Abstract
Background
Nutritional status is a critical determinant of clinical outcomes in patients with chronic heart failure (CHF), potentially contributing to adverse prognosis and suboptimal therapeutic response. Despite growing interest, the interplay between malnutrition and frailty syndrome (FS) in the CHF population remains inadequately elucidated.
Aims
The study aims to evaluate the association between malnutrition risk and the presence of FS in individuals hospitalized with CHF.
Methods
A total of 200 patients (mean age: 72.28 years) hospitalized due to CHF were enrolled. Data collection included retrospective analysis of medical records and application of validated instruments: the Mini Nutritional Assessment (MNA) for nutritional evaluation and the Fried phenotype criteria for frailty assessment.
Results
Based on MNA, 63.5% of participants demonstrated normal nutritional status, 35.0% were at risk of malnutrition and 1.5% were malnourished (mean MNA score: 24.25). According to the Fried phenotype, 35% were classified as pre‐frail and 65% as frail (mean frailty score: 2.67). A statistically significant association was identified between MNA score and frailty status (P < 0.05), with the highest prevalence of frailty observed in malnourished individuals (P = 0.002). No significant correlations were found between frailty and anthropometric parameters such as calf circumference (P = 0.17), arm circumference (P = 0.687) or body mass index (BMI) (P = 0.643).
Conclusions
These findings highlight the clinical importance of routine, comprehensive nutritional screening in patients with CHF. Early identification and management of malnutrition may play a pivotal role in mitigating frailty and enhancing clinical outcomes in this high‐risk population.
Keywords: heart failure, frailty syndrome, malnutrition, prolonged stay
This abstract presents a study of 200 heart failure patients (average age 72.3 years), assessing the relationship between malnutrition and frailty. Nutritional status was evaluated using the Mini Nutritional Assessment: 63.5% had normal nutrition, 35% were at risk, and 1.5% were malnourished. Frailty was measured using Fried's criteria: 35% were pre‐frail and 65% frail. A significant correlation (P < 0.05) was found between malnutrition and frailty. Early identification and treatment of malnutrition may reduce frailty and improve heart failure outcomes.
Introduction
In older patients with heart failure (HF), physical frailty and malnutrition often coexist, creating a vicious cycle in which each condition exacerbates the other, leading to poorer clinical outcomes. Evidence indicates that malnutrition is prevalent in HF, affecting up to 69% of patients across various demographics, including age, sex and left ventricular ejection fraction (LVEF). Among hospitalized HF patients, this prevalence reaches approximately 50%, with even higher rates observed in older adults. 1 This is particularly concerning given the frequent hospitalizations associated with HF, which further increase the risk of malnutrition. 2 Malnutrition is a well‐documented complication in HF patients and is associated with a worse prognosis. As a result, current HF treatment guidelines recommend including nutritional assessments and personalized interventions as part of comprehensive disease management programmes. 3 Despite various approaches to nutritional assessment, there is still no clear consensus on how to best diagnose malnutrition in HF patients.
In older patients, factors such as age‐related anorexia, deteriorating oral health and changes in taste perception significantly contribute to the risk of malnutrition. Ageing anorexia, characterized by a reduced appetite and decreased food intake, is often compounded by age‐related physiological changes and medications that further reduce appetite. 4 Chronic malnutrition can lead to sarcopenia, a condition marked by loss of muscle mass, which in turn results in decreased strength, walking speed and overall physical activity. 5 When combined with age‐related anorexia, the effects of chronic malnutrition can be even more pronounced.
These age‐related factors elevate the risk of malnutrition in older HF patients, making it a more pressing concern compared with younger individuals. The interaction between malnutrition and frailty plays a critical role in perpetuating the cycle of frailty. Frailty itself is a significant predictor of poor outcomes in HF patients. 6 The close association between malnutrition and frailty, particularly in the context of sarcopenia and cachexia, is well established. 7 Consequently, recent HF guidelines advocate for dietary interventions to manage both frailty and cachexia. 8 , 9 However, these guidelines also emphasize the need for further research to develop optimal nutritional therapies tailored to individual patient conditions, including comorbidities and the severity of HF.
