0.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR-30e-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.22[0.09,0.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16[0.06,0.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary Heart Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ealcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrug treatment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACEI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eloopdiuretics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThiazides\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eα-receptor antagonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eβ-receptor antagonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-platelet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003ePatient Demographics\u003c/h2\u003e\u003cp\u003eThe outcomes showcased no significant disparities in age or HTN Classification groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e as well as in the age, gender, SBP, DBP, BMI, or proportions of patients with CKD, cancer, Smoking, and drug treatment with ACEI, ARNI, MRA, Thiazides, α-receptor antagonist, Nitrates between the EH and EH-LVDD groups (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe characteristics of High-throughput sequencing patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eHypertension patients\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary Heart Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal dysfunction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension Classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eHypertension patients with LVDD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary Heart Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal dysfunction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension Classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHowever, there were more patients with concomitant coronary heart disease (37/88 to 35/149, P\u0026thinsp;=\u0026thinsp;0.003), diabetes (18/88 to 11/149, P\u0026thinsp;=\u0026thinsp;0.003), and alcohol history (10/88 to 5/149, P\u0026thinsp;=\u0026thinsp;0.014) in the EH-LVDD group, unlike the EH group. For the drug treatment, the EH-LVDD group had more patients with ARB (33/88 to 32/149, P\u0026thinsp;=\u0026thinsp;0.008), loop diuretics (10/88 to 5/149, P\u0026thinsp;=\u0026thinsp;0.014), and β-receptor antagonist (24/88 to 23/149, P\u0026thinsp;=\u0026thinsp;0.027) than the EH patients, while fewer patients with CCB (49/88 to 63/149, P\u0026thinsp;=\u0026thinsp;0.046), Statins (48/88 to 53/149, P\u0026thinsp;=\u0026thinsp;0.004) and anti-platelet drugs (43/88 to 51/149, P\u0026thinsp;=\u0026thinsp;0.026) in the EH-LVDD group.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eC-miRNAs Identified between Non-LVDD and LVDD patients and related KEGG Pathways\u003c/h3\u003e\n\u003cp\u003eThe HTS analysis between Non-LVDD and LVDD patients found 32 significant DE-miRNAs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Under the selection criteria, the two groups had high expression levels and an absolute Fold Change\u0026thinsp;\u0026ge;\u0026thinsp;1.5. Four miRNAs were selected for further identification in the serum of 237 EH patients from four hospitals\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the four selected c-miRNA expressions, indicating higher miR-19b-3p/30e-5p level (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.039) in the LVDD group. Meanwhile, miR-15a-5p/21-5p level (p\u0026thinsp;=\u0026thinsp;0.151, p\u0026thinsp;=\u0026thinsp;0.017) was lower in the LVDD group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo identify gene networks potentially regulated by the selected miRNAs, we performed in silico target prediction analysis using TargetScan (v5.0) and miRanda (v3.3a) algorithms to predict putative miRNA target sites. Only overlapping targets predicted by both algorithms were retained for further functional analysis. The KEGG Pathway of these miRNA targets was also annotated. In these validated miRNAs, Target prediction defined key elements that contributed to cardiac injury: LPAR1, LPA3-modulated lysophosphatidic acid signaling enhances postnatal heart regeneration in mice, GNB5 related to Ras signaling, LPAR1 in actin cytoskeleton, PDE3A, and ATP2B1 in cGMP-PKG signaling regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eExpression of Serum Biomarkers and Clinical Factors in the Patients\u003c/h2\u003e\u003cp\u003eUnlike the Non-LVDD group, the levels of TC [4.