Written by Abhishek Shastry
The biopsychosocial model is a way of thinking about pathological manifestations as an interaction between biological, psychological, and socio-environmental influences. It has become the current and prominent paradigm for how we view illness and has expanded significantly over multiple areas of medicine and healthcare. Biological factors included in this model take account of genetic background (nuclear and mitochondrial), local and systemic metabolomic environments, and abnormal physiology and histology, among other factors. Psychological factors include stressors (e.g. life changes: relationship conflicts, financial strains), risk behaviours, coping mechanisms, family histories, etc. Socio-environmental factors include relationships, education, housing, employment, climate and pollution, and other factors that can affect health. This model distinguishes itself from a purely biomedical view of health, a symptom-sign-oriented method of diagnosing and treating medical issues that has been shown to be problematic and ineffective.
Currently, we have a large but incomplete understanding of the hereditary factors that can modulate disease proliferation and development. There are a host of different genetic variations that underly disease, including single-nucleotide polymorphisms, insertions or deletions, structural variants, inversions, among others. However, the genetic background – the combination of evolutionarily- and environmentally – influenced tweaks and adjustments made at various points in the human genome – has recently been found to have an immense influence over how individuals present risk factors and how they develop disease. This phenomenon is found in ethnic group studies, largely because of the huge variety of biopsychosocial factors that individuals of various ethnic groups may experience. For example, congregate data in the United States have demonstrated that Caucasians have a moderate rate of abdominal obesity relative to other ethnic groups, but experience a lower rate of cardiovascular disease and type II diabetes. In contrast, Native (Indigenous) Americans have similar rates of abdominal obesity but considerably higher rates of cardiovascular disease and type II diabetes. Although obesity is a primary risk factor for the development of a variety of cardiometabolic diseases, this demonstrates that there must be an internal genetic and physiological environment characteristically unique in ethnic groups. It is estimated that genetic factors contribute to 40-70% of the interindividual  risk of obesity, and that mitochondrial economy (how well our powerhouses of the cell use oxygen to create energy and heat) plays a significant role in an individual’s response to a calorie-rich diet.
We see an increasing trend towards overweight and obese phenotypes in the Canadian population – as of 2018, and using the Body Mass Index (BMI) as a measure of relative height-weight distribution (which has its own problems as a tool -- among them are that humans have increased in height and weight composition both throughout their individual lives and throughout history; and that BMI does not account for body fat location or fat accumulation due to life cycle changes (i.e. puberty) , over one-fourth of Canadians are obese, and over one-third are overweight. This may be due to an increase in sedentary and leisure time, screen time, a shift towards processed and high-calorie foods, among other factors. However, these are only derivations from social determinants that continue to need to be addressed.
Grouped together like this, a risk profile can be created to better understand how individuals develop disease. Risk profiles are a tool of preventative medicine, to address potential pathological events down the line and address root causes. Personalized medicine seeks to use these profiles to influence the treatments clinicians prescribe to their patients. For example, it is known that drugs that are traditionally prescribed to treat certain conditions are drastically ineffective and only help a minority of the affected population (e.g. statins, prescribed to lower cholesterol, are estimated to only benefit 1 in 50 consumers). In addition, clinical studies typically only utilize white and male Western participants, which creates a bias in drug efficacy, especially as it is known that certain drugs are harmful to particular ethnic groups. To circumvent this, the study of gene interactions that are derived from a patient’s genetic background, environmental exposures, metabolomics, and cellomics, among other factors, to select and develop drugs that target specific pathways that may be dysregulated in the individual patient. A gene regulatory network can be created, so that the most significant (determined in terms of differential gene regulation or other methods) can be outlined, and drug candidates that are most relevant to the key driver gene or its effectors can be utilized.
In addition, there are increased interests into the use of CRISPR gene editing to treat disease and potential disease-gene targets. CRISPR-Cas9 is a gene editing tool that uses a guide RNA (that matches the gene to be cut) to guide the Cas9 protein, and cuts the gene of interest; in trying to repair this cut, the DNA repair proteins try to add random nucleotides, making the gene itself non-functional. A recent clinical trial by Intellia Therapeutics showed that CRISPR-Cas9 treatment of the TTR gene (whose protein misfolds are responsible for Transthyretin amyloidosis, a buildup of transthyretin amyloid proteins in the tissue), causes a 96% reduction in TTR protein levels, as opposed to the 80% decrease found in current drug treatments. In another study, two patients with Sickle Cell Anemia and Transfusion-dependent β-thalassemia had their BCL11A genes CRISPR, which led to higher levels of fetal hemoglobin expression and the elimination of relevant symptoms. Although these results are preliminary, combining this technology, gene regulatory networks, and risk profiles to treat medicine in a precise and personalized way shows immense promise for the future, especially in underrepresented groups in healthcare research and treatment. Health inequalities caused by scientific and medical negligence, as well as the use of dogmatic science that has not been validated across the diversity of the human population, pose a great burden for healthcare outcomes. Ultimately, it appears that proactive healthcare, dealing with the issues that impact human health upstream and before disease actually manifests, and utilizing the tools of personalized medicine are all predicted to be crucial in the fight for better health.
