Researchers at NYU Tandon School of Engineering have utilised a probabilistic physiological modelling approach to investigate the pulse events underlying leptin secretion. Understanding the patterns of leptin secretion from fat cells throughout the body could help scientists identify hidden health issues in patients with obesity, monitor health development after treatment and test drug effectiveness.
"Instead of only relying on visual inspection of leptin by statistical modelling of leptin secretion events, we quantify the underlying pulsatile physiological signalling to enable extending investigations of leptin signalling in health and disease and in response to medications," said Associate Professor of Biomedical Engineering, Dr Rose Faghih, who led the study.
Leptin typically signals the brain when enough food has been consumed, helping to regulate energy and appetite. But in some individuals with obesity, this signalling system seems to malfunction - a condition known as leptin resistance, where high levels of leptin fail to curb hunger. Why this resistance develops remains unclear, but recent findings on leptin's high-frequency, wave-like release patterns offer intriguing possibilities for future research.
Hormones like cortisol and insulin, which manage stress and blood sugar, are known to follow rhythmic release patterns, adapting to daily cycles. Leptin follows a similar - but unknown - rhythm. Faghih and colleagues set out to analyse leptin's pulses, examining how often these leptin pulses occur and their relative strength. By breaking down the pulses into their timings and amplitudes, researchers could better understand these patterns, which could lead to breakthroughs in treating leptin resistance and, by extension, obesity.
The researchers applied statistical models with different complexities to fit the data and compared the performance of these distribution models for each subject using pre-established metrics. They aimed to find the best model for these distributions which would be important in monitoring leptin secretion and detecting deviations from a normal secretion pattern.
The results show that each of these models can capture the general shape of the distribution alone for each subject despite the complex process of leptin secretion which can be affected by various factors. These distribution models suggest several possible features of leptin secretion, such as the rarity of extremely small or large pulses.
They also allow researchers to see the effects of drugs by identifying changes in the model parameters before and after treatment. They compared leptin behaviour before and after treatment with bromocriptine, a medication that affects neuroendocrine signalling. After treatment, subtle shifts in one model of leptin's pulse timings (the diffusion model) suggested that it might be possible to influence these patterns with medication.
Such findings open the door to exploring hormonal treatments for obesity that work by re-establishing leptin's natural rhythms. The research is a key first step in reliable long-term leptin monitoring that could help doctors and patients fight obesity.
The outcomes were featured in the paper, ‘Marked Point Process Secretory Events Statistically Characterize Leptin Pulsatile Dynamics’, published in the Journal of the Endocrine Society. To access this paper, please click here
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