Hemoglobin to red blood cell distribution width ratio inversely associated with heart failure in NHANES 2011–2018

Hemoglobin to red blood cell distribution width ratio inversely associated with heart failure in NHANES 2011–2018 | 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 Hemoglobin to red blood cell distribution width ratio inversely associated with heart failure in NHANES 2011–2018 Feng Jiang, Xiaobo Zhu, Chen Su, Qiang Wang, Junjie Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7809317/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 Hemoglobin-to-red cell distribution width ratio (HRR), a composite index reflecting both oxygen-carrying capacity and erythrocyte heterogeneity, has recently been proposed as a prognostic biomarker in cardiovascular disease. However, its association with heart failure (HF) in the general population remains unclear. We analyzed data from NHANES 2011–2018, applying survey-weighted logistic regression and restricted cubic spline (RCS) models to examine the association between HRR and self-reported HF. Discriminative ability was evaluated using receiver operating characteristic (ROC) curves. Higher HRR was independently and nonlinearly associated with lower prevalence of HF after adjustment for demographics, laboratory indices, cardiometabolic risk factors, and comorbidities. ROC analysis showed that HRR (AUC = 0.671, p < 0.01) had greater discriminative ability than hemoglobin (AUC = 0.600), BMI (AUC = 0.620), and triglycerides (AUC = 0.557), though slightly inferior to red cell distribution width (RDW) (AUC = 0.694). In a nationally representative sample, HRR demonstrated an independent inverse association with HF and provided additional discriminative value beyond hemoglobin alone. These findings suggest that HRR may serve as a readily available biomarker to aid in HF risk stratification. heart failure NHANES red cell distribution width hemoglobin-to-red blood cell distribution width ratio hemoglobin Figures Figure 1 Figure 2 Figure 3 Introduction Heart failure (HF) is a complex clinical syndrome that results from any structural or functional impairment of ventricular filling or ejection of blood, affecting more than 64 million people globally and representing a significant cause of morbidity, mortality, and health care burden( 1 , 2 ). Despite advances in pharmacological and device-based therapies, prognosis continues to be poor, underscoring the need for simple, inexpensive, and accessible biomarkers to aid in risk stratification and early detection( 3 ). Hemoglobin (Hb) is a well-established indicator of oxygen-carrying capacity, and lower Hb levels have been consistently linked with adverse cardiovascular outcomes. RDW, which quantifies variability in erythrocyte size, reflects ineffective erythropoiesis, systemic inflammation, and oxidative stress. Elevated RDW has been associated with incident HF and poor prognosis across multiple populations( 4 ). Although both Hb and RDW have demonstrated clinical relevance, each provides only a partial perspective on the underlying pathophysiology. In this context, the hemoglobin-to-red blood cell distribution width ratio (HRR) has emerged as a novel biomarker of systemic inflammation( 5 ). By capturing both oxygen transport and erythropoietic efficiency, HRR may mitigate the limitations of single markers and offer additional predictive value. Recent studies have demonstrated that HRR, as a composite index, offers enhanced stability and predictive value over either component alone and is associated with various inflammation-related diseases, including diabetes, atrial fibrillation, and several malignancies( 6 – 8 ). Despite its potential, the relationship between HRR and HF remains insufficiently explored. Given that both anemia and inflammation are prevalent and clinically relevant in HF pathophysiology, HRR may serve as an integrative biomarker reflecting the interplay of these two processes. However, its association with the prevalence of HF in large, population-based settings has not been clearly established. To address this gap, the objective of this study was to evaluate the association between HRR and HF using data from the National Health and Nutrition Examination Survey (NHANES) 2011–2018. In addition, we evaluated the discriminative performance of HRR compared with Hb, RDW, and other conventional cardiometabolic markers, aiming to clarify its potential utility as a novel, clinically accessible biomarker for HF risk assessment. Materials and methods Study design The NHANES is a nationally representative, cross-sectional survey designed to assess the health and nutritional status of the non-institutionalized civilian population in the United States. It combines detailed interviews with standardized physical examinations and laboratory assessments. All participants provided informed consent prior to data collection, and the NHANES protocol was approved by the National Center for Health Statistics (NCHS) Ethics Review Board. Therefore, no additional ethical approval was required for this secondary analysis. Figure 1 outlines the participant selection process. Initially, a total of 39,156 participants from the 2011–2018 NHANES cycles were considered. Among them, 16,591 individuals were excluded due to missing data regarding HF status. Of the remaining 22,565 participants with available HF data, 1,992 were further excluded due to missing Hb or RDW values, which are required to calculate the HRR. This yielded 20,573 participants with complete HRR data. To control for potential confounding factors, an additional 5,451 participants were excluded due to missing covariate data, including body mass index (BMI, n = 263), glycated hemoglobin (HbA1c, n = 49), biochemistry profiles (n = 388), alcohol consumption history (n = 4,224), and other unknown or refused responses (n = 527). Ultimately, a total of 15,122 participants were included in the final analysis. Participants were categorized into tertiles according to HRR distribution: Q1 (0.26–1.00), Q2 (1.00–1.12), and Q3 (1.12–1.50). Exposure and outcome variables Exposure and outcome variables The complete blood count (CBC) data were obtained from the NHANES mobile examination center and analyzed using the Beckman Coulter automated hematology analyzer. This standardized method provides measurements of hematologic parameters including Hb, RDW. The specific procedures for specimen collection, handling, and analysis are detailed in the NHANES Laboratory/Medical Technologists Procedures Manual, publicly available on the NHANES website. In this study, the exposure variable—HRR—was calculated by dividing the Hb level (g/dL) by the RDW value (%), both obtained from the CBC profile. HF was determined based on participants’ responses to the questionnaire item “Has a doctor or other health professional ever told you that you had congestive heart failure?” (variable: MCQ160b). Individuals answering “Yes” (code = 1) were classified as having HF, while those answering “No” (code = 2) were classified as not having HF. Participants with responses of “Refused,” “Don’t know,” or missing data were excluded from the analysis. Covariates Building on previous related research, this study included age, sex, race, CBC parameters (leukocyte count, neutrophil count, lymphocyte count, and platelet count), biochemical markers (glycated hemoglobin, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, creatinine, fasting glucose, total cholesterol, and triglycerides), BMI, hypertension, diabetes, smoking status, alcohol consumption, and a history of coronary heart disease (CVD) as covariates. Hypertension was defined as a self-reported history of being told by a doctor or other health professional that the participant had high blood pressure. Diabetes was defined based on a self-reported diagnosis from a doctor or health professional. BMI was calculated as weight in kilograms divided by height in meters squared. Smoking status was defined as having smoked at least 100 cigarettes in one's lifetime. Drinking status was defined as ever having consumed four or more alcoholic drinks in a single day. CVD was defined by a self-reported history of being told by a doctor or other health professional that the participant had coronary heart disease. Statistical analysis All analyses were performed following the analytical guidelines provided by NHANES, incorporating the complex survey design features, including sampling weights, stratification, and clustering. Examination weights recommended by NHANES were applied to adjust for unequal probabilities of selection and to correct for nonresponse, ensuring the representativeness of the U.S. population. Continuous variables with a normal distribution were presented as weighted means with standard deviations (mean ± SD), while categorical variables were reported as unweighted counts and weighted percentages. Group comparisons for continuous variables were performed using either the Student’s t-test or the Mann–Whitney U test, depending on the distributional characteristics. The chi-square test was used to assess differences in categorical variables between groups, where appropriate. A p-value of < 0.05 was regarded as indicative of statistical significance. Utilizing survey-weighted logistic regression models, we examined the association between HRR and the prevalence of HF. To assess the robustness of the association, three progressively adjusted models were constructed. Model 1 was an unadjusted model without any covariate adjustment. Model 2 was adjusted for age, sex, and race. Model 3 was further adjusted for potential confounders, including selected complete blood count parameters, biochemical markers, BMI, hypertension, diabetes, smoking status, alcohol consumption, and history of CVD. Results from these models are presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). The OR represents the ratio of the odds of HF occurrence for a given level of HRR compared with a reference level, with values below 1.0 indicating a lower odds and values above 1.0 indicating a higher odds. The 95% CI quantifies the precision of the OR estimate, providing a range within which the true association is expected to lie with 95% certainty; CIs that do not cross 1.0 are considered statistically significant. All statistical analyses were conducted using R software (version 4.4.3). ROC curve analysis was performed using the pROC package to compare the predictive performance of HRR and other biomarkers. The optimal