scholarly journals Compositional and genetic alterations in Graves’ disease gut microbiome reveal specific diagnostic biomarkers

2021 ◽  
Author(s):  
Qiyun Zhu ◽  
Qiangchuan Hou ◽  
Shi Huang ◽  
Qianying Ou ◽  
Dongxue Huo ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Cohort Analysis ◽  
High Specificity ◽  
Genetic Alterations ◽  
Small Sample ◽  
Classification Model ◽  
Graves Disease ◽  
Pilot Studies ◽  
Taxonomic Markers ◽  
Small Sample Sizes

AbstractGraves’ Disease is the most common organ-specific autoimmune disease and has been linked in small pilot studies to taxonomic markers within the gut microbiome. Important limitations of this work include small sample sizes and low-resolution taxonomic markers. Accordingly, we studied 162 gut microbiomes of mild and severe Graves’ disease (GD) patients and healthy controls. Taxonomic and functional analyses based on metagenome-assembled genomes (MAGs) and MAG-annotated genes, together with predicted metabolic functions and metabolite profiles, revealed a well-defined network of MAGs, genes and clinical indexes separating healthy from GD subjects. A supervised classification model identified a combination of biomarkers including microbial species, MAGs, genes and SNPs, with predictive power superior to models from any single biomarker type (AUC = 0.98). Global, cross-disease multi-cohort analysis of gut microbiomes revealed high specificity of these GD biomarkers, notably discriminating against Parkinson’s Disease, and suggesting that non-invasive stool-based diagnostics will be useful for these diseases.

scholarly journals Ecological and Evolutionary Alterations in Graves’ Disease Gut Microbiome Reveal Specific Diagnostic Biomarkers

2020 ◽  
Author(s):  
Qiyun Zhu ◽  
Qiangchuan Hou ◽  
Shi Huang ◽  
Qianying Ou ◽  
Yoshiki Vázquez-Baeza ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Small Sample Size ◽  
Meta Analysis ◽  
Small Sample ◽  
Classification Model ◽  
Graves Disease ◽  
Metagenomic Sequencing ◽  
Pilot Studies ◽  
Shotgun Metagenomics ◽  
Microbial Biomarkers

Abstract Background: Graves’ Disease (GD) is the most common organ-specific autoimmune disease and has been linked in small pilot studies to taxonomic markers within the gut microbiome. However, the small sample size and limited resolution of the 16S rRNA amplicon sequencing technology limited the universality and significance of these studies. Larger cohorts and integration of different levels of analysis from high-resolution shotgun metagenomics data were therefore needed to explore the GD patients’ alteration in gut microbial taxonomy, genes, pathways and functions, metabolites, and mutational spectra, to establish robust microbial biomarkers at these levels for GD diagnosis.Results: Here, we studied 162 gut microbiomes of mild and severe Graves’ disease (GD) patients and healthy controls. Shotgun metagenomic sequencing and inferred metabolomics were applied to describe the intestinal microbial characteristics and microbial mutations of GD patients. Then, combined biomarkers were identified from machine learning. Finally, we performed a meta-analysis to confirm the specificity of these biomarkers across different metabolic and autoimmune diseases. Taxonomic and functional analyses based on metagenome-assembled genomes (MAGs) and MAG-annotated genes, together with predicted metabolic functions and metabolite profiles, revealed a bipartite co-occurrence network of MAGs, genes and clinical indexes separating healthy from GD subjects. A supervised classification model identified a combination of biomarkers including microbial species, MAGs, genes and SNPs, with predictive power superior to models from any single biomarker type (AUC=0.98). Global, cross-disease meta-analysis of gut microbiomes revealed high specificity of these GD biomarkers, notably discriminating against Parkinson’s Disease, and suggesting that non-invasive stool-based diagnostics will be useful for these diseases.Conclusions: This work extended our understanding of the microbial ecology and evolution of GD pathogenesis, and developed a useful predictive model for GD diagnosis based on intestinal microbial biomarkers.

scholarly journals Genetic Mutations in Young Nonsmoking Patients With Oral Cavity Cancer: A Systematic Review

OTO Open ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 2473974X2097018
Author(s):  
Rohini R. Bahethi ◽  
Katelyn O. Stepan ◽  
Rachel Pinotti ◽  
Ryan Li ◽  
Nishant Agrawal ◽  
...  
Keyword(s):  
Risk Factors ◽  
Oral Cavity ◽  
Similar Rate ◽  
Genetic Alterations ◽  
Small Sample ◽  
Genetic Mutations ◽  
Case Control Studies ◽  
Level Of Evidence ◽  
Tp53 Mutations ◽  
Small Sample Sizes

