SummaryCentrosome separation, critical for bipolar spindle formation and subsequent chromosome segregation during mitosis, occurs via distinct prophase and prometaphase pathways [1–3]. Kinesin-5 (Eg5), a microtubule (MT) motor, pushes centrosomes apart during bipolar spindle assembly [4]; its suppression results in monopolar spindles and mitotic arrest [5, 6]. Forces that antagonize Eg5 in prophase are unknown. Here we identify a new force generating mechanism mediated by the guanine nucleotide exchange factor (GEF) Tiam1, dependent on its ability to activate the GTPase Rac. We reveal that Tiam1 and Rac localize to centrosomes during prophase and prometaphase, and Tiam1, acting through Rac, ordinarily retards centrosome separation. Importantly, both Tiam1-depleted cells in culture and Rac1-deficient epithelial cells in vivo escape the mitotic arrest induced by Eg5 suppression. Moreover, Tiam1-depleted cells transit more slowly through prometaphase and display increased chromosome congression errors. Significantly, Eg5 suppression in Tiam1-depleted cells rectifies not only their increased centrosome separation but also their chromosome congression errors and mitotic delay. These findings identify Tiam1-Rac signaling as the first antagonist of centrosome separation during prophase, demonstrate its requirement in balancing Eg5-induced forces during bipolar spindle assembly in vitro and in vivo, and show that proper centrosome separation in prophase facilitates subsequent chromosome congression.

BackgroundAlthough covariate adjustment in the analysis of randomised trials can be beneficial, adjustment for continuous covariates is complicated by the fact that the association between covariate and outcome must be specified. Misspecification of this association can lead to reduced power, and potentially incorrect conclusions regarding treatment efficacy.MethodsWe compared several methods of adjustment to determine which is best when the association between covariate and outcome is unknown. We assessed (a) dichotomisation or categorisation; (b) assuming a linear association with outcome; (c) using fractional polynomials with one (FP1) or two (FP2) polynomial terms; and (d) using restricted cubic splines with 3 or 5 knots. We evaluated each method using simulation and through a re-analysis of trial datasets.ResultsMethods which kept covariates as continuous typically had higher power than methods which used categorisation. Dichotomisation, categorisation, and assuming a linear association all led to large reductions in power when the true association was non-linear. FP2 models and restricted cubic splines with 3 or 5 knots performed best overall.ConclusionsFor the analysis of randomised trials we recommend (1) adjusting for continuous covariates even if their association with outcome is unknown; (2) keeping covariates as continuous; and (3) using fractional polynomials with two polynomial terms or restricted cubic splines with 3 to 5 knots when a linear association is in doubt.

BackgroundDigital self-monitoring, particularly of weight, is increasingly prevalent. The associated data could be reused for clinical and research purposes.ObjectiveThe aim was to compare participants who use connected smart scale technologies with the general population and explore how use of smart scale technology affects, or is affected by, weight change.MethodsThis was a retrospective study comparing 2 databases: (1) the longitudinal height and weight measurement database of smart scale users and (2) the Health Survey for England, a cross-sectional survey of the general population in England. Baseline comparison was of body mass index (BMI) in the 2 databases via a regression model. For exploring engagement with the technology, two analyses were performed: (1) a regression model of BMI change predicted by measures of engagement and (2) a recurrent event survival analysis with instantaneous probability of a subsequent self-weighing predicted by previous BMI change.ResultsAmong women, users of self-weighing technology had a mean BMI of 1.62 kg/m2 (95% CI 1.03-2.22) lower than the general population (of the same age and height) (P<.001). Among men, users had a mean BMI of 1.26 kg/m2 (95% CI 0.84-1.69) greater than the general population (of the same age and height) (P<.001). Reduction in BMI was independently associated with greater engagement with self-weighing. Self-weighing events were more likely when users had recently reduced their BMI.ConclusionsUsers of self-weighing technology are a selected sample of the general population and this must be accounted for in studies that employ these data. Engagement with self-weighing is associated with recent weight change; more research is needed to understand the extent to which weight change encourages closer monitoring versus closer monitoring driving the weight change. The concept of isolated measures needs to give way to one of connected health metrics.

Caries and obesity were highly prevalent in this population. Caries in childhood was not shown to be associated with obesity in adolescence and there was no cross-sectional association between the two diseases in adolescence. A CRFA is not precluded, however, the results suggest that additional interventions, specific for each disease, are required to prevent obesity and caries.