Classifying early infant feeding status from clinical notes using natural language processing and machine learning

The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status. We trained 6 machine learning models to classify infant feeding status: logistic regression, random forest, XGBoost gradient descent, k-nearest neighbors, and support-vector classifier. Model comparison was evaluated based on overall accuracy, precision, recall, and F1 score. Our modeling corpus included an even number of clinical notes that was a balanced sample across each class. We manually reviewed 999 notes that represented 746 mother-infant dyads with a mean gestational age of 38.9 weeks and a mean maternal age of 26.6 years. The most frequent feeding status classification present for this study was exclusive breastfeeding [n = 183 (18.3%)], followed by exclusive formula bottle feeding [n = 146 (14.6%)], and exclusive feeding of expressed mother's milk [n = 102 (10.2%)], with mixed feeding being the least frequent [n = 23 (2.3%)]. Our final analysis evaluated the classification of clinical notes as breast, formula/bottle, and missing. The machine learning models were trained on these three classes after performing balancing and down sampling. The XGBoost model outperformed all others by achieving an accuracy of 90.1%, a macro-averaged precision of 90.3%, a macro-averaged recall of 90.1%, and a macro-averaged F1 score of 90.1%. Our results demonstrate that natural language processing can be applied to clinical notes stored in the electronic health records to classify infant feeding status. Early identification of breastfeeding status using NLP on unstructured electronic health records data can be used to inform precision public health interventions focused on improving lactation support for postpartum patients.

A systematic review of contaminants in donor human milk

Donor human milk (DHM) from a milk bank is the recommended feeding method for preterm infants when the mother's own milk (MOM) is not available. Despite this recommendation, information on the possible contamination of donor human milk and its impact on infant health outcomes is poorly characterised. The aim of this systematic review is to assess contaminants present in DHM samples that preterm and critically ill infants consume. The data sources used include PubMed, EMBASE, CINAHL and Web of Science. A search of the data sources targeting DHM and its potential contaminants yielded 426 publications. Two reviewers (S. T. and D. L.) conducted title/abstract screening through Covidence software, and predetermined inclusion/exclusion criteria yielded 26 manuscripts. Contaminant types (bacterial, chemical, fungal, viral) and study details (e.g., type of bacteria identified, study setting) were extracted from each included study during full-text review. Primary contaminants in donor human milk included bacterial species and environmental pollutants. We found that bacterial contaminants were identified in 100% of the papers in which bacterial contamination was sought (16 papers) and 61.5% of the full data set (26 papers), with the most frequently identified genera being Staphylococcus (e.g., Staphylococcus aureus and coagulase-negative Staphylococcus) and Bacillus (e.g., Bacillus cereus). Chemical pollutants were discovered in 100% of the papers in which chemical contamination was sought (eight papers) and 30.8% of the full data set (26 papers). The most frequently identified chemical pollutants included perfluoroalkyl substances (six papers), toxic metal (one paper) and caffeine (one paper). Viral and fungal contamination were identified in one paper each. Our results highlight the importance of establishing standardisation in assessing DHM contamination and future studies are needed to clarify the impact of DHM contaminants on health outcomes.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup

OBJECTIVE

Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space.

PROCESS

A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members.

CONCLUSIONS

Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Call for Improved Fourth Trimester Care After Stillbirth

Women who experience stillbirths are at increased risk for severe maternal morbidity and mortality, which makes the postpartum period a critical time in which to address health conditions and prevent complications. However, research on the health care needs of women who experience stillbirths is scarce, and these women are often excluded from research on the postpartum period. Therefore, the purpose of this commentary is to identify gaps in the research on postpartum care after stillbirth, explain why current fourth trimester care guidelines in the United States are inadequate, and advocate for nursing research and practice to improve understanding of health care needs in the fourth trimester.

An Automated Workflow Composition System for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing

Liquid chromatography-mass spectrometry (LC-MS) metabolomics studies produce high-dimensional data that must be processed by a complex network of informatics tools to generate analysis-ready data sets. As the first computational step in metabolomics, data processing is increasingly becoming a challenge for researchers to develop customized computational workflows that are applicable for LC-MS metabolomics analysis. Ontology-based automated workflow composition (AWC) systems provide a feasible approach for developing computational workflows that consume high-dimensional molecular data. We used the Automated Pipeline Explorer (APE) to create an AWC for LC-MS metabolomics data processing across three use cases. Our results show that APE predicted 145 data processing workflows across all the three use cases. We identified six traditional workflows and six novel workflows. Through manual review, we found that one-third of novel workflows were executable whereby the data processing function could be completed without obtaining an error. When selecting the top six workflows from each use case, the computational viable rate of our predicted workflows reached 45%. Collectively, our study demonstrates the feasibility of developing an AWC system for LC-MS metabolomics data processing.

Untargeted Metabolomic Analysis of Lactation-Stage-Matched Human and Bovine Milk Samples at 2 Weeks Postnatal

Epidemiological data demonstrate that bovine whole milk is often substituted for human milk during the first 12 months of life and may be associated with adverse infant outcomes. The objective of this study is to interrogate the human and bovine milk metabolome at 2 weeks of life to identify unique metabolites that may impact infant health outcomes. Human milk (n = 10) was collected at 2 weeks postpartum from normal-weight mothers (pre-pregnant BMI < 25 kg/m2) that vaginally delivered term infants and were exclusively breastfeeding their infant for at least 2 months. Similarly, bovine milk (n = 10) was collected 2 weeks postpartum from normal-weight primiparous Holstein dairy cows. Untargeted data were acquired on all milk samples using high-resolution liquid chromatography-high-resolution tandem mass spectrometry (HR LC-MS/MS). MS data pre-processing from feature calling to metabolite annotation was performed using MS-DIAL and MS-FLO. Our results revealed that more than 80% of the milk metabolome is shared between human and bovine milk samples during early lactation. Unbiased analysis of identified metabolites revealed that nearly 80% of milk metabolites may contribute to microbial metabolism and microbe-host interactions. Collectively, these results highlight untargeted metabolomics as a potential strategy to identify unique and shared metabolites in bovine and human milk that may relate to and impact infant health outcomes.

Maternal Gut Microbiota in the Postpartum Period: A Systematic Review

Studies have demonstrated the importance of the gut microbiota during pregnancy, and there is emerging literature on the postpartum maternal gut microbiota. The primary objective of this paper was to synthesize the literature on the postpartum gut microbiome composition and diversity measured in stool samples from healthy mothers of predominantly term infants. The secondary objectives were (1) to identify biological and environmental factors that influence postpartum maternal gut microbiota and (2) to assess health conditions and clinical intermediate measures associated with postpartum gut microbiota changes in all mothers. Electronic searches were conducted November 9, 2020 and updated July 25, 2021 without publication time limits on PubMed, Embase, CINHAL, Scopus, Cochrane Library, BioArchives, and OpenGrey.eu. Primary research on maternal gut microbiota in the postpartum (up to one year after childbirth) were eligible. Postpartum gut microbiota comparisons to pregnancy or non-pregnancy gut microbiota were of interest, therefore, studies examining these in addition to the postpartum were included. Studies were excluded if they were only conducted in animals, infants, pregnancy, or microbiome of other body locations (e.g., vaginal). Data extraction of microbial composition and diversity were completed and synthesized narratively. Studies were assessed for risk of bias. A total of 2512 articles were screened after deduplication and 27 were included in this review. Of the 27 included studies, 22 addressed the primary objective. Firmicutes was the predominant phylum in the early (<6 weeks) and late postpartum (6 weeks to 1 year). In early postpartum, Bacteroides was the predominant genus. Findings from longitudinal assessments of alpha and beta diversity from the early to the late postpartum varied. Nineteen of the 27 studies assessed biological and environmental factors influencing the postpartum gut microbial profile changes. Timing of delivery, probiotic supplementation, triclosan exposure, and certain diets influenced the postpartum gut microbiota. Regarding health conditions and intermediate clinical measures assessed in 8 studies; inflammatory bowel disease, postpartum depression, early-onset preeclampsia, gestational diabetes, excessive gestational weight gain, and anthropometric measures such as body mass index and waist-to-hip ratio were related to gut microbiota changes. There is limited data on the maternal postpartum gut microbiota and how it influences maternal health. We need to understand the postpartum maternal gut microbiome, establish how it differs from non-pregnancy and pregnancy states, and determine biological and environmental influencers. Future research of the gut microbiome's significance for the birthing parent in the postpartum could lead to a new understanding of how to improve maternal short and long-term health.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

BACKGROUND

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).

