Multilevel analysis of the influence of maternal smoking and alcohol consumption on the facial shape of English adolescents

This cross-sectional study aims to assess the influence of maternal smoking and alcohol consumption during pregnancy on the facial shape of non-syndromic English adolescents and demonstrate the potential benefits of using multilevel principal component analysis (mPCA). A cohort of 3755 non-syndromic 15-year-olds from the Avon Longitudinal Study of Parents and Children (ALSPAC), England, were included. Maternal smoking and alcohol consumption during the 1st and 2nd trimesters of pregnancy were determined via questionnaire at 18 weeks gestation. 21 facial landmarks, used as a proxy for the main facial features, were manually plotted onto 3D facial scans of the participants. The effect of maternal smoking and maternal alcohol consumption (average 1–2 glasses per week) was minimal, with 0.66% and 0.48% of the variation in the 21 landmarks of non-syndromic offspring explained, respectively. This study provides a further example of mPCA being used effectively as a descriptive analysis in facial shape research. This is the first example of mPCA being extended to four levels to assess the influence of environmental factors. Further work on the influence of high/low levels of smoking and alcohol and providing inferential evidence is required

Initial Investigations of the cranial size and shape of adult Eurasian otters (Lutra lutra) in Great Britain

Three-dimensional (3D) surface scans were carried out in order to determine the shapes of the upper sections of (skeletal) crania of adult Eurasian otters (Lutra lutra) from Great Britain. Landmark points were placed on these shapes using a graphical user interface (GUI) and distance measurements (i.e., the length, height, and width of the crania) were found by using the landmark points. Male otters had significantly larger skulls than females (P < 0.001). Differences in size also occurred by geographical area in Great Britain (P < 0.05). Multilevel Principal Components Analysis (mPCA) indicated that sex and geographical area explained 31.1% and 9.6% of shape variation in “unscaled” shape data and that they explained 17.2% and 9.7% of variation in “scaled” data. The first mode of variation at level 1 (sex) correctly reflected size changes between males and females for “unscaled” shape data. Modes at level 2 (geographical area) also showed possible changes in size and shape. Clustering by sex and geographical area was observed in standardized component scores. Such clustering in a cranial shape by geographical area might reflect genetic differences in otter populations in Great Britain, although other potentially confounding factors (e.g., population age-structure, diet, etc.) might also drive regional differences. This work provides a successful first test of the effectiveness of 3D surface scans and multivariate methods, such as mPCA, to study the cranial morphology of otters

Investigating facial shape using multilevel principal component analysis

SUMMARY Aims: 1. To determine the influence of geographical location, sex, height, Body Mass Index (BMI), age (14-16 years old), pubertal stage, metabolic factors, atopy, breathing disorders, maternal smoking and alcohol consumption during pregnancy on facial shape. 2. To explore the usefulness of Multilevel Principal Component Analysis (mPCA) in facial shape research. Method: The influence of geographical location and sex was assessed using 21 landmarks on 3D facial scans of subjects from Croatia (n=73), England (n=79), Wales (n=50) and Finland (n=47). The influence of sex, height, BMI, age (14-16 years old), pubertal stage, metabolic factors, atopy, breathing disorders, maternal smoking and alcohol consumption during pregnancy on adolescent facial shape was assessed using 1000 and 7160 quasi-landmarks on 3D facial scans of the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n=1411). The results of mPCA were compared to those using landmarks only, conventional Principal Component Analysis (PCA), Discriminate Function Analysis (DFA) and Partial Least Squares Regression (PLSR). mPCA was also assessed as a variable selection tool prior to PLSR. Results: mPCA provided more meaningful information in the exploratory phase of data analysis than conventional PCA and DFA. However, the results must be interpreted with caution when group sizes are imbalanced. All variables reached significance, except for age, in their respective mPCA models. Geographical location, sex, height, BMI and fasting insulin explained greater than 5% of the total variation. These variables also reached significance in the PLSR models. Therefore 5% may be a useful threshold for PLSR variable selection. Conclusions: Sex, geographical location, height, BMI and fasting insulin had the most influence on facial shape. mPCA appears to be a useful tool for visualising the maximum variation between groups of subjects when group sizes are balanced and as a variable selection tool to inform more sophisticated models such as PLSR