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08 vs. 4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06, P\u0026thinsp;=\u0026thinsp;0.026], LDL (2.84 [2.24, 3.40] vs. 2.22 [1.77, 2.93], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), HDL (1.12 [0.97, 1.32] vs. 1.03 [0.87, 1.17], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Scr (0.82 [0.69, 0.94] vs. 0.74 [0.63, 0.87], P\u0026thinsp;=\u0026thinsp;0.019), CysC (2.06 [1.59, 2.72] vs. 1.82 [1.49, 2.25], P\u0026thinsp;=\u0026thinsp;0.012) and AST (22.75 [18.42, 27.80] vs. 19.80 [16.95, 23.00], P\u0026thinsp;=\u0026thinsp;0.001) were escalated in the LVDD group, and GFR levels (50.43 [38.07, 60.84] vs. 54.71 [42.96, 65.57], P\u0026thinsp;=\u0026thinsp;0.020) were elevated in the Non-LVDD group. However, no significant disparities were noted between both groups in TG, UA, BUN, Alb, ALT, and Hb levels (\u003cb\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05;\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor the Echocardiographic examination, IVS in diastole (10 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] vs. 9.81 [9.00,10.65], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MPA (23 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] vs. 22.00 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], P\u0026thinsp;=\u0026thinsp;0.003), A (1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 vs. 0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20, P\u0026thinsp;=\u0026thinsp;0.016) and E/e (13.06 [10.81, 15.12] vs. 11.50 [9.49, 12.99], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were escalated in the LVDD group. While, LAD (36.50 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] vs. 38.00 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], P\u0026thinsp;=\u0026thinsp;0.017), RV (25.5 [21.0, 31.21] vs. 29.90 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LVEF% (58 [55, 62] vs. 64.00 [60, 68], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FS% (29.9 [28.6, 35] vs. 35.00 [30.40, 37.00], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and e (5.44 [4.59, 6.89] vs. 6.16 [5.12, 7.37], P\u0026thinsp;=\u0026thinsp;0.008) were diminished in the LVDD group. No significant differences were observed in the LVPWT, E, E/A, PASP, and Vmax-t between both groups (P\u0026thinsp;\u003cb\u003e\u0026gt;\u0026thinsp;0.05;\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eEchocardiographic parameters of hypertension patients in the study group.\u003c/b\u003eData are expressed as median (quartile range) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. IVS:interventricular septum; LVPWT: ;RV: right ventricle diameter; LVEF:left ventricular ejection fraction;FS:Fractional Shortening; MPA:Main Pulmonary Artery; PASP:Pulmonary artery systolic pressure. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 has statistically significant.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEchocardiographic parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLVDD(n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-LVDD(n\u0026thinsp;=\u0026thinsp;149)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft atrial diameter(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.50[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.00[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVS in diastole(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.81[9.00,10.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVPWT(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.75[9.00,10.21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRV(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.5[21.0,31.21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.90[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVEF%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58[55,62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.00[60,68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFS%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.9[28.6,35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.00[30.40,37.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPA(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.00[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE(m/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA(m/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.80[5.68,8.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.31[5.93,8.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ee(cm/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.44[4.59,6.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.16[5.12,7.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE/e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.06[10.81,15.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.50[9.49,12.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePASP(mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.14[31.52,39.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.77[31.00,37.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.535\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCorrelations of C-miRNAs and Echocardiographic Parameters\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the levels of RV (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.200, P\u0026thinsp;=\u0026thinsp;0.002), LVEF (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.240, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FS(r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.181, P\u0026thinsp;=\u0026thinsp;0.005), A (r\u0026thinsp;=\u0026thinsp;0.138, P\u0026thinsp;=\u0026thinsp;0.034), e (r =- 0.135, P\u0026thinsp;=\u0026thinsp;0.038), and E/e (r\u0026thinsp;=\u0026thinsp;0.198, P\u0026thinsp;=\u0026thinsp;0.002) were significantly related to miR-19b-3p expression. For the miR-30e-5p, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the significantly related to LVEF (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.137, P\u0026thinsp;=\u0026thinsp;0.035), E (r\u0026thinsp;=\u0026thinsp;0.176, P\u0026thinsp;=\u0026thinsp;0.007), E/e (r\u0026thinsp;=\u0026thinsp;0.170, P\u0026thinsp;=\u0026thinsp;0.009), FS (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.181, P\u0026thinsp;=\u0026thinsp;0.005) and A (r\u0026thinsp;=\u0026thinsp;0.141, P\u0026thinsp;=\u0026thinsp;0.030). FS (r = -0.148, P\u0026thinsp;=\u0026thinsp;0.024), and LVEF (r = -0.182, P\u0026thinsp;=\u0026thinsp;0.005) was significantly related to miR-15a-5p.