Is maternal caffeine intake associated with neonatal anthropometry?In this cohort study of 2055 women from 12 clinical sites, measures of caffeine consumption (plasma caffeine and paraxanthine and self-reported consumption) were associated with neonatal size at birth. Increasing caffeine measures were significantly associated with lower birth weight, shorter length, and smaller head, arm, and thigh circumference.In this study, caffeine consumption during pregnancy, even in amounts less than the recommended 200 mg per day, was associated with smaller neonatal anthropometric measurements. Higher caffeine consumption during pregnancy has been associated with lower birth weight. However, associations of caffeine consumption, based on both plasma concentrations of caffeine and its metabolites, and self-reported caffeinated beverage intake, with multiple measures of neonatal anthropometry, have yet to be examined.To evaluate the association between maternal caffeine intake and neonatal anthropometry, testing effect modification by fast or slow caffeine metabolism genotype.A longitudinal cohort study, the National Institute of Child Health and Human Development Fetal Growth Studies–Singletons, enrolled 2055 nonsmoking women at low risk for fetal growth abnormalities with complete information on caffeine consumption from 12 US clinical sites between 2009 and 2013. Secondary analysis was completed in 2020.CYP1A2*1F).Caffeine was evaluated by both plasma concentrations of caffeine and paraxanthine and self-reported caffeinated beverage consumption measured/reported at 10-13 weeks gestation. Caffeine metabolism defined as fast or slow using genotype information from the single nucleotide variant rs762551 , length , and head , arm , and thigh  circumference. Similar reductions were observed for paraxanthine quartiles, and for continuous measures of caffeine and paraxanthine concentrations. Compared with women who reported drinking no caffeinated beverages, women who consumed approximately 50 mg per day (~ 1/2 cup of coffee) had neonates with lower birth weight , smaller arm  and thigh  circumference, and smaller anterior flank skin fold . Results did not differ by fast or slow caffeine metabolism genotype.A total of 2055 participants had a mean (SD) age of 28.3 (5.5) years, mean (SD) body mass index of 23.6 (3.0), and 580 (28.2%) were Hispanic, 562 (27.4%) were White, 518 (25.2%) were Black, and 395 (19.2%) were Asian/Pacific Islander. Delivery occurred at a mean (SD) of 39.2 (1.7) gestational weeks. Compared with the first quartile of plasma caffeine level (≤28 ng/mL), neonates of women in the fourth quartile (>659 ng/mL) had lower birth weight (β = −84.3 g; 95% CI, −145.9 to −22.6 g; In this cohort study, small reductions in neonatal anthropometric measurements with increasing caffeine consumption were observed. Findings suggest that caffeine consumption during pregnancy, even at levels much lower than the recommended 200 mg per day of caffeine, are associated with decreased fetal growth. This cohort study examines the association between maternal caffeine consumption and multiple measures of neonatal anthropometry using data from the National Institute of Child Health and Human Development Fetal Growth Studies–Singletons study. However, in 1 meta-analysis,3 4 of 9 studies reported null or contrary results.7 These inconsistent associations may have been owing to the reliance of most studies on self-reported measures of caffeine intake.3 Coffee varies in its caffeine content based on preparation method, and serving size of caffeinated beverages may vary across respondents.8 Additionally, some studies of caffeine consumption did not control for important confounders such as smoking.9 Further, there are variations in individual caffeine metabolism, such that people with fast metabolism, those with a genetic variant leading to more rapid caffeine metabolism, may be at higher risk for adverse pregnancy outcomes, potentially because of higher exposure to paraxanthine, the primary metabolite in caffeine.11Caffeine consumption during pregnancy has been an ongoing topic of debate. As of 2010, the American College of Obstetricians and Gynecologists recommends that pregnant women limit caffeine consumption to less than 200 mg per day.11 Our aim was to examine associations of caffeine consumption, based on both plasma concentrations of caffeine and its metabolites and self-reported caffeinated beverage intake, with multiple measures of neonatal anthropometry. Our secondary aim was to evaluate whether the association between caffeine consumption and neonatal anthropometry may be moderated by genetic variations in fast vs slow caffeine metabolism.To our knowledge, no studies have examined the association between caffeine intake and neonatal anthropometric measures beyond weight, length, and head circumference, and few have analyzed plasma concentrations of caffeine and its metabolites or genetic variations in the rate of metabolism associated with neonatal size.Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies–Singletons (NCT00912132) was designed to prospectively assess fetal growth in a racially/ethnically diverse cohort of pregnant women.12 Nonsmoking women with low-risk pregnancies, body mass index  of 19.0-29.9, and no history of prepregnancy chronic conditions were enrolled at 12 US clinical sites between 8 and 13 weeks of gestation between 2009 and 2013.13 Secondary analysis was completed in 2020. Women were interviewed and provided blood samples. Fetal growth was tracked via ultrasonographic examinations across 6 visits. Of the 2334 women enrolled, we excluded 14 women found ineligible after enrollment, 186 with pregnancies that did not end in a live birth or with unknown birth outcomes, and 33 participants lacking information on plasma caffeine measures or self-reported caffeine consumption, leaving 2101 women and their neonates with self-reported caffeine consumption for analysis. For analyses using measured plasma caffeine and paraxanthine, we excluded 46 additional women who did not consent to have their blood samples used, leaving 2055 participants. Approval for human subjects research was obtained from the NICHD and the institutional review boards of all participating sites, and participants provided written informed consent. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.The 16 Head circumference was measured by placing a tape around the head anteriorly from the forehead above the eyebrows and posteriorly at the maximum protrusion of the occiput.18 Abdominal circumference was measured by placing the measuring tape on the abdomen cephalward of the umbilicus, perpendicular to the trunk’s long mid-axis.22 Mid-upper arm circumference and mid-upper thigh circumference were measured on the right side of the body with the tape held perpendicular to the long axis and at the mid-point of the limb. All measurements were taken at least twice. A third measurement was taken if either value was higher than a prespecified technical error rate.25 Skin fold measures of abdominal flank, anterior thigh, subscapula, and triceps were taken using calipers. For skin fold outcomes, we excluded 136 neonates from 1 clinical site that used the wrong calipers for measurement. We calculated percent fat mass using a validated formula that combines proportions of neonatal anthropometric measures [0.39055  + 0.0453  – 0.03237  + 0.054657].26 We examined percent fat mass as an outcome among neonates in whom the formula had been validated—those delivered at term (at least 37 weeks of gestation) or with birth weight greater than or equal to 2000 g (n = 1791). Small for gestational age was defined as birth weight below the tenth percentile for GA using the Duryea reference.27Birth weight was abstracted from medical records. Neonatal anthropometric measures were obtained generally within 1 to 3 days after birth  by research staff who were trained and credentialed in a standardized manner. Length was measured from the soles of the feet to the top of the head using an electronic infant scale .Blood was collected at enrollment, processed into plasma, and stored at −80 °C. A detailed description of caffeine and paraxanthine extraction and measurement is available in the eMethods in the 28 we converted servings per day to milligrams per day by multiplying the number of servings by the mean caffeine content of 8 ounces of coffee (96 mg), 8 ounces of tea (48 mg), 12 ounces of soda (40 mg), or 12 ounces of energy drink (108 mg) to create a summary variable. We calculated total caffeine consumption by summing milligrams per day from all caffeine sources.At enrollment participants reported whether they had consumed any caffeinated beverages in the past week  and how many cups (8 oz) or cans or bottles (12 oz or 16 oz) consumed per day. Less than 1 serving  was coded as half a serving. Using USDA guidelines for average caffeine content of each beverage,CYP1A2), which regulates caffeine metabolism.31 DNA was extracted from stored buffy coat specimens collected at enrollment and genotyping was performed using genotyping equipment .Participants were classified as having fast metabolism based on their genotype of AA and CC and slow metabolism based on their genotype of CA for the autosomal single nucleotide variant (SNV) rs762551 in the cytochrome P450 gene (Covariates collected at enrollment included age (years), prepregnancy BMI, race/ethnicity , parity , married or living with partner (yes/no), educational level , insurance type (private/managed care or other), and infant sex. For all outcomes except birth weight, we adjusted for number of days elapsed between delivery and neonatal examination.2 for categorical variables and t tests for continuous variables were conducted to evaluate demographic differences across caffeine concentration quartiles. Pearson correlation statistics were used to compare plasma caffeine and paraxanthine with self-reported consumption. Caffeine and paraxanthine concentrations represented machine-observed values and were analyzed as quartiles  and continuous exposures. Continuous measures were logarithm-transformed, due to skewness, and standardized after adding an appropriate positive value ) because of negative values produced during the measurement phase. We did not substitute 0 for negative values to minimize bias associated with constraining an exposure to a lower limit.33Descriptive analysis using χUsing adjusted generalized linear models, we tested associations between quartiles and continuous measures of caffeine and paraxanthine concentrations and their sum relative to each neonatal outcome. To assess nonlinearity of associations for continuous exposures, we modeled log-transformed exposures as restricted cubic splines with 5 knots. The β coefficients of these models represent the change in neonatal anthropometric measure per SD change in exposure. We assessed risk of SGA using adjusted log-linear regression models.13 in separate models, we tested for interactions between race/ethnicity and all caffeine measures for each outcome.Based on the distribution of self-reported caffeine consumption, we categorized participants as consuming no caffeine, consuming 1-50 mg per day, or consuming more than 50 mg per day, fitting models to test associations between self-reported first trimester caffeinated beverage consumption and neonatal anthropometric measures. To ensure