was successfully added to your cart.



Improving Outcomes and Reducing Healthcare Costs for Diabetes and Its Comorbidities With Pharmacogenetics Guided Medication Therapy

By | Diabetes, PGx in the News, Pharmacogenetic Testing, Precision Medicine, Provider | No Comments
[clickToTweet tweet=”New White Paper Available from MD Labs: ‘Benefits of Pharmacogenetics in Diabetes Management'” quote=”‘Pharmacogenomics, the study of genetic mediators of medication response, has demonstrated success in improving pharmacotherapy outcomes across a range of medical conditions.'”]
Volume 5 White Paper – Improving Outcomes and Reducing Healthcare Costs for Diabetes and Its Comorbidities with Pharmacogenetics Guided Medication Therapy


MD Labs logo


Pharmacogenetics in Practice

Volume 5


Improving Outcomes and Reducing Healthcare Costs for Diabetes and Its Comorbidities With Pharmacogenetics Guided Medication Therapy




Diabetes continues to be a growing epidemic in the United States and one of the more costly conditions to treat long-term. Data from the 2017 Centers for Disease Control (CDC) report on diabetes indicates that 30.3 million people (9.4%) in the United States, and over 25% of those aged 65 or older, had diabetes in 2015, with an estimated 1.5 million new cases each year.1 Moreover, an estimated 33.9% of US adults had prediabetes, including nearly half of those aged 65 years or older. The social and economic burden of diabetes is likewise staggering. Diabetes was the seventh leading cause of death in the United States in 2015. Total direct and indirect estimated costs of diagnosed diabetes was $245 billion, accounting for over 20% of health care dollars in the US.2


Copyright Rxight

Care for diabetes patients is complicated by the presence of comorbid conditions. Approximately half of adults with diabetes have at least one comorbid chronic disease and 40% of elderly adults with diabetes have four or more comorbid conditions,3,4 exceeding all other major chronic conditions except ischemic heart disease in extent of comorbidities.3 Hyperlipidemia (77% comorbidity), hypertension (49% comorbidity), and coronary artery disease (11% comorbidity), are among the most common comorbid conditions due to their pathophysiological overlap with diabetes.5,6 Unsuccessful management of these conditions in diabetic patients can result in significantly greater cost burden and reductions in quality of life.7 Indeed, cardiovascular disease is the primary cause of mortality in diabetes.8 Conversely, effective management of cardiovascular conditions can enhance the management of diabetes.9 Other conditions such as depression (19% comorbidity)3,6 also worsen diabetes outcomes by causing poorer adherence to medications and self-management.10 Similar to cardiovascular conditions, effective management of these conditions can reduce economic burden and can improve glycemic outcomes and quality of life.10-14


Pharmacogenomics, the study of genetic mediators of medication response, has demonstrated success in improving pharmacotherapy outcomes across a range of medical conditions. Briefly, pharmacogenomic testing evaluates variants in genes known to affect the pharmacokinetics or mechanism of action of a medication. Specifically, much research has focused on the CYP450 genes, which encode enzymes involved in first pass metabolism of many medications. Variation in these genes can increase or reduce clearance of medications from the body, predisposing individuals to a decrease in medication efficacy and an increase in the prevalence of sometimes life-threatening adverse drug reactions. Pharmacogenomic implementation guidelines have been published for medications used to treat a number of common chronic conditions such as depression, cardiovascular disease, and chronic pain, among others.15-20


Surprisingly, genetic variation in pharmacogenomic genes is quite common; virtually every individual (90-99%)21-22 carries at least one clinically actionable variant. Pharmacogenomic variation does not typically pose health risks until one is exposed to a drug for which the variant is relevant. However, increased exposure to medications due to medical comorbidities will increase the occurrence of gene-drug interactions. The frequency of comorbidity and polypharmacy in the diabetic population makes this condition a prime target for pharmacogenetic intervention. In this white paper, we will review the evidence supporting the association of genetic variation with medication outcomes for medications used to treat diabetes and its common comorbidities: hypertension, hyperlipidemia, major adverse cardiovascular events, and depression. Given that type 1 diabetes accounts for only 5-10% of all diabetes cases,1 this review will primarily focus on management of type 2 diabetes (T2DM).


Pharmacogenomics of Diabetes Management


Pharmacotherapy for diabetes mellitus consists of a number of agents that differ in their pharmacokinetics, mechanism of action, efficacy, and side effect profile.



Metformin is considered first-line monotherapy for type 2 diabetes by both the American Diabetes Association (ADA) and the American Association of Clinical Endocrinologists (AACE).8 Metformin is not metabolized, thus avoiding the polymorphic CYP450 family of enzymes. Some studies have focused on genetic variation in organic cation transporters responsible for metformin uptake into the bloodstream and hepatocytes or secretion into the renal tubular cells, with mixed results.23-28 Another gene, ATM, which encodes a serine/threonine kinase, initially showed promise for predicting metformin effects on HbA1C values,29,30 but failed to replicate in a later large study.31 More research is needed to conclusively demonstrate an effect of these polymorphisms on metformin response.



Sulfonylurea medications are generally divided into first-generation medications (including chlorpropamide, tolazamide, and tolbutamide) and second-generation medications (including glipizide, glyburide, and glimepiride). While they remain the second most commonly prescribed oral medications for the treatment of T2DM, their place in treatment algorithms has become more controversial with the approval of newer medications that are less prone to cause hypoglycemia. The ADA recommends sulfonylurea use equally to other second-line agents, while the AACE recommends their use only after lower-risk medications have been tried.8


All of the second-generation sulfonylureas (as well as tolbutamide) are metabolized by CYP2C9, and several studies have demonstrated an impact of CYP2C9 genetic variation on serum levels of these medications. For example, in 2010, Zhou et al found that carriers of the CYP2C9*2 and *3 alleles were 3-4 times more likely to achieve target HbA1c after 18 months of initiation of a combination of sulfonylurea and metformin, compared with patients with the normal function genotype.32 Variant allele carriers were also less likely to experience treatment failure with sulfonylurea monotherapy compared to functional allele carriers. Similarly, Becker et al found that CYP2C9*2 and *3 carriers needed a lower tolbutamide dose to achieve optimal outcome.33 Furthermore, a study of Indian patients with T2DM showed that CYP2C9*2 and *3 allele carriers had a better response to glyburide without an increase in risk of hypoglycemia.34 Finally, a small study of glimepiride in Asian patients showed a greater mean change in HbA1c levels at 6 months among CYP2C9*3 carriers.35



Pioglitazone and rosiglitazone are the two thiazolidinedione medications that are FDA-approved for T2DM. While the ADA considers these medications second-line agents, the AACE lists them as fifth line agents due to their risk of causing edema, heart failure, and fractures.8 Both medications are metabolized primarily by CYP2C8 and to a lesser extent by CYP2C9 and CYP3A4.36 These medications are distributed into the hepatocytes by an organic anion transporter encoded by the SLCO1B1 gene, which has genetic polymorphisms known to impact response to statin medications.18 Only one study has examined the impact of these genes on thiazolidinedione outcomes. Dawed et al found that the CYP2C8*3 variant was associated with reduced glycemic response to rosiglitazone, while the SLCO1B1 rs4149056 variant was associated with improved glycemic response.37


Newer Classes

Newer classes of oral glucose lowering agents that have come to market in the last 10 years include DPP- 4 inhibitors, SGLT2 inhibitors, and GLP1 agonists. Because of their recent entry to the market, most of these medications have not yet been extensively studied for pharmacogenomic associations.


DPP-4 inhibitors include sitagliptin, saxagliptin, linagliptin and alogliptin. They are considered second- line agents by the ADA, but fourth line agents by the AACE.8 Like metformin, most are renally excreted and avoid metabolism by CYP450 enzymes.36 One exception, saxagliptin, is metabolized by CYP3A4/5 but has not yet been evaluated for an association with any of the known polymorphisms in CYP3A4 and CYP3A5. One recent study implicated a single polymorphism near the CTRB1 and CTRB2 genes in response to DPP-4 inhibitors38 but this finding has not yet been replicated.


SGLT2 inhibitors include dapagliflozin, empagliflozin and canagliflozin. They are considered second line agents by the ADA and third line medications by the AACE.8 Dapagliflozin and canagliflozin are metabolized by UGT enzymes,39-40 while empagliflozin is primarily excreted unchanged with only minor metabolism by UGT enzymes.41 Only one study has evaluated pharmacogenetic associations with SGLT2 inhibitors, but found no effect of polymorphisms in the SLC5A2 gene on empagliflozin treatment response.42 GLP1 agonists are some of the newest oral glucose lowering agents and include exenatide, liraglutide, lixisenatide, dulaglutide, and semaglutide. They are considered second line agents by the ADA and second or third line medications by the AACE.8/sup> They are metabolized by ubiquitous proteolytic enzymes that are unlikely to be affected by genetic variation. No studies have yet evaluated these medications for pharmacogenetic associations.



The pharmacogenomics of oral glucose lowering agents continues to be an emerging field of inquiry. Research into predictors of metformin response continues to expand, and it is likely that research efforts will begin focusing on some of the newer classes of medications. While more research is needed to support the use of pharmacogenomics to predict patient outcomes with other classes of diabetes medications, there are sufficient data to support the use of pharmacogenomics with sulfonylureas and, to a less extent, thiazolidinediones.


