bahareh honarmand rahaghi

Ovarian reserve is one of the most important factors that influences the success of assisted reproductive technology (ART). Recently, the role of anti-müllerian hormone (AMH) in ART has been investigated as a marker for the prediction of ovarian response. We aim to examine this relationship within a large Iranian population.

In this cross-sectional study, we obtained data from 1000 infertile couples who referred to the Research and Clinical Centre of Yazd Infertility Clinic for in vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI). Serum AMH levels, oocyte count, numbers of fertilised oocytes, endometrial thickness, and percentage of mature oocytes were measured. The relationship between AMH serum levels and the number and quality of oocytes and embryos in ART cycles was analysed.

In the linear regression model, the log of the variables total dose of gonadotropin, two pronuclei (2PN), log oestradiol, total embryos, duration of stimulation, number of embryos transferred, protocol, and cause of infertility were significant predictors of log AMH.

There appears to be a relationship between serum AMH levels in the early follicular phase and ovarian reserve. Higher serum AMH levels were also associated with shorter ART cycles.

The development of type 2 diabetes mellitus (T2DM) is associated with lifestyle factors, including dietary patterns. A diet rich in macro- and micronutrients has been reported to reduce the risk of T2DM. Therefore, this study aimed to identify the dietary factors most closely associated with T2DM in subjects within the MASHAD cohort using a decision tree algorithm.  This cross-sectional study was conducted on 9704 individuals from the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD), of whom 5936 participants completed a 24h dietary recall questionnaire. Macronutrients and micronutrients were estimated using Diet Plan 6 software. A decision tree algorithm was utilized to evaluate the most crucial dietary nutrient intakes concerning T2DM.  The algorithm showed a high specificity (81.34%) but low sensitivity (34.21%), which could predict T2DM with a low-to-moderate diagnostic ability (AUC=0.58). Based on the decision tree, eight features, including dietary potassium, total sugar, sucrose, riboflavin, thiamin, sodium, total nitrogen, and magnesium, were T2DM’s most critical dietary components.  Based on the results, consuming sugar, salt, and vitamin B was the most critical related dietary intake to T2DM. Dietary interventions may be a cost-effective strategy for preventing T2DM.