[圣诞特刊]Individual differences in normal body temperature: longitudinal big data analysis of patient records [正常体温的个体差异：患者记录的纵向大数据分析]

• 分享：

BMJ 2017; 359 doi: https://doi.org/10.1136/bmj.j5468 (Published 13 December 2017)
Cite this as: BMJ 2017;359:j5468

Authors
Ziad Obermeyer, Jasmeet K Samra, Sendhil Mullainathan

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.