Heart failure (HF) is a global cardiovascular disorder with a steadily increasing incidence worldwide. HF has emerged as one of the leading causes of hospitalization in the United States, with a steadily increasing societal burden [1]. According to the World Health Organization, the prevalence of HF among adults globally during 2017–2020 was approximately 2.5%. HF is a leading cause of disability and mortality, accounting for 13.4% of all sudden deaths in 2018 [2]. Although a declining trend in the relative mortality of HF was observed prior to 2012, the fatality rate since then has risen continuously [3]. Projections indicate that by 2030, the number of HF patients in the United States will exceed 8 million, with a prevalence approaching 3%, and particularly affecting the elderly population [4]. Consequently, further research is urgently needed to improve the prediction of HF.

Malnutrition is a common comorbidity associated with HF, with detrimental effects across diverse environmental contexts and regardless of the health status of patients. Malnutrition is also closely associated with the mortality and disability rates of cardiovascular diseases [5]. The controlling nutritional status (CONUT) index and allostatic load (AL) are two important tools for assessing an individual’s nutritional status and physiological system imbalance. Although they have shown some promise in predicting HF, their efficacy remains to be fully evaluated. Previous studies have reported a robust correlation between CONUT and HF [6, 7, 8], with the strength and direction of the association varying across different HF subtypes [9]. A significant positive correlation was observed between albumin concentration and the readmission rate, and hence albumin concentration may represent an independent risk factor for HF [10, 11]. In contrast, there is still only limited research on the relationship between AL and HF. A positive correlation was found between AL and HF, with age identified as a potential effect modifier [12]. Compared to CONUT, AL incorporates neuroendocrine components in addition to cardiovascular and metabolic parameters [13]. Whether this innovative inclusion enhances the predictive accuracy for HF warrants further investigation.

Comorbidity research in HF has recently emerged as a prominent focus in both domestic and international studies, contributing to a deeper understanding of HF pathophysiology. With regard to psychosocial factors, depression poses a significant global health burden and is characterized by persistent low mood, anhedonia, and diminished interest [14, 15]. Metabolic syndrome is a pathological condition closely associated with cardiovascular diseases. It is primarily defined by insulin resistance, atherosclerosis-related dyslipidemia, central obesity, and hypertension [16]. Recent studies have demonstrated significant correlations between components of metabolic syndrome and depression [17], with oxidative stress and inflammatory mediation playing pivotal roles [18].

In the present study, we therefore investigated whether psychosocial factors can influence the development of HF by altering metabolic profiles. We conducted subgroup analyses to examine the relationships between CONUT, AL, and HF across different depressive states using various models. These models incorporated covariates presumed to be associated with HF, including demographic and cardiovascular variables. Furthermore, by employing net reclassification improvement (NRI) and integrated discrimination improvement (IDI) methodologies, we compared the performance of the models for predicting HF, with the aim of providing clinicians with enhanced tools for HF risk stratification.

HF is a clinical syndrome characterized by impaired capacity for cardiac pumping function or filling. This disease imposes a substantial socioeconomic burden in the United States and globally, with rising annual incidence and mortality rates [15]. The 2021 American Heart Association Heart Disease and Stroke Statistics Update projected that the prevalence of HF in the U.S. was likely to increase from 2.4% in 2012 to 3% in 2030, while the total treatment costs would increase from 30.7 billion dollars in 2012 to 69.8 billion dollars in 2030 [4, 25]. HF is therefore a critical public health challenge demanding urgent attention. To better understand the risk factors associated with HF, the current study incorporated two nutritional and physiological assessment indices—CONUT and AL—while taking into account the potential influence of psychiatric factors. Specifically, depression was evaluated as a possible mediating variable due to its potential effect on metabolic components.

The CONUT and AL indices demonstrated significant positive associations with HF in this study. Compared to Model 1 (unadjusted) the ORs for both indices showed a downward trend across models that were adjusted for demographic and cardiovascular factors, suggesting potential confounding effects. When categorized by CONUT and AL levels, significantly elevated ORs were observed in the high CONUT/AL groups compared to their low-level counterparts. Notably, the moderate malnutrition subgroup in the CONUT stratification maintained a significant association with HF after adjusting for all confounders, consistent with this factor being closely related to HF. These findings align with similar conclusions reported by Chen et al. [9] and Agra Bermejo et al. [26].

Our subgroup analyses identified depressive status as a potential biological regulatory factor that attenuated the association between CONUT and HF. CONUT comprises serum albumin, total lymphocyte count, and total cholesterol levels, while depressive symptoms show positive correlations with metabolic abnormalities such as reduced HDL, increased lymphocyte count, and hyperlipidemia [17, 24, 27]. Although the serum albumin concentration shows a negative correlation with the incidence of depression [28], the low prevalence of hypoalbuminemia in the sample population contributed to an overall downward trend in CONUT associated with depression in the NHANES database cohort. Individuals with depression exhibit irregular eating patterns, leading to nutritional imbalances characterized by excessive intake of high-calorie, high-fat, and low-protein foods [29]. This is likely to contribute to a decline in CONUT. Currently, there is both domestic and international consensus on the positive correlation between depression and HF. Individuals with depression can activate the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic-adrenal medullary (SAM) axis. This directly induces myocardial cell hypertrophy and fibrosis, as well as increasing myocardial oxygen consumption, thereby promoting myocardial remodeling and accelerating the onset and progression of HF. The renin-angiotensin-aldosterone system (RAAS) is also activated, which induces vasoconstriction, increases cardiac afterload, and accelerates myocardial remodeling. Individuals with depression maintain a chronic low-grade inflammatory state, characterized by the sustained release of pro-inflammatory cytokines such as interleukin (IL)-6, which contributes to myocardial fibrosis and impairs myocardial contractility [30]. The attenuation of the CONUT-HF association under depressive states, as a hypothetical finding, warrants further validation through large-scale longitudinal studies.

We used RCS curves to visualize the relationship between continuous CONUT/AL and HF. This revealed a nonlinear association for CONUT, with a critical threshold value of 1.5. Beyond this threshold, CONUT emerged as a potential moderating factor for HF. Below the threshold, the ORs for HF exhibited a gradual decline with increasing CONUT. The nutritional status in the population under the threshold is relatively good or only mildly inadequate, and hence the body still has the compensatory capacity to maintain myocardial structure and function. This may explain why we observed a transient downward trend. However, once the threshold is exceeded, the degree of malnutrition worsens, the body enters a state of decompensation, and myocardial structure and function can undergo accelerated degeneration. As a consequence, the ORs for association with HF showed a significant increase. Secondly, factors such as reverse causality or survival bias may be involved. In cases of mild malnutrition, the body remains in a compensated state. This could lead to a transient increase in nutritional intake, explaining why the ORs exhibited a gradual decline before reaching the threshold. Finally, after exceeding the threshold, the nutritional status declined sharply, leading to poorer patient prognosis, increased short-term mortality, and study withdrawal, thereby indirectly introducing selection bias in the sample. Although attenuated at lower levels, the overall OR trend for HF progressively increased, with a steeper increase post-threshold compared to the pre-threshold decline, while maintaining OR values $>$1. These findings suggest that severe malnutrition significantly increases the risk of HF, which calls for increased monitoring of individuals with elevated CONUT to reduce mortality. This conclusion agrees with those of earlier studies that employed nutritional indices for HF prediction [6, 31, 32]. However, the potential HF-protective effect of low CONUT requires validation in prospective cohort studies. In contrast, AL exhibited a linear relationship with HF, showing a progressive increase in the OR with rising AL values. The high AL group demonstrated significantly stronger associations with HF than the low AL group. As an indicator of cumulative physiological stress burden, AL has previously been linked to both preserved and reduced ejection fraction HF [12]. The current study integrated demographic and cardiovascular factors into the analysis, building upon prior experience that combined physiological and socioenvironmental parameters [4, 33, 34, 35]. Although we observed a decrease in the OR for the association of AL with HF after demographic adjustment, the specificity and sensitivity of this predictive model were substantially improved.

