Obesity and Diabetes Final Paper

Obesity and Diabetes Risk

Mod 2 – SLP

RES500 (2020JUN01FT-1)

July 5, 2020

Obesity and Diabetes Risk

Module two of the Session Long Project will continue the assessment of the relationship between obesity (IV) and diabetes risk (DV; type 2 diabetes or T2DM) in selected Americans aged 35-45 years. The relational study’s research question: Is obesity (IV) associated with diabetes risk (DV) among Americans aged 35-45? The paper will also examine the literature on the subject as shown in the evidence table (Appendix A), gaps in qualitative and quantitative data, discuss the advantages and disadvantages of prospective cohort study design, and identify bias and threats to validity. First, a brief reminder of the relationship between obesity and diabetes.

Gaps in Qualitative and Quantitative Data

Qualitative data.

Qualitative data can be monitored and recorded through observation and interviews. It can provide valuable insights into human behavior and socio-cultural aspects of the disease to improve disease management. In the study reported by Kim et al, the researchers were able to collect data on the member’s lifestyle, financial status, individual characteristics, including smoking habits, intake of alcohol, and physical activity. Participants who developed type 2 diabetes during this study reported being current smokers and heavy drinkers. It was also reported that they had a lower level of income, and a higher prevalence of hypertension, hyperglycemia, and a family medical history of diabetes (2018).

Quantitative data.

Quantitative research collects information in a numerical form that can be put into categories, or then used in rating order to create actual data tables and charts. This valuable knowledge helps us use statistics to analyze our results, explain trends and relationships when making decisions. In 1996, a 19-year longitudinal study was performed on women to explore the correlation of trajectories between BMI, early onset of being overweight, and obese-years in early adulthood, and eventual risk of type 2 diabetes.

Participants were evaluated every three years. In 2008, the mean study reported that the prevalence rate of obesity at baseline was 6.5 % and increased to 25.7 % (Luo et al., 2019). Of the 11,192 women who were non-diabetic at baseline, 162 women developed type 2 diabetes over an average of 16 years of follow-up. During the final study, the mean BMI rose from 22.8 kg / m2 at baseline to 26.9 kg / m2 (Luo et al., 2019).

Observational Prospective Cohort

The prospective cohort design of the experiment is observational because once the members of the sample are selected, they are monitored over some time, usually, several years, to determine if and when they become affected and if their exposure status influences outcomes. The studies found in Appendix A are cohort studies, to establish whether or not if obesity plays a role in developing Type 2 Diabetes in ages 35-45.

Prospective Cohort: Advantages and Disadvantages

Advantages.

Some of the benefits of prospective cohort studies are that they can help identify risk factors for being diagnosed with a new disease as they are a cumulative study over time, and findings are obtained at routine update intervals, therefore reducing mistakes. In a cohort study, subjects are known to be disease-free at the beginning of the observation period when their exposure status is established. In the studies listed in Appendix A, all have examined and reported multiple new onsets of health issues, such as cardiovascular disease.

Disadvantages.

There are several disadvantages to cohort studies. Cohort studies normally consist of large numbers of participants over an extended period. It can be expensive if compensating, and time-consuming keeping data on so many. Failure to address such a large amount of data may result in undesirable outcomes, especially if the reason for failure to comply is associated with the dependent variable or outcome. For example in the study to examine the relationship between BMI and mortality in type 2 diabetes patients, the researchers had to conclude their study due to time sensitivity. Drug treatments are the potential confounders to study the impact of fat distribution, as certain glucose-lowering medications are associated with weight, longevity, and smoking, which is negatively linked with BMI (Zaccardi et al., 2017).

Bias and Threats to Validity

The Korean research found that the pathways involving diabetes preventive capacity to lose weight were associated with increased sensitivity to insulin, thereby hindering or inhibiting beta-cell failure. The explanation of why weight loss has a more significant effect on lower risks to the occurrence of T2DM in apparently healthy weight subjects and whether ethnic disparities exist. Unfortunately, the data can not be applied to other ethnicities, because this analysis only included Korean subjects. (Kim et al., 2018)

Threats to internal validity.

At the beginning of the women’s study, the baseline BMI was obtained, but none were recorded thereafter. The participants self-reported their weights, however, there is a chance at inaccuracy. Incapable of calculating the timing of obesity for women who may have been obese at the starting point meant that these women had to be removed from the obese-year analyses (Luo et al., 2019). There was no other data documented for comparison.

Threats to external validity.

After the research, there were participants with significant weight loss. Researchers couldn't remove medically induced subjects from the weight loss category. Nonetheless, as this was a broad cohort, and the healthy weight group appeared to be more daily exercisers than the weight gain group, the findings may have been skewed by subjects with extreme medical conditions (Luo et al., 2019).

Conclusion

Prospective observational cohort studies play a significant role in the derivation of data. It provides a temporal aspect, which enables a series of events to be documented. This has demonstrated several impacts that can be examined for a given onset and or the prevalence of the disease in individuals that can be measured over time. Nonetheless, study quality might be affected by selection, attrition, or information bias based on the independent and dependent variables. This study design will adequately address the research question as to whether or not obesity plays a role in the development of Type 2 Diabetes in ages 35-45, as the health outcome has yet to be determined.

References

Bohula, E., Scirica, B., Inzucchi, S., McGuire, D., Keech, A., Smith, S., Steering Committee Investigators. (2018, November 24). Effect of lorcaserin on prevention and remission of type 2 diabetes in overweight and obese patients (CAMELLIA-TIMI 61): A randomised, placebo-controlled trial. Retrieved July 03, 2020, from https://www.ncbi.nlm.nih.gov/pubmed/30293771

Chan, M. (2017). Obesity and diabetes: The slow-motion disaster. The Milbank Quarterly, 95(1), 11-14. doi:10.1111/1468-0009.12238