cut-off value of HRR was determined using the Youden index, based on the ROC curve analysis. RCS models were constructed using the rms package to assess the potential nonlinear relationship between HRR and the probability of HF. The spline term’s nonlinearity was tested by examining the overall significance of the spline function. GAM models were implemented with the mgcv package to further visualize the smoothed association between HRR and HF risk. Results Baseline Characteristics Table 1 summarizes the baseline characteristics of participants across different levels of HRR. Significant differences were observed in demographic, clinical, and laboratory parameters (all p < 0.05). Participants with lower HRR tended to be older, more often female and Non-Hispanic Black, and had a higher prevalence of hypertension, diabetes, CVD, and HF. They also showed lower hemoglobin levels, higher RDW, and unfavorable metabolic profiles, including elevated glycated hemoglobin (HbA1c) and BMI. In contrast, those with higher HRR were generally younger, predominantly male and Non-Hispanic White, and exhibited lower rates of comorbid conditions. Overall, lower HRR was associated with a higher burden of cardiovascular and metabolic risk factors, as well as increased prevalence of HF. Table 1 Baseline characteristics of the study population by HRR tertiles. All Q1 Q2 Q3 p-value Age (year) < 0.001 < 60 73.2 63.4 71.5 81.5 60–80 23.1 29.2 24.8 17.4 ≥ 80 3.7 7.4 3.7 1.1 Gender (%) < 0.001 Male 50.8 21.9 42.6 78.5 Female 49.2 78.1 57.4 21.5 Race (%) < 0.001 Mexican American 8.2 7.3 7.6 9.4 Other Hispanic 6.0 6.5 5.9 5.7 Non-Hispanic White 68.3 58.8 70.7 72.9 Non-Hispanic Black 10.0 20 8.4 4.2c Other Race 7.5 7.4 7.4 7.7 BMI < 0.001 < 25 28.5 26.9 31.3 27.3 25–29.9 32.6 27.2 31.9 36.9 ≥ 30 38.9 45.9 36.8 35.8 Leukocyte (10⁹/L) 7.32 ± 2.88 7.43 ± 4.00 7.31 ± 2.59 7.26 ± 2.04 0.04 Neutrophil (10⁹/L) 4.35 ± 1.72 4.43 ± 1.86 4.33 ± 1.74 4.30 ± 1.60 0.03 Lymphocyte (10⁹/L) 2.15 ± 1.90 2.18 ± 3.10 2.18 ± 1.48 2.12 ± 0.71 0.13 Platelet (10⁹/L) 238.52 ± 59.68 251.68 ± 70.32 238.08 ± 57.76 229.49 ± 50.62 < 0.001 Hb (g/dL) 14.24 ± 1.44 12.73 ± 1.12 14.11 ± 0.74 15.43 ± 0.91 < 0.001 RDW (%) 13.44 ± 1.20 14.51 ± 1.57 13.26 ± 0.63 12.81 ± 0.59 < 0.001 HbA1c (%) 5.63 ± 0.93 5.77 ± 0.94 5.62 ± 0.87 5.54 ± 0.95 < 0.001 ALT (U/L) 24.97 ± 20.11 20.86 ± 15.99 23.34 ± 14.42 29.28 ± 25.36 < 0.001 AST (U/L) 24.83 ± 15.49 23.57 ± 16.41 23.85 ± 11.41 26.57 ± 17.53 < 0.001 BUN (mmol/L) 4.99 ± 1.88 5.12 ± 2.44 4.98 ± 1.72 4.91 ± 1.53 < 0.01 Cr (µmol/L) 78.25 ± 29.50 76.97 ± 45.77 75.86 ± 23.23 81.16 ± 16.17 < 0.001 Glucose (mmol/L) 5.54 ± 1.83 5.60 ± 1.90 5.49 ± 1.70 5.54 ± 1.88 0.02 Cholesterol (mmol/L) 4.98 ± 1.08 4.90 ± 1.09 5.01 ± 1.06 5.02 ± 1.08 < 0.001 Triglyceride (mmol/L) 1.70 ± 1.40 1.55 ± 1.05 1.60 ± 1.18 1.89 ± 1.73 < 0.001 Hypertension (%) < 0.001 Yes 32.4 40.9 30.7 27.8 No 67.6 59.1 69.3 72.2 Diabetes (%) < 0.001 Yes 10.4 15 9.9 7.5 No 89.6 85 90.1 92.5 Smoking (%) < 0.001 Yes 47.6 43.4 46.9 51.2 No 52.4 56.6 53.1 48.8 Drinking (%) < 0.001 Yes 15.7 12.9 14.2 19.1 No 84.3 87.1 85.8 80.9 CVD (%) < 0.001 Yes 3.6 5.2 4.0 2.3 No 96.4 94.8 96.0 97.7 HF (%) < 0.001 Yes 2.3 4.5 1.8 1.1 No 97.7 95.5 98.2 98.9 ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; Cr, creatinine. Hb, hemoglobin; RDW, red cell distribution width. BMI, body mass index; CVD, cardiovascular disease; HF, heart failure. Univariate Analysis Table 2 displays the results of weighted univariate logistic regression analyses assessing the associations between individual variables and HF. A significant inverse association was observed between HRR and HF (OR: 0.03, 95% CI: 0.02–0.05, p < 0.001), suggesting that lower HRR levels were associated with increased odds of HF. Older age, higher BMI (≥ 30 kg/m²), elevated HbA1c, glucose, neutrophil count, blood urea nitrogen, and creatinine were all positively associated with HF risk (all p < 0.01). Participants with hypertension, diabetes, smoking or drinking history, and pre-existing CVD showed significantly higher odds of HF, with CVD having the strongest association (OR: 26.66, 95% CI: 20.16–35.24, p < 0.001). Conversely, higher lymphocyte counts, total cholesterol, and platelet counts were negatively associated with HF. These findings indicate that HRR, alongside several cardiometabolic and inflammatory markers, may be strongly associated with HF in the general population. Table 2 Weighted univariate analysis between HRR and HF statistics OR (95% CI) p-value HRR 1.07 ± 0.15 0.03 (0.02, 0.05) < 0.001 Age (year) < 60 10191 (73.16%) 1 60–80 4115 (23.12%) 6.79 (5.01, 9.20) < 0.001 ≥ 80 816 (3.72%) 15.34 (11.69, 20.12) < 0.001 Gender (%) Male 7923 (50.84%) 1 Female 7199 (49.16%) 0.83 (0.64, 1.07) 0.14 Race (%) Mexican American 2012 (8.22%) 1 Other Hispanic 1540 (5.97%) 1.55 (0.89, 2.69) 0.12 Non-Hispanic White 6207 (68.26%) 2.43 (1.60, 3.70) < 0.001 Non-Hispanic Black 3288 (10%) 3.00 (1.86, 4.83) < 0.001 Other Race 2075 (7.54%) 1.47 (0.70, 3.09) 0.30 BMI < 25 4252 (28.5%) 1 25–29.9 4909 (32.57%) 1.25 (0.85, 1.85) 0.26 ≥ 30 5961 (38.93%) 2.72 (1.80, 4.13) < 0.001 Hb (g/dL) 14.24 ± 1.44 0.80 (0.74, 0.88) < 0.001 RDW (%) 13.44 ± 1.20 1.41 (1.32, 1.50) < 0.001 Leukocyte (10⁹/L) 7.32 ± 2.88 1.02 (0.97, 1.09) 0.40 Neutrophil (10⁹/L) 4.35 ± 1.72 1.14 (1.09, 1.19) < 0.001 Lymphocyte (10⁹/L) 2.15 ± 1.9 0.71 (0.53, 0.96) 0.03 Platelet (10⁹/L) 238.52 ± 59.68 0.99 (0.99, 0.99) < 0.001 HbA1c (%) 5.63 ± 0.93 1.47 (1.39, 1.55) < 0.001 ALT (U/L) 24.97 ± 20.11 1.00 (1.00, 1.01) 0.08 AST (U/L) 24.83 ± 15.49 1.01 (1.00, 1.01) 0.02 BUN (mmol/L) 4.99 ± 1.80 1.32 (1.26, 1.37) < 0.001 Cr (µmol/L) 78.25 ± 29.5 1.01 (1.00, 1.01) < 0.01 Glucose (mmol/L) 5.54 ± 1.83 1.17 (1.14, 1.2) < 0.001 Cholesterol (mmol/L) 4.98 ± 1.08 0.63 (0.51, 0.77) < 0.001 Triglyceride (mmol/L) 1.7 ± 1.4 1.07 (1.03, 1.11) < 0.01 Hypertension (%) Yes 5488 (32.39%) 8.82 (6.49, 11.99) < 0.001 No 9634 (67.61%) 1 Diabetes (%) Yes 2111 (10.4%) 8.82 (6.49, 11.99) < 0.001 No 13011 (89.6%) 1 Smoking (%) Yes 7286 (47.59%) 2.52 (1.91, 3.34) < 0.001 No 7836 (52.41%) 1 Drinking (%) Yes 2477 (15.72%) 2.29 (1.69, 3.10) < 0.001 No 12645 (84.28%) 1 CVD (%) Yes 636 (3.65%) 26.66 (20.16, 35.24) < 0.001 No 14486 (96.35%) 1 ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; Cr, creatinine. Hb, hemoglobin; RDW, red cell distribution width. BMI, body mass index; CVD, cardiovascular disease; HF, heart failure. ROC Curve Analysis As shown in Fig. 2 , ROC analysis showed that RDW had the highest discriminative ability for HF (AUC = 0.694), followed by HRR (AUC = 0.671). Both were significantly superior to Hb (AUC = 0.600, p < 0.01), BMI (AUC = 0.620, p < 0.01), and Triglyceride (AUC = 0.557, p < 0.01). Although the AUC of HRR was slightly lower than that of RDW, its discriminative performance was notably better than Hb and Triglyceride, suggesting that HRR, as a composite indicator, could partially enhance the predictive ability of Hb. The proposed cut-off value of HRR for predicting HF was 1.06, which yielded a sensitivity of 0.752 and a specificity of 0.511. Multivariable Logistic Regression Analysis Table 3 presents the association between HRR and the prevalence of HF across three logistic regression models. In the unadjusted model, HRR was significantly associated with lower odds of HF (OR: 0.03; 95% CI: 0.02–0.05; p < 0.001). This inverse association remained robust after adjusting for age, sex, and race in Model 2 (OR: 0.03; 95% CI: 0.01–0.06; p < 0.001), and persisted in the fully adjusted Model 3 that accounted for demographic, hematological, biochemical, and clinical covariates (OR: 0.08; 95% CI: 0.03–0.22; p < 0.001). When HRR was categorized into tertiles, individuals in the middle and highest HRR groups showed significantly lower odds of HF compared to those in the lowest group. In the fully adjusted model, the odds ratios for HF were 0.57 (95% CI: 0.43–0.76) and 0.44 (95% CI: 0.29–0.69) for the second and third tertiles, respectively (p < 0.001). These results indicate a strong and independent inverse association between HRR and HF, with a clear dose-response trend across HRR tertiles. Table 3 Association between HRR and HF. Non-adjusted OR (95% CI) p-value Model l OR (95% CI) p-value Model 2 OR (95% CI) p-value HRR 0.03 (0.02, 0.05) < 0.001 0.03 (0.01, 0.06) < 0.001 0.08 (0.03, 0.22) < 0.001 HRR tertile Q1 1 1 1 Q2 0.40 (0.31, 0.50) < 0.001 0.44 (0.34, 0.56) < 0.001 0.57 (0.43, 0.76) < 0.001 Q3 0.25 (0.17, 0.35) < 0.001 0.30 (020, 0.44) < 0.001 0.44 (0.29, 0.69) < 0.001 p for trend < 0.001 < 0.001 < 0.001 RCS Analysis To further explore the potential nonlinear association between HRR and HF, RCS regression models were constructed based on the complex sampling design of NHANES. As shown in Fig. 3 , a strong inverse association between HRR and the probability of HF was observed across all models. In the unadjusted model (Fig. 3 A), the risk of HF decreased steeply with increasing HRR, showing a nonlinear dose-response relationship (p for non-linearity < 0.001). This inverse trend remained consistent after adjusting for age, sex, and race (Model 1, Fig. 3 B; p for non-linearity < 0.001). Further adjustment for potential confounders, including hematologic, biochemical, anthropometric, and clinical covariates (Model 2, Fig. 3 C), yielded a similar curve pattern (p for non-linearity < 0.001), confirming the robustness of the association. Overall, the spline curves suggest a stable and monotonic decrease in heart failure risk as HRR increases, supporting HRR as a potentially independent and nonlinear predictor of HF in the general population. Subgroup Analysis To further examine the robustness of the association between HRR and HF across different population subgroups, stratified analyses and interaction tests were performed (Table 4 ). The inverse association between HRR and HF remained consistent across all subgroups, including age, gender, BMI categories, hypertension, diabetes, smoking, drinking status, and history of cardiovascular disease (all p < 0.05 in subgroup-specific estimates). Notably, the protective association of higher HRR with reduced HF risk was observed in both males (OR: 0.09, 95% CI: 0.02–0.38) and females (OR: 0.06, 95% CI: 0.01–0.21), as well as across age groups < 60 and 60–80 years. The association also held in participants with and without comorbid conditions such as hypertension and diabetes. No statistically significant interactions were detected in any of the subgroup comparisons (all p for interaction > 0.05), suggesting that the relationship between HRR and HF was not significantly modified by the variables examined. Table 4 Subgroup analysis and interaction tests of HRR and HF. Subgroup OR (95% CI), p-value p for interaction Age 0.65 < 60 0.04 (0.01, 0.21), < 0.001 60–80 0.09 (0.02, 0.33), < 0.001 ≥ 80 0.15 (0.01, 1.54), 0.12 Gender 0.54 Male 0.09 (0.02, 0.38), < 0.01 Female 0.06 (0.01, 0.21), < 0.001 BMI 0.81 < 25 0.12 (0.01, 1.42), 0.10 25–29.9 0.09 (0.01, 0.66), 0.02 ≥ 30 0.07 (0.02. 