Tarraga Lopez PJ; Sadek MI; Madrona Marcos F; Simon MA; Villar Inarejos MJ; Tarraga Marcos ML

Research Article

Austin Cardiol. 2019; 4(1): 1020.

Obesity and Heart Failure in a Health Area

Tarraga Lopez PJ¹*, Sadek MI², Madrona Marcos F³, Simon MA⁴, Villar Inarejos MJ⁵, Tarraga Marcos ML⁶

¹Department of Medical Sciences, Faculty of Medicine, University of Castilla la Mancha, Spain

²Doctor CS Zone 5 A Albacete. Spain

³Medico CS Zone 5 A Albacete Spain

⁴Department of Cardiologist, University Hospital Albacete, Spain

⁵Department of Emergency Physician Albacete University Hospital, Spain

⁶Nurse Lozano Blesa Hospital, Saragossa, Spain

*Corresponding author:Tarraga Lopez PJ, Department of Medical Sciences, Faculty of Medicine, University of Castilla la Mancha, Spain

Received: August 24, 2019; Accepted: October 04, 2019; Published: October 11, 2019

Abstract

Objective: To analyze the relationship of the Body Mass Index (BMI) with heart failure in a health area.

Method: Observational descriptive study of the 161 patients who had been diagnosed in the Health Area between January 2014 and December 2016.

Among other demographic, clinical and analytical data, the BMI was analyzed based on weight and height at the first visit to the unit, using the formula: weight (in kilograms)/square of height (in meters). Once obtained, the relationship between BMI and 2-year survival was evaluated. Four subgroups of patients were analyzed, based on their BMI, based on the criteria defined by the World Health Organization (WHO) in 1999 (Technical Report Series, No. 854, Geneva: 1999): low weight (BMI ‹ 20.5), normal weight (BMI of 20.5 to ‹ 25.5), overweight (BMI of 25.5 to ‹ 30) and obesity (BMI ≥ 30).

Statistical analysis was carried out using the statistical package SPSS® 24.0 for Windows. The association between BMI as a continuous variable and 2-year mortality.

Results: Of the participants, 81 were obese (50.8%), being 33 men and 48 women. The average age of the obese is 80.32 +/- 9.23 years.

The main causes of heart failure in 62.2% had diagnosed some type of heart disease, being: 29.2% Ischemic heart disease, 46.6% cardiac arrhythmias and 20.5% valvulopathies.

BMI as a continuous variable was significantly associated with mortality (p ‹ 0.001), age (0.002), ischemic disease (0.001), gender (0.004), hypertension (0.002), diabetes (0.003) and dyslipidemia (0.004). The relation of BMI with the use of Digoxin, As a Diuretics and Spironolactone treatments has also been seen with higher BMI plus utilization. BMI is also associated with the number of admissions, greater number of concomitant chronic diseases and mortality.

The scores obtained in the MLWHFQ quality of life questionnaire at the initial visit; the patients with low weight were those who obtained the highest score, which corresponds to a worse quality of life. There were no significant differences between the scores obtained by patients of normal weight, overweight and obese, although these showed a tendency to obtain a higher score.

Conclusions: BMI has been shown to be associated with mortality, ischemic disease, sex, hypertension, diabetes and dyslipidemia in patients with heart failure.

Introduction

The changes in the diet and the increase of the sedentary progressive and generalized own of our times have brought about a progressive increase of the incidence and the prevalence of the obesity in the population. General [1]. This increase has a heterogeneous geographical distribution and mainly affects western countries, both the United States [2] and Europe are being victims of this epidemic. According to the Register of the Spanish Society for the Study of Obesity (SEEDO) [3] between 1999 and 2000, the prevalence of obesity (Body Mass Index [BMI]≥ 30) in the adult Spanish population was 14.5%, predominantly among women, and increased with age, up to 20-30% of those over 55 years.

Obesity is a known independent risk factor for heart failure [1] (CHF) that has reached epidemic proportions: the World Health Organization estimates that more than one billion adults worldwide are overweight and 300 million of them are clinically obese The incidence and prevalence of obesity and heart failure are so high that it is not uncommon to find both disorders in the same patient. In fact, several cohorts of patients with heart failure have revealed that 15-35% of these patients are obese and that 30-60% have overweight problems [2]. Epidemiological studies have clearly shown a close relationship between obesity and increased risk of Cardiovascular Disease (CVD) and mortality in the general population.

Discovering the relationship between obesity and heart failure is proving complex. A recent epidemiological study, derived from the Framingham Heart Study, clearly indicates that obesity and overweight are very predictive variables of subsequent clinical heart failure [1]. Although obesity causes abnormalities in diastolic and systolic function and is supposed to increase the risk of mortality in patients with established heart failure, our group and others have shown that, paradoxically, BMI is inversely related to long-term mortality in patients with chronic heart failure [2,4,5-11]. However, it is important to note that BMI is not the only conventional risk factor for CVD that presents a paradoxical association in the clinical outcomes of patients with heart failure. High concentrations of low density lipoproteins, as well as total cholesterol, have also been associated with a survival advantage in heart failure. These systematic findings on various risk factors for CVD in patients with heart failure justify the use of the term reverse epidemiology [12-15].

