Ignite Creation Date:
2024-10-26 @ 3:38 PM
Last Modification Date:
2024-10-26 @ 3:38 PM
Study NCT ID:
NCT06561815
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-08-13
Brief Title:
Body Composition Estimated by Bioelectrical Impedance Analysis in Patients with Acute Coronary Syndrome
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Body Composition Estimated by Bioelectrical Impedance Analysis in Patients with Acute Coronary Syndrome
Status:
RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
This study addresses the critical issue of obesity and its impact on patients with acute coronary syndrome ACS While obesity is a known risk factor for cardiovascular diseases emerging evidence suggests that obese patients with coronary artery disease may have better survival outcomes-a phenomenon known as the 34obesity paradox34
Our research aims to explore this paradox by examining the body composition of ACS patients using bioimpedance analysis BIA We will evaluate parameters such as lean mass body fat and fluid volume to assess their relationship with clinical outcomes including mortality and the incidence of heart and kidney failure
By focusing on body composition rather than just BMI this study seeks to provide a more accurate understanding of how these factors influence patient outcomes Conducted at a major hospital in Argentina the study will contribute valuable insights into the role of body composition in the prognosis of ACS potentially informing more personalized treatment strategies
Our research aims to explore this paradox by examining the body composition of ACS patients using bioimpedance analysis BIA We will evaluate parameters such as lean mass body fat and fluid volume to assess their relationship with clinical outcomes including mortality and the incidence of heart and kidney failure
By focusing on body composition rather than just BMI this study seeks to provide a more accurate understanding of how these factors influence patient outcomes Conducted at a major hospital in Argentina the study will contribute valuable insights into the role of body composition in the prognosis of ACS potentially informing more personalized treatment strategies
Detailed Description:
1 Introduction
It is indisputable that obesity is a global epidemic According to the latest data from the World Health Organization WHO the prevalence of obesity has tripled since 1975 In 2016 there were 650 million obese people worldwide representing 13 of those over 18 years of age Its definition is simple a body mass index greater than 30 Moreover it is a preventable condition
In developing countries its prevalence is steadily increasing According to data from the Permanent Household Survey conducted in Argentina the prevalence of obesity in 2013 was 208 this represented an increase of 156 compared to the 2009 edition prevalence of 180 and 425 compared to the 2005 edition prevalence of 146 The obesity indicator was higher among men 229 than among women 188 and higher among older people with a maximum of 296 in the 50 to 64 age group compared to younger individuals 77
The health importance of obesity is mainly due to its relationship with cardiovascular disease In a classic study analyzing 457785 men and 588369 women published in 1999 it was found that the total mortality risk due to obesity was 258 and 20 times higher respectively The relative risk of cardiovascular death in obese men was 29 with a 95 confidence interval CI95 of 237 to 356 Obesity increases the metabolism of free fatty acids reduces insulin sensitivity increases sympathetic activity promotes inflammation and causes a state of higher coagulability all of these factors can contribute to the development of coronary disease
Despite the well-known predisposition to cardiovascular disease caused by obesity once established its relationship with body weight is more complex In 2002 a study analyzing the impact of obesity in patients undergoing percutaneous coronary intervention PCI was published Gruber et al analyzed data from 9633 patients and observed that obese individuals were generally younger but had more cardiovascular risk factors hypertension diabetes hypercholesterolemia and smoking Although angiographic success rates were similar in all groups the presence of normal BMI was associated with higher cardiovascular mortality For long-term death BMI presented in multivariable analysis an odds ratio OR of 094 with a CI95 of 094 - 098 showing to be a protective factor This effect was named the obesity paradox which could be formulated as follows obese individuals have a higher risk of coronary disease but obese coronary patients have a lower risk of mortality This was confirmed in a more recent study in patients with ST-elevation acute coronary syndrome STEMI where after analyzing data from 50149 patients hospital mortality rates of 77 were observed for individuals with normal weight and 43 44 and 61 for subjects with class I II and III obesity respectively
The first explanations for the obesity paradox revolved around a higher risk of bleeding due to increased anticoagulation and the possible presence of other non-cardiovascular diseases in low-weight patients Later attention was given to adiponectin a mediator predominantly produced by adipose tissue although it can also be synthesized in cardiac myocytes in response to angiotensin II In rodents adiponectin increases fatty acid oxidation in muscle improving insulin sensitivity Despite being produced in adipocytes blood levels of adiponectin are inversely proportional to BMI and hypoadiponectinemia in obese individuals is related to insulin resistance and higher levels of plasma C-reactive protein Adiponectin levels below 40 mgdl can double the risk of coronary disease
