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Medicine Feb 2024Traditional observational and in vivo studies have suggested an etiological link between gastroesophageal reflux disease (GERD) and the development of extraesophageal...
Traditional observational and in vivo studies have suggested an etiological link between gastroesophageal reflux disease (GERD) and the development of extraesophageal diseases (EEDs), such as noncardiac chest pain. However, evidence demonstrating potential causal relationships is lacking. This study evaluated the potential causal relationship between GERD and EEDs, including throat and chest pain, asthma, bronchitis, chronic rhinitis, nasopharyngitis and pharyngitis, gingivitis and periodontal disease, cough, using multiple Mendelian randomization (MR) methods, and sensitivity analysis was performed. The Mendelian randomization Pleiotropy RESidual Sum and Outlier and PhenoScanner tools were used to further check for heterogeneous results and remove outliers. MR with inverse-variance weighted (IVW) showed a significant causal relationship between GERD and EEDs after Bonferroni correction. IVW results indicated that GERD increased the risk of chronic rhinitis, nasopharyngitis and pharyngitis (odds ratio [OR] = 1.482, 95% confidence interval [CI] = 1.267-1.734, P < .001], gingivitis and periodontal disease (OR = 1.166, 95% CI = 1.046-1.190, P = .001), throat and chest pain (OR = 1.585, 95% CI = 1.455-1.726, P < .001), asthma (OR = 1.539, 95% CI = 1.379-1.717, P < .001), and bronchitis (OR = 1.249, 95% CI = 1.168-1.335, P < .001). Sensitivity analysis did not detect pleiotropy. Leave-one-out analysis shows that MR results were not affected by individual single nucleotide polymorphisms. The funnel plot considers the genetic instrumental variables to be almost symmetrically distributed. This MR supports a causal relationship among GERD and EEDs. Precise moderation based on causality and active promotion of collaboration among multidisciplinary physicians ensure high-quality diagnostic and treatment recommendations and maximize patient benefit.
Topics: Humans; Nasopharyngitis; Mendelian Randomization Analysis; Gastroesophageal Reflux; Pharyngitis; Asthma; Bronchitis; Chest Pain; Gingivitis; Periodontal Diseases; Rhinitis; Genome-Wide Association Study
PubMed: 38363933
DOI: 10.1097/MD.0000000000037054 -
Molecular Oncology Jun 2024Multiple strategies are continuously being explored to expand the drug target repertoire in solid tumors. We devised a novel computational workflow for...
Multiple strategies are continuously being explored to expand the drug target repertoire in solid tumors. We devised a novel computational workflow for transcriptome-wide gene expression outlier analysis that allows the systematic identification of both overexpression and underexpression events in cancer cells. Here, it was applied to expression values obtained through RNA sequencing in 226 colorectal cancer (CRC) cell lines that were also characterized by whole-exome sequencing and microarray-based DNA methylation profiling. We found cell models displaying an abnormally high or low expression level for 3533 and 965 genes, respectively. Gene expression abnormalities that have been previously associated with clinically relevant features of CRC cell lines were confirmed. Moreover, by integrating multi-omics data, we identified both genetic and epigenetic alternations underlying outlier expression values. Importantly, our atlas of CRC gene expression outliers can guide the discovery of novel drug targets and biomarkers. As a proof of concept, we found that CRC cell lines lacking expression of the MTAP gene are sensitive to treatment with a PRMT5-MTA inhibitor (MRTX1719). Finally, other tumor types may also benefit from this approach.
Topics: Humans; Colorectal Neoplasms; Gene Expression Regulation, Neoplastic; Cell Line, Tumor; Transcriptome; Gene Expression Profiling; DNA Methylation
PubMed: 38468448
DOI: 10.1002/1878-0261.13622 -
Frontiers in Molecular Biosciences 2023Pancreatic cancer (PC) is a devastating malignancy characterized by its aggressive nature and poor prognosis. However, the relationship of PC with peripheral...
Unveiling the intricate causal nexus between pancreatic cancer and peripheral metabolites through a comprehensive bidirectional two-sample Mendelian randomization analysis.
