Gestational diabetes mellitus (GDM) is a serious and frequent pregnancy complication that can lead to short and long-term risks for both mother and fetus. Different health organizations proposed different algorithms for the screening, diagnosis, and management of GDM. Medical Nutrition Therapy (MNT), together with physical exercise and frequent self-monitoring, represents the milestone for GDM treatment in order to reduce maternal and fetal complications. The pregnant woman should benefit from her family support and make changes in their lifestyles, changes that, in the end, will be beneficial for the whole family. The aim of this manuscript is to review the literature about the Medical Nutrition Therapy in GDM and its crucial role in GDM management.

Background and Objectives: Gestational diabetes mellitus (GDM) represents one of the most common complications during pregnancy, being associated with numerous maternal and neonatal complications. The study aimed to analyze maternal and neonatal complications associated with GDM. The risk factors of GDM and of the maternal and neonatal complications were studied in order to prevent their occurrence. Materials and Methods: The study included 97 women in the study, who underwent an oral glucose tolerance test (OGTT) between weeks 24–28 of pregnancy, consequently being divided into two groups: pregnant women with and without GDM. Statistical analysis was performed using the SPSS 26.0 software and MATLAB fitglm, the results being considered statistically significant if p < 0.05. Results: We observed statistically significant differences between the group of women with and without GDM, regarding gestational hypertension (17.6% vs. 0%), preeclampsia (13.72% vs. 0%), and cesarean delivery (96.1% vs. 78,3%). Data on the newborn and neonatal complications: statistically significant differences were recorded between the two groups (GDM vs. no GDM) regarding the average weight at birth (3339.41 ± 658.12 g vs. 3122.83 ± 173.67 g), presence of large for gestational age (21.6% vs. 0%), macrosomia (13.7% vs. 0%), excessive fetal growth (35.3% vs. 0%), respiratory distress (31.4% vs. 0%), hospitalization for at least 24 h in the Neonatal Intensive Care Unit (9.80% vs. 0%), and APGAR score <7 both 1 and 5 min following birth (7.8% vs. 0%). Additionally, the frequency of neonatal hypoglycemia and hyperbilirubinemia was higher among newborns from mothers with GDM. Conclusions: The screening and diagnosis of GDM is vital, and appropriate management is required for the prevention of maternal and neonatal complications associated with GDM. It is also important to know the risk factors for GDM and attempt to prevent their appearance.

In recent decades, it has become clear that the gut is more than just a digestive organ; it also functions as an immune organ with regulatory capabilities and acts as a “second brain” that influences brain function due to the presence and regulatory roles of the gut microbiota (GM). The GM is a crucial component of its host and significantly impacts human health. Dysbiosis, or microbial imbalance, has been closely linked to various diseases, including gastrointestinal, neurological, psychiatric, and metabolic disorders. The aim of this narrative review is to highlight the roles of the GM in maintaining metabolic health. Sleep is a vital biological necessity, with living organisms having evolved an internal sleep–wake rhythm that aligns with a roughly 24 h light/dark cycle, and this is known as the circadian rhythm. This cycle is essential for tissue repair, restoration, and overall optimal body functioning. Sleep irregularities have become more prevalent in modern society, with fast-paced lifestyles often disrupting normal sleep patterns. Urban living factors, such as fast food consumption, shift work, exposure to artificial light and nighttime noise, medications, and social activities, can adversely affect circadian rhythms, with dysbiosis being one of the many factors incriminated in the etiology of sleep disorders.

