BUY ready-to-submit essays - 100% undetectable and plagiarism-free guaranteed!
Note: All our papers are written by real people, not generated by AI. They pass Turnitin, SafeAssign, and LopesWrite
2
Need Help Writing an Essay?
Tell us about your essay and we will find the best writer for your paper. Thousands of students improved their grades with us.
Write My Essay For MeTable of Contents Chapter 1. INTRODUCTION 3 1.1. Statement of the Problem 3 1.2. Purpose of the Study 4 1.3. Objectives of the Study 4 1.4. Hypotheses 5 1.5. Significance of the Study 6 Chapter 2. LITERATURE REVIEW 7 2.1. Diabetes Mellitus (DM) 7 2.1.1. Pathophysiology of Diabetes Mellitus 8 2.1.2. Types of DM 10 2.1.3. Epidemiology of Diabetes Mellitus 12 2.1.4. Diagnosis of DM 13 2.1.5. Complications of Diabetes Mellitus 14 2.1.6. Cost of Diabetes in the U.S. 16 2.1.7. Management of DM 17 2.2. Selenium Intake 24 2.2.1. Selenium Function 26 2.2.2. Metabolic Fate of Selenium 27 2.3. Relationship between Selenium and Diabetes Mellitus 30 2.3.1. Selenium and GDM 34 2.3.2. Gender in Diabetes-Selenium Relationship 35 2.3.3. Age in Diabetes-Selenium Relationship 39 Chapter 3. METHODS 40 Research Design 40 NHANES 41 Target Population 41 Survey Objectives 41 Data Collection Procedure 42 Sample 43 Data 43 Dietary Intake 44 Blood levels of selenium 45 Hemoglobin A1c (HbA1c) 46 Fasting blood glucose 47 3.5. Statistical Analysis 47 Chapter 4. RESULTS 49 Chapter 5. DISSCUSSION 56 Discussion of Hypotheses 60 Chapter 6. CONCLUSIONS AND RECOMMENDATIONS 62 Conclusions 62 Recommendations 64 Limitation of the Study 65 REFERENCES 66
Chapter 1. INTRODUCTION
1.1. Statement of the Problem
Diabetes Mellitus (DM) is one of the most common and costly chronic diseases. It is a disease in which the body’s ability to produce or respond to the hormone insulin is impaired, resulting in abnormal metabolism of carbohydrates and elevated levels of glucose in the blood and urine (Alavi et al., 2014). The number of adults with DM has quadrupled worldwide in less than 4 decades, reaching 422 million cases (World Health Organization, 2016). More than 23.1 million people in the United States are diagnosed with DM, at an estimated cost of more than $245 billion per year (American Diabetes Association, 2013; Like, 2017). The CDC estimates that another 7.2 million people have DM but remain undiagnosed, while another 84.1 million adults 18 years and older have prediabetes (Centers for Disease Control and Prevention, 2017). With high prevalence and implicated costs, DM is a serious problem that justifies more investment to reduce such burdens. Selenoproteins, consisting essentially of selenium, play an important role in oxidative stress. In human physiology, increased oxidative stress reduces insulin secretion and increases insulin resistance. The relationship of selenium and its indirect influence on insulin secretion / production are thus suggestive of a possible relationship between this component and DM.
Selenium has shown a non-linear relationship with DM. Some studies show lower selenium levels in patients with DM (Rajpathak, Rimm, Morris, & Hu, 2005) while other studies have shown an increase of selenium concentrations in patients with DM (Bleys, Navas-Acien, & Guallar, 2007). It is believed that the relationship between selenium and DM, has a U-shaped curve which means that it is important to achieve optimal levels of selenium (Wang, Yang, Wei, Lei, & Zeng, 2015). These optimum levels are not similar in healthy males and females, with males having higher levels than females (Jain & Choi, 2016). Only a few studies have analyzed these differences in diabetic populations. Studies are also lacking that assess the relationship between selenium and key indicators of DM management, specifically, blood glucose and hemoglobin A1c levels; and furthermore, whether, age and gender might play a role in defining this relationship.
1.2. Purpose of the Study
1.3. Objectives of the Study
The objectives of the study are as follows:
1. To assess and compare selenium intake and blood levels in males and females with DM aged 40 years;
2. To determine whether there is a relationship between serum selenium concentrations and levels of fasting blood glucose and hemoglobin A1c, and whether the relationship is similar in males versus females with DM aged 40 years;
1.4. Hypotheses
The following hypotheses will be tested:
1. There are gender differences in the concentration of serum selenium in persons with DM aged 40 years, with females having significantly lower serum selenium levels;
2. Selenium intake of both US men and women, either from food and/or supplements, will exceed the RDA by more than double. There will be significant differences between females and males regarding selenium intake with female consuming significantly less selenium than males;
3. Higher selenium levels will be associated with higher fasting plasma glucose. The relationship will be stronger in males compared to females;
4. There will be a positive relationship between HbA1c and serum selenium levels in males and females with DM aged 40 years old. The relationship will be stronger in males versus females.
5. Age will be related to serum selenium levels in people with DM. Oldest age clusters ( 80 years old) will have the highest serum selenium levels.
1.5. Significance of the Study
Understanding the currently unclear relationship between Se and DM could help in the development of guidelines and policies aimed at preventing and controlling DM in the population, thereby lowering health costs associated with treatments for DM and its complications and allowing people to live a more healthy and productive life. This knowledge could also help in raising important questions for further research that could aid in the establishment of dietary-guided interventions. The cost of diabetes treatment itself and associated co-morbidities can be significantly lowered if dietary factors that lead to the development of disease, and those that impact management, are better understood.
Chapter 2. LITERATURE REVIEW
2.1. Diabetes Mellitus (DM)
Diabetes often goes undetected because symptoms can be attributed to many other causes and some patients experience no symptoms or fail to heed warning signs. However, often when people have a physical examination, they are screened for diabetes with a fasting plasma glucose test (FPG) (Riaz, Alam, Raza, Hasnain, & Akhtar, 2009). The most frequent symptoms are polyuria, polydipsia, weight loss, sometimes with polyphagia, and blurred vision. Long-term complications include nephropathy leading to renal failure, retinopathy leading to loss of vision, peripheral neuropathy that can lead to ulcers and amputations and increased incidence of cerebrovascular and cardiovascular disease, through atherosclerotic processes (American Diabetes, 2013).
2.1.1. Pathophysiology of Diabetes Mellitus
Type 1 diabetes is characteristic of the pediatric age. However, it can appear in adults in the late thirties or early forties. In this type of diabetes there is a destruction of the pancreatic cells by environmental or infectious agents. In individuals who are genetically susceptible, the immune system is stimulated to produce immune responses against altered β-cells or against molecules in β-cells that are like viral proteins. About 80% of individuals with type 1 diabetes have circulating antibodies against islet cells. Furthermore, most of these patients will also have anti-insulin antibodies prior to receiving insulin therapy (Al-Goblan et al., 2014).
On the other hand, type 2 diabetes presents a distinct pathophysiology of type 1. There are different factors that influence the increased number of people diagnosed with type 2 diabetes: increased prevalence of obesity in all age groups, sedentary lifestyle in both sexes, “westernized” food patterns and urbanization. In Type 2 DM there is a dysfunction of the β cells which results in metabolic imbalance, leading to glucose accumulation in the blood and resulting hyperglycemia. Insulin resistance is characterized by a decrease in the biological effects of insulin, not allowing storage of glucose in muscle tissue and simultaneously not acting properly in suppressing endogenous production of glucose by the liver. Insulin resistance is strongly associated with obesity and sedentary lifestyles (Lim et al., 2011).
Insulin resistance leads to an increase of plasma fatty acids, leading to decreased glucose transport and increased fat breakdown. As such, fatty acids such as hormones, cytokines and non-esterified acids may influence insulin’s action. These fatty acids are responsible for regulating adipocytes. When there is an increase in adipocytes number, insulin loses its ability to suppress lipolysis. Increased release and circulating levels of non-esterified fatty acids and glycerol aggravate the insulin resistance of the two target organs (muscle and liver) (Shah et al., 2016). Anyone with excess weight or obesity has some resistance to insulin. However, diabetes only develops when there is insufficient insulin secretion that matches the degree of resistance. In other words, insulin may be high but not enough to normalize glycemic levels.
Gestational diabetes (GDM) is another form of diabetes that appears during pregnancy without prior evidence of type 1 or type 2 diabetes. Pregnancy is accompanied by progressive insulin resistance that is offset by increased secretion of insulin by the pancreas. Simultaneously pregnancy is also characterized by an increased inflammatory pattern (when compared to the non-pregnant state) which is associated with adverse outcomes such as GDM. However, the pathophysiologic mechanism remains unclear. Some authors suggest that there is exacerbation of beta cell dysfunction genetically predisposed to changes in these cells (Law, K. P., & Zhang, H., 2017). Additionally, there are risk factors associated with the disease, such as obesity, which leads to altered production of proinflammatory cytokines by adipocytes. This production leads to a state of low-grade inflammation. It usually goes away after giving birth, but these women are at an increased risk of developing gestational diabetes again in a future pregnancy.
2.1.2. Types of DM
2.1.2.1. Type 1 DM
Type 1 DM, also known as insulin-dependent DM (IDDM), is characterized by a complete insulin deficiency caused by T-cell–mediated autoimmune destruction of pancreatic β-cells (De Ferranti et al., 2014). This type of DM results in lifelong dependence on exogenous insulin. There is considerable variability in the presentation of Type 1 DM: Children often present acutely, with severe symptoms of polyuria, polydipsia, and ketoacidosis. On the contrary, in adults, Type 1 DM has a more gradual onset, where initially symptoms appear to corroborate a diagnosis of type 2 diabetes (Chiang et al., 2014).
Although it is not widely adopted, Type 1 DM has been divided in two smaller groups: Type A, affecting 90% of the individuals, has a detectable serological autoimmune response; and type B, the idiopathic form, where no humoral autoimmunity is present (Eisenbarth, 2007). It has already been proven that there is a genetic predilection in Type 1 DM. In predisposed individuals, early-life environmental triggering factors such as infections, nutrition or even chemicals can activate self-targeting immune cascades (Zaccardi et al., 2015).
2.1.2.2. Type 2 DM
Type 2 DM, also known as non–insulin dependent DM (NIDDM), represents almost 90 to 95% of affected individuals. In this form of DM, individuals have insulin resistance, and sometimes some kind of insulin production deficiency (American Diabetes Association, 2013). The deficient production of insulin and gradual decline in pancreatic β cell function characterizes Type 2 DM. Studies have shown that at the time of diagnosis only 20% of β cell function still happens.
