Association between maternal multimorbidity and neurodevelopment of offspring: a prospective birth cohort study from the Japan Environment and Children's Study.

OBJECTIVES: To investigate the association between multimorbidity during pregnancy and neurodevelopmental delay in offspring using data from a Japanese nationwide birth cohort study. DESIGN: This study was a prospective birth cohort study. SETTING: This study population included 104 059 fetal records who participated in The Japan Environment and Children's Study from 2011 to 2014. PARTICIPANTS: Pregnant women whose children had undergone developmental testing were included in this analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: Neurodevelopment of offspring was assessed using the Japanese version of the Ages and Stages Questionnaire, third edition, comprising five developmental domains. The number of comorbidities among the pregnant women was categorised as zero, single disease or multimorbidity (two or more diseases). Maternal chronic conditions included in multimorbidity were defined as conditions with high prevalence among women of reproductive age. A multivariate logistic regression analysis was conducted to examine the association between multimorbidity in pregnant women and offspring development. RESULTS: Pregnant women with multimorbidity, single disease and no disease accounted for 3.6%, 30.6% and 65.8%, respectively. The ORs for neurodevelopmental impairment during the follow-up period were similar for infants of mothers with no disease comorbidity and those with a single disease comorbidity. However, the ORs for neurodevelopmental impairment were significantly higher for children born to mothers with multimorbidity compared with those born to healthy mothers. CONCLUSION: An association was observed between the number of comorbidities in pregnant women and developmental delay in offspring. Multimorbidity in pregnant women may be associated with neurodevelopmental delay in their offspring. Further research is required in this regard in many other regions of the world.

Modeling soybean growth: A mixed model approach.

The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth. These tools are widely used in many fields but very rarely in plant biology. During model formulation, a nonlinear function describes the shape of growth, and random effects describe genetic and environmental effects and their variability. Genetic relationships among the varieties were also integrated into the model using a genetic relationship matrix. The SAEM algorithm was chosen as an efficient alternative to MCMC methods, which are more commonly used in the domain. It was implemented to infer the expected growth patterns in the analyzed population and the expected curves for each variety through a maximum-likelihood and a maximum-a-posteriori approaches, respectively. The obtained estimates can be used to predict the growth curves for each variety. We illustrate the strengths of the proposed approach using simulated data and soybean plant growth data obtained from a soybean cultivation experiment conducted at the Arid Land Research Center, Tottori University. In this experiment, plant height was measured daily using drones, and the growth was monitored for approximately 200 soybean cultivars for which whole-genome sequence data were available. The NLMEM approach improved our understanding of the determinants of soybean growth and can be successfully used for the genomic prediction of growth pattern characteristics.

Genomic prediction and genome-wide association study using combined genotypic data from different genotyping systems: application to apple fruit quality traits.

With advances in next-generation sequencing technologies, various marker genotyping systems have been developed for genomics-based approaches such as genomic selection (GS) and genome-wide association study (GWAS). As new genotyping platforms are developed, data from different genotyping platforms must be combined. However, the potential use of combined data for GS and GWAS has not yet been clarified. In this study, the accuracy of genomic prediction (GP) and the detection power of GWAS increased for most fruit quality traits of apples when using combined data from different genotyping systems, Illumina Infinium single-nucleotide polymorphism array and genotyping by random amplicon sequencing-direct (GRAS-Di) systems. In addition, the GP model, which considered the inbreeding effect, further improved the accuracy of the seven fruit traits. Runs of homozygosity (ROH) islands overlapped with the significantly associated regions detected by the GWAS for several fruit traits. Breeders may have exploited these regions to select promising apples by breeders, increasing homozygosity. These results suggest that combining genotypic data from different genotyping platforms benefits the GS and GWAS of fruit quality traits in apples. Information on inbreeding could be beneficial for improving the accuracy of GS for fruit traits of apples; however, further analysis is required to elucidate the relationship between the fruit traits and inbreeding depression (e.g. decreased vigor).

The association between flooring materials and childhood asthma: A prospective birth cohort in the Japan Environment and Children's Study.

