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Birth Weight

Birth Weight is a measure of the weight of an infant at the time of delivery.
It is an important indicator of an infant's health and a critical factor in determining appropriate medical care and monitoring.
This MeSH term encompasses data and research related to factors that can influence birth weight, such as maternal health, gestational age, and environmental conditions.
Researchers can use PubCompare.ai to streamline their birth weight studies, comparing protocols and data across literature, preprints, and patents to uncover insights and optimze their research more efficently.

Most cited protocols related to «Birth Weight»

1
Nationwide Birth Cohort Recruitment
To ensure generalizability and ability to extrapolate the results of JECS to Japanese population, the 15 Regional Centers are selected to cover wide geographical areas. The study locations’ urbanization and land development are diverse, from urban and suburban to rural areas as well as from agricultural and fishery to commercial and industrial uses.
Regional Centers were selected in a competitive process in which universities and other research institutions were invited to submit proposals for covered areas and population, recruitment methods, organization structures, regional liaison, and the resources. Each Regional Center consists of one or more study areas. The population of the selected study areas is 130,000 to 600,000. Assuming birth rate of the study areas to be 1%, each Regional Center will see 1,300 to 6,000 annual births, 4,400 on average. JECS aims half of all the births in the area to be covered. Selected Regional Centers are required to recruit 3,000 to 9,000 pregnant women in three years, totaling to 100,000 participants in 15 Regional Centers (Figure 1). In order to ensure the maximum contact with eligible participants, Regional Centers have formed JECS regional liaison involving local governments and health care providers. All the study areas are contained within administrative units, e.g. municipalities, further enhancing local government cooperation. This makes it easier to obtain basic health statistics in the study areas, for example, total number of births, sex ratios, birth weights, morbidities, and mortalities. It also helps us maximize follow-up and retention rates.
Kawamoto T., Nitta H., Murata K., Toda E., Tsukamoto N., Hasegawa M., Yamagata Z., Kayama F., Kishi R., Ohya Y., Saito H., Sago H., Okuyama M., Ogata T., Yokoya S., Koresawa Y., Shibata Y., Nakayama S., Michikawa T., Takeuchi A, & Satoh H. (2014). Rationale and study design of the Japan environment and children’s study (JECS). BMC Public Health, 14, 25.
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Publication 2014
Japanese Pregnant Women Retention (Psychology) Urbanization
2
Preterm Growth Chart Revision
To revise the growth chart, thorough literature searches were performed to find published and unpublished population-based preterm size at birth (weight, length, and/or head circumference) references. The inclusion criteria, defined a priori, designed to minimize bias by restriction [13 ], were to locate population-based studies of preterm fetal growth, from developed countries with:
a) Corrected gestational ages through fetal ultrasound and/or infant assessment and/or statistical correction;
b) Data percentiles at 24 weeks gestational age or lower;
c) Sample of at least 25,000 babies, with more than 500 infants aged less than 30 weeks;
d) Separate data on females and males;
e) Data available numerically in published form or from authors,
f) Data collected within the past 25 years (1987 to 2012) to account for any secular trends.
Fenton T.R, & Kim J.H. (2013). A systematic review and meta-analysis to revise the Fenton growth chart for preterm infants. BMC Pediatrics, 13, 59.
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Publication 2013
Birth Weight Care, Prenatal Females Fetal Growth Fetal Ultrasonography Gestational Age Head Infant Males
3
Evaluating Background Correction Methods for DNA Methylation Analysis
The effect of different background correction methods on reproducibility was assessed using data from 20 pairs of duplicate samples that were part of a previously published study of methylation in 891 infant whole blood samples (12 (link)). As part of this study, duplicate samples were located on separate 96 well plates that underwent independent bisulfite conversion, hybridization and array scanning. One sample was excluded due to poor data quality, leaving 19 duplicate pairs (38 samples) for evaluation.
The effect of different background correction methods on measurement accuracy was assessed using data from methylation control mixture samples for this same study (12 (link)), where purified human 100% methylated and unmethylated DNA (Zymo Research, Irving CA) were mixed together in different proportions to create laboratory control samples with specific methylation levels: 0%, 5%, 10%, 20%, 40%, 50%, 60%, 80% and 100% methylated Replicates for each methylation level (n = 10, 3, 2, 3, 3, 2, 3, 3 and 10, respectively) were independently assayed on different arrays.
To avoid possible impact on evaluations, we excluded 69 075 probes, which include non-specific bind probes, common (MAF > 0.05) SNPs at CpG target regions, probes on sex chromosomes and probes with multimodal methylation distributions identified using ENmix R package. We also excluded probes with low quality methylation values where the number of beads was less than 3 or detection P-value greater than 0.05.
