Ecologically mediated differences in electric organ discharge drive evolution in a sodium channel gene in South American electric fishes.

Active electroreception-the ability to detect objects and communicate with conspecifics via the detection and generation of electric organ discharges (EODs)-has evolved convergently in several fish lineages. South American electric fishes (Gymnotiformes) are a highly species-rich group, possibly in part due to evolution of an electric organ (EO) that can produce diverse EODs. Neofunctionalization of a voltage-gated sodium channel gene accompanied the evolution of electrogenic tissue from muscle and resulted in a novel gene (scn4aa) uniquely expressed in the EO. Here, we investigate the link between variation in scn4aa and differences in EOD waveform. We combine gymnotiform scn4aa sequences encoding the C-terminus of the Nav1.4a protein, with biogeographic data and EOD recordings to test whether physiological transitions among EOD types accompany differential selection pressures on scn4aa. We found positive selection on scn4aa coincided with shifts in EOD types. Species that evolved in the absence of predators, which likely selected for reduced EOD complexity, exhibited increased scn4aa evolutionary rates. We model mutations in the protein that may underlie changes in protein function and discuss our findings in the context of gymnotiform signalling ecology. Together, this work sheds light on the selective forces underpinning major evolutionary transitions in electric signal production.

Association of screen time and cardiometabolic risk in school-aged children.

Screen use has become a pervasive behaviour among children and has been linked to adverse health outcomes. The objective of this study was to examine the association between screen time and a comprehensive total cardiometabolic risk (CMR) score in school-aged children (7-12-years), as well as individual CMR factors. In this longitudinal study, screen time was measured over time (average duration of follow-up was 17.4 months) via parent-report. Anthropometric measurements, blood pressure, and biospecimens were collected over time and used to calculate CMR score [sum of age and sex standardized z-scores of systolic blood pressure (SBP), glucose, log-triglycerides, waist circumference (WC), and negative high-density lipoprotein cholesterol (HDL-c)/square-root of 5]. Generalized estimating equations (GEE) were used to examine the association between screen time and total CMR score as well as individual CMR factors. A total of 567 children with repeated measures were included. There was no evidence of an association between parent-reported child screen time and total CMR score (adjusted ß = -0.01, 95% CI [-0.03, 0.005], 0.16). Screen time was inversely associated HDL-c (adjusted ß = -0.008, 95% CI [-0.011, -0.005], p = 0.016), but there was no evidence that the other CMR components were associated with screen time. Among children 7-12 years, there was no evidence of an association between parent-reported child screen time and total CMR, but increased screen time was associated with slightly lower HDL-c. Research is needed to understand screen-related contextual factors which may be related to CMR factors.

The association between screen time and cardiometabolic risk in young children.

OBJECTIVES: While studies exist on the association between screen time and cardiometabolic risk among adolescents, research examining the effect of screen time on cardiometabolic risk in young children is lacking. The primary objective of this study was to examine the association between daily screen time and cardiometabolic risk (CMR) [sum of age- and sex-standardized z-scores of systolic blood pressure (SBP), glucose, log-triglycerides, waist circumference (WC), and negative high-density lipoprotein (HDL) cholesterol divided by the square root of five] in young children. Secondary objectives included examining individual CMR risk factors, including waist-to-height ratio and non high-density lipoprotein (non-HDL) cholesterol, as well as the individual cut-offs of these risk factors. Additional analyses include examining the association between screen time and CMR by handheld/non-handheld devices. METHODS: A study was conducted among young children 3 to 6 years from the TARGet Kids! practice-based research network in Toronto and Montreal, Canada. Children with one or more measures of screen time and CMR were included in this study. Generalized estimating equation (GEE) multivariable linear regressions and multivariable logistic regressions, using published cut-offs, were conducted to evaluate these associations. RESULTS: Data from 1317 children [mean age 52 months (SD = 13.36), 44.34% female] were included for analyses. There was no evidence of associations between screen time and total CMR score or individual risk factors (p > 0.05) after adjusting for confounders. A statistically significant, but small association between daily screen time and non-HDL cholesterol was found (B = 0.046; CI = [0.017 to 0.075]; p = 0.002. CONCLUSIONS: Though no relationship was reported between daily screen time and the majority of CMR factors in early childhood, there was an association between daily screen time and non-HDL cholesterol. As the relationship between daily screen time and CMR factors may not be apparent in early childhood, studies to evaluate longer-term cardiometabolic effects of screen time are needed. Although there is an evidence-based rationale to reduce screen time in early childhood, prevention of cardiometabolic risk may not be the primary driver.