Racial Profiling in Psychiatry: Does It Help Patients?

Is race relevant for psychopharmacology?
If these data on the genetics of race are convincing, then the importance of race to psychopharmacology becomes obvious. Researchers have described a series of drug metabolizing enzymes, drug transporters, and drug receptors that affect drug response. A series of liver enzymes, the cytochrome P-450 system, metabolizes a wide variety of psychiatric medications (Table). Many variants exist that produce a range of phenotypes, from poor metabolizers to extensive metabolizers. Poor metabolizers experience higher drug concentrations from a given dose, potentially increasing both efficacy and side effects. These drug metabolizing enzymes can be under tight genetic control, with heritability greater than 90%.^14,15 As with many genetic variations, researchers have found that P-450 variants occur at different frequencies in different populations (Table). Racial patterns have also been found in receptors and transporters, especially the serotonin transporter.^4,16,17

These findings seem to provide a justification for racial profiling in psychiatric prescribing. Asian patients, more likely to be poor metabolizers with 2C19 than whites, should respond to lower doses of diazepam, but might require higher doses of drugs metabolized by 2D6, such as fluoxetine(Drug information on fluoxetine). With drug metabolizing enzymes under genetic control, and modern genomics showing that genetic differences exist between races, it would make sense that drug metabolism will vary from race to race.

The devil is in the details
Given the history of race in America, it is not surprising that concerns about race, genetics, and pharmacology have persisted. When evolutionary biologist Armand Leroi published an editorial in the New York Times defending the importance of race in medicine in March 2005, a collection of prominent scholars wrote critical replies.¹⁸ Why does the controversy endure?

First, genomics has not definitively proven that race is a real, stable category. Many of the studies that have found racial differences are, in part, self-fulfilling prophecies. It was not by chance that HapMap researchers first sampled populations that corresponded to Caucasian (Provo), African (Ibadan), and Asian (Tokyo and Beijing). Had they sampled one group of short persons and one group of tall persons, they would also have found genetic differences between the groups. The conclusion should not be that there is a short race and a tall race, an Asian race and a Caucasian race. For example, had the HapMap researchers started in London and sampled one person every 35 miles from there to Tokyo, they would have ended up with a sample of 270 persons that showed a gradient of continuous genetic change, not one that suggested 3 racial clusters.

Similar problems appear with data interpretation. The letter in the New England Journal of Medicine about the correlation of self-reported race and genetics could have been interpreted quite differently. While most white persons (in Cleveland, at least) do seem to be of European descent, some who self- reported as white actually had 70% to 80% African ancestry. Meanwhile, individuals who self-reported as black had indeterminate ancestry, anywhere from less than 10% to more than 90% African ancestry. The data do not indicate that white and black are natural categories. Instead, it appears obvious that "African American" persons have such diverse ancestry that meaningful generalizations cannot be made.

Many other studies have identified limitations of self-reported race. In the 2000 census, more than 7 million people reported multiple races, and more than 800,000 reported being both black and white.¹⁹ Another study found that one third changed their self-identified racial and ethnic categories over time.²⁰ Presumably these ambiguities will only increase over time as diverse populations continue to mix.

Another problem is that it remains unclear how best to apply population-based data to individuals. Suppose it is true that 18% of blacks are poor metabolizers with P-450 2C19, while only 3% of whites are. What does this mean for individual black patients? Not that they are poor metabolizers, but that they have an increased risk of being poor metabolizers. An overwhelming majority of both groups (82% of blacks and 97% of whites) would have normal metabolism. All patients should be treated similarly regardless of race.

This has been demonstrated most clearly for antihypertensive medications.²¹ Many clinical trials have found racial differences in drug response for diuretics, ß-blockers, angiotensin- converting enzyme inhibitors, and many other medications. Captopril(Drug information on captopril), for instance, lowers diastolic blood pressure more in white patients than in black patients (10.7 vs 8.0 mm Hg; P < .001). However, in every study the standard deviation within each racial group far exceeded the differences between the groups. A meta-analysis showed that 80% to 95% of black and white patients had indistinguishable responses to each medication. While racial differences did exist, they were not relevant for most patients. To treat individuals differently because of a slightly different likelihood of being a poor metabolizer would do them a disservice.

Moving forward
Where does this leave clinicians? Many advocates of pharmacogenetics hope to avoid the problems of racial profiling by moving toward personalized medicine. In this ideal future, clinicians will genotype every patient they treat, identifying every relevant drug-metabolizing variant without relying on assumptions about race, ethnicity, or ancestry.^22-24 The technology for such personalized medicine is starting to appear: a gene chip for analyzing variants of P-450 2D6 is now available, and others will surely follow.

But even this might not usher in an era of personalized medicine. Many factors, beyond drug metabolizing genotypes, influence patients' responses to medications. Clinicians will need to integrate information about every relevant metabolic pathway with data about drug transporters and receptors. Moreover, a wide range of environmental exposures, including tobacco smoke, charcoal-broiled beef, and many fruits and vegetables can increase the activity of drug-metabolizing enzymes.²⁵

Because of the interplay between genetics and environment, some researchers argue that we should focus not on genotype but on phenotype. Dubbing this new field "pharmacometabonomics," they argue that clinicians should monitor assays of gene expression,²⁶ although this might require retesting patients every time their environmental exposures change.

Another frustratingly familiar confounder is the problem of noncompliance. Studies of noncompliance have found rates as high as 20% to 50% in general populations and 60% to 80% in psychiatric patients.²⁷ The fact that a patient's race might affect his or her chance of being a poor metabolizer by 10% to 20% pales in comparison to the fact that he is likely to take less than half of the prescribed medication. Spending more time with a patient or implementing psychosocial intervetions to improve compliance will probably be more cost-effective than spending $1000 on pharmacogenetic or pharmacometabonomic assays.

Race or ethnicity should not be irrelevant for psychiatrists. Both continue to be social variables of enormous significance, influencing the distribution of health risks and social resources and generating enormous disparities in wealth, disease rates, treatment, and mortality.²⁸ A patient's racial or ethnic background might be relevant for understanding attitudes toward mental illness or willingness to start medications or engage in therapy, but these risks, resources, and attitudes are also influenced by socioeconomic status, education, religious beliefs, family background, and many other factors. The solution cannot be simple-minded racial profiling or cultural competence, in which all members of one group are treated in a standardized way. Instead, especially in psychiatry, we must practice individual profiling: the more we know about an individual, the better we can treat that patient.

What do I do in my own practice? I work in a setting of incredible diversity: the Psychiatric Emergency Service at Cambridge Hospital in Massachusetts. In recent weeks, I have treated patients from Boston, Cape Verde, Egypt, Brazil, Florida, India, Haiti, and countless other local and remote locations. Unlike Satel, I do not jump from a patient's race to a prediction about his drug response. Instead, I take a conservative but generalized approach: start low and go slow. This process will eventually find the right drug and dosage for every patient, without resorting to unreliable assumptions about race and ethnicity.

Does this increase the time it takes to find the optimal dose for each patient? Possibly. Is it overly cautious? Possibly. If my 42-year-old patient responded to 10 mg of fluoxetine, would I be more likely to suspect poor metabolism if he were black than if he were white? Possibly. If I really wanted to know, I could test the genotype directly. But with my empiric approach, I do not need to know or assume anything about race or genotype; I find this the most honest way to practice.