that results were not being affected by women who consumed more than 2 cups per day (>200 mg), we performed 2 sensitivity analyses: first, we removed the individuals with the highest consumption (>200 mg/d) from analyses (n = 16), and second, we split the group who consumed more than 50 mg per day into 51 to 100 mg per day (n = 329) and more than 100 mg per day (n = 148).Because of observed race/ethnic differences in fetal growth from the NICHD cohort,CYP1A2 genotype on the associations between caffeine consumption and neonatal anthropometry, we coded 2-way interaction terms for all caffeine exposures and rate of caffeine metabolism. All interaction models were adjusted for genetic principal components generated from multidimensional scaling analysis to account for population structure. In consideration of allele frequency differences among racial/ethnic groups, we stratified models by race/ethnicity to avoid population-stratification bias in effect estimates. Caffeine metabolism analyses included only women with genetic information to determine genotype of the CYP1A2 gene (n = 1516).To test for potential moderation of caffeine metabolism P < .05 in 2-tailed tests. All analyses were conducted in SAS version 9.4 (SAS Institute Inc).Final models were not adjusted for plasma cotinine concentrations because it was not significantly associated with caffeine measures and did not change the results. Results were considered statistically significant at P < .001) and more likely to be non-Hispanic White , parous , and married   of women reported consuming no caffeinated beverages in the first trimester, while 751 of 2055 women (35.7%) reported drinking at least 50 mg  and 477 of 2055 women (22.7%) reported drinking more than 50 mg per day .A total of 2055 participants had a mean (SD) age of 28.3 (5.5) years, mean (SD) body mass index of 23.6 (3.0), and 580 (28.2%) were Hispanic, 562 (27.4%) were White, 518 (25.2%) were Black, and 395 (19.2%) were Asian/Pacific Islander. Delivery occurred at a mean (SD) of 39.2 (1.7) gestational weeks. There were no clear trends in demographic characteristics across quartiles, although women in the highest quartile (Q4) vs lowest quartile (Q1) were older  . Plasma P < .05 for trend) (P = .04 for trend), shorter length , and smaller head circumference , mid-upper arm circumference , and mid-upper thigh circumference . Similar negative associations were observed for the highest vs lowest quartile of paraxanthine, with an 83.7-g reduction in birth weight , shorter length , and smaller head , arm , and thigh circumference . Additionally, the third quartiles of paraxanthine concentrations were associated with shorter length , and smaller head circumference , and mid-upper arm circumference  compared with the first quartile. Similar results were observed when assessing quartiles for the sum, with smaller birth weight , shorter length , and smaller head , arm , and thigh circumference   . For cafP = .004), length , and smaller head , and arm circumference . For log paraxanthine, there was a decrease in birth weight , length , and smaller head , arm , and thigh circumference . For each SD increment increase in the log of the sum, there was a decrease in birth weight , length , and smaller head , arm , and thigh circumference   . Risk ofP = .04) . There wP = .04) .) and women who reported drinking more than 50 mg per day had neonates with lower birth weight  and smaller mid-upper arm circumference , mid-upper thigh circumference , and anterior thigh skin fold   consuming coffee and 41% (870 of 2101) consuming soda. Analyses were conducted based on reports at the enrollment visit, but caffeinated beverage intake was reported across visits and remained consistent for 98% of women in the second and third trimesters. Compared with women who reported no caffeinated beverage intake, women who reported drinking at least 50 mg per day had neonates with smaller subscapular skin folds  . ResultsIn testing interactions between metabolism rate and caffeine or paraxanthine quartiles, there were no significant interactions for any neonatal anthropometric measure indicating that the association between caffeine concentrations and neonatal anthropometry did not vary by rate of caffeine metabolism. Results were consistent for self-reported measures.In this cohort study of pregnant women with low caffeine consumption, even small increases in plasma caffeine concentrations and its major metabolite paraxanthine, were associated with lower birth weight, finding that smaller size was manifested by shorter length, and smaller head, arm and thigh circumferences at birth. The decreases in bone and muscle measures, but not skin folds and fat mass, may indicate decreases in lean tissue as caffeine consumption increases. Results were consistent with self-reported caffeine consumption, in which consumption of at least 50 mg  per day was associated with lower birth weight and smaller neonatal anthropometric measurements, even when excluding individuals who consumed higher amounts (>200 mg). Associations between caffeine and neonatal anthropometric measures did not vary by fast or slow caffeine metabolism.34 although associations with measured caffeine have not previously been established. Pooled statistics in these analyses demonstrated a graded risk in low birth weight that increases with each additional cup of coffee (100 mg) consumed per day, suggesting that even low amounts of caffeine consumption during pregnancy are associated with smaller offspring birth size. Similarly, we observed no threshold effect for caffeine consumption, as shown by associations between caffeine biomarkers and anthropometric measurements, and the finding that even low consumption of caffeinated beverages was associated with less lean tissue, which may have long-term implications for cardiometabolic risk.35 To our knowledge, few studies have explored caffeine consumption in association with neonatal anthropometric measures beyond birth weight and length.37 Our results are consistent with 2 studies that additionally explored head circumference,36 although our sample was larger, and our detailed measures specifically characterized changes in lean and fat tissue. In addition, we observed these associations in a cohort of pregnant women with low mean caffeine consumption (36 mg/d). Our findings are in contrast to null associations with neonatal weight, length, and head circumference observed in another study conducted in a low-consumption sample,37 however, that study included only 100 women, possibly lacking power to detect an association.Our findings of decreased birth weight and length associated with both plasma caffeine and paraxanthine concentrations and caffeinated beverage intake are consistent with meta-analyses that have reported a dose-response association between self-reported maternal caffeine consumption and the risk of low birth weight, SGA, and fetal growth restriction,38 Consistent with this finding, we observed higher risk for SGA in the fourth quartile of measured caffeine and paraxanthine concentrations, although results were not significant, likely because of low overall consumption in the sample. However, SGA is an extreme of birth weight, and does not describe incremental changes that may signal a negative association between caffeine and neonatal size. When evaluating outcomes continuously, a 2018 study noted linear decreases in birth weight, length, and head circumference with increasing self-reported caffeine consumption, as observed in our study.36 We were unable to directly calculate plasma caffeine or paraxanthine concentrations to the amount of caffeine consumed. However, it is estimated that among pregnant women in their first trimester, consuming 55 mg of caffeine translates to a mean blood caffeine concentration of 1859 ng/mL an hour after consumption.39 In the context of our study, when comparing the fourth with the first caffeine quartiles, a 630 ng/mL increase in caffeine concentration translated to an 84 g reduction in birth weight and a 0.44 cm decrease in length. Thus, our results indicate that even small increases in caffeine consumption in the first trimester may translate to reductions in neonatal anthropometric measures, and our findings were robust in multiple analyses.Other studies assessing caffeinated beverage intake instead of measured caffeine and paraxanthine concentrations have suggested increased risk of negative growth outcomes such as SGA and fetal growth restriction only after consumption of 200 to 300 mg per day.39 Because the fetus lacks CYP1A2 enzymes for metabolism, caffeine and paraxanthine accumulate in fetal tissues.40 Caffeine is hypothesized to alter fetal growth via disruption of neuroendocrine processes that cause uteroplacental vasoconstriction, hinder organ development, and permanently alter the stress response.40 In the long term, these disruptions may put offspring at higher risk for rapid weight gain after birth, childhood obesity, and chronic disease.42 Even low maternal caffeine intake (>50 mg/d) is associated with higher risk of excess growth in infancy and overweight in early childhood and altered fat deposition that may put children of caffeine consumers at higher risk of later cardiometabolic disease.43The long-term implications of our findings are unclear, considering the relatively small estimates we observed. Caffeine metabolism slows throughout pregnancy.44 we did not observe a modifying effect of caffeine metabolism. However, this null finding may, in part, be owing to the low level of consumption in our sample. To date, only 1 study has examined caffeine metabolism genotypes in the context of pregnancy, finding that differences in associations between caffeine and neonatal anthropometry only differed by metabolism rate among high-consumption groups (≥300 mg/d).10 Additionally, our sample was racially/ethnically diverse, which necessitated further stratification by race, limiting the power to detect a small effect. Although we used a validated SNV to define fast and slow metabolism, there are likely multiple genes involved in caffeine metabolism, and in a low-consumption sample, a single SNV may not be a sensitive indicator of metabolism alone.Although evidence supports high interindividual variation in the rate of caffeine metabolism,38 our self-reported measure included caffeinated coffee, tea, soda, and energy drinks. Another strength is the numerous, rigorously collected anthropometric measures, which allowed us to investigate associations with neonatal lean and fat measures. In addition, by limiting our sample to nonsmokers without chronic disease, we reduced unmeasured confounding in our analyses.A major strength of our study was the ability to investigate caffeine intake from multiple measures including plasma caffeine and paraxanthine concentrations, self-reported caffeine consumption, and genetic information on caffeine metabolism. Unlike previous studies which relied mostly on coffee consumption,45 there was low correlation between self-reported caffeinated beverage intake and plasma caffeine and paraxanthine in our sample, possibly because of variability in caffeine amounts from beverage intake,8 differential rates of metabolism, and lack of information on timing of last consumption. By using biomarker data, we overcame many of these limitations and recorded caffeine exposure from consuming certain foods, such as chocolate and decaffeinated beverages, which may contain small amounts of caffeine. We measured plasma caffeine and paraxanthine once in pregnancy, although it should be noted that self-reported caffeine remained constant for 98% of the sample in the second and third trimesters. This finding is consistent with other caffeine studies that found stable reported consumption across trimesters,5 and little variability in results based on timing of exposure.4 Thus, measuring caffeine in the first trimester may be a good proxy of consumption throughout pregnancy, but evaluation of caffeine biomarker changes across trimesters may be warranted in future studies.This study has limitations. Similar to other studies assessing first trimester consumption,1 may be associated with decreased fetal growth.In this cohort study, we observed small reductions in neonatal anthropometric measurements with increasing caffeine consumption. Our results suggest that caffeine consumption during pregnancy, even at levels much lower than the recommended 200 mg per day of caffeine


