Association between the dietary index for gut microbiota and heart failure: NHANES 2007-2018

Association between the dietary index for gut microbiota and heart failure: NHANES 2007-2018 | 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 Association between the dietary index for gut microbiota and heart failure: NHANES 2007-2018 Sihan Wang, Qi Cheng, Yingting Wu, Xinyue Gong, Ying Zhu, Kehui Xu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6861182/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 With the global incidence of heart failure (HF) on a continuous upward trend, greater attention has been placed on the part played by gut microbiota in this condition. The Dietary Index for Gut Microbiota (DI-GM) is an evidence-supported tool created to evaluate the influence of diet on gut microbiota. Nevertheless, the possible association between DI-GM and the risk of HF demands more in-depth exploration. This study aimed to examine the relationship between DI-GM and the risk of HF while also assessing its capability to forecast the occurrence and progression of the disease. Methods This study encompassed 30,349 people aged 20 years or above. The participants were sourced from the National Health and Nutrition Examination Survey (NHANES) database covering the period from 2007 to 2018. To evaluate the association between the DI-GM and the risk of HF, several statistical techniques were utilized. These techniques included weighted multivariable logistic regression, restricted cubic splines (RCS), threshold effect evaluation, and subgroup analysis. Additionally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression approach was applied to pinpoint covariates associated with the risk of HF. To gauge the efficacy of the nomogram model, receiver operating characteristic (ROC) curves were used for the evaluation. Results After accounting for all confounding variables, a negative association was discovered between the DI-GM and the risk of HF. This negative correlation was more evident in the cohort with a high DI-GM value (OR = 0.78, 95% CI: 0.64–0.96, P < 0.05). An analysis using RCS showed a significant non-linear negative relationship between DI-GM and the risk of HF ( P -nonlinearity = 0.030). A scrutiny of the threshold effect posited that the safeguarding influence of DI-GM reached a stable condition once the score went beyond 2.00. The forecasting model, chosen via LASSO regression, exhibited robust discriminatory ability. It achieved an area under the curve (AUC) of 0.891 (95% CI: 0.881-0.900). Conclusion Elevated DI-GM scores are linked to a decreased incidence of HF. Maintaining a DI-GM score of 2 or higher can improve the efficacy of HF prevention. Dietary index for gut microbiota Heart failure RCS NHANES LASSO Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Heart failure (HF) is the end-stage manifestation of cardiovascular disorders. It is characterized by elevated rates of occurrence, impairment, and fatality ( 1 ). Currently, approximately 64 million people worldwide suffer from HF. With the global population aging and an increase in risk factors, the prevalence of HF continues to rise each year ( 2 , 3 ). A large-scale study conducted on the Chinese population revealed a stepwise increase in the prevalence of HF with age: 0.57% in individuals aged 25–64, a significant rise to 3.86% in those aged 65–79, and as high as 7.55% in those aged 80 and above. In the United States, the current number of HF patients exceeds 6 million, and by 2030, this number is projected to increase by 33%, surpassing 8 million cases ( 4 , 5 ). In addition, over the past several decades, there has been a notable increase in the medical expenses related to HF. This considerable rise has imposed a considerable financial strain on both individuals and society as a whole ( 6 ). Over the past few years, the link between the gut microbiome and cardiovascular disorders has come to the forefront as a crucial research domain. Recent evidence shows that HF patients often exhibit reduced gut microbiota diversity and an increased abundance of opportunistic pathogens. This dysbiosis is closely associated with heightened systemic inflammation, accelerated myocardial fibrosis, and overactivation of the neuroendocrine system ( 7 , 8 ). The underlying mechanism primarily involves the "gut-heart axis." First, gut barrier dysfunction leads to the translocation of endotoxins into the bloodstream, triggering systemic low-grade inflammation that exacerbates myocardial cell damage and cardiac remodeling. Second, microbiota metabolites, such as trimethylamine-N-oxide (TMAO), can promote atherosclerosis by regulating cholesterol metabolism and platelet activity. Moreover, a decrease in certain bacteria responsible for generating short-chain fatty acids (SCFAs) could potentially undermine the energy provision and anti-apoptotic safeguard these bacteria provide to myocardial cells ( 9 – 11 ). Notably, HF-induced gut congestion and hypoxia create a vicious cycle that further exacerbates dysbiosis and metabolic abnormalities ( 12 ). Consequently, modulating the balance of gut microbiota may emerge as a new therapeutic objective for the prevention and management of HF. In 2016, after conducting a literature review, Kase's research group created a new dietary evaluation tool named The Dietary Index for Gut Microbiota (DI-GM) ( 13 ). This tool was purpose-built to assess the possible regulatory impacts of diet on the gut microbiota. DI-GM is calculated by integrating dietary data and microbiome analysis, using statistical models or machine learning to identify key dietary-microbiota associations. Foods are assigned positive or negative weights based on their impact on beneficial or pathogenic bacteria, generating a composite score. A high DI-GM diet is typically rich in fiber, polyphenols, and fermented foods, which are linked to increased microbiota diversity and a higher abundance of anti-inflammatory bacteria. In contrast, a low DI-GM score is associated with processed foods, high sugar and fat diets, and the proliferation of pro-inflammatory bacteria. This particular index has shown significant significance in the areas of clinical investigation, public health projects, and customized dietary interventions. Nonetheless, research on the connection between DI-GM and the risk of HF remains scarce. Revealing the fundamental relationship between DI-GM and the risk of HF can offer novel biological perspectives and clinical proof about how metabolites of gut microbiota affect cardiac compensatory mechanisms. Against this backdrop, our research intends to utilize data from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2007 to 2018. Our objectives are to comprehensively examine the relationship between DI-GM and the risk of HF, assess the ability of DI-GM to predict HF, and construct a risk prediction model. 2. Methods 2.1 Data Source The NHANES is a long-term national health monitoring program in the United States that collects representative samples from the non-institutionalized population. Participants undergo a thorough physical examination, health and nutrition surveys, and laboratory tests. Researchers can apply for access to the database to support various health-related studies and inform policy development ( 14 ). 2.2 Study population This research employed data sourced from the NHANES database covering the period from 2007 to 2018. In total, the dataset consisted of 59,842 participants. Participants who were minors, that is, those below 20 years old, were removed from the sample. Additionally, due to the lack of available data for DI-GM and HF, 3,981 and 81 participants respectively were not incorporated into the analysis. Participants with incomplete information regarding body mass index (BMI) (n = 316), educational level (n = 29), or marital status (n = 11) were also excluded. After these exclusions, 30,349 participants were left for analysis, as depicted in Fig. 1 . 2.3 Assessment of the dietary index for gut microbiota In the context of the NHANES initiative, dietary information was gathered via the 24-hour recall approach, namely the Automated Multiple-pass Method (AMPM). This method was devised by the United States Department of Agriculture (USDA) ( 15 ). To minimize the recall bias stemming from both interviewers and participants, interviewers were given standardized training. Moreover, standardized procedures and instruments were employed during the data collection process ( 16 ). DI-GM is a recently devised tool for assessing dietary quality. It offers a way to gauge the impact of dietary patterns on gut health and the makeup of the microbiome ( 13 ). The framework of this instrument is founded on 14 particular foods or nutrients. Among the beneficial components of the diet are avocados, broccoli, chickpeas, coffee, cranberries, fermented dairy products, fiber, green tea (NHANES lacks explicit green tea consumption data, hence excluded from this study's statistical analysis), soybeans, and whole grains. Conversely, the detrimental elements consist of red meat, processed meats, refined grains, and high-fat diets where fat accounts for ≥ 40% of the total energy intake. The assessment of dietary components relies on gender-specific medians. Based on prior studies, DI-GM scores were classified into four distinct categories: 0 to 3 points, 4 points, 5 points, and 6 points or more ( 17 ). More detailed information on DI-GM can be found in Supplementary Table S1 . 2.4 Definition of heart failure The assessment of HF was conducted by healthcare professionals through the NHANES MCQ questionnaire, asking participants “Has anyone ever told you had congestive HF?” to confirm whether they have the condition ( 18 ). 2.5 Covariates Based on previous studies ( 19 – 21 ), several potential confounders were selected for inclusion in the research, which are categorized into three main groups: demographic baseline data, laboratory test data, and health status. The basic demographic information encompasses various components. These include gender, age ranges (below 40 years, 40 to 60 years, and 60 years and above), race (Mexican American, non-Hispanic White, non-Hispanic Black, other Hispanic, and other races), educational level (less than high school, high school or equivalent, and college or higher), marital status (married or living with a partner, widowed, divorced or separated, and never married), and the poverty-to-income ratio (PIR) (below 1.30, between 1.30 and 3.50, and 3.50 and higher. A lower PIR suggests a less favorable family financial state) ( 22 ). Laboratory examination data include waist circumference (WC, measured in centimeter (cm)), BMI (computed as kilograms divided by the square of meters and categorized into intervals: below 25, 25 to 30, and 30 and above), total cholesterol (TC, quantified in millimoles per liter), and high-density lipoprotein cholesterol (HDL-C, also quantified in millimoles per liter). In health-related studies, several factors can introduce confounding variables. These factors encompass smoking status, alcohol consumption, diabetes, physical activity, high blood pressure, and coronary heart disease (CHD) ( 18 , 23 ). Smoking behaviors can be divided into three distinct groups. Non-smokers are those who have smoked fewer than 100 cigarettes throughout their lives. Former smokers are individuals who had smoked more than 100 cigarettes previously but have since quit. Current smokers are people who have smoked more than 100 cigarettes and continue to smoke, either on an occasional or regular basis ( 24 ). The assessment of whether a participant can be considered a drinker depends on their alcohol consumption in the previous year. If the total amount of alcohol consumed throughout the past year reaches 12 drinks or more, the participant is labeled as a "drinker ( 25 )". Physical activity is defined as a categorical variable. An affirmative response implies that the person participated in moderate-intensity physical exercise within the last 30 days, leading to a minor elevation in either the respiration rate or the heart rate ( 23 ). The diagnosis of CHD was based on self-report in the MCQ questionnaire. The definition of hypertension is adopted in the following three ways: 1) During the questionnaire interview, professional staff ask the survey respondents, "Have you ever been informed by medical staff that you have hypertension?" Individuals who answer "yes" are defined as the population with hypertension; 2) Survey respondents who are currently taking antihypertensive medications; 3) A participant having a resting systolic blood pressure of 140 mmHg or higher, or a diastolic blood pressure of 90 mmHg or greater ( 26 ). Diabetes was defined according to three criteria: 1) Self-reported diabetes diagnosis; 2) Use of antidiabetic medications (excluding insulin-sensitizing agents) or insulin therapy; 3) Meeting any of the following biochemical thresholds: Glycated hemoglobin (HbA1c) ≥ 6.5%; Fasting blood glucose (FBG) ≥ 7.0 mmol/L; 2-hour postprandial glucose (2hPG) ≥ 11.1 mmol/L. Prediabetes was defined as: 1) HbA1c 5.7%-6.4%; 2) FBG 5.6–6.9 mmol/L or 2hPG 7.8–11.0 mmol/L ( 27 ). 