Type 2 diabetes mellitus (T2DM) is characterized by high blood sugar levels. On the other hand, obesity is often characterized by an excess increase in the accumulation of body fat. According to Daryabor et al. (2020), type 2 diabetes mellitus and obesity are great contributors to the burden of disease globally. These two conditions strain healthcare systems in different areas of the world and pose significant risks to people’s health. Health complications associated with obesity include mental health issues, cardiovascular diseases, cancers, and musculoskeletal disorders. Because excess body fat can result in insulin resistance and poor glucose metabolism, obesity often serves as a forerunner to type 2 diabetes mellitus. On the other hand, T2DM not only demands substantial resources for management and treatment but also results in complications such as heart disease, kidney failure, blindness, and stroke (Ruze et al., 2023). This essay explores the intricate association between obesity and T2DM, recognizing their interconnection as significant health issues.
The population health topic I selected is the association between obesity and type 2 diabetes mellitus. My research question is: What is the association between obesity and the increase of type 2 diabetes mellitus among adults aged 30-50 in major urban areas? I chose this topic and research area due to the increasing prevalence of obesity and T2DM among people living in urban areas as a result of lifestyle and environmental influences. Understanding the association between these two complications provides valuable insights for targeted prevention and intervention strategies to counter the rise of T2DM in urban areas (Ruze et al., 2023).
The epidemiologic study design that would be most appropriate to explore the correlation between obesity and type 2 diabetes mellitus would be a prospective cohort study (Daryabor et al., 2020). A prospective cohort study entails tracking people over a specified period to evaluate their obesity and subsequent development of type 2 diabetes mellitus. This methodology establishes a temporal sequence by capturing data longitudinally. This enables a researcher to fathom the causal linkages between obesity and T2DM. These studies offer a more robust framework for inference as they allow for the direct observation of outcomes as they happen and reduce recall bias (Daryabor et al., 2020). Researchers can gain valuable insights into the progression from obesity to T2DM and formulate strategies for targeted prevention and intervention to counter the rise of T2DM in urban areas
I would conduct baseline assessments to gather comprehensive information from participants at the beginning of the study. According to Curley et al. (2024), baseline assessments enable a contextual understanding of the study population and may include data such as age, gender, and socioeconomic status. Next, anthropometric measurements are taken to assess body composition and identify individuals classified as obese. These measurements may include weight, height, and circumference of the participant’s waist (Friis & Sellers, 2020). Moreover, eating habits, physicality levels, and lifestyle factors are evaluated to ascertain the risk of T2DM among the chosen cohort. In addition, the medical histories of the chosen cohort are recorded to identify pre-existing health conditions they may have before the research. These baseline assessments not only allow for identifying confounding variables that may influence the association between obesity and T2DM but also provide a basis for later analyses.
For the sake of updating participants’ level of exposure and outcomes, annual follow-up assessments are also crucial to the research’s methodology is vital. In-person interviews, physical examinations, and laboratory glucose level measurements are some of the techniques used in these follow-up assessments (Curley et al., 2024). These regular assessments help researchers track any minor differences in the general health status of participants over time. This enables the researchers to monitor obesity and the potential development of T2DM over a specific period. According to Ruze et al. (2023), these assessments facilitate the identification of any emerging associations between obesity and T2DM, which may contribute to a better understanding of the relationship between the two. In addition, the assessments also ensure that the data collected is accurate and reliable.
To implement methodological strategies for exposure and outcome classification in a prospective cohort study, participants are initially grouped based on their exposure status at the beginning, distinguishing between the obese and those who are not. According to Daryabor et al. (2020), participants are studied and assessed over a specific to track the occurrence of type 2 diabetes mellitus. This longitudinal strategy helps researchers to directly observe and study how obesity influences the development of type 2 diabetes mellitus over a certain period, facilitating the occurrence of temporal relationships, which are necessary for determining causality.
Rigorous stratification techniques and statistical changes should be used to bolster the validity of the study findings. The mentioned adjustments usually aim to account for confounding factors that might potentially obscure the link between obesity and type 2 diabetes mellitus. Researchers can mitigate the influence of extraneous factors by combing for variables such as age, gender, history of diabetes in the participants’ family, and social and economic status (Ruze et al., 2023). Moreover, variables such as age, gender, and family history of T2DM allow for the study of how the linkage between obesity and T2DM is susceptible to change in different population subgroups. This may be aided by stratification, which offers a more nuanced analysis of the mentioned variables.
One strength of prospective cohort studies is the ability to directly assess incidence rates and relative risks. This is very effective in facilitating the establishment of causal relationships. In addition, prospective cohort studies enable the simultaneous evaluation of different risk factors and outcomes, leading to more comprehensive research findings (Daryabor et al., 2020). Despite the mentioned strengths, prospective cohort studies also have limitations. One limitation is that it requires adequate time to collect data. The studies also require substantial resources, which makes them very expensive and resource-intensive. Another challenge may be the loss of follow-up among cohort members over time, leading to biased results and decreased generalizability of findings (Friis & Sellers, 2020).
Obtaining informed consent from the participants ensures the confidentiality of the data collected, and minimizing harm or discomfort associated with data collection procedures are some of the ethical considerations that should be carefully considered during this study (Goldstein et al., 2018). While carrying out prospective cohort studies, researchers must strictly adhere to ethical guidelines regarding participant autonomy, beneficence, and justice.
The research topic and research area were chosen due to the increasing prevalence of obesity and T2DM among people living in urban areas as a result of lifestyle and environmental influences. To implement methodological strategies for exposure and outcome classification in a prospective cohort study, participants are initially grouped based on their exposure status at the beginning, distinguishing between the obese and those who are not. Understanding the association between these two complications provides valuable insights for targeted prevention and intervention strategies to counter the rise of T2DM in urban areas. While carrying out prospective cohort studies, researchers must strictly adhere to ethical guidelines regarding participant autonomy, beneficence, and justice.
Curley, A. L., Niedz, B. A., & Erikson, A. (Eds.). (2024). Population-based nursing: Concepts and competencies for advanced practice. Springer Publishing Company. https://www.gop.com/
Friis, R. H., & Sellers, T. (2020). Epidemiology for public health practice. Jones & Bartlett Learning. https://samples.jblearning.com/9781284175431/9781284191127_FMxx_Friis_Secured.pdf
Goldstein, C. E., Weijer, C., Brehaut, J. C., Fergusson, D. A., Grimshaw, J. M., Horn, A. R., & Taljaard, M. (2018). Ethical issues in pragmatic randomized controlled trials: A recent literature review identifies gaps in ethical argumentation. BMC medical ethics, 19, 1-10. https://doi.org/10.1186/s12910-018-0253-x
Daryabor, G., Atashzar, M. R., Kabelitz, D., Meri, S., & Kalantar, K. (2020). The effects of type 2 diabetes mellitus on organ metabolism and the immune system. Frontiers in immunology, 11, 546198. https://doi.org/10.3389/fimmu.2020.01582
Ruze, R., Liu, T., Zou, X., Song, J., Chen, Y., Xu, R., … & Xu, Q. (2023). Obesity and type 2 diabetes mellitus: Connections in epidemiology, pathogenesis, and treatments. Frontiers in endocrinology, 14, 1161521. https://doi.org/10.3389/fendo.2023.1161521