Functional hypothalamic amenorrhea: gut microbiota composition and the effects of exogenous estrogen administration.

Functional hypothalamic amenorrhea (FHA) is characterized by estrogen deficiency that significantly impacts metabolic, bone, cardiovascular, mental, and reproductive health. Given the importance of environmental factors such as stress and body composition, and particularly considering the importance of estrogens in regulating the gut microbiota, some changes in the intestinal microenvironment are expected when all of these factors occur simultaneously. We aimed to assess whether the gut microbiota composition is altered in FHA and to determine the potential impact of hormonal replacement therapy (HRT) on the gut microbiota. This prospective observational study included 33 patients aged 18-34 yr with FHA and 10 age-matched healthy control women. Clinical, hormonal, and metabolic evaluations were performed at baseline for the FHA group only, whereas gut microbiota profile was assessed by 16S rRNA gene amplicon sequencing for both groups. All measurements were repeated in patients with FHA after receiving HRT for 6 mo. Gut microbiota alpha diversity at baseline was significantly different between patients with FHA and healthy controls (P < 0.01). At the phylum level, the relative abundance of Fusobacteria was higher in patients with FHA after HRT (P < 0.01), as was that of Ruminococcus and Eubacterium at the genus level (P < 0.05), which correlated with a decrease in circulating proinflammatory cytokines. FHA is a multidimensional disorder that is interconnected with dysbiosis through various mechanisms, particularly involving the gut-brain axis. HRT appears to induce a favorable shift in the gut microbiota in patients with FHA, which is also associated with a reduction in the systemic inflammatory status.NEW & NOTEWORTHY Our study marks the first comprehensive analysis of gut microbiota composition in FHA and the impact of HRT on it, along with biochemical, anthropometric, and psychometric aspects. Our results indicate distinct gut microbiota composition in patients with FHA compared with healthy individuals. Importantly, HRT prompts a transition toward a more beneficial gut microbiota profile and reduced inflammation. This study validates the concept of FHA as a multifaceted disorder interlinked with dysbiosis, particularly involving the gut-brain axis.

Circulating irisin levels in functional hypothalamic amenorrhea: a new bone damage index? A pilot study.

PURPOSE: Patients with functional hypothalamic amenorrhea (FHA) could commonly have bone damage, often preceded by metabolic alterations due to a relative energy deficit state. To date, there are no markers capable of predicting osteopenia before it is manifested on DXA. Irisin is a myokine that promotes the differentiation of osteoblastic cells and appears to be inversely correlated with the incidence of bone fragility and fractures in postmenopausal women. The aim of this study was to measure irisin levels in FHA patients and to correlate it with bone density parameters. METHODS: Thirty-two patients with FHA and 19 matched controls underwent the same clinical and laboratory evaluation. RESULTS: Irisin and body mass index (BMI) were significantly lower in the case group than in healthy controls (2.03 ± 0.12 vs. 2.42 ± 0.09 p < 0.05 and 19.43 ± 2.26 vs. 22.72 ± 0.67 p < 0.05, respectively). Additionally, total body mass density (BMD g/cm2) was significantly lower in the case group than in the healthy controls (1.09 ± 0.08 vs. 1.14 ± 0.05, p < 0.05), without signs of osteopenia. CONCLUSIONS: The FHA group showed lower irisin levels associated with significantly reduced BMD parameters that did not reach the severity of osteopenia. Therefore, we could speculate that irisin could predict DXA results in assessing modifications of body composition parameters. Future research is warranted to study these parameters in a larger population to confirm our results, so that irisin could be used as a predictor and screening method for bone deprivation. Furthermore, irisin is strictly related to energy metabolism and could be an indirect marker of nutritional status in FHA patients, identifying earlier states of energy deficit.

The Role of IONM in Reducing the Occurrence of Shoulder Syndrome Following Lateral Neck Dissection for Thyroid Cancer.

Lateral neck dissection (LND) leads to a significant morbidity involving accessory nerve injury. Modified radical neck dissection (MRND) aims at preservation of the accessory nerve, but patients often present with negative functional outcomes after surgery. The role of neuromonitoring (IONM) in the prevention of shoulder syndrome has not yet been defined in comparison to nerve visualization only. We retrospectively analyzed 56 thyroid cancer patients who underwent MRND over a period of six years (2015-2020) in a high-volume institution. Demographic variables, type of surgical procedure, removed lymph nodes and the metastatic node ratio, pathology, adoption of IONM and shoulder functional outcome were investigated. The mean number of lymph nodes removed was 15.61, with a metastatic node ratio of 0.2745. IONM was used in 41.07% of patients, with a prevalence of 68% in the period 2017-2020. IONM adoption showed an effect on post-operative shoulder function. There were no effects in 89.29% of cases, and temporary and permanent effects in 8.93% and 1.79%, respectively. Confidence intervals and two-sample tests for equality of proportions were used when applicable. Expertise in high-volume centres and IONM during MRND seem to be correlated with a reduced prevalence of accessory nerve lesions and limited functional impairments. These results need to be confirmed by larger prospective randomized controlled trials.

A model with space-varying regression coefficients for clustering multivariate spatial count data.

Multivariate spatial count data are often segmented by unobserved space-varying factors that vary across space. In this setting, regression models that assume space-constant covariate effects could be too restrictive. Motivated by the analysis of cause-specific mortality data, we propose to estimate space-varying effects by exploiting a multivariate hidden Markov field. It models the data by a battery of Poisson regressions with spatially correlated regression coefficients, which are driven by an unobserved spatial multinomial process. It parsimoniously describes multivariate count data by means of a finite number of latent classes. Parameter estimation is carried out by composite likelihood methods, that we specifically develop for the proposed model. In a case study of cause-specific mortality data in Italy, the model was capable to capture the spatial variation of gender differences and age effects.

A Model-Based Approach to Simultaneous Clustering and Dimensional Reduction of Ordinal Data.

The literature on clustering for continuous data is rich and wide; differently, that one developed for categorical data is still limited. In some cases, the clustering problem is made more difficult by the presence of noise variables/dimensions that do not contain information about the clustering structure and could mask it. The aim of this paper is to propose a model for simultaneous clustering and dimensionality reduction of ordered categorical data able to detect the discriminative dimensions discarding the noise ones. Following the underlying response variable approach, the observed variables are considered as a discretization of underlying first-order latent continuous variables distributed as a Gaussian mixture. To recognize discriminative and noise dimensions, these variables are considered to be linear combinations of two independent sets of second-order latent variables where only one contains the information about the cluster structure while the other one contains noise dimensions. The model specification involves multidimensional integrals that make the maximum likelihood estimation cumbersome and in some cases infeasible. To overcome this issue, the parameter estimation is carried out through an EM-like algorithm maximizing a composite log-likelihood based on low-dimensional margins. Examples of application of the proposal on real and simulated data are performed to show the effectiveness of the proposal.