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PGx in the News

Cardinal Health Selects Rxight to Feature in 2018 RBC Showcase

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Independent Pharmacies Embracing Pharmacogenetics

RENO, Nev.  — According to the Centers for Disease Control (CDC), 82% of American adults take at least one medication and 29% take five or more.  In addition, the CDC reports over 2 million adverse drug reactions each year leading to emergency room visits, with an estimated 100,000 deaths annually.


In response to this serious health issue,Cardinal Health announced that it has chosen the Rxight® (pronounced “RIGHT”) pharmacogenetic program to feature in their exclusive Showcase Area during their 2018 RBC conference being held in San Diego June 27-30. The Rxight program helps determine if the medications people take and the dosage levels are right for them based on their DNA. Rxight provides medication guidance on over 230 prescription and OTC medications.


“Cardinal Health recognizes the importance of pharmacogenetics and the role of pharmacists in helping their providers make more informed prescribing decisions to help achieve desired outcomes sooner and avoid adverse effects,” said Chuck Dushman, Vice President of Marketing for MD Labs. “Cardinal is now making it easier for pharmacies to incorporate pharmacogenetics.”


Pharmacogenetics uses information about a person’s genetic profile to help choose the medication and drug doses that are likely to work best for them, helping to avoid ineffective or potentially dangerous drug reactions.  After their provider has authorized the lab test, the patient visits a pharmacy for a simple cheek swab and to purchase the Rxight test. The pharmacist receives the results from the lab within 7 business days. Each patient receives their results during a Personalized Medication Review with a pharmacist trained in pharmacogenetics. The pharmacist will then coordinate care with the patient’s provider.


The Rxight test provides information on medications covering 50 therapeutic classes, including blood pressure, cholesterol, blood thinners, other heart issues, ADHD, anxiety, depression, diabetes, pain, ED, acid reflux, oncology and more. It is highly likely that someone is taking one of these medications now or will need to take them in the future.


“Being featured by Cardinal as their pharmacogenetics program of choice for their thousands of national physician and pharmacy customers is a great way to grow the awareness and availability of this life-saving technology,” said Matthew Rutledge, co-founder of MD Labs. “Together we will help pharmacists bring the promise of precision medicine to their communities.”


To learn more about the Rxight test and to learn how you can provide pharmacogenetics to your patients, visit the Rxight booth in the RBC Showcase area or contact the Rxight team at info@Rxight.com/ 888-888-1932.


About MD Labs and Rxight

Developed by MD Labs, a leading high-complexity CLIA certified laboratory specializing in clinical lab testing, Rxight is the leading pharmacogenetic program used by pharmacists. Founded in 2011, MD Labs serves healthcare providers across the United States with state-of-the-art technology and testing for a wide array of analytes. Their team of PhDs and clinical technologists are able to keep ahead of the needs of the medical community. To learn more, please visit Rxight.com.


About Cardinal Health

Cardinal Health, Inc. is a global, integrated healthcare services and products company, providing customized solutions for hospitals, healthcare systems, pharmacies, ambulatory surgery centers, clinical laboratories and physician offices worldwide. The company provides clinically proven medical products, pharmaceuticals and cost-effective solutions that enhance supply chain efficiency from hospital to home. Cardinal Health connects patients, providers, payers, pharmacists and manufacturers for integrated care coordination and better patient management. For more information, visit cardinalhealth.com.

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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
'Pharmacogenomics, the study of genetic mediators of medication response, has demonstrated success in improving pharmacotherapy outcomes across a range of medical conditions.'Click To Tweet
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


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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


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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

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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).

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Dangerous Drug Combinations are Missed by Half of Pharmacies

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A recent article in the Chicago Tribune “Pharmacies miss half of dangerous drug combinations” discussed their two year study on over 250 pharmacies to determine how often stores would dispense dangerous drug pairs without warning patients. The results showed that more than 50% of pharmacies prescribed dangerous combinations without notifying the patients.
According to the article, speed of medication delivery, fatigue and competition are major influences. For instance, one pharmacist said she filled out prescriptions every two seconds, showing pharmacists succumb to the demands of the customer’s, drug warnings are ignored and the prescribers make errors due to fatigue.
In response to the Tribune research, CVS, Walgreens and Wal-Mart each stated that they would take “significant steps to improve patient safety at its stores nationwide.” Combined, the initiative would impact 22,000 drugstores nationwide and involve additional training for 123,000 pharmacists and technicians, according to the article.
The “warning light fatigue” has implications for drug-gene interactions – the take away is “you can’t exclusively rely on your physician or your pharmacist.” As we look ahead, the Rxight genetic testing solutions and how our customers engage with them, may be more important than we ever considered.

South Dakota Health System Drives Costs Down with PGx Testing

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Pharmacogenetic testing is becoming the new frontier in precision medication. It provides critical guidance to physicians when determining the most appropriate therapeutics and optimal dose of medications tailored to each individual patient. Despite the clear benefits of PGx, few health systems have implemented it as there are quite a few hurdles to overcome: convincing insurers to cover the cost, providing rapid turnaround of test results, and collecting more data on the cost effectiveness of a PGx testing program.


Avera Health, a health system in Sioux Falls, South Dakota, is finding solution to such challenges. Avera Health’s largest hospital, 545-bed Avera McKennan Hospital and University Health Center in Sioux Falls, performs pharmacogenomic testing on surgery patients to determine how well these patients metabolize opioid pain medications, according to TDR Insider
“Pharmacogenomic Testing a Success at South Dakota Health System” (Jan 2017).


For rapid turnaround of the testing, the lab performs genotyping and enters the data into the EMR so that the physician knows how the patient metabolizes medications in a timely manner. Such quick results lead to patients having the appropriate medication within 24 hours and to more data which can be presented to insurers. Our administration recognized the potential of pharmacogenomic testing to improve patient care and to reduce costs through quicker treatment success and fewer adverse effects,” said Krista Bohlen, PharmD, the Director of Personalized Pharmaceutical Medicine at the Avera Institute for Human Genetics. our laboratory reports the test results, then physicians follow the dosing guidelines published by the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Pharmacogenetics Working Group of the Royal Dutch Association for the Advancement of Pharmacy,” Bohlen said. “In addition to these guidelines, we also evaluate primary literature.”








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Pharmacy Practice News: “Matching the Right Prescription to the Right Patient”

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Pharmacogenetic testing is gaining traction as more gene-medication responses are being analyzed and are integrated into patient electronic health records. One of latest examples is the testing of cytochrome P450 2C19 (CYP2C19) and how the drug is used in antiplatelet and other pharmacotherapies.


According to  Pharmacy Practice News “PGx Helps Pharmacists Match Right Rx to Right Patient” (Feb 7, 2017), seven major health systems have been testing patients preemptively for cytochrome P450 2C19 (CYP2C19) genotype in patients undergoing percutaneous coronary intervention (PCI).


The findings were reported at the 2016 American Heart Association’s Scientific Sessions on behalf of the National Human Genome Research Institute-funded IGNITE (Implementing Genomics in Practice) Pharmacogenetics Working Group investigators, part of a consortium of 16 health systems that have implemented various pharmacogenomics tests to enhance therapeutic decision-making.


CYP2C19 Genotyping for Clopidogrel, PPI Pharmacotherapy


The most common drug-gene test is clopidogrel and CYP2C19. The IGNITE investigators reported that approximately 31% of patients had a variant that reduces clopidogrel activation and efficacy. More than half of those patients were put on an alternative medication treatment plan as a result. The patients who received a different treatment plan suffered fewer adverse reactions to the medications in comparison to the group that received clopidogrel treatment.


Other drug-gene tests include opioids and CYP2D6; thiopurines and thiopurine methyltransferase (TPMT); and simvastatin and SLCO1B1; proton pump inhibitor (PPIs) and CYP2C19.  Testing for PPI metabolism is particularly crucial, as there are a large number of patients receiving PPI pharmacotherapy, according to the article.


Pharmacists Play a Central Collaborative Role


“Pharmacists are right at the center,” noted James M. Hoffman, PharmD the chief patient safety officer at St. Jude Children’s Research Hospital in Memphis, Tenn, highlighting the collaboration required in integrating pharmacogenetic information to improve drug therapy. “We’re at a very different place than we were, say, five years ago. Now we have many more models of practice, with resources like CPIC (Clinical Pharmacogenetics Implementation Consortium).”


Integration of Rxight® PGx Testing


The integration of the Rxight® pharmacogenetic testing program into healthcare systems is a goal of MD Labs.  The Rxight® testing model is designed to provide a one-time test of 18 genes and their alleles to guide the patient and treatment team in tailoring pharmacotherapy to the patient’s unique genotype. Specifically, Rxight® analyzes the patient’s genetically modulated metabolism of over 200 medications and 50 clinically significant pharmacological classes including clopidogrel and other anti-platelet drugs, as well as simvastatin, common opioids, and PPIs in order to find the correct dose or a safer more effective therapeutic alternative – thus enhancing safety, reducing costs, and improving patient compliance.

The Human Genome and Era of Personalized Medicine

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When the first human genome was sequenced early in the 2000’s, there was great hope that personalized medicine would soon become a reality.  The reason for optimism was that as more people had their genomes sequenced, the more data scientists and researchers would have on individual gene variation. It soon became apparent that certain genetic variations are responsible for people’s varied response to prescribed medications. This entails that one patient may handle a specific dosage and frequency of a drug treatment plan appropriately, while another patient may metabolize the medication in a completely different way and suffer severe adverse reactions. These differences are based on each individual’s genetic makeup.  It is this variation that has led to the field of pharmacogenomics, or pharmacogenetics, as it is often called.


With advances in gene sequencing techniques in the past five years or more, there has been a tremendous increase in the number of genomes sequenced and studied.  One particularly fruitful area has been the study of variation among humans in drug metabolism genes.


Genes are made of DNA.  The DNA sequence of the genes, determines the composition of proteins and enzymes that do most of the work of the cell. Simply put, every unique DNA sequence specifies how that unique protein is made.


Every human has drug metabolism genes (made up of DNA) that encode specific enzymes (made up of protein) responsible for metabolism of particular drugs or group of drugs. Through sophisticated techniques, advanced genome-wide studies have allowed scientists to make connections from gene variations (genotype) to specific symptoms due to treatment with a particular drug (phenotype). A simple explanation of the genotype-phenotype relationship is eye color.  You may have brown eyes, so that means your genotype codes for brown eyes, while your phenotype is the observed result of the gene’s expression.


Why Pharmacogenetic Testing?

Pharmacogenetics concerns itself with the study of gene variation and its effect on phenotype. The goal is to prevent side effects – phenotypes – that are damaging or dangerous.  In the past, drugs were prescribed by a trial and error process. This method is effective to a certain extent, but it can be costly, patients may suffer adverse reactions…  The study of pharmacogenetics and the process of pharmacogenetic testing, will eventually eliminate the trial and error process of prescribing medicines.


Pharmacogenetics testing helps physicians strategically target patient care based on the patient’s genetic code and is ushering in a new era of personalized medicine.  In the future, the promise of pharmacogenetics is to test every individual for drug metabolism gene variations to prevent uncomfortable or harmful side effects so that medicines can be used with more precision. The goal of personalized medicine is to usher in a new era of precision medicine and to strategically target patient care based on the patient’s genetic code.  These advances will help bring patients medicines that are prescribed with a knowledge of how the patients react to the medications.









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Plavix and genetic testing

University of Maryland Offers Genetic Testing as Standard of Care for Heart Stent Patients

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Plavix (clopidogrel) is a blood thinner that is used to reduce the risk of heart disease and stroke.  It is a popular drug and is considered to be safe for most patients. However, there is real risk that patient may not be able to metabolize the drug appropriately, and thus receive no benefit from the medication – or, conversely – suffer adverse reactions to Plavix such as statin myopathy, according to researchers at the University of Maryland, as reported in November at the American Heart Association’s Scientific Sessions in New Orleans.


In response to these findings, the University of Maryland Medical Center has launched a program to conduct genetic tests for the liver enzyme CYP2C19 before prescribing Plavix and other medications to patients who have received a heart stent. The enzyme affects the patient’s ability to metabolize the drug. Sixty percent of the patients with reduced CYP2C19 function were given an alternative medication. The result was reduction of the percentage of heart attacks and death by nearly half compared with those who continued taking clopidogrel.


Alternative medications, while not statistically as effective at Plavix, were found to be adequate replacements, according to the UMMC researchers.  Thus, the trial and error process was reduced and patient receive the appropriate medication early in treatment.


“This is a true personalized medicine initiative,” says Mark R. Vesely, MD, an associate professor of medicine at UM SOM and an interventional cardiologist at UMMC who was a co-investigator of the study. “The test provides the ability to optimize therapy for a specific patient by helping us tailor our treatment based on the patient’s unique genetic profile.”


Source:  University of Maryland Medical Center “University of Maryland Medical Center Offers Genetic Testing as Standard of Care to Help Improve Outcomes for Heart Stent Patients” (January 2017).

