Search Results (38)

Background/Objectives: Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and requires sustained patient engagement to achieve optimal maternal and neonatal outcomes. This narrative review synthesized current evidence on patient engagement strategies and developed clinical principles for implementing patient-centered care across the GDM care continuum. Methods: A domain-based narrative review was conducted using the Consensus literature search platform. A total of 5359 records were identified. After multi-phase screening for relevance and eligibility, 37 studies, including systematic reviews, randomized controlled trials, observational studies, and qualitative research, were included. Evidence was synthesized across four domains: universal screening and linkage to care, whole-person assessment, digital patient engagement, and postpartum transition. Results: Universal screening consistently detected more women with GDM than risk-based approaches, although greater detection alone did not consistently improve maternal or neonatal outcomes. Evidence supported structured patient education, culturally responsive communication, self-management support, and active clinician feedback as key components of effective engagement. Digital health interventions, including telehealth, mobile applications, and remote monitoring, improved adherence, self-management, patient satisfaction, and, in many studies, glycemic control when integrated with clinician oversight. Postpartum follow-up remained a persistent gap despite evidence supporting reminder systems and coordinated care transitions. These findings informed a stepped clinical care pathway tailored to low-resource community health settings. Conclusions: Current evidence supports several components of patient-centered GDM care that informed a proposed stepped clinical care pathway integrating early identification, whole-person assessment, structured education, self-management support, digital engagement, and coordinated postpartum care. Many recommended strategies can be implemented using existing personnel and low-cost digital technologies, although additional implementation research is needed to evaluate culturally tailored interventions and long-term effectiveness in underserved populations. Full article

Background: Precision medicine aims to improve early, individualized risk stratification using biologically relevant biomarkers. In early pregnancy, markers reflecting placental function remain limited. Kisspeptin, a placentally derived peptide that rises during normal gestation, has emerged as a potential indicator of pregnancy viability. Objectives: We aimed to evaluate evidence on maternal serum kisspeptin levels and placental KISS1/KISS1R expression in early pregnancy loss, and to assess its potential relevance as a precision biomarker within personalized pharmacy and individualized monitoring frameworks. Methods: A systematic search of PubMed, Scopus, Web of Science, and Google Scholar (up to 2025) was conducted according to the PRISMA 2020 guidelines. Studies assessing circulating kisspeptin and/or placental expression in early pregnancy loss versus viable pregnancies were included. A formal meta-analysis was not performed due to substantial heterogeneity in study design, biological material, assay methods, gestational age, and outcome reporting formats. Under these conditions, quantitative pooling was considered methodologically inappropriate; therefore, qualitative synthesis was performed. Results: Six studies met the inclusion criteria. Most reported significantly lower maternal serum kisspeptin levels in early pregnancy loss, with good discriminatory accuracy. Immunohistochemical analyses showed reduced placental and choriodecidual KISS1/KISS1R expression in miscarriage and recurrent pregnancy loss, indicating disrupted local signaling. Concordant systemic and tissue findings suggest that circulating kisspeptin reflects placental dysfunction. Conclusions: Kisspeptin appears to be a promising precision biomarker for monitoring early pregnancy viability and supporting individualized pharmaceutical care. Standardized assays and large prospective studies are needed before routine clinical implementation. Full article

Background/Objectives: Hypertensive disorders of pregnancy (HDPs) affect approximately one in seven hospital deliveries in the United States and increase the risk of pregnancy-associated mortality. Home blood pressure monitoring (HBPM) for patients with HDPs has emerged as a model of care poised to improve ascertainment of blood pressure and triage of care during pregnancy and postpartum periods. However, the strength of evidence supporting HBPM approaches has been variable. This rapid review aimed to understand how HBPM approaches for pregnant and postpartum populations with HDPs have been evaluated in order to strengthen future research. Methods: Search criteria included peer-reviewed literature in English and French published during 2018–2024 that assessed HBPM approaches for pregnant and postpartum populations in high-income countries. A total of 370 records were screened and reviewed to identify 52 eligible articles. Key study characteristics, methodologies, and outcome measures were extracted. Identified outcome measures were mapped by outcome type (implementation, health service, and client) to assess gaps in evaluation of HBPM approaches. Results: A range of study designs were employed to evaluate HBPM approaches: experimental (17%), observational (52%), qualitative (10%), mixed method (10%), and economic (11%) designs. Over a third employed a comparison group, most of which compared HBPM approaches to usual antepartum or postpartum care. Only 11 studies reported on impact outcomes (long-term blood pressure control, adverse maternal and perinatal outcomes). Significant gaps were identified among the implementation outcomes examined. While patient engagement measures were common, assessment of provider adherence and engagement was limited. Hospital admissions and emergency department visits were often employed as proxies to measure HBPM effectiveness, efficiency, and safety. However, no studies adequately reported effectiveness measures for remote patient triage. Conclusions: Our results call for improved HBPM metrics to ensure patients are receiving high-quality care responsive to their clinical condition. Future studies on HBPM approaches should prioritize more transparent reporting on health actor engagement. A composite measure including both patient and provider adherence to monitoring and triage processes will provide stronger evidence on the effectiveness of HBPM for pregnant and postpartum patients and share impactful learning for health systems interested in adopting HBPM approaches. Full article

