Abstract
Aims
Heart failure (HF) is a major public health challenge. Malnutrition has a significant effect on HF prognosis. Understanding the impact of social and clinical factors on the risk of malnutrition is necessary because it may aid in improving the health status of HF patients.
Methods and results
Three hundred twenty patients with HF who were hospitalized in a heart centre in Shiraz, Iran, from March to November 2022 were studied. Two validated questionnaires were used to evaluate malnutrition and social support: (1) Mini‐Nutritional Assessment Short Form and (2) Medical Outcomes Study Social Support Survey. The participants were then divided into three groups: those with normal nutritional status (scores 12–14), those at risk of malnutrition (scores 7–11), and those who were malnourished (scores 0–6). The potential correlates of malnutrition (including socio‐demographic, clinical, comorbidities, and laboratory factors) were included in the study. Then, ordinal logistic regression was used to investigate the correlates of malnutrition. The mean age of the participants was 64.2 ± 11.2 years, and more than half were male and married. Normal nutritional status was seen in 110 (34.4%) participants, 151 (47.2%) were at risk of malnutrition, and 58 (18.1%) were malnourished. The mean social support score of the participants was 61.65 ± 12.91. According to the adjusted odds ratios (95% confidence intervals) obtained from multivariate analysis, increased risk of malnutrition was associated with having a lower social support score [0.95 (0.93–0.97), P‐value ≤ 0.001], lower body mass index [0.91 (0.86–0.97), P‐value = 0.004], higher New York Heart Association classification [1.26 (1.02–1.56), P‐value = 0.03], longer duration of disease [1.006 (1.001–1.01), P‐value = 0.006], and lower serum albumin level [0.25 (0.08–0.75), P‐value = 0.01].
Conclusions
Besides the clinical conditions affecting the risk of malnutrition in patients with HF, social support may play an important role. Including this factor in HF guidelines and developing educational programmes may help improve HF patients' health.
Keywords: Heart failure, Malnutrition, Social support, Clinical factors, Comorbidity
Introduction
Heart failure (HF), viewed as the endpoint of most cardiovascular diseases, is highly prevalent and responsible for substantial mortality and morbidity. 1 , 2 , 3 Worldwide, 64.3 million patients live with HF, while its prevalence is increasing due to population growth, aging, and improved survival of patients with cardiovascular disease. 4 , 5 In the United States, the prevalence of HF is estimated at 8 million by 2030. 6 Despite improvements in the management of HF, the mortality rate is still high in these patients. 7 In a recent study, the 5 year mortality rate in hospitalized patients with HF was 75%, which is unacceptably high. 1 Malnutrition, a common complication of HF, is associated with adverse health outcomes and poor prognosis in these patients. 8 , 9 Thus, a better understanding of malnutrition and its correlates can help improve HF prognosis.
Patients with HF are prone to malnutrition due to malabsorption following intestinal wall oedema, anorexia, increased energy requirements, and increased catabolism due to the effect of cytokines. 10 According to a study that evaluated malnutrition in patients with HF using CONUT (the Controlling Nutritional Status score, which is determined based on serum albumin level, total cholesterol level, and total lymphocyte counts), PNI (Prognostic Nutritional Index, which is determined based on serum albumin level and lymphocyte count), and GNRI [Geriatric Nutritional Risk Index, which is determined based on body mass index (BMI) and serum albumin in older patients], almost 20% of hospitalized patients had moderate to severe malnutrition. 11 In another study, 3386 HF patients were evaluated using these three criteria; malnutrition was found in 57% of patients using one of these criteria and 5% using all three. 12 Malnutrition is associated with decreased quality of life and increased morbidity, readmission, duration of hospitalization, and mortality in patients with HF. 13 In a recent study on 467 patients with chronic HF using the Mini‐Nutritional Assessment Short Form (MNA‐SF) instrument, mortality was 6.5 times higher in patients with moderate to severe malnutrition than in those without. 14 Malnutrition is associated with older age, HF severity, high levels of N‐terminal pro‐brain natriuretic peptide, lower BMI, lower haemoglobin, and more comorbidities in HF patients. 14 Previous research has paid little attention to the role of social determinants in malnutrition.