BACKGROUND: Childhood asthma is known to be affected by a range of factors, including conditions in the indoor environment. While flooring material influences indoor air conditions, the potential association between flooring materials and childhood asthma remains poorly understood in Japan. OBJECTIVE: The present study aims to assess the association between childhood asthma incidence and the primary flooring material with the ongoing prospective nationwide birth cohort data of the Japan Environment and Children's Study (JECS). METHODS: The JECS gathered data on mothers and children through 15 Regional Centres across Japan. The present study assessed flooring materials used in the home and asthma incidence at age four among children born between 2011 and 2014. We implemented logistic regressions, setting asthma incidence among the children as the outcome and home floor type as the exposure. Additional analyses were conducted, stratifying the home's age as a proxy for tatami age, to assess whether the potential effect of tatami flooring on asthma risk is influenced by its age. RESULTS: The present study included total of 75,629 infants. For tatami flooring, the main multivariable regression and additional sub-group regression for homes over ten years old produced odds ratios of 1.09; 95% Confidence Interval (CI) [1.01-1.17] and 1.10; 95% CI [1.00-1.21] compared with flooring, respectively. CONCLUSION: These results imply that exposure to tatami flooring, particularly in older homes, may be associated with childhood asthma incidence. Moreover, our study highlights the importance of evaluating the relationship between regional and cultural differences between asthma and flooring materials.

Heavy metals and trace elements in maternal blood and prevalence of congenital limb abnormalities among newborns: the Japan Environment and Children's Study.

BACKGROUND: Heavy metals such as lead (Pb) and cadmium (Cd) have been associated with adverse pregnancy and developmental outcomes, including congenital abnormalities. This study investigated the association between exposure to heavy metals and trace elements during fetal life and congenital limb abnormalities in infants. METHODS: This study is based on a prospective ongoing nationwide birth cohort from the Japan Environment and Children's Study (JECS). The concentrations of Cd, Pb, mercury (Hg), selenium (Se), and manganese (Mn) were measured in maternal blood collected during the mid-late trimesters. Inclusion criteria were available from questionnaires filled in during pregnancy, including information about congenital limb abnormalities at birth or at one month. To examine the associations with limb anomalies and individual chemicals, logistic regression models were applied following log-transformation or division into quartiles of Cd, Pb, Hg, Se, and Mn concentrations. To assess the associations with the heavy metals and trace elements mixture, quantile g-computation was employed. All models were adjusted for age, maternal smoking history, maternal alcohol intake, history of smoking, and infant sex. RESULTS: Data from 90,163 participants were included in the analysis, of whom 369 had congenital limb abnormalities in any of the collected information, and 89,794 had none. Among the 369 cases of congenital limb abnormalities, there were 185 and 142 cases of polydactyly and syndactyly, respectively. The median concentrations of Pb, Cd, Hg, Se, and Mn were 5.85, 0.66, 3.64, 168, and 15.3 ng/g, respectively. There were no associations between maternal blood concentrations of Pb [adjusted odd ratio = 0.83; 95% confidence interval = 0.61, 1.11], Cd [0.87; 0.68, 1.10], Hg [0.88; 0.73, 1.07], Se [1.07; 0.44, 2.59], and Mn [0.91; 0.64, 1.30] with congenital limb abnormalities. No significant association was observed between the mixture of heavy metals and trace elements [0.85; 0.72, 1.02] and any congenital limb abnormalities. Moreover, there was no association with all polydactylies and all syndactylies, or any type of abnormality as a subdivision. CONCLUSION: At the maternal exposure levels of Cd, Pb, Hg, Se, and Mn assessed in the present study, no association was identified with the risk of developing congenital limb abnormalities in children.

Maternal pre-pregnancy body mass index and related factors: A cross-sectional analysis from the Japan Environment and Children's Study.

