Temperature Biorhythm Atlas

Paper

Phenome-Wide Association Study of Actigraphy in the UK Biobank
Brooks TG, Lahens NF, Grant GR, Sheline YI, FitzGerald GA, Skarke C
medRxiv 2021.12.09.21267558; doi: https://doi.org/10.1101/2021.12.09.21267558
2021.

Dec. 11th, 2021 version 1

Abstract

Using data from 82,412 participants in the UK Biobank, robustness of diurnal rhythms was captured by diurnal changes in wrist temperature, as measured during one week of actigraphy. Rhythm amplitude was prospectively associated to a phenome-wide scan of diagnoses in Cox proportional hazard models.

Chronic diseases, such as type 2 diabetes and hypertension, are diagnosed at substantial higher incidences in subjects with weaker circadian rhythms of wrist temperature. The phenome-wide scan establishes a comprehensive, disease-specific repository for clinicians, researchers, and the public to make these data actionable for individualized approaches of disease risk management.

Results

Phenotypes were categorized according to PheCODE v1.2. Select a phenotype below to explore selected results related to the chosen phenotype; see paper for full methods and supplemental data for full results. Top actigraphy-associated phenotypes include diabetes mellitus, hypertension, renal failure, chronic airway obstruction, pneumonia, anxiety disorders, and disorders of lipoid metabolism.

{{phenotype}}

Acceleration and Temperature traces

Acceleration and wrist temperature were measured using an wrist-worn Axivity AX3 for one-week periods (Doherty, et al 2017). Below are the average acceleration and wrist temperature values by case/control status across the day.

Generated from {{phenotype_results.trace_match_counts}} case-controls pairs, matched by age and sex. Temperature values are normalized to have mean 0 in each individual. Solid curves give median value for the population at that time of day, and shaded regions indicate the middle 50% of the population.

Do diurnal rhythms predict {{phenotype}}?

To determine if diurnal rhythms predict later diagnosis with the phenotypes, the individuals who had no record of the phenotype at the time of actigraphy measurement were selected. Among these, diagnosis with the phenotype was modelled through a Cox proportional hazards model with rhythm robustness (wrist temperature amplitude) as a factor. Effect sizes are expressed in terms of a 2 SD (1.8°C) decrease in temperature amplitude.