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMLR Analysis\u003c/h2\u003e\u003cp\u003eAmong the variables that could distinguish the two groups (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the level of miR-19b-3p (OR\u0026thinsp;=\u0026thinsp;1.565, 95% CI: 1.098\u0026ndash;2.231), miR-21-5p (OR\u0026thinsp;=\u0026thinsp;0.713, 95% CI: 0.611\u0026ndash;0.831), and miR-15a-5p (OR\u0026thinsp;=\u0026thinsp;0.762, 95% CI: 0.656\u0026ndash;0.885) was significantly related to LVDD in the unadjusted model. Furthermore, we adjusted for CHD, DM, alcohol, ARB, CCB, loop diuretics, β-receptor antagonists, Statins, anti-platelet, eGFR, and AST, showing significant disparities between both groups (\u003cb\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the MLR analysis, miR-19b-3p (OR\u0026thinsp;=\u0026thinsp;4.359, 95% CI: 2.154\u0026ndash;8.822), miR-21-5p (OR\u0026thinsp;=\u0026thinsp;0.664, 95% CI: 0.516\u0026ndash;0.854), and miR-15a-5p (OR\u0026thinsp;=\u0026thinsp;0.728, 95% CI: 0.579\u0026ndash;0.915) level still showed a significant odds ratio in the adjusted model (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Furthermore, multicollinearity was assessed among CHD, DM, alcohol, ARB, CCB, loop diuretics, β-receptor antagonists, Statins, Anti-platelet, eGFR, AST, and miRNAs by the VIF. No VIF values exceeded 10, indicating no significant multicollinearity among the included variables. The Hosmer\u0026ndash;Lemeshow tests manifested significant goodness of fit for the adjusted model (P\u0026thinsp;=\u0026thinsp;0.214).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eMultivariate analysis using logistic regression: predictive factors for the risk of LVDD in elderly essential hypertension.\u003c/b\u003e Multivariate analysis was adjusted for CHD,DM, alcohol, ARB, CCB, loopdiuretics, β-receptor antagonist, Statins, Anti-platelet, eGFR, AST, which showed significant differences between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR-19b-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.565(1.098,2.231)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.359(2.154,8.822)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR-30e-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.549(0.948,2.532)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.396(0.860,2.268)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR-21-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.713(0.611,0.831)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.664(0.516,0.854)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR-15a-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.762(0.656,0.885)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.728(0.579,0.915)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eThe Value of the C-miRNAs Level for LVDD Diagnosis in Elderly Patients with EH\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the effect of individual and combined miRNAs on LVDD diagnosis. Among these individual miRNAs, we observed that miR-19b-3p presented the best AUC of 0.735 (95% CI: 0.669-0.800), a Youden index of 0.356, a sensitivity of 88.64%, and a specificity of 46.98% (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Furthermore, the combined analysis of miR-19b-3p/21-5p/30e-5p demonstrated superior diagnostic performance, yielding an AUC of 0.915 (95% CI: 0.872\u0026ndash;0.947), better than that of miRNA alone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eAUC for individual/combination miRNAs.\u003c/b\u003e Receiver operating characteristic (ROC) curve analysis was used to calculate the area under the curve (AUC) of individual and combined miRNAs for diagnosing LVDD in elderly hypertension patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe under area of ROC curve (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003esensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003especifcity(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYouden\u003c/p\u003e\u003cp\u003eindex\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR\u0026minus;19b\u0026minus;3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.735(0.669\u0026minus;0.800)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.356\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR\u0026minus;21\u0026minus;5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.587(0.514\u0026ndash;0.661)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR\u0026minus;30e\u0026minus;5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.580(0.506\u0026ndash;0.655)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR\u0026minus;15a\u0026minus;5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.556(0.481\u0026ndash;0.631)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR\u0026minus;19b\u0026minus;3p\u0026thinsp;+\u0026thinsp;miR\u0026minus;21\u0026minus;5p\u0026thinsp;+\u0026thinsp;miR\u0026minus;30e\u0026minus;5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.915(0.872\u0026ndash;0.947)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR\u0026minus;19b\u0026minus;3p\u0026thinsp;+\u0026thinsp;miR\u0026minus;21\u0026minus;5p\u0026thinsp;+\u0026thinsp;miR\u0026minus;15a\u0026minus;5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.898(0.854\u0026ndash;0.943)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80.680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiR\u0026minus;19b\u0026minus;3p\u0026thinsp;+\u0026thinsp;miR\u0026minus;21\u0026minus;5p\u0026thinsp;+\u0026thinsp;miR\u0026minus;15a\u0026minus;5p\u0026thinsp;+\u0026thinsp;miR\u0026minus;30e\u0026minus;5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.914(0.871\u0026ndash;0.947)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study focused on early heart functional injury in elderly HTN patients. Using the HTS approach; we analyzed c-miRNA differential expressions in elderly HTN patients versus those with HTN complicated by LVDD. The DE-miRNAs were further quantitatively evaluated for the diagnostic value in LVDD with HTN. Our results elucidated that LVDD patients displayed significantly repressed miR-21-5p/15a-5p and overexpressed miR-30e-5p/19b-3p compared to EH patients. In addition, these four miRNA expressions were related to some echocardiographic parameters, suggesting their potential utility as diagnostic biomarkers for LVDD. Moreover, ROC curve analysis showcased that miR-19b-3p expression could moderately predict LVDD in EH, providing evidence for the c-miRNAs to diagnose elderly HTN patients with LV injury early. Combining miRNA biomarkers (miR-19b-3p/21-5p/30e-5p) revealed robust predictive accuracy for LV dysfunction and injury.\u003c/p\u003e\u003cp\u003emiRNAs have emerged as promising biomarkers for EH, with c-miRNA profiles exhibiting distinct deregulation patterns in pathophysiological states. Notably, c-miRNA dysregulation often precedes clinical disease manifestation, highlighting their utility in early detection [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Accumulating evidence underscores miRNAs' involvement in EH pathogenesis [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Karolina et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] demonstrated that MetS traits exhibit miRNA-level interconnectivity, with certain miRNAs uniquely related to individual components. Specifically, miR-130a/195/92a were linked with HT but not other MetS features. Subsequent research validated miR-92a's association with HT. Two Japanese prospective targeted investigations studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] found that serum miR-27a/126/133/221/222 were dysregulated before HT diagnosis, implicating pathways related to vascular dysfunction, inflammation, and oxidative stress in preclinical stages.\u003c/p\u003e\u003cp\u003eChronic uncontrolled HTN induces cardiac remodeling via sustained afterload elevation, manifesting as LV or left atrial structural and functional alterations. Early LVDD, characterized by impaired LV relaxation, frequently arises from inadequately managed HT or its comorbidities, including obesity, type 2 diabetes, and dyslipidemia [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Being an independent predictor of major adverse cardiovascular events in HTN populations, LVDD necessitates rigorous diagnostic criteria for effective risk stratification. Standardized Doppler echocardiography, per ASE/EACVI guidelines, remains the cornerstone for LVDD assessment.\u003c/p\u003e\u003cp\u003eOur prior investigation demonstrated elevated c-miR-21 levels in elderly patients with EH and LA dilation, with significant correlations between miR-21 expression and LA structural changes. These findings implicated miR-21 in the pathophysiological mechanisms and therapeutic monitoring of HTN-related early cardiac injury in EH populations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Herein, we further carried out HTS to investigate c-miRNA differential expressions in elderly patients with HTN versus those with HTN complicated by LVDD. Enrichment analyses found that the differential expression of miRNAs-related genes mainly involves vascular structural cell proliferation and migration, inflammatory cell function activation, and tissue remodeling (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u0026ndash;D).