Skip to Main Content
MCPHS Library Logo

Annotated Bibliographies

Learn about the anatomy of an annotated bibliography. See an example of those requirements and recommendations in practice.
Sometimes, it can be hard to understand how to put everything together even if you understand all of the tips. Use the example annotated bibliography on this page as a model for your own annotated bibliography. This example uses APA (7th Edition) citation style.


  1.  Each reference in your bibliography follows the rules for your citation style, such as APA style.
  2.  Your notes for each reference come immediately after your reference. Your notes are indented so that each citation is easy to find.
  3.  The references in your bibliography appear in the same order they would in the reference list at the end of your paper. In APA style, that means that they are listed in alphabetical order by the first author’s last name.
  4.  Explain why each reference is useful for your paper.
  5.  Include specific data, facts, or other unique information you want to use in your paper.
  6.  Include limitations of each source.
  7.  You might notice some themes in what you are writing. Or, you might notice that different sources conflict. These themes and conflicts will be useful for organizing your literature review when you write your paper.

Example Annotated Bibliography

Feng, C., Osgood, N. D., & Dyck, R. F. (2018). Low birth weight, cumulative obesity dose, and the risk of incident type 2 diabetes. Journal of Diabetes Research, 2018, Article 8435762.

This study shows the complexity of identifying risk factors and, unlike other studies, closely examines the roles of obesity and birth weight. In this large cohort study of people in Britain, the researchers examined the roles of several demographic factors as well as a few different measures of weight and BMI changes. This study did not see a link between low birth weight and the risk of developing diabetes unless changes in BMI were also taken into account. The interplay between low birth weight and changes in BMI over time was a significant risk factor for developing diabetes. Other notable findings were that it mattered when people became obese; people who became obese as teenagers were at higher risk than people who became obese later in life. The study had several important limitations to note. First, the number of participants dropped drastically over time; they had an 82.4% drop-out rate over the 50 years of the study. Although they used statistical methods to estimate missing data and were careful to compare like timespans with like timespans, this lack of data could skew the results. The second major limitation is that the authors did not distinguish between different types of diabetes. Other limitations were more minor. Based on what the authors share about how they calculated another factor, which did not end up being significant, it is unclear if only people with married mothers were included in the study. The authors used the changes in BMI between the different data collection periods to estimate the average age of onset, but the data collection points were not evenly spaced and the authors did not explain why they collected data at those points in time instead of others. Again, that information could skew the data. In spite of its limitations, this article shows how no one factor alone can predict if a person will develop diabetes. It can be a caution in assigning too much importance to specific measures.

Ivarsdottir, E. V., Steinthorsdottir, V., Daneshpour, M. S., Thorleifsson, G., Sulem, P., Holm, H., Sigurdsson, S., Hreidarsson, A. B., Siggurdson, G., Bjarnason, R., Thorsson, A. V., Benediktsson, R., Eyjolfsson, G., Sigurdardottir, O., Zeinali, S., Azizi, F., Thorsteinsdottier, U., Gudbjartsson, D. F., & Stefansson, K. (2017). Effect of sequence variants on variance in glucose levels predicts type 2 diabetes risk and accounts for heritability. Nature Genetics, 49(9), 1398-1402.

In this study, the authors investigated the relationship between genetic variants that are known to affect blood glucose levels to see if they were also associated with type 2 diabetes. In addition to looking for differences between specific people, the authors also accounted for differences between samples taken from a single individual. The real value of this article is in the supplementary materials the authors shared. The authors included a copy of the code they used to calculate the differences between all of their samples. It can be modified to be used with other genetic traits. In addition, the authors also linked out to a set of open access genetic data sets for type 2 diabetes research. Both sets of information make this article super useful for additional genetic research.

Min, D., & Cho, E. (2018). Associations among health behaviors, body mass index, hypertension, and diabetes mellitus: A path analysis. Medicine, 97(22), Article e10981.

