Next Article in Journal
The Effects of Working Memory Training on Brain Activity
Previous Article in Journal
How Neuronal Noises Influence the Spiking Neural Networks’s Cognitive Learning Process: A Preliminary Study
Previous Article in Special Issue
Postnatal Ethanol-Induced Neurodegeneration Involves CB1R-Mediated β-Catenin Degradation in Neonatal Mice
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Brain Sciences
Volume 11
Issue 2
10.3390/brainsci11020154
brainsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prenatal Alcohol Exposure and the Facial Phenotype in Adolescents: A Study Based on Meconium Ethyl Glucuronide

by
Janina Maschke
1,*,
Jakob Roetner
1,
Tamme W. Goecke
2,3,
Peter A. Fasching
2,
Matthias W. Beckmann
2,
Oliver Kratz
1,
Gunther H. Moll
1,
Bernd Lenz
4,5,
Johannes Kornhuber
4,†,
Anna Eichler
1,† and
IMAC-Mind-Consortium
‡
1
Department of Child and Adolescent Mental Health, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
2
Department of Obstetrics and Gynecology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
3
Department of Gynecology, RoMed Klinikum Rosenheim, 83022 Rosenheim, Germany
4
Department of Psychiatry and Psychotherapy, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany
5
Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
*
Author to whom correspondence should be addressed.
†
Shared senior authorship.
‡
Group is provided in the acknowledgment.
Brain Sci. 2021, 11(2), 154; https://doi.org/10.3390/brainsci11020154
Submission received: 14 December 2020 / Revised: 12 January 2021 / Accepted: 19 January 2021 / Published: 25 January 2021
(This article belongs to the Special Issue Recent Advances in Fetal Alcohol Spectrum Disorder (FASD))
Browse Figures
Review Reports Versions Notes

Abstract

:
Here, we explore the effects of prenatal alcohol exposure (PAE) in adolescence. We investigated associations between meconium ethyl glucoronide (EtG) and facial malformation. For 129 children (66/63 male/female; M = 13.3, SD = 0.32, 12–14 years), PAE was implemented by newborn meconium EtG and maternal self-reports during the third trimester. Cognitive development was operationalized by standardized scores (WISC V). The EtG cut-off values were set at ≥10 ng/g (n = 32, 24.8% EtG10+) and ≥112 ng/g (n = 20, 15.5% EtG112+). The craniofacial shape was measured using FAS Facial Photographic Analysis Software. EtG10+− and EtG112+-affected children exhibited a shorter palpebral fissure length (p = 0.031/p = 0.055). Lip circularity was smaller in EtG112+-affected children (p = 0.026). Maternal self-reports were not associated (p > 0.164). Lip circularity correlated with fluid reasoning (EtG10+ p = 0.031; EtG112+ p = 0.298) and working memory (EtG10+ p = 0.084; EtG112+ p = 0.144). The present study demonstrates visible effects of the facial phenotype in exposed adolescents. Facial malformation was associated with a child’s cognitive performance in the alcohol-exposed group. The EtG biomarker was a better predictor than maternal self-reports.

1. Introduction

Prenatal alcohol exposure (PAE) is a known risk factor for child development and can cause adverse and irreversible damage to the child [1]. Alcohol consumption is one of the major preventable causes of childhood health and developmental problems and continues to be a public health challenge [2,3]. The prevalence of self-reported alcohol use in pregnancy is about 10% worldwide [4]. Further research has reported a 20% prevalence of alcohol consumption during pregnancy in European countries [5,6]. The prevalence of fetal alcohol syndrome (FAS) in a general population has been reported to be 1.1 per 1000 (0.11%), and 7.7 per 1000 (0.77%) for fetal alcohol spectrum disorder (FASD) [4,7]. Alcohol, as a teratogenic substance, can damage brain development throughout pregnancy and cause structural abnormalities (i.e., facial malformation) or affect the brain at the neuro-biochemical level in the form of brain dysfunction with different consequences [8]. This means that PAE has structural and functional consequences for the central nervous system (CNS). A global decrease in intelligence or developmental delay is summarized by the FASD diagnosis criterion ‘CNS abnormality’. There is a homogeneous reduction in action and verbal IQ [9,10]. In addition to the impairment of global intelligence, PAE might impact child learning and memory abilities. The reduced memory function affects the short- and long-term memory and is linked to slower information processing and a reduced learning capacity [11,12,13]. Further performance deviations relate to language abilities, fine motor skills, spatial-visual perception, executive functions, arithmetic skills, awareness, and social skills [14,15]. Current research based on ethanol metabolites in the meconium found that even small amounts of these metabolites significantly influence cognitive development in childhood and adolescence [16,17,18].
The duration of alcohol consumption during pregnancy and the timing of prenatal alcohol exposure differ from mother to mother, and numerous other developmental risk factors are significant. Therefore, the negative effects vary greatly between children with FASD, and each child is affected differently [13]. A recent study on FASD children revealed that all affected mothers drunk before pregnancy recognition, but only 10% continued alcohol consumption during the second and third trimester [19]. Therefore, there is no safe-time-point or safe-amount for prenatal alcohol consumption: The range from ‘visible’ to ‘invisible’ effects caused by smaller or higher amounts of prenatally consumed alcohol is wide. The basis for valid studies on the developmental consequences of intrauterine alcohol exposure is reliable alcohol-exposure measures. Most studies rely on a clinical FAS diagnosis or maternal self-reports. However, it has been shown that maternal self-reports are inaccurate compared to biomarkers [20,21]. Other studies suggest that metabolites could be a more effective marker than the maternal self-report for predicting negative development outcomes [16,20,22].
For FAS diagnosis, in addition to CNS and growth abnormalities, craniofacial changes are used as physical diagnostic criteria [2,23,24,25,26,27]. Animal studies have reported that the facial shape is formed up to day 16 of pregnancy [28], making facial malformations an early marker of intrauterine alcohol exposure. A short palpebral fissure (PFL), a weakly modeled or flat philtrum, and a thin upper lip are the most frequently observed characteristics for alcohol embryopathy and children suffering from FASD (see Figure 1); these typical facial features disappear with an increasing age and become less clear over time [14,29]. Muggli [2] and Tan [23] found that low to moderate PAE (operationalized by maternal self-reports) in 1- and 15-month-old children influences the craniofacial shape. Muggli [2] observed that the craniofacial differences concentrated around the midface, especially the nose, eyes, and lips [2].Tan [23] observed similar structural changes in the face, including a smaller forehead, a larger maxilla on one side of the face, and a short and upturned nose [25]. In clinical settings, professionals use the Lip Philtrum Guide to observe the upper lip size and the philtrum. A sliding digital caliper or ruler is used to examine the palpebral fissure length (PFL). However, these manual measurements are error-prone [29,30,31,32]. To address this, Astley and Clarren [30] developed a software tool (the FAS Facial Photographic Analysis Software) to examine facial changes in children. The software was developed for research and health care professionals [32].
It can be summarized that the facial phenotype changes due to early intrauterine alcohol exposure; these changes have a high interindividual variance—in addition to others, depending on the drinking amount and timing—which cannot be accurately measured by maternal self-reports because of invalid reporting behavior. We are aware of two published studies that have associated self-report operationalized alcohol exposure with facial anomalies in infants [2,26]. We are aware of no studies using biomarkers to measure alcohol exposure or studies analyzing facial differences in adolescence.
The present study examined the association between PAE, operationalized by an ethanol metabolite in newborn meconium (EtG), and craniofacial differences in 12 to 14-year-old adolescents. These associations were compared with associations between the maternal self-report on PAE and craniofacial shapes. We also analyzed the functional relevance of detected facial differences in fluid reasoning and working memory cognitive performance.

2. Materials and Methods

2.1. Study Design

The study is a cooperation project between the Department of Obstetrics and Gynecology, Psychiatry and Psychotherapy, and Child and Adolescent Mental Health at the University Hospital Erlangen. The first part of the study (Franconian Maternal Health Evaluation Studies (FRAMES)) was performed at the Department of Obstetrics and Gynecology and took place from 2005 to 2007; 1100 women were recruited in the third trimester of their pregnancy [33,34]. From 2012 to 2015, a random subsample of n = 501 was contacted for re-participation. Since the risk groups (prenatal alcohol consumption and/or prenatal depression) were underrepresented in this sample, n = 117 additional women were contacted (oversampling). Finally, n = 245 FRAMES mothers and their children (39.6%; child age: M = 7.74, SD = 0.74, range: 6.00–10.0) took part in the FRANCES I study (Franconian Cognition and Emotion Studies) at the Department of Child and Adolescent Mental Health. The 245 participating women did not differ from the 373 non-participating women at the time of birth (FRAMES) in terms of the family status (χ2(1) = 0.16, p = 0.690), school level (χ2(1) = 0.08, p = 0.774), or family income (χ2(2) = 0.97, p = 0.616). From 2019, the mothers and their adolescent children were contacted again to ask them to take part in a second follow-up (FRANCES II). The recruiting is ongoing. For the present paper, there were n = 185 re-participants (75.5%). The re-participating women did not differ from the n = 60 non-re-participating women in terms of the family status (χ2(1) = 1.43, p = 0.232) or family income (χ2(2) = 0.62, p = 0.735) at the time of birth (FRAMES). However, their school level was higher (χ2(1) = 7.96, p = 0.005).
In the FRANCES I and II follow-up study, multiple parameters were examined in a multi-level-design (clinical, neuropsychological, neurophysiological, and neurobiological outcomes) observing the cognitive, emotional, and social development. In the FRANCES II follow-up, the facial malformations of young adolescents were measured. The study was approved by the Ethik-Kommission from the Friedrich-Alexander Universität Erlangen—Nürnberg (FAU) (353_18B) and conducted in accordance with the Declaration of Helsinki. Mothers and children gave approved consent. Participation in the study was voluntary.

