The bidirectional regulatory effect of TXNRD2 methylation in patients with chronic heart failure and its nonlinear dose-response relationship with key clinical parameters

The bidirectional regulatory effect of TXNRD2 methylation in patients with chronic heart failure and its nonlinear dose-response relationship with key clinical parameters | 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 Research Article The bidirectional regulatory effect of TXNRD2 methylation in patients with chronic heart failure and its nonlinear dose-response relationship with key clinical parameters Ruonan Zhao, Lin Ma, Ruiping Wang, Rongqiang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5211334/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jun, 2025 Read the published version in Journal of Cardiothoracic Surgery → Version 1 posted 7 You are reading this latest preprint version Abstract Objective The research focused on examining CpG methylation within the TXNRD2 promoter area in chronic heart failure (CHF) patients, aiming to correlate methylation levels with clinical indexes to guide CHF treatment. Methods Whole blood samples from 20 CHF patients and 20 healthy controls were analyzed using MALDI-TOF-MS. Methylation levels of CpGs in the TXNRD2 -FA42 region were compared between CHF patients, healthy controls, and CHF patients with varying cardiac functions. Results TXNRD2 -FA42_CpG_3 methylation was lower in CHF patients ( P =0.0407), while TXNRD2 -FA42_CpG_8 was higher ( P =0.0183) compared to controls. Conclusion TXNRD2 promoter methylation in CHF patients exhibited bidirectional regulation, potentially influencing coagulation, renal function, and blood routine. These results deepen understanding of CHF pathogenesis and suggest new treatment approaches. selenoprotein thioredoxin reductase 2 (TXNRD2) chronic heart failure DNA methylation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Chronic heart failure (CHF) can be a serious consequence of numerous cardiovascular diseases, often developing in the advanced stages. It is also a severe, life-threatening clinical syndrome associated with high morbidity and mortality rates, frequent hospitalizations, substantial medical costs, and a notable decline in patients' quality of life [ 1 ] . According to a survey conducted in 2000, In China, the occurrence rate of chronic heart failure stood at 0.9% (with men at 0.7% and women at 1.0%), escalating as age increased [ 2 ] . Based on the medical insurance data of 50 million urban workers in six provinces of China in 2017, An analysis revealed that chronic heart failure occurs at a rate of 275 per 100,000 person-years in China, with 287 in males and 261 in females, and 3 million new cases of chronic heart failure occurred every year [ 3 ] . Efforts to reduce its socioeconomic burden have, therefore, become a major public health priority in our country as well as globally. Nowadays, exploring the molecular mechanisms and specific therapeutic measures of chronic heart failure has become a research hotspot. Within these epigenetic alterations, DNA methylation has garnered significant interest. Selenoproteins are now recognized as playing a crucial role in regulating DNA methylation, and some related studies have shown that selenoproteins are correlated with the mechanisms of cardiovascular and cerebrovascular diseases, tumors, and other diseases [4; 5] . Selenium is an important trace element which has various benefits for human health. It exerts its biological functions in the human body mainly in the form of selenoproteins [ 6 ] . So far, Twenty-five selenoproteins, such as glutathione peroxidase (GPx), thioredoxin reductase (TrxR), and selenoprotein R, selenoprotein P, selenoprotein K, and selenoprotein T, have been identified [7; 8; 9; 10] . This study focused on selenoprotein thioredoxin reductase 2 ( TXNRD2 ), a protein present in both the cytoplasmic lysate and mitochondria in mammalian cells. Essential for cellular redox homeostasis, TXNRD2 is also highly expressed in the heart [ 11 ] . Based on this research background, Our approach involved the assessment of methylation levels of CpGs in the promoter region of TXNRD2 in peripheral blood of patients with chronic heart failure using Matrix-assisted laser desorption ionizing flight time mass spectrometry (MALDI-TOF-MS) analysis. By analyzing these epigenetic changes, we investigated the differences in the degree of methylation in CpGs located within the promoter region of TXNRD2-FA42 and further explored the potential mechanisms by which these variations might affect chronic heart failure patients. 1. Materials and Methods 1.1 Study population Twenty chronic heart failure patients were recruited by the Ningxia Autonomous Region People's Hospital Stroke Center, classified into NYHA functional classes I/II (n = 10) and III/IV (n = 10). The study also included a control group of 20 healthy participants who were examined at a hospital health screening center during the same period. This study recruited participants meeting the following inclusion criteria: (1) diagnosis confirmed by the "China Chronic Heart Failure Diagnostic and Treatment Guidelines 2018 on chronic heart failure" [ 12 ] , (2) NYHA functional class I to IV, (3) age 18–80 years, and (4) good adherence. We excluded patients with (1) severe complications involving the heart, liver, kidneys, or other vital organs, (2) active or acutely exacerbated cardiovascular diseases, (3) concomitant malignant tumors, (4) recent surgeries or major traumatic injuries, and (5) pregnancy or breastfeeding. Every participant took part willingly and provided their signed informed consent. The Ningxia Autonomous Region People's Hospital's Ethics Committee sanctioned the design and methodology of the study. 1.2 DNA extraction, quantification, and quality control Each participant, fasting, had five milliliters of new blood drawn from their elbow vein in the morning, which was then administered EDTA as a blood thinning agent. Using a QIAGEN DNA extraction kit from Germany, genomic DNA was isolated from the blood specimens. A spectrophotometer was employed to gauge the concentration of the isolated DNA, with 100 ng being used in subsequent analyses. Additionally, the DNA's integrity was assessed via electrophoresis, employing a 0.8% agarose gel. 1.3 Primer design and synthesis We obtained the TXNRD2 gene's promoter region sequence from the NCBI website ( https://www.ncbi.nlm.nih.gov/gene/ ), covering 2000 base pairs before and 1000 base pairs after the transcription initiation point, summing up to 3000 base pairs.In this paper, we used the online prediction network of CpG islands ( http://www.ebi.ac.uk/Tools/seqstats/emboss_cpgplot ) to predict potential CpG islands, and the primer design software from the official website of Agena ( HTTP:/WWWW.EpideSigner.com/Index.HTML ) was used to carry out the primer design (synthesized by Beijing Liuho Huada Gene Technology Co. ). TXNRD2 -FA42 with high CpG content was selected for PCR amplification in this study, and this primer expanded the 1476bp-1864bp region of the TXNRD2 -FA42 promoter region, which was 389bp in length and contained eight CpG sites; in addition, the exact position of this amplified fragment on the chromosome was identified as chr22:19942293–19942681.(Fig. 1 ) 1.4 Methylation assay for methylation detection The Agena MassARRAY methylation experiment involved several steps: bisulfite modification, PCR amplification, in vitro transcription, RNaseA-specific digestion, and MALDI-TOF mass spectrometry. During this process, the methylation status of CpGs in the DNA template was first converted into a sequence difference within the RNA fragments generated by in vitro transcription. This difference was then detected by MALDI-TOF mass spectrometry based on the distinct molecular weights of the RNA fragments. MALDI-TOFMS was used to analyze the processed spectral chip using the spectrometry platform provided by Beijing Boao Jingdian Biotechnology Co., Ltd. Results were analyzed using TYPER4.0 software. 1.5 Data analysis Statistical analysis was conducted using the SPSS 23.0 and Stata 17.0 software. The normality of all measured data was evaluated through the Kolmogorov-Smirnov test. Information adhering to a normal distribution was presented as an average plus or minus the standard deviation (x ± SD) and examined among different groups using the Student's t-test.Data deviating from a normal distribution were displayed as median figures and analyzed through the Mann-Whitney U test. The categorical information was presented in percentage terms and analyzed through the chi-square test to compare groups. Limited cubic spline models were employed to investigate the dose-response correlations with clinical markers, encompassing blood pressure, lipid concentrations, blood clotting, liver performance, proteins, electrolytes, and total blood counts. These models integrated the 25th, 50th, and 75th percentiles of methylation levels from crucial CpG loci into three distinct nodes.For each variable, Hazard ratios (HR) and 95% confidence intervals (CI) were determined, considering a P-value below 0.05 as statistically significant. 