| Abstract
Objective
Background
Design
Setting
Participants
Methods
Findings
Conclusion
Acknowledgments
References
|
Editor's
Note: This article was originally scheduled for publication in the
Spring 2001 issue but was delayed through no fault of the authors.
It appears for the first time in April 2002.
Abstract
This descriptive-correlational study describes energy expenditure, nutritional
intake, and body mass characteristics of 17 women who were one- to two-year
survivors of kidney transplantation. Study data were collected via
mailed questionnaires and clinic records. The Nutritional Analysis Tool
(Version 2.0) was used to analyze calorie, protein, carbohydrate, fat,
and cholesterol intake; a Caltrac monitor assessed energy expenditure.
Obesity was prevalent, with the consumption of calories, protein, carbohydrates,
and fats exceeding the recommended daily allowance. There were no
associations among energy expended, nutrients consumed, and body mass;
however, older women were less active and consumed less protein and calories
than younger women. Excessive consumption of calories, fat, and carbohydrate
among study participants added to their prevalence of obesity and subsequent
cardiovascular risk.
Objective
The purpose of this study was to describe the energy expenditure, nutritional
intake, and body mass of women one to two years following kidney transplantation
and to examine relationships among these variables. Improved understanding
of how these variables interrelate would provide a basis for intervention
to counteract the fat accumulation that commonly follows transplantation.
Background
Renal transplant recipients are at great risk for the onset of obesity
post transplantation. Posttransplant weight gain occurs in both obese and
non-obese recipients; however, the prevalence is greater in obese recipients,
and particularly in those that are female or black (Johnson
et al., 1993; Pischon & Sharma, 2001). Obesity
is often the result of immunosuppressive medications that stimulate the
appetite and cause a redistribution of body fat, starting within the first
posttransplant year (Rao, 1988). However, augmented
by indulgence in foods that had been forbidden on the highly restricted
dialysis diet and an imbalance between dietary intake and physical activities,
weight gain is an even more common occurrence after transplant Obesity
represents an energy imbalance resulting from an excess of energy input
(fuel from food) over energy output (energy requirement or expenditure)
(Williams, 1994). While there are many health risks
associated with obesity, the risks for cardiovascular (CV) disease, hypertension,
and type 2 diabetes (DM) are substantially raised (American
Obesity Association [AOA], 1999) – all critical health concerns that
threaten posttransplant health outcomes. Moreover, CV disease is the leading
cause of death among renal transplant recipients who have a functioning
graft (USRDS, 2001) and the contribution of its
influence on risk for CV complications has been documented both pre (Rice
et al., 2001) and post transplant (Martin et al., 2001). Therefore,
since obesity is often associated with inappropriate dietary intake and
physical activities, it is important to assess specific health-related
lifestyle behaviors to identify strategies that reduce obesity and decrease
the morbidity and mortality associated with CV disease in these patients.
Researchers have examined relationships among energy expenditure, dietary
consumption, and body composition in the general population (Wareham,
1998; Westerterp, 1997) and in chronic renal
failure patients (Monteon et al., 1986). Few studies
have examined the problem of obesity in kidney transplant recipients with
primary emphasis on relationships between dietary intake and steroid therapy
(Broyer et al., 1981; Whittier
et al., 1985), dietary intake and body composition (Patel, 1998), body
composition and physical activity (Van den Ham et al.,
2000), and dietary intake, body composition and obesity (Lopes
et al., 1999); yet, none have examined associations among physical
activity, dietary consumption, and body mass in this population. Thus,
the current study was aimed at exploring existing associations among these
three variables in women post kidney transplant.
The research questions that guided this nutrition study were the following:
1) What are the nutritional and energy expenditure characteristics of women
one year after a kidney transplant? and 2) What are the associations among
physical activity, dietary consumption, and body mass of women one year
after a kidney transplant?
Design
An exploratory descriptive-correlational design was used to obtain cross-sectional
information about the energy expenditure, nutritional characteristics,
and body mass of women who have had a kidney transplant. The study involved
secondary analysis of data from a subsample of patients being analyzed
in a larger, institutional review board approved study that examined CV
risk characteristics of women one to two years after a kidney transplant.