Objective This investigation aims to review the known genetic mutations associated with oral cavity squamous cell carcinoma (OCSCC) in young adults with limited environmental risk factors (YLERs). Data Sources A comprehensive search strategy was designed to identify studies in MEDLINE (Ovid), Embase (Ovid), and Scopus from database inception to May 2017 that included adults ≤50 years of age with OCSCC and minimal tobacco use history (≤10 pack-years) who had their tumors genetically sequenced or mutational profiles analyzed. Review Methods Identified articles were screened by 2 reviewers. Quality of evidence was graded by the MINORS criteria for case-control studies; other studies were graded by assigning a level of evidence for gene mutation literature. Results Thirteen studies met our inclusion criteria, and 130 patients met our criteria for age and tobacco history. TP53 was the most commonly evaluated gene (10 of 13 studies) and the most frequently observed mutation. One study reported that nonsmokers had significantly fewer TP53 mutations, while 9 studies found no difference in the prevalence of TP53 mutations. No other mutations were found specific to this cohort. Conclusions TP53 mutations may occur at a similar rate in YLERs with OCSCC as compared with older patients or those with risk factors. However, few studies have aimed to characterize the genetic landscape of oral cavity tumors in this population, often with small sample sizes. Future studies are needed to explore unidentified genetic alterations leading to tumor susceptibility or alternative mechanisms of carcinogenesis.

scholarly journals The Affect Misattribution Procedure

2017 ◽  
Vol 64 (3) ◽  
pp. 215-230 ◽  
Author(s):  
Sarah Teige-Mocigemba ◽  
Manuel Becker ◽  
Jeffrey W. Sherman ◽  
Regina Reichardt ◽  
Karl Christoph Klauer
Keyword(s):  
Implicit Association Test ◽  
Research Question ◽  
Small Sample ◽  
Association Test ◽  
Implicit Association ◽  
Pilot Studies ◽  
Evaluative Priming ◽  
Priming Task ◽  
Affect Misattribution Procedure ◽  
Small Sample Sizes

Abstract. The Affect Misattribution Procedure (AMP) has been forwarded as one of the most promising alternatives to the Implicit Association Test and the evaluative-priming task for measuring attitudes such as prejudice indirectly. We investigated whether the AMP is indeed able to detect an evaluative out-group bias. In contrast to recent conclusions about the robustness of AMP effects, six out of seven pilot studies indicated that participants did not show any prejudice effects in the AMP. Yet, these pilot studies were not fully conclusive with regard to our research question because they investigated different domains of prejudice, used small sample sizes, and employed a modified AMP version. In a preregistered, high-powered AMP study, we therefore examined whether the standard AMP does reveal prejudice against Turks, the biggest minority in Germany, and found a significant, albeit very small prejudice effect. We discuss possible reasons for the AMP’s weak sensitivity to evaluations in socially sensitive domains.

scholarly journals Altered White Matter Microstructural Organization in Post-Traumatic Stress Disorder across 3,049 Adults: Results from the PGC-ENIGMA PTSD Consortium

2019 ◽  
Author(s):  
Emily L Dennis ◽  
Seth G Disner ◽  
Negar Fani ◽  
Lauren E Salminen ◽  
Mark Logue ◽  
...  
Keyword(s):  
White Matter ◽  
Stress Disorder ◽  
Meta Analysis ◽  
Cohort Analysis ◽  
Structural Connectivity ◽  
Small Sample ◽  
Post Traumatic Stress ◽  
History Of ◽  
Small Sample Sizes ◽  
And Function

AbstractA growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed, which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3,049 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1,446 individuals with PTSD and 1,603 controls (2152 males/897 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen’sd=−0.12,p=0.0021). The tapetum connects the left and right hippocampus, structures for which structure and function have been consistently implicated in PTSD. Results remained significant/similar after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.

Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

2018 ◽  
Author(s):  
Prathiba Natesan ◽  
Smita Mehta
Keyword(s):  
Behavioral Intervention ◽  
Effect Size ◽  
Rate Ratio ◽  
Visual Analysis ◽  
Single Case ◽  
Small Sample ◽  
Statistical Analyses ◽  
Data Types ◽  
Ratio Effect ◽  
Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

No Evidence that Experiencing Physical Warmth Promotes Interpersonal Warmth: Two Failures to Replicate Williams and Bargh (2008)

2018 ◽  
Author(s):  
Christopher Chabris ◽  
Patrick Ryan Heck ◽  
Jaclyn Mandart ◽  
Daniel Jacob Benjamin ◽  
Daniel J. Simons
Keyword(s):  
Null Hypothesis ◽  
Small Sample ◽  
Sample Sizes ◽  
Double Blind ◽  
Bayesian Analyses ◽  
Physical Warmth ◽  
Small Sample Sizes ◽  
Interpersonal Warmth

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

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