2. Results 2.1 Comparison of baseline characteristics and clinical parameters between CHF patients and healthy controls The research group consisted of 20 individuals diagnosed with chronic heart failure (CHF) along with 20 competent medical professionals. No notable disparities were observed in terms of age (t = 1.042, P = 0.304) or gender (χ² = 0.921, P = 0.337) between the control and case groups. Variations in clinical parameters among the groups are depicted in Table 1 . Table 1 Comparison of baseline characteristics and clinical parameters between chronic heart failure patients and healthy controls Variables Patients with CHF (n = 20) Patients without CHF (n = 20) t P -value Age, years 61.35 ± 14.61 66.05 ± 13.92 -1.042 0.304 Male, n(%) 10(50%) 13(65%) 0.946 0.350 LDH (U/L) 228.95 ± 64.04 234.15 ± 40.11 0.308 0.760 ALB (g/L) 36.75 ± 5.41 38.34 ± 4.61 -1.004 0.322 PA (mg/L) 192.50 ± 73.30 193.53 ± 97.17 -0.037 0.971 CHE (U/L) 6265.25 ± 1264.48 6538.65 ± 2152.40 -0.490 0.628 CA (mmol/L) 2.15 ± 013 2.17 ± 0.11 0.602 0.551 PT (S) 1234 ± 150 1147 ± 055 2.436 0.023 PTA (%) 84.00 ± 19.61 93.32 ± 9.87 -1.899 0.068 PTR 1.07 ± 0.13 1.00 ± 0.05 2.442 0.022 PT-INR 1.08 ± 0.14 1.50 ± 2.24 -0.832 0.411 APTT (S) 29.37 ± 3.90 26.82 ± 2.23 2.538 0.017 DD (mg/L) 0.94 ± 1.07 1.39 ± 1.56 -1.057 0.297 LDH: lactate dehydrogenase, ALB: albumin, PA: serum prealbumin, CHE: Cholinesterase, CA: calcium, PT: Prothrombin time, PTA: Prothrombin time activity, PTR: Prothrombin time ratio, PT-INR: Prothrombin Time-International Normalization Ratio, APTT: Activated partial thromboplastin time, DD: D-Dimer, HGB: Hemoglobin 2.2 Comparison of baseline characteristics and clinical parameters of CHF with different cardiac functions This study compared clinical parameter indexes between 20 patients with CHF. Patients were classified into Class I/II and Class III/IV categories, and the differences in their indexes are presented in Table 2 . Table 2 Evaluating the fundamental traits and clinical metrics of patients with chronic I/II (n = 10) III/IV (n = 10) t P -value LDH (U/L) 211.90 ± 42.72 246.00 ± 78.65 -1.205 0.304 ALB (g/L) 38.60 ± 3.74 34.90 ± 6.34 1.59 0.35 PA (mg/L) 222.22 ± 58.07 165.74 ± 77.90 1.774 0.76 CHE (U/L) 6441.80 ± 1416.41 6088.70 ± 1140.17 0.614 0.322 CA (mmol/L) 2.15 ± 0.10 2.14 ± 016 0.253 0.971 PT (S) 11.75 ± 1.20 12.93 ± 1.59 -1.874 0.628 PTA (%) 91.06 ± 16.59 76.93 ± 20.63 1.688 0.551 PTR 1.02 ± 0.10 1.13 ± 0.14 -1.874 0.023 PT-TNR 1.03 ± 0.12 1.14 ± 0.15 -1.856 0.068 APTT (S) 29.19 ± 3.67 29.54 ± 4.29 -0.196 0.022 DD (mg/L) 0.78 ± 1.15 1.10 ± 1.01 -0.671 0.511 HGB (g/L) 116.74 ± 43.77 119.90 ± 26.68 -0.195 0.848 LDH: lactate dehydrogenase, ALB: albumin, PA: serum prealbumin, CHE: Cholinesterase, CA:calcium, PT: Prothrombin time, PTA: Prothrombin time activity, PTR: Prothrombin time ratio, PT-INR: Prothrombin Time-International Normalization Ratio, APTT: Activated partial thromboplastin time, DD: D-Dimer, HGB: Hemoglobin 2.3 Comparison of methylation levels of selenoprotein gene TXNRD2 -FA42 promoter region in CHF patients and healthy controls Prism 8.0 software was used to generate violin plots comparing the levels of methylation of the TXNRD2 -FA42 promoter region in selenoprotein genes between CHF patients and healthy controls. In Fig. 2 , green represents healthy controls, while red represents patients with CHF. The results revealed significant differences ( P < 0.05) in methylation levels between the two groups for TXNRD2 -FA42_CpG_3 ( P = 0.0407) and TXNRD2 -FA42_CpG_8 ( P = 0.0183). 2.4 Comparison of the methylation level of TXNRD2 -FA42 promoter region of selenoprotein gene in CHF patients with different cardiac functions A total of twenty individuals suffering from chronic heart failure (CHF) were divided into two categories based on their heart performance (Class I/II versusClass III/IV) utilized for contrasting the methylation intensities of the TXNRD2 -FA42 promoter area in the selenoprotein gene, as depicted in violin diagrams. Referring to Fig.In Fig. 3 , green signifies CHF patients classified as Class I/II, whereas red denotes those in Class III/IV.The study revealed no notable disparities in methylation rates between the two cohorts (P > 0.05). 2.5 The Relationship between methylation of TXNRD2 and renal function The dose-response relationship between methylation levels at TXNRD2 -FA42_CpG_3 and TXNRD2 -FA42_CpG_8 and renal function parameters was assessed using restricted cubic spline regression. The solid line in Fig. 4 demonstrates the ORs for each renal function parameter. The results shown in Fig. 4 demonstrated a non-linear inverted U-shaped relationship between blood uric acid (UA) and methylation levels at TXNRD2 -FA42_CpG_3 ( P 0.05 overall). TXNRD2 -FA42_CpG_8 methylation did not show a significant association with any renal function parameters. Furthermore, the OR value of UA initially increased rapidly and then gradually decreased as the methylation of TXNRD2 -FA42_CpG_3 increased.(Fig. 4 A) However, no significant correlations were found between eGFR(Epidermal Growth Factor Receptor), K(kalium), and the methylation levels of TXNRD2 -FA42_CpG_3 and TXNRD2 -FA42_CpG_8 ( P > 0.05 for both overall and non-linear effects). 2.6 The Link Between TXNRD2 methylation and coagulation indexes The relationship between methylation levels of TXNRD2 -FA42_CpG_3 and TXNRD2 -FA42_CpG_8 and coagulation function was investigated by Restricted cubic spline regression. The dose-response relationship was shown, which was further visualized using spline curves (Fig. 5 ). The solid line in the figure represents the ORs for each coagulation parameter. The analysis revealed a significant linear relationship between thrombin time (TT) and the methylation level at the TXNRD2 -FA42_CpG_8 position ( P 0.05 for overall effect, P < 0.05 for non-linearity). Specifically, the OR of TT initially increased rapidly with increasing methylation of TXNRD2 -FA42_CpG_8, followed by a plateau.(Fig. 5 B) In contrast, no significant correlation was found between the methylation level of TXNRD2 -FA42_CpG_3 and TT ( P > 0.05 for both overall and non-linear effects). Furthermore, no significant correlations were observed between prothrombin time (PT) and the methylation levels of either TXNRD2 -FA42_CpG_3 or TXNRD2 -FA42_CpG_8 ( P > 0.05 for both overall and non-linear effects). 2.7 The correlation Between TXNRD2 methylation and routine blood indicators Restricted cubic spline regression for analysis and visualization of dose-response relationships between methylation levels at TXNRD2 -FA42_CpG_3 and TXNRD2 -FA42_CpG_8 and various blood routine indicators (Fig. 6 ). The solid lines in the figure represent the ORs for each blood count parameter. The analysis revealed a U-shaped linear relationship ( P < 0.05 for both overall and non-linear effects) between mean platelet volume (MPV) and the methylation level at the TXNRD2 -FA42_CpG_3 position. This relationship can be described by a dose-response effect. Specifically, the OR of MPV initially decreased slowly and then increased rapidly as the methylation level of TXNRD2 -FA42_CpG_3 increased.(Fig. 6 A) In contrast, no significant correlation was found between MPV and the methylation level of TXNRD2 -FA42_CpG_8 ( P > 0.05 for both overall and non-linear effects)(Fig. 6 AB). An inverted U-shaped non-linear relationship, but no clear dose-response association ( P > 0.05 for all effects), was observed between the percentage of lymphocytes (LYMPH)(Fig. 6 D) and the methylation level at the TXNRD2 -FA42_CpG_8 position. There was also no significant correlation between the methylation levels at TXNRD2 -FA42_CpG_3 ( P > 0.05 for overall effect, P < 0.05 for non-linear effect) and LYMPH.(Fig. 6 C) Finally, no significant correlations were found between hemoglobin (HGB) and the methylation levels of either TXNRD2 -FA42_CpG_3 or TXNRD2 -FA42_CpG_8 ( P > 0.05 for all effects).(Fig. 6 EF) 2.8 Connecting TXNRD2 methylation with indicators of liver functionality Dose-response relationship between Restricted cubic spline regression analysis and visualization of methylation levels at TXNRD2 -FA42_CpG_3 and TXNRD2 -FA42_CpG_8 and various liver function indices (Fig. 7 ). The solid lines in the figure represent the ORs for each liver function parameter. The goal was to identify potential sensitive thresholds for liver function tests based on methylation levels and to explore areas with significant differences between patients and healthy controls. However, the analysis revealed no significant correlations between the methylation levels of TXNRD2 -FA42_CpG_3 and TXNRD2 -FA42_CpG_8 and the following liver function parameters: alanine aminotransferase (ALT) and cholinesterase (CHE) ( P > 0.05 for both overall and non-linear effects). 2.9 ROC curve judgment diagnostic capability In this study, we investigated the ability of DNA methylation levels at specific CpG sites to predict CHF. We analyzed the area under the receiver operating characteristic curve (AUC) for various clinical indicators, including methylation levels of CHF-related CpG sites, in two groups of subjects. The first group consisted of patients already diagnosed with CHF, while the second group included healthy controls. For the CHF group, the AUC values for TXNRD2 -FA42_CpG_3, UA, and MPV were 0.329, 0.690, and 0.556, respectively. These results suggest that UA and MPV may be better predictors of CHF compared to TXNRD2 -FA42_CpG_3 methylation levels in this group.(Fig. 8 ) Similarly, we evaluated the potential of these markers to predict the development of CHF. Here, TXNRD2 -FA42_CpG_8 showed the most promising results (AUC: 0.746) compared to TT and LYMPH (AUCs: 0.463 and 0.435, respectively).