0.24), < 0.001 Hypertension 0.50 Yes 0.08 (0.03, 0.23), < 0.001 No 0.12 (0.02, 0.89), 0.05 Diabetes 0.55 Yes 0.12 (0.03, 0.54), < 0.01 No 0.06 (0.02, 0.22), < 0.001 Smoking 0.62 Yes 0.07 (0.02, 0.28), < 0.001 No 0.07 (0.02, 0.26), < 0.001 Drinking 0.94 Yes 0.11 (0.02, 0.81), 0.04 No 0.06 (0.02, 0.15), < 0.001 CVD 0.12 Yes 0.08 (0.02, 0.42), < 0.01 No 0.07 (0.03, 0.21), < 0.001 BMI, body mass index; CVD, cardiovascular disease; HF, heart failure. Discussion This nationally representative analysis demonstrates that a higher HRR is independently and non-linearly associated with a lower prevalence of self-reported HF. The relationship remained robust after extensive adjustment for demographic characteristics, complete blood-count indices, biochemical markers, adiposity, traditional cardiometabolic risk factors and pre-existing cardiovascular disease, and was consistent across all examined subgroups without evidence of effect modification. The univariable OR of 0.03 arises from modeling HRR on a 1-unit scale within a narrow empirical range. Therefore, it should not be interpreted as a literal 97% risk reduction. When expressed on clinically interpretable scales, the association corresponds to about 30% lower odds per 0.1-unit, consistent with the graded trend observed across tertiles. HRR also showed notable discriminatory ability for HF status, outperforming Hb and triglycerides, though slightly inferior to RDW. Notably, the biological plausibility of HRR may be stronger for adverse outcomes and disease progression rather than the initial incidence of HF, since anemia and inflammation are well-established drivers of ventricular remodeling and prognosis( 9 ). Biologically, the finding is plausible. Hb captures oxygen-carrying capacity, whereas RDW reflects ineffective erythropoiesis, inflammation and oxidative stress( 10 , 11 ). Experimental work indicates that IL-6–mediated iron sequestration blunts erythropoietin signaling, simultaneously reducing Hb and widening RDW, offering a mechanistic rationale for the monotonic risk gradient we observed( 12 ). Thus, a low HRR integrates both impaired oxygen delivery and systemic inflammation—two processes central to ventricular dysfunction and adverse remodeling. Moreover, the heterogeneous pathobiology of HF phenotypes should be considered. In heart failure with reduced ejection fraction (HFrEF), ischemic injury, neurohormonal activation, and maladaptive remodeling predominate; anemia and systemic inflammation may exacerbate myocyte loss and chamber dilation, thereby linking low HRR with disease severity( 13 ). In contrast, heart failure with preserved ejection fraction (HFpEF) is strongly driven by comorbidities such as obesity, hypertension, and diabetes, leading to microvascular inflammation, myocardial stiffening, and impaired relaxation. In this setting, reduced HRR may capture chronic low-grade inflammation and endothelial dysfunction that contribute to diastolic dysfunction( 14 ). Thus, although HRR does not differentiate between phenotypes in NHANES, its biological relevance may extend to both HFrEF and HFpEF through distinct pathways. In the present study, RDW demonstrated the strongest predictive ability for HF, consistent with previous findings that RDW is a robust hematological marker reflecting chronic inflammation, oxidative stress, and impaired erythropoiesis( 4 ). Nevertheless, HRR provided additional explanatory value by integrating Hb levels with RDW, thereby capturing both oxygen-carrying capacity and red blood cell heterogeneity. This composite index may overcome the limitations of single-parameter models and thus holds promise for risk stratification and mechanistic exploration in HF( 15 ). In contrast, BMI and triglyceride exhibited relatively weak discriminatory power, suggesting that metabolic factors alone may not sufficiently explain HF susceptibility in this population( 16 ). Instead, hematological and inflammation-related pathways appear to play a more central role in the pathophysiology of HF, which further underscores the relevance of HRR as a potentially valuable biomarker( 17 ). This study builds upon previous findings that have independently associated either Hb or RDW with the incidence and prognosis of HF, while extending the evidence base for HRR as a ratio index that may provide incremental value despite its slightly lower AUC compared with RDW( 4 , 11 ). Its utility may therefore lie more in complementing existing markers and aiding in mechanistic interpretation rather than serving as a standalone predictor of HF incidence. A recent study by Yin et al. demonstrated that lower HRR levels were significantly associated with increased mortality and cardiovascular hospitalization in patients with HF, suggesting that HRR may serve as a novel prognostic marker in this population( 15 ). To the best of our knowledge, no prior research has systematically assessed HRR in relation to HF outcomes within a nationally representative, survey-weighted NHANES cohort. The consistency of the association across sex, age, BMI categories and major comorbidities suggests that HRR may serve as a broadly applicable marker. Because HRR is inexpensive and automatically available from the complete blood count, it could be incorporated into electronic health-record alerts or opportunistic screening protocols to prompt earlier echocardiographic evaluation or intensified management of modifiable risk factors( 18 ). In specialty clinics it might complement natriuretic peptides and inflammatory markers for refined risk stratification( 19 ). This study has several strengths, including a large and ethnically diverse sample, rigorous application of complex survey methodology, comprehensive adjustment for potential confounders, and the use of convergent analytic strategies encompassing univariate, multivariable, RCS analyses, and stratified subgroup analyses. Nonetheless, several limitations should be acknowledged. First, the cross-sectional design precludes any inference of causality and raises the potential for reverse causation. Second, HF status was determined by self-report, which may introduce misclassification bias. Moreover, self-report does not allow for discrimination between HFrEF and HFpEF. This limitation is particularly relevant because these phenotypes differ substantially in etiology, pathobiology, and prognosis. Although prior validation studies of NHANES self-reported diagnoses have demonstrated reasonable accuracy, future studies incorporating imaging-confirmed HF phenotypes are warranted( 20 ). Third, residual confounding cannot be entirely excluded due to unmeasured variables, such as iron status indices or circulating natriuretic peptide levels. Additionally, reliance on a single baseline measurement may not adequately reflect long-term hematologic dynamics. Finally, because NHANES excludes institutionalized individuals, the observed associations may not generalize to frailer populations, and external validity in non-U.S. cohorts or imaging-confirmed HF phenotypes remains to be established( 21 ). Prospective studies are needed to determine whether low HRR predicts incident HF or adverse outcomes and whether interventions that modify Hb or RDW can favorably influence risk. Incorporating HRR into multivariable prediction models alongside established biomarkers also merits investigation. Conclusion In summary, higher HRR was independently and non-linearly associated with a lower prevalence of HF in a nationally representative U.S. population. Owing to its availability from routine blood counts, negligible cost and stability across subgroups, HRR holds promise as a convenient biomarker for HF screening and risk stratification. Longitudinal and mechanistic studies are needed to confirm causality and determine whether modifying HRR can favorably influence HF development and progression. Declarations Conflict of Interest All authors state no conflict of interest. Clinical trial number not applicable. Human Ethics and Consent to Participate declarations not applicable. Funding This work was supported by Xuzhou Medical University Affiliated Hospital Development Fund (ZX202511), the Changzhou Sci & Tech Program (CJ20220165), Young Talent Development Plan of Changzhou Health Commission (CZQM2022027), and Changzhou Longcheng Medical Star Health Youth Sci & Tech Talent Support Project. Author Contribution Feng Jiang contributed to the drafting and writing of the manuscript. Xiaobo Zhu was responsible for data acquisition and organization from the NHANES database. Chen Su conducted the statistical analysis using R. Qiang Wang contributed to manuscript review and editing. Junjie Zhang conceived and designed the study, and was involved in manuscript revision and final approval. All authors read and approved the final version of the manuscript. Data Availability The data used in this study are publicly available from the NHANES, conducted by the U.S. Centers for Disease Control and Prevention (CDC). NHANES datasets from the 2011–2018 cycles can be accessed at the following links:https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2011; https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2013; https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2015; https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2017. References Collaborators GBDCoD. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1736–88. Bui AL, Horwich TB, Fonarow GC. Epidemiology and risk profile of heart failure. Nat Rev Cardiol. 2011;8(1):30–41. Tsutsui H. Recent advances in the pharmacological therapy of chronic heart failure: Evidence and guidelines. Pharmacol Ther. 2022;238:108185. Felker GM, Allen LA, Pocock SJ, Shaw LK, McMurray JJ, Pfeffer MA, et al. Red cell distribution width as a novel prognostic marker in heart failure: data from the CHARM Program and the Duke Databank. J Am Coll Cardiol. 2007;50(1):40–7. Huang L, Li L, Ouyang QR, Chen P, Yu M, Xu L. Association between the hemoglobin-to-red cell distribution width ratio and three-month unfavorable outcome in older acute ischemic stroke patients: a prospective study. Front Neurol. 