Setting
This study was conducted at a mid-southern University Transplant Center
that serves patients who require transplantation and primarily reside in
a five-state region. Patients of this center represent all sociodemographic
categories; however, the majority are Caucasians and African-Americans
of lower socioeconomic status.
Participants
The sample consisted of 17 women who received a kidney transplant between
June 1997 and May 1998 and volunteered to participate in a sub-study focused
on posttransplant nutritional characteristics. This sample represents 59%
(17 of 29) of the participants from the parent study. This time period
allowed us to examine women who have survived past the first post-transplant
year. There was no exclusion of participants based on race or ethnicity;
however, exclusion criteria did include the following: a) persons with
comorbid conditions that prohibited full participation in the intervention;
b) those who no longer received medical care at the University Transplant
Center; c) any who refused participation in the study; and d) any unable
to comprehend instructions and respond in English.
Methods
This study was conducted using instruments designed to measure three indices
of health-related lifestyle behaviors in renal transplant recipients –
energy expenditure, nutritional intake, and body mass. Energy expenditure
was assessed using the Caltrac Motion Sensor, nutritional intake using
a Food and Calorie Expenditure Diary and the Nutritional Analysis Tool
program; body mass was assessed using the most current height and weight
documented on the clinic medical record. Each measure will be described
in turn.
Energy expenditure was operationalized as physical activity in kilocalories/day.
The Caltrac Motion Sensor was used to collect data on energy expenditure
during a 24-hour time period. This instrument measures the number
of calories that one expends over a given time period. Calories expended
are measured by the sensor’s detection of physical movement. Clients
were given verbal and written instructions on Caltrac operation.
After explaining the purpose of the Caltrac, and giving instructions on
how to clear, activate, and record data from the device, clients were asked
to wear it on their waistline for one full 24-hour day. To document the
most accurate data participants were asked to wear it on a “typical” day
in terms of their physical activity level. When the 24-hour period
was complete, clients were instructed to record the Caltrac reading (calories
expended) on the form provided and to then send the results and sensor
to the researcher by return mail.
Nutritional intake was operationalized as the food and beverages documented
as dietary intake on the Food and Caloric Expenditure Diary. More
specifically, nutritional intake represents the dietary intake of protein,
fat, and carbohydrate grams/day (% of each nutrient/day) consumed during
a typical day. Nutritionists examining dietary intake of various
populations have commonly suggested the use of 3- to 5-day diaries in order
to provide the most reliable representations of dietary habits; however,
such a request would have posed a burden for current participants. Therefore,
study participants were asked to record their food intake for breakfast,
lunch, dinner, and snacks within the same 24-hour time period during which
the Caltrac was worn. They were asked to eat normally without dietary
modification to provide an accurate representation of their normal daily
intake.
Dietary intake data from the Food and Caloric Expenditure Diary were then
entered into the Nutritional Analysis Tool (NAT) for evaluation.
The NAT is a web-based program that allows an individual to analyze their
dietary intake for various nutrients. Hewes and Painter (Painter,
2000) of the University of Illinois Department of Food Science and
Human Nutrition developed NAT version 1.0 after Painter became intrigued
with nutrient data that was being assembled by the USDA. The
program was originally developed for use in his own program for students
and health professionals interested in analyzing the nutrient content of
foods. In response to popular interest, version 2.0 was placed on-line
in July of 1999. Version 2.0 includes an advanced ability to assess
nutritive components of fast foods and specific brand name foods.
NAT analyzes each food and beverage item, number of servings, and serving
size. The report provides an analysis of the nutrient grams consumed
and percentages of the RDA for the particular nutrient in both table format
and printable text.
Body mass was operationalized as the body mass index (BMI). BMI was
computed as the ratio between the metric measures of weight (kilograms)
and height (meters) using the formula: kg/m² (Williams,
1994). A BMI of 20 to 25 kg/m² is the desired health maintenance
range for adults; health risks associated with obesity are estimated to
begin with a BMI in the range of 25 to 30 kg/m² (Williams).