Offspring of mothers with gestational diabetes mellitus (GDM) have an increased risk of neonatal complications like birth trauma due to macrosomia or postnatal hypoglycemia, as well as long-term metabolic sequelae. Neonatal body composition may be a sensitive marker of metabolic effects on the fetus caused by suboptimal glycemic control during pregnancy.To determine body composition in offspring of mothers with GDM compared to a reference cohort of healthy term neonates and to assess whether increased body fat would be associated with postnatal hypoglycemia.This prospective, observational, cross-sectional study included 311 full-term, singleton infants born between June 2014 and July 2015. Body composition was measured within 96 h of birth using air displacement plethysmography. Results are indicated as median (1st Quartile – 3rd Quartile).p = 0.04), fat mass  as well as fat mass/total body mass  were significantly higher in infants following maternal GDM than in controls. In GDM offspring, anthropometric parameters, fat mass or BF% did not differ between infants with or without postnatal hypoglycemia. In this cohort, SDS for birth weight, fat mass, fat free mass, BF% or postnatal hypoglycemia were not associated with maternal blood glucose levels measured at an oral glucose tolerance test.Of 311 infants, 40 (12.9%) were born to mothers with GDM. Birth weight standard deviation scores (SDS) (0.24 vs. − 0.07, SDS for birth weight, neonatal fat mass, and BF% were significantly higher in newborns following maternal GDM. In these infants born to mothers with GDM, body composition did not differ between those with or without postnatal hypoglycemia. The prevalence of gestational diabetes mellitus (GDM) is increasing worldwide . CurrentThe Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study enrolling 23,316 participants showed strong continuous associations between maternal glucose levels and increased birth weight or cord-blood C-peptide levels . These fNeonatal adiposity may be an indicator of poor glycemic control during pregnancy, however, birth weight or body mass index (BMI) at birth correlate poorly with neonatal adiposity as indicated by considerable variability in neonatal body composition parameters such as fat mass (FM), lean mass (FFM) and the proportion of FM divided by total body mass (BF%) in neonates of similar weight and length , 16. ThuThe aim of this study was to determine body composition in infants of mothers with and without GDM using air displacement plethysmography (ADP) soon after birth and to assess, whether BF% or FM are associated with postnatal hypoglycemia. We used ADP as this is the gold standard for measuring neonatal body composition, is fast, non-invasive and without ionizing radiation, making it suitable for large epidemiological studies with high reproducibility and accuracy , 19.This was a prospective cross-sectional study of term, singleton infants (≥37 0/7 weeks of pregnancy) born between June 2014 and July 2015 at Tuebingen University Women’s and Children’s Hospital, Germany. Infants were recruited by the study team on the maternity ward if they fulfilled inclusion criteria , preferably on the day after birth, to enable measurements at the latest 96 h after birth. The aim was to address as many parents as possible, but recruitment was restricted by limited availability of the study team. Infants with major congenital anomalies  or severe disease  were excluded. Recruited neonates were divided by pregnancy history into a healthy reference group and those with evidence of maternal GDM as identified by the national GDM screening program.In Germany, a nationwide screening for GDM was introduced in 2012 for all pregnant women. In week 24–28 of pregnancy, a screening test involving oral administration of 50 g glucose is performed and, if the capillary glucose level exceeds 135 mg/dl (7.5 mmol/l) after 1 hour, followed by an oral glucose tolerance test . The diagnostic criteria for GDM applied herein are based on those from the International Association of Diabetes and Pregnancy Study Group (IADPSG)  = . The reference population was the British 1990 growth reference ) if normally distributed, or as median and interquartile range (1st Quartile – 3rd Quartile) if not. In case that within a table a minority of parameters were normally distributed, all data in that table were presented as median (Q1-Q3) to improve clarity of presentation. Between-group comparisons were performed using two-sided t-test or Wilcoxon test for non-normally distributed data and Fisher’s exact test for categorical outcomes. To assess correlations between AUC for maternal blood glucose levels during 75 oGTT and birth weight SDS, fat mass and BF% were assessed by linear regression and Pearson correlation coefficients were calculated. Analyses were performed with GraphPad Prism® 8.1.0  and the level of significance was There were 3170 deliveries at Tuebingen University Women’s Hospital during the one-year recruitment period; measurements of body composition using ADP could be performed in 311 healthy infants with gestational age > 37 weeks. These included a reference cohort of 271 healthy, singleton term infants of GDM-free mothers  and 40 2) compared to the reference cohort.Mothers with GMD had a significantly higher pre-pregnancy weight, pre-pregnancy BMI and were more often overweight or obese . There was no significant difference in anthropometric parameters between infants born during the study period and not included in the study and the reference study or GDM cohort. Demographic data of the study population are shown in Table SDS birth weight in infants born to mothers with GDM was significantly higher than in the reference cohort  and fat mass  was significantly higher than in the reference cohort (Table p = 0.036). Birth weight, SDS birth weight, BF% and fat mass were not different in both sex in the GDM cohort  was documented in 17/35 (49%) newborns of mothers with GDM during the first 24 postnatal hours, the age at the last measured hypoglycemia was 1 h (1 h–6 h) and the number of documented hypoglycemic blood glucose results was 1 [.5 mmol/lSDS birth weight, neonatal hypoglycemia count or BF% of infants born to mothers with GDM were not associated with the AUC of 75 g oGGT, see Fig. The aim of this cross-sectional observational study was to determine the difference in body composition in healthy singletons born to mothers with GDM compared to a reference cohort of neonates born to mothers without GDM. Furthermore, we wanted to investigate whether postnatal hypoglycemia in newborns of mothers with GDM is associated with neonatal body composition, since an elevated BF% could serve as a surrogate marker for suboptimal glycemic control during pregnancy.p = 0.02) and BF%  between infants of mothers with GMD compared to the reference cohort. These differences were statistically significant, but mean differences were small and probably of little clinical relevance. We are aiming to re-examine these children’s anthropometrics and body composition in subsequent years to verify whether the small difference at birth translates into clinically relevant differences later in life. In a meta-analysis of 10 studies using different body composition techniques , infants of mothers with all types of diabetes mellitus during pregnancy were found to have 83 g or 22% more pooled fat mass and 2.2% higher BF% after birth compared to infants of mothers with normal glucose tolerance [Significant differences were found in fat mass (370 g vs. 333 g, olerance . In contolerance . Our GDMIn our GDM cohort, there were no associations between birth weight, SDS birth weight/BF% and postnatal hypoglycemia. We had hoped that increased BF% could be used as a predictor for postnatal hypoglycemia in infants born to diabetic mothers, to enable better identification of those at greatest risk. Overall, infants born to mothers with all types of diabetes mellitus during pregnancy have a high risk of neonatal hypoglycemia , 13, 14.In our GDM cohort, SDS birth weight, BF%, and the number of hypoglycemic episodes were not associated with area under the curve of blood glucose levels following a standard 75 g oGTT. In a prospective study of 50 women with GDM, the fasting value of 100 g oGTT correlated with FM and BF% of their infants, who had their body composition measured via total body electrical conductivity . The HAPSex is known to be an important determinant of body weight and body composition at birth and also throughout life , 30, 31.In our reference cohort, a significantly higher median BF% and lower FFM was found in girls compared to boys. In newborns of mothers with GDM, BF% was not different between boys and girls. Girls’ body composition in the GDM and reference cohort did not differ, but the BF% and FM in the GDM cohort were significantly higher in boys than in the reference cohort. More boys were found in the group with hypoglycemia, but this was not significant.As previously published, BF% and FM were significantly associated with maternal BMI before pregnancy in our reference cohort . In agreA strength of this study is its rather large sample size of the reference cohort, which was representative of all newborns born during the study period. That 12.6% of mothers had GDM is within the expected range for Germany . A limitAdditional information, e.g., detailed daily blood glucose profiles, maternal food intake or physical activity during pregnancy would have been helpful in retrospect to identify further important factors related to body composition at birth.This cross-sectional study demonstrates that singleton neonates from mothers with GDM who were managed on the maternity ward had increased BF% values compared to healthy term controls. Newborns of mothers with GDM who suffered from hypoglycemic episodes showed no significant differences in body composition compared to those without hypoglycemia, but this finding may be biased by recruitment occurring only on the maternity ward, thereby excluding infants needing i.v. dextrose. Furthermore, the incidence of hypoglycemia was not associated with the AUC of maternal glucose levels during the 75 g oGTT. Continued monitoring of body composition in this cohort of offspring of mothers with GDM into child- and adulthood is desirable to investigate long-term effects.


