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Note: Tables and figures
of the article can be access and seen in the PDF file.
Introduction
There
are two sets of criterion that determine if a
participant has the metabolic syndrome. The recently
released Adult Treatment Panel III provided a definition
of the metabolic syndrome in adult and emphasized the
importance of this syndrome which put individuals at
risk for Type II diabetes, cardiovascular diseases, and
other life-threatening diseases. According to ATP III
criteria (ATP III, 2001), a participant has the
metabolic syndrome if he or she meets three
or more of the following criteria:
-
Abdominal obesity: waist circumference >
102 cm in men and > 88 cm in
women
-
Hypertriglyceridemia:
 150
mg/dl (1.695 mmol/l)
-
Low
HDL cholesterol: < 40 mg/dl (1.036 mmol/l) in
men and < 50 mg/dl (1.295 mmol/l)
in women
-
High blood pressure:
130/85
mmHg
-
High fasting glucose: 110
mg/dl ( 6.1
mmol/l)
Secondly, the World Health Organization (WHO) has also
stipulated the guidelines for diagnosing the metabolic
syndrome. According to WHO criteria (Alberti
et al., 1998), a participant has the metabolic syndrome
if he or she has diabetes, impaired glucose
tolerance, impaired fasting glucose, or
insulin resistance plus two or more of the
following abnormalities:
-
High blood pressure:
160/90
mmHg
-
Hyperlipidemia: triacylglyceride
concentration
150
mg/dl (1.695 mmol/l) and/or HDL
cholesterol < 35 mg/dl (0.9 mmol/l) in
men and < 39 mg/dl (1.0 mmol/l) in
women
-
Central obesity: waist-to-hip ratio of >
0.90 in men or > 0.85 in women and/or BMI
> 30 kg/m2
-
Microalbuminuria: urinary albumin excretion rate
20
µg/min or an albumin-to-creatinine ratio
20
mg/g.
In
addition, the International Diabetes Federation (IDF,
2005) has established criteria for someone having the
metabolic syndrome (Selig, 2006). Percentages of
individuals who are prone to have metabolic syndrome in
accordance with ATP III and WHO guidelines are
calculated. Table 1 provides the percentages of each
criterion that could contribute to someone having
metabolic syndrome under these guidelines. From this
group, more subjects have the potential of being
diagnosed with metabolic syndrome using ATP III criteria
rather than WHO criteria.
However, the definition of the metabolic syndrome given
by ATP III includes that hyperglycemia does not
necessarily meet the criterion for a diagnosis of
diabetes (Selig, 2006). Therefore, this study was to
establish its prevalence and its individual components
relevant to overweight Hispanic adults, to find the role
of insulin sensitivity on the metabolic syndrome data in
the secondary treatment option, and whether there is any
relationship between the treatment options and the
gender. The present study is necessary as previous
studies have been conducted to find out the prevalence
of metabolic syndrome across ethnic groups (Foldout
Feature, 2004).
Identifying Risk Factors
Patients suffering from metabolic syndrome are subjected
to three or more risk factors that include excessive
abdominal fat, hypertension, low levels of HDL
cholesterol, elevated triacylglyceride levels, and
abnormal blood sugar.
They can die more than
three-and a-half times as likely from risks associated
with coronary heart disease, as well as an increased
risk of liver and kidney disease, and possibly cancer.
Generalized metabolic disorder in which the body’s
inability to use insulin efficiently is closely
associated with metabolic syndrome. Even if some people
are genetically disposed, others can still develop the
syndrome due to physical inactivity and excess body fat.
Furthermore, there are non-drug treatments for metabolic
syndrome that include weight loss, dietary changes and
increased physical activity. A study (Dennison,
2005) found that programs focusing on weight loss and
exercise decreased the incidence of metabolic syndrome
by almost 41 percent, whereas the incidence rate among
patients on drug therapy was reduced by only 17 percent.
Accordingly, better dietary and exercise habits are
parts of treatment for metabolic syndrome and the
prevention of diabetes.