Values greater than or equal to 40 are indicative of severe or “morbid”
obesity (Williams). The BMI for each participant
was computed using the most recently documented medical record measurements
of height and weight recorded on the clinic chart. Weight in pounds
and height in inches were determined by trained clinic nursing staff.
Height and weight were converted to metric units and BMI using the formula,
kg/m² (Williams).
Participant data were transferred from Caltrac, Food & Caloric Expenditure
Diaries, Nutritional Analysis Tool reports, and medical records onto a
Microsoft Excel spreadsheet (Microsoft Corporation, 1992) for the PC (Microsoft
Excel Software, version 4.0) for storage until data collection was completed.
At this point data were transferred to the Digital Equipment Corporation
VAX/VMS Version V5.5-2 mainframe computer system located in the Biomedical
Information Transfer (BIT) Center on the University campus. Data
analyses were conducted using Statistical Analysis System (SAS) software
version 6.0, which is available through the VAX-8800 system (128 MB memory)
of the 4 node VAX cluster.
All study variables were analyzed using univariate and bivariate statistical
procedures in the SAS software package. The mean, standard deviation,
median, range, normality and proportions are the primary numerical summaries
that were used to describe the study sample. Finally, Pearson and
Spearman correlation coefficients were estimated for all pairs of variables
of interest. Given the exploratory nature of the study and the very
small sample size, a liberal a priori level of significance was established
at p < .10 for all statistical analyses to reduce the likelihood of
committing Type 1 error.
Findings
The sample consisted of 17 adult female volunteers who had received a kidney
transplant between June 1997 and May 1998. The mean age of participants
was 45.82+13.15 years. Participants were primarily African-American
(n=13, 77%) and currently or previously married (n=11, 65%); all except
one had at least a high school education (n=16, 94%). Table 1 provides
a detailed reporting of data describing the sample.
Table 1. Descriptive
Data Describing Study Sample (n=17)
| Variable |
Mean ± S.D. |
n (%) |
| Age |
45.82 ± 13.15 |
|
Race
African-American
Caucasian-American |
|
13 (77%)
4 (24%) |
Marital
Status
Married
Separated
Divorced
Widowed
Never Married |
|
9 (53%)
0
1 (6%)
1 (6%)
6 (35% |
Education
Grade School or less
Some High School
High School Graduate
Some College
College
Graduate |
|
0
1(6%)
8 (47%)
5 (29%)
3 (18%) |
| Height |
63.29 ± 3.86 |
|
| Weight |
189.35 ± 40.51 |
|
Obesity was prevalent in this sample as the majority of participants had
BMI’s higher than 30 (M=33.28+7.94). These women reported higher daily
energy expenditure (M=2,471.77 cal+731.85) than their caloric consumption
(M=2,025.63 cal+1,008.36); however, the caloric consumption was higher
than the recommended daily allowance (RDA) of consumed calories (101%).
Although their diets were low in cholesterol (87% RDA), protein, fat, and
carbohydrate intakes were excessive in comparison to the RDA at 160%, 116%,
and 115%, respectively. Table 2 provides a detailed reporting of
data describing these variables.
Table 2. Descriptive
Data Describing Study Variables
| Variables |
Mean ± S.D. |
| Body Mass Index
Calories Expended
Calories Consumed
% of Recommended Calories
Protein (g)
% of Recommended Protein
Fat (g)
% of Recommended Fat
Carbohydrates (g)
% of Recommended Carbohydrates
Cholesterol (mg)
% of Recommended Cholesterol
|
33.29 ± 7.94
2471.77 ± 731.85
2025.63 ± 1008.36
100.49 ± 39.46
80.06 ± 33.92
160.11 ± 67.87
81.95 ± 52.54
115.53 ± 71.19
276.68 ± 110.00
115.28 ± 45.83
260.10 ± 217.19
86.70 ± 72.40
|
Correlational analyses identified inverse associations between age and
the number of calories expended (r = -.51, p = .04), calories consumed
(r = -.41, p = .10), and the RDA percentage of protein consumed (r = -.41,
p = .10), indicating that as participant age increased, there was decreased
physical activity and a decreased intake of protein and calories. This
finding was not surprising since that older adults commonly eat less and
are less active than younger adults. There was also a high level of intercorrelation
among the variables ‘calories consumed’ and 'RDA intake percentages for
calories, protein, fats, and carbohydrates' (r < .46, p < .06).
