Gonadal dysfunction in women with diabetes mellitus.
Maria ZaimiOlympia MichalopoulouKaterina StefanakiParaskevi KazakouVasiliki VasileiouTheodora PsaltopoulouDimitrios S KaragiannakisStavroula A PaschouPublished in: Endocrine (2024)
It is well known that both type 1 and type 2 diabetes mellitus (DM) are related to increased risk for cardiovascular (CV) and chronic kidney disease (CKD). However, besides these prominently presented complications, DM has also been associated with reproductive dysfunctions. It seems that these disorders are met in up to 40% of women with DM and consist of delayed menarche, all types of menstrual disorders, such as amenorrhea, oligomenorrhea, menstrual irregularity, as well as menorrhagia, infertility, characteristics of polycystic ovary syndrome (PCOS) and early (or rarely late) menopause. In type 1 DM (T1DM), insulin treatment, although it has reduced the rates of insulinopenic-induced hypogonadotropic hypogonadism, an entity commonly presented in many women with the disease in the past decades, when it is used in excess it can also promote hyperandrogenism. Regarding type 2 DM (T2DM), insulin resistance (IR) and hyperinsulinemia have mainly been implicated in the pathogenesis of reproductive dysfunctions, as insulin can act as gonadotropin on the theca cells of the ovary and can lead to hyperandrogenism and inhibition of proper ovulation. This review aims to detail the reproductive dysfunctions associated with DM and provide scientific data to enlighten the underlying pathogenetic mechanisms.
Keyphrases
- polycystic ovary syndrome
- glycemic control
- insulin resistance
- type diabetes
- chronic kidney disease
- adipose tissue
- metabolic syndrome
- high fat diet
- postmenopausal women
- skeletal muscle
- end stage renal disease
- risk factors
- weight loss
- high fat diet induced
- cell death
- endothelial cells
- artificial intelligence
- tyrosine kinase
- signaling pathway
- big data
- high glucose
- endoplasmic reticulum stress
- smoking cessation
- combination therapy
- cell proliferation
- cardiovascular risk factors
- stress induced
- deep learning