Social support is a multi‐faceted concept defined as the protection and assistance given to people. 15 It positively affects the outcome of many chronic diseases, including HF. 15 , 16 , 17 , 18 Higher social support improves self‐care behaviours, adherence to nutritional regimens and medications, and patient cooperation in symptom management and reduces readmission rates in HF patients. 15 , 19 It was also seen that in patients hospitalized with HF, social support was significantly lower compared with outpatients. 19 Despite the importance of the role of social determinants in malnutrition, very little is currently known about the relationship between social support and malnutrition. As a result, this study aimed to investigate the predictors of malnutrition in HF patients, with a particular emphasis on the role of social support.
Methods
Study design and participants
This cross‐sectional study was performed on 320 HF patients admitted to Al‐Zahra Heart Hospital, Shiraz, Iran, from March to November 2022. Stratified random sampling was used, the numbers of women and men and different age groups were considered, and patients were selected based on the file number. The sample size was calculated with the assumption of type 1 error and precision equal to 5% and 10%, respectively. The minimum required sample size was estimated to be 256 patients. Considering an 80% response rate, this number was equal to 320 patients. Patients who agreed to participate and had a documented diagnosis of HF with <40% left ventricular ejection fraction (LVEF), were 20 years old or older, and had a negative history of severe inflammation, infection, trauma, malignancy, and chemotherapy were enrolled in the study. Patients who could not cooperate due to mental or psychological disorders were excluded from the study. The investigation conforms with the principles outlined in the Declaration of Helsinki. 20 This study was approved by the Ethics Committee of Shiraz University of Medical Sciences (http://ethics.research.ac.ir/IR.SUMS.MED.REC.1399.211). Participants were informed about the study's objectives and provided written informed consent to participate.
Data collection, measurements, and tools
Socio‐demographic information, medical history, and clinical data were collected through face‐to‐face interviews in an interview room using a structured questionnaire and based on documented information from patients' medical records. Each interview was done by trained medical students and averaged 30 min. Two validated questionnaires were used in order to evaluate malnutrition and social support:
MNA‐SF
MNA‐SF, a validated questionnaire 21 consisting of six questions, was used to evaluate malnutrition. Then the participants were divided into three groups: those with normal nutritional status (scores 12–14), those at risk of malnutrition (scores 7–11), and those who were malnourished (scores 0–6).
-
2
Medical Outcomes Study Social Support Survey (MOS‐SSS; Sherburne and Stewart)
MOS‐SSS, a validated questionnaire 22 consisting of nineteen 5‐point Likert scales, was used to evaluate social support. MOS‐SSS measures four dimensions of social support: emotional‐informational support, tangible support, positive social interaction, and affectionate support. Higher scores demonstrate higher perceived social support.
The study included the potential correlates of malnutrition based on our literature review. These are the factors and their definitions: age (40–65 and ≥65), gender, marital status (single, married, and widowed/divorced), socio‐economic status (low, moderate, and high), educational level (primary or less, elementary and high, and academic), living status (alone, with family, and nursing home), BMI (underweight: <18.5, normal: 18.5–24.9, overweight: 25–29.9, and obese: ≥30), having insurance, tobacco smoking (smoker and non‐smoker), alcohol consumption, number of hospitalizations, duration of disease, having polypharmacy (concurrent use of five or more drugs), 23 having comorbidities (including diabetes, hypertension, hyperlipidaemia, and cerebrovascular accident), having pre‐existing heart disease (including myocardial infarction, valvular heart disease, percutaneous coronary intervention, and coronary bypass graft surgery), serum albumin level, haemoglobin, blood sugar, blood urea nitrogen, creatinine, sodium, potassium, triglyceride, total cholesterol, low‐density lipoprotein, high‐density lipoprotein, LVEF, and New York Heart Association (NYHA) classification.