Despite the well‐recognized importance of both frailty and malnutrition in determining HF prognosis, there remains a notable gap in research exploring their combined prevalence and impact. 6 , 10 Further studies are essential to understand how best to manage these interrelated conditions and improve clinical outcomes in this vulnerable patient population. Therefore, the aim of this research was to investigate the relationship between nutritional status (risk of malnutrition) and the occurrence of frailty syndrome (FS) among patients with CHF. Second, the aim was to evaluate relationship of malnutrition and frailty on prolong stay at the hospital in HF patients.
Methods
Participants
The study involved 200 consecutively enrolled patients diagnosed with HF who were admitted to a cardiology department due to an acute exacerbation of their condition. To qualify for the study, participants had to be at least 60 years old, have a confirmed HF diagnosis according to the European Society of Cardiology (ESC) guidelines, 8 have experienced HF for at least 6 months, be classified in New York Heart Association (NYHA) functional class II–IV and have been recently hospitalized for HF. Patients were excluded if they were in NYHA class I or declined to participate.
Data collection
Patients were recruited from the Institute of Cardiology of the University Clinical Hospital in Wroclaw between September 2022 and June 2023. Assessment of nutritional status and frailty was performed between 24 and 48 h after admission, once patients were clinically stabilized. Data were collected during hospitalization through structured patient interviews and review of medical records. Information on clinical characteristics, comorbidities, HF severity and outpatient pharmacotherapy was obtained at the time of assessment. Multimorbidity was defined as the presence of two or more chronic conditions, including but not limited to previous myocardial infarction, diabetes mellitus, chronic obstructive pulmonary disease (COPD) or asthma, coronary artery disease, hypertension, family history of cardiovascular disease, chronic kidney disease, stroke or other cerebrovascular diseases, connective tissue diseases, cancer, peptic ulcer disease and liver diseases. HF severity was assessed using the following clinical parameters: NYHA functional class (II–IV), N terminal pro brain natriuretic peptide (NT‐proBNP) levels at admission, LVEF, categorized as preserved, mildly reduced or reduced according to ESC guidelines. Data on chronic outpatient pharmacotherapy (used within 30 days prior to admission) were collected. Drug classes recorded included angiotensin‐converting‐enzyme inhibitors, angiotensin receptor blockers, angiotensin receptor neprilysin inhibitors, beta‐blockers, mineralocorticoid receptor antagonists, sodium‐glucose cotransporter 2 inhibitors, diuretics, calcium channel blockers, alpha‐blockers, statins, anticoagulants and antiplatelet agents. The study was approved by the Bioethics Committee of the Wroclaw Medical University (No. KB‐651/2022).
Research instruments
The Mini Nutritional Assessment (MNA) was established in 1994 by experts in nutrition, geriatrics and medicine to evaluate the nutritional status of older adults, particularly those aged 65 years and above. 11 It serves as a practical tool for identifying individuals at risk of malnutrition or who are already malnourished, applicable in both outpatient and hospital settings. The MNA is widely used by healthcare professionals and researchers to enable early intervention and improve patient outcomes. The assessment consists of two main components. The initial screening section includes six questions about weight changes, appetite, dietary intake, mobility, perceived health status and neuropsychological issues, with a maximum score of 14 points. If further evaluation is needed, a detailed assessment follows, addressing aspects like dietary habits, skin condition, medication use and anthropometric measurements, with a maximum score of 16 points. The total possible score for the MNA is 30 points, with results interpreted as follows: scores from 24 to 30 indicate good nutritional status, 17 to 23 suggest a risk of malnutrition and 0 to 16 indicate malnutrition. 12
The Fried Scale, introduced by Fried et al. in 2001, is designed to evaluate the risk of frailty in older adults. 13 It serves as a tool to identify individuals with diminished resilience to everyday stressors, which can increase the likelihood of falls, hospital admissions and lower quality of life. The scale assesses five key criteria: unintentional weight loss over the past year, reduced muscle strength (assessed by hand grip strength using a dynamometer), low physical activity levels, fatigue or low energy and a slower walking speed. Individuals meeting three or more criteria are classified as frail while those with one or two criteria are considered to be at an intermediate or pre‐frail stage. The Fried Scale thus helps healthcare providers recognize older adults who may need targeted medical care and interventions. 14
Statistical analysis
Comparison of categorical variables between groups was conducted using the χ 2 test (with Yates correction for 2 × 2 tables) or Fisher's exact test when the χ 2 test assumptions for expected counts were not met. Multivariate analysis of the effect of potential predictors on a dichotomous variable was performed using logistic regression. The results were rearranged in the form of OR (odds ratio) parameters along with 95% confidence intervals. A significance level of 0.05 was applied in the analysis, with all P values below 0.05 interpreted as indicating significant associations. The analysis was performed using R software, version 4.4.2.
