Good Example Of Methods: Research Paper

Type of paper: Research Paper

Topic: Diabetes, Sports, Exercise, Education, Study, Medicine, Glucose, Sugar

Pages: 2

Words: 550

Published: 2020/11/30

Background:

The prevalence of Diabetes in the United States is 9.3 %, which has been growing rapidly compared to 8.3 % in 2010 (CDC, 2014). The dramatic increase in the rate of diabetes in the last couple of years has been attributed to the lifestyle and physical activity of people. Physical activity is considered a cornerstone in the prevention and treatment of diabetes and its impact on the cardiovascular system, which still the leading cause of morbidity and mortality in diabetics (Miller et al., 2012).
Inflammation is the underlying pathophysiology of insulin resistance and diabetic complications in heart, kidney, eye and nerves. A lot of studies in the last years have been looking for the correlation between inflammatory markers (i.e. WBCs, neutrophils) and insulin resistance in diabetics. Studies showed that the inflammatory markers as WBC’s and neutrophil are inversely correlated to the pathogenesis of diabetes. (Bulum et al., 2014; Jiang et al., 2014; Płaczkowska, Pawlik-Sobecka and Kokot, 2014). Furthermore, the association between exercise and inflammatory markers in diabetics has been much of interest recently. Some studies had difficulties in documenting exercise, when subjects were self–reporting; other studies used an accelerometer to get more accurate measures of the physical activity (Loprinzi and Pariser, 2013).
Exercise intensity is measured in kilocalories burned per minute of activity or in a unit called the metabolic equivalent (MET). MET is the ratio of the metabolic rate during exercise to the metabolic rate at rest. Evidence from various studies indicates that physical activity provides a very effective approach to avert or delay the development of diabetes mellitus. Cardiovascular exercise also has a bearing on inflammation. There are blood markers that are associated with inflammation such as white blood cell count, homocysteine, C-reactive protein (CRP), fibrinogen and others. CRP is the most comprehensively studied inflammatory blood marker (Yeh, 2003). Recent studies reported lower levels of CRP in subjects who were physically active and those who had normal body weight (Church et al., 2002; Loprinzi and Pariser, 2013). These studies have focused on the relationship between fitness and inflammatory markers. For example, Kokkinos & Myers (2010) indicate that the CRP level is inversely proportional to fitness level. They indicate that in a study involving 722 men, CRP levels were noted to be inversely related to the fitness levels. A reduction in CRP rate of nearly 80% was noted in the most-fit group as compared to the least-fit group. Church, Barlow, Earnest, Priest, Blair and Kampert (2002) indicate that physical activity provides a higher degree of protection in women as compared to men. Although many researchers have focused on the implication of physical fitness to inflammatory markers for diabetes, very few, if any, stratified their results by sex. It remains unclear how the effect of exercise on inflammatory markers is affected by gender. The aim of this study was to look at the relation between exercise capacity using MET and inflammatory markers in diabetics and non-diabetics. Further on, analyzed our data according to the gender difference between diabetics and non–diabetics.

Design and participants
The population of our study consisted of 285 subjects with stable coronary artery disease. The subjects were enrolled in the HEARTS (Slowing HEART disease with lifestyle and omega-3 fatty acids) clinical trial. Coronary artery disease was defined as > 50% stenosis in at least one coronary artery at catheterization, previous myocardial infarction, stable angina, significant non-calcified plaque in at least one coronary artery at MDCTA, abnormal exercise tolerance test (ETT). It is also defined as an area of reversible ischemia on nuclear imaging study or pharmacologic stress, with subsequent revascularization, angioplasty, abnormal exercise treadmill test (ETT) with or without nuclear imaging or echocardiography. All subjects were receiving standard of care for CAD, which included a statin drug. The protocol was approved by the BIDMC IRB and all subjects signed informed consent. All subjects underwent
Data was obtained from The HEARTS (Slowing HEART disease with lifestyle and omega-3 fatty acids) clinical trial. A total of 285 subjects with coronary artery disease, they include diabetic and non-diabetics.The evaluation will be performed at the General Clinical Research Center (GCRC) of the Beth Israel Deaconess Medical Center (BIDMC). All subjects underwent a maximal, graded exercise treadmill stress test. The protocol was approved by the BIDMC IRB and all subjects signed informed consent. We looked at the relation between exercise capacity by using MET as a measure of exercise intensity based on oxygen consumption and inflammatory markers in Diabetics and non-diabetics subjects. We also examined the gender differences between male and female subjects in each group. Male and female were analyzed separately according to their diabetic status.