2.2. Sample Characteristics

The participants were between the ages of 12 and 14 years (M = 13.3, SD = 0.32, range 12 to 14 years). The overall median school type was 4 (1 = primary degree (i.e., ‘Förderschule’), and school ranges were 2 = secondary degree (‘Hauptschule’), 3 = intermediary degree (‘Realschule’), and 4 = upper secondary degree (‘Gymnasium’)) [35]. Photographs of n = 165 participants were taken. From these (n = 165) participants, n = 36 children had to be excluded: n = 29 children had no valid EtG measurements; n = 3 children were outliers regarding >4 SD around the mean; n = 1 was excluded due to missing the sticker for the photograph; and n = 3 children with one parent being non-Caucasian (China, Nigeria, and Peru) (these children were excluded because of established ethnic differences in facial abnormalities due to PAE). Consequently, the present paper reports the data of 129 children who had valid EtG measurements and for whom facial photographs were taken. Sample characteristics are reported in Table 1. Out of the 129 children, n = 32 children (24.8%) had a meconium EtG ≥ 10 ng/g (EtG10+) and n = 20 children (15.5%) had a meconium EtG ≥ 112 ng/g (EtG112+). The EtG+ (≥10 ng/g) children were compared to the non-exposed children within the sample (EtG negative: EtG10− n = 97, EtG112− n = 109). For maternal self-report, there were n = 32 mothers (24.8%) who reported alcohol consumption during pregnancy.

2.3. Instruments and Measures

Prenatal alcohol consumption: Meconium EtG is an ethanol metabolite that accumulates in the fetal gut, starting at week 20 of gestation until birth. Intrauterine alcohol exposure can be detected in the meconium of a newborn as a third-trimester marker [36]. Within the first 2–24 h after birth, about 1 g of meconium was removed from the newborn and frozen at −80 °C for up to 30 months until analysis. The procedure for taking, storing, and determining the EtG samples/values in the meconium was described in Bakdash [37]. The positive detection limit for prenatal alcohol exposure varies slightly from study to study, but at least 10 ng/g EtG indicates alcohol exposure in the third trimester [37,38]. Therefore, the first cut-off value was set at 10 ng/g EtG. Other studies have found further effects with a higher cut-off [22,33,38]. Therefore, the second positive cut-off of EtG ≥ 112 ng/g was set to study the effects of higher levels of alcohol consumption within the sample and was defined in line with Grimm [22].
Maternal self-reports regarding alcohol consumption during pregnancy were recorded within the FRAMES study. In the third trimester, the women answered an interview question about their drinking behavior throughout pregnancy (no, I do not drink in general; no, I did not drink during pregnancy; yes, I rarely drank during pregnancy; yes, I drank one glass/day during pregnancy; and yes, I drank more than one glass/day during pregnancy) (category frequencies reported in [20]). Two groups were designed for data analysis based on the women’s self-reports: No drinking (‘I don’t drink in general’ + ‘I didn’t drink during pregnancy’) vs. drinking (‘I rarely drank during pregnancy’ + ‘I drank one glass/day during pregnancy’. No woman reported ‘Yes, I drank more than one glass day during pregnancy’). For the maternal self-report, all mothers (except for one mother) who reported prenatal alcohol consumption stated that they ‘rarely’ consumed alcohol [39]. Maternal self-reports during pregnancy on alcohol consumption behavior indicated a trend, with child meconium EtG for EtG ≥ 10 ng/g χ2(1) = 3.68, p = 0.055. However, this was not significantly correlated, with child meconium EtG for EtG ≥ 112 ng/g χ2(1) = 1.32, p = 0.251.
Facial malformations: Photographic measurements of facial features were conducted analogously to Astley and colleagues with the FAS Facial Photographic Analysis Software (Version 2.1.0) [29] by placing a small paper sticker on the patient’s forehead between the eyebrows (see Figure 1; sticker size 12.70 mm × 19.05 mm). Two pictures were taken with a digital camera (Canon, Model IXUS 145, 14.0 Megapixels, Tokyo, Japan) while the study participants were in a seating position: One close up, with the patient’s head filling up the entire frame, and both ears being easily visible, and one frontal, with a three-fourths view. The facial expression of the child was always relaxed, with no smile, eyes wide open, and no glasses, and the hair was pinned back (Figure 1). All photographs were taken without the flashlight, and the camera was always secured on a tripod. The height of the tripod was dependent on the height of the subject. The camera lens had to align with the midface section of the subject’s head. All photographs were uploaded to the FAS Facial Photographic Analysis Software (Version 2.1.0) and analyzed following the program instructions. The palpebral fissure length (PFL), inner canthal distance (ICD), and thin upper lip (lip circularity) were measured by two independent raters using the computer mouse: The PFL by measuring the paper sticker and the distance between the exocanthion and endocanthion of the right eye and the left eye; the ICD by measuring the distance of the endocanthion of the right eye and the left eye; and the lip circularity by bordering the upper lip with the mouse. The program obtains a pixel value, a real length value (in millimeter), and an age- and sex-standardized z-score. The real length of the PFL value is calculated using the equation PFL (mm) = ((length of the sticker, mm/length of the sticker, pixel) × (PFL, pixel)) × 1.07 [30]. The real length of the ICD value was calculated using the equation ICD (mm) = ((length of sticker, mm/length of sticker, pixel) × (ICD, pixel)) [30]. For the PFL measurement, an adjustment value of 1.07 was added to the formula to adjust for the foreshortening effect. This is not necessary for the ICD measurement as it is recorded at the midline of the face. Therefore, no foreshortening effect is present [30,40]. The formula for the z-score was computed using the equation PFL/ICD z-score = ((population mean PFL/ICD in mm)—(subject’s PFL/ICD in mm))/((population mean PFL/ICD in mm)—(population 1 SD PFL/ICD in mm)) [41]. The z-score was computed according to Hall (1989); all z-scores were age- and gender-dependent [42,43,44]. The underlying population was Caucasian. The upper lip was outlined with the computer mouse by each rater and was converted into the circularity value by the program. The circularity value was calculated using Circularity = (perimeter2/area) [45]. Higher PFL scores (i.e., longer eye length) decrease the chance of the child having FAS [46]. On the contrary, higher ICD scores (i.e., a larger distance between the two eyes) indicate a chance of the child having FAS, and higher lip circularity scores (i.e., smaller upper lip) increase the chance of the child suffering from FAS [30]. The described facial malformation criteria were rated by two independent and blind-trained raters; the interrater correlations for all three measures were highly significant, with r > 0.70 and p < 0.001 (Table 2), indicating the sufficient validity of the standardized rating method. For further analyses, the mean scores of both raters were calculated. Child lip circularity was significantly correlated with PFL (r = −0.203, p < 0.05), while child ICD was not associated. The PFL and ICD scores were independent (r = 0.091–0.068). The correlations between the length and z-scores for ICD (r = 0.999, p < 0.01) and PFL (r = 0.992, p < 0.01) were high.
Cognitive Development: For the measurement of ‘fluid reasoning’ and ‘working memory’, we used the Wechsler Intelligence Test for Children (WISC V) [9], which is an individually administered test for children between 6 and 16 years of age. The test age- and sex-specific standard norms are based on a sample of about 1100 German children and adolescents from 2016. The WISC generates five primary standardized index scores (MW = 10, SD = 3). In the present study, the ‘Fluid Reasoning Index Score’ (‘Matrix Reasoning’ and ‘Figure Weights’ subtests; ability to detect an underlying conceptual relationship and to use reasoning to identify and apply rules) and the ‘Working Memory Index Score’ (‘Digit Span’ and the ‘Picture span’ subtests; ability to register, maintain, and manipulate visual and auditory information) were used to assess functional cognitive abnormalities.

2.4. Confounders

We selected relevant confounders in concordance with theoretical considerations and earlier empirical results [2,23,26]. The following variables were tested in t-tests (Table 1, differences for EtG-positive versus -negative groups) and correlations (Table 3, with facial indices): Child’s sex; child’s age (years); childbirth weight (gram); current child’s weight (kg); current child’s height (cm); child’s current head circumference (cm); family socioeconomic status (combination of maternal and paternal education level (4-level: <9, 9, 10, or 13 years) and net family income (6-level: <1000 to >5000) during primary school age, sum-index theoretical range: 3–14); maternal age at delivery (years); and maternal smoking during pregnancy (yes (≥1 cigarette per day was interpreted as prenatal smoking)/no (≤1 cigarette per day was interpreted as no prenatal smoking)). EtG10+ and EtG10− children significantly differed in age and birth weight; child’s sex, birth weight, current weight, height, and head circumference measures were significantly correlated with facial characteristics. Afterward, these variables were controlled for in the ANCOVA analyses.

2.5. Statistical Analysis

Statistical analyses were carried out using IBM SPSS Statistics (Version 24.0, Armonk, NY: IBM Corp, 2016). Descriptive data are reported as means (M) and the standard deviation (SD). Uncontrolled mean differences were tested by t-tests and differences in frequencies by chi-squared tests (χ2), and are reported in descriptive data tables. Facial PFL, ICD, and circularity indices and continuous EtG scores were tested for normal distributions by the Shapiro–Wilk test in the total and subgroups. Lip circularity (total group: W = 0.945, p < 0.001) and continuous EtG data (total group: W = 0.297, p < 0.001) were not normally distributed. Therefore, we logarithmized (ln) the data. PFL (mm and Z), ICD (mm and Z), and circularity measures were tested in separate analyses of covariance (ANCOVA). Variables that were significantly (p < 0.05) correlated with at least one facial index or which significantly differentiated between EtG-positive versus -negative children within at least one cut-off group were controlled for in ANCOVA analyses. These criteria were applied to the following: Child’s age; childbirth weight; child’s sex; child’s weight; child’s height; and child’s head circumference (Table 1 and Table 3). The global analyses were run separately for the EtG10±, the EtG112±, and the self-report± groups. Post-hoc F-tests (univariate ANCOVA results) indicated significant group-differences. Facial markers, which were significantly predicted by prenatal variables in the foregoing ANCOVAs, were associated with cognitive ‘Fluid Reasoning’ and ‘Working Memory’ indices in confounder controlled partial correlations. The level of significance was defined as p < 0.05 (two-tailed) and p < 0.1 indicated a trend.

