The NHANES is a nationally representative continuous health survey project conducted by the National Center for Health Statistics (NCHS) under the Centers for Disease Control and Prevention (CDC). The database covers a wide range of information on demographics, physical measurements, laboratory tests, and health questionnaires, providing a rich data resource for medical research. No additional informed consent or ethical review was required as all study participants provided informed consent and the NCHS Institutional Review Board approved the study. All methods were performed in accordance with relevant guidelines and regulations.

In this study, data from the NHANES database spanning the years 2001–2010 were carefully screened to identify the elderly population aged 60 years or older. The inclusion criteria were designed to ensure the availability of complete data regarding vitamin D levels, the CAR, and disease-related markers of CHF. Conversely, individuals with severe hepatic and renal disorders, malignancies, autoimmune diseases (specifically rheumatoid arthritis), and other conditions that could potentially impact vitamin D metabolism and the inflammatory response were excluded. Following this rigorous screening procedure, a total of 4128 elderly individuals were selected for inclusion in the study. The sample selection process is presented in Fig. 1 through a detailed flowchart, which demonstrates the steps and criteria employed to determine the final study sample.

Fig. 1.

Flow diagram of selecting populations for analysis. CHF, congestive heart failure; CAR, C-reactive protein to albumin ratio; NHANES, National Health and Nutrition Examination Survey.

Flowchart illustrating the process of selecting study participants aged 60 years and above from the NHANES database between 2001 and 2010, including inclusion and exclusion criteria, and the final inclusion of 4128 individuals.

Serum samples from NHANES 2001–2010 were evaluated by liquid chromatography-tandem mass spectrometry. Serum total vitamin D levels (nmol/L) were calculated by totaling the levels of 25-hydroxyvitamin D2 and 25-hydroxyvitamin D3. Vitamin D deficiency was defined as 25(OH)D 20 ng/mL, vitamin D insufficiency was defined as 20 ng/mL $\le$ 25(OH)D 30 ng/mL, and vitamin D sufficiency was defined as 25(OH)D $\ge$30 ng/mL [20]. Participants with missing data on serum vitamin D concentration were removed from this study.

CHF disease history was obtained through an interview. Participants were asked, ‘Has a doctor or other health professional ever told you that you have congestive heart failure?’ Patients were considered to have CHF if they answered ‘yes’ to the above question [21].

CAR was defined as serum CRP (mg/dL)/serum albumin (g/dL) and both CRP and albumin (ALB) were collected at a mobile screening center and sent to the laboratory for analysis. Prior to collection, blood samples were screened against specific exclusion criteria [9, 22].

In our study, several potential covariates were considered, including demographic factors, living status variables, disease status indicators, and biochemical indices. Demographic information encompassed sex, age, race, marital status, and educational attainment. Living status variables involved smoking habits, alcohol consumption patterns, and body mass index (BMI). Disease status included hypertension, diabetes mellitus, cancer, hepatic and renal diseases, and autoimmune diseases (specifically rheumatoid arthritis). The information regarding hypertension, diabetes mellitus, cancer, hepatic and renal diseases, and rheumatoid arthritis was obtained through questionnaires. In terms of biochemical indices, total cholesterol (TC) and aspartame nontransferable (AST) were utilized. All data collection and handling processes adhered to a standardized protocol to systematically investigate the potential impact of these covariates on the relationship between the CAR and CHF.

CAR was categorized into quartiles (Q1 $\le$0.0262, Q2 0.0262–0.055, Q3 0.055–0.1167, Q4 $>$0.1167) to evaluate dose-response relationships, with Q1 (lowest CAR) as the reference group. Continuous variables were tested for normality using the Shapiro-Wilk test; non-normally distributed data were reported as median (IQR). Categorical variables were presented as frequencies (%). Group comparison: Analysis of Variance (ANOVA) with Bonferroni correction compared normally distributed variables across CAR quartiles; Kruskal-Wallis test was used for non-normally distributed variables across multiple groups, with Mann-Whitney U tests for post-hoc pairwise comparisons when significant group differences were found. Correlation & regression: Pearson’s correlation analysis explored the relationship between vitamin D (25(OH)D, ng/mL), CAR, and the relationship between CHF and vitamin D, and CAR using spearman analysis. Multivariate logistic regression models assessed independent effects of CAR and vitamin D (treated as a continuous variable) on CHF, with stepwise adjustment: Model 1: Unadjusted; Model 2: Adjusted for gender, age, race; Model 3: Model 2 + education, alcohol, smoking, BMI; Model 4: Model 3 + hypertension, diabetes, TC. Mediation & ROC: Hayes’ PROCESS Model 4 (adjusted for Model 4 covariates) tested the mediating role of vitamin D, using 5000 bootstrap samples for 95% confidence intervals (CIs) of indirect effects. Receiver operating characteristic (ROC) curves with 1000-bootstrap resampling evaluated CAR’s predictive accuracy for CHF, calculating the area under the curve (AUC) and identifying the optimal cut-off via Youden’s index (sensitivity + specificity –1). Subgroup analysis: Likelihood ratio tests evaluated effect modification by demographic (age, sex, race) and clinical (hypertension, diabetes) factors to assess interaction effects. All analyses were performed using SPSS 27.0 (IBM Corp., Armonk, NY, USA), with statistical significance set at p 0.05.