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PGx as Applied to ACS Protocol

PGx as Applied to ACS Protocol: Maximizing CMS Incentive Payments to Hospitals

By | CMS Cardiac Bundle, PGx in the News, Provider | No Comments

Anti-platelet pharmacotherapy is an established standard of care for Acute Coronary Syndrome (ACS) to reduce thrombotic risk. Incorporating pharmacogenetic testing and pharmacist engagement into the prescribing protocol allows the provider to understand the patient’s genetic phenotype to help determine whether the patient will achieve the optimal therapeutic outcome – and thus mitigate potentially serious adverse effects or sub-optimal treatment response.

Using PGx/ACS Protocol to Enhance Quality-Based Payments

The new PGx/ACS Protocol can help maximize CMS quality-based incentive payments. As discussed in Becker’s Hospital Review “How managing medications based on genetics can enhance quality-based payments” (January 17 2017), pharmacogenetic testing can reduce drug-related complications and readmission rates, thus sparing added costs to hospitals and providers.
A recent report out of the University of Illinois Hospital & Health Sciences System demonstrated that PGx reduced 90-Day ER and Hospital Readmission Rates by 68% leading to $2,043 savings per patient and almost $600K overall savings to UIC. This becomes even more important as the new CMS Cardiac Bundle takes effect this coming July.

Contact MD Labs for Information on the PGx/ACS Protocol and
its Turnkey PGx Testing Program

Contact MD Labs for details on the PGx/ACS Protocol and how to implement it with the Rxight® PGx program. 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 proven to help reduce readmission rates.
MD Labs’ Rxight® Program provides turnkey implementation and includes pharmacist training in PGx and certification to conduct PGx consultations. Additionally, the Rxight® program is integrated within the hospital lab offerings for establishing collection procedures. DNA test kits are provided by MD Labs, and results are accessible via online provider and patient portals.
Assess your hospital’s cost saving opportunity by calculating the 90-day hospital readmission costs due to drug-related complications (clopidogrel and coumadin), over the last 3 years. Call MD Labs at 1-888-888-1932, or email info@Rxight.com to get started. Rxight.com

Mitigating the Dangers of Statin Myopathy with PGx Testing

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  • Statins are the most commonly prescribed drugs in the United States and are extremely effective in reducing major cardiovascular events in the millions of Americans with hyperlipidemia, according to recent research in Journal of Pharmacogenomics and Pharmacoproteonomics: “Pharmacogenetics of Statin-Induced Myopathy: A Focused Review of the Clinical Translation of Pharmacokinetic Genetic Variants,” Talameh and, Kitzmiller (2014)


    Unfortunately, 25-60% of all users cannot tolerate or discontinue statin therapy due to statin-induced myopathy (SIM), and “[p]atients will continue to experience SIM at unacceptably high rates or experience unnecessary cardiovascular events (as a result of discontinuing or decreasing their statin therapy) until strategies for predicting or mitigating SIM are identified,” according to the research.


    A promising strategy for predicting or mitigating SIM is pharmacogenetic testing, particularly of pharmacokinetic genetic variants as SIM is related to statin exposure, the investigators stated, noting that research investments in pharmacokinetic genetic variants have the potential to make a profound impact on public health.” Future directions, specific to the research on pharmacokinetic genetic variants, could speed the translation into clinical practice.








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  • NPR: Personalizing Medical Treatment with Genetic Testing Data

    By | PGx in the News | No Comments

    Scientists are now using big data analytics to mine electronic health records for clues as to what treatments work best for different individuals, as discussed in a piece aired by National Public Radio on January 10 2016 “Electronic Health Records May Help Customize Medical Treatments.”


    The interview included Dr. Tracy Lieu, who heads the Kaiser Permanente research division in Oakland, Calif., Dr. David Ledbetter, the Chief Scientific Officer Geisinger Health System in Pennsylvania, and Dr. Harlan Krumholz, a professor of medicine at Yale University who researches cardiology and health care.


    While computerized medical records are hardly new (they date back to the 1970s), the potential now is that patients can proactively take part in mining the records, and that patients’ genetic information is key to this data mining, Leiu stated.


    “Even though this is primarily a research project, we’re identifying genomic variants that are actually important to people’s health and health care today,” Ledbetter added.


    Geisinger patient Jody Christ was also featured. She had volunteered to get the genetic screen during one of her routine medical visits, as her doctor had been concerned about her high cholesterol. Her screening – known as exome testing – told her and her providers that she had inherited a genetic trait that elevated her cholesterol. The genetic diagnosis led directly to a series of screenings.


    Krumholz is excited at the prospect of being able to look at physical symptoms in medical records and then look for genetic variations that could be responsible. While the system is not yet robust it bodes very well for the inclusion of PGx testing as a vital piece in the customization of patient electronic records.








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