An opponent process for alcohol addiction based on changes in endocrine gland mass

Structure-function relationship of the pituitary gland in anorexia nervosa and intense physical activity

Patients with Anorexia Nervosa (AN) and athletes share intense physical activity and pituitary hormonal disturbances related to absolute (AN) or relative (athletes) undernutrition. Pituitary gland (PG) structure evaluations in those conditions are scarce, and did not differentiate anterior from posterior lobe. We evaluated the structure-function relationship of anterior and posterior PG in AN and athletes, and potential reversibility of this alteration in a group of weight-recovered patients (AN_Rec). Manual delineation of anterior (AP) and posterior (PP) PG was performed on T1-weighted MR images in 17 women with AN, 15 women with AN_Rec, 18 athletes women and 25 female controls. Anthropometric, hormonal, and psychometric parameters were explored and correlated with PG volumes. AP volume (APV) was lower in AN (448 ± 82 mm3), AN_Rec (505 ± 59 mm3), and athletes (540 ± 101 mm3) vs. Controls (615 ± 61 mm3, p < 0.00001, p < 0.00001 and p = 0.02, respectively); and smaller in AN vs. AN_Rec (p = 0.007). PP volume did not show any differences between the groups. APV was positively correlated with weight (R = 0.36, p = 0.011) in AN, and luteinizing hormone (R = 0.35, p = 0.014) in total group. In AN, mean growth hormone (GH) was negatively correlated with global pituitary volume (R = 0.31, p = 0.031) and APV (R = 0.29, p = 0.037). Absolute and relative undernutrition led to a decreased anterior pituitary gland volume, which was reversible with weight gain, correlated with low bodyweight, and blockade of gonadal hypothalamic-pituitary axis. Intriguing inverse correlation between anterior pituitary gland volume and GH plasma level could suggests a low storage capacity of anterior pituitary gland and increased reactivity to low insulin-like growth factor type 1.

Cross-talk between the HPA axis and addiction-related regions in stressful situations

Addiction is a worldwide problem that has a negative impact on society by imposing significant costs on health care, public security, and the deactivation of the community economic cycle. Stress is an important risk factor in the development of addiction and relapse vulnerability. Here we review studies that have demonstrated the diverse roles of stress in addiction. Term searches were conducted manually in important reference journals as well as in the Google Scholar and PubMed databases, between 2010 and 2022. In each section of this narrative review, an effort has been made to use pertinent sources. First, we will provide an overview of changes in the Hypothalamus-Pituitary-Adrenal (HPA) axis component following stress, which impact reward-related regions including the ventral tegmental area (VTA) and nucleus accumbens (NAc). Then we will focus on internal factors altered by stress and their effects on drug addiction vulnerability. We conclude that alterations in neuro-inflammatory, neurotrophic, and neurotransmitter factors following stress pathways can impact related mechanisms on craving and relapse susceptibility.

Rules for body fat interventions based on an operating point mechanism

Interventions to reduce fat are important for human health. However, they can have opposing effects such as exercise that decreases fat but increases food intake, or coherent effects such as leptin resistance which raises both. Furthermore, some interventions show an overshoot in food intake, such as recovery from a diet, whereas others do not. To explain these properties we present a graphical framework called the operating point model, based on leptin control of feeding behavior. Steady-state fat and food intake is given by the intersection of two experimental curves - steady-state fat at a given food intake and ad libitum food intake at a given fat level. Depending on which curve an intervention shifts, it has opposing or coherent effects with or without overshoot, in excellent agreement with rodent data. The model also explains the quadratic relation between leptin and fat in humans. These concepts may guide the understanding of fat regulation disorders.

Dynamics of thyroid diseases and thyroid-axis gland masses

Thyroid disorders are common and often require lifelong hormone replacement. Treating thyroid disorders involves a fascinating and troublesome delay, in which it takes many weeks for serum thyroid-stimulating hormone (TSH) concentration to normalize after thyroid hormones return to normal. This delay challenges attempts to stabilize thyroid hormones in millions of patients. Despite its importance, the physiological mechanism for the delay is unclear. Here, we present data on hormone delays from Israeli medical records spanning 46 million life-years and develop a mathematical model for dynamic compensation in the thyroid axis, which explains the delays. The delays are due to a feedback mechanism in which peripheral thyroid hormones and TSH control the growth of the thyroid and pituitary glands; enlarged or atrophied glands take many weeks to recover upon treatment due to the slow turnover of the tissues. The model explains why thyroid disorders such as Hashimoto's thyroiditis and Graves' disease have both subclinical and clinical states and explains the complex inverse relation between TSH and thyroid hormones. The present model may guide approaches to dynamically adjust the treatment of thyroid disorders.

A Mechanism for Ovulation Number Control

Every menstrual cycle, many follicles begin to develop but only a specific number ovulate. This ovulation number determines how many offspring are produced per litter, and differs between species. The physiological mechanism that controls ovulation number is unknown; a class of mathematical models can explain it, but these models have no physiological basis. Here, we suggest a physiological mechanism for ovulation number control, which enables selection of a specific number of follicles out of many, and analyze it in a mathematical model of follicular growth. The mechanism is based on a signal, intra-follicular androgen concentration, that measures follicle size relative to the other follicles. This signal has a biphasic effect, suppressing follicles that are too large or too small compared to others. The ovulation number is determined by the androgen inhibitory thresholds. The model has a scaling symmetry that explains why the dominant follicles grow linearly with time, as observed in human ultrasound data. This approach also explains how chronic hyperandrogenism disrupts ovulation in polycystic ovary syndrome (PCOS), a leading cause of infertility. We propose specific experiments for testing the proposed mechanism.