Tobacco exposure remains a significant issue for public health, especially for pregnant women. It increases the risk for premature labor, low birth weight and small for gestational age (SGA), among other effects. To reduce these risks, many countries have enacted public policies to curb tobacco exposure. Peru enacted anti-tobacco laws that forbid smoking in public places, require prevention text and images in products and publicity, along with restriction of sales to adults. We evaluated the effect of the implementation of this law on newborn outcomes: birth weight, prematurity and SGA.This was a quasi-experimental study that utilized data from the Peruvian Live Birth Registry. Children born to mothers from urban areas were the intervention group, while children born to mothers from rural areas were considered the control group. Only singletons with information on birth weight and gestational age, born to mothers aged 12 to 49 years were included in the study. In addition, newborns with birth weights greater than + 4 standard deviations (SD) or less than − 4 SD from the gestational age-specific mean were excluded. To measure the effect of legislation on birth weight we performed a difference in differences analysis.A total of 2,029,975 births were included in the analysis. After adjusting for characteristics of the mother and the child, and contextual variables, the anti-tobacco law in Peru reduced the incidence of prematurity by 30 cases per 10,000 live births (95% CI: 19 to 42).The reform had negligible effects on overall birth weights and on the incidence of SGA. This modest result suggests the need for a more aggressive fight against tobacco, prohibiting all types of advertising and promotion of tobacco products, among others measures. During the last century, heavy advertising was associated with a significant increase in tobacco consumption and exposure to tobacco smoke. By the end of the twentieth century, smoking was considered a worldwide pandemic .Tobacco is well known as a risk factor in the development of many diseases –8, and pTobacco exposure is associated with preterm birth, small for gestational age (SGA) and low birth weight, which can result in chronic disease and infant death –18. BeinIn order to reduce these risks, many countries have created public policies to protect the population from exposure to tobacco, including banning smoking in public places, the inclusion of images and warning phrases about the health effects of tobacco consumption on cigarette boxes, increasing cigarette taxes, etc. . Many stAnti-tobacco laws have shown positive effect on hospitalizations for respiratory tract infection and asthma in children –30. LikeIn Latin American countries, laws have also been created to reduce tobacco exposure. In Peru, a series of restrictions was passed into law after 1991 –52. As aLike most Latin American countries, the anti-tobacco laws in Peru have not been formally evaluated with respect to an objective health outcome. We are therefore interested in evaluating the effect of this policy on birth weights, prematurity and SGA.The study design was quasi-experimental, due to the non-random nature of the intervention. We utilized data from the Peruvian Live Birth Registry, from 2005 to 2016. Children of mothers residing in urban areas were considered the intervention group, since bars and restaurants in urban areas would be affected by the new smoke-free regulations. These exposed children were compared to children of mothers residing in rural settings, where there would be few public places affected by the new prohibitions and little enforcement of the new law. Between 2016 and 2018, district and national level authorities in Peru carried out at least 175 unannounced visits to restaurants with the objective of enforcing anti-tobacco laws. None of these enforcements were carried out in rural areas –55. LikeRegistration of all births is mandatory in Peru . RegistrWe considered the following inclusion criteria: single births, with complete information on birth weight and gestational age, as well as information on the co-variables of interest, and born from mothers between 12 and 49 years of age. We also excluded birth weights deviating more than 4 standard deviations from the corresponding mean for each recorded gestational age , using aIn 2003, the World Health Organization (WHO) developed the “Framework Convention on Tobacco Control” (FCTC) to “protect present and future generations from the devastating health, social, environmental and economic consequences of smoking and exposure to smoke.” So far, 181 countries have adhered to this Convention, committing themselves to generate national strategies, plans and policies to reduce exposure to tobacco .– Sale forbidden to people under 18 years old”; finally, in chapter 4) advertising, promotion, and sponsorship of tobacco, the law determines that all advertising of tobacco products must include warning phrases about the health effects of smoking, and that tobacco brands are prohibited from sponsoring events aimed at minors. The law also included penalties if the established regulations were not obeyed [Peru also signed the CMCT in 2004. Prior to this agreement, there were already some laws to reduce exposure to tobacco in this country –50, but t obeyed . Finallyt obeyed .In Peru, the implementation of new laws requires the approval of regulations, technical standards, which are detailed in supplementary support 2008 law: Coded as “1” for births occurring after July 5th, 2008 (date of publication of the regulation for the law No. 28705) from mothers residing in urban areas and “0” otherwise.Birth weight in grams, (ii) Preterm birth, coded as “1” for gestational age less than 37 weeks and “0” otherwise, and (iii) Small for gestational age, coded as “1” for birth weight lower than the corresponding 10th percentile for gestational age and “0” otherwise, using as the reference the data on Canadian births [We evaluated three outcome variables using data from the Peruvian Live Birth Registry: (i) n births , 64.The Registry did not contain information about the method of ascertaining gestational age . We consulted health personnel in charge of the database, and found that clinical estimates were based on information available, whether reported last menstrual period, ultrasound or Capurro Method at the discretion of the attending physician.Other factors included in the analysis were: maternal factors , child factors , district level covariates . The 2011 official classification was used to define the urban/rural status of the district. Districts were classified as rural when their municipalities were not located within the district, or when more than 50% of their population live in rural areas . The perData were analyzed using STATA 15.1 . Records with missing data were for gestational age, birth weight, place of birth, parity, education of mother, and gender of newborn were excluded from the analysis. We first explored urban/rural differences in the outcomes and co-variables of interest. We assessed parallel pre-intervention trends in the treated and control group prior to carrying out analyses for the three outcomes .Covariatesid= maternal characteristics: age, marital status, level of education, parity; and child characteristics: gender, year of birth, place of delivery, person that assisted the delivery.εid= non-observed characteristics of the district.μd = non-observed characteristics of the child.β3 = effect of the anti-tobacco law on birth weight. A positive sign of the coefficient would correspond to a gain in birth weight.Where:The outcomes for the second and third models were prematurity  and SGA. They were evaluated using mixed effects logistic regression. The average marginal effects were estimated in order to obtain the effect of the anti-tobacco law on the absolute scale. The mixed effects logistic differences in differences model used is:p = probability of prematurity or SGA.Where:The remaining variables correspond to those presented in eq. 1, except altitude. Altitude was analyzed as a linear and quadratic term for the prematurity model and as a linear term for SGA , as required for the validity of the difference in differences analysis.After excluding births prior to 2005 and after 2013, 2,729,681births remained for the second part of the analysis. The average birth weight and prevalence of prematurity were lower in rural areas compared to urban areas, whereas the percentage of SGA in rural areas was higher than in urban areas. Among characteristics of the child, there were a higher percentage of births assisted by health professionals in urban areas, as well as a higher percentage of institutional deliveries. In terms of maternal characteristics, a higher level of education was observed in urban areas. Poverty levels and altitude were higher in rural areas  in birth weight and a negligible reduction in prevalence of SGA , and again a significant reduction in the proportion of prematurity by 25 cases per 10,000 live births .The data analyzed from the database of live births in Peru, shows that the 2008 and 2010 anti-tobacco laws in Peru do not have a discernible effect on birth weights and proportion of SGA, however, we demonstrated a modest effect of these laws in reducing the proportion of premature births by approximately 30 cases per 10,000 live births.We identified five studies that reported similar results regarding negligible effects on birth weights after implementing anti-smoking laws in Norway, Ireland, two studies in the United States, and Uruguay , 46, 68.With regards to the premature births proportion, eight studies in Belgium, Scotland, Ireland, Spain, England, Canada, and in two USA states found a positive effect, like our study –42, 45. As in the case of our study, w study showing no effect in the proportion of SGA, in the United States , while sIn countries where the studies identified an effect of the anti-tobacco laws on birth weights, preterm delivery or SGA, smoking is generally more common than in Peru. The average number of cigarettes smoked per person per year for these countries is: 2060 for Hungary, 2441 for Belgium, 828 for Scotland, 976 for Ireland, 1017 for the USA, 828 for England, 1460 for the Netherlands, 1021 for Canada, and 1499 for Spain . This fip-value decreases, but nonetheless we think that this value is under-adjusted. With respect to preterm birth when child-level covariates are not adjusted, this estimate becomes 28 prevented cases per 10,000 births, which is a negligible change. With the 6 months lag, this gets even stronger, to 44 prevented cases per 10,000 births. At a 1 year lag, this drop to 13 prevented cases per 10,000 births, but remains statistically significant. For SGA when child-level covariates are not adjusted, this becomes 3 more cases per 10,000 births, not distinct from 0, but with the 6 months lag increases SGA cases significantly. At a 1 year lag, this drops to 23 prevented cases per 10,000 births (see supplementary support The use of rural areas as a control group could be a relative limitation. It was used under the assumption that the law would have little effect in these areas, as there is no much smoking in these areas, and there are fewer closed spaces that could be considered public areas. There is also no active enforcement of these laws outside of major cities in Peru , 54, 57.The main strength of our study is the use of national databases, with a high coverage that went from 53.74% in 2005 to 83.40% in 2016 , which aThe implementation of anti-tobacco laws in Peru was associated with a small but significant reduction in the frequency of prematurity. In spite of our modest results, the abundance of data supporting the positive effect of anti-tobacco laws on delivery outcomes means that at least a subpopulation of children born from high risk women likely benefitted from the anti-tobacco laws in Peru.The evidence found in this study justifies the implementation of anti-tobacco laws for the benefit of public health, with the promotion of 100% smoke-free environments. This should include the fight against smoking, prohibiting all types of advertising, promotion of tobacco products, as well as the sponsorship of all kinds of activities or events, to prevent the initiation of smoking among children and adolescents.Additional file 1. Supporting Information.


