Metabolic syndrome components and face shape variation in elderly

The aim of this study was to identify the metabolic syndrome (MS) components mostly influencing face shape in elderly individuals. This is a cross-sectional epidemiological study carried out with elderly individuals living in Aiquara County, Bahia State. Facial images at frontal view and MS were cl...

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Bibliographic Details
Main Authors: Lélia Lessa Teixeira Pinto, Thaís Barros do Carmo, Alessandra Santos Sales, Lorena Andrade Nunes, Cezar Augusto Casotti
Format: Article
Language:English
Published: Universidade Federal de Santa Catarina 2020-11-01
Series:Revista Brasileira de Cineantropometria e Desempenho Humano
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Online Access:https://periodicos.ufsc.br/index.php/rbcdh/article/view/74390
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Summary:The aim of this study was to identify the metabolic syndrome (MS) components mostly influencing face shape in elderly individuals. This is a cross-sectional epidemiological study carried out with elderly individuals living in Aiquara County, Bahia State. Facial images at frontal view and MS were classified according to the National Cholesterol Education Program's Adult Treatment Panel III (revised version). Discriminant function, cross validation and distance Mahalanobis D2 were used to extract face shape variations due to MS. Principal Component Analysis (PCA) was used to evaluate MS components’ influence on face shape. The total of 193 elderly individuals were selected; there were significant differences in face shape due to MS (p <0.01) in both sexes. PCA 1 showed HDL-C in men, which accounted for 37% of the total variation. HDL-C in biplot is associated with individuals who do not have MS and with elderly individuals with MS - there was correlation between waist circumference and triglycerides. PCA 1 represented 33.2% of the total variation in women; this outcome is explained by triglycerides. There was association between blood glucose and waist circumference in biplot. HDL-C is related to women who do not have MS. Facial variations affected by MS did not derive from the action of any of the MS components, but from the association between them. Thus, geometric morphometrics emerges as a promising method that makes it possible identifying heart disease and metabolic risk factors according to face shape features.
ISSN:1415-8426
1980-0037