Pharmacogenomics of Cardiovascular Medications


Hyperlipidemia and hypertension are two of the most common diabetes comorbidities. Together with hyperglycemia, these conditions constitute the “three H’s” of metabolic syndrome. These two conditions are significant predictors of overall cardiovascular health in diabetics and ADA guidelines emphasize both hyperlipidemia and hypertension management as a key component of effective diabetes treatment.43


Hyperlipidemia is the most common diabetes comorbidity and is a more important risk factor for cardiovascular disease than hyperglycemia.44 While the prevalence of LDL-C levels is similar in diabetics and non-diabetics, diabetic patients have a 2-3 fold increased risk of elevated triglycerides and low HDL-C compared to the general population.44 Moreover, for the same serum lipid levels, individuals with diabetes have a greater risk of cardiovascular disease than non-diabetic individuals.45 Similarly, the risk of coronary artery disease in patients with comorbid diabetes and hypertension is three-fold higher than in patients with either diabetes or hypertension alone46 and the risk of stroke, nephropathy, and retinopathy is also increased.47


Treatment of these underlying risk factors reduces the risk of cardiovascular disease in diabetic individuals. Statin treatment reduced the risk of CVD-related mortality by 19-25% in two studies.45 Likewise, the UKPDS 36 study showed that for each 10 mm Hg decrease in systolic blood pressure (SBP), there was a corresponding decrease in deaths related to diabetes (15% decrease), myocardial infarction (11% decrease), microvascular complications (13% decrease), and any complication (12% decrease).48 Improving efficacy and adherence to statin and antihypertensive therapy via pharmacogenetic-guided treatment would be expected to result in further decreases in major adverse cardiovascular events.



Statins are the preferred treatment for hyperlipidemia and are effective at lowering lipid levels and consequent risk of cardiovascular disease in individuals with diabetes.44 Medications in the statin class include atorvastatin, fluvastatin, lovastatin, pravastatin, rosuvastatin, and simvastatin. Like many medication classes, statins are not uniformly metabolized in the same fashion. Fluvastatin is primarily metabolized by CYP2C9, while atorvastatin, lovastatin, and simvastatin are metabolized by CYP3A4/5. Pravastatin and rosuvastatin do not undergo CYP-mediated metabolism.49

Cytochrome P450 variation has been shown to have an impact on statin efficacy and safety profiles. Carriers of the CYP3A4*22 variant, which reduces activity of the CYP3A4/5 enzyme system, have higher plasma concentrations of simvastatin and was associated with reduced dose requirement and greater reductions in total cholesterol and LDL-c levels.49 Carriers of the CYP3A5*1 allele, which increases activity of the CYP3A4/5 enzyme system, had reduced response to simvastatin, lovastatin, and atorvastatin.50 Genetic variation in CYP2C9 has been shown to increase serum levels of fluvastatin,51,52 and has been shown to increase adverse event rates by over 6-fold.53


Another well-known example of hyperlipidemia pharmacogenomics is the SLCO1B1 gene, which encodes a protein that transports statins into the hepatocyte. As the hepatocyte is both the site of action and the site of metabolism for most statin medications, this protein plays a critical role in their clinical effect.49 Individuals who carry two copies of the SLCO1B1 rs4149056 variant, which reduces transport of statin into the hepatocyte, have shown a 120-221% increase in simvastatin AUC compared with wild-type patients.54 Moreover, these individuals have a nearly 17-fold increase in likelihood of statin-induced myopathy compared to individuals with the normal genotype.55 This increase in myopathy risk may explain the two-fold increase in discontinuation rates among variant carriers.56 These data have led the Clinical Pharmacogenetic Implementation Consortium (CPIC) to establish clinical guidelines for the use of SLCO1B1 genotyping to inform simvastatin prescribing.18


SLCO1B1 variation has also been found to impact efficacy of some statins. Carriers of the rs4149056 allele have shown reduced total cholesterol and LDL-c response with pravastatin and atorvastatin, while SLCO1B1*14 carriers showed improved response to fluvastatin.49 Pharmacokinetic variability, though not necessarily clinical outcomes, has been observed as a function of SLCO1B1 genotype with all of the statins except lovastatin.49


Angiotensin Converting Enzyme Inhibitors

Angiotensin converting enzyme (ACE) inhibitors, along with angiotensin II receptor blockers (ARBs), are considered first line therapy for hypertension in diabetes due to their nephroprotective effects.8 ACE facilitates the conversion of angiotensin I to angiotensin II, which limits the production of aldosterone, thereby reducing sodium and water reabsorption in the kidney. While some studies have evaluated associations between ACE inhibitor efficacy and genetic variants in the ACE gene (in particular, rs1799572), the results have been conflicting.57,58 As a class, ACE inhibitors largely avoid CYP-mediated metabolism, reducing the impact of metabolism-related pharmacogenomic variants. However, studies have found that SLCO1B1 variation can increase enalapril serum levels59 and may increase risk of enalapril-induced cough by as much as 7-fold.60 Though more research is needed, SLCO1B1 is an intriguing target as a pharmacogenetic predictor of enalapril tolerability.


Angiotensin II Receptor Blockers

Angiotensin II receptor blockers (ARBs) target the angiotensin II receptor. However, studies have failed to show a consistent association between genetic variation in angiotensin receptor genes and ARB response577 Unlike ACE inhibitors, many ARBs are metabolized by the CYP450 enzyme system. In particular, losartan, azilsartan, candesartan, valsartan, and irbesartan are all metabolized to greater or lesser degrees by CYP2C9 and CYP3A4/5.61 Pharmacogenomic variation in these genes has been shown to impact the clinical profile of these medications. For example, data shows that variants in CYP2C9 can impact losartan pharmacokinetics, reducing its conversion to its pharmacologically active form.57 Corresponding reductions in losartan efficacy have also been observed.62-64 In particular, one study looked at the impact of CYP2C9 variation on losartan response in type I diabetic patients with nephropathy, finding that individuals carrying the CYP2C9*3 variant showed less improvement after 4 months of losartan therapy. CYP2C9 variation has also been shown to impact irbesartan response65-66 and changes in pharmacokinetic parameters as a function of CYP2C9 genotype have been reported with candesartan67,68 and valsartan.69 Thus, CYP2C9 genotype may be an important predictor of response to ARBs in hypertensive diabetics.



β-blockers are extensively prescribed for heart failure and continue to be prescribed as an effective therapy for the treatment of hypertension in diabetes, especially as an adjunctive medication.8 β-blockers that have been evaluated for pharmacogenomic associations include carvedilol, metoprolol, and propranolol. A considerable amount of data has shown significant impacts of CYP450 variation on β-blocker pharmacokinetics and outcomes. The FDA-approved drug label for metoprolol notes the impact of CYP2D6 poor metabolism on pharmacokinetics and cardioselectivity of metoprolol, while the Dutch Pharmacogenetics Working Group has published metoprolol dosing guidelines as a function of CYP2D6 metabolizer status.70 The impact of CYP2D6 genotype on carvedilol pharmacokinetics and clinical response has also been extensively demonstrated.57,71 While CYP2D6 variation has been shown to impact propranolol pharmacokinetics, its effect on response is still somewhat controversial.72-74 Several well- studied pharmacogenomic variants have been shown to impact β-blocker pharmacodynamics, including variants in genes encoding the β-adrenergic receptors themselves (ADRB1 and ADRB2).57,75,76


Anticoagulant/Antiplatelet Medications

When diabetes-related cardiovascular disease progresses to the point where intervention is required, anticoagulant and antiplatelet medications are often prescribed to manage risk of thrombosis and stroke, particularly after procedures such as heart valve replacement and stent placement. Clopidogrel and warfarin are some of the most commonly prescribed medications in these situations and both are among the best-known examples of pharmacogenomics.


Clopidogrel is one of several medications to have a black box warning in the FDA-approved drug label that explicitly calls out differential risk as a function of patient genotype, noting that “effectiveness of Plavix depends on conversion to an active metabolite by the cytochrome P450 (CYP) system, principally CYP2C19.” The black box warning further notes that clinical tests are available to identify CYP2C19 poor metabolizers and recommends that practitioners “consider use of another platelet P2Y12 inhibitor in patients identified as CYP2C19 poor metabolizers.”77 CYP2C19 genotype may also interact with diabetes status to further reduce antiplatelet response to clopidogrel.78


Copyright Rxight

In late 2017, a landmark study was published by Cavallari et al that assessed clinical outcomes following clinical implementation of CYP2C19 testing in 1,815 patients who underwent percutaneous coronary intervention (PCI).79 Those with a normal genotype received standard therapy (approximately 85% received clopidogrel, with the rest receiving an alternative antiplatelet medication). However, if a patient carried a loss-of-function (LOF) allele, their provider was alerted and alternative anti-platelet therapy was suggested (approximately 61% received alternative antiplatelet therapy, with the rest receiving clopidogrel). After 12 months, patients with a LOF allele who received clopidogrel were 2.3 times more likely to experience a major adverse cardiovascular event than patients with a LOF allele who received alternative therapy (p = 0.013). Patients with an LOF allele who received alternative therapy had similar outcomes to those without an LOF allele.


These data indicate that CYP2C19 testing is an effective predictor of post-PCI clopidogrel outcomes. Prospective use of CYP2C19 genotyping can reduce risk of major adverse cardiovascular risk post-PCI and can also be used to triage individuals to the most appropriate and cost-effective therapy. For example, CYP2C19 normal metabolizers taking alternative therapies may be able to be triaged to the less expensive clopidogrel, reserving the more expensive alternative medications for individuals carrying LOF alleles.


The case of warfarin has been somewhat more controversial. FDA labeling has included CYP2C9 genotype-guided dosing information since 2007.80 CPIC guidelines were published in 2011 and updated in 2017.20 However, trials examining clinical utility of warfarin have produced mixed results. Early studies failed to meet the primary endpoint of time in therapeutic range,81-82 although these studies have also been criticized for failing to mirror standard clinical practice.83 The COAG trial did show a trend toward fewer adverse events in the genotype-guided arm81 and the GIFT trial was launched to further explore these clinical outcomes.84 This study, which enrolled 1,650 patients randomized to either CYP2C9-guided dosing or standard clinical dosing, demonstrated a 27% reduction in the composite endpoint of venous thromboembolism, major bleeding, INR ≥ 4, or death in the genotype-guided arm compared to the clinical dosing arm. These data argue powerfully for the clinical implementation of warfarin despite earlier failures with less clinically relevant endpoints.


Economic Outcomes

To date, cost-effectiveness analyses for cardiovascular medications have been limited to clopidogrel and warfarin only. These analyses generally follow a similar pattern of comparing pharmacogenomic-guided therapy with one or more alternative approaches that do not require pharmacogenomic testing.