Statistical analysis
First, data were checked to detect missing values, outliers, extreme values, and internal and external inconsistencies. Multiple imputations were done for the variable ‘albumin’, which has more than 30% missing data. To describe quantitative variables, mean and standard deviation were used, and to express categorical variables, frequencies were used. In order to check the normality of variables, the Kolmogorov–Smirnov test was used. Then, the χ 2 test, the Kruskal–Wallis test, independent sample t‐test, one‐way ANOVA test, and Spearman's correlation coefficient test were used for univariate analysis. Ordinal logistic regression was used to investigate the predictors of malnutrition. Variables with a univariate P‐value of <0.3 were selected as potential correlates of malnutrition, and a complete model was fitted; then, to fit the final model, backward elimination was used. We considered a P‐value of <0.05 to be statistically significant. Data analysis was done using the Stata statistical software, Version 17.
Results
Data from 320 participants were analysed. The mean age of the participants was 64.2 ± 11.2 years, and more than half were male [198 (61.9%)] and married [242 (75.6%)]. The LVEF mean was 27.9 ± 7.7, and 162 (50.6%) patients had NYHA classes III and IV.
Malnutrition
Normal nutritional status was seen in 110 (34.4%) participants, 151 (47.2%) were at risk of malnutrition, and 58 (18.1%) were malnourished (Table 1 ).
Table 1.
Socio‐demographic and baseline characteristics of the study participants by nutritional and social support status
| Variable | Nutritional status (MNA‐SF), N (%) | P‐value a | Social support | P‐value b | ||
|---|---|---|---|---|---|---|
|
Normal 110 (34.4) |
At risk of malnutrition 151 (47.2) |
Malnourished 58 (18.1) |
||||
| Age | 0.04 | 0.04 | ||||
| 40–65 | 68 (39.1) | 82 (47.1) | 24 (13.8) | 62.9 ± 12.7 | ||
| ≥65 | 42 (29.0) | 69 (47.6) | 34 (23.4) | 60.1 ± 12.9 | ||
| Gender | 0.04 | 0.94 | ||||
| Male | 78 (39.6) | 87 (44.2) | 32 (16.2) | 61.6 ± 12.4 | ||
| Female | 32 (26.2) | 64 (52.5) | 26 (21.3) | 61.5 ± 13.7 | ||
| Marital status | ≤0.001 | ≤0.001 | ||||
| Single | 10 (52.6) | 6 (31.6) | 3 (15.8) | 66.7 ± 18.9 | ||
| Married | 91 (37.8) | 116 (48.1) | 34 (14.1) | 62.6 ± 11.7 | ||
| Divorced/widowed | 9 (15.3) | 29 (49.2) | 21 (35.6) | 56.0 ± 13.8 | ||
| Socio‐economic status | 0.08 | 0.59 | ||||
| Low | 47 (36.4) | 51 (39.5) | 31 (24.0) | 60.8 ± 13.3 | ||
| Moderate | 55 (32.2) | 92 (53.8) | 24 (14.0) | 62.3 ± 12.0 | ||
| High | 8 (42.1) | 8 (42.1) | 3 (15.8) | 61.1 ± 17.2 | ||
| Educational level | ≤0.001 | 0.08 | ||||
| Primary or less | 56 (31.6) | 74 (41.8) | 47 (26.6) | 60.4 ± 13.2 | ||
| Elementary and high | 43 (37.4) | 61 (53.0) | 11 (9.6) | 62.3 ± 11.9 | ||
| Academic | 11 (40.7) | 16 (59.3) | 0 (0.0) | 66.1 ± 13.7 | ||
| Living status | 0.29 | ≤0.001 | ||||
| Alone | 12 (48.0) | 9 (36.0) | 4 (16) | 58.4 ± 17.6 | ||
| With family | 92 (33.5) | 134 (48.7) | 49 (17.8) | 62.4 ± 12.1 | ||
| Nursing home | 2 (16.7) | 6 (50.0) | 4 (33.3) | 49 ± 13.8 | ||
| Body mass index | 25.1 (4.3) | 24.7 (5.5) | 23.8 (7.2) | 0.04 | −0.14 | 0.01 |
| Tobacco smoking | 0.77 | 0.01 | ||||
| Smoker | 26 (36.1) | 35 (48.6) | 11 (15.3) | 58.1 ± 13.3 | ||
| Non‐smoker | 82 (33.7) | 115 (47.3) | 46 (18.9) | 62.6 ± 12.6 | ||
| Alcohol | 0.47 | 0.85 | ||||
| Yes | 5 (35.7) | 5 (35.7) | 4 (28.6) | 60.9 ± 22.1 | ||
| No | 102 (34.6) | 143 (48.5) | 50 (16.9) | 61.5 ± 12.5 | ||
| Having insurance | 0.3 | 0.52 | ||||
| Yes | 104 (36) | 130 (45) | 55 (19) | 61.7 ± 12.7 | ||
| No | 6 (20) | 21 (70) | 3 (10) | 60.2 ± 14.1 | ||
MNA‐SF, Mini‐Nutritional Assessment Short Form.