3. Results

Table 4 reports the ANCOVA results. All global ANCOVA models for the prediction of a child’s facial features by alcohol consumption parameter and relevant confounders were significant (p < 0.05, Table 4). Post-hoc F-tests showed that affected children with EtG ≥ 10 ng/g with significance (p = 0.031, ηp2 = 0.038) and with EtG levels ≥ 112 ng/g by trend (p = 0.055, ηp2 = 0.030) displayed a smaller PFL than controls (Figure 2). The association was not significant for maternal self-reports (p = 0.164, ηp2 = 0.016). Beyond the EtG status, the best predictors for child PFL were the child’s sex (p = 0.049, ηp2 = 0.032), birth weight (p = 0.003, ηp2 = 0.070), and current height (p = 0.004, ηp2 = 0.066). Child ICD did not differ between exposed and non-exposed children (EtG10+: p = 0.777, ηp2 = 0.001; EtG112+: p = 0.607, ηp2 = 0.002). The only significant predictor was the child’s current head circumference (p = 0.037, ηp2 = 0.036). PFL and ICD results were similar for FAS Facial Analysis Software Length- and z-scores. For child lip circularity, there was a significant difference between alcohol-exposed and non-exposed children for the 112 ng/g cut-off (p = 0.026, ηp2 = 0.040; Figure 3). This effect was not found for the 10 ng/g cut-off (p = 0.340, ηp2 = 0.008) or maternal self-report (p = 0.156, ηp2 = 0.017). Lip circularity was also associated with the child’s current height (p = 0.043, ηp2 = 0.034) and head circumference (p = 0.045, ηp2 = 0.033).
Facial markers that were significantly predicted by child meconium EtG levels (PFL and lip circularity) were tested for functional relevance in association with cognitive outcomes. When controlling for relevant confounders in partial correlations (Table 5), affected children’s lip circularity was associated with their cognitive outcomes (Figure 4 and Figure 5): The fluid reasoning index reached significance within the EtG10+ group (rp = −0.509, p = 0.031), while within the EtG112+ group, a medium correlation reached no significance (rp = −0.391, p = 0.298). When interpreting the effect size measure, lip circularity correlations with a high effect (r > 0.500) were, within the EtG112+ group, present for the working memory index (a statistical trend was also seen in the EtG10+ group: r = −0.418, p = 0.084). There was no statistical significance for these correlations. The PFL scores of EtG+ children were not significantly correlated with cognitive outcomes, and correlation coefficients were low (r < 0.129).

4. Discussion

The present study examined the effects of PAE on structural facial changes and associated impairments in cognitive functions in young adolescents. The meconium biomarker EtG and maternal self-reports during the third trimester were used to identify prenatal alcohol exposure during pregnancy. For both cut-offs (10 ng/g was statistically significant, and 112 ng/g was significant by trend), exposed children had a smaller PFL value. For child lip circularity, the 112 ng/g cut-off was significant. Maternal self-reports did not show any statistical significance. This may be associated with the inaccuracy of the self-report, as discussed in previous papers [20,46].
Furthermore, it has been reported that mothers who consumed a higher amount of alcohol during pregnancy are the most likely to answer the self-report inaccurately, thus skewing the results [33]. Earlier research has already shown that maternal self-reports are inclined to underreport PAE: In their review, Lange and colleagues reported that underestimation was four-times the actual value when using maternal self-reports in comparison to meconium biomarker data [47]. One study found cognitive outcomes to be better predicted by the meconium biomarker than by maternal self-reports during pregnancy [16]. Furthermore, predictive quality comparisons have been missing until now. The present study contributes results for the facial phenotype. The PFL and the lip circularity measure are two of the three common facial features employed for FAS diagnosis. As expected, the EtG biomarker was associated with PFL and lip circularity. The ICD—not listed as a facial feature in the FAS diagnostic guidelines—was of negligible relevance. We observed that only the head circumference of the patient determines the ICD. The greater the head circumference of the patient, the greater the distance between the patient’s eyes, and thus the greater the ICD.
The results lend validity to the EtG meconium marker and the harm of prenatal alcohol consumption for child development. In detail, these results found support for the notion that a lower consumption of alcohol during pregnancy (EtG10+) has a greater impact on a shortened PFL length. Furthermore, it was observed that greater amounts of alcohol consumption (EtG112+) had a considerable impact on lip circularity, resulting in a smaller upper lip. Muggli [2] suggests that certain FAS features might only develop at higher levels of alcohol consumption. A baby’s face already starts to develop at day 17–18 after fertilization [48]. The neural crest development is affected by ethanol during all stages, which causes facial malformations in the baby [27]. In mice studies, it has been observed that the timing of prenatal alcohol consumption greatly affects the facial changes in the fetus [27]. It was detected that alcohol exposure to the fetus at gestational day 7, which is equivalent to gestational day 15–17 in humans [49], causes changes in the midface, specifically, a smooth philtrum and a stretched upper lip [27]. This can be compared to exposure at gestational day 8.5, which only mildly influences the midface area and philtrum, but causes a shorter upper lip [27]. Previous studies also display these variabilities in facial malformations with heavy PAE [25,48,50]. Additionally, factors such as oxidative stress, growth factor involvement, epigenetic influences, and a decreased biosynthesis of retinoic acid might also influence the development of the infant’s face [2,51]. Despite these diverse influencing factors, the significant association between the EtG biomarker and the child’s facial characteristics is impressive and indicates a stable association.
The question of how much alcohol was consumed during pregnancy when giving birth to an EtG-positive child is difficult to answer. Goecke [33] reported that 20.4% of their study sample had a positive EtG marker [33]. In contrast, only 0.77% of the general population are diagnosed with FASD [7]. The high prevalence of positive EtG compared to the lower prevalence of diagnosed FASD children might be an indicator that not every child has symptomatic FASD or may be diagnosed falsely. There is a high correlation between FASD and symptomatic attention deficit hyperactivity disorder (ADHS) [52]. Seventy percent of children who suffer from FASD present ADHS symptoms [52]. Furthermore, the EtG biomarker indicates the amount of ethanol metabolite that was present in meconium, but we cannot precisely reflect how much of this originated from the consumption of alcohol or through specific food (e.g., fresh fruit juice and fresh fruit) [53,54]. Therefore, positive EtG cases might not be clinically and pathologically relevant. The consumption of alcohol during pregnancy impacts the child negatively, but it does not necessarily lead to diagnostic FASD [55]; the alcohol-induced vulnerability of the child cumulates with further risk factors, increasing the chance of FASD [56].
For example, a low socioeconomic background increases the vulnerability of developing diagnostic FASD [57]. Most (70.9%) of the children born to prenatally alcohol-consuming mothers from a low socioeconomic background develop FASD, whereas, in children born to mothers from a high socioeconomic background, only 4.5% develop FASD [57,58].
Until now, few studies have attempted to link meconium EtG levels with concrete prenatal drinking amounts. In contrast, there are results for other meconium ethanol metabolites: Mothers of fatty acid ethyl esters (FAEE)—EtG and FAEE are highly correlated with each other [53]. Mothers of EtG and FAEE-positive children drank an average of 10 drinks/week and no fewer than four drinks/week [59]. One or two drinks per day were not enough to find positive FAEE meconium [60]. A particularly crucial point in this context is that the facial phenotype forms during early pregnancy [28], whereas the EtG meconium levels only indicate the intrauterine alcohol exposure during the last trimester of pregnancy [37]. Therefore, we can only estimate how much alcohol was consumed over the course of the pregnancy. Other research suggests that most women reduce their alcohol intake once they find out that they are pregnant [61]. Proportions, as well as quantities, of drinking, usually decrease during the course of pregnancy [62]. An additional problem may be that some women consumed alcohol in the first weeks of pregnancy, but stopped the consumption of alcohol before the third trimester, thus indicating no EtG meconium levels. However, a recent study investigated the prenatal drinking patterns of 19 mothers of FASD children. They concluded that only 10.5% of the mothers continued drinking after pregnancy recognition—based on their self-reports [63]. Nonetheless, both variables are associated, consistent with the hypothesis that mothers who consumed alcohol in late pregnancy were already consuming alcohol in early pregnancy. A recent study confirmed the assumption by identifying a ‘low-to-moderate PAE sustained across gestation’ and a ‘high PAE sustained across gestation’ type with the highest effects on child development [24].
Our findings do not stop at the point that prenatal alcohol consumption is associated with a child biomarker. An additional focus was placed on the functional relevance of this association. Within the EtG-positive groups, the lip circularity measure was correlated with child fluid reasoning and working memory competence. All correlations between lip circularity measures and cognitive functioning were r > 0.30 for practical relevance, with partial significance. Research has shown that the brain is severely impacted when the mother consumes alcohol during pregnancy [64]. Children with FASD perform more poorly on IQ tests, compared to unaffected children, especially in terms of verbal learning, working memory, and fluid intelligence [65,66]. The brain and the face develop simultaneously during pregnancy. Therefore, a correlation between brain impairments and facial changes is not unexpected. The lip circularity was superior to the PFL measure in predicting cognitive impairments, meaning a thinner upper lip was the better criterion for cognitive impairment in young adolescents. These results are important for demonstrating the developmental consequences of early prenatal alcohol exposure and validating the lip circularity measure as a facial FAS diagnosis criterion, but must be interpreted carefully to avoid stigmatization. At the same time, when comparing the interrater-accuracy of the different values, it was found that the lip circularity had the greatest inaccuracy.
The ICD and PFL values were calculated by the FAS Facial Photographic Analysis software by setting points on the photograph. In contrast, the lip circularity value was enumerated by outlining the lip with a computer mouse. Lining the lip with a computer mouse might cause a greater inaccuracy between the different independent-raters as it can be difficult for some raters to trace the lip accurately. This method might be prone to more errors, rather than just that associated with setting points on the photograph. This could be the reason why the lip circularity exhibited a greater inaccuracy for the different independent-raters. Therefore, it is of great importance that the same individual trains each independent-rater extensively beforehand, to ensure highly accurate results. Conversely, in the research paper by Astley, only the PFL measurements were extensively examined, and not the lip circularity measurements [42].
The present study is the first study to validate FAS facial diagnosis software as a potential digital tool for clinical practice in a German general population sample. The digitally determined FAS facial features were significantly correlated with PAE measures and developmental outcomes. Even in young adolescents, when facial characteristics begin to decrease and become less clear [14,29], standardized z-scores were as reliable as absolute length measures in millimeters. However, the most critical finding was that the association of meconium EtG and facial features only became significant when relevant covariates were controlled for, including the child’s age, sex, weight, height, and head circumference. The actual PFL z-scores are based on samples of American children, taking age and gender into account [44]; lip circularity measures are not standardized. When using the FAS Facial Photographic Analysis software to measure the facial changes in patients, it is important to study confounders beforehand and to include them in the analysis. There is a need to expand existing standard norms—minimally by weight, height, and head circumference specifications—before the software can be systematically used in clinical settings. In future studies, including these confounders in the z-score calculation might be beneficial as the factors height, weight, and head circumference are correlated with the eye size [67,68]. Furthermore, future studies should try to replicate the research in larger cohorts. Additionally, when replicating this research in a larger cohort, the sexes should be separated in the analyses.