Results
The average age of the studied group was 72.28 years (6.63 SD), 29.5% were women and 70.5% were men. Most participants were in NYHA class II (44.5%) or III (27.5%). The mean LVEF was 43.37%. The most common conditions included hypertension (88%), coronary artery disease (63%) and diabetes (57%). The analysis showed overweight in 37% of patients and obese in 38%. Central obesity was present in 82.5% of participants. The data are shown in Table 1.
Table 1.
Characteristics of sociodemographic and clinical data.
| Parameter | Total (N = 200) | |
|---|---|---|
| Age (years) | Mean (SD) | 72.28 (6.63) |
| Median (quartiles) | 73 (67–76) | |
| Range | 60–91 | |
| n | 200 | |
| Sex | Woman | 59 (29.5%) |
| Man | 141 (70.5%) | |
| Marital status | Single | 68 (34.0%) |
| In a relationship | 132 (66.0%) | |
| NYHA class | II | 89 (44.5%) |
| II/III | 21 (10.5%) | |
| III | 55 (27.5%) | |
| III/IV | 7 (3.5%) | |
| IV | 28 (14.0%) | |
| LVEF (%) | Mean (SD) | 43.37 (13.19) |
| Median (quartiles) | 44 (32–55) | |
| Range | 14–75 | |
| n | 200 | |
| Comorbidities a | Previous heart attack | 83 (41.5%) |
| Diabetes | 114 (57.0%) | |
| COPD/asthma | 38 (19.0%) | |
| Coronary artery disease | 126 (63.0%) | |
| Hypertension | 176 (88.0%) | |
| Cardiovascular diseases in the family | 52 (26.0%) | |
| Kidney diseases | 76 (38.0%) | |
| Stroke/cerebrovascular disease | 24 (12.0%) | |
| Connective tissue diseases | 45 (22.5%) | |
| Cancer | 39 (19.5%) | |
| Peptic ulcer disease | 23 (11.5%) | |
| Liver diseases | 19 (9.5%) | |
| BMI | Weight normal | 50 (25.0%) |
| Overweight | 74 (37.0%) | |
| Obesity | 76 (38.0%) | |
| Central obesity | No | 35 (17.5%) |
| Yes | 165 (82.5%) | |
Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; LVEF, left ventricular ejection fraction; NYHA, New York Heart Association.
Multiple‐choice question—percentages do not add up to 100%.
Table 2 presents findings from the Fried scale and MNA questionnaire. The mean Fried score was 2.67, with a median of 3 (range: 1–5). A substantial portion of participants (65%) were categorized as ‘frail’, and 35% as ‘pre‐frail’. The MNA results indicated an average score of 24.25, with a median of 24.5. Most patients (63.5%) were classified as having adequate nutrition, while 35% were at risk of malnutrition, and 1.5% were malnourished.
Table 2.
Fried scale and MNA results.
| Parameter | Total (N = 200) | |
|---|---|---|
| Fried (points) | Mean (SD) | 2.67 (0.9) |
| Median (quartiles) | 3 (2–3) | |
| Range | 1–5 | |
| n | 200 | |
| Fried | Pre‐frail | 70 (35.0%) |
| Frail | 130 (65.0%) | |
| Parameter | Total (N = 200) | |
|---|---|---|
| MNA (points) | Mean (SD) | 24.25 (2.63) |
| Median (quartiles) | 24.5 (22.5–26) | |
| Range | 14–28 | |
| n | 200 | |
| MNA | Malnutrition | 3 (1.5%) |
| Risk of malnutrition | 70 (35.0%) | |
| Proper nutrition | 127 (63.5%) | |
Abbreviation: MNA, Mini Nutritional Assessment.
Based on the analysis, a statistically significant relationship was observed between nutritional risk (MNA) and FS (FRIED). The percentage of patients with FS was the highest among malnourished patients and the lowest among properly nourished patients (P = 0.002). The data are shown in Table 3.
Table 3.