2025;16:1534564. Lai T, Liang Y, Guan F, Hu K. Trends in hemoglobin-to- red cell distribution width ratio and its prognostic value for all-cause, cancer, and cardiovascular mortality: a nationwide cohort study. Sci Rep. 2025;15(1):7685. Wang J, Chen Z, Yang H, Li H, Chen R, Yu J. Relationship between the Hemoglobin-to-Red Cell Distribution Width Ratio and All-Cause Mortality in Septic Patients with Atrial Fibrillation: Based on Propensity Score Matching Method. J Cardiovasc Dev Dis. 2022;9(11). Coradduzza D, Medici S, Chessa C, Zinellu A, Madonia M, Angius A et al. Assessing the Predictive Power of the Hemoglobin/Red Cell Distribution Width Ratio in Cancer: A Systematic Review and Future Directions. Med (Kaunas). 2023;59(12). O'Meara E, Rouleau JL, White M, Roy K, Blondeau L, Ducharme A, et al. Heart failure with anemia: novel findings on the roles of renal disease, interleukins, and specific left ventricular remodeling processes. Circ Heart Fail. 2014;7(5):773–81. Diaz-Canestro C, Pentz B, Sehgal A, Montero D. Sex differences in cardiorespiratory fitness are explained by blood volume and oxygen carrying capacity. Cardiovasc Res. 2022;118(1):334–43. Semba RD, Patel KV, Ferrucci L, Sun K, Roy CN, Guralnik JM, et al. Serum antioxidants and inflammation predict red cell distribution width in older women: the Women's Health and Aging Study I. Clin Nutr. 2010;29(5):600–4. Lecour S. Remote ischaemic conditioning modulates platelet reactivity: the need to optimize the therapy is more important than ever before. Cardiovasc Res. 2021;117(2):346–7. Xiao X, Wu W, Mao Q, Li B, Wang J, Liu S, et al. Single-cell transcriptomic profiling reveals cell type heterogeneity between HFpEF and HFrEF. Commun Biol. 2025;8(1):1436. Sorop O, Heinonen I, van Kranenburg M, van de Wouw J, de Beer VJ, Nguyen ITN, et al. Multiple common comorbidities produce left ventricular diastolic dysfunction associated with coronary microvascular dysfunction, oxidative stress, and myocardial stiffening. Cardiovasc Res. 2018;114(7):954–64. Rahamim E, Zwas DR, Keren A, Elbaz-Greener G, Ibrahimli M, Amir O et al. The Ratio of Hemoglobin to Red Cell Distribution Width: A Strong Predictor of Clinical Outcome in Patients with Heart Failure. J Clin Med. 2022;11(3). Suthahar N, Meems LMG, Groothof D, Bakker SJL, Gansevoort RT, van Veldhuisen DJ, et al. Relationship between body mass index, cardiovascular biomarkers and incident heart failure. Eur J Heart Fail. 2021;23(3):396–402. Tseliou E, Terrovitis JV, Kaldara EE, Ntalianis AS, Repasos E, Katsaros L, et al. Red blood cell distribution width is a significant prognostic marker in advanced heart failure, independent of hemoglobin levels. Hellenic J Cardiol. 2014;55(6):457–61. Ghazi L, Yamamoto Y, Riello RJ, Coronel-Moreno C, Martin M, O'Connor KD, et al. Electronic Alerts to Improve Heart Failure Therapy in Outpatient Practice: A Cluster Randomized Trial. J Am Coll Cardiol. 2022;79(22):2203–13. Lala A, Mentz RJ. Making Time for Diastole While Getting the Work Done. J Card Fail. 2023;29(7):985. Camplain R, Kucharska-Newton A, Loehr L, Keyserling TC, Layton JB, Wruck L, et al. Accuracy of Self-Reported Heart Failure. The Atherosclerosis Risk in Communities (ARIC) Study. J Card Fail. 2017;23(11):802–8. Yao H, Wang H, Li T, Feng X, Liu D, Zhang Y, et al. Weekend catch-up sleep and frailty in US adults: a cross-sectional study from NHANES 2017–2020. BMC Public Health. 2025;25(1):1481. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7809317","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551424723,"identity":"22b95267-3d2f-41f6-906d-45dfa0f9c4cc","order_by":0,"name":"Feng Jiang","email":"","orcid":"","institution":"Wujin Hospital Affiliated with Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Jiang","suffix":""},{"id":551424728,"identity":"0d1c27da-376e-4205-83dd-dcae357833f1","order_by":1,"name":"Xiaobo Zhu","email":"","orcid":"","institution":"Wujin Hospital Affiliated with Jiangsu 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Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYFCCAwkMEgwMcgzMpGoxJkULBCQ2EK1UvvHAsweWOYfT57fzHvzAUGMTTVALY8OBdAPJbYdzNxzmS5ZgOJaWS9A6ZoYDaRJgLcw8BhKMDYcJa2GDakmXb+Yx/kGUFh6olgSGwzxmxNkiAdGSbrgBqMUigRi/yM84kyYtuc1aXr7/jPGNDzU2hLUwSJxJYJaAcRIIKgcB/vYDjB+IUjkKRsEoGAUjFgAARes8OlD2l6kAAAAASUVORK5CYII=","orcid":"","institution":"Wujin Hospital Affiliated with Jiangsu University","correspondingAuthor":true,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-10-08 15:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7809317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7809317/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96919079,"identity":"9d82daa8-bd7b-49c8-8d5c-97481151f70e","added_by":"auto","created_at":"2025-11-27 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1","display":"","copyAsset":false,"role":"figure","size":776627,"visible":true,"origin":"","legend":"\u003cp\u003eResearch flowchart. HF, heart failure; Hb, hemoglobin; RDW, red blood cell distribution width; HRR, hemoglobin-to-red blood cell distribution ratio; BMI, body Mass Index; HbA1c, glycated hemoglobin.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7809317/v1/99d6105188c47bb55b84af59.jpg"},{"id":96886991,"identity":"96289c3b-b411-4c33-b24a-d10ac24b2b83","added_by":"auto","created_at":"2025-11-27 08:37:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":456871,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves evaluating the predictive performance of HRR and other biomarkers for HF.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7809317/v1/d47636c9d7b4f706908c6ad5.jpg"},{"id":96886993,"identity":"c2b9bf2b-ddcc-48de-b8ed-05d84df3a357","added_by":"auto","created_at":"2025-11-27 08:37:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":638268,"visible":true,"origin":"","legend":"\u003cp\u003eRCS plots showing the association between HRR and probability of HF. The shaded area indicates the 95% CI. A shows the unadjusted model; B adjusts for age, sex, and race; C further adjusts for complete blood count parameters, biochemical indicators, BMI, hypertension, diabetes, smoking, alcohol use, and history of cardiovascular disease.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7809317/v1/61ccddc1f76d3183fef1f242.jpg"},{"id":106093910,"identity":"0a036bb7-18af-4272-8d52-5fc750003afa","added_by":"auto","created_at":"2026-04-03 11:40:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3084864,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7809317/v1/0f5a783b-bc88-45e8-b716-0c1435ca9604.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hemoglobin to red blood cell distribution width ratio inversely associated with heart failure in NHANES 2011–2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart failure (HF) is a complex clinical syndrome that results from any structural or functional impairment of ventricular filling or ejection of blood, affecting more than 64\u0026nbsp;million people globally and representing a significant cause of morbidity, mortality, and health care burden(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite advances in pharmacological and device-based therapies, prognosis continues to be poor, underscoring the need for simple, inexpensive, and accessible biomarkers to aid in risk stratification and early detection(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHemoglobin (Hb) is a well-established indicator of oxygen-carrying capacity, and lower Hb levels have been consistently linked with adverse cardiovascular outcomes. RDW, which quantifies variability in erythrocyte size, reflects ineffective erythropoiesis, systemic inflammation, and oxidative stress. Elevated RDW has been associated with incident HF and poor prognosis across multiple populations(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Although both Hb and RDW have demonstrated clinical relevance, each provides only a partial perspective on the underlying pathophysiology.\u003c/p\u003e\u003cp\u003eIn this context, the hemoglobin-to-red blood cell distribution width ratio (HRR) has emerged as a novel biomarker of systemic inflammation(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). By capturing both oxygen transport and erythropoietic efficiency, HRR may mitigate the limitations of single markers and offer additional predictive value. Recent studies have demonstrated that HRR, as a composite index, offers enhanced stability and predictive value over either component alone and is associated with various inflammation-related diseases, including diabetes, atrial fibrillation, and several malignancies(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Despite its potential, the relationship between HRR and HF remains insufficiently explored. Given that both anemia and inflammation are prevalent and clinically relevant in HF pathophysiology, HRR may serve as an integrative biomarker reflecting the interplay of these two processes. However, its association with the prevalence of HF in large, population-based settings has not been clearly established.\u003c/p\u003e\u003cp\u003eTo address this gap, the objective of this study was to evaluate the association between HRR and HF using data from the National Health and Nutrition Examination Survey (NHANES) 2011\u0026ndash;2018. In addition, we evaluated the discriminative performance of HRR compared with Hb, RDW, and other conventional cardiometabolic markers, aiming to clarify its potential utility as a novel, clinically accessible biomarker for HF risk assessment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eThe NHANES is a nationally representative, cross-sectional survey designed to assess the health and nutritional status of the non-institutionalized civilian population in the United States. It combines detailed interviews with standardized physical examinations and laboratory assessments. All participants provided informed consent prior to data collection, and the NHANES protocol was approved by the National Center for Health Statistics (NCHS) Ethics Review Board. Therefore, no additional ethical approval was required for this secondary analysis.