5. Conclusions

This is the first study to use meconium EtG to test the hypothesis that subliminal PAE has a detectable—if even subclinical—effect on craniofacial structures, enduring into early adolescence. The results suggest that prenatal alcohol consumption has a sustained impact on craniofacial development: When including relevant confounders (age, sex, height, weight, and head circumference), we observed that the PFL and lip circularity were affected in young adolescence children. These characteristics are associated with the FAS Diagnostic Guidelines. In this context, the EtG meconium biomarker is a more valid predictor than maternal self-reports. The present study not only shows the consequences of intrauterine alcohol exposure on the development of the face, but also displays the correlation between facial and cognitive changes. We conclude that the FAS Facial Photographic Analysis software can be used in clinical settings to reduce measurement errors in lip-philtrum-guide and ruler use by well-trained independent raters, following future studies on adequate standardized norms. Overall, it can be concluded that prenatal alcohol consumption is a major risk factor for the unborn child, accompanied by multi-level changes. Therefore, avoiding alcohol during pregnancy is the safest option.

Author Contributions

Conceptualization, M.W.B., P.A.F., T.W.G., J.K., G.H.M., O.K., B.L., I.-M.-C., and A.E.; methodology, M.W.B., P.A.F., T.W.G., J.K., G.H.M., O.K., B.L., and A.E.; software, J.K., B.L., A.E., J.R., and J.M.; investigation, A.E. and J.M.; data curation, A.E., J.R., and J.M.; writing—original draft preparation, A.E., J.R., J.K., and J.M.; writing—review and editing, M.W.B., P.A.F., T.W.G., J.K., G.H.M., O.K., B.L., A.E., J.R., and J.M.; visualization, J.R. and J.M.; validation: J.R. and J.M.; supervision, B.L. and A.E.; project administration, A.E., O.K., and G.H.M.; funding acquisition, M.W.B., P.A.F., J.K., G.H.M., O.K., B.L., I.-M.-C., and A.E. All authors have read and agreed to the published version of the manuscript.

Funding

This project was partly funded by the Federal Ministry of Education and Research (IMAC-Mind: Improving Mental Health and Reducing Addiction in Childhood and Adolescence through Mindfulness: Mechanisms, Prevention and Treatment; 01GL1745B subproject TP1), the Else Kröner-Fresenius-Stiftung (2019_A45, grant to Anna Eichler), and the Universitätsbund Erlangen-Nürnberg (no grant number, grant to Anna Eichler).

Institutional Review Board Statement

The study was approved by the Ethik-Kommission from the Friedrich-Alexander Uni-versität Erlangen—Nürnberg (FAU) (353_18B) and conducted in accordance with the Declaration of Helsinki.

Informed Consent Statement

Mothers and children gave approved consent.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