In the case of clopidogrel, one cost-effectiveness analysis compared pharmacogenomic-guided therapy antiplatelet selection (with normal allele carriers receiving clopidogrel and LOF allele carriers receiving alternative antiplatelet therapy) with universal clopidogrel therapy, universal alternative antiplatelet therapy, and platelet-reactivity guided therapy.85 The analysis evaluated costs over the lifetime of a 60-year-old patient undergoing a PCI and found that pharmacogenomic-
guided testing resulted in the lowest overall cost over a lifetime and with the highest number of quality-adjusted life years gained. Another study evaluated the cost-effectiveness of genotype-guided antiplatelet therapy over a 30-day and 1-year window, finding that genotype-guided treatment was cost effective over 30 days and 1 year in 62 and 70% of simulations, respectively.86 A 2015 review of CYP2C19-guided antiplatelet therapy found that genotype-guided therapy was cost-effective in all seven studies that were reviewed, not including the two studies described previously.87


Analyses of warfarin cost-effectiveness have produced largely, though not universally, positive results. Seven cost-effectiveness analyses have demonstrated genotype-guided anticoagulant therapy to be cost- effective relative to standard warfarin treatment and/or universal use of direct oral anticoagulant therapy,88-94 with two analyses finding standard warfarin dosing to be superior95-96 and one analysis finding standard warfarin dosing and genotype-guided dosing to be essentially equivalent.97



Pharmacogenomics has a significant role to play in the management of cardiovascular risk factors in diabetes. Pharmacogenomic testing for clopidogrel and warfarin is now well established, with recent trials providing definitive evidence for clinical utility of genotyping prior to prescription of these medications. Pharmacogenomic testing for CYP3A4, CYP3A5 and SLCO1B1 can also be used to optimize the selection and dosing of statin medications for hyperlipidemia. In particular, SLCO1B1 already has published guidelines on its clinical implementation and routine testing is occurring at many major medical centers. Similarly, testing for SLCO1B1, CYP2D6, and CYP2C9 for the individualization of ACE inhibitors, beta-blockers, and ARBs, respectively, may reduce the time to effective dose (thus improving efficacy), while decreasing the risk of side effects (thus increasing adherence), resulting in better hypertension outcomes. Thus, adoption of pharmacogenomics is likely to result in significant improvements in cardiovascular health and quality of life among individuals with type 2 diabetes, with a reduced overall cost to the healthcare system. Moreover, by targeting a high-cardiovascular-risk population such as patients with type 2 diabetes, cost-effectiveness of pharmacogenomic testing is likely to exceed universal testing of the general population.


Pharmacogenomics of Antidepressant Medications


Few conditions have a greater impact on global functioning and quality of life than depression. In diabetes, comorbid depression is associated with poorer cognitive functioning, worsened glycemic outcomes, decreased adherence to behavioral and pharmacotherapeutic interventions, greater frequency and severity of diabetes complications (such as lower extremity amputation), and decreased quality of life.10,98-100 The prevalence of depression in type 2 diabetes is nearly twice as high (19.1%) compared to those without diabetes (10.7%).101 The relationship between depression and diabetes is likely bidirectional, with depression as both a risk factor for and a consequence of diabetes102 and the two conditions share neurobiological pathways.98


Depression is primarily treated through pharmacotherapy and/or psychotherapy. Overall efficacy of antidepressants in both diabetics and the general population is poor, with only 50% of individuals responding to their first trial of an antidepressant, while intolerability and nonadherence to medications are quite common.103-104 Antidepressant use has been shown to be effective in reducing depressive symptoms, improving glycemic control, and improving quality of life for diabetic patients.14,105,106


Clinical Outcomes

Pharmacogenomic testing in psychiatry is supported by an abundance of data. CPIC guidelines exist for tricyclic antidepressants (TCAs)15 and selective serotonin reuptake inhibitors (SSRIs)16 and over 30 neuropsychiatric medications contain pharmacogenomic information in their FDA-approved package inserts.107 Psychiatric pharmacogenomic testing has been evaluated in multiple published double-blind randomized controlled trials, with all showing statistically significant108-110 or trending111 improvements in depressive symptomatology in the genotype-guided arm relative to the treatment-as-usual arm. Naturalistic studies have likewise shown that genotype-guided treatment can improve antidepressant response by as much as 56% over treatment-as-usual.112-115 Studies have also demonstrated improved antidepressant adherence with genotype-guided treatment relative to standard treatment.108,116,117 Outside of depression, evidence also exists for the use of pharmacogenomics to predict antidepressant outcomes in diabetic neuropathy.118


Economic Outcomes

Psychiatric pharmacogenomic testing has been demonstrated to reduce healthcare medication costs. One large study that compared 2,168 genotype-guided patients to 10,880 matched controls demonstrated savings of over $1,000 USD per person in medication expenditures over a one year timeframe.116 Interestingly, diabetes and cardiovascular medications made up ~28% and ~16% of these savings, respectively. This is likely due to the aforementioned effect of improved mental wellbeing on physical health.14,105,106

Copyright Rxight

Pharmacogenomic testing has also been demonstrated to positively impact and lower healthcare utilization costs. One study found that patients taking genetically inappropriate medications accrued an additional ~$5,200 in healthcare utilization costs relative to patients on genetically appropriate medications,119 while another study found that CYP2D6 abnormal metabolizers had longer hospitalization stays and more adverse events, resulting in increased healthcare costs.120 Additionally, prospective use of psychiatric pharmacogenomic testing reduced healthcare utilization in two studies.117,121



Psychiatric pharmacogenomic testing can improve patient outcomes in depression, leading to increased medication adherence and reduced healthcare costs. Psychiatric pharmacogenomic testing has shown a direct impact on medication costs associated with diabetes and cardiovascular disease and is likely to produce similar benefits in healthcare utilization and patient quality of life. The evidence supporting the clinical utility of pharmacogenomic testing for psychiatric medications may be the most extensive of any disease state.


Future Directions: Genetic Predictors of Diabetes Risk


While not directly related to pharmacogenomics, a brief discussion of genetic predictors of diabetes diagnosis is warranted. Monogenic forms of diabetes (i.e. inherited forms of diabetes caused by single genes) may represent one underutilized area of genetic testing in diabetes. The most well characterized form of monogenic diabetes is maturity-onset-diabetes of the young (MODY), a term encompassing 13 subtypes of the condition, each characterized by a single gene causing the illness presentation. As the clinical presentation of MODY is similar to both type 1 and type 2 diabetes, it is often misdiagnosed and may make up 1-2% of all diabetes cases.122 Neonatal diabetes mellitus, which usually presents in the first six months of life, is also caused by known, detectable mutations.122


Type 2 diabetes mellitus, on the other hand, is multifactorial and is the result of a complex interplay between environment and numerous, largely uncharacterized genetic variants. Indeed, the NHGRI-EBI Catalog of published genome-wide association studies lists well over 1500 statistically significant associations, including variants in TCF7L2, KCNJ11, PPAR-γ, and FTO. However, each of these variants confers a very small increase in absolute risk for type 2 diabetes, making them presently useless as diagnostic markers. However, as the field advances, polygenic risk scores may be developed that, when validated, can assist in the prediction and early detection of type 2 diabetes so that interventions can occur before the disease progresses.



The treatment of type 2 diabetes mellitus, with its myriad of risk factors, treatment options, complications, and comorbidities is incredibly complex, resulting in significant social and economic burden in the United States. Pharmacogenomic testing has been demonstrated to significantly improve the treatment of cardiovascular and psychiatric conditions associated with diabetes, and data on the pharmacogenomics of glycemic agents themselves continue to accumulate. As clinicians adopt the use of pharmacogenomic testing for the management of pharmacotherapy in diabetic patients, it is likely that significant gains will be seen in medication efficacy, tolerability, and adherence, resulting in overall improved outcomes and reduced healthcare costs for the patient and healthcare system.




