By Karthika Swamy Cohen
Polycystic ovary syndrome (PCOS), which can cause infertility in women, is generally associated with abnormal reproductive and metabolic hormone levels. The causes for this are unclear and can be influenced by both environmental and genetic factors.
At a minisymposium at the SIAM Annual Meeting taking place in Pittsburgh, Pa., this week, Erica Graham of Bryn Mawr College talked about another less known effect of PCOS.
“PCOS is characterized by three features. One of the common causes is androgen (or testosterone) excess,” Graham explained. “In addition to that there is some level of ovulatory dysfunction. Many PCOS women are also insulin resistant.”
Insulin resistance leads to elevated insulin or hyperinsulinemia as the body tries to produce more to compensate. While the precise role of insulin in ovulatory function is unclear, the ovulatory dysfunction that comes with the condition is linked to increased ovarian androgen production.
“We are Looking to understand the role of insulin particularly when it comes to ovulatory dysfunction,” said Graham. “How do androgen levels get high? How does all this lead to anovulation?"
To get answers to these questions, one first needs to understand how these processes occur in physiology and the dynamics underlying normal ovulation
Under normal conditions, neurons in the brain’s hypothalamus are stimulated to produce and release follicle stimulating hormone (FSH) and luteinizing (LH) hormone; circulating levels of these hormones then bind to receptors on ovarian cells to regulate follicle development, as well as production of the steroid hormones estradiol, progesterone, and testosterone.
Testosterone concentrations in women are much lower than in men, and lack of sensitive assays makes it hard to measure the hormone in women. In addition, follicle selection is a mystery. No single data set describes all relevant hormone dynamics for an entire cycle length. But these challenges are where mathematical analysis proves useful. As Graham put it, “There are portions of this model that are made up because we have no idea of some things – but that's the idea of math modeling.”
Graham's model describes a physiological role for testosterone in the normal ovulatory cycle, lending insights into the role of hyperinsulinemia in pathological regulation of the cycle.
The model illustrates the menstrual cycle, incorporating brain signals that are regulated by the pituitary-ovarian axis, the mechanisms of ovarian testosterone production, the role of testosterone, and finally, the influence of elevated insulin in ovulatory dysfunction.
Interplay between pituitary hormones and follicular dynamics.
Graham described her model as three subystems. The first of these—pituitary regulation—is defined as a two-compartment model. This describes the dynamics of the pituitary hormones. It is built into the model that testosterone may play a role in allowing basal LH production, and prevent inhibition of LH synthesis by progesterone in the pituitary. Progesterone is also essential for the release of LH and FSH. Estradiol, on the other hand, inhibits this release.
The second subsystem is follicle dynamics. Here, the ovulatory cycle is broadly categorized into three major phases: the follicular phase when growth and development occurs along with selection of a dominant follicle for ovulation; the ovulatory phase triggered by a surge in serum LH, stimulating the rupture of the dominant follicle and release of the ovum; and the luteal phase, which is characterized by extensive remodeling and vascularization of the ruptured follicle, leading to production of large amounts of progesterone. While previous models have consisted of up to 12 stages, Graham’s group condensed it to three phases to capture the essential dynamics. In addition, they added in the notion of LH support to the model, since LH is the primary ovulation signal that accounts for transition to the ovulatory stage, and hence may be affected by insulin.
The third piece of the puzzle is ovarian steroidogenesis, which involves a semimechanistic model that incorporates the influence of hyperinsulinemia. The model is developed in three parts: a model of intracellular androgen production, a model reduction to testosterone dynamics, and extension of the reduced model to incorporate follicle dynamics.
The results of Graham’s model suggest increased ovulatory disruption with elevated insulin-mediated testosterone production. The model also indicates that variations in the response of ovarian follicles to essential signals can alter the degree to which hyperinsulinemia disrupts the ovulatory cycle, and provides insight into various PCOS phenotypes and the gravity of ovulatory dysfunction.
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||Karthika Swamy Cohen is the managing editor of SIAM News.