With the public-funded research project IMAC-Mind: Improving Mental Health and Reducing Addiction in Childhood and Adolescence through Mindfulness: Mechanisms, Prevention and Treatment (2017−2021 BMBF-Förderkennzeichen (subproject)), the Federal Ministry of Education and Research contributes to improving the prevention and treatment of children and adolescents with substance use disorders and associated mental disorders. Project coordination was realized by the German Center of Addiction Research in Childhood and Adolescence at the University Medical Center Hamburg-Eppendorf. The consortium comprises seven projects in Germany. Principal Investigators are Rainer Thomasius (Coordinator, University Medical Center Hamburg-Eppendorf), Tobias Banaschewski und Herta Flor (both Central Institute for Mental Health, Mannheim), Johannes Kornhuber (Friedrich-Alexander-Universität Erlangen-Nürnberg), Michael Klein (Catholic University of Applied Sciences, Cologne), Olaf Reis (University Medicine of Rostock), Tanja Legenbauer (Ruhr-University Bochum), and Antonia Zapf (University Medical Center Hamburg-Eppendorf). Further members of the consortium are Nicolas Arnaud, Christiane Baldus, Anne Daubmann, Simone Franz, Amra Hot, Sabrina Kunze, Kathrin Simon-Kutscher, Léa Josette Laurenz, Sophie Luise Schiller, Michael Supplieth (University Medical Center Hamburg-Eppendorf), Frauke Nees, Karina Abenova, Karl Gottfried, Stella Guldner, Sabina Millenet, Maren Prignitz (Central Institute for Mental Health, Mannheim), Bernd Lenz, Peter Fasching, Matthias Beckmann, Verena Nadine Buchholz, Anna Eichler, Lothar Häberle, Christiane Mühle, Patrick Stelzl, Bernhard Volz (Friedrich-Alexander-University Erlangen-Nürnberg), Jihan Hassan, Lina-Sophia Falkenberg, Daria Kunst (Catholic University of Applied Sciences, Cologne), Lucie Waedel (University Medicine of Rostock), Martin Holtmann, Regina Herdering, Carina Maria Huhn, Lea Kretschmar, and Laura Mokros (Ruhr-University Bochum). For more information, please visit our homepage www.IMAC-Mind.de. The present work was performed in partial fulfillment of the requirements for obtaining the degree Phil. for Janina Maschke.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Muggli, E.; O’Leary, C.; Forster, D.; Anderson, P.; Lewis, S.; Nagle, C.; Craig, J.M.; Donath, S.; Elliott, E.; Halliday, J. Study protocol: Asking QUestions about Alcohol in pregnancy (AQUA): A longitudinal cohort study of fetal effects of low to moderate alcohol exposure. BMC Pregnancy Childbirth 2014, 14, 302. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Muggli, E.; Matthews, H.; Penington, A.; Claes, P.; O’Leary, C.; Forster, D.; Donath, S.; Anderson, P.J.; Lewis, S.; Nagle, C.; et al. Association Between Prenatal Alcohol Exposure and Craniofacial Shape of Children at 12 Months of Age. JAMA Pediatr. 2017. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Hoyme, H.E.; Kalberg, W.O.; Elliott, A.J.; Blankenship, J.; Buckley, D.; Marais, A.S.; Manning, M.A.; Robinson, L.K.; Adam, M.P.; Abdul-Rahman, O.; et al. Updated Clinical Guidelines for Diagnosing Fetal Alcohol Spectrum Disorders. Pediatrics 2016, 138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Popova, S.; Lange, S.; Probst, C.; Gmel, G.; Rehm, J. Estimation of national, regional, and global prevalence of alcohol use during pregnancy and fetal alcohol syndrome: A systematic review and meta-analysis. Lancet Glob. Health 2017, 5, e290–e299. [Google Scholar] [CrossRef] [Green Version]
  5. Lanting, C.I.; van Dommelen, P.; van der Pal-de Bruin, K.M.; Bennebroek Gravenhorst, J.; van Wouwe, J.P. Prevalence and pattern of alcohol consumption during pregnancy in the Netherlands. BMC Public Health 2015, 15, 723. [Google Scholar] [CrossRef] [Green Version]
  6. Melchior, M.; Hersi, R.; van der Waerden, J.; Larroque, B.; Saurel-Cubizolles, M.J.; Chollet, A.; Galera, C. Maternal tobacco smoking in pregnancy and children’s socio-emotional development at age 5: The EDEN mother-child birth cohort study. Eur. Psychiatry 2015. [Google Scholar] [CrossRef]
  7. Lange, S.; Probst, C.; Gmel, G.; Rehm, J.; Burd, L.; Popova, S. Global Prevalence of Fetal Alcohol Spectrum Disorder among Children and Youth: A Systematic Review and Meta-analysis. JAMA Pediatr. 2017, 171, 948–956. [Google Scholar] [CrossRef]
  8. De Gruyter, S. (Ed.) Teratogenität des Alkohols; Walter de Gruyter GmbH: Berlin, Germany, 2013; p. 112. [Google Scholar] [CrossRef]
  9. Petermann, F. Wechsler Intelligence Scale for Children, 5th ed.; Deutschsprachige Adaptation der WISC-V; von David, W., Frankfurt, A.M., Eds.; Pearson Assessment: Frankfurt, Germany, 2017. [Google Scholar]
  10. Feldmann, R. Psychopathologie bei Kindern und Jugendlichen mit Fetalem Alkohol-Syndrom; Urban & Vogel: München, Germany, 2006. [Google Scholar]
  11. Burden, M.J.; Jacobson, J.L.; Westerlund, A.; Lundahl, L.H.; Morrison, A.; Dodge, N.C.; Klorman, R.; Nelson, C.A.; Avison, M.J.; Jacobson, S.W. An event-related potential study of response inhibition in ADHD with and without prenatal alcohol exposure. Alcohol. Clin. Exp. Res. 2010, 34, 617–627. [Google Scholar] [CrossRef]
  12. Coles, C.D. Discriminating the effects of prenatal alcohol exposure from other behavioral and learning disorders. Alcohol Res. Health 2011, 34, 42–50. [Google Scholar]
  13. Dörrie, N.; Föcker, M.; Freunscht, I.; Hebebrand, J. Fetal alcohol spectrum disorders. Eur. Child Adolesc. Psychiatry 2014, 23, 863–875. [Google Scholar] [CrossRef] [Green Version]
  14. Landgraf, M.N.; Albers, L.; Rahmsdorf, B.; Vill, K.; Gerstl, L.; Lippert, M.; Heinen, F. Fetal alcohol spectrum disorders (FASD)—What we know and what we should know—The knowledge of German health professionals and parents. Eur. J. Paediatr. Neurol. 2018. [Google Scholar] [CrossRef]
  15. Römer, P.; Mathes, B.; Reinelt, T.; Stoyanova, P.; Petermann, F.; Zierul, C. Systematic review showed that low and moderate prenatal alcohol and nicotine exposure affected early child development. Acta Paediatr. 2020. [Google Scholar] [CrossRef] [PubMed]
  16. Eichler, A.; Hudler, L.; Grunitz, J.; Grimm, J.; Raabe, E.; Goecke, T.W.; Fasching, P.A.; Beckmann, M.W.; Kratz, O.; Moll, G.H.; et al. Effects of prenatal alcohol consumption on cognitive development and ADHD-related behaviour in primary-school age: A multilevel study based on meconium ethyl glucuronide. J. Child Psychol. Psychiatry Allied Discip. 2018, 59, 110–118. [Google Scholar] [CrossRef] [PubMed]
  17. Min, M.O.; Singer, L.T.; Minnes, S.; Wu, M.; Bearer, C.F. Association of fatty acid ethyl esters in meconium and cognitive development during childhood and adolescence. J. Pediatr. 2015, 166, 1042–1047. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Peterson, J.; Kirchner, H.L.; Xue, W.; Minnes, S.; Singer, L.T.; Bearer, C.F. Fatty acid ethyl esters in meconium are associated with poorer neurodevelopmental outcomes to two years of age. J. Pediatr. 2008, 152, 788–792. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Popova, S.; Lange, S.; Temple, V.; Poznyak, V.; Chudley, A.E.; Burd, L.; Murray, M.; Rehm, J. Profile of Mothers of Children with Fetal Alcohol Spectrum Disorder: A Population-Based Study in Canada. Int. J. Environ. Res. Public Health 2020, 17. [Google Scholar] [CrossRef] [PubMed]
  20. Eichler, A.; Grunitz, J.; Grimm, J.; Walz, L.; Raabe, E.; Goecke, T.W.; Beckmann, M.W.; Kratz, O.; Heinrich, H.; Moll, G.H.; et al. Did you drink alcohol during pregnancy? Inaccuracy and discontinuity of women’s self-reports: On the way to establish meconium ethyl glucuronide (EtG) as a biomarker for alcohol consumption during pregnancy. Alcohol 2016, 54, 39–44. [Google Scholar] [CrossRef]
  21. Lamy, S.; Hennart, B.; Houivet, E.; Dulaurent, S.; Delavenne, H.; Benichou, J.; Allorge, D.; Marret, S.; Thibaut, F. Assessment of tobacco, alcohol and cannabinoid metabolites in 645meconium samples of newborns compared to maternal self-reports. J. Psychiatr. Res. 2017, 90, 86–93. [Google Scholar] [CrossRef]
  22. Grimm, J.; Stemmler, M.; Golub, Y.; Schwenke, E.; Goecke, T.W.; Fasching, P.A.; Beckmann, M.W.; Kratz, O.; Moll, G.H.; Kornhuber, J.; et al. The association between prenatal alcohol consumption and preschool child stress system disturbance. Dev. Psychobiol. 2020. [Google Scholar] [CrossRef]
  23. Tan, D.W.; Foo, Y.Z.; Downs, J.; Finlay-Jones, A.; Leonard, H.; Licari, M.K.; Mullan, N.; Symons, M.; Varcin, K.J.; Whitehouse, A.J.O.; et al. A preliminary investigation of the effects of prenatal alcohol exposure on facial morphology in children with Autism Spectrum Disorder. Alcohol 2020, 86, 75–80. [Google Scholar] [CrossRef]
  24. Bandoli, G.; Jones, K.; Wertelecki, W.; Yevtushok, L.; Zymak-Zakutnya, N.; Granovska, I.; Plotka, L.; Chambers, C. Patterns of Prenatal Alcohol Exposure and Alcohol-Related Dysmorphic Features. Alcohol. Clin. Exp. Res. 2020. [Google Scholar] [CrossRef] [PubMed]
