Impacts of Smoking on the Onset and Development of Type 2 Diabetes Mellitus
Diabetes is a lifelong chronic condition that creates an influence on the way by which the human body utilizes glucose or sugar. Also referred to as a metabolic syndrome, this condition is primarily characterized by increased blood glucose levels, besides insulin resistance and a relative less secretion of insulin (Brethauer et al., 2013). Some of the common symptoms of the condition comprise of frequent urination, increased thirst, unexplained and sudden loss of weight, increased hunger, and a sense of fatigue. There is mounting evidence to correlate the onset of T2DM with obesity and a sedentary lifestyle (Johnson et al., 2013). While some people are found to be genetically predisposed to the metabolic syndrome, several lifestyle factors also increase the likelihood of the disease to affect people. According to Clair et al. (2013) smoking has been considered to increase the risks of T2DM among people. According to government reports, every year an estimated 100 Canadians die due to smoking related diseases. Individuals suffering from T2DM face greater risks due to smoking (Diabetes Canada, 2018). Smoking results in the hardening of the blood vessels, specifically the arteries and result in an impairment of the ability of blood to distribute oxygen throughout the human body (American Diabetes Association, 2014). The research question was formulated with the intent to explore effects of smoking habits on the onset and progression of type 2 diabetes mellitus. It was expected that the literature search would contribute substantial knowledge in this domain and inform clinical practice in future.
What are the impacts of smoking on the onset and development of type 2 diabetes mellitus?
The SPIDER tool for qualitative research was adopted for this research study that helped in classifying the research question into different elements for facilitating formulation of key phrases, inclusion and exclusion criteria that are mentioned below (Methley et al., 2014):
- S(ample)- adults having smoking habits
- PI(phenomenon of interest)- onset of T2DM
- D(esign)- inductive approach, phenomenology
- E(valuation)- laboratory tests, views
- R(esearch)- qualitative
Teenagers who smoke regularly were not included in the sample owing to the fact that the usual age of onset of T2DM is middle or old age. Furthermore, laboratory tests (specifically blood tests) were considered as the primary evaluation measures since they help in determining the blood glucose levels in a person. Time limitation of 5 years was used as an inclusion criteria in order to maintain the research currency (Aromataris & Riitano, 2014). The complete inclusion and exclusion criteria are given in Appendix 1.
Following breaking of the research question into the corresponding SPIDER tool components for effectively searching primary scholarly articles that were relevant to the research question of interest. The key phrases and search terms were recognised with the use of synonyms and Medical Subject Headings (MeSH) terms, followed by their input into four search engines and electronic databases namely, CINAHL Plus, PubMed, Cochrane Library, and EMBASE (refer to appendix 2-5) (Leydesdorff & Opthof, 2013). The bibliography of the retrieved articles were thoroughly investigated via the snowballing procedure to extract more prospective literature that could be included in the final list of findings.
Adoption of the SPIDER Tool for Qualitative Research
Development of a research question has been identified critical in the effective exploration of research evidences from databases. In the words of Hastings and Fisher (2014) while the PICO framework (Population, Intervention, Comparison, and Outcome) plays a crucial part in collecting confirmations for evidence-based practice, extraction of articles for a qualitative research is more difficult. The SPIDER tool was used for designing the research question as it took into account all the individual elements that were present in the question. The SPIDER tool demonstrated greater sensitivity for retrieval of articles in all the databases and search engines (Stern, Jordan & McArthur, 2014). On the other hand, the PICO tool did not produce equivalent number of scientific articles, when compared to the SPIDER tool. Certain problems were also encountered while deciding upon the search terms and phrases that were meant to be input into the databases.