2.6 Statistical analysis Since the NHANES project employs a weighted sampling approach, in our analysis, we utilized one-sixth of the sample weight related to the dietary intake on the first day (WTDRD1). When handling missing data, we applied the multiple imputation with chained equations (MICE) technique ( 15 ). In the course of the statistical analysis, the participants were divided into four distinct groups according to their DI-GM levels. For variables of a continuous nature, we carried out weighted t-tests. When it came to categorical variables, weighted chi-square tests were employed. Continuous variables are presented as mean values along with standard errors (SE), whereas categorical variables are shown as percentages. In order to examine the link between DI-GM and HF, we conducted a weighted multivariable logistic regression analysis. We utilized three different models to assess this connection. Model 1, the initial model, did not incorporate any adjustments for covariates. In contrast, Model 2, the subsequent model, was calibrated to account for demographic variables such as gender, years of age, and race. Conversely, Model 3 was further adjusted for a variety of confounding factors. These factors encompassed educational level, marital status, PIR, BMI, WC, smoking status, alcohol consumption, TC, diabetes, hypertension, CHD, and physical activity. The connection between DI-GM and HF was evaluated using odds ratios (OR) and 95% confidence intervals (CI). For every model, these measurements were calculated to gauge the strength and statistical significance of the relationship. We utilized restricted cubic splines (RCS) to explore the non-linear correlation between DI-GM and HF. After finishing the RCS analysis, we conducted a threshold effect assessment on the results. The objective of this assessment is to identify possible thresholds or particular turning points at which the relationship between DI-GM and HF changes. This provides valuable insights into how fluctuations in DI-GM levels can have distinct impacts on the probability of developing HF among various subgroups in the population. Additionally, we scrutinized every individual dietary component within the DI-GM to evaluate its unique association with the risk of HF. To investigate differences among groups, we conducted subgroup analyses and tests for interaction impacts. In the end, we made use of the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify relevant variables for building the predictive model. After that, several methods were applied to evaluate the performance of the predictive model. Receiver operating characteristic (ROC) curves: To evaluate the model's capacity to distinguish between HF and non-HF instances, we utilized Decision Curve Analysis (DCA). This method examines the clinical utility of the model by scrutinizing the net benefit of making decisions based on its predictions at different threshold probabilities. To ensure the reliability of the model, calibration curves were utilized to assess the extent to which the predicted probabilities align with the real-world observed outcomes. Nomograms were utilized to make the model's results visible. These instruments offered a graphical representation of the predictive elements that was easily comprehensible. The entirety of the statistical analyses was carried out utilizing the R software (version 4.3.3). 3. Results 3.1 Characteristics of the study population This study included 30,349 participants, with 966 diagnosed with HF, as shown in Table 1. Participants were categorized based on four DI-GM levels. The findings of the research indicated that as the scores of the DI-GM rose, there was a marked elevation in the mean age of the participants in the study and their HDL-C levels. In contrast, the WC demonstrated a declining trend. Nevertheless, there was no substantial disparity in TC levels among the various groups ( P = 0.152). Remarkably, both the DI-GM score and the measures associated with the advantages and disadvantages of gut microbiota displayed significant variations across the groups ( P < 0.001). Except for CHD, Significant differences were detected among the groups in terms of gender, race, educational level, marital status, PIR, BMI, and health-related measures. Table 1 should appear here; due to its length exceeding A4 size, it is provided at the end of the manuscript. 3.2 Association Between DI-GM and HF This research employed weighted logistic regression to examine the association between the DI-GM and the risk of HF. The results were presented in Table 2. In the unadjusted model (Model 1), for every single-unit increment in the DI-GM, the risk of HF decreased by 7% (OR = 0.93, 95% CI: 0.89-0.96, P < 0.001). In Model 2, once age, gender, and race were factored in, a single-unit increase in DI-GM was linked to approximately an 11% reduction in the likelihood of HF (OR = 0.89, 95% CI: 0.86-0.99, P < 0.001). In Model 3, when further adjustments were made for other confounding factors, the inverse relationship between DI-GM and the risk of HF still held statistical significance (OR = 0.94, 95% CI: 0.90-0.98, P < 0.05). These results implied that even when multiple variables were accounted for, DI-GM served as an independent safeguard against HF. After the initial data gathering, an in-depth analysis was carried out. This involved categorizing the DI-GM. The group of participants whose DI-GM score was between 0 and 3 was set as the reference cohort. When the DI-GM score reached 6 or higher, there was a notable reduction in the risk of HF (OR = 0.78, 95% CI: 0.64-0.96, P < 0.05). This discovery suggested that higher DI-GM scores offered a stronger protective influence against HF. Additionally, the trend test produced statistically significant outcomes across all the analytical frameworks. Subsequent exploration of dietary elements that exerted either favorable or unfavorable effects on gut microbiota yielded the following findings. Regarding the beneficial constituents, dietary fiber and whole grains were notably linked to a decreased likelihood of HF (OR = 0.98, 95% CI: 0.98-0.99, P < 0.001; OR = 0.92, 95% CI: 0.87-0.98, P < 0.05). Upon initial inspection, avocados, broccoli, chickpeas, and fermented dairy products appeared to confer protective advantages. However, after accounting for confounding factors, no substantial correlations were detected, and the magnitudes of the effects were negligible. There was a modest association between coffee consumption and a decreased likelihood of HF, yet the protective effect was quite small. In each model, cranberries and soybeans consistently produced an OR of 1.00, along with non-significant P -values. This suggested that there was no distinct connection between these two food items and the risk of HF. When taking into account adverse factors, within specific models, only processed meat was associated with a heightened risk of HF (OR = 1.06, 95% CI: 1.02-1.10, P < 0.05). On the other hand, refined grains and red meat did not show any significant relationship with the risk of HF. Conversely, a diet rich in fat had an OR of 0.99 across all three models, along with statistically significant P -values. (Supplementary Table S2). 3.3 Dose-Response Relationship Between DI-GM and HF Risk Following the adjustment for all confounding variables, we utilized RCS to explore the possible non-linear association between DI-GM and the risk of HF. The outcomes indicated a notable non-linear inverse link between DI-GM and the risk of HF ( P -nonlinearity = 0.030) (Figure 2). This discovery motivated us to conduct a threshold effect analysis. A segmented logistic regression model (Model 2) was utilized to determine the turning point of DI-GM, which was found to be 2.00. Specifically, when the DI-GM value was 2.00 or lower, a one-unit increment in DI-GM led to a notable reduction in the likelihood of HF (OR = 0.672, 95% CI: 0.489-0.950, P < 0.05). Conversely, when the DI-GM value exceeded 2.00, the relationship between DI-GM and HF was less pronounced (OR = 0.953, 95% CI: 0.908-0.999, P < 0.05). Moreover, the likelihood ratio test demonstrated a significant disparity between the two-segment model and the standard model ( P < 0.05). This implied that the segmented model offered a superior fit for elucidating the relationship between DI-GM and HF. In the process of model analysis, all potential confounding factors were adequately considered, guaranteeing that the outcomes were both trustworthy and stable (Table 3). 3. 4 Subgroup Analysis To conduct a more in-depth exploration of the disparities in the connection between the DI-GM and the risk of HF among different groups, we carried out subgroup and interaction analyses. We sorted the data according to age, BMI, PIR, race, smoking status, alcohol consumption, CHD, hypertension, and diabetes. After taking into account all relevant confounding variables, we found that there was a notable inverse correlation between DI-GM and the risk of HF in several groups. These groups included individuals aged 60 years or older, those with a BMI of 30 or higher, non-smokers, non-drinkers, and people without diabetes ( P < 0.05). Moreover, as shown in Figure 3, a significant interaction was detected among the subgroups classified by smoking status ( P -interaction < 0.05). 3. 5 LASSO (Least Absolute Shrinkage and Selection Operator) Regression and Nomogram Model In order to construct the most effective prediction model, we utilized LASSO regression for the purpose of feature selection. Figure 4A and 4B depict the distribution of coefficients from the LASSO regression and the outcomes of cross-validation. The dashed line on the left shows the minimum value of λ (λ min ), and the dashed line on the right indicates the standard error of λ (λ SE ). Using λ SE (log(λ SE ) = -5.3504) as a basis, we chose nine variables—age, marital status, PIR, BMI, TC, smoking status, hypertension, diabetes, and CHD—to be incorporated into the nomogram model (Supplementary Table S3). The final prediction nomogram was developed by combining the results of the LASSO analysis with the DI-GM. As depicted in Figure 5A, the red dot serves as an example: an unmarried 22-year-old individual free from CHD, diabetes, and hypertension, who has never smoked, has a TC level of 5 mmol/L, a BMI of 23 kg/m 2 , a moderate degree of PIR, and a DI-GM score of 8. Under these circumstances, the likelihood of developing HF is just 1.2%. Subsequently, we assessed the nomogram model using ROC analysis. When contrasted with the standalone use of the DI-GM, the integrated model exhibited greater discriminatory ability. The area under the curve (AUC) was calculated to be 0.891 (95% CI: 0.881-0.900). Moreover, it had a sensitivity of 79.4% and a specificity of 81.9% (as shown in Figure 5B and Supplementary Table S4). The calibration plot indicated that the results of the model's training and validation were highly consistent with the optimal reference model (Supplementary Figure S1). Moreover, DCA demonstrated that the prediction model yields significant clinical advantages within the anticipated probability intervals (Supplementary Figure S2). These results further confirmed the robust predictive capacity of the model in identifying patients suffering from HF. 4. Discussion In the present research, we utilized the NHANES 2007-2018 dataset to examine data from 30,349 residents of the United States. Our aim was to explore the association between the DI-GM and the risk of HF. The main results are presented below: 1) As the DI-GM scores went up, the incidence of HF among the participants declined; 2) After adjusting for all potential confounders, an inverse association was discovered between the DI-GM and the risk of HF. This connection was more prominent among groups that had higher DI-GM scores; 3) It was determined that dietary fiber and whole-grain products are the main dietary elements linked to a decreased likelihood of developing HF; 4) Through the application of RCS and piecewise logistic regression, it was found that when the DI-GM was 2.00 or less, there was a significant inverse relationship with the risk of HF. This relationship weakened once the DI-GM exceeded 2.00; 5) A clear inverse relationship was detected between DI-GM and the risk of HF among particular populations. These populations included people aged 60 years or older, those having a BMI of 30 or higher, non-smokers, non-drinkers, and individuals without diabetes. Furthermore, smoking status significantly interacted with this relationship; 6) The prediction model developed in this study, based on DI-GM and selected feature variables, demonstrated strong diagnostic capabilities in identifying HF risk. To date, no published research has delved into the connection between DI-GM and the risk of HF. Our research is the first to assess this relationship, and its results align with prior studies on the link between gut microbiota and the risk of HF. For instance, a research carried out by Kai Luo and