Objective: This study assessed sociodemographic, health-seeking and social services related factors associated with first-trimester prenatal care. Study Design: This cross-sectional study used Phase 8 pooled data from the Kentucky Pregnancy Risk Assessment Monitoring System (PRAMS) for 2017 to 2020 and 2022. A logistic regression model was used to estimate unadjusted and adjusted odds ratios and 95% confidence intervals. Results: Among the 3502 women in the analytic sample, 89.3% had first-trimester prenatal care. Most respondents were White (weighted percentage, 83.3%), between the ages of 25 and 29 (31.2%), had more than a high school education (59.5%), were married (59.8%), lived in an urban area (59.4%), and had public insurance (53.9%). Maternal education was associated with the highest odds of first-trimester prenatal care, relative to other covariates, and was highest among those who completed more than high school (aOR 4.23, 95% CI 2.72–6.59) and high school (aOR 3.09, 95% CI 2.06–4.64) relative to less than high school. Private insurance, having a healthcare visit one year prior to pregnancy, and WIC receipt during pregnancy were associated with higher odds of first-trimester prenatal care. Conclusions: The findings of this study suggest that sociodemographic factors and access to healthcare and social services are important factors in first-trimester prenatal care. Full article

(1) Background: While the Robson Ten-Group Classification System (RTGCS) is widely used to assess and monitor caesarean section rates, its association with exclusive breastfeeding (EBF) outcomes at hospital discharge remains insufficiently explored. This study aimed to examine differences in EBF rates at hospital discharge across RTGCS groups among births attended at Hospital Universitario de la Ribera (Spain). (2) Methods: This retrospective observational study was conducted at a public hospital between 1 January 2010 and 31 December 2023. EBF at hospital discharge was analysed across Robson groups. Sociodemographic, obstetric, and neonatal variables were collected. A binomial logistic regression model was developed to identify predictors of EBF at discharge. Model fit was assessed using the Hosmer–Lemeshow goodness-of-fit test and Nagelkerke’s R². (3) Results: The study analysed 23,081 births classified using the RTGCS, and 23,037 were included in the breastfeeding analysis. The overall EBF rate at discharge was 74.10%, with significant variation across Robson groups. Group 1 showed the highest EBF rate (78.339%) whereas Group 8 had the lowest (56.485%). Key factors positively associated with EBF included non-Spanish origin, nulliparity, cephalic presentation, singleton pregnancy, and term gestation. (4) Conclusions: Group 1 demonstrated the highest EBF rates, whereas Groups 8, 5, and 7 exhibited the lowest. These findings are essential for informing the development of targeted support strategies to improve breastfeeding outcomes in high-risk populations. Full article

The use of health monitoring software applications (apps) and devices is gaining popularity in obstetrics. The attitude and acceptance of different healthcare professionals regarding telemonitoring during pregnancy and the early phase of labor have not been sufficiently investigated. This study aims to assess healthcare professionals’ views on telemonitoring during pregnancy and childbirth, as well as data processing in the telemonitoring process. The study is part of an international project called `Newlife`, funded by the European Council and nationally funded by the Swiss State Secretariat for Education, Research and Innovation and Innosuisse. Eleven physicians from the fields of obstetrics and neonatology and five prenatal care nurses and five midwives were interviewed. First, participants were asked to fill out a written questionnaire with open and closed-ended answers, containing questions with a 5-point Likert scale. In a second step, a personal oral interview was conducted with all respondents. The study had an exploratory, qualitative focus. Questionnaire responses were summarized using descriptive statistics, while interview recordings were transcribed verbatim and systematically coded to identify recurring themes. Of the respondents (n = 20), five (25.0%) reported previous experience with telemonitoring in their professional practice, and all of them considered it useful. Regarding attitudes and acceptance, 57.1% (n = 12) of respondents would welcome telemonitoring during pregnancy and 52.4% (n = 11) during the early phase of labor, while 33.3% expressed no clear opinion. Rejection of telemonitoring was indicated by 9.6% (n = 2) during pregnancy, and 19.0% (n = 4) during early labor. In terms of perceived benefits, respondents highlighted early detection of problems (n = 13, 61.9%), improved prenatal care (n = 11, 52.4%), and better opportunities for data analysis and research (n = 12, 47.1%). Perceived risks included technical challenges and susceptibility to errors (n = 14, 66.7%), the lack of human contact and personal support (n = 14, 66.7%), and potentially inaccurate measurements (n = 12, 57.1%). This study offers insights into healthcare professionals’ attitudes and acceptance of telemonitoring in healthcare during pregnancy and the early stages of labor. There is a generally positive outlook but concerns and preferences exist. Addressing these considerations is essential for developing effective and user-friendly telemonitoring systems that benefit both healthcare professionals and pregnant women. Full article