Exercise

All subjects underwent a maximal, graded exercise treadmill stress test. Exercise capacity was measured by the metabolic equivalent of task (MET). MET was calculated using the speed and grade of the treadmill.

Inflammatory markers and Diabetics status

The blood and plasma were drawn, and serum was prepared, after a 10-12 hour fast. Glucose, chemical profile, total white blood cell count, absolute neutrophil, lymphocyte and monocyte counts, lipid panel and TSH were measured at Quest Diagnostics (Cambridge, MA) which functioned as our central lab.
The fasting blood drawn at baseline performed at (fasting glucose, comprehensive metabolic panel, liver enzymes, and CBC). Subjects with history of type 1 diabetes and/ or history of ketoacidosis were excluded. Subjects with type 2 diabetes must have a fasting glucose of ≤ 200 mg/dl at screening and cannot be treated with thiazolidinedione class agents or insulin or Extendin-4 (Byetta) therapy.

Statistical Analysis:

Results are expressed as mean ± SD. Continuous variables were compared using unpaired t tests. Normality tests were conducted using the Shapiro-Wilk test. For skewed variables (all lipid levels), log-transformed values were used in all analyses and reported as a geometric mean ± SD. A probability value P ≤ 0.05 was considered statistically significant.
Our models were based on six dependent variables associated with inflammation and endothelial dysfunction: WBCs, neutrophils, lymphocytes, monocytes, platelets and microalbuminuria (MCR). Univariate correlations were determined for each of the dependent variables against several independent variables. These include body mass index (BMI), weight, waist circumference, systolic blood pressure, diastolic blood pressure, diabetes status, hemoglobin A1c level, glucose level, metabolic equivalent of tasks (METs). Others include Bruce duration, total cholesterol level (TC), LDL-C level, HDL-C level, triglycerides level, creatinine clearance (CRCL), glomerular filtration rate (eGFR), amount of alcohol intake per week (ETOH), aspartate aminotransferase (AST) level and alanine aminotransferase (ALT) level. Subsequently, we used separate age and gender adjusted elimination backward multivariate linear regression models for each of the dependent variables against the independent variables that had a p-value <0.3 in the first simple univariate analysis. In those subsequent models, we have selected p-value <0.1 for the independent variables to stay in the model. We have determined a p-value of <0.05 for the independent variables to be considered a significantly independent variable. Because sex was significantly different in the models, we stratified our analyses by sex and repeated the GLM models adjusting for age and diabetes.

Results:

The baseline characteristics by diabetes status are shown in Table 1. Compared to non-diabetics, diabetics had a significantly higher BMI and waist circumference. Diabetics also had higher levels of glucose, A1C, and urine MCR compared to non-diabetics as would be expected. In contrast, the exercise capacity of diabetics was significantly lower than that of non-diabetics. There were no significant differences in levels of WBC or its subsets or lipid levels in diabetics compared to non-diabetics.

