Beyond Genetics: Application of the Biopsychosocial Model to Gestational Diabetes Mellitus

The biopsychosocial model of health was created by psychiatrist George Engel, and is used to demonstrate the multiple factors (biological, psychological, social) that make up one's health outcomes (Borrell-Carrió et al., 2004). These factors (apart from genetics) are not fixed and interact with each other over time, resulting in how an individual's health presents itself. Engel believed that in order to fully understand a patient and make an accurate diagnosis, a practitioner must consider the patient’s lived experience and the biological, psychological, and social dimensions of their illnesses (Borrell-Carrió et al., 2004). I will be using the biopsychosocial model of health to explain how multiple factors can affect a population of my interest: pregnant mothers who have gestational diabetes mellitus (GDM). GDM is hyperglycemia that develops during pregnancy and resolves after delivery.

Hart, I. (2018). Biopsychosocial model of health. Safety & Health Practitioner. Retrieved from https://www.shponline.co.uk/occupational-health/common-health-issues-in-the-workplace/attachment/biopsychosocial-model-of-health/

Biological: Genetics play a role in one's risk of developing GDM, and there are certain ethnic groups that are predisposed to it. Ménard et al. (2020) studied ethnicity and immigration status as risk factors for GDM in a group of 1,387 women who were attending the Montreal Diet Dispensary Program. After adjusting for income, parity, age, infant sex, education, smoking, BMI, weight gain, and marital status, researchers found an increased rate of GDM in Asian women when compared to Caucasian women. There was also a significantly higher rate of GDM in women born in South Asia, East Asia and Pacific than North American-born women. Dyck et al. (2020) studied the epidemiology of GDM and pre-GDM (type 1 or type 2 diabetes occurring before pregnancy) among hundreds of thousands of First Nations and non-First Nations women in Saskatchewan between 1980 and 2009. Information on other factors, such as maternal age, parity, sex of offspring, previous stillborn, previous high birthweight infant, previous GDM, area of residence, and income quintile were also collected and data was adjusted for these factors. The study found that the risk of GDM and pre-GDM were both significantly higher for First Nations women than non-First Nations women. The researchers suggest that increased risk of GDM in First Nations women is at least partly related to determinants that they were unable to measure, and in some way may be related to the legacy of colonization. Other biological factors that affect the risk of GDM include increased BMI, unhealthy diet, and lack of exercise (McIntyre et al., 2019). Psychological: As mentioned above, a woman's diet and level of exercise plays a large role in her risk of developing GDM. A pregnant woman's attitude and beliefs about diet during would greatly impact these lifestyle factors. Also, a woman's mental state, emotions, and coping skills during pregnancy could impact behaviours such as unhealthy eating and binge eating. A woman who feels depressed or has a low mood may also feel reluctant to eat healthy foods or exercise regularly. Hosler et al. (2011) looked at the effect of stressful life events in the 12 months before a baby is born, such as a death in the family, divorce, and family conflicts on the risk of GDM. They found that women who had 5 or more stressful events in this time period were significantly more likely to develop GDM than women with no stressful events. This data demonstrates the psychological component of GDM risk factors according to the biopsychosocial model. Social: Social determinants of health such as income, education, and environment can also impact a woman's risk of developing GDM. A study by Rönö et al. (2019) looked at the relationship between income and educational level and the risk of developing GDM. The study included 5,962 Finnish women who gave birth for the first time between 2009 and 2015. Women were divided into five income categories according to pre-pregnancy annual income and four educational categories. Data was adjusted for smoking status, age, pre-pregnancy BMI, and cohabitating status. Researchers found that the incidence of GDM showed an inverse association with both income and educational level, meaning that the women studied were more likely to develop GDM if they had low income and low educational level. Kahr et al. (2016) conducted a study in Harris County, Texas, and looked at the density of fast-food restaurants and convenience stores within each ZIP code area. They then recruited women pregnant with singletons within the county and followed them throughout their pregnancy and delivery. 8,912 participants with validating ZIP code data were eligible for the study. Data was adjusted for age, race/ethnicity, gravidity, income, and BMI. Data showed that GDM correlated with the density of fast-food restaurants within a neighbourhood. The researchers also divided the density of fast-food restaurants by neighbourhood into groups above and below the median density, as well as into four quartiles. The rate of GDM in neighbourhoods with fast-food restaurant density above median was greater than those below the median, as well as in the fourth quartile (highest density) when compared to the first quartile (lowest density).

Castillo-Castrejon, M. & Powell, T. L. (2017). Placental nutrient transport in gestational diabetic pregnancies. Frontiers in Endocrinology, 8(306). Further Thoughts & Conclusion: The data presented in the studies outlined above demonstrate how the biopsychosocial model can be applied to a woman’s risk of developing GDM. There is evidence that biological, psychological, and social factors can all influence the risk of GDM, alluding to Engel’s theory that multiple factors work together to produce health outcomes. I did not receive any feedback from classmates on my unit 4 forum post about the biopsychosocial model, however, I did read another classmate’s work on using the biopsychosocial model to explore the opioid crisis during the Covid-19 pandemic. The classmate stated that factors leading to opioid use have been exacerbated by the pandemic, and I feel that the same logic could be applied to the steadily increasing rates of GDM. Due to the pandemic, many people are staying home, eating unhealthy foods due to lack of resources or stress/anxiety, and are not getting out to exercise as much. In terms of “stressful events” as mentioned in the study explained above by Hosler et al. (2011), the vast majority of the population has had stressful events in the past year, whether it be death or illness of a friend or family member due to Covid-19, fear of becoming sick, social isolation, or financial stress due to losing one’s job or having reduced working hours. Financial issues due to Covid-19 have also put many people in a lower income bracket, and as mentioned above, income is an important risk factor for GDM. As such, the consequences of the pandemic could possibly result in increased rates of GDM for multiple reasons. References: Borrell-Carrió, F., Suchman, A. L. Epstein, R. M. (2004). The biopsychosocial model 25 years later: Principles, practice, and scientific inquiry. Annals of Family Medicine, 2(6), 576-582. Dyck, R. F., Karunanayake, C., Pahwa, P., Stang, M., & Osgood, N. D. (2020). Epidemiology of diabetes in pregnancy among First Nations and non-First Nations women in Saskatchewan, 1980-2013. Part 2: Predictors and early complications; results from the DIP:ORRIIGENSS project. Canadian Journal of Diabetes, 44, 605-614. Hosler, A. S., Nayak, S. G., & Radigan, A. M. (2011). Stressful events, smoking exposure and other maternal risk factors associated with gestational diabetes mellitus. Paediatric and Perinatal Epidemiology, 25, 566-574. Lehman, B. J., David, D. M., & Gruber, J. A. Rethinking the biopsychosocial model of health: Understanding health as a dynamic system. Social and Personality Psychology Compass,11(8). McIntyre, H. D., Catalano, P., Zhang, C., Desoye, G., Mathieson, E. R., & Damm, P. (2019).

Gestational diabetes mellitus. Nature Reviews, 5(47).

Ménard, V., Sotunde, O. F., & Weiler, H. A. (2020). Ethnicity and immigration status as risk

factors for gestational diabetes mellitus, anemia and pregnancy outcomes among food

insecure women attending the Montreal Diet Dispensary Program. Canadian Journal of Diabetes, 44, 139-145. Rönö, K., Masalin, S., Kautiainen, H., Gissler, M., Raina, M., Eriksson, J. G., Laine, M. K.

(2019). Impact of maternal income on the risk of gestational diabetes mellitus in primiparous women. Diabetic Medicine, 36(2), 214-220.