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Pharmacogenomics of Diabetes Mellitus

21 Jan 2021 5:58 PM | Anonymous

By: Miriam Bisada, PharmD Candidate 2021 and Yostena Khalil, PharmD Candidate 2022; St. Louis College of Pharmacy at University of Health Sciences and Pharmacy in St. Louis.

Mentor: Erica F. Crannage, Pharm.D., FCCP, BCPS, BCACP; Associate Professor, St. Louis College of Pharmacy at University of Health Sciences and Pharmacy in St. Louis; Clinical Pharmacist Mercy Clinic-Family Medicine

Diabetes mellitus is a disease of abnormal carbohydrate metabolism related to relative or absolute impairment in insulin secretion. Pharmacogenomics, the study of drug responses impacted by genes, is a drastically growing field especially after the Human Genome Project (HGP) mapped DNA for the entire human genome in 2000. However, the clinical utilization of pharmacogenomics has been limited to severe idiosyncratic adverse drug reactions, variations in drug metabolism, and chemotherapy interventions.1 The International Diabetes Foundation estimated 463 million in the world were diagnosed with diabetes in 2019 and predicted 578 million by 2030.2 Through the efforts of the Genome-Wide Association Study (GWAS), 50 genetic loci were found associated with various glycemic traits and at least 90 loci with type 2 diabetes.3 Over the past 20 years, many monogenic forms of diabetes have been identified which have great response to targeted treatments. However, diabetes remains a complex polygenic disease with many variants contributing to the risk and prevalence.

Type 1 diabetes mellitus (T1DM) is likely to be triggered at an early age by the development of autoantibodies against islet cells, insulin, and/or glutamic acid decarboxylase.4 The greater the number of types of beta cell autoantibodies, the higher the risk of progression of diabetes.5 There are several genetic mutations that have been identified which increase a patient’s likelihood of developing type 1 diabetes mellitus. A patient is more likely to develop type 1 diabetes mellitus if they have variants of the HLA-DQA1, HLA-DQB1, and HLA-DRB1 genes.6 These genes belong to the human leukocyte antigen (HLA) complex which is a group of proteins encoded by the MHC gene complex, a large locus of vertebrate DNA, in humans. These proteins are responsible for the regulation of the immune system, which aids our immune system to recognize foreign substances and differentiate them from our own. Along with genetics, environmental factors, such as a virus, can also trigger the progression of type 1 DM.7

Unlike T1DM, diet and exercise can significantly decrease the development of diabetes type 2 (T2DM), but these measures alone have not been effective at curtailing the increase in prevalence of T2DM, especially with the growing obesity pandemic.8 T2DM is an insulin resistant disease and patients with a first-degree diagnosed relative are three times likely at risk for T2DM. Genes that are candidates for disease susceptibility involve pancreatic β cell function, insulin action/glucose metabolism, or other metabolic conditions that increase T2DM risk. There are more than 50 candidate genes affecting T2DM, but only few are promising for future use in clinical practice; PPARγ, ABCC8, KCNJ11, and CALPN10 mutations are associated with T2DM. CALPN10, encodes intracellular calcium dependent cysteine protease, has been linked with T2DM diagnosis.

Metformin is the first-line treatment of T2DM but causes gastrointestinal (GI) effects in about 10% of patients. Patients had better response to metformin with reduced GLUT2 transport (up to 0.5% HbA1c); however, side effects were more pronounced in patients with reduced function of SLC22A1 genes had 2.4 times higher odds (95% confidence interval (CI) =1.48–3.93, P=0.001) of developing GI side effects.9 There was also a 0.33% greater reduction in HbA1c for patients taking metformin that carried an C allele for SLC2A2 single-nucleotide polymorphism rs8192675 (reduces the expression of GLUT2 that alters metformin action) than patients that didn’t.8 SLC22A1 (encodes OCT1 transporter), SLC29A4 (encodes PMAT transporter), and SLC6A4 (encodes serotonin transporter) genes are expressed in human gut responsible for transporting of metformin. The GWAS found that patients with three or more alleles of these genes affect their tolerance of metformin with odds of 2.15 (95% CI, 1.2–4.12).8 However, current metformin pharmacogenomics data has a 3 evidence level which means it is not clinically practical yet as the results have not been replicated or have a clear evidence based association.12 Patients with loss-of-function variants in CYP2C9 have higher exposure to sulfonylureas and thus experience a greater glycemic response.10 ATP binding cassette, subfamily C, member 8 (ABCC8), has a high affinity for sulfonylurea receptors along with KCNJ11 as both regulate hormones released like insulin and glucagon in beta cells. Peroxisome proliferator-activated receptor-γ (PPARγ) is widely studied because of its adipocyte and lipid metabolism effects as one form can decrease insulin sensitivity and increase T2DM risk. PPARγ is the therapeutic target of thiazolidinediones (TZD) class of medications. Variants in PPARγ where patients carry alleles experienced an increased glycemic response to TZDs with odds of 2.32 ([95% CI = 1.10–4.87] P = 0.03).9 Patients with altered CYP2C8 and SLCO1B1 activity have a different response to pioglitazone and rosiglitazone in glycemic response and side effects that might be driven from genotype-based differential drug responsiveness.13 Overall, TZD have a grade C level of evidence on the CPIC guidelines and a 3 PharmGKB level of evidence so there are no prescribing actions because dosing based on PGX data has weak evidence or unclear.12 Unlike T1DM, T2DM has defined genes linked to drug mechanisms like ABCC8, which encodes the high-affinity sulfonylurea receptor, and KCNJ11, both of which are known to be ATP-sensitive potassium channels to regulate insulin and glucagon release.

