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Gastroesophageal Reflux Disease

Gastroesophageal Reflux Disease (GERD) is a chronic condition where stomach contents flow back into the esophagus, causing symptoms such as heartburn, regurgitation, and difficulty swallowing.
This condition can lead to complications if left untreated.
PubCompare.ai leverages AI to optimize GERD research by identifying the best protocols from literature, preprints, and patents, ensuring enhanced reproducibility and accuaracy.
By using artificial intelligence, PubCompare.ai helps users find the most effective procedures and products for their GERD research, ultimately advancing the understanding and treatment of this common digestive disorder.

Most cited protocols related to «Gastroesophageal Reflux Disease»

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Publication 2018
Adolescent Adult Child Diagnosis Eosinophilia Gastroesophageal Reflux Disease Hypersensitivity Prepulse Inhibition Tests, Diagnostic
Leveraging the strong genetic correlation between GERD and related traits, we applied a multitrait GWAS model combining GWASs for body mass index (BMI), major depressive disorder (MDD), education attainment and GERD (and BE), to identify more susceptibility loci for GERD and BE (figure 1). The brief description for each input GWAS in the multitrait GWAS analysis and the equivalent effect sample size is shown in online supplemental table ST1. Candidate loci for GERD and BE achieving genome-wide significance (p<5e-8) were sent for replication in the independent 23andMe cohort (4 62 753 cases; 1 127 474 controls). Findings from these GWAS analyses were followed up with transcriptome-wide association studies (TWAS) and tissue enrichment analyses. TWAS analysis allows us to infer if there is a relationship between predicted gene-expression levels and GERD/BE risk. Tissue enrichment analyses allow us to assess whether the relevant GERD/BE-associated genes showed differential enrichment across 44 human tissues including oesophageal-related tissues. We finally applied a simple heuristic to dissect aetiological heterogeneity in GERD by separating GERD risk loci into obesity-driven and depression-driven categories; we then assessed these categories for differences in predicted gene expression in various tissues and for their ability to predict BE/EA susceptibility.
Publication 2021
DNA Replication Esophagus Gastroesophageal Reflux Disease Gene Expression Genes Genetic Heterogeneity Genome Genome-Wide Association Study Homo sapiens Index, Body Mass Obesity Reproduction Susceptibility, Disease Tissues Transcriptome Unipolar Depression

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Publication 2009
Animals Gastroesophageal Reflux Disease Mice, Laboratory Pregnant Women SLC47A2 protein, human
The University of North Carolina (UNC) EoE clinico-pathologic database was used for this study. This database contains clinical, endoscopic, and pathologic characteristics on over 1,200 patients with esophageal eosinophilia from any cause from 2000 to 2007. It also contains information on patients in whom esophageal eosinophilia was excluded with a normal esophageal biopsy. Because this study was designed primarily to test reliability of determining eosinophil counts, subjects were selected based on their initial eosinophil counts (eos/hpf) performed for clinical care, and specifically chosen to represent a wide range of esophageal eosinophil counts, from 0 to >300 eos/hpf. The esophageal eosinophilia could have been from any cause, including EoE or gastroesophageal reflux disease, and clinical characteristics were not relevant for the purposes of this study. After identification of appropriate patients, archived pathology slides were pulled for review. This study was approved by the University of North Carolina Institutional Review Board.
Publication 2009
Biopsy Endoscopy Eosinophil Eosinophilia Ethics Committees, Research Gastroesophageal Reflux Disease Patients
We reduced the number of chronic conditions used, in order to simulate studies that were based on fewer chronic conditions. We used the 11 minimum chronic conditions as suggested by Diederichs et al),12 (link) the 12 most prevalent chronic conditions in our study (hypertension, hyperlipidaemia, ischaemic heart disease, type 2 diabetes, obesity, osteoarthritis, chronic back pain, asthma, depression, anxiety, gastro-oesophageal reflux disease and malignant neoplasms) as suggested by Fortin et al11 and the 24 listed chronic conditions with a tick box. We then compared these results with those generated using all diagnosed chronic conditions.
BEACH substudies have a single stage cluster design, with each GP having 30 patients clustered around them. The cluster effect was accounted for using SAS V.9.3.
Publication 2014
Anxiety Asthma Back Pain Chronic Condition Chronic Pain Degenerative Arthritides Diabetes Mellitus, Non-Insulin-Dependent Gastroesophageal Reflux Disease High Blood Pressures Hyperlipidemia Malignant Neoplasms Myocardial Ischemia Obesity Patients Ticks