Socioeconomic status and smoking are reportedly associated with underweight and obesity; however, their associations among pregnant women are unknown. This study aimed to investigate whether socioeconomic factors, namely educational attainment, household income, marital status, and employment status, were associated with pre-pregnancy body mass index (BMI) categories, including severe-moderate underweight (BMI ≤ 16.9 kg/m2), mild underweight (BMI, 17.0-18.4 kg/m2), overweight (BMI, 25.0-29.9 kg/m2), and obese (BMI ≥ 30.0 kg/m2) among Japanese pregnant women using data from the Japan Environment and Children's Study (JECS). In total, pregnant women were included 96,751. Age- and parity-adjusted multivariable multinomial logistic regression analyses assessed socioeconomic factors and smoking associations with falling within abnormal BMI categories (normal BMI as the reference group). Lower education and lower household were associated with overweight and obesity, and, especially, lowest education and household income had relatively higher point estimate relative ratios (RRs) of 3.97 and 2.84, respectively. Regarding the risks for underweight, however, only junior high school education had a significantly higher RR for severely to moderately underweight. Regarding occupational status, homemakers or the unemployed had a higher RR for severe-moderate underweight, overweight, and obesity. Unmarried, divorced, or bereaved women had significantly higher RRs for mildly underweight status. Quitting smoking early in pregnancy/still smoking had higher RRs for all four not having normal BMI outcomes; however, quitting smoking before pregnancy had a higher RR only for obese individuals. Lower educational attainment and smoking are essential intervention targets for obesity and severe-moderate underweight prevention in younger women. Lower household income is also a necessary target for obesity.

Machine learning for genomic and pedigree prediction in sugarcane.

Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome prediction (GP) models of crops with highly heterozygous polyploid genomes. This study incorporates non-additive genetic effects and pedigree information using machine learning methods to track sugarcane breeding lines and enhance the prediction by assessing the degree of association between genotypes. This study measured the stalk biomass and sugar content of 297 clones from 87 families within a breeding population used in the Japanese sugarcane breeding program. Subsequently, we conducted analyses based on the marker genotypes of 33,149 single-nucleotide polymorphisms. To validate the accuracy of GP in the population, we first predicted the prediction accuracy of the best linear unbiased prediction (BLUP) based on a genomic relationship matrix. Prediction accuracy was assessed using two different cross-validation methods: repeated 10-fold cross-validation and leave-one-family-out cross-validation. The accuracy of GP of the first and second methods ranged from 0.36 to 0.74 and 0.15 to 0.63, respectively. Next, we compared the prediction accuracy of BLUP and two machine learning methods: random forests and simulation annealing ensemble (SAE), a newly developed machine learning method that explicitly models the interaction between variables. Both pedigree and genomic information were utilized as input in these methods. Through repeated 10-fold cross-validation, we found that the accuracy of the machine learning methods consistently surpassed that of BLUP in most cases. In leave-one-family-out cross-validation, SAE demonstrated the highest accuracy among the methods. These results underscore the effectiveness of GP in Japanese sugarcane breeding and highlight the significant potential of machine learning methods.

AI-assisted selection of mating pairs through simulation-based optimized progeny allocation strategies in plant breeding.

Emerging technologies such as genomic selection have been applied to modern plant and animal breeding to increase the speed and efficiency of variety release. However, breeding requires decisions regarding parent selection and mating pairs, which significantly impact the ultimate genetic gain of a breeding scheme. The selection of appropriate parents and mating pairs to increase genetic gain while maintaining genetic diversity is still an urgent need that breeders are facing. This study aimed to determine the best progeny allocation strategies by combining future-oriented simulations and numerical black-box optimization for an improved selection of parents and mating pairs. In this study, we focused on optimizing the allocation of progenies, and the breeding process was regarded as a black-box function whose input is a set of parameters related to the progeny allocation strategies and whose output is the ultimate genetic gain of breeding schemes. The allocation of progenies to each mating pair was parameterized according to a softmax function, whose input is a weighted sum of multiple features for the allocation, including expected genetic variance of progenies and selection criteria such as different types of breeding values, to balance genetic gains and genetic diversity optimally. The weighting parameters were then optimized by the black-box optimization algorithm called StoSOO via future-oriented breeding simulations. Simulation studies to evaluate the potential of our novel method revealed that the breeding strategy based on optimized weights attained almost 10% higher genetic gain than that with an equal allocation of progenies to all mating pairs within just four generations. Among the optimized strategies, those considering the expected genetic variance of progenies could maintain the genetic diversity throughout the breeding process, leading to a higher ultimate genetic gain than those without considering it. These results suggest that our novel method can significantly improve the speed and efficiency of variety development through optimized decisions regarding the selection of parents and mating pairs. In addition, by changing simulation settings, our future-oriented optimization framework for progeny allocation strategies can be easily implemented into general breeding schemes, contributing to accelerated plant and animal breeding with high efficiency.

Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean.

KEY MESSAGE: We proposed models to predict the effects of genomic and environmental factors on daily soybean growth and applied them to soybean growth data obtained with unmanned aerial vehicles. Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G × E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G × E and has been implemented in genomic prediction models, no reaction norm models have been applied to plant growth data. Here, we propose a novel reaction norm model for plant growth using spline and random forest models, in which daily growth is explained by environmental factors one day prior. The proposed model was applied to soybean canopy area and height to evaluate the influence of drought stress levels. Changes in the canopy area and height of 198 cultivars were measured by remote sensing using unmanned aerial vehicles. Multiple drought stress levels were set as treatments, and their time-series soil moisture was measured. The models were evaluated using three cross-validation schemes. Although accuracy of the proposed models did not surpass that of single-trait genomic prediction, the results suggest that our model can capture G × E, especially the latter growth period for the random forest model. Also, significant variations in the G × E of the canopy height during the early growth period were visualized using the spline model. This result indicates the effectiveness of the proposed models on plant growth data and the possibility of revealing G × E in various growth stages in plant breeding by applying statistical or machine learning models to time-series phenotype data.

A comparison of opioid dose between home palliative care and hospital palliative care.

OBJECTIVE: While opioids are a key part of palliative care, few studies have evaluated opioid demand in the home care context. This study aims to compare opioid usage in home care and hospital care settings. METHODS: This cross-sectional study retrospectively recruited patients receiving palliative care in home care and hospital settings, between November 2018 and October 2020. Opioid prescriptions were standardized to oral morphine equivalent (OME) doses at 7 and 14 days prior to death and analyzed. Additional analysis performed multivariable linear regression on the outcome of OME at 7 days, adjusting for medical setting and confounders in patients with opioid prescriptions. RESULTS: After 21 exclusions, 209 patients (48 home care and 161 hospital care) were eligible for analysis. The home care group had a higher mean age (74.8 years) and Palliative Prognosis Score (50), than the hospital group (70.1 and 40, respectively). Mean OME at 7 and 14 days before death was numerically higher in the home care group (72.8 mg/day and 53.0 mg/day, respectively) than the hospital care group (57.7 mg/day and 35.7 mg/day). Student's t-test produced p-values of 0.49 and 0.32, and the Wilcoxon rank sum test found p-values of 0.24 and 0.11 at 7 and 14 days, respectively. Multivariable regression analysis of the home care group found mean OME of 40.7 mg/day; 95% confidence interval [-0.62, 82.0 (mg/day)], p = 0.06. Additional analysis found a p-value of 0.06 for medical setting. CONCLUSIONS: We did not find a statistically significant difference in opioid use between home care and hospital care. However, the numerically higher rate of use in the home care group suggests that further research is warranted.

The Population-Attributable Fractions of Small-for-Gestational-Age Births: Results from the Japan Birth Cohort Consortium.

