Key Clinical Factors Predicting Adipokine and Oxidative Stress Marker Concentrations among Normal, Overweight and Obese Pregnant Women Using Artificial Neural Networks

Oxidative Stress in Pregnancy

Recent years have seen an increased interest in the role of oxidative stress (OS) in pregnancy. Pregnancy inherently heightens susceptibility to OS, a condition fueled by a systemic inflammatory response that culminates in an elevated presence of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in the circulatory system. The amplified OS in pregnancy can trigger a series of detrimental outcomes such as underdevelopment, abnormal placental function, and a host of pregnancy complications, including pre-eclampsia, embryonic resorption, recurrent pregnancy loss, fetal developmental anomalies, intrauterine growth restriction, and, in extreme instances, fetal death. The body's response to mitigate the uncontrolled increase in RNS/ROS levels requires trace elements that take part in non-enzymatic and enzymatic defense processes, namely, copper (Cu), zinc (Zn), manganese (Mn), and selenium (Se). Determination of ROS concentrations poses a challenge due to their short half-lives, prompting the use of marker proteins, including malondialdehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase (GPx), glutathione reductase (GR), catalase (CAT), and glutathione (GSH). These markers, indicative of oxidative stress intensity, can offer indirect assessments of pregnancy complications. Given the limitations of conducting experimental studies on pregnant women, animal models serve as valuable substitutes for in-depth research. This review of such models delves into the mechanism of OS in pregnancy and underscores the pivotal role of OS markers in their evaluation.

Inflammation and Oxidative Stress Induced by Obesity, Gestational Diabetes, and Preeclampsia in Pregnancy: Role of High-Density Lipoproteins as Vectors for Bioactive Compounds

Inflammation and oxidative stress are essential components in a myriad of pathogenic entities that lead to metabolic and chronic diseases. Moreover, inflammation in its different phases is necessary for the initiation and maintenance of a healthy pregnancy. Therefore, an equilibrium between a necessary/pathologic level of inflammation and oxidative stress during pregnancy is needed to avoid disease development. High-density lipoproteins (HDL) are important for a healthy pregnancy and a good neonatal outcome. Their role in fetal development during challenging situations is vital for maintaining the equilibrium. However, in certain conditions, such as obesity, diabetes, and other cardiovascular diseases, it has been observed that HDL loses its protective properties, becoming dysfunctional. Bioactive compounds have been widely studied as mediators of inflammation and oxidative stress in different diseases, but their mechanisms of action are still unknown. Nonetheless, these agents, which are obtained from functional foods, increase the concentration of HDL, TRC, and antioxidant activity. Therefore, this review first summarizes several mechanisms of HDL participation in the equilibrium between inflammation and oxidative stress. Second, it gives an insight into how HDL may act as a vector for bioactive compounds. Third, it describes the relationships between the inflammation process in pregnancy and HDL activity. Consequently, different databases were used, including MEDLINE, PubMed, and Scopus, where scientific articles published in the English language up to 2023 were identified.

Using community-based geographical information system (GIS) to recruit older Asian Americans in an Alzheimer's disease study

OBJECTIVE

This study aims to show the usefulness of incorporating a community-based geographical information system (GIS) in recruiting research participants for the Asian Cohort for Alzheimer's Disease (ACAD) study for using the subgroup of Korean American (KA) older adults. The ACAD study is the first large study in the USA and Canada focusing on the recruitment of Chinese, Korean and Vietnamese older adults to address the issues of under-representation of Asian Americans in clinical research.

METHODS

To promote clinical research participation of racial/ethnic minority older adults with and without dementia, we used GIS by collaborating with community members to delineate boundaries for geographical clusters and enclaves of church and senior networks, and KA serving ethnic clinics. In addition, we used socioeconomic data identified as recruitment factors unique to KA older adults which was analysed for developing recruitment strategies.