To demonstrate the effect of ENmix background correction method on epigenome-wide association studies (EWAS), we re-analyzed raw blood DNA methylation data from 889 infants in relation to maternal smoking (12 (link)). We preprocessed the data with different methods or combinations of methods: raw data, Q5 background correction, ENmix_oob background correction, ENmix and dye bias correction (ENmixD), ENmix+dye bias correction+quantile normalization (ENmixDQ) and ENmix+dye bias correction+quantile normalization+BMIQ (ENmixDQB). We used a robust linear regression model to test for association between maternal smoking and infant DNA methylation level adjusting for the following variables: cell type proportion (CD8T, CD4T, NK, Bcell, Mono and Gran) estimated using the Houseman method (13 (link)) from minfi R package, gestational age in weeks, sex, education in two categories, birth weight, maternal age, maternal BMI, parity, experimental batch, cleft phenotype and baby birth year.
Xu Z., Niu L., Li L, & Taylor J.A. (2015). ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Research, 44(3), e20.
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Publication 2015
Birth Birth Weight BLOOD Cells Crossbreeding DNA Methylation Epigenome Gestational Age Granisetron Homo sapiens hydrogen sulfite Infant Methylation Mothers Multimodal Imaging Phenotype Sex Chromosomes Single Nucleotide Polymorphism
4
Comprehensive Growth Reference for Preterm Infants
A search of the literature was conducted on three databases (Pub Med, the Cochrane Library, EMBASE from 1980 to June 2002) using the subject headings: infant, (premature, very low birthweight), anthropometry, growth, birthweight, head, cephalometry, gestational age, newborn, and reference values. Articles selected included surveys of intrauterine and post term growth. Reference lists of relevant articles were searched.
To improve on the Babson graph, two types of data were needed: infant size measured at the time of birth for the intrauterine section and term infant measurements for the post-term section. Population studies with large sample sizes were preferred to improve generalizability. The World Health Organization has recommended that gestational age of infants be described as completed weeks [7 (link)], so data stated in this manner were favored. Numerical data were preferred over graphic depiction to ensure accuracy.
, & Fenton T.R. (2003). A new growth chart for preterm babies: Babson and Benda's chart updated with recent data and a new format. BMC Pediatrics, 3, 13.
Publication 2003
Birth Birth Weight cDNA Library Cephalometry Gestational Age Head Infant Infant, Newborn Infant, Postmature Infant, Very Low Birth Weight Premature Birth
5
Systematic Exposure Modeling for Global Burden of Disease
For each risk factor, we systematically searched for published studies, household surveys, censuses, administrative data, ground monitor data, or remote sensing data that could inform estimates of risk exposure. To estimate mean levels of exposure by age-sex-location-year, specific methods varied across risk factors (appendix 1 sections 2.1, 4). For many risk factors, exposure data were modelled using either spatiotemporal Gaussian process regression or DisMod-MR 2.1,17 (link), 18 (link) which are Bayesian statistical models developed over the past 12 years for GBD analyses. For most risk factors, the distribution of exposure across individuals was estimated by modelling a measure of dispersion, usually the SD, and fitting an ensemble of parametric distributions to the predicted mean and SD. Ensemble distributions for each risk were estimated based on individual-level data. Details for each risk factor modelling for mean, SD, and ensemble distribution are available in appendix 1 (section 4). Because of the strong dependency between birthweight and gestational age, exposure for these risks was modelled as a joint distribution using the copula method.19
In many cases, exposure data were available for the reference method of ascertainment and for alternative methods, such as tobacco surveys reporting daily smoking versus total smoking; in these cases, we estimated the statistical relationship between the reference and alternative methods of ascertainment using network meta-regression and corrected the alternative data using this relationship.
Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. (2020). Lancet (London, England), 396(10258), 1223-1249.
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Publication 2020
Birth Weight Gestational Age Households Joints Nicotiana tabacum

Most recents protocols related to «Birth Weight»

1
Monitoring Bisphenol A Levels in Women
Not available on PMC !

Example 21

There is growing evidence that bisphenol A (BPA) may adversely affect humans. BPA is an endocrine disruptor that has been shown to be harmful in laboratory animal studies. As reported by Rochester J (Reproductive Toxicology, 2013) BPA has been shown to affect many endpoints of fertility, including poor ovarian response, viability of oocytes, and reduced yield of viable oocytes. BPA has also been correlated with PCOS, endometrial disorders, an increased rate of miscarriages, premature delivery, and lower birth weights.

