Effect of Vitamin D Adjunct Therapy on the Progression of Latent Tuberculosis to Active Disease: a Double-Blind, Randomized Control Trial
Assessment of the relationship between the progression of tuberculosis and vitamin D deficiency could be a meaningful way of determining the intervention measures of such progression. The aim of this paper is to determine the effect of Vitamin D Adjunct Therapy on the Progression of Latent Tuberculosis to Active Disease with Double-Blind, as a randomized control trial. To obtain perfect results, a sample size of 221 participants was used. …..
Tuberculosis (TB) is typically caused by a number of mycobacteria strains, especially the Mycobacterium tuberculosis. The disease is widespread and usually fatal. It mainly targets the human lungs. The disease can however other body parts. The main mode of spreading the disease is through the air as tuberculosis patients sneeze, cough, or transmit their respiratory fluids to other people through the air. TB has been found to be a result of immortality. In the year 2009, it caused close to 1.68 million deaths globally (Martineau, 2012). It has been estimated that the world prevalence of latent Mycobacterium tuberculosis infections stands at about 32 per cent. TB is also considered dangerous in the sense that it remains the second leading cause of death from a single infectious agent, specifically after HIV/AIDS. According to Martineau (2012), latent Mycobacterium tuberculosis carries a 5 to 20 per cent lifetime reactivation disease risk. In other words, one in every three people around the world has latent TB. In this case, the infection is contained by the immune system such that, those individuals having the latent TB do not develop or spread the disease. About 10 per cent will progress from latent to active TB but the risk is greater among those with certain risk factors, such as HIV/AIDS and tobacco usage. Those who develop active TB may only portray mild symptoms for several months. During this time, such patients can infect between 10 and 15 people every year.
Interleukin-32 & Vitamin Background
Drug-resistant organisms’ emergence triggered the development of new and better agents for enhancing antimicrobial response as with respect to active TB therapy (Martineau, 2012). Vitamin D was used for the treatment of TB during the pre-antibiotic era. Studies have gone to an extent of identifying a certain protein that seems to play a major role in the protection of people infected with the Mycobacterium tuberculosis. Mycobacterium tuberculosis is the bacteria, which causes TB, including the development of the active form of TB. Interleukin-32 is said to have been discovered and considered as a biomarker of enough host defense against tuberculosis ( University of California – Los Angeles Health Sciences, 2014). According to the University of California – Los Angeles Health Sciences (2014) an “An estimated one-third of the world’s population is infected with tuberculosis, but the disease is latent in 90 to 95 percent of infected people, meaning that they experience no symptoms and are not contagious.” The study confirmed that interleukin-32 assists in the maintenance of such latent state as well in the prevention of active TB infection. It was estimated that about 9 million individuals around the world suffered from TB in 2012. In the same case, about 1.3 million deaths related to TB were recorded ( University of California – Los Angeles Health Sciences, 2014).
As TB re-emergence as a global health threat, a new urgency that develops new approaches for identifying people at risk maintained that treat active diseases and immunity have arisen with the past few years. It has therefore become easier for doctors to identify the people with the greatest risk regarding TB infection. Such discovery could further provide the way forward to new strategies for the treatment of TB. The importance of people having to maintain sufficient vitamin D levels as a way of combating the TB causing pathogen could ultimately be underscored. Interleukin-32 as a protective protein has been found to have the capabilities of inducing the termination of TB bacterium when sufficient vitamin D is present ( University of California – Los Angeles Health Sciences, 2014).
Due to the lack of proper ways of differentiating people with latent TB from healthy people, has been difficult to determine the difference between the two groups. Proper ways of predicting the reason for not developing active TB among the latently infected individuals has been discovered ( University of California – Los Angeles Health Sciences, 2014). It is highly astonishing today regarding the many differences found to exist between health people and people with latent TB. Studies examining the gene expression profiles specifically in people with latent TB and others with active TB showed that interleukin-32 was responsible for the latent TB because it acts as a protective marker for host defense.