(Fig. 9 ) 2.10 Binary Logistic Regression Analysis of Factors Influencing CHF This study investigated the association between CHF and various biochemical markers, as well as methylation levels at specific CpG sites within the TXNRD2 gene ( TXNRD2 _FA42_CpG_3 and TXNRD2 _FA42_CpG_8). Clinical data were divided into quartiles (Q1-Q4) based on the 25th (P25), 50th (P50), and 75th (P75) percentiles. These quartile values for each biochemical index were used as independent variables in a binary logistic regression model. CHF status (presence or absence) served as the dependent variable. Hazard ratios with 95% CI were calculated for each quartile compared to the reference group (Q1) to assess the relationship between these factors and CHF. The analysis revealed a significantly increased risk of CHF for patients with UA levels in the highest quartile (Q4) compared to the lowest quartile (Q1) (OR: 9.333, 95% CI: 1.193, 72.991). No significant associations were found for other biochemical markers. Interestingly, higher methylation levels at TXNRD2 _FA42_CpG_3 were associated with a trend towards a lower risk of CHF, although this effect was not statistically significant. Conversely, higher methylation levels at TXNRD2 _FA42_CpG_8 were associated with an increased likelihood of developing CHF.(Fig. 10 ) 3. Discussion China's aging population and advancements in cardiovascular disease (CVD) treatment have led to a significant rise in CHF prevalence. As CVD survival rates increase, CHF, a progressive disease primarily affecting the elderly, is becoming an epidemic. Therefore, identifying reliable predictors of CHF is crucial for improving patient management across all stages, from pathogenesis to prognosis. Selenium, a component of the polypeptide sequence within the amino acid selenocysteine (Sec), is identified as the genetic code's 21st amino acid and encoded by TGA [ 13 ] . Proteins incorporating Sec in their polypeptide chains are known as selenoproteins [14; 15] . In the human genome, 25 selenoproteins are encoded, typically conserved well, with 24 of these also being found in mice. Nonetheless, latest studies suggest variations in their physiological functions between humans and mice [ 16 ] . Adequate selenium levels are essential for the synthesis of the required amounts of selenoproteins, especially since the absence or deficiency of these proteins may have cardiac consequences [ 17 ] . Seleniumoproteins play critical roles in defending antioxidants, managing thyroid activities, protein structuring, and immune responses.The pathogenesis of chronic heart failure involves oxidative stress, myocardial fibrosis, and cardiomyocyte apoptosis, with Trx2 playing a significant role in these processes. Huang et al [ 18 ] found that cardiac-specific knockout of Trx2 led to early dilated cardiomyopathy in mice, resulting in death from chronic heart failure by four months of age. Furthermore, their analysis of failing myocardium in human cases of dilated cardiomyopathy revealed reduced Trx2 expression alongside increased ASK-1 activity. Thus, Trx2 may offer protective benefits in chronic heart failure by inhibiting mitochondrial ROS production and modulating ASK-1 activity in cardiomyocytes [ 18 ] . In this study, we investigated the methylation levels of CpG sites in the TXNRD2 -FA42 gene and their potential impact on CHF. We employed MALDI-TOF MS to measure methylation levels all CpG sites in the subjects. The relationship between methylation levels at different methylation sites and various clinical markers OR was observed using ROC curves generated by the RCS model. Binary logistic regression was then applied to analyze the factors influencing CHF development. Among the renal function indicators, UA is known to be a pro-oxidant. Elevated UA levels play a significant role in promoting CHF progression and increasing mortality risk in CHF patients to a certain extent [19; 20] . Our analysis revealed a non-linear relationship between blood UA levels and methylation levels at the TXNRD2 -FA42_CpG_3 locus. Coagulation function is often compromised in chronic heart failure due to factors like liver fibrosis, impaired liver function, and altered hemodynamics [ 21 ] . Previous research has confirmed that hepatocellular damage begins during the cardiac compensation phase in CHF, leading to reduced synthesis of major coagulation factors and impaired overall coagulation function [ 22 ] . Consistent with these findings, our study did not observe significant differences in activated partial thromboplastin time (APTT), PT, or international normalized ratio (INR) between the two groups. However, we did find a linear correlation and dose-response relationship between TT and the methylation level at the TXNRD2 -FA42_CpG_8 locus. MPV is an important indicator of platelet activation in routine blood tests. Increased MPV is associated with larger and more active platelets, and recent studies have linked elevated MPV levels to higher mortality and poorer prognosis in cardiovascular disease patients [23; 24] . In CHF, increased platelet activation can contribute to both thromboembolic events and the progression of the disease through immune and inflammatory mechanisms [ 25 ] . A study by Hayati Kandis [ 23 ] et al. involving 136 CHF patients followed for 12 months found significantly elevated MPV levels in decompensated patients, with these levels being an independent predictor of in-hospital and 6-month mortality. Furthermore, a recent prospective study suggested that lymphocyte count is an independent predictor of mortality risk in CHF patients. The study followed participants for a mean duration of 4.7 years and revealed a significant correlation between lymphocyte counts and death in people with chronic heart failure [ 26 ] . Our study observed a higher OR of MPV with lower TXNRD2 -FA42_CpG_3 methylation and a higher OR of LYMPH with lower TXNRD2 -FA42_CpG_8 methylation. It's important to recognize the constraints present in this study. Initially, the limited size of the sample might have resulted in selection bias. Expanding the size of the study group could enhance the applicability of our results. Additionally, our present knowledge regarding the link between thioredoxin reductase and chronic heart failure is still restricted. Additional studies are needed to clarify the exact function of this enzyme and determine its cause and effect. In conclusion, our study suggests that methylation levels in the TXNRD2 promoter region may have a dual regulatory effect on coagulation, renal function, and routine blood parameters in patients with CHF. However, it is important to acknowledge that the association between TXNRD2 promoter methylation abnormalities and CHF remains to be definitively established. Further research is needed to clarify this potential link. Abbreviations CHF Chronic heart failure PT Prothrombin time PTA Prothrombin time activity PTR Prothrombin time ratio APTT Activated partial thromboplastin time CHE Cholinesterase MPV Mean platelet volume HGB Hemoglobin MCV Mean volume of red blood cells LYMPH Lymphocyte MALDI-TOF-MS Matrix-assisted laser desorption/ionization time-of-fight mass spectrometry TBIL Total bilirubin PT Prothrombin time APTT Activated partial thromboplastin time TG Triglyceride TC Serum total cholesterol LDL Low-density lipoproteinQi et al. LDH lactate dehydrogenase ALB albumin PA serum prealbumin CA calcium DD D-Dimer UA Uric Acid TT Thrombin Time eGFR Epidermal Growth Factor Receptor K kalium Declarations Acknowledgements Our gratitude goes to the participants of this research for their collaborative efforts. Our gratitude extends to the School of Public Health at Shaanxi University of Chinese Medicine for their assistance. Funding The funding for this study came from the Chinese Academy of Se-enriched Industry's Special R&D Program Project (2020FXZX05-01) and the Subject Innovation Team at Shaanxi University of Chinese Medicine. Author contributions Ruonan Zhao holds the responsibility fordata visualization and writing; Rongqiang Zhang and Ruiping Wang contributed to writing-review and editing; Ruiping Wang provided sample data and made resource contributions; Lin Ma oversees certain aspects of data management; Rongqiang Zhang helped in supervision, performed project administration and helped in funding acquisition. All authors have read and agreed to the published version of the manuscript. Availability of data and materials Data will be available upon request from the corresponding author. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Medical Ethics Committee of Ningxia Hui Autonomous Region People’s Hospital (ZDYF-046; 2021). Consent for publication Informed consent was obtained from all subjects involved in the study. Competing interests The authors declare that they have no competing interests. References Hua, W., Yujia, L., & Jief, Y. (2023). Epidemiology of heart failure. Journal of Clinical Cardiology, 39 (4), 243-247(in Chinese) Dongfeng, G., Guangyong, H., Jiang, H., , & (2003). Investigation of prevalence and distributing feature of chronic heart failure in Chinese adult population. Chinese Journal of Cardiology (01), 6-9(in Chinese) Wang, H., Chai, K., Du, M., Wang, S., Cai, J. P., Li, Y., . . . Yang, J. (2021). Prevalence and Incidence of Heart Failure Among Urban Patients in China: A National Population-Based Analysis. 