Use of a Structured Interview to Assess the Association of Inadequate Energy and Protein Intake with Subclinical Congestion in Heart Failure Patients

Summary

This study evaluates the association between subclinical congestion and dietary intake in patients with heart failure using combined diagnostic tools, including bioelectrical impedance and Doppler ultrasound, as well as 24 h dietary recalls, showing that subclinical congestion is linked to reduced energy, protein, and micronutrient intake.

Abstract

Although previous studies have suggested a relationship between subclinical congestion and poor dietary intake, evidence on this topic remains limited. This study aimed to evaluate whether subclinical congestion is associated with inadequate dietary intake in patients with heart failure (HF). A cross-sectional study was conducted in 122 ambulatory patients at the Heart Failure Clinic of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán between April 2023 and January 2025. Subclinical congestion was assessed using the Venous Excess Ultrasound Score (VExUS) and bioelectrical impedance vector analysis (BIVA). Dietary intake was evaluated through three nonconsecutive 24 h dietary recalls. Inadequate dietary intake was defined as consumption below 60% of the standard energy requirement (25-30 kcal/kg) and a protein intake below 1.2 g/kg/day. Patients with subclinical congestion showed significantly lower total energy intake (1098.42 vs. 1478 kcal, p = 0.001) and protein intake (0.84 vs. 1.44 g/kg, p = 0.001), alongside a higher carbohydrate intake (64.3% vs. 49.1%, p < 0.001) and lower fiber intake (10.90 g vs. 16.83 g, p = 0.008), particularly soluble fiber (0.53 vs. 3.01 g, p < 0.001). Subclinical congestion was strongly associated with inadequate dietary intake (odds ratio [OR] = 10.04; 95% confidence interval [CI]: 1.03-97.75; p = 0.047). Additionally, lack of appetite emerged as an independent risk factor for insufficient intake (OR = 11.37; 95% CI: 2.14-60.30; p = 0.004). In conclusion, subclinical congestion in HF patients was associated with significantly lower energy and protein intake, higher carbohydrate consumption, and reduced fiber intake. These findings highlight the potential role of nutritional assessment in the early identification and management of subclinical congestion in HF.

Introduction

Congestion is a leading cause of hospitalization in patients with heart failure (HF), and its recurrence is strongly associated with worse prognosis¹. While pulmonary and peripheral oedema are classical clinical signs, subclinical congestion, characterized by elevated cardiac filling pressures without overt symptoms, can develop weeks before decompensation and often remains undetected with conventional assessment methods². This early-stage congestion may promote gastrointestinal dysfunction through mechanisms such as intestinal wall oedema, increased mucosal permeability, bacterial translocation, and systemic inflammation, all of which can contribute to symptoms like nausea, early satiety, and cardiac cachexia³. However, the presence of nonspecific symptoms such as dyspnea or fatigue does not necessarily imply congestion, as they may result from other prevalent conditions in HF patients, such as poor physical conditioning, anaemia, or depressive symptoms⁴. Therefore, objective tools are essential for accurately identifying true fluid overload and differentiating it from other causes of symptom burden in this complex population⁵. This is especially relevant in cases of subclinical congestion, which can be identified through whole-body bioimpedance analysis and is much more common than previously thought, affecting nearly two-thirds of ambulatory patients with heart failure with reduced ejection fraction (HFrEF). Notably, this early, often unnoticed fluid accumulation is associated with increased rates of worsening heart failure events and worse clinical outcomes, highlighting the importance of using sensitive, noninvasive assessments in routine care⁶. Malnutrition, often intertwined with frailty and other comorbidities, has been consistently recognized as an independent predictor of adverse outcomes in HF, reinforcing the need for early detection and targeted interventions. Despite this, routine assessment of dietary intake is rarely incorporated into follow-up visits for patients with HF, even though evaluating nutrition is essential for identifying inadequate intake, recognizing early signs of catabolism, and guiding individualized dietary strategies that may prevent further functional decline^3,4.

Previous studies have suggested that congestion may influence nutritional status. For example, a study conducted in 2020 reported a significant association between higher B-type natriuretic peptide (BNP) levels at discharge and inadequate dietary intake, based on hospital meal consumption analysis⁷. As BNP reflects elevated cardiac filling pressures, this association may indicate persistent subclinical congestion impacting nutritional intake at discharge. Despite these insights, evidence on the relationship between subclinical congestion, dietary intake, and clinical outcomes, especially in stable ambulatory patients, remains limited. Therefore, further research is warranted to clarify these interactions using validated diagnostic tools for assessing congestion. This study aimed to determine whether subclinical congestion is associated with poor nutrient intake in patients with HF, hypothesizing that early detection and nutritional evaluation may play a critical role in patient management and prognosis.