his team showed that among Hispanic/Latino adults in the United States, left ventricular diastolic dysfunction (LVDD), an early sign of HF, was related to a decrease in beneficial gut bacteria and an uptick in potentially detrimental bacteria. Additionally, various gut microbiota-related metabolites were closely associated with LVDD, enhancing its predictive ability to some extent (28). Anna L Beale and colleagues also found that, compared to healthy individuals, HF patients had significantly lower gut microbiota diversity and richness (29). A review of multiple studies on the gut microbiota in HF patients reported a general decline in gut microbiota richness and notable changes in its composition. In particular, a decrease in bacteria responsible for generating SCFAs could heighten inflammatory reactions. Subsequently, this speeds up the advancement of HF (30). Additionally, a multitude of animal research studies validated these results (31-33). To sum up, our research offers considerable empirical data regarding the influence of gut microbiome variety on the likelihood of developing HF. It highlights the crucial role that the gut microbiome plays in both the initiation and progression of HF. Dietary patterns, a vital element in regulating the gut microbiome, have the potential to alter the makeup and metabolic capabilities of gut microorganisms. This phenomenon occurs due to disparities in the kinds and ratios of nutrients ingested (34). Our study supports this idea, suggesting that changes in dietary structure can help reduce the risk of HF. Notably, we observed that certain dietary components, which are typically considered unfavorable to the gut microbiota, such as high-fat diets, showed an association between higher scores and a reduced risk of HF. Several factors may explain this counterintuitive finding: First, a "high-fat diet" is not synonymous with unhealthy fat intake alone. Certain high-fat foods, such as those containing monounsaturated and polyunsaturated fats, can also provide cardiovascular-protective nutrients (35, 36). Second, individuals with existing cardiovascular risk may have already modified their diet (e.g., by reducing fat intake), whereas those without such risks may consume high-fat foods more casually. This reverse causality could explain the observed unexpected result. Lastly, while the P -value is statistically significant, the change in the OR is minimal (only 0.99). Although this may reach statistical significance in large samples, the actual clinical impact might be limited, highlighting the importance of considering the practical range and magnitude of score changes. Among the beneficial components, only dietary fiber and whole grains showed a significant association with a reduced risk of HF. This finding may be influenced by the following factors: First, individual beneficial foods are often closely linked to an overall healthy dietary pattern. Isolating a single component may not fully capture its effect within the broader dietary context. The true protective effect may depend on the synergistic interaction of multiple components rather than any single factor (37). Second, the DI-GM score derived from 24-hour dietary recall data may be prone to measurement errors or recall bias, which could potentially dilute the true association. The main pathophysiological mechanisms of HF include myocardial remodeling, excessive activation of the neuroendocrine system, and chronic inflammation, which collectively lead to the progressive deterioration of cardiac pumping function. The DI-GM scoring mechanism assesses the makeup of the gut microbiota along with its metabolic by-products. This assessment serves as an indicator for a possible link with cardiac performance. It is quite possible that there are multiple mechanisms that account for the connection between DI-GM and the probability of developing HF. Research suggests that gut microbiota dysbiosis can influence disease progression through a bidirectional "gut-heart axis." On one hand, reduced gut microbiota diversity impairs the intestinal barrier, promoting the translocation of endotoxins into the bloodstream. This activates the Toll-like receptor 4 (TLR4) signaling pathway, triggering systemic inflammation that exacerbates myocardial cell apoptosis and fibrosis. On the other hand, TMAO, a metabolite generated by the microbiota, speeds up the advancement of coronary atherosclerosis. It does so by disrupting the reverse cholesterol transport mechanism and facilitating the creation of foam cells. TMAO also directly inhibits myocardial mitochondrial energy metabolism, reducing cardiac contractile reserve (38, 39). In addition, specific microbial metabolites have dual regulatory effects on cardiac function. SCFAs activate G protein-coupled receptors (GPR41/43), which help inhibit the overactivation of the renin-angiotensin-aldosterone system, thereby reducing cardiac afterload (40). Experimental studies have confirmed that butyrate improves intestinal barrier function, reduces microglial-mediated inflammation, and modulates N-methyl-D-aspartic acid (NMDA) receptor activity in the paraventricular nucleus (PVN). These effects help reduce sympathetic overactivation in HF, improving cardiac function (41). Secondary bile acids, such as lithocholic acid, regulate myocardial energy metabolism via the farnesoid X receptor (FXR), enhancing fatty acid oxidation efficiency (42). Furthermore, recent studies have identified that phenylacetylglutamine (PAGln) exacerbates HF by increasing sympathetic nervous system activity, interfering with calcium ion signaling, inducing inflammation and cardiac fibrosis, and increasing oxidative stress. These combined effects negatively impact myocardial cells and cardiac structure, driving the onset and progression of HF (43). The outcomes of this study have substantial clinical implications. Firstly, the RCS results demonstrate that DI-GM scores have a non-linear negative correlation with HF risk, with the RCS curve showing steeper changes in certain DI-GM ranges. This is likely due to the RCS model fitting the data using cubic splines, which results in different slopes for various intervals. The distribution of DI-GM in the sample may cause denser values in some intervals and sparser values in others. In denser areas, the sample distribution provides more information, leading to a more pronounced reduction in risk, whereas in sparser areas, the curve changes more gradually due to fewer data points (44). Secondly, additional piecewise regression analysis revealed that elevated DI-GM scores were linked to a decreased likelihood of HF. The protective impact was more evident when the DI-GM score was under 2.00 and weakened as the score went above 2.00. This discovery offers crucial empirical support for future research on the function of dietary interventions in preventing cardiovascular ailments. Thirdly, an examination of the advantageous elements of gut microbiota uncovered a notable link between the intake of dietary fiber and whole grains and a reduced risk of HF. This implies that those afflicted with HF might be able to enhance their prognosis by boosting their consumption of dietary fiber and whole grains. Finally, the prediction model developed in this study shows potential for identifying individuals at risk of HF before clinical symptoms appear, offering more targeted intervention strategies for clinical practice. This study has both strengths and limitations. To begin with, the data utilized in this research were obtained from the NHANES database. This database has a substantial sample size, which greatly improves the representativeness of the study's results. Second, this study used LASSO regression analysis, and the feature variables selected to construct a prediction model, combined with DI-GM, demonstrated good diagnostic ability in identifying HF risk. However, this study has several limitations. First, as NHANES is a survey based on sampling, its generalizability may be hindered by selection bias. Second, the inherent limitations of DI-GM itself must also be recognized. The calculation logic of DI-GM may need to be revalidated or adjusted as nutritional intake standards or policies evolve, which could affect its timeliness. Furthermore, considering the significant individual variation in daily dietary intake, using a 24-hour dietary recall as an assessment tool may not fully capture actual nutritional intake. To improve the timeliness and applicability of DI-GM, future studies could integrate machine learning or big data analysis techniques to develop a prediction model capable of dynamically updating and adapting to changes in nutritional standards or policies over time. Moreover, advanced technologies such as molecular biology and genomics should be incorporated to further explore the biological mechanisms linking dietary nutritional factors to the development of HF. This could ultimately translate research findings into personalized nutrition interventions and precise policy recommendations. 5. Conclusion In general, this research discovered a notable non-linear inverse relationship between higher DI-GM scores and the risk of HF. More precisely, when the DI-GM score was 2.00 or below, a one-unit increment in the DI-GM score was linked to a substantial decrease in the risk of HF. However, once the DI-GM score went beyond 2.00, the extent of risk reduction became less pronounced. This suggested that DI-GM had a stronger protective effect against HF in lower ranges, with this effected diminishing in higher ranges. Additionally, our model demonstrated the ability to identify individuals at risk of HF before clinical symptoms appear, offering more targeted intervention opportunities for clinical practice. Modifying dietary patterns to maintain gut microbiota diversity may offer new therapeutic targets for preventing and improving the prognosis of HF patients. Subsequent research ought to conduct a more all-encompassing investigation into the particular mechanisms by which the gut microbiota impacts HF. By utilizing metabolomics and metagenomic sequencing technologies, future studies could precisely identify key microbial metabolites and their associated signaling pathways involved in HF development. Abbreviations DI-GM dietary index for gut microbiota PIR poverty income ratio BMI body mass index TC total cholesterol CHD coronary heart disease. Declarations Ethics approval and consent to participate This research project obtained approval from the Ethics Review Board of the National Center for Health Statistics. Additionally, all participants were obligated to furnish written informed consent prior to their involvement. We confirm that the manuscript has been read and approved by all named authors. We further confirm that the order of authors listed in the manuscript has been approved by all of us. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and materials The data used in this study are all from the publicly available data of the National Health and Nutrition Examination Survey, which can be obtained here: https://www.cdc.gov/nchs/nhanes/index.htm. Competing interests The authors declare no competing interests. Funding This research was supported by the Key Project of Humanities and Social Sciences Research in Anhui Provincial Universities (2023AH050722), Provincial Quality Engineering Project for Educating People in the New Era in Anhui Province (2024szsfkc095) and Teaching Research Project of Anhui Provincial Quality Engineering (2021jyxm0797). Authors' contributions S.H.W.: Writing original draft; Writing - review & editing; Data Curation; Data analysis and visualization. Q.C.: Writing original draft; Writing - review & editing. Y.T.W.: Methodology; Writing - review & editing. X.Y.G.: Validation; Methodology. Y.Z.: Validation; Methodology. K.H.X.: Writing - review; Validation. L.H.: Funding acquisition; Supervision. J.C.: Project administration; Funding acquisition. All authors read and approved the final manuscript. Acknowledgements We express heartfelt gratitude to the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC) for creating and conducting the NHANES and making the data publicly available. Additionally, we would like to thank all team members for their invaluable contributions to this article. References Katagiri M, Yamada S, Katoh M, Ko T, Ito M, Komuro I. Heart failure pathogenesis elucidation and new treatment method development. 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Desquilbet L, Mariotti F. Dose-response analyses using restricted cubic spline functions in public health research. Stat Med. 2010;29(9):1037–57. 