  25. Suttie, M.; Foroud, T.; Wetherill, L.; Jacobson, J.L.; Molteno, C.D.; Meintjes, E.M.; Hoyme, H.E.; Khaole, N.; Robinson, L.K.; Riley, E.P.; et al. Facial dysmorphism across the fetal alcohol spectrum. Pediatrics 2013, 131, e779–e788. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Howe, L.J.; Sharp, G.C.; Hemani, G.; Zuccolo, L.; Richmond, S.; Lewis, S.J. Prenatal alcohol exposure and facial morphology in a UK cohort. Drug Alcohol Depend. 2019, 197, 42–47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Lipinski, R.J.; Hammond, P.; O’Leary-Moore, S.K.; Ament, J.J.; Pecevich, S.J.; Jiang, Y.; Budin, F.; Parnell, S.E.; Suttie, M.; Godin, E.A.; et al. Ethanol-induced face-brain dysmorphology patterns are correlative and exposure-stage dependent. PLoS ONE 2012, 7, e43067. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Theiler, K. The House Mouse Atlas of Embryonic Development; Springer: Berlin/Heidelberg, Germany, 1989. [Google Scholar]
  29. Astley, S.J.; Clarren, S.K. A fetal alcohol syndrome screening tool. Alcohol. Clin. Exp. Res. 1995, 19, 1565–1571. [Google Scholar] [CrossRef] [PubMed]
  30. Astley, S.J.; Clarren, S.K. Measuring the facial phenotype of individuals with prenatal alcohol exposure: Correlations with brain dysfunction. Alcohol Alcohol. 2001, 36, 147–159. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Astley, S.J.; Clarren, S.K. A case definition and photographic screening tool for the facial phenotype of fetal alcohol syndrome. J. Pediatr. 1996, 129, 33–41. [Google Scholar] [CrossRef]
  32. Astley, S.J.; Clarren, S.K. FAS Diagnostic & Prevention Network. Available online: https://depts.washington.edu/fasdpn/ (accessed on 1 July 2020).
  33. Goecke, T.W.; Burger, P.; Fasching, P.A.; Bakdash, A.; Engel, A.; Haberle, L.; Voigt, F.; Faschingbauer, F.; Raabe, E.; Maass, N.; et al. Meconium Indicators of Maternal Alcohol Abuse during Pregnancy and Association with Patient Characteristics. BioMed Res. Int. 2014, 2014, 702848. [Google Scholar] [CrossRef] [PubMed]
  34. Reulbach, U.; Bleich, S.; Knorr, J.; Burger, P.; Fasching, P.A.; Kornhuber, J.; Beckmann, M.W.; Goecke, T.W. Prä-, peri- und postpartale Depressivität. Erste Erkenntnisse ausFRAMES (Franconian Maternal Health Evaluation Study). Fortschr. Neurol. Psychiatr. 2009, 77, 708–713. [Google Scholar] [CrossRef]
  35. Fertig, M.; Kluve, J. The Effect of Age at School Entry on Educational Attainment in Germany. RWI Leibniz-Institut für Wirtschaftsforschung. IZA Discussion Paper No. 1507. 2005. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=678905 (accessed on 1 February 2020).
  36. Comasco, E.; Rangmar, J.; Eriksson, U.J.; Oreland, L. Neurological and Neuropsychological Effects of Low and Moderate Prenatal Alcohol Exposure. Acta Physiol. 2017. [Google Scholar] [CrossRef]
  37. Bakdash, A.; Burger, P.; Goecke, T.W.; Fasching, P.A.; Reulbach, U.; Bleich, S.; Hastedt, M.; Rothe, M.; Beckmann, M.W.; Pragst, F.; et al. Quantification of fatty acid ethyl esters (FAEE) and ethyl glucuronide (EtG) in meconium from newborns for detection of alcohol abuse in a maternal health evaluation study. Anal. Bioanal. Chem. 2010, 396, 2469–2477. [Google Scholar] [CrossRef] [PubMed]
  38. Himes, S.K.; Dukes, K.A.; Tripp, T.; Petersen, J.; Raffo, C.; Burd, L.; Odendaal, H.; Elliott, A.J.; Hereld, D.; Signore, C.; et al. Clinical Sensitivity and Specificity of Meconium Fatty Acid Ethyl Ester, Ethyl Glucuronide, and Ethyl Sulfate for Detecting Maternal Drinking During Pregnancy. Clin. Chem. 2015. [Google Scholar] [CrossRef] [PubMed]
  39. Flak, A.L.; Su, S.; Bertrand, J.; Denny, C.H.; Kesmodel, U.S.; Cogswell, M.E. The association of mild, moderate, and binge prenatal alcohol exposure and child neuropsychological outcomes: A meta-analysis. Alcohol. Clin. Exp. Res. 2014, 38, 214–226. [Google Scholar] [CrossRef] [PubMed]
  40. Farkas, L.G.; Katic, M.J.; Forrest, C.R. Anthropometric proportion indices in the craniofacial regions of 73 patients with forms of isolated coronal synostosis. Ann. Plast. Surg. 2005, 55, 495–499. [Google Scholar] [CrossRef]
  41. Astley, S.J. Canadian palpebral fissure length growth charts reflect a good fit for two school and FASD clinic-based U. S. populations. J. Popul. Ther. Clin. Pharmacol. 2011, 18, e231–e241. [Google Scholar]
  42. Astley, S. FAS Facial Photographic Anaysis Software. Available online: https://depts.washington.edu/fasdpn/htmls/face-software.htm (accessed on 1 February 2020).
  43. Astley, S.J. Palpebral fissure length measurement: Accuracy of the FAS facial photographic analysis software and inaccuracy of the ruler. J. Popul. Ther. Clin. Pharmacol. 2015, 22, e9–e26. [Google Scholar]
  44. Hall, J.G.; Froster-Iskenius, U.G.; Allanson, J.E. Handbook of Normal Physical Measurements; Oxford University Press: New York, NY, USA, 1989. [Google Scholar]
  45. Astley, S.J.; Stachowiak, J.; Clarren, S.K.; Clausen, C. Application of the fetal alcohol syndrome facial photographic screening tool in a foster care population. J. Pediatr. 2002, 141, 712–717. [Google Scholar] [CrossRef] [Green Version]
  46. Todorow, M.; Moore, T.E.; Koren, G. Investigating the effects of low to moderate levels of prenatal alcohol exposure on child behaviour: A critical review. J. Popul. Ther. Clin. Pharmacol. 2010, 17, e323–e330. [Google Scholar]
  47. Lange, S.; Shield, K.; Koren, G.; Rehm, J.; Popova, S. A comparison of the prevalence of prenatal alcohol exposure obtained via maternal self-reports versus meconium testing: A systematic literature review and meta-analysis. BMC Pregnancy Childbirth 2014, 14, 127. [Google Scholar] [CrossRef] [Green Version]
  48. Smith, S.M.; Garic, A.; Flentke, G.R.; Berres, M.E. Neural crest development in fetal alcohol syndrome. Birth Defects Res. Part C Embryo Today Rev. 2014, 102, 210–220. [Google Scholar] [CrossRef] [Green Version]
  49. Hill, M. Carnegie Stage Comparison. Available online: https://jeltsch.org/carnegie_stage_comparison (accessed on 16 December 2020).
  50. Halliday, J.L.; Muggli, E.; Lewis, S.; Elliott, E.J.; Amor, D.J.; O’Leary, C.; Donath, S.; Forster, D.; Nagle, C.; Craig, J.M.; et al. Alcohol consumption in a general antenatal population and child neurodevelopment at 2 years. J. Epidemiol. Community Health 2017. [Google Scholar] [CrossRef] [PubMed]
  51. Petrelli, B.; Bendelac, L.; Hicks, G.G.; Fainsod, A. Insights into retinoic acid deficiency and the induction of craniofacial malformations and microcephaly in fetal alcohol spectrum disorder. Genesis 2019, 57, e23278. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Koren, G.; Ornoy, A. Searching for the Fetal Alcohol Behavioral Phenotype. Glob. Pediatr. Health 2020, 7. [Google Scholar] [CrossRef]
  53. Abernethy, C.; McCall, K.E.; Cooper, G.; Favretto, D.; Vaiano, F.; Bertol, E.; Mactier, H. Determining the pattern and prevalence of alcohol consumption in pregnancy by measuring biomarkers in meconium. Arch. Dis. Child. Fetal Neonatal Ed. 2017. [Google Scholar] [CrossRef]
  54. Joya, X.; Friguls, B.; Ortigosa, S.; Papaseit, E.; Martínez, S.E.; Manich, A.; Garcia-Algar, O.; Pacifici, R.; Vall, O.; Pichini, S. Determination of maternal-fetal biomarkers of prenatal exposure to ethanol: A review. J. Pharm. Biomed. Anal. 2012, 69, 209–222. [Google Scholar] [CrossRef]
  55. Chen, W.J.; Maier, S.E.; Parnell, S.E.; West, J.R. Alcohol and the developing brain: Neuroanatomical studies. Alcohol Res. Health 2003, 27, 174–180. [Google Scholar]
  56. May, P.A.; Blankenship, J.; Marais, A.S.; Gossage, J.P.; Kalberg, W.O.; Joubert, B.; Cloete, M.; Barnard, R.; De Vries, M.; Hasken, J.; et al. Maternal alcohol consumption producing fetal alcohol spectrum disorders (FASD): Quantity, frequency, and timing of drinking. Drug Alcohol Depend. 2013, 133, 502–512. [Google Scholar] [CrossRef] [Green Version]
  57. Bingol, N.; Schuster, C.; Fuchs, M.; Iosub, S.; Turner, G.; Stone, R.K.; Gromisch, D.S. The influence of socioeconomic factors on the occurrence of fetal alcohol syndrome. Adv. Alcohol Subst. Abus. 1987, 6, 105–118. [Google Scholar] [CrossRef]
  58. McCormack, C.; Monk, C. Considering Prenatal Alcohol Exposure in a Developmental Origins of Health and Disease Framework. Am. J. Psychiatry 2020, 177, 1025–1028. [Google Scholar] [CrossRef]
  59. Bearer, C.F.; Jacobson, J.L.; Jacobson, S.W.; Barr, D.; Croxford, J.; Molteno, C.D.; Viljoen, D.L.; Marais, A.S.; Chiodo, L.M.; Cwik, A.S. Validation of a new biomarker of fetal exposure to alcohol. J. Pediatr. 2003, 143, 463–469. [Google Scholar] [CrossRef] [Green Version]
  60. Derauf, C.; Katz, A.R.; Easa, D. Agreement between maternal self-reported ethanol intake and tobacco use during pregnancy and meconium assays for fatty acid ethyl esters and cotinine. Am. J. Epidemiol. 2003, 158, 705–709. [Google Scholar] [CrossRef] [Green Version]
