A leptin-based Bayesian inference of a pro-satiety state reflects a basal circadian rhythm in women with obesity
Bibliographic record
Abstract
Introduction: Leptin, primarily secreted by adipose tissue, is a critical hormone involved in regulating energy balance and food intake by inducing satiety. Although several hormones influence satiety, leptin plays a dominant role in long-term satiety regulation. Methods: We apply a state-space estimation framework using Bayesian filtering to infer continuous, long-term pro-satiety states from plasma leptin concentrations collected from premenopausal women with obesity. Our approach adopts methodologies previously applied to biosignals such as skin conductance and cortisol data to estimate latent states, leveraging the features in the leptin secretory pulses and plasma leptin levels. Additionally, we investigate the potential influence of meals, sleep, and bromocriptine treatment on the pro-satiety states. We introduce the High Satiety Index (HSI), a direct, long-term satiety measure based on leptin secretion dynamics, minimizing biases inherent in conventional assessment methods. Results: Comparisons of the estimated state in different time windows show that the pro-satiety state inferred by leptin secretion is significantly higher during sleep, aligning with a circadian rhythm. The estimated state does not show a significant variation in response to meal intake or bromocriptine treatment. Discussion: The leptin-based estimator reflects basal variations of satiety in women with obesity. This framework shows the feasibility of applying Bayesian filtering to track satiety and can be further developed to perform a multimodal estimation.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".