10.1002/sim.3841 . Tables Table 1 Baseline characteristics of subjects by DI-GM in NHANES 2007-2018. Characteristic DI-GM P -value 1 (0-3 score) 2 (4 score) 3 (5 score) 4 (≥ 6 score) Age (years) 48.08 ± 17.63 48.23 ± 17.75 49.61 ± 17.66 51.92 ± 17.27 < 0.001 Waist circumference (cm) 101.09 ± 16.88 99.66 ± 16.35 99.25 ± 15.85 98.38 ± 15.51 < 0.001 TC (mmol/L) 4.98 ± 1.05 4.96 ± 1.04 4.99 ± 1.07 5.00 ± 1.05 0.152 HDL-C (mmol/L) 1.34 ± 0.40 1.35 ± 0.40 1.37 ± 0.40 1.41 ± 0.42 < 0.001 DI-GM 2.54 ± 0.68 4.00 ± 0.00 5.00 ± 0.00 6.62 ± 0.84 < 0.001 Beneficial to gut microbiota 0.87 ± 0.84 1.37 ± 0.97 2.11 ± 0.88 3.37 ± 0.99 < 0.001 Unfavorable to gut microbiota 1.67 ± 0.93 2.63 ± 0.97 2.89 ± 0.88 3.26 ± 0.75 < 0.001 Age, n (%) < 0.001 < 40 2742 (26.91) 2367 (23.23) 2321 (22.77) 2761 (27.09) 40-60 2377 (23.71) 2871 (28.63) 2343 (23.37) 2436 (24.29) ≥ 60 2255 (22.26) 3218 (31.76) 2349 (23.19) 2309 (22.79) Gender, n (%) < 0.001 Male 3850 (26.12) 3704 (25.13) 3350 (22.73) 3836 (26.02) Female 3524 (22.58) 3802 (24.36) 3663 (23.47) 4620 (29.60) Race/ethnicity, n (%) < 0.001 Mexican American 1045 (22.97) 1287 (28.29) 1144 (25.14) 1074 (23.60) Other Hispanic 808 (25.53) 853 (26.95) 720 (22.75) 784 (24.77) Non-Hispanic White 2821 (22.34) 2861 (22.65) 2920 (23.12) 4027 (31.89) Non-Hispanic Black 1999 (30.60) 1725 (26.41) 1441 (22.06) 1367 (20.93) Other Races 701 (20.18) 780 (22.46) 788 (22.69) 1204 (34.67) Education level, n (%) < 0.001 Less than high school 1952 (26.63) 1581 (21.57) 1640 (22.38) 2156 (29.42) High school or equivalent 2026 (29.10) 1588 (22.81) 1604 (23.04) 1745 (25.06) College graduate or above 3396 (21.15) 5287 (32.93) 3769 (23.47) 3605 (22.45) Marital status, n (%) < 0.001 Married/Living with a partner 4188 (23.25) 5304 (29.45) 4219 (23.42) 4300 (23.87) Divorced/Separated/Widowed 1628 (24.22) 1892 (28.14) 1509 (22.45) 1694 (25.20) Never married 1558 (27.75) 1260 (22.44) 1285 (22.89) 1512 (26.93) PIR, n (%) < 0.001 < 1.3 2450 (27.70) 1927 (21.79) 2027 (22.92) 2441 (27.60) 1.3-3.5 3321 (25.23) 3557 (27.02) 2970 (22.56) 3315 (25.18) ≥ 3.5 1603 (19.22) 2972 (35.63) 2016 (24.17) 1750 (20.98) BMI, n (%) < 0.001 < 25 kg/m 2 1889 (21.96) 2634 (30.62) 1975 (22.96) 2103 (24.45) 25-30 kg/m 2 2319 (23.26) 2895 (29.04) 2329 (23.36) 2425 (24.33) ≥ 30 kg/m 2 3166 (26.88) 2927 (24.85) 2709 (23.00) 2978 (25.28) Smoking status, n (%) < 0.001 Never 3865 (22.87) 4879 (28.87) 3979 (23.54) 4177 (24.72) Former 1706 (23.42) 2280 (31.30) 1622 (22.27) 1676 (23.01) Current 1803 (29.25) 1297 (21.04) 1412 (22.90) 1653 (26.81) Alcohol consumption, n (%) 0.004 Yes 4239 (24.40) 4184 (24.08) 3997 (23.01) 4953 (28.51) No 3135 (24.16) 3322 (25.60) 3016 (23.24) 3503 (27.00) Diabetes, n (%) < 0.001 Yes 1466 (27.21) 1403 (26.04) 1180 (21.90) 1338 (24.84) Pre 2316 (23.56) 2803 (28.51) 2280 (23.19) 2433 (24.75) No 3592 (23.74) 4250 (28.09) 3553 (23.48) 3735 (24.69) Physical activity, n (%) 0.036 Yes 2768 (24.59) 2853 (25.34) 2602 (23.11) 3035 (26.96) No 4606 (24.13) 4653 (24.37) 4411 (23.11) 5421 (28.40) Hypertension, n (%) 0.013 Yes 3281 (25.23) 3177 (24.43) 2965 (22.80) 3583 (27.55) No 4093 (23.60) 4329 (24.96) 4048 (23.34) 4873 (28.10) CHD, n (%) 0.259 Yes 298 (24.39) 274 (22.42) 297 (24.30) 353 (28.89) No 7076 (24.29) 7232 (24.83) 6716 (23.06) 8103 (27.82) Heart failure, n (%) 0.003 Yes 262 (27.12) 243 (25.16) 241 (24.95) 220 (22.77) No 7112 (24.20) 7263 (24.72) 6772 (23.05) 8236 (28.03) Continuous variables: Values are expressed as mean ± standard error. Categorical variables: Values are expressed as numbers (percentage). Abbreviations: TC, total cholesterol; HDL-C, high density lipoprotein cholesterol; DI-GM, dietary index for gut microbiota; PIR, poverty income ratio; BMI, body mass index; CHD, coronary heart disease. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial1.docx Supplementary Material 1 (Figure S1) SupplementaryMaterial2.docx Supplementary Material 2 (Figure S2) SupplementaryMaterial3.docx Supplementary Material 3 (Table S1) SupplementaryMaterial4.docx Supplementary Material 4 (Table S2) SupplementaryMaterial5.docx Supplementary Material 5 (Table S3) SupplementaryMaterial6.docx Supplementary Material 6 (Table S4) 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. 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2","display":"","copyAsset":false,"role":"figure","size":197648,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic splines were used to evaluate the potential non-linear relationship with DI-GM of heart failure.\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6861182/v1/dfe50760423d50a85a875fe2.png"},{"id":88096697,"identity":"eada4451-bdd6-4cfe-9b0c-f117a888e220","added_by":"auto","created_at":"2025-08-01 10:58:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":480671,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis and interaction analysis of the relationship between DI-GM and heart failure.\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6861182/v1/b988324acaa0102231687377.png"},{"id":88093955,"identity":"b42113d7-aea6-45ad-9d35-f898f9d2a10e","added_by":"auto","created_at":"2025-08-01 10:42:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":189453,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO regression analysis was used to screen the factors most associated with heart failure. (A) Plot for LASSO regression coefficients; (B) Crossvalidation plot.\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-6861182/v1/8a3607fcd58012ae65b68eb6.png"},{"id":88096702,"identity":"b34058e3-5d02-4242-a921-fa2195c61918","added_by":"auto","created_at":"2025-08-01 10:58:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":310675,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram model established for predicting the risk of heart failure and its ROC curve. (A) The nomogram model was constructed based on the feature variables selected by LASSO regression and DI-GM, and the red points were examples; (B) ROC curve was used to evaluate the diagnostic efficacy of DI-GM and nomogram model in this study.\u003c/p\u003e","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-6861182/v1/826a350a0af8594c3fd9a6cc.png"},{"id":91498456,"identity":"d669c5d6-1def-423f-8cd0-c006dd27a9c9","added_by":"auto","created_at":"2025-09-17 06:54:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1475017,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6861182/v1/dc547211-fb74-4b26-ae80-0455d685caf5.pdf"},{"id":88093944,"identity":"0bf0796d-a16d-4e92-a342-ceb4575158a9","added_by":"auto","created_at":"2025-08-01 10:42:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":70207,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material 1 (Figure S1)\u003c/p\u003e","description":"","filename":"SupplementaryMaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6861182/v1/313c4be0959153a89ad9818c.docx"},{"id":88096698,"identity":"17b3aef4-44ad-44ec-b476-d4a7e3619c01","added_by":"auto","created_at":"2025-08-01 10:58:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":75471,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material 2 (Figure S2)\u003c/p\u003e","description":"","filename":"SupplementaryMaterial2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6861182/v1/9993c07a22e9319903c1bd97.docx"},{"id":88094801,"identity":"72dc600f-c38c-4231-9969-cbda187beeb0","added_by":"auto","created_at":"2025-08-01 10:50:52","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":28714,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material 3 (Table S1)\u003c/p\u003e","description":"","filename":"SupplementaryMaterial3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6861182/v1/0588a4fa41b54f94dad118cd.docx"},{"id":88094802,"identity":"f992380e-7a4c-43de-b104-0e62ed1d211f","added_by":"auto","created_at":"2025-08-01 10:50:52","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":46314,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material 4 (Table S2)\u003c/p\u003e","description":"","filename":"SupplementaryMaterial4.docx","url":"https://assets-eu.researchsquare.com/files/rs-6861182/v1/b1df4fe08b4ad566e432dd96.docx"},{"id":88093951,"identity":"f4d00f7a-7158-47ad-a7e9-83578734da46","added_by":"auto","created_at":"2025-08-01 10:42:52","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":22496,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material 5 (Table S3)\u003c/p\u003e","description":"","filename":"SupplementaryMaterial5.docx","url":"https://assets-eu.researchsquare.com/files/rs-6861182/v1/8a2645b46a6b7b59c72226c2.docx"},{"id":88094810,"identity":"6eda99f8-5fd7-41c1-b1d1-1121fb585ccc","added_by":"auto","created_at":"2025-08-01 10:50:53","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":20665,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material 6 (Table S4)\u003c/p\u003e","description":"","filename":"SupplementaryMaterial6.docx","url":"https://assets-eu.researchsquare.com/files/rs-6861182/v1/50b1cefcd78c99e80879857a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between the dietary index for gut microbiota and heart failure: NHANES 2007-2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHeart failure (HF) is the end-stage manifestation of cardiovascular disorders. It is characterized by elevated rates of occurrence, impairment, and fatality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Currently, approximately 64\u0026nbsp;million people worldwide suffer from HF. With the global population aging and an increase in risk factors, the prevalence of HF continues to rise each year (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). A large-scale study conducted on the Chinese population revealed a stepwise increase in the prevalence of HF with age: 0.57% in individuals aged 25\u0026ndash;64, a significant rise to 3.86% in those aged 65\u0026ndash;79, and as high as 7.55% in those aged 80 and above. In the United States, the current number of HF patients exceeds 6\u0026nbsp;million, and by 2030, this number is projected to increase by 33%, surpassing 8\u0026nbsp;million cases (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In addition, over the past several decades, there has been a notable increase in the medical expenses related to HF. This considerable rise has imposed a considerable financial strain on both individuals and society as a whole (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOver the past few years, the link between the gut microbiome and cardiovascular disorders has come to the forefront as a crucial research domain. Recent evidence shows that HF patients often exhibit reduced gut microbiota diversity and an increased abundance of opportunistic pathogens. This dysbiosis is closely associated with heightened systemic inflammation, accelerated myocardial fibrosis, and overactivation of the neuroendocrine system (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The underlying mechanism primarily involves the \"gut-heart axis.\" First, gut barrier dysfunction leads to the translocation of endotoxins into the bloodstream, triggering systemic low-grade inflammation that exacerbates myocardial cell damage and cardiac remodeling. Second, microbiota metabolites, such as trimethylamine-N-oxide (TMAO), can promote atherosclerosis by regulating cholesterol metabolism and platelet activity. Moreover, a decrease in certain bacteria responsible for generating short-chain fatty acids (SCFAs) could potentially undermine the energy provision and anti-apoptotic safeguard these bacteria provide to myocardial cells (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Notably, HF-induced gut congestion and hypoxia create a vicious cycle that further exacerbates dysbiosis and metabolic abnormalities (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Consequently, modulating the balance of gut microbiota may emerge as a new therapeutic objective for the prevention and management of HF. In 2016, after conducting a literature review, Kase's research group created a new dietary evaluation tool named The Dietary Index for Gut Microbiota (DI-GM) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This tool was purpose-built to assess the possible regulatory impacts of diet on the gut microbiota. DI-GM is calculated by integrating dietary data and microbiome analysis, using statistical models or machine learning to identify key dietary-microbiota associations. Foods are assigned positive or negative weights based on their impact on beneficial or pathogenic bacteria, generating a composite score. A high DI-GM diet is typically rich in fiber, polyphenols, and fermented foods, which are linked to increased microbiota diversity and a higher abundance of anti-inflammatory bacteria. In contrast, a low DI-GM score is associated with processed foods, high sugar and fat diets, and the proliferation of pro-inflammatory bacteria. This particular index has shown significant significance in the areas of clinical investigation, public health projects, and customized dietary interventions.\u003c/p\u003e\u003cp\u003eNonetheless, research on the connection between DI-GM and the risk of HF remains scarce. Revealing the fundamental relationship between DI-GM and the risk of HF can offer novel biological perspectives and clinical proof about how metabolites of gut microbiota affect cardiac compensatory mechanisms. Against this backdrop, our research intends to utilize data from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2007 to 2018. Our objectives are to comprehensively examine the relationship between DI-GM and the risk of HF, assess the ability of DI-GM to predict HF, and construct a risk prediction model.