Our objective was to analyze the relationship between BMI and the evolution of Heart Failure during a 2-year follow-up in patients with heart failure treated on an outpatient basis in our health area by Primary Care Physicians and Nurses, assess whether this relationship was affected by the number of hospital readmissions and, finally, assess whether the BMI has an influence on the quality of life, since there are discrepancies in the publications in this regard.

Methods

Descriptive observational study of the 161 patients who had been diagnosed in the Health Area with a population over 14 years of 15,000 inhabitants, between January 2014 and December 2016, those who had BMI at the first visit were analyzed and of his vital situation after 2 years of follow-up. The criterion for inclusion in the unit was heart failure as the main diagnosis of the patient. Among other demographic, clinical, and analytical data, BMI was analyzed based on weight and height on the first visit to the unit, using the formula: weight (in kilograms)/square of height (in meters). Once obtained, the relationship between BMI and 2-year survival was evaluated. Four subgroups of patients were analyzed, based on their BMI, based on the criteria defined by the World Health Organization (WHO) in 1999 (Technical Report Series, no 854; Geneva: 1999): low weight (BMI ‹ 20.5), normal weight (BMI of 20.5 to ‹ 25.5), overweight (BMI of 25.5 to ‹ 30) and obesity (BMI ≥ 30).

Statistical analysis was performed using the SPSS® 24.0 statistical package for Windows. The association between BMI as a continuous variable and 2-year mortality has been analyzed using the Mann- Whitney U test and the Kruskal-Wallis test for those without normal distribution. The logistic regression has been used to calculate the Odds Ratio (OR). In the multivariable logistic regression analysis [16] mortality and readmissions at 1 and 2 years have been introduced as a dependent variable, and as independent variables, BMI (as a continuous variable), age, sex, the etiology of heart failure, the presence of diabetes and high blood pressure and the treatments received (beta blockers, Angiotensin Conversion Enzyme Inhibitors [ACEI] or Angiotensin II Receptor Antagonists [ARA-II], loops diuretics, spironolactone, digoxin and statins). The method used was “by conditional backward steps”.

The relationship between the different established BMI groups with readmissions and mortality at 1 and 2 years was analyzed using the χ² test (linear by linear association for the joint analysis of the four groups) or by the Fisher test, in function of the number of patients. Comparisons between groups have been made using the χ² test for categorical variables and the Kruskal-Wallis test for continuous variables, after checking that they had no normal distribution.

The quality of life of the patients was evaluated using the Minnesota Living With Heart Failure Questionnaire [15] (MLWHFQ), previously used in Spain [16], during the study inclusion visit.

The MLWHFQ consists of [21] questions whose objective is to find out to what extent heart failure affects the physical, psychic and socioeconomic aspects of patients’ lives; the questions refer to signs and symptoms of heart failure, social relationships, physical and sexual activity, work and emotions; The range of possible answers for each question ranges from 0 (no) to 5 (very much), so that the higher the score, the worse the quality of life.

The study has been carried out in compliance with the personal data protection law and in accordance with the international clinical research recommendations of the Helsinki Declaration of the World Medical Association.

Results

161 patients diagnosed with HF in our Health Zone have participated. Average age 81.24 +/- 9.59 years (average age/standard deviation), 54% are women.

Of the participants 81 were obese (50.8%), with 33 men and 48 women. The average age of the obese is 80.32 +/- 9.23 years.

The main causes of Heart Failure in 62.2% were diagnosed with some type of heart disease, being: 29.2% Ischemic Heart Disease, 46.6% Cardiac Arrhythmias and 20.5% Valvulopathies.

Tables 2 and 3 show the clinical characteristics and the treatments received by the patients.

Table 1: Clinical characteristics of the patients.




  
    Insuf Cardiac evolution    medium time 
    23,5    months 
  
  
    HTA 
    86% 
  
  
    Diabetes 
    54% 
  
  
    Hyperlipemia 
    63% 
  
  
    Tobacco 
    15% 
  
  
    BMI1 
    30,89 
  
  
    TAS1 
    130,58 
  
  
    TAD1 
    70,25 
  
  
    FC1 
    73,89 
  
  
    CT 
    154,65 
  
  
    TG 
    115,94 
  
  
    LDL 
    81,30 
  
  
    HDL 
    48,72 
  
  
    Glycemia 
    105,21 
  
  
    Hgbglicada 
    6,25 
  
  
    Na 
    140,08



Table 1: Clinical characteristics of the patients.

Table 2: Clinical characteristics based on body mass index (% percentages).