RESULTS

GIS maps show a visualisation of the heterogeneity of the sociodemographic characteristics and the resources of faith-based organisations and KA serving local clinics. We addressed these factors that disproportionately affect participation in clinical research and successfully recruited the intended participants (N=60) in the proposed period.

DISCUSSION

Using GIS maps to locate KA provided innovative inroads to successful research outreach efforts for a pilot study that may be expanded to other underserved populations across the USA in the future. We will use this tool subsequently on a large-scale clinical genetic epidemiology study.

POLICY IMPLICATION

This approach responds to the call from the National Institute on Aging to develop strategies to improve the health status of older adults in diverse populations. Our study will offer a practical guidance to health researchers and policymakers in identifying understudied and hard-to-reach specific Asian American populations for clinical studies or initiatives. This would further contribute in reducing the health and research disparity gaps among older minority populations.

Maternal Metabolic Biomarkers are Associated with Obesity and Excess Gestational Weight Gain

OBJECTIVE

The purpose of this study was to evaluate the independent contribution of maternal obesity and gestational weight gain (GWG) in excess of the Institute of Medicine's guidelines on levels of maternal serum inflammatory and metabolic measures.

STUDY DESIGN

Banked maternal serum samples from 120 subjects with documented prepregnancy or first trimester body mass index (BMI) were utilized for analyte analyses. Validated, BMI-specific formulas were utilized to categorize GWG as either insufficient, at goal or excess based on the Institute of Medicine guidelines with gestational age adjustments. Serum was analyzed for known inflammatory or metabolic pathway intermediates using the Luminex xMap system with the MILLIPLEX Human Metabolic Hormone Magnetic Bead Panel. Measured analytes included interleukin-6, monocyte chemoattractant protein-1, and tumor necrosis factor-α and metabolic markers amylin, c-peptide, ghrelin, gastric inhibitory polypeptide, glucagon-like peptide-1, glucagon, insulin, leptin, pancreatic polypeptide, and peptide YY. Kruskal-Wallis ANOVA and Pearson's correlation coefficients were calculated for each marker.

RESULTS

C-peptide, insulin, and leptin all varied significantly with both obesity and GWG while glucagon-like peptide-1 varied by BMI but not GWG. These analytes covaried with other metabolic analytes, but not with inflammatory analytes.

CONCLUSION

Maternal metabolic biomarkers at delivery vary significantly with both obesity and GWG. Taken together, these findings suggest that GWG (with and without comorbid obesity) is an important mediator of measurable metabolites in pregnancy but is not necessarily accompanied by inflammatory measures in serum. These findings are consistent with GWG being an independent risk factor for metabolic disturbances during pregnancy.

Gestational Weight Gain Influences the Adipokine-Oxidative Stress Association during Pregnancy

INTRODUCTION AND OBJECTIVE

The weight gained during pregnancy could determine the immediate and future health of the mother-child dyad. Excessive gestational weight gain (EGWG) due to abnormal adipose tissue (AT) accumulation is strongly associated with adverse perinatal outcomes as gestational diabetes, macrosomia, obesity, and hypertension further in life. Dysregulation of adipokine, AT dysfunction, and an imbalance in the prooxidant-antioxidant systems are critical features in altered AT accumulation. This study was aimed to investigate the association between adipokines and oxidative stress markers in pregnant women and the influence of the GWG on this association.

METHODS

Maternal blood samples were obtained in the third trimester of pregnancy (n = 74) and serum adipokines (adiponectin, leptin, and resistin), oxidative damage markers: 8-oxo-2'-deoxyguanosine (8-oxodG), lipohydroperoxides (LOOH), malondialdehyde (MDA), and carbonylated proteins (CP), and glucose a metabolic marker were measured.

