The obesity paradox in heart failure (HF) is criticized because of the limitations of body mass index (BMI) in correctly characterizing overweight and obese patients, necessitating a better evaluation of nutritional status. The aim of this study was to assess nutritional status, BMI, and significance in terms of HF survival. Anthropometry and biochemical nutritional markers were assessed in 55 HF patients. Undernourishment was defined as the presence of ≥2 of the following indexes below the normal range: triceps skinfold, subscapular skinfold, arm muscle circumference, albumin, and total lymphocyte count. Patients were also stratified by BMI and followed for a median of 26.7 months. Across BMI strata, no patient was underweight, 31% were normal weight, 42% were overweight, and 27% were obese. Undernourishment was present in 53% of normal-weight patients, 22% of overweight patients, and none of the obese patients (p = 0.001). Undernourished patients had significantly higher mortality (p = 0.009) compared to well-nourished patients. In multivariate analysis, only undernutrition (hazard ratio 3.149, 95% confidence interval 1.367 to 7.253), New York Heart Association functional class (hazard ratio 3.374, 95% confidence interval 1.486 to 7.659), and age (hazard ratio 1.115, 95% confidence interval 1.045 to 1.189) remained in the model. Among nutritional indicators, subscapular skinfold was the best predictor of mortality; patients with subscapular skinfold in the fifth percentile had higher mortality (p = 0.0001). In conclusion, BMI does not indicate true nutritional status in HF. Classifying patients as well nourished or undernourished may improve risk stratification.
The clinical syndrome of heart failure (HF) often leads to undernourishment and wasting, which are strong risk factors for death. The exact prevalence of malnutrition in the HF population is unknown, but is estimated to be between 20% and 70%, depending on the assessment criteria. There is no universally accepted definition of malnutrition or a gold standard for nutritional assessment. The term “malnutrition” is used to describe a host of nutritional abnormalities, although it typically refers to protein-energy malnutrition. Nutritional assessment can be based on anthropometry, bioimpedance, clinical or biochemical variables, or scores that combine some of these variables and must be adapted not only to a specific population or patient but also to the available local resources. Assessment methods and diagnostic examinations, which are inexpensive, rapidly performed, well tolerated by patients, and require low staff assignments, are preferred. A further assessment of the body composition of HF patients, analyzing the nutritional status rather than body mass index (BMI), could shed light on the obesity paradox. The aim of this study was to assess the nutritional status of a cohort of HF patients, its correspondence with BMI, and its significance in terms of survival.
Methods
Fifty-five ambulatory HF patients from a specialized HF clinic at a university hospital were consecutively enrolled from April to October 2008. All patients had established diagnoses of HF according to European Society of Cardiology guidelines and ≥1 hospital admission for symptoms of HF within the past year. This study included patients with either depressed (<45%) or preserved (≥45%) left ventricular ejection fractions (LVEF). An HF clinical score based on Framingham criteria, consisting in a summation of major and minor signs and symptoms, was calculated to assess the severity of the disease. Demographic and clinical data were prospectively collected at enrollment using specially designed case record forms. The study was approved by the local ethics committee, and informed consent was obtained from each patient. All patients were followed up regularly at the HF clinic according to their clinical status. Information about deceased patients was obtained from medical records and the patients’ physicians and relatives.
