Introduction
Chronic heart failure (CHF) is a persistent state of heart failure and a major cause of death from various cardiovascular diseases. According to the European Heart Association’s Guidelines for Diagnosis and Treatment of Acute and Chronic Heart Failure, the prevalence of heart failure in developed countries ranges from 1.5 to 2.0% [
1]. According to the Report on Cardiovascular Health and Diseases in China 2021: an Updated Summary, there were 8.9 million patients with heart failure in China [
2]. Heart failure is a chronic and recurring disease, with a high 30-day readmission rate of up to 20–25% and a five-year survival rate of 56.7% [
3,
4]. The development of CHF is a lengthy process with a poor overall prognosis.
The concept of frailty is increasingly being considered in the study and treatment of CHF patients. Frailty is an age-related clinical syndrome characterized by reduced physiological reserves in stressful situations, constituting a state of vulnerability that involves a higher risk of adverse events [
5]. It adversely impacts the mortality of heart failure patients [
6], seriously interferes with the recovery process and increases the risk of suicide in CHF patient [
7]. Frailty encompasses both physical and mental aspects, and research indicates a close correlation between physical frailty in CHF patients and cognitive impairment [
8]. While physical frailty and cognitive impairment were previously studied separately, recent research has highlighted their close relationship [
9].
Thus, the concept of cognitive frailty (CF) was proposed to combine physical frailty and cognitive impairment. CF is characterized by the coexistence of physical frailty and cognitive impairment, with a Clinical Dementia Rating (CDR) score of 0.5, but excluding Alzheimer’s disease or other forms of dementia [
10]. Previous research has shown that CF is prevalent in patients with cardiovascular disease and can have a negative impact on their clinical outcome, functional status and quality of life [
11]. A study of Japanese heart failure patients over the age of 65 found that those with CF had a 1.55 times higher risk of poor prognostic outcomes in the first year compared to those without CF [
12]. Previous studies indicated that CF could be reversible [
13], thus, identifying and responding to risk factors for CF can help to slow down or even reverse its progression. However, the associated factors discovered in current studies of CF, such as age, education level, etc., are not intervenable [
14]. A review shows that social status, nutrition status, physical and cognitive activities and functional status are factors shown to be associated with CF [
15]. However, the effect of depression in CF remains controversial and we need to explore future [
16,
17]. Social support has been shown to be an influential factor for CF in hypertensive patients [
18] and other studies have found the effects of BMI and nutrition status of CF [
19]. This study includes some known influencing factors of CF and hypothesizes that these factors are also relevant for patients with CHF. Therefore, this study aims to investigate the prevalence and modifiable risk factors of CF in patients with CHF in China. We hope to draw the attention of medical staff and improve the prognosis of CHF patients by intervening on modifiable risk factors of CF and to improve the recovery process of patients and reduce mortality.
Discussion
This study aimed to examine the prevalence of cognitive frailty (CF) in patients with chronic heart failure (CHF) and identify any modifiable risk factors. Our findings revealed a high prevalence of CF in this population, with BMI, blood pressure level, nutrition status and social support being identified as significant modifiable risk factors for CF.
In this study, it was found that the total prevalence of CF in patients with CHF was 49.4% [95%CI = 43.5–55.4%], with reversible cognitive frailty at 20.7% and potentially reversible cognitive frailty at 28.8%. Researchers from South Korea [
29] and Japan [
12] evaluated heart failure patients over the age of 65 and discovered that 34.5% and 23% of patients had CF, respectively. They did not investigate the two types of CF separately. However, Yao et al. investigated the prevalence of frailty and cognitive impairment in hospitalized patients with cardiovascular diseases and discovered that only 8% of patients had both conditions [
30]. This difference may be due to the fact that Yao’s study included hospitalized patients with various cardiovascular disease, whereas our study only included CHF patients. Meanwhile, we used different assessment tools. Currently, cognitive and frailty assessment tools are commonly used to assess CF, but uniform assessment standards have yet to be developed. As the terminal stage of various cardiovascular diseases, CHF has a long course that involves many different systems and mechanisms. These include brain changes, vascular mechanisms, inflammation, hormones, sarcopenia, oxidative stress process, mitochondrial dysfunction and intestinal microbiome changes, all of which have a significant impact on physical frailty and cognitive function [
11]. Simultaneously, it was found that the prevalence of CF in the elderly and middle-young patients undergoing maintenance hemodialysis was 35.8% and 8.8%, respectively, indicating that middle-young patients are equally susceptible to CF [
19]. Our study found that the prevalence of CF was 17.1% among middle-young CHF patients, suggesting that the prevalence of CF in CHF patients of different age ranges should not be ignored. Previous studies on CF have focused on the elderly population, with little attention paid on middle-young demographic. Despite the limited number of middle-young patients included in this study, we found a significant prevalence of CF in this population. While CF has been extensively studied in the field of geriatrics, more research is needed to understand the current status of CF in middle-aged individuals. For middle-young CHF patients with CF, appropriate interventions should be implemented promptly to reverse CF, prevent its worsening with age, and reduce the risk of poor cardiovascular outcomes.
In this study, it was found that BMI was a modifiable risk factor of CF in patients with CHF (
OR = 0.826, 95%CI = 0.726–0.938). Rietman et al. conducted a cohort research which discovered a slight linear correlation between BMI and CF, with higher BMI being associated with more severe CF [
31]. However, other studies have found opposite results, with a higher BMI being linked to a lower risk of CF [
32]. Therefore, the relationship between BMI and CF remains contentious. By reviewing data from the CLHLS project, Ju et al. discovered a U-shaped correlation between BMI, waist circumference (WC), and frailty in elderly Chinese women [
33]. However, the biological mechanism underlying this relationship remains unknown. Further research is needed to identify additional indicators with predictive value. The study found that only being overweight was a risk factor for CF, while being underweight may also affect CF in patients with CHF. However, the specific effect and degree of influence of BMI require further investigation.