Current methods of detecting BPA in blood are done through mass spectrometry. Monitoring of BPA levels in blood may help reduce or eliminate certain sources of BPA in a women's environment, aiding in overall health.

In some embodiments the disclosed device focuses on detecting levels of BPA toxin from menstrual blood or cervicovaginal fluid.

US11864740B2. Sample collection and preservation devices, systems and methods (2024-01-09). NextGen Jane, Inc. [US]. Inventors: Ridhi Tariyal [US], Stephen K. Gire [US].
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Patent 2024
Animals, Laboratory bisphenol A BLOOD Endocrine Disruptors Endometrial Diseases Fertility Homo sapiens Mass Spectrometry Medical Devices Menstruation Miscarriage Oocytes Ovary Polycystic Ovary Syndrome Premature Birth Reproduction Toxic Substances, Environmental Toxins, Biological
2
Delayed-Onset Postpartum Preeclampsia Retrospective Study
We conducted a retrospective cohort study of all women readmitted to Meir Medical Center, from January 2014 through March 2020, with new delayed-onset of postpartum preeclampsia. Delayed-onset postpartum preeclampsia was defined as a new diagnosis of preeclampsia that occurred 48 h to 6 weeks postpartum. Preeclampsia was defined according to the American College of Obstetricians and Gynecologists criteria as blood pressure of 140 mm Hg systolic or 90 mm Hg diastolic or higher on two or more occasions more than 6 h apart, accompanied by proteinuria or end organ dysfunction, or blood pressure 160 mm Hg systolic or 110 mmHg diastolic or higher [9 (link)]. Excluded from the study women with prior diagnosis of preeclampsia, gestational hypertension, or chronic hypertension as well as women with prior chronic diseases.
The control group included randomly recruited healthy parturients with uncomplicated pregnancies, who came, during 2020, to the hospital for a routine screening hearing test for their newborns in the neonatal clinic, during postpartum period, on days 2–11.
.Data were collected by electronic medical record review and included: maternal age, gravity and parity, characteristics of current pregnancy (gestational age at delivery, mode of delivery, neonatal birth weight) and hemoglobin level on the day of labor. The postpartum hospitalization data included postpartum day of readmission and clinical features on presentation including pulse rate (beat per minute, bpm), blood pressure and serum laboratory values of liver function and platelet count. Pulse rate (bpm) and blood pressure of the control group were measured after at least 10 min of rest in a sitting position.
The study was approved by the Meir Medical Center Institutional Review Board on 17th March 2020, number MMC-0048–20. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was not obtained from subjects due to the study nature.
Ravid D., Ovadia M., Asali A., Nisim S., Gershnabel S.F., Biron-Shental T, & Weitzner O. (2023). Changes in maternal heart rate in delayed post-partum preeclampsia. BMC Women's Health, 23, 99.
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Publication 2023
Audiometry Birth Weight Blood Pressure Diagnosis Diastole Disease, Chronic Ethics Committees, Research Gestational Age Gravity Gynecologist Hemoglobin High Blood Pressures Hospitalization Infant, Newborn Liver Obstetric Delivery Obstetrician Obstetric Labor Platelet Counts, Blood Pre-Eclampsia Pregnancy Pulse Rate Serum Systole Transient Hypertension, Pregnancy Woman
3
Preterm Birth and Neonatal Outcomes Analysis
Preterm birth was the primary outcome of this study, which was defined as births before 37 completed weeks of gestation. The World Health Organization (WHO) further subdivided preterm birth based on gestational age: extremely preterm (< 28 weeks), very preterm (28 to < 32 weeks), and moderate or late preterm (32 to < 37 weeks) [23 (link)]. Secondary outcomes were NICU admission, low birthweight and small for gestational age. Low birthweight was defined as a birthweight < 2500 g, and small for gestational age was defined as a birthweight less than the 10th percentile. The following variables were collected: maternal age at delivery (years), race [Asian, Black (Black or African American), White, other (American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, and more than one race)], education [less than 12 grade, high school/general educational development (GED), some college or associate degree (AA), bachelor or higher], pre-pregnancy weight (lb), pre-pregnancy body mass index (BMI) (BMI < 18.5 kg/m2, underweight; BMI = 18.5–24.9 kg/m2, normal; BMI = 25.0–29.9 kg/m2, overweight; BMI = 30.0–34.9 kg/m2, obesity), delivery weight (lb), weight gain (lb), smoking before pregnancy (yes or no), smoking status 1st/2nd/3rd trimester (mother-reported smoking in the three trimesters of pregnancy, yes or no), hypertension eclampsia (yes or no), gestational hypertension (yes or no), pre-pregnancy hypertension (yes or no), number of prenatal visits, the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC, receipt of WIC food for the mother during this pregnancy, yes or no), plurality, prior birth now living, prior birth now dead, prior other terminations, total birth order, gestational age (weeks), newborn sex (female or male), birth weight (g), infertility treatment used (yes or no), pregnancy method (natural pregnancy, pregnancy via ART), method of delivery [spontaneous, non-spontaneous (forceps, vacuum, cesarean)], preterm birth [extremely preterm, very preterm, moderate or late preterm; spontaneous, indicated (forceps, vacuum, cesarean)], NICU admission, low birthweight (yes or no), and small for gestational age (yes or no). WIC is a program intended to help low income pregnant women, infants, and children through age 5 receive proper nutrition by providing vouchers for food, nutrition counseling, health care screenings and referrals; it is administered by the U.S. Department of Agriculture (https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/UserGuide2019-508.pdf). Infertility treatment referred to using fertility enhancing drugs, artificial insemination, intrauterine insemination, or using ART. ART included in vitro fertilization (IVF), gamete intrafallopian transfer (GIFT), and zygote intrafallopian transfer (ZIFT). Information on variables is available at https://www.cdc.gov/nchs/nvss/index.htm.