A fetal growth restriction is related to adverse child outcomes. We investigated risk ratios and population-attributable fractions (PAF) of small-for-gestational-age (SGA) infants in the Japanese population. Among 28,838 infants from five ongoing prospective birth cohort studies under the Japan Birth Cohort Consortium, two-stage individual-participant data meta-analyses were conducted to calculate risk ratios and PAFs for SGA in advanced maternal age, pre-pregnancy underweight, and smoking and alcohol consumption during pregnancy. Risk ratio was calculated using modified Poisson analyses with robust variance and PAF was calculated in each cohort, following common analyses protocols. Then, results from each cohort study were combined by meta-analyses using random-effects models to obtain the overall estimate for the Japanese population. In this meta-analysis, an increased risk (risk ratio, [95% confidence interval of SGA]) was significantly associated with pre-pregnancy underweight (1.72 [1.42-2.09]), gestational weight gain (1.95 [1.61-2.38]), and continued smoking during pregnancy (1.59 [1.01-2.50]). PAF of underweight, inadequate gestational weight gain, and continued smoking during pregnancy was 10.0% [4.6-15.1%], 31.4% [22.1-39.6%], and 3.2% [-4.8-10.5%], respectively. In conclusion, maternal weight status was a major contributor to SGA births in Japan. Improving maternal weight status should be prioritized to prevent fetal growth restriction.

Hokkaido birth cohort study in Japan on the growth trajectory of children born with low birth weight until 7 years of age.

BACKGROUND: Low birth weight (LBW) is a significant global health concern with potential health risks and developmental implications for infants. Catch-up growth, an accelerated growth following an inhibition period, may partially compensate for growth deficits in LBW children. AIMS: This study investigated the prevalence of LBW and catch-up growth in height, weight, and body mass index (BMI) among LBW children in Japan, identified factors associated with LBW, and explored the potential for catch-up growth at different ages up to seven years. STUDY DESIGN AND SUBJECTS: The Hokkaido birth cohort study included 20,926 pregnant Japanese women recruited during their first trimester from 37 hospitals and clinics. Follow-up assessments were conducted in children up to seven years of age, tracking LBW children's growth and development using the Maternal and Child Health Handbook, and providing valuable insights into catch-up growth patterns. OUTCOME MEASURES: LBW was defined as a neonatal birth weight of <2500 g. The primary outcomes were catch-up growth in height, weight, and BMI at different ages. Z-scores were calculated to assess growth parameters with catch-up growth, defined as a change in z-score (> 0.67) between two time points. RESULTS AND CONCLUSIONS: A LBW was prevalent in 7.6 % of the cohort, which was lower than that reported in other Japanese studies. Among LBW children, 19.3 % achieved catch-up growth in height by age seven, and 10.6 % in weight. Catch-up growth in LBW children could partially offset these deficits. Further research will help understand the long-term outcomes and inform interventions for healthy development.

The association between prenatal per-and polyfluoroalkyl substance levels and Kawasaki disease among children of up to 4 years of age: A prospective birth cohort of the Japan Environment and Children's study.

Kawasaki disease (KD) is common among pediatric patients and is associated with an increased risk of later cardiovascular complications, though the precise pathophysiology of KD remains unknown. Per- and polyfluoroalkyl substances (PFAS) have gathered notoriety as the causal pathogens of numerous diseases as well as for their immunosuppressive effects. The present epidemiological study aims to assess whether PFAS may affect KD risk. We evaluated research participants included in the ongoing prospective nationwide birth cohort of the Japan Environment and Children's Study (JECS). Among the over 100,000 pregnant women enrolled in the JECS study, 28 types of PFAS were measured in pregnancy in a subset of participants (N = 25,040). The JECS followed their children born between 2011 and 2014 (n total infants = 25,256; n Kawasaki disease infants = 271), up to age four. Among the 28 types of PFAS, those which were detected in >60 % of participants at levels above the method reporting limit (MRL) were eligible for analyses. Multivariable logistic regressions were implemented on the seven eligible PFAS, adjusting for multiple comparison effects. Finally, we conducted Weighted Quantile Sum (WQS) and Bayesian kernel machine regression (BKMR) to assess the effects of the PFAS mixture on KD. Therefore, we ran the BKMR model using kernel mechanical regression equations to examine PFAS exposure and the outcomes of KD. Upon analysis, the adjusted multivariable regression results did not reach statistical significance for the seven eligible substances on KD, while odds ratios were all under 1.0. WQS regression was used to estimate the mixture effect of the seven eligible PFAS, revealing a negative correlation with KD incidence; similarly, BKMR implied an inverse association between the PFAS mixture effect and KD incidence. In conclusion, PFAS exposure was not associated with increased KD incidence.