  61. Muggli, E.; O’Leary, C.; Donath, S.; Orsini, F.; Forster, D.; Anderson, P.J.; Lewis, S.; Nagle, C.; Craig, J.M.; Elliott, E.; et al. “Did you ever drink more?” A detailed description of pregnant women’s drinking patterns. BMC Public Health 2016, 16, 683. [Google Scholar] [CrossRef] [Green Version]
  62. Dukes, K.; Tripp, T.; Willinger, M.; Odendaal, H.; Elliott, A.J.; Kinney, H.C.; Robinson, F.; Petersen, J.M.; Raffo, C.; Hereld, D.; et al. Drinking and smoking patterns during pregnancy: Development of group-based trajectories in the Safe Passage Study. Alcohol 2017, 62, 49–60. [Google Scholar] [CrossRef] [PubMed]
  63. Popova, S.; Dozet, D.; Burd, L. Fetal Alcohol Spectrum Disorder: Can We Change the Future? Alcohol. Clin. Exp. Res. 2020, 44, 815–819. [Google Scholar] [CrossRef] [PubMed]
  64. Caputo, C.; Wood, E.; Jabbour, L. Impact of fetal alcohol exposure on body systems: A systematic review. Birth Defects Res. Part C Embryo Today Rev. 2016, 108, 174–180. [Google Scholar] [CrossRef] [PubMed]
  65. Aragón, A.S.; Coriale, G.; Fiorentino, D.; Kalberg, W.O.; Buckley, D.; Gossage, J.P.; Ceccanti, M.; Mitchell, E.R.; May, P.A. Neuropsychological characteristics of Italian children with fetal alcohol spectrum disorders. Alcohol. Clin. Exp. Res. 2008, 32, 1909–1919. [Google Scholar] [CrossRef] [Green Version]
  66. Adnams, C.M.; Kodituwakku, P.W.; Hay, A.; Molteno, C.D.; Viljoen, D.; May, P.A. Patterns of cognitive-motor development in children with fetal alcohol syndrome from a community in South Africa. Alcohol. Clin. Exp. Res. 2001, 25, 557–562. [Google Scholar] [CrossRef]
  67. Foster, P.J.; Broadway, D.C.; Hayat, S.; Luben, R.; Dalzell, N.; Bingham, S.; Wareham, N.J.; Khaw, K.T. Refractive error, axial length and anterior chamber depth of the eye in British adults: The EPIC-Norfolk Eye Study. Br. J. Ophthalmol. 2010, 94, 827–830. [Google Scholar] [CrossRef]
  68. Song, H.T.; Kim, Y.J.; Lee, S.J.; Moon, Y.S. Relations between age, weight, refractive error and eye shape by computerized tomography in children. Korean J. Ophthalmol. 2007, 21, 163–168. [Google Scholar] [CrossRef] [Green Version]
Brainsci 11 00154 g001 550
Figure 1. Typical facial abnormalities in fetal alcohol syndrome (FAS) children and FAS software measures (modeled in a non-affected adult person).
Figure 1. Typical facial abnormalities in fetal alcohol syndrome (FAS) children and FAS software measures (modeled in a non-affected adult person).
Brainsci 11 00154 g001
Brainsci 11 00154 g002 550
Figure 2. PFL length in millimeters and z-score for EtG 10 ng/g (EtG10+ n = 32) and 112 ng/g (EtG112+ n = 20) cut-off values. Higher PFL value = longer eyes. Univariate ANCOVA results: + p < 0.01, * p < 0.05, and ** p < 0.01.
Figure 2. PFL length in millimeters and z-score for EtG 10 ng/g (EtG10+ n = 32) and 112 ng/g (EtG112+ n = 20) cut-off values. Higher PFL value = longer eyes. Univariate ANCOVA results: + p < 0.01, * p < 0.05, and ** p < 0.01.
Brainsci 11 00154 g002
Brainsci 11 00154 g003 550
Figure 3. Lip circularity for the EtG 10 ng/g (EtG10+ n = 32) and 112 ng/g (EtG112+ n = 20) cut-offs. Higher lip circularity value = smaller upper lip size. Univariate ANCOVA results: * p < 0.05
Figure 3. Lip circularity for the EtG 10 ng/g (EtG10+ n = 32) and 112 ng/g (EtG112+ n = 20) cut-offs. Higher lip circularity value = smaller upper lip size. Univariate ANCOVA results: * p < 0.05
Brainsci 11 00154 g003
Brainsci 11 00154 g004 550
Figure 4. (a) Lip circularity versus fluid reasoning for EtG-negative individuals (10 ng/g cut-off, EtG-negative n = 97). (b) Lip circularity versus fluid reasoning for EtG10+ individuals (EtG10+ n = 32). (c) Lip circularity versus fluid reasoning for the EtG112+ (EtG112+ n = 20) cut-off. Higher lip circularity value = smaller upper lip size. * p < 0.05.
Figure 4. (a) Lip circularity versus fluid reasoning for EtG-negative individuals (10 ng/g cut-off, EtG-negative n = 97). (b) Lip circularity versus fluid reasoning for EtG10+ individuals (EtG10+ n = 32). (c) Lip circularity versus fluid reasoning for the EtG112+ (EtG112+ n = 20) cut-off. Higher lip circularity value = smaller upper lip size. * p < 0.05.
Brainsci 11 00154 g004
Brainsci 11 00154 g005 550
Figure 5. (a) Lip circularity versus working memory for EtG-negative individuals (the 10 ng/g cut-off, EtG-negative n = 97). (b) Lip circularity versus fluid reasoning for EtG10+ individuals (EtG10+ n = 32). (c) Lip circularity versus working memory for EtG112+ (EtG112+ n = 20) cut-offs. Higher lip circularity value = smaller upper lip size. + p < 0.10.
Figure 5. (a) Lip circularity versus working memory for EtG-negative individuals (the 10 ng/g cut-off, EtG-negative n = 97). (b) Lip circularity versus fluid reasoning for EtG10+ individuals (EtG10+ n = 32). (c) Lip circularity versus working memory for EtG112+ (EtG112+ n = 20) cut-offs. Higher lip circularity value = smaller upper lip size. + p < 0.10.
Brainsci 11 00154 g005
Table
Table 1. Frequency (n), means (M), and standard deviations (SD) of sample characteristics.
Table 1. Frequency (n), means (M), and standard deviations (SD) of sample characteristics.
TotalEtG10−EtG10+Statistics EtG112−EtG112+Statistics Self−Self+Statistics
n:1299732t(df) a,cpd10920t(df) a,cpd9732t(df) a,cpd
Prenatal:
EtG (ng/g)82.7 (301.2)--333.4 (537.0)------5.28 (17.4)504.7 (622.7)−19.0 (127)0.000 **1.1392.5 (342.6)52.9 (99.4)−1.44 (127)0.1530.16
sex (m/f) a66/6351/4615/170.310.5760.4957/52 9/11 0.360.5490.5346/51 20/122.190.1390.13
birth weight (grams)3439 (450.9)3382 (411.8)3613 (522.2)−2.28 (44.4)0.027 *0.493408 (441.7)3612 (473.0)−1.88 (127)0.062 +0.453393 (451.8)3579 (424.9)−2.05 (127)0.042 *0.42
maternal age at delivery (years)32.7 (4.53)32.5 (4.73)33.1 (3.92)−0.63 (127)0.5330.1432.7 (4.63)32.6 (4.06)0.15 (127)0.8810.0232.1 (4.61)34.5 (3.79)−2.67 (127)0.009 **0.57
maternal smoking pregnancy d0.65 (2.31)0.60 (2.21)0.81 (2.63)−0.45 (127)0.6510.190.53 (2.09)1.30 (3.26)−1.37 (127)0.1730.280.60 (2.21)0.81 (2.63)−0.45 (127)0.6510.09
Today:
age (years)13.30 (0.32)13.2 (0.27)13.4 (0.39)−2.47 (41.3)0.018 *0.6013.20 (0.29)13.41 (0.42)−1.91 (22.3)0.068 +0.5813.31 (0.31)13.29 (0.34)−0.77 (127)0.440.06
weight (kg)50.9 (10.2)49.94 (9.61)53.64 (11.38)−1.81 (127)0.073 +0.3550.2 (9.95)54.7 (10.7)−1.84 (127)0.068 +0.4450.8 (10.3)51.1 (9.95)−0.13 (127)0.8960.03
height (cm)161.4 (7.83)161.1 (7.70)162.24 (8.32)−0.70 (127)0.4870.14161.0 (7.57)163.4 (9.12)−1.22 (127)0.2260.29161.6 (7.63)160.7 (8.49)0.56 (127)0.5750.11
head (cm)55.0 (1.68)54.87 (1.77)55.23 (1.37)−1.07 (127) 0.2850.2354.9 (1.72)55.3 (1.47)−0.87 (127)0.3870.2554.84 (1.64) 55.32 (1.77)−1.40 (127)0.1620.28
family status b11.5 (2.14)11.39 (2.26)11.8 (1.74)−0.89 (127)0.3740.2011.47 (2.19)11.6 (1.90)−0.25 (127)0.8010.0911.4 (2.17)11.9 (2.02)−1.27 (127)0.2040.24
fluid reasoning 108.2 (14.8)110.4 (11.7)101.7 (20.3)2.23 (37.0)0.032 *0.61110.2 (11.3) 97.0 (24.0)2.37 (19.5)0.028 *0.70108.4 (15.3)107.5 (12.8)0.27 (120)0.7860.06
working memory110.7 (12.8)111.2 (12.3)109.1 (14.8)0.70 (112)0.4840.15111.3 (12.1)107.2 (17.2)0.88 (16.1)0.3920.28111.2 (12.2)109.3 (14.8)0.66 (112)0.5120.14
PFL Len (mm)27.4 (1.29)27.5 (1.28)27.2 (1.32)1.12 (127)0.2640.2327.45 (1.24)27.16 (1.57)0.94 (127)0.3510.2027.45 (1.37)27.28 (1.02)0.74 (70.6)0.4640.14
Z−1.93 (1.00)−1.86 (0.98)−2.14 (1.03)1.38 (127)0.1720.28−1.88 (0.95)−2.17 (1.23)1.19 (127)0.2370.26−1.90 (1.06)−2.03 (0.80)0.80 (68.8)0.4290.14
ICD Len (mm)31.9 (2.47)31.76 (2.57)32.2 (2.16)−0.90 (127)0.3710.1931.8 (2.52)32.4 (2.17)−0.98 (127)0.3210.2631.6 (2.35)32.7 (2.68)−2.20 (127)0.029 *0.44
Z0.40 (1.03)0.35 (1.07)0.55 (1.00)−0.93 (127)0.3540.190.34 (1.05)0.62 (0.88)−1.05 (127)0.2970.290.29 (0.98)0.74 (1.11)−2.18 (127)−0.4510.43
lip circularity57.4 (12.0)56.9 (10.7)58.9 (15.3)−0.69 (41.5)0.4160.1556.5 (10.7)62.2 (17.0)−1.44 (21.8)0.1640.4058.0 (11.7)55.3 (12.9)1.12 (127)0.2630.22
Notes. a ‘Child sex’ statistic: Chi-squared χ2(1) test. EtG: Ethyl glucuronide. b Socioeconomic status: FRANCES I, combination of maternal/paternal education level (4-level: <9, 9, 10, or 13 years) and net family income (6-level: <1000 to >5000) (sum-index, theoretical range: 3–14). c School types represent ranked ordinal data. Therefore, a Mann–Whitney U-test was performed. d Cigarettes/day. Child fluid reasoning and working memory: WISC V, theoretical range: MW = 100, SD = 15 [1]. PFL: palpebral fissure length; the elliptic space between the medial and lateral canthi of the two open lids. Higher PFL value = longer eyes. ICD = inner canthal distance; distance between the two medial canthi of the eyes. Higher ICD value = increased distance between the eyes. Circularity = thinness of the upper lip. Higher Circularity value = smaller upper lip size. Len = true length in mm. Z = calculated z-score, according to Hall (1989) [44]; all z-scores are age- and gender-dependent and calculated by the program. χ2: df = x; t: df = x. + p < 0.10, * p < 0.05, and ** p < 0.01.
