Excessive visceral adiposity is associated with metabolic and reproductive abnormalities. Adipose tissue is an active endocrine gland and participates in multiple mechanisms in the reproductive function of women. The nature of the complex interaction of obesity with the female reproductive function remains a challenge. Several links have been implicated in the gonadal dysfunction of obese women, like insulin resistance and hyperinsulinemia, via which ovarian androgen production is stimulated resulting in hyperandrogenemia, increased peripheral aromatization of androgens to estrogens, altered gonadotrophin secretion, decreased sex hormone binding globulin, decreased GH and IGFBPs, increased leptin levels and altered neuroregulation of the hypothalamic-pituitary-gonadal axis. The impact of obesity in these mechanisms and their influence on female reproductive function are discussed in this article.

Download full-text PDF

Source

Publication Analysis

Top Keywords

reproductive function
12
function women
8
female reproductive
8
obesity gonadal
4
function
4
gonadal function
4
women excessive
4
excessive visceral
4
visceral adiposity
4
adiposity associated
4

Similar Publications

In the Drosophila brain, neuronal diversity originates from approximately 100 neural stem cells, each dividing asymmetrically. Precise mapping of cell lineages at the single-cell resolution is crucial for understanding the mechanisms that direct neuronal specification. However, existing methods for high-resolution lineage tracing are notably time-consuming and labor-intensive.

View Article and Find Full Text PDF

Cell lineage analysis is primarily undertaken to understand cell fate specification and diversification along a cell lineage tree. Built with dual repressible markers, twin-spot mosaic analysis with repressible cell markers (MARCM) labels the two daughter cells made by a common precursor in distinct colors. The power of twin-spot MARCM to systematically subdivide complex lineages is exemplified in studies of Drosophila neural stem-cell lineages.

View Article and Find Full Text PDF

GEMLI: Gene Expression Memory-Based Lineage Inference from Single-Cell RNA-Sequencing Datasets.

Methods Mol Biol

January 2025

Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland.

Gene expression memory-based lineage inference (GEMLI) is a computational tool allowing to predict cell lineages solely from single-cell RNA-sequencing (scRNA-seq) datasets and is publicly available as an R package on GitHub. GEMLI is based on the occurrence of gene expression memory, i.e.

View Article and Find Full Text PDF

Computational Methods for Lineage Reconstruction.

Methods Mol Biol

January 2025

Centro Nacional de Análisis Genómico, Barcelona, Spain.

The recent development of genetic lineage recorders, designed to register the genealogical history of cells using induced somatic mutations, has opened the possibility of reconstructing complete animal cell lineages. To reconstruct a cell lineage tree from a molecular recorder, it is crucial to use an appropriate reconstruction algorithm. Current approaches include algorithms specifically designed for cell lineage reconstruction and the repurposing of phylogenetic algorithms.

View Article and Find Full Text PDF

During development, cells undergo a sequence of specification events to form functional tissues and organs. To investigate complex tissue development, it is crucial to visualize how cell lineages emerge and to be able to manipulate regulatory factors with temporal control. We recently developed TEMPO (Temporal Encoding and Manipulation in a Predefined Order), a genetic tool to label with different colors and genetically manipulate consecutive cell generations in vertebrates.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!