In the diabetics after adjustment for age and gender and BMI, Table 5 shows that waist was the best predictor and directly correlated with WBC (p =0.0002) and neutrophil (p=0.001). It was the second best predictor for monocytes (diastolic BP is best) while MET was the best predictor and inversely correlated with lymphocytes (p=0.025). TG was of borderline significance in predicting MCR.
Next, we stratified our results by diabetes status. The baseline characteristics of men are shown in Table 7. Compared to non-diabetics, diabetics had higher HbA1c and glucose levels while MET in the diabetic men were lower than non-diabetics while the amount of alcohol per unit in a week was more in non-diabetics than diabetics.
When we stratified our data by gender, we saw a similar pattern in diabetic males (Table 9) for WBC, neutrophils, and monocytes. In contrast to the total group, sys BP was borderline inversely correlated with lymphocytes, and there were no predictors for MCR. The baseline characteristics by diabetes status for women are shown in Table 10. Compared to non-diabetics., diabetics had higher HbA1c and glucose levels while MET in the diabetic women showed no significant correlation.
In contrast to diabetics male, in diabetic women alcohol was strongly predicting WBC with (p=0.001), and monocytes with (p=0.001). The second best predictor for lymphocyte (p=0.012) (Table 12), LDL-C predicted neutrophils and lymphocytes (p=0.013) and (p=0.004), respectively. Total cholesterol was directly correlated with MCR (p=0.014).

Conclusion:

There non-diabetic subjects have higher exercise capacity than in the diabetic subjects. While there was no significant difference between diabetics and non–diabetics in levels of Inflammatory markers (WBC, Neutrophil, monocytes, and platelets).

Discussion:

The study conducted is reliable because of several factors. First, the sample is representative enough. The sample comprised of 285 subjects, all with stable coronary artery disease.The sample included diabetics and non-diabetics. The health status of the individuals, as reported, is credible because it was conducted in a reputable institution, the General Clinical Research Center (GCRC). The male and female subjects were also examined separately and analyzed according to their diabetes status. The results point to a relatively high degree of credibility because the measurements were as expected in terms of glucose level. Diabetics also had higher levels of glucose, A1C, and urine MCR compared to non-diabetics. On average, diabetics are in worse physical shape than non-diabetics. This fact was indicated by the BMI and waist circumference measures. The significance of this finding is to prompt fitness interventions for diabetics. Further in the research, it is apparent that physical fitness has an inverse relationship with the level of progression of diabetes. Inflammatory markers such as CRP and MET also indicate that there were univariate correlations for diabetics and non-diabetics. The data was then stratified by gender, indicating that, in contrast to diabetics male, in diabetic women alcohol was strongly predicting WBC with (p=0.001), and monocytes with (p=0.001). This study is significant because the findings can be used to influence further research. The stratification of this research by gender provides a foundation for other researchers to go deeper in determining how physical exercise interventions are likely to affect men and women with diabetes. They also enable educators and students interested in this area to expand their knowledge.

References

Bulum, T., Vrhovac, R., Kolarić, B. and Duvnjak, L. (2014). Association of hematological parameters with insulin resistance in type 1 diabetes. NCBI [Pubmed], 39(2), pp.119-126.
CDC, (2014). 2014 Statistics Report | Data & Statistics | Diabetes | CDC. [online] Cdc.gov. Available at: http://www.cdc.gov/diabetes/data/statistics/2014statisticsreport.html [Accessed 27 Feb. 2015].
Church, T.S., Barlow, C.E, Earnest C.P., Kampert J.B., Priest E.L., Blair S.N. (2002). Associations Between Cardiorespiratory Fitness and C-Reactive Protein in Men. Arteriosclerosis, Thrombosis, and Vascular Biology, 22(11), pp.1869-1876.
Loprinzi, P. and Pariser, G. (2013). Physical activity intensity and biological markers among adults with diabetes: considerations by age and gender. Journal of Diabetes and its Complications, 27(2), pp.134-140.
Miller, T., Gilligan, S., Herlache, L. and Regensteiner,, J. (2012). Sex Differences in Cardiovascular Disease Risk and Exercise in Type 2 Diabetes. US National Library of Medicine National Institutes of Health, 60(4), pp.664-670. [Pubmed]
Jiang, H., Yan, W., Li, C., Wang, A., Dou, J. and Mu, Y. (2014). Elevated White Blood Cell Count Is Associated with Higher Risk of Glucose Metabolism Disorders in Middle-Aged and Elderly Chinese People. IJERPH, 11(5), pp.5497-5509.
Yeh, E. (2003). Coming of Age of C-Reactive Protein: Using Inflammation Markers in Cardiology. Circulation, 107(3), pp.370-371.

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