  1. Centers for Disease Control and Prevention (CDC). National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States. Atlanta, GA: Centers for Disease Control and Prevention; 2014. US Dep. Heal. Hum. Serv. 2009–2012 (2017). doi:10.1177/1527154408322560
  2. American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care 36, 1033–46 (2013).
  3. Druss, B. G. et al. Comparing the national economic burden of five chronic conditions. Health Aff. (Millwood). 20, 233–41 (2001).
  4. Wolff, J. L., Starfield, B. & Anderson, G. Prevalence, Expenditures, and Complications of Multiple Chronic Conditions in the Elderly. Arch. Intern. Med. 162, 2269 (2002).
  5. Lin, P., Kent, D. M., Winn, A. N., Cohen, J. T. & Neumann, P. J. Multiple Chronic Conditions in Type 2 Diabetes Mellitus: Prevalence and Consequences. Am. J. Manag. Care 21, e23–e34 (2015).
  6. Piette, J. D. & Kerr, E. A. The impact of comorbid chronic conditions on diabetes care. Diabetes Care 29, 725–731 (2006).
  7. Vaidya, V., Gangan, N. & Sheehan, J. Impact of cardiovascular complications among patients with Type 2 diabetes mellitus: a systematic review. Expert Rev. Pharmacoecon. Outcomes Res. 15, 487–497 (2015).
  8. DiPiro, J. T. et al. Pharmacotherapy: A Pathopysiological Approach. (McGraw Hill, 2017).
  9. Laiteerapong, N., Huang, E. S. & Chin, M. H. Prioritization of care in adults with diabetes and comorbidity.  Ann. N. Y. Acad. Sci. 1243, 69–87 (2011).
  10. Egede, L. E. & Hernández-Tejada, M. A. Effect of comorbid depression on quality of life in adults with Type 2 diabetes. Expert Rev. Pharmacoecon. Outcomes Res. 13, 83–91 (2013).
  11. Bhattacharya, R., Shen, C., Wachholtz, A. B., Dwibedi, N. & Sambamoorthi, U. Depression treatment decreases healthcare expenditures among working age patients with comorbid conditions and type 2 diabetes mellitus along with newly-diagnosed depression. BMC Psychiatry 16, 247 (2016).
  12. Crowley, M. J. et al. Factors associated with persistent poorly controlled diabetes mellitus: Clues to improving management in patients with resistant poor control. Chronic Illn. 10, 291–302 (2014).
  13. van der Feltz-Cornelis, C. M. et al. Effect of interventions for major depressive disorder and significant depressive symptoms in patients with diabetes mellitus: A systematic review and meta-analysis. Gen. Hosp. Psychiatry 32, 380–395 (2010).
  14. Radojkovic, J. et al. Improvement of Glycemic Control in Insulin-Dependent Diabetics with Depression by Concomitant Treatment with Antidepressants. Med. Sci. Monit. 22, 2133–43 (2016).
  15. Hicks, J. K. et al. Clinical pharmacogenetics implementation consortium guideline (CPIC) for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants: 2016 update. Clin. Pharmacol. Ther. (2016). doi:10.1002/cpt.597
  16. Hicks, J. K. et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6 and CYP2C19 genotypes and dosing of selective serotonin reuptake inhibitors. Clin. Pharmacol. Ther. 98, 127– 134 (2015).
  17. Scott, S. A. et al. Clinical pharmacogenetics implementation consortium guidelines for CYP2C19 genotype and clopidogrel therapy: 2013 update. Clin. Pharmacol. Ther. 94, 317–323 (2013).
  18. Ramsey, L. B. et al. The clinical pharmacogenetics implementation consortium guideline for SLCO1B1 and simvastatin-induced myopathy: 2014 update. Clin. Pharmacol. Ther. 96, 423–8 (2014).
  19. Crews, K. R. et al. Clinical pharmacogenetics implementation consortium guidelines for cytochrome P450 2D6 genotype and codeine therapy: 2014 Update. Clin. Pharmacol. Ther. 95, 376–382 (2014).
  20. Johnson, J. A. et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Pharmacogenetics-Guided Warfarin Dosing: 2017 Update. Clin. Pharmacol. Ther. 102, 397–404 (2017).
  21. Van Driest, S. et al. Clinically Actionable Genotypes Among 10,000 Patients With Preemptive Pharmacogenomic Testing. Clin. Pharmacol. Ther. 95, 423–431 (2014).
  22. Ji, Y. et al. Preemptive Pharmacogenomic Testing for Precision Medicine: A Comprehensive Analysis of Five Actionable Pharmacogenomic Genes Using Next-Generation DNA Sequencing and a Customized CYP2D6 Genotyping Cascade. J. Mol. Diagnostics 18, 438–445 (2016).
  23. Gong, L., Goswami, S., Giacomini, K. M., Altman, R. B. & Klein, T. E. Metformin pathways: pharmacokinetics and pharmacodynamics. Pharmacogenet. Genomics 22, 820–7 (2012).
  24. Shu, Y. et al. Effect of Genetic Variation in the Organic Cation Transporter 1, OCT1, on Metformin Pharmacokinetics. Clin Pharmacol Ther. 83, 273–280 (2010).
  25. Jablonski, K. A. et al. Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the diabetes prevention program. Diabetes 59, 2672–81 (2010).
  26. Zhou, K. et al. Organic Cation Transporter 1 and Glycemic Response to Metformin : A GoDARTS Study.Diabetes 58, 1434–1439 (2009).
  27. Xiao, D. et al. The Impacts of SLC22A1 rs594709 and SLC47A1 rs2289669 Polymorphisms on Metformin Therapeutic Efficacy in Chinese Type 2 Diabetes Patients. Int. J. Endocrinol. 2016, (2016).
  28. Chen, Y. et al. Effect of Genetic Variation in the Organic Cation Transporter 2, OCT2, on the Renal Elimination of Metformin. Pharmacogenet. Genomics 19, 497–504 (2009).
  29. GoDARTS and UKPDS Diabetes Pharmacogenetics Study Group et al. Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat. Genet. 43, 117–20 (2011).
  30. Van Leeuwen, N. et al. A gene variant near ATM is significantly associated with metformin treatment response In type 2 diabetes: A replication and meta-analysis of five cohorts. Diabetologia 55, 1971–1977 (2012).
  31. Florez, J. C. et al. The C allele of ATM rs11212617 does not associate with metformin response in the diabetes prevention program. Diabetes Care 35, 1864–1867 (2012).
  32. Zhou, K. et al. Loss-of-function CYP2C9 variants improve therapeutic response to sulfonylureas in type 2 diabetes: A go-DARTS study. Clin. Pharmacol. Ther. 87, 52–56 (2010).
  33. Becker, M. L. et al. Cytochrome P450 2C9 *2 and *3 polymorphisms and the dose and effect of sulfonylurea in type II diabetes mellitus. Clin. Pharmacol. Ther. 83, 288–92 (2008).
  34. Surendiran, A. et al. Influence of CYP2C9 gene polymorphisms on response to glibenclamide in type 2 diabetes mellitus patients. Eur. J. Clin. Pharmacol. 67, 797–801 (2011).
  35. Suzuki, K. et al. Effect of CYP2C9 genetic polymorphisms on the efficacy and pharmacokinetics of glimepiride in subjects with type 2 diabetes. Diabetes Res. Clin. Pract. 72, 148–154 (2006).
  36. Elk, N. & Iwuchukwu, O. F. Using Personalized Medicine in the Management of Diabetes Mellitus.Pharmacotherapy 37, 1131–1149 (2017).
  37. Dawed, A. Y. et al. CYP2C8 and SLCO1B1 variants and therapeutic response to thiazolidinediones in patients with Type 2 diabetes. Diabetes Care 39, 1902–1908 (2016).
  38. ’t Hart, L. M. et al. The CTRB1/2 locus affects diabetes susceptibility and treatment via the incretin pathway. Diabetes 62, 3275–81 (2013).
  39. Janssen Pharmaceuticals. Invokamet package insert. 1–54 (2014).
  40. AstraZeneca Pharmaceuticals. Farxiga package insert. (2014).
  41. Boehringer Ingelheim Pharmaceuticals. Jardiance package insert. 1–28 (2014).
  42. Zimdahl, H. et al. Influence of common polymorphisms in the SLC5A2 gene on metabolic traits in subjects at increased risk of diabetes and on response to empagliflozin treatment in patients with diabetes. Pharmacogenet. Genomics 27, 135–142 (2017).
  43. Association, A. D. Standards of Medical Care in Diabetes-2017: Summary of Revisions. Diabetes Care 40,S4–S5 (2017).
  44. Carmena, R. Type 2 diabetes, dyslipidemia, and vascular risk: Rationale and evidence for correcting the lipid imbalance. Am. Heart J. 150, 859–870 (2005).
  45. Chaiyakunapruk, N., Boudreau, D. & Ramsey, S. D. Pharmaco-economic impact of HMG-CoA reductase inhibitors in type 2 diabetes. J. Cardiovasc. Risk 8, 127–132 (2001).
  46. Assmann, G. & Schulte, H. The Prospective Cardiovascular Münster (PROCAM) study: prevalence ofhyperlipidemia in persons with hypertension and/or diabetes mellitus and the relationship to coronary heart disease. Am. Heart J. 116, 1713–24 (1988).
  47. Grossman, Y., Shlomai, G. & Grossman, E. Treating hypertension in type 2 diabetes. Expert Opin Pharmacother 15, 2131–2140 (2014).
  48. Adler, A. I. et al. Association of systolic blood pressure with macrovascular and microvascular complications of type 2 diabetes (UKPDS 36): prospective observational study. BMJ 321, 412–9 (2000).
  49. Maxwell, W. D. et al. Impact of Pharmacogenetics on Efficacy and Safety of Statin Therapy for Dyslipidemia. Pharmacotherapy 37, 1172–1190 (2017).
  50. Kivistö, K. T. et al. Lipid-lowering response to statins is affected by CYP3A5 polymorphism.Pharmacogenetics 14, 523–5 (2004).
  51. Kirchheiner, J. et al. Influence of CYP2C9 polymorphisms on the pharmacokinetics and cholesterol-lowering activity of (-)-3S,5R-fluvastatin and (+)-3R,5S-fluvastatin in healthy volunteers. Clin. Pharmacol. Ther. 74, 186–94 (2003).
  52. Zhou, Q., Ruan, Z., Yuan, H. & Zeng, S. CYP2C9*3(1075A>C), MDR1 G2677T/A and MDR1 C3435T are determinants of inter-subject variability in fluvastatin pharmacokinetics in healthy Chinese volunteers. Arzneimittelforschung. 62, 519–24 (2012).
  53. Miroševic Skvrce, N. et al. CYP2C9 and ABCG2 polymorphisms as risk factors for developing adverse drug reactions in renal transplant patients taking fluvastatin: a case-control study. Pharmacogenomics 14, 1419– 31 (2013).
  54. Pasanen, M. K., Neuvonen, M., Neuvonen, P. J. & Niemi, M. SLCO1B1 polymorphism markedly affects the pharmacokinetics of simvastatin acid. Pharmacogenet. Genomics 16, 873–9 (2006).
  55. SEARCH Collaborative Group et al. SLCO1B1 variants and statin-induced myopathy–a genomewide study.N. Engl. J. Med. 359, 789–99 (2008).
  56. Donnelly, L. A. et al. Common nonsynonymous substitutions in SLCO1B1 predispose to statin intolerance in routinely treated individuals with type 2 diabetes: a go-DARTS study. Clin. Pharmacol. Ther. 89, 210–6 (2011).
  57. Eadon, M. T. & Chapman, A. B. A Physiologic Approach to the Pharmacogenomics of Hypertension. Adv. Chronic Kidney Dis. 23, 91–105 (2016).
  58. Flaten, H. K. & Monte, A. A. The Pharmacogenomic and Metabolomic Predictors of ACE Inhibitor and Angiotensin II Receptor Blocker Effectiveness and Safety. Cardiovasc. drugs Ther. 31, 471–482 (2017).
  59. Tian, L. et al. Effect of organic anion-transporting polypeptide 1B1 (OATP1B1) polymorphism on the single- and multiple-dose pharmacokinetics of enalapril in healthy Chinese adult men. Clin. Ther. 33, 655–63 (2011).
  60. Luo, J.-Q. et al. SLCO1B1 Variants and Angiotensin Converting Enzyme Inhibitor (Enalapril)-Induced Cough: a Pharmacogenetic Study. Sci. Rep. 5, 17253 (2015).
  61. Yang, R. et al. Drug Interactions with Angiotensin Receptor Blockers: Role of Human Cytochromes P450.Curr. Drug Metab. 17, 681–91 (2016).
  62. Joy, M. S. et al. CYP2C9 genotype and pharmacodynamic responses to losartan in patients with primary and secondary kidney diseases. Eur. J. Clin. Pharmacol. 65, 947–53 (2009).