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Source\u003c/h2\u003e\u003cp\u003eThe NHANES is a long-term national health monitoring program in the United States that collects representative samples from the non-institutionalized population. Participants undergo a thorough physical examination, health and nutrition surveys, and laboratory tests. Researchers can apply for access to the database to support various health-related studies and inform policy development (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study population\u003c/h2\u003e\u003cp\u003eThis research employed data sourced from the NHANES database covering the period from 2007 to 2018. In total, the dataset consisted of 59,842 participants. Participants who were minors, that is, those below 20 years old, were removed from the sample. Additionally, due to the lack of available data for DI-GM and HF, 3,981 and 81 participants respectively were not incorporated into the analysis. Participants with incomplete information regarding body mass index (BMI) (n\u0026thinsp;=\u0026thinsp;316), educational level (n\u0026thinsp;=\u0026thinsp;29), or marital status (n\u0026thinsp;=\u0026thinsp;11) were also excluded. After these exclusions, 30,349 participants were left for analysis, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Assessment of the dietary index for gut microbiota\u003c/h2\u003e\u003cp\u003eIn the context of the NHANES initiative, dietary information was gathered via the 24-hour recall approach, namely the Automated Multiple-pass Method (AMPM). This method was devised by the United States Department of Agriculture (USDA) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). To minimize the recall bias stemming from both interviewers and participants, interviewers were given standardized training. Moreover, standardized procedures and instruments were employed during the data collection process (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). DI-GM is a recently devised tool for assessing dietary quality. It offers a way to gauge the impact of dietary patterns on gut health and the makeup of the microbiome (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The framework of this instrument is founded on 14 particular foods or nutrients. Among the beneficial components of the diet are avocados, broccoli, chickpeas, coffee, cranberries, fermented dairy products, fiber, green tea (NHANES lacks explicit green tea consumption data, hence excluded from this study's statistical analysis), soybeans, and whole grains. Conversely, the detrimental elements consist of red meat, processed meats, refined grains, and high-fat diets where fat accounts for \u0026ge;\u0026thinsp;40% of the total energy intake. The assessment of dietary components relies on gender-specific medians. Based on prior studies, DI-GM scores were classified into four distinct categories: 0 to 3 points, 4 points, 5 points, and 6 points or more (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). More detailed information on DI-GM can be found in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Definition of heart failure\u003c/h2\u003e\u003cp\u003eThe assessment of HF was conducted by healthcare professionals through the NHANES MCQ questionnaire, asking participants \u0026ldquo;Has anyone ever told you had congestive HF?\u0026rdquo; to confirm whether they have the condition (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Covariates\u003c/h2\u003e\u003cp\u003eBased on previous studies (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), several potential confounders were selected for inclusion in the research, which are categorized into three main groups: demographic baseline data, laboratory test data, and health status. The basic demographic information encompasses various components. These include gender, age ranges (below 40 years, 40 to 60 years, and 60 years and above), race (Mexican American, non-Hispanic White, non-Hispanic Black, other Hispanic, and other races), educational level (less than high school, high school or equivalent, and college or higher), marital status (married or living with a partner, widowed, divorced or separated, and never married), and the poverty-to-income ratio (PIR) (below 1.30, between 1.30 and 3.50, and 3.50 and higher. A lower PIR suggests a less favorable family financial state) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Laboratory examination data include waist circumference (WC, measured in centimeter (cm)), BMI (computed as kilograms divided by the square of meters and categorized into intervals: below 25, 25 to 30, and 30 and above), total cholesterol (TC, quantified in millimoles per liter), and high-density lipoprotein cholesterol (HDL-C, also quantified in millimoles per liter). In health-related studies, several factors can introduce confounding variables. These factors encompass smoking status, alcohol consumption, diabetes, physical activity, high blood pressure, and coronary heart disease (CHD) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Smoking behaviors can be divided into three distinct groups. Non-smokers are those who have smoked fewer than 100 cigarettes throughout their lives. Former smokers are individuals who had smoked more than 100 cigarettes previously but have since quit. Current smokers are people who have smoked more than 100 cigarettes and continue to smoke, either on an occasional or regular basis (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The assessment of whether a participant can be considered a drinker depends on their alcohol consumption in the previous year. If the total amount of alcohol consumed throughout the past year reaches 12 drinks or more, the participant is labeled as a \"drinker (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\". Physical activity is defined as a categorical variable. An affirmative response implies that the person participated in moderate-intensity physical exercise within the last 30 days, leading to a minor elevation in either the respiration rate or the heart rate (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The diagnosis of CHD was based on self-report in the MCQ questionnaire. The definition of hypertension is adopted in the following three ways: 1) During the questionnaire interview, professional staff ask the survey respondents, \"Have you ever been informed by medical staff that you have hypertension?\" Individuals who answer \"yes\" are defined as the population with hypertension; 2) Survey respondents who are currently taking antihypertensive medications; 3) A participant having a resting systolic blood pressure of 140 mmHg or higher, or a diastolic blood pressure of 90 mmHg or greater (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Diabetes was defined according to three criteria: 1) Self-reported diabetes diagnosis; 2) Use of antidiabetic medications (excluding insulin-sensitizing agents) or insulin therapy; 3) Meeting any of the following biochemical thresholds: Glycated hemoglobin (HbA1c)\u0026thinsp;\u0026ge;\u0026thinsp;6.5%; Fasting blood glucose (FBG)\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L; 2-hour postprandial glucose (2hPG)\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L. Prediabetes was defined as: 1) HbA1c 5.7%-6.4%; 2) FBG 5.6\u0026ndash;6.9 mmol/L or 2hPG 7.8\u0026ndash;11.0 mmol/L (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\u003cp\u003eSince the NHANES project employs a weighted sampling approach, in our analysis, we utilized one-sixth of the sample weight related to the dietary intake on the first day (WTDRD1). When handling missing data, we applied the multiple imputation with chained equations (MICE) technique (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In the course of the statistical analysis, the participants were divided into four distinct groups according to their DI-GM levels. For variables of a continuous nature, we carried out weighted t-tests. When it came to categorical variables, weighted chi-square tests were employed. Continuous variables are presented as mean values along with standard errors (SE), whereas categorical variables are shown as percentages. In order to examine the link between DI-GM and HF, we conducted a weighted multivariable logistic regression analysis. We utilized three different models to assess this connection. Model 1, the initial model, did not incorporate any adjustments for covariates. In contrast, Model 2, the subsequent model, was calibrated to account for demographic variables such as gender, years of age, and race. Conversely, Model 3 was further adjusted for a variety of confounding factors. These factors encompassed educational level, marital status, PIR, BMI, WC, smoking status, alcohol consumption, TC, diabetes, hypertension, CHD, and physical activity. The connection between DI-GM and HF was evaluated using odds ratios (OR) and 95% confidence intervals (CI). For every model, these measurements were calculated to gauge the strength and statistical significance of the relationship. We utilized restricted cubic splines (RCS) to explore the non-linear correlation between DI-GM and HF. After finishing the RCS analysis, we conducted a threshold effect assessment on the results. The objective of this assessment is to identify possible thresholds or particular turning points at which the relationship between DI-GM and HF changes. This provides valuable insights into how fluctuations in DI-GM levels can have distinct impacts on the probability of developing HF among various subgroups in the population. Additionally, we scrutinized every individual dietary component within the DI-GM to evaluate its unique association with the risk of HF. To investigate differences among groups, we conducted subgroup analyses and tests for interaction impacts. In the end, we made use of the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify relevant variables for building the predictive model. After that, several methods were applied to evaluate the performance of the predictive model. Receiver operating characteristic (ROC) curves: To evaluate the model's capacity to distinguish between HF and non-HF instances, we utilized Decision Curve Analysis (DCA). This method examines the clinical utility of the model by scrutinizing the net benefit of making decisions based on its predictions at different threshold probabilities. To ensure the reliability of the model, calibration curves were utilized to assess the extent to which the predicted probabilities align with the real-world observed outcomes. Nomograms were utilized to make the model's results visible. These instruments offered a graphical representation of the predictive elements that was easily comprehensible. The entirety of the statistical analyses was carried out utilizing the R software (version 4.3.3).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included 30,349 participants, with 966 diagnosed with HF, as shown in\u0026nbsp;Table 1. Participants were categorized based on four DI-GM levels. The findings of the research indicated that as the scores of the DI-GM rose, there was a marked elevation in the mean age of the participants in the study and their HDL-C levels. In contrast, the WC demonstrated a declining trend. Nevertheless, there was no substantial disparity in TC levels among the various groups (\u003cem\u003eP\u003c/em\u003e = 0.152). Remarkably, both the DI-GM score and the measures associated with the advantages and disadvantages of gut microbiota displayed significant variations across the groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Except for CHD, Significant differences were detected among the groups in terms of gender, race, educational level, marital status, PIR, BMI, and health-related measures.