Electrophorus electricus) to paralyze prey muscles and the use of venoms that paralyze prey by disrupting the neuromuscular junction. The eel’s strategy depends on the recently discovered ability of eels to activate prey motor neuron efferents with high-voltage pulses. Usually, eels use high voltage to cause brief, whole-body tetanus, thus preventing escape while swallowing prey whole. However, when eels struggle with large prey, or with prey held precariously, they often curl to bring their tail to the opposite side. This more than doubles the strength of the electric field within shocked prey, ensuring maximal stimulation of motor neuron efferents. Eels then deliver repeated volleys of high-voltage pulses at a rate of approximately 100 Hz. This causes muscle fatigue that attenuates prey movement, thus preventing both escape and defense while the eel manipulates and swallows the helpless animal. Presumably, the evolution of enough electrical power to remotely activate ion channels in prey efferents sets the stage for the selection of eel behaviors that functionally “poison” prey muscles.In this paper, I draw an analogy between the use of electricity by electric eels ( My goal in this paper is to highlight the remarkable similarities between one particular form of an eel’s electrical attack  and the Electric eels give off two types of discharges ,12,13,14However, advances in multi-channel data recording and high-speed video systems have allowed for a detailed examination of these predator-prey interactions . One of With this new view of the eel’s effect on prey, it is helpful to invoke a metaphor. Namely, what is seen on the tension traces by an experimenter is also “seen” by natural selection. With such control over muscle activity, one can easily imagine how an experimenter in a physiology laboratory might devise a strategy to disable the prey’s muscles for a period of time. The same could be said for natural selection acting on eel behavior. The strategy is to deliver multiple trains of high-frequency stimulation to the motor neuron efferent fibers—a procedure commonly used for research , and forBefore describing the details, it helps to put this unique behavior in context by briefly outlining a more typical electric eel attack. Electric eels are gape-limited predators that immobilize nearby animals with a high-frequency volley of high voltage, which causes tetanus  through It is in these difficult cases that eels essentially “poison” prey muscles in a manner analogous to venom. First, the eel grasps any part of the struggling prey tightly in its jaws, and then curls into a circle to bring the negative pole of its huge electric organ to the opposite side of the animal . Thus poIn addition to confirming the predicted electrical effect of curling, these experiments also serendipitously revealed how eels “meter” their metabolically expensive high voltage. The trigger for the initiation of curling behavior, and the cue for determining the duration of the behavior, is prey movement. For example, jiggling the wires to cause the electrodes to simulate prey movement elicited curling and the accompanying high-voltage volleys. In the absence of movement, the eel would uncurl and attempt to swallow the prey, whereas additional movements would elicit additional curls and high-voltage volleys . Note that this finding is very similar to the description provided by Wigger and colleagues  that spiThe key aspect of this curling behavior, and the focus of this paper, is the details of the high-voltage delivery and its subsequent effect on prey. Once curled, an electric eel gives off repeated volleys of high-voltage discharges at a rate of roughly 100 Hz. This kind of stimulation regimen is commonly used in the laboratory in order to study fatigue . In the To test this possibility, an experiment was arranged that simulated the electric eel’s output when curled . Figure Given that an electric eel already has a grip on prey when this paralyzing technique is used, one might wonder why it is necessary in the first place. The answer becomes obvious when natural predatory encounters are observed, and the limitations on the eel’s prey handling ability are considered. Two types of encounters serve to highlight the utility of the behavior: (1) small electric eels feeding on fish, and (2) larger electric eels feeding on more difficult prey.Fish are often initially captured lengthwise, or held precariously , making them difficult or impossible to swallow without repositioning. Although eels are able to firmly grasp prey, unlike a snake, they cannot manipulate prey without releasing their grip entirely. Fish often escape in the process . The solLarge electric eels also use this technique when presented with particularly difficult prey. For example, when large crayfish with large claws were offered to a large electric eel, the eel invariably used the curling technique and extended the duration of the curled configuration for nearly a minute, all the while delivering repeated volleys of high-voltage pulses as described above . BecausIt is possible that, in addition to inducing involuntary fatigue, large electric eels with the most powerful electric organs cause additional disruptions to the nervous system and muscles, which help to incapacitate prey. However, this does not detract from the venom analogy, as many venoms are aptly described as a “witch’s brew” of components  that work together to efficiently immobilize prey—or on other occasions, to deter potential predators. There is no doubt, for example, that the purpose of a different electric eel behavior—the leaping defensive shock ,26—functIn closing, I do not suggest that the eel’s electrical attack falls under the technical umbrella of venom. Rather my goal is to highlight the remarkable similarity in functions that have evolved from a similar underlying substrate—the ability for one animal to remotely activate the ion channels of another. Nature is replete with organisms that achieve this result through chemical means, and it is remarkable that the same end can be achieved with electricity.