Data are presented as n (%) or mean ± standard deviation for categorical variables and median (interquartile range) or rho = correlation coefficient for quantitative variables.
P‐values are obtained from the χ 2 test or the Kruskal–Wallis test.
P‐values are obtained from independent sample t‐test, one‐way ANOVA test, or Spearman's correlation coefficient test.
According to univariate analysis, a higher risk of malnutrition was seen in those who had a longer duration of disease, higher number of hospitalizations, polypharmacy, history of diabetes mellitus, and cerebrovascular accident (Table 2 ; P = 0.02, P = 0.02, P = 0.003, P = 0.03, and P = 0.003, respectively). It was also shown that those who were malnourished had lower haemoglobin, higher blood urea nitrogen, and higher serum creatinine levels (Table 3 ; P = 0.004, P = 0.003, and P = 0.02, respectively). The associations between malnutrition and LVEF and NYHA classification (as two main clinical predictors of HF severity) were also evaluated. More than half of those malnourished (51.7%) had NYHA class IV (Table 4 ; P = 0.002).
Table 2.
Clinical characteristics and comorbidities of the study participants by nutritional and social support status
| Variable | Nutritional status (MNA‐SF), N (%) | P‐value a | Social support | P‐value b | ||
|---|---|---|---|---|---|---|
|
Normal 110 (34.4) |
At risk of malnutrition 151 (47.2) |
Malnourished 58 (18.1) |
||||
| Duration of disease (months) | 24.0 (36.0) | 36.0 (60.0) | 60.0 (99.0) | 0.02 | −0.15 | 0.002 |
| Number of hospitalizations | 1.0 (2.0) | 2.0 (2.0) | 2.0 (2.0) | 0.02 | −0.08 | 0.14 |
| Polypharmacy | 0.003 | ≤0.001 | ||||
| Yes | 32 (23.2) | 73 (52.9) | 33 (23.9) | 58.1 ± 12.6 | ||
| No | 36 (44.4) | 28 (34.6) | 17 (21.0) | 66.1 ± 14.7 | ||
| Comorbidity | 0.94 | 0.38 | ||||
| Yes | 91 (34.1) | 127 (47.6) | 49 (18.4) | 61.3 ± 13.3 | ||
| No | 19 (36.5) | 24 (46.2) | 9 (17.3) | 63.1 ± 10.6 | ||
| Number of comorbidities | 1.0 (1.0) | 2.0 (1.0) | 2.0 (5.0) | 0.19 | −0.11 | 0.04 |
| Hypertension | 0.26 | 0.85 | ||||
| Yes | 68 (33.3) | 103 (50.5) | 33 (16.2) | 61.5 ± 13.4 | ||
| No | 42 (36.5) | 48 (41.7) | 25 (21.7) | 61.8 ± 11.9 | ||
| Diabetes | 0.03 | 0.01 | ||||
| Yes | 26 (27.4) | 44 (46.3) | 25 (26.3) | 58.8 ± 14.2 | ||
| No | 84 (37.5) | 107 (47.8) | 33 (14.7) | 62.8 ± 12.1 | ||