  63. Nordestgaard, B. G. et al. Effect of ACE insertion/deletion and 12 other polymorphisms on clinical outcomes and response to treatment in the LIFE study. Pharmacogenet. Genomics 20, 77–85 (2010).
  64. Yin, T. et al. Genetic variations of CYP2C9 in 724 Japanese individuals and their impact on the antihypertensive effects of losartan. Hypertens. Res. 31, 1549–57 (2008).
  65. Hallberg, P. et al. The CYP2C9 genotype predicts the blood pressure response to irbesartan: results from the Swedish Irbesartan Left Ventricular Hypertrophy Investigation vs Atenolol (SILVHIA) trial. J. Hypertens. 20, 2089–93 (2002).
  66. Chen, G. et al. CYP2C9 Ile359Leu polymorphism, plasma irbesartan concentration and acute blood pressure reductions in response to irbesartan treatment in Chinese hypertensive patients. Methods Find. Exp. Clin. Pharmacol. 28, 19–24 (2006).
  67. Uchida, S. et al. Altered pharmacokinetics and excessive hypotensive effect of candesartan in a patient with the CYP2C91/3 genotype. Clin. Pharmacol. Ther. 74, 505–8 (2003).
  68. Hanatani, T. et al. CYP2C9*3 influences the metabolism and the drug-interaction of candesartan in vitro.Pharmacogenomics J. 1, 288–92 (2001).
  69. Cabaleiro, T. et al. Evaluation of the relationship between sex, polymorphisms in CYP2C8 and CYP2C9, and pharmacokinetics of angiotensin receptor blockers. Drug Metab. Dispos. 41, 224–9 (2013).
  70. Dean, L. Metoprolol Therapy and CYP2D6 Genotype. Medical Genetics Summaries (2017).
  71. Lymperopoulos, A., McCrink, K. A. & Brill, A. Impact of CYP2D6 Genetic Variation on the Response of the Cardiovascular Patient to Carvedilol and Metoprolol. Curr. Drug Metab. 17, 30–6 (2015).
  72. Lai, M. L. et al. Propranolol disposition in Chinese subjects of different CYP2D6 genotypes. Clin. Pharmacol. Ther. 58, 264–8 (1995).
  73. Huang, C.-W., Lai, M.-L., Lin, M.-S., Lee, H.-L. & Huang, J.-D. Dose-response relationships of propranolol in Chinese subjects with different CYP2D6 genotypes. J. Chin. Med. Assoc. 66, 57–62 (2003).
  74. Zhang, F. et al. Influence of CYP2D6 and β2-adrenergic receptor gene polymorphisms on the hemodynamic response to propranolol in Chinese Han patients with cirrhosis. J. Gastroenterol. Hepatol. 31, 829–34 (2016).
  75. Arwood, M. J., Cavallari, L. H. & Duarte, J. D. Pharmacogenomics of hypertension and heart disease. Curr. Hypertens. Rep. 17, 586 (2015).
  76. Mottet, F., Vardeny, O. & de Denus, S. Pharmacogenomics of heart failure: a systematic review.Pharmacogenomics 17, 1817–1858 (2016).
  77. Sanofi Aventis. Clopidogrel package insert. 1–27 (2017).
  78. Carreras, E. T. et al. Diabetes mellitus, CYP2C19 genotype, and response to escalating doses of clopidogrel. Insights from the ELEVATE-TIMI 56 Trial. Thromb. Haemost. 116, 69–77 (2016).
  79. Cavallari, L. H. et al. Multisite Investigation of Outcomes With Implementation of CYP2C19 Genotype- Guided Antiplatelet Therapy After Percutaneous Coronary Intervention. JACC. Cardiovasc. Interv. 1–11 (2017). doi:10.1016/j.jcin.2017.07.022
  80. Bristol Myers Squibb. Warfarin package insert. (2017).
  81. Kimmel, S. E. et al. A pharmacogenetic versus a clinical algorithm for warfarin dosing. N. Engl. J. Med. 369,2283–93 (2013).
  82. Verhoef, T. I. et al. A randomized trial of genotype-guided dosing of acenocoumarol and phenprocoumon.N. Engl. J. Med. 369, 2304–12 (2013).
  83. Cavallari, L. H. Time to revisit warfarin pharmacogenetics. Future Cardiol. 13, 511–513 (2017).
  84. Gage, B. F. et al. Effect of Genotype-Guided Warfarin Dosing on Clinical Events and Anticoagulation Control Among Patients Undergoing Hip or Knee Arthroplasty: The GIFT Randomized Clinical Trial. JAMA 318, 1115–1124 (2017).
  85. Jiang, M. & You, J. H. Cost-effectiveness analysis of personalized antiplatelet therapy in patients with acute coronary syndrome. Pharmacogenomics 17, 701–13 (2016).
  86. Borse, M. S. et al. CYP2C19-guided antiplatelet therapy: a cost-effectiveness analysis of 30-day and 1-year outcomes following percutaneous coronary intervention. Pharmacogenomics 18, 1155–1166 (2017).
  87. Jiang, M. & You, J. H. S. Review of pharmacoeconomic evaluation of genotype-guided antiplatelet therapy.Expert Opin. Pharmacother. 16, 771–9 (2015).
  88. You, J. H. S. Pharmacogenetic-guided selection of warfarin versus novel oral anticoagulants for stroke prevention in patients with atrial fibrillation: A cost-effectiveness analysis. Pharmacogenet. Genomics 24, 6–14 (2014).
  89. You, J. H. S. Universal versus genotype-guided use of direct oral anticoagulants in atrial fibrillation patients: a decision analysis. Pharmacogenomics 16, 1089–100 (2015).
  90. Verhoef, T. I. et al. Cost-effectiveness of pharmacogenetic-guided dosing of warfarin in the United Kingdom and Sweden. Pharmacogenomics J. 16, 478–484 (2016).
  91. You, J. H. S., Tsui, K. K. N., Wong, R. S. M. & Cheng, G. Cost-effectiveness of dabigatran versus genotype- guided management of warfarin therapy for stroke prevention in patients with atrial fibrillation. PLoS One 7, e39640 (2012).
  92. Pink, J., Pirmohamed, M., Lane, S. & Hughes, D. A. Cost-effectiveness of pharmacogenetics-guided warfarin therapy vs. alternative anticoagulation in atrial fibrillation. Clin. Pharmacol. Ther. 95, 199–207 (2014).
  93. Patrick, A. R., Avorn, J. & Choudhry, N. K. Cost-effectiveness of genotype-guided warfarin dosing for patients with atrial fibrillation. Circ. Cardiovasc. Qual. Outcomes 2, 429–36 (2009).
  94. Kim, D.-J., Kim, H.-S., Oh, M., Kim, E.-Y. & Shin, J.-G. Cost Effectiveness of Genotype-Guided Warfarin Dosing in Patients with Mechanical Heart Valve Replacement Under the Fee-for-Service System. Appl. Health Econ. Health Policy 15, 657–667 (2017).
  95. Nshimyumukiza, L. et al. Dabigatran versus warfarin under standard or pharmacogenetic-guided management for the prevention of stroke and systemic thromboembolism in patients with atrial fibrillation: a cost/utility analysis using an analytic decision model. Thromb. J. 11, 14 (2013).
  96. Martes-Martinez, C. et al. Cost-Utility Study of Warfarin Genotyping in the VACHS Affiliated Anticoagulation Clinic of Puerto Rico. P. R. Health Sci. J. 36, 165–172 (2017).
  97. Verhoef, T. I. et al. Cost-effectiveness of pharmacogenetic-guided dosing of phenprocoumon in atrial fibrillation. Pharmacogenomics 14, 869–83 (2013).
  98. Doyle, T., Halaris, A. & Rao, M. Shared Neurobiological Pathways Between Type 2 Diabetes and Depressive Symptoms: a Review of Morphological and Neurocognitive Findings. Curr. Diab. Rep. 14, 1–12 (2014).
  99. Danna, S. M., Graham, E., Burns, R. J., Deschênes, S. S. & Schmitz, N. Association between depressive symptoms and cognitive function in persons with diabetes mellitus: A systematic review. PLoS One 11, 1– 14 (2016).
  100. O’Neill, S. M., Kabir, Z., McNamara, G. & Buckley, C. M. Comorbid depression and risk of lower extremity amputation in people with diabetes: systematic review and meta-analysis. BMJ open diabetes Res. care 5, e000366 (2017).
  101. Roy, T. & Lloyd, C. E. Epidemiology of depression and diabetes: A systematic review. J. Affect. Disord. 142,S8–S21 (2012).
  102. Renn, B. N., Feliciano, L. & Segal, D. L. The bidirectional relationship of depression and diabetes: A systematic review. Clin. Psychol. Rev. 31, 1239–1246 (2011).
  103. Warden, D., Rush, A. J., Trivedi, M. H., Fava, M. & Wisniewski, S. R. The STAR*D Project results: a comprehensive review of findings. Curr. Psychiatry Rep. 9, 449–59 (2007).
  104. Bryan, C. et al. The impact of diabetes on depression treatment outcomes. Gen. Hosp. Psychiatry 32, 33–41
  105. Roopan, S. & Larsen, E. R. Use of antidepressants in patients with depression and comorbid diabetes mellitus: A systematic review. Acta Neuropsychiatr. 29, 127–139 (2017).
  106. Vega, C. et al. Impact of adherence to antidepressants on healthcare outcomes and costs among patients with type 2 diabetes and comorbid major depressive disorder. Curr. Med. Res. Opin. 33, 1879–1889 (2017).
  107. U.S. Food and Drug Administration. Table of Pharmacogenomic Biomarkers in Drug Labeling. (2017). Available at: https://www.fda.gov/Drugs/ScienceResearch/ucm572698.htm.
  108. Singh, A. B. Improved Antidepressant Remission in Major Depression via a Pharmacokinetic Pathway Polygene Pharmacogenetic Report. Clin. Psychopharmacol. Neurosci. 13, 150–6 (2015).
  109. Pérez, V. et al. Efficacy of prospective pharmacogenetic testing in the treatment of major depressive disorder: Results of a randomized, double-blind clinical trial. BMC Psychiatry 17, 1–13 (2017).
  110. Bradley, P. et al. Improved efficacy with targeted pharmacogenetic-guided treatment of patients with depression and anxiety: A randomized clinical trial demonstrating clinical utility. J. Psychiatr. Res. 96, 100– 107 (2018).
  111. Winner, J. G., Carhart, J. M., Altar, C. A., Allen, J. D. & Dechairo, B. M. A prospective, randomized, double- blind study assessing the clinical impact of integrated pharmacogenomic testing for major depressive disorder. Discov. Med. 16, 219–27 (2013).
  112. Hall-Flavin, D. K. et al. Using a pharmacogenomic algorithm to guide the treatment of depression. Transl.Psychiatry 2, e172 (2012).
  113. Hall-Flavin, D. K. et al. Utility of integrated pharmacogenomic testing to support the treatment of major depressive disorder in a psychiatric outpatient setting. Pharmacogenet. Genomics 23, 535–48 (2013).
  114. Altar, C. A. et al. Clinical Utility of Combinatorial Pharmacogenomics-Guided Antidepressant Therapy: Evidence from Three Clinical Studies. Mol. Neuropsychiatry 1, 145–155 (2015).
  115. Boland, J. R., Duffy, B. & Myer, N. M. Clinical utility of pharmacogenetics-guided treatment of depression and anxiety. Pers. Med. Psychiatry (2017). doi:10.1016/j.pmip.2017.11.001
  116. Winner, J. G. et al. Combinatorial pharmacogenomic guidance for psychiatric medications reduces overall pharmacy costs in a 1 year prospective evaluation. Curr. Med. Res. Opin. 31, 1633–1643 (2015).
  117. Fagerness, J. et al. Pharmacogenetic-guided psychiatric intervention associated with increased adherence and cost savings. Am. J. Manag. Care 20, 146–156 (2014).
  118. Chaudhry, M. et al. Impact of CYP2D6 genotype on amitriptyline efficacy for the treatment of diabetic peripheral neuropathy: a pilot study. Pharmacogenomics 18, 433–443 (2017).
  119. Winner, J., Allen, J. D., Anthony Altar, C. & Spahic-Mihajlovic, A. Psychiatric pharmacogenomics predicts health resource utilization of outpatients with anxiety and depression. Transl. Psychiatry 3, e242 (2013).
  120. Chen, S. et al. The cytochrome P450 2D6 (CYP2D6) enzyme polymorphism: screening costs and influence on clinical outcomes in psychiatry. Clin. Pharmacol. Ther. 60, 522–34 (1996).
  121. Rundell, J. R., Harmandayan, M. & Staab, J. P. Pharmacogenomic testing and outcome among depressed patients in a tertiary care outpatient psychiatric consultation practice. Transl. Psychiatry 1, e6 (2011).
  122. Kleinberger, J. W. & Pollin, T. I. Personalized medicine in diabetes mellitus: Current opportunities and future prospects. Ann. N. Y. Acad. Sci. 1346, 45–56 (2015).