Beyond possible explanations for the obesity paradox it has been found that BMI does not always reflect fat mass and body composition may not be represented by this simple index In a study where both BMI and waist circumference were measured in patients with ACS a trend was found towards a higher number of events in patients with lower BMI but higher waist circumference In another study waist circumference showed a higher correlation with myocardial infarction size than BMI
There is limited evidence on the role of body composition in patients with ACS In a retrospective study in China the Clínica Universidad de Navarra formula was used to estimate body fat BF in patients with impaired renal function where sex is considered 0 in men and 1 in women BF -44988 0503 age 10689 sex 3172 BMI - 0026 BMI2 0181 BMI sex - 002 BMI age - 0005 BMI2 sex 000021 BMI2 age They found that in multivariable analysis increased body fat and decreased lean mass were associated with a higher risk of death They also found higher mortality in patients with higher BMI but without a significant increase with intermediate BMI values Another study conducted in the United States analyzed fat mass based on estimation by body folds in 570 patients with stable coronary disease and found that both reduced fat mass and lean mass were associated with higher mortality
Adiposity and lean mass can be evaluated non-invasively cheaply and quickly using bioimpedance analysis BIA Total body composition estimation through total body bioimpedance can be carried out using the equation V ρ x S2R where V is the conduction volume representing total body water or fat-free mass ρ is the specific resistivity of the conductor S is height and R is total body resistance measured with four surface electrodes placed on one wrist and one ankle
In Latin America at least two BIA validation studies have been conducted to estimate body composition In the study by Fjeld et al in Peru the correlation coefficient for cross-validation of formulas for calculating body composition was 096 In Argentina in 2008 Rodríguez et al conducted a study aimed at comparing body composition estimated by two simple anthropometric methods BIA and dual-energy X-ray absorptiometry DXA and studying the correlations between them in a pediatric population They found good correlation between simple anthropometric methods waist circumference and bioimpedance and DXA but the results were not interchangeable even between BIA and DXA
In cross-validation studies of formulas to estimate body composition from bioimpedance high correlation coefficients and small standard errors of estimation were found with acceptable precision Due to this it is considered that despite their limitations they can be extrapolated to different populations However it is important to note that although the estimates are validated and highly accurate for evaluating populations they are less reliable for determining body composition in individual subjects
Very few studies have measured body composition using BIA in patients with stable coronary disease Puri et al estimated the percentage of BF using this method in 477 individuals divided into three groups normal with established coronary disease and at high risk of coronary disease BF was higher in the at-risk group for coronary disease 307 compared to 254 in patients with established coronary disease and 239 in controls However most strikingly despite a high correlation between BF percentage and BMI r 08 34 of men and 44 of women with normal BMI had increased BF This suggests that BMI significantly underestimates obesity in certain patients
In a cross-sectional study conducted on 161 patients undergoing coronary angiography for stable coronary disease characteristics were compared between those with coronary lesions and those without BMI and body composition estimated by BIA were taken into account There were no differences in total weight and BMI but BF was higher among those with coronary lesions while lean mass was higher among those without lesions
Only one study has used BIA in patients with ACS It was found that visceral fat is a better risk indicator for the no-reflow phenomenon after PCI in STEMI patients compared to BMI and total BF
Given the limited experience with BIA in patients with ACS and the effects that body composition may have on clinical outcomes and as a confounding factor with BMI this study proposes to investigate this topic in greater depth
2 Objectives
21 General To describe the body composition of patients with ACS estimated through BIA and evaluate its association with clinical outcomes
22 Specific
Describe the body composition of patients with ACS in terms of lean mass body fat and fluid volume estimated through BIA
Analyze the relationship between body composition parameters with age sex and BMI
Determine if there is an association between a higher percentage of body fat and mortality during hospitalization and at one year
Determine if there is an association between a lower percentage of lean mass and mortality during hospitalization and at one year
Determine if a lower percentage of body water is associated with a higher incidence of renal failure during hospitalization
Determine if a higher percentage of body water is associated with a higher incidence of heart failure during hospitalization
3 Hypotheses
There is a difference in the percentage of body fat between those who die and those who survive during hospitalization and at one year
The percentage of lean mass is lower among those who die and those who survive during hospitalization and at one year