This finding suggests that even diets high in recommended nutrients were
also high in calories, fat, and carbohydrates. However, of all study variables,
neither BMI, calories expended, nor RDA percentage of cholesterol intake
were associated with any other variable. Table 3 provides a detailed reporting
of the correlational data describing associations among these study variables.
Conclusions
In conclusion, study findings show that study participants demonstrated
inappropriate dietary habits, inadequate energy expenditure, and excessive
body mass. Also, despite the fact that calories expended exceeded calories
consumed, the excessive consumption of recommended calories, fat, and carbohydrate
intake added to the prevalence of obesity in this sample of post transplant
women.
The fact that there were no associations among energy expenditure, nutritional
intake, and body mass indicates that there may be other unexplored factors
(such as immunosuppressant type) that likely contribute to post transplant
obesity. In addition, further research would be required to demonstrate
the effectiveness of various approaches to reducing the fat accumulation
which leads to the development of obesity in kidney transplant recipients.
The absence of anticipated associations could also be explained by methodological
limitations such as limited variability in values for all three variables.
Most participants
Table 3. Correlational
Data Describing Associations Among Study Variables
| Variables |
Age |
BMI |
Calories
Expended |
Calories
Consumed |
%RDA
Calories |
%RDA
Protein |
%RDA
Fat |
%RDA
Carb. |
| Age |
- |
|
|
|
|
|
|
|
| Body Mass Index |
-.25 (.33) |
- |
|
|
|
|
|
|
| Calories Expended |
-.51(.04) |
.11(.67) |
- |
|
|
|
|
|
| Calories Consumed |
-.41(.10) |
-.13(.63) |
.25(.34) |
- |
|
|
|
|
| % RDA Calories |
-.32(.20) |
-.16(.54) |
.22(.40) |
.99(.0001) |
- |
|
|
|
| % RDA Protein |
-.41(.10) |
.10(.71) |
.31(.23) |
.68(.003) |
.67(.003) |
- |
|
|
| % RDA Fat |
-.39(.13) |
-.01(.98) |
.20(.44) |
.86(.0001) |
.88(.0001) |
.46(.06) |
- |
|
| % RDA Carbohydrate |
-.27(.30) |
-.33(.19) |
.22(.39) |
.90(.0001) |
.90(.0001) |
.60(.01) |
.63(.01) |
- |
| % RDA Cholesterol |
-.13(.61) |
.24(.36) |
-.04(.87) |
.17(.52) |
.05(.85) |
.27(.29) |
.21(.42) |
-.20(.45) |
demonstrated limited physical
activity, less than ideal nutritional habits, and general obesity. Given
the low socioeconomic status, of this mid-southern, primarily African-American
sample, these findings are not expected to be the result of sampling bias,
but instead thought to be representative of the patients commonly served
by this transplant center. A larger sample size would have possibly provided
more variability and a greater likelihood of evidence for the anticipated
relationships.
Nevertheless, findings suggest that there is hope for improving outcomes
of kidney transplant recipients by promoting practice of healthier lifestyle
behaviors. Improved lifestyle behaviors would include implementing varied
approaches to better control the problem of imbalanced energy expenditure
versus dietary consumption and the subsequent excessive fat accumulation
in women after a kidney transplant. Finally, outcomes can be improved by
establishing and implementing targeted nutritional support protocols (Moore
et al., 1996). These combined approaches would foster improved overall
health and cardiovascular outcomes for women who survive kidney transplantation.
Acknowledgments
This study was supported by funding to the second author from the University
of Tennessee Center for Women’s Health Improvement and the National Institute
for Nursing Research (NR 04687-R01-S02). The authors would like to express
appreciation to Dr. Donna Hathaway for mentorship to Dr. Martin on the
NINR grant and to Dr. Mona N. Wicks for statistical assistance with this
project.
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