Individual differences in normal body temperature: longitudinal big data analysis of patient records

Abstract

Objective: To estimate individual level body temperature and to correlate it with other measures of physiology and health.

Design: Observational cohort study.

Setting: Outpatient clinics of a large academic hospital, 2009-14.

Participants: 35 488 patients who neither received a diagnosis for infections nor were prescribed antibiotics, in whom temperature was expected to be within normal limits.

Main outcome measures: Baseline temperatures at individual level, estimated using random effects regression and controlling for ambient conditions at the time of measurement, body site, and time factors. Baseline temperatures were correlated with demographics, medical comorbidities, vital signs, and subsequent one year mortality.

Results: In a diverse cohort of 35 488 patients (mean age 52.9 years, 64% women, 41% non-white race) with 243 506 temperature measurements, mean temperature was 36.6°C (95% range 35.7-37.3°C, 99% range 35.3-37.7°C). Several demographic factors were linked to individual level temperature, with older people the coolest (-0.021°C for every decade, P<0.001) and African-American women the hottest (versus white men: 0.052°C, P<0.001). Several comorbidities were linked to lower temperature (eg, hypothyroidism: -0.013°C, P=0.01) or higher temperature (eg, cancer: 0.020, P<0.001), as were physiological measurements (eg, body mass index: 0.002 per m/kg², P<0.001). Overall, measured factors collectively explained only 8.2% of individual temperature variation. Despite this, unexplained temperature variation was a significant predictor of subsequent mortality: controlling for all measured factors, 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).

Conclusions: Individuals' baseline temperatures showed meaningful variation that was not due solely to measurement error or environmental factors. Baseline temperatures correlated with demographics, comorbid conditions, and physiology, but these factors explained only a small part of individual temperature variation. Unexplained variation in baseline temperature, however, strongly predicted mortality.

Fig 1

Relation of temporal and environmental factors to measured body temperature. Coefficients estimated by random effects regression are shown for ambient temperature, dew point, hour, and month, compared with reference categories: median temperature 10th (12.2°C), median dew point 10th (4.7˚C), 12 pm, and April, respectively

Fig 2

Physiological measurements and relation to temperature effects, by sex. Dots represent centiles of individual temperature effect and vertical bar 95% confidence intervals