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the participant selection process. Initially, a total of 39,156 participants from the 2011\u0026ndash;2018 NHANES cycles were considered. Among them, 16,591 individuals were excluded due to missing data regarding HF status. Of the remaining 22,565 participants with available HF data, 1,992 were further excluded due to missing Hb or RDW values, which are required to calculate the HRR. This yielded 20,573 participants with complete HRR data. To control for potential confounding factors, an additional 5,451 participants were excluded due to missing covariate data, including body mass index (BMI, n\u0026thinsp;=\u0026thinsp;263), glycated hemoglobin (HbA1c, n\u0026thinsp;=\u0026thinsp;49), biochemistry profiles (n\u0026thinsp;=\u0026thinsp;388), alcohol consumption history (n\u0026thinsp;=\u0026thinsp;4,224), and other unknown or refused responses (n\u0026thinsp;=\u0026thinsp;527). Ultimately, a total of 15,122 participants were included in the final analysis. Participants were categorized into tertiles according to HRR distribution: Q1 (0.26\u0026ndash;1.00), Q2 (1.00\u0026ndash;1.12), and Q3 (1.12\u0026ndash;1.50).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExposure and outcome variables\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eExposure and outcome variables\u003c/div\u003e\u003cp\u003eThe complete blood count (CBC) data were obtained from the NHANES mobile examination center and analyzed using the Beckman Coulter automated hematology analyzer. This standardized method provides measurements of hematologic parameters including Hb, RDW. The specific procedures for specimen collection, handling, and analysis are detailed in the NHANES Laboratory/Medical Technologists Procedures Manual, publicly available on the NHANES website. In this study, the exposure variable\u0026mdash;HRR\u0026mdash;was calculated by dividing the Hb level (g/dL) by the RDW value (%), both obtained from the CBC profile. HF was determined based on participants\u0026rsquo; responses to the questionnaire item \u0026ldquo;Has a doctor or other health professional ever told you that you had congestive heart failure?\u0026rdquo; (variable: MCQ160b). Individuals answering \u0026ldquo;Yes\u0026rdquo; (code\u0026thinsp;=\u0026thinsp;1) were classified as having HF, while those answering \u0026ldquo;No\u0026rdquo; (code\u0026thinsp;=\u0026thinsp;2) were classified as not having HF. Participants with responses of \u0026ldquo;Refused,\u0026rdquo; \u0026ldquo;Don\u0026rsquo;t know,\u0026rdquo; or missing data were excluded from the analysis.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eBuilding on previous related research, this study included age, sex, race, CBC parameters (leukocyte count, neutrophil count, lymphocyte count, and platelet count), biochemical markers (glycated hemoglobin, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, creatinine, fasting glucose, total cholesterol, and triglycerides), BMI, hypertension, diabetes, smoking status, alcohol consumption, and a history of coronary heart disease (CVD) as covariates.\u003c/p\u003e\u003cp\u003eHypertension was defined as a self-reported history of being told by a doctor or other health professional that the participant had high blood pressure. Diabetes was defined based on a self-reported diagnosis from a doctor or health professional. BMI was calculated as weight in kilograms divided by height in meters squared. Smoking status was defined as having smoked at least 100 cigarettes in one's lifetime. Drinking status was defined as ever having consumed four or more alcoholic drinks in a single day. CVD was defined by a self-reported history of being told by a doctor or other health professional that the participant had coronary heart disease.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003e All analyses were performed following the analytical guidelines provided by NHANES, incorporating the complex survey design features, including sampling weights, stratification, and clustering. Examination weights recommended by NHANES were applied to adjust for unequal probabilities of selection and to correct for nonresponse, ensuring the representativeness of the U.S. population.\u003c/p\u003e\u003cp\u003eContinuous variables with a normal distribution were presented as weighted means with standard deviations (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), while categorical variables were reported as unweighted counts and weighted percentages. Group comparisons for continuous variables were performed using either the Student\u0026rsquo;s t-test or the Mann\u0026ndash;Whitney U test, depending on the distributional characteristics. The chi-square test was used to assess differences in categorical variables between groups, where appropriate. A p-value of \u0026lt;\u0026thinsp;0.05 was regarded as indicative of statistical significance.\u003c/p\u003e\u003cp\u003eUtilizing survey-weighted logistic regression models, we examined the association between HRR and the prevalence of HF. To assess the robustness of the association, three progressively adjusted models were constructed. Model 1 was an unadjusted model without any covariate adjustment. Model 2 was adjusted for age, sex, and race. Model 3 was further adjusted for potential confounders, including selected complete blood count parameters, biochemical markers, BMI, hypertension, diabetes, smoking status, alcohol consumption, and history of CVD. Results from these models are presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). The OR represents the ratio of the odds of HF occurrence for a given level of HRR compared with a reference level, with values below 1.0 indicating a lower odds and values above 1.0 indicating a higher odds. The 95% CI quantifies the precision of the OR estimate, providing a range within which the true association is expected to lie with 95% certainty; CIs that do not cross 1.0 are considered statistically significant.\u003c/p\u003e\u003cp\u003eAll statistical analyses were conducted using R software (version 4.4.3). ROC curve analysis was performed using the pROC package to compare the predictive performance of HRR and other biomarkers. The optimal cut-off value of HRR was determined using the Youden index, based on the ROC curve analysis. RCS models were constructed using the rms package to assess the potential nonlinear relationship between HRR and the probability of HF. The spline term\u0026rsquo;s nonlinearity was tested by examining the overall significance of the spline function. GAM models were implemented with the mgcv package to further visualize the smoothed association between HRR and HF risk.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics of participants across different levels of HRR. Significant differences were observed in demographic, clinical, and laboratory parameters (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Participants with lower HRR tended to be older, more often female and Non-Hispanic Black, and had a higher prevalence of hypertension, diabetes, CVD, and HF. They also showed lower hemoglobin levels, higher RDW, and unfavorable metabolic profiles, including elevated glycated hemoglobin (HbA1c) and BMI. In contrast, those with higher HRR were generally younger, predominantly male and Non-Hispanic White, and exhibited lower rates of comorbid conditions. Overall, lower HRR was associated with a higher burden of cardiovascular and metabolic risk factors, as well as increased prevalence of HF.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the study population by HRR tertiles.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (year)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e73.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.2c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLeukocyte (10⁹/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeutrophil (10⁹/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLymphocyte (10⁹/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlatelet (10⁹/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e238.52\u0026thinsp;\u0026plusmn;\u0026thinsp;59.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e251.68\u0026thinsp;\u0026plusmn;\u0026thinsp;70.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e238.08\u0026thinsp;\u0026plusmn;\u0026thinsp;57.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e229.49\u0026thinsp;\u0026plusmn;\u0026thinsp;50.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHb (g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRDW (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALT (U/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.97\u0026thinsp;\u0026plusmn;\u0026thinsp;20.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.86\u0026thinsp;\u0026plusmn;\u0026thinsp;15.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.34\u0026thinsp;\u0026plusmn;\u0026thinsp;14.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.28\u0026thinsp;\u0026plusmn;\u0026thinsp;25.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAST (U/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.83\u0026thinsp;\u0026plusmn;\u0026thinsp;15.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.57\u0026thinsp;\u0026plusmn;\u0026thinsp;16.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.85\u0026thinsp;\u0026plusmn;\u0026thinsp;11.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.57\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBUN (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCr (\u0026micro;mol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78.25\u0026thinsp;\u0026plusmn;\u0026thinsp;29.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.97\u0026thinsp;\u0026plusmn;\u0026thinsp;45.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.86\u0026thinsp;\u0026plusmn;\u0026thinsp;23.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.16\u0026thinsp;\u0026plusmn;\u0026thinsp;16.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlucose (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCholesterol (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTriglyceride (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrinking (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCVD (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHF (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; Cr, creatinine. Hb, hemoglobin; RDW, red cell distribution width. BMI, body mass index; CVD, cardiovascular disease; HF, heart failure.