  
     
    BMI (-18,5)
    BMI1 (18,5-25)
    BMI2 (25.1-30)
    BMI3 (+30,1)
    p
  
  
    Age
    67,4 +/-6,2
    73,2+/-7,1
    82,6 +/-5,8
    84,5+/-6,7
    < 0,002
  
  
    man
    80%
    65%
    45%
    32%
    < 0,004
  
  
    Ischemic Cause
    27,5%
    64,5%
    57%
    48%
    < 0,001
  
  
    HTA
    11%
    18%
    30%
    41%
    < 0,002
  
  
    Diabetes
    12%
    13%
    28%
    47%
    < 0,003
  
  
    Dislypemia
    16%
    18%
    30%
    44%
    < 0,004



Table 2: Clinical characteristics based on body mass index (% percentages).

Table 3: Treatments based on body mass index (absolute number).




  
    
       
    Low weight
    Normal weight
    overweight
    obesity
    p
  
  
    Beta blockers
    14
    16
    46
    24
    NS
  
  
    IECA ó ARA II
    8
    14
    31
    47
    NS
  
  
    spironolactone
    5
    12
    34
    49
    < 0,002
  
  
    Digoxin
    0
    0
    45
    55
    < 0,006
  
  
    Diurétics handle
    14
    10
    23
    53
    < 0,001
  
  
    statins
    10
    21
    34
    35
    NS



Table 3: Treatments based on body mass index (absolute number).

Mortality at 2 years of follow-up was 19.1%.

BMI as a continuous variable was significantly associated with mortality (p ‹ 0.001), age (0.002), ischemic disease (0.001), sex (0.004), HT (0.002), Diabetes (0.003) and dyslipidemia (0.004) Table 2.

BMI has also been seen to be related to the use of Digoxin, As a Diuretics and Spironolactone treatments with a higher BMI plus utilization Table 3.

BMI is also associated with the number of admissions, higher number of concomitant chronic diseases and mortality Table 4.

Table 4: Income, Mortality, causes and Chronic diseases (% Percentages).




  
     
    Low weight
    Normal weight
    overweight
    obesity
    p
  
  
    Hospital income in 1 year
    8%
    12%
    42%
    48%
    < 0,001
  
  
    Hospital income in 3 years
    7,5%
    11,5%
    30%
    51%
    < 0,003
  
  
    Mortality
    0
    7%
    47%
    46%
    < 0,001
  
  
    Concomitant chronic diseases
    5%
    8%
    25%
    62%
    < 0,001
  
  
    Respiratory infection as a cause of income
    0
    8%
    22%
    0
    NS
  
  
    Cardiac Decompensation as a cause of Income
    5%
    21%
    32%
    52%
    < 0,003



Table 4: Income, Mortality, causes and Chronic diseases (% Percentages).

After adjusting for age, sex, etiology, hypertension, diabetes, and the different treatments received, the BMI remained as an independent predictor of heart failure (OR = 0.92 [0.88-0.97]) Table 5, which This means that, after adjustment, the risk of heart failure due to any cause is greater than 8% for each 1-point increase in BMI.

Table 5: Multivariable logistic regression analysis (by conditional backward steps).




  
    Variables that remain in the model
    OR
    IC del 95%
  
  
    Age
    1,35
    1,21-1,40
  
  
    Diabetes
    2,47
    1,31-3,61
  
  
    BMI
    0,92
    0,88-0,99
  
  
    Beta blockers
    0,64
    0,25-0,74
  
  
    Estatinas
    0,38
    0,27-0,77
  
  
    IECA o ARA-II
    0,39
    0,25-0,87
  
  
    ARA-II:    Angiotensin II Receptor Antagonists; LVEF: Left Ventricular Ejection    Fraction; ACEI: Angiotensin Conversion Enzyme Inhibitors; BMI: Body Mass    Index.



Table 5: Multivariable logistic regression analysis (by conditional backward
steps).

The scores obtained in the MLWHFQ quality of life questionnaire at the initial visit; the patients with low weight were those who obtained the highest score, which corresponds to a worse quality of life. There were no significant differences between the scores obtained by normal weight, overweight and obese patients, although these showed some tendency to obtain a higher score Table 6.

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Citation:Tarraga Lopez PJ, Sadek MI, Madrona Marcos F, Simon MA, Villar Inarejos MJ, Tarraga Marcos ML. Obesity and Heart Failure in a Health Area. Austin Cardiol. 2019; 4(1): 1020.

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Insuf Cardiac evolution medium time	23,5 months
HTA	86%
Diabetes	54%
Hyperlipemia	63%
Tobacco	15%
BMI1	30,89
TAS1	130,58
TAD1	70,25
FC1	73,89
CT	154,65
TG	115,94
LDL	81,30
HDL	48,72
Glycemia	105,21
Hgbglicada	6,25
Na	140,08

Obesity and Heart Failure in a Health Area

Abstract

Introduction

Methods

Results

Discussion

References