Pancreatic cancer (PC) is a devastating malignancy characterized by its aggressive nature and poor prognosis. However, the relationship of PC with peripheral metabolites remains not fully investigated. The study aimed to explore the causal linkage between PC and peripheral metabolite profiles. Employing publicly accessible genome-wide association studies (GWAS) data, we conducted a bidirectional two-sample Mendelian randomization (MR) analysis. The primary analysis employed the inverse-variance weighted (IVW) method. To address potential concerns about horizontal pleiotropy, we also employed supplementary methods such as maximum likelihood, weighted median, MR-Egger regression, and MR pleiotropy residual sum and outlier (MR-PRESSO). We ascertained 20 genetically determined peripheral metabolites with causal linkages to PC while high-density lipoprotein (HDL) and very low-density lipoprotein (VLDL) particles accounted for the vast majority. Specifically, HDL particles exhibited an elevated PC risk while VLDL particles displayed an opposing pattern. The converse MR analysis underscored a notable alteration in 17 peripheral metabolites due to PC, including branch chain amino acids and derivatives of glycerophospholipid. Cross-referencing the bidirectional MR results revealed a reciprocal causation of PC and X-02269 which might form a self-perpetuating loop in PC development. Additionally, 1-arachidonoylglycerophosphocholine indicated a reduced PC risk and an increase under PC influence, possibly serving as a negative feedback regulator. Our findings suggest a complex interplay between pancreatic cancer and peripheral metabolites, with potential implications for understanding the etiology of pancreatic cancer and identifying novel early diagnosis and therapeutic targets. Moreover, X-02269 may hold a pivotal role in PC onset and progression.
PubMed: 37954977
DOI: 10.3389/fmolb.2023.1279157 -
Public Health Nutrition Dec 2023To quantify the full life cycle impacts of ultra-processed foods (UPF) for key environmental, economic and nutritional indicators to identify trade-offs between UPF...
OBJECTIVE
To quantify the full life cycle impacts of ultra-processed foods (UPF) for key environmental, economic and nutritional indicators to identify trade-offs between UPF contribution to broad-scope sustainability.
DESIGN
Using 24-h dietary recalls along with an input-output database for the Australian economy, dietary environmental and economic impacts were quantified in this national representative cross-sectional analysis. Food items were classified into non-UPF and UPF using the NOVA system, and dietary energy contribution from non-UPF and UPF fractions in diets was estimated. Thereafter, associations between nutritional, environmental and economic impacts of non-UPF and UPF fractions of diets were examined using a multi-dimensional nutritional geometry representation.
SETTING
National Nutrition and Physical Activity Survey 2011-2012 of Australia.
PARTICIPANTS
Respondents (n 5344) aged > 18 years with 1 d of 24-h dietary recall data excluding respondents with missing values and outlier data points and under reporters.
RESULTS
Australian diets rich in UPF were associated with reduced nutritional quality, high greenhouse gas emissions, energy use, and increased employment and income associated with the food supply chains. The environmental and economic impacts associated with the UPF portion of diets become more distinct when the diets are standardised to average protein recommendation.
CONCLUSION
Increased consumption of UPF has socio-economic benefits, but this comes with adverse effects on the environment and public health. Consideration of such trade-offs is important in identifying policy and other mechanisms regarding UPF for establishing healthy and sustainable food systems.
Topics: Humans; Food, Processed; Cross-Sectional Studies; Nutrition Surveys; Fast Foods; Food Handling; Australia; Diet; Energy Intake
PubMed: 37881877
DOI: 10.1017/S136898002300232X -
Frontiers in Endocrinology 2024Low levels of high-density lipoprotein cholesterol (HDL-C) are commonly seen in patients with type 2 diabetes mellitus (T2DM). However, it is unclear whether there is an...
BACKGROUND
Low levels of high-density lipoprotein cholesterol (HDL-C) are commonly seen in patients with type 2 diabetes mellitus (T2DM). However, it is unclear whether there is an independent or causal link between HDL-C levels and T2DM. This study aims to address this gap by using the The National Health and Nutrition Examination Survey (NHANES) database and Mendelian randomization (MR) analysis.