Using existing data from a study of aging in Korea, this study identified specific risk factors for developing type 2 diabetes for people who have lower-than-normal weight levels or higher-than-normal weight levels. They looked at correlations between several dietary behaviors, exercise, demographic factors, and changes in body-mass index (BMI). Overall, they found a 2.4% incidence rate for diabetes in their study population, which consisted of middle-aged and older people in Korea who had not previously been diagnosed with diabetes. Regardless of weight, increases in a person’s BMI correlated with higher risk of developing diabetes. For people who were underweight, eating regular meals played a complex role; it was associated with a lower chance of developing type 2 diabetes, but also with increasing the person’s BMI. The study is helpful because it shows the complexity of identifying risk factors for developing type 2 diabetes and deciding what factors might be appropriate targets for public health programs. Rather than focusing on a single potential risk factor, this study looked at the interplay between several possible risk factors, which means the results reflect reality more closely. The study is limited in that the number of people studied was relatively small (a few thousand) and only a small portion of that group were underweight (about 130 people.) It is unclear if the results would hold for the larger population or for a different population.

Muller, N., Heller, T., Freitag, M. H., Gerste, B., Haupt, C. M., Wolf, G., & Muller, U. A. (2015). Healthcare utilization of people with type 2 diabetes in Germany: An analysis based on health insurance data. Diabetic Medicine, 32(7), 951-957.

This study demonstrates the high costs associated with having type 2 diabetes for both the individual and for insurance companies. It helps establish the argument that diabetes should be a public health priority and a priority for policy-makers and insurance companies. In addition, this study clearly showed that age correlates with both diabetes incidence and prevalence. This study looked at health-care related costs in insurance data for people in Germany who used the most common German insurance provider at the time. Based on that data, the researchers found that nearly 34% of people who had type 2 diabetes had secondary diabetes-related conditions. In addition, people with type 2 diabetes were more likely to use healthcare services on both outpatient and in-patient bases. People with type 2 diabetes were twice as likely to be admitted to the hospital and, when taking both outpatient and inpatient information into account, stayed at the hospital three times as long as people without any form of diabetes. (The researchers had excluded people with type 1 diabetes and gestational diabetes from their sample.) The researchers did not include standard deviations or confidence intervals; the data may not be fully representative. The data used in this study was collected in 2010; newer data might show different trends. The study’s findings may not be generalizable to other areas of the world due to differences in diabetes’ prevalence and incidence, the health care structures in place, and environmental factors. It still indicates that diabetes can be an expensive disease. In spite of some limitations, the information in this article suggests that targeting diabetes could have wide benefits.

Wang, T., Huang, T., Li, Y., Zheng, Y., Manson, J. E., Hu, F. B., & Qi, L. (2016). Low birthweight and risk of type 2 diabetes: A Mendelian randomisation study. Diabetologia, 59(9), 1920-1927.

In this meta-analysis, the researchers pooled longitudinal data about two studies of healthcare workers in the United States to evaluate the connection between genetic differences and risk of developing diabetes. Building on a study that showed certain genetic variants increased the likelihood that a person would have a low birth weight, this study examined participant’s genetic make-up to determine their likelihood of having a low birth weight and then investigated how many people with each variation had diabetes at the end of the study. Overall, these calculated risk factors did correlate with increased risk of developing diabetes; having a higher risk factor increased the chances that the person would develop diabetes. However, only two of the five variants the authors considered correlated with increased risk of developing diabetes and they did not include the variant that had the strongest correlation to having a low birth weight. Even when taking into account other behavioral and demographic factors, genetic makeup was a significant risk factor for diabetes. This reinforces the idea that diabetes has several interacting causes. That said, the authors were only looking at people in the U.S. with European heritage; different populations might have different results. This study is important because it shows that social and behavioral factors are not the sole causes for diabetes; traditional public health programs cannot completely eliminate diabetes. In addition, the study’s results might explain some of why people with similar outward appearances or behaviors have different outcomes.

Connect with the MCPHS Libraries via Social Media: Instagram