Protocol

This study was conducted according to the guidelines of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMNSZ) and approved by the Biomedical Research Ethics Committee (reference number 4504). Written and verbal informed consent was obtained from all participants prior to enrolment.

Study design and population

This was a cross-sectional study. Patients aged ≥ 18 years with a confirmed diagnosis of heart failure for at least six months, clinically stable, with no medication adjustments in the preceding three months, and no hospital admissions or acute decompensation within the last month who attended the HF Clinic at INCMNSZ between April 2023 and January 2025 were included. The exclusion criteria comprised current use of nutritional supplements, pregnancy, active cancer diagnosis, and gastrointestinal diseases associated with malabsorption. A total of 122 patients were recruited, and after applying the selection criteria, 117 were included in this study. The recruitment and selection of participants are depicted in the study flow diagram (Figure 1).

Assessment of subclinical congestion

Subclinical congestion was defined as the presence of objective signs of fluid overload detected by both the Venous Excess Ultrasound Score (VExUS) and Bioelectrical Impedance Vector Analysis (BIVA), in the absence of overt clinical signs of congestion (such as peripheral oedema, pulmonary rales, jugular venous distension, or orthopnea). Patients presenting only with nonspecific symptoms, such as fatigue or exertional dyspnea, were not classified as clinically congested. The clinicians who evaluated the patients were not blinded to the BIVA or VExUS results.

Venous Excess Ultrasound Score (VExUS) procedure

The VExUS assessment was performed by a trained cardiologist using a hybrid portable ultrasound device that offers pulsed and continuous Doppler with a low-frequency (2.5-5 MHz) sectorial transducer. Four main veins were measured: the inferior vena cava, portal vein, hepatic vein, and suprarenal vein. According to these measurements, the degree of congestion can be obtained, ranging from 0 to 3, where a degree equal to or greater than 1 is considered congestion. A more detailed description of the VExUS protocol has been described elsewhere⁸.

Bioelectrical Impedance Vector Analysis (BIVA) procedure

The bioelectrical impedance we used analyzes the whole body, measuring fat mass, lean, and body water across the entire body; it allows for detecting imbalances and tracking localized changes. BIVA was performed using a monofrequency (50 kHz), tetrapolar bioelectrical impedance device. Resistance and reactance measurements were obtained and standardized to the subject's height. According to the resistance and reactance calculations, we obtained the phase angle and vector. If the vector is below the 75^th percentile, it indicates that congestion is present. The procedure followed the standardized protocols described elsewhere⁹.

Dietary intake assessment

Dietary intake assessment was performed on the same day as the clinical visit and congestion evaluation. The questionnaire was evaluated using three non-consecutive 24 h dietary recalls (Supplementary 1) administered in person by trained nutrition professionals.

The multiple-pass method was used to enhance the accuracy of dietary recall. Patients were first asked to provide a free and uninterrupted list of all foods and beverages consumed during the previous 24 h. Subsequently, interviewers probed for commonly forgotten items, including snacks, beverages, sauces, and condiments, to minimize omissions. Patients then reported the time of consumption and the eating occasion for each item recalled. Detailed information regarding food preparation methods, portion sizes, ingredients, and, when applicable, product types was collected to allow for precise nutrient estimation. Finally, the entire recall was reviewed with each patient to verify completeness and accuracy.

The recalls were performed on random days, ensuring that one recall included a weekend day to account for variability in dietary patterns. The average intake from the three recalls was used for analysis.

Dietary adequacy was defined as an intake of at least 60% of the estimated energy requirements (25-30 kcal/kg/day) and a protein intake of 1.2 g/kg/day, based on clinical nutrition guidelines for patients with chronic diseases¹⁰. Lower intake was classified as inadequate, considering its association with increased malnutrition risk.

Nutritional analysis

Dietary data collected from the 24 h recalls were entered into the Food Processor Nutrition Analysis Software (version 7) for nutrient composition analysis. This software was previously used in another study to evaluate dietary phosphorus intake in patients treated with peritoneal dialysis, with good results¹¹. The software database includes comprehensive information on macronutrients, micronutrients, and fiber content. Inputting detailed food descriptions, preparation methods, and portion sizes enabled the precise calculation of total energy intake, macronutrient distribution, and fiber intake for each participant.

The dietary assessment and analysis were performed using ESHA's Food Processor software following a standardized workflow to ensure reproducibility and consistency. As part of the data entry process, an individual patient profile was created by selecting person > New, where demographic and anthropometric variables, including age, sex, weight, height, and relevant clinical information, were recorded. Measurement units were standardized to grams, milliliters, and kilograms, and individualized nutritional goals were defined when required, such as energy requirements of 25-30 kcal/kg/day and protein intake of 1.2 g/kg/day.