\u003c/p\u003e\n\u003cp\u003eTable 1 should appear here; due to its length exceeding A4 size, it is provided at the end of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Association Between DI-GM and HF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research employed weighted logistic regression to examine the association between the DI-GM and the risk of HF. The results were presented in Table 2. In the unadjusted model (Model 1), for every single-unit increment in the DI-GM, the risk of HF decreased by 7% (OR = 0.93, 95% CI: 0.89-0.96, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). In Model 2, once age, gender, and race were factored in, a single-unit increase in DI-GM was linked to approximately an 11% reduction in the likelihood of HF (OR = 0.89, 95% CI: 0.86-0.99, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). In Model 3, when further adjustments were made for other confounding factors, the inverse relationship between DI-GM and the risk of HF still held statistical significance (OR = 0.94, 95% CI: 0.90-0.98, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). These results implied that even when multiple variables were accounted for, DI-GM served as an independent safeguard against HF. After the initial data gathering, an in-depth analysis was carried out. This involved categorizing the DI-GM. The group of participants whose DI-GM score was between 0 and 3 was set as the reference cohort. When the DI-GM score reached 6 or higher, there was a notable reduction in the risk of HF (OR = 0.78, 95% CI: 0.64-0.96, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). This discovery suggested that higher DI-GM scores offered a stronger protective influence against HF. Additionally, the trend test produced statistically significant outcomes across all the analytical frameworks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubsequent exploration of dietary elements that exerted either favorable or unfavorable effects on gut microbiota yielded the following findings. Regarding the beneficial constituents, dietary fiber and whole grains were notably linked to a decreased likelihood of HF (OR = 0.98, 95% CI: 0.98-0.99, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; OR = 0.92, 95% CI: 0.87-0.98, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Upon initial inspection, avocados, broccoli, chickpeas, and fermented dairy products appeared to confer protective advantages. However, after accounting for confounding factors, no substantial correlations were detected, and the magnitudes of the effects were negligible. There was a modest association between coffee consumption and a decreased likelihood of HF, yet the protective effect was quite small. In each model, cranberries and soybeans consistently produced an OR of 1.00, along with non-significant \u003cem\u003eP\u003c/em\u003e-values. This suggested that there was no distinct connection between these two food items and the risk of HF. When taking into account adverse factors, within specific models, only processed meat was associated with a heightened risk of HF (OR = 1.06, 95% CI: 1.02-1.10, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). On the other hand, refined grains and red meat did not show any significant relationship with the risk of HF. Conversely, a diet rich in fat had an OR of 0.99 across all three models, along with statistically significant \u003cem\u003eP\u003c/em\u003e-values. (Supplementary Table S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Dose-Response Relationship Between DI-GM and HF Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the adjustment for all confounding variables, we utilized RCS to explore the possible non-linear association between DI-GM and the risk of HF. The outcomes indicated a notable non-linear inverse link between DI-GM and the risk of HF (\u003cem\u003eP\u003c/em\u003e-nonlinearity = 0.030) (Figure 2). This discovery motivated us to conduct a threshold effect analysis. A segmented logistic regression model (Model 2) was utilized to determine the turning point of DI-GM, which was found to be 2.00. Specifically, when the DI-GM value was 2.00 or lower, a one-unit increment in DI-GM led to a notable reduction in the likelihood of HF (OR = 0.672, 95% CI: 0.489-0.950, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Conversely, when the DI-GM value exceeded 2.00, the relationship between DI-GM and HF was less pronounced (OR = 0.953, 95% CI: 0.908-0.999, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Moreover, the likelihood ratio test demonstrated a significant disparity between the two-segment model and the standard model (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). This implied that the segmented model offered a superior fit for elucidating the relationship between DI-GM and HF. In the process of model analysis, all potential confounding factors were adequately considered, guaranteeing that the outcomes were both trustworthy and stable (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Subgroup Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo conduct a more in-depth exploration of the disparities in the connection between the DI-GM and the risk of HF among different groups, we carried out subgroup and interaction analyses. We sorted the data according to age, BMI, PIR, race, smoking status, alcohol consumption, CHD, hypertension, and diabetes. After taking into account all relevant confounding variables, we found that there was a notable inverse correlation between DI-GM and the risk of HF in several groups. These groups included individuals aged 60 years or older, those with a BMI of 30 or higher, non-smokers, non-drinkers, and people without diabetes (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Moreover, as shown in Figure 3, a significant interaction was detected among the subgroups classified by smoking status (\u003cem\u003eP\u003c/em\u003e-interaction \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;LASSO (Least Absolute Shrinkage and Selection Operator) Regression and Nomogram Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to construct the most effective prediction model, we utilized LASSO regression for the purpose of feature selection. Figure 4A and 4B depict the distribution of coefficients from the LASSO regression and the outcomes of cross-validation. The dashed line on the left shows the minimum value of \u0026lambda; (\u0026lambda;\u003csub\u003emin\u003c/sub\u003e), and the dashed line on the right indicates the standard error of \u0026lambda; (\u0026lambda;\u003csub\u003eSE\u003c/sub\u003e). Using \u0026lambda;\u003csub\u003eSE\u003c/sub\u003e (log(\u0026lambda;\u003csub\u003eSE\u003c/sub\u003e) = -5.3504) as a basis, we chose nine variables\u0026mdash;age, marital status, PIR, BMI, TC, smoking status, hypertension, diabetes, and CHD\u0026mdash;to be incorporated into the nomogram model (Supplementary Table S3). The final prediction nomogram was developed by combining the results of the LASSO analysis with the DI-GM. As depicted in Figure 5A, the red dot serves as an example: an unmarried 22-year-old individual free from CHD, diabetes, and hypertension, who has never smoked, has a TC level of 5 mmol/L, a BMI of 23 kg/m\u003csup\u003e2\u003c/sup\u003e, a moderate degree of PIR, and a DI-GM score of 8. Under these circumstances, the likelihood of developing HF is just 1.2%. Subsequently, we assessed the nomogram model using ROC analysis. When contrasted with the standalone use of the DI-GM, the integrated model exhibited greater discriminatory ability. The area under the curve (AUC) was calculated to be 0.891 (95% CI: 0.881-0.900). Moreover, it had a sensitivity of 79.4% and a specificity of 81.9% (as shown in Figure 5B and Supplementary Table S4). The calibration plot indicated that the results of the model\u0026apos;s training and validation were highly consistent with the optimal reference model (Supplementary Figure S1). Moreover, DCA demonstrated that the prediction model yields significant clinical advantages within the anticipated probability intervals (Supplementary Figure S2). These results further confirmed the robust predictive capacity of the model in identifying patients suffering from HF.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the present research, we utilized the NHANES 2007-2018 dataset to examine data from 30,349 residents of the United States. Our aim was to explore the association between the DI-GM and the risk of HF. The main results are presented below: 1)\u0026nbsp;As the DI-GM scores went up, the incidence of HF among the participants declined; 2) After adjusting for all potential confounders, an inverse association was discovered between the DI-GM and the risk of HF. This connection was more prominent among groups that had higher DI-GM scores; 3) It was determined that dietary fiber and whole-grain products are the main dietary elements linked to a decreased likelihood of developing HF; 4) Through the application of RCS and piecewise logistic regression, it was found that when the DI-GM was 2.00 or less, there was a significant inverse relationship with the risk of HF. This relationship weakened once the DI-GM exceeded 2.00; 5) A clear inverse relationship was detected between DI-GM and the risk of HF among particular populations. These populations included people aged 60 years or older, those having a BMI of 30 or higher, non-smokers, non-drinkers, and individuals without diabetes. Furthermore, smoking status significantly interacted with this relationship; 6) The prediction model developed in this study, based on DI-GM and selected feature variables, demonstrated strong diagnostic capabilities in identifying HF risk.\u003c/p\u003e\n\u003cp\u003eTo date, no published research has delved into the connection between DI-GM and the risk of HF. Our research is the first to assess this relationship, and its results align with prior studies on the link between gut microbiota and the risk of HF. For instance, a research carried out by Kai Luo and his team showed that among Hispanic/Latino adults in the United States, left ventricular diastolic dysfunction (LVDD), an early sign of HF, was related to a decrease in beneficial gut bacteria and an uptick in potentially detrimental bacteria. Additionally, various gut microbiota-related metabolites were closely associated with LVDD, enhancing its predictive ability to some extent (28). Anna L Beale and colleagues also found that, compared to healthy individuals, HF patients had significantly lower gut microbiota diversity and richness (29). A review of multiple studies on the gut microbiota in HF patients reported a general decline in gut microbiota richness and notable changes in its composition. In particular, a decrease in bacteria responsible for generating SCFAs could heighten inflammatory reactions. Subsequently, this speeds up the advancement of HF (30). Additionally, a multitude of animal research studies validated these results (31-33).\u003c/p\u003e\n\u003cp\u003eTo sum up, our research offers considerable empirical data regarding the influence of gut microbiome variety on the likelihood of developing HF. It highlights the crucial role that the gut microbiome plays in both the initiation and progression of HF. Dietary patterns, a vital element in regulating the gut microbiome, have the potential to alter the makeup and metabolic capabilities of gut microorganisms. This phenomenon occurs due to disparities in the kinds and ratios of nutrients ingested (34). Our study supports this idea, suggesting that changes in dietary structure can help reduce the risk of HF. Notably, we observed that certain dietary components, which are typically considered unfavorable to the gut microbiota, such as high-fat diets, showed an association between higher scores and a reduced risk of HF. Several factors may explain this counterintuitive finding: First, a \u0026quot;high-fat diet\u0026quot; is not synonymous with unhealthy fat intake alone. Certain high-fat foods, such as those containing monounsaturated and polyunsaturated fats, can also provide cardiovascular-protective nutrients (35, 36). Second, individuals with existing cardiovascular risk may have already modified their diet (e.g., by reducing fat intake), whereas those without such risks may consume high-fat foods more casually. This reverse causality could explain the observed unexpected result. Lastly, while the \u003cem\u003eP\u003c/em\u003e-value is statistically significant, the change in the OR is minimal (only 0.99). Although this may reach statistical significance in large samples, the actual clinical impact might be limited, highlighting the importance of considering the practical range and magnitude of score changes. Among the beneficial components, only dietary fiber and whole grains showed a significant association with a reduced risk of HF. This finding may be influenced by the following factors: First, individual beneficial foods are often closely linked to an overall healthy dietary pattern. Isolating a single component may not fully capture its effect within the broader dietary context. The true protective effect may depend on the synergistic interaction of multiple components rather than any single factor (37). Second, the DI-GM score derived from 24-hour dietary recall data may be prone to measurement errors or recall bias, which could potentially dilute the true association.\u003c/p\u003e\n\u003cp\u003eThe main pathophysiological mechanisms of HF include myocardial remodeling, excessive activation of the neuroendocrine system, and chronic inflammation, which collectively lead to the progressive deterioration of cardiac pumping function. The DI-GM scoring mechanism assesses the makeup of the gut microbiota along with its metabolic by-products. This assessment serves as an indicator for a possible link with cardiac performance. It is quite possible that there are multiple mechanisms that account for the connection between DI-GM and the probability of developing HF. Research suggests that gut microbiota dysbiosis can influence disease progression through a bidirectional \u0026quot;gut-heart axis.