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eUnivariate Analysis\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the results of weighted univariate logistic regression analyses assessing the associations between individual variables and HF. A significant inverse association was observed between HRR and HF (OR: 0.03, 95% CI: 0.02\u0026ndash;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that lower HRR levels were associated with increased odds of HF. Older age, higher BMI (\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;), elevated HbA1c, glucose, neutrophil count, blood urea nitrogen, and creatinine were all positively associated with HF risk (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Participants with hypertension, diabetes, smoking or drinking history, and pre-existing CVD showed significantly higher odds of HF, with CVD having the strongest association (OR: 26.66, 95% CI: 20.16\u0026ndash;35.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, higher lymphocyte counts, total cholesterol, and platelet counts were negatively associated with HF. These findings indicate that HRR, alongside several cardiometabolic and inflammatory markers, may be strongly associated with HF in the general population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWeighted univariate analysis between HRR and HF\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003estatistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHRR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03 (0.02, 0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (year)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10191 (73.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4115 (23.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.79 (5.01, 9.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e816 (3.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.34 (11.69, 20.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7923 (50.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7199 (49.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83 (0.64, 1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2012 (8.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1540 (5.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.55 (0.89, 2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6207 (68.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.43 (1.60, 3.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3288 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00 (1.86, 4.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2075 (7.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.47 (0.70, 3.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4252 (28.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4909 (32.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25 (0.85, 1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5961 (38.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.72 (1.80, 4.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHb (g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.80 (0.74, 0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRDW (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.41 (1.32, 1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLeukocyte (10⁹/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02 (0.97, 1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeutrophil (10⁹/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14 (1.09, 1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLymphocyte (10⁹/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71 (0.53, 0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlatelet (10⁹/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e238.52\u0026thinsp;\u0026plusmn;\u0026thinsp;59.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99 (0.99, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.47 (1.39, 1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALT (U/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.97\u0026thinsp;\u0026plusmn;\u0026thinsp;20.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (1.00, 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAST (U/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.83\u0026thinsp;\u0026plusmn;\u0026thinsp;15.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01 (1.00, 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBUN (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32 (1.26, 1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCr (\u0026micro;mol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.25\u0026thinsp;\u0026plusmn;\u0026thinsp;29.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01 (1.00, 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlucose (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17 (1.14, 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCholesterol (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63 (0.51, 0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTriglyceride (mmol/L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.07 (1.03, 1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5488 (32.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.82 (6.49, 11.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9634 (67.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2111 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.82 (6.49, 11.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13011 (89.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7286 (47.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.52 (1.91, 3.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7836 (52.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrinking (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2477 (15.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.29 (1.69, 3.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12645 (84.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCVD (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e636 (3.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.66 (20.16, 35.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14486 (96.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; Cr, creatinine. Hb, hemoglobin; RDW, red cell distribution width. BMI, body mass index; CVD, cardiovascular disease; HF, heart failure.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eROC Curve Analysis\u003c/h3\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, ROC analysis showed that RDW had the highest discriminative ability for HF (AUC\u0026thinsp;=\u0026thinsp;0.694), followed by HRR (AUC\u0026thinsp;=\u0026thinsp;0.671). Both were significantly superior to Hb (AUC\u0026thinsp;=\u0026thinsp;0.600, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), BMI (AUC\u0026thinsp;=\u0026thinsp;0.620, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and Triglyceride (AUC\u0026thinsp;=\u0026thinsp;0.557, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Although the AUC of HRR was slightly lower than that of RDW, its discriminative performance was notably better than Hb and Triglyceride, suggesting that HRR, as a composite indicator, could partially enhance the predictive ability of Hb. The proposed cut-off value of HRR for predicting HF was 1.06, which yielded a sensitivity of 0.752 and a specificity of 0.511.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMultivariable Logistic Regression Analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the association between HRR and the prevalence of HF across three logistic regression models. In the unadjusted model, HRR was significantly associated with lower odds of HF (OR: 0.03; 95% CI: 0.02\u0026ndash;0.05; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This inverse association remained robust after adjusting for age, sex, and race in Model 2 (OR: 0.03; 95% CI: 0.01\u0026ndash;0.06; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and persisted in the fully adjusted Model 3 that accounted for demographic, hematological, biochemical, and clinical covariates (OR: 0.08; 95% CI: 0.03\u0026ndash;0.22; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When HRR was categorized into tertiles, individuals in the middle and highest HRR groups showed significantly lower odds of HF compared to those in the lowest group. In the fully adjusted model, the odds ratios for HF were 0.57 (95% CI: 0.43\u0026ndash;0.76) and 0.44 (95% CI: 0.29\u0026ndash;0.69) for the second and third tertiles, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results indicate a strong and independent inverse association between HRR and HF, with a clear dose-response trend across HRR tertiles.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between HRR and HF.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-adjusted OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel l OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 2 OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHRR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.03 (0.02, 0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03 (0.01, 0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08 (0.03, 0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHRR tertile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQ1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQ2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.40 (0.31, 0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44 (0.34, 0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.57 (0.43, 0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eQ3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25 (0.17, 0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30 (020, 0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.44 (0.29, 0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ep for trend\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRCS Analysis\u003c/h2\u003e\u003cp\u003eTo further explore the potential nonlinear association between HRR and HF, RCS regression models were constructed based on the complex sampling design of NHANES. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a strong inverse association between HRR and the probability of HF was observed across all models. In the unadjusted model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), the risk of HF decreased steeply with increasing HRR, showing a nonlinear dose-response relationship (p for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This inverse trend remained consistent after adjusting for age, sex, and race (Model 1, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; p for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further adjustment for potential confounders, including hematologic, biochemical, anthropometric, and clinical covariates (Model 2, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), yielded a similar curve pattern (p for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming the robustness of the association. Overall, the spline curves suggest a stable and monotonic decrease in heart failure risk as HRR increases, supporting HRR as a potentially independent and nonlinear predictor of HF in the general population.