Algorithmic complexity of growth hormone release in humans

Area of Science:

• Endocrinology
• Computational Biology
• Biophysics

Background:

• Hormones, including growth hormone (GH), are typically secreted in pulsatile patterns, crucial for cellular regulation.
• Acromegaly, caused by GH-producing tumors, is associated with irregular GH release patterns.
• The underlying mechanism for disordered GH secretion in acromegaly remains unclear.

Purpose of the Study:

• To investigate the temporal dynamics of growth hormone (GH) secretion in acromegaly.
• To differentiate between deterministic and stochastic patterns of GH release in healthy subjects versus acromegaly patients.
• To test the hypothesis that irregular GH release in acromegaly stems from random tumor cell activity.

Main Methods:

• Analysis of temporal patterns in GH secretion using algorithmic complexity (a context-free grammar measure).
• Comparison of patient data with random surrogate datasets to assess stochasticity.
• Evaluation of GH release patterns in both healthy individuals and acromegaly patients.

Main Results:

• GH secretion patterns in acromegaly patients did not significantly differ from stochastic processes.
• Healthy subjects exhibited a clear deterministic structure in their temporal GH secretion patterns.
• Algorithmic complexity analysis revealed distinct differences between normal and acromegalic GH release dynamics.

Conclusions:

• The findings support the hypothesis that GH release in acromegaly is driven by random events within tumor cells.
• Tumorous GH secretion may become independent of normal hypothalamic regulation.
• Pulsatile hormone secretion dynamics can serve as a biomarker for endocrine disorders.