\u0026quot; On one hand, reduced gut microbiota diversity impairs the intestinal barrier, promoting the translocation of endotoxins into the bloodstream. This activates the Toll-like receptor 4 (TLR4) signaling pathway, triggering systemic inflammation that exacerbates myocardial cell apoptosis and fibrosis. On the other hand, TMAO, a metabolite generated by the microbiota, speeds up the advancement of coronary atherosclerosis. It does so by disrupting the reverse cholesterol transport mechanism and facilitating the creation of foam cells. TMAO also directly inhibits myocardial mitochondrial energy metabolism, reducing cardiac contractile reserve (38, 39). In addition, specific microbial metabolites have dual regulatory effects on cardiac function. SCFAs activate G protein-coupled receptors (GPR41/43), which help inhibit the overactivation of the renin-angiotensin-aldosterone system, thereby reducing cardiac afterload (40). Experimental studies have confirmed that butyrate improves intestinal barrier function, reduces microglial-mediated inflammation, and modulates N-methyl-D-aspartic acid (NMDA) receptor activity in the paraventricular nucleus (PVN). These effects help reduce sympathetic overactivation in HF, improving cardiac function (41). Secondary bile acids, such as lithocholic acid, regulate myocardial energy metabolism via the farnesoid X receptor (FXR), enhancing fatty acid oxidation efficiency (42). Furthermore, recent studies have identified that phenylacetylglutamine (PAGln) exacerbates HF by increasing sympathetic nervous system activity, interfering with calcium ion signaling, inducing inflammation and cardiac fibrosis, and increasing oxidative stress. These combined effects negatively impact myocardial cells and cardiac structure, driving the onset and progression of HF (43).\u003c/p\u003e\n\u003cp\u003eThe outcomes of this study have substantial clinical implications. Firstly, the RCS results demonstrate that DI-GM scores have a non-linear negative correlation with HF risk, with the RCS curve showing steeper changes in certain DI-GM ranges. This is likely due to the RCS model fitting the data using cubic splines, which results in different slopes for various intervals. The distribution of DI-GM in the sample may cause denser values in some intervals and sparser values in others. In denser areas, the sample distribution provides more information, leading to a more pronounced reduction in risk, whereas in sparser areas, the curve changes more gradually due to fewer data points (44). Secondly, additional piecewise regression analysis revealed that elevated DI-GM scores were linked to a decreased likelihood of HF. The protective impact was more evident when the DI-GM score was under 2.00 and weakened as the score went above 2.00. This discovery offers crucial empirical support for future research on the function of dietary interventions in preventing cardiovascular ailments. Thirdly, an examination of the advantageous elements of gut microbiota uncovered a notable link between the intake of dietary fiber and whole grains and a reduced risk of HF. This implies that those afflicted with HF might be able to enhance their prognosis by boosting their consumption of dietary fiber and whole grains. Finally, the prediction model developed in this study shows potential for identifying individuals at risk of HF before clinical symptoms appear, offering more targeted intervention strategies for clinical practice.\u003c/p\u003e\n\u003cp\u003eThis study has both strengths and limitations. To begin with, the data utilized in this research were obtained from the NHANES database. This database has a substantial sample size, which greatly improves the representativeness of the study\u0026apos;s results. Second, this study used LASSO regression analysis, and the feature variables selected to construct a prediction model, combined with DI-GM, demonstrated good diagnostic ability in identifying HF risk. However, this study has several limitations. First, as NHANES is a survey based on sampling, its generalizability may be hindered by selection bias. Second, the inherent limitations of DI-GM itself must also be recognized. The calculation logic of DI-GM may need to be revalidated or adjusted as nutritional intake standards or policies evolve, which could affect its timeliness. Furthermore, considering the significant individual variation in daily dietary intake, using a 24-hour dietary recall as an assessment tool may not fully capture actual nutritional intake. To improve the timeliness and applicability of DI-GM, future studies could integrate machine learning or big data analysis techniques to develop a prediction model capable of dynamically updating and adapting to changes in nutritional standards or policies over time. Moreover, advanced technologies such as molecular biology and genomics should be incorporated to further explore the biological mechanisms linking dietary nutritional factors to the development of HF. This could ultimately translate research findings into personalized nutrition interventions and precise policy recommendations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn general, this research discovered a notable non-linear inverse relationship between higher DI-GM scores and the risk of HF. More precisely, when the DI-GM score was 2.00 or below, a one-unit increment in the DI-GM score was linked to a substantial decrease in the risk of HF. However, once the DI-GM score went beyond 2.00, the extent of risk reduction became less pronounced. This suggested that DI-GM had a stronger protective effect against HF in lower ranges, with this effected diminishing in higher ranges. Additionally, our model demonstrated the ability to identify individuals at risk of HF before clinical symptoms appear, offering more targeted intervention opportunities for clinical practice. Modifying dietary patterns to maintain gut microbiota diversity may offer new therapeutic targets for preventing and improving the prognosis of HF patients. Subsequent research ought to conduct a more all-encompassing investigation into the particular mechanisms by which the gut microbiota impacts HF. By utilizing metabolomics and metagenomic sequencing technologies, future studies could precisely identify key microbial metabolites and their associated signaling pathways involved in HF development.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDI-GM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edietary index for gut microbiota\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePIR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epoverty income ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etotal cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCHD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecoronary heart disease.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research project obtained approval from the Ethics Review Board of the National Center for Health Statistics. Additionally, all participants were obligated to furnish written informed consent prior to their involvement.\u003c/p\u003e\n\u003cp\u003eWe confirm that the manuscript has been read and approved by all named authors. We further confirm that the order of authors listed in the manuscript has been approved by all of us.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are all from the publicly available data of the National Health and Nutrition Examination Survey, which can be obtained here:\u0026nbsp;https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Key Project of Humanities and Social Sciences Research in Anhui Provincial Universities (2023AH050722), Provincial Quality Engineering Project for Educating People in the New Era in Anhui Province (2024szsfkc095) and Teaching Research Project of Anhui Provincial Quality Engineering (2021jyxm0797).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.H.W.: Writing original draft; Writing - review \u0026amp; editing; Data Curation; Data analysis and visualization. Q.C.: Writing original draft; Writing - review \u0026amp; editing. Y.T.W.: Methodology; Writing - review \u0026amp; editing. X.Y.G.: Validation; Methodology. Y.Z.: Validation; Methodology. K.H.X.: Writing - review; Validation. L.H.: Funding acquisition; Supervision. J.C.: Project administration; Funding acquisition. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express heartfelt gratitude to the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC) for creating and conducting the NHANES and making the data publicly available. Additionally, we would like to thank all team members for their invaluable contributions to this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKatagiri M, Yamada S, Katoh M, Ko T, Ito M, Komuro I. 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Stat Med. 2010;29(9):1037\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/sim.3841\u003c/span\u003e\u003cspan address=\"10.1002/sim.3841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Baseline characteristics of subjects by DI-GM in NHANES 2007-2018.\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 416px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDI-GM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0-3 score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(4 score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(5 score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026ge;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6 score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e48.08 \u0026plusmn; 17.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e48.23 \u0026plusmn; 17.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e49.61 \u0026plusmn; 17.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e51.92 \u0026plusmn; 17.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eWaist circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e101.09 \u0026plusmn; 16.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e99.66 \u0026plusmn; 16.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e99.25 \u0026plusmn; 15.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e98.38 \u0026plusmn; 15.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.98 \u0026plusmn; 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.96 \u0026plusmn; 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.99 \u0026plusmn; 1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5.00 \u0026plusmn; 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.34 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.35 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.37 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.41 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eDI-GM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.54 \u0026plusmn; 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.00 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5.00 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6.62 \u0026plusmn; 0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eBeneficial to gut microbiota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.87 \u0026plusmn; 0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.37 \u0026plusmn; 0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.11 \u0026plusmn; 0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.37 \u0026plusmn; 0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eUnfavorable to gut microbiota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.67 \u0026plusmn; 0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.63 \u0026plusmn; 0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.89 \u0026plusmn; 0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.26 \u0026plusmn; 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eAge, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026lt; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2742 (26.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2367 (23.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2321 (22.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2761 (27.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e40-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2377 (23.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2871 (28.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2343 (23.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2436 (24.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026ge; 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2255 (22.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3218 (31.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2349 (23.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2309 (22.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3850 (26.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3704 (25.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3350 (22.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3836 (26.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3524 (22.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3802 (24.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3663 (23.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4620 (29.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eRace/ethnicity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1045 (22.