AIM OF REVIEW

This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

Perinatal Health Outcomes Across Rural and Nonrural Counties Within a Single Health System Catchment

Background

Perinatal health outcomes are influenced by a variety of socioeconomic, behavioral, and economic factors that reduce access to health services. Despite these observations, rural communities continue to face barriers, including a lack of resources and the fragmentation of health services.

Objective

To evaluate patterns in health outcomes, health behaviors, socioeconomic vulnerability, and sociodemographic characteristics across rural and nonrural counties within a single health system catchment area.

Methods

Socioeconomic vulnerability metrics, health care access as determined by licensed provider metrics, and behavioral data were obtained from FlHealthCHARTS.gov and the County Health Rankings. County-level birth and health data were obtained from the Florida Department of Health. The University of Florida Health Perinatal Catchment Area (UFHPCA) was defined as all Florida counties where ≥5% of all infants were delivered at Shands Hospital between June 2011 and April 2017.

Results

The UFHPCA included 3 nonrural and 10 rural counties that represented more than 64,000 deliveries. Nearly 1 in 3 infants resided in a rural county, and 7 out of 13 counties did not have a licensed obstetrician gynecologist. Maternal smoking rates (range 6.8%-24.8%) were above the statewide rate (6.2%). Except for Alachua County, breastfeeding initiation rates (range 54.9%-81.4%) and access to household computing devices (range 72.8%-86.4%) were below the statewide rate (82.9% and 87.9%, respectively). Finally, we found that childhood poverty rates (range 16.3%-36.9%) were above the statewide rate (18.5%). Furthermore, risk ratios suggested negative health outcomes for residents of counties within the UFHPCA for each measure, except for infant mortality and maternal deaths, which lacked sample sizes to adequately test.

Conclusions

The health burden of the UFHPCA is characterized by rural counties with increased maternal death, neonatal death, and preterm birth, as well as adverse health behaviors that included increased smoking during pregnancy and lower levels of breastfeeding relative to nonrural counties. Understanding perinatal health outcomes across a single health system has potential to not only estimate community needs but also facilitate planning of health care initiatives and interventions in rural and low-resource communities.

Association between early life antibiotic exposure and development of early childhood atopic dermatitis

Background

Atopic dermatitis (AD) is a chronic, inflammatory skin disease commonly onset during infancy.

Objective

We examine the association between pre-and postnatal antibiotic exposure and the development of AD.

Methods

A retrospective, observational study analyzed 4106 infants at the University of Florida from June 2011 to April 2017.

Results

Antibiotic exposure during the first year of life was associated with a lower risk of AD. The association was strongest for exposure during the first month of life. There were no significant differences in the rates of AD in infants with or without exposure to antibiotics in months 2 through 12, when examined by month. Antibiotic exposure during week 2 of life was associated with lower risk of AD, with weeks 1, 3, and 4 demonstrating a similar trend.

Limitations

Retrospective data collection from a single center, use of electronic medical record, patient compliance with prescribed medication, and variable follow-up.

Conclusions

Early life exposures, such as antibiotics, may lead to long-term changes in immunity. Murine models of atopic dermatitis demonstrate a "critical window" for the development of immune tolerance to cutaneous microbes. Our findings suggest that there may also be a "critical window" for immune tolerance in human infants, influenced by antibiotic exposure.

Assessment of human milk in the era of precision health

PURPOSE OF REVIEW

Precision health provides an unprecedented opportunity to improve the assessment of infant nutrition and health outcomes. Breastfeeding is positively associated with infant health outcomes, yet only 58.3% of children born in 2017 were still breastfeeding at 6 months. There is an urgent need to examine the application of precision health tools that support the development of public health interventions focused on improving breastfeeding outcomes.

RECENT FINDINGS

In this review, we discussed the novel and highly sensitive techniques that can provide a vast amount of omics data and clinical information just by evaluating small volumes of milk samples, such as RNA sequencing, cytometry by time-of-flight, and human milk analyzer for clinical implementation. These advanced techniques can run multiple samples in a short period of time making them ideal for the routine clinical evaluation of milk samples.

SUMMARY

Precision health tools are increasingly used in clinical research studies focused on infant nutrition. The integration of routinely collected multiomics human milk data within the electronic health records has the potential to identify molecular biomarkers associated with infant health outcomes.

Implementation of a Family Support Grant to Subsidize Caregiving Needs and Support Attendance at American Society for Nutrition's Annual Professional Scientific Conference

Attendance at professional society meetings facilitates networking, collaboration, and success in academic/scientific fields. Insufficient funds, support, or resources for caregiving can inhibit attendance for parents/caretakers, who may become professionally disadvantaged by not attending professional society conferences. The American Society for Nutrition (ASN) offered a family support grant for caregiving needs during the annual conference (maximum: $750); however, the perceived impact of caregiving funds on attendance outcomes is unknown. The objective of this study was to assess the need of family support for attendance to the ASN annual conference among applicants and to assess recipients' experience and usage of funds. Applicants completed a pre-conference survey assessing requested funds, out-of-pocket caregiving expenses to attend the meeting, the influence of receiving the grant on attendance, and additional factors. Recipients completed a post-conference survey assessing use of the funds and impact of the grant on attending/participating. Grant applications (n = 110) were majority women, aged 26-45 y, married, at the trainee or assistant professor level, from diverse racial/ethnic backgrounds, and with parenting noted as the primary responsibility. Thirty-seven percent of applicants were currently lactating or expressing milk. The average amount requested was $650 US dollars, and >60% of respondents indicated plans to use funds to bring a family member/friend to the conference. Seventy-seven percent of respondents indicated that receiving the grant would influence their attendance. The post-conference survey (n = 25) indicated that recipients felt that receiving the grant was helpful in attending the conference (92%), specifically attending scientific sessions (96%) and poster sessions (80%). Recipients indicated the grant helped them network with attendees (88%), visit the exhibitor hall (72%), and participate in career development activities (64%). The ASN family support grant aided attendance and supported recipients' participation in conference activities, particularly early-career women who are parents, with the goal of supporting diversity and inclusivity in scientific/academic fields. This trial was registered at www.clinicaltrials.gov as NCT03432585.

Enhancing Quality Measurement With Clinical Information: A Use Case of Body Mass Index Change Among Children Taking Second Generation Antipsychotics

OBJECTIVE

We sought to examine the extent to which body mass index (BMI) was available in electronic health records for Florida Medicaid recipients aged 5 to 18 years taking Second-Generation Antipsychotics (SGAP). We also sought to illustrate how clinical data can be used to identify children most at-risk for SGAP-induced weight gain, which cannot be done using process-focused measures.