Blood samples were obtained from venipuncture, with the patient at rest, from 9 am to 2 pm . Several nutritional markers were measured: albumin by spectrophotometric bromocresol green method (Modular DPE system; Roche Diagnostics GmbH, Mannheim, Germany); prealbumin by nephelometry (ProSpec system; Siemens Healthcare Diagnostics, Marburg, Germany); total proteins by spectrophotometry biuret method (Modular DPE system); uric acid by spectrophotometry, uricase-peroxidase (Modular DPE system); total lymphocyte count by automated cell counter (Sysmex España s.l., Roche Diagnostics, Barcelona, Spain); and insulin-like growth factor–1 by chemiluminescence (Siemens Healthcare Diagnostics, Llanberis, United Kingdom). The threshold values for malnutrition for albumin, prealbumin, and total lymphocyte counts were obtained from the Spanish Society of Parenteral and Enteral Nutrition (albumin <35 g/L, prealbumin ≤0.15 g/L, total lymphocyte count ≤1.2 × 10 9 ). The lower limits of reference values given by our laboratory were used for the rest of the parameters (total proteins <60 g/L, uric acid <210 μmol/L, insulin-like growth factor–1 <118 μg/L). N-terminal pro–B-type natriuretic peptide (NT–pro-BNP) levels were measured by electrochemiluminescence immunoassay using an Elecsys 1010 analyzer (Roche Diagnostics GmbH). The intra-assay coefficients of variation for NT–pro-BNP were 1.8% for 221 pg/ml and 3.1% for 4,250 pg/ml, and the interassay coefficients of variation were 5.5% for 187 pg/ml, 7.0% for 3,120 pg/ml, and 7.3% for 12,376 pg/ml.
Triceps skinfold and subscapular skinfold (SS) were measured 3 times to the nearest 0.1 mm with a Lange skinfold caliper (Cambridge Scientific Instruments, Cambridge, Massachusetts) while the patient was in a relaxed position. Skinfold thickness was defined as the mean of the sum of the values of the 3 skinfold measurements. All measurements were performed by a single observer according to standard techniques. Triceps skinfold was measured in the arm between the olecranon and the clavicular acromion. SS was measured below the inferior end of the scapula, at a 45° angle from the vertical. Arm muscle circumference was calculated as arm circumference (cm) − [0.314 × triceps skinfold (mm)]. The lower limits of normal values for anthropometric parameters were obtained by calculating the fifth percentile for each age and gender group of the reference population.
For the purposes of this study, we chose a nutritional assessment that involved different nutritional markers most used in published research: albumin, which is a biochemical indicator of protein reserves; total lymphocyte count, an immunologic parameter related to protein depletion and loss of defenses; and anthropometric variables including triceps skinfold and SS, which measure subcutaneous fat and reflect the caloric aspect of malnutrition, and arm muscle circumference, which estimates the muscular compartment. The presence of ≥2 of these indexes less than the normal ranges was defined as undernourishment. Patients were also stratified by BMI using the World Health Organization classification. Weight and height were measured during enrollment, and BMI was calculated as weight in kilograms divided by the square of height in meters. Subjects were stratified according to baseline BMI.
Descriptive analyses were performed at the first step. Categorical variables were described by frequencies and percentages. Continuous variables were described by means and SDs or medians and interquartile ranges in case of skewed distribution. Baseline characteristics of patients across the 3 BMI categories were examined using chi-square tests for categorical variables. The comparison of continuous variables between groups was carried out using analysis of variance for unpaired data once normality was demonstrated (Kolmogorov-Smirnov test); otherwise, a nonparametric test (Mann-Whitney and Kruskal-Wallis tests) was used. To identify independent predictors of death, a multivariate Cox proportional-hazards analysis was performed, adjusting for classic confounders and the covariates statistically significant at the univariate analysis (p <0.10 was the criterion for entry into multivariate analysis). A backward stepwise method was used to define the final model and the independent predictors of death. Significant predictors of mortality were expressed in terms of hazard ratios (HR) and 95% confidence intervals (CIs). Kaplan-Meier survival curves were plotted, and the groups were compared using the log-rank test. Statistical analyses were performed using SPSS version 11 (SPSS, Inc., Chicago, Illinois). A 2-sided p value <0.05 was considered significant.
Results
Patient classification according to BMI identified 31% of patients in the normal-weight range, 42% as overweight, and 27% as obese. Patients were followed for a median of 26.7 months (range 18.4 to 28.5), and 47% (n = 26) died during this period.