Such discoveries show that people with latent TB could have immune systems that are activated. The immune systems in such cases are responsible for protecting from active TB infection, which could consequently develop. The breakthrough in this case came as a result of interleukin-32, which seem to be used to cover the reliance of vitamin D as a therapy for TB ( University of California – Los Angeles Health Sciences, 2014). In essence, high levels of interleukin-32 make people most likely to develop latent TB. In this regard, it is difficult for such people to develop active TB because interleukin-32 has the ability of stimulating an immune system that is capable of killing the TB-causing bacteria. Nevertheless, this is only found to act effectively when sufficient levels of vitamin D are present. The need for vitamin D to be present depicts the association between IL-32 and the vitamin D antimicrobial pathway ( University of California – Los Angeles Health Sciences, 2014).
Vitamin D is known to be produced in the human skin. The production of vitamin D occurs after exposure to sunlight. Insufficient production of vitamin D, especially among dark-skinned people, leads to high risk of developing active TB ( University of California – Los Angeles Health Sciences, 2014). A study by the University of California – Los Angeles Health Sciences ( 2014), pointed out that “American adults — particularly members of ethnic minorities with darker-pigmented skin and lighter-skinned people who receive minimal sun exposure — lack sufficient levels of vitamin D, and vitamin D deficiency has been found to be associated with a higher risk for active TB. “ The assertions in this case seem to point out that both interleukin-32 and vitamin D are critical in the prevention of latent TB development to active TB among people.
Vitamin D has been known to promote the immune response to TB in both vitro and several clinical trials that have been established to examine the effect of vitamin D adjunct therapy in treating active TB. Nevertheless, vitamin D alongside its metabolites do not posses any antimycobacterial activity especially in the absence of cells (Martineau, 2012). Its active metabolite, known as 1,25-dihydroxyvitamin D has been known to induce antimycobacterial activity in molecular phagocytes (Martineau, 2012). When the levels of vitamin D are typically low, IL-32 is found to be incapable of killing the TB-causing bacteria. Vitamin D is thus applicable for further IL-32 development since IL-32 has been known to be a potential diagnostic marker in specifying the patients having latent TB, but they are at a risk of developing active TB. Besides, it has been identifies as useful and a promising therapy for the treatment of TB through the stimulation of antimicrobial activity.
This study is based on the notion that none of the previous studies has examined the effect of vitamin D on the progression of latent TB to active disease. This aspect has lead to the research gap being studied. A unique opportunity for exploring vitamin D adjunct therapy is thus presented. The opportunity could probably improve outcomes in people with latent TB and prevent them from developing active TB. The usefulness of vitamin D has been shown on other diseases. Studies related to what is being studied tried to figure out whether vitamin D supplementation slows the progression increase the quality of life among patients with latent or active TB infection (Lönnermark, et al., 2012).
In the study of the effect of vitamin D adjunct therapy on the progression of Latent TB to active TB using a Double-Blind Randomized Control Trial, the following advanatages are realized. They include:
There are also some benefits attributed to the same study. Such benefits include:
The study was done to accomplish some specific aims. The key aim was to evaluate the effects of vitamin D adjunct therapy on the progression of latent TB to active TB. The study also aims to evaluate the efficacy of TB progression intervention when some key potential risk factors in the progression of latent TB to active TB are present. The major risk factors considered include HIV/AIDS, tobacco use, IL-32 gene expression, and vitamin D status.
The primary design for the study will essentially be the simple parallel-group design. Simple parallel-group design involves a clinical study in which two treatments are compared. In this study, a test therapy will be compared with a standard therapy. The allocation of the subjects being studied will be achieved through randomization. Two groups would be incorporated, which include the control group and the treatment group. Stratified randomization would be incorporated in the primary design. This will involve constrained randomization whereby strata would be constructed with respect to the prognostic variables used, like age and gender, and their respective values. A specified number of strata would then be constructed based on the two prognostic variables for both the treatment and the control groups (Walter N. Kernan, Makuch, Brass, & Horwitz, 1998). The study will be double blinded in that both the researcher and the subjects participating in the study will be unaware of the nature of the intervention being provided. The double-blind trials will produce objective results because the expectations of both the researcher and the participants will not have any effect on the study outcomes (Friedman, Furberg, & DeMets, 2010). The overall combination of these aspects of the primary design will ensure that the study has sufficient power as well as enhancing the possibility of sub-group analysis. The primary design will be as presented in figure 1 below.
Figure 1: The Primary Design
Why Simple Parallel-Group Design
The design choice is made because it is usually straightforward, ensures easy allocation, and it will have the ability to ensure follow-up. The design does not require an equal number of subjects in each group, despite that similar numbers are often observed. This aspect will be important for the actual study because the sample size involves an odd number of subjects. Essentially, 221 respondents will be used for the study (Foulkes, 2008).