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup Analysis\u003c/h2\u003e\u003cp\u003eTo further examine the robustness of the association between HRR and HF across different population subgroups, stratified analyses and interaction tests were performed (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The inverse association between HRR and HF remained consistent across all subgroups, including age, gender, BMI categories, hypertension, diabetes, smoking, drinking status, and history of cardiovascular disease (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in subgroup-specific estimates). Notably, the protective association of higher HRR with reduced HF risk was observed in both males (OR: 0.09, 95% CI: 0.02\u0026ndash;0.38) and females (OR: 0.06, 95% CI: 0.01\u0026ndash;0.21), as well as across age groups\u0026thinsp;\u0026lt;\u0026thinsp;60 and 60\u0026ndash;80 years. The association also held in participants with and without comorbid conditions such as hypertension and diabetes. No statistically significant interactions were detected in any of the subgroup comparisons (all p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the relationship between HRR and HF was not significantly modified by the variables examined.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubgroup analysis and interaction tests of HRR and HF.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubgroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI), p-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep for interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04 (0.01, 0.21), \u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09 (0.02, 0.33), \u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15 (0.01, 1.54), 0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09 (0.02, 0.38), \u0026lt; 0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06 (0.01, 0.21), \u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12 (0.01, 1.42), 0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09 (0.01, 0.66), 0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07 (0.02. 0.24), \u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.08 (0.03, 0.23), \u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12 (0.02, 0.89), 0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12 (0.03, 0.54), \u0026lt; 0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06 (0.02, 0.22), \u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07 (0.02, 0.28), \u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07 (0.02, 0.26), \u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrinking\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11 (0.02, 0.81), 0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06 (0.02, 0.15), \u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCVD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.08 (0.02, 0.42), \u0026lt; 0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07 (0.03, 0.21), \u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eBMI, body mass index; CVD, cardiovascular disease; HF, heart failure.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis nationally representative analysis demonstrates that a higher HRR is independently and non-linearly associated with a lower prevalence of self-reported HF. The relationship remained robust after extensive adjustment for demographic characteristics, complete blood-count indices, biochemical markers, adiposity, traditional cardiometabolic risk factors and pre-existing cardiovascular disease, and was consistent across all examined subgroups without evidence of effect modification.\u003c/p\u003e\u003cp\u003eThe univariable OR of 0.03 arises from modeling HRR on a 1-unit scale within a narrow empirical range. Therefore, it should not be interpreted as a literal 97% risk reduction. When expressed on clinically interpretable scales, the association corresponds to about 30% lower odds per 0.1-unit, consistent with the graded trend observed across tertiles. HRR also showed notable discriminatory ability for HF status, outperforming Hb and triglycerides, though slightly inferior to RDW. Notably, the biological plausibility of HRR may be stronger for adverse outcomes and disease progression rather than the initial incidence of HF, since anemia and inflammation are well-established drivers of ventricular remodeling and prognosis(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBiologically, the finding is plausible. Hb captures oxygen-carrying capacity, whereas RDW reflects ineffective erythropoiesis, inflammation and oxidative stress(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Experimental work indicates that IL-6\u0026ndash;mediated iron sequestration blunts erythropoietin signaling, simultaneously reducing Hb and widening RDW, offering a mechanistic rationale for the monotonic risk gradient we observed(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Thus, a low HRR integrates both impaired oxygen delivery and systemic inflammation\u0026mdash;two processes central to ventricular dysfunction and adverse remodeling.\u003c/p\u003e\u003cp\u003eMoreover, the heterogeneous pathobiology of HF phenotypes should be considered. In heart failure with reduced ejection fraction (HFrEF), ischemic injury, neurohormonal activation, and maladaptive remodeling predominate; anemia and systemic inflammation may exacerbate myocyte loss and chamber dilation, thereby linking low HRR with disease severity(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In contrast, heart failure with preserved ejection fraction (HFpEF) is strongly driven by comorbidities such as obesity, hypertension, and diabetes, leading to microvascular inflammation, myocardial stiffening, and impaired relaxation. In this setting, reduced HRR may capture chronic low-grade inflammation and endothelial dysfunction that contribute to diastolic dysfunction(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Thus, although HRR does not differentiate between phenotypes in NHANES, its biological relevance may extend to both HFrEF and HFpEF through distinct pathways.\u003c/p\u003e\u003cp\u003eIn the present study, RDW demonstrated the strongest predictive ability for HF, consistent with previous findings that RDW is a robust hematological marker reflecting chronic inflammation, oxidative stress, and impaired erythropoiesis(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Nevertheless, HRR provided additional explanatory value by integrating Hb levels with RDW, thereby capturing both oxygen-carrying capacity and red blood cell heterogeneity. This composite index may overcome the limitations of single-parameter models and thus holds promise for risk stratification and mechanistic exploration in HF(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In contrast, BMI and triglyceride exhibited relatively weak discriminatory power, suggesting that metabolic factors alone may not sufficiently explain HF susceptibility in this population(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Instead, hematological and inflammation-related pathways appear to play a more central role in the pathophysiology of HF, which further underscores the relevance of HRR as a potentially valuable biomarker(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study builds upon previous findings that have independently associated either Hb or RDW with the incidence and prognosis of HF, while extending the evidence base for HRR as a ratio index that may provide incremental value despite its slightly lower AUC compared with RDW(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Its utility may therefore lie more in complementing existing markers and aiding in mechanistic interpretation rather than serving as a standalone predictor of HF incidence. A recent study by Yin et al. demonstrated that lower HRR levels were significantly associated with increased mortality and cardiovascular hospitalization in patients with HF, suggesting that HRR may serve as a novel prognostic marker in this population(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). To the best of our knowledge, no prior research has systematically assessed HRR in relation to HF outcomes within a nationally representative, survey-weighted NHANES cohort. The consistency of the association across sex, age, BMI categories and major comorbidities suggests that HRR may serve as a broadly applicable marker. Because HRR is inexpensive and automatically available from the complete blood count, it could be incorporated into electronic health-record alerts or opportunistic screening protocols to prompt earlier echocardiographic evaluation or intensified management of modifiable risk factors(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In specialty clinics it might complement natriuretic peptides and inflammatory markers for refined risk stratification(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study has several strengths, including a large and ethnically diverse sample, rigorous application of complex survey methodology, comprehensive adjustment for potential confounders, and the use of convergent analytic strategies encompassing univariate, multivariable, RCS analyses, and stratified subgroup analyses. Nonetheless, several limitations should be acknowledged. First, the cross-sectional design precludes any inference of causality and raises the potential for reverse causation. Second, HF status was determined by self-report, which may introduce misclassification bias. Moreover, self-report does not allow for discrimination between HFrEF and HFpEF. This limitation is particularly relevant because these phenotypes differ substantially in etiology, pathobiology, and prognosis. Although prior validation studies of NHANES self-reported diagnoses have demonstrated reasonable accuracy, future studies incorporating imaging-confirmed HF phenotypes are warranted(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Third, residual confounding cannot be entirely excluded due to unmeasured variables, such as iron status