Type 2 DM is caused by a combination of genetic and environmental factors such as obesity, lack of exercise, stress and aging (Kaku, Rasmussen, Clauson, & Seino, 2010). Family history it is also important, as it increases by 2.4 times the risk of having this chronic disease (Pratley, 2013). This type of DM may take years to develop. It is usually preceded by pre-DM, in which levels of are above normal but not high enough for the diagnosis of DQAM (Riaz et al., 2009). Some individuals can have adequate control of glucose levels with weight reduction, exercise and oral glucose-lowering agents, avoiding daily intake of insulin (American Diabetes Association, 2013).
2.1.2.3. Other Specific Types of DM
Diabetes mellitus can be secondary to other conditions such as diseases of the exocrine pancreas (such as pancreatitis, cystic fibrosis or hemochromatosis), endocrinopathies (such as Cushing’s syndrome, acromegaly or pheochromocytoma), drug-induced (such as glucocorticoids, neuroleptics, alpha-interferons or pentamidine) or genetic defects of the β-cell function (such as maturity onset diabetes of the young – MODY- forms) (Kerner & Brückel, 2014). MODY is sometimes classified as Type 2 DM variation or secondary DM but is often considered a separate condition. Wolfram syndrome is a genetic disorder that causes IDDM, vision problems, deafness and DM insipidus (Riaz et al., 2009). Several hormones (such as growth hormone, cortisol, glucagon or epinephrine) antagonize insulin action. High concentration of these hormones, like acromegaly, Cushing’s syndrome, glucagonoma or even pheochromocytoma, can cause DM (American Diabetes Association, 2013)
2.1.2.4. Gestational DM (GDM)
GDM is a temporary metabolic disorder and is one of the most common complications in pregnancy. Any previously nondiabetic woman can develop this temporary disease. Hormonal changes play its role, but weight excess and family history contributes (Spaight, Gross, Horsch, & Puder, 2016). Associated complications can affect either the mother or the baby. This includes preeclampsia, prematurity, macrosomia and breathing difficulties in the infant. Although it has a temporary character, it increases the risk of future development of Type 2 DM both in the mother and the child (Riaz et al., 2009).
2.1.3. Epidemiology of Diabetes Mellitus
Diabetes mellitus is a chronic disease with high social, human and economic costs. There is an increasing growth in developed and rapidly expanding countries around the world. The prevalence of DM has been increasing as a result of the effect of several factors, such as the aging of the world population, longevity, obesity and sedentary lifestyles (Whiting, Guariguata, Weil, & Shaw, 2011).
Data from the WHO states that an estimated 422 million adults were diagnosed with DM in 2014, compared to 108 million diagnosed in 1980 (World Health Organization, 2016). This means that the global prevalence has almost doubled, rising from 4.7% to 8.5% in adults. Unfortunately, DM was responsible for 1.5 million deaths worldwide in 2012, and 47% of these deaths occurred before the age of 70 years (World Health Organization, 2016). Expectations are that this number is going to rise to 592 million by 2035 (Forouhi et al., 2014).
Separate global estimates of diabetes prevalence of type 1 and type 2 do not exist because sophisticated laboratory tests are needed to distinguish both (World Health Organization, 2016). For example, C-peptide test can distinguish autoimmune from metabolic disease. People with type 2 diabetes have C-peptide, which is a byproduct of insulin production, but people with type 1 diabetes and latent autoimmune diabetes of adulthood do not nor have a very low level. However, it is known that most of the people affected by DM have the type 2 and recent literature verifies existence in children as well (Forouhi et al., 2014).
In the U.S., an estimated 30.3 million people of all ages, which represents 9.4% of the U.S. population, had DM in 2015 (Mayer-Davis et al., 2017). Also, in 2015, there were an estimated 1.5 million new cases of DM (6.7 per 1,000 persons) among U.S. adults aged 18 years or older. In the 2017 National DM Statistics Report, 7.2 million, or 23.8%, did not know or weren’t aware that they had this disease. The highest prevalence is among the elderly, as it reaches 25.2% among those aged 65 years or older (Centers for Disease Control Prevention, 2017). If the current epidemic of DM is not addressed and controlled, the U.S. Centers for Disease Control and Prevention estimates that 1 in 3 people could have DM by the year 2050 (Seaquist, 2014).
2.1.4. Diagnosis of DM
Recently, the ADA together with the WHO and other authoritative bodies have approved the use of HbA1c for the diagnosis of DM with a cut-off of above 6.5% in a standardized laboratory (Forouhi et al., 2014). The diagnosis of DM in an asymptomatic person should not be made on the basis of a single abnormal value of fasting blood glucose or HbA1c, and should be done after a second analysis conducted one to two weeks later (Dorcely et al., 2017).
2.1.5. Complications of Diabetes Mellitus
Diabetes Mellitus is an important health problem that is associated with significant complications and morbidity. It has a severe impact on the quality of life of the patient and in worst cases leads to death. Most described complications are: microvascular: retinopathy, nephropathy and neuropathy and macrovascular: ischemic heart and cerebral disease and peripheral vasculopathy. The chronic complications of DM are mainly due to inadequate control, time of evolution and genetic factors of the disease (Maric-Bilkan, 2017).
The prevalence of the most common DM complications among people with DM is not clear, but it is estimated that cardiovascular diseases such as heart disease and stroke cause up to 65% of all deaths in people with DM. More than 70% of people with DM have high blood pressure or are being treated with medications for hypertension (McKown, 2016). One of the major complications, extremity amputations, will occur in 15% of people with DM during their lifetime. These people are 10 to 20 times more likely to have lower-extremity amputation (LEA) than those without DM (McKown, 2016).
Type 2 DM is heavily marked by gender difference. A recent study showed that the prevalence of DM was reversed depending upon the stage of reproductive life, and this means that, there are more men with diabetes before puberty, while there are more women with diabetes after menopause. The prevalence of pre-diabetic syndromes such as impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) differs by sex, with IFG being more prevalent in men while IGT is more prevalent in women (Mauvais-Jarvis, 2015).
Diabetic nephropathy is a chronic complication of DM affecting 20% to 30% of diabetic people and accounts for approximately half of the new cases of renal failure in subjects with dialysis it is associated with significantly increased mortality (Forouhi et al., 2014). Complications have sexual differences as men; frequently describe erectile dysfunction, low testosterone levels and depression, anxiety or stress that can interfere with sexual feelings. On the other hand, women have increased risk of eating disorders, so that DM complications occur more frequently (Joshi & Karule, 2018).
2.1.6. Cost of Diabetes in the U.S.
The cost of DM is high and rising in every country around the world, increasing exponentially with the global rise in the prevalence of obesity and unhealthy lifestyles (Forouhi et al., 2014). In 2012, the total amount of direct and indirect costs in the U.S.A. was estimated as $245 billion. Of this total, $176 billion were in direct medical costs and $69 billion in reduced work productivity (Centers for Disease Control Prevention, 2017).
The National DM Statistic Report also refers to a total of $14.2 million spent in 2014 in emergency department visits among adults aged 18 years or older, including 207,000 for hyperglycemic crisis (9.5 per 1,000 persons with DM). In terms of hospitalizations, a total of 7.2 million hospital discharges were reported with DM as any listed diagnosis (Centers for Disease Control Prevention, 2017).
Indirect costs can come through reduced employment, productivity and quality of life. The indirect costs associated with DM include workdays missed due to health conditions (absenteeism), reduced work productivity, reduced workforce participation due to disability, and productivity lost due to premature mortality (Fu, Qiu, Radican, & Wells, 2009). People with diagnosed diabetes incur average medical expenditures of about $13,700 per year, of which about $7,900 is attributed to diabetes (American Diabetes Association, 2013).
In 2015, DM was the seventh leading cause of death in the U.S.A. Seventy-nine thousand, five hundred and thirty-five death certificates were issued with DM as the underlying cause of death (24.7 per 100,000 persons). More than that, DM was mentioned in 252,806 death certificates in 2015 (78.7 per 100,000 persons as any cause of death (Centers for Disease Control Prevention, 2017).
The ADA also reports that 84 million Americans have prediabetes and are at risk for developing Type 2 DM. Among them, 90% do not even know they have the disease; African Americans and Hispanics are over 50% more likely to have DM as non-Hispanic whites (American Diabetes Association, 2013). For a typical American family, with three members and a median income of $64,000, DM burden, which includes hospital inpatient care, prescription medications to treat complications of diabetes, anti-diabetic agents and diabetes supplies and physician office visits, represents 4.8% of income. Improved understanding of the economic cost of DM helps policymakers make decisions to reduce the DM burden (American Diabetes Association, 2013).
Most of the cost for DM care in the U.S., 62.4%, is provided by government insurance. However, 34.4% is provided by private insurance, and 3.2% of these affected do not have any protection. When costs are estimated among U.S. states, California had the largest population with diabetes and thus the highest costs, at $39.47 billion in 2017 (American Diabetes Association, 2013).
Fortunately, a recent report based on data from the National Health Interview Survey, the National Hospital Discharge Survey and the U.S. Renal Data found that DM complications have declined between 1990 and 2010, with a 68% reduction in acute myocardial infarction (Gregg et al., 2014). Although DM is a devastating disease, its burden can be reduced with appropriate management and individualized care (Seaquist, 2014).
2.1.7. Management of DM
Dietary intake and physical exercises are two major determinants of energy balance and are considered as the basis for the treatment of patients with diabetes. As such, part of the management of diabetes involves the practice of physical activity, a healthy diet, and habits like adequate rest that will be essential for maintaining energy levels and well-being. Simultaneously with nutritional intervention, the comorbidities that may exist in a diabetic patient also have to be considered. Dietary recommendations may contribute to the achievement of optimum blood glucose, blood pressure, lipid profile and weight and several studies have demonstrated the metabolic benefits of nutritional recommendations in reducing HbA1c (Marín-Peñalver, J et al., 2016).
2.1.7.1. Diet
Being a key component of diabetes management, nutrition achieves general purposes such as weight maintenance, and specific, such as glycemic control. At the same time, “eating” has an associated cultural component that is very strong and, therefore, the changes and limitations of dietary intake have an impact on the quality of life. The most current guidelines emphasize the importance of a healthy diet, similar to what is recommended for the non-diabetic population, with the goal of cardio-vascular protection (Wiebe, J. C., et al., 2016).
Current recommendations agree that one of the goals of nutrition therapy is to adopt energy intake for each patients’ needs, which will depend primarily on age, baseline body weight and physical activity (Wiebe, J. C., et al, 2016). Body mass index is a tool that is frequently used in clinical practice to classify patients (Siddiqui, K., Bawazeer, N., & Joy, SS, 2014) and the ADA recommends that overweight and obese type 2 diabetic adults should reduce energy intake while maintaining a healthy eating pattern that promotes weight loss (ADA, 2019). Indeed, most patients with type 2 DM are overweight or obese, which will favor insulin resistance and affect its secretion, aggravating diabetes. In these cases, in addition to an adjustment of the distribution of macro and micronutrients, we should also seek weight reduction by decreasing the total energy intake by 500 to 1000 kcal (ADA, 2019).