The design will be useful because the study will involve a randomized control trial. The implication in this case is that other common designs will not be idea or appropriate for the study. Some comparable design that are not appropriate include withdrawal design, randomized content design, crossover design, factorial design, and cluster-randomized design. Regarding the withdrawal design, it is considerably unethical to discontinue treatment unless infection has fully resolved. The randomized consent design in on the other hand not blinded, while for this study, a double-blinded design is required (Foulkes, 2008). The crossover design takes much time to initiate conversion from latent TB to active TB. The last two designs are not idea as well because for the factorial design, the proportion of individuals expressing IL-32 would remain as unknown, while the cluster-randomized design applies best for a group level implementation in which capturing the level of population effects is critical.
Why Stratified Randomization?
Typically, any human population sample obtained through randomization would consist of both males and females. Such sample would also consist of subjects of various age groups. Stratified randomization would thus be important in identifying these two prognostic variables that characterize any human population sample. Stratified randomization would therefore be important for the researcher to incorporate various strata regarding the age and gender of the 221 subjects in the study sample (Walter N. Kernan, Makuch, Brass, & Horwitz, 1998). Such prognostic variables may come out to be major determinants and contributors of the final results especially given the double-blinded nature of the study. Essentially, it will create even-sized groups as well as balanced variables that could affect the study outcome.
Why Double Blinding?
The study will be carried out to establish a solution to a problem that has never been covered before in depth. The existence of research gap regarding the study topic implies that a double blind study is necessary since both the researcher and the respondents are not aware of the study results. Various control variables will be incorporated to enhance the study outcome. Double blinding will therefore attempt to eliminate subjective and unrecognized biases that would be initiated by the study subjects and the researcher (Friedman, Furberg, & DeMets, 2010).
An effective recruitment will involve a systematic enrollment process. The enrollement process will be initiated within a period of eighteen months. The enrollment will be done in various hospitals and clinics within the state of Georgia. This will be done by various team members. The key team members will include mainly professionals from the Schools of Medicine and Public Health in the Grady Health System and Emory University. An additional support as well as expertise will be required. These will be obtained from Atlanta TB Prevention Coalition members who include mainly the CDC. They will collaborate with the professional for better results. The recruitment process will be facilitated by assistance from the Georgia’s Department of Public Health.
Screening will be critical in the enrollment criteria. The screening process will be done to determine the health status of the subjects. This will be a meaningful way of determining the subjects to be included in the control group and those belonging to the treatment group. Adult with new diagnosis cases of TB will be considered for the enrollment. The identification criteria will be through positive tuberculin skin test (TST). Interferon-gamma release assay, and specifically the QuantiFERON-TB Gold In-tube test. While in many cases screening is done to determine potential subjects, it will mainly be used to group the subjects accordingly. The screening will test the IL-32 gene expression profiles among the subjects. This will be done irrespective of their prevailing health conditions. Basically, the eligibility requirements will determine the exclusion criteria to be used (Van Spall, 2007). The major criteria to be used would include a subject’s active disease, BCG vaccination, vitamin D deficiency, as well as by individuals taking certain antiretrovirals and who can hardly make adjustments to their dosages.
Determination of the sample size would involve choosing the number of subjects to take part in the study for a meaningful statistical sample. In the study, determining the sample size will be critical for the empirical analysis. For this study, a sample size of 221 participants would be adequate. A superior trial will be used in this case. The use of superior trial will be done to demonstrate the effectiveness on one intervention compared to the other with respect to the determined sample size. To determine the effectiveness of one intervention method, the following hypotheses would be formulated.
Hypotheses for Superiority Trial and the respective formulas
Ho: PN = PS
Ha: PN ≠ PS
Where: PN = the proportion of subjects with outcomes in new treatment arm
PS = the proportion of subjects with outcome in the standard treatment arm
The following formula would be used to calculate the statistical superiority for the random sample:
The clinical superiority for the random sample will as well be calculated using the following equation (Zhong, 2009):
Where: α =0.05
Zα/2 = 1.96
Zβ = 0.84
Z1- α = 1.645
p = old treatment = 0.6
P0 = new treatment = 0.42
d = effect difference = 0.6 – 0.42 = 0.18
δ0 = clinically acceptable difference = 0.048
Based on the above values, the statistical superiority would be lower than the clinical superiority with 89 and 170 respectively.
