Data integration and analysis for circadian medicine

Baum, Lena; Johns, Marco; Poikela, Maija; Möller, Ralf; Ananthasubramaniam, Bharath; Praßer, Fabian

doi:10.1111/apha.13951

Cited by 8 publications

(7 citation statements)

References 98 publications

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“…Interestingly, the study of circadian rhythms, a field which is by nature interdisciplinary and relevant to several preclinical and clinical specialties, has become a preferred environment for multimodal multidimensional data analysis, 6 with research projects focusing on outcomes as diverse as personalized treatment for ICU patients with circadian network disturbances, 7 targeted timed exercise interventions for diabetes management, 8 and studying the effects of hypercaloric snacking at different times in the day on hypothalamic control of energy homeostasis and thus circadian metabolic disruption by metabolomic profiling. 9 In another subspecialty, Dumont et al describe how screening, −omics, and genome-wide association studies have successfully been applied to identify the members of a diverse molecular family of proteins, PACSINs, involved in the regulation of cell cytoskeleton, intracellular trafficking and signaling, to be associated with mammalian pathophysiology.…”

Section: Subcellular Physiologymentioning

confidence: 99%

Subcellular physiology

Persson,

Bondke Persson

2024

Acta Physiologica

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Section: Subcellular Physiologymentioning

confidence: 99%

Subcellular physiology

Persson,

Bondke Persson

2024

Acta Physiologica

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“…In addition, it should be noted that time of day is circular and, although it may be tempting to discretize observations into “morning” or “evening” (or something similar), time is best handled as a circular predictor to avoid information loss. The development of statistical methods to describe periodic processes is an active area of research [ 75 , 87 , 88 ].…”

Section: Challenges For the Use Of Ehr For Circadian Medicinementioning

confidence: 99%

Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges

Kervezee,

Dashti,

Pilz

et al. 2024

PLOS Digit Health

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A wealth of data is available from electronic health records (EHR) that are collected as part of routine clinical care in hospitals worldwide. These rich, longitudinal data offer an attractive object of study for the field of circadian medicine, which aims to translate knowledge of circadian rhythms to improve patient health. This narrative review aims to discuss opportunities for EHR in studies of circadian medicine, highlight the methodological challenges, and provide recommendations for using these data to advance the field. In the existing literature, we find that data collected in real-world clinical settings have the potential to shed light on key questions in circadian medicine, including how 24-hour rhythms in clinical features are associated with—or even predictive of—health outcomes, whether the effect of medication or other clinical activities depend on time of day, and how circadian rhythms in physiology may influence clinical reference ranges or sampling protocols. However, optimal use of EHR to advance circadian medicine requires careful consideration of the limitations and sources of bias that are inherent to these data sources. In particular, time of day influences almost every interaction between a patient and the healthcare system, creating operational 24-hour patterns in the data that have little or nothing to do with biology. Addressing these challenges could help to expand the evidence base for the use of EHR in the field of circadian medicine.

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“…These rhythms are orchestrated by a symphony of biological clocks in most tissues that drive tissue-specific temporal programs [2]. Characterization of these rhythms in a human subject under health and disease (in the relevant tissue) requires measurements at regular time intervals over one or more cycles, also known as a time-series [3]. Would you want to give repeated samples of internal tissues to quantify your circadian rhythm?…”

Section: Introductionmentioning

confidence: 99%

Time series-free rhythm profiling using COFE reveals multi-omic circadian rhythms in in-vivo human cancers

Ananthasubramaniam,

Venkataramanan

2024

Preprint

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The study of the ubiquitous circadian rhythms in human physiology typically requires regular measurements across time. Repeated sampling of the different internal tissues that house circadian clocks is both practically and ethically infeasible. Here, we present a novel unsupervised machine learning approach (COFE) that can use single high-throughput (omics) samples from individuals to reconstruct circadian rhythms across cohorts. COFE can both assign time-labels to samples, and identify rhythmic data features used for temporal reconstruction. With COFE, we discovered widespread de novo circadian gene expression rhythms in eleven different human adenocarcinomas using data from the TCGA database. The arrangement of peak times of core clock gene expression was conserved across the cancers and resembled a healthy functional clock except for the mistiming of few key genes. The commonly rhythmic genes were involved in metabolism and the cell cycle. Although these rhythms were synchronized with the cell cycle in many cancers, they were uncoupled with clocks in healthy matched tissue. Moreover, rhythms in the transcriptome were strongly associated with the cancer-relevant proteome. The targets of most of FDA-approved and potential anti-cancer drugs were rhythmic in tumor tissue with different amplitudes and peak times, emphasizing the utility of considering "time" in cancer therapy. Our approach thus creates new opportunities to repurpose data without time-labels to study circadian rhythms.

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Data integration and analysis for circadian medicine

Abstract: Data integration, data sharing, and standardized analyses are important enablers for data-driven medical research. Circadian medicine is an emerging field with a particularly high need for coordinated and systematic collaboration between researchers from different disciplines.

Cited by 8 publications

References 98 publications

Subcellular physiology

Subcellular physiology

Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges

Time series-free rhythm profiling using COFE reveals multi-omic circadian rhythms in in-vivo human cancers

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