Subjects and Methods
Subjects and Protocol
This
study included 44 individuals who lived in Laredo, Webb
County, Texas, US, of which 23 were males and 21 were
females. Twenty-two of the subjects, 12 males and 10
females, had been treated with Actos; the rest had not
been administered any secondary medication. They were
frequently monitored and health data was collected
afterward. They were all selected by the utilization of
a convenience sample technique. Criteria for inclusion
included being on Actos for treatment of metabolic
syndrome or diabetes mellitus. The patients were of
Hispanic background ages ranging from 22 to 86 with a
roughly equal gender representation. Half of the sample
was taking Actos and the other half were not. Data such
as total cholesterol, high density lipoprotein (HDL),
low density lipoprotein (LDL), triacylglycerides,
fasting blood glucose collected prior to each and every
three to four months scheduled visit with the provider.
Blood pressure, height, weight, and abdominal girth
measurements were all taken on the scheduled appointment
day.
Actos Treatment
A
thiazolidinedione antidiabetic agent found in
Pioglitazone (Actos) contributes to its medicinal
action. It decreases insulin resistance in the liver,
resulting in increased insulin-dependent glucose
disposal and decreased hepatic glucose output. Actos
also reduces the hyperglycemia, hyperinsulinemia, and
hypertriglyceridemia characteristic of insulin-resistant
conditions such as Type II diabetes in animal models of
diabetes (Actos® Lilly, 2005). The metabolic changes
occurred by pioglitazone result in increased
responsiveness of insulin-dependent tissues as observed
in the models. Actos has not lowered blood glucose level
in animal models that lack endogenous insulin. The US
Food and Drug Administration (FDA or USFDA) reviewed the
safety of medications including troglitazone and
reported that 35 patients out of approximately 1.5
million have either died or required liver transplant.
As a result, Rezulin has been removed from the market.
Experiments showed that rosiglitaone (Avandia) and
pioglitazone (Actos) introduced as alternatives to
troglitazone (Rezulin), appear to have low risk of
hepatotoxicity. This treatment enhances the ability of
smooth muscle to metabolize sugar, thereby reducing
insulin resistance. This study analyzes the long-term
relief from secondary treatments of Actos administered
on patients.
Results
Statistical Analysis
The
impact of gender in physical and metabolic
characteristics after secondary Actos treatment was
examined using the general linear model and other
possible relevant statistical determinations. Some
auxiliary analyses have been done to show the effects of
secondary medications on weight, cholesterol, as well as
on the gender.
For
each category of data, the percentages of the average
metabolic syndrome data reductions using means standard
error of means (SEM) among Weight, Girth, B/P (blood
pressure), BMI (body mass index), FBS (fasting blood
sugar), Chol. (Cholesterol), HDL (high-density
lipoprotein), LDL (low-density lipoprotein), and TG (triacylglycerides
or Trigl.) have been computed in Table 2. For B/P, it
might be more informative if analyses were done for each
blood pressure reading. However, the lower B/P reading
is considered more critical than upper reading; hence,
the same is considered for the analysis of the
individual metabolic syndrome health data.
Physical Characteristics of Subjects
Table 3 shows the physical and metabolic
characteristics of subjects based on their gender
after secondary treatment of Actos medication
similar to findings of (Cruz et al., 2004). In this
regard, means and standard error of means (SEM’s)
are calculated for each gender group with or without
secondary treatment of Actos medication.
The
treatment is considered effective if either mean or SEM
decreases following the secondary dose of medication.
Besides age and height, the table shows that the
treatment is 66.67 and 100 percent effective for females
and males, respectively. Actos treats males more
effectively.
Prevalence of Features of the Metabolic Syndrome
Table 3
gives a summary of the individual syndrome data
improvement following the secondary treatment of
medication.
It
revealed that the secondary treatment helped 77.78
percent of males, 66.67 percent of females, and 55.56
percent of all individuals without the secondary
treatment to improve metabolic syndrome; thus,
concluding secondary dose of medication is necessary for
a greater relief. Table 3 shows that there is an
improvement of metabolic syndrome following the Actos
treatment on the adults; this reduction is larger for
males than for their female counterparts. The results
are gender independent if there is no secondary
treatment of medication.