Regarding the distribution of macronutrients, there is no consensus about the percentages of each group for people with diabetes. Older recommendations focus on defining an optimal amount of macronutrients. For instance, in 2002 ADA recommended that 50 to 60% of this energy should come from carbohydrates, 25-35% from fat and 15 to 20% from protein (Ajala, O et al. 2013) .However, most present guidelines do not define an optimal amount of each dietary components. Moreover, guidelines focus on food quality rather than type of carbohydrates, they still lack of agreement regarding glycemic load and glycemic index and recommend fat similar as for the general population. According to “Lifestyle Management: Standards of Medical Care in Diabetes-2019” (ADA, 2019), the objectives of nutritional therapy are to promote and to support healthy dietary patterns, emphasizing a variety of foods rich in nutrients in appropriate portion sizes. Moreover, these objectives are planned to improve health in general and to achieve and to maintain the corporal weight goals, glycemic goals, blood pressure and lipid profile, delaying and avoiding complications of diabetes.
If there is no optimal percentage of calories from carbohydrates, proteins and fats, the distribution of macronutrients should be based on the individualized assessment of current consumption patterns, preferences and metabolic goals. Thus, healthy dietary patterns that contain foods rich in nutrients, less focused on specific nutrients (ADA, 2019), should be preferred. The Mediterranean, Dietary Approaches to Stop Hypertension (DASH), and plant-based food diets are examples of healthy patterns with positive results. Most individuals with type 2 diabetes report moderate intake of carbohydrate (about 45%) (ADA, 2019). Regarding protein, there seems to be no evidence that their daily intake adjustment (15-20%) improves health (except for individuals with kidney disease) or glycemic control but higher protein intakes (20-30%) appear to increase satiety in type 2 diabetics (ADA 2019). Lastly, according to the National Academy of Medicine the dietary fat intake should be 20-35% being in the diabetics controversial (ADA 2019).
The carbohydrate group, in addition to the amount ingested, the type is also a determinant of glycemic control (Marín-Peñalver, J et al., 2016). Thus, carbohydrate intake is preferable to be obtained from fruits, vegetables, legumes, whole grain and dairy products, as opposed to products high in salt, fat or simple sugars. Proteins do not directly affect the control of blood glucose but they seem to increase the insulin response, therefore it is not advisable to use them in situations of hypoglycemia. Lastly, also in the lipid group is important the type (quality), being more relevant than the quantity; mono and polyunsaturated fatty acids are better.
Through the macronutrients ingestion we can also obtain micronutrients, essential for the individual, daily, for the normal functioning of the organism. This group includes the trace elements, such as selenium. These elements participate in functions at the cellular and subcellular levels such as immune regulation, nerve conduction, muscle contractions, among others. Several studies have observed direct associations between trace elements and DM. The action of insulin on reducing blood glucose appears to be enhanced by these elements and the proposed mechanisms for this promotion of insulin are several: activation of insulin receptor sites, acting as cofactors or components for enzymatic systems involved in glucose metabolism, increasing insulin sensitivity and acting as antioxidants (prevent tissue peroxidation) (Siddiqui, K., Bawazeer, N., & Joy, S. S., 2014).
Currently, eating patterns in United States do not meet the Dietary Guidelines (US Department of Health and Human Services). Around 75% of the population has a low consumption of vegetables, fruit, dairy products and oils. Moreover, nearly 50% of the population exceeds the ingestion of total grain and total protein foods recommendations, however, the recommendations for the subgroups within each of these food groups are not being achieved. Additionally, most of the American population exceed recommendations for added sugars, saturated fats, and sodium.
2.1.7.2. Physical Activity
Physical activity is expression used to describe a movement that increases energy utilization being extremely important in the diabetes control plan. Exercise is a specific type of physical activity, designed and planned to improve physical capability. Thus, both are important in this pathology (ADA, 2019).
Although rates of diabetes are increasing, the importance of exercise in patients with type 2 diabetes is already known. The lack of physical activity is considered a risk factor for the development of insulin resistance and type 2 diabetes. Exercise interventions alone proved to be as effective in preventing the progression of type 2 diabetes as food programs (Stanford, K. I., & Goodyear, L. J., 2014). Thus, physical activity in patients with type 2 diabetes improves control of blood glucose, body weight, lipid profile, blood pressure, cardiovascular disease, mortality and quality of life in general (Stanford, KI, & Goodyear, LJ, 2014).
One of the mechanisms that explains the improvement in the health of type 2 diabetics through exercise is the adaptation of the skeletal muscle, which, in turn, decreases insulin resistance in skeletal muscle (Stanford, KI, & Goodyear, LJ, 2014). In people with this pathology, the uptake of glucose stimulated by insulin in the skeletal muscle is impaired. However, the uptake of glucose stimulated by the exercise in people with type 2 diabetes is practically normal. Thus, exercise-induced adaptations in skeletal muscle seem to be essential to combat type 2 diabetes progression, helping in its management (Stanford, K. I., & Goodyear, L. J., 2014).
According to ADA, people with diabetes should perform aerobic and resistance exercise regularly (ADA, 2019). The aerobic activity should ideally last for at least 10 minutes and the goal is 30 minutes (or more) per day for adults with type 2 diabetes. It is also recommended exercise daily or at least day-on day-off, for a reduction in insulin resistance. Additionally, ADA says that over time, activities should progress in intensity, frequency and / or duration for at least 150 minutes per week of moderate physical activity. However, most adults with type 2 diabetes would not be able to participate in intense exercise and therefore they should be committed to moderate exercise with the recommended duration. As such, adults with type 2 diabetes should practice 2 to 3 sessions per week of resistance exercise as it improves strength, balance and ability to participate in daily activities throughout life.
2.1.7.3. Medication
In the last decade, the core of diabetes therapy, in addition to lifestyle modifications, was the use of metformin, sulphonylurea, or insulin (Haddad, J et al., 2018). It was believed that diabetes was characterized by two major deficiencies: 1) insulin resistance with increased hepatic glucose production and reduction of insulin use by tissues such as muscle and 2) decreased production of insulin by pancreatic beta cells. However, today there are several classes of anti-diabetic agents. As such, I will address metformin, insulin secretagogues, alpha-glucosidase inhibitors, thiazolidinediones and dipeptidyl peptidase-4 inhibitors.
Metformin is considered the first-line agent for the treatment of type 2 diabetes, in the absence of contraindications (Haddad, J et al., 2018). Its mechanism of action involves altering the composition of the intestinal microbiota and activation of the mucosal AMP-activated protein kinase (AMPK), which maintains the integration of the intestinal barrier (Marín-Peñalver, J et al., 2016). Apparently, in hepatocytes, these combined effects decrease the levels of lipopolysaccharide (LPS) in circulation and in the liver. Metformin is distributed from the intestine to the liver, where it inhibits gluconeogenesis.
Insulin secretagogues like sulfonylureas and meglitinides are other 2 different classes of oral hypoglycemic drugs with a common mechanism of action: both stimulate pancreatic beta cells in insulin secretion (Marín-Peñalver, J et al., 2016). Sulfonylureas are first-line or second-line classic drugs for type 2 diabetics and are used as a benchmark for comparison of efficacy and safety of other hypoglycemic drugs. In turn, meglitinides stimulate insulin secretion through a similar mechanism however, they require more frequent dosing. Its mechanism of action is to increase the secretion of insulin regulated by ATP-sensitive potassium channels located on the membrane of pancreatic beta cells.
Another class is the alpha-glucosidase inhibitors – acarbose, miglitol, and voglibose – whose mechanism of action is unique (Marín-Peñalver, J et al., 2016). They reduce postprandial triglycerides but their effects on LDL and HDL cholesterol are insignificant. Regarding their mechanisms of action, they have a similar structure to natural oligosaccharides. Thus, they have a high affinity for alpha-glucosidases which are enzymatic complexes located in the brush border membrane of the small intestine that hydrolyze the oligosaccharides into monosaccharides. They produce a reversible inhibition of membrane-bound intestinal alpha-glucoside hydrolase enzymes. This provokes a delay in absorption and digestion of carbohydrates with a decrease in postprandial blood glucose.
Thiazolidinediones increase insulin sensitivity by acting on adipose tissue, muscle and liver to increase glucose utilization and decrease its production. They bind to peroxisome proliferator-activated receptors (PPARs) that are increased in diabetic individuals. They may also improve blood glucose levels by preserving the function of beta-pancreatic cells. They are effective in combination therapy (Marín-Peñalver, J et al., 2016). Lastly, dipeptidyl peptidase-4 (DPP4) inhibitors are used in monotherapy, in patients with inadequate diet and exercise control, and in dual therapy in combination with metformin, thiazolidinediones and insulin. Agents that inhibit DPP4, an enzyme that rapidly inactivates incretins, increase the active levels of these hormones, improving islet function and glycemic control in type 2 diabetes.
Although pharmacological options are increasingly extensive and offer several therapeutic possibilities, lifestyle interventions are essential in approaching these patients and are necessary to achieve therapeutic goals (Haddad, J et al., 2018).
2.2. Selenium Intake
Selenium is nutritionally essential for humans, being naturally present in different foods (Monsen, 2000). It may appear as an added form or be given as a nutritional supplement. Brazil nuts are the natural source richest in selenium (5300µg / 100g) (Foundation, 2001), followed by seafoods and organ meats. Other dietary sources include muscle meats, cereals and dairy products (Monsen, 2000). In the American diet the largest sources of Se are bread, grains, meat, poultry, fish and eggs (Foundation, 2001) .
Selenium comes in two forms, inorganic – selenate and selenite – and inorganic – selenomethionine (Se-meth) and selenocysteine (Se-cyst). Both forms can be good dietary sources of selenium. Most selenium in animal tissues is present in the form of selenomethionine or selenocysteine. selenomethionine can be incorporated non-specifically with the amino acid methionine, into body proteins. selenomethionine cannot be synthesized in humans, being initially synthesized in plants and incorporated randomly in place of methionine in a variety of proteins obtained from animal and plant sources (Monsen, 2000).
The amount of selenium in a particular type of plant-based food depends on the amount of selenium in the soil, as well as other factors such as pH, amount of organic matter in the soil and the form of selenium that can be absorbed by plants (Foundation, 2001). Soils contain selenites and inorganic selenates that plants will accumulate and convert into organic forms, mostly selenomethionine, selenocystheine and its methylated derivatives. Thus, selenium concentrations in plant-based foods vary widely according to geographic location (Foundation, 2001). In addition, the selenium content in soils will also affect the amount of selenium that the animals ingest, with consequent variation of selenium in products of animal origin. However, foods of animal origin will not be so affected by these variations of Se in soil because they can maintain normal concentrations of selenium in your body due to homeostatic mechanisms (Foundation, 2001).