Table 1 presents a summary of the baseline characteristics of the study population, stratified by the presence or absence of CHF. Among the included elderly adults, 28.78% had vitamin D deficiency, 42.44% had vitamin D insufficiency, and 28.78% had sufficient vitamin D levels. Further analysis indicated that the rates of vitamin D deficiency and insufficiency were higher in men compared to women. Racial disparities were also observed, with non-Hispanic whites having the highest prevalence of vitamin D deficiency. Out of the 4128 participants, 247 were diagnosed with CHF, resulting in an overall CHF prevalence of 5.98% in the study population. The mean age of the participants was 70 years. Regarding gender distribution, 55.81% of the participants were male, and 44.19% were female. Statistical analyses revealed that men, individuals with higher educational attainment, drinkers, smokers, those with hypertension, and patients with vitamin D deficiency had a significantly higher risk of developing CHF compared to their counterparts (all p 0.05). Notably, within the CHF group, although non-drinkers accounted for a higher proportion (72.87%) compared to drinkers (27.13%), this does not contradict the observed higher risk of CHF in drinkers when compared across groups (CHF vs. No-CHF). This within-group proportion highlights the complexity of analyzing risk factors and underscores the importance of considering both within- and between-group perspectives in interpreting such data.

Clinical baseline characteristics of the study population stratified by the presence or absence of CHF. This includes the distribution of vitamin D levels (deficiency, insufficiency, sufficiency), CHF prevalence, age, gender distribution, and the risk differences of CHF among various demographic and clinical characteristics (e.g., higher risk of CHF in males, higher education, drinkers, smokers, hypertensive patients, and vitamin D deficient individuals).

Table 2 displays the CAR values of the participants stratified into quartiles. The median CAR value was 0.055. Q1 represents values below 0.0262, Q2 encompasses values ranging from 0.0262 to 0.055, Q3 includes values from 0.055 to 0.1167, and Q4 corresponds to values above 0.1167. The level of CAR was found to be correlated with multiple factors, including age, gender, BMI, smoking status, and alcohol consumption. Specifically, there was a statistically significant association between CAR levels and age (p = 0.035), but the trend was not a simple increase with age. Men exhibited higher CAR levels compared to women. A positive correlation was detected between BMI and CAR levels, such that the higher the BMI, the higher the CAR level. Smokers showed a progressive increase in proportion across higher CAR quartiles (p 0.001), suggesting smokers were associated with elevated CAR levels. Additionally, drinking status distribution differed significantly across CAR quartiles (p = 0.008), indicating a potential association. Moreover, the prevalence of CHF increased in tandem with the increasing CAR values of the participants. The CHF prevalence rates for Q1, Q2, Q3, and Q4 were 4.03%, 4.85%, 6.41%, and 8.66% respectively, with a statistically significant difference (p 0.001).

Pearson correlation analysis showed that vitamin D level was significantly negatively correlated with CAR (r = –0.12, p = 0.004), i.e., the lower the level of vitamin D, the higher the level of CAR; Given that CHF is a dichotomous variable, a Spearman correlation analysis was performed. The results showed a significant negative correlation between CHF and vitamin D ($\rho$ = –0.061, p 0.01), implying that higher levels of vitamin D were associated with a lower risk of developing CHF. On the contrary, there was a significant positive correlation between CHF and CAR ($\rho$ = 0.080, p 0.01), suggesting that higher levels of CAR are associated with an increased likelihood of CHF. This suggests that vitamin D may influence CAR levels by modulating the inflammatory response, which in turn may have an impact on the development of CHF.

The associations between CAR and CHF, as well as vitamin D and CHF, are presented in Table 3, derived from multivariate logistic regression analyses. In the unadjusted model, CAR was significantly and positively associated with CHF (odds ratio (OR) = 2.09, 95% CI: 1.49–2.94, p 0.001). This association persisted after adjusting for gender, age, and race in Model 2 (OR = 2.10, 95% CI: 1.49–2.96, p 0.001). Model 3, which additionally adjusted for education, alcohol consumption, smoking, and BMI, also showed a significant association (OR = 2.05, 95% CI: 1.45–2.88, p 0.001). Even after further adjusting for hypertension, diabetes, and TC in Model 4, the significant correlation remained (OR = 2.16, 95% CI: 1.51–3.09, p 0.001). These results collectively demonstrate a robust positive correlation between CAR and CHF across all models. Meanwhile, the negative correlation between vitamin D and CHF, along with corresponding OR, CI, and p-values, is also detailed in Table 3.

