Pub Date : 2024-01-23DOI: 10.1101/2024.01.23.24301624
James K Ruffle, Michelle Henderson, Cho Ee Ng, Trevor Liddle, Amy P. K. Nelson, Parashkev Nachev, Charles H Knowles, Yan Yiannakou
Objective: Functional bowel disorders (FBDs) are multi-dimensional diseases varying in demographics, symptomology, lifestyle, mental health, and susceptibility to treatment. The patient lived experience is an integration of these factors, best understood with appropriately multivariate models. Methods: In a large patient cohort (n=1175), we developed a machine learning framework to better understand the lived experience of FBDs. Iterating through 59 factors available from routine clinical care, spanning patient demography, diagnosis, symptomatology, life-impact, mental health indices, healthcare access requirements, COVID-19 impact, and treatment effectiveness, machine models were used to quantify the predictive fidelity of one feature from the remainder. Bayesian stochastic block models were used to delineate the network community structure underpinning the lived experience of FBDs. Results: Machine models quantified patient personal health rating (R2 0.35), anxiety and depression severity (R2 0.54), employment status (balanced accuracy 96%), frequency of healthcare attendance (R2 0.71), and patient-reported treatment effectiveness variably (R2 range 0.08-0.41). Contrary to the view of many healthcare professionals, the greatest determinants of patient-reported health and quality-of-life were life-impact, mental wellbeing, employment status, and age, rather than diagnostic group and symptom severity. Patients responsive to one treatment were more likely to respond to another, leaving many others refractory to all. Conclusions: The assessment of patients with FBDs should be less concerned with diagnostic classification than with the wider life impact of illness, including mental health and employment. The stratification of treatment response (and resistance) has implications for clinical practice and trial design, in need of further research.
{"title":"The lived experience of functional bowel disorders: a machine learning approach","authors":"James K Ruffle, Michelle Henderson, Cho Ee Ng, Trevor Liddle, Amy P. K. Nelson, Parashkev Nachev, Charles H Knowles, Yan Yiannakou","doi":"10.1101/2024.01.23.24301624","DOIUrl":"https://doi.org/10.1101/2024.01.23.24301624","url":null,"abstract":"Objective: Functional bowel disorders (FBDs) are multi-dimensional diseases varying in demographics, symptomology, lifestyle, mental health, and susceptibility to treatment. The patient lived experience is an integration of these factors, best understood with appropriately multivariate models.\u0000Methods: In a large patient cohort (n=1175), we developed a machine learning framework to better understand the lived experience of FBDs. Iterating through 59 factors available from routine clinical care, spanning patient demography, diagnosis, symptomatology, life-impact, mental health indices, healthcare access requirements, COVID-19 impact, and treatment effectiveness, machine models were used to quantify the predictive fidelity of one feature from the remainder. Bayesian stochastic block models were used to delineate the network community structure underpinning the lived experience of FBDs.\u0000Results: Machine models quantified patient personal health rating (R2 0.35), anxiety and depression severity (R2 0.54), employment status (balanced accuracy 96%), frequency of healthcare attendance (R2 0.71), and patient-reported treatment effectiveness variably (R2 range 0.08-0.41). Contrary to the view of many healthcare professionals, the greatest determinants of patient-reported health and quality-of-life were life-impact, mental wellbeing, employment status, and age, rather than diagnostic group and symptom severity. Patients responsive to one treatment were more likely to respond to another, leaving many others refractory to all.\u0000Conclusions: The assessment of patients with FBDs should be less concerned with diagnostic classification than with the wider life impact of illness, including mental health and employment. The stratification of treatment response (and resistance) has implications for clinical practice and trial design, in need of further research.","PeriodicalId":501258,"journal":{"name":"medRxiv - Gastroenterology","volume":"313 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-18DOI: 10.1101/2024.01.18.24301488
Mahmud Omar, Mohammad Omar, Saleh Nassar, Adi Lahat, Kassem Sharif
Background Celiac disease, a gluten-triggered autoimmune disorder, is known for its systemic inflammatory effects. Epidemiological data suggest an association with type 2 inflammatory diseases like asthma, allergic rhinitis, and atopic dermatitis, however, genetic associations remain unclear, prompting this study to explore their potential genetic interplay.
{"title":"Genetic Associations Between Celiac Disease and Type 2 Inflammatory Diseases: A Mendelian Randomization Analysis","authors":"Mahmud Omar, Mohammad Omar, Saleh Nassar, Adi Lahat, Kassem Sharif","doi":"10.1101/2024.01.18.24301488","DOIUrl":"https://doi.org/10.1101/2024.01.18.24301488","url":null,"abstract":"<strong>Background</strong> Celiac disease, a gluten-triggered autoimmune disorder, is known for its systemic inflammatory effects. Epidemiological data suggest an association with type 2 inflammatory diseases like asthma, allergic rhinitis, and atopic dermatitis, however, genetic associations remain unclear, prompting this study to explore their potential genetic interplay.","PeriodicalId":501258,"journal":{"name":"medRxiv - Gastroenterology","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139518747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.1101/2024.01.12.24301260
Yan Zhou, Zi-Han Yin, Ming-Sheng Sun, Yang-Yang Wang, Chen Yang, Shu-Hao Li, Fan-Rong Liang, Fang Liu
Objective To explore the current status, trends, and frontiers of POI research from 2011 to the present based on bibliometric analysis.
