A population-based cross-sectional study of the association between facial morphology and cardiometabolic risk factors in adolescence

Abstract

ObjectiveTo determine whether facial morphology is associated with fasting insulin, glucose and lipids independent of body mass index (BMI) in adolescents.DesignPopulation-based cross-sectional study.SettingAvon Longitudinal Study of Parents and Children (ALSPAC), South West of England.ParticipantsFrom the ALSPAC database of 4747 three-dimensional facial laser scans, collected during a follow-up clinic at the age of 15, 2348 white British adolescents (1127 males and 1221 females) were selected on the basis of complete data on cardiometabolic parameters, BMI and Tanner's pubertal stage.Main outcome measuresFasting insulin, glucose and lipids (triglycerides, high-density lipoprotein cholesterol (HDLc) and low-density lipoprotein cholesterol (LDLc)).ResultsOn the basis of the collection of 63 x, y and z coordinates of 21 anthropometric landmarks, 14 facial principal components (PCs) were identified. These components explained 82% of the variation in facial morphology and were used as exposure variables. With adjustment for age, gender and pubertal stage, seven PCs were associated with fasting insulin, none with glucose, three with triglycerides, three with HDLc and four with LDLc. After additional adjustment for BMI, four PCs remained associated with fasting insulin, one with triglycerides and two with LDLc. None of these associations withstood adjustment for multiple comparisons.ConclusionsThese initial hypotheses generating analyses provide no evidence that facial morphology is importantly related to cardiometabolic outcomes. Further examination might be warranted. Facial morphology assessment may have value in identifying other areas of disease risk.

Face morphology: Can it tell us something about body weight and fat?

“…In [30] Ferrario et al observed an increase in some facial dimensions in a study on the face morphology of obese adolescents. Djordjevic et al in [31] reported an analysis of facial morphology of a large population of adolescents under the influence of confounding variables: though the statistical univariate analysis showed that four principal face components (face height, asymmetry of the nasal tip and columella basis, asymmetry of the nasal bridge, depth of the upper eyelids) correlated with insulin levels, the regression coefficients were weak, and no significance persisted in the multivariate analysis.…”

Three-Dimensional Soft-Tissue Facial Morphometry in Caucasian Obese Adults

“…Indeed, quantitative soft-tissue facial data in the three dimensions can currently be obtained by digital, computerized anthropometry [10][11][12][13][14][15][16][17][18][19][20][21]. Current technology allows fast and non-invasive optical scans of facial surface, providing a global assessment of patients.…”

“…In children and adolescents previous studies reported bimaxillary prognathism and relatively greater horizontal and anteroposterior facial measurements in obese subjects compared to normalweighted peers [23][24][25][26]. Nevertheless, no evidence that facial morphology is importantly related to cardiometabolic outcomes was found in a large cohort of adolescents [17]. Studies on obese adults mostly focused on patients with obstructive sleep apnea (OSA) [26], while investigations on the relationships among obesity, facial morphometry and MS have not been conducted so far.…”

What’s in a Smile? Initial Analyses of Dynamic Changes in Facial Shape and Appearance

“…Much research into the "science of a smile" also focuses on the effects of aging and biological sex on the shape and appearance [4][5] and also the dynamics [6][7][8] of smiling. Recent investigations have been greatly enhanced by the use of three-dimensional (3D) imaging techniques [9][10][11][12][13] that allow both static and dynamic imaging of the face. Clinically, this research has led to improved understanding of orthognathic surgery [9], malocclusion [10], associations between facial morphology and cardiometabolic risk [11], lip shape during speech [12], facial asymmetry [13], and sleep apnea [14] (to name but a few examples).…”

“…Recent investigations have been greatly enhanced by the use of three-dimensional (3D) imaging techniques [9][10][11][12][13] that allow both static and dynamic imaging of the face. Clinically, this research has led to improved understanding of orthognathic surgery [9], malocclusion [10], associations between facial morphology and cardiometabolic risk [11], lip shape during speech [12], facial asymmetry [13], and sleep apnea [14] (to name but a few examples). Clearly also, facial simulation is of much interest for human-computer interfaces (see, e.g., Refs.…”