The glycaemic and insulin indices assess postprandial glycaemic and insulin response to foods, respectively, which may not reflect the long-term effects of diet on insulin response. We developed and evaluated the validity of four empirical indices to assess the insulinaemic potential of usual diets and lifestyles, using dietary, lifestyle and biomarker data from the Nurses’ Health Study (NHS, n 5812 for hyperinsulinaemia, n 3929 for insulin resistance). The four indices were as follows: the empirical dietary index for hyperinsulinaemia (EDIH) and the empirical lifestyle index for hyperinsulinaemia (ELIH); the empirical dietary index for insulin resistance (EDIR) and the empirical lifestyle index for insulin resistance (ELIR). We entered thirty-nine FFQ-derived food groups in stepwise linear regression models, and defined indices as patterns most predictive of fasting plasma C-peptide, for the hyperinsulinaemia pathway (EDIH and ELIH), and of theTAG:HDL-cholesterol ratio, for the insulin-resistance pathway (EDIR and ELIR). We evaluated the validity of indices in two independent samples from NHS-II and Health Professionals Follow-up Study (HPFS) using multivariable-adjusted linear regression analyses to calculate relative concentrations of biomarkers. The EDIH is comprised of eighteen food groups; thirteen were positively associated with C-peptide and five were inversely associated. The EDIR is comprised of eighteen food groups; ten were positively associated with TAG:HDL-cholesterol and eight were inversely associated. Lifestyle indices had fewer dietary components, and included BMI and physical activity as components. In the validation samples, all indices significantly predicted biomarker concentrations – for example, the relative concentrations of the corresponding biomarkers comparing extreme index quintiles in the HPFS were EDIH, 1·29 (95 % CI 1·22, 1·37); ELIH, 1·78 (95 % CI 1·68, 1·88); EDIR, 1·44 (95 % CI 1·34, 1·55); and ELIR, 2·03 (95 % CI 1·89, 2·19); all P trend<0·0001. The robust associations of these novel hypothesis-driven indices with insulin response biomarker concentrations suggest their usefulness in assessing the ability of whole diets and lifestyles to stimulate and/or sustain insulin secretion.

In the behavioral literature on human movement, a distinction is made between the learning of parameters and the learning of new movement forms or topologies. Whereas the target articles by Thach, Smith, and Houk et al. provide evidence for cerebellar involvement in parametrization learning and adaptation, the evidence in favor of its involvement in the generation of new movement patterns is less straightforward. A case is made for focusing more attention on the latter issue in the future. This would directly help to bridge the gap between current neurophysiological approaches to the role of the cerebellum and the behavioral expressions of human motor learning, [HOUK et al.; SMITH; THACH]

Although the application of the emulation model to the control of simple positioning movements is relatively straightforward, extending the scheme to actions requiring multisegmental, interlimb coordination complicates matters a bit. Special consideration of the demands in this case, both on sensory processing and on the process model (two key elements of the Kalman filter), are discussed.