Lu L., He L., Hu J, & Li J. (2023). Association between very advanced maternal age women with gestational diabetes mellitus and the risks of adverse infant outcomes: a cohort study from the NVSS 2014–2019. BMC Pregnancy and Childbirth, 23, 158.
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Publication 2023
African American Alaskan Natives American Indians Artificial Insemination Asian Americans Birth Birth Weight Child Eclampsia Fertility Agents Fertilization in Vitro Food Forceps Gamete Intrafallopian Transfer Gestational Age High Blood Pressures Index, Body Mass Infant Infant, Newborn Insemination Males Mothers Native Hawaiians Obesity Obstetric Delivery Pacific Islander Americans Pregnancy Pregnant Women Prehypertension Premature Birth Screening Sterility, Reproductive Transient Hypertension, Pregnancy Vacuum Woman Zygote Intrafallopian Transfer
4
Gestational Diabetes and Preterm Birth in Very Advanced Maternal Age
Continuous data were tested for normality using the Kolmogorov-Smirnov test, and the continuous data of normal distribution were described as mean ± standard deviation (Mean ± SD), and the t-test was used for comparisons between groups. Non-normally distributed continuous variables were shown by median and quartile [M (Q1, Q3)], and the Wilcoxon rank sum test was used for comparisons between groups. Categorical data of groups were compared with the Pearson’s χ2 test, and expressed as cases and the constituent ratio [n (%)]. Statistical power was calculated (power = 1). Missing data were imputed using multiple imputation (Supplementary Table 1). Data before imputation were also used for multivariate analyses to conduct sensitivity analyses.
In order to study the association between GDM and preterm birth among vAMA women, we established three models, and odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. Model 1 was a univariate model. Model 2 was a multivariate model adjusting for maternal age at delivery, race, education, and newborn sex. Then all variables were included in a multivariable model for stepwise regression, and the following variables were adjusted for in Model 3: maternal age at delivery, race, education, newborn sex, pre-pregnancy weight, pre-pregnancy BMI, delivery weight, weight gain, smoking before pregnancy, hypertension eclampsia, gestational hypertension, pre-pregnancy hypertension, number of prenatal visits, plurality, total birth order, prior birth now living, prior other terminations, birth weight, pregnancy method, and method of delivery. Subgroup analyses were then performed based on race and use of infertility treatment to demonstrate if and how the association between GDM and preterm birth in vAMA women varied by race and use of infertility treatment. Further, preterm birth was subdivided into extremely preterm, very preterm, and moderate or late preterm birth. Logistic regression was used to investigate the association between GDM and different stages of preterm birth. Model 1 was a univariate model. Model 2 was a multivariate model correcting for maternal age at delivery, race, education, and newborn sex. Model 3 was a multivariate model correcting for maternal age at delivery, race, education, newborn sex, delivery weight, smoking status 2nd trimester, hypertension eclampsia, gestational hypertension, pre-pregnancy hypertension, number of prenatal visits, WIC, plurality, prior other terminations, total birth order, birth weight, pregnancy method, and method of delivery. As for the associations of GDM with NICU admission, low birthweight and small for gestational age in vAMA women, analytical methods were the same as those for the association between GDM and preterm birth, and subgroup analyses by race and use of infertility treatment were also conducted for these outcomes.
All statistical analyses were two-sided, and P < 0.05 was considered to be statistically significant. All analyses were completed by SAS 9.4 software (SAS Institute Inc., Cary, NC, USA).
Lu L., He L., Hu J, & Li J. (2023). Association between very advanced maternal age women with gestational diabetes mellitus and the risks of adverse infant outcomes: a cohort study from the NVSS 2014–2019. BMC Pregnancy and Childbirth, 23, 158.
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Publication 2023
Birth Weight Eclampsia Gestational Age High Blood Pressures Hypersensitivity Infant, Newborn Obstetric Delivery Pregnancy Prehypertension Premature Birth Sterility, Reproductive Transient Hypertension, Pregnancy Woman
5
Hypertensive Disorders in Pregnancy Risks
The primary outcome was the risk of HDP. It was attempted to define HDP as sustained (on at least two occasions 6 h apart) blood pressure ≥ 140/90 mmHg after 20 weeks, with or without proteinuria and other signs or symptoms of preeclampsia and without a history of hypertension. As secondary outcomes, we analyzed some other perinatal risks and neonatal risks. Other perinatal risks included gestational diabetes mellitus (GDM), placenta previa, premature rupture of membrane, anemia, miscarriage, and stillbirth. Miscarriage was defined as the spontaneous loss of clinical pregnancy before 28 weeks of gestational age. Stillbirth was defined as the absence of signs of life at or after 28 weeks of gestation. Neonatal risks included preterm birth (<37 weeks’ gestation), extremely preterm birth (<32 weeks’ gestation), low birth weight (<2500 g), very low birth weight (<1500 g), and macrosomia (birth weight ≥4000 g) (6 (link)).
Fan L., Li N., Liu X., Li X., Cai H., Pan D., Wang T., Shi W., Qu P, & Shi J. (2023). Hormone replacement treatment regimen is associated with a higher risk of hypertensive disorders of pregnancy in women undergoing frozen-thawed embryo transfer. Frontiers in Endocrinology, 14, 1133978.
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Publication 2023
Anemia Birth Weight Blood Pressure Fetal Membranes, Premature Rupture Gestational Age Gestational Diabetes High Blood Pressures Infant, Newborn Infant, Very Low Birth Weight Placenta Previa Pre-Eclampsia Pregnancy Premature Birth Spontaneous Abortion