Background/Objectives: Diabetes mellitus (DM) in pregnancy, including type 1 (T1DM), type 2 (T2DM), and gestational DM (GDM), represents an increasing health burden due to its maternal and fetal complications. Despite the increment in the global prevalence estimates of DM in pregnancy, in Romania, it has not been comprehensively described. This study aimed to analyze the prevalence and dynamics of DM in pregnancy in Romania between 2014 and 2024, using national databases, and to identify prevention strategies for reducing maternal and fetal complications. Methods: Data were obtained from the Romanian National Public Health Institute through two distinct sources: Database 1, consisting of reports from public and medical units associated with the National Health Insurance House and Database 2, based on the reports from general practitioners. Pregnancies complicated by DM were assessed by type, age group, and environmental settlement. Additional data were extracted on pregnancies with insufficient prenatal care and those of socially vulnerable individuals. Results: From 2014 to 2024, the prevalence of DM in pregnancy in Romania was consistently lower than European and global estimates, ranging from 1.01‰ to 3.08‰ in Database 1 and from 0.84‰ to 5.88‰ in Database 2, respectively. GDM accounted for the majority of cases, accounting for 65–88% of reported DM in pregnancy. The highest incidence was observed in the 20–39 years age group, with a growing proportion in women aged ≥40 years. Urban-rural disparities decreased over the decade, with rural cases reaching parity by 2024. Vulnerable populations included adolescents, women with insufficient prenatal care, and those with social risk factors, predominantly from rural areas. Conclusions: Although the reported prevalence of DM in pregnancy in Romania is lower than international figures, the true burden is likely underestimated. GDM remains the leading type of DM in pregnancy, mirroring global trends. Strengthening the reporting system, standardizing diagnostic criteria, and targeting high-risk groups through preconceptional counselling, lifestyle interventions, advanced monitoring technologies, and improving social support through the involved authorities are crucial steps to reduce maternal and fetal morbidity. Full article

Background: Postpartum depression is a common mental health condition that can occur up to one year after childbirth. Recent studies have increasingly used machine learning techniques to predict its occurrence; however, few have comprehensively explored the use of electronic health record data, particularly in tertiary care settings where such data can be fragmented. Methods: We analyzed electronic health record data from 12,284 women who delivered at The Birth Center at Atrium Health Wake Forest Baptist Medical Center, excluding those with missing data or no prenatal or postpartum visits. To define the target variable, we examined different combinations of depression screening tools (Edinburgh Postnatal Depression Scale and Patient Health Questionnaire-9), along with diagnosis codes specific to postpartum depression. We then trained a random forest classification model to predict postpartum depression. Results: The model achieved an area under the receiver operating characteristic curve of 0.733 ± 0.008, which is comparable to previous studies. Adding socioeconomic features from census tract data did not improve predictive performance, underscoring the importance of individual-level data. Incorporating national survey data, such as the Pregnancy Risk Assessment Monitoring System, also did not improve performance due to limited overlap in data features. Interestingly, model performance was slightly lower among Hispanic patients (area under the curve = 0.713 ± 0.040), although this difference was not statistically significant (p = 0.17), likely due to the small sample size. A similar, but statistically significant trend was observed in the larger national survey dataset (area under the curve = 0.699 ± 0.019 for Hispanic patients versus 0.735 ± 0.010 for White patients, p < 0.01). Conclusions: While our model demonstrates moderate predictive capability, further validation and prospective testing are needed before clinical implementation. This work also identified an optimal approach for digital phenotyping postpartum depression in electronic health record data and highlighted key gaps in data quality and completeness. These findings emphasize the importance of robust data when developing predictive models for real-world clinical use. Full article