RESULTS

Women with EGWG had low adiponectin levels than women with adequate weight gain (AWG) or insufficient weight gain (IWG). Multiple linear regression models revealed a positive association between adiponectin and 8-oxodG in women with AWG (B = 1.09, 95% CI: 164-222, p = 0.027) and IWG (B = 0.860, 95% CI: 0.199-1.52, p = 0.013) but not in women with EGWG. In women with EGWG, leptin was positively associated with LOOH (p = 0.018), MDA (p = 0.005), and CP (p = 0.010) oxidative markers.

CONCLUSION

Our findings suggest that concurrent mechanisms regulate adipokine production and oxidative stress in pregnant women and that this regulation is influenced by GWG, probably due to an excessive AT accumulation.

The causes of adverse changes of testosterone levels in men

INTRODUCTION

As men age, progressive testosterone deficiency syndrome becomes an increasingly common problem. However, the decreased testosterone levels are not only the result of advanced age.

AREAS COVERED

PubMed search of published data on testosterone, nutritional deficiency, stress, sleep, and obesity. Many factors impact the male HPG axis (the hypothalamic-pituitary-adrenal), including body weight, calorific and nutritional value of a diet, the amount and quality of sleep, as well as the level of stress. In the case of persons of healthy weight, a below-average calorific value of a diet may decrease the levels of testosterone in men. On the other hand, the same caloric deficiency in obese persons may result in a neutral or positive impact on testosterone levels.

EXPERT OPINION

Many factors, including external, environmental and internal factors, influence testosterone levels. Undoubtedly, nutritional deficiency, and particularly of such nutrients as zinc, magnesium, vitamin D, together with low polyphenols intake, affects the HPG axis. The levels of mental and oxidative stress can also adversely impact the axis. Hence, a diagnosis of the cause of disturbance in testosterone levels depends on many factors and requires a broad range of research, as well as a change of patients' lifestyle.

Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks

Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician's diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.

Skeletal muscle as a protagonist in the pregnancy metabolic syndrome

The pregnant woman normally shows clinical manifestations similar to a metabolic syndrome (MS), due to her metabolic and hemodynamic adaptations in order to share nutrients with the child. If those adjustments are surpassed, a kind of pregnancy MS (PregMS) could appear, characterized by excessive insulin resistance and vascular maladaptation. Skeletal muscle (SKM) must be a protagonist in the PregMS: SKM strength and mass have been associated inversely with MS incidence in non-pregnant patients, and in pregnant women muscular activity modulates metabolic and vascular adaptations that favor better outcomes. Of note, a sedentary lifestyle affects exactly in the other way. Those effects may be explained not only by the old paradigm of SKM being a great energy consumer and store, but because it is an endocrine organ whose chronic activity or deconditioning correspondingly releases myokines modulating insulin sensitivity and cardiovascular adaptation, by direct or indirect mechanisms not well understood. In this document, we present evidence to support the concept of a PregMS and hypothesize on the role of the SKM mass, fiber types composition and myokines in its pathophysiology. Also, we discuss some exercise interventions in pregnancy as a way to test our hypotheses.

Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors

Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with major nutritional and neurodevelopmental sequelae. Since predicting which neonates will develop perforation is still challenging; clinicians might benefit considerably with an early diagnosis tool and the identification of critical factors. The aim of this study was to forecast IP related to NEC and to investigate the predictive quality of variables; based on a machine learning-based technique. The Back-propagation neural network was used to train and test the models with a dataset constructed from medical records of the NICU; with birth and hospitalization maternal and neonatal clinical; feeding and laboratory parameters; as input variables. The outcome of the models was diagnosis: (1) IP associated with NEC; (2) NEC or (3) control (neither IP nor NEC). Models accurately estimated IP with good performances; the regression coefficients between the experimental and predicted data were R² > 0.97. Critical variables for IP prediction were identified: neonatal platelets and neutrophils; orotracheal intubation; birth weight; sex; arterial blood gas parameters (pCO₂ and HCO₃); gestational age; use of fortifier; patent ductus arteriosus; maternal age and maternal morbidity. These models may allow quality improvement in medical practice.