indices or circulating natriuretic peptide levels. Additionally, reliance on a single baseline measurement may not adequately reflect long-term hematologic dynamics. Finally, because NHANES excludes institutionalized individuals, the observed associations may not generalize to frailer populations, and external validity in non-U.S. cohorts or imaging-confirmed HF phenotypes remains to be established(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProspective studies are needed to determine whether low HRR predicts incident HF or adverse outcomes and whether interventions that modify Hb or RDW can favorably influence risk. Incorporating HRR into multivariable prediction models alongside established biomarkers also merits investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, higher HRR was independently and non-linearly associated with a lower prevalence of HF in a nationally representative U.S. population. Owing to its availability from routine blood counts, negligible cost and stability across subgroups, HRR holds promise as a convenient biomarker for HF screening and risk stratification. Longitudinal and mechanistic studies are needed to confirm causality and determine whether modifying HRR can favorably influence HF development and progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eAll authors state no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003enot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003cp\u003enot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by Xuzhou Medical University Affiliated Hospital Development Fund (ZX202511), the Changzhou Sci \u0026amp; Tech Program (CJ20220165), Young Talent Development Plan of Changzhou Health Commission (CZQM2022027), and Changzhou Longcheng Medical Star Health Youth Sci \u0026amp; Tech Talent Support Project.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFeng Jiang contributed to the drafting and writing of the manuscript. Xiaobo Zhu was responsible for data acquisition and organization from the NHANES database. Chen Su conducted the statistical analysis using R. Qiang Wang contributed to manuscript review and editing. Junjie Zhang conceived and designed the study, and was involved in manuscript revision and final approval. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study are publicly available from the NHANES, conducted by the U.S. Centers for Disease Control and Prevention (CDC). NHANES datasets from the 2011\u0026ndash;2018 cycles can be accessed at the following links:https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2011; https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2013; https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2015; https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2017.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCollaborators GBDCoD. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1736\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBui AL, Horwich TB, Fonarow GC. Epidemiology and risk profile of heart failure. Nat Rev Cardiol. 2011;8(1):30\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsutsui H. Recent advances in the pharmacological therapy of chronic heart failure: Evidence and guidelines. Pharmacol Ther. 2022;238:108185.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFelker GM, Allen LA, Pocock SJ, Shaw LK, McMurray JJ, Pfeffer MA, et al. Red cell distribution width as a novel prognostic marker in heart failure: data from the CHARM Program and the Duke Databank. J Am Coll Cardiol. 2007;50(1):40\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang L, Li L, Ouyang QR, Chen P, Yu M, Xu L. Association between the hemoglobin-to-red cell distribution width ratio and three-month unfavorable outcome in older acute ischemic stroke patients: a prospective study. Front Neurol. 2025;16:1534564.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLai T, Liang Y, Guan F, Hu K. Trends in hemoglobin-to- red cell distribution width ratio and its prognostic value for all-cause, cancer, and cardiovascular mortality: a nationwide cohort study. Sci Rep. 2025;15(1):7685.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Chen Z, Yang H, Li H, Chen R, Yu J. Relationship between the Hemoglobin-to-Red Cell Distribution Width Ratio and All-Cause Mortality in Septic Patients with Atrial Fibrillation: Based on Propensity Score Matching Method. J Cardiovasc Dev Dis. 2022;9(11).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoradduzza D, Medici S, Chessa C, Zinellu A, Madonia M, Angius A et al. Assessing the Predictive Power of the Hemoglobin/Red Cell Distribution Width Ratio in Cancer: A Systematic Review and Future Directions. Med (Kaunas). 2023;59(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO'Meara E, Rouleau JL, White M, Roy K, Blondeau L, Ducharme A, et al. Heart failure with anemia: novel findings on the roles of renal disease, interleukins, and specific left ventricular remodeling processes. Circ Heart Fail. 2014;7(5):773\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiaz-Canestro C, Pentz B, Sehgal A, Montero D. Sex differences in cardiorespiratory fitness are explained by blood volume and oxygen carrying capacity. Cardiovasc Res. 2022;118(1):334\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSemba RD, Patel KV, Ferrucci L, Sun K, Roy CN, Guralnik JM, et al. Serum antioxidants and inflammation predict red cell distribution width in older women: the Women's Health and Aging Study I. Clin Nutr. 2010;29(5):600\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLecour S. Remote ischaemic conditioning modulates platelet reactivity: the need to optimize the therapy is more important than ever before. Cardiovasc Res. 2021;117(2):346\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao X, Wu W, Mao Q, Li B, Wang J, Liu S, et al. Single-cell transcriptomic profiling reveals cell type heterogeneity between HFpEF and HFrEF. Commun Biol. 2025;8(1):1436.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSorop O, Heinonen I, van Kranenburg M, van de Wouw J, de Beer VJ, Nguyen ITN, et al. Multiple common comorbidities produce left ventricular diastolic dysfunction associated with coronary microvascular dysfunction, oxidative stress, and myocardial stiffening. Cardiovasc Res. 2018;114(7):954\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahamim E, Zwas DR, Keren A, Elbaz-Greener G, Ibrahimli M, Amir O et al. The Ratio of Hemoglobin to Red Cell Distribution Width: A Strong Predictor of Clinical Outcome in Patients with Heart Failure. J Clin Med. 2022;11(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuthahar N, Meems LMG, Groothof D, Bakker SJL, Gansevoort RT, van Veldhuisen DJ, et al. Relationship between body mass index, cardiovascular biomarkers and incident heart failure. Eur J Heart Fail. 2021;23(3):396\u0026ndash;402.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTseliou E, Terrovitis JV, Kaldara EE, Ntalianis AS, Repasos E, Katsaros L, et al. Red blood cell distribution width is a significant prognostic marker in advanced heart failure, independent of hemoglobin levels. Hellenic J Cardiol. 2014;55(6):457\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhazi L, Yamamoto Y, Riello RJ, Coronel-Moreno C, Martin M, O'Connor KD, et al. Electronic Alerts to Improve Heart Failure Therapy in Outpatient Practice: A Cluster Randomized Trial. J Am Coll Cardiol. 2022;79(22):2203\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLala A, Mentz RJ. Making Time for Diastole While Getting the Work Done. J Card Fail. 2023;29(7):985.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCamplain R, Kucharska-Newton A, Loehr L, Keyserling TC, Layton JB, Wruck L, et al. Accuracy of Self-Reported Heart Failure. The Atherosclerosis Risk in Communities (ARIC) Study. J Card Fail. 2017;23(11):802\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao H, Wang H, Li T, Feng X, Liu D, Zhang Y, et al. Weekend catch-up sleep and frailty in US adults: a cross-sectional study from NHANES 2017\u0026ndash;2020. BMC Public Health. 2025;25(1):1481.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"heart failure, NHANES, red cell distribution width, hemoglobin-to-red blood cell distribution width ratio, hemoglobin","lastPublishedDoi":"10.21203/rs.3.rs-7809317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7809317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHemoglobin-to-red cell distribution width ratio (HRR), a composite index reflecting both oxygen-carrying capacity and erythrocyte heterogeneity, has recently been proposed as a prognostic biomarker in cardiovascular disease. However, its association with heart failure (HF) in the general population remains unclear. We analyzed data from NHANES 2011\u0026ndash;2018, applying survey-weighted logistic regression and restricted cubic spline (RCS) models to examine the association between HRR and self-reported HF. Discriminative ability was evaluated using receiver operating characteristic (ROC) curves. Higher HRR was independently and nonlinearly associated with lower prevalence of HF after adjustment for demographics, laboratory indices, cardiometabolic risk factors, and comorbidities. ROC analysis showed that HRR (AUC\u0026thinsp;=\u0026thinsp;0.671, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) had greater discriminative ability than hemoglobin (AUC\u0026thinsp;=\u0026thinsp;0.600), BMI (AUC\u0026thinsp;=\u0026thinsp;0.620), and triglycerides (AUC\u0026thinsp;=\u0026thinsp;0.557), though slightly inferior to red cell distribution width (RDW) (AUC\u0026thinsp;=\u0026thinsp;0.694). In a nationally representative sample, HRR demonstrated an independent inverse association with HF and provided additional discriminative value beyond hemoglobin alone. These findings suggest that HRR may serve as a readily available biomarker to aid in HF risk stratification.\u003c/p\u003e","manuscriptTitle":"Hemoglobin to red blood cell distribution width ratio inversely associated with heart failure in NHANES 2011–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 08:37:10","doi":"10.21203/rs.3.rs-7809317/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":"449a5c9c-734c-4c28-b5e1-4dc5ea1aa251","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T17:25:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-27 08:37:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7809317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7809317","identity":"rs-7809317","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}