Worldwide trends in underweight and obesity from 1990 to 2022 : a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

A list of authors and their affiliations appears online. A supplementary appendix is herewith attached.Background: Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods: We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI 2 SD above the median). Findings: From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation: The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesity.peer-reviewe

Six RNA binding proteins (RBPs) related prognostic model predicts overall survival for clear cell renal cell carcinoma and it is associated with immune infiltration

The aim of this article was to construct an accurate prognostic model by using RNA-binding proteins (RBPs) to predict overall survival (OS) for patients with clear cell renal cell carcinoma (ccRCC) as well as to reveal its associations with immune infiltration. Expression profiles based on RNA-binding proteins (RBPs) and  clinical follow-up parameters were obtained from the Cancer Genome Atlas (TCGA) and the ArrayExpress databases. Through univariate COX and LASSO regression analyses, the RBPs based signature was developed. A total of six RBPs (CLK2, IGF2BP2, RNASE2, EZH2, PABPC1L, RPL22L1) were eventually used to establish a prognostic signature. Based on this signature, ccRCC patients were classified into high-risk and low-risk subgroups and significant OS was obtained in both the internal and external datasets (p&lt;0.05). AUCs of its ROC curve were all above 0.70 and this signature was an independent prognostic factor of OS for ccRCC (p&lt;0.05). Nomograms were also constructed to visualize the relationships among individual predictors and 1-, 3- and 5-year OS for ccRCC. Furthermore, the established RBPs based signature was strongly related to critical clinicopathologic characteristics such as grade (p=8.921e−12), stage (p=1.421e−11), M (p=1.662e−05), and T stage (p=7.907e−10). Moreover, 12 kinds of tumor-infiltrating immune cells were significantly linked to high-risk and low-risk groups classified by our constructed model (all p&lt;0.05). Our study successfully identified six RBPs as a robust prognostic signature in ccRCC by both external and internal verification. Besides, our established model displayed significant associations with immune infiltration. In addition to original clinical parameters, our findings may further help clinicians in predicting patients’ survival status and creating individualized treatment plans.</jats:p

Optimal infostation deployment for spatio-temporal information dissemination

A growing number of applications require disseminating information around specific geographical areas within a limited valid time. For example, the store in the mall area expects to publish the time-limited sales promotion to all the potential clients in the nearby area, before the discount activity end. In this paper, we study the problem of deploying infostation for geographical information dissemination. It aims to achieve the desired dissemination ratio under the given time constraint and to minimize the infostation deployment cost. Inspired by several observations in recent studies on realistic mobility model, we build a mobility graph to reflect the statistical characteristic of users movement in a area. Based on this graph, we formulate the infostation deployment problem as an optimization problem. Then, we prove it is NP-hard by reducing it to the classical vertex cover problem and then develop a greedy heuristic algorithm DGREEDY with the polynomial time complexity. Extensive simulations based on the real human mobility traces have been carried out to show the efficacy of our approach.1 ? 2010 IEEE.EI