MATERIALS AND METHODS
Data from the NHANES survey (2007-2018) with 9,420 participants were analyzed using specialized software. Logistic regression models and restricted cubic splines (RCS) were used to assess the relationship between HDL-C and T2DM incidence, while considering covariates. Genetic variants associated with HDL-C and T2DM were obtained from genome-wide association studies (GWAS), and Mendelian randomization (MR) was used to evaluate the causal relationship between HDL-C and T2DM. Various tests were conducted to assess pleiotropy and outliers.
RESULTS
In the NHANES study, all groups, except the lowest quartile (Q1: 0.28-1.09 mmol/L], showed a significant association between HDL-C levels and reduced T2DM risk (all P < 0.001). After adjusting for covariates, the Q2 [odds ratio (OR) = 0.67, 95% confidence interval (CI): (0.57, 0.79)], Q3 [OR = 0.51, 95% CI: (0.40, 0.65)], and Q4 [OR = 0.29, 95% CI: (0.23, 0.36)] groups exhibited average reductions in T2DM risk of 23%, 49%, and 71%, respectively. In the sensitivity analysis incorporating other lipid levels, the Q4 group still demonstrates a 57% reduction in the risk of T2DM. The impact of HDL-C levels on T2DM varied with age (P for interaction = 0.006). RCS analysis showed a nonlinear decreasing trend in T2DM risk with increasing HDL-C levels (P = 0.003). In the MR analysis, HDL-C levels were also associated with reduced T2DM risk (OR = 0.69, 95% CI = 0.52-0.82; P = 1.41 × 10), and there was no evidence of pleiotropy or outliers.
CONCLUSION
This study provides evidence supporting a causal relationship between higher HDL-C levels and reduced T2DM risk. Further research is needed to explore interventions targeting HDL-C levels for reducing T2DM risk.
Topics: Humans; Diabetes Mellitus, Type 2; Cholesterol, HDL; Risk Factors; Mendelian Randomization Analysis; Nutrition Surveys; Triglycerides; Genome-Wide Association Study; Cholesterol, LDL
PubMed: 38455653
DOI: 10.3389/fendo.2024.1272314 -
Genes Oct 2023It is well known that the microbiome data are ridden with outliers and have heavy distribution tails, but the impact of outliers and heavy-tailedness has yet to be...
It is well known that the microbiome data are ridden with outliers and have heavy distribution tails, but the impact of outliers and heavy-tailedness has yet to be examined systematically. This paper investigates the impact of outliers and heavy-tailedness on differential abundance analysis (DAA) using the linear models for the differential abundance analysis (LinDA) method and proposes effective strategies to mitigate their influence. The presence of outliers and heavy-tailedness can significantly decrease the power of LinDA. We investigate various techniques to address outliers and heavy-tailedness, including generalizing LinDA into a more flexible framework that allows for the use of robust regression and winsorizing the data before applying LinDA. Our extensive numerical experiments and real-data analyses demonstrate that robust Huber regression has overall the best performance in addressing outliers and heavy-tailedness.
Topics: Microbiota
PubMed: 38002943
DOI: 10.3390/genes14112000 -
BioData Mining Sep 2023There are not currently any univariate outlier detection algorithms that transform and model arbitrarily shaped distributions to remove univariate outliers. Some...