| Cerebrovascular accident | 0.003 | 0.002 | ||||
| Yes | 3 (12.5) | 11 (45.8) | 10 (41.7) | 54.0 ± 13.6 | ||
| No | 107 (36.3) | 140 (47.5) | 48 (16.3) | 62.2 ± 12.6 | ||
| Hyperlipidaemia | 0.48 | 0.11 | ||||
| Yes | 48 (37.8) | 55 (43.3) | 24 (18.9) | 60.2 ± 12.2 | ||
| No | 62 (32.3) | 96 (50.0) | 34 (17.7) | 62.5 ± 13.2 | ||
| Valvular heart disease | 0.92 | 0.32 | ||||
| Yes | 10 (31.3) | 16 (50) | 6 (18.8) | 59.5 ± 13.1 | ||
| No | 100 (34.8) | 135 (47) | 52 (18.1) | 61.8 ± 12.8 | ||
| Myocardial infarction | 0.07 | 0.04 | ||||
| Yes | 75 (34.9) | 108 (50.2) | 39 (14.9) | 60.6 ± 12.1 | ||
| No | 35 (33.7) | 43 (41.3) | 26 (25.0) | 63.7 ± 14.1 | ||
| Coronary artery bypass graft surgery | 0.10 | 0.003 | ||||
| Yes | 17 (26.6) | 30 (46.9) | 17 (26.6) | 55.6 ± 12.7 | ||
| No | 93 (36.5) | 121 (47.5) | 41 (16.1) | 63.1 ± 12.5 | ||
| Percutaneous coronary intervention | 0.23 | 0.04 | ||||
| Yes | 58 (34.5) | 85 (50.6) | 25 (14.9) | 60.2 ± 11.1 | ||
| No | 52 (34.4) | 66 (43.7) | 33 (21.9) | 63.2 ± 14.5 | ||
MNA‐SF, Mini‐Nutritional Assessment Short Form.
Data are presented as n (%) or mean ± standard deviation for categorical variables and median (interquartile range) or rho = correlation coefficient for quantitative variables.
P‐values are obtained from the χ 2 test or the Kruskal–Wallis test.
P‐values are obtained from independent sample t‐test or Spearman's correlation coefficient test.
Table 3.
Laboratory characteristics of the study participants by nutritional status
| Variable | Nutritional status (MNA‐SF), N (%) | P‐value | ||
|---|---|---|---|---|
|
Normal 110 (34.4) |
At risk of malnutrition 151 (47.2) |
Malnourished 58 (18.1) |
||
| Serum albumin (g/dL) | 4.2 (0.5) | 4.1 (0.3) | 4.1 (0.7) | 0.12 |
| Haemoglobin (g/dL) | 13.3 (2.5) | 13.0 (2.0) | 12.4 (2.0) | 0.004 |
| Blood urea nitrogen (mg/dL) | 19.5 (7.0) | 19.0 (6.0) | 26.0 (20.0) | 0.003 |
| Serum creatinine (mg/dL) | 1.2 (0.4) | 1.2 (0.3) | 1.3 (0.7) | 0.02 |
| Triglyceride (mg/dL) | 101.0 (39.0) | 99.0 (70.7) | 100.0 (53.0) | 0.69 |
| Total cholesterol (mg/dL) | 130.0 (120.0) | 115.0 (65.5) | 104 (76.5) | 0.13 |
| Low‐density lipoprotein (mg/dL) | 69.0 (25.0) | 69.0 (39.0) | 64.0 (45.0) | 0.58 |
| High‐density lipoprotein (mg/dL) | 43.5 (9.3) | 49.5 (16.3) | 49.5 (28.5) | 0.23 |
MNA‐SF, Mini‐Nutritional Assessment Short Form.