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Uric acid and incident atrial fibrillation of 14 years population-based cohort study: The Suita Study. J Arrhythm, 37 (5), 1215-1219.http://doi.org/10.1002/joa3.12612. Siqin, Z., Qiu, L., Tao, W., Xiaorong, L., Ning, Z., Xiaoli, N., . . . Yongmei, H. (2005). Correlation between B natriuretic peptide and hem odynam ics in patients with decompensated heart failure and clinical significance. Chinese Journal of Cardiology (06), 502-504(in Chinese) Hongxia, L., & Jianhua, C. (2011). Role of Blood Plasma Brain Natriuretic Peptide in Diagnosis and Prognosis of Patients with Heart Failure. Chinese Journal of General Practice, 9 (08), 1209-1210(in Chinese) Kandis, H., Ozhan, H., Ordu, S., Erden, I., Caglar, O., Basar, C., . . . Aydin, M. (2011). The prognostic value of mean platelet volume in decompensated heart failure. Emerg Med J, 28 (7), 575-578.http://doi.org/10.1136/emj.2009.088401. Ki, Y. J., Park, S., Ha, S. I., Choi, D. H., & Song, H. (2014). Usefulness of mean platelet volume as a biomarker for long-term clinical outcomes after percutaneous coronary intervention in Korean cohort: a comparable and additive predictive value to high-sensitivity cardiac troponin T and N-terminal pro-B type natriuretic peptide. Platelets, 25 (6), 427-432.http://doi.org/10.3109/09537104.2013.835393. Hua, W., Chengxin, B., & Jief, Y. (2006). Thromboembolkm in Congestive Heart Failure. Chinese Journal of Thrombosis and Hemostasis (04), 173-175(in Chinese) Michael. (2011). Death risk of heart failure patients based on lymphocyte count. Clinical Journal of Diabetes World, 5 (03), 129 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Jun, 2025 Read the published version in Journal of Cardiothoracic Surgery → Version 1 posted Editorial decision: Revision requested 19 Nov, 2024 Reviews received at journal 17 Nov, 2024 Reviewers agreed at journal 09 Nov, 2024 Reviewers invited by journal 06 Nov, 2024 Editor assigned by journal 06 Oct, 2024 Submission checks completed at journal 06 Oct, 2024 First submitted to journal 06 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5211334","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379819901,"identity":"1fde96f7-c88f-4a9b-af6c-c244bd673e15","order_by":0,"name":"Ruonan Zhao","email":"","orcid":"","institution":"Shaanxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ruonan","middleName":"","lastName":"Zhao","suffix":""},{"id":379819903,"identity":"8b40772d-a912-4cdf-a85a-b0224ead864a","order_by":1,"name":"Lin Ma","email":"","orcid":"","institution":"Shaanxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Ma","suffix":""},{"id":379819907,"identity":"488558e5-1775-42d2-aa88-89fa405e8027","order_by":2,"name":"Ruiping Wang","email":"","orcid":"","institution":"Shaanxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ruiping","middleName":"","lastName":"Wang","suffix":""},{"id":379819909,"identity":"93b79aa8-6953-45b3-94e4-0638302d5c71","order_by":3,"name":"Rongqiang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBAC+xlQBhuUlmNjbz+AVwsjuhZjPp4zCcRpgYHEeRIOBni1MEs3P3v4dY91Yh/72WMSP3fUprdJMCQw/KjYhlMLm8wxc2OZZ+nGbDx5aZK9Z47ntkk3HmDsOXMbpxYeiQQzaYkDh+XYGHLMJHjbjuW2yRxIYGZsw61FQiL9G0gLDxv/GzPJv23H0tkkEgzwajGQyDGT/ACyBciQ5m2rSSBGS5k0wwGgXyTeGFvLth0wbAMG8kF8frGfkb5N8scB68T5/TmGN9+21cnLt7cffPCjArcWEGDmYWAG0SwSDAyHwSIH8KoHAsYfEC3MHxgY6ggpHgWjYBSMghEIAL7gU/2XewYcAAAAAElFTkSuQmCC","orcid":"","institution":"Shaanxi University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Rongqiang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-10-06 05:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5211334/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5211334/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13019-025-03495-7","type":"published","date":"2025-06-04T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70918277,"identity":"2bc4a0d5-c560-436e-80e7-62bbccb16903","added_by":"auto","created_at":"2024-12-09 08:24:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8731337,"visible":true,"origin":"","legend":"\u003cp\u003eTarget gene sequence and methylation site information of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-5211334/v1/75b8e6c18b26e2ef80268dd9.png"},{"id":70918330,"identity":"eaafd9f3-1647-4b15-8ff4-461cd80aa1ff","added_by":"auto","created_at":"2024-12-09 08:24:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25175124,"visible":true,"origin":"","legend":"\u003cp\u003eAnalyzing the differences in methylation levels of the \u003cem\u003eTXNRD2\u003c/em\u003e-FA42 promoter region between patients with chronic heart failure and healthy individuals.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-5211334/v1/741df94108a3e37942e06ee6.png"},{"id":70918280,"identity":"6e8f0576-3a44-480d-a668-26d8016c0968","added_by":"auto","created_at":"2024-12-09 08:24:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22112098,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of methylation levels in \u003cem\u003eTXNRD2\u003c/em\u003e-FA42 promoter region of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42 in patients with chronic heart failure with different cardiac functions.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-5211334/v1/6aa873aba4728c9125790df7.png"},{"id":70918332,"identity":"e6c9253c-2a46-43be-ac77-739bc0274fe5","added_by":"auto","created_at":"2024-12-09 08:24:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2586228,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between methylation levels in the promoter region of selenoprotein genes and renal performance.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-5211334/v1/0d920c0cb8a6ff704245d624.png"},{"id":70918384,"identity":"e3e768b7-563b-4d5d-b8b7-b49dee340fc5","added_by":"auto","created_at":"2024-12-09 08:24:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1749594,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationship between selenoprotein gene promoter region methylation levels and coagulation indices.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-5211334/v1/0ca049c209692987cfef8272.png"},{"id":70918326,"identity":"ddf6d921-0401-4e07-9a8d-7d2bdb4393d5","added_by":"auto","created_at":"2024-12-09 08:24:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1442997,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between methylation levels in the promoter region of selenoprotein genes and blood metrics.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-5211334/v1/3b11a087aaea21d7c4f4112b.png"},{"id":70919485,"identity":"0a464c1f-93ec-4abc-9795-8fc4c84e4620","added_by":"auto","created_at":"2024-12-09 08:32:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1690711,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationship between methylation level in promoter region of selenium gene and liver function indexes.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-5211334/v1/ca3a716d333ed1d0c71e5a3c.png"},{"id":70918278,"identity":"a6c520b3-6c13-4495-b58f-4338aa62ec2e","added_by":"auto","created_at":"2024-12-09 08:24:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1217761,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3, UA, MPV for diagnosing chronic heart failure.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-5211334/v1/d8a47a99c33ce2eaf403ce8a.png"},{"id":70918385,"identity":"cc9b9f94-2be3-4ec3-8930-3e46dca965c5","added_by":"auto","created_at":"2024-12-09 08:24:10","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1538722,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8, TT, LYMPH for diagnosing chronic heart failure.\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-5211334/v1/bc027313c13a692b59d3f9b4.png"},{"id":70918276,"identity":"e062db40-44aa-4f3b-a6ef-0cb39dec74df","added_by":"auto","created_at":"2024-12-09 08:24:05","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1449824,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of binary logistic regression analysis of study loci and indicators of clinical variability with heart failure.\u003c/p\u003e","description":"","filename":"Fig.10.png","url":"https://assets-eu.researchsquare.com/files/rs-5211334/v1/be88302aac5937e2584d4e9d.png"},{"id":70918056,"identity":"e657e41f-2d33-418c-9d36-fe8076681c93","added_by":"auto","created_at":"2024-12-09 08:23:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":741006,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5211334/v1/777b8ebf-fea7-4aeb-b0cf-31a0c351bf80.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The bidirectional regulatory effect of TXNRD2 methylation in patients with chronic heart failure and its nonlinear dose-response relationship with key clinical parameters","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic heart failure (CHF) can be a serious consequence of numerous cardiovascular diseases, often developing in the advanced stages. It is also a severe, life-threatening clinical syndrome associated with high morbidity and mortality rates, frequent hospitalizations, substantial medical costs, and a notable decline in patients' quality of life\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to a survey conducted in 2000, In China, the occurrence rate of chronic heart failure stood at 0.9% (with men at 0.7% and women at 1.0%), escalating as age increased\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Based on the medical insurance data of 50\u0026nbsp;million urban workers in six provinces of China in 2017, An analysis revealed that chronic heart failure occurs at a rate of 275 per 100,000 person-years in China, with 287 in males and 261 in females, and 3\u0026nbsp;million new cases of chronic heart failure occurred every year\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Efforts to reduce its socioeconomic burden have, therefore, become a major public health priority in our country as well as globally.\u003c/p\u003e \u003cp\u003eNowadays, exploring the molecular mechanisms and specific therapeutic measures of chronic heart failure has become a research hotspot. Within these epigenetic alterations, DNA methylation has garnered significant interest. Selenoproteins are now recognized as playing a crucial role in regulating DNA methylation, and some related studies have shown that selenoproteins are correlated with the mechanisms of cardiovascular and cerebrovascular diseases, tumors, and other diseases\u003csup\u003e[4; 5]\u003c/sup\u003e. Selenium is an important trace element which has various benefits for human health. It exerts its biological functions in the human body mainly in the form of selenoproteins\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. So far, Twenty-five selenoproteins, such as glutathione peroxidase (GPx), thioredoxin reductase (TrxR), and selenoprotein R, selenoprotein P, selenoprotein K, and selenoprotein T, have been identified\u003csup\u003e[7; 8; 9; 10]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study focused on selenoprotein thioredoxin reductase 2 (\u003cem\u003eTXNRD2\u003c/em\u003e), a protein present in both the cytoplasmic lysate and mitochondria in mammalian cells. Essential for cellular redox homeostasis, \u003cem\u003eTXNRD2\u003c/em\u003e is also highly expressed in the heart\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Based on this research background, Our approach involved the assessment of methylation levels of CpGs in the promoter region of \u003cem\u003eTXNRD2\u003c/em\u003e in peripheral blood of patients with chronic heart failure using Matrix-assisted laser desorption ionizing flight time mass spectrometry (MALDI-TOF-MS) analysis. By analyzing these epigenetic changes, we investigated the differences in the degree of methylation in CpGs located within the promoter region of \u003cem\u003eTXNRD2-FA42\u003c/em\u003e and further explored the potential mechanisms by which these variations might affect chronic heart failure patients.\u003c/p\u003e"},{"header":"1. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Study population\u003c/h2\u003e \u003cp\u003eTwenty chronic heart failure patients were recruited by the Ningxia Autonomous Region People's Hospital Stroke Center, classified into NYHA functional classes I/II (n\u0026thinsp;=\u0026thinsp;10) and III/IV (n\u0026thinsp;=\u0026thinsp;10). The study also included a control group of 20 healthy participants who were examined at a hospital health screening center during the same period.\u003c/p\u003e \u003cp\u003eThis study recruited participants meeting the following inclusion criteria: (1) diagnosis confirmed by the \"China Chronic Heart Failure Diagnostic and Treatment Guidelines 2018 on chronic heart failure\"\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, (2) NYHA functional class I to IV, (3) age 18\u0026ndash;80 years, and (4) good adherence.\u003c/p\u003e \u003cp\u003eWe excluded patients with (1) severe complications involving the heart, liver, kidneys, or other vital organs, (2) active or acutely exacerbated cardiovascular diseases, (3) concomitant malignant tumors, (4) recent surgeries or major traumatic injuries, and (5) pregnancy or breastfeeding. Every participant took part willingly and provided their signed informed consent. The Ningxia Autonomous Region People's Hospital's Ethics Committee sanctioned the design and methodology of the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.2 DNA extraction, quantification, and quality control\u003c/h3\u003e\n\u003cp\u003eEach participant, fasting, had five milliliters of new blood drawn from their elbow vein in the morning, which was then administered EDTA as a blood thinning agent. Using a QIAGEN DNA extraction kit from Germany, genomic DNA was isolated from the blood specimens. A spectrophotometer was employed to gauge the concentration of the isolated DNA, with 100 ng being used in subsequent analyses. Additionally, the DNA's integrity was assessed via electrophoresis, employing a 0.8% agarose gel.\u003c/p\u003e\n\u003ch3\u003e1.3 Primer design and synthesis\u003c/h3\u003e\n\u003cp\u003eWe obtained the \u003cem\u003eTXNRD2\u003c/em\u003e gene's promoter region sequence from the NCBI website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), covering 2000 base pairs before and 1000 base pairs after the transcription initiation point, summing up to 3000 base pairs.In this paper, we used the online prediction network of CpG islands (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ebi.ac.uk/Tools/seqstats/emboss_cpgplot\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/Tools/seqstats/emboss_cpgplot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to predict potential CpG islands, and the primer design software from the official website of Agena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eHTTP:/WWWW.EpideSigner.com/Index.HTML\u003c/span\u003e\u003cspan address=\"http://HTTP:/WWWW.EpideSigner.com/Index.HTML\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to carry out the primer design (synthesized by Beijing Liuho Huada Gene Technology Co. ). \u003cem\u003eTXNRD2\u003c/em\u003e-FA42 with high CpG content was selected for PCR amplification in this study, and this primer expanded the 1476bp-1864bp region of the \u003cem\u003eTXNRD2\u003c/em\u003e-FA42 promoter region, which was 389bp in length and contained eight CpG sites; in addition, the exact position of this amplified fragment on the chromosome was identified as chr22:19942293\u0026ndash;19942681.(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e1.4 Methylation assay for methylation detection\u003c/h3\u003e\n\u003cp\u003eThe Agena MassARRAY methylation experiment involved several steps: bisulfite modification, PCR amplification, in vitro transcription, RNaseA-specific digestion, and MALDI-TOF mass spectrometry. During this process, the methylation status of CpGs in the DNA template was first converted into a sequence difference within the RNA fragments generated by in vitro transcription. This difference was then detected by MALDI-TOF mass spectrometry based on the distinct molecular weights of the RNA fragments. MALDI-TOFMS was used to analyze the processed spectral chip using the spectrometry platform provided by Beijing Boao Jingdian Biotechnology Co., Ltd. Results were analyzed using TYPER4.0 software.\u003c/p\u003e\n\u003ch3\u003e1.5 Data analysis\u003c/h3\u003e\n\u003cp\u003eStatistical analysis was conducted using the SPSS 23.0 and Stata 17.0 software. The normality of all measured data was evaluated through the Kolmogorov-Smirnov test. Information adhering to a normal distribution was presented as an average plus or minus the standard deviation (x ± SD) and examined among different groups using the Student's t-test.Data deviating from a normal distribution were displayed as median figures and analyzed through the Mann-Whitney U test. The categorical information was presented in percentage terms and analyzed through the chi-square test to compare groups. Limited cubic spline models were employed to investigate the dose-response correlations with clinical markers, encompassing blood pressure, lipid concentrations, blood clotting, liver performance, proteins, electrolytes, and total blood counts. These models integrated the 25th, 50th, and 75th percentiles of methylation levels from crucial CpG loci into three distinct nodes.For each variable, Hazard ratios (HR) and 95% confidence intervals (CI) were determined, considering a P-value below 0.05 as statistically significant.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"2. Results","content":"\u003ch2\u003e2.1 Comparison of baseline characteristics and clinical parameters between CHF patients and healthy controls\u003c/h2\u003e\u003cp\u003eThe research group consisted of 20 individuals diagnosed with chronic heart failure (CHF) along with 20 competent medical professionals. No notable disparities were observed in terms of age (t = 1.042, P = 0.304) or gender (χ² = 0.921, P = 0.337) between the control and case groups. Variations in clinical parameters among the groups are depicted in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline characteristics and clinical parameters between chronic heart failure patients and healthy controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\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\u003ePatients with CHF (n = 20)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients without CHF (n = 20)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\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\u003e61.35 ± 14.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.05 ± 13.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.042\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n(%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(50%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(65%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228.95 ± 64.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234.15 ± 40.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB (g/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.75 ± 5.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.34 ± 4.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA (mg/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192.50 ± 73.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193.53 ± 97.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHE (U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6265.25 ± 1264.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6538.65 ± 2152.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.490\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15 ± 013\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17 ± 0.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (S)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1234 ± 150\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1147 ± 055\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.436\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTA (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.00 ± 19.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.32 ± 9.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.899\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 ± 0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 ± 0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.442\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT-INR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 ± 0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 ± 2.