Currie, G. P., Lee, D. K. C., & Lipworth, B. J. (2006). Long-Acting β2-Agonists in Asthma. Drug Safety, 29(8), 647–656. https://doi.org/10.2165/00002018-200629080-00002.
Dunham-Snary, K. J., Sandel, M. W., Sammy, M. J., Westbrook, D. G., Xiao, R., McMonigle, R. J., Ratcliffe, W. F., Penn, A., Young, M. E., & Ballinger, S. W. (2018). Mitochondrial - nuclear genetic interaction modulates whole body metabolism, adiposity and gene expression in vivo. EBioMedicine, 36, 316–328. https://doi.org/10.1016/j.ebiom.2018.08.036.
Eichler E. E. (2019). Genetic Variation, Comparative Genomics, and the Diagnosis of Disease. The New England journal of medicine, 381(1), 64–74. https://doi.org/10.1056/NEJMra1809315.
Engel, G. L. (1977). The need for a new medical model: a challenge for biomedicine. Science, 196, 129–136.
Fetterman, J. L., Zelickson, B. R., Johnson, L. W., Moellering, D. R., Westbrook, D. G., Pompilius, M., Sammy, M. J., Johnson, M., Dunham-Snary, K. J., Cao, X., Bradley, W. E., Zhang, J., Wei, C. C., Chacko, B., Schurr, T. G., Kesterson, R. A., Dell'italia, L. J., Darley-Usmar, V. M., Welch, D. R., & Ballinger, S. W. (2013). Mitochondrial genetic background modulates bioenergetics and susceptibility to acute cardiac volume overload. The Biochemical journal, 455(2), 157–167. https://doi.org/10.1042/BJ20130029.
Frangoul, H., Altshuler, D., Cappellini, M. D., Chen, Y.-S., Domm, J., Eustace, B. K., Foell, J., de la Fuente, J., Grupp, S., Handgretinger, R., Ho, T. W., Kattamis, A., Kernytsky, A., Lekstrom-Himes, J., Li, A. M., Locatelli, F., Mapara, M. Y., de Montalembert, M., Rondelli, D., … Corbacioglu, S. (2020). CRISPR-Cas9 Gene Editing for Sickle Cell Disease and β-Thalassemia. New England Journal of Medicine, 384(3), 252–260. https://doi.org/10.1056/NEJMoa2031054.
Gillmore, J. D., Gane, E., Taubel, J., Kao, J., Fontana, M., Maitland, M. L., Seitzer, J., O’Connell, D., Walsh, K. R., Wood, K., Phillips, J., Xu, Y., Amaral, A., Boyd, A. P., Cehelsky, J. E., McKee, M. D., Schiermeier, A., Harari, O., Murphy, A., … Lebwohl, D. (2021). CRISPR-Cas9 In Vivo Gene Editing for Transthyretin Amyloidosis. New England Journal of Medicine, 385(6), 493–502. https://doi.org/10.1056/NEJMoa2107454.
Green, A. R., Carrillo, J. E., & Betancourt, J. R. (2002). Why the disease-based model of medicine fails our patients. The Western journal of medicine, 176(2), 141–143.
Hutchinson, R. N., & Shin, S. (2014). Systematic review of health disparities for cardiovascular diseases and associated factors among American Indian and Alaska Native populations. PloS one, 9(1), e80973. https://doi.org/10.1371/journal.pone.0080973.
Loos R. J. (2009). Recent progress in the genetics of common obesity. British journal of clinical pharmacology, 68(6), 811–829. https://doi.org/10.1111/j.1365-2125.2009.03523.x.
Nuttall F. Q. (2015). Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutrition today, 50(3), 117–128. https://doi.org/10.1097/NT.0000000000000092.
Osei, K., & Gaillard, T. (2017). Disparities in Cardiovascular Disease and Type 2 Diabetes Risk Factors in Blacks and Whites: Dissecting Racial Paradox of Metabolic Syndrome. Frontiers in endocrinology, 8, 204. https://doi.org/10.3389/fendo.2017.00204.
Schork, N. J. (2015). Personalized medicine: Time for one-person trials. Nature, 520(7549), 609–611. https://doi.org/10.1038/520609a.
Statistics Canada. (2019). Overweight and obese adults, 2018 [Infographic]. https://www150.statcan.gc.ca/n1/pub/82-625-x/2019001/article/00005-eng.htm.
van der Wijst, M. G. P., de Vries, D. H., Brugge, H., Westra, H.-J., & Franke, L. (2018). An integrative approach for building personalized gene regulatory networks for precision medicine. Genome Medicine, 10(1), 96. https://doi.org/10.1186/s13073-018-0608-4.
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