Influence of MNA scale on the occurrence of FS.
| Fried | MNA | Mr | ||
|---|---|---|---|---|
| Malnutrition (N = 3) | Risk of malnutrition (N = 70) | Proper nutrition (N = 127) | ||
| Pre‐frail | 0 (0.00%) | 15 (21.43%) | 55 (43.31%) | P = 0.002 |
| Frail | 3 (100.00%) | 55 (78.57%) | 72 (56.69%) | |
Note: P—Fisher's exact test
Abbreviations: FS, frailty syndrome; MNA, Mini Nutritional Assessment.
The study examined the relationship between obesity indicators [body mass index (BMI) and central obesity] and FS. Results indicated no statistically significant association between BMI (P = 0.643), central obesity (P = 0.38) and FS. Detailed data are presented in Table 4.
Table 4.
Influence of body weight (based on BMI scale) on the occurrence of frailty.
| Fried | BMI | Mr | ||
|---|---|---|---|---|
| Normal weight (N = 50) | Overweight (N = 74) | Obesity (N = 76) | ||
| Pre‐frail | 15 (30.00%) | 26 (35.14%) | 29 (38.16%) | P = 0.643 |
| Frail | 35 (70.00%) | 48 (64.86%) | 47 (61.84%) | |
| Fried | Central obesity | Mr | ||
|---|---|---|---|---|
| No (N = 35) | Yes (N = 165) | |||
| Pre‐frail | 15 (42.86%) | 55 (33.33%) | P = 0.38 | |
| Frail | 20 (57.14%) | 110 (66.67%) | ||
Note: P—χ 2 test.
Abbreviation: BMI, body mass index.
The analysis of the relationship between calf circumference (P = 0.17) and arm circumference (P = 0.687) with frailty (FRIED) revealed no statistically significant association. No relationship has been shown between multimorbidity and frailty (P = 0.756). Data are shown in Table 5.
Table 5.
The relationship between calf circumference, arm circumference and multimorbidity on the prevalence of frailty.
| Fried | Calf circumference | Mr | ||
|---|---|---|---|---|
| <31 cm (N = 10) | ≥31 cm (N = 190) | |||
| Pre‐frail | 1 (10.00%) | 69 (36.32%) | P = 0.17 | |
| Frail | 9 (90.00%) | 121 (63.68%) | ||
| Fried | Arm circumference | Mr | ||
|---|---|---|---|---|
| <21 cm (N = 4) | 21–22 cm (N = 3) | >22 cm (N = 193) | ||
| Pre‐frail | 1 (25.00%) | 0 (0.00%) | 69 (35.75%) | P = 0.687 |
| Frail | 3 (75.00%) | 3 (100.00%) | 124 (64.25%) | |
| Fried | Multimorbidity | Mr | ||
|---|---|---|---|---|
| No multimorbidity (N = 12) | Multimorbidity (N = 188) | |||
| Pre‐frail | 5 (41.67%) | 65 (34.57%) | P = 0.756 | |
| Frail | 7 (58.33%) | 123 (65.43%) | ||
Note: P—Fisher's exact test.
In the following part of the study, a logistic regression analysis was conducted. The multivariate logistic regression model showed that the risk of malnutrition or undernutrition raises the odds of frail 5.885 times (because OR = 5.885) compared with normal nutrition.
It was also noted that each additional year of life raises the odds of frail by 18.4% (because OR = 1.184). NYHA class III raises the odds of frail 2703 times (because OR = 2703) relative to class II, while NYHA IV raises the odds of frail 8036 times (because OR = 8036) relative to class II. The data are included in Table 6.
Table 6.
Univariate analysis of variables affecting the occurrence of frailty syndrome.