Simple and Partial Correlation between Actos Treatment
and the Metabolic Syndrome Data
Multiple regression analysis allows researchers to
address the relationships among variables (Polit, 1996).
Multiple linear regression analysis is used to access
the independent contribution of Actos treatment to each
metabolic syndrome variable (Shannon & Davenport, 2001).
A partial correlation is the portion of a bivariate
correlation that remains after the influence of a third
variable has been removed.
The
first outputs which appear in Tables 5 and 6 summarize
the results of relationships between various syndrome
data such as Weight, Girth, B/P, BMI, FBS, Cholesterol (Chol.),
HDL, LDL, and TG (Trigl.). The Pearson correlation
coefficient for Chol. and HDL is 0.57237, indicating a
moderate positive correlation between Chol. and HDL. The
square of 0.57237 is 0.3276; thus, the coefficient of
determination is approximately 33%. That is,
approximately 33 percent of the variance in Chol. can be
attributed to HDL, or vice versa. A correlation exists
between Chol. and HDL, however, this relationship is not
as strong as the one we have seen
for BMI and Weight. The correlation matrix for female
subjects and metabolic syndrome health data after
secondary treatment of Actos is shown in Table 4.
Similarly, the correlation matrix for male subjects and
metabolic syndrome health data after secondary treatment
of Actos is shown in Table 5.
A
significant problem in multiple regression occurs when
there is a very high correlation between some of the
predictor variables. The best solution is to determine
the cause of multicollinearity and avoid them. For
females, there are high correlations among variables:
Weight/Girth, Weight/BMI, and LDL/Chol., and for males:
Weight/BMI, LDL/Chol. Chol and FBS are negatively
correlated, suggesting that as FBS decreases, the faster
it varies with Chol. Tables 4 and 5 show the correlation
matrices for both females and males, and these
comparisons are more insignificant for males than
females under secondary treatment. The most important
aspect to notice from these tables is that the
correlation is significant if it is greater than 0.111.
Further Auxiliary Analyses
The
weight comparison before and after the secondary
medication revealed that they are linearly correlated
with a strong correlation coefficient ( 0.9982)
for primary data with medication vs. secondary data with
or without medication.
Further
comparison of weight in the absence of secondary dose
had the same result as that of those with secondary
dose. This time, the linear correlation coefficient was
0.9833 (0.9833).
Regression Analysis and
Correlation
A total
of 35 subjects were divided into two groups on the basis
of administration of secondary medication. Two
scatter-plots have been drawn for their weights before
and after (the observation period) for the two groups.
The results concluded from these two graphs are
summarized below. Similar analyses can be done for other
metabolic syndrome data.
-
There was a weight gain (at
1.0115) for the (12) subjects not administered
secondary medication compared to a weight gain at a
lower rate (at 0.975) for the (23) subjects
administered secondary medications. This concluded
that the administration of secondary medication was
necessary for the subjects to maintain a successful
body weight.
-
Weight comparison of
the subjects before and after the secondary
medication was strongly linear (coefficient of
determination
 when
compared to that of the subjects not administered
secondary medication (coefficient of determination,
 .
Chi-Square Test of Independence
(vs. Multiple Regression)
Many major methods that
address the entire period are bivariate correlation and
regression, multiple regression, factor analyses, and
chi-square. The use of nonparametric methods is on the
rise, although the frequency of their occurrence is
still low. Special autocorrelation and advanced multiple
regression appear to be new to the discipline. The
chi-square ( )
distribution is of interest both because of its role in
data analyses and its relation to the normal, t
and F distribution. This test will serve as a
hypothesis test to decide whether two variables, namely,
weight gain/loss, and gender are associated. Assumptions
are:
1.
All expected
frequencies are 1 or greater
2.
At most 20 percent
of the expected frequencies are less than 5
3.
