Dataset Information

Factors Predicting Atypical Development of Nighttime Bladder Control.

ABSTRACT: To derive latent classes (longitudinal "phenotypes") of frequency of bedwetting from 4 to 9 years and to examine their association with developmental delay, parental history of bedwetting, length of gestation and birth weight.The authors used data from 8,769 children from the U.K. Avon Longitudinal Study of Parents and Children cohort. Mothers provided repeated reports on their child's frequency of bedwetting from 4 to 9 years. The authors used longitudinal latent class analysis to derive latent classes of bedwetting and examined their association with sex, developmental level at 18 months, parental history of wetting, birth weight, and gestational length.The authors identified 5 latent classes: (1) "normative"-low probability of bedwetting; (2) "infrequent delayed"-delayed attainment of nighttime bladder control with bedwetting

SUBMITTER: Sullivan S

PROVIDER: S-EPMC4631123 | biostudies-literature | 2015 Nov-Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Factors Predicting Atypical Development of Nighttime Bladder Control.

Sullivan Sarah S Joinson Carol C Heron Jon J

Journal of developmental and behavioral pediatrics : JDBP 20151101 9

<h4>Objective</h4>To derive latent classes (longitudinal "phenotypes") of frequency of bedwetting from 4 to 9 years and to examine their association with developmental delay, parental history of bedwetting, length of gestation and birth weight.<h4>Method</h4>The authors used data from 8,769 children from the U.K. Avon Longitudinal Study of Parents and Children cohort. Mothers provided repeated reports on their child's frequency of bedwetting from 4 to 9 years. The authors used longitudinal laten ...[more]

PMID: 26468941

Similar Datasets

Project description:BackgroundNon-dipping or reverse dipping patterns are known to be associated with adverse cardiovascular prognosis among the general population and clinical cohort. Few large sized studies have explored factors including sleep duration and sleep quality related to nighttime blood pressure (BP) and nocturnal dipping patterns.MethodsAmong 5,360 patients enrolled in Korean multicenter nationwide prospective Registry of ambulatory BP monitoring (KORABP), 981 subjects with complete data on sleep duration, sleep quality assessed using a 4-point Likert scale, and clinical variables were included in the analysis. Phenotypes of nighttime BP pattern were categorized as extreme dipper, dipper, non-dipper, and reverse dipper. Hypertension was defined as a 24-h ambulatory BPs were 130/80 mmHg or higher.ResultsAmong 981 subjects, 221 were normotensive, 359 were untreated hypertensive, and 401 were treated hypertensive. Age of the participants were 53.87 ± 14.02 years and 47.1% were female. In overall patients, sleep duration was 431.99 ± 107.61 min, and one to four points of sleep quality were observed in 15.5%, 30.0%, 30.4%, and 24.2%, respectively. Of the 760 hypertensive patients, extreme dipper, dipper, non-dipper, and reverse dipper were observed in 58 (7.63%), 277 (36.45%), 325 (42.76%), and 100 (13.16%), respectively. In multiple linear regression analysis, sleep duration (β = 0.0105, p < 0.001) and sleep quality (β = -0.8093, p < 0.001) were associated with nighttime systolic BP and sleep quality was associated with extent of nighttime systolic BP dipping (β = 0.7622, p < 0.001) in hypertensive patients. In addition, sleep quality showed positive association with dipper pattern (odds ratio [OR] = 1.16, 95% confidence interval [CI] = 1.03-1.30) and showed negative association with reverse dipper pattern (OR = 0.73, 95% CI = 0.62-0.86) in multiple logistic regression analyses.ConclusionWhen adjusted covariates, less sleep duration and poor sleep quality were positively associated with nighttime systolic BP. Additionally, sleep quality was the independent associated factor for dipper and reverse dipper phenotypes. The study also found that male sex, low estimated glomerular filtration rate, high ambulatory BP, low office BP, and poor sleep quality were associated with blunted nighttime SBP dipping.

Project description:ObjectivesThis study aimed to develop and validate a hypoxia signature for predicting survival outcomes in patients with bladder cancer.MethodsWe downloaded the RNA sequence and the clinicopathologic data of the patients with bladder cancer from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/repository?facetTab=files) and the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) databases. Hypoxia genes were retrieved from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). Differentially expressed hypoxia-related genes were screened by univariate Cox regression analysis and Lasso regression analysis. Then, the selected genes constituted the hypoxia signature and were included in multivariate Cox regression to generate the risk scores. After that, we evaluate the predictive performance of this signature by multiple receiver operating characteristic (ROC) curves. The CIBERSORT tool was applied to investigate the relationship between the hypoxia signature and the immune cell infiltration, and the maftool was used to summarize and analyze the mutational data. Gene-set enrichment analysis (GSEA) was used to investigate the related signaling pathways of differentially expressed genes in both risk groups. Furthermore, we developed a model and presented it with a nomogram to predict survival outcomes in patients with bladder cancer.ResultsEight genes (AKAP12, ALDOB, CASP6, DTNA, HS3ST1, JUN, KDELR3, and STC1) were included in the hypoxia signature. The patients with higher risk scores showed worse overall survival time than the ones with lower risk scores in the training set (TCGA) and two external validation sets (GSE13507 and GSE32548). Immune infiltration analysis showed that two types of immune cells (M0 and M1 macrophages) had a significant infiltration in the high-risk group. Tumor mutation burden (TMB) analysis showed that the risk scores between the wild types and the mutation types of TP53, MUC16, RB1, and FGFR3 were significantly different. Gene-Set Enrichment Analysis (GSEA) showed that immune or cancer-associated pathways belonged to the high-risk groups and metabolism-related signal pathways were enriched into the low-risk group. Finally, we constructed a predictive model with risk score, age, and stage and validated its performance in GEO datasets.ConclusionWe successfully constructed and validated a novel hypoxia signature in bladder cancer, which could accurately predict patients' prognosis.

Dataset Information

Factors Predicting Atypical Development of Nighttime Bladder Control.

Publications

Factors Predicting Atypical Development of Nighttime Bladder Control.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets