This transcript has been edited for clarity.
Every branch of science has its constants. Physics has the speed of light, the gravitational constant, the Planck constant. Chemistry gives us Avogadro’s number, Faraday’s constant, the charge of an electron. Medicine isn’t quite as reliable as physics when it comes to these things, but insofar as there are any constants in medicine, might I suggest normal body temperature: 37° Celsius, 98.6° Fahrenheit.
Sure, serum sodium may be less variable and lactate concentration more clinically relevant, but even my 7-year-old knows that normal body temperature is 98.6°.
Except, as it turns out, 98.6° isn’t normal at all.
How did we arrive at 37.0° C for normal body temperature? We got it from this guy – German physician Carl Reinhold August Wunderlich, who, in addition to looking eerily like Luciano Pavarotti, was the first to realize that fever was not itself a disease but a symptom of one.
In 1851, Dr. Wunderlich released his measurements of more than 1 million body temperatures taken from 25,000 Germans – a painstaking process at the time, which employed a foot-long thermometer and took 20 minutes to obtain a measurement.
The average temperature measured, of course, was 37° C.
We’re more than 150 years post-Wunderlich right now, and the average person in the United States might be quite a bit different from the average German in 1850. Moreover, we can do a lot better than just measuring a ton of people and taking the average, because we have statistics. The problem with measuring a bunch of people and taking the average temperature as normal is that you can’t be sure that the people you are measuring are normal. There are obvious causes of elevated temperature that you could exclude. Let’s not take people with a respiratory infection or who are taking Tylenol, for example. But as highlighted in this, we can do a lot better than that.
The study leverages the fact that body temperature is typically measured during all medical office visits and recorded in the ever-present electronic medical record.
Researchers from Stanford identified 724,199 patient encounters with outpatient temperature data. They excluded extreme temperatures – less than 34° C or greater than 40° C – excluded patients under 20 or above 80 years, and excluded those with extremes of height, weight, or body mass index.
You end up with a distribution like this. Note that the peak is clearly lower than 37° C.
But we’re still not at “normal.” Some people would be seeing their doctor for conditions that affect body temperature, such as infection. You could use diagnosis codes to flag these individuals and drop them, but that feels a bit arbitrary.
I really love how the researchers used data to fix this problem. They used a technique called LIMIT (Laboratory Information Mining for Individualized Thresholds). It works like this:
Take all the temperature measurements and then identify the outliers – the very tails of the distribution.
Look at all the diagnosis codes in those distributions. Determine which diagnosis codes are overrepresented in those distributions. Now you have a data-driven way to say that yes, these diagnoses are associated with weird temperatures. Next, eliminate everyone with those diagnoses from the dataset. What you are left with is a normal population, or at least a population that doesn’t have a condition that seems to meaningfully affect temperature.
So, who was dropped? Well, a lot of people, actually. It turned out that diabetes was way overrepresented in the outlier group. Although 9.2% of the population had diabetes, 26% of people with very low temperatures did, so everyone with diabetes is removed from the dataset. While 5% of the population had a cough at their encounter, 7% of the people with very high temperature and 7% of the people with very low temperature had a cough, so everyone with cough gets thrown out.
The algorithm excluded people on antibiotics or who had sinusitis, urinary tract infections, pneumonia, and, yes, a diagnosis of “fever.” The list makes sense, which is always nice when you have a purely algorithmic classification system.
What do we have left? What is the real normal temperature? Ready?
It’s 36.64° C, or about 98.0° F.
Of course, normal temperature varied depending on the time of day it was measured – higher in the afternoon.
The normal temperature in women tended to be higher than in men. The normal temperature declined with age as well.
In fact, the researchers built awhere you can enter your own, or your patient’s, parameters and calculate a normal body temperature for them. Here’s mine. My normal temperature at around 2 p.m. should be 36.7° C.
So, we’re all more cold-blooded than we thought. Is this just because of better methods? Maybe. But studies have actually shown, possibly because of the lower levels of inflammation we face in modern life (thanks to improvements in hygiene and antibiotics).
Of course, I’m sure some of you are asking yourselves whether any of this really matters. Is 37° C close enough?
Sure, this may be sort of puttering around the edges of physical diagnosis, but I think the methodology is really interesting and can obviously be applied to other broadly collected data points. But these data show us that thin, older individuals really do run cooler, and that we may need to pay more attention to a low-grade fever in that population than we otherwise would.
In any case, it’s time for a little re-education. If someone asks you what normal body temperature is, just say 36.6° C, 98.0° F. For his work in this area, I suggest we call it Wunderlich’s constant.
Dr. Wilson is associate professor of medicine and public health at Yale University, New Haven, Conn., and director of Yale’s Clinical and Translational Research Accelerator. He has no disclosures.
A version of this article appeared on.