Grand Rounds

Is Individual Baseline Temperature Variation Associated with Subsequent Mortality?

BACKGROUND AND PURPOSE:

Medical professionals are given ranges of ‘normal’ temperatures, but these reflect human population data and not individual variation
- Most studies on temperature conducted from 1950 or before
There is research in fruit flies and mice that lower body temperature may be associated with longevity
The modern electronic health record allows for research on a more individualized basis
Obermeyer et al. (BMJ, 2017) estimated individual level body temperature and correlated this data with other physiologic and health parameters

Observational cohort study
Dataset from large US based academic hospital
- Routine outpatient visits
- No evidence of infection or antibiotic prescriptions
- Temperatures within normal limits
Temperature recordings controlled for ambient conditions at the time of measurement, body site, and time factors
Baseline temperature was correlated with demographics, medical comorbidities, vital signs, and subsequent one year mortality

35,488 patients were included
Data obtained from outpatient visits from 2009-2014
243,506 temperature measurements
- Mean age 52.9 years
- 64% women
- 41% non-white race
Mean temperature was 36.6°C (95% range 35.7-37.3°C; 99% range 35.3-37.7°C)
Older people had the coolest temperatures
- –0.021°C for every decade (P<0.001)
African-American women had the hottest temperatures vs white men
- 0.052°C (P<0.001)
Lower temperature was linked to many comorbidities such as hypothyroidism
- –0.013°C (P=0.01)
Higher temperature was associated with
- Cancer: 0.020 (P<0.001)
- BMI: 0.002 per m/kg2 (P<0.001)
Overall, measured factors collectively explained only 8.2% of individual temperature variation
Unexplained temperature variation was a significant predictor of subsequent mortality
- An increase of 0.149°C (1 SD of individual temperature in the data) was linked to 8.4% higher one year mortality (P=0.014)
- Not associated with increase in inflammatory marker reactive protein

Authors recognize study limitations including the fact that a single center has a single climactic zone and data based on hospital visits so potentially sicker cohort of patients than general population
In this study, the authors provide an example of how ‘big data’ experiments can generate new medical knowledge
Individuals’ baseline temperatures correlate with disease or other physiological measurements
However, unexplained individual temperature variation correlates with a higher risk for mortality
Results suggest baseline temperature could be a potential predictive tool in medicine

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