The prevalence of undernourishment in our population was 25.4%. Nutritional data showed that 53% of normal-weight patients, 22% of overweight, and none of the obese group were undernourished (p = 0.001; Figure 1 ) . Table 1 lists the demographic and clinical data of the study population. First, we performed a multivariate analysis including the classic HF prognostic factors (age, gender, New York Heart Association functional class, estimated glomerular filtration rate, hemoglobin, LVEF, NT–pro-BNP, and BMI) and undernourishment as defined in the methods. Only undernourishment (HR 3.149, 95% CI 1.367 to 7.253), New York Heart Association functional class (HR 3.374, 95% CI 1.486 to 7.659), and age (HR 1.115, 95% CI 1.045 to 1.189) remained in the model. Figure 2 shows the Kaplan-Meier survival curves of nourished and undernourished patients. The latter had significantly higher mortality than well-nourished patients (p = 0.009; Figure 2 ).
| Variable | Total Population | Nourished | Undernourished | p Value |
|---|---|---|---|---|
| (n = 55) | (n = 41) | (n = 14) | ||
| Age (years) | 73.7 ± 8.7 | 72.7 ± 9.0 | 76.7 ± 7.4 | 0.13 |
| Men | 36 (66%) | 28 (68%) | 8 (57%) | 0.45 |
| Ischemic cause of HF | 21 (38.2%) | 17 (41.5%) | 4 (28.6%) | 0.39 |
| NYHA class | 0.95 | |||
| II | 58% | 56% | 64% | |
| III or IV | 42% | 44% | 36% | |
| Clinical disease severity score | 1.3 (1–2.5) | 1.5 (1–2.5) | 1.0 (1.0–2.5) | 0.83 |
| LVEF (%) | 46 ± 17 | 41 ± 16 | 58 ± 16 | 0.001 |
| Atrial fibrillation | 21 (38%) | 13 (32%) | 8 (57%) | 0.09 |
| Diabetes mellitus | 29 (52.7%) | 22 (53.7%) | 7 (50.0%) | 0.75 |
| Dyslipidemia ⁎ | 26 (47.3%) | 21 (51.2%) | 5 (35.7%) | 0.51 |
| Hypertension ⁎ | 46 (83.6%) | 34 (82.9%) | 12 (85.7%) | 0.81 |
| BMI (kg/m 2 ) | 27.2 ± 4.3 | 28.3 ± 4.1 | 24.1 ± 3.6 | 0.001 |
| Waist circumference (cm) | 100.9 ± 11.3 | 103.4 ± 11.3 | 93.7 ± 8.1 | 0.005 |
| Hemoglobin (mg/dl) | 12.8 ± 1.7 | 13.0 ± 1.6 | 12.0 ± 1.9 | 0.049 |
| Estimated glomerular filtration rate (ml/min/1.73 m 2 ) | 56.2 (38.0–60.0) | 60.0 (49.4–60.0) | 37.5 (26.8–57.1) | 0.005 |
| NT–pro-BNP (ng/L) | 2,417 (1,137–3,830) | 2,413 (812–3,856) | 3,266 (1,695–4,079) | 0.16 |
⁎ For this study, dyslipidemia and hypertension were considered when the diagnosis was registered in the patient’s clinical records and was receiving specific treatment.
Next, in a univariate analysis, we studied all the nutritional markers to dissect those related to mortality. Among these, the best variable for predicting mortality was SS (HR 5.713, 95% CI 2.209 to 14.778, p <0.0001; Table 2 ). Patients with SS in the fifth percentile or lower had significantly higher mortality (p = 0.0001; Figure 3 ) . In fact, all overweight patients with SS in the fifth percentile or lower died during the follow-up period.
| Nutritional Marker ⁎ | HR (95% CI) | p Value |
|---|---|---|
| Total lymphocyte count | 2.515 (1.143–5.536) | 0.022 |
| SS | 5.713 (2.209–14.778) | <0.0001 |
| Triceps skinfold | 2.660 (0.794–8.909) | 0.113 |
| Arm muscle circumference | 0.830 (0.378–1.819) | 0.641 |
| Prealbumin | 1.966 (0.580–6.664) | 0.278 |
| Total proteins | 2.660 (0.794–8.909) | 0.113 |
| Uric acid | 2.210 (0.293–16.684) | 0.442 |
| Insulin-like growth factor–1 | 2.090 (0.744–5.870) | 0.162 |
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