Data are presented as median (interquartile range). P‐values are obtained from the Kruskal–Wallis test.
Table 4.
Nutritional and social support status of the study participants by left ventricular ejection fraction (LVEF) and New York Heart Association (NYHA) classification
| Variable | LVEF a | P‐value b | NYHA classification c | P‐value d | ||||
|---|---|---|---|---|---|---|---|---|
| Class I | Class II | Class III | Class IV | |||||
| Nutritional status (MNA‐SF) | Normal | 30.0 (10.0) | 0.07 | 29 (26.4) | 28 (25.5) | 24 (21.8) | 29 (26.4) | 0.002 |
| At risk of malnutrition | 30.0 (15.0) | 24 (15.9) | 57 (37.7) | 26 (17.2) | 44 (29.1) | |||
| Malnourished | 25.0 (15.0) | 7 (12.1) | 12 (20.7) | 9 (15.5) | 30 (51.7) | |||
| Social support | +0.18 | ≤0.001 | 64.7 ± 14.0 | 61.2 ± 11.5 | 60.9 ± 13.3 | 60.6 ± 13.1 | 0.21 | |
MNA‐SF, Mini‐Nutritional Assessment Short Form.
Data are presented as median (interquartile range) or rho = correlation coefficient.
P‐values are obtained from the Kruskal–Wallis test or Spearman's correlation coefficient test.
Data are presented as n (%) or mean ± standard deviation.
P‐values are obtained from the χ 2 test or the one‐way ANOVA test.
According to the adjusted odds ratios (95% confidence intervals) obtained from multivariate analysis, there was a significant correlation between increased risk of malnutrition and having a lower social support score [0.95 (0.93–0.97), P‐value ≤ 0.001], lower BMI [0.91 (0.86–0.97), P‐value = 0.004], higher NYHA classification [1.26 (1.02–1.56), P‐value = 0.03], longer duration of disease [1.006 (1.001–1.01), P‐value = 0.006], and lower serum albumin level [0.25 (0.08–0.75), P‐value = 0.01] (Table 5 ).
Table 5.
Predictors of malnutrition using ordinal logistic regression
| Variable | Malnutrition (MNA‐SF) | |||
|---|---|---|---|---|
| Crude odds ratio (95% CI) | P‐value a | Adjusted odds ratio (95% CI) | P‐value b | |
| Social support | 0.95 (0.94–0.95) | ≤0.001 | 0.95 (0.93–0.97) | ≤0.001 |
| Age | ||||
| 40–65 | Ref | — | — | — |
| ≥65 | 1.68 (1.11–2.56) | 0.01 | — | — |
| Gender | ||||
| Male | Ref | — | — | — |
| Female | 1.65 (1.08–2.54) | 0.02 | — | — |
| Marital status | ||||
| Single | Ref | — | — | — |
| Married | 1.57 (0.62–3.99) | 0.33 | — | — |
| Divorced/widowed | 5.22 (1.86–14.65) | 0.002 | — | — |
| Educational level | ||||
| Primary or less | Ref | — | — | — |
| Elementary and high | 0.56 (0.36–0.88) | 0.01 | — | — |
| Academic | 0.42 (0.20–0.90) | 0.02 | — | — |
| Living status | ||||
| Alone | Ref | — | — | — |
| With family | 1.63 (0.73–3.64) | 0.22 | — | — |
| Nursing home | 3.85 (1.03–14.34) | 0.04 | — | — |
| Body mass index | 0.93 (0.89–0.98) | 0.01 | 0.91 (0.86–0.97) | 0.004 |
| Left ventricular ejection fraction | 0.96 (0.95–0.97) | ≤0.001 | — | — |
| New York Heart Association class | 1.45 (1.38–1.52) | ≤0.001 | 1.26 (1.02–1.56) | 0.03 |
| Duration of disease (months) | 1.006 (1.002–1.01) | 0.001 | 1.006 (1.001–1.01) | 0.006 |
| Number of hospitalizations | 1.15 (1.04–1.28) | 0.006 | — | — |
| Polypharmacy | ||||
| Yes | 1.95 (1.15–3.32) | 0.01 | — | — |
| No | Ref | — | — | — |
| Number of comorbidities | 1.28 (1.08–1.52) | 0.004 | — | — |
| Diabetes | ||||
| Yes | 1.77 (1.12–2.81) | 0.01 | — | — |
| No | Ref | — | — | — |
| Cerebrovascular accident | ||||
| Yes | 3.75 (1.68–8.34) | 0.001 | — | — |
| No | Ref | — | — | — |
| Myocardial infarction | ||||
| Yes | 0.75 (0.48–1.18) | 0.22 | — | — |
| No | Ref | — | — | — |
| Coronary artery bypass graft surgery | ||||
| Yes | 1.71 (1.01–2.89) | 0.04 | — | — |