\u003c/p\u003e\u003cp\u003eThroughout a median follow-up of 19.1 months (IQR: 17.7\u0026ndash;20.7), the highest miR-19b-3p quartile patients exhibited the poorest survival outcomes (Log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings support c-miR-19b-3p as a prognostic biomarker for AHF and suggest its association with ventricular hypertrophy in AHF patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Xue et al. reported that reductions in c-miR-19 levels throughout dietary weight-loss interventions correlated with decreased ASCVD risk, particularly in individuals without sleep disturbances or with elevated physical activity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The miR-19b-3p target prediction LPAR1 gene in mice could mediate lysophosphatidic acid signaling and promote postnatal heart regeneration [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our research also indicated that RV, LVEF, A, e, and E/e were significantly correlated with miR-19b-3p level. MiR-19b-3p was significantly related to LVDD in the univariate (ULR) and MLR analysis, and it showed a moderate diagnostic value in LVDD with AUC 0.735 (95% CI: 0.669\u0026ndash;0.800).\u003c/p\u003e\u003cp\u003eMiRNA-21 is among the most extensively investigated miRNAs in HT, with a current meta-analysis (546 cases, 436 controls) demonstrating overexpressed miR-21 in HT patients [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The modulation of cytochrome by miR-21 helped to reduce ROS production and protect the mitochondria from oxidative stress. A recombinant adeno-associated virus-mediated miR-21 overexpression in spontaneously hypertensive rats reduced systolic blood pressure by nearly 15 mm Hg after one month [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. miR-21-5p expression was elevated in doxorubicin-treated cardiomyocytes and murine myocardial tissue, with cardiac overexpression demonstrating protective effects against doxorubicin-provoked injury [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Here, we identified GNA12 as a miR-21-5p target, potentially activated by mechanical stretch and oxidative stress to function as a stress-response mediator, offering cardioprotection via mitochondrial apoptotic pathway inhibition [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, miR-21-5p was significantly impaired in LVDD patients, confirming it as an independent correlate of LVDD, as evidenced by ULR and MLR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, miR-21-5p exhibited only marginal diagnostic utility for LVDD (AUC 0.587; 95% CI: 0.514\u0026ndash;0.661).\u003c/p\u003e\u003cp\u003eMiR-30e-5p, belonging to the miR-30 family, is dysregulated in EH. Bioinformatic analysis revealed its significant downregulation in LV tissue of SD rats post-MI compared to non-infarcted controls, with experimental studies demonstrating its anti-inflammatory and cardioprotective role via PTEN suppression [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. ADAM9, a validated miR-30e-5p target, conversed the protective effects of miR-30e-5p overexpression in angiotensin II-treated cardiomyocytes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Herein, miR-30e-5p may target the predicted Atp2b1 gene. Wang et al. proved that alternative splicing of the Atp2b1 gene in cGMP/PKG/Ca2\u0026thinsp;+\u0026thinsp;signaling could decrease the risk of cardiomyopathy and HF in diabetes patients [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Figures\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;D show that LVEF, E, A, E/e, and FS were significantly correlated with miR-30e-5p expression. MiR-30e-5p showed moderate diagnostic value in LVDD with AUC 0.580 (95% CI: 0.506\u0026ndash;0.655).\u003c/p\u003e\u003cp\u003eMiR-15a-5p was overexpressed in patients with adverse cardiac events in arrhythmogenic RV cardiomyopathy [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In a study evaluating whether c-miRNAs could be potential biomarkers for diffuse myocardial fibrosis in cases having hypertrophic cardiomyopathy, miR-15a-5p presented a moderate diagnostic value for this condition [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Fen et al. demonstrated that exosomes derived from Ang-II-treated cardiomyocytes transfer miR-15a-5p to cardiac fibroblasts, where it targets Dyrk2, leading to NFAT dephosphorylation, enhancing cell viability and overexpressing α-smooth muscle actin, collagen type I/III α1, thereby promoting myocardial fibrosis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our results elucidated GNB5 as a putative miR-15a-5p target gene, with experimental evidence suggesting its role in promoting myofibroblast transition and pathological remodeling, where sustained activation contributes to HF progression [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Despite the absence of significant intergroup differences in miR-15a-5p expression between non-LVDD and LVDD cohorts, our findings revealed significant correlations between miR-15a-5p levels, LVEF, and FS. MiR-15a-5p was significantly associated with LVDD in the ULR and MLR analyses, and it showed a moderate diagnostic value in LVDD with an AUC of 0.556 (95% CI: 0.481\u0026ndash;0.631).