In this work a simulation study is performed to gain insights in the patterns of induced radiofrequency (RF) currents for various implant‐like structures at 1.5 T. The previously introduced transfer matrix (TM) is used to determine why certain current patterns have a tendency to naturally occur. This can benefit current safety assessment techniques and may enable the identification of critical exposure conditions.The induced current on an elongated implant can be determined by multiplication of the incident electric field along the implant with its TM. The eigenmode spectrum of the TMs for various lengths and various types of implants are determined. The eigenvector with the highest eigenvalue describes the incident electric field pattern that induces the highest current which in turn will lead to highest heating. Subsequently, a statistical probability analysis is performed using a wide range of potential incident electric field distributions in a representative human subject model during a 1.5 T MR exam which are determined by means of electromagnetic FDTD simulations. These incident electric field distributions and the resulting induced current patterns are projected onto eigenvectors of the TM to determine which eigenmodes of the implant dominate the current patterns.The eigenvectors of the TM of bare and insulated wires resemble sinusoidal harmonics of a string fixed at both ends similar to the natural‐current distribution on thin antennas(1). The currents on implants shorter than 20 cm are generally dominated by the first harmonic . This is firstly because for these implant lengths (relative to the RF wavelength), the first eigenvalue is more than three times bigger than the second showing the ability of an implant to accommodate one eigenmode better than another. Secondly, the incident electric fields have a high likelihood  to project predominantly on this first eigenmode.The eigenmode spectrum of the TM of an implant provides insight into the expected shape of induced current distributions and worst‐case exposure conditions. For short implants, the first eigenvector is dominant. In addition, realistic incident electric field distributions project more heavily on this eigenvector. Both effects together cause significant currents to always resemble the dominant eigenmode of the TM for short implants at 1.5 T. One of the main safety risks is the hazardous interaction of the implant with the transmit RF field of the MRI system. Conductive structures  when exposed to the RF electric field component of the RF field tangential to the implant will accommodate currents.This tip heating can be calculated from the incident RF electric field with an implant characteristic called the transfer function(TF).The extension of the TF to the TM was originally developed for MR based TF determination.Firstly, the TMs of various elongated implant structures will be determined through electromagnetic simulations. Subsequently, the eigenmode spectrum of these TMs is calculated. Secondly, realistic incident electric field distributions inside a representative patient model, which has been extensively researched,22.AM¯¯, relates the induced current pattern over the implant (described by vector I¯) to the incident electric field along the implant (described by vector E¯) that caused it, through:The TM, I¯ and E¯ are distributions discretized to 5 mm resolution of respectively the induced current in the implant and the tangential component of the electric field incident on the implant. This corresponds to the resolution with which the TM is resolved. Bare and partially insulated wires of various lengths are investigated. Their TMs are computed from electromagnetic simulations where currents are induced in the various implants by electric field sourcesThe vectors Note that the simulation method of subsequently repositioned localized electric field excitations for TM determination used here is equivalent to the piecewise excitation method7 S/m. The 0.5mm thick insulation layer has a relative permittivity of 3 and is nonconductive. These generic structures were used in this exploration because their TMs will vary significantly and show different responses to an incident electric field. Furthermore, the bare and insulated wires of 20cm length have known properties and have been used in other works.The conductive parts of the wires are made of copper with a conductivity (at 64MHz) of 5.8·10LU factorization. These eigenvectors give the characteristic eigen current modes in the implant. One can always decompose (part of) the incident electric field in the first k eigenvectors  of the TM. Therefore,Once the TM of an implant has been simulated, the eigenvalues and eigenvectors of this matrix of size NxN are determined with MATLAB  using vi stands for the ith eigenvector of M¯¯. αi is the coefficient in the eigenvector decomposition. It is computed by taking the normalized inner product of the corresponding eigenvector with the incident field, i.e. vi¯·Einc¯/Einc¯. Er¯ is the part of the electric field that is not decomposable into the first k eigenvectors of the TM and is generally extremely small after inclusion of only a few eigenvectors. Hence,Here It is clear that particularly the eigenvector with the highest eigenvalue has the potential to manifest itself as induced current pattern when it is ‘excited’ because the contributions of the various eigenvectors are weighted by the corresponding eigenvalue. This occurs when the inner product between the electric field and this eigenvector is large. The first two eigenvectors of the various structures are shown in Table 2.BAnother FDTD electromagnetic simulation is performed to determine realistic incident electric field distributions. These distributions are determined from a simulation of the “Duke” model from the Virtual FamilyzE along the implant is extracted. To speed up this process the electric field inside the Duke model is linearly interpolated to a 2 × 2 × 5 mm resolution. The resolution in the z‐direction corresponds to the resolution of the TM, i.e. the width of the plane wave boxes used in the piecewise excitation method. For all positions, the wires are assumed to be z‐oriented because this configuration will for the application presented here generally lead to worst case exposure conditions and are furthermore most likely to occur for linear implants. In some regions other components of the electric field distribution inside the “Duke” model will also be significant, but since only z‐directed implants are considered in the analyses presented here these components will not induce currents. All connected Ez field distributions of 10 cm, 20 cm, 30 cm, and 40 cm length along the z‐axis inside the human model are extracted from the interpolated electric field, i.e. the complex Ez values inside, respectively, all 20, 40, 60, and 80 voxel stacks in the z‐direction within the Duke model for the 10 cm, 20 cm, 30 cm, and 40 cm wire create the distribution of Einc fields. The result is a collection of millions of possible incident E‐fields along an implant. Despite being obtained from a single simulation, this large number is assumed to be a representative collection of potential electric field exposures. The discussion section will present a verification of this assumption.From the electric field distribution inside the Duke model, that is predominantly z‐oriented, the potential electric field exposures along the implant are extracted. For this purpose, the complete set of possible positions of the implant within the model is evaluated and for each position the incident electric field 2.CEz¯ distributions will result in the corresponding current patterns. Note that the highest average Ez¯ does not necessarily correspond to the highest current.The TM is applied to gain insight into the current patterns that can be excited on an implant given the collection of potential incident electric field exposures. Multiplication of the transfer matrix with all possible incident vi¯ onto a particular Ez¯ distribution will be denoted with αi, i.e. vi¯·Einc¯\Einc¯. This projection purely displays how efficient an external field can excite certain current modes and contains no information on the ability of an implant to accommodate a certain mode.Next to a direct calculation of the currents on the wires a more detailed analysis of the resulting current patterns is performed based on the eigenvectors of the TM. Firstly, all the electric field distributions are decomposed into the eigenvectors of the TM to identify modes that are ‘excited’ more heavily. The projection of eigenvector Ez¯ with the TM the actual contribution of a particular eigenvector to this distribution will be denoted by βi, i.e. vi¯·Iind¯\Iind¯. In these projections the weighting due to the magnitude of the corresponding eigenvalue are incorporated. The magnitude of the eigenvalue is a quantitative measure of the ability of an implant to accommodate the corresponding eigenvector.Secondly, after obtaining the induced current on the wire by multiplication of 3Figure st eigenvalue that is three or more times bigger than the 2nd eigenvalue. For the 20 cm and 10 cm bare wires the second eigenvalue is only 27% and 33% of the first eigenvalue respectively. In the other structures this dominance is also found with an exception of the bare wire connected on one end to a conductive block, which has a second eigenvector that is 57% of the first. The dominant first eigenvalue shows that one specific current pattern  will have the largest contribution to the overall current distribution. The dominance of certain modes depends on the length of the implant and the wavelength of the RF field in the medium and is related to resonance effects.Table 1+max of the birdcage coil to 30 μT in the center of the imaging domain for this particular simulation.After the simulation of the incident electric field in the Duke model in total 2.83 million electric field exposures of 10 cm, 2.27 million electric field exposures of 20 cm, 1.85 million electric field exposures of 30 cm, and 1.53 million electric field exposures of 40 cm are extracted. When integrating the electric fields over the implant length, the highest electric potentials are 268V, 445V, 572V, and 657V for 10 cm, 20 cm 30 cm, and 40 cm length respectively after scaling the Bα1 and α2. The ratio between α1 and α2 indicates how strongly an incident electric field couples to the first eigenvector with respect to the second. The distributions of these ratios for all possible electric field exposures are shown in Fig. α2/α1 equals 1. The large majority of the potential incident electric field distributions results in a α2/α1 ratio smaller or much smaller than 1 (i.e. the first eigenvector is dominant) with a maximum likelihood around α2/α1= 0.176 and 0.171 for the bare and insulated wire respectively. Only in 1.7 and 1.8% of the cases, for the bare and insulated wires, respectively, the projection of the incident field on the second eigenvector is larger.These electric field distributions are projected onto the first and second eigenvector of the TM of the various implants. The projections are denoted by α2>α1, are respectively only 73%, 47%, 39%, and 41% of the highest electric potentials in general. So even though it can happen that the second mode is excited more effectively by an incident field then this will not correspond to the worst‐case exposure.Looking at all histograms, it is clear that for all wire lengths exciting the first eigenvector is more likely than exciting the second eigenvector. This likelihood goes down with increasing implant length from ca. 98% for 10 cm wires to 81% for 40 cm wires. On top of this, in the less likely situation that the incident electric field excites the second eigenvector stronger, the amplitude of that electric field distribution tends to be lower: the worst‐case electric fields that have a larger projection onto the second eigenvector (of a bare wire) compared to the first, i.e. β1 and β2, describe to which eigenvector the induced currents are more similar. These values will be influenced by both the effectiveness of the electric field in exciting a certain eigenvector and the ability of the implant to support this mode. In Fig. β2/β1. In these scatter plots each point represents one potential incident electric field distribution . The horizontal position of each point represents the β2/β1 ratio. If β2/β1< 1 the current distribution will be more like the first eigenvector. If β2/β1> 1, the current will be more like the second eigenvector or contain even contributions from higher eigenvectors. The vertical position of each point represents the largest current amplitude along the wire for this incident electric field distribution. For 10 cm wires, the large majority of potential incident electric field distributions results in a current distribution along the wire that is highly similar to its first eigenvector (β2/β1<< 1). Only a very small minority of the potential incident electric fields realizes current distributions along the implant that are more similar to the second eigenvector. However, in these situations the maximum amplitude of the induced current is rather low. From Fig. β2/β1> 1. For all lengths, the amplitude of current distributions that are more similar to the second eigenvector are well below the maximum amplitude of induced currents in general. In addition, this situation is less likely to occur. The distribution of current maxima is on the verge of crossing the β2/β1 = 1 line for the 40 cm bare wire. This indicates that at 1.5 T wires longer than approximately 40 cm will start to have a dominant eigenvector with two current maxima.Multiplication of the incident electric fields with the TM results in the total induced currents on the implant. The current distributions are, like the incident electric fields, projected onto the first two eigenvectors of the TM. These projections, denoted by 4Numerical simulations are performed to determine the TM of various implants. These TMs are subsequently multiplied with a large number of potential incident electric field distributions. These distributions are obtained by extracting the electric field values along the z‐directed wire for all physically possible positions inside a human model. The electric field distribution inside this model is also determined by numerical electromagnetic simulations. The resulting current distributions always tend to appear in a pattern similar to the first dominant eigenvector of the TM of this implant. This especially holds for implants shorter than 20 cm. The dominant eigenvectors are always current patterns with one maximum along the implant for the lengths and implants investigated in this work. Note that for longer implants and/or larger frequencies, it is likely that the largest eigenvector may in fact show two maxima and the second eigenvector only has one. This essentially interchanges the eigenvectors when ordered based on the magnitude of the corresponding eigenvalue. The first and second eigenvectors might both contribute significantly for longer implants. The current in the implant can always be decomposed into the eigenvectors of the TM, but generally since the eigenvalues decrease quickly in magnitude a decomposition with only the first few eigenvectors leads to an accurate approximation. It should still be verified that the dominance of one eigenmode found for the generic models investigated in this work holds for other  implants. Particularly the implant with the IPG already at a shorter length has a relatively big contribution from higher eigenmodes. The IPG was simulated as a conductive block shorted with respect to the lead, which corresponds to the situation where induced currents in the lead are guided towards the casing of the IPG. Depending on how the currents are routed in a particular implant this behavior might change which will have an effect on the eigenmode spectrum of the transfer matrix. The relative dominance of certain eigenmodes will therefore be dependent on the characteristics of the connection between the lead and the IPG.Knowledge of the TM of an implant is a prerequisite to determine eigenvector resonances and the corresponding current distributions. If an implant with unknown characteristics is present this knowledge might be available or attainable by means of simulationsThe TM of the implants was determined through simulations in a background of high permittivity medium as described in safety standard.In this work the electric field distribution inside a human subject is only simulated for one specific model at one specific imaging position in one specific RF coil. Extensions to more imaging positions of the Duke model and the Ella model are performed to investigate if the found results are representative for other scenarios. For these 8 simulations again, the electric field distributions for every possible z‐oriented location (in a discretized sense) are extracted and decomposed into the eigenvectors of the various investigated structures. The distributions of fractions between the first and the second coefficient in this eigenvector decomposition are displayed in Fig. Future work should verify if the millions of incident electric field distributions extracted from these situations are representative for even more imaging positions, body models, various coils, and/or implant orientations.5The eigenvectors of the transfer matrix of wire‐like implants show similarities to naturally occurring current patterns on thin dipole antennas resembling sinusoidal standing wave patterns. The endpoints of these dipoles are grounded (open) when attached to a ‘large’ conductive component like an IPG and closed when embedded in a poor conductor like human tissue. This eigenvector spectrum explains why currents induced on short (L ≲ 20 cm) implants have a tendency to appear in one specific pattern. Firstly, the relatively large first eigenvalue shows dominance of the first eigenvector. This is a reflection of the ability of an implant to support one current pattern better than another. The second reason is the likelihood of realistic RF exposures to match these modes. A large set of potential incident electric field distributions was used to show that the large majority of these potential incident fields projects most strongly on the first eigenmode. For all tested implants over 80% of electric field distributions excites the dominant mode more heavily (a number which increases up to 98% for 10 cm wires).If, by coincidence, the second eigenmode is excited more heavily, the corresponding electric field distribution has an integral value far from the worst case  found in the Duke model. Furthermore, the maximum of the induced current will not reach values close to the potential maximally induced current inside the Duke model  which can be translated in an approximately four foldSo, if strong heating is present due to an implant the current pattern causing it, will reflect the dominant eigenvector of its TM. Most severe heating will occur when the incident electric field projects predominantly onto this dominant eigenvector. These insights can be used to aid current monitoring techniques, identify hazardous situations and strategiesThe authors have no conflicts to disclose.


Participants read sentences that biased towards a figurative or literal reading of idioms and continued with resolutions that were congruent or incongruent with these biases . While interpretations of high-literality idioms were strengthened by supporting contexts and showed costs for incongruent resolutions, low-literality idioms did not show this effect. Rather, interpreting low-literality idioms in a literal manner showed a cost regardless of context. We conclude that biasing contexts are used in a flexible process of real-time idiom processing and meaning constitution, but this effect is mediated by idiom literality.In a self-paced reading study, we investigated how effects of biasing contexts in idiom processing interact with effects of idiom literality. Specifically, we tested if idioms with a high potential for literal interpretation (e.g.,  However, a literal interpretation, denoting an event in which a male cow is grabbed by its horns, is also available following standard meaning composition. In recent years, a number of studies have shown that the availability of these two meanings can be influenced by linguistic context, and while there is a general consensus that context can facilitate access to the figurative meaning of an idiom , an idiom like to be on cloud nine has a low potential for literal interpretation in the absence of any contextual information . Figuratively, it means to be very happy, and literally, a context is needed in order to create a situation in which a person might find themselves “on” a cloud called “nine.” This variance in literality could mediate contextual effects on literal and figurative interpretations and particularly help to explain the varying effects of context on literal interpretations in previous studies. For high-literality idioms, supporting contexts could strengthen both the figurative and the literal interpretation, since both interpretations have a high potential to start with. For low-literality idioms, supporting contexts could still strengthen the figurative interpretation, but fail to affect the literal interpretation.Idiom literality refers to the variation between idioms in their potential to be interpreted literally. While break the ice [with his peers/on the lake]…). By comparing the costs and benefits of processing these expected and unexpected variations in sentences containing idioms of varying literality, our study looked closely at how literal and figurative meanings are integrated into sentential meaning in online processing.We tested our assumptions in a self-paced reading study in English with idioms of varying literality. Following up on the methods used in a self-paced reading study conducted on phrasal verbs . Though few studies have looked at both literal and figurative interpretations on a phrasal level, particularly without the impact of biasing context, Ortony et al.  was not activated when the words were presented in idiomatic sentences. When the same final words were presented in literal sentences , their literal word meaning was activated. That is, an N400 effect for semantically related words  was found in literal sentences, but no N400 effect was found in idiomatic sentences, a result suggesting that literal interpretation was rendered unnecessary in this context. In another EEG study, Canal et al. . Their self-paced reading study showed that figurative interpretations benefitted from the presence of biasing contexts, but literal interpretations did not show differences regardless of whether the biasing context was figurative or literal. Furthermore, they found that the greatest processing costs occurred when a figurative interpretation followed a literal biasing context. Thus, unlike the previous studies discussed above  slows down processing, several studies have found the opposite pattern. Cronk and Schweigert . However, for unpredictable idioms, literal constituent meaning was activated in both high- and low-literality idioms. Titone and Connine interpreted the results to mean that competition between figurative and literal meanings may stay active when relevant, for instance in high-literality idioms, but this competition can decay where not necessary , followed contexts that either biased a literal or a figurative interpretation  and preceded resolutions that either corresponded to a literal or a figurative interpretation . Low-literality idioms like lose one’s cool (meaning figuratively to lose control of one’s emotions) also followed the same type of biasing contexts  and preceded corresponding resolutions .Our study had a total of three manipulations: idiom type , biasing context , and resolution . High-literality idioms, like We predicted that our results for figurative interpretations should replicate those found by Holsinger and Kaiser . Namely,Participants.Fifty-two native speakers of American English  received financial compensation for taking part in the study. Participants were recruited at the University of Maryland from the Department of Linguistics subject pool and grew up in monolingual households. A total of 6 participants were left-handed, and all participants had normal hearing and normal to corrected vision.Materials.The experiment consisted of 22 target and 78 filler trials. Sentences in target trials began with a biasing context (noun phrase + relative clause) followed by the infinitive form of the idiom, followed by a resolution  congruent or incongruent with contextual expectations, and ended with two additional short phrases shared across all conditions (see Appendix). Two example stimuli with all four conditions are provided in Table Sixty-eight sentences varying in length and structure were selected as filler trials, and ten of these sentences also contained idioms. Additionally, ten filler trials consisted of basic arithmetic questions *5 = ?). One third of all trials, including all target trials, were followed by comprehension questions targeting differing types of sentential information. The comprehension and arithmetic questions were included to hold participants’ attention and ensure concentration throughout the task, and correctness of response was considered in the analysis. All experimental items were divided into 4 lists, counterbalanced so that each idiom occurred only once per list, but idiom-type and context- and ending-conditions were balanced between the lists. In order to do so with an uneven number of experimental items, the lists varied slightly by resolution-type in the ratio of congruent and incongruent endings , but were equivalent in all other aspects. The order was then randomized and reversed for each list so that there were 8 lists in total. All lists contained the same filler items.Three regions of interest, also labeled in Table break the ice), but they differed in levels of literality . For all 30 pre-selected idioms, biasing sentences were constructed and subjected to norming studies.Before deciding on the 22 target idioms, a total of 30 idioms were pre-selected from the 300 idioms in the English-German Database of Idiom Norms (Beck and Weber Norming Two norming studies were conducted on the pre-selected 30 idioms and their biasing sentences in order