Assessing the Potential for Genome-Assisted Breeding in Red Perilla Using Quantitative Trait Locus Analysis and Genomic Prediction.

Red perilla is an important medicinal plant used in Kampo medicine. The development of elite varieties of this species is urgently required. Medicinal compounds are generally considered target traits in medicinal plant breeding; however, selection based on compound phenotypes (i.e., conventional selection) is expensive and time consuming. Here, we propose genomic selection (GS) and marker-assisted selection (MAS), which use marker information for selection, as suitable selection methods for medicinal plants, and we evaluate the effectiveness of these methods in perilla breeding. Three breeding populations generated from crosses between one red and three green perilla genotypes were used to elucidate the genetic mechanisms underlying the production of major medicinal compounds using quantitative trait locus analysis and evaluating the accuracy of genomic prediction (GP). We found that GP had a sufficiently high accuracy for all traits, confirming that GS is an effective method for perilla breeding. Moreover, the three populations showed varying degrees of segregation, suggesting that using these populations in breeding may simultaneously enhance multiple target traits. This study contributes to research on the genetic mechanisms of the major medicinal compounds of red perilla, as well as the breeding efficiency of this medicinal plant.

Prenatal metal concentrations and physical abnormalities in the Japan Environment and Children's Study.

BACKGROUND: The association between prenatal metal exposure and congenital anomalies is unclear. We aimed to examine the association between exposure to cadmium, lead, mercury, selenium, and manganese and physical abnormalities. METHODS: Data from 89,887 pregnant women with singleton pregnancies who participated in the Japan Environment and Children's Study (JECS) were used. The correlation between maternal blood metal concentrations and physical abnormalities during the second or third trimester was investigated using logistic regression models. Physical anomalies included those observed at birth or at 1 month, primarily from ICD-10 Chapter 17, particularly congenital anomalies associated with environmental factors (e.g., hypospadias, cryptorchidism, cleft lip and palate, digestive tract atresia, congenital heart disease, and chromosomal abnormalities) and minor abnormalities. RESULTS: After adjusting for covariates, the OR (95% CIs) of physical abnormalities for a one-unit rise in Mn concentrations in all individuals were 1.26 (1.08, 1.48). The OR (95% CIs) of physical abnormalities in the 4th quartile (≥18.7 ng/g) were 1.06 (1.01, 1.13) (p-value for the trend = 0.034) compared with those in the 1st quartile (≤12.5 ng/g). CONCLUSION: In Japan, maternal blood Mn concentrations above threshold during pregnancy may slightly increase the incidence of physical abnormalities. IMPACT: Physical abnormalities (including minor anomalies and congenital anomalies) are associated with prenatal manganese concentrations. They are not associated with cadmium, lead, mercury, and selenium concentrations.

Genetic Dissection of Seasonal Changes in a Greening Plant Based on Time-Series Multispectral Imaging.

Good appearance throughout the year is important for perennial ornamental plants used for rooftop greenery. However, the methods for evaluating appearance throughout the year, such as plant color and growth activity, are not well understood. In this study, evergreen and winter-dormant parents of Phedimus takesimensis and 94 F1 plants were used for multispectral imaging. We took 16 multispectral image measurements from March 2019 to April 2020 and used them to calculate 15 vegetation indices and the area of plant cover. QTL analysis was also performed. Traits such as the area of plant cover and vegetation indices related to biomass were high during spring and summer (growth period), whereas vegetation indices related to anthocyanins were high in winter (dormancy period). According to the PCA, changes in the intensity of light reflected from the plants at different wavelengths over the course of a year were consistent with the changes in plant color and growth activity. Seven QTLs were found to be associated with major seasonal growth changes. This approach, which monitors not only at a single point in time but also over time, can reveal morphological changes during growth, senescence, and dormancy throughout the year.