目的 基于文献计量分析，探讨 2011 年至今 POI 研究的现状、趋势和前沿。
{"title":"Global research trends in postoperative ileus from 2011 to 2023: A Scientometric study","authors":"Yan Zhou, Zi-Han Yin, Ming-Sheng Sun, Yang-Yang Wang, Chen Yang, Shu-Hao Li, Fan-Rong Liang, Fang Liu","doi":"10.1101/2024.01.12.24301260","DOIUrl":"https://doi.org/10.1101/2024.01.12.24301260","url":null,"abstract":"<strong>Objective</strong> To explore the current status, trends, and frontiers of POI research from 2011 to the present based on bibliometric analysis.","PeriodicalId":501258,"journal":{"name":"medRxiv - Gastroenterology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.1101/2023.12.15.23299964
Emilie Steinbach, Eugeni Belda, Rohia Alili, Solia Adriouch, Jejunal Luminal and Colonic Mucosa-Associated Microbiota in Metabolic Diseases (Je/Col-MiMe) Group, Benoit Chassaing, Tiphaine Le Roy, Karine Clement
The intestinal microbiota is recognised as an important player in the development and maintenance of obesity. Most studies focus on faecal microbiota because of its accessibility. However, the small intestine is a major site for nutrient sensing and absorption and few studies have examined the composition and function of the microbiota in this segment of the digestive tract. We conducted a clinical research project on 30 age- and sex-matched participants with (N=15) and without (N=15) obesity. Duodenojejunal fluid was obtained by aspiration during fibroscopy. Phenotyping included clinical variables related to metabolic status, lifestyle and psychosocial factors using validated questionnaires. Metagenomic analyses of the oral, duodenojejunal and faecal microbiome, as well as metabolomic data from duodenojejunal fluid and faeces, were integrated with clinical and lifestyle data. The results show associations between duodenojejunal microbiota and lifestyle as well as clinical phenotypes. These associations had larger effect sizes than the associations between these variables and faecal microbiota. We also observed that the duodenojejunal microbiota of obese patients had a higher diversity. In addition, we observed differences in the abundance of several species of the duodenojejunal microbiota between control individuals and patients suffering from obesity. In conclusion, our results support the relevance of studying the role of the small intestinal microbiota in the development of metabolic diseases.
{"title":"Comparative Analysis of the Duodenojejunal Microbiome with the oral and fecal microbiome unveils its role in Human Severe Obesity.","authors":"Emilie Steinbach, Eugeni Belda, Rohia Alili, Solia Adriouch, Jejunal Luminal and Colonic Mucosa-Associated Microbiota in Metabolic Diseases (Je/Col-MiMe) Group, Benoit Chassaing, Tiphaine Le Roy, Karine Clement","doi":"10.1101/2023.12.15.23299964","DOIUrl":"https://doi.org/10.1101/2023.12.15.23299964","url":null,"abstract":"The intestinal microbiota is recognised as an important player in the development and maintenance of obesity. Most studies focus on faecal microbiota because of its accessibility. However, the small intestine is a major site for nutrient sensing and absorption and few studies have examined the composition and function of the microbiota in this segment of the digestive tract. We conducted a clinical research project on 30 age- and sex-matched participants with (N=15) and without (N=15) obesity. Duodenojejunal fluid was obtained by aspiration during fibroscopy. Phenotyping included clinical variables related to metabolic status, lifestyle and psychosocial factors using validated questionnaires. Metagenomic analyses of the oral, duodenojejunal and faecal microbiome, as well as metabolomic data from duodenojejunal fluid and faeces, were integrated with clinical and lifestyle data.\u0000The results show associations between duodenojejunal microbiota and lifestyle as well as clinical phenotypes. These associations had larger effect sizes than the associations between these variables and faecal microbiota. We also observed that the duodenojejunal microbiota of obese patients had a higher diversity. In addition, we observed differences in the abundance of several species of the duodenojejunal microbiota between control individuals and patients suffering from obesity. In conclusion, our results support the relevance of studying the role of the small intestinal microbiota in the development of metabolic diseases.","PeriodicalId":501258,"journal":{"name":"medRxiv - Gastroenterology","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138741773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.1101/2023.12.15.23299944
Abhishek Abhishek, Georgina Nakafero, Matthew J Grainge, Tim Card, Maarten W Taal, Guruprasad Aithal, Christopher P Fox, Christain D Mallen, Stevenson D Matthew, Richard D Riley
Background and aim: To develop and validate a prognostic model for risk-stratified monitoring of 5-aminosalicylate (5-ASA) toxicity. Methods: This nationwide retrospective cohort study used data from the Clinical Practice Research Datalink (CPRD) Aurum and Gold for model development and validation respectively. It included adults newly diagnosed with inflammatory bowel disease (IBD) and established on 5-ASAs between 01/01/2007 and 31/12/2019. 5-ASA discontinuation with abnormal monitoring blood test result was the outcome of interest. Patients prescribed 5-ASAs for ≥6 months i.e., established on treatment, were followed-up for up to five years. Penalised Cox-regression was used to develop the risk equation. Model performance was assessed in terms of calibration and discrimination. Statistical analysis was performed using STATA (StataCorp LLC). Results: 14,109 and 7,523 participants formed the development and validation cohorts with 401 and 243 events respectively. 185, 172, and 64 discontinuations were due to cytopenia, elevated creatinine and elevated liver enzymes respectively in the derivation cohort. Hazardous alcohol intake, chronic kidney disease, thiopurine use, and blood test abnormalities before follow-up were strong prognostic factors. The optimism adjusted R2D in development data was 0.08. The calibration slope and Royston D statistic (95% Confidence Interval) in validation cohort were 0.90 (0.61-1.19) and 0.57 (0.37-0.77) respectively. Conclusion: This prognostic model utilises information available during routine clinical care and can be used to inform decisions on the interval between monitoring blood-tests. The results of this study ought to be considered by guideline writing groups to risk-stratify blood test monitoring during established 5-ASA treatment.
