Turning the tables of sex distinction in craniofacial identification: Why females possess thicker facial soft tissues than males, not vice versa

Abstract

Males are universally reported to possess larger facial soft-tissue thickness (FSTT) than females, however, this observation oversimplifies the raw data yielding an underpowered assessment of FSTT sex-patterning where: differences are small (η(2) < 5%) and inconsistent (females are routinely larger than males at the cheeks). Here we investigate body-size normalized data to assess whether more general and improved understanding of FSTT sex-variation in humans is possible. FSTTs were measured in 52 healthy living Australians aged 18 to 30 years using B-mode ultrasound. Participants' stature and body mass were also measured. Sex differences were calculated before and after normalization by the aforementioned body-composition variables. Methods were repeated in three other independent samples to evaluate reproducibility: 100 American Whites and 60 American Blacks measured by B-mode ultrasound; and 50 Turkish residents measured by regular supine CT. Compared to raw mean differences (F < M, by -6%), females displayed much thicker FSTTs than males when normalized for body mass (F > M, by +16%). Consequently, while the sexes share similar raw values, females possess much larger FSTTs for their relatively lighter bodies. The relative FSTT difference was 2.7× larger than the raw mean difference. Sex differences in FSTT are of larger magnitude and reversed direction in mass normalized data. Contrary to popular thought, females possess much larger FSTTs than males owing to their generically lighter bodies (-18 kg). These data patterns help explain why the pooling of sex-categorized FSTT does not jeopardize the sex-difference-it is encoded more strongly in terms relative to body mass.

Facial soft tissue thickness (FSTT) estimation models—And the strength of correlations between craniometric dimensions and FSTTs

“…Craniometrics narrowly correlated highest with weight (mean r on absolute values = 0.37), followed by height (0.36; Supplementary File 3). Normalisation of FSTTs by height, weight and BMI showed general consistency with prior findings where weight normalisation provides the largest percentage sex difference in FSTTs (18%) and the most consistent results across all facial landmarks [21] (Table 4). The size of the sex difference rose from 10 to 18% after weight normalization and signs at all 18 landmarks unanimously converted to positive values (females larger than males; note here that mixed signs were observed across the landmarks for raw data based sex differences).…”

“…Due to missing values, only 51 of 71 subjects could be used in this part of the analysis. Sex trends were additionally examined both in the raw data and the size-adjusted (bodyweight normalized) data after Stephan et al [21] using R [24] (n = 61).…”

In vivo facial soft tissue thicknesses of adult Australians

“…2). Due to one outlying BMI result (extremely large BMI far from the distribution cluster at the right most extreme), one subject was excluded from further analysis as previously reported elsewhere [40]. This resulted in a final tally of 52 adults in the main dataset for analysis (n1), including 35 female and 17 male subjects (Table 1 and Fig.…”

“…Traditional arithmetic means, and shorths and 75-Shormaxes [35,49] were calculated for the data in R [45] using the TDStats v.2013.1 script [35] for updated v.2017.1 see [50]. As sex differences for these data were calculated and found to be negligible elsewhere [40], we focus on and only report the sex collapsed data in this paper.…”

“…In conjunction with sexual dimorphism analysis mentioned above, height, weight, and BMI were also recorded for each participant, results of which are presented elsewhere [40].…”