In the United States, the entire population has a selenium intake greater than 55 ug /day. High serum selenium concentrations were associated with increased serum lipids in a representative survey of US adults ≥ 40 years old conducted in 2003-2004 (Laclaustra, M. et al., 2010). This increase in selenium intake may be attributed to natural sources as well as fortified foods, supplements and fertilizers that lead to an average selenium concentration of 136 μg / L. In fact, vitamin and mineral supplements appear to have contributed to this increase in selenium levels. However, enhanced selenium intake in individuals with a complete status of selenoproteins does not have big potential to add health benefits and can result in toxic effects (Laclaustra, M. et al., 2010). As such, selenium supplements should be avoided in the U.S. since it may increase the risk of type 2 diabetes. Furthermore, U.S. adults that has been previously exposed to a high background ingestion of selenium, had their prevalence of diabetes increased.
Changes in dietary eating patterns can have significant effects on selenium status. It can result from changes in food imports – for example variations in selenium concentrations in wheat – and in eating habits – for example change in fish consumption, poultry and whole-wheat products. Furthermore, the level of selenium intake varies among populations. For instance, Eastern European countries tended to have lower selenium intake than their western counterparts and varied throughout the Middle East (even within the same country) (Stoffaneller & Morse, 2015). This may be partially explained by the differences in food patterns of each one. However, it seems that selenium deficiency is widespread among these populations, except for healthy subjects with optimum weight. Additionally, most studies suggest that selenium losses in food confectionery are scarce and the absorption of selenium from food is about 80% being independent of exposure and not affected by nutritional status.
2.2.1. Selenium Function
Selenium functions as a redox center, part of the family of selenium-dependent glutathione peroxidases, transforming hydrogen peroxide and damaging lipids and phospholipid hydroperoxides into harmless products (Wang et al., 2014). Selenium also exerts its biological effect through several selenoproteins, functioning as antioxidants and participating in thyroid hormone metabolism, redox reactions, reproduction and immune function (Thomson, 2004).
The Recommended Dietary Allowance (RDA) for selenium is based on the amount of selenium that is required to maximize selenoprotein glutathione peroxidase synthesis, assessed by plateau on the activity of the isoform of this enzyme, in plasma. For men and women, both young adults and adults over the age of 51, the RDA is 55 µg/day (Monsen, 2000). A recent study involving 5,423 participants in China, showed that the daily average intake of selenium was of 43.51μg (Wei et al., 2015). Controversy, in US, the daily average of selenium ingestion is 151.2 µg and 107.5 µg, in male and female over 20 years of age respectively. This consumption is both from food and supplements with both averages exceeding the RDA by more than double (Rocourt & Cheng, 2013).
The association of plasma levels and selenium intake with DM has not yet been fully elucidated, with studies pointing to conflicting results (Mueller, Mueller, Wolf, & Pallauf, 2009; Wei et al., 2015). Until 1957 selenium was considered a toxin (Gupta & Gupta, 2016). However, after this date, and with the research results published in the meantime, the protective effect of selenium in cases of cancer became clear.
2.2.2. Metabolic Fate of Selenium
The metabolic fate of selenium varies according to the form ingested and the overall status of selenium in the individual and in the population. Organic selenium appears to be more bioavailable and maintains a higher level post supplementation, possibly because of its better absorption. It can also be stored and has lower renal clearance. Supplementation studies in adults corroborate this concept. Generally, selenium is well absorbed. When under normal feeding conditions, absorption is not a limiting factor to bioavailability. The Se-meth form is better absorbed than selenite and possibly the same is true when compared to Se-cyst. However, under optimum conditions, the absorption rate for Se-meth is like selenite, and the true absorption of selenium from food is 50-80% (Daniels, 1996).
The organic selenium is more effective at increasing selenium levels in adults than inorganic selenium. The organic selenium is able to increase plasma, erythrocyte and blood selenium levels more quickly and to a greater degree. Accordingly, the results of previous studies seem to suggest that selenium metabolism varies according to the form of selenium ingested and that the organic form, particularly Se-meth, is more bioavailable than inorganic. In terms of storage, skeletal muscle contains the largest selenium pool, accounting for about 40-50% of total selenium in subjects with intakes within the US safe and adequate range (50-200 µg / day) (Daniels, 1996).
It is well known that the differences inherent to sex induce variations observed at the biological level. Thus, it is not surprising that selenium biology also exhibits significant sex dimorphisms. Several factors appear to contribute to the interaction between selenium and gender. The most well characterized and studied are sex hormones, alcohol and tobacco consumption, hypertension, glucose intolerance, dyslipidemia, obesity, diet and lifestyle. In addition to the biosynthesis of selenoenzymes and selenoproteins they present sex-specific differences in a dose-dependent manner (Arnaud et al., 2012).
More specifically, at the intestinal level there appear to be differences in the absorption of Se-meth, 96% for women and 76% for men. However, the associated mechanism remains poorly understood. It has also been recognized that women have higher selenoprotein glutathione peroxidase activity in their circulation compared to men but not selenium levels. Regarding supplementation, it appears that there is a greater urinary excretion of selenium in women compared to men, supplemented with the same dose, indicating a sexual dimorphism in selenium metabolism in other body tissues (Seale et al., 2018).
Several studies have been carried out to describe the differences in selenium levels. However, there will be studies that may not be representative of the US population. According to the Third National Health and Nutrition Examination Survey (NHANES III), which included 18,597 participants, men presented serum selenium levels higher than women (Niskar et al., 2003). However, in 2011, a study was conducted to investigate independent determinants of selenium levels in 1,997 men and 1,905 women in two large prospective U.S. cohorts. It was concluded that sex was an independent determinant of selenium levels (Park et al., 2012).
Another important factor to take into account in the metabolic fate of selenium will be age. Despite the recommendations (RDA) for men and women either young or older than 55 years be the same, the pattern of ingestion in the elderly may be slightly different compared to young adults. Typically, the mean selenium intake in adults is 76 +/- 124µg / day. For men and women between ages of 51 to 70 years, the mean intake is 135 µg/day and 94 µg/day, respectively, and for men and women older than age 70 it is about 112 ug/day and 83 µg/day , respectively. Selenium deficiency is not common in the US, however, glutathione peroxidase (a marker of selenium status) appears to be decreased in the elderly (Bernstein & Munoz, 2014)
2.3. Relationship between Selenium and Diabetes Mellitus
Type 2 diabetes is the most common form of diabetes and is caused by a progressive increase in insulin resistance. Its relation to oxidative stress seems to reflect the excess of reactive oxygen species levels in hyperglycemia. As such, oxidative stress has a huge impact on the etiology, pathogenesis and complications of type 2 DM. Thus, this component may play a protective role against type 2 DM. However, the relationship of selenium to type 2 DM is rather complex. On one hand, an overexpression of selenium-dependent glutathione peroxidases in the islets can protect pancreatic β cells from oxidative stress, stimulate pancreatic β cell gene expression and improve islet function. On the other hand, high concentrations of selenium in the body can interfere with insulin signaling, a critical point in the regulation of glucose levels and in the prevention of diabetes.
High serum selenium concentrations, achieved from dietary intake, are associated with a higher occurrence of diabetes, higher fasting plasma glucose and increased glycosylated hemoglobin levels (Rocourt & Cheng, 2013). Wei et al. (2015) found a positive and significant association between dietary intake of selenium and diabetes, for the highest quartile of dietary selenium intake in comparison with the lowest quartile (OR=1.52, 95% CI: 1.01 to 2.28) (Wei et al., 2015). These results are supported by another study performed in rural China involving 1,856 subjects with 65 or more years old, which showed that, a long-term, higher level of exposure to selenium may be associated with a higher risk of diabetes. In other words, the authors found a significant increased risk of DM from the second selenium quartile to the fourth quartile (OR= 2.65, 95% CI: 1.48 to 4.73; OR= 2.47, 95% CI: 1.37 to 4.45); OR= 3.30, 95% CI: 1.85 to 5.88) by performing a logistic regression model used to estimate odds ratios for diabetes between the four Se quartile groups for all participants (SU et al., 2016).
However, some studies showed different results. A study conducted in Italy involving a cohort followed for 16 years with 226 women of type 2 DM and 395 age-matched control women, found that toenail and dietary selenium were uncorrelated and found no association between toenail selenium and subsequent development of diabetes (Vinceti et al., 2015). Furthermore, a randomized control trial with placebo versus selenium supplementation in patients diagnosed with DM, fasting plasma glucose, glycosylated hemoglobin Alc, and high-density lipoprotein cholesterol were shown to be statistically significantly higher in the selenium recipient arm (Faghihi et al., 2014). Also, in a randomized trial aiming to access the diabetogenic power of selenium involving 473 participants over a period of 6 months taking 100, 200 or 300 µg selenium/d as high-selenium or placebo yeast, selenium supplementation, independently of the dose, had no effect on plasma adiponectin concentration (Rayman et al., 2012).
Additionally, another study reporting the incidence of type 2 DM in U.S. men and women, found that individuals with higher toenail selenium levels are at lower risk for type 2 DM (Park et al., 2012). However, this study did not account for the potential confounder effect of food supplementation. This aspect may be important because high levels of Se consumption from supplements may have different effects than those of modest dietary doses. Also, a 20 years Swedish study involving 1925 Swedish men who were 50 years old, the incident cases of DM were not associated with higher selenium concentration, nor was a significant odds ratio for DM development found (H. Gao et al., 2014). Moreover, in a study aiming to assess the effects of selenium supplementation for 12 weeks on biomarkers of inflammation and oxidative stress in patients with diabetic nephropathy, was shown that taking selenium supplements had no significant effects on fasting plasma glucose (FPG), quantitative insulin sensitivity check index (QUICKI) and lipid profiles compared with the placebo (Bahmani, Kia, Soleimani, Asemi, & Esmaillzadeh, 2016).
There are also inconclusive studies, unable to adjudicate for or against selenium supplementation because of a lack of evidence or statistically significant results. A meta-analysis of randomized controlled trials showed that no association existed between selenium and DM. Therefore selenium supplementation was not recommended for these groups of patients (Mao, Zhang, & Huang, 2014).