The Pharmacogenetics of Antiplatelet Agent: Meta-Analyses of Aspirin and Clopidogrel Loss-of-Function Alleles

By | Anticoagulants, Pharmacogenetic Testing, Provider | No Comments

Antiplatelet agents combined with aspirin have been shown to play a significant role in mitigating the effects of coronary disease, and ample research has found that distinct genetic determine patients’ response to clinically significant antiplatelet agents.
Platelets play a decisive role during the formation of an initial hemostatic plug through their intricate response to injury. When inappropriately activated, platelets contribute to pathological thrombus formation. Arterial thrombus formation can then lead to tissue ischemia causing potentially fatal coronary and cerebrovascular events.

Interindividual Genetic Variation Impacts Aspirin Antiplatelet Efficacy

Aspirin is regarded as “the cornerstone for secondary cardiovascular prevention,” the efficacy of which has long been established. as noted in Current Pharmaceutical Design, “Pharmacogenetics of the Antiplatelet Effect of Aspirin” (2012).
The researchers aver that there is considerable interindividual variation in response to aspirin, thus reducing its efficacy in treating heart disease in some patients.

P1A2 and P2Y1 Association with Decreased Aspirin Antiplatelet Efficacy

Specifically, the review conducted by Current Pharmaceutical Design examined polymorphisms of genes that contributed highly to antiplatelet responses. These were P1A2 from glycoprotein GP IIb/IIIa, and the P2Y1 polymorphism from AD receptor (ADP) genes.
P1A2 was characterized as having an association with coronary thrombus formation. One study showed P1A2 allele was related with a shorter baseline bleeding time in comparison to a wild type allele. After measuring bleeding after aspirin ingestion, there was a a reduced antiplatelet effect.
Another study supported this finding by discovering an enhanced thrombin formation in P1A2 carriers compared to P1A1/A1 homozygotes before and after aspirin ingestion. The review concluded that P1/A2 polymorphism is a prothrombotic platelet phenotype responding inadequately to aspirin.
Polymorphism P2Y1 was utilized in an arachidonic acid-induced optical platelet aggregometry to assess its antiplatelet effect of aspirin. The results showed that the T allele of the C893T P2Y1 polymorphism was substantially linked with a decreased antiplatelet effect of aspirin.

CYP2C19 Mediates Clopidogrel Non-Response

Evidence for association of CYP2C19 with clopidogrel response was investigated in the Journal of Human Genetics “Pharmacogenomics of Anti-Platelet Therapy: How Much Evidence is Enough for Clinical Implementation?” (June 2013).
The study established CYP2C19 as a genetic factor contributing to the creation of the active metabolite of clopidogrel. A corresponding analysis detailing the associations of CYP2C19 alleles and increasing residual on-treatment platelet reactivity corroborated this finding. The study concluded that patients with even one reduced function of CYP2C19 and taking clopidogrel as treatment for percutaneous coronary intervention may be “associated with increased risk of major adverse cardiovascular events as a consequence of aspirin antiplatelet inefficacy.
The International Journal of Environmental Research and Public Health “Pharmacokinetic and Pharmacodynamics Responses to Clopidogrel” (February 2017) also reviewed the connection between CYP2C19 and clopidogrel. The review was based on the authors’ argument that genetic polymorphisms impact the absorbtion and metabolism of clopidogrel and that the P2Y12 receptor may interfere with its antiplatelet activity.
In one meta-analysis, it was found there was a critical relation between CYPC219 loss-of-function in diverse patients with frequent cardiovascular events. In another meta-analysis, CYPC219 was identified as having a having a crucial part in reducing the active metabolite of clopidogrel.

CYP3A4/5 Mediates Clopidogrel Non-Response

In addition to analyzing clopidogrel, the review also analyzed CYP3A4/5. The authors found that the CYP3A5*3 allele has an influence on clopidogrel metabolism because of its possible dependence on CYP2C19 and CYP3A4 inhibitors. In the study, the patients with a CYP3A5*3/3 genotype displayed enhanced platelet reactivity compared to those with a CYP3A5*1 allele in CYP2C19 poor metabolizers. An additional study reported CYP3A5*3 on clopidogrel response is prominently in patients with the CYP2C19 loss-of-function.

Benefits of Individualizing Antiplatelet Therapy with Pharmacogenetic Testing

Research has been conclusive in identifying potential antiplatelet pharmacogenetic applications pointing to effective individualized treatments, according to the studies.
The review by the International Journal of Environmental Research and Public Health asserted there is an “inter-individual variability” in clopidogrel’s antiplatelet effects. They concluded inadequate platelet responsiveness to clopidogrel has a role in accumulating the risk of cardiovascular events, and therefore increasing drug dosage or switching to alternative drug medications may be more beneficial for patients. Similarly, the review published in Current Pharmaceutical Design concludes by recommending utilization of antiplatelet pharmacogenetics in clinical practice. “The promise of pharmacogenetics lies in the prospect of improving treatment efficacy and safety.”

The Prospect of Pharmacogenetics in Pediatrics

By | Opioids, Other, Pharmacogenetic Testing, Provider | No Comments

Pharmacogenetics of Opioids as a Potential Alternative in Pediatric Pain Management

Opioid and codeine treatment in pain management for children has been a primary concern in clinical settings, specifically for surgical pain management. The concerns are the adverse reactions caused by the opioids, such as respiratory depression. Current Opinion in Anesthesiology, “Codeine and Opioid Metabolism: Implications and Alternatives for Pediatric Pain Management” (2017), reviews how important clinical factors and genetic polymorphisms affect the metabolism of opioids after surgical operations.


Adverse Side Effects of Codeine

Codeine’s efficacy has been questioned in the pain management of children. Current Opinion in Anesthesiology identifies the adverse reactions of it. The prominent ones are respiratory depression, anoxic brain injuries, and even death occurring in children. With reported doses of codeine, significant respiratory depression was found in newborns in a report by Canadian Pharmacists Journal “Pain Management in Children: A Transition from Codeine to Morphine for Moderate to Severe Pain in Children” (2012).

Opioids in Pain Management

Opioids are the cornerstone of pain and chronic pain management. “Successful pain management provides adequate analgesia without excessive adverse reactions affirms Clinical Biochemistry “Pharmacogenetics of Chronic Pain Management” (2014). Drug metabolism and responses are influenced by numerous factors, including pharmacogenetics. Genetic variations contribute to the distinct inter individual responses to pain medications.