Simple random
sample
Since
the size of the sample was small; all possible
chi-square tests did not satisfy the three assumptions
above. A group of 23 subjects administered secondary
medication was analyzed using this test. The null and
alternative hypotheses were:
 :
Weight gain/loss and gender are not associated.
 :
Weight gain/loss and gender are associated.
 .
From this test, we
concluded not to reject the null hypothesis,.
Thus, at the 5 percent significance level the data did
not provide sufficient evidence to conclude that an
association existed between weight gain/loss and gender
after secondary medication. Similar analysis can be done
for other pairs of metabolic syndrome data if they
satisfy the three assumptions, 1, 2, and 3.
Analysis of Cholesterol Levels vs.
their Gender
The
level of cholesterol became a much discussed topic among
the visits to the physicians. Therefore, the level of
Cholesterol among the subjects and their gender was
worth studying. Cholesterol (Chol.) levels of the
subjects before and after secondary medication were
analyzed on the basis of their gender using the line
graphs in Figures 3 and 4. First this was done for males
and then, for females. Figures 3 and 4 represent the
analyses for males and females.
The two
line graphs showed that there was a slight increase in
the cholesterol level after the subjects were
administered secondary dose before the levels started
falling. This characteristic was independent of gender.
This increase diminished as the ages of the subjects
increased.
Aggregate Test Indicator (ATI)
We
wanted to find out how the subjects did on average in
terms of metabolic syndrome variables. Hence, an
aggregate test indicator in short (ATI) was introduced.
The following value assignments were made on the basis
of: +1 signifying that there was an increase of test
data, -1 signifying that there was a decrease of test
data, and 0 signifying that there was no change in test
data. +2, -2, +1, -1, and 0 were assigned if there were
changes in low and high readings of data in B/P. For LDL,
+ 1 was for a decrease and -1 was for an increase were
assigned, accordingly. Note that the scale above may be
improved on considering the extent of individual effect.
That is, +1   ,
-1  ,
and 0 if,
no change. If the changes in health set are
 then
the aggregate test indicator for
 indicators
was defined by We
chose  to
be +1 ,
-1 ,
and 0 if,
no change. (These
may
be improved). Aggregate test indicator (ATI) for all
subjects (males and females) after secondary dose of
medication were computed in Table 7.
Figure
5 depicts individual indicators for all subjects (males
and females). The columns above the horizontal axis
indicates favorable reductions of health variables, and
the columns that appear below the axis are unfavorable.
We computed the average of these variables to determine
aggregate test indicator. These columns were identified
on the basis of the individual’s gender and age as
listed at the bottom of each column in Figures 5, 6, and
7.
The mean of the aggregate test
indicator for all subjects (males and females) was:
Thus,
on average, the aggregate test indicator decreased as
secondary medication was administered. Medication was
effective as there was a reduction of the aggregate test
indicator for all of the subjects. Figure 6 is for male
subjects.
The
mean of aggregate test indicator for males was 0.80.
Thus, aggregate test indicator decreased for male after
administration of secondary medication. Secondary
medication was effective as there was a reduction of the
aggregate test indicator for the male subjects. Figure 7
is for female subjects. The mean of aggregate test
indicator for females was 0.67. Thus, the aggregate test
indicator decreased for female after administration of
secondary medication. Secondary dose of medication was
effective as there was a reduction of aggregate test
indicator for females.
Discussions and Conclusions
The
purpose of this study was to establish the prevalence of
the metabolic syndrome in the South Texas region and of
individual variables which contributed to the obesity of
Hispanics ranging from 22 years to 86 years of age (at
risk for) Type II diabetes. According to the data, 8
percent of the participants using ATP III criterion and
11 percent of the participants using WHO criterion can
be identified as potentially having metabolic syndrome.
Individual physical characteristics determined that the
secondary treatment helped 77.78 percent of males, 66.67
percent of females, and 55.56 percent of all subjects
without the secondary treatment to reduce metabolic
syndrome data. Accordingly, the secondary dose of
medication was needed for stronger long-term relief from
Actos treatment. This was more effective for males than
for their female counterparts.