Patients with lower body water estimated by BIA have a higher risk of acute renal failure ARF during hospitalization
Patients with higher body water estimated by BIA have a higher risk of heart failure during hospitalization
4 Methodology
41 Study Design This is a prospective cohort study of patients with ACS
42 Population The study population will include all adult patients with ACS
43 Data Collection Patient data will be collected from the electronic medical record BIA will be performed within the first 72 hours of admission
44 Data Analysis Statistical analysis will be conducted using R software Categorical variables will be compared using the chi-square test or Fisher39s exact test as appropriate Continuous variables will be analyzed using t-tests or Mann-Whitney U tests for independent samples depending on their distribution Multivariable analysis will be conducted using logistic regression
5 Ethical Considerations The study protocol will be submitted to the Research Ethics Committee of the Hospital for approval before implementation All participants will sign an informed consent form before inclusion in the study
It is indisputable that obesity is a global epidemic According to the latest data from the World Health Organization WHO the prevalence of obesity has tripled since 1975 In 2016 there were 650 million obese people worldwide representing 13 of those over 18 years of age Its definition is simple a body mass index greater than 30 Moreover it is a preventable condition
In developing countries its prevalence is steadily increasing According to data from the Permanent Household Survey conducted in Argentina the prevalence of obesity in 2013 was 208 this represented an increase of 156 compared to the 2009 edition prevalence of 180 and 425 compared to the 2005 edition prevalence of 146 The obesity indicator was higher among men 229 than among women 188 and higher among older people with a maximum of 296 in the 50 to 64 age group compared to younger individuals 77
The health importance of obesity is mainly due to its relationship with cardiovascular disease In a classic study analyzing 457785 men and 588369 women published in 1999 it was found that the total mortality risk due to obesity was 258 and 20 times higher respectively The relative risk of cardiovascular death in obese men was 29 with a 95 confidence interval CI95 of 237 to 356 Obesity increases the metabolism of free fatty acids reduces insulin sensitivity increases sympathetic activity promotes inflammation and causes a state of higher coagulability all of these factors can contribute to the development of coronary disease
Despite the well-known predisposition to cardiovascular disease caused by obesity once established its relationship with body weight is more complex In 2002 a study analyzing the impact of obesity in patients undergoing percutaneous coronary intervention PCI was published Gruber et al analyzed data from 9633 patients and observed that obese individuals were generally younger but had more cardiovascular risk factors hypertension diabetes hypercholesterolemia and smoking Although angiographic success rates were similar in all groups the presence of normal BMI was associated with higher cardiovascular mortality For long-term death BMI presented in multivariable analysis an odds ratio OR of 094 with a CI95 of 094 - 098 showing to be a protective factor This effect was named the obesity paradox which could be formulated as follows obese individuals have a higher risk of coronary disease but obese coronary patients have a lower risk of mortality This was confirmed in a more recent study in patients with ST-elevation acute coronary syndrome STEMI where after analyzing data from 50149 patients hospital mortality rates of 77 were observed for individuals with normal weight and 43 44 and 61 for subjects with class I II and III obesity respectively
The first explanations for the obesity paradox revolved around a higher risk of bleeding due to increased anticoagulation and the possible presence of other non-cardiovascular diseases in low-weight patients Later attention was given to adiponectin a mediator predominantly produced by adipose tissue although it can also be synthesized in cardiac myocytes in response to angiotensin II In rodents adiponectin increases fatty acid oxidation in muscle improving insulin sensitivity Despite being produced in adipocytes blood levels of adiponectin are inversely proportional to BMI and hypoadiponectinemia in obese individuals is related to insulin resistance and higher levels of plasma C-reactive protein Adiponectin levels below 40 mgdl can double the risk of coronary disease
Beyond possible explanations for the obesity paradox it has been found that BMI does not always reflect fat mass and body composition may not be represented by this simple index In a study where both BMI and waist circumference were measured in patients with ACS a trend was found towards a higher number of events in patients with lower BMI but higher waist circumference In another study waist circumference showed a higher correlation with myocardial infarction size than BMI
There is limited evidence on the role of body composition in patients with ACS In a retrospective study in China the Clínica Universidad de Navarra formula was used to estimate body fat BF in patients with impaired renal function where sex is considered 0 in men and 1 in women BF -44988 0503 age 10689 sex 3172 BMI - 0026 BMI2 0181 BMI sex - 002 BMI age - 0005 BMI2 sex 000021 BMI2 age They found that in multivariable analysis increased body fat and decreased lean mass were associated with a higher risk of death They also found higher mortality in patients with higher BMI but without a significant increase with intermediate BMI values Another study conducted in the United States analyzed fat mass based on estimation by body folds in 570 patients with stable coronary disease and found that both reduced fat mass and lean mass were associated with higher mortality