| Variable | N | Frail | OR | 95% CI | P | ||
|---|---|---|---|---|---|---|---|
| MNA | Normal nutritional status | 127 | 72 | 1 | Ref. | — | — |
| Risk of malnutrition/malnutrition | 73 | 58 | 5.885 | 2.491 | 13.901 | <0.001* | |
| Age (years) | — | — | 1.184 | 1.107 | 1.266 | <0.001* | |
| Sex | Woman | 59 | 40 | 1 | Ref. | — | — |
| Man | 141 | 90 | 0.882 | 0.363 | 2.142 | 0.781 | |
| Marital status | Alone | 68 | 48 | 1 | Ref. | — | — |
| In relationship | 132 | 82 | 0.591 | 0.259 | 1.347 | 0.211 | |
| NYHA class | II | 89 | 47 | 1 | ref. | — | — |
| III | 76 | 53 | 2.703 | 1.248 | 5.854 | 0.012* | |
| IV | 35 | 30 | 8.036 | 2.416 | 26.728 | 0.001* | |
| LVEF (%) | — | — | 0.98 | 0.95 | 1.012 | 0.216 | |
| Multimorbidity | No | 12 | 7 | 1 | Ref. | — | — |
| Yes | 188 | 123 | 0.495 | 0.099 | 2.484 | 0.393 | |
| BMI | Normal weight | 50 | 35 | 1 | Ref. | — | — |
| Overweight | 74 | 48 | 0.577 | 0.187 | 1.782 | 0.339 | |
| Obesity | 76 | 47 | 0.428 | 0.126 | 1.455 | 0.174 | |
| Central obesity | No | 35 | 20 | 1 | Ref. | — | — |
| Yes | 165 | 110 | 2.886 | 0.885 | 9.415 | 0.079 | |
Note: P—multivariate logistic regression.
Abbreviations: CI, confidence interval; LVEF, left ventricular ejection fraction; MNA, Mini Nutritional Assessment; NYHA, New York Heart Association; OR, odds ratio.
Statistically significant relationship (P < 0.05).
A multivariate logistic regression model showed that overweight reduces the odds of malnutrition or risk of malnutrition by 63.1% (OR = 0.369) relative to normal weight, and obesity reduces the odds of malnutrition or risk of malnutrition by 61.9% (because OR = 0.381) relative to normal weight. The data are presented in Table 7.
Table 7.
Multivariate analysis of variables affecting nutritional status.
| Variable | N | MNA < 24 | OR | 95% CI | P | ||
|---|---|---|---|---|---|---|---|
| Age (years) | — | — | 0.956 | 0.911 | 1.002 | 0.063 | |
| Sex | Woman | 59 | 23 | 1 | Ref. | — | — |
| Man | 141 | 50 | 0.701 | 0.329 | 1.493 | 0.357 | |
| Marital status | Alone | 68 | 23 | 1 | Ref. | — | — |
| In relationship | 132 | 50 | 1.263 | 0.639 | 2.494 | 0.502 | |
| NYHA class | II | 89 | 30 | 1 | Ref. | — | — |
| III | 76 | 32 | 1.455 | 0.746 | 2.837 | 0.272 | |
| IV | 35 | 11 | 0.956 | 0.395 | 2.317 | 0.921 | |
| LVEF (%) | — | — | 0.989 | 0.964 | 1.016 | 0.432 | |
| Multimorbidity | No | 12 | 3 | 1 | Ref. | — | — |
| Yes | 188 | 70 | 1.678 | 0.394 | 7.146 | 0.484 | |
| BMI | Normal weight | 50 | 25 | 1 | Ref. | — | — |
| Overweight | 74 | 23 | 0.369 | 0.149 | 0.912 | 0.031* | |
| Obesity | 76 | 25 | 0.381 | 0.148 | 0.984 | 0.046* | |
| Central obesity | No | 35 | 15 | 1 | Ref. | — | — |
| Yes | 165 | 58 | 1.333 | 0.486 | 3.655 | 0.576 | |
Note: P—multivariate logistic regression.
Abbreviations: CI, confidence interval; LVEF, left ventricular ejection fraction; MNA, Mini Nutritional Assessment; NYHA, New York Heart Association; OR, odds ratio.
Statistically significant relationship (P < 0.05).
In the next step, an analysis was conducted to identify variables influencing the prolongation of hospitalization (>5 days) within the study group. The multivariate logistic regression model revealed that frailty increases the likelihood of prolonged hospitalization by 4.063 times (OR = 4.063). Being in a relationship reduces the odds of prolonged hospitalization by 58.1% (OR = 0.419) compared with being single. Furthermore, NYHA class III increases the likelihood of prolonged hospitalization by 3.088 times (OR = 3.088) relative to class II, while NYHA class IV raises the odds by 4.734 times (OR = 4.734) compared with class II. The analysis also indicated that each additional percentage point of LVEF decreases the odds of prolonged hospitalization by 5.7% (OR = 0.943). Additionally, obesity lowers the likelihood of prolonged hospitalization by 79.5% (OR = 0.205) compared with individuals with normal weight. The detailed data are presented in Table 8.
Table 8.
