Olaseni Prince

Background: The evaluation of metabolic syndrome (MetS) is critical among children and adolescents as it can predict cardiovascular disease in adulthood. The TG/HDL-C ratio has the best predictive accuracy among the different lipid ratios. This study aims to evaluate the prevalence and factors associated with a high TG/HDL-C ratio and the predictive accuracy for pediatric MetS. Methods: This is a cross-sectional study of adolescents aged 9–21 to determine the prevalence of and associations between an elevated TG/HDL-C ratio and MetS. A ROC curve analysis was performed to determine the predictive accuracy of TG/HDL-C with MetS. Results: Of the 604 subjects, 29.8% had elevated TG/HDL-C, MetS was identified in 28.2%, and 54.5% were obese. Among patients with MetS, those with obesity were more likely to have an elevated TG/HDL-C ratio compared to those with a normal BMI (64% vs. 14%, p < 0.05). Gender, obesity, ethnicity, and METS are significantly associated with a high TG/HDL-C ratio. TG/HDL-C has a good discriminatory ability to distinguish patients with MetS from those without (AUC 0.65, p < 0.05). Conclusions: The TG/HDL-C ratio was able to distinguish between children and adolescents with MetS. Hence, the TG/HDL-C ratio may be used in the rapid risk assessment of pediatric MetS, especially in those with obesity.

Objective This study aimed to assess the association between neonatal antibiotic exposure and the risk of childhood obesity. Study Design This retrospective cohort study enrolled neonates born between 2011 and 2015 and followed up until 5 years. The incidence of obesity at 5 years old, and other characteristics were compared between the antibiotic-exposed and unexposed groups. Chi-square test was conducted on categorical variables and Student's t-test for normally distributed continuous variable. Significant variables (p < 0.05) in bivariate analysis were modelled in a stepwise multivariate logistic regression analysis to ascertain independent predictors of obesity at 5 years. Results Of the 1,447 subjects, 749 (51.8%) received ampicillin and gentamicin, and 333 (23%) were obese. Neonates exposed to antibiotics were more likely to be obese compared with those unexposed (26 vs. 20%, p = 0.01). In the adjusted model, this association persisted (adjusted odds ratio: 1.37, p = 0.02). Conclusion Neonatal antibiotic exposure is associated with early childhood obesity and may play a significant role in the weight trajectories of these children. Hence, antibiotic stewardship in this period cannot be overemphasized. Key Points

Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with R_t<1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country.