It has been suggested that identification of the key terms gets facilitated by breaking the main topic into principal concepts that get easily translated into keywords (Lewis, 2015). Furthermore, the thesaurus was also used to recognise the synonyms of the identified search terms in order to retrieve articles that contained relevant information on the alternative terms. These search terms were combined with the use of boolean operators AND that helped in narrowing the search results (McGowan et al., 2016). While CINAHL Plus was used owing to the presence of more than 300 journals, EMBASE was used for extracting biomedical articles that addressed the research question. Use of Cochrane Library was considered imperative owing to its role in providing evidence that informs healthcare decision making.
The first search was conducted in CINAHL Plus, by combining all the key phrases with Boolean operator (Appendix 2). This search gave a total hit of 12 articles. Thus, search in the first database resulted in narrowing down the scholarly literature to specific hits. This was followed by using the same search terms in EMBASE database that resulted in 44 articles (Appendix 3). EMBASE was selected suitable for retrieving relevant literature owing to the fact that it had been identified as a multi-purpose, updated and versatile biomedical database that covered almost all important biomedical works published since 1947, till date. However, most of the articles were related to all risk factors that increase the susceptibility of T2DM amid adults. This lead to conduction of a similar search in Cochrane Library. Combination of the key terms in the Cochrane Library resulted in 24 trials that were published between January 2013 and November 2018 (Appendix 4). This database was selected owing to the assortment of different databases related to healthcare, medicine and other specialities.
Short Summary of Search Strategy
In order to negate chances of missing out any scientific literature that were relevant to the phenomenon being investigated, the search engine of PubMed was used at last. Bramer et al. (2013) opined that one major advantage that PubMed offers over any other search engine is the fact that it translates the original search formulation and mechanically adds field names, pertinent MeSH (Medical Subject Headings) terms, boolean operators, alternative expressions, and stores the subsequent terms suitably, thus augmenting the search design in a significant manner. Final input of the search terms and key phrases in PubMed search engine resulted in 517 article hits (Appendix 5). Extensive information was expected following the input of the key phrases in the databases since extensive research has been conducted to determine the risk factors for the incidence and progress of T2DM. In order to limit the articles that were obtained in the searches, limiters such as, publication language, publication date, study subjects, and age group were predetermined (Gonzalez Aleu & Van Aken, 2016).
Following the extraction of all articles from the four search engines and electronic databases, the duplicate articles were removed. The duplicates were divided into two categories namely, type I and type II. While type I duplicates included those articles that were simultaneously present in more than one search engine and/or database, type II included those articles that were published in dissimilar issues or journals (Moher et al. 2015). A total of 597 articles were obtained from the four electronic databases. Type I duplicates were more prevalent in this scenario, which when excluded resulted in 312 articles. The scholarly articles that met the inclusion criteria were screened, following the removal of duplicates.
The titles and the abstracts were then matched in order to determine their relevance to the research question. Any article that did not provide a definite answer to the research question was removed. Some of such articles either focused on establishing the association between smoking and all kinds of chronic disease, or correlated T2D with all types of lifestyle factors (Look AHEAD Research Group, 2013). This step was succeeded by obtaining the full texts of the remaining articles to assess their eligibility. The final assessment of the eligibility of the literature resulted in four hits, all of which were true to the research question and helped in drawing definite conclusions on the phenomenon under investigation.
For the purpose of literature search, both primary and secondary articles are usually taken into account. We considered inclusion of primary scientific articles best for this activity owing to the fact that all primary scientific literature contain accounts of the exploration conducted personally by individual researchers, or in the form of a collaboration by a group of researchers, published in peer-reviewed scientific journal. On the other hand, the secondary articles such as, reviews, meta-analysis were not selected for this activity since the data might be outdated and not cover the accurate sample population that was considered relevant for this research (adult smokers) (Smith, 2015). Upon reflecting, I realised that selection of the research question was quite a challenging task since T2DM is a broad topic and had been associated with a range of genetic and lifestyle risk factors that have been identified responsible for making a group of population more likely to suffer from the metabolic syndrome.
