Browse
Submit Data
Databases
API
Help

Dataset Information

17 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The epidemiology of delirium: challenges and opportunities for population studies.

ABSTRACT: Delirium is a serious and common acute neuropsychiatric syndrome that is associated with short- and long-term adverse health outcomes. However, relatively little delirium research has been conducted in unselected populations. Epidemiologic research in such populations has the potential to resolve several questions of clinical significance in delirium. Part 1 of this article explores the importance of population selection, case-ascertainment, attrition, and confounding. Part 2 examines a specific question in delirium epidemiology: What is the relationship between delirium and trajectories of cognitive decline? This section assesses previous work through two systematic reviews and proposes a design for investigating delirium in the context of longitudinal cohort studies. Such a design requires robust links between community and hospital settings. Practical considerations for case-ascertainment in the hospital, as well as the necessary quality control of these programs, are outlined. We argue that attention to these factors is important if delirium research is to benefit fully from a population perspective.

SUBMITTER: Davis DH

PROVIDER: S-EPMC3837358 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Challenges and opportunities in understanding dementia and delirium in the acute hospital.

Project description:In an Essay, Andrew Jackson and colleagues discuss challenges in the diagnosis and management of older people with dementia and delirium in acute hospitals.

| S-EPMC5349650 | biostudies-literature

Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes.

Project description:Only recently has it become clear that epithelial ovarian cancer (EOC) is comprised of such distinct histotypes--with different cells of origin, morphology, molecular features, epidemiologic factors, clinical features, and survival patterns-that they can be thought of as different diseases sharing an anatomical location. Herein, we review opportunities and challenges in studying EOC heterogeneity.The 2014 World Health Organization diagnostic guidelines incorporate accumulated evidence that high- and low-grade serous tumors have different underlying pathogenesis, and that, on the basis of shared molecular features, most high grade tumors, including some previously classified as endometrioid, are now considered to be high-grade serous. At the same time, several studies have reported that high-grade serous EOC, which is the most common histotype, is itself made up of reproducible subtypes discernable by gene expression patterns.These major advances in understanding set the stage for a new era of research on EOC risk and clinical outcomes with the potential to reduce morbidity and mortality. We highlight the need for multidisciplinary studies with pathology review using the current guidelines, further molecular characterization of the histotypes and subtypes, inclusion of women of diverse racial/ethnic and socioeconomic backgrounds, and updated epidemiologic and clinical data relevant to current generations of women at risk of EOC.

| S-EPMC5718213 | biostudies-other

Rare variant association studies: considerations, challenges and opportunities.

Project description:Genome-wide association studies (GWASs) have successfully uncovered thousands of robust associations between common variants and complex traits and diseases. Despite these successes, much of the heritability of these traits remains unexplained. Because low-frequency and rare variants are not tagged by conventional genome-wide genotyping arrays, they may represent an important and understudied component of complex trait genetics. In contrast to common variant GWASs, there are many different types of study designs, assays and analytic techniques that can be utilized for rare variant association studies (RVASs). In this review, we briefly present the different technologies available to identify rare genetic variants, including novel exome arrays. We also compare the different study designs for RVASs and argue that the best design will likely be phenotype-dependent. We discuss the main analytical issues relevant to RVASs, including the different statistical methods that can be used to test genetic associations with rare variants and the various bioinformatic approaches to predicting in silico biological functions for variants. Finally, we describe recent rare variant association findings, highlighting the unexpected conclusion that most rare variants have modest-to-small effect sizes on phenotypic variation. This observation has major implications for our understanding of the genetic architecture of complex traits in the context of the unexplained heritability challenge.

| S-EPMC4337325 | biostudies-literature

Opportunities and challenges for transcriptome-wide association studies.

Project description:Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn's disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.

| S-EPMC6777347 | biostudies-literature

Mendelian randomization studies of depression: evidence, opportunities, and challenges.