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More about "Birth Weight"

Birth weight is a crucial metric that provides invaluable insights into an infant's health and development.
This key measurement, recorded at the time of delivery, serves as a critical factor in determining appropriate medical care and monitoring for newborns.
Researchers studying birth weight can leverage a wealth of data and research related to various factors that can influence this important indicator, such as maternal health, gestational age, and environmental conditions.
To streamline and optimize their birth weight studies, researchers can utilize PubCompare.ai, a leading AI-driven platform that enables the comparison of protocols and data across literature, preprints, and patents.
This powerful tool can help uncover valuable insights and enhance the efficiency of birth weight research, whether it's conducted using statistical software like SAS version 9.4, SPSS version 20, Stata 14, or other popular packages.
By comparing data and protocols from a vast array of sources, including SAS 9.4, SPSS version 22.0, and Stata 13, researchers can identify the most effective and reproducible approaches, ultimately leading to more robust and insightful findings.
PubCompare.ai's AI-driven comparisons empower researchers to streamline their studies, accelerate their discovery process, and optimize their birth weight research for maximum impact.
Whether you're exploring the influence of maternal health, investigating gestational age factors, or delving into environmental conditions that can impact birth weight, PubCompare.ai is the go-to tool to enhance your research efficiency and uncover valuable insights that can advance our understanding of this critical indicator of infant well-being.
Embark on your birth weight research journey with confidence and ease, leveraging the power of PubCompare.ai to drive your studies forward.