Most recents protocols related to «Gastroesophageal Reflux Disease»

Before randomization, comprehensive medication reviews were conducted by BCGPs for all participants using participant-reported medical conditions and information on dose, frequency, indication, duration of treatment, tolerability, and adverse drug reactions for all prescription medications, vitamins, and supplements. The BCGP medication review process involved 1) assessing the clinical appropriateness of each medication using the Beers Criteria [13 ] and Medication Appropriateness Index (MAI); [16 (link)] 2) evaluating potential drug-drug and drug-disease interactions in accord with the above and also taking into account prescription label information; and 3) assessing whether medication regimens followed relevant disease-specific evidence-based guidelines [13 , 17 (link), 18 (link)]. Of note, blood laboratory work results, electronic medical records, and previous therapies (e.g., medication failures) were not available to BCGPs when devising baseline recommendations, but were available to the clinician member of the MTM team. Following randomization, the MTM recommendations were only shared with those participants randomized to the intervention group (N = 46). Recommendations for the control group were recorded in the study database but not shared with those participants.
During the INCREASE study period, the pharmacy team of two BCGPs utilized drug and health information resources (e.g., Lexicomp and UpToDate [Wolters Kluwer Health Inc. Riverwoods, IL]), Beers Criteria [13 ], relevant guidelines (e.g., Diabetes Standards of Care [17 (link)] and Clinical Practice Guidelines for Hypertension [18 (link)]), and clinical judgement to justify their recommendations. Each recommendation was reviewed by both BCGPs and a consensus pharmacy recommendation was decided via discussion. Detailed information for each recommendation was then entered into a series of pre-specified study protocol data collection forms, allowing for systematic categorization of recommendations as either: 1) medication discontinuation with or without tapering; 2) switch to a different medication; 3) dose adjustment (e.g., decrease dose, adjust dose for organ function/tolerability, or increase dose); 4) new medication initiation; 5) drug or disease monitoring recommendation (e.g., vital signs, falls risk, sedation); or 6) a non-pharmacologic recommendation (e.g., sleep hygiene, avoiding gastroesophageal reflux triggers, referral for diagnostic workup). Baseline recommendations were also categorized by pharmacologic class and over the counter (OTC) or supplement status of the medication prompting a baseline MTM recommendation. A full schematic for medication categorization is available in the supplementary material (see Supplementary Table S1).
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Publication 2023
BLOOD Clinical Reasoning Diabetes Mellitus Diagnosis Dietary Supplements Drug Interactions Drug Reaction, Adverse Drugs, Non-Prescription Gastroesophageal Reflux Disease High Blood Pressures Medication Review Pharmaceutical Preparations Precipitating Factors Sedatives Signs, Vital Treatment Protocols Vitamins

