Age characterization through handwriting is an important research field with several potential applications. It can, for instance, characterize normal aging process on one hand and detect significant handwriting degradation possibly related to early pathological states. In this work, we propose a novel approach to characterize age and gender from online handwriting styles. Contrary to previous works on handwriting style characterization, our contribution consists of a two-layer clustering scheme. At the first layer, we perform a writerindependent clustering on handwritten words, described by global features. At the second layer, we perform a clustering that considers style variation at the previous level for each writer, to provide a measure of his/her handwriting stability across words. We investigated different clustering algorithms and their effectiveness for each layer. The handwriting style patterns inferred by our novel technique show interesting correlations between handwriting, age and gender.