p-value log Hazard Ratio (95% CI)
per SD
Incidence Rate Increase (95% CI)
For 2 SD worse rhythms
Overall {{fmt_p(phenotype_results.predictive_tests.p)}} {{fmt_ci(-phenotype_results.predictive_tests.std_logHR, phenotype_results.predictive_tests.std_logHR_se)}} {{fmt(Math.exp(-2*phenotype_results.predictive_tests.std_logHR)*100-100)}}% {{fmt_range(Math.exp(2*(-phenotype_results.predictive_tests.std_logHR - 1.96*phenotype_results.predictive_tests.std_logHR_se))*100-100, Math.exp(2*(-phenotype_results.predictive_tests.std_logHR + 2 * 1.96*phenotype_results.predictive_tests.std_logHR_se))*100-100)}}
Males {{fmt_p(phenotype_results.predictive_tests_by_sex.male_p)}} {{fmt_ci(-phenotype_results.predictive_tests_by_sex.std_male_logHR, phenotype_results.predictive_tests_by_sex.std_male_logHR_se)}} {{fmt(Math.exp(-2*phenotype_results.predictive_tests_by_sex.std_male_logHR)*100-100)}}% {{fmt_range(Math.exp(2*(-phenotype_results.predictive_tests_by_sex.std_male_logHR - 1.96*phenotype_results.predictive_tests_by_sex.std_male_logHR_se))*100-100, Math.exp(2*(-phenotype_results.predictive_tests_by_sex.std_male_logHR + 2 * 1.96*phenotype_results.predictive_tests_by_sex.std_male_logHR_se))*100-100)}}
Females {{fmt_p(phenotype_results.predictive_tests_by_sex.female_p)}} {{fmt_ci(-phenotype_results.predictive_tests_by_sex.std_female_logHR, phenotype_results.predictive_tests_by_sex.std_female_logHR_se)}} {{fmt(Math.exp(-2*phenotype_results.predictive_tests_by_sex.std_female_logHR)*100-100)}}% {{fmt_range(Math.exp(2*(-phenotype_results.predictive_tests_by_sex.std_female_logHR - 1.96*phenotype_results.predictive_tests_by_sex.std_female_logHR_se))*100-100, Math.exp(2*(-phenotype_results.predictive_tests_by_sex.std_female_logHR + 2 * 1.96*phenotype_results.predictive_tests_by_sex.std_female_logHR_se))*100-100)}}
Under 65 {{fmt_p(phenotype_results.predictive_tests_by_age.under_65_p)}} {{fmt_ci(-phenotype_results.predictive_tests_by_age.under_65_std_logHR, phenotype_results.predictive_tests_by_age.under_65_std_logHR_se)}} {{fmt(Math.exp(-2*phenotype_results.predictive_tests_by_age.under_65_std_logHR)*100-100)}}% {{fmt_range(Math.exp(2*(-phenotype_results.predictive_tests_by_age.under_65_std_logHR - 1.96*phenotype_results.predictive_tests_by_age.under_65_std_logHR_se))*100-100, Math.exp(2*(-phenotype_results.predictive_tests_by_age.under_65_std_logHR + 2 * 1.96*phenotype_results.predictive_tests_by_age.under_65_std_logHR_se))*100-100)}}
Over 65 {{fmt_p(phenotype_results.predictive_tests_by_age.over_65_p)}} {{fmt_ci(-phenotype_results.predictive_tests_by_age.over_65_std_logHR, phenotype_results.predictive_tests_by_age.over_65_std_logHR_se)}} {{fmt(Math.exp(-2*phenotype_results.predictive_tests_by_age.over_65_std_logHR)*100-100)}}% {{fmt_range(Math.exp(2*(-phenotype_results.predictive_tests_by_age.over_65_std_logHR - 1.96*phenotype_results.predictive_tests_by_age.over_65_std_logHR_se))*100-100, Math.exp(2*(-phenotype_results.predictive_tests_by_age.over_65_std_logHR + 2 * 1.96*phenotype_results.predictive_tests_by_age.over_65_std_logHR_se))*100-100)}}

There were {{phenotype_results.predictive_tests.N_cases}} cases and {{phenotype_results.predictive_tests.N_controls}} controls included. Results broken down by sex were not run due to low case counts in one or both sexes. Results broken down by age were not run due to low case counts.

Predictive tests were not run for this phenotype due to low case counts among participants with no prior record of the phenotype at the time of actigraphy recordings.

Prevalence by activity rhythm

Below, incidence of {{phenotype}} stratified by the temperature amplitude across the population (without controlling for other factors, such as sex or age).

Top, distribution of amplitudes among cases and controls. Vertical dashed lines give mean values. Bottom, incidence proportion by amplitude in black line. Gray bands denote the 95% CI and dashed horizontal line gives the overall population incidence. Note that temperature amplitudes are rare below 1°C or above 4°C and therefore the confidence intervals are quite large in these regions and care should be taken in interpreting patterns in these regions.

PheCODE Definition

Diagnoses of {{phenotype}} were derived from the PheCODE {{phenotype_results.phecode.phecode}}. This includes the following sub-PheCODEs:

{{phenotype_results.phecode.phecodes.split(";").join(', ')}}

From ICD10 codes, diagnoses were identified from the codes (with case counts from each in parentheses):

Note that some subjects may have had multiple ICD10 codes contributing to this PheCODE and so the total of all the ICD10 case counts may not match that of the PheCODE.

Subjects were further excluded based off diagnoses predating the actigraphy measurement. The following diagnosis sources always occur prior to the date of actigraphy measurement and therefore were used for exclusions.

From ICD9 codes, exclusions were identified from the codes:

{{phenotype_results.phecode.ICD9_codes.split(";").join(', ')}}

From self-reported conditions during the initial assessment interview, exclusions were identified from the following conditions:

{{phenotype_results.phecode.self_reported_condition_codes.split(';').join(', ')}}.

Lastly, subjects were excluded if they had records of any of the following PheCODEs prior to their actigraphy measurement:

{{phenotype_results.phecode.controls_excluded_phecode.split(';').join(', ')}}.
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