There are not currently any univariate outlier detection algorithms that transform and model arbitrarily shaped distributions to remove univariate outliers. Some algorithms model skew, even fewer model kurtosis, and none of them model bimodality and monotonicity. To overcome these challenges, we have implemented an algorithm for Skew and Tail-heaviness Adjusted Removal of Outliers (STAR_outliers) that robustly removes univariate outliers from distributions with many different shape profiles, including extreme skew, extreme kurtosis, bimodality, and monotonicity. We show that STAR_outliers removes simulated outliers with greater recall and precision than several general algorithms, and it also models the outlier bounds of real data distributions with greater accuracy.Background Reliably removing univariate outliers from arbitrarily shaped distributions is a difficult task. Incorrectly assuming unimodality or overestimating tail heaviness fails to remove outliers, while underestimating tail heaviness incorrectly removes regular data from the tails. Skew often produces one heavy tail and one light tail, and we show that several sophisticated outlier removal algorithms often fail to remove outliers from the light tail. Multivariate outlier detection algorithms have recently become popular, but having tested PyOD's multivariate outlier removal algorithms, we found them to be inadequate for univariate outlier removal. They usually do not allow for univariate input, and they do not fit their distributions of outliership scores with a model on which an outlier threshold can be accurately established. Thus, there is a need for a flexible outlier removal algorithm that can model arbitrarily shaped univariate distributions.Results In order to effectively model arbitrarily shaped univariate distributions, we have combined several well-established algorithms into a new algorithm called STAR_outliers. STAR_outliers removes more simulated true outliers and fewer non-outliers than several other univariate algorithms. These include several normality-assuming outlier removal methods, PyOD's isolation forest (IF) outlier removal algorithm (ACM Transactions on Knowledge Discovery from Data (TKDD) 6:3, 2012) with default settings, and an IQR based algorithm by Verardi and Vermandele that removes outliers while accounting for skew and kurtosis (Verardi and Vermandele, Journal de la Société Française de Statistique 157:90-114, 2016). Since the IF algorithm's default model poorly fit the outliership scores, we also compared the isolation forest algorithm with a model that entails removing as many datapoints as STAR_outliers does in order of decreasing outliership scores. We also compared these algorithms on the publicly available 2018 National Health and Nutrition Examination Survey (NHANES) data by setting the outlier threshold to keep values falling within the main 99.3 percent of the fitted model's domain. We show that our STAR_outliers algorithm removes significantly closer to 0.7 percent of values from these features than other outlier removal methods on average.Conclusions STAR_outliers is an easily implemented python package for removing outliers that outperforms multiple commonly used methods of univariate outlier removal.
PubMed: 37667378
DOI: 10.1186/s13040-023-00342-0 -
BMC Pulmonary Medicine Dec 2023The relationship between gastroesophageal reflux disease (GERD) and the susceptibility as well as the prognosis of idiopathic pulmonary fibrosis (IPF) has been...
BACKGROUND
The relationship between gastroesophageal reflux disease (GERD) and the susceptibility as well as the prognosis of idiopathic pulmonary fibrosis (IPF) has been previously suggested, with the potential confounding factor of smoking not adequately addressed. In light of this, we conducted a Mendelian randomization (MR) study to investigate the causal effects of GERD on the susceptibility and prognosis of IPF while excluding smoking.
METHODS
We chose GERD as the exposure variable and employed genome-wide association data to examine its association with susceptibility, forced vital capacity (FVC), diffusing capacity of the lung for carbon monoxide (DLco), and transplant-free survival (TFS) in patients with IPF as the outcome variables. MR analyses were performed using the inverse variance weighted (IVW) method, and sensitivity analyses were conducted using the MR-PRESSO outlier test, Cochran's Q test, MR-Egger intercept test, and leave-one-out sensitivity analysis. Additionally, to mitigate the potential effects of smoking on our MR estimates, we conducted a multivariable MR (MVMR) analysis by adjusting for smoking.
RESULTS
The univariable MR analysis demonstrated no causal effect of GERD on FVC (β = 26.63, SE = 48.23, P = 0.581), DLco (β = 0.12, SE = 0.12, P = 0.319), and TFS (HR = 0.87, 95% CI = 0.56 to 1.35, P = 0.533) in patients with IPF. Furthermore, sensitivity analysis revealed no evidence of heterogeneity, horizontal pleiotropy, or outlier single nucleotide polymorphisms. The MVMR analysis showed no causal effect of GERD on susceptibility to IPF after adjusting for smoking (OR = 1.30, 95% CI = 0.93 to 1.68, P = 0.071). These findings were consistent in the replication cohort.
CONCLUSIONS
The link between GERD and its potential impact on susceptibility to IPF may not be of a direct causal nature and could be influenced by factors such as smoking. Our findings did not reveal any evidence of a causal relationship between GERD and the FVC, DLco, and TFS of patients with IPF.
Topics: Humans; Smoking; Mendelian Randomization Analysis; Genome-Wide Association Study; Idiopathic Pulmonary Fibrosis; Prognosis; Gastroesophageal Reflux
PubMed: 38129814
DOI: 10.1186/s12890-023-02788-8 -
Brain and Behavior May 2024High salt intake has been proposed as a risk factor for dementia. However, causal relationship between salt intake and dementia remains uncertain.