Table
Table 2. FAS software score correlations (Pearson’s r) between two independent raters, n = 129.
Table 2. FAS software score correlations (Pearson’s r) between two independent raters, n = 129.
Rater 1
PFLICDCircularity
Lengthz-scoreLengthz-score
Rater 2PFL aLength0.884 **0.887 **0.1270.128−0.090
z-score0.869 **0.876 **0.0970.098−0.079
ICDLength0.0470.0390.970 **0.970 **−0.132
z-score0.0370.0290.964 **0.965 **−0.120
Circularity −0.195 *−0.186 *−0.036−0.0370.727 **
Notes. a Left-Right-Mean Score. Length in mm. z-score (see [43,44]): Age and gender norms. PFL = palpebral fissure length; the elliptic space between the medial and lateral canthi of the two open lids. Higher PFL value = longer eyes. ICD = inner canthal distance; distance between the two medial canthi of the eyes. Higher ICD value = increased distance between the eyes. Circularity = thinness of the upper lip. Higher circularity value = smaller upper lip size. Length = true length in mm, calculated by the program. Z = z-score calculated by the program. * p < 0.05, and ** p < 0.01.
Table
Table 3. Testing potential confounders in Pearson’s correlations and t-tests (r/t(p)), n = 129.
Table 3. Testing potential confounders in Pearson’s correlations and t-tests (r/t(p)), n = 129.
PFLICDCircularity a
LenZLenZ
EtG a−0.060 (0.500)−0.083 (0.350)0.086 (0.335)0.089 (0.313)0.085 (0.337)
sex (male/female)3.09 (0.002) **3.00 (0.002) **2.60 (0.011) *2.51 (0.013) *−1.55 (0.124)
age (years)0.113 (0.200)0.038 0(.669)0.091 (0.305)0.080 (0.365)−0.169 (0.055) +
birth weight (grams)0.308 (0.000) **0.293 (0.001) **0.253 (0.004) **0.247 (0.005) **−0.054 (.544)
weight (grams)0.256 (0.003) **0.223 (0.011) *0.204 (0.020) *0.202 (0.022) *−0.162 (0.066) +
height (cm)0.382 (0.000) **0.349 (0.000) **0.203 (0.021) *0.198 (0.025) *−0.239 (0.006) **
head (cm)0.300 (0.001) **0.284 (0.001) **0.348 (0.000) **0.338 (0.000) **−0.008 (0.925)
family status −0.056 (0.531)−0.052 (0.556)−0.154 (0.081) +−0.162 (0.066) +−0.059 (0.503)
maternal age at delivery−0.067 (0.450)−0.075 (0.396)0.069 (0.439)0.070 (0.431)0.088 (0.320)
maternal smoking pregnancy0.021 (0.816)0.022 (0.802)0.005 (0.959)0.004 (0.965)0.032 (0.721)
Notes. a ln(x + 1) transformed continuous EtG data, and ln(x) transformed lip circularity data. Socioeconomic status: Combination of maternal/paternal education level (4-level: <9, 9, 10, or 13 years) and net family income (6-level: <1000 to >5000) (sum-index, theoretical range: 3–14). t: df = 127. Maternal smoking during pregnancy: yes = smoking ≥ 1 cigarette per day. PFL = palpebral fissure length; the elliptic space between the medial and lateral canthi of the two open lids Higher PFL value = longer eyes. ICD = inner canthal distance; distance between the two medial canthi of the eyes. Higher ICD value = increased distance between the eyes. Circularity = thinness of the upper lip. Higher circularity value = smaller upper lip size. Len = true length. Z = calculated z-score, according to Hall (1989) [44], where all z-scores are age-dependent and calculated by the program. + p < 0.10, * p < 0.05, and ** p < 0.01.
Table
Table 4. EtG-associated differences in facial parameters (ANCOVAs, n = 129, df: 7/121).
Table 4. EtG-associated differences in facial parameters (ANCOVAs, n = 129, df: 7/121).
EtG10EtG112Self-Report
Fpηp2Fpηp2Fpηp2
PFL Length6.720.000 **0.2806.530.000 **0.2746.180.000 **0.263
EtG/Self4.790.031 *0.0383.760.055 +0.0301.960.1640.016
sex3.940.049 *0.0324.090.045*0.0335.160.025 *0.041
age0.060.8060.0010.010.9160.0000.020.8880.000
birth weight9.150.003 **0.0708.300.005 **0.0647.580.007 **0.059
height8.590.004 **0.0669.550.002 **0.0738.490.004 **0.066
weight0.070.7990.0010.320.8950.0000.000.9600.000
head circumference0.140.7110.0010.120.7330.0010.250.6190.002
PFL z-score5.990.000 **0.2575.830.000 **0.2525.460.000 **0.240
EtG/Self4.760.031 *0.0383.880.051 +0.0311.890.1720.015
sex3.510.063 +0.0283.650.058 +0.0294.660.033 *0.037
age0.350.5580.0030.530.4690.0040.970.3270.008
birth weight8.560.004 **0.0667.770.006 **0.0607.030.009 **0.055
height7.800.006 **0.0618.720.004 **0.0677.730.006 **0.060
weight0.020.8870.0000.010.9350.0000.000.9610.000
head circumference0.230.6630.0020.200.6560.0020.360.5490.003
ICD Len3.350.003 **0.1623.380.002 **0.1643.750.001 **0.178
EtG/Self0.080.7770.0010.270.6070.0022.430.1220.020
sex2.060.1540.0172.110.1490.0171.700.1950.014
age0.010.9040.0000.020.8830.0000.020.8810.000
birth weight2.250.1360.0182.240.1370.0181.950.1650.016
height0.160.6860.0010.150.6970.0010.320.5750.003
weight0.230.6330.0020.220.6410.0020.220.6380.002
head circumference4.470.037 *0.0364.520.036 *0.0364.020.047 *0.032
ICD z-score3.170.004 **0.1553.210.004 **0.1573.560.002 **0.171
EtG/Self0.120.7260.0010.350.5540.0032.410.1230.020
sex1.840.1660.0161.990.1610.0161.580.2120.013
age0.060.8080.0000.070.7880.0010.070.7930.001
birth weight2.160.1450.0182.160.1450.0181.900.1710.015
height0.150.7010.0010.130.7160.0010.280.5940.002
weight0.270.6050.0020.260.6120.0020.270.6040.002
head circumference4.140.044 *0.0334.200.043 *0.0343.710.056 *0.030
Circularity2.310.031 *0.1182.980.006 **0.1472.490.020 *0.126
EtG/Self0.920.3400.0085.090.026 *0.0402.040.1560.017
sex3.120.080 +0.0252.920.090 +0.0243.080.082 +0.025
age2.250.1360.0182.920.090 +0.0241.560.2140.13
birth weight0.260.6610.0020.440.5060.0040.020.8960.000
height3.830.053 +0.0314.200.043 *0.0344.860.029 *0.039
weight0.370.5430.0030.500.4790.0040.220.6440.002
head circumference3.770.055 +0.0034.100.045 *0.0334.170.043 *0.033
Notes. N: EtG10− (97) vs. EtG10+ (32); EtG112− (109) vs. EtG112+ (20); Self− (97) vs. Self+ (32). All analyses controlled for the child’s age, childbirth weight, child’s sex, child’s weight, child’s height, and child’s head circumference as covariates. EtG = ethyl glucuronide. PFL = palpebral fissure length; the elliptic space between the medial and lateral canthi of the two open lids. Higher PFL value = longer eyes. ICD = inner canthal distance; distance between the two medial canthi of the eyes. Higher ICD value = increased distance between the eyes. Circularity = thinness of the upper lip. Higher circularity value = smaller upper lip size. Len = true length- Z = calculated z-score by program. + p < 0.10, * p < 0.05, and ** p < 0.01.
Table
Table 5. Partial correlations (r p) of child PFL and lip circularity with the fluid reasoning and working memory index within the affected EtG+ groups.
Table 5. Partial correlations (r p) of child PFL and lip circularity with the fluid reasoning and working memory index within the affected EtG+ groups.
nFluid Reasoning IndexWorking
Memory Index
EtG10+32
(df = 16)
PFL len 0.113 (0.654)0.058 (0.818)
PFL z-score 0.117 (0.644)0.044 (0.861)
Circularity −0.509 (0.031 *)−0.418 (0.084 +)
EtG112+20
(df = 7)
PFL len −0.010 (0.980)−0.129 (0.741)
PFL z-score 0.007 (0.986)−0.129 (0.741)
Circularity −0.391 (0.298)−0.528 (0.144)
Notes. All correlations controlled for the child’s age, childbirth weight, child’s sex, child’s weight, child’s height, and child’s head circumference as covariates. EtG = ethyl glucuronide. PFL = palpebral fissure length; the elliptic space between the medial and lateral canthi of the two open lids. Higher PFL value = longer eyes. ICD = inner canthal distance; distance between the two medial canthi of the eyes. Higher ICD value = increased distance between the eyes. Circularity = thinness of the upper lip. Higher circularity value = smaller upper lip size. Len = true length in mm. Z = calculated z-score by program. + p < 0.10, * p < 0.05.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Maschke, J.; Roetner, J.; Goecke, T.W.; Fasching, P.A.; Beckmann, M.W.; Kratz, O.; Moll, G.H.; Lenz, B.; Kornhuber, J.; Eichler, A.; et al. Prenatal Alcohol Exposure and the Facial Phenotype in Adolescents: A Study Based on Meconium Ethyl Glucuronide. Brain Sci. 2021, 11, 154. https://doi.org/10.3390/brainsci11020154

AMA Style

Maschke J, Roetner J, Goecke TW, Fasching PA, Beckmann MW, Kratz O, Moll GH, Lenz B, Kornhuber J, Eichler A, et al. Prenatal Alcohol Exposure and the Facial Phenotype in Adolescents: A Study Based on Meconium Ethyl Glucuronide. Brain Sciences. 2021; 11(2):154. https://doi.org/10.3390/brainsci11020154

Chicago/Turabian Style

Maschke, Janina, Jakob Roetner, Tamme W. Goecke, Peter A. Fasching, Matthias W. Beckmann, Oliver Kratz, Gunther H. Moll, Bernd Lenz, Johannes Kornhuber, Anna Eichler, and et al. 2021. "Prenatal Alcohol Exposure and the Facial Phenotype in Adolescents: A Study Based on Meconium Ethyl Glucuronide" Brain Sciences 11, no. 2: 154. https://doi.org/10.3390/brainsci11020154

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop