DNAmTIMP1 as a Predictor of Cardiovascular Disease and Mortality: A Prospective Analysis from the NHANES

DNAmTIMP1 as a Predictor of Cardiovascular Disease and Mortality: A Prospective Analysis from the NHANES | 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 DNAmTIMP1 as a Predictor of Cardiovascular Disease and Mortality: A Prospective Analysis from the NHANES Xiaochen Lu, Fanbiao Liu, Ding Ding, Jian Zhuo, Yue Hou, Zixin Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6427282/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background TIMP1(tissue inhibitor metalloproteinase 1 ) represents a tissue metalloprotease that modulates the degradation of the extracellular matrix by inhibiting the activity of matrix metalloproteinases. Furthermore, it has the capacity to activate signaling pathways that lead to an overproduction of collagen, thereby enhancing extracellular matrix synthesis through the interaction between surface CD63 and integrin beta1 on cardiac fibroblasts, which results in increased ventricular stiffness, fosters cardiac fibrosis, and ultimately contributes to a decline in relaxation performance. DNA methylation constitutes an epigenetic modification. Alterations in the methylation landscape considerably influence gene expression changes associated with cardiac architecture and functionality. Recently, an evaluation of TIMP1 based on DNA methylation, referred to as DNAmTIMP1, was utilized to assess TIMP1 tools. Our latest study revealed a strong link between DNAmTIMP1 and the likelihood of cardiovascular disease, along with continued mortality in individuals of middle and advanced age. Methods We established a comprehensive, population-representative cohort derived from the National Health and Nutrition Examination Survey conducted between 1999 and 2002, which encompasses data pertaining to DNA methylation predicted tissue inhibitor metalloproteinase 1 used to predict the time of death for GrimAge and GrimAge2,which is used to predict human mortality risk and the rate of biological aging as an epigenetic biomarker based on DNA methylation[16]. By applying logistic regression and Cox proportional hazards models, we methodically examined the associations between DNAmTIMP1 and cardiovascular disease (CVD) risk, along with mortality outcomes. Results The study group was made up of 2466 individuals, with cardiovascular disease (CVD) having a weighted prevalence of 18.9%. Significantly, each one-kilobase increase in DNAmTIMP1 was linked to a 54% increase in the likelihood of developing CVD [odds ratio (OR): 1.54, 95% confidence interval (CI): 1.12–2.21, P <0.001]. Throughout a median follow-up duration of 207 months, there were 1309 recorded fatalities (53.1%), of which 345 cases (14.0%) were attributable to CVD. Participants exhibiting the longest DNAmTIMP1 demonstrated a 80% increasing in the risk of all-cause mortality (hazard ratio (HR): 1.80, 95% CI: 1.28–2.53, P < 0.001) and a 102% augment in mortality due to CVD (HR: 2.02, 95% CI: 1.05–3.90, P =0.035) when compared to those with the shortest DNAmTIMP1. Conclusion This study provides evidence of a significant positive association between DNAmTIMP1 levels and both cardiovascular disease (CVD) risk and mortality in a middle-aged to older US population. Higher DNAmTIMP1 levels correlate with an increased likelihood of CVD and a higher risk of all-cause and CVD-related mortality. These findings suggest that DNAmTIMP1 may serve as a valuable biomarker for CVD risk assessment and long-term mortality prediction. Further research is needed to elucidate the underlying mechanisms linking DNAmTIMP1 to CVD and mortality, and to explore potential interventions to improve cardiovascular outcomes and reduce mortality rates. DNA methylation Tissue inhibitor metalloproteinase 1 Cardiovascular disease Mortality NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cardiovascular diseases (CVDs) led to 20.5 million deaths worldwide in 2021, maintaining their position as the leading cause of death, with a troubling upward trend over the years[ 1 ]. According to the American Heart Association and the National Institutes of Health, global deaths from cardiovascular disease have risen by 12.5% over the last ten years, making up about one-third of all deaths globally. Biomarkers include NLR, CRP and IL-6 exert an essential role in sensitive and prognosis in assessment of cardiovascular disease. The increasing prevalence of CVD underscores the need for effective biomarker for early prediction of the disease and improved prognosis. DNA methylation facilitates the formation of "CpG islands," characterized by elevated concentrations of contiguous cytosine-guanine nucleotides within the DNA sequence[ 2 ]. The methylation occurring within this specific region is frequently linked to the "silencing" of gene expression and governs the spatio-temporal specificity of gene expression[ 3 ]. DNA methylation constitutes a fundamental epigenetic modification. It is instrumental in the regulation of gene transcription and influences the progression of physiological pathologies[ 4 ]. In the context of cardiovascular diseases, alterations in the DNA methylation landscape significantly impact the expression of genes pertinent to cardiac function and structural adaptability. Recent search elucidates that in models of ischemic heart disease, hypermethylation within critical promoter regions of cardiac genes correlates with diminished gene expression and compromised myocardial contractility, thereby suggesting that dysregulation of DNA methylation adversely affects cardiac remodeling[ 5 ]. Furthermore, studies have identified that individuals with congenital heart disease exhibit distinct DNA methylation profiles[ 6 ]. Tissue inhibitors of metalloproteinases (TIMPs) represent a family of four distinct proteins that primarily function to inhibit matrixin proteinases, thereby modulating the turnover of the extracellular matrix and maintaining tissue homeostasis, in addition to influencing essential biological processes such as inflammation and apoptosis [ 7 – 8 ]. TIMPs can also activate pathways that lead to excessive collagen synthesis and augmented extracellular matrix production through mediating interactions between CD63 and integrin β1 on cardiac fibroblasts, ultimately resulting in increased ventricular stiffness, fostering cardiac fibrosis, and culminating in impaired diastolic function, thus playing a pivotal role in cardiac pathologies such as heart failure and atrial fibrillation [ 9 – 10 ]. For instance, Zhang et al found that the upregulation of TIMP-1 expression in fibrotic myocardial tissue is closely associated with impaired diastolic function, and posited its role in facilitating fibroblast activation and advancing myofibroblast differentiation, thereby exacerbating the pronounced pathological remodeling observed in cardiac fibrosis[ 10 ]. Grosse et al. investigated the serum levels of TIMP-1 in patients who have heart failure and atrial fibrillation compared to common individuals with structurally normal hearts, revealing that TIMP-1 levels correlate with increased extracellular matrix deposition and the extent of atrial fibrosis [ 11 ]. These observations highlight that TIMP-1 is not limited to affecting ECM degradation as an MMP inhibitor in cardiac pathologic development, but also directly affects cell differentiation and ECM remodeling[ 10 – 13 ]. Also, TIMPs are associated with the prognosis and progression of the disease. A significant proportion of research has concentrated on the hypermethylation of TIMP-3 and TIMP-2 in oncological patients, correlating with the differentiation of benign, progressing, aggressive tumor behavior and metastasis [ 14 – 15 ]. For patients with lower-grade glioma, hyper-methylation of TIMP1 suggested enhanced overall and relapse-free survival, pointing to its potential as a prognostic marker. A recent study by Lu et al. presented an innovative approach for predicting plasma TIMP-1 protein concentrations by analyzing the methylation levels at 7 CpG sites[ 16 ]. However, the relationship between DNAmTIMP1 expression levels and cardiovascular disease (CVD) remains unexplored. In our research, we intend to investigate the relationship between DNAmTIMP1 and cardiovascular disease (CVD), along with long-term mortality in middle-aged and senior individuals living in the United States. Methods Study design and population Our research involves a cross-sectional analysis using data from 2,466 participants gathered during the 1999–2000 and 2001–2002 cycles of the National Health and Nutrition Examination Survey (NHANES). The goal of this initiative is to evaluate the health and nutritional condition of both adults and children throughout the United States. The Research Ethics Review Board of the National Center for Health Statistics (NCHS) has endorsed the NHANES protocol, which is supervised by the CDC. The acquisition of informed written consent from all participants is mandated by the program to safeguard their rights. Individuals were excluded from the study if they met any of the criteria listed below, as shown in Fig. 1 : (1)missing CVD information and ༈2༉missing DNAmTIMP1 data. In this research, CVD diagnoses relied on self-reported physician assessments gathered through a standardized questionnaire[ 17 – 18 ]. Participants were inquired, “Have you ever been diagnosed with congestive heart failure, coronary heart disease, angina, myocardial infarction, or stroke by a healthcare professional?” People who responded with a yes were identified as having cardiovascular disease. Study flowchart showing the inclusion and exclusion process DNA methylation measurements and DNA mTIMP1 calculations DNA methylation measurements were conducted on individuals aged 50 or older who provided blood samples for DNA purification. DNA was extracted from the whole blood samples and stored at -80°C until analysis. At Duke University, Dr. Yongmei Liu's laboratory carried out the DNA methylation measurements. For the conversion to sulfite, 500 ng of DNA underwent processing via the Zymo EZ DNA Methylation Kit (cat# D5001, Zymo Research, Irvine, CA, USA), following the guidelines set by the manufacturer, utilizing PCR conditions tailored for Illumina's Infinium Methylation Assay (30 seconds at 95°C, 60 minutes at 50°C, for sixteen cycles). The methylation data were acquired using the Illumina Infinium MethylationEPIC BeadChip v1.0 (cat# WG317-1001, Illumina, San Diego, CA, USA). Following the protocol provided by the manufacturer, a 4 µL aliquot of DNA treated with sulfite was hybridized to the BeadChip. Once hybridization was complete, the samples were denatured and amplified for an overnight duration of 20 to 24 hours. The next steps included breaking up, precipitating, and resuspending the samples, then incubating them overnight and hybridizing them to the EPIC BeadChip for 16 to 24 hours. Following this, the BeadChip underwent rinsing to clear away any non-hybridized DNA and was labeled with nucleotides to assist in primer extension. Using the Illumina iScan system (Illumina, San Diego, CA, USA), the BeadChip was imaged in accordance with the Infinium HD methylation protocol[ 16 ]. Determination of Mortality Rates The National Center for Health Statistics (NCHS) linked mortality details from the NHANES program to the National Death Index (NDI) as of December 31, 2019. Deaths specific to certain diseases are classified according to the International Statistical Classification of Diseases, 10th Edition (ICD-10). Covariates Demographic factors included age, gender, racial/ethnic background (white, black, Mexican American, other races), and educational attainment (below high school,9-11Grade, High school grade, Some college, College Graduate),The poverty-to-income ratio (PIR), alcohol consumption status (yes or no), and smoking status (never, ever, current). Laboratory tests measured total cholesterol (TC) and triglycerides (TG), blood potassium, sodium, chloride, creatinine, blood urea nitrogen, red blood cell count, white blood cell count, and platelet count. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI) were documented during the physical examination. Information on the use of medications, including antihypertensive and hypoglycemic drugs, was also gathered. Hypertension was defined as self-reported diagnosis, use of antihypertensive medication, and SBP ≥ 140 mmHg or DBP ≥ 90 mmHg [ 19 ]. Diabetes mellitus (DM) was diagnosed on the basis of self-reported diagnosis, use of insulin or oral hypoglycemic medication, fasting blood glucose ≥ 7.0 mmol/L, 2-hour blood glucose ≥ 11.1 mmol/L after oral glucose tolerance test, or HbA1c ≥ 6.5%. Statistical analysis: All analyses incorporated sample weights as a result of NHANES's complex sampling design. Three groups were formed by classifying participants according to DNAmTIMP1 level tertiles (T1-T3). Mean ± SD or median (interquartile range) represents continuous variables, which are analyzed with one-way ANOVA or Mann–Whitney tests as suitable. Numbers (percentages) are used to describe categorical variables, and they are compared with the chi-square test. The association between DNAmTIMP1 and CVD was assessed using logistic regression analyses across three models. In Model I, adjustments were made for age, whereas Model II also accounted for gender, race/ethnicity, BMI, PIR, education, alcohol intake, and smoking status, In Model III, additional adjustments were made for hypertension, diabetes, total cholesterol, triglycerides, systolic and diastolic blood pressure, creatinine, and the use of medications including antihypertensives and hypoglycemics. Cox proportional hazards models which is a semi-parametric regression model and widely used in survival analysis were used to assess the association of DNAmTIMP1 with mortality and three models were built in the same fashion as above: Models I and II were controlled for the same confounding variables mentioned earlier, while Model III included additional adjustments for CVD. Restricted cubic splines were used to evaluate the possible nonlinear relationship between DNAmTIMP1, cardiovascular disease, and mortality outcomes. R software (version 4.4.2) was utilized for data analysis, and statistical significance was defined as a two-sided p value below 0.05. Results Basic Characteristics of Study Participants The present research involved the participation of 24,66 individuals, whose average age was 66.0 ± 10.1 years. Among them, 1,255, accounting for 50.9% of the total, were of the male gender. Table 1 provides an overview of the participants' baseline characteristics. Individuals with the longest DNA mTIMP1 (T3) were generally older and had a higher likelihood of being male, smokers, white, and of lower socioeconomic status. Participants in the T3 category exhibited elevated levels of systolic blood pressure, leukocytes, creatinine, random glucose, blood potassium, and blood urea nitrogen. Conversely, they showed reduced levels of diastolic blood pressure, red blood cells, platelets, albumin, total cholesterol, and triglycerides. Association of DNAmTIMP1 with CVD prevalence Out of the 24,66 participants, 467 were diagnosed with cardiovascular disease (CVD), resulting in a weighted prevalence of 18.9%. Table 2 elaborates on the cross-sectional association between DNAmTIMP1 and cardiovascular disease risk in older and middle-aged individuals. The incidence of cardiovascular disease was notably greater in the longer DNAmTIMP1 group as opposed to the shorter group, with a weighted prevalence of 29.0% versus 9.85%(P < 0.001). Following the consideration of potential confounding factors: an increase of 1 kilobase in DNA mTIMP1 was linked to a 54% elevated risk of cardiovascular disease (CVD). [ratio of ratios (OR): 1.54, 95% confidence interval (CI): 1.12–2.21, P < 0.001]. People belonging to Group T3 demonstrated a 262% higher likelihood of developing cardiovascular disease (CVD) when contrasted with those in the T1 group (OR: 3.62, 95% CI: 1.28–10.25, P = 0.011). The results of the restricted cubic spline (RCS) analysis indicated that there was a linear dose-response relationship between DNAmTIMP1 and the risk of cardiovascular disease (CVD) (non-linear P: 0.176) Association of DNAmTIMP1 with Mortality During a median follow-up time of 207 months, there were 1309 deaths, which made up 53.1% of the participants. Out of these deaths, 345 were due to cardiovascular diseases, accounting for 14.0% of the total group. In Table 3 , The impact of DNAmTIMP1 on both overall mortality and cardiovascular disease mortality is summarized. Upon accounting for various factors, it was observed that for every 1-kilobase increment in the length of DNAmTIMP1, there was a corresponding 38% rise in the rate of overall mortality[HR = 1.38, 95% confidence interval (CI):1.22–1.57, P < 0.001]. Concurrently, the mortality rate associated with CVD escalated by 53%[HR = 1.53, 95% confidence interval (CI): 1.22,1.93, P < 0.001]. When juxtaposed with the T1 group, people involved in the T3 group showed an 80% higher propensity towards overall mortality[HR = 1.80, 95% confidence interval (CI): 1.28–2.53) p < 0.001]. Moreover, the risk pertaining to CVD mortality in the T3 group was augmented by 102%[HR = 2.02, 95% confidence interval (CI): 1.05,3.90, p = 0.035]. The restricted cubic spline (RCS) analysis results indicated a nonlinear relationship between DNAmTIMP1 and increased overall mortality risk. However, there was a linear relationship between DNAmTIMP1 and the heightened risk of cardiovascular disease (CVD) mortality. (The p-value for nonlinearity was 0.196, as illustrated in Fig. 3.) Subgroup and sensitivity analysis We performed subgroup analyses of the data to assess the consistency of the relationship between DNAmTIMP1 and mortality across various population groups. Figure 4A illustrates that cardiovascular disease (CVD) notably amplifies the link between DNAmTIMP1 and overall mortality. Specifically, this association is more pronounced in individuals with CVD than in those without the condition. Regarding the prediction of CVD mortality by TIMP1, factors such as age, sex, alcohol consumption, smoking status, and the presence of hypertension or diabetes did not substantially influence the results (Fig. 4B). After removing participants who passed away during the first two years of follow-up, sensitivity analyses continued to demonstrate the robust predictive capacity of TIMP1 for distinguishing between all-cause mortality and cardiovascular disease (CVD) mortality, as detailed in Table 4 . Disccusion This research is the initial exploration of the link between DNAmTIMP1 and the risk of cardiovascular disease (CVD), among middle-aged and older adults in the United States, including long-term mortality. The main results indicated (1) a positive association between DNAmTIMP1 levels and both cardiovascular disease risk and mortality, and (2) a distinct linear dose-response relationship, where increased DNAmTIMP1 levels are tied to a higher likelihood of risk of these outcomes. These findings highlight DNAmTIMP1 as a potentially significant biomarker for CVD risk stratification and long-term mortality prediction. Worldwide, cardiovascular diseases (CVD) continue to be the top cause of death, responsible for 18.6 million fatalities in 2019, which is 32% of all deaths globally[1]. DNA methylation, a fundamental epigenetic mechanism, has been strongly linked to cardiovascular risk and mortality[19-20]. Emerging evidence supports the role of specific DNA methylation patterns as reliable biomarkers for predicting cardiovascular risk, providing critical insights into disease pathogenesis and potential prevention strategies. For example, alterations in DNA methylation have been observed in congenital heart diseases such as Tetralogy of Fallot, where hypermethylation in the promoter regions of NKX2.5 and HAND2 is associated with downregulation of these genes, thereby contributing to developmental cardiac abnormalities[21]. Furthermore, studies on acute coronary syndrome (ACS) have identified 26 differentially methylated positions (DMPs) linked to established genetic loci (e.g., PRKCZ, EHBP1L1) that influence CVD pathophysiology[22]. Moreover, an epigenome-wide association study (EWAS) focusing on whole blood DNA methylation has demonstrated the potential of CpG sites as informative biomarkers for predicting cardiovascular risk, mortality, and all-cause mortality across diverse cohorts[23]. These observations suggest highlight the utility of epigenetic markers in improving our understanding of the systemic and multifactorial nature of cardiovascular diseases. In recent years, numerous biomarkers have been identified as being associated with cardiovascular events, including inflammatory markers, heart-specific proteins, and novel molecular markers [6]. TIMP1, a tissue inhibitor of metalloproteinases, is closely linked to myocardial fibrosis. Studies have shown that higher levels of TIMP-1 are linked to greater cardiac fibrosis and ECM buildup in heart failure patients[11]. Furthermore, previous research has emphasized TIMP1's potential as a marker for acute coronary syndrome and heart attacks, where elevated levels are indicative of an increased inflammatory burden and a higher likelihood of adverse cardiac events. Despite the growing focus on the role of TIMP1 in cardiac pathology, its specific association with cardiovascular disease progression and prognosis remains insufficiently explored. Our findings reveal a strong relationship between high DNAmTIMP1 expression and cardiovascular disease risk. Specifically, for every 1000-base increase in DNAmTIMP1, the risk of CVD increases by 54%, while mortality risk rises by 33% among middle-aged and older adults. These results suggest that the longer the DNAmTIMP1 sequence, the higher the risk of CVD-related and all-cause mortality. Therefore, DNAmTIMP1 emerges as a significant epigenetic marker for evaluating CVD progression and its associated outcomes. Recent studies have emphasized several major risk factors for CVD, including age, hypertension, dyslipidemia, diabetes, obesity, and smoking, which were also observed in this study. When analyzed as independent subgroups, they were not shown to have a significant impact on TMIP1mort predicting all-cause mortality versus CVD mortality. Earlier research has shown a significant link between alterations in DNA methylation and risk factors for cardiovascular disease, including lipid metabolism and inflammation[24]. For instance, a study has shown that individuals with higher methylation levels of the APOA5 gene promoter, which regulates lipid metabolism, have a significantly increased risk of cardiovascular disease[25]. Similarly, findings from the CARDIA study revealed that cumulative cardiovascular health (CVH) during young adulthood is associated with specific DNA methylation patterns observed in midlife, which subsequently influence long-term cardiovascular risk and mortality[26]. These pieces of research underscore the potential of epigenomic markers in the early detection and prevention of CVD. In this context, TIMP1 shows a pivotal role in myocardial fibrosis and serves as a promising biomarker for understanding epigenetic regulation in CVD. Elevated concentrations of TIMP1 are strongly correlated with increased extracellular matrix (ECM) accumulation and cardiac fibrosis, particularly in patients who have heart failure. The recent research highlights DNAmTIMP1 as a biomarker capable of capturing long-term biological exposure and cumulative effects. Advanced methylation detection methods, characterized by high sensitivity, specificity, and throughput, offer a robust and reliable approach for quantifying DNAmTIMP1 levels and assessing its association with CVD risk and mortality[27]. The study further revealed that DNA methylation levels are modulated by various factors, including reactive oxygen species (ROS), inflammatory mediators, and lifestyle-related variables. In conditions such as ischemia-reperfusion injury, heart failure, or chronic inflammation (e.g., mediated by TNF-α and IL-6), excessive ROS generation activates the ROS signaling pathway and inhibits the oxidation activity of TET (Ten-Eleven Translocation) proteins, thereby disrupting DNA demethylation processes[28-29]. These disruptions not only alter DNA methylation levels but also upregulate TIMP1 expression, exacerbating cardiac pathologies such as myocardial fibrosis and tissue remodeling. Moreover, lifestyle factors and nutritional metabolites play a significant role in modulating DNA methylation. Heavy metals found in cigarettes (e.g., cadmium and lead) can alter DNA methyltransferase activity, leading to aberrant DNA methylation patterns[30]. Similarly, methyl donors such as folate and S-adenosylmethionine (SAM) are critical for maintaining methylation reactions[31]. Cardiometabolic abnormalities, including diabetes and hyperlipidemia, further disrupt TET protein activity by altering alpha-ketoglutaric acid levels within the TCA cycle, which consequently influences DNA methylation status[32-33]. These epigenetic alterations activate downstream signaling pathways, such as calcium signaling and pro-inflammatory responses, while impairing mitochondrial function, reducing cardiomyocyte repair capacity, and promoting fibrotic tissue proliferation. Overall, DNAmTIMP1's predictive value probably stems from its capacity to encapsulate these complex epigenetic changes and mirror the intricate relationship between biological metabolism and systemic inflammation. Upcoming studies should aim to clarify the exact regulatory processes involved in TIMP1 methylation site changes and investigate treatments that could enhance CVD outcomes and lower death rates. Our study's ability to objectively measure long-term biomarker exposure using DNAm-predicted TIMP1 levels is one of its key strengths. Furthermore, having a large, nationally representative group with extended follow-up improves the applicability of the results. Using multiple statistical models and sensitivity analyses across different subgroups enhances the validity and robustness of the findings. By adopting these methodologies, the study minimizes biases inherent in traditional biomarker assessments. Nonetheless, it is important to recognize several limitations. Firstly, due to its cross-sectional nature, the study cannot conclusively establish causal relationships between DNAmTIMP1 and cardiovascular disease risk or mortality. Secondly, while TIMP1 was measured using DNA methylation techniques, it remains uncertain whether these methylation levels directly reflect TIMP1 expression in cardiac tissues. Thirdly, the study primarily focused on middle-aged and elderly individuals, necessitating further validation in broader populations, including younger individuals and diverse ethnic groups. Conclusions In conclusion, there is a positive association between DNAmTIMP1 and a higher risk of cardiovascular disease (CVD) and death, with a notable link between its expression and gender. The findings propose that DNAmTIMP1 might be a significant biomarker for cardiovascular risk assessment and long-term mortality prediction in the populations concerned. Further investigations should target understanding the underlying processes that link DNAmTIMP1 to disease morbidity and mortality. Additionally, efforts should be directed toward identifying effective interventions that could improve cardiovascular disease prognosis and reduce mortality. Declarations Ethical Approval and Informed Consent to Participat e This study is a cross - sectional analysis based on data from 2466 participants in the National Health and Nutrition Examination Survey (NHANES) from 1999 - 2000 and 2001 - 2002. The NHANES program is designed to assess the health and nutritional status of adults and children in the United States. The NHANES protocol is managed by the Centers for Disease Control and Prevention (CDC) and has been approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS). This program ensures the protection of participants' rights through informed written consent obtained from all relevant individuals. Consent for Publication This section is not applicable. Availability of Data and Materials All data used in this study are from NHANES. The official webpage of NHANES is NHANES Questionnaires, Datasets, and Related Documentation. Conflict of Interest The authors declare that they have no competing interests. Funding This work was supported by the Science and Technology Bureau of Nantong City under Grant Number MSZ2023085 and Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant Number SJCX24_2050. Authors' Contributions Xiaochen Lu, Fanbiao liu, and Ding Ding wrote the main manuscript and prepared the figures and tables. Haihua Geng and Hongzhuan Sheng provided guidance. All authors reviewed the manuscript. Clinical trial number: Not applicable. We would like to acknowledge the invaluable support from the National Health and Nutrition Examination Survey (NHANES) for providing the dataset used in this study. The dataset was made publicly available by the National Center for Health Statistics, and we are grateful for the researchers who collected and curated these data. References World Heart Federation. World Heart Report 2023: Confronting the World’s Number One Killer. Geneva, Switzerland: World Heart Federation; 2023. Bird A. CpG-rich islands and the function of DNA methylation. Nature . 1986;321:209–213. He Y, et al. Spatiotemporal DNA methylome dynamics of the developing mouse fetus. Nature . 2020;583:752–759. Law JA, Jacobsen SE. Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nat Rev Genet . 2010;11:204–220. Pepin A, et al. Genome-wide cardiac DNA methylation in patients with end-stage heart failure reveals metabolic gene reprogramming. [Preprint]. 2023. Xia S, et al. DNA Methylation Variation Is Identified in Monozygotic Twins Discordant for Congenital Heart Diseases. ScienceDirect . 2024. Gomez DE, Alonso DF, Yoshiji H, Thorgeirsson UP. Tissue inhibitors of metalloproteinases: structure, regulation and biological functions. Eur J Cell Biol . 1997;68:315-321. Brew K, Nagase H. The tissue inhibitors of metalloproteinases (TIMPs): An ancient family with structural and functional diversity. Biochim Biophys Acta . 2010;1803:55–71. Burstein B, Nattel S. Atrial Fibrosis: Mechanisms and Clinical Relevance in Atrial Fibrillation. J Am Coll Cardiol . 2008;51:802-809. Takawale A, et al. Tissue Inhibitor of Matrix Metalloproteinase-1 Promotes Myocardial Fibrosis by Mediating CD63-Integrin β1 Interaction. Hypertension . 2017;69(6):1092-1103. Grosse E, et al. Tissue inhibitor of metalloproteinase-1 (TIMP-1) as a biomarker for predicting the occurrence and progression of atrial fibrillation. Eur J Heart Fail . 2012. Warner RB, Najy AJ, Jung YS, et al. Establishment of Structure-Function Relationship of Tissue Inhibitor of Metalloproteinase-1 for Its Interaction with CD63: Implication for Cancer Therapy. Sci Rep . 2020;10:2099. Gomez DE, Alonso DF, Yoshiji H, Thorgeirsson UP. Tissue inhibitors of metalloproteinases: structure, regulation and biological functions. Eur J Cell Biol . 1997;68:315-321. Ben Néjima D, Ben Zarkouna Y, Gammoudi A, et al. Prognostic impact of polymorphism of matrix metalloproteinase-2 and metalloproteinase tissue inhibitor-2 promoters in breast cancer in Tunisia: case-control study. Tumour Biol . 2015;36(5):3815-3822. Lu YC, Chang JT, Liao CT, et al. OncomiR-196 promotes an invasive phenotype in oral cancer through the NME4-JNK-TIMP1-MMP signaling pathway. Mol Cancer . 2014;13:218. Lu AT, Seeboth A, Tsai PC, et al. DNA methylation-based estimator of telomere length. Aging . 2019;11(16):5895–923. Ye L, Zhang C, Duan Q, Shao Y, Zhou J. Association of magnesium depletion score with cardiovascular disease and its association with longitudinal mortality in patients with cardiovascular disease. J Am Heart Assoc . 2023;12(18):e030077. Abdalla SM, Yu S, Galea S. Trends in cardiovascular disease prevalence by income level in the United States. JAMA Netw Open . 2020;3(9):e2018150. Wang X, et al. Mechanisms and Advances of Epigenetic Regulation in Cardiovascular Disease. Front Biosci (Landmark Ed) . 2024. Epigenetic Regulation of Cardiovascular Diseases Induced by Behavioral and Environmental Risk Factors: Mechanistic, Diagnostic, and Therapeutic Insights. PMC . 2023. Bahado-Singh R, et al. Cell-free DNA in maternal blood and artificial intelligence: accurate prenatal detection of fetal congenital heart defects. Am J Obstet Gynecol . 2024;228:76e71-76e81. Wang Y, et al. Genome-wide DNA methylation profiling in blood reveals epigenetic signature of incident acute coronary syndrome. Nat Commun . 2024;15:4567. Pastorino M, Campitelli M, Longo M, et al. DNA methylation in cardiovascular disease and heart failure: novel prediction models? Clinical Epigenetics . 2024;16:115. Rath S, Hawsawi YM, Alzahrani F, Khan MI. Epigenetic regulation of inflammation: The metabolomics connection. Semin Cell Dev Biol . 2024;154:355-364. Li W, Wang Y, Huang R, et al. Association of lipid metabolism-related gene promoter methylation with risk of coronary artery disease. Mol Biol Rep . 2022;49(10):9373–8. Keshawarz A, Bui H, Joehanes R, et al. Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study. Sci Rep . 2023;13:12952. Lu Y, Horvath S, et al. Meta-analyses of epigenetic age acceleration and GrimAge components of schizophrenia or first-episode psychosis. Nat Commun . 2024. Anene-Nzelu CG, Stenzig J, Foo RS-Y. DNA methylation in heart failure. In: Translational Epigenetics. Elsevier; 2021:55-75. doi:10.1016/B978-0-12-822258-4.00016-X Pietrzak K, Foksinski M, Flisiak R, et al. The role of oxidative stress in the pathogenesis of non-alcoholic fatty liver disease - the impact on DNA methylation. Oxid Med Cell Longev . 2018;2018:6819374. doi:10.1155/2018/6819374. Maugeri A, Barchitta M. How dietary factors affect DNA methylation: Lesson from epidemiological studies. Medicina (Kaunas) . 2020;56(8):374. doi:10.3390/medicina56080374. Choi SW, Friso S. Folate and DNA methylation: a mechanistic link between folate deficiency and colorectal cancer? Cancer Epidemiol Biomarkers Prev . 2004;13(4):511-519. Liu R, Zhao E, Yu H, et al. Methylation across the central dogma in health and diseases: new therapeutic strategies. Sig Transduct Target Ther . 2023;8:310. Wu D, et al. Glucose-regulated phosphorylation of TET2 by AMPK reveals a pathway linking diabetes to cancer. Nature . 2018;559:637–641. Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6427282","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456668226,"identity":"fe8e2162-4628-4f05-9a2a-1e783b45a68d","order_by":0,"name":"Xiaochen Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYNACAxs5+/bGxocfiFPOzNjAUJBmbMBzuNlYgngtHw4nbpBIbxPgIUaDfP/6448LDA4bm0s+bGOQYLCT020goMXgxmPG5hkG6XKWsxPbHhQwJBubHSCkReIwYzOPgbUxw+3EdgMJhgOJ2whpkZ8B1sKc2HDzYJsEDzFaGM43g7Q4J264wUikFoMbzIazZxikGUv2JAID2YAIv8j3H3zwueCPjRw/+/GHDz9U2MkR1MIgkcDAjGQpIeUgwH8AWcsoGAWjYBSMAiwAAESpRKmjsv3eAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":true,"prefix":"","firstName":"Xiaochen","middleName":"","lastName":"Lu","suffix":""},{"id":456668227,"identity":"87b9d7b9-a2fb-4797-b821-84bfda2e2844","order_by":1,"name":"Fanbiao Liu","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Fanbiao","middleName":"","lastName":"Liu","suffix":""},{"id":456668228,"identity":"2b350296-6ad6-4c89-aa96-fa9720d6cb85","order_by":2,"name":"Ding Ding","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Ding","middleName":"","lastName":"Ding","suffix":""},{"id":456668229,"identity":"4041e254-bdaf-4e7c-8d10-9c2516db5b3b","order_by":3,"name":"Jian Zhuo","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhuo","suffix":""},{"id":456668230,"identity":"501d0483-e5fa-47d4-a0ae-097d932c7d5a","order_by":4,"name":"Yue Hou","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Hou","suffix":""},{"id":456668231,"identity":"5d0c772b-8a8f-4923-a334-75211d1af5ed","order_by":5,"name":"Zixin Wang","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Zixin","middleName":"","lastName":"Wang","suffix":""},{"id":456668234,"identity":"49d83aae-6810-4d28-9260-566422310e6d","order_by":6,"name":"Haihua Geng","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Haihua","middleName":"","lastName":"Geng","suffix":""},{"id":456668236,"identity":"983ffe27-29c3-4c44-ab3d-bdb3a2b7ef51","order_by":7,"name":"Hongzhuan Sheng","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Hongzhuan","middleName":"","lastName":"Sheng","suffix":""}],"badges":[],"createdAt":"2025-04-11 10:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6427282/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6427282/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83039509,"identity":"30170f9f-d8aa-4bfd-91f9-8fd368740c35","added_by":"auto","created_at":"2025-05-19 10:37:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27641,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart showing the inclusion and exclusion process\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6427282/v1/50ac3a857968eea66e0a1f09.jpg"},{"id":83041246,"identity":"814dace1-7b7b-413c-bb9a-870a9645c5d1","added_by":"auto","created_at":"2025-05-19 10:45:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19020,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis for the dose–response association between DNAmTIMP1 and risk of CVD\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6427282/v1/49b96704e4beb587a84fe77c.jpg"},{"id":83041245,"identity":"3ad5081c-13d9-416f-9f93-7a1f5163033b","added_by":"auto","created_at":"2025-05-19 10:45:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30794,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis for the dose–response association between DNAmTIMP1 and risk of all-cause (A) and CVD (B) mortality\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6427282/v1/395d0e21b57b5faf37ab81b2.jpg"},{"id":83039511,"identity":"b4e4311c-1850-4ccb-bb14-587ab319f2f5","added_by":"auto","created_at":"2025-05-19 10:37:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62196,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association between the DNAmTIMP1 and all-cause (A) and CVD (B) mortality\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6427282/v1/37d10aaaae338b43892dea58.jpg"},{"id":85465347,"identity":"3de6dd17-1ac8-46db-bf6f-a66bbdcbd833","added_by":"auto","created_at":"2025-06-26 08:17:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":739367,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6427282/v1/2160d5be-8e23-49a3-9257-e0a23f7a9fbd.pdf"},{"id":83041247,"identity":"35bcf1f5-60a0-44d6-b808-70b95fc5311a","added_by":"auto","created_at":"2025-05-19 10:45:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":524962,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6427282/v1/d0f5953608edf35b34cc0f63.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"DNAmTIMP1 as a Predictor of Cardiovascular Disease and Mortality: A Prospective Analysis from the NHANES","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) led to 20.5\u0026nbsp;million deaths worldwide in 2021, maintaining their position as the leading cause of death, with a troubling upward trend over the years[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the American Heart Association and the National Institutes of Health, global deaths from cardiovascular disease have risen by 12.5% over the last ten years, making up about one-third of all deaths globally. Biomarkers include NLR, CRP and IL-6 exert an essential role in sensitive and prognosis in assessment of cardiovascular disease. The increasing prevalence of CVD underscores the need for effective biomarker for early prediction of the disease and improved prognosis.\u003c/p\u003e \u003cp\u003eDNA methylation facilitates the formation of \"CpG islands,\" characterized by elevated concentrations of contiguous cytosine-guanine nucleotides within the DNA sequence[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The methylation occurring within this specific region is frequently linked to the \"silencing\" of gene expression and governs the spatio-temporal specificity of gene expression[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. DNA methylation constitutes a fundamental epigenetic modification. It is instrumental in the regulation of gene transcription and influences the progression of physiological pathologies[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the context of cardiovascular diseases, alterations in the DNA methylation landscape significantly impact the expression of genes pertinent to cardiac function and structural adaptability. Recent search elucidates that in models of ischemic heart disease, hypermethylation within critical promoter regions of cardiac genes correlates with diminished gene expression and compromised myocardial contractility, thereby suggesting that dysregulation of DNA methylation adversely affects cardiac remodeling[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, studies have identified that individuals with congenital heart disease exhibit distinct DNA methylation profiles[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTissue inhibitors of metalloproteinases (TIMPs) represent a family of four distinct proteins that primarily function to inhibit matrixin proteinases, thereby modulating the turnover of the extracellular matrix and maintaining tissue homeostasis, in addition to influencing essential biological processes such as inflammation and apoptosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. TIMPs can also activate pathways that lead to excessive collagen synthesis and augmented extracellular matrix production through mediating interactions between CD63 and integrin β1 on cardiac fibroblasts, ultimately resulting in increased ventricular stiffness, fostering cardiac fibrosis, and culminating in impaired diastolic function, thus playing a pivotal role in cardiac pathologies such as heart failure and atrial fibrillation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For instance, Zhang et al found that the upregulation of TIMP-1 expression in fibrotic myocardial tissue is closely associated with impaired diastolic function, and posited its role in facilitating fibroblast activation and advancing myofibroblast differentiation, thereby exacerbating the pronounced pathological remodeling observed in cardiac fibrosis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Grosse et al. investigated the serum levels of TIMP-1 in patients who have heart failure and atrial fibrillation compared to common individuals with structurally normal hearts, revealing that TIMP-1 levels correlate with increased extracellular matrix deposition and the extent of atrial fibrosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These observations highlight that TIMP-1 is not limited to affecting ECM degradation as an MMP inhibitor in cardiac pathologic development, but also directly affects cell differentiation and ECM remodeling[\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Also, TIMPs are associated with the prognosis and progression of the disease. A significant proportion of research has concentrated on the hypermethylation of TIMP-3 and TIMP-2 in oncological patients, correlating with the differentiation of benign, progressing, aggressive tumor behavior and metastasis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For patients with lower-grade glioma, hyper-methylation of TIMP1 suggested enhanced overall and relapse-free survival, pointing to its potential as a prognostic marker. A recent study by Lu et al. presented an innovative approach for predicting plasma TIMP-1 protein concentrations by analyzing the methylation levels at 7 CpG sites[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the relationship between DNAmTIMP1 expression levels and cardiovascular disease (CVD) remains unexplored. In our research, we intend to investigate the relationship between DNAmTIMP1 and cardiovascular disease (CVD), along with long-term mortality in middle-aged and senior individuals living in the United States.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003e Our research involves a cross-sectional analysis using data from 2,466 participants gathered during the 1999\u0026ndash;2000 and 2001\u0026ndash;2002 cycles of the National Health and Nutrition Examination Survey (NHANES). The goal of this initiative is to evaluate the health and nutritional condition of both adults and children throughout the United States. The Research Ethics Review Board of the National Center for Health Statistics (NCHS) has endorsed the NHANES protocol, which is supervised by the CDC. The acquisition of informed written consent from all participants is mandated by the program to safeguard their rights.\u003c/p\u003e \u003cp\u003eIndividuals were excluded from the study if they met any of the criteria listed below, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: (1)missing CVD information and ༈2༉missing DNAmTIMP1 data. In this research, CVD diagnoses relied on self-reported physician assessments gathered through a standardized questionnaire[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Participants were inquired, \u0026ldquo;Have you ever been diagnosed with congestive heart failure, coronary heart disease, angina, myocardial infarction, or stroke by a healthcare professional?\u0026rdquo; People who responded with a yes were identified as having cardiovascular disease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStudy flowchart showing the inclusion and exclusion process\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA methylation measurements and DNA mTIMP1 calculations\u003c/h3\u003e\n\u003cp\u003eDNA methylation measurements were conducted on individuals aged 50 or older who provided blood samples for DNA purification. DNA was extracted from the whole blood samples and stored at -80\u0026deg;C until analysis. At Duke University, Dr. Yongmei Liu's laboratory carried out the DNA methylation measurements. For the conversion to sulfite, 500 ng of DNA underwent processing via the Zymo EZ DNA Methylation Kit (cat# D5001, Zymo Research, Irvine, CA, USA), following the guidelines set by the manufacturer, utilizing PCR conditions tailored for Illumina's Infinium Methylation Assay (30 seconds at 95\u0026deg;C, 60 minutes at 50\u0026deg;C, for sixteen cycles). The methylation data were acquired using the Illumina Infinium MethylationEPIC BeadChip v1.0 (cat# WG317-1001, Illumina, San Diego, CA, USA). Following the protocol provided by the manufacturer, a 4 \u0026micro;L aliquot of DNA treated with sulfite was hybridized to the BeadChip. Once hybridization was complete, the samples were denatured and amplified for an overnight duration of 20 to 24 hours. The next steps included breaking up, precipitating, and resuspending the samples, then incubating them overnight and hybridizing them to the EPIC BeadChip for 16 to 24 hours. Following this, the BeadChip underwent rinsing to clear away any non-hybridized DNA and was labeled with nucleotides to assist in primer extension. Using the Illumina iScan system (Illumina, San Diego, CA, USA), the BeadChip was imaged in accordance with the Infinium HD methylation protocol[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDetermination of Mortality Rates\u003c/h3\u003e\n\u003cp\u003eThe National Center for Health Statistics (NCHS) linked mortality details from the NHANES program to the National Death Index (NDI) as of December 31, 2019. Deaths specific to certain diseases are classified according to the International Statistical Classification of Diseases, 10th Edition (ICD-10).\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eDemographic factors included age, gender, racial/ethnic background (white, black, Mexican American, other races), and educational attainment (below high school,9-11Grade, High school grade, Some college, College Graduate),The poverty-to-income ratio (PIR), alcohol consumption status (yes or no), and smoking status (never, ever, current). Laboratory tests measured total cholesterol (TC) and triglycerides (TG), blood potassium, sodium, chloride, creatinine, blood urea nitrogen, red blood cell count, white blood cell count, and platelet count. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI) were documented during the physical examination. Information on the use of medications, including antihypertensive and hypoglycemic drugs, was also gathered. Hypertension was defined as self-reported diagnosis, use of antihypertensive medication, and SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Diabetes mellitus (DM) was diagnosed on the basis of self-reported diagnosis, use of insulin or oral hypoglycemic medication, fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, 2-hour blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L after oral glucose tolerance test, or HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003eAll analyses incorporated sample weights as a result of NHANES's complex sampling design. Three groups were formed by classifying participants according to DNAmTIMP1 level tertiles (T1-T3). Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (interquartile range) represents continuous variables, which are analyzed with one-way ANOVA or Mann\u0026ndash;Whitney tests as suitable. Numbers (percentages) are used to describe categorical variables, and they are compared with the chi-square test. The association between DNAmTIMP1 and CVD was assessed using logistic regression analyses across three models. In Model I, adjustments were made for age, whereas Model II also accounted for gender, race/ethnicity, BMI, PIR, education, alcohol intake, and smoking status, In Model III, additional adjustments were made for hypertension, diabetes, total cholesterol, triglycerides, systolic and diastolic blood pressure, creatinine, and the use of medications including antihypertensives and hypoglycemics. Cox proportional hazards models which is a semi-parametric regression model and widely used in survival analysis were used to assess the association of DNAmTIMP1 with mortality and three models were built in the same fashion as above: Models I and II were controlled for the same confounding variables mentioned earlier, while Model III included additional adjustments for CVD. Restricted cubic splines were used to evaluate the possible nonlinear relationship between DNAmTIMP1, cardiovascular disease, and mortality outcomes. R software (version 4.4.2) was utilized for data analysis, and statistical significance was defined as a two-sided p value below 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBasic Characteristics of Study Participants\u003c/h2\u003e \u003cp\u003eThe present research involved the participation of 24,66 individuals, whose average age was 66.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1 years. Among them, 1,255, accounting for 50.9% of the total, were of the male gender. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides an overview of the participants' baseline characteristics. Individuals with the longest DNA mTIMP1 (T3) were generally older and had a higher likelihood of being male, smokers, white, and of lower socioeconomic status. Participants in the T3 category exhibited elevated levels of systolic blood pressure, leukocytes, creatinine, random glucose, blood potassium, and blood urea nitrogen. Conversely, they showed reduced levels of diastolic blood pressure, red blood cells, platelets, albumin, total cholesterol, and triglycerides.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation of DNAmTIMP1 with CVD prevalence\u003c/h3\u003e\n\u003cp\u003eOut of the 24,66 participants, 467 were diagnosed with cardiovascular disease (CVD), resulting in a weighted prevalence of 18.9%. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e elaborates on the cross-sectional association between DNAmTIMP1 and cardiovascular disease risk in older and middle-aged individuals. The incidence of cardiovascular disease was notably greater in the longer DNAmTIMP1 group as opposed to the shorter group, with a weighted prevalence of 29.0% versus 9.85%(P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Following the consideration of potential confounding factors: an increase of 1 kilobase in DNA mTIMP1 was linked to a 54% elevated risk of cardiovascular disease (CVD). [ratio of ratios (OR): 1.54, 95% confidence interval (CI): 1.12\u0026ndash;2.21, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. People belonging to Group T3 demonstrated a 262% higher likelihood of developing cardiovascular disease (CVD) when contrasted with those in the T1 group (OR: 3.62, 95% CI: 1.28\u0026ndash;10.25, P\u0026thinsp;=\u0026thinsp;0.011). The results of the restricted cubic spline (RCS) analysis indicated that there was a linear dose-response relationship between DNAmTIMP1 and the risk of cardiovascular disease (CVD) (non-linear P: 0.176)\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of DNAmTIMP1 with Mortality\u003c/h2\u003e \u003cp\u003eDuring a median follow-up time of 207 months, there were 1309 deaths, which made up 53.1% of the participants. Out of these deaths, 345 were due to cardiovascular diseases, accounting for 14.0% of the total group. In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, The impact of DNAmTIMP1 on both overall mortality and cardiovascular disease mortality is summarized. Upon accounting for various factors, it was observed that for every 1-kilobase increment in the length of DNAmTIMP1, there was a corresponding 38% rise in the rate of overall mortality[HR\u0026thinsp;=\u0026thinsp;1.38, 95% confidence interval (CI):1.22\u0026ndash;1.57, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. Concurrently, the mortality rate associated with CVD escalated by 53%[HR\u0026thinsp;=\u0026thinsp;1.53, 95% confidence interval (CI): 1.22,1.93, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. When juxtaposed with the T1 group, people involved in the T3 group showed an 80% higher propensity towards overall mortality[HR\u0026thinsp;=\u0026thinsp;1.80, 95% confidence interval (CI): 1.28\u0026ndash;2.53) p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. Moreover, the risk pertaining to CVD mortality in the T3 group was augmented by 102%[HR\u0026thinsp;=\u0026thinsp;2.02, 95% confidence interval (CI): 1.05,3.90, p\u0026thinsp;=\u0026thinsp;0.035].\u003c/p\u003e \u003cp\u003eThe restricted cubic spline (RCS) analysis results indicated a nonlinear relationship between DNAmTIMP1 and increased overall mortality risk. However, there was a linear relationship between DNAmTIMP1 and the heightened risk of cardiovascular disease (CVD) mortality. (The p-value for nonlinearity was 0.196, as illustrated in Fig.\u0026nbsp;3.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup and sensitivity analysis\u003c/h2\u003e \u003cp\u003eWe performed subgroup analyses of the data to assess the consistency of the relationship between DNAmTIMP1 and mortality across various population groups. Figure\u0026nbsp;4A illustrates that cardiovascular disease (CVD) notably amplifies the link between DNAmTIMP1 and overall mortality. Specifically, this association is more pronounced in individuals with CVD than in those without the condition.\u003c/p\u003e \u003cp\u003eRegarding the prediction of CVD mortality by TIMP1, factors such as age, sex, alcohol consumption, smoking status, and the presence of hypertension or diabetes did not substantially influence the results (Fig.\u0026nbsp;4B). After removing participants who passed away during the first two years of follow-up, sensitivity analyses continued to demonstrate the robust predictive capacity of TIMP1 for distinguishing between all-cause mortality and cardiovascular disease (CVD) mortality, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Disccusion","content":"\u003cp\u003eThis research is the initial exploration of the link between DNAmTIMP1 and the risk of cardiovascular disease (CVD), among middle-aged and older adults in the United States, including long-term mortality. The main results indicated (1) a positive association between DNAmTIMP1 levels and both cardiovascular disease risk and mortality, and (2) a distinct linear dose-response relationship, where increased DNAmTIMP1 levels are tied to a higher likelihood of risk of these outcomes. These findings highlight DNAmTIMP1 as a potentially significant biomarker for CVD risk stratification and long-term mortality prediction.\u003c/p\u003e\n\u003cp\u003eWorldwide, cardiovascular diseases (CVD) continue to be the top cause of death, responsible for 18.6 million fatalities in 2019, which is 32% of all deaths globally[1]. DNA methylation, a fundamental epigenetic mechanism, has been strongly linked to cardiovascular risk and mortality[19-20]. Emerging evidence supports the role of specific DNA methylation patterns as reliable biomarkers for predicting cardiovascular risk, providing critical insights into disease pathogenesis and potential prevention strategies. For example, alterations in DNA methylation have been observed in congenital heart diseases such as Tetralogy of Fallot, where hypermethylation in the promoter regions of NKX2.5 and HAND2 is associated with downregulation of these genes, thereby contributing to developmental cardiac abnormalities[21]. Furthermore, studies on acute coronary syndrome (ACS) have identified 26 differentially methylated positions (DMPs) linked to established genetic loci (e.g., PRKCZ, EHBP1L1) that influence CVD pathophysiology[22]. Moreover, an epigenome-wide association study (EWAS) focusing on whole blood DNA methylation has demonstrated the potential of CpG sites as informative biomarkers for predicting cardiovascular risk, mortality, and all-cause mortality across diverse cohorts[23]. These observations suggest highlight the utility of epigenetic markers in improving our understanding of the systemic and multifactorial nature of cardiovascular diseases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;In recent years, numerous biomarkers have been identified as being associated with cardiovascular events, including inflammatory markers, heart-specific proteins, and novel molecular markers [6]. TIMP1, a tissue inhibitor of metalloproteinases, is closely linked to myocardial fibrosis. Studies have shown that higher levels of TIMP-1 are linked to greater cardiac fibrosis and ECM buildup in heart failure patients[11]. Furthermore, previous research has emphasized TIMP1\u0026apos;s potential as a marker for acute coronary syndrome and heart attacks, where elevated levels are indicative of an increased inflammatory burden and a higher likelihood of adverse cardiac events. Despite the growing focus on the role of TIMP1 in cardiac pathology, its specific association with cardiovascular disease progression and prognosis remains insufficiently explored. Our findings reveal a strong relationship between high DNAmTIMP1 expression and cardiovascular disease risk. Specifically, for every 1000-base increase in DNAmTIMP1, the risk of CVD increases by 54%, while mortality risk rises by 33% among middle-aged and older adults. These results suggest that the longer the DNAmTIMP1 sequence, the higher the risk of CVD-related and all-cause mortality. Therefore, DNAmTIMP1 emerges as a significant epigenetic marker for evaluating CVD progression and its associated outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent studies have emphasized several major risk factors for CVD, including age, hypertension, dyslipidemia, diabetes, obesity, and smoking, which were also observed in this study. When analyzed as independent subgroups, they were not shown to have a significant impact on TMIP1mort predicting all-cause mortality versus CVD mortality.\u003c/p\u003e\n\u003cp\u003eEarlier research has shown a significant link between alterations in DNA methylation and risk factors for cardiovascular disease, including lipid metabolism and inflammation[24]. For instance, a study has shown that individuals with higher methylation levels of the APOA5 gene promoter, which regulates lipid metabolism, have a significantly increased risk of cardiovascular disease[25]. Similarly, findings from the CARDIA study revealed that cumulative cardiovascular health (CVH) during young adulthood is associated with specific DNA methylation patterns observed in midlife, which subsequently influence long-term cardiovascular risk and mortality[26]. These pieces of research underscore the potential of epigenomic markers in the early detection and prevention of CVD. In this context, TIMP1 shows a pivotal role in myocardial fibrosis and serves as a promising biomarker for understanding epigenetic regulation in CVD. Elevated concentrations of TIMP1 are strongly correlated with increased extracellular matrix (ECM) accumulation and cardiac fibrosis, particularly in patients who have heart failure. The recent research highlights DNAmTIMP1 as a biomarker capable of capturing long-term biological exposure and cumulative effects. Advanced methylation detection methods, characterized by high sensitivity, specificity, and throughput, offer a robust and reliable approach for quantifying DNAmTIMP1 levels and assessing its association with CVD risk and mortality[27].\u003c/p\u003e\n\u003cp\u003eThe study further revealed that DNA methylation levels are modulated by various factors, including reactive oxygen species (ROS), inflammatory mediators, and lifestyle-related variables. In conditions such as ischemia-reperfusion injury, heart failure, or chronic inflammation (e.g., mediated by TNF-\u0026alpha; and IL-6), excessive ROS generation activates the ROS signaling pathway and inhibits the oxidation activity of TET (Ten-Eleven Translocation) proteins, thereby disrupting DNA demethylation processes[28-29]. These disruptions not only alter DNA methylation levels but also upregulate TIMP1 expression, exacerbating cardiac pathologies such as myocardial fibrosis and tissue remodeling. Moreover, lifestyle factors and nutritional metabolites play a significant role in modulating DNA methylation. Heavy metals found in cigarettes (e.g., cadmium and lead) can alter DNA methyltransferase activity, leading to aberrant DNA methylation patterns[30]. Similarly, methyl donors such as folate and S-adenosylmethionine (SAM) are critical for maintaining methylation reactions[31]. Cardiometabolic abnormalities, including diabetes and hyperlipidemia, further disrupt TET protein activity by altering alpha-ketoglutaric acid levels within the TCA cycle, which consequently influences DNA methylation status[32-33]. These epigenetic alterations activate downstream signaling pathways, such as calcium signaling and pro-inflammatory responses, while impairing mitochondrial function, reducing cardiomyocyte repair capacity, and promoting fibrotic tissue proliferation. Overall, DNAmTIMP1\u0026apos;s predictive value probably stems from its capacity to encapsulate these complex epigenetic changes and mirror the intricate relationship between biological metabolism and systemic inflammation. Upcoming studies should aim to clarify the exact regulatory processes involved in TIMP1 methylation site changes and investigate treatments that could enhance CVD outcomes and lower death rates.\u003c/p\u003e\n\u003cp\u003eOur study\u0026apos;s ability to objectively measure long-term biomarker exposure using DNAm-predicted TIMP1 levels is one of its key strengths. Furthermore, having a large, nationally representative group with extended follow-up improves the applicability of the results. Using multiple statistical models and sensitivity analyses across different subgroups enhances the validity and robustness of the findings. By adopting these methodologies, the study minimizes biases inherent in traditional biomarker assessments. Nonetheless, it is important to recognize several limitations. Firstly, due to its cross-sectional nature, the study cannot conclusively establish causal relationships between DNAmTIMP1 and cardiovascular disease risk or mortality. Secondly, while TIMP1 was measured using DNA methylation techniques, it remains uncertain whether these methylation levels directly reflect TIMP1 expression in cardiac tissues. Thirdly, the study primarily focused on middle-aged and elderly individuals, necessitating further validation in broader populations, including younger individuals and diverse ethnic groups.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, there is a positive association between DNAmTIMP1 and a higher risk of cardiovascular disease (CVD) and death, with a notable link between its expression and gender. The findings propose that DNAmTIMP1 might be a significant biomarker for cardiovascular risk assessment and long-term mortality prediction in the populations concerned. Further investigations should target understanding the underlying processes that link DNAmTIMP1 to disease morbidity and mortality. Additionally, efforts should be directed toward identifying effective interventions that could improve cardiovascular disease prognosis and reduce mortality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eEthical Approval and Informed Consent to Participat\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e This study is a cross - sectional analysis based on data from 2466 participants in the National Health and Nutrition Examination Survey (NHANES) from 1999 - 2000 and 2001 - 2002. The NHANES program is designed to assess the health and nutritional status of adults and children in the United States. The NHANES protocol is managed by the Centers for Disease Control and Prevention (CDC) and has been approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS). This program ensures the protection of participants\u0026apos; rights through informed written consent obtained from all relevant individuals.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e This section is not applicable.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e All data used in this study are from NHANES. The official webpage of NHANES is NHANES Questionnaires, Datasets, and Related Documentation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the Science and Technology Bureau of Nantong City under Grant Number MSZ2023085 and\u0026nbsp;Postgraduate Research \u0026amp; Practice Innovation Program of Jiangsu Province under Grant Number SJCX24_2050.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e Xiaochen Lu, Fanbiao liu, and Ding Ding wrote the main manuscript and prepared the figures and tables. Haihua Geng and Hongzhuan Sheng provided guidance. All authors reviewed the manuscript.\u003c/li\u003e\n \u003cli\u003eClinical trial number: Not applicable.\u003c/li\u003e\n \u003cli\u003eWe would like to acknowledge the invaluable support from the National Health and Nutrition Examination Survey (NHANES) for providing the dataset used in this study. The dataset was made publicly available by the National Center for Health Statistics, and we are grateful for the researchers who collected and curated these data. \u0026nbsp;\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Heart Federation. World Heart Report 2023: Confronting the World\u0026rsquo;s Number One Killer. Geneva, Switzerland: World Heart Federation; 2023.\u003c/li\u003e\n\u003cli\u003eBird A. CpG-rich islands and the function of DNA methylation. \u003cem\u003eNature\u003c/em\u003e. 1986;321:209\u0026ndash;213.\u003c/li\u003e\n\u003cli\u003eHe Y, et al. Spatiotemporal DNA methylome dynamics of the developing mouse fetus. \u003cem\u003eNature\u003c/em\u003e. 2020;583:752\u0026ndash;759.\u003c/li\u003e\n\u003cli\u003eLaw JA, Jacobsen SE. Establishing, maintaining and modifying DNA methylation patterns in plants and animals. \u003cem\u003eNat Rev Genet\u003c/em\u003e. 2010;11:204\u0026ndash;220.\u003c/li\u003e\n\u003cli\u003ePepin A, et al. Genome-wide cardiac DNA methylation in patients with end-stage heart failure reveals metabolic gene reprogramming. [Preprint]. 2023.\u003c/li\u003e\n\u003cli\u003eXia S, et al. DNA Methylation Variation Is Identified in Monozygotic Twins Discordant for Congenital Heart Diseases. \u003cem\u003eScienceDirect\u003c/em\u003e. 2024.\u003c/li\u003e\n\u003cli\u003eGomez DE, Alonso DF, Yoshiji H, Thorgeirsson UP. Tissue inhibitors of metalloproteinases: structure, regulation and biological functions. \u003cem\u003eEur J Cell Biol\u003c/em\u003e. 1997;68:315-321.\u003c/li\u003e\n\u003cli\u003eBrew K, Nagase H. The tissue inhibitors of metalloproteinases (TIMPs): An ancient family with structural and functional diversity. \u003cem\u003eBiochim Biophys Acta\u003c/em\u003e. 2010;1803:55\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eBurstein B, Nattel S. Atrial Fibrosis: Mechanisms and Clinical Relevance in Atrial Fibrillation. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. 2008;51:802-809.\u003c/li\u003e\n\u003cli\u003eTakawale A, et al. Tissue Inhibitor of Matrix Metalloproteinase-1 Promotes Myocardial Fibrosis by Mediating CD63-Integrin \u0026beta;1 Interaction. \u003cem\u003eHypertension\u003c/em\u003e. 2017;69(6):1092-1103.\u003c/li\u003e\n\u003cli\u003eGrosse E, et al. Tissue inhibitor of metalloproteinase-1 (TIMP-1) as a biomarker for predicting the occurrence and progression of atrial fibrillation. \u003cem\u003eEur J Heart Fail\u003c/em\u003e. 2012.\u003c/li\u003e\n\u003cli\u003eWarner RB, Najy AJ, Jung YS, et al. Establishment of Structure-Function Relationship of Tissue Inhibitor of Metalloproteinase-1 for Its Interaction with CD63: Implication for Cancer Therapy. \u003cem\u003eSci Rep\u003c/em\u003e. 2020;10:2099.\u003c/li\u003e\n\u003cli\u003eGomez DE, Alonso DF, Yoshiji H, Thorgeirsson UP. Tissue inhibitors of metalloproteinases: structure, regulation and biological functions. \u003cem\u003eEur J Cell Biol\u003c/em\u003e. 1997;68:315-321.\u003c/li\u003e\n\u003cli\u003eBen N\u0026eacute;jima D, Ben Zarkouna Y, Gammoudi A, et al. Prognostic impact of polymorphism of matrix metalloproteinase-2 and metalloproteinase tissue inhibitor-2 promoters in breast cancer in Tunisia: case-control study. \u003cem\u003eTumour Biol\u003c/em\u003e. 2015;36(5):3815-3822.\u003c/li\u003e\n\u003cli\u003eLu YC, Chang JT, Liao CT, et al. OncomiR-196 promotes an invasive phenotype in oral cancer through the NME4-JNK-TIMP1-MMP signaling pathway. \u003cem\u003eMol Cancer\u003c/em\u003e. 2014;13:218.\u003c/li\u003e\n\u003cli\u003eLu AT, Seeboth A, Tsai PC, et al. DNA methylation-based estimator of telomere length. \u003cem\u003eAging\u003c/em\u003e. 2019;11(16):5895\u0026ndash;923.\u003c/li\u003e\n\u003cli\u003eYe L, Zhang C, Duan Q, Shao Y, Zhou J. Association of magnesium depletion score with cardiovascular disease and its association with longitudinal mortality in patients with cardiovascular disease. \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e. 2023;12(18):e030077.\u003c/li\u003e\n\u003cli\u003eAbdalla SM, Yu S, Galea S. Trends in cardiovascular disease prevalence by income level in the United States. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. 2020;3(9):e2018150.\u003c/li\u003e\n\u003cli\u003eWang X, et al. Mechanisms and Advances of Epigenetic Regulation in Cardiovascular Disease. \u003cem\u003eFront Biosci (Landmark Ed)\u003c/em\u003e. 2024.\u003c/li\u003e\n\u003cli\u003eEpigenetic Regulation of Cardiovascular Diseases Induced by Behavioral and Environmental Risk Factors: Mechanistic, Diagnostic, and Therapeutic Insights. \u003cem\u003ePMC\u003c/em\u003e. 2023.\u003c/li\u003e\n\u003cli\u003eBahado-Singh R, et al. Cell-free DNA in maternal blood and artificial intelligence: accurate prenatal detection of fetal congenital heart defects. \u003cem\u003eAm J Obstet Gynecol\u003c/em\u003e. 2024;228:76e71-76e81.\u003c/li\u003e\n\u003cli\u003eWang Y, et al. Genome-wide DNA methylation profiling in blood reveals epigenetic signature of incident acute coronary syndrome. \u003cem\u003eNat Commun\u003c/em\u003e. 2024;15:4567.\u003c/li\u003e\n\u003cli\u003ePastorino M, Campitelli M, Longo M, et al. DNA methylation in cardiovascular disease and heart failure: novel prediction models? \u003cem\u003eClinical Epigenetics\u003c/em\u003e. 2024;16:115.\u003c/li\u003e\n\u003cli\u003eRath S, Hawsawi YM, Alzahrani F, Khan MI. Epigenetic regulation of inflammation: The metabolomics connection. \u003cem\u003eSemin Cell Dev Biol\u003c/em\u003e. 2024;154:355-364.\u003c/li\u003e\n\u003cli\u003eLi W, Wang Y, Huang R, et al. Association of lipid metabolism-related gene promoter methylation with risk of coronary artery disease. \u003cem\u003eMol Biol Rep\u003c/em\u003e. 2022;49(10):9373\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eKeshawarz A, Bui H, Joehanes R, et al. Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study. \u003cem\u003eSci Rep\u003c/em\u003e. 2023;13:12952.\u003c/li\u003e\n\u003cli\u003eLu Y, Horvath S, et al. Meta-analyses of epigenetic age acceleration and GrimAge components of schizophrenia or first-episode psychosis. \u003cem\u003eNat Commun\u003c/em\u003e. 2024.\u003c/li\u003e\n\u003cli\u003eAnene-Nzelu CG, Stenzig J, Foo RS-Y. DNA methylation in heart failure. In: Translational Epigenetics. Elsevier; 2021:55-75. doi:10.1016/B978-0-12-822258-4.00016-X\u003c/li\u003e\n\u003cli\u003ePietrzak K, Foksinski M, Flisiak R, et al. The role of oxidative stress in the pathogenesis of non-alcoholic fatty liver disease - the impact on DNA methylation. \u003cem\u003eOxid Med Cell Longev\u003c/em\u003e. 2018;2018:6819374. doi:10.1155/2018/6819374.\u003c/li\u003e\n\u003cli\u003eMaugeri A, Barchitta M. How dietary factors affect DNA methylation: Lesson from epidemiological studies. \u003cem\u003eMedicina (Kaunas)\u003c/em\u003e. 2020;56(8):374. doi:10.3390/medicina56080374.\u003c/li\u003e\n\u003cli\u003eChoi SW, Friso S. Folate and DNA methylation: a mechanistic link between folate deficiency and colorectal cancer? \u003cem\u003eCancer Epidemiol Biomarkers Prev\u003c/em\u003e. 2004;13(4):511-519.\u003c/li\u003e\n\u003cli\u003eLiu R, Zhao E, Yu H, et al. Methylation across the central dogma in health and diseases: new therapeutic strategies. \u003cem\u003eSig Transduct Target Ther\u003c/em\u003e. 2023;8:310.\u003c/li\u003e\n\u003cli\u003eWu D, et al. Glucose-regulated phosphorylation of TET2 by AMPK reveals a pathway linking diabetes to cancer. \u003cem\u003eNature\u003c/em\u003e. 2018;559:637\u0026ndash;641.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"DNA methylation, Tissue inhibitor metalloproteinase 1, Cardiovascular disease, Mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-6427282/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6427282/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTIMP1(tissue inhibitor metalloproteinase 1 ) represents a tissue metalloprotease that modulates the degradation of the extracellular matrix by inhibiting the activity of matrix metalloproteinases. Furthermore, it has the capacity to activate signaling pathways that lead to an overproduction of collagen, thereby enhancing extracellular matrix synthesis through the interaction between surface CD63 and integrin beta1 on cardiac fibroblasts, which results in increased ventricular stiffness, fosters cardiac fibrosis, and ultimately contributes to a decline in relaxation performance. DNA methylation constitutes an epigenetic modification. Alterations in the methylation landscape considerably influence gene expression changes associated with cardiac architecture and functionality. Recently, an evaluation of TIMP1 based on DNA methylation, referred to as DNAmTIMP1, was utilized to assess TIMP1 tools. Our latest study revealed a strong link between DNAmTIMP1 and the likelihood of cardiovascular disease, along with continued mortality in individuals of middle and advanced age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe established a comprehensive, population-representative cohort derived from the National Health and Nutrition Examination Survey conducted between 1999 and 2002, which encompasses data pertaining to DNA methylation predicted tissue inhibitor metalloproteinase 1 used to predict the time of death for GrimAge and GrimAge2,which is used to predict human mortality risk and the rate of biological aging as an epigenetic biomarker based on DNA methylation[16]. By applying logistic regression and Cox proportional hazards models, we methodically examined the associations between DNAmTIMP1 and cardiovascular disease (CVD) risk, along with mortality outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study group was made up of 2466 individuals, with cardiovascular disease (CVD) having a weighted prevalence of 18.9%. Significantly, each one-kilobase increase in DNAmTIMP1 was linked to a 54% increase in the likelihood of developing CVD [odds ratio (OR): 1.54, 95% confidence interval (CI): 1.12–2.21, P \u0026lt;0.001]. Throughout a median follow-up duration of 207 months, there were 1309 recorded fatalities (53.1%), of which 345 cases (14.0%) were attributable to CVD.\u003c/p\u003e\n\u003cp\u003eParticipants exhibiting the longest DNAmTIMP1 demonstrated a 80% increasing in the risk of all-cause mortality (hazard ratio (HR): 1.80, 95% CI: 1.28–2.53, P \u0026lt; 0.001) and a 102% augment in mortality due to CVD (HR: 2.02, 95% CI: 1.05–3.90, P =0.035) when compared to those with the shortest DNAmTIMP1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides evidence of a significant positive association between DNAmTIMP1 levels and both cardiovascular disease (CVD) risk and mortality in a middle-aged to older US population. Higher DNAmTIMP1 levels correlate with an increased likelihood of CVD and a higher risk of all-cause and CVD-related mortality. These findings suggest that DNAmTIMP1 may serve as a valuable biomarker for CVD risk assessment and long-term mortality prediction. Further research is needed to elucidate the underlying mechanisms linking DNAmTIMP1 to CVD and mortality, and to explore potential interventions to improve cardiovascular outcomes and reduce mortality rates.\u003c/p\u003e","manuscriptTitle":"DNAmTIMP1 as a Predictor of Cardiovascular Disease and Mortality: A Prospective Analysis from the NHANES","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 10:37:30","doi":"10.21203/rs.3.rs-6427282/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"97257be1-25e8-41e4-a858-f31fd90e5cb5","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-26T08:09:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-19 10:37:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6427282","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6427282","identity":"rs-6427282","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}