BACKGROUND
High salt intake has been proposed as a risk factor for dementia. However, causal relationship between salt intake and dementia remains uncertain.
PURPOSE
The aim of this study was to employ a mendelian randomization (MR) design to investigate the causal impact of salt intake on the risk of dementia.
METHODS
Genome-wide association study (GWAS) data of exposures and outcomes (any dementia, cognitive performance, different types of dementia, Alzheimer's disease [AD], and Parkinson's disease) were obtained from the IEU database. MR estimates were generated though inverse-variance weighted model. MR-Egger, weighted median, and MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) method also used in our study. Sensitivity analyses included Cochran's Q test, MR-Egger intercept, MR-PRESSO global test and outlier test, leave-one-out analysis, and funnel plot assessment.
RESULTS
Our MR analysis provided evidence of a causal association between high salt added to food and dementia (odds ratio [OR] = 1.73, 95% confidence interval [CI]: 1.21-2.49, and p = .003), dementia in AD (OR = 2.10, 95% CI: 1.15-3.83, and p = .015), and undefined dementia (OR = 2.61, 95% CI: 1.26-5.39, and p = .009). Higher salt added was also associated with increased risk of AD (OR = 1.80, 95% CI: 1.12-2.87, and p = .014) and lower cognitive performance (β = -.133, 95% CI: -.229 to -.038, and p = .006).
CONCLUSION
This study provides evidence suggesting that high salt intake is causally associated with an increased risk of developing dementia, including AD and undefined dementia, highlighting the potential importance of reducing salt consumption as a preventive measure.
Topics: Humans; Mendelian Randomization Analysis; Dementia; Sodium Chloride, Dietary; Genome-Wide Association Study; White People; Risk Factors; Alzheimer Disease
PubMed: 38702903
DOI: 10.1002/brb3.3516 -
BMJ Open Jul 2023To measure differences at various deciles in days alive and out of hospital to 90 days (DAOH) and explore its utility for identifying outliers of performance among...
OBJECTIVES
To measure differences at various deciles in days alive and out of hospital to 90 days (DAOH) and explore its utility for identifying outliers of performance among district health boards (DHBs).
METHODS
Days in hospital and mortality within 90 days of surgery were extracted by linking data from the New Zealand National Minimum Data Set and the births and deaths registry between 1 January 2011 and 31 December 2021 for all adults in New Zealand undergoing acute laparotomy (AL-a relatively high-risk group), elective total hip replacement (THR-a medium risk group) or lower segment caesarean section (LSCS-a low-risk group). DAOH was calculated without censoring to zero in cases of mortality. For each DHB, direct risk standardisation was used to adjust for potential confounders and presented in deciles according to baseline patient risk. The Mann-Whitney U test assessed overall DAOH differences between DHBs, and comparisons are presented between selected deciles of DAOH for each operation.
RESULTS
We obtained national data for 35 175, 52 032 and 117 695 patients undergoing AL, THR and LSCS procedures, respectively. We have demonstrated that calculating DAOH without censoring zero allows for differences between procedures and DHBs to be identified. Risk-adjusted national mean DAOH Scores were 64.0 days, 79.0 days and 82.0 days at the 0.1 decile and 75.0 days, 82.0 days and 84.0 days at the 0.2 decile for AL, THR and LSCS, respectively, matching to their expected risk profiles. Differences between procedures and DHBs were most marked at lower deciles of the DAOH distribution, and outlier DHBs were detectable. Corresponding 90-day mortality rates were 5.45%, 0.78% and 0.01%.
CONCLUSION
In New Zealand after direct risk adjustment, differences in DAOH between three types of surgical procedure reflected their respective risk levels and associated mortality rates. Outlier DHBs were identified for each procedure. Thus, our approach to analysing DAOH appears to have considerable face validity and potential utility for contributing to the measurement of perioperative outcomes in an audit or quality improvement setting.
Topics: Pregnancy; Adult; Humans; Female; Cross-Sectional Studies; New Zealand; Cesarean Section; Hospitals; Treatment Outcome
PubMed: 37491100
DOI: 10.1136/bmjopen-2022-063787