Background/Objectives: The reciprocal relationship between Systemic Lupus Erythematosus (SLE) and pregnancy continues to elude the scientific community’s approaches for a clear understanding. Multiple studies have reached dissimilar results regarding the impact that SLE exerts on pregnancy, whilst the potential risks of lupus pregnancies continue to encumber women of childbearing age. Whether SLE predisposes to a complicated pregnancy and conversely whether pregnancy impacts the progression of the disease is aimed to be assessed by this systematic review. Methods: A thorough search of original research articles was conducted using online databases (PubMed, Google Scholar), initially identifying 877 potential studies. Results: Upon further assessment for relevance and eligibility, 65 articles were selected for detailed analysis. Conclusions: We concluded that, even though advanced approaches have optimized SLE prognosis and treatment, the complexity of the disease requires further extensive study in order to grasp the mechanism behind the susceptibility to adverse complications. SLE pregnancy cannot be considered without risk. Comprehensive, multidisciplinary, and continuous monitoring of the disease course prior to, during, and after pregnancy is necessary to ensure optimal recovery and minimal maternal and fetal complications. Tailored treatments and novel biomarkers would move us towards precise patient-centered care that addresses each patient’s unique disease profile and pregnancy needs, ultimately improving both maternal and fetal outcomes in women with systemic lupus erythematosus. Full article

(1) Background: Gestational diabetes mellitus (GDM) is a glucose metabolism disorder that typically develops in the second half of pregnancy, transforming a normal pregnancy into a high-risk condition, with both short- and long-term complications for the mother and the fetus. Achieving optimal glycaemic control during pregnancy is essential for preventing these outcomes and could be realized using continuous glucose monitoring systems (CGMSs). This systematic review aims to evaluate the role of the CGMS as a potential diagnostic aid and predictor of maternal and fetal outcomes in GDM. (2) Methods: Following the PRISMA guidelines (protocol ID: CRD42024559169), we performed a literature search using the terms “(continuous glucose monitoring system OR CGMS) AND (gestational diabetes mellitus OR GDM)” in the PubMed, Web of Science, and Scopus databases. (3) Results: Twelve studies were included, all reporting data on CGMS use in pregnancies complicated by GDM. The data included in our analysis are heterogeneous, the results suggesting that the CGMS may offer several advantages such as improved glycaemic control (by avoiding hyper- and hypoglycaemia), better gestational weight management, timely initiation of pharmacologic treatment, lower rates of preeclampsia, and improved neonatal outcomes. (4) Conclusions: the CGMS offers a more detailed assessment of both maternal and fetal exposure to high glucose levels, which could lead to earlier detection of those at risk for GDM complications and better guide treatment regimens, especially timely pharmacological intervention. While the current data are heterogeneous, reporting both limited or no benefits and superior benefits compared to the classic monitoring, larger longitudinal studies are mandatory to validate these findings and to better refine the role of CGMS in the monitoring and management of GDM. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Dexamethasone, widely used as an exogenous glucocorticoid in clinical and animal practice, has recently been recognized as an environmental contaminant of concern. Existing evidence documents its ability to induce persistent dyslipidemia in adult offspring. In this study, plasma cholesterol levels in male rats exposed to dexamethasone prenatally (PDE) were increased. Meanwhile, developmental tracking revealed a reduction in hepatic low-density lipoprotein receptor (LDLR) promoter H3K27 acetylation (H3K27ac) and corresponding transcriptional activity across gestational-to-postnatal stages. Mechanistic investigations established glucocorticoid receptor/histone deacetylase2 (GR/HDAC2) axis-mediated epigenetic programming of LDLR through H3K27ac modulation in PDE offspring, potentiating susceptibility to hypercholesterolemia. Additionally, in peripheral blood mononuclear cells (PBMC) of PDE male adult offspring, LDLR H3K27ac level and expression were also decreased and positively correlated with those in the liver. Clinical studies further substantiated that male newborns prenatally treated with dexamethasone exhibited increased serum cholesterol levels and consistent reductions in LDLR H3K27ac levels and corresponding transcriptional activity in PBMC. This study establishes a complete evidence chain linking PDE with epigenetic programming and cholesterol metabolic dysfunction, proposing PBMC epigenetic biomarkers as a novel non-invasive monitoring tool for assessing the developmental toxicity of chemical exposures during pregnancy. This has significant implications for improving environmental health risk assessment systems. Full article