The association between selenium and DM has been controversial, with some studies that concluding that selenium may be a protective factor, and other studies that show the contrary. However, many of the studies conducted were not primarily aimed at evaluating selenium and its effects on the development of DM, but they were part of more general studies, or even studies of cancer protection with selenium supplementation, which could induce abnormally high levels of selenium intake. It is important to note here the hypothesis that selenium concentrations have a U-shaped effect, in which the optimum dose should be maintained in order to enhance the beneficial and protective effects of selenium (M. P. Rayman & Stranges, 2013). These findings are supported by a recent review including a total of five studies and 13,460 participants. In this review, a non-linear association was found between selenium and T2DM, indicating that a U-shaped curve is a possible explanation for the relationship between selenium and DM (Wang et al., 2015).
In a view of its important function in protection against oxidative stress, selenium was suggested to play a protective role against Type 2 DM. Although some studies have tried to clarify this association, their findings are still inconclusive and contradictory. Some studies found that selenium could reduce the prevalence of DM (Rajpathak et al., 2005), others suggested that high levels of selenium are related to increased prevalence of this chronic disease (Bleys et al., 2007). A placebo-controlled trial of selenium supplementation in patients with DM has shown that selenium supplementation in patients with DM may be associated with adverse effects on blood glucose homeostasis (Faghihi et al., 2014).
Selenium is also capable of influencing carbohydrate and lipid metabolism and thus may impact the DM risk (Steinbrenner, 2013). Selenium concentration in humans are frequently achieved from serum, plasma, or toenails. In a cohort study with a mean follow-up of 16 years, without intervention, no associations between toenail selenium and subsequent development of DM were found (Vinceti et al., 2015). The finding of another study, a Swedish cohort study, suggested that even when analyzing participants who were 50 years or older, researchers failed to demonstrate any association between dietary selenium in the development of disturbances in glucose metabolism or DM (H. Gao et al., 2014). A meta-analysis of randomized controlled trials of selenium supplementation involving 20,294 participants also failed to find any support for the routine application of selenium supplementation for Type 2 DM prevention (Mao et al., 2014).
In contrast with the previous studies where no association was found, research describing the incidence of Type 2 DM in U.S., found that toenail selenium are lower among diabetic men with or without CVD than among healthy controls (Rajpathak et al., 2005). On the other hand, (Ogawa-Wong et al. 2016) reported that there is an increased risk of DM in individuals with high baseline selenium levels. Also, a meta-analysis of observational studies with a total of 13,460 participants described a U-shaped association between selenium serum levels and Type 2 DM, where both relatively low levels and high levels of serum selenium were positively associated with Type 2 DM (Wang et al., 2015). Lu et al. (2016) assessed the connection between high concentrations of selenium and increased risk of DM. In this health center –based controlled study, with a sample of 847 adults who were over 40 years old in Northern parts of Taiwan, it was demonstrated that selenium levels were positively connected with DM predominance. Also, it was found that the association existing between the selenium and DM did not depend on insulin resistance. Similarly, a cross-sectional study of 5,423 subjects of Chinese origin showed the association between selenium and DM (Gong, Q., Yang, Lei & Yang, 2015). In the study, the primary characteristics, the biochemical test outcomes and the dietary ingestion were analyzed from individual participants. Results showed a positive relationship between DM and selenium dietary ingestion.
2.3.1. Selenium and GDM
2.3.2. Gender in Diabetes-Selenium Relationship
Due to the sexual dimorphism in the regulation of selenium, it is not a surprise that some studies have been trying to establish a relationship between selenium, DM and gender.
Selenium is co-translationally inserted into the growing peptide chain of selenoproteins in the form of 21st proteinogenic amino acid, selenocysteine. This difference in relation to other minerals is fundamental for all aspects associated with the metabolism of selenium and the differences found between the tissues of males and females in expression patterns (Schomburg & Schweizer, 2009a). However, none of the genes coding for selenoproteins or for factors that incorporate selenocysteine are present on sex chromosomes (A. Seale, N. Ogawa-Wong, & J. Berry, 2018). Thus, the differences found in the expression of selenoproteins and factors that incorporate selenocysteine are under the regulation of other factors such as sex hormones, directly influencing the production of testosterone. Additionally, the regulation of sex hormones implies that the metabolism of selenomethionine and the consequent formation of selenocysteine and availability for the synthesis of selenoproteins is different between sexes.
Regarding nutritional intakes, retention of selenium can be determined by the differences in the amount of selenium ingested along with the sum of what is lost through urine and feces. Thus, the greater the selenium intake, the greater the urine excretion. However, women do not retain selenomethionine as well as men (Stipanuk & Caudill, 2013). There is a higher dose-dependent urinary selenium excretion compared to men. However, the differences in selenium intake between males and females is not significant when the intake is calculated per kilogram of body weight (Wendel, 2012).
Studies using data from the third National Health and Nutrition Examination Survey (NHANES) found correlations among these parameters. For instance, NHANES III, found a correlation between T2DM risk and high selenium in males, but not females, a finding which was supported by NHANES 2003–2004 (Laclaustra, Navas-Acien, Stranges, Ordovas, & Guallar, 2009). Furthermore, a study conducted in 2017 found a high correlation of total selenium and selenoprotein P concentrations in young and elderly men, and in elderly women, but not in young women, indicating a specific sexual dimorphism in these biomarkers of selenium status in young subjects (Hybsier et al., 2017). As such, understanding gender differences in biological function of selenoproteins may help understand the contribution of individual selenoproteins in metabolic diseases. Human clinical trials hint at the possibility that the relationship between selenium and DM is sex-specific. In the Nutritional Prevention of Cancer (NPC) trial, the increased risk of DM in response to Selenium supplementation was limited to males (Stranges et al., 2007). On the other hand, but still supporting the hypothesis the selenium and DM interrelationship is sex-specific, another study reported a correlation between lower baseline Selenium (GPx1 polymorphisms) and T2DM incidence among elderly French men, but not women (Akbaraly et al., 2010).
Although sex differences in the association between selenoprotein S (SelS)and metabolic diseases have not yet been described, there are sex differences in the amount of selenium necessary to reach maximal hepatic SelS expression (Stoedter, Renko, Hog, & Schomburg, 2010). The different SelS expression may contribute to low effectiveness of Selenium supplementation. One cannot exclude the possibility that sex differences in the regulation of SelS, may result in sex-specific outcomes in metabolic diseases such as DM (Y. Gao et al., 2004). Wei et al. (2015), found a small positive association between selenium intake and DM in males, but this did not reach statistical significance. In females a significant positive association between selenium intake and DM was not found. It has been suggested that selenium metabolic pathogenesis influences carbohydrate and lipid metabolism, strengthening the idea that selenium does in fact play a role in Type 2 DM. However, the sexually dimorphic association between selenium and metabolic diseases reveals the complexity of the processes. To reduce potentially undesirable effects of selenium supplementation, and to further improve dietary guidelines, there is an urgent need to understand selenium metabolism and selenoproteins in both male and female subjects (Ogawa-Wong, 2016).
The relationship between selenium, Type 2 DM, and gender, has not been much studied yet. Studies that investigate this relationship are scarce. Selenium is an essential trace element for its antioxidant, anti-inflammatory, chemo preventive and antiviral activities. Selenium plays an important role in the metabolism of thyroid and glucose, immunity cellular and reproductive function (Margaret P Rayman et al., 2012). It is possible to believe that gender differences are going to be found, because both immunity and reproduction processes differ according to sex (Schomburg & Schweizer, 2009b).
It has been reported that selenoprotein biosynthesis discriminates among the individual selenoproteins, and impaired selenoprotein biosynthesis becomes phenotypically evident (Schomburg & Schweizer, 2009b). Akbaraly et al.(2010) conducted a study in an elderly French sample, and found that males’ plasma selenium concentrations were significantly associated with a lower risk of developing dysglycemia. On the contrary, no association was found in women, where only high systolic blood pressure, high BMI, alcohol intake, use of lipid-lowering drugs and low HDL cholesterol were related to the incidence of dysglycemia (Akbaraly et al., 2010). As noted above, differences in the pattern of selenoprotein absorption between men and women were observed, suggesting a sexual dysmofirm in the selenium regulation in the body.
Gender specific differences are not yet fully clarified regarding the DM-selenium relationship. In a Cochrane Review including only randomized control trials with healthy adults (1,100,000 from 55 studies), no gender-specific relationship between DM and selenium was found (Vinceti et al., 2015). However, here again the results are not linear. In a study, conducted in three European countries and involving couples to study the differences between genders, it was found that women with metabolic syndrome had increased plasma selenium. This association remains significantly even after adjustments for age, country group, social status, physical activity, energy intake, alcohol consumption, smoking, menopausal status, uses of oral contraceptive pills or hormonal replacement therapy. (Arnaud et al., 2012).
This gender dimorphism has recently been described to happen only in young adults, with differences tending to disappear with advanced age. This study found a high correlation of total selenium and selenium protein P concentrations in young and elderly men, and in elderly women, but not in young women. This result suggests that selenium concentrations and correlations with diseases such as cancer or DM should be done with caution, especially in young women (Hybsier et al., 2017).
The literature is scarce in studies that report the effect of gender as a cofactor in the relationship between DM and selenium. In addition to there being little research in this area, those that exist do not have as main objective this evaluation. The effect of the gender is applied to study models as a possible confounder. As such it may be important to assess the associations of age, selenium and indicators of DM management, being age as a factor that influence these associations.
2.3.3. Age in Diabetes-Selenium Relationship
The literature is not clear, and very scarce in studies that report the effect of age as a factor in the relationship between DM and selenium. Most studies were done in animals and few were applied to human testing. Zelenka and Fajmonova, (2005), conducted a study in chickens, and showed that that selenium concentration in the body increased with age. Richie et al. (2012) conducted similar research in rats to study age related changes in selenium and prostate problems, and their results showed that there is a positive relationship between age and selenium levels, suggesting that this compound may be playing a mechanistic role.
However, and supporting what the animal testing studies indicate, selenium concentrations seem to increase in older human individuals. Recent studies, like the one conducted by Jain and Choi (2015) that included data from National Health and Nutrition Examination Survey for the period 2011–2012, were particularly important to identify the effect of age status on the levels of selenium. Results showed that levels of selenium were lower in adolescents aged 12–19 years than adults aged 20–64 years. Letsiou et al. (2014) study showed no age-related changes in selenium levels in their 400 individuals’ sample.
Older people usually have reduced appetite and energy expenditure, associated with a decline in their biological and physiological functions, such as the loss of lean mass, dysregulation of hormonal levels and cytokines, among others. Selenium is present in several proteins, called selenium-containing proteins. Thus, with a reduction in the intake of certain nutrients, the protein in the elderly is compromised and consequently the ingestion of selenium associated with it. However, these differences within studies may be partially explained because, recently a systematic review found that the ingestion of Se was significantly lower in elderly sarcopenic when compared to nonsarcopenic elderly. These associations may be explained by the potential action of Se on muscle tissue. It is believed that Se may have an effect on muscle synthesis and function through selenoproteins. Due to its cytoprotective properties, it has the ability to upregulate antioxidant selenium enzymes. Therefore, it is believed that with its supplementation can prevent certain metabolic diseases such as DM in elderly.
