Context: Physiological gynecomastia is common and affects a large proportion of otherwise healthy adolescent boys. It is thought to be caused by an imbalance between estrogen and testosterone, although this is rarely evident in analyses of serum. Objective: This study aimed to describe the frequency of physiological gynecomastia and to determine possible etiological factors (eg, auxology and serum hormone levels) in a longitudinal setup. Design, Settings, and Participants: A prospective cohort study of 106 healthy Danish boys (5.8–16.4 years) participated in the longitudinal part of the COPENHAGEN Puberty Study. The boys were examined every 6 months during an 8-year follow-up. Median number of examinations was 10 (2–15). Main outcome measurements: Blood samples were analyzed for FSH, LH, testosterone, estradiol, SHBG, inhibin B, anti-Müllerian hormone, IGF-1, and IGF binding protein-3 by immunoassays. Auxological parameters, pubertal development, and the presence of gynecomastia were evaluated at each visit. Results: Fifty-two of 106 boys (49%) developed gynecomastia, of which 10 (19%) presented with intermittent gynecomastia. Boys with physiological gynecomastia reached peak height velocity at a significantly younger age than boys who did not develop gynecomastia (13.5 versus 13.9 years, P = .027), and they had significantly higher serum levels of IGF-1 (P = .000), estradiol (P = .013), free testosterone (P < .001), and FSH (P = .030) during pubertal transition. However, no differences in serum LH or in the estradiol to testosterone ratio were found. Conclusions: Gynecomastia is frequent in pubertal boys. Increased IGF-1 levels and pubertal growth appear to be associated, whereas changes in estrogen to testosterone ratio seem negligible.

We introduce a stochastic model of diffeomorphisms, whose action on a variety of data types descends to stochastic evolution of shapes, images and landmarks. The stochasticity is introduced in the vector field which transports the data in the large deformation diffeomorphic metric mapping framework for shape analysis and image registration. The stochasticity thereby models errors or uncertainties of the flow in following the prescribed deformation velocity. The approach is illustrated in the example of finite-dimensional landmark manifolds, whose stochastic evolution is studied both via the Fokker–Planck equation and by numerical simulations. We derive two approaches for inferring parameters of the stochastic model from landmark configurations observed at discrete time points. The first of the two approaches matches moments of the Fokker–Planck equation to sample moments of the data, while the second approach employs an expectation-maximization based algorithm using a Monte Carlo bridge sampling scheme to optimise the data likelihood. We derive and numerically test the ability of the two approaches to infer the spatial correlation length of the underlying noise.