After stratifying CAR into quartiles, the strong correlation between CAR and CHF was still evident, and the trend test was statistically significant (p 0.05). In Model 4, comparing the highest quartile of CAR with the lowest quartile revealed a 96% increase in the incidence of CHF (OR = 1.96, 95% CI: 1.31–2.95, p 0.001). Moreover, a significant negative correlation was found between vitamin D and CHF. After adjusting for the variables in Model 4, each unit increase in the serum concentration of vitamin D was associated with a 0.97-fold reduction in the odds of developing CHF (OR = 0.97; 95% CI: 0.95–0.99; p 0.001). In Model 1, for the category of 20 ng/mL $\le$ 25(OH)D 30 ng/mL, the statistical software output shows an OR of 0.77, with a 95% CI of 0.57$\sim$1.03, and a p-value of 0.076.

These findings suggest that both vitamin D and CAR are independent risk factors for CHF. This further corroborates the crucial role of vitamin D and CAR in the pathogenesis of CHF, and demonstrates that their effects on CHF remain significant even after controlling for other common risk factors.

The results of the linear regression analyses exploring the relationship between the CAR and vitamin D are presented in Table 4. In the unadjusted model, CAR was negatively associated with vitamin D levels ($\beta$ = –1.68; 95% CI: –2.82– –0.54; p = 0.004), indicating that as CAR increased, vitamin D levels tended to decrease. However, after adjusting for variables such as gender, age, race, education, drinking status, smoking, BMI, hypertension, diabetes, and TC, this significant association was no longer observed.

To accurately evaluate the predictive power of the CAR level for CHF, we calculated the AUC of the ROC curve for the study subjects. The results are presented in Fig. 2. Our study findings revealed that the AUC value of the CAR level was 0.597. While this suggests CAR has a marginal predictive ability for CHF compared to random guessing (AUC = 0.5), the modest AUC indicates that CAR alone holds limited clinical predictive value for CHF. These results warrant cautious interpretation, and additional research is needed to explore biomarker combinations or refined models with enhanced predictive accuracy. Through further analysis, the optimal cut-off value of CAR was determined to be 0.149. At this cut-off value, the sensitivity for predicting CHF was 59.1% and the specificity was 56.4% (since specificity = 1 – 0.436), achieving an optimal balance between sensitivity and specificity for prediction.

Fig. 2.

ROC curves and the AUC values of CAR in diagnosing CHF. AUC, area under the curve; ROC, receiver operating characteristic.

To evaluate the stability of the association between the CAR and CHF and to identify potential differences among different subgroups, we conducted subgroup analyses. The results of these analyses are presented in Table 5. In the sensitivity test, we observed that the ‘p-value for interaction’ was greater than 0.05. This finding indicated that the positive association between CAR and CHF was generally stable and consistent across the overall population.

Mediation analyses were performed to assess whether vitamin D mediates the association between CAR and CHF disease occurrence. Mediation analyses were performed by establishing three pathways. For this purpose, we set up three pathways: (1) exposure to mediator; (2) mediator to outcome (direct effect); and (3) exposure to outcome (total effect). The total effect reflects the sum of the direct and mediated (indirect) effects. The percentage of mediated effects was calculated using the following formula: (mediated effect/total effect) $\times$ 100 [23]. Mediation analyses were performed to assess whether vitamin D mediated the association between CAR and CHF development. The model for mediation analysis is shown in Fig. 3 and the pathway for mediation analysis is shown in Table 6.

Fig. 3.

Path diagram of the mediation analysis models. In the mediation analysis framework, the “c - path” represents the total effect of EXPOSURE (CAR) on OUTCOME (CHF), while the “c’ - path” denotes the direct effect, excluding the influence mediated by vitamin D.

Mediation effect analysis models and results for the role of vitamin D in the association between CAR and CHF. This includes the total effect of CAR on CHF, the direct effect, the mediating effect of vitamin D, and the proportion of the total effect mediated by vitamin D (3.00%), indicating that vitamin D plays a partial mediating role in the relationship between CAR and CHF.

The results of the mediation effect analysis indicated that the total effect of the CAR on CHF was significant ($\beta$ = 0.081, 95% CI: 0.052–0.111, p 0.01). This demonstrates that the overall impact of CAR on congestive heart failure is very significant. When vitamin D was introduced as a mediator variable, the direct effect of CAR on CHF remained significant ($\beta$ = 0.079, 95% CI: 0.05–0.108, p 0.01), yet the effect value decreased, the p-value is much less than 0.05, indicating that CAR has a significant direct impact on CHF. The mediating effect of vitamin D was also significant ($\beta$ = 0.002, 95% CI: 0.001–0.005, p = 0.039). Vitamin D partially mediated the association between CAR and CHF, but its contribution was low (3.00%).

This finding holds significant importance for clinical practice. Physicians can utilize this cut-off value to assess the risk of patients, enabling them to promptly identify individuals at potential risk of CHF and take appropriate preventive and therapeutic measures. For instance, if a patient’s CAR level exceeds this cut-off value, it indicates that the patient is at a high risk of developing CHF, and further in-depth examination and close monitoring of the patient are necessary.