Report on Search Outcomes
Thus, selection of the correct research topic was a major challenge. Following research question formulation, I determined what resources were available for the study such as, literature, and time. The key to the research was related to obtaining an overarching theoretical setting for the results. Selection of the research methodology was another thought-provoking situation. I can correlate adoption of a positivist research philosophy to the fact that it emphasises on gaining factual knowledge through observations, including several measurements. Weinberg (2013) stated that positivism depends on a set of observations that are quantifiable and result in statistical analyses, thus all the articles that were considered relevant to the research question were quantitative. This helped us to determine the association between smoking habits among adults and their likelihood of getting diagnosed with T2DM.
Mixed method research designs are usually adopted for conducting research activities that comprise of collection, analysis, and integration of qualitative and quantitative articles. This kind of research approach is usually put to use under circumstances where the integration provides a sound understanding of the problem under investigation (Zohrabi, 2013). However, we did not adopt a mixed method technique owing to some of its disadvantages. These kind of research designs are extremely multi-faceted and complex, require more time and resources for completion and implementation into real time settings. Furthermore, we also found it difficult to plan and apply one type of research method by drawing on the results of another. Additionally, it also becomes difficult to resolve inconsistencies that originate in the interpretation of mixed method findings.
The reflection also helped us realise that a major drawback of the research conducted by us was related to selection bias. This refers to the kind of bias that gets introduced due to selection of data for research analysis in a way that prevents the data being representative of the entire sample that is intended to be explored. Personally, I have a bias towards primary research articles. Thus, while conduction of this research, I did not consider the probability of including secondary articles, despite their potential advantage of containing data from several studies that have been critically appraised. Adoption of the snowballing procedure was advantageous since the process was less time consuming and simple, and also provided us with the opportunity of findings scholarly literature that were difficult to retrieve with the use of the electronic databases (Wohlin, 2014). However, this process made us exert little or no control over the article selection method. Additionally, we could not guarantee representativeness of the entire sample by adopting this procedure. Generation of sampling bias was another potential drawback that we feared while using this procedure. Hence, it is highly possible that we included articles that were almost similar in their characteristics and study design. Also, I did not consider searching the MEDLINE electronic database that has been widely accepted as an important bibliographic database that contains current academic journals on biomedicine and life sciences, in more than 40 languages. This might have resulted in the exclusion of articles that were relevant to the research question of interest.
Comparison of SPIDER Tool vs PICO Framework
Akter et al. (2015) conducted a study in order to examine the association of the status of smoking and smoking intensity and the overall process of smoking cessation with the risk of developing type 2 diabetes. Thus, the study main attempts to draw a relationship with smoking and the development of diabetes. In other words, it can be said that the aim of the paper directly aligns with the scope of this research and hence this primary research article was included in this study. The study included 53,930 Japanese employees under the age group of 15 to 83 years. The inclusion criteria were people who received health check-up and did not have diabetes at the baseline or are receiving medication of diabetes. The authors mainly used Cox proportional-hazards regression models in order to study the association with the smoking and the diabetes development. The analysis of the results during the 3.9 years of median follow up highlighted that at least 2,441 (4.5%) of individuals developed type-2 diabetes mellitus (T2DM). Of this percentage, 1.16% is non-smokers and 1.34% are former smokers or current smokers. Although the relative risk of diabetes was higher among the individuals with lower BMI and the overall attributable risk of diabetes was higher with the individuals with higher BMIs. The hazard ratio among the smokers was high in comparison to the non-smokers. At the end, the authors concluded that the cigarette smoking is associated with an increased risk of developing T2DM and this risk persist even after 10 years of smoking cessation (Akter et al., 2015).
