Opioid use disorder

Over 80% of global consumption of prescription opioid pain killers (drugs that bind and act through one of the opioid receptors) is in the United States, which accounts for just under 5% of the world’s population [5]. It is estimated that almost 100 million US residents took an opioid last year, with between 5% and 10% of these people likely to become addicted or at least transition to opioid use disorder (OUD). This in turn helps explain the fact that upwards of 50,000 people died of a drug overdose in 2016, or 2% of all deaths in the US. An increasing proportion of this is now attributed to fentanyl, a synthetic drug initially developed for surgical sedation, which is 50 times more potent than heroin and is available off prescription (on the street) or laced into counterfeit prescription drugs. A microscopic increase in the dosage of fentanyl in one bad pill can be lethal to unsuspecting addicts just trying to cope with chronic pain [6].

There are three potential targets for prediction of opioid response. One is identification of individuals at high risk of addiction; another is identification of poor metabolizers, who have reduced conversion of ingested drug into a bioactive form like morphine and hence are not receiving analgesic benefits; and conversely, the third is identification of ultrarapid metabolizers, who produce so much active drug that they are at risk of serious adverse events, including respiratory depression, severe nausea and constipation, and dysphoria [7,8]. Guidelines for dosing are now provided by the Clinical Pharmacogenetic Implementation Consortium (CPIC) based on the known large effect of allelic variation at the CYP2D6 locus on metabolism of at least codeine and tramadol and possibly oxycodone and hydrocodone [9]. Approximately 7% of the population fall into either the poor or ultrarapid metabolizer categories and may benefit from genetic assessment, but there are over 70 known SNP variants and copy-number variations, so genotyping is currently only performed at fewer than 10 medical centers in the US.

Prediction of OUD or addiction is a much more difficult problem [10]. Variants in the OPRM1 gene, which encodes the μ opioid receptor expressed in the central nervous system, have been repeatedly associated with measures of response to pain medication, but these findings were from small studies and did not reach anything close to genome-wide significance [11]. Nevertheless, the San Diego company Proove Biosciences developed a test based on 11 SNPs broadly associated with addiction, mostly in neurotransmitter receptors and transporters, but the company was forced into receivership in August of 2017 after facing charges of kickbacks and over legitimate concerns surrounding the validity of the aggressively marketed test [12]. Extrapolation of their sensitivity analysis [13] on the assumption of 8% prevalence of OUD implies a precision of just 30%: in other words, the majority of the small percentage of patients recommended not to take opioids would actually not be at high risk of addiction.

By contrast, if a negative predictor could identify two-thirds of the population with half the risk of progression, allowing the social services and other health workers to give more resources and attention to monitoring of the one-third of higher-risk individuals, then successful intervention in just half of these cases would reduce the OUD rate by 25%. Alternatively, if the aim is to generate a test with a precision of at least 50%, then targeting 5% of the population would require a score defining this group with an odds ratio of 8.7, and highly effective treatment would also reduce OUD by 25% (see Table 2). It is remarkable that no genome-wide association study (GWAS) for opioid addiction based on hospital records has yet been reported and conceivable that phenome-wide association studies (PheWASs) [14] on a million patients will have the requisite power for development of a PRS.

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Table 2. Approximate observed prevalence, relative risk, response, and the NNT.

https://doi.org/10.1371/journal.pgen.1008060.t002

Hypertension

One billion people worldwide, including 75 million US residents, suffer from hypertension. It is well established that above 115 mmHg of systolic blood pressure (SBP), every increase of 20 mmHg doubles the risk of cardiovascular disease (CVD). There is much debate over guidelines, but the 8th Joint National Committee (JNC) report [15] called for a combination of lifestyle changes and pharmaceutical intervention—generally including a thiazide diuretic and possibly one or more of an angiotensin converting enzyme (ACE) inhibitor, or a blocker of the angiotensin receptor, beta-adrenoreceptor, or calcium channels—to keep SBP below 150, particularly in elderly persons over the age of 60. Formerly [16], this target was 140, or even 130 in patients with chronic kidney disease or diabetes, as there is strong evidence that doing so almost halves the risk of cardiovascular death. As of December 2017, the American Heart Association (AHA) now regards almost half of all US residents as in need of reducing their blood pressure [17,18].

A randomized control trial involving over 9,000 patients [19] evaluated the effectiveness of intensive therapy with an average of almost three drugs targeting SBP less than 120 mmHg compared with standard treatment with an average of two drugs targeting SBP less than 140 in elderly individuals with incident or preclinical heart disease (generally overweight and with a high Framingham risk score). It was stopped after just 4 years because of a clear benefit of intensive therapy showing a 25% reduction in primary end points (myocardial infarction, stroke, heart failure) from 2.19% to 1.65% events per year. Between one-quarter and one-third of these events led to cardiovascular deaths, which were also significantly reduced. Follow-up analyses [20] also revealed that there was no difference between the treatment arms in self-perception of effectiveness of medication (which can either be taken to mean that there is not likely to be any obstacle to adherence to intensive treatment or that there is no clear benefit to it in terms of general daily health). Furthermore, economic microsimulation showed that the average additional cost of intensive therapy of approximately US$1,000 per year, or around just 5% of the annual total cost of care for these individuals, is well below the “acceptable” threshold of US$50,000 per quality additional life-year (QALY) gained [21], given that median life expectancy is 12 years and an average extension of 3 months was estimated from the data. Such data argue for general introduction of intensive blood pressure control in this high-risk CVD population.

Table 2 shows that targeting intensive therapy to the 5% identified at highest polygenic risk, who have an odds ratio of 3.3 according to Khera and colleagues [2], would prevent 4% of all events, which is 15% of those preventable if everyone is treated. Two-thirds of the events could be avoided if half the population is treated aggressively. However, it also needs to be recognized that the rate of drug-induced, serious adverse events—mostly declining kidney function, often leading to chronic failure, syncope (fainting), and hypotension—was considerably higher (1.4× to 2.5×, depending on criteria) in the intensive treatment group than the decline in cardiovascular events [19]. Furthermore, the NNT for primary events per year is just over 60 for all-cause deaths, and cardiovascular deaths is double that, at around 15 over a decade. This means that the 3 months of QALYs per patient is more likely to reflect 1 in 15 or more patients avoiding an event such that they meet their normal life expectancy rather than meeting an early death, with ambiguous benefit to the remainder.

Meta-analysis of the FINRISK, Framingham cohort, and UK Biobank studies [22,23] has identified PRSs based on either 46,000 or 1.7 million SNPs for which the top and bottom quintiles are differentiated by 15 years in the age of cumulative incidence of 10% acute coronary events, with that threshold never met in low-risk women. Assuming a constant response rate to therapy of 25% for all genetic risk thresholds, targeting the top quintile would prevent one-third of deaths in men, halving the NNT (Table 2).

Type 2 diabetes

The goal for therapeutic intervention in diabetes is not so much about prevention of disease onset as about prevention of progression to serious morbidities and mortality due mainly to CVD but also retinopathy, limb amputation, and internal organ damage. The frontline treatment has long been metformin [24], which reduces blood glucose for the most part by inhibition of mitochondrial gluconeogenesis. Its prescription to the vast majority of type 2 diabetics is largely based on perceptions following a 1998 study [25] implying a 35% reduction in mortality in overweight patients, reinforced by the 2002 United Kingdom Diabetes Prevention Program conclusion that metformin reduced diabetes incidence 18% over a decade [26]. The drug is well tolerated by over 80% of patients and relatively inexpensive at around US$50 a year, so even if only a minority of patients benefit, there is little perceived harm in widespread usage. However, meta-analyses have failed to confirm that there is in fact long-term benefit, with one prominent study in 2012 [27] concluding that the drug is as likely to increase all-cause mortality by 31% as to reduce it by 25%. Part of the difficulty is that the early analyses excluded patients also receiving sulfonylureas, which multiple studies have associated with elevated mortality and cardiovascular event rate and are given as supplementary therapy to patients whose glucose levels are not controlled by metformin (such nonresponders may be more likely to have poor outcomes) [27]. Similarly, the ACCORD study [28] found that intensive control of glycemia and lipids for long-term diabetics can increase all-cause mortality, emphasizing the importance of new strategies to guide prescription.

In the past decade, a new generation of drugs have emerged that act more broadly to reduce the so-called ominous octet of diabetes pathologies [29]: hepatic glucose production, pancreatic glucagon secretion by α cells, insulin secretion by β cells, neurotransmitter dysfunction, cardiac endothelial damage, reduced glucose uptake in muscle, elevated lipolysis, and renal glucose reabsportion. Because hyperglycemia is not thought to be the primary cause of cardiovascular pathophysiology, which is responsible for 80% of diabetes mortality, therapeutic intervention increasingly focuses on CVD. Agonists of the GLP-1 receptor and SGLT2 inhibitors have been shown in large randomized clinical trials [30,31] to reduce stroke, myocardial infarction, and cardiovascular death by approximately 20% in diabetic patients with CVD, independent of Hb1Ac (and, hence, blood glucose) reduction. Drugs like liraglutide (Victoza) and exenatide (Byetta) are currently over 100 times the cost of metformin. Although the price will presumably reduce when they become generic in the next few years, it would seem desirable to identify that subset of the diabetic population with CVD who are most likely to benefit and to assess whether patients at highest risk of CVD are more responsive. One study found that a lipid PRS did not differentiate metformin-responsive low-density lipoprotein (LDL) reduction [32], but it would be more informative to know whether the type 2 diabetes or CVD PRS identifies patients who are more likely to respond to the new generation of GLP-1 receptor and SGLT2 inhibitor drugs. Economic modeling has already argued that the combination of metformin plus a DPP-4 inhibitor, another drug that reduces mortality around 20%, though expensive at approaching ₤20,000/QALY gained [33], is within the accepted guidelines for cost-effectiveness.

Given that adiposity, measured both generally as body mass index (BMI) and centrally as waist-to-hip ratio (WHR) adjusted for BMI (WHRadjBMI), is strongly associated with metabolic syndrome, it has been argued that weight reduction is among the most important public health goals for the coming decades. Mendelian randomization studies of the UK Biobank with a PRS genetic instrument explaining approximately 2% of BMI indicate that an increase of one standard deviation unit (approximately 5 kg/m²) causally doubles the risk of type 2 diabetes [34], and similar results are seen in the DIAGRAM study [35] for WHR, with an independent PRS explaining approximately 1% of the trait. Although it is notoriously difficult to maintain a 10% reduction in weight long term, recent studies indicate that intensive lifestyle intervention is effective and can produce remission of diabetes [36]. Moreover, even moderate exercise significantly reduces cardiovascular mortality across urban and rural settings in low-, moderate-, and high-income countries [37].

Depression

Major depressive disorder (MDD) has rapidly become the second-leading source of morbidity globally. At least 1 in 20 people will lose meaningful quality of life-years to depression, and total costs to the healthcare system alone in the US have been estimated at up to US$100 billion per year [38]. Even though meta-analyses provide little indication that antidepression medications (ADMs) lead to better rates of long-term remission than behavioral therapies [39,40], there are several reasons why the adoption of ADMs is only likely to increase in the coming years: the short-term benefits for symptom relief are superior, there is a severe shortage of trained therapists, and insurance coverage for mental health is unreliable [41]. However, only around half of patients will respond to the first ADM they are prescribed, and most of the two-thirds who do benefit will need to test three to five drugs before finding a regimen that is both effective and tolerated. Including hospitalization, this may cost tens of thousands of dollars, so once again, there are enormous economic (and clinical) benefits to be gained from genomic predictive evaluation.

The heritability of MDD (at about 30%) is one of the lowest of all common disorders, in part reflecting broad heterogeneity in environmental contributions, and accordingly, GWAS has made few inroads. Despite a sample size including 120,000 cases, the largest studies to date have discovered just 15 replicated loci for MDD [42], and a genome-wide PRS explains less than 1% of the variance [43] and thus shows little predictive potential. An even larger GWAS for neuroticism [44] turned up 116 independent loci, and the polygenic score is highly correlated with that for depression, but it still explains just a small fraction of the variance. Furthermore, the genetic correlation between Asian- and European-ancestry populations [45] is notably less than 1, and somewhat differential genetic risk is implicated for early- and late-onset disease [46].

Similarly, GWAS has not yet identified any replicated genome-wide significant loci for ADM response [47,48], even though common variants explain over 40% of the variance. There is some preliminary evidence [49] that quartiles of polygenic scores for openness, neuroticism, and conscientiousness differentiate response to selective serotonin reuptake inhibitors (SSRIs) and possibly predict remission, but differences in sign of effect at 4 and 8 weeks of treatment caution that many more than 1,000 subjects need to be evaluated. There is also an extensive body of literature evaluating two dozen candidate genes for pharmacogenetic modulatory effects. These mostly target serotonin production and signaling (because they are the targets of the major classes of ADM) as well as drug metabolism and bioavailability. Meta-analyses [50,51] implicate variation in the serotonin transporter and receptors (the 5HTTLPR in SLC6A4, HTR2A) as well as cytochrome P450s and a few other loci, though with apparent heterogeneity across ethnicities, unexpected effects such as a heterozygote advantage for V66M in BDNF, and overall lack of Bonferroni-adjusted significance. It should be noted that, with odds ratios in the vicinity of 1.5, current sample sizes of fewer than 3,000 patients are underpowered, so much larger pharmacogenetics GWASs are urgently required. Despite this, CPIC has released guidelines for implementation of pharmacogenetics for SSRI dosing based on CYP2D6 and CYP2C19 genotypes [52], and at least four companies are offering combinatorial pharmacogenetics diagnostic tests (CPGx: the Neuropharmagen [53], GeneSight [54], Admera PGxPsych, and MD labs Rχight panels). I could not find publications describing clinical assessment of the latter two, but the first two both show meaningful improvements in 12-week response (for example, reducing depression symptoms in 50% of patients when physician-ordered ADMs are congruent with test predictions, relative to 35% of those who are not given a CPGx test and 39% for all tested patients regardless of congruence). One of the companies also reports cost-effectiveness of the test, saving almost US$4,000 in medication costs per patient treated solely by a primary care provider, relative to US$2,500 for the one-time diagnostic [55]. Because these are proprietary tests, it is impossible from the published data to know which variants are most useful, and clearly much larger independent studies will be needed to comprehensively evaluate utility across dozens of drugs. It is highly likely, though, that much of the benefit comes from the negative predictive value of avoiding ADMs that are predicted either to lead to intolerable side effects or not to alleviate symptoms. Potential healthcare savings in this domain run plausibly to tens of billions of dollars per year once validated mature pharmacogenetic tests are available.

Osteoporosis

In the US alone, over 1.5 million postmenopausal women suffer clinically significant bone fractures each year. Lifetime risk of both hip and vertebral fractures is over 15% [56], with hip fracture seriously impeding the mobility of half of the patients for an extended period of time, leading to a requirement for long-term ambulatory care in a third of cases. It is not clear whether the 10% elevation in mortality after a fall leading to hip fracture is a consequence of fracture or comorbid with underlying disease. Hormone replacement therapy with estrogen and progesterone was consistently found to reduce fracture rates by around 40%, but following the Women’s Health Initiative’s [57] detection of meaningfully elevated rates of invasive breast cancer and cardiovascular morbidity, it is no longer widely adopted. Instead, bisphosphonate inhibitors of bone resorption such as alendronate [58], which are similarly effective without the serious adverse effects, have become standard of care, also showing cost-effectiveness in high-risk groups over the age of 70 [59].