to choose the items that were a) most appropriately biased for a literal or figurative reading of the idioms following the given contexts and b) most plausible . The selected 22 idioms had mean constituent frequencies of 3.25 per million and were rated as highly familiar by L1 speakers (average of 6.5).Norming Study 1: Strength of Context In a multiple-choice test, 40 American English participants were presented with the idioms preceded by either their figuratively or literally biasing contexts and were asked to choose the meaning of the highlighted phrase (the idiom) that most appropriately fits the provided sentential context. Each participant saw only one biasing context for each idiom, and four answer choices were available: the literal meaning, the correct figurative meaning, an incorrect figurative meaning, and “I don’t know”.idiom literality  and biasing context  as fixed effects. Our model confirmed that figurative contexts were completed consistently more often than literal contexts  and that for literal contexts, the high-literality idioms were completed consistently more often than low-literality idioms . Thus, in a number of cases participants responded with figurative interpretations even when a literal context rendered them inappropriate, thereby in fact ignoring contextual information. If this preference for figurative interpretations in the norming study indeed implies that figurative contexts bias more than literal contexts, it is possible that we will not see effects of literal bias in our self-paced reading results. Nonetheless, in order to best capture the potential individual variation based on item, biasing strength, included as the by-item average of correct completions based on context, was later included as a predictor in the regression models in our analysis of reading times where warranted.For the final selection of 11 high literality idioms, the percentages of completions consistent with the biasing context were 98% for figurative contexts and 52% for literal contexts respectively; for the final selection of 11 low literality idioms, the percentages of consistent completions were 97% for figurative contexts and 30% for literal contexts respectively. Linear models were conducted on the average correct completions based on context using Norming Study 2: Plausibility Eighty American English participants were asked to rate the 30 pre-selected idioms with their four biasing sentence contexts , biasing context , and resolution type  were included as fixed effects. Our model showed that ratings for literal endings were overall lower than those for figurative endings , and literal contexts were likewise rated lower than figurative contexts . Additionally, an interaction of resolution by context suggested that congruent endings  had higher ratings than incongruent resolutions , and a three-way interaction suggests that low-literality idioms were rated worse than high-literality idioms in the condition with a literal bias and resolution , whereas low-literality idioms showed higher plausibility ratings in cases where endings were congruent with their contexts.For the final selection of 22 items, the mean plausibility rating was 4.15 (SD= 1.17), and item averages ranged from 2.16 to 6.85, suggesting that while no items were rated as completely implausible, some were rated as very plausible. We again used simple linear models to verify any differences between items based on our predictive variables. Using the mean plausibility rating as the dependent factor, A further look at the comments section at the end of the ratings study suggested that participants were likely responding to preferential use of idioms rather than the plausibility of the sentences as described in the instructions, and it is possible that this norming study did not fully capture plausibility as intended. The differences between both congruent and incongruent endings as well as the preference for low-literality idioms with a figurative resolution also aligns with these expectations, as both cases represent unlikely uses of idioms. In spite of the difficulties in capturing this effect, in order to account for possible item differences based on plausibility, as collected in this norming study, the average plausibility ratings were included as a predictor in the regression models of the reading data where justified.Procedure Participants gave informed consent following the ethical guidelines of The University of Maryland College Park and were tested individually in quiet rooms at the Language Science Center. The experiment was programmed and executed using E-Prime , resolution type , and idiom literality  on normalized reaction times in each of the three previously identified areas of interest: idiom, resolution, and resolution + 1 regions. Trials were only included in the analyses if answers to the directly following comprehension questions were answered correctly. One participant fell below a threshold of answering 85% of questions correctly (68% correct) and was therefore excluded from the analysis. Two idioms (play the field and turn the tide) were also excluded as the idiom recognition test following the reading study revealed that more than half of participants were not familiar with these idioms. Outliers were excluded based on a visual inspection of reading times in each section. First, theoretically irrelevant times were excluded, second, inspection of the mean and general distribution of overall reading times were used to determine critical reading times for each region on an individual basis. Critical reading times were identified as 150–1500 ms in the idiom section, 150–1800 ms in the resolution section, and 150–1500 ms in the resolution + 1 section, and 3.89%, 2.62%, and 4.85% of the data was excluded in each region respectively. The difference in size between regions is in line with the mean length, difficulty, and average reading times in each region. Table We used R , resolution type , and idiom literality . All three theoretically relevant factors were kept in the models, and items and participants were included as random factors with random slopes. A maximally justified random effects structure was determined for each region by stepwise selection and model comparison , average lexical frequency, and norming values for idiom familiarity, plausibility, and strength of context were also included in full models, and were eliminated from the models if backward stepwise selection showed that they did not improve the model. While these additional factors did not contribute to the interpretation of the results, their inclusion in the final model was justified by model comparisons, and any additional effects found were more likely to be caused by our manipulations rather than these factors. After models were selected, extreme outliers with a distance of greater than 2.5 standard deviations from zero were removed in order to improve model fit  and trial order  suggest that idioms with more letters took longer to read and that reading times improved over the course of the experiment. These effects are consistent throughout the successive regions but did not contribute to the overall findings other than to justify their inclusion in the models and will therefore be listed in the tables for models but not discussed further in the text.Reading Times in the Idiom Region.The model for reading times for the idiom region did not include The predicted log reading times for the idiom region, collapsed across literality, can be seen in Fig. region length and, marginally, trial order effects were significant. Additionally, idiom familiarity improved overall reading times in this region , suggesting that reading times improve with increasing familiarity. Resolution type was also significant as a main effect  and as an interaction with biasing context .Reading Times in the Resolution Region. For the resolution region, Table biasing context , its effect on resolution type can be examined more clearly. The same model used in the original analysis was run using the change in coding listed above, and the effect of resolution type is significant when contexts are literal  and not figurative . Namely, as displayed in Fig. In order to better interpret this interaction, a post hoc analysis was conducted using simple slopes  and, marginally, idiom literality  were present. Additionally, the effects of region length and trial order were generally in line with those found in the previous regions. In addition to main effects, several interactions were also significant, setting our data apart from earlier studies. Both biasing context by resolution type  and idiom literality by resolution type  were significant, and the three-way interaction of biasing context by resolution type by idiom literality was marginally significant .The final model output for the idiom literality on biasing context as well as its involvement in the three-way interaction with resolution type was investigated by recoding idiom literality  and re-running the original model. Results showed that resolution type was significant in the case of low-literality idioms  but not in high-literality idioms . This confirms the pattern  and not for the low-literality idioms .In order to better interpret the interactions, we performed post hoc analyses using simple slopes on the relevant variables individually. First, the effect of see Fig.  in low-lbiasing context was also recoded , and the original model was used again, keeping high-literality idioms as the intercept. The results display main effects of resolution type for both literal  and figurative contexts  in opposite directions. This finding suggests that, for high-literality idioms, congruently resolving endings were read more quickly than incongruently resolving endings . While this is a reflection of the marginally significant three-way interaction, these results confirm the trends displayed in the high-literality idioms (right) in Fig. In order to investigate the nature of this interaction in high-literality idioms, idiom literality and then biasing context according to literality, biasing context was recoded  and applied to the original model with no other changes. Where contexts were figurative, both the effects of idiom literality  and resolution type  were significant. This result suggests that figurative contexts cause significant benefits and costs where resolutions do not match these expectations and that figurative contexts improve readings for high-literality idioms more than low-literality idioms. In the case of literal contexts, these factors were involved in a two-way interaction between these factors , which was already explored in the first post hoc analysis.In order to investigate any possible effects not already summarized by the recoding of idiom, the resolution, and the resolution + 1 regions), we were able to see effects of context and literality as both figurative and literal interpretations were integrated with contextual meaning. Our study fills some of the gaps in similar research conducted in the field and provides evidence that context has a limited role in meaning activation, and factors such as idiom literality can supersede standard processing and integration mechanisms under certain conditions. We will briefly discuss the implications of the reading times in the first two regions before focusing on the final region.With this self-paced reading study, we aimed to further examine the activation and role of literal and figurative meaning during the time-course of idiomatic processing. Specifically, we asked how context impacts access to these interpretations and what limits the level of literality of an idiom can impose in both cases where contextual expectations are met and turn out to be false. By examining three regions of interest (the idiom region) between idiom types based on context, which suggests similar processing during reading. The well-documented effect that idioms are read faster following a supporting context  are both in line with earlier studies . However, as discussed briefly in the introduction, this factor overlaps critically with other idiomatic properties such as ambiguity (e.g., Cacciari and Tabossi Overall, our findings fill in some of the gaps in current research on idiom processing, specifically in addressing questions of figurative and literal meaning constitution on a phrasal level. Our data support a processing strategy that is sensitive to context, but in which these contextual effects are mediated by idiom literality. We conclude that processing follows a single, adaptive pattern until a threshold of information can be reached that suppresses or qualitatively changes the nature of literal computation. This threshold can be mediated by context, pushing more ambiguous, high-literality idioms across this threshold only when context also supports such a strategy, and by literality, which can supersede context in strength if low-literality idioms cause the threshold to be reached earlier, for example. Future studies are still needed to provide more evidence on what happens earlier in processing concerning context and literality, among other properties, and what additional factors can impact a change in processing strategies.