Individual Factors Including Age, BMI, and Heritable Factors Underlie Temperature Variation in Sickness and in Health: An Observational, Multi-cohort Study
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Abstract
Background
Aging affects immunity, potentially altering fever response to infection. We assess effects of biological variables on basal temperature, and during COVID-19 infection, proposing an updated temperature threshold for older adults ≥65 years.
Methods
Participants were from 4 cohorts: 1 089 unaffected adult TwinsUK volunteers; 520 adults with emergency admission to a London hospital with RT-PCR confirmed SARS-CoV-2 infection; 757 adults with emergency admission to a Birmingham hospital with RT-PCR confirmed SARS-CoV-2 infection and 3 972 adult community-based COVID Symptom Study participants self-reporting a positive RT-PCR test. Heritability was assessed using saturated and univariate ACE models; mixed-effect and multivariable linear regression examined associations between temperature, age, sex, and body mass index (BMI); multivariable logistic regression examined associations between fever (≥37.8°C) and age; receiver operating characteristic (ROC) analysis was used to identify temperature threshold for adults ≥ 65 years.
Results
Among unaffected volunteers, lower BMI (p = .001), and increasing age (p < .001) was associated with lower basal temperature. Basal temperature showed a heritability of 47% (95% confidence interval 18%–57%). In COVID-19+ participants, increasing age was associated with lower temperatures in Birmingham and community-based cohorts (p < .001). For each additional year of age, participants were 1% less likely to demonstrate a fever ≥37.8°C (OR 0.99; p < .001). Combining healthy and COVID-19+ participants, a temperature of 37.4°C in adults ≥65 years had similar sensitivity and specificity to 37.8°C in adults <65 years for discriminating infection.
Conclusions
Aging affects temperature in health and acute infection, with significant heritability, indicating genetic factors contribute to temperature regulation. Our observations suggest a lower threshold (37.4°C/97.3°F) for identifying fever in older adults ≥65 years.
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SciScore for 10.1101/2021.01.26.21250480: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Data analysis and graphics were performed in the R statistical environment (version 4.0) using the Tidyverse[32], Open Mx[30] and lme4[33] packages, in Stata (version 15.0) and in Python (version 3.9). Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences …SciScore for 10.1101/2021.01.26.21250480: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Data analysis and graphics were performed in the R statistical environment (version 4.0) using the Tidyverse[32], Open Mx[30] and lme4[33] packages, in Stata (version 15.0) and in Python (version 3.9). Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Observations from our community-based cohort may have relevance when considering methods for community case detection and limitation of spread (such as quarantining or screening for travel). Strengths and Limitations: Our study combines three types of data: a classic research cohort (TwinsUK), routinely collected clinical data from hospital cohorts, and a cohort of community ‘citizen science’ data. The TwinsUK cohort benefits from a large number of observations, collected using rigorous study procedures. Reassuringly, associations observed in this cohort were recapitulated in the “real-life” hospital and community-based data. This concordance increases generalisability from the academic sphere to clinical and community-based settings. For historical reasons, the TwinsUK cohort is predominantly female. Females generally show greater individual temperature variability than males. The app cohort was also predominantly female. However, both hospital cohorts were predominantly male, reflecting that men are more severely affected by COVID-19. However, our models did not show any association with sex and recorded temperature, suggesting that these differences in cohort structure would not affect results. Although the app cohort only contained people with self-reported COVID-19 testing status, it is possible that maximal temperature logged in the app was not synchronous with COVID-19 infection or could be the result of another infection. Only those reporting “fever or chills” were pr...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from scite Reference Check: We found no unreliable references.
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