The Obesity and Coronary Heart Disease Relationship

Introduction

Research requires attention to many details to meet the scientific community’s requirements in reliability and validity. Assessing variables and potential biases is part of the planning research process. In the case considered, the study covers 9,400 people and investigates the possible link between obesity and coronary heart disease (CHD). The researchers also included data on measuring cholesterol levels in participants. Even though the researchers have already collected data on obesity and CHD, they must accurately describe the studied exposure variables and disease, envisage confounding variables, and prevent bias.

Exposure Variable

When planning and designing studies, assessing which variables will be investigated and how they relate to each other is necessary. In particular, exposure and outcome variables are linked as a causal relationship where exposure is considered a cause and outcome variable – effect (Rajvir, 2018). In the case under study, the researchers initially characterized the participants by the presence or absence of obesity to investigate the subsequent possibility of the appearance of CHD. Therefore, obesity is considered a possible cause, that is, an exposure variable, and CHD is the disease and outcome variable in this research.

Relative Risk

The data collected during the considered study allows for determining the relative risk (RR). It presents the probability ratios of a particular event in exposed and non-exposed groups (Tenny & Hoffman, 2022). Its calculation is valuable as it will demonstrate the likelihood of developing CHD in people exposed to obesity. Each group’s probabilities will initially be calculated, and after that, their ratio will be determined to estimate RR.

1. Probability in the exposure group = 79 (having CHD) / 824 (total in group) = 0.096.
2. Probability in the non-exposure group = 286 (having CHD) / 8576 (total in group) = 0.033.
3. RR = 0.096 / 0.033 = 2.9.

Confounding Variables

Additional factors not included in the research may influence the study. Notably, a confounding variable is an external influence on an experiment, that is, a force that manipulates variables and can distort the results of a study (“Confounding variable,” 2021). Therefore, researchers should envisage this type of variable and make efforts to control them, if possible, to reduce inaccuracy. Several factors can affect the results of the research, which investigates the association between obesity and CHD. For instance, age is a confounding variable since the older a person is, the higher the risk of heart disease and gaining weight. Another factor is lifestyle – eating high-fat foods and lack of exercise also increase the threat of CHD and obesity. As a result, many factors in such a study can distort the results.

Cholesterol Level Impact on Research

The researchers also measured participants’ cholesterol levels, determining normal and high levels. It is critical to understand whether cholesterol is the confounding variable, and three specific criteria exist. According to Bovbjerg (2020), firstly, such a variable should have a statistical association with exposure, that is, be more common in the exposure group. Using the collected data, the percentage of participants with high cholesterol is as follows:

• Obese with CHD: 55 of 79 = 69.6%.
• Obese without CHD: 51 of 745 = 6.8%.
• Not obese with CHD: 5 of 286 = 1.7%.
• Not obese without CHD: 5 of 8290 = 0.06%.

Since high cholesterol is more common in the exposure group, it has the necessary association and meets the first criterion for confounding variables.

Consideration should be given to the remaining criteria to ensure cholesterol is or is not a confounding variable. The second criterion is the causal relationship between variable and outcome (Bovbjerg, 2020). The third criterion is that the variable should not be on the causal path between exposure and outcome (Bovbjerg, 2020). According to the UK’s National Health Service (2020), high cholesterol contributes to CHD, and therefore there is a causal relationship between them. Moreover, the calculations prove the association – there are higher percent of participants with high cholesterol among those with CHD in both groups. Considering the third criterion, it is noteworthy that high cholesterol intermediates the relationship between obesity and heart disease (Bakhtiyari et al., 2022). As a result, the high weight grows the cholesterol level, which increases the likelihood of CHD. Therefore, cholesterol is on the causal pathway and is not a confounding variable.

Possible Sources of Bias

Random and systematic errors can occur in studies and influence their validity and reliability. The study considered in the current paper follows a prospective cohort study design. According to Bovbjerg (2020), such a design is robust and less prone to bias. Nevertheless, there are several possible sources of such errors for this investigation. The most likely errors are selection, confusion, and information biases (Ramirez-Santana, 2018). Selection biases usually occur in the initial stages of participant selection; they can influence the sample’s representativeness and distort the study’s result. Information bias can arise if data is improperly collected or evaluation tools are selected wrongly, which also interferes with the accuracy of conclusions. Finally, confusion bias arises from confounding variables affecting the data and the result. Researchers must strive for objectivity and accuracy and carefully prepare for the study to avoid bias.

Conclusion

Thus, the current paper analyzed a study of the association between obesity and CHD, examining its variables and potential sources of bias. This study treats obesity as an exposure variable and CHD as an outcome disease. The RR was calculated for the connection of these variables – 2.9. Confounding variables, such as age or lifestyle, may influence the study. At the same time, the cholesterol level measured by the researchers is not such a confounder. Potential biases are selection, information, and confusion, which can distort the results.

References

Bakhtiyari, M., Kazemian, E., Kabir, K., Hadaegh, F., Aghajanian, S., Mardi, P., Ghahfarokhi, N. T., Ghanbari, A., Mansournia, M. A., & Azizi, F. (2022). Contribution of obesity and cardiometabolic risk factors in developing cardiovascular disease: A population-based cohort study. Scientific Reports, 12(1), 1-10. Web.

Bovbjerg, M. L. (2020). Foundations of epidemiology [eBook edition]. Oregon State University. Web.

Confounding variable. (2021). Data Science. Web.

National Health Service. (2020). Coronary heart disease: Causes. Web.

Rajvir, B. (2018). Community medicine preparatory manual for undergraduates (2^nd ed.). Elsevier Health Sciences.

Ramirez-Santana, M. (2018). Limitations and biases in cohort studies. In R. M. Barría (Ed.), Cohort studies in health sciences (pp. 29-46). IntechOpen. Web.

Tenny, S., & Hoffman, M. R. (2022). Relative risk. StatPearls [Internet]. Web.