METHODS

Electronic health record (EHR) data and Medicaid claims were linked from 2013 to 2019. We quantified sociodemographic differences between children with and without pre- and post-BMI values. We developed a linear regression model of post-BMI to examine pre-post changes in BMI among 4 groups: 1) BH/SGAP+ children had behavioral health conditions and were taking SGAP; 2) BH/SGAP- children had behavioral health conditions without taking SGAP; 3) children with asthma; and 4) healthy children.

RESULTS

Of 363,360 EHR-Medicaid linked children, 18,726 were BH/SGAP+. Roughly 4% of linked children and 8% of BH/SGAP+ children had both pre and post values of BMI required to assess quality of SGAP monitoring. The percentage varied with gender and race-ethnicity. The R2 for the regression model with all predictors was 0.865. Pre-post change in BMI differed significantly (P < .0001) among the groups, with more BMI gain among those taking SGAP, particularly those with higher baseline BMI.

CONCLUSION

Meeting the 2030 Centers for Medicare and Medicaid Services goal of digital monitoring of quality of care will require continuing expansion of clinical encounter data capture to provide the data needed for digital quality monitoring. Using linked EHR and claims data allows identifying children at higher risk for SGAP-induced weight gain.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

Using machine learning analysis to assist in differentiating between necrotizing enterocolitis and spontaneous intestinal perforation: A novel predictive analytic tool

PURPOSE

Necrotizing enterocolitis (NEC) and spontaneous intestinal perforation (SIP) are devastating diseases in preterm neonates, often requiring surgical treatment. Previous studies evaluated outcomes in peritoneal drain placement versus laparotomy, but the accuracy of the presumptive diagnosis remains unknown without bowel visualization. Predictive analytics provide the opportunity to determine the etiology of perforation and guide surgical decision making. The purpose of this investigation was to build and evaluate machine learning models to differentiate NEC and SIP.

METHODS

Neonates who underwent drain placement or laparotomy NEC or SIP were identified and grouped definitively via bowel visualization. Patient characteristics were analyzed using machine learning methodologies, which were optimized through areas under the receiver operating characteristic curve (AUROC). The model was further evaluated using a validation cohort.

RESULTS

40 patients were identified. A random forest model achieved 98% AUROC while a ridge logistic regression model reached 92% AUROC in differentiating diseases. When applying the trained random forest model to the validation cohort, outcomes were correctly predicted.

CONCLUSIONS

This study supports the feasibility of using a novel machine learning model to differentiate between NEC and SIP prior to any intended surgical interventions.

LEVEL OF EVIDENCE

level II TYPE OF STUDY: Clinical Research Paper.

Metabolomic Profiling of Biological Reference Materials using a Multiplatform High-Resolution Mass Spectrometric Approach

The number of metabolomics studies have increased dramatically in recent years, spanning from basic/mechanistic research to the identification and validation of clinical biomarkers. Developments in analyte separation techniques and the growth of databases are largely responsible for the rapid growth of metabolomics, although broad differences in analytical workflows can result in difficulty when comparing data across studies. The establishment of baseline metabolomics data for human reference materials using complementary/orthogonal data acquisition strategies can help to alleviate some of these challenges. To this end, we report nontargeted semiquantitative metabolomics data for 22 commercially available materials including plasma (healthy, diabetic, hypertriglyceridemic, African-American), serum (female, male, pregnant, among others), feces (meconium, vegan, omnivore), urine (smokers' and nonsmokers'), breast milk, saliva, and vaginal fluid, using ultrahigh-performance liquid chromatography-tandem mass spectrometry in positive and negative electrospray ionization, as well as gas chromatography-electron ionization-mass spectrometry. Significant differences were observed in the metabolomic fingerprints between all sample types. Post hoc comparisons between relevant sample types support the relevance of these materials and the validity of nontargeted strategies in global metabolomics. As the number and variety of reference materials continues to increase, it is imperative that their adoption is matched. The results of this study may inform future biomedical research by highlighting several metabolites across matrixes and treatments/states that could serve as clinical biomarkers or important biochemical pathway intermediates. Furthermore, our work can serve as a metric for systems suitability, quality assurance, and quality control across the community via the dissemination of high-quality and publicly available annotated metabolomics data.

Association between atopic dermatitis and race from infancy to early childhood: a retrospective cohort study

BACKGROUND

Atopic dermatitis (AD) is a common pediatric skin condition with significant morbidity. It is unclear what factors contribute to racial differences in disease prevalence.

METHODS

A single-site, retrospective cohort study of infants born from June 1, 2011, to April 30, 2017, was performed.

RESULTS

Of the 4016 infants included, 39.2% (n = 1574) were Black, 38.5% (n = 1543) White (non-Hispanic), 7.1% (n = 286) Hispanic, 5.3% (n = 213) Asian, 6.5% (n = 262) "other" race, 3.4% (n = 135) multiracial, and 0.1% (n = 3) not reported. Prevalence of AD differed by race, with 37.0% (n = 583) of Black, 25.8% (n = 55) of Asian, 24.1% (n = 69) of Hispanic, 23.0% (n = 31) of multiracial, 19.1% (n = 50) of "other" race, and 17.9% (n = 276) of White patients diagnosed (P < 0.0001). Delivery mode, NICU stay, and gestational age were all significantly associated with race. In modeling AD with logistic regression, race was significantly associated with the development of AD (P < 0.0001, OR Black = 2.6 [2.2-3.2], OR Asian = 1.6 [1.1-2.2], OR Hispanic = 1.4 [1.0-1.9], OR multiracial 1.4 [0.91-2.2], OR "other" 0.97 [0.67-1.4], and OR White 1.0).

CONCLUSIONS

Racial differences in rates of AD arise early in life. Diagnosis is associated with race rather than delivery mode, insurance type, and gestational age. Further investigation into these disparities and interventions to mitigate them should focus on infancy and early childhood.

Acceptability of Research and Health Care Visits During the COVID-19 Pandemic: Cross-sectional Survey Study

BACKGROUND

The COVID-19 pandemic has had a widespread impact on attendance in biomedical research and health care visits.

OBJECTIVE

This study aimed to identify when and how American adults might feel comfortable about resuming in-person research and health care visits.

METHODS

Cross-sectional questionnaire data were collected from 135 adults (age: median 48 years; women: n=113, 83.7%; White participants: n=92, 68.2%) who were engaged in health-related research.

RESULTS

More than half of the respondents (65/122, 53.3%) felt that the COVID-19 pandemic positively affected their desire to participate in research. Although 73.6% (95/129) of respondents also indicated a willingness to attend in-person health care visits while Centers for Disease Control and Prevention (CDC) guidelines are implemented, 85.8% (109/127) indicated a willingness to attend in-person, outdoor visits, and 92.2% (118/128) reported a willingness to attend drive-through visits (with CDC guidelines implemented during both visit types). Videoconferencing was the most preferred format for intervention visits; however, adults over the age of 65 years preferred this format less than younger adults (P=.001).

CONCLUSIONS

Researchers and clinicians should continue to provide opportunities for continuing the conduction of remote-based interventions while enforcing CDC guidelines during in-person visits.

Implications of the vaginal microbiome and potential restorative strategies on maternal health: a narrative review

The vaginal microbiome undergoes dramatic shifts before and throughout pregnancy. Although the genetic and environmental factors that regulate the vaginal microbiome have yet to be fully elucidated, high-throughput sequencing has provided an unprecedented opportunity to interrogate the vaginal microbiome as a potential source of next-generation therapeutics. Accumulating data demonstrates that vaginal health during pregnancy includes commensal bacteria such as Lactobacillus that serve to reduce pH and prevent pathogenic invasion. Vaginal microbes have been studied as contributors to several conditions occurring before and during pregnancy, and an emerging topic in women's health is finding ways to alter and restore the vaginal microbiome. Among these restorations, perhaps the most significant effect could be preterm labor (PTL) prevention. Since bacterial vaginosis (BV) is known to increase risk of PTL, and vaginal and oral probiotics are effective as supplemental treatments for BV prevention, a potential therapeutic benefit exists for pregnant women at risk of PTL. A new method of restoration, vaginal microbiome transplants (VMTs) involves transfer of one women's cervicovaginal secretions to another. New studies investigating recurrent BV will determine if VMTs can safely establish a healthy Lactobacillus-dominant vaginal microbiome. In most cases, caution must be taken in attributing a disease state and vaginal dysbiosis with a causal relationship, since the underlying reason for dysbiosis is usually unknown. This review focuses on the impact of vaginal microflora on maternal outcomes before and during pregnancy, including PTL, gestational diabetes, preeclampsia, and infertility. It then reviews the clinical evidence focused on vaginal restoration strategies, including VMTs.

Estimating drug consumption during a college sporting event from wastewater using liquid chromatography mass spectrometry

Consumption of licit and/or illicit compounds during sporting events has traditionally been monitored using population surveys, medical records, and law enforcement seizure data. This pilot study evaluated the temporal and geospatial patterns in drug consumption during a university football game from wastewater using liquid chromatography tandem mass spectrometry (LC-MS/MS). Untreated wastewater samples were collected from three locations within or near the same football stadium every 30 min during a university football game. This analysis leveraged two LCMS/ MS instruments (Waters Acquity TQD and a Shimadzu 8040) to analyze samples for 58 licit or illicit compounds and some of their metabolites. Bayesian multilevel models were implemented to estimate mass load and population-level drug consumption, while accounting for multiple instrument runs and concentrations censored at the lower limit of quantitation. Overall, 29 compounds were detected in at least one wastewater sample collected during the game. The 10 most common compounds included opioids, anorectics, stimulants, and decongestants. For compounds detected in more than 50% of samples, temporal trends in median mass load were correlated with the timing of the game; peak loads for cocaine and tramadol occurred during the first quarter of the game and for phentermine during the third quarter. Stadium-wide estimates of the number of doses of drugs consumed were rank ordered as follows: oxycodone (n = 3246) > hydrocodone (n = 2260) > phentermine (n = 513) > cocaine (n = 415) > amphetamine (n = 372) > tramadol (n = 360) > pseudoephedrine (n = 324). This analysis represents the most comprehensive assessment of drug consumption during a university football game and indicates that wastewater-based epidemiology has potential to inform public health interventions focused on reducing recreational drug consumption during large-scale sporting events.

Perspectives of Pregnant and Breastfeeding Women on Participating in Longitudinal Mother-Baby Studies Involving Electronic Health Records: Qualitative Study

BACKGROUND

Electronic health records (EHRs) hold great potential for longitudinal mother-baby studies, ranging from assessing study feasibility to facilitating patient recruitment to streamlining study visits and data collection. Existing studies on the perspectives of pregnant and breastfeeding women on EHR use have been limited to the use of EHRs to engage in health care rather than to participate in research.

OBJECTIVE

The aim of this study is to explore the perspectives of pregnant and breastfeeding women on releasing their own and their infants' EHR data for longitudinal research to identify factors affecting their willingness to participate in research.

METHODS

We conducted semistructured interviews with pregnant or breastfeeding women from Alachua County, Florida. Participants were asked about their familiarity with EHRs and EHR patient portals, their comfort with releasing maternal and infant EHR data to researchers, the length of time of the data release, and whether individual research test results should be included in the EHR. The interviews were transcribed verbatim. Transcripts were organized and coded using the NVivo 12 software (QSR International), and coded data were thematically analyzed using constant comparison.

RESULTS

Participants included 29 pregnant or breastfeeding women aged between 22 and 39 years. More than half of the sample had at least an associate degree or higher. Nearly all participants (27/29, 93%) were familiar with EHRs and had experience accessing an EHR patient portal. Less than half of the participants (12/29, 41%) were willing to make EHR data available to researchers for the duration of a study or longer. Participants' concerns about sharing EHRs for research purposes emerged in 3 thematic domains: privacy and confidentiality, transparency by the research team, and surrogate decision-making on behalf of infants. The potential release of sensitive or stigmatizing information, such as mental or sexual health history, was considered in the decisions to release EHRs. Some participants viewed the simultaneous use of their EHRs for both health care and research as potentially beneficial, whereas others expressed concerns about mixing their health care with research.

CONCLUSIONS

This exploratory study indicates that pregnant and breastfeeding women may be willing to release EHR data to researchers if researchers adequately address their concerns regarding the study design, communication, and data management. Pregnant and breastfeeding women should be included in EHR-based research as long as researchers are prepared to address their concerns.

Perspectives of pregnant and breastfeeding women on longitudinal clinical studies that require non-invasive biospecimen collection - a qualitative study

BACKGROUND

Investigation of the microbiome during early life has stimulated an increasing number of cohort studies in pregnant and breastfeeding women that require non-invasive biospecimen collection. The objective of this study was to explore pregnant and breastfeeding women's perspectives on longitudinal clinical studies that require non-invasive biospecimen collection and how they relate to study logistics and research participation.

METHODS

We completed in-depth semi-structured interviews with 40 women who were either pregnant (n = 20) or breastfeeding (n = 20) to identify their understanding of longitudinal clinical research, the motivations and barriers to their participation in such research, and their preferences for providing non-invasive biospecimen samples.

RESULTS

Perspectives on research participation were focused on breastfeeding and perinatal education. Participants cited direct benefits of research participation that included flexible childcare, lactation support, and incentives and compensation. Healthcare providers, physician offices, and social media were cited as credible sources and channels for recruitment. Participants viewed lengthy study visits and child protection as the primary barriers to research participation. The barriers to biospecimen collection were centered on stool sampling, inadequate instructions, and drop-off convenience.

CONCLUSION

Women in this study were interested in participating in clinical studies that require non-invasive biospecimen collection, and motivations to participate center on breastfeeding and the potential to make a scientific contribution that helps others. Effectively recruiting pregnant or breastfeeding participants for longitudinal microbiome studies requires protocols that account for participant interests and consideration for their time.

Duration of neonatal intensive care unit exposure associated with decreased risk of atopic dermatitis

BACKGROUND/OBJECTIVES

Premature infants have lower rates of atopic dermatitis (AD) compared with full-term infants, though little is known about the factors contributing to this association. We explored the infant and environmental factors that may contribute to the association between prematurity and atopic dermatitis, including mode of delivery, birthweight, gestation, and duration of stay in the neonatal intensive care unit (NICU).

METHODS

This was a single-center retrospective study. Independent samples t tests or chi-square tests were used to compare groups on continuous and categorical variables, respectively. Logistic regression then examined the association of the predictor variables with AD.

RESULTS

Four thousand sixteen mother-infant dyads were included. Infants had a higher risk of developing AD if they were delivered vaginally (P = .013), did not stay in the NICU (P < .001), had a longer gestation (P = .001), or had a higher birthweight (P = .002). In modeling atopic dermatitis with the predictor variables, only NICU length of stay remained significantly associated with a lower risk of AD (P = .004).

CONCLUSION

Infants had a lower risk of developing AD if they had a longer stay in the NICU.

COVID-19 Impacts Mental Health Outcomes and Ability/Desire to Participate in Research Among Current Research Participants

OBJECTIVE

This study aimed to examine the impact of coronavirus disease 2019 (COVID-19) on current research participants' mental health outcomes, ability to adhere to behavioral intervention recommendations, and desire to participate in research.

METHODS

A quantitative/qualitative cross-sectional survey was used among adults currently enrolled in health-related research (N = 250; 85% women; > 50% currently enrolled in behavioral weight loss intervention).

RESULTS

COVID-19 was perceived as a severe threat by most (62.3%). Related to COVID-19, 29.6% of participants reported moderate/severe symptoms of anxiety/depression, and 68.4% reported moderate/severe posttraumatic stress disorder (PTSD) symptomatology, with women more likely to demonstrate moderate/severe anxiety/depression (P = 0.047) and PTSD symptomatology (P = 0.028) relative to men. Those with moderate/severe levels of anxiety/depression (P = 0.0154) and distress (P = 0.0330) were more likely to report a decreased desire to participate in research. Among those in behavioral interventions, individuals perceiving COVID-19 as a moderate/severe threat or experiencing moderate/severe depression or PTSD symptomatology were 4 to 19 times more likely to report that COVID-19 affected their ability to adhere to behavioral recommendations. Qualitative analysis identified four themes describing COVID-19's impact on research experiences: transition, remote intervention delivery, ability to adhere to program goals, and research participation interest.

CONCLUSIONS

These data suggest that participants engaged in health-related research perceive COVID-19 as a significant threat, affecting mental health, desire to participate in research, and ability to adhere to intervention recommendations.

A Qualitative Study of Pregnant Women's Perspectives on Antibiotic Use for Mom and Child: Implications for Developing Tailored Health Education Interventions

The overutilization of antibiotics during pregnancy and early life are associated with adverse health outcomes for mothers and infants. In this study, we explored pregnant women’s opinions and concerns of antibiotics and how perceptions may affect their health-related decision-making. We conducted 18 in-depth, semi-structured interviews with pregnant women and used the Health Belief Model (HBM) as a framework to analyze the data. We found that mothers generally understood the benefits of antibiotics and were aware that antibiotics are clinically effective for treating bacterial infections. Importantly, perceived barriers related to antibiotic use included concerns regarding the impact of antibiotics on breastfeeding efficacy, microbial health, and societal factors such as antimicrobial resistance. The prescription of antibiotics by a healthcare provider was a cue to action for women, as they trusted providers to recommend medications that were safe for them and their infants. Overall, mothers shared that receiving education on the effects of antibiotics would improve their self-efficacy and decision-making surrounding the use of antibiotics for treating illness. Implications for tailored perinatal health education interventions to enhance antibiotic use, knowledge, and decision-making are discussed.

Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models

BACKGROUND

Febrile neutropenia (FN) has been associated with high mortality among adults with cancer. Current systems for early detection of inpatient FN mortality are based on scoring indexes that require intensive physicians' subjective evaluation.

OBJECTIVE

In this study, we leveraged machine learning techniques to build a FN mortality risk evaluation tool focused on FN admissions without physicians' subjective evaluation.

METHODS

We used the National Inpatient Sample and Nationwide Inpatient Sample (NIS) that included mortality data among adult inpatients who were diagnosed with FN during a hospital admission. Machine learning techniques that we compared included linear models (ridge logistic regression and linear support vector machine) and non-linear models (gradient boosting tree and neural network). The primary outcome for this study was death among individuals with a recorded FN admission. Model comparison was evaluated based on areas under the receiver operating characteristic curve (AUROC) and model performance was estimated using 30 % test set created via stratified split.

RESULTS

Our analysis detected 126,013 adult admissions within the NIS data that were diagnosed with FN, among which 5,856 were declared as deceased (4.6 %). Our machine learning results demonstrate linear models and non-linear models achieved areas under the receiver operating characteristic (AUROC) around 92 % in survival prediction.

CONCLUSIONS

We developed machine learning models that do not require physicians' subjective evaluation for FN mortality risk prediction.

Postnatal pediatric systemic antibiotic episodes during the first three years of life are not associated with mode of delivery

BACKGROUND

Delivery by cesarean section (C-section) is associated with adverse short-term and long-term infant outcomes. Given that antibiotics during early life are prescribed for infant outcomes that are more likely among c-section deliveries, we hypothesized that postnatal antibiotic exposure will be greater among c-section infants compared to vaginally delivered infants.

OBJECTIVE

The aim of this paper was to evaluate if mode of infant delivery was associated with patterns of systemic antibiotic exposure in children during their first three years.

METHODS

Pediatric electronic health records from UFHealth, 2011 to 2017 were reviewed. We included singleton, term infants (37-42 weeks gestation) with a birth weight ≥ 2500 grams, with documented mode of delivery and well visits on record. Infants with a neonatal intensive care unit stay were excluded. Both oral and intravenous antibiotics for a 10-day duration were classified as a single episode. The primary outcome was antibiotic episodes in the first three years of life, and a sub-analysis was performed to compare broad-spectrum versus narrow-spectrum antibiotic exposures.

RESULTS

The mean number of antibiotic episodes in 4,024 full-term infants was 0.34 (SD = 0.79) and 24.1% of infants had at least one antibiotic episode. Penicillins were the most prescribed antibiotic in children 0-1 years (66.9%) and cephalosporins were the most common antibiotic prescribed for children 1-3 years (56.2%). We did not detect a meaningful or significant rate ratio (RR) between mode of delivery and overall antibiotic episodes 1.14 (95% CI 0.99, 1.31), broad-spectrum episodes 1.19 (95% CI 0.93, 1.52, or narrow-spectrum episodes 1.14 (95% CI 0.97, 1.34).

CONCLUSION

Our results do not support the hypothesis that postnatal antibiotic exposure was greater among infants delivered by cesarean section compare to infants delivered vaginally during the first three years of life.

Initial microbial community of the neonatal stomach immediately after birth

The purpose of this prospective cross-sectional cohort pilot study is to explore the initial microbial community of gastric aspirate fluid as collected immediately after birth and its relationships with mode of delivery and preterm birth. Twenty-nine gastric aspirate samples collected immediately after birth from infants born between 24-40 weeks gestation were analyzed for microbial composition. Total microbial content was low in many samples, with a substantial number sharing taxonomic composition with negative controls. qPCR targeting the 16S rRNA gene showed that infants delivered vaginally had a higher microbial load than infants delivered by C-section. Some pre-term samples showed high relative abundance of genus Ureaplasma, consistent with previous literature that has implicated infections with this taxon as a potential cause of pre-term birth. Vaginally born term infant samples, by contrast, had significantly higher levels of genus Lactobacillus with Lactobacillus crispatus the most dominant species. Microbial evaluation showed that vaginally born term infant gastric aspirate samples had higher levels of lactobacilli than pre-terms. Samples from many infants had low microbial load near the edge of the detection limit.

Objectively measured pediatric obesity prevalence using the OneFlorida Clinical Research Consortium

We characterized the prevalence of obesity among Florida children 2-19years old using electronic health records (EHRs). The obesity prevalence for 331,641 children was 16.9%. Obesity prevalence at 6-11years (19.5%) and 12-19years (18.9%) were approximately double the prevalence of obesity among children 2-5years (9.9%). The highest prevalence of severe obesity occurred in rural Florida (21.7%) and non-Hispanic children with multiple races had the highest obesity prevalence (21.1%) across all racial/ethnic groups. Our results highlight EHR as a low-cost alternative to estimate the prevalence of obesity and severe obesity in Florida children, both overall and within subpopulations.

The gut microbiota in infants of obese mothers increases inflammation and susceptibility to NAFLD

Maternal obesity is associated with increased risk for offspring obesity and non-alcoholic fatty liver disease (NAFLD), but the causal drivers of this association are unclear. Early colonization of the infant gut by microbes plays a critical role in establishing immunity and metabolic function. Here, we compare germ-free mice colonized with stool microbes (MB) from 2-week-old infants born to obese (Inf-ObMB) or normal-weight (Inf-NWMB) mothers. Inf-ObMB-colonized mice demonstrate increased hepatic gene expression for endoplasmic reticulum stress and innate immunity together with histological signs of periportal inflammation, a histological pattern more commonly reported in pediatric cases of NAFLD. Inf-ObMB mice show increased intestinal permeability, reduced macrophage phagocytosis, and dampened cytokine production suggestive of impaired macrophage function. Furthermore, exposure to a Western-style diet in Inf-ObMB mice promotes excess weight gain and accelerates NAFLD. Overall, these results provide functional evidence supporting a causative role of maternal obesity-associated infant dysbiosis in childhood obesity and NAFLD.

The Neonatal Microbiome and Its Partial Role in Mediating the Association between Birth by Cesarean Section and Adverse Pediatric Outcomes

BACKGROUND

Cesarean sections (CS) are among the most commonly performed surgical procedures in the world. Epidemiologic data has associated delivery by CS with an increased risk of certain adverse health outcomes in children, such as asthma and obesity.

OBJECTIVE

To explore what is known about the effect of mode of delivery on the development of the infant microbiome and discuss the potentially mediating role of CS-related microbial dysbiosis in the development of adverse pediatric health outcomes. Recommendations for future inquiry are also provided.

METHODS

This study provides a narrative overview of the literature synthesizing the findings of literature retrieved from searches of PubMed and other computerized databases and authoritative texts.

RESULTS

Emerging evidence suggests that mode of delivery is involved in the development of the neonatal microbiome and may partially explain pediatric health outcomes associated with birth by CS. Specifically, the gut microbiome of vaginally delivered infants more closely resembles their mothers' vaginal microbiome and thus more commonly consists of potentially beneficial microbiota such as Lactobacillus, Bifidobacterium, and Bacteroides. Conversely, the microbiome of infants born via CS shows an increased prevalence of either skin flora or potentially pathogenic microbial communities such as Klebsiella, Enterococcus, and Clostridium.

CONCLUSIONS

Mode of delivery plays an important role in the development of the postnatal microbiome but likely tells only part of the story. More comprehensive investigations into all the pre- and perinatal factors that have the potential to contribute to the neonatal microbiome are warranted.

What Are Optimal Cesarean Section Rates in the U.S. and How Do We Get There? A Review of Evidence-Based Recommendations and Interventions

Cesarean sections (CSs) are the most commonly performed surgical procedures in the world today. Global epidemiological studies from the last decade suggest that the optimal CS rates in developed countries exist somewhere between 15% and 19%. Despite these findings, CS rates in the United States have remained stable at slightly over 32% over the past 10 years. Using primary and secondary literature published from 2010 to 2015, this review discusses how optimal CS rates were developed. In addition, we define a category of potentially avoidable CS (i.e., those conducted on nulliparous low-risk women who present with vertex infants at term) and explore how CS in this population appear to be one of the main drivers of high CS rates overall. The institutional, provider, and patient-related factors, which may be related to higher-than-recommended rates of CS, particularly those conducted in low-risk women, will be discussed. This review will then delve into clinician and patient-oriented interventions that have been shown to effectively reduce the rate of potentially avoidable CS. Our analysis showed that large-scale, multifaceted interventions that include audit and feedback cycles as well as peer review strategies were the most effective in decreasing rates of potentially avoidable CS. This review concludes with an agenda for future research into interventions that aim to achieve optimal CS rates.

Personalization of the Microbiota of Donor Human Milk with Mother's Own Milk

The American Academy of Pediatrics recommends that extremely preterm infants receive mother's own milk (MOM) when available or pasteurized donor breast milk (DBM) when MOM is unavailable. The goal of this study was to determine whether DBM could be inoculated with MOM from mothers of preterm infants to restore the live microbiota (RM). Culture dependent and culture independent methods were used to analyze the fluctuations in the overall population and microbiome, respectively, of DBM, MOM, and RM samples over time. Using MOM at time = 0 (T0) as the target for the restoration process, this level was reached in the 10% (RM-10) and 30% (RM-30) mixtures after 4 h of incubation at 37°C, whereas, the larger dilutions of 1% (RM-1) and 5% (RM-5) after 8 h. The diversity indexes were similar between MOM and DBM samples, however, different genera were prevalent in each group. Interestingly, 40% of the bacterial families were able to expand in DBM after 4 h of incubation indicating that a large percentage of the bacterial load present in MOM can grow when transferred to DBM, however, no core microbiome was identified. In summary, the microbiome analyses indicated that each mother has a unique microbiota and that live microbial reestablishment of DBM may provide these microbes to individual mothers' infants. The agreement between the results obtained from the viable bacterial counts and the microbiome analyses indicate that DBM incubated with 10-30% v/v of the MOM for 4 h is a reasonable restoration strategy.

Exploring the contribution of maternal antibiotics and breastfeeding to development of the infant microbiome and pediatric obesity

Pediatric obesity, a significant public health concern, has been associated with adult premature mortality and the development of type 2 diabetes and cardiovascular disease. Evidence has suggested that the gut microbiota is associated with pediatric obesity. Establishment of the infant gut microbiome is dependent on a dynamic maternal-infant microbiota exchange during early life. The objective of this review is to describe maternal factors such as feeding practices and antibiotic use that may influence the infant gut microbiome and risk for obesity. The complex components in human milk have many nutritional benefits to the infant; however, the microbiome in human milk may be an important factor to help regulate the infant's weight. We discuss maternal antibiotics and the effects on breast milk as critical exposures that alter the infant's gut microbiome and influence the risk of pediatric obesity.

Polymorphisms in stearoyl coa desaturase and sterol regulatory element binding protein interact with N-3 polyunsaturated fatty acid intake to modify associations with anthropometric variables and metabolic phenotypes in Yup'ik people

SCOPE

n-3 polyunsaturated fatty acid (n-3 PUFA) intake is associated with protection from obesity; however, the mechanisms of protection remain poorly characterized. The stearoyl CoA desaturase (SCD), insulin-sensitive glucose transporter (SLC2A4), and sterol regulatory element binding protein (SREBF1) genes are transcriptionally regulated by n-3 PUFA intake and harbor polymorphisms associated with obesity. The present study investigated how consumption of n-3 PUFA modifies associations between SCD, SLC2A4, and SREBF1 polymorphisms and anthropometric variables and metabolic phenotypes.

MATERIALS AND METHODS

Anthropometric variables and metabolic phenotypes were measured in a cross-sectional sample of Yup'ik individuals (n = 1135) and 33 polymorphisms were tested for main effects and interactions using linear models that account for familial correlations. n-3 PUFA intake was estimated using red blood cell nitrogen stable isotope ratios. SCD polymorphisms were associated with ApoA1 concentration and n-3 PUFA interactions with SCD polymorphisms were associated with reduced fasting cholesterol levels and waist-to-hip ratio. SLC2A4 polymorphisms were associated with hip circumference, high-density lipoprotein and ApoA1 concentrations. SREBF1 polymorphisms were associated with low-density lipoprotein and HOMA-IR and n-3 PUFA interactions were associated with reduced fasting insulin and HOMA-IR levels.

CONCLUSION

The results suggest that an individual's genotype may interact with dietary n-3 PUFAs in ways that are associated with protection from obesity-related diseases in Yup'ik people.

Linkage and association analysis of circulating vitamin D and parathyroid hormone identifies novel loci in Alaska Native Yup'ik people

Background

Vitamin D deficiency is a well-documented public health issue with both genetic and environmental determinants. Populations living at far northern latitudes are vulnerable to vitamin D deficiency and its health sequelae, although consumption of traditional native dietary pattern rich in fish and marine mammals may buffer the effects of reduced sunlight exposure. To date, few studies have investigated the genetics of vitamin D metabolism in circumpolar populations or considered genediet interactions with fish and n-3 fatty acid intake.

Methods

We searched for genomic regions exhibiting linkage and association with circulating levels of vitamin D and parathyroid hormone (PTH) in 982 Yup’ik individuals from the Center for Alaska Native Health Research Study. We also investigated potential interactions between genetic variants and a biomarker of traditional dietary intake, the δ15N value.

Results

We identified several novel regions linked with circulating vitamin D and PTH as well as replicated a previous linkage finding on 2p16.2 for vitamin D. Bioinformatic analysis revealed multiple candidate genes for both PTH and vitamin D, including CUBN, MGAT3, and NFKBIA. Targeted association analysis identified NEBL as a candidate gene for vitamin D and FNDC3B for PTH. We observed significant associations between a variant in MXD1 and vitamin D only when an interaction with the δ15N value was included. Finally, we integrated pathway level information to illustrate the biological validity of the proposed candidate genes.

Conclusion

We provide evidence of linkage between several biologically plausible genomic regions and vitamin D metabolism in a circumpolar population. Additionally, these findings suggest that a traditional dietary pattern may modulate genetic effects on circulating vitamin D.

Electronic supplementary material

The online version of this article (doi:10.1186/s12263-016-0538-y) contains supplementary material, which is available to authorized users.

Alterations in human milk leptin and insulin are associated with early changes in the infant intestinal microbiome

BACKGROUND

Increased maternal body mass index (BMI) is a robust risk factor for later pediatric obesity. Accumulating evidence suggests that human milk (HM) may attenuate the transfer of obesity from mother to offspring, potentially through its effects on early development of the infant microbiome.

OBJECTIVES

Our objective was to identify early differences in intestinal microbiota in a cohort of breastfeeding infants born to obese compared with normal-weight (NW) mothers. We also investigated relations between HM hormones (leptin and insulin) and both the taxonomic and functional potentials of the infant microbiome.

DESIGN

Clinical data and infant stool and fasting HM samples were collected from 18 NW [prepregnancy BMI (in kg/m(2)) <24.0] and 12 obese (prepregnancy BMI >30.0) mothers and their exclusively breastfed infants at 2 wk postpartum. Infant body composition at 2 wk was determined by air-displacement plethysmography. Infant gastrointestinal microbes were estimated by using 16S amplicon and whole-genome sequencing. HM insulin and leptin were determined by ELISA; short-chain fatty acids (SCFAs) were measured in stool samples by using gas chromatography. Power was set at 80%.

RESULTS

Infants born to obese mothers were exposed to 2-fold higher HM insulin and leptin concentrations (P < 0.01) and showed a significant reduction in the early pioneering bacteria Gammaproteobacteria (P = 0.03) and exhibited a trend for elevated total SCFA content (P < 0.06). Independent of maternal prepregnancy BMI, HM insulin was positively associated with both microbial taxonomic diversity (P = 0.03) and Gammaproteobacteria (e.g., Enterobacteriaceae; P = 0.04) and was negatively associated with Lactobacillales (e.g., Streptococcaceae; P = 0.05). Metagenomic analysis showed that HM leptin and insulin were associated with decreased bacterial proteases, which are implicated in intestinal permeability, and reduced concentrations of pyruvate kinase, a biomarker of pediatric gastrointestinal inflammation.

CONCLUSION

Our results indicate that, although maternal obesity may adversely affect the early infant intestinal microbiome, HM insulin and leptin are independently associated with beneficial microbial metabolic pathways predicted to increase intestinal barrier function and reduce intestinal inflammation. This trial was registered at clinicaltrials.gov as NCT01693406.

Higher Intake of PUFAs Is Associated with Lower Total and Visceral Adiposity and Higher Lean Mass in a Racially Diverse Sample of Children

BACKGROUND

Polyunsaturated fatty acids (PUFAs) are associated with protection from obesity-related phenotypes in adults; however, the relation between reported intake of PUFAs with body-composition outcomes in children remains unknown.

OBJECTIVE

Our objective was to examine how self-reported intakes of PUFAs, including total, n-6 (ω-6), and n-3 (ω-3) PUFAs and ratios of n-6 to n-3 PUFAs and PUFAs to saturated fatty acids (SFAs), are associated with measures of adiposity and lean mass (LM) in children. We hypothesized that higher self-reported intakes of PUFAs and the ratio of PUFAs to SFAs would be positively associated with LM and negatively associated with total adiposity.

METHODS

Body composition and dietary intake were measured in a racially diverse sample of 311 children (39% European American, 34% African American, and 27% Hispanic American) aged 7-12 y. Body composition and abdominal fat distribution were measured by dual-energy X-ray absorptiometry and computed tomography scans, respectively. Self-reported dietary intakes (including total PUFAs, n-3 PUFAs, n-6 PUFAs, and SFAs) were assessed by using two 24-h recalls. Independent-sample t tests and multiple linear regression analyses were conducted.

RESULTS

Total PUFA intake was positively associated with LM (P = 0.049) and negatively associated with percentage of body fat (%BF; P = 0.033) and intra-abdominal adipose tissue (IAAT; P = 0.022). A higher ratio of PUFAs to SFAs was associated with higher LM (P = 0.030) and lower %BF (P = 0.028) and IAAT (P = 0.048). Intakes of n-3 and n-6 PUFAs were positively associated with LM (P = 0.017 and P = 0.021, respectively), and the ratio of n-6 to n-3 PUFAs was negatively associated with IAAT (P = 0.014). All results were independent of biological, environmental, and genetic covariates.

CONCLUSIONS

Our results show that a higher self-reported intake of PUFAs and a higher ratio of PUFAs to SFAs are positively associated with LM and negatively associated with visceral adiposity and %BF in a healthy cohort of racially diverse children aged 7-12 y. This trial was registered at clinicaltrials.gov as NCT00726778.

Associations of maternal weight status prior and during pregnancy with neonatal cardiometabolic markers at birth: the Healthy Start study

Background

Maternal obesity increases adult offspring risk for cardiovascular disease; however the role of offspring adiposity in mediating this association remains poorly characterized.

Objective

To investigate the associations of maternal pre-pregnant body mass index (maternal BMI) and gestational weight gain (GWG) with neonatal cardio-metabolic markers independent of fetal growth and neonatal adiposity.

Methods

A total of 753 maternal-infant pairs from the Healthy Start study, a large multi-ethnic pre-birth observational cohort were used. Neonatal cardio-metabolic markers included cord blood glucose, insulin, glucose-to-insulin ratio (Glu/Ins), total and high-density lipoprotein cholesterol (HDL-c), triglycerides, free fatty acids and leptin. Maternal BMI was abstracted from medical records or self-reported. GWG was calculated as the difference between the first pre-pregnant weight and the last weight measurement before delivery. Neonatal adiposity (percent fat mass) was measured within 72 hours of delivery using whole body air displacement plethysmography.