Nutrition status is another modifiable risk factor of CF (
OR = 0.810, 95%CI = 0.671–0.979). Numerous studies have shown that malnutrition is a significant risk factor for CF [
34], and it is negatively correlated with nutrition status. Malnutrition can lead to weight loss, muscle tissue loss, body wasting, and other physical frailties, ultimately contributing to the development of CF. Furthermore, the lack of essential nutrients such as serum proteins, vitamins, and trace elements can affect cognitive function, which is also a crucial factor in the development and progression of physical frailty and cognitive impairment [
35]. Zupo et al. found that malnourished elderly people with CF had a higher mortality rate [
36], highlighting the urgent need to improve the nutrition status of patients with CF. Consequently, we aim to substantiate the impact of nutrition status on CF in patients with CHF through forthcoming clinical trials.
Patients with hypertension had a 2.323 times higher likelihood of developing CF compared to those without hypertension (95%CI = 1.105–4.882). Wang et al. found that 9.8% of elderly hypertensive patients had CF [
18], While up to 28% of hypertension and diabetes patients who underwent physical examination in community health service centers had CF [
34]. These findings suggest that the prevalence of CF in elderly hypertensive patients in China should not be ignored. Scholars have identified that frequent morbidity, such as hypertension and heart disease, as influential factor for CF [
37]. This may indicate that hypertension, heart disease and CF are interrelated and interconnected. As a major cause of cardiovascular disease, hypertension can damage brain capillaries, leading to cognitive impairment and accelerating the development of dementia. These risk factors for cognitive impairment are associated with the onset and deterioration of PF. Consequently, there is a necessity to initiate clinical trials to investigate the impact of hypertension on CF in patients with CHF.
We have found that social support is a modifiable risk factor for CF (
OR = 0.745, 95%CI = 0.659–0.842). Studies have shown that low social support is an independent risk factor of CF in the elderly [
38]. Apart from CHF, Wang et al. found that social support was also an independent factor impacting CF in hypertensive patients [
18]. We speculate that social support may be a factor influencing CF in other diseases. Lack of social support may lead to various psychological problems. However, this study did not find evidence that depression, anxiety, or other mental states are independent factors influencing CF in CHF patients. Hou et al. found that depressive symptoms do not directly affect CF in older adults, but rather, feelings of loneliness are the link between the two [
39]. Loneliness can reflect social support to some extent. However, Wang et al. found that psychological distress could regulate the relationship between social support and the prevalence of CF [
17]. Therefore, the relationship between mental state and CF remains vague and requires further study.
The study did not find old age and low education level to be significant risk factors for CF, despite their correlation in the univariate analysis. This discrepancy may be attributed to the specific population and region included in the study. Although we transformed some continuous variables, we re-analyzed the data using the original continuous variables. Variables associated with CHF were treated as additional independent variables associated with CF risk. The prevalence of HFrEF was higher in the non-CF group (41%) compared to the CF groups (27% and 25%), while the prevalence of HFpEF was higher in the CF groups. This would suggest that CF is more prevalent in diastolic dysfunction rather than systolic dysfunction, but further studies are needed. Importantly, more than half of the Non-CF patients had CHF for ≤ 1 year, while the CF groups had a longer duration. This is significant in the regression analysis, where we seek to identify modifiable risk factors for CF. Additionally, the Non-CF group had a higher prevalence in the lower NYHA classes. In this study, we also found a strong correlation between CHF and CF, particularly through the indicators associated with CHF. As a result, it is crucial to monitor the condition of CHF patients and provide them with relief from CF while maintaining disease stability.
In this study, we broadened the scope of our research by including patients above the age of 18. This is because middle-aged and young people are also at risk of cognitive frailty, which has been previously overlooked in studies that mainly focused on the elderly. We also categorized patients into two groups: reversible cognitive frailty and potentially reversible cognitive frailty to better understand the status quo of CF in CHF patients. We identified modifiable risk factors that influence CF in patients with CHF. Nonetheless, clinical trials are imperative to ascertain whether these modifiable risk factors have the potential to reverse CF in CHF patients. This study adds to the current situation in domestic research on cognitive frailty in CHF patients above the age of 18.
Although this study has yielded valuable insights, there are still some limitations that need to be addressed. Firstly, it is a single-center study, and the majority of patients are from nearby towns and cities, which limits the generalizability of the findings. Future multi-center studies with large sample sizes are needed to overcome this limitation. Secondly, this study is constrained by its cross-sectional design, which impedes our ability to establish the predictive value of the aforementioned factors for CF. We urge and encourage researchers to delve further into the relationship among these factors. Future investigations could benefit from non-clinical population controls and longitudinal follow-up studies to provide a more comprehensive understanding of these associations. Thirdly, prevalence estimates obtained from convenience sampling are strongly subjected to sampling bias. In future studies, we intend to employ a more judicious and rational sampling method to enhance the robustness and generalizability of our findings. Finally, no unique influencing factors connected to CHF are discovered in this investigation, which may be related to the disease severity of the included patients. Additionally, the baseline data included in this study are incomplete, and there may be undiscovered influencing factors that require further investigation.
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