It was
necessary to find any relationship among Girth, B/P,
BMI, FBS, Cholesterol (Chol.), HDL, LDL, and TG (Trigl.)
with one another in determining Weight. This occurs when
the predictors are highly correlated; however,
multicollinearity could be an issue. One way of checking
multicollinearity is to regress each predicator
variable, in turn, against all other predictors and to
examine the values.
If thevalue
goes above 90 percent multicollinearity, it is said to
be a problem. We compared the results from bivariate and
multivariate regressions. It is safe to conclude that
our analysis was free of any possible multicollinearity
effect except for males: Girth/BMI, Chol./LDL, and for
females: Weight/Girth, Chol./LDL, Weight/BMI. Tables 5
and 6 present the intercorrelation among the criterion
variables. Furthermore, for males, correlations in
between health data apparently were more insignificant
compared to that of females. The metabolic syndrome
health variables displayed have substantially strong
correlations with one another; if linear correlation
coefficients,
 s
were between 0.60 and 0.80, they were significant beyond
the 0.01 level. This study showed that there was a
reduction of metabolic syndrome data following the
secondary dose of Actos treatment in the adults. These
reductions were higher for males than their female
counterparts. Furthermore, for the two gender groups
considered in the study, there was a weight gain (at
1.0115) for the 12 subjects not administered secondary
medication, compared to a weight gain at a lower rate
(at 0.975) for the (23) subjects administered secondary
medications. This means that the administration of
secondary medication was necessary for subjects to
maintain a successful body weight of the subjects.
Weight comparison of the subjects before and after the
secondary medication was strongly linear (coefficient of
determination,compared
to that of the subjects not administered secondary
medication .
The
chi-square ()
test of independence conducted for the data showed that,
at the 5 percent significance level, the data did not
provide sufficient evidence to conclude that an
association existed between weight (gain/loss) and
gender after medication. However, there was a slight
initial increase in the cholesterol level after the
subjects were administered secondary medication before
it started decreasing. This characteristic was gender
independent and the increase got smaller as the ages of
subjects increased. For all other subjects, regardless
of gender, the secondary treatment helped to reduce
their metabolic syndrome variables. The aggregate test
indicator (ATI) which measures the effectiveness of the
secondary medication showed that males are doing better
as compared to females.
Further Analyses and Remedial Actions
Since
the sample size for each category in the study was
 11,
the correlation was significant if it is greater than
0.111. It is possible that multicollinearity made the
regression coefficients unstable and unreliable. The
 value
and the standard error may change depending upon which
other predictors were in the model. The impact of
multicollinearity can be reduced by increasing the
sample size.
This
study suggested an effective intervention for the
prevention of chronic disease in patients based on their
gender and secondary dose of medication. It was
concluded that poverty-struck women in the age group 15
to 49 years had greater health status than their wealthy
counterparts in a country like Jamaica. The poverty is
synonymous with rural area and women, and in spite of
this reality this study revealed that the secondary
treatment was more effective for males than for females
even though this disparity still may exist (Bourne &
Rhule, 2009). The results from this sample of Hispanic
adults from the South Texas region who were at risk for
Type II diabetics showed that the metabolic syndrome was
highly prevalent. Thus, interventions with individuals
who have metabolic syndrome may have important
implications for the healthcare sector and its
staggering cost in treatment and prevention. However, a
long-term study is needed to solidify these findings
with a larger cohort of patients and more follow-up
monitoring.
Acknowledgments and Disclaimer
Texas
A&M International University's Texas Center for Border
Economic and Enterprise Development (TCBEED), Laredo,
Texas 78041 deserves our appreciation for approving this
award for this project with funding. Dr. F.M. Canseco
School of Nursing in the College of Nursing and Health
Sciences gathered data for this research and deserves
our appreciation. The cooperation received from the
office of a local physician and staff has been
tremendous. This article stems from the final project
report of this award submitted to TCBEED. Any opinions,
findings, conclusions, or recommendations expressed in
this article are those of the authors and do not
necessarily reflect the views of Texas A&M International
University or the Texas Center for Border Economic and
Enterprise Development.
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