Objective: Utilizing the Health Care Access Barriers (HCAB) Theoretical Framework, this study examined latent profiles of barriers to prenatal care among pregnant women in Alabama and whether these profiles mediate or moderate the relationship between pregnancy intentionality and early prenatal care initiation. Methods: An explanatory mixed-method design was employed, integrating quantitative analysis of Alabama Pregnancy Risk Assessment Monitoring System (PRAMS) Phase 8 data (2016–2021) with qualitative insights from expert interviews. Latent class analysis (LCA) identified subgroups based on reported barriers. Multivariable logistic regression assessed the association between pregnancy intentionality and early prenatal care initiation, controlling for covariates. A Firth-penalized multivariable logistic regression model tested interaction effects. Results: Planned pregnancy was associated with higher odds of early prenatal care initiation (OR = 0.78, 95% CI [0.49, 1.23], p = 0.286), though this association was not statistically significant. Barrier profiles did not significantly moderate or mediate the relationship. The interaction term was nonsignificant (OR = 5.19, 95% CI [0.22, 828.94], p = 0.309), and the mediation pathway was also not supported (indirect effect = 0.012, p = 0.518). Expert interviews emphasized ongoing systemic and cognitive barriers that hinder timely access. Conclusions: Although pregnancy intentionality was not a statistically significant predictor of early prenatal care initiation, qualitative findings highlighted persistent barriers that continue to constrain access. These results underscore the need for multilevel strategies to address informational and logistical challenges. Future research should evaluate additional pathways that influence care-seeking behaviors. Full article

Objective: To examine trends in perinatal smoking across rural and urban areas and investigate whether structural and intermediary health factors explain rural-urban disparities. Methods: This cross-sectional study used data from the Pregnancy Risk Assessment Monitoring System (PRAMS) collected between 2009 and 2021 in the United States. Perinatal smoking patterns were based on self-reported smoking before, during, and after pregnancy. Weighted prevalence estimates with 95% confidence intervals (CIs) were calculated for persistent smoking and cessation, stratified by rural–urban residence. Temporal trends were analyzed using logistic regression. Multivariable weighted logistic regression was performed on Phase 8 data (2016–2021) to examine associations between rural–urban status and perinatal smoking patterns, adjusting for maternal age, year of delivery, region, and structural (e.g., education, Tobacco 21 policy) and intermediary (e.g., perinatal stressors) health determinants. All analyses accounted for the complex survey design. Results: Although perinatal smoking declined over time, prevalence remained consistently higher among rural mothers. From 2009 to 2021, persistent smoking decreased significantly in both rural and urban areas (p < 0.001). Smoking cessation rates remained stable (p = 0.087), with no significant difference by rural–urban status (p = 0.475). After adjustment, rural women were 45% more likely to smoke persistently than urban women (OR = 1.45, 95% CI: 1.35–1.56) and 26% less likely to quit smoking. Conclusions: While perinatal smoking declined overall, rural mothers remained more likely to smoke throughout pregnancy. Structural and intermediary determinants partially explained this persistent rural–urban disparity. Full article

Female fertility and reproductive system disorders are influenced by a complex interplay of biological, physiological, and environmental factors. Minerals have emerged as crucial yet often overlooked elements that impact fertility and the prevalence of reproductive system disorders. Background/Objectives: This review aims to provide a comprehensive overview of the multifaceted role of minerals in female fertility, focusing on key areas such as oocyte quality, ovulation, embryo development, oxidative stress, miscarriage, hormonal regulation, environmental exposure, and in-vitro fertilization (IVF) outcomes. Methods: A systematic review was conducted, focusing on randomized controlled trials (RCTs), prospective cohort studies, case-control studies, nested case-control, and observational studies examining mineral supplementation and nutrition in women planning pregnancy or utilizing assisted reproduction technologies (ARTs). Relevant literature was sourced from multiple electronic databases, including PubMed, Scopus, Google Scholar, Web of Science, and the Cochrane Library, using keywords related to minerals and female fertility. The quality of studies was assessed using the Newcastle–Ottawa Scale (NCO) for non-randomized studies and the Risk of Bias (RoB) tool for RCTs. This systematic review has been registered on PROSPERO (registration number is CDR 42024547656). Results: From an initial pool of 20,830 records, 39 articles met the inclusion criteria and were analyzed. The studies addressed various reproductive outcomes influenced by minerals: embryo development, oocyte quality, oxidative stress, miscarriage, hormonal regulation, IVF outcomes, environmental exposure, and minerals as biomarkers. The analysis revealed that minerals like selenium, zinc, and copper are essential for maintaining reproductive health, while exposure to toxic metals such as cadmium and lead is detrimental. Conclusions: This review highlights the crucial role of both mineral supplementation and serum mineral status in female fertility. The findings provide key insights for clinicians to improve reproductive health through targeted mineral intake and monitoring. Further research is needed to refine guidelines for supplementation and serum levels in women with fertility issues. Full article