Data-driven assessment of the human ovarian reserve

Human ovarian physiology is still poorly understood, with the factors and mechanisms that control initiation of follicular recruitment and loss remaining particularly unclear. Conventional hypothesis-led studies provide new data, results and insights, but datasets from individual studies are often small, allowing only limited interpretation. Great power is afforded by the aggregation of data from multiple studies into single datasets. In this paper, we describe how modern computational analysis of these datasets provides important new insights into ovarian function and has generated hypotheses that are testable in the laboratory. Specifically, we can hypothesize that age is the most important factor for variations in individual ovarian non-growing follicle (NGF) populations, that anti-Müllerian hormone (AMH) levels generally rise and fall in childhood years before peaking in the mid-twenties, and that there are strong correlations between AMH levels and both NGF populations and rates of recruitment towards maturation, for age ranges before and after peak AMH levels.