Adiposity and lean mass can be evaluated non-invasively cheaply and quickly using bioimpedance analysis BIA Total body composition estimation through total body bioimpedance can be carried out using the equation V ρ x S2R where V is the conduction volume representing total body water or fat-free mass ρ is the specific resistivity of the conductor S is height and R is total body resistance measured with four surface electrodes placed on one wrist and one ankle
In Latin America at least two BIA validation studies have been conducted to estimate body composition In the study by Fjeld et al in Peru the correlation coefficient for cross-validation of formulas for calculating body composition was 096 In Argentina in 2008 Rodríguez et al conducted a study aimed at comparing body composition estimated by two simple anthropometric methods BIA and dual-energy X-ray absorptiometry DXA and studying the correlations between them in a pediatric population They found good correlation between simple anthropometric methods waist circumference and bioimpedance and DXA but the results were not interchangeable even between BIA and DXA
In cross-validation studies of formulas to estimate body composition from bioimpedance high correlation coefficients and small standard errors of estimation were found with acceptable precision Due to this it is considered that despite their limitations they can be extrapolated to different populations However it is important to note that although the estimates are validated and highly accurate for evaluating populations they are less reliable for determining body composition in individual subjects
Very few studies have measured body composition using BIA in patients with stable coronary disease Puri et al estimated the percentage of BF using this method in 477 individuals divided into three groups normal with established coronary disease and at high risk of coronary disease BF was higher in the at-risk group for coronary disease 307 compared to 254 in patients with established coronary disease and 239 in controls However most strikingly despite a high correlation between BF percentage and BMI r 08 34 of men and 44 of women with normal BMI had increased BF This suggests that BMI significantly underestimates obesity in certain patients
In a cross-sectional study conducted on 161 patients undergoing coronary angiography for stable coronary disease characteristics were compared between those with coronary lesions and those without BMI and body composition estimated by BIA were taken into account There were no differences in total weight and BMI but BF was higher among those with coronary lesions while lean mass was higher among those without lesions
Only one study has used BIA in patients with ACS It was found that visceral fat is a better risk indicator for the no-reflow phenomenon after PCI in STEMI patients compared to BMI and total BF
Given the limited experience with BIA in patients with ACS and the effects that body composition may have on clinical outcomes and as a confounding factor with BMI this study proposes to investigate this topic in greater depth
2 Objectives
21 General To describe the body composition of patients with ACS estimated through BIA and evaluate its association with clinical outcomes
22 Specific
Describe the body composition of patients with ACS in terms of lean mass body fat and fluid volume estimated through BIA
Analyze the relationship between body composition parameters with age sex and BMI
Determine if there is an association between a higher percentage of body fat and mortality during hospitalization and at one year
Determine if there is an association between a lower percentage of lean mass and mortality during hospitalization and at one year
Determine if a lower percentage of body water is associated with a higher incidence of renal failure during hospitalization
Determine if a higher percentage of body water is associated with a higher incidence of heart failure during hospitalization
3 Hypotheses
There is a difference in the percentage of body fat between those who die and those who survive during hospitalization and at one year
The percentage of lean mass is lower among those who die and those who survive during hospitalization and at one year
Patients with lower body water estimated by BIA have a higher risk of acute renal failure ARF during hospitalization
Patients with higher body water estimated by BIA have a higher risk of heart failure during hospitalization
4 Methodology
41 Study Design This is a prospective cohort study of patients with ACS
42 Population The study population will include all adult patients with ACS
43 Data Collection Patient data will be collected from the electronic medical record BIA will be performed within the first 72 hours of admission
44 Data Analysis Statistical analysis will be conducted using R software Categorical variables will be compared using the chi-square test or Fisher39s exact test as appropriate Continuous variables will be analyzed using t-tests or Mann-Whitney U tests for independent samples depending on their distribution Multivariable analysis will be conducted using logistic regression
5 Ethical Considerations The study protocol will be submitted to the Research Ethics Committee of the Hospital for approval before implementation All participants will sign an informed consent form before inclusion in the study
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None