| No | Ref | — | — | — |
| Serum albumin | 0.35 (0.10–1.16) | 0.08 | 0.25 (0.08–0.75) | 0.01 |
| Haemoglobin | 0.81 (0.72–0.91) | 0.001 | — | — |
| Blood urea nitrogen | 1.03 (1.01–1.05) | ≤0.001 | — | — |
| Serum creatinine | 1.60 (1.08–2.35) | 0.01 | — | — |
CI, confidence interval; MNA‐SF, Mini‐Nutritional Assessment Short Form; Ref, reference.
Data are presented as crude and adjusted odds ratios and their 95% CIs. Dependent variable = malnutrition (MNA‐SF) (1 = normal/2 = at risk of malnutrition/3 = malnourished).
P‐values are obtained from univariate analysis.
P‐values are obtained from multivariable modelling.
Social support
The mean social support score of the participants was 61.65 ± 12.91. A lower level of social support was seen in those who were older, divorced or widowed, and living in nursing rooms and had a longer duration of disease, polypharmacy, and more comorbid conditions (Tables 1 and 2 ; P = 0.04, P ≤ 0.001, P ≤ 0.001, P = 0.002, P ≤ 0.001, and P = 0.04, respectively). It was also shown that there was a positive correlation between social support and LVEF (Table 4 ; P ≤ 0.001).
Discussion
This comprehensive study was carried out to evaluate malnutrition and its relationship with social support, socio‐demographic, and clinical factors in patients with HF. The results may improve HF prognosis and survival. Our findings showed that 65.6% of the participants were malnourished or at risk of malnutrition. It should be considered that the risk of malnutrition in itself is associated with increased morbidity and mortality. 24 Our findings revealed that an increased risk of malnutrition is associated with a lower social support score, lower BMI, higher NYHA classification, longer disease duration, and lower serum albumin level.
Concerning the first research question, it was found that 110 (34.4%) of the participants had normal nutritional status, 151 (47.2%) of them were at risk of malnutrition, and 58 (18.1%) were malnourished. In general, there is no standard way to assess malnutrition in patients with HF, which has caused the existing estimates of malnutrition to have a wide range. According to previous studies, the prevalence of any degree of malnutrition in HF patients is between 6% and 60%, and the prevalence of severe malnutrition is between 3% and 9%. 14 The findings of a study that assessed malnutrition in 1307 hospitalized patients with HF using three nutritional scores revealed that approximately 20% of patients had moderate to severe malnutrition. 11 This difference in the prevalence of malnutrition using different tools can be due to the difference in the components of each tool and index. More research is needed to develop standardized tools to assess malnutrition in HF patients for clinical and research purposes.
As mentioned earlier, social factors may affect malnutrition risk. In the present study, multivariate analysis showed that lower social support scores are associated with an increased risk of malnutrition. This could be explained by the positive impact of social support on improvement in self‐care behaviours, adherence to recommended therapy, and patients' cooperation in disease management. 15 , 19 In reviewing the literature, few data were found on the positive impact of social support on decreasing malnutrition risk in other study groups, 25 but to the best of our knowledge, this is the first study to look at the link between malnutrition and social support in HF patients. Therefore, future studies on the current topic are recommended.