Recently, a monoclonal antibody inhibitor of sclerostin that simultaneously increases bone formation and decreases bone resorption, romosozumab, was shown in two randomized clinical trials to reduce both vertebral and nonvertebral fracture rates [60,61]. Direct comparison of this treatment with alendronate alone in high-risk women with bone mineral density (BMD) in the bottom decile and a history of fracture demonstrated remarkable efficacy consistent with an NNT for vertebral fracture of less than 20 (6.2% relative to 11.9% over a 2-year follow-up, which would likely be around 10 relative to placebo). However, the US Food and Drug Administration (FDA) has disallowed registration of the drug because the data show a worrying elevation of the serious adverse cardiovascular event rate from 1.9% to 2.5%. A Japanese study argued that as few as four SNPs can differentiate high- and low-risk deciles for vertebral fracture [62] by as much as 10-fold. Concerted robust genomic and clinical evaluation thus has potential to stratify patients for treatment: if a polygenic score achieves a relative risk of five for the top decile, then 20% of cases could be prevented with a precision of over 50%. An analysis of the UK Biobank for osteoporosis and general bone fracture is less promising [63] but did not target the low-BMD group. In any case, genetics could rescue a treatment that has the potential to prevent loss of quality of life for hundreds of thousands of women each year while also ensuring that the costs to the healthcare system are contained.

Discussion

With the advent of clinically relevant polygenic scores [2], the time has come for research designed not just to identify high-risk groups but also to evaluate therapeutic response rates across the distribution of scores. The oft-reported measures of sensitivity and specificity are not generally likely to be clinically useful, as scores that identify the upper two deciles of the risk distribution generally have sensitivities less than 50%. Fig 1 assumes case-control comparisons but might actually be more informative in relation to the prediction of therapeutic responses in patients already diagnosed with disease. In some situations, response may be highly correlated with risk of disease, but that cannot be assumed, so more GWASs of disease progression therapeutic response are needed.

Although it is often assumed that all patients should receive treatment, considerations of cost, adverse side effects, development of resistance, and patient choice all mitigate in some cases toward preferential treatment of the patients at highest risk of progression to life-threatening disease and/or most likely to respond to therapy. Meaningful reductions in the NNT while ensuring high prevention rates will be most attainable if therapy can be targeted to individuals predicted to have the highest response rates. It should generally be possible to prevent 20% to 50% of cases by treating between 5% and 20% of the patients. Conversely, negative prediction may identify those least likely to benefit, which is likely to become increasingly important in relation to expensive next-generation biologics and in markets emphasizing cost control.

Two further critical components of this evidence-based approach to clinical intervention are the costs of treatment and patient rights. Economic modeling of healthcare costs is notoriously difficult, but it is nevertheless clear that either the government or private health insurers must bear the short-term costs of expensive new-generation medications such as biologics. Any consequent rationing of options must be evaluated alongside patient rights and desires. Box 1 argues that some individuals are likely to demand treatment irrespective of where they fit in terms of risk if the drug has been approved for general use; others may prefer not to take drugs as far as possible and will see genomic information as empowering [64, 65]. Genetic risk scores are an important step toward personalized genomic medicine and will need to be implemented with respect to the specific clinical and economic circumstances of each disease.

Supporting information

Acknowledgments

I am especially grateful to Professors Arcadi Navarro, David Comas, and Jaume Bertranpetit for hosting me at the PRBB and Universitat Pompeu Fabra (CEXS-UPF) in Barcelona in Fall 2017 while the article was researched and drafted. Also thanks to Patrick Howard for coding the Accurator.