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Publication 2023
Europeans Gastroesophageal Reflux Disease Gender Patients Pharmaceutical Preparations
We performed a questionnaire-based cohort study in postmenopausal women recruited within the outpatient consultation at the University Hospital Marburg, Germany. The women visited the gynecological department for numerous medical reasons, but the majority took part in routine checkups or menopausal counseling including bone densitometry (in our hospital performed by the department of gynecological endocrinology). Participants between the ages of 45 and 65 were offered participation, although the last menstruation had to be at least one year ago.
We excluded women with irregular bleeding, smokers, history of any recent sex steroid treatment, including Menopause Hormone Therapy (MHT) or any differential diagnosis that might be connected to chronic cough (e.g. cancer, chronic bronchitis, gastro-esophageal reflux, chronic heart failure, therapy with ace-inhibitors).
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Publication 2023
Angiotensin-Converting Enzyme Inhibitors Bones Bronchitis, Chronic Congestive Heart Failure Cough Densitometry Differential Diagnosis Gastroesophageal Reflux Disease Gonadal Steroid Hormones Malignant Neoplasms Menopause Menstruation Outpatients System, Endocrine Therapeutics Therapy, Hormone Replacement Woman
We utilized a two pronged approach to covariate selection: 1) covariates were selected based on prior knowledge1 (link),4 (link),6 (link),7 (link),9 (link),10 (link),16 (link)–20 (link),24 (link),26 (link),44 (link),51 (link),52 , 2) in recognition that our knowledge of COVID-19 is evolving, we also employed an algorithmic approach to identify covariates in data domains consisting of diagnoses, medications and laboratory test results. Pre-defined and algorithmically selected covariates were used in modeling and were assessed in the year prior to T0.
Pre-defined covariates consisted of age, race (white, black, and other), sex, ADI, body mass index, smoking status (current, former, and never), and measures of healthcare utilization (number of outpatient encounters as well as long-term care utilization1 (link),16 (link),18 ). Additionally, several comorbidities including cancer, cardiovascular disease, chronic kidney disease, chronic lung disease, diabetes, and hypertension were used as pre-defined covariates. Laboratory values consisting of estimated glomerular filtration rate, systolic, and diastolic pressure were also used as pre-defined covariates. Continuous variables were transformed into restricted cubic spline functions to account for possible non-linear relationships.
To supplement our pre-defined covariates, we utilized algorithmically selected covariates from high dimensional data domains consisting of diagnoses, medications, and laboratory test results53 (link). Data from patient encounter, prescription, and laboratory domains collected in the year prior to T0 were organized into 540 diagnostic groups, 543 medication types, and 62 laboratory test abnormalities. From these three domains (diagnoses, medications, and laboratory test results) we selected variables which occurred in at least 100 participants within each exposure group in acknowledgment of the fact that exceedingly rare variables (those that occurred in fewer than 100 participants in these cohorts) may not substantially influence the examined associations. Univariate relative risks between each variable and exposure was estimated and 100 variables with the highest relative risks were selected for use in statistical analyses54 (link). The algorithmic selection process described above was used to independently select high dimensional covariates in each comparison (for example, the COVID-19 vs contemporary control and the COVID-19 vs historical control analyses to assess incident GERD).
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Publication 2023
Cardiovascular Diseases Chronic Kidney Diseases Congenital Abnormality COVID 19 Cuboid Bone Diabetes Mellitus Diagnosis Dietary Supplements Disease, Chronic Gastroesophageal Reflux Disease Glomerular Filtration Rate High Blood Pressures Index, Body Mass Long-Term Care Lung Lung Diseases Malignant Neoplasms Outpatients Patients Pharmaceutical Preparations Pressure, Diastolic Systole
We selected IVs based on three generally recognized assumptions: (i) IVs need to be strongly associated with exposure, (ii) IVs are independent of confounders, and (iii) IVs are solely related to outcomes through exposure without a direct association with outcomes (17 (link)). We first screened the IVs for MR analysis using a genome-wide significance threshold (P < 5e−8). In addition, to increase the confidence of our findings, we additionally screened more IVs under a locus-wide significance threshold (P < 1e−5) for a secondary MR analysis. The SNPs within a window size of 10,000 kb were pruned under the threshold of r2 < 0.001 to mitigate linkage disequilibrium (LD), thus ensuring the independence of each IV. Smoking, alcohol consumption, and body mass index (BMI) might be potential confounders influencing GERD, anxiety disorders, and depression (23 (link)–27 (link)). Therefore, we retrieved SNPs associated with these confounders (P < 5e−8) from the IEU Open GWAS project database and excluded them from the IVs. The accession number of these confounders is shown in Table 2. Then, palindromic SNPs, outcome-related SNPs (P < 0.05), and SNPs not present in outcome GWAS summary data were removed from the IVs. Finally, we calculated the F-statistic of IVs to assess the degree of weak instrumental bias. Only IVs with F > 10 were retained to avoid bias caused by weak IVs (28 (link)).
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Publication 2023
Anxiety Disorders Debility Gastroesophageal Reflux Disease Genome Genome-Wide Association Study Index, Body Mass Single Nucleotide Polymorphism

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More about "Gastroesophageal Reflux Disease"

Gastroesophageal Reflux Disease (GERD) is a chronic digestive condition where stomach contents flow back into the esophagus, leading to symptoms like heartburn, regurgitation, and difficulty swallowing.
This common disorder can progress to complications if left untreated.
To optimize GERD research, PubCompare.ai leverages artificial intelligence (AI) to identify the best protocols from scientific literature, preprints, and patents, enhancing reproducibility and accuracy.
Utilizing SAS version 9.4, SPSS software, and other statistical tools, researchers can analyze data related to GERD, including the impact of factors like collagen IV-coated wells and glycochenodeoxycholic acid on disease progression.
SPSS v22 and Stata 13 are also widely used for GERD-related studies.
By incorporating AI-driven protocol comparisons, PubCompare.ai helps users discover the most effective procedures and products for their GERD research, ultimately advancing the understanding and treatment of this common digestive disorder.
This AI-powered platform can be a valuable resource for clinicians, researchers, and patients seeking to optimize their GERD-related studies and improve patient outcomes.