Diabetes mellitus (DM) is a complex chronic disease, with a prevalence that has reached alarming proportions in recent decades. In this study, we aimed to analyze the association of type 2 diabetes mellitus (T2DM) with certain insulin resistance (IR) indicators, according to the gender of the participants enrolled in the PREDATORR study. Biomarkers such as the triglyceride–glucose (TyG) index and its derivates, triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-c), and metabolic score for insulin resistance (METS-IR), as well as recent indicators, like cholesterol, HDL, the glucose (CHG) index and its derivates, CHG–body mass index (CHG-BMI), and CHG–waist circumference (CHG-WC), as well as its newly proposed derivates, such as CHG–waist-to-height ratio (CHG-WHtR), CHG–neck circumference (CHG-NC), and CHG–neck-to-height ratio (NHtRs were analyzed in 2080 subjects, divided into two groups, according to gender). Univariate and multivariate logistic regression was used to identify the relationships between IR indicators and T2DM. Regardless of gender, all the analyzed indicators presented statistically significantly higher values in T2DM (+) compared to T2DM (−). For both studied groups, CHG–WHtR had the largest AUROC curve: in males, the AUROC curve was 0.809, the cut-off value being 3.22, with a 70.7% sensitivity and 75.3% specificity; in females, the AUROC curve was 0.840, the cut-off value was 3.20, with a 79.3% sensitivity and 75.5% specificity, respectively. Regardless of gender, the age-adjusted model for multivariate logistic regression analysis demonstrated that TyG and CHG were predictive factors for T2DM.

Background: Type 2 diabetes mellitus (T2DM) is associated with increased mortality and morbidity, including cardiovascular diseases and obstructive sleep apnea (OSA). The aim of this study was to assess the associations between cardiovascular risk, chronic diabetes complications and the risk of OSA in adult patients with T2DM. Methods: The study included 529 patients with T2DM in whom moderate-to-severe OSA risk was assessed using the STOP-Bang questionnaire, dividing the subjects into two groups: group 1: STOP-Bang score <5, and group 2: STOP-Bang score ≥5, respectively. In all the subjects, cardiovascular risk was assessed using the UKPDS risk engine. Statistical analysis was performed using SPSS 26.0, the results being statistically significant if p value was <0.05. Results: 59% of the subjects scored ≥5 on the STOP-Bang questionnaire. We recorded statistically significant differences between the two groups regarding diabetes duration, HbA1c, HOMA-IR, albuminuria, as well as cardiovascular risk at 10 years for both coronary heart disease (CHD) and stroke (p < 0.05). Furthermore, through logistic regression, adjusting for confounding factors, we demonstrated that the STOP-Bang score ≥ 5 is a risk factor for 10-year fatal and nonfatal CHD risk. Conclusions: It is extremely important to screen and diagnose OSA in patients with T2DM, in order to improve the primary and secondary prevention of cardiovascular events in these patients.

Metabolic syndrome (MetS) represents a huge burden on the health system. This study aimed to investigate the association between MetS and certain indirect insulin resistance (IR) indicators according to gender. The triglyceride–glucose index (TyG), TyG–body mass index (TyG–BMI), the TyG–waist-to-height ratio (TyG–WHtR), TyG–waist circumference (TyG–WC), the triglyceride to high-density-lipoprotein cholesterol index (TG/HDL-c) and recently proposed indicators such as the metabolic score for IR (MetS-IR), TyG–neck circumference (TyG–NC) and the TyG–neck-circumference-to-height ratio (TyG–NHtR) were evaluated in 2594 subjects enrolled in the PREDATORR study. Univariate and multivariate logistic regression was performed to identify the association between MetS and the indirect IR indicators, as well as the risk factors. The participants were divided into two groups, according to gender. Data were analyzed using SPSS version 26.0. TyG, TyG–WC, TyG–NC, TyG–NHtR and TG/HDL-c had higher values in the male group, while TyG–BMI, TyG–WHtR and MetS-IR had approximately equal values in the two studied groups, but also statistically significantly higher values in MetS (+) vs. MetS (−) subjects (p < 0.001). For both studied groups, the multivariate logistic regression analysis demonstrated that TyG and MetS-IR were independent predictors for MetS. Both in the female and in the male group, TyG had the largest area under the receiver operating characteristic (AUROC) curve. Thus, in females, the TyG AUROC curve was 0.890; 95% CI 0.873–0.907; p < 0.001; cut-off value 8.51, with 81.4% sensitivity and 80.0% specificity. In males, the TyG AUROC curve was 0.880; 95% CI 0.861–0.899; p < 0.001; cut-off value 8.69, with 78.5% sensitivity and 84.6% specificity. All of the analyzed indirect IR indicators had statistically significantly higher values in MetS (+) vs. MetS (−) subjects. TyG and MetS-IR are independent predictive factors for MetS, regardless of the subject’s gender.