Involvement of CYP2D6 in Codeine

Those with two nonfunctional alleles of CYP2D6 are considered poor metabolizers. Extensive metabolizers have one or two effective CYP2D6 alleles and those with duplicated CYP2D6 alleles are ultra rapid metabolizers. Canadian Pharmacists Journal indicates the functions of CYP2D6 are similar in both children and adults.

The review also acknowledges the safety concerns of CYP2D6 ultra rapid metabolizers from several studies. One study demonstrated how a breastfed newborn infant died after his mother consumed Tylenol #3 for postpartum pain. Toxicology testing found the mother had abnormally high concentration levels of morphine in her breast milk. Genotype testing found the mother was an ultra rapid metabolizer of codeine. The study concluded since the mother was an ultra rapid metabolizer, higher than normal morphine levels crossed into the breast milk and resulted in the infant dying from morphine intoxication.

Another study found a two-year-old child who also died of morphine intoxication. The child was prescribed codeine in recommended dosages after having his tonsils removed. Genotype testing revealed the child was an ultra rapid metabolizer of codeine. However, there were also other contributing factors; the child had bronchopneumonia and sleep apnea. The study concluded these factors “may have increased his risk of hypoxemia, leading to alterations in opioid receptors and increased sensitivity to morphine.”

Canadian Pharmacists Journal concludes these studies show ultra rapid metabolizers of codeine are correlated with a higher risk of morphine intoxication among children.


Alternatives to Prevent Adverse Drug Reactions

Canadian Pharmacists Journal argues morphine as a safer alternative compared to codeine. They argue morphine has “demonstrated efficacy and relative safety when used appropriately in pain management in both adults and children.” A study they analyzed found morphine treatment more effective than a placebo for children in postoperative pain.

Current Opinion in Anesthesiology also outlines the possible alternatives to prevent the risks of opioids, such as pharmacogenetics. They indicate personalized opioid therapy for pain management is “distant from reality”, but current CYP2D6 pharmacogenetic research on codeine is hopeful. The review summarizes, “pharmacogenetics has the potential to guide anesthesia providers on perioperative opioid selection and dosing to maximize efficacy and safety.”

Pharmacogenetics Emerging as a Method to Guide Medication Therapy

By | Gene Panel, Opioids, Other, Pharmacogenetic Testing, Provider | No Comments

According to a recent article published in American Family Physician “Pharmacogenetics: Using Genetic Data to Guide Drug Therapy (2015), pharmacogenetics is being more widely used by family physicians and the number of patients who are interested in acquiring genetic information is growing.


The Components of Pharmacogenetics Testing

Pharmacogenetics involves genetic variations that code for drug metabolizing enzymes. It also involves how a medication breaks down in the body and how the body responds to the medication. The most common forms of genetic variations are single nucleotide polymorphisms.

The differences in single nucleotide polymorphisms or other polymorphisms result in diverse types of genes or alleles, the American Family Physician explains. Individuals inherit these alleles that “govern expression of the gene and the cor¬responding enzyme or protein.” As a result, these genetic differences influence how the drug reacts in the body and how the body metabolizes the drug.


Genetic Variability Can Alter the Effects of Drugs

Studies have demonstrated there is a connection between genetic variations and changes in drug levels and effects.

CYP2D6 and Opioids

The enzyme activity of CYP2D6 is volatile because of single nucleotide polymorphisms and other variations of CYP2D6. American Family Physician indicates codeine metabolism occurs in 90% of patients and results in normal morphine formation. However, 1% to 2% of people are ultra rapid metabolizers of codeine signifying they have an increased risk of morphine toxicity.

American Family Physician analyzed a study involving the death of a breastfed infant and a mother who was an ultra rapid metabolizer of codeine. The study demonstrated the infant died of morphine intoxication. There was opioid toxicity in the breast milk, which passed onto the infant.

They recommend pharmacogenetic testing for patients who are possible poor or ultra rapid metabolizers of opioids.


CYP2C19 and Clopidrogrel

Clopidogrel is primarily metabolized in the enzyme CYP2C19. CYP2C19 is highly polymorphic and 80% of individuals metabolize clopidogrel normally. However, 18% to 45% of people have intermediate enzyme activity and 2% to 15% have poor enzyme activity.

American Family Physician presents meta-analyses of CYP2C19 poor metabolizers. Poor CYP2C19 metabolizers taking clopidogrel treatment and undergoing percutaneous coronary intervention have a higher risk of cardiovascular death, myocardial infraction, stroke, and stent thrombosis.

These results lead to the recommendation that clinicians should consider alternative treatments, such as pharmacogenetic testing of CYP2C19 to guide antiplatelet therapy.


The Benefits of Pharmacogenetics

American Family Physician examined the clinical implications of pharmacogenetic testing and the various resources available and developing to support the usage of pharmacogenetics in clinical settings. They conclude “pharmacogenetic testing can be a practical tool to optimize drug therapy and avoid medication adverse effects.”

side effects of opioids

Pharmacogenomic Data May Help Guide Opioid Pharmacotherapy in Patients with Cancer-related Pain

By | Cancer Treatments, Other, Pain Medications, Pharmacogenomics, Provider | No Comments

Opioids are the most potent analgesics and are used to treat severe pain, specifically pain associated with cancer – a significant factor in reducing quality of life and clinical outcomes in such patients as detailed in Cancer Control “Clinical Implications of Opioid Pharmacogenomics in Patients with Cancer” (October 2015).


Inter-individual Differences in Genetically Modulated Opioid Response

The study reviewed clinical studies involving the pharmacodynamics and pharmacokinetics of opioids. It examined the opioid agents morphine, codeine, tramadol, oxycodone, fentanyl, and hydrocodone and the relationship to single nucleotide polymorphisms (SNPs): OPRM1, COMT (specifically COMT Val Met), CYP2D6, CYP3A4/5, and ABCB1, which the study claimed are responsible for the inter-individual differences in opioid response.

The authors specifically found that OPRM1, COMT Val Met, and ABCB1 are most strongly correlated with morphine response. One study combined OPRM1 and ABCB1 and found that patients with both of these genetic variants were the best responders as indicated in patients’ measures of pain intensity. In another study, patients with OPRM1 and COMT Val Met needed the lowest morphine dose compared to other genotypes. All three together demonstrated no difference in morphine dose requirements.


CYP2D6 Variants Correlate with Drug Efficacy

Similarly, the presence of CYP2D6 variants correlated positively with variations in codeine and tramadol efficacy. CYP2D6 is responsible in converting the analgesic properties of codeine and tramadol. In studies investigating codeine pharmacotherapy in cancer patients, analgesic differences and adverse effects were found for CYP2D6 poor, intermediate, and extensive metabolizers.

The authors concluded CYP2D6 testing helps in finding which patients respond positively to codeine. Studies with tramadol focusing on non-cancer pain populations identified CYP2D6 poor metabolizers as having a decreased analgesic response compared to extensive metabolizers. However, the authors noted there has been no specific study relating to tramadol’s analgesic efficacy in cancer populations, arguing tramadol will likely have decreased clinical benefit in patients who are poor CYP2D6 metabolizers.


Call for Preemptive Genotyping in Clinical Practice

The authors assert that these findings “suggest genotyping patients for some of these genetic variants may help predict responses to pain treatments with good rates of sensitivity and specificity and with greater benefits for patients and decreased health care utilization.” Furthermore, the authors assert that utilizing pharmacogenomics data combined with a preemptive genotyping be a “key element” in guiding treatment decisions for cancer patients.

Utility of PGx Testing in Hospitals Bolstered by Research on the Pharmacogenetics (PGx) of Antiplatelet Response

By | CMS Cardiac Bundle, Provider | No Comments

MD Labs Has Turnkey PGx Program for Hospital Implementation

Target Audience: Hospital Executives; Hospital-based Cardiologists, Quality Directors and Pharmacists
There is mounting evidence on the cost saving opportunities of applying Pharmacogenetic (PGx) testing following percutaneous coronary intervention (PCI) and coronary stent procedures. In concert with an interventional cardiologist, MD Labs has developed a PGx protocol for Catheter Labs that U.S. hospitals are in the process of adopting. This is of particular importance given the new CMS Cardiac Bundle being introduced into hospitals.
The benefits of this protocol are bolstered by studies such as the one in Expert Opinion on Drug Metabolism & Toxicology “The pharmacogenetic control of antiplatelet response: candidate genes and CYP2C19” (July 2015) which surveyed clinical outcomes of using pharmacogenetics to guide antiplatelet therapy used for preventing ischemic events in patients with acute coronary syndromes (ACS), percutaneous coronary intervention (PCI) and other indications. The pharmacogenetics of available antiplatelet agents – aspirin, clopidogrel, prasugrel and ticagrelor – were analyzed.

CYP2C19 Implicated in Clopidogrel Response Variability

The authors found abundant data in its literature meta-analysis supporting the clinical validity of CYP2C19 and clopidogrel response variability among ACS/PCI patients, stating “[t]he increased risks for reduced clopidogrel efficacy among ACS/PCI patients that carry CYP2C19 loss-of-function alleles should be considered when genotype results are available.” It was also found that “insufficient candidate genes” have thus far been implicated for prasugrel or ticagrelor.

The Clinical Utility of Pre-emptive PGx Testing for Plavix

The authors concluded by citing the need for pre-emptive PGx testing for clopidogrel (Plavix), for which they found a “clear association” with CYP2C19, explaining that pre-emptive pharmacogenetics testing would circumvent the issue of the need for rapid turnaround which is one of the frequently cited barriers to implementing CYP2C19 genetic testing for antiplatelet therapy.
A  pre-emptive approach – as offered by genotyping platforms such as MD Labs’ Rxight® – would integrate CYP2C19 genotype data into cath labs and the patient EMRs to alerts prescribers through clinical decision support at the point-of-care if and when clopidogrel is ordered and the patient carries an at-risk CYP2C19 genotype.
“Although this model has inherent challenges … pre-emptive CYP2C19 genetic testing has recently been deployed at several academic medical centers,” the authors stated. The authors called for “an ongoing effort towards the application of clinical pharmacogenetics by increasing clinician education and acceptance.”