Project description:BackgroundMajor depressive disorder (MDD) poses a significant social and economic burden worldwide. Identifying exposures, risk factors, and biological mechanisms that are causally connected to MDD can help build a scientific basis for disease prevention and development of novel therapeutic approaches.MethodsIn this systematic review, we assessed the evidence for causal relationships between putative causal risk factors and MDD from Mendelian randomization (MR) studies, following PRISMA. We assessed methodological quality based on key elements of the MR design: use of a full instrumental variable analysis and validation of the three key MR assumptions.ResultsWe included methodological details and results from 52 articles. A causal link between lifestyle, metabolic, inflammatory biomarkers, particular pathological states and MDD is supported by MR investigations, although results for each category varied substantially.ConclusionsWhile this review shows how MR can offer useful information for examining prospective treatment targets and better understanding the pathophysiology of MDD, some methodological flaws in the existing literature limit reliability of results and probably underlie their heterogeneity. We highlight perspectives and recommendations for future works on MR in psychiatry.

| S-EPMC10666459 | biostudies-literature

Opportunities and Challenges in Understanding Atherosclerosis by Human Biospecimen Studies.

Project description:Over the last few years, new high-throughput biotechnologies and bioinformatic methods are revolutionizing our way of deep profiling tissue specimens at the molecular levels. These recent innovations provide opportunities to advance our understanding of atherosclerosis using human lesions aborted during autopsies and cardiac surgeries. Studies on human lesions have been focusing on understanding the relationship between molecules in the lesions with tissue morphology, genetic risk of atherosclerosis, and future adverse cardiovascular events. This review will highlight ways to utilize human atherosclerotic lesions in translational research by work from large cardiovascular biobanks to tissue registries. We will also discuss the opportunities and challenges of working with human atherosclerotic lesions in the era of next-generation sequencing.

| S-EPMC9300954 | biostudies-literature

Challenges for Cryptosporidium Population Studies.

Project description:Cryptosporidiosis is ranked sixth in the list of the most important food-borne parasites globally, and it is an important contributor to mortality in infants and the immunosuppressed. Recently, the number of genome sequences available for this parasite has increased drastically. The majority of the sequences are derived from population studies of Cryptosporidium parvum and Cryptosporidium hominis, the most important species causing disease in humans. Work with this parasite is challenging since it lacks an optimal, prolonged, in vitro culture system, which accurately reproduces the in vivo life cycle. This obstacle makes the cloning of isolates nearly impossible. Thus, patient isolates that are sequenced represent a population or, at times, mixed infections. Oocysts, the lifecycle stage currently used for sequencing, must be considered a population even if the sequence is derived from single-cell sequencing of a single oocyst because each oocyst contains four haploid meiotic progeny (sporozoites). Additionally, the community does not yet have a set of universal markers for strain typing that are distributed across all chromosomes. These variables pose challenges for population studies and require careful analyses to avoid biased interpretation. This review presents an overview of existing population studies, challenges, and potential solutions to facilitate future population analyses.

| S-EPMC8229070 | biostudies-literature

The Opportunities and Challenges of Integrating Population Histories Into Genetic Studies for Diverse Populations: A Motivating Example From Native Hawaiians.

Project description:There is a well-recognized need to include diverse populations in genetic studies, but several obstacles continue to be prohibitive, including (but are not limited to) the difficulty of recruiting individuals from diverse populations in large numbers and the lack of representation in available genomic references. These obstacles notwithstanding, studying multiple diverse populations would provide informative, population-specific insights. Using Native Hawaiians as an example of an understudied population with a unique evolutionary history, I will argue that by developing key genomic resources and integrating evolutionary thinking into genetic epidemiology, we will have the opportunity to efficiently advance our knowledge of the genetic risk factors, ameliorate health disparity, and improve healthcare in this underserved population.

| S-EPMC8503554 | biostudies-literature

Opportunities and challenges for the use of common controls in sequencing studies.

Project description:Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.

| S-EPMC9765323 | biostudies-literature

Meta-analysis of clinical metabolic profiling studies in cancer: challenges and opportunities.

Project description:Cancer cell metabolism has received increasing attention. Despite a boost in the application of clinical metabolic profiling (CMP) in cancer patients, a meta-analysis has not been performed. The primary goal of this study was to assess whether public accessibility of metabolomics data and identification and reporting of metabolites were sufficient to assess which metabolites were consistently altered in cancer patients. We therefore retrospectively curated data from CMP studies in cancer patients published during 5 recent years and used an established vote-counting method to perform a semiquantitative meta-analysis of metabolites in tumor tissue and blood. This analysis confirmed well-known increases in glycolytic metabolites, but also unveiled unprecedented changes in other metabolites such as ketone bodies and amino acids (histidine, tryptophan). However, this study also highlighted that insufficient public accessibility of metabolomics data, and inadequate metabolite identification and reporting hamper the discovery potential of meta-analyses of CMP studies, calling for improved standardization of metabolomics studies.

| S-EPMC5048364 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data