\u003c/p\u003e\u003cp\u003eCombining multiple miRNAs may offer a strategic advantage over single-miRNA approaches for improving risk stratification and long-term outcomes in cardiovascular disease. Beyond their diagnostic and prognostic applications, miRNA-based therapies hold promise for enabling integration with emerging platforms and computational tools to advance precision medicine [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In patients with CPTH complicated by RV dysfunction, correlation analyses between dysregulated miRNAs and echocardiographic parameters highlighted the diagnostic potential of miR-20a-5p/93-5p/17-5p/3202. Notably, a tripartite miRNA panel derived from these four biomarkers demonstrated robust predictive performance, underscoring the added value of combinatorial miRNA strategies in clinical decision-making [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLmitations\u003c/h2\u003e\u003cp\u003eAlthough this study was conducted across multiple centers, it was limited to a single geographic region (northern Hebei Province), restricting the finding generalizability to broader populations. Future multi-region prospective cohort studies should validate these results in ethnically and geographically diverse cohorts. Our current analysis primarily focused on EH populations, with findings warranting further investigation in patients stratified by varying stages of LVDD severity. Subsequent studies will expand inclusion criteria to encompass LVDD subtypes to comprehensively assess the diagnostic utility and clinical relevance of these miRNA biomarkers.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our ROC curve analysis demonstrated that miRNA expression profiles possess predictive capacity for LVDD in patients with EH. Notably, miR-19b-3p emerged as the most specific and robust single biomarker for LVDD development, outperforming other assessed miRNAs. Additionally, miRNAs exhibit critical roles in intercellular communication through direct paracrine signaling or by modulating downstream mediators of cell-to-cell signaling [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Importantly, integrating miR-19b-3p/21-5p/30e-5p significantly enhanced diagnostic accuracy for LVDD, as evidenced by improved AUC values compared to individual biomarkers. These findings underscore the additive value of combinatorial miRNA panels in augmenting risk stratification and diagnostic precision beyond conventional echocardiographic assessment, providing a mechanistic and translational foundation for miRNA-based clinical decision-making in EH-related LVDD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under the Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDifferentially Expressed\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEssential Hypertensive\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFold-change\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFractional Shortening\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHTN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLVDD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeft Ventricular Diastolic Dysfunction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLVEF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeft Ventricular Ejection Fraction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRight Ventricle\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe study was authorized by the Ethics Committee at Heibei General Hospital (2019027) and followed the Helsinki Declaration and Good Clinical Practice guidelines, defined by the International Conference on Harmonisation. Patients signed informed consent prior to enrollment. Study registration was completed on ResMan [approval number ChiCTR1900026699].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analysed during the current study are available in GEO (https://www.ncbi.nlm.nih/) with accession numbers: GSE305634. All data generated for analysis are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Hebei General Hospital, Fourth Hospital of Hebei Medical University, Shijiazhuang People\u0026apos;s Hospital, and Handan Central Hospital for their assistance in data collection for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by these grants including: S\u0026amp;T Program of Hebei(Grant No. 19277787D,199776249D); Natural Science Foundation of Hebei (Grant No. H2023307018),Medical Science Research Project of Hebei (Grant No. 20220061).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conficts of interest related to this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Geriatric Cardiology, Hebei General Hospital, Shijiazhuang, Hebei, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eHebei Key Laboratory of Forensic Medicine, Department of Forensic Medical, Hebei Medical University, Shijiazhuang, Hebei, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGFP conducted the miRNA quantitative real-time reverse transcription polymerase chain reactions and data analysis;YFL and XWY enrolled patients and recorded clinical data; YW performed the statistical analysis and wrote the manuscript; YFG designed the study and reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan der Veen, P. H. et al. Hypertensive Target Organ Damage and Longitudinal Changes in Brain Structure and Function: The Second Manifestations of Arterial Disease-Magnetic Resonance Study. \u003cem\u003eHypertension\u003c/em\u003e \u003cb\u003e66\u003c/b\u003e (6), 1152\u0026ndash;1158 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNadar, S. K., Tayebjee, M. H., Messerli, F. \u0026amp; Lip, G. Y. Target organ damage in hypertension: pathophysiology and implications for drug therapy. \u003cem\u003eCurr. Pharm. Des.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (13), 1581\u0026ndash;1592 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyodele, O. E., Alebiosu, C. O., Salako, B. L., Awoden, O. G. \u0026amp; Abigun, A. D. 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KEGG: kyoto encyclopedia of genes and genomes. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (1), 27\u0026ndash;30 (2000).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"miRNA, Elderly, Hypertension, Heart Function Injury","lastPublishedDoi":"10.21203/rs.3.rs-7320316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7320316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOur objective is to investigate associations between miRNA expression profiles and echocardiographic cardiac structural parameters and to evaluate the diagnostic utility of miRNA biomarkers for identifying left ventricular diastolic dysfunction (LVDD) in older adults with essential hypertension (EH).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eClinical data and serum samples were collected from 10 older adults with EH (5 LVDD, 5 non-LVDD controls). Then, miRNA high-throughput sequencing (HTS) was carried out on the serum samples to define differentially expressed miRNAs (DE-miRNAs) between both groups. In the subsequent validation experiment, we collected serum samples, basic clinical data, and echocardiographic data from 237 elderly EH patients (149 Non-LVDD and 88 LVDD) from four tertiary A-level hospitals in Hebei Province. Serum DE-miRNA levels were quantified via RT-PCR, and associations with echocardiographic parameters were analyzed via Spearman's rank correlation. The diagnostic efficacy of miRNAs for LVDD was assessed using the ROC curve.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe HTS results identified 34 DE-miRNAs between both groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Four serum-abundant miRNAs (miR-19b-3p/21-5p/15a-5p/30e-5p) were selected for diagnostic performance validation, revealing that serum circulating miR (c-miR)-19b-3p/21-5p/30e-5p were significantly differential expressed between both groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Spearman analysis manifested significant associations between miR-19b-3p and LV function parameters (LVEF, FS, e', A, E/e') and right ventricular (RV) structural parameters (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). miR-30e-5p showed significant correlations with LV function indices (LVEF, FS, E, A, E/e'; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). miR-15a-5p was significantly related to LV function parameters (LVEF and FS; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate regression analysis showcased that serum c-miR-19b-3p/21-5p/15a-5p served as independent risk factors for LVDD in elderly hypertension (HTN) patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ROC curve analysis manifested that miR-19b-3p exhibited the maximum AUC value (0.735) among the four miRNAs for diagnosing LVDD in elderly HTN patients, with diagnostic sensitivity and specificity of 88.64% and 46.98%, respectively. Combinatorial analysis showed that the combined detection of miR-19b-3p/21-5p/30e-5p significantly improved the diagnostic AUC to 0.915 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with enhanced sensitivity (86.36%) and specificity (89.93%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study revealed that c-miR-19b-3p serum levels in elderly HTN patients demonstrated moderate diagnostic value (AUC\u0026thinsp;=\u0026thinsp;0.735) for identifying LVDD. The combined use of miR-19b-3p/21-5p/30e-5p as a diagnostic panel significantly enhanced the predictive efficacy for LVDD occurrence (AUC\u0026thinsp;=\u0026thinsp;0.915). These findings establish a theoretical foundation for miRNA clinical application in early diagnosis and therapeutic management of cardiac functional impairment in elderly HTN patients.\u003c/p\u003e","manuscriptTitle":"Diagnostic Value of miRNA Expression for Elderly Hypertension with Early Heart Function Injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 08:32:36","doi":"10.21203/rs.3.rs-7320316/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f63d8124-c6b9-4e0a-8ec5-984e1ea0af3b","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59436744,"name":"Health sciences/Biomarkers"},{"id":59436745,"name":"Health sciences/Cardiology"},{"id":59436746,"name":"Health sciences/Diseases"},{"id":59436747,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-04-15T05:26:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 08:32:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7320316","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7320316","identity":"rs-7320316","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}