Analysis of factors affecting prolonged hospitalization (>5 days) in patients with HF.
| Variable | N | Prolong hospitalization | OR | 95% CI | P | ||
|---|---|---|---|---|---|---|---|
| Fried | Pre‐frail | 70 | 7 | 1 | Ref. | — | — |
| Frail | 130 | 39 | 4.063 | 1.363 | 12.108 | 0.012* | |
| MNA | Normal nutritional status | 127 | 30 | 1 | Ref. | — | — |
| Risk of malnutrition/malnutrition | 73 | 16 | 0.44 | 0.18 | 1.074 | 0.071 | |
| Age (years) | — | — | 0.978 | 0.916 | 1.045 | 0.517 | |
| Sex | Woman | 59 | 12 | 1 | Ref. | — | — |
| Man | 141 | 34 | 0.608 | 0.218 | 1.691 | 0.34 | |
| Marital status | Alone | 68 | 21 | 1 | Ref. | — | — |
| In relationship | 132 | 25 | 0.419 | 0.178 | 0.985 | 0.046* | |
| NYHA class | II | 89 | 11 | 1 | Ref. | — | — |
| III | 76 | 21 | 3.088 | 1.193 | 7.991 | 0.02* | |
| IV | 35 | 14 | 4.734 | 1.498 | 14.96 | 0.008* | |
| LVEF (%) | — | — | 0.943 | 0.908 | 0.979 | 0.002* | |
| Multimorbidity | No | 12 | 0 | 1 | Ref. | — | — |
| Yes | 188 | 46 | — | — | — | — | |
| BMI | Normal weight | 50 | 17 | 1 | Ref. | — | — |
| Overweight | 74 | 20 | 0.76 | 0.255 | 2.27 | 0.623 | |
| Obesity | 76 | 9 | 0.205 | 0.057 | 0.731 | 0.015* | |
| Central obesity | No | 35 | 12 | 1 | Ref. | — | — |
| Yes | 165 | 34 | 0.606 | 0.169 | 2.171 | 0.442 | |
Note: P—multivariate logistic regression.
Abbreviations: BMI, body mass index; CI, confidence interval; LVEF, left ventricular ejection fraction; MNA, Mini Nutritional Assessment; NYHA, New York Heart Association; OR, odds ratio.
Statistically significant relationship (P < 0.05).
Discussion
The relationship between malnutrition and frailty is well‐documented, particularly in older adults, and both conditions are closely interconnected. The association between frailty and the risk of malnutrition in older hospitalized adults is indeed significant. Research consistently shows that these conditions often coexist and exacerbate each other, particularly in hospitalized older adults, who are at a higher risk for poor health outcomes due to the combination of frailty, muscle loss and inadequate nutrition. 15 Both malnutrition and frailty are linked to increased systemic inflammation, a key underlying mechanism that contributes to the progression of these conditions. Chronic inflammation, often characterized by elevated levels of pro‐inflammatory cytokines such as tumour necrosis factor‐α and interleukin‐6, can impair muscle function, decrease appetite and increase the breakdown of proteins, which contribute to both malnutrition and frailty A study on advanced HF patients demonstrated that markers of inflammation, such as high‐sensitivity C‐reactive protein, are associated with increased mortality risk. This underscores the importance of inflammation in the prognosis of HF and its potential role in the development of frailty. 16 Understanding the shared mechanisms between these two conditions is critical to developing more effective interventions.
Frailty is a multidimensional syndrome that includes physical, cognitive, social, and mental components, and it is increasingly recognized as a major risk enhancer in patients with HF. Research shows that frailty affects approximately 40%–80% of all HF patients, and its prevalence increases with age and disease severity. 17 The relationship between HF and frailty is driven by multiple interconnected pathophysiological mechanisms. Chronic inflammation, characterized by elevated cytokine levels in HF, accelerates muscle catabolism, thereby contributing to sarcopenia and the development of frailty. 18 Frailty, characterized by reduced physiological reserve and increased vulnerability to stressors, is particularly detrimental in CHF patients who already face challenges related to their cardiovascular condition. Research indicates that frailty is prevalent among CHF patients, with a notable association with increased hospital readmissions and mortality rates. 19 The substantial prevalence of frailty (65% of participants) in this cohort underscores the importance of recognizing frailty as a critical factor in the management of CHF.