The main research method that was used in this study is multi-centre study under Japan Epidemiology Collaboration on Occupational Health. The study mainly included 12 companies from different industries. Thus selection of the diverse group of candidates from diverse age group and different work culture further helped to draw a connection between job role and the diabetes development. The analysis of the results revealed that irrespective of the job role of the employees, the role of smoking over the development of T2DM cannot be ignored among the adults. The large sample size (80,469 individuals) can be regarded as the main strength of the study. According to Parahoo (2014) high sample size helps in the generalization of the data and at the same time helps to limit the biased response. However, the main limitation of of the study is, Akter et al. (2015) used self-administered questionnaire in order to measure the smoking status. According to Quinlan, Babin, Carr and Griffin (2018) self-administered questionnaire results in questionnaire bias. Moreover, according to Akter et al. (2015), total number of the cigarettes smoked per day was mentioned as a continuous variable while in the other companies, the respondent was allowed to select from three different options. These discrepancies the questionnaire format might give rise of biased results as different type of questionnaire format was used for the collection of the data and this might bring-in health inequality. Another limitation of the study is its relatively small follow-up period and this might increase the chances of getting biased results.
Limitations of the Study
Kim et al. (2014) conducted a study on a major d health problem, T2DM that is creating fiscal burden to each of the nation’s healthcare system. The aim of the study was to examine the effect of the early onset and the duration of smoking on the risk of developing T2DM. The aim of the study, coincided with the scope of the research because it aims to find association between smoking the risk of developing diabetes mellitus. The main method that is use to conduct the study is cross-sectional research under the National Health and Nutrition Examination Survey which is set under the set-up of South Korea (2010) and the United States (2009 to 2010). According to Lewis (2015), in cross-sectional study design, the data of all the variables is collected once. This helps in the measurement of the prevalence for all the factors under investigation and thereby helping to conduct the public healthcare assessment. Thus choosing cross-sectional study design can be highlighted as the main strength of the research. The participants included by Kim et al. (2014) included patients with diabetes between the age group of 20 years and above. Kim et al. (2014) used cox proportional models which are stratified by sex and country in order to estimate the ration of hazards. The results of the study highlighted that at least 7.1% of the South Korean men, 5.5% of South Korean women, 15.5 % of men, and 12.4% of women of the United States has T2DM. Moreover, the study also indicated that 34% of U.S men and 21% of U.S women stated again before the age of 20. The participants who developed T2DM were older and are married and have high BMI and belongs to low income family and has less education along with low rate of physical activity. The population who began smoking before the age of 20 have higher tendency of developing T2DM in comparison to people to begin smoking after 20 years or did not smoke. However, no direct association was found in the development of diabetes among the women and smoking. At the end, the author concluded that the early on-set of smoking increases the tendency of developing T2DM among the South Korean and US men and the type 2 diabetes risk increase in the total number of years of smoking. The study also showed that people who started smoking at young age and have developed T2DM consecutively, have mild physical activity, poor financial status and lack of proper education. Thus, the author found link between the education and the financial backup behind the healthy lifestyle. The study provided insight about the tobacco policy and education program. In the cross-sectional design the data from two countries are taken, South Korea and the United States and this promoted the generalizing of the data. However, one of the significant limitation of the study is, the survey data was not collected at the same time and they do not reflect past health-related barrier rather focus on the predicting variable. Moreover, the association of the other risk factor behind the diabetes development like physical activity, diet is not studies in details and this might be highlighted under the selection bias of the author.