One of the project's objectives was to identify the relation between clinical factors and malnutrition risk. According to our findings, increased risk of malnutrition was associated with higher NYHA class. This finding was consistent with data obtained from previous studies. A recent study on the relationship between prognostic nutrition index and NYHA classification in patients with coronary heart disease showed that moderate and severe malnutrition were associated with NYHA class III and IV. 26 In another study, Rodrigo R. P. Duarte et al. demonstrated that even after controlling for the LVEF, those who were malnourished had a 2.5‐fold increased risk of HF severity by NYHA classification. 27 These findings could be explained by the muscle weakness (including respiratory muscles) caused by malnutrition; in this condition, patients are more prone to dyspnoea and fatigue during daily activities. In contrast, LVEF was not associated with malnutrition risk. This finding was in line with previous studies that did not observe any relationship between LVEF and nutritional status. 11 , 13 The current study also demonstrated that a longer disease duration is associated with an increased risk of malnutrition. However, previous studies did not show any relationship between the risk of malnutrition and disease duration. 28 This study also showed that lower BMI was one of the predictors of malnutrition. In accordance with the present result, previous studies have shown that HF patients who are malnourished tend to have a lower BMI. 11 , 12 The findings of a study conducted on 3386 HF patients confirm this finding but also demonstrate that although malnutrition is more common in patients with HF who have a lower BMI, its prevalence in overweight and obese patients is also high and is associated with higher mortality rates in these patients. Therefore, malnutrition cannot simply be considered equivalent to a low BMI. 12
Concerning the potential relationship between the risk of malnutrition and laboratory characteristics, the present study demonstrated that a lower serum albumin level is related to an increased risk of malnutrition. This finding was in line with data obtained from previous studies in patients with HF that showed those with malnutrition had a lower level of serum albumin. 11 , 13 Although albumin is known as a serum marker that can reflect nutritional status, as its concentration is affected by fluid retention in patients with HF, it cannot be used alone to evaluate nutritional status.
This study has some limitations. First, the cross‐sectional nature of our data made us unable to distinguish whether variables were temporal or not. Second, due to our sample size, we had some limitations in choosing confounding factors for multivariable analyses. Besides these limitations, this study had some strengths. First, to evaluate malnutrition, among six malnutrition screening tools (CONUT, GNRI, PNI, Malnutrition Universal Screening Tool, Subjective Global Assessment, and MNA‐SF), we used MNA‐SF, which has the highest specificity (99%) and the lowest misclassification rate (2%) in identifying moderate to severe malnutrition in patients with HF. 14 Second, besides face‐to‐face interviews, we reviewed patients' medical records and documented laboratory tests and echocardiograms in data collection. Third, confounding factors were chosen based on clinician judgement, a literature review, and a univariate P‐value of <0.3, and the final model was fitted using backward elimination.
Conclusions
Being malnourished or at risk of malnutrition has a significant role in the quality of life, hospitalization and readmission rates, morbidity, and mortality of patients with HF. This study shows that malnutrition and the risk of malnutrition are highly prevalent among hospitalized patients with HF, so we emphasize that malnutrition must be prioritized in hospitals, particularly for patients with HF. As a result, an appropriate malnutrition screening tool is suggested for early evaluation of nutritional risks and a way to avoid further complications and functional decline. The current data highlight the importance of social and clinical factors in preventing and managing malnutrition. In addition, we demonstrate that correlates of malnutrition could be used to assess malnutrition in HF patients. As a result, this study suggests including these factors in developing guidelines for HF management.
Conflict of interest
None declared.
Funding
This study was supported by Shiraz University of Medical Sciences.
Acknowledgements
The authors would like to thank all participants.
Sharifi, M. H. , Afshari, M. , Vardanjani, H. M. , Nikmanesh, A. , and Nikoo, M. H. (2024) Correlates of malnutrition in patients with heart failure: the role of social support. ESC Heart Failure, 11: 719–726. 10.1002/ehf2.14612.
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