Association between dim light melatonin onset predicted from gene expression profiles with sleep time and chronotype preference: A pilot study
Bibliographic record
Abstract
Chronotherapeutic approaches that optimize the timing of therapy to enhance efficacy and minimize side effects are becoming mainstream. The widespread adoption of chronotherapeutic approaches is hindered by the lack of accessible, valid tools to determine circadian time. Building on evidence that gene expression profiles predict circadian time, this pilot study assessed associations between circadian phase predictions from a single blood sample, actigraphy-estimated sleep, and chronotype in a real-world setting. Twelve adults (mean age 51 y, 8 women) reporting short sleep (<7 h/night) and at risk for metabolic syndrome participated. CD14+ monocytes were isolated from 20 ml blood samples, pelleted, and stored at -80°C before RNA sequencing. Sleep was monitored over two weeks using the ActiGraph GT9X-BT, and chronotype preference was assessed with the Composite Scale of Morningness. Spearman's correlations analyzed correlations between predicted dim light melatonin onset (DLMO), sleep, and chronotype preference. Moderate-to-strong association was found between gene expression-based DLMO predictions and sleep, supporting the utility of peripheral blood mononuclear cell gene expression profiles for estimating circadian phase. This approach shows promise for improving chronotherapy implementation in middle-aged adults with chronic health conditions and short sleep. This study was part of a larger study that was registered with Clinicaltrials.gov as NCT03596983.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".