The study will be based on two major arms. These arms include the treatment arm and the control arm. The treatment arm includes the subjects whose reaction is expected to change with respect to the experiment activities. The treatment are and its results are based on the findings obtained from the control arm whose results are definite. The treatment arm will that include subjects with directly observed rifabutin and isoniazid. In this case, the rate will be 900mg per week for a period of three months and not inferior to SOT Isonaizid over a period of six months. The treatment arm will also include subjects adminstering vitamin D adjunct therapy at the rate of 70,000 IU per week within a period of three weeks. The control arm will include subjects with directly observed rifabutin plus isoniazid at 900 mg per week within a period of three weeks. This will also include placebo, which will entail blinding.
A follow up will be necessary in order to ensure that each subject responds to the study accordingly. The follow up will ensure that meaningful and unbiased results are obtained. This will be done in such a way that after treatment and every six months thereafter, the participants are tested for active disease. Testing for active TB will be done through Xpert MTB or through the RIF assay technique. Termination will then occur after active TB disease has been confirmed. This could as well be done after a period of three years.
Data analysis will be done using both primary and secondary data analysis techniques. The primary data analysis will be the fundamental way of determining the problem being studied based on the basic variable. Secondary analysis will involve the same analysis, but after incorporating the secondary variables.
Primary Data Analysis
Primary data analysis will involve a comparison of the proportion of subjects who progressed to active disease while receiving vitamin D adjunct therapy to those receiving standard antibiotic therapy. This analysis will assume that other factors/variables do not contribute to the progression from latent TB to active TB. Only the primary variables will be considered for this analysis.
Secondary Data Analysis
Secondary data analysis will incorporate other variables that could contribute to the progression of latent TB to active TB disease. In this case, a comparison of the interaction of vitamin D adjunct therapy with HIV/AIDS, tobacco use, & IL-32 gene expression will be examined. The key idea in this case will be to determine the extent to which each of these secondary variables inter or promote the progression process of latent TB to active TB among the subjects being studied.
Methods to be used
The choice of research method will be based on various factors. One of such factors is time and money availability whereby it would be necessary to carry out the study with respect to the set budget and time. Besides, it would be critical to consider the aims of the study. The research may be forced to try and twist the research data so it may conform to the research hypothesis. This requirement implies that an applicable method would be necessary. The method chosen would have to come up with the best results possible. Another consideration for the methods choice would be the researcher’s or participants’ know about the problem being investigated. The use of the double-blind technique is based on this aspect. For instance, interviews may not be used because the researcher and the participants do not know about the study outcome, especially given that medical investigation on each subject would be performed.
Typically, logistic regression would be used following the nature of the study and choice of the research methods. Logistic regression is basically a type of model for statistical classification. It is mostly used in referring to a problem whereby the dependant variable is specifically binary. Binary in this case implies that the available categories are two in number. The choice of this regression technique was done because the study would use two dependent variables as applicable in logistic regression. The major variables to be used in this model include categorical predictor, which include vitamin D adjunct or a standard antibiotic therapy. The categorical outcomes could also be one of two possibilities. The outcome could thus be progression to active TB disease or no progression to TB disease. Every possible categorical outcome will result from the categorical dependent variable. The parameters to be estimated by the model are typically qualitative. Here, the probabilities that describe a single trial and respective possible outcome would be modeled solely as a function of the predictor variable, using the logistic function. The logistic regression model will thus be used to refer to the study problem whereby the dependant variable is binary.
Why Logistic Regression Model
The use of logistic regression model is attributed to a number of factors. One of such factors is that the progression to active disease has known confounding factors. The study also aims to observe the interaction between vitamin D adjunct therapy and specified variables. The model will be useful in this case because it is the only model that can measure the interaction of one categorical dependent variable with at least one independent variable. This happens through the use of probability scores mainly as the dependent variable’s predictor value. The logistic regression will grant the researcher a meaningful way to evaluate confounding and interaction outcome, as well as the possibility of predicting the outcome using a single model.
The logistic regression model will incorporate aspects like test statistics, exposure variable, and control variables. The logistic regression model takes values, specifically between zero and one and thus, it is interpreted as a probability function.
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