While we’re busy debating the pros and cons of clinical genome sequencing and tossing around buzzwords like “personalized” and “translational” medicine, I’ve recently caught some health care providers ignoring the archaic skills of communication and common sense. So while we await genome analysis apps on our smartphones and DNA sequence annotators in our doctors’ offices, here are 3 suggestions on how to provide personalized medicine right now:
1. Read the patient’s chart (paper or digital)
2. Listen to the patient
3. Look at the patient
Disclaimer: Today’s blog is anecdotal and non-scientific, but may identify a trend.
My Missing Thyroid
A few weeks ago, I had a long-overdue check-up, with a nurse practitioner. It was my first visit to the practice, which had provided excellent urgent care.
On the medical history form, I described my circa 1993 thyroid cancer in intimate histological detail: papillary in left lobe, follicular in the right.
The NP spent an impressive 45 minutes asking questions and listening to me – or so I thought. During the brief physical exam, I told her all about my thyroid cancer, my daily Synthroid dose, and even brought her hand to my throat, having noticed that dentists get very excited at my lack of a thyroid gland. No thyroid tests needed, said I. My endocrinologist had recently done them.
So I was surprised when, early the next morning, a Saturday, my cell phone quacked.
How much can you help yourself by getting your genome sequenced?
A lot, a little, not at all?
Scenario 1 (big help): You discover you have a greatly elevated risk of Disease X. You do various things to reduce that risk that actually reduce it.
Scenario 2: (a little help): You discover you have a greatly elevated risk of Rare Disease X. You do various things to reduce that risk but they don’t help. At least, when Disease X starts, you will be less upset.
Scenario 3 (no help): You discover that you have a greatly elevated risk for a common easily-noticed disease (such as obesity). You already watched your weight, this changes nothing. Scenario 4 (harm): You discover that you have a greatly elevated risk of Scary Disease X (e.g., bipolar disorder). It is depressing news. Later studies show that the gene/disease association was a mistake. (Many gene/disease associations have failed to replicate.)
A recent Wired article tries to answer this question for one person: Raymond McCauley, a bioinformatics scientist who had his genome sequenced four years ago and learned he was “four or five times more likely than most people to develop age-related macular degeneration (AMD)”. The article says “of all the ailments described in the 23andme profile, AMD has one of the strongest genetic associations”. If I found this in my genetic profile, I would want to know the confidence interval of the increased risk. Is it a factor of 4.5 plus or minus 1? Or 4.5 plus or minus 8? This isn’t easy to figure out. In addition to the question of variability, there can easily be bias (= estimate is too high). Let’s say I do 100 gene/disease association studies.