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.832\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT (S)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.37 ± 3.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.82 ± 2.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.538\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD (mg/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 ± 1.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 ± 1.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.057\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eLDH: lactate dehydrogenase, ALB: albumin, PA: serum prealbumin, CHE: Cholinesterase,\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eCA: calcium, PT: Prothrombin time, PTA: Prothrombin time activity, PTR: Prothrombin time ratio, PT-INR: Prothrombin Time-International Normalization Ratio, APTT: Activated partial thromboplastin time, DD: D-Dimer, HGB: Hemoglobin\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\n\u003ch3\u003e2.2 Comparison of baseline characteristics and clinical parameters of CHF with different cardiac functions\u003c/h3\u003e\n\u003cp\u003eThis study compared clinical parameter indexes between 20 patients with CHF. Patients were classified into Class I/II and Class III/IV categories, and the differences in their indexes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluating the fundamental traits and clinical metrics of patients with chronic\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI/II (n = 10)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIII/IV (n = 10)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e211.90 ± 42.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e246.00 ± 78.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.205\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB (g/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e38.60 ± 3.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e34.90 ± 6.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA (mg/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e222.22 ± 58.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e165.74 ± 77.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.774\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHE (U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e6441.80 ± 1416.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e6088.70 ± 1140.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e2.15 ± 0.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e2.14 ± 016\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (S)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e11.75 ± 1.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e12.93 ± 1.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.874\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTA (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e91.06 ± 16.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e76.93 ± 20.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.688\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.02 ± 0.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.13 ± 0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.874\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT-TNR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.03 ± 0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.14 ± 0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.856\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT (S)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e29.19 ± 3.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e29.54 ± 4.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.196\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD (mg/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e0.78 ± 1.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.10 ± 1.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.671\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB (g/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e116.74 ± 43.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e119.90 ± 26.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.195\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eLDH: lactate dehydrogenase, ALB: albumin, PA: serum prealbumin, CHE: Cholinesterase, CA:calcium, PT: Prothrombin time, PTA: Prothrombin time activity, PTR: Prothrombin time ratio, PT-INR: Prothrombin Time-International Normalization Ratio, APTT: Activated partial thromboplastin time, DD: D-Dimer, HGB: Hemoglobin\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 Comparison of methylation levels of selenoprotein gene\u003c/b\u003e \u003cb\u003eTXNRD2\u003c/b\u003e\u003cb\u003e-FA42 promoter region in CHF patients and healthy controls\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePrism 8.0 software was used to generate violin plots comparing the levels of methylation of the \u003cem\u003eTXNRD2\u003c/em\u003e-FA42 promoter region in selenoprotein genes between CHF patients and healthy controls. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, green represents healthy controls, while red represents patients with CHF. The results revealed significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) in methylation levels between the two groups for \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 (\u003cem\u003eP\u003c/em\u003e = 0.0407) and \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 (\u003cem\u003eP\u003c/em\u003e = 0.0183).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.4 Comparison of the methylation level of\u003c/b\u003e \u003cb\u003eTXNRD2\u003c/b\u003e\u003cb\u003e-FA42 promoter region of selenoprotein gene in CHF patients with different cardiac functions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of twenty individuals suffering from chronic heart failure (CHF) were divided into two categories based on their heart performance (Class I/II versusClass III/IV) utilized for contrasting the methylation intensities of the \u003cem\u003eTXNRD2\u003c/em\u003e-FA42 promoter area in the selenoprotein gene, as depicted in violin diagrams. Referring to Fig.In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, green signifies CHF patients classified as Class I/II, whereas red denotes those in Class III/IV.The study revealed no notable disparities in methylation rates between the two cohorts (P \u0026gt; 0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.5 The Relationship between methylation of\u003c/b\u003e \u003cb\u003eTXNRD2\u003c/b\u003e \u003cb\u003eand renal function\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe dose-response relationship between methylation levels at \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 and \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 and renal function parameters was assessed using restricted cubic spline regression. The solid line in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates the ORs for each renal function parameter. The results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrated a non-linear inverted U-shaped relationship between blood uric acid (UA) and methylation levels at \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 for non-linearity). No clear dose-response relationship was observed for the other parameters (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 overall). \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 methylation did not show a significant association with any renal function parameters. Furthermore, the OR value of UA initially increased rapidly and then gradually decreased as the methylation of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 increased.(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) However, no significant correlations were found between eGFR(Epidermal Growth Factor Receptor), K(kalium), and the methylation levels of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 and \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for both overall and non-linear effects).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.6 The Link Between\u003c/b\u003e \u003cb\u003eTXNRD2\u003c/b\u003e \u003cb\u003emethylation and coagulation indexes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe relationship between methylation levels of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 and \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 and coagulation function was investigated by Restricted cubic spline regression. The dose-response relationship was shown, which was further visualized using spline curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The solid line in the figure represents the ORs for each coagulation parameter. The analysis revealed a significant linear relationship between thrombin time (TT) and the methylation level at the \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 position (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 for overall effect). There was also evidence of a non-linear dose-response association (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for overall effect, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 for non-linearity). Specifically, the OR of TT initially increased rapidly with increasing methylation of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8, followed by a plateau.(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) In contrast, no significant correlation was found between the methylation level of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 and TT (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for both overall and non-linear effects). Furthermore, no significant correlations were observed between prothrombin time (PT) and the methylation levels of either \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 or \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for both overall and non-linear effects).