Our study compared the predictive efficacy of CONUT and AL for HF using ROC curves, AUC, NRI, and IDI. In the depressive population, AL-based prediction models demonstrated superior performance over CONUT in both the unadjusted model and in models adjusted for demographic factors. The predictive performance of CONUT models improved significantly following adjustment for cardiovascular risk factors, surpassing that of AL models adjusted for comparable factors. Among non-depressed individuals, the unadjusted AL model initially outperformed CONUT. However, following adjustments for demographic and cardiovascular factors, the predictive performance of CONUT models showed significant improvement and exceeded that of AL models. Notably, when demographic and cardiovascular factors were jointly adjusted, the AUC values of AL and CONUT reached optimally comparable levels. The inclusion of additional factors may capture more predictive information, thereby enhancing the model’s discriminative ability. In the non-depression subgroup, the predictive performance after incorporating additional factors was generally higher in the CONUT-related models compared to the AL-related models. However, the opposite pattern was observed in the depression subgroup. Subgroup analysis exploring the relationships between CONUT, AL, and HF did not identify any specific mediating factors, except that depression status was found to modulate the association between CONUT and HF. Further comparison of the models with NRI and IDI revealed no significant differences between the two models in terms of their efficacy of HF prediction across different depressive states. These findings suggest that adjusting for demographic and cardiovascular factors substantially improves the predictive performance of both CONUT and AL models. The integration of nutritional-metabolic and physiological homeostasis indicators, supplemented by socioenvironmental and psychiatric factors, offers good perspectives for the prediction of HF.

One of the strengths of this study was the inclusion of depressive status as a mediating factor in determining the predictive efficacy of CONUT and AL for HF. We propose a novel framework that incorporates psychiatric factors into HF prediction, thereby advancing the interdisciplinary integration of mental health and cardiovascular disease research. While CONUT serves as a risk factor for HF, our proposed CONUT prediction model also takes into account the influence of depression. Therefore, researchers should actively stratify samples according to depression status in order to reduce the occurrence of false-negative results.

This study had several limitations. Firstly, the small HF sample sizes in the high CONUT and AL groups could introduce bias. Further research with larger cohorts is required to ensure adequate statistical power. Secondly, the cross-sectional study design prevented the investigation of causal relationships between the indices and HF, highlighting the need for longitudinal designs that incorporate temporal dynamics. Thirdly, the cross-sectional design invariably led to the inclusion of confounding factors that may affect the associations between CONUT/AL and HF, thereby reducing the precision and sensitivity of our predictive models. Fourth, the NHANES database does not include specific clinical diagnostic data for HF or N-terminal pro–B-type natriuretic peptide (NT-proBNP) results related to HF. Consequently, it was not possible to assess diagnostic consistency for HF using metrics such as kappa $\approx$ 0.7, or to conduct sensitivity analyses. Fifth, the relatively small sample size of the moderately to severely malnourished population may limit the robustness of the conclusions. Finally, the diagnosis of depression in this study was primarily based on a PHQ $\ge$10, without the support of auxiliary diagnostic criteria such as clinical symptoms. This approach may introduce diagnostic bias, thereby compromising the reliability of conclusions regarding the mediating role of depressive status.

In the general population, CONUT and AL both emerged as potential moderating factors for HF. However, the associations between the two indices and HF differed significantly across different depressive states. While the positive correlation persisted in the non-depressed population, the association was attenuated in the depressed population.

Subgroup analyses of the included factors further confirmed that depression acts as a potential biological regulatory factor, influencing the positive association between CONUT and HF. While CONUT and AL are both clearly excellent predictors of HF, depressive status also appears to play a pivotal role. Therefore, it is essential to consider the influence of psychosocial factors on the predictive value of CONUT and AL for HF. Nutritional-metabolic, socio-environmental, and psychological factors should all be incorporated into predictive models.

The study’s original contributions are contained within the article/supplementary material. For additional information, please contact the corresponding authors.

LL, conception, design of the work, acquisition, analysis, interpretation of data for the work; YZ, design of the work, analysis, and review; AL, design of the work and review; JS, design of the work and review. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

This study was carried out in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Beijing Anzhen Hospital (ethical approval number: 2025224X). This study is a retrospective analysis based on a public database, and no additional informed consent from subjects is required.

Not applicable.

This research received no external funding.

The authors declare no conflict of interest.

Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.31083/RCM45879.