Chapter 3. METHODS
Research Design
This study will utilize data from the National Health and Nutrition Examination Survey (NHANES) 2015-2016. Details about NHANES can be found on the CDC/National Center for Health Statistics website, in National Health and Nutrition Examination Survey heading, at https://www.cdc.gov/nchs/nhanes.htm. This data source is provided by the NCHS, National Center for Health Statistics, a part of the Centers for Disease Control and Prevention (CDC). An overview of NHANES 2015-2016 follows.
NHANES
NHANES is a program that aims to assess the health and nutritional status of adults and children in the United States and employs a complex, multistage probability design. The project started in 1971 and took about four years until all samples were collected and organized. The project took between 4 and 6 years until the entire sample was collected and analyzed. From 1999 the project began to be held every year, which is usually the cycle of data collection. Since 1999-2000, the data from NHANES have been publicly available bi-annually, although data is collected every year (CDC, 2016).
Target Population
The sample includes civilian, non-institutionalized persons living in the 50 states and the District of Columbia. The NHANES sampling design has been changed regularly to sample larger numbers of the population subgroups ( Hispanics, non-Hispanic Blacks, Non-Hispanic Asians, older adults, and low-income Whites) to improve the reliability and precision of assessment of health status indicators particular public health interest.
Survey Objectives
The major objectives of NHANES are to:
· Estimate the number and percentage of persons in the U.S. population and in designated subgroups with selected diseases and risk factors;
· Monitor trends in the prevalence, awareness, treatment, and control of selected diseases;
· Monitor trends in risk behaviors and environmental exposures;
· Study the relationship between diet, nutrition, and health;
· Explore emerging public health issues and new technologies; and
· Provide baseline health characteristics that can be linked to mortality data from the National Death Index or other administrative records (e.g., enrollment and claims data from the Centers for Medicare & Medicaid Services).
Data Collection Procedure
For an effective sampling of the United States citizens, NHANES employs compound, multistage probability scheme. This entails: picking the Primary Sampling units in which case composed of regions or neighboring counties. After the selection, NHANES selects elements within these units which are composed of a number of families. Next is selecting particular families within the subdivisions. The last phase is selecting of persons within these families.
For surveys carried out in various thirty counties between 2015 and 2016, only 9,971 finished the interview with 9,544 being tested. In order to aid in oversampling of the Asian people, there was a translation of the review materials to Mandarin Chinese. To uphold cultural variances, the employees took part in traditional competency coaching. In this case, native translators were employed and given the required translation materials such as a list of vocabularies. Also, a medical expert translator cellphone service was availed as a backup of needs not catered for.
Sample
Participants will be selected from NHANES database according to the following inclusionary criteria: having selenium serum evaluation, fasting plasma glucose, HbA1c, having been diagnosed with DM as self-report in questionnaire, being aged 40 years old. All ethnicities and both genders will be included.
Exclusionary criteria are as follows: pregnancy, adults aged under 40 years, and missing data in the analyses. Any participant that does not have complete information on the demographic, laboratory, examination and questionnaire component of the survey will be eliminated from the study.
Data
Of the data collected in NHANES 2015- 2016, the following will be utilized: Socio-demographic data (age, gender, ethnicity, marital status, Pregnancy status, educational level, and annual household income), dietary intake, blood level of selenium, fasting blood glucose, hemoglobin blood A1C, and laboratory data.
Descriptions of how these data were collected follow
Socio-Demographic Data (age, gender,
ethnicity, educational level, and annual household income)
Qualified interviewers used a Computer-Assisted Personal Interview (CAPI) system to ask for family and sample personal demographic questionnaires. The respondents had to choose either English or Spanish. Hand cards with different choices printed in Spanish, English and Mandarin Chinese were used to for some of the questions. The survey participants were directed by the interviews in filling the appropriate hand cards based on their language of preference. In addition, the questioners aided the respondents by reading the choices recorded on the hand cards. Direct interviews involved emancipated minors and the people aged 16 and above were. A proxy was used for those aged below 16 years as well as those who could not respond to the questions by themselves.
Dietary Intake
The examination convention and information gathering techniques are completely recorded in the NHANES dietary questioner systems manuals (In-person meeting and telephone follow-up meeting).
The in-person meet was led in a private room in the NHANES MEC. A lot of estimating guides (different glasses, bowls, mugs, bottles, family unit spoons, estimating containers and spoons, a ruler, thickness sticks, bean sacks, and circles) was accessible in the MEC dietary meeting space for the member to use for detailing measures of sustenance’s (NHANES Measuring Guides for the Dietary Recall Interview). Endless supply of the in-person talk with, members were given estimating mugs, spoons, a ruler, and a sustenance display booklet, which contained two-dimensional illustrations of the different estimating guides accessible in the MEC, to use for detailing nourishment sums amid the phone meet. Phone dietary meetings were gathered 3 to 10 days following the MEC dietary meeting and were commonly planned on an alternate day of the week as the MEC meet. Just few members (n=120) were met around the same time of the week for both day 1 and day 2 meets because of their planning accessibility. Any member who did not have a phone was given a without toll number to call so the review could be directed.
What We Eat in America information was gathered utilizing USDA’s dietary information accumulation instrument, the Automated Multiple Pass Method (AMPM) (http://www.ars.usda.gov/nea/bhnrc/fsrg). The AMPM was intended to give productive and exact methods for gathering admissions for vast scale national reviews. The AMPM is a completely automated review technique that utilizes a 5-step meet delineated underneath:
1. Quick List – Participant reviews all nourishments and refreshments devoured the day preceding the meeting (midnight to midnight).
2. Forgotten Foods – Participant is gotten some information about utilization of sustenance’s generally overlooked amid the Quick List step.
3. Time and Occasion – Time and eating event are gathered for every nourishment.
4. Detail Cycle – For every nourishment, a nitty gritty depiction, sum eaten, and increments to the sustenance are gathered. Eating events and times between eating events are looked into to inspire overlooked nourishments.
5. Final probe – Additional sustenance’s not recalled before are gathered.
Blood levels of selenium
Inductively coupled plasma dynamic reaction cell mass spectrometry (ICP-DRC-MS) is observed to be one of the techniques which can be used when it comes to measuring of a panel of 3 elements. One has to pass a liquid sample into the nebulizer and the spray chamber which is then carried out by an argon stream. After these, there is use of radio-frequency power which leads to the creation of plasma and the process which follows is that the sample is atomized by the thermal power. This power also ionizes this sample into atoms. What follows is that the ions together with argon will enter into a spectrum which would lead to separation of ICP from the spectrum. The next step is that the ions usually passes through a Dynamic Reaction Cell which is quiet crucial when it comes to the separation of the isotopes of an element. Some of the isotopes which are measured is selenium (m/z 78).
Hemoglobin A1c (HbA1c)
Without any manual pre-treatment, both SA1c which is stable and LA1c which is liable, can be determined on a chromatogram. This allows accurate measurement of Hb1c, which is a stable form. Having a hemolysis solution, the analyzer is able to dilute the whole blood specimen. In addition, a slight quantity of the treated specimen is injected onto the HPLC analytical column. By maximumly exploiting the difference in ionic interactions between the hemoglobin components and the cation exchange group on the column resin surface, separation is attained. With the use of step-wise elution method, where elution buffers are used with distinct salt concentration, the hemoglobin functions( A1c) is successively eliminated from the column material. The photometer flow cell allows passage of the hemoglobin components and the analyzer alters in absorbance at 415nm. Moreover, the analyzer combines and decreases the raw data . it further calculates the percentage of each hemoglobin fraction. It takes 3 minutes for analysis to take place.
Fasting plasma glucose(FPG)
In an enzymatic method, hexokinase in the presence of ATP which is a phosphate donor converts glucose to glucose-6-phosphate. In the presence of NADP+, Glucose-6-phosphate dehydrogenase then transforms G-6-P to gluconate -6-p. during this conversion reaction, NADP+ is reduced to NADPH hence resulting to increase in absorbance at 340nm(secondary wavelength= 700nm) is calculated. This results to an endpoint reaction that is particularly for glucose.
3.5. Statistical Analysis
The SUDAAN Statistical Software for Analyzing Correlated Data will be used to organize and analyze the data extracted from the NHANES 2015-2016 database and to make it available to work for the study. Descriptive statistics according to data distribution will be used. Mean and standard deviation (±SD) as well as frequency distribution will also be used. To describe categorical data, proportions will be used. Chi-square tests will be conducted to test relationships across categorical variables. Linear regression correlation will be used to test relationships between continuous variables. Paired t-test will be used to test differences in mean HbA1c, selenium levels and glucose between groups. Throughout all analyses performed, a p-value of 0.05 and a confidence interval of 95% will be used to determine statistical significance.
Chapter 4. RESULTS
demographic Characteristics
The final sample comprised of one thousand, eleven hundred, and forty eight (1148) subjects. Tables 1 and 2 show the distribution of the demographic and socio-economic characteristics of the subjects. The majority of the subjects (74.8%) were between 40 and 69 years of age. The percentage of males was almost equivalent to females (49.9% vs. 50.1%, respectively). In terms of race/ethnicity, non-Hispanic Whites made up the highest percentage (34.5%) of the sample, followed by non-Hispanic Blacks, Mexican Americans, other Hispanics, and then those of other races (multi-racial).
Table 1. demographic Characteristics
| Number | Percent | |
| Age Group | ||
| 40-49 years | 284 | 24.7 |
| 50-59 years | 285 | 24.8 |
| 60-69 years | 290 | 25.3 |
| 70-79 years | 180 | 15.7 |
| 80 years or more | 109 | 9.5 |
| Total | 1148 | 100.0 |
| Gender | ||
| Male | 573 | 49.9 |
| Female | 575 | 50.1 |
| Total | 1148 | 100.0 |
| Race/Ethnicity | ||
| Mexican American | 194 | 16.9 |
| Other Hispanic | 169 | 14.7 |
| Non-Hispanic White | 396 | 34.5 |
| Non-Hispanic Black | 232 | 20.2 |
| Other Race – Including Multi-racial | 157 | 13.7 |
| Total | 1148 | 100.0 |
Table 2 illustrates the educational levels, and the annual household incomes of the subjects. Regarding education levels, the largest percentage (48.5%) had at least a high school graduate/GED to some college or an AA degree. The annual household income level varied considerably, with the highest percentage of subjects (20.2%) earning annual incomes of $19,999 or less, followed by 17.0% earning $100,000 or more, with the lowest percentage (3.4%) earning $20,000 or more, but less than $75.000.