Conclusion
Luo et al. (2013) conducted a study in order to draw the relationship between smoking and T2DM. The scope of the research coincided with the scope of the research and hence this primary study was included. The main aim of the study design is to find the relationship between the smoking cessation and development of T2DM. The study was conducted with taking the post-menopausal women who are aged between 50 to 79 years and are enrolled under the Women’s Health initiative between September 1993 to December 1998. An average of 11 years of association was used to calculate the relationship between the smoking cessation and development of T2DM. This cross-sectional study also used the Cox proportional hazard multivariable-adjusted regression models in order to analyze the study results. The comparison of the result was done with the women who never smoked in their life. The analysis of the results highlighted significant hazard ration of 1.28 with 95% of confidence interval among the women who are current smokers. However, the hazard ration was less among the women who quit smoking after 3 years of follow up. Among the former smokers, the risk of developing T2DM decreased with time and this relationship was equal to the women who have never smoked in their life. In the new smoking quitter groups, who have low cumulative exposure, the risk of diabetes was not elevated following the smoking cessation. In conclusion, the author concluded that the risk of diabetes is women who are former smokers return to that of the women who have never smoked cigarette in their life of have quit smoking before 10 years. Overall from the study conducted among the post-menopausal women, it can be said that the smoking and risk of developing diabetes is also high among the women however, with increase in the years of smoking cessation, the risk of developing T2DM decreases. The main limitations of the study is, the study only focused over the post-menopausal women. According to Brand et al. (2013), the post-menopausal women have high risk of developing type 2 diabetes irrespective of their smoking habits. Menopause occurring at early span of tine, increases the risk of developing T2DM further. Heianza et al. (2013) conducted a similar study, which indicated that pre-diabetic women after menopause develop diabetes. The cross-sectional study conducted by Luo et al. (2013) failed to undertaken the time of menopause and the pre-diabetic condition as important confounding factor behind the diabetes development. This might be highlighted as a selection bias of the authors. Moreover, Luo et al. (2013) used self-reported diabetes status and this might decrease the specificity of the diabetes classification and might lead to certain degree of misclassification. According to Luo et al. (2013), the authors excluded the strong association between the adiposity and the T2DM development, this might be included under the selection bias, and this gave rise of misinterpretation of the data. Whether the selected group of participants were under the nicotine replacement therapy was also not included in the study.
Future Directions for Research
The aim of the study conducted by Spijkerman et al. (2014) were to study the association if smoking and the susceptibility of occurring T2DM and its relation with the large number of confounding factors. The study also aims to explore the potential effect modifiers and the intermediate factors. The aim of the study, coincided with the scope of the research and hence the study was included in this research. The critical analysis of this study will help to throw light on how smoking influences the development of diabetes in association with the other confounding factors. The main study design followed by the authors is a prospective case-cohort study with eight European countries, which have recorded diabetes reports of 12,403 along with random sub-cohort of 16,835 individuals. The individuals with missing data and final analysis included 10,327 cases along with 13,863 sub-cohort individuals. The smoking status of the individuals was used in the form of never, former and current and never smokers were used as placebo. The country-specific Prentice-weight Cox regression models along with random-effects meta-analysis were used to calculate the hazard ratio of T2DM. The results indicated that among men, type 2 diabetes were 1.40 were former smokers and 1.43 were from current smokers and this percentage ratio was independent, education, physical activity, diet and substance abuse. In women, this association was weak in comparison to men with hazard ration estimating to 1.18 and 1.14 among the former and the current smokers respectively. There was some evidence of the hazard effect modification by the BMI. The association however found to be slightly stronger in normal weight men in comparison to those who are suffering from adiposity. The authors concluded that the people who are current smokers or had past smoking habits are more likely to develop T2DM in comparison to the population with no smoking habits. This association was independent of the educational level, amount of physical activity, alcohol consumption and diet plan (Spijkerman et al., 2014). The authors highlighted modifiable risk factor for the T2DM development and thus encouraged smoking cessation for diabetes prevention. The main strength of the article is its high sample size. Large sample size collected from different countries helped in generalization of the data (Parahoo, 2014). However, the main limitation of the study is the selection bias. The people who were excluded from the data analysis were younger and had lower BMI along with larger waist circumference along with lower consumption of alcohol, vegetables, fruits, meat and fist and higher consumption of coffee and tea. The excluded group of individuals were less frequently never smokers and physically inactive. This selection bias might have resulted in the over-estimation of the association between the confounding factors. The authors also failed to study the effect of the lifestyle factors on the T2DM generation (Spijkerman et al., 2014).
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