CMS Cardiac Bundle Paves Way for PGx Testing for ACS Patients

With the coming of the CMS Cardiac Bundle program for hospitals (effective Oct 1, 2017) there is now added financial incentive to implement PGx testing as part of the standard of care for cardiac patients about to undergo antiplatelet pharmacotherapy.
1,200 participating hospitals in 98 metropolitan areas in the U.S. are mandated to be held financially accountable for the costs of heart attacks and bypass surgery under the CMS protocol for cardiac care.  There is therefore significant incentive to reduce costs through various measures such as integrating PGx testing into standing orders for coronary stent procedures and percutaneous coronary interventions (PCIs). For these treatments, anti-platelet pharmacotherapy is an established standard of care to reduce thrombotic risk, with Plavix (clopidogrel) as a first-line agent tested.

Implementing PGx in Your Hospital

A crucial part of MD Labs’ Rxight® turnkey program is the incorporation of PGx trained and certified pharmacists as part of the protocol to serve as expert resources for physicians and to provide consultations with the patients; pharmacist involvement in patient care has been shown to reduce hospital readmission rates.  DNA test kits are provided by MD Labs, and results are accessible via online provider and patient portals. Contact MD Labs (1-888-888-1932 or info@rxight.com) for details on the PGx protocol as applied to PCI and stent procedures, and how to integrate the Rxight® pharmacogenetics program into your cath lab.

Another Study Confirms Financial Benefit of Pharmacogenetic Testing for Patients Receiving a Stent

By | CMS Cardiac Bundle, Provider | No Comments

Contact MD Labs to Learn How You Can Implement PGx in Your Cath Lab

Target Audience: Hospital Executives, Hospital-based Cardiologists, Quality Directors and Pharmacists
Evidence continues to grow demonstrating the financial utility and cost-saving opportunity of applying Pharmacogenetic (PGx) testing following coronary stent procedures and percutaneous coronary intervention (PCI). MD Labs, working with an interventional cardiologist, has developed a PGx protocol for Catheter Labs that is being adopted by hospitals around the country. This is especially important given the new CMS Cardiac Bundle being introduced into hospitals.
The benefits of this protocol are reinforced by the article “Financial Analysis of CYP2C19 Genotyping in Patients Receiving Dual Antiplatelet Therapy Following Acute Coronary Syndrome and Percutaneous Coronary Intervention” published in the Journal of Managed Care and Specialty Pharmacy (Jul 2015). This study, discussed in the article, analyzed the financial impact of CYP2C19 genotyping for a set of patients with ACS who received percutaneous coronary intervention and coronary stent implantation and were treated with clopidogrel, prasugrel, or ticagrelor in a managed care setting.

CYP2C19 Metabolism Determines Clinical Response and Adverse Events in Plavix Users

Diminished CYP2C19 activity impairs clopidogrel metabolism and thereby increases risk of adverse clinical outcomes. Specifically, slow and intermediate CYP2C19 metabolizers treated with clopidogrel suffer higher cardiovascular event rates – including myocardial infarction, stent thrombosis, and stroke – than patients with normal CYP2C19 genotypes, and conversely rapid metabolizers are found to be hypo-responsive. It was concluded from the study that clopidogrel should be used as a first-line agent for all but this subset of patients.

Pharmacogenetics Reduces Costs by an Estimated $444K Annually, per One Thousand Patients

A budget impact analysis based on market share rates was conducted using overall and average cost per patient modelling based on the rate of CYP2C19 genotyping in a theoretical patient cohort. The magnitude of the financial impact from CYP2C19 genotype-guided antiplatelet therapy was emphasized, and it was expected that use of CYP2C19 genotyping would displace market share from clopidogrel to either prasugrel or ticagrelor. Total estimated annual costs of adverse clinical outcomes (e.g., MI, bleeding, stroke) and antiplatelet treatment were measured. The analysis showed an estimated annual savings of roughly $444,852 when PGx was employed in all patients in the theoretical 1,000 person cohort versus none.
Contact MD Labs to learn more. 1-888-888-1932 or info@Rxight.com

More Time To Prepare for the CMS Cardiac Bundle Program – Start Date Pushed to Oct 1 2017

By | CMS Cardiac Bundle, Provider | No Comments

Contact MD Labs to Learn How You Can Implement PGx in Your Cath Lab

Target Audience: Hospital Executives, Hospital-based Cardiologists, Quality Directors and Pharmacists
The CMS (Centers for Medicare & Medicaid Services) has pushed the implementation of its bundled payment initiatives for cardiac care from July 1 to Oct. 1, 2017, according to an interim final rule posted to the Federal Register “Medicare Program; Advancing Care Coordination Through Episode Payment Models (EPMs); Cardiac Rehabilitation Incentive Payment Model; and Changes to the Comprehensive Care for Joint Replacement Model; Delay of Effective Date.”

New Start Date Gives Hospitals Additional Preparation Time

The three-month delay “allow[s] time for additional review, to ensure that the agency has adequate time to undertake notice and comment rulemaking to modify the policy if modifications are warranted, and to ensure that in such a case participants have a clear understanding of the governing rules and are not required to take needless compliance steps,” the interim rule stated.

The Utility of PGx Testing for Hospital Cost Reduction

Under the CMS bundled payment initiative, participating hospitals in 98 metropolitan areas in the U.S. are mandated to be held financially accountable for the costs of heart attacks and bypass surgery, and thus have incentive to reduce costs through various measures such as integrating PGx testing into standing orders for coronary stent procedures and percutaneous coronary interventions (PCIs). For these treatments, anti-platelet pharmacotherapy is an established standard of care to reduce thrombotic risk, with Plavix (clopidogrel) as a first-line agent tested.
Pharmacogenetic testing is shown to reduce drug-related complications and readmission rates, thus sparing added costs to hospitals and providers, as discussed in a recent report out of the University of Illinois Hospital & Health Sciences System, which demonstrated that pharmacogenetic testing reduced 90-Day ER and Hospital Readmission Rates by 68 percent.

Contact MD Labs for Information on the CMS Initiative and its Turnkey PGx Testing Program

Contact MD Labs (1-888-888-1932) or info@Rxight.com) for details on the PGx protocol as applied to PCI and stent procedures, and how to integrate its Rxight® PGx program into your hospital lab.
A cornerstone of MD Labs’ Rxight® program is incorporating PGx trained and certified pharmacist as part of the protocol, as pharmacist involvement in patient care is also proven to help reduce readmission rates. The Rxight® Program provides turnkey implementation and includes pharmacist training in PGx and certification to conduct PGx consultations. DNA test kits are provided by MD Labs, and results are accessible via online provider and patient portals.

Pharmacogenetics of Methylphenidate (Ritalin) in ADHD

By | Provider | No Comments

Methylphenidate (MPH), branded as Ritalin, Concerta, Daytrana, Methylin, and Aptensio, is the most frequently used pharmacological treatment in children with attention-deficit/hyperactivity disorder (ADHD). There is, however, considerable interindividual variability exists in clinical outcomes, which may arise from underlying genetic influences coupled with environmental influences, as discussed in a January 2017 article in The Pharmacogenomics Journal “Pharmacogenetics of methylphenidate response and tolerability in ADHD.”
The study is the first of its kind examining multiple SNPs across genes encoding the main components of the dopaminergic system to identify genetic factors that moderate response variability in ADHD treatment, according to the authors.
Specifically, the study was based in the analysis of 57 single-nucleotide polymorphisms (SNPs) in nine dopamine-related candidate genes (TH, DBH, COMT, DAT1 and DRD1-5) as potential predictors of methylphenidate efficacy and tolerability, and additionally considered teratogenic and postnatal xenotoxins (specifically maternal nicotine use)  as factors.
In analyzing the clinical efficacy of MPH, researchers found a “[a]dverse events after MPH treatment were significantly associated with variation in DBH  and DRD2. This study suggests that the geneticallly modulated dopaminergic system together with xenobiological and teratogenic influences may moderate MPH treatment effects.
“[C]linical response to MPH may be the result of a much more complex matrix of factors, including both genetic and environmental risks,” the authors concluded, calling for [f]urther pharmacogenetic studies with larger samples are required to fully validate these results and to disentangle the impact of prenatal xenotoxins on clinical response to MPH in genetically susceptible individuals.”

African Genetic Diversity: Implications for Cytochrome P450-mediated Drug Metabolism

By | Provider | No Comments

Continental African populations are characterized by high levels of genetic diversity in a high proportion of patients who experience adverse reactions to a range of pharmacotherapeutic approaches when compared to Caucasian and Asian populations, which the majority of research on pharmacogenetics have hitherto considered for analysis.
A February 2017 article  in EBioMedicine “African Genetic Diversity: Implications for Cytochrome P450-mediated Drug Metabolism and Drug Development” (Rajman, I., et al.) addressed the necessity for such research, presenting findings on the identification of CYP alleles of potential clinical relevance in the African population based in literature review.
Specifically, the study employed a statistical method known as PCA (principle component analysis) grounded in text mining publications to find references to global populations including Africa for the purpose of comparative analysis. Sixteen CYP variants were considered.
The authors, who confirmed that there is in fact greater diversity in CYP distribution in Africa than in other continental populations, identified a necessity for optimization of drug therapy and drug development for Africa, which currently “carries a high burden of adverse drug reactions owing to the use of old, poorly optimized drugs.”
Pharmacogenetic findings on African-Americans are often erroneously extrapolated to the African population, the article states, noting that it is not representative of the variety of populations present in the African continent.  “The African continent cannot … be treated as a single entity in drug research and development, nor can African-American populations be considered an adequate proxy for pharmacogenetic differences across Africa,” the authors stated,
The researchers called for a need to study in depth CYP variants in Africans and, moreover, to raise awareness of the greater genetic variation among this population when applied to drug metabolism and efficacy,  stating “[t]he involvement of clinicians in genomic research will facilitate this translation process, helping to ensure that patients are treated with efficacious doses of therapeutic drugs.”