An uncommon subtype of T2DM known as Maturity Onset Diabetes of the Young (MODY) occurs before the age of 25 and accounts for <5% of all the T2DM cases.7 Currently, there are six forms of MODY which are caused by mutations in GCK, HNF, and NEURODI genes involved in metabolism of glucose, regulation of insulin, glucose transport, and development of fetal pancreas.8 It was thought that MODY3 severe diabetes, to be T1DM, but changed to T2DM diagnosis once realized that the mutations in MODY3 are sensitive to sulfonylureas. Given the autosomal dominant inheritance of MODY, early genetic diagnosis may reduce long-term complications. Revealing genetic mutations could help us better diagnose and personalize treatments.

With a growing focus on genetic studies and pharmacogenomics, our understanding is expanding to where we could match treatments based on a patient's genomic makeup in the near future. A world of personalized medicine practice through global research and data-based medicine could reduce healthcare cost with more rapid identification of needed preventative strategies and/or ideal treatments. The Type 1 Diabetes TrialNet has a strategy to gene test high risk patients and use preventive measures to avoid onset of disease since T1DM is not curable.8 Early findings remain promising; however, we are not at a point where the benefits of using genetic information for diabetes is robust enough to be generalizable to all patients and is outweighed by the cost.8 Although genetic links to diseases, drug mechanisms and effects have been reported in literature, more comprehensive robust studies are needed before pharmacogenomics for diabetes can be utilized in routine clinical disease management. Pharmacists, as interprofessional team members with a goal to optimize medications, should continue to contribute to and monitor future pharmacogenomic research efforts to fulfill the promise of the Human Genome Project and find consistent results across populations for genetic conclusion to better our patient outcomes.

References

  1. Pearson ER. Pharmacogenetics and target identification in diabetes. Current opinion in genetics & development. 2018; 50: 68–73. https://doi.org/10.1016/j.gde.2018.02.005
  2. International Diabetes Federation. IDF Diabetes Atlas, 9th edn. Brussels,
  3. Belgium: 2019. Available at: https://www.diabetesatlas.org/en/sections/worldwide-toll-of-diabetes.html
  4. Floyd JS, Psaty BM. The application of genomics in diabetes: Barriers to discovery and implementation. Diabetes Care. 2016;39(11): 1858–1869.
  5. Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2020. Atlanta, GA: Centers for Disease Control and Prevention, U.S. Dept of Health and Human Services; 2020.
  6. Pociot F, Lernmark Å. Genetic risk factors for type 1 diabetes. Lancet (London, England), 2016;387(10035): 2331–2339. https://doi.org/10.1016/S0140-6736(16)30582
  7. National Library of Medicine. Type 1 diabetes: MedlinePlus Genetics. (2020, August 18). Page last updated on 18 August 2020. Retrieved December 22, 2020, from https://medlineplus.gov/genetics/condition/type-1-diabetes/#causes
  8. World Health Organization: Available at: https://www.who.int/genomics/about/Diabetis-fin.pdf accessed on December 11, 2020.
  9. Pearson ER. Diabetes: Is There a Future for Pharmacogenomics Guided Treatment? Clinical Pharmacology & Therapeutics. 2019; 106(2): 329–337. https://doi.org/10.1002/cpt.1484
  10. Zhou K, Pedersen HK, Dawed AY, Pearson ER. Pharmacogenomics in diabetes mellitus: insights into drug action and drug discovery. Nature Reviews Endocrinology. 2016; 12(6): 337–346. https://doi.org/10.1038/nrendo.2016.51
  11. Pollastro C, Ziviello C, Costa V, Ciccodicola A. Pharmacogenomics of Drug Response in Type 2 Diabetes: Toward the Definition of Tailored Therapies? PPAR Research. 2015; 1–10. https://doi.org/10.1155/2015/415149
  12. Caudle K, Klein T, Hoffman J, et al. Incorporation of Pharmacogenomics into Routine Clinical Practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline Development Process. Current Drug Metabolism, 2014; 15(2): 209–217. https://doi.org/10.2174/1389200215666140130124910
  13. Clinical annotation level of evidence – PharmGKB. Available at: https://www.pharmgkb.org/
  14. Dawed, A., Donnelly, L., Tavendale, R., et al. Zhou, K. CYP2C8 and SLCO1B1 Variants and Therapeutic Response to Thiazolidinediones in Patients with Type 2 Diabetes. 2016; 39:1902–1908. Retrieved January 18, 2021, from https://care.diabetesjournals.org/content/39/11/1902


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