Table 2. Socio-economic Characteristics
| Number | Percent | |
| Educational Level | ||
| Less than 9th Grade | 167 | 14.5 |
| 9th-11th Grade (includes 12th grade with no diploma) | 141 | 12.3 |
| High School Graduate/GED | 266 | 23.2 |
| Some College or AA Degree | 290 | 25.3 |
| College Graduate or Above | 283 | 24.7 |
| Not Reported | 1 | 0.1 |
| Total | 1148 | 100.0 |
| Annual Household Income | ||
| $19,999 or less | 232 | 20.2 |
| $20,000-34,999 | 205 | 17.9 |
| $35,000-54,999 | 185 | 16.1 |
| $55,000-74,999 | 115 | 10.0 |
| $20,000 or more | 39 | 3.4 |
| $75,000-99,999 | 100 | 8.7 |
| $100,000 or more | 195 | 17.0 |
| Not Reported | 77 | 6.7 |
| Total | 1148 | 100.0 |
Selenium Intake
Selenium intake (mcg) based on A1C for males and females is presented in Table 3. Mean selenium intake was, 8.31.7 mcg for females with diabetes, 6.30.8 mcg for those with pre-diabetes and 5.70.7 mcg in (nondiabetic) females. Among males, mean intake was 7.51.3 mcg in (diabetic), 10.01.1 mcg in (prediabetic) and 10.51.4 mcg in (nondiabetic). Similarly to these findings, selenium intake based on the FPG for the females according to results in Table 4 was 5.51.5 mcg in (diabetic), 6.51.1 mcg in(prediabetic), and 6.81.2 mcg in (nondiabetic) while for the males was 9.41.7 mcg in (diabetic), 8.01.3 mcg in (prediabetic), and 11.52.2 mcg in (nondiabetic). Although selenium intake in females seemed to trend downward from diabetic to non-diabetic, there are no significant differences in selenium intakes by diabetes classification based on A1C or FPG in either gender.
Table 3. Selenium Intake (mcg) by Gender and Disease Classification Based on A1C Levels
| Females | ||
| Diabetic1Mean±SEMn=88 | Prediabetic2Mean±SEMn=198 | Nondiabetic3Mean±SEMn=243 |
| 8.3a1.7 | 6.3a0.8 | 5.7a0.7 |
| Males | ||
| Diabetic1Mean±SEMn=104 | Prediabetic2Mean±SEMn=214 | Nondiabetic3Mean±SEMn=219 |
| 7.5a1.3 | 10.0a1.1 | 10.5a1.4 |
1A1C≥6.5% 2A1C≥5.7% &<6.5%) 3A1C<5.7%
aNo significant differences found (p>0.05)
Table 4. Selenium Intake (mcg) by Gender and Disease Classification Based on Fasting Plasma Glucose (FPG) Levels
| Females | ||
| Diabetic1Mean±SEMn=43 | Prediabetic2Mean±SEMn=111 | Nondiabetic3Mean±SEMn=89 |
| 5.5a1.5 | 6.5a1.1 | 6.8a1.2 |
| Males | ||
| Diabetic1Mean±SEMn=61 | Prediabetic2Mean±SEMn=127 | Nondiabetic3Mean±SEMn=79 |
| 9.4a1.7 | 8.0a1.3 | 11.5a2.2 |
1FPG≥126.0 mg/dL 2FPG≥100.0 & <126.0 mg/dL 3FPG<100.0 mg/dL
aNo significant differences found (p>0.05)
Serum Selenium Levels and Gender
Table 5 shows serum selenium levels by A1C levels and gender. Serum selenium (mcg/L) by A1C Levels for females was 130.31.7 (diabetic), 128.51.2(prediabetic) and 125.70.9 (nondiabetic) while for the males was 138.81.7(diabetic), 130.01.0(prediabetic) and 129.41.1(nondiabetic). No significant differences in serum selenium were found among diabetic, prediabetic and nondiabetic females. However, males with diabetes had significantly higher serum selenium levels than those who were prediabetic or nondiabetic.
Table 5. Serum Selenium (mcg/L) by Gender and Disease Classification Based on A1C Levels
| Females | ||
| Diabetic1Mean±SEMn=97 | Prediabetic2Mean±SEMn=212 | Nondiabetic3Mean±SEMn=266 |
| 130.3a1.7 | 128.5a1.2 | 125.7a0.9 |
| Males | ||
| Diabetic1Mean±SEMn=108 | Prediabetic2Mean±SEMn=231 | Nondiabetic3Mean±SEMn=234 |
| 138.8a1.7 | 130.0b1.0 | 129.4b1.1 |
1A1C≥6.5% 2A1C≥5.7% &<6.5%) 3A1C<5.7%
a,bMeans with different letters as superscript are significantly different (p0.05)
Serum selenium levels by FPG levels and gender are presented in Table 6 serum selenium (mcg/L) by FPG Levels for females were 130.22.7 (diabetic), 128.91.3(prediabetic) and 128.01.7 (nondiabetic) while for the males were 140.32.3(diabetic), 129.01.2(prediabetic) and 128.61.6 (nondiabetic). No significant differences in serum selenium were found among diabetic, prediabetic and nondiabetic females. However, as with selenium levels by AIC level, males with diabetes had significantly higher serum selenium levels than those who were prediabetic or nondiabetic.
Table 6. Serum Selenium (mcg/L) by Disease Classification Based on Fasting plasma Glucose (FPG) Levels
| Females | ||
| Diabetic1Mean±SEMn=47 | Prediabetic2Mean±SEMn=119 | Nondiabetic3Mean±SEMn=95 |
| 130.2a2.7 | 128.9a1.3 | 128.0a1.7 |
| Males | ||
| Diabetic1Mean±SEMn=64 | Prediabetic2Mean±SEMn=133 | Nondiabetic3Mean±SEMn=82 |
| 140.3a2.3 | 129.0b1.2 | 128.6c1.6 |
1FPG≥126.0 mg/dL 2FPG≥100.0 & <126.0 mg/dL 3FPG<100.0 mg/dL
a,b,cMeans with different letters as superscript are significantly different (p<0.05)
Selenium Intake and Age
Table 7. Selenium Intake (mcg) by Age in Subjects with Diabetes Classified on the Basis of A1C Levels1
| Age | ||||
| 40-49 yearsMean±SEMn=24 | 50-59 yearsMean±SEMn=45 | 60-69 yearsMean±SEMn=71 | 70-79 yearsMean±SEMn=37 | ≥80 yearsMean±SEMn=15 |
| 17.6a±4.6 | 7.6b±2.0 | 6.8b±1.6 | 4.6b±1.7 | 5.6b±2.2 |
1A1C≥6.5%
a,bMeans with different letters as superscript are significantly different (p<0.05)
Table 8 shows selenium intake levels by age for subjects with diabetes classified on the basis of FPG levels. Mean selenium intake ranged from 16.8±6.6 to 7.4±3.6 mcg with subjects aged 40-49 years had a significantly higher mean selenium intake 16.8a±6.6 mcg than those aged 50-59 (5.8±1.7 mcg) years.
Table 8. Selenium Intake (mcg) by Age in Subjects with Diabetes Classified on the Basis of FPG Levels1
| Age | ||||
| 40-49 yearsMean±SEMn=11 | 50-59 yearsMean±SEMn=23 | 60-69 yearsMean±SEMn=35 | 70-79 yearsMean±SEMn=24 | ≥80 yearsMean±SEMn=11 |
| 16.8a±6.6 | 5.8b±1.7 | 7.5ab±2.0 | 6.2ab±1.9 | 7.4ab±3.6 |
1FPG≥126.0 mg/dL
a,bMeans with different letters as superscript are significantly different (p<0.05)
Serum Selenium Levels and Age
Serum selenium levels by age are presented in Tables 9 and 10. Specifically, Table 9 shows serum selenium levels by age for subjects with diabetes classified on the basis of A1C levels. The mean serum selenium was from133.0±3.4 mcg/L to 131.7±3.5 mcg/L. Subjects aged 70-79 years had a significantly higher mean serum selenium 138.2±2.5 mcg/L than those aged 40-49 years 133.0±3.4 mcg/L.
Table 9. Serum Selenium (mcg/L) by Age in Subjects with Diabetes Classified on the Basis of A1C Levels1
| Age | ||||
| 40-49 yearsMean±SEMn=25 | 50-59 yearsMean±SEMn=47 | 60-69yearsMean±SEMn=74 | 70-79 yearsMean±SEMn=39 | ≥80 yearsMean±SEMn=20 |
| 133.0a±3.4 | 136.4a±3.1 | 133.4a±2.0 | 138.2b±2.5 | 131.7a±3.5 |
1A1C≥6.5%
a,bMeans with different letters as superscript are significantly different (p<0.05)
Table 10 shows serum selenium levels by age for subjects with diabetes classified on the basis of PFG levels. The mean serum selenium was from132.5±6.5 mcg/L to 135.4±4.7 mcg/L. Subjects aged 70-79 and 80 and above years old had a significantly higher mean serum selenium 137.6±3.7, 135.4±4.7 mcg/L, respectively than those aged 40-49 years 132.5±6.5 mcg/L.
Table 10. Serum Selenium (mcg/L) by Age in Subjects with Diabetes Classified on the Basis of Fasting Plasma Glucose (FPG) Levels1
| Age | ||||
| 40-49 yearsMean±SEMn=12 | 50-59 yearsMean±SEMn=24 | 60-69 yearsMean±SEMn=36 | 70-79 yearsMean±SEMn=25 | ≥80 yearsMean±SEMn=14 |
| 132.5ab±6.5 | 138.1ab±3.9 | 134.9ab±3.2 | 137.6a±3.7 | 135.4b±4.7 |
1FPG≥126.0 mg/dL
a,bMeans with different letters as superscript are significantly different (p<0.05)
Chapter 5. DISCUSSION
Concerted efforts to establish the relationship between serum selenium and biochemical indicators (fasting blood glucose and hemoglobin A1C) of DM management with respect to age or gender have not been conclusive. Most of the researchers have thrown their weight and explored the relationship between serum selenium levels and DM but not much has been done with respect to age and gender. Therefore, this study aimed to assess the relationship between serum selenium levels and DM with age and gender as potential predictive factors. The relationship between selenium and DM remains complex and this has resulted in mixed views amongst researchers with some suggesting that selenium levels are highest in the males with others holding the contrary opinion. Similarly, studies have revealed a positive association between selenium level and DM with others suggesting a contrary view, hence the matter of the controversy.
The current study utilized a sample from NHANES (2015-2016) of one thousand, eleven hundred, and forty eight (1148) subjects, aged ≥ 40 years old which was comparable to some of the studies that investigated the relationship between serum selenium and diabetes such as a cross sectional analysis of one thousand, eleven hundred, and fifty nine (1159) adults ≥40 years old from NHANES 2003–2004 (Laclaustra, Navas-Acien, Stranges, Ordovas, & Guallar, 2009). Additionally, a prospective cohort analyses study reporting of 142,550 person of U.S. men and woman (Park et al., 2012). However, the current study is not comparable in sample size to previous studies that examined the variables in question. Demographic characteristics were much different either in population, age or gender such as a longitudinal cohort of 1925 Swedish men who were 50 years old and did not have diabetes at baseline (H. Gao et al., 2014).
In examining selenium intake, subjects were found to have levels (quantify how far below or above RDA) for Exploration of a possible gender difference in intake yielded no significant differences in selenium intakes by diabetes classification based on AIC or FPG. (Prior to talking about differences, tackle the issue of Se intake itself. This was your first objective of the study. Was intake lower/higher than RDA etc., and possible explanations for the findings. How did your findings for intake compare to earlier studies (with references)?)
The results (Specifically which results?)of this study are similar to several previous research studies. First, they are supported by a study that was carried out in Italy involving a cohort that was followed for 16 years with 226 with 2 DM and 395 age-matched control women. This study from Italy established that there is no correlation or association between the selenium and diabetes mellitus (Vinceti et al, 2015). Similarly, Swedish study that involved 1925 men that were 50 years and above established that there was no correlation between selenium concentration and DM (H. GAO Et Al., 2014). Furthermore, another study showed that taking selenium supplements significant effects on fasting plasma glucose (FPG) hence supporting the results presented in table 4(Bahmani, Kia, Soleimani, Asemi &Esmaillzadeh, 2016).
A key objective of the current study was to assess the impact of gender on serum selenium levels. It is known that selenium is an essential trace mineral known for its antioxidant function that plays a protective role against diabetes mellitus, and authors have suggested that levels might therefore be higher/lower (indicate the correct one ) in people with diabetes (There needs to be a statement to make the connection between what exists in the literature and what you found) .However, the results from this study (Table 5) suggest otherwise in that that there was no significant difference in serum selenium among diabetic, prediabetic and nondiabetic females. More interestingly, males with diabetes had significantly higher serum selenium levels than those who were prediabetic or nondiabetic.
Findings from the current study can be compared to a 2009 study byLaclaustra and colleagues. The researchers examined participants (State the number of participants).and found a correlation between T2DM risk and high selenium in males, but not females, a finding which was reported earlier (NHANES 2003–2004) by the same authors. Similarly, another study reported a correlation between lower baseline selenium (GPx1 polymorphisms) and T2DM incidence among elderly French men, but not women (Akbaraly et al., 2010). Wei et al. (2015), found a small positive association between selenium intake and DM in males, but this did not reach statistical significance. In females, a significant positive association between selenium intake and DM was not found. Although these earlier studies support the results of the current study, the sexually dimorphic association between selenium and metabolic diseases remains a complex process that demands more research to explain some of the issues that surround metabolism across the sexes and how they influence the production of insulin. The possible variation that has been identified by both previous and current studies may be defined by the differences in the immunity and reproduction processes across the sexes (Schomburg & Schweizer, 2009b).
In a Cochrane review including only randomized control trials with healthy adults (1,100,000 from 55 studies), no gender-specific relationship between DM and selenium was found (Vinceti et al., 2015). This gender dimorphism tends to appear in young adults but disappears as one advances in age and this calls for caution in considering correlations between serum selenium and DM.
Lastly this study sought to establish whether there was a relationship between serum selenium and biochemical factors FBG and AIC taking into consideration the age factor. This is the area that has limited research. However, this study considered age a crucial factor largely in part due to the understanding that the metabolic process and rate (Of what? ) differs with age and therefore it is necessary to consider aspect of age. In the current study, (results presented in Table 9) we examined serum selenium levels by age for people with diabetes classified on the basis of A1C levels and found that subjects aged 70-79 years had a significantly higher mean serum selenium than those aged 40-49 years (What about the other age clusters?). Also, serum selenium levels by age for diabetics classified on the basis of PBG levels. Subjects aged 70-79 and 80 and above years old had a significantly higher mean serum selenium than those aged 40-49 years. These (Were there any human studies to which you could compare these findings? If not state that to the best of out knowledge, there were no studies in humans that examined this question, however, research with animal models showed…) findings corroborated those of a previous a study that was carried out in the chickens, which showed that selenium concentration in the body increases with age (Richie et al, 2012). Another study was conducted on the rats which showed that there was a positive relationship between age and selenium.
In humans, advanced age is associated with reduced appetite and energy expenditures which are associated with reduced biological and physiological functions, such as loss of lean mass, dysregulation of hormonal levels and cytokines, among others.. This has implications for selenium in that it might be necessary to advise reduced intake of serum selenium in people who are 80 years old and above because instead of playing a protective role, High Se intake might increase susceptibility to DM or complicate management of the disease. This (Will need a reference for this relationship.) could be the reason why patients at an advanced age have higher serum selenium. (Would like to see additional discussion here, as this was your major finding. If there is no explanation in the literature, state that, and the fact that this warrants further investigation.)
From the above analyses, considering all the factors, that a higher serum selenium level might be associated to some degree, with diabetes manifestation. However, as with many of the studies on selenium and DM there remains a gap in understanding the specific mechanism of action.are inconclusive. (What about the differences seen in results using FBG vs A1C? Important to address this, especially as you included it as an area for future research.)
DISCUSSION OF HYPOTHESES
In hypothesis one, it was posited that there are gender differences in the concentration of serum selenium in persons with DM aged 40 years, with females having significantly lower serum selenium levels. However, in this study, there was no significant difference in selenium intakes by diabetes classification based on A1C or FPG in either gender. Selenium intake by A1C levels and gender and as well as Fasting Plasma Glucose (FPG) levels and gender, was the same in males and females. The results failed to support hypothesis one. The evidence from the sample did not support that females indeed have lower serum selenium concentration than males.
The second hypothesis posited that selenium intake in both men and women, either from food and/or supplements, will exceed the RDA by more than double. There will be significant differences between females and males regarding selenium intake with female consuming significantly less selenium than males. Considering (What was the actual finding?) this hypothesis, the result of this study did not support the hypothesis. The same results were repeated on for serum selenium levels by FPG levels and gender. Based on these results hypotheses two was/was not (Which one?(
supported.
The third hypothesis stated that higher selenium levels will be associated with higher fasting plasma glucose levels. The relationship will be stronger in males compared to females. From the results of this study, there is no evidence that supported hypothesis three. On the contrary, when assessing serum selenium levels by FPG and gender, the results showed that there was no significant difference in selenium levels among diabetic, prediabetic and nondiabetic females. From these results, no relationship was found to support that higher selenium levels were associated with higher fasting plasma glucose. The results failed to support hypothesis three.
The fourth hypothesis posited that there will be a positive relationship between HbA1c and serum selenium levels in males and females with DM aged 40 years old. The relationship will be stronger in males versus females. There was no significant difference in HbAIc and serum selenium levels in females with DM aged 40 but there was a significant difference in HbAIc and serum selenium in males with DM aged 40. This shows that results supported only part of the hypothesis and did not meet the other part of the hypothesis. However, we can deduce that the relationship was stronger (Not from the results reported here. It does not appear that you did a correlation to determine the strength of the relationship. Check your analyses to confirm)in males than females because part of the males partly satisfies the hypothesis.
The fifth hypothesis stated that age will be related to serum selenium levels in people with DM. Oldest age clusters ( 80 years old) will have the highest serum selenium levels. In this study, it was found that the oldest clusters 80 years old and above with DM had the highest levels of serum selenium levels. According to the results serum selenium levels by FPG for the people with diabetes, age group 70-79 and 80 years old above had significantly higher serum selenium. On the basis of this finding the hypothesis was accepted.
CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS
Conclusions
In conclusion, this study demonstrated the following:
1. There were no gender differences in the concentration of serum selenium in persons with DM aged 40 based on A1C or FPG. What about intake? List in order of objectives.
2. For serum selenium levels by A1C levels and gender, there were no significant differences in serum selenium found among diabetic, prediabetic and nondiabetic females
3. For serum selenium levels by A1C levels and gender, diabetic males had significantly higher serum selenium levels than those who were prediabetic or nondiabetic (p0.05).
4. For serum selenium levels by fasting plasma glucose (FPG) levels and gender, there were no significant differences in serum selenium found among diabetic, prediabetic and nondiabetic females
5. For serum selenium levels by fasting plasma glucose (FPG) levels and gender, males with diabetes had significantly higher serum selenium levels than males without diabetes..
6. Considering serum selenium by age in diabetics classified on the basis of fasting plasma glucose (FPG) Levels, oldest age clusters 70-79 and 80 years old had the highest serum selenium levels compared to those in the other age groups.
Recommendations
Based on the results of this study, the following recommendations are made for future studies:
1. Given the complexity of age in underrating how intake of the serum selenium levels affects the DM, more research needs to be done to dig deeper into how intake of the serum selenium (?? )is distributed across the ages and how different levels identified across the gaps can be explained in detail.
2. Given the different results for serum selenium levels by FPG levels and gender where there was no significant difference on the part of the female but a significant difference for the males, who had higher serum selenium, additional research is warranted to help identify why this disparity exists across the genders, and to elucidate potential implications.
3. When using A1C and FPG as indicators of DM managements, each showed varied results. There is a need for research to carry out a detailed analysis of what causes variety when A1C and FPG are used. A detailed explanation will be critical for this case.
4. Lastly, there is a need to demystify the relationship between males and females and how sex factor influences the intake of serum selenium. In this question, research will try to explain dimorphism of the sex or gender on DM and how they sequentially relate to A1C, FPG, serum selenium and selenium intake levels.
Limitations of the Study
As with most studies, this research comes with no exception with regard to limitations. First, the causal relationship between serum selenium and DM has not been established in this research. No general conclusion that has been reached whether indeed serum selenium causes DM because of the conflicting perceptions that at some point it causes DM and other times it plays a role of protecting DM. Second, this study has failed to bring to attention the concept of the insulin which is associated with diabetes. It was significant to have this included to help understand how it influences diabetes what necessary intervention that can be taken to increase or reduce insulin in the body. Third, this study has failed to establish the risks associated with and also provide human intervention mechanism that can be applied to mitigate this particular risk. Lastly, there might be unmeasured factors thus indicating residual effects in this study. For example, there were potential influences of long-term disease on lowering serum selenium levels over time, but we did not collect time elapsed from disease diagnosis among diabetic individuals.
