Dietary intake of micronutrients are predictor of premenstrual syndrome, a machine learning method.

BACKGROUND & AIMS: Premenstrual syndrome (PMS) is a common disorder affecting 30-40% of women of reproductive age. Many modifiable risk factors associated with PMS involve nutrition and poor eating habits. This study aims to explore the correlation between micronutrients and PMS in a group of Iranian women and to build a predictor model showing the PMS using nutritional and anthropometric variables. METHODS: This is a cross-sectional study which was conducted on 223 females in Iran. Anthropometric indices were measured, including Body Mass Index (BMI) and skinfold thickness. Machine learning methods were used to assess participants' dietary intakes, Food Frequency Questionnaire (FFQ) and analyze the data. RESULTS: After applying different variable selection techniques, we have created machine learning models such as KNN. KNN achieved 80.3% accuracy rate and 76.3% F1 score indicating that our model is a curate and valid proof to show a strong relationship between input variables (sodium intake, Skin fold thickness of suprailiac, irregular menstruation, total calorie intake, total fiber intake, trans fatty acids, painful menstruation (dysmenorrhea), total sugar intake, total fat intake, and biotin) and the output variable (PMS). We sorted these effective variables based on their 'Shapley values' and figured out that Na intake, suprailiac skinfold thickness, biotin intake, total fat intake and total sugar intake have a major impact on having PMS. CONCLUSIONS: Dietary intake and anthropometric measurements are highly associated with the occurrence of PMS, and in our model, these variables can predict PMS in women with a high accuracy rate.

A comparison of the effects of cinnamon, ginger, and metformin consumption on metabolic health, anthropometric indices, and sexual hormone levels in women with poly cystic ovary syndrome: A randomized double-blinded placebo-controlled clinical trial.

Background/objectives: Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder in women that can alter blood glucose, lipid profile and sexual hormonal level. Therefore, the evaluation of potential therapeutic agents in this population is important. The aim of the study was to determine the effects of cinnamon, ginger, and metformin intake on improvement of sex hormones level, metabolic health (lipid profiles, insulin level and fasting blood glucose) and anthropometric indices (weight, body mass index (BMI), etc.) in women with PCOS. Methods: A total of 100 women with PCOS were randomly assigned to one of the following four groups: cinnamon (500 mg of cinnamon, 3 × day), ginger (500 mg of ginger, 3 × day), metformin (500 mg of metformin 3 × day) or placebo. However, 17 participants were excluded for various reasons and consequently, 83 participants were considered for analysis. Sexual hormones, anthropometrics, glycemic and lipid markers were evaluated before and after the 8-week intervention. Results: Weight and BMI decreased significantly in all intervention groups. The consumption of metformin and cinnamon significantly decreased insulin resistance (HOMA-IR) in comparison to the placebo and ginger groups (P < 0.05). Moreover, a significant decrease in FSH (follicle-stimulating hormone) and LH (Luteinizing hormone) levels were observed in the ginger compared to the placebo group. While metformin and cinnamon significantly lowered testosterone levels (P < 0.05), none of the groups experienced a significant change in DHEA (dehydroepiandrosterone). Conclusion: Our findings suggest that in women with PCOS, cinnamon supplementation causes similar reductions in insulin resistance and testosterone level to that of metformin. Ginger supplementation decreased FSH and LH, hormonal effects not seen following metformin consumption. Overall, cinnamon and ginger supplementations may potentially be used as alternative treatment in women with PCOS. Clinical trial registration: [https://www.irct.ir/trial/28548], identifier [IRCT20171227038105N1].