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1287 (28.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1144 (25.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1074 (23.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e808 (25.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e853 (26.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e720 (22.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e784 (24.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2821 (22.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2861 (22.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2920 (23.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4027 (31.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1999 (30.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1725 (26.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1441 (22.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1367 (20.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eOther Races\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e701 (20.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e780 (22.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e788 (22.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1204 (34.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eEducation level, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1952 (26.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1581 (21.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1640 (22.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2156 (29.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eHigh school or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2026 (29.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1588 (22.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1604 (23.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1745 (25.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eCollege graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3396 (21.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5287 (32.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3769 (23.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3605 (22.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eMarital status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eMarried/Living with a partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4188 (23.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5304 (29.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4219 (23.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4300 (23.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eDivorced/Separated/Widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1628 (24.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1892 (28.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1509 (22.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1694 (25.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1558 (27.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1260 (22.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1285 (22.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1512 (26.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003ePIR, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026lt; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2450 (27.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1927 (21.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2027 (22.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2441 (27.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e1.3-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3321 (25.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3557 (27.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2970 (22.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3315 (25.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026ge; 3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1603 (19.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2972 (35.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2016 (24.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1750 (20.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eBMI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026lt; 25 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1889 (21.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2634 (30.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1975 (22.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2103 (24.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e25-30 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2319 (23.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2895 (29.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2329 (23.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2425 (24.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026ge; 30 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3166 (26.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2927 (24.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2709 (23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2978 (25.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3865 (22.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4879 (28.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3979 (23.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4177 (24.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1706 (23.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2280 (31.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1622 (22.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1676 (23.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1803 (29.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1297 (21.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1412 (22.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1653 (26.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4239 (24.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4184 (24.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3997 (23.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4953 (28.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3135 (24.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3322 (25.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3016 (23.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3503 (27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1466 (27.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1403 (26.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1180 (21.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1338 (24.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2316 (23.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2803 (28.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2280 (23.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2433 (24.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3592 (23.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4250 (28.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3553 (23.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3735 (24.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003ePhysical activity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2768 (24.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2853 (25.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2602 (23.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3035 (26.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4606 (24.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4653 (24.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4411 (23.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5421 (28.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3281 (25.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3177 (24.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2965 (22.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3583 (27.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4093 (23.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4329 (24.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4048 (23.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4873 (28.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eCHD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e298 (24.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e274 (22.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e297 (24.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e353 (28.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7076 (24.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7232 (24.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6716 (23.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8103 (27.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eHeart failure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e262 (27.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e243 (25.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e241 (24.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e220 (22.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7112 (24.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7263 (24.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6772 (23.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8236 (28.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eContinuous variables: Values are expressed as mean \u0026plusmn; standard error.\u003c/p\u003e\n\u003cp\u003eCategorical variables: Values are expressed as numbers (percentage).\u003c/p\u003e\n\u003cp\u003eAbbreviations: TC, total cholesterol; HDL-C, high density lipoprotein cholesterol; DI-GM, dietary index for gut microbiota; PIR, poverty income ratio; BMI, body mass index; CHD, coronary heart disease.\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":"Dietary index for gut microbiota, Heart failure, RCS, NHANES, LASSO","lastPublishedDoi":"10.21203/rs.3.rs-6861182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6861182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWith the global incidence of heart failure (HF) on a continuous upward trend, greater attention has been placed on the part played by gut microbiota in this condition. The Dietary Index for Gut Microbiota (DI-GM) is an evidence-supported tool created to evaluate the influence of diet on gut microbiota. Nevertheless, the possible association between DI-GM and the risk of HF demands more in-depth exploration. This study aimed to examine the relationship between DI-GM and the risk of HF while also assessing its capability to forecast the occurrence and progression of the disease.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study encompassed 30,349 people aged 20 years or above. The participants were sourced from the National Health and Nutrition Examination Survey (NHANES) database covering the period from 2007 to 2018. To evaluate the association between the DI-GM and the risk of HF, several statistical techniques were utilized. These techniques included weighted multivariable logistic regression, restricted cubic splines (RCS), threshold effect evaluation, and subgroup analysis. Additionally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression approach was applied to pinpoint covariates associated with the risk of HF. To gauge the efficacy of the nomogram model, receiver operating characteristic (ROC) curves were used for the evaluation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAfter accounting for all confounding variables, a negative association was discovered between the DI-GM and the risk of HF. This negative correlation was more evident in the cohort with a high DI-GM value (OR\u0026thinsp;=\u0026thinsp;0.78, 95% CI: 0.64\u0026ndash;0.96, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). An analysis using RCS showed a significant non-linear negative relationship between DI-GM and the risk of HF (\u003cem\u003eP\u003c/em\u003e-nonlinearity\u0026thinsp;=\u0026thinsp;0.030). A scrutiny of the threshold effect posited that the safeguarding influence of DI-GM reached a stable condition once the score went beyond 2.00. The forecasting model, chosen via LASSO regression, exhibited robust discriminatory ability. It achieved an area under the curve (AUC) of 0.891 (95% CI: 0.881-0.900).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eElevated DI-GM scores are linked to a decreased incidence of HF. Maintaining a DI-GM score of 2 or higher can improve the efficacy of HF prevention.\u003c/p\u003e","manuscriptTitle":"Association between the dietary index for gut microbiota and heart failure: NHANES 2007-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-01 10:42:48","doi":"10.21203/rs.3.rs-6861182/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":"57da84e0-ca4a-4918-a295-b6829bf0d441","owner":[],"postedDate":"August 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-17T06:53:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-01 10:42:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6861182","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6861182","identity":"rs-6861182","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}