Obesity prevalence and time trend among youngsters in China, 1982-2002

Purpose of present study is to describe the prevalence and trend of overweight and obesity, as well as its coexistence with stunting, among youngsters in China, from 1982 to 2002. Data from children 7-17 years of age from three cross-sectional national surveys: 1982 China National Nutrition Survey (5 334 boys and 4 793 girls), 1992 China National Nutrition Survey (8 048 boys and 7 453 girls) and 2002 China National Nutrition and Health Survey (23 242 boys and 21 638 girls) were used in this study. Overweight and obesity were defined according to age, sex specific BMI cut-off points from the International Obesity Task Force, while stunting was defined as height-for-age below -2 standard deviation from the NCHS/WHO reference median value. Results: Overweight prevalence of Chinese youngsters was 1.2%, 3.7% and 4.4%, while the obesity prevalence was 0.2%, 0.9% and 0.9% in 1982, 1992 and 2002, respectively. Both the overweight and obesity prevalence and their increment were higher among boys in urban areas. In 1982, 28.4% of overweight and 69.6% of obese youngsters were stunted, this decreased to 22.0% and 46.4% in 1992, and then to 5.7% and 7.7% in 2002, respectively. Conclusion: The prevalence of overweight and obesity in Chinese youngsters were low in 1982. There has been a rapid increase since then. If this trend continues, overweight will soon reach epidemic proportions. Stunting among overweight and obese youngsters decreased dramatically at the same time

Analysis of Long Noncoding RNAs in Aila-Induced Non-Small Cell Lung Cancer Inhibition

Non-small cell lung cancer (NSCLC) has the highest morbidity and mortality among all carcinomas. However, it is difficult to diagnose in the early stage, and current therapeutic efficacy is not ideal. Although numerous studies have revealed that Ailanthone (Aila), a natural product, can inhibit multiple cancers by reducing cell proliferation and invasion and inducing apoptosis, the mechanism by which Aila represses NSCLC progression in a time-dependent manner remains unclear. In this study, we observed that most long noncoding RNAs (lncRNAs) were either notably up- or downregulated in NSCLC cells after treatment with Aila. Moreover, alterations in lncRNA expression induced by Aila were crucial for the initiation and metastasis of NSCLC. Furthermore, in our research, expression of DUXAP8 was significantly downregulated in NSCLC cells after treatment with Aila and regulated expression levels of EGR1. In conclusion, our findings demonstrate that Aila is a potent natural suppressor of NSCLC by modulating expression of DUXAP8 and EGR1

Mapping Peptidergic Cells in Drosophila: Where DIMM Fits In

The bHLH transcription factor DIMMED has been associated with the differentiation of peptidergic cells in Drosophila. However, whether all Drosophila peptidergic cells express DIMM, and the extent to which all DIMM cells are peptidergic, have not been determined. To address these issues, we have mapped DIMM expression in the central nervous system (CNS) and periphery in the late larval stage Drosophila. At 100 hr after egg-laying, DIMM immunosignals are largely congruent with a dimm-promoter reporter (c929-GAL4) and they present a stereotyped pattern of 306 CNS cells and 52 peripheral cells. We assigned positional values for all DIMM CNS cells with respect to reference gene expression patterns, or to patterns of secondary neuroblast lineages. We could assign provisional peptide identities to 68% of DIMM-expressing CNS cells (207/306) and to 73% of DIMM-expressing peripheral cells (38/52) using a panel of 24 markers for Drosophila neuropeptide genes. Furthermore, we found that DIMM co-expression was a prevalent feature within single neuropeptide marker expression patterns. Of the 24 CNS neuropeptide gene patterns we studied, six patterns are >90% DIMM-positive, while 16 of 22 patterns are >40% DIMM-positive. Thus most or all DIMM cells in Drosophila appear to be peptidergic, and many but not all peptidergic cells express DIMM. The co-incidence of DIMM-expression among peptidergic cells is best explained by a hypothesis that DIMM promotes a specific neurosecretory phenotype we term LEAP. LEAP denotes Large cells that display Episodic release of Amidated Peptides

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p