As frailty is associated with poorer clinical outcomes, addressing malnutrition through targeted nutritional interventions could potentially improve physical functioning, reduce frailty, and enhance overall prognosis. 20 Malnutrition in HF is linked to adverse outcomes, including increased hospitalizations, diminished quality of life, and higher mortality rates. 21 Malnourished patients had longer hospital stays, with a mean increase of 4.67 days compared with their well‐nourished counterparts. 22 In the study of Chien et al., malnutrition was frequently and strongly associated with systemic inflammation in patients hospitalized for acute HF with preserved ejection fraction. 23
A meta‐analysis indicates that the prevalence of malnutrition in HF patients ranges from 16% to 90% depending on the population and assessment methods used. 24 In a study utilizing the National Inpatient Sample (NIS) database, out of 1 110 085 HF patients, 3.29% were identified as malnourished, with 2.20% classified as severely malnourished. 21 Based on literature data, 20% of hospitalized patients with HF have moderate to severe malnutrition as defined by various indices. 5 , 24 In this analysis, the MNA questionnaire was used to assess malnutrition as a recommended tool for assessing nutritional status in elderly patients. In our own study, malnutrition affected 1.5%, while 35% were patients at nutritional risk. It is important to note that the study concerned hospitalized patients and was conducted after the patients were admitted to hospital.
Available research indicates that frail patients are significantly more likely to experience malnutrition or be at risk of malnutrition compared with pre‐frail individuals. Patients at risk of malnutrition, as assessed by the MNA scale, had a three‐fold increased likelihood of developing frailty compared with those with normal nutritional status. 25 Iida et al. emphasize that nutritional status plays a significant role in predicting the prognosis of patients with HF. The study suggests that frail patients with HF are more susceptible to the adverse effects of malnutrition and the impact of poor nutrition on their outcomes. The severity of physical frailty can alter the strength of this association. 5
In this study, we also showed that the nutritional status of patients with HF is associated with the risk of developing FS. Research indicates a substantial overlap between frailty and malnutrition in hospitalized patients. In one of them, 33.5% of older hospitalized patients were found to be both frail and at risk for malnutrition. These patients exhibited worse health‐related quality of life and longer length of stay compared with those who were only at risk of malnutrition or only frail. 26 Patients who are both malnourished and frail experience higher rates of hospitalization. The FRAGILE‐HF study emphasizes that frail patients with poor nutritional status have a higher cumulative incidence of adverse outcomes, including increased hospital readmissions. 27 Interestingly, a multivariate regression model showed that among the clinical variables, it is obesity (OR = 0.205) that lowers the risk of prolonged hospitalization in the subject group of patients. Ohori et al. in their study documented an association between body fat mass and cardiovascular events in patients with HF. Patients with higher percent body fat had a lower risk of cardiovascular events. 28 The findings of our study provide compelling evidence for the significant relationship between malnutrition and FS among patients with CHF. With 63.5% of participants demonstrating normal nutritional status and 35% at risk of malnutrition, alongside the striking 65% prevalence of frailty, these results highlight the pervasive nature of these interrelated conditions in this patient population. The statistically significant association (P < 0.05) between MNA scores and the Fried phenotype for frailty emphasizes the critical role that nutritional status plays in the overall health and prognosis of CHF patients. The finding that the highest percentage of frail individuals were those categorized as malnourished reinforces the idea that inadequate nutrition not only contributes to but may also exacerbate frailty. This vicious cycle of malnutrition leading to frailty and vice versa highlights the need for integrated management strategies that simultaneously address both conditions.
A recent systematic review and meta‐analysis demonstrated that poorer nutritional status was associated with elevated levels of HF biomarkers (BNP, NT‐proBNP) and inflammatory markers (CRP). These findings support the hypothesis that malnutrition in HF patients may play a key role in the development of FS and worsen overall prognosis. 29
In frailty context, anthropometry is a useful and easy‐to‐apply tool to assess nutritional status, functional decline and chronic health conditions, which are important risk factors. 30 Interestingly, the study did not find significant associations between other anthropometric measures, such as BMI, calf circumference, arm circumference and FS. This suggests that traditional measures of body composition may not adequately capture the nuances of malnutrition and frailty in this specific patient population. The lack of significant correlation with BMI, in particular, challenges the conventional understanding that higher body weight correlates with better nutritional status. In Butt. et al.'s study, the authors revisit the concept of the obesity paradox in the context of HF with reduced ejection fraction. The obesity paradox refers to the observation that, in certain populations, individuals with higher BMI may have better outcomes and survival rates than those with normal or lower BMI. The authors state that BMI and waist‐to‐height ratio showed that greater adiposity was associated with a higher risk of the primary outcome and HF hospitalization and also highlights the complexities and controversies surrounding obesity paradox, urging for a more nuanced understanding and interpretation of body weight and health outcomes in HF patients. 31 In the context of HF, it is essential to recognize that individuals can present as obese yet still be malnourished, further complicating the clinical picture.