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.7 The correlation Between\u003c/b\u003e \u003cb\u003eTXNRD2\u003c/b\u003e \u003cb\u003emethylation and routine blood indicators\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRestricted cubic spline regression for analysis and visualization of dose-response relationships between methylation levels at \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 and \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 and various blood routine indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The solid lines in the figure represent the ORs for each blood count parameter. The analysis revealed a U-shaped linear relationship (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 for both overall and non-linear effects) between mean platelet volume (MPV) and the methylation level at the \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 position. This relationship can be described by a dose-response effect. Specifically, the OR of MPV initially decreased slowly and then increased rapidly as the methylation level of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 increased.(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) In contrast, no significant correlation was found between MPV and the methylation level of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for both overall and non-linear effects)(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eAB). An inverted U-shaped non-linear relationship, but no clear dose-response association (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for all effects), was observed between the percentage of lymphocytes (LYMPH)(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD) and the methylation level at the \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 position. There was also no significant correlation between the methylation levels at \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for overall effect, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 for non-linear effect) and LYMPH.(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) Finally, no significant correlations were found between hemoglobin (HGB) and the methylation levels of either \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 or \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for all effects).(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eEF)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.8 Connecting\u003c/b\u003e \u003cb\u003eTXNRD2\u003c/b\u003e \u003cb\u003emethylation with indicators of liver functionality\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDose-response relationship between Restricted cubic spline regression analysis and visualization of methylation levels at \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 and \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 and various liver function indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The solid lines in the figure represent the ORs for each liver function parameter. The goal was to identify potential sensitive thresholds for liver function tests based on methylation levels and to explore areas with significant differences between patients and healthy controls. However, the analysis revealed no significant correlations between the methylation levels of \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 and \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 and the following liver function parameters: alanine aminotransferase (ALT) and cholinesterase (CHE) (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for both overall and non-linear effects).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 ROC curve judgment diagnostic capability\u003c/h2\u003e \u003cp\u003eIn this study, we investigated the ability of DNA methylation levels at specific CpG sites to predict CHF. We analyzed the area under the receiver operating characteristic curve (AUC) for various clinical indicators, including methylation levels of CHF-related CpG sites, in two groups of subjects. The first group consisted of patients already diagnosed with CHF, while the second group included healthy controls. For the CHF group, the AUC values for \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3, UA, and MPV were 0.329, 0.690, and 0.556, respectively. These results suggest that UA and MPV may be better predictors of CHF compared to \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 methylation levels in this group.(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, we evaluated the potential of these markers to predict the development of CHF. Here, \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 showed the most promising results (AUC: 0.746) compared to TT and LYMPH (AUCs: 0.463 and 0.435, respectively).(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Binary Logistic Regression Analysis of Factors Influencing CHF\u003c/h2\u003e \u003cp\u003eThis study investigated the association between CHF and various biochemical markers, as well as methylation levels at specific CpG sites within the \u003cem\u003eTXNRD2\u003c/em\u003e gene (\u003cem\u003eTXNRD2\u003c/em\u003e_FA42_CpG_3 and \u003cem\u003eTXNRD2\u003c/em\u003e_FA42_CpG_8). Clinical data were divided into quartiles (Q1-Q4) based on the 25th (P25), 50th (P50), and 75th (P75) percentiles. These quartile values for each biochemical index were used as independent variables in a binary logistic regression model. CHF status (presence or absence) served as the dependent variable. Hazard ratios with 95% CI were calculated for each quartile compared to the reference group (Q1) to assess the relationship between these factors and CHF. The analysis revealed a significantly increased risk of CHF for patients with UA levels in the highest quartile (Q4) compared to the lowest quartile (Q1) (OR: 9.333, 95% CI: 1.193, 72.991). No significant associations were found for other biochemical markers. Interestingly, higher methylation levels at \u003cem\u003eTXNRD2\u003c/em\u003e_FA42_CpG_3 were associated with a trend towards a lower risk of CHF, although this effect was not statistically significant. Conversely, higher methylation levels at \u003cem\u003eTXNRD2\u003c/em\u003e_FA42_CpG_8 were associated with an increased likelihood of developing CHF.(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e "},{"header":"3. Discussion","content":"\u003cp\u003eChina's aging population and advancements in cardiovascular disease (CVD) treatment have led to a significant rise in CHF prevalence. As CVD survival rates increase, CHF, a progressive disease primarily affecting the elderly, is becoming an epidemic. Therefore, identifying reliable predictors of CHF is crucial for improving patient management across all stages, from pathogenesis to prognosis.\u003c/p\u003e\u003cp\u003eSelenium, a component of the polypeptide sequence within the amino acid selenocysteine (Sec), is identified as the genetic code's 21st amino acid and encoded by TGA\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Proteins incorporating Sec in their polypeptide chains are known as selenoproteins\u003csup\u003e[14; 15]\u003c/sup\u003e. In the human genome, 25 selenoproteins are encoded, typically conserved well, with 24 of these also being found in mice. Nonetheless, latest studies suggest variations in their physiological functions between humans and mice\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Adequate selenium levels are essential for the synthesis of the required amounts of selenoproteins, especially since the absence or deficiency of these proteins may have cardiac consequences\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Seleniumoproteins play critical roles in defending antioxidants, managing thyroid activities, protein structuring, and immune responses.The pathogenesis of chronic heart failure involves oxidative stress, myocardial fibrosis, and cardiomyocyte apoptosis, with Trx2 playing a significant role in these processes. Huang et al\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e found that cardiac-specific knockout of Trx2 led to early dilated cardiomyopathy in mice, resulting in death from chronic heart failure by four months of age. Furthermore, their analysis of failing myocardium in human cases of dilated cardiomyopathy revealed reduced Trx2 expression alongside increased ASK-1 activity. Thus, Trx2 may offer protective benefits in chronic heart failure by inhibiting mitochondrial ROS production and modulating ASK-1 activity in cardiomyocytes\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we investigated the methylation levels of CpG sites in the \u003cem\u003eTXNRD2\u003c/em\u003e-FA42 gene and their potential impact on CHF. We employed MALDI-TOF MS to measure methylation levels all CpG sites in the subjects. The relationship between methylation levels at different methylation sites and various clinical markers OR was observed using ROC curves generated by the RCS model. Binary logistic regression was then applied to analyze the factors influencing CHF development. Among the renal function indicators, UA is known to be a pro-oxidant. Elevated UA levels play a significant role in promoting CHF progression and increasing mortality risk in CHF patients to a certain extent\u003csup\u003e[19; 20]\u003c/sup\u003e. Our analysis revealed a non-linear relationship between blood UA levels and methylation levels at the \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 