The nutrient output configuration was established prior to data analysis by selecting preferences > Nutrients to View or by customizing columns within reports. This configuration allowed for the evaluation of total energy intake, macronutrient distribution expressed as percentages, protein intake normalized to body weight (g/kg), total dietary fiber, soluble and insoluble fiber, and sodium intake. This setup ensured uniformity across all dietary analyses.

Dietary data collection was conducted independently of the software using the multiple-pass 24 h recall method. In Step 1 of the recall procedure, patients were asked to freely recall all foods and beverages consumed during the previous 24 h. In Step 2, interviewers systematically probed for commonly forgotten items, such as snacks, beverages, sauces, and condiments. In Step 4, detailed information regarding food preparation methods, portion sizes, ingredients, and product types was collected. This structured approach was used to improve recall precision before dietary data were entered into Food Processor.

All reported foods and beverages were entered into the software using the search function, and appropriate items were selected from generic, branded, or restaurant databases when applicable. Portion sizes were specified using standardized units or household measures that reflected actual consumption. To estimate usual dietary intake, recalls were completed on two to three nonconsecutive days within the same patient profile, including at least one weekend day, with each recall assigned to its corresponding date.

Daily and averaged nutrient intakes were generated by selecting reports > Spreadsheet, Nutrient Totals, or Intakes vs. Goals, allowing for evaluation of both single-day intake and multi-day averages across selected recalls.

All dietary data were subsequently exported using reports > Export in CSV, Excel, or PDF format for statistical analysis and inclusion in manuscript materials.

Dietary intake was therefore assessed using three nonconsecutive 24 h multi-pass recalls and analyzed with Food Processor, with the nutrient database updated to the corresponding version and multi-day average intakes used for analysis.

In this study, we selected a dietary intake questionnaire instead of commonly used nutritional risk tools because these tools rely primarily on biochemical markers (e.g., albumin and lymphocyte count) and anthropometric measurements to estimate nutritional risk, but they do not capture information on actual food intake or eating patterns. In contrast, the three non-consecutive 24 h dietary recalls employed in our methodology provided a direct and detailed evaluation of patients' consumption, making it possible to identify both qualitative and quantitative insufficiencies in macro and micronutrient intake. This approach was better suited to the study objective of examining dietary intake in relation to subclinical congestion.

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Statistical analysis

Categorical variables were summarized using frequencies and percentages. Continuous variables were expressed as means with standard deviations or medians with interquartile ranges, depending on data distribution. The Kolmogorov-Smirnov test with Lilliefors correction assessed normality¹³. Comparisons between groups (adequate vs. inadequate intake) were performed using Pearson's chi-square or Fisher's exact test for categorical variables, and Student's t-test or Mann-Whitney U test for continuous variables as appropriate. The significance threshold was p < 0.05. For the multivariate analysis, multiple linear regression was performed, in which subclinical congestion, dyspnea, and lack of appetite were included as covariates (potential confounding factors) to determine whether poor dietary intake was independently associated with any of these variables in a statistically significant manner. Jamovi software version 2.7.5 was used for statistical analysis.

Results

Study population

A total of 117 patients met the inclusion criteria and were enrolled in the study. Among them, 54% were women, and the median age was 66.5 years. The patient selection process is detailed in the flowchart presented in Figure 1. The baseline characteristics of the study population, stratified by the presence or absence of subclinical congestion, are summarized in Table 1. No significant differences were observed be...

Discussion

This study proposes that subclinical congestion, even in the absence of overt clinical signs, is significantly associated with inadequate dietary intake in patients with heart failure. Our results revealed that individuals with subclinical congestion consumed fewer calories and less protein, along with higher carbohydrate intake and reduced fiber intake, than those without congestion. These findings raise the possibility that nutritional status is intertwined with the mechanisms driving subclinical congestion; however, t...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
White paper	NA	NA	Any brand can be used
Pen	NA	NA	Any brand can be used
ESHA food processor software	Trustwell	https://www.trustwell.com/products/food-processor-nutrition-analysis-software/dietitian-purchase/?__hstc=245337562.66dbd18d36f4c f64e594f71fe9ffa176.175502016180 4.1755020161804.1755020161804.1 &__hssc=245337562.1.17550201 61804&__hsfp=4246053562	ESHA Food Processor is a software application that provides comprehensive food and nutrient analysis. The software allows users to analyze recipes, menus, and individual foods for their nutritional content, including calories, macronutrients, and micronutrients.
Computer system	Microsoft	Microsoft 10	Microsoft 10 or higher