Circadian, Sleep and Caloric Intake Phenotyping in Type 2 Diabetes Patients With Rare Melatonin Receptor 2 Mutations and Controls: A Pilot Study
Bibliographic record
Abstract
Background: Melatonin modulates circadian rhythms in physiology and sleep initiation. Genetic variants of the MTNR1B locus, encoding the melatonin MT2 receptor, have been associated with increased type 2 diabetes (T2D) risk. Carriers of the common intronic MTNR1B rs10830963 T2D risk variant have modified sleep and circadian traits such as changes of the melatonin profile. However, it is currently unknown whether rare variants in the MT2 coding region are also associated with altered sleep and circadian phenotypes, including meal timing. Materials and Methods: In this pilot study, 28 individuals (50% male; 46-82 years old; 50% with rare MT2 mutations [T2D MT2]) wore actigraphy devices and filled out daily food logs for four weeks. We computed circadian, sleep, and caloric intake phenotypes, including sleep duration, timing, and regularity (assessed by the Sleep Regularity Index [SRI]), composite phase deviations (CPD) as a sleep timing-based proxy for circadian misalignment, and caloric intake patterns throughout the day. Using regression analyses, we estimated age- and sex-adjusted mean differences (MD) and 95% confidence intervals (95%CI) between the two patient groups. Secondary analyses also compare T2D MT2 to 15 healthy controls. Results: Patients with rare MT2 mutations had a later sleep onset (MD=1.23, 95%CI=0.42;2.04), and midsleep time (MD=0.91, 95%CI=0.12;1.70), slept more irregularly (MD in SRI=-8.98, 95%CI=-16.36;-1.60), had higher levels of behavioral circadian misalignment (MD in CPD=1.21, 95%CI=0.51;1.92), were more variable in regards to duration between first caloric intake and average sleep offset (MD=1.08, 95%CI=0.07;2.08), and had more caloric episodes in a 24-hr day (MD=1.08, 95%CI=0.26;1.90), in comparison to T2D controls. Secondary analyses showed similar patterns between T2D MT2 and non-diabetic controls. Conclusion: This pilot study suggests that compared to diabetic and non-diabetic controls, T2D MT2 patients display a number of adverse sleep, circadian, and caloric intake phenotypes, including more irregular behavioral timing than T2D. A prospective study is needed to determine the role of these behavioral phenotypes in T2D onset and severity, especially in view of rare MT2 mutations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".