locus. Coagulation function is often compromised in chronic heart failure due to factors like liver fibrosis, impaired liver function, and altered hemodynamics\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Previous research has confirmed that hepatocellular damage begins during the cardiac compensation phase in CHF, leading to reduced synthesis of major coagulation factors and impaired overall coagulation function\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Consistent with these findings, our study did not observe significant differences in activated partial thromboplastin time (APTT), PT, or international normalized ratio (INR) between the two groups. However, we did find a linear correlation and dose-response relationship between TT and the methylation level at the \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 locus. MPV is an important indicator of platelet activation in routine blood tests. Increased MPV is associated with larger and more active platelets, and recent studies have linked elevated MPV levels to higher mortality and poorer prognosis in cardiovascular disease patients\u003csup\u003e[23; 24]\u003c/sup\u003e. In CHF, increased platelet activation can contribute to both thromboembolic events and the progression of the disease through immune and inflammatory mechanisms\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. A study by Hayati Kandis \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003eet al. involving 136 CHF patients followed for 12 months found significantly elevated MPV levels in decompensated patients, with these levels being an independent predictor of in-hospital and 6-month mortality. Furthermore, a recent prospective study suggested that lymphocyte count is an independent predictor of mortality risk in CHF patients. The study followed participants for a mean duration of 4.7 years and revealed a significant correlation between lymphocyte counts and death in people with chronic heart failure\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Our study observed a higher OR of MPV with lower \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 methylation and a higher OR of LYMPH with lower \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 methylation.\u003c/p\u003e\u003cp\u003eIt's important to recognize the constraints present in this study. Initially, the limited size of the sample might have resulted in selection bias. Expanding the size of the study group could enhance the applicability of our results. Additionally, our present knowledge regarding the link between thioredoxin reductase and chronic heart failure is still restricted. Additional studies are needed to clarify the exact function of this enzyme and determine its cause and effect.\u003c/p\u003e\u003cp\u003eIn conclusion, our study suggests that methylation levels in the \u003cem\u003eTXNRD2\u003c/em\u003e promoter region may have a dual regulatory effect on coagulation, renal function, and routine blood parameters in patients with CHF. However, it is important to acknowledge that the association between \u003cem\u003eTXNRD2\u003c/em\u003e promoter methylation abnormalities and CHF remains to be definitively established. Further research is needed to clarify this potential link.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCHF Chronic heart failure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePT \u0026nbsp;Prothrombin time\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePTA \u0026nbsp;Prothrombin time activity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePTR \u0026nbsp;Prothrombin time ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAPTT \u0026nbsp;Activated partial thromboplastin time\u003c/p\u003e\n\u003cp\u003eCHE Cholinesterase\u003c/p\u003e\n\u003cp\u003eMPV Mean platelet volume\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHGB Hemoglobin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMCV Mean volume of red blood cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLYMPH Lymphocyte\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMALDI-TOF-MS Matrix-assisted laser desorption/ionization time-of-fight mass spectrometry\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTBIL Total bilirubin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePT Prothrombin time\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAPTT Activated partial thromboplastin time\u003c/p\u003e\n\u003cp\u003eTG Triglyceride\u003c/p\u003e\n\u003cp\u003eTC Serum total cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLDL Low-density lipoproteinQi et al.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLDH lactate dehydrogenase \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eALB albumin\u003c/p\u003e\n\u003cp\u003ePA serum prealbumin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCA calcium \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDD D-Dimer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUA Uric Acid\u003c/p\u003e\n\u003cp\u003eTT Thrombin Time\u003c/p\u003e\n\u003cp\u003eeGFR Epidermal Growth Factor Receptor\u003c/p\u003e\n\u003cp\u003eK kalium\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur gratitude goes to the participants of this research for their collaborative efforts. Our gratitude extends to the School of Public Health at Shaanxi University of Chinese Medicine for their assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe funding for this study came from the Chinese Academy of Se-enriched Industry\u0026apos;s Special R\u0026amp;D Program Project (2020FXZX05-01) and the Subject Innovation Team at Shaanxi University of Chinese Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRuonan Zhao holds the responsibility fordata visualization and writing;\u003c/p\u003e\n\u003cp\u003eRongqiang Zhang and Ruiping Wang contributed to writing-review and editing; Ruiping Wang provided sample data and made resource contributions;\u003c/p\u003e\n\u003cp\u003eLin Ma oversees certain aspects of data management;\u003c/p\u003e\n\u003cp\u003eRongqiang Zhang helped in supervision, performed project administration and helped in funding acquisition.\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData will be available upon request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Medical Ethics Committee of Ningxia Hui Autonomous Region People\u0026rsquo;s Hospital (ZDYF-046; 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHua, W., Yujia, L., \u0026amp; Jief, Y. 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Death risk of heart failure patients based on lymphocyte count. \u003cem\u003eClinical Journal of Diabetes World, 5\u003c/em\u003e(03), 129\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"selenoprotein thioredoxin reductase 2 (TXNRD2), chronic heart failure, DNA methylation","lastPublishedDoi":"10.21203/rs.3.rs-5211334/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5211334/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research focused on examining CpG methylation within the \u003cem\u003eTXNRD2\u003c/em\u003e promoter area in chronic heart failure (CHF) patients, aiming to correlate methylation levels with clinical indexes to guide CHF treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole blood samples from 20 CHF patients and 20 healthy controls were analyzed using MALDI-TOF-MS. Methylation levels of CpGs in the \u003cem\u003eTXNRD2\u003c/em\u003e-FA42 region were compared between CHF patients, healthy controls, and CHF patients with varying cardiac functions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_3 methylation was lower in CHF patients (\u003cem\u003eP\u003c/em\u003e=0.0407), while \u003cem\u003eTXNRD2\u003c/em\u003e-FA42_CpG_8 was higher (\u003cem\u003eP\u003c/em\u003e=0.0183) compared to controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTXNRD2\u003c/em\u003e promoter methylation in CHF patients exhibited bidirectional regulation, potentially influencing coagulation, renal function, and blood routine. These results deepen understanding of CHF pathogenesis and suggest new treatment approaches.\u003c/p\u003e","manuscriptTitle":"The bidirectional regulatory effect of TXNRD2 methylation in patients with chronic heart failure and its nonlinear dose-response relationship with key clinical parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 08:23:19","doi":"10.21203/rs.3.rs-5211334/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-19T06:36:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-17T15:52:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276914629259750746954385829995728273315","date":"2024-11-09T15:05:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-06T20:15:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-07T00:54:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-07T00:53:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cardiothoracic Surgery","date":"2024-10-06T05:16:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"733e582a-03b5-4fa0-afb2-d8c65c5d2ced","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:04:09+00:00","versionOfRecord":{"articleIdentity":"rs-5211334","link":"https://doi.org/10.1186/s13019-025-03495-7","journal":{"identity":"journal-of-cardiothoracic-surgery","isVorOnly":false,"title":"Journal of Cardiothoracic Surgery"},"publishedOn":"2025-06-04 15:57:47","publishedOnDateReadable":"June 4th, 2025"},"versionCreatedAt":"2024-12-09 08:23:19","video":"","vorDoi":"10.1186/s13019-025-03495-7","vorDoiUrl":"https://doi.org/10.1186/s13019-025-03495-7","workflowStages":[]},"version":"v1","identity":"rs-5211334","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5211334","identity":"rs-5211334","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}