In our study, we also found no association between multimorbidity and the presence of FS. Interestingly, the available data indicate that patients with multimorbidity had a significantly higher incidence of FS than patients without FS, although these results apply to cardiac surgery patients. 32
The interconnected issues of frailty, sarcopenia, cachexia and malnutrition greatly impact the progression of HF. Each of these conditions worsens the others, creating a harmful cycle that negatively affects the patients' health, quality of life and chances of survival. Recognizing the distinctions and commonalities among these conditions can help develop more effective and personalized treatment approaches for both men and women with HF. Timely identification and comprehensive management of these issues are essential for enhancing overall outcomes in HF patients. 33
The novelty of our study lies in the simultaneous evaluation of frailty and nutritional status and their combined impact on clinical outcomes, particularly length of hospital stay, in a well‐defined cohort of older CHF patients. While previous studies have addressed frailty or malnutrition separately, our findings underscore the added value of using the MNA tool—which showed a stronger association with frailty than traditional anthropometric measures. Furthermore, the identification of frailty and malnutrition as independent predictors of prolonged hospitalization highlights the clinical importance of integrating both assessments into routine care for HF patients.
Conclusions
The results of this study reveal a significant relationship between malnutrition and FS in patients with CHF. Patients who were malnourished or at risk of malnutrition demonstrated a higher prevalence of frailty, emphasizing the impact of nutritional status on physical vulnerability in this population. The study identified that malnutrition, as measured by MNA, is strongly correlated with frailty, highlighting the importance of addressing nutritional deficiencies in CHF patients.
Although a considerable portion of patients exhibited frailty, no statistically significant associations were found between frailty and other physical measures such as BMI, central obesity, calf circumference or arm circumference. This suggests that while nutritional status plays a critical role in the development of frailty, traditional anthropometric measures like BMI may not adequately capture the risk of frailty in this group.
The findings underscore the need for integrating comprehensive nutritional assessments into the routine care of CHF patients. Interventions targeting malnutrition could be crucial in reducing the incidence of frailty and improving the overall prognosis in these patients. Further research is needed to explore effective nutritional interventions and their role in managing both frailty and CHF, particularly in older adults where these conditions frequently co‐occur.
Study limitations
The presented study has some limitations. At first, it only included patients from a single cardiology department, which may not be representative of the broader population of HF patients. Additionally, participants were required to be at least 60 years old, which limits generalizability to younger populations or those with different health statuses. Second, the study utilized data collected during hospitalization, which provides a snapshot of patient status at a single point in time. Although assessments were performed within 24–48 h after admission and after clinical stabilization, the acute nature of hospitalization may have still influenced the frailty and nutritional status measurements. Furthermore, specific data regarding clinical presentation—particularly the presence or absence of congestion at the time of evaluation—were not systematically collected, which may have affected the interpretation of factors influencing the length of hospital stay. Additionally, detailed information on pharmacological treatments administered prior to admission and during the 24–48 h evaluation window was not recorded; because certain medications (e.g., diuretics, corticosteroids and appetite‐modifying agents) can influence nutritional status and physical performance, their absence may have impacted our findings.
While the study involved 200 participants, the sample size may still be insufficient to detect smaller effect sizes or subtle relationships between variables, especially in subgroups with specific characteristics such as those with only mild HF or early stages of frailty.
Funding
This study was funded by the National Science Centre, Poland [grant No. 2021/41/B/NZ7/01698), recipient: I. U.].
Conflict of interest statement
There is no conflict of interest.
Lomper, K. , Jędrzejczyk, M. , Wleklik, M. , and Uchmanowicz, I. (2025) Relationship of malnutrition and frailty on prolonged stay at the hospital in heart failure patients. ESC Heart Failure, 12: 3614–3623. 10.1002/ehf2.15390.
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