Results

In covariate adjusted models, maternal BMI was positively associated with cord blood insulin (p=0.01) and leptin (p<0.001) levels and inversely associated with cord blood HDL-c (p=0.05) and Glu/Ins (p=0.003). Adjustment for fetal growth or neonatal adiposity attenuated the effect of maternal BMI on neonatal insulin, rendering the association non-significant. However, maternal BMI remained associated with higher leptin (p<0.0011), lower HDL-c (p=0.02) and Glu/Ins (p=0.05), independent of neonatal adiposity. GWG was positively associated with neonatal insulin (p=0.02), glucose (p=0.03) and leptin levels (p<0.001) and negatively associated with Glu/Ins (p=0.006). After adjusting for neonatal adiposity, GWG remained associated with higher neonatal glucose (p=0.02) and leptin levels (p=0.02) and lower Glu/Ins (p=0.048).

Conclusions

Maternal weight prior and/or during pregnancy is associated with neonatal cardio-metabolic makers including leptin, glucose, and HDL-c at delivery, independent of neonatal adiposity. Our results suggest that intrauterine exposure to maternal obesity influences metabolic processes beyond fetal growth and fat accretion.

Linkage and association analysis of obesity traits reveals novel loci and interactions with dietary n-3 fatty acids in an Alaska Native (Yup'ik) population

OBJECTIVE

To identify novel genetic markers of obesity-related traits and to identify gene-diet interactions with n-3 polyunsaturated fatty acid (n-3 PUFA) intake in Yup'ik people.

MATERIAL AND METHODS

We measured body composition, plasma adipokines and ghrelin in 982 participants enrolled in the Center for Alaska Native Health Research (CANHR) Study. We conducted a genome-wide SNP linkage scan and targeted association analysis, fitting additional models to investigate putative gene-diet interactions. Finally, we performed bioinformatic analysis to uncover likely candidate genes within the identified linkage peaks.

RESULTS

We observed evidence of linkage for all obesity-related traits, replicating previous results and identifying novel regions of interest for adiponectin (10q26.13-2) and thigh circumference (8q21.11-13). Bioinformatic analysis revealed DOCK1, PTPRE (10q26.13-2) and FABP4 (8q21.11-13) as putative candidate genes in the newly identified regions. Targeted SNP analysis under the linkage peaks identified associations between three SNPs and obesity-related traits: rs1007750 on chromosome 8 and thigh circumference (P=0.0005), rs878953 on chromosome 5 and thigh skinfold (P=0.0004), and rs1596854 on chromosome 11 for waist circumference (P=0.0003). Finally, we showed that n-3 PUFA modified the association between obesity related traits and two additional variants (rs2048417 on chromosome 3 for adiponectin, P for interaction=0.0006 and rs730414 on chromosome 11 for percentage body fat, P for interaction=0.0004).

CONCLUSIONS

This study presents evidence of novel genomic regions and gene-diet interactions that may contribute to the pathophysiology of obesity-related traits among Yup'ik people.

Evidence for novel genetic loci associated with metabolic traits in Yup'ik people

OBJECTIVES

To identify genomic regions associated with fasting plasma lipid profiles, insulin, glucose, and glycosylated hemoglobin in a Yup'ik study population, and to evaluate whether the observed associations between genetic factors and metabolic traits were modified by dietary intake of marine derived omega-3 polyunsaturated acids (n-3 PUFA).

METHODS

A genome-wide linkage scan was conducted among 982 participants of the Center for Alaska Native Health Research study. n-3 PUFA intake was estimated using the nitrogen stable isotope ratio (δ(15) N) of erythrocytes. All genotyped SNPs located within genomic regions with LOD scores > 2 were subsequently tested for individual SNP associations with metabolic traits using linear models that account for familial correlation as well as age, sex, community group, and n-3 PUFA intake. Separate linear models were fit to evaluate interactions between the genotype of interest and n-3 PUFA intake.

RESULTS

We identified several chromosomal regions linked to serum apolipoprotein A2, high density lipoprotein-, low density lipoprotein-, and total cholesterol, insulin, and glycosylated hemoglobin. Genetic variants found to be associated with total cholesterol mapped to a region containing previously validated lipid loci on chromosome 19, and additional novel peaks of biological interest were identified at 11q12.2-11q13.2. We did not observe any significant interactions between n-3 PUFA intake, genotypes, and metabolic traits.

CONCLUSIONS

We have completed a whole genome linkage scan for metabolic traits in Native Alaskans, confirming previously identified loci, and offering preliminary evidence of novel loci implicated in chronic disease pathogenesis in this population.

Obesity polymorphisms identified in genome-wide association studies interact with n-3 polyunsaturated fatty acid intake and modify the genetic association with adiposity phenotypes in Yup'ik people

n-3 Polyunsaturated fatty acids (n-3 PUFAs) have anti-obesity effects that may modulate risk of obesity, in part, through interactions with genetic factors. Genome-wide association studies (GWAS) have identified genetic variants associated with body mass index (BMI); however, the extent to which these variants influence adiposity through interactions with n-3 PUFAs remains unknown. We evaluated 10 highly replicated obesity GWAS single nucleotide polymorphisms (SNPs) for individual and cumulative associations with adiposity phenotypes in a cross-sectional sample of Yup'ik people (n = 1,073) and evaluated whether genetic associations with obesity were modulated by n-3 PUFA intake. A genetic risk score (GRS) was calculated by adding the BMI-increasing alleles across all 10 SNPs. Dietary intake of n-3 PUFAs was estimated using nitrogen stable isotope ratio (δ(15)N) of red blood cells, and genotype-phenotype analyses were tested in linear models accounting for familial correlations. GRS was positively associated with BMI (p = 0.012), PBF (p = 0.022), ThC (p = 0.025), and waist circumference (p = 0.038). The variance in adiposity phenotypes explained by the GRS included BMI (0.7 %), PBF (0.3 %), ThC (0.7 %), and WC (0.5 %). GRS interactions with n-3 PUFAs modified the association with adiposity and accounted for more than twice the phenotypic variation (~1-2 %), relative to GRS associations alone. Obesity GWAS SNPs contribute to adiposity in this study population of Yup'ik people and interactions with n-3 PUFA intake potentiated the risk of fat accumulation among individuals with high obesity GRS. These data suggest the anti-obesity effects of n-3 PUFAs among Yup'ik people may, in part, be dependent upon an individual's genetic predisposition to obesity.

Genetic polymorphisms in carnitine palmitoyltransferase 1A gene are associated with variation in body composition and fasting lipid traits in Yup'ik Eskimos

Variants of carnitine palmitoyltransferase 1A (CPT1A), a key hepatic lipid oxidation enzyme, may influence how fatty acid oxidation contributes to obesity and metabolic outcomes. CPT1A is regulated by diet, suggesting interactions between gene variants and diet may influence outcomes. The objective of this study was to test the association of CPT1A variants with body composition and lipids, mediated by consumption of polyunsaturated fatty acids (PUFA). Obesity phenotypes and fasting lipids were measured in a cross-sectional sample of Yup'ik Eskimo individuals (n = 1141) from the Center of Alaska Native Health Research (CANHR) study. Twenty-eight tagging CPT1A SNPs were evaluated with outcomes of interest in regression models accounting for family structure. Several CPT1A polymorphisms were associated with HDL-cholesterol and obesity phenotypes. The P479L (rs80356779) variant was associated with all obesity-related traits and fasting HDL-cholesterol. Interestingly, the association of P479L with HDL-cholesterol was still significant after correcting for body mass index (BMI), percentage body fat (PBF), or waist circumference (WC). Our findings are consistent with the hypothesis that the L479 allele of the CPT1A P479L variant confers a selective advantage that is both cardioprotective (through increased HDL-cholesterol) and associated with reduced adiposity.