{"title":"Monitoring for 5-aminosalicylate toxicity: prognostic model development and validation.","authors":"Abhishek Abhishek, Georgina Nakafero, Matthew J Grainge, Tim Card, Maarten W Taal, Guruprasad Aithal, Christopher P Fox, Christain D Mallen, Stevenson D Matthew, Richard D Riley","doi":"10.1101/2023.12.15.23299944","DOIUrl":"https://doi.org/10.1101/2023.12.15.23299944","url":null,"abstract":"Background and aim: To develop and validate a prognostic model for risk-stratified monitoring of 5-aminosalicylate (5-ASA) toxicity. Methods: This nationwide retrospective cohort study used data from the Clinical Practice Research Datalink (CPRD) Aurum and Gold for model development and validation respectively. It included adults newly diagnosed with inflammatory bowel disease (IBD) and established on 5-ASAs between 01/01/2007 and 31/12/2019. 5-ASA discontinuation with abnormal monitoring blood test result was the outcome of interest. Patients prescribed 5-ASAs for ≥6 months i.e., established on treatment, were followed-up for up to five years. Penalised Cox-regression was used to develop the risk equation. Model performance was assessed in terms of calibration and discrimination. Statistical analysis was performed using STATA (StataCorp LLC). Results: 14,109 and 7,523 participants formed the development and validation cohorts with 401 and 243 events respectively. 185, 172, and 64 discontinuations were due to cytopenia, elevated creatinine and elevated liver enzymes respectively in the derivation cohort. Hazardous alcohol intake, chronic kidney disease, thiopurine use, and blood test abnormalities before follow-up were strong prognostic factors. The optimism adjusted R2D in development data was 0.08. The calibration slope and Royston D statistic (95% Confidence Interval) in validation cohort were 0.90 (0.61-1.19) and 0.57 (0.37-0.77) respectively. Conclusion: This prognostic model utilises information available during routine clinical care and can be used to inform decisions on the interval between monitoring blood-tests. The results of this study ought to be considered by guideline writing groups to risk-stratify blood test monitoring during established 5-ASA treatment.","PeriodicalId":501258,"journal":{"name":"medRxiv - Gastroenterology","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138742188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.1101/2023.12.16.23300077
Samuel S. Minot, Koshlan Mayer-Blackwell, Andrew Fiore-Gartland, Andrew Johnson, Steven Self, Parveen Bhatti, Lena Yao, Lili Liu, Xin Sun, Yi Jinfa, James Kublin
Background The human gut microbiome develops rapidly during infancy, a key window of development coinciding with maturation of the adaptive immune system. However, little is known of the microbiome growth dynamics over the first few months of life and whether there are any generalizable patterns across human populations. We performed metagenomic sequencing on stool samples (n=94) from a cohort of infants (n=15) at monthly intervals in the first six months of life, augmenting our dataset with seven published studies for a total of 4,441 metagenomes from 1,162 infants.
{"title":"Strain-level characterization of health-associated bacterial consortia that colonize the human gut during infancy","authors":"Samuel S. Minot, Koshlan Mayer-Blackwell, Andrew Fiore-Gartland, Andrew Johnson, Steven Self, Parveen Bhatti, Lena Yao, Lili Liu, Xin Sun, Yi Jinfa, James Kublin","doi":"10.1101/2023.12.16.23300077","DOIUrl":"https://doi.org/10.1101/2023.12.16.23300077","url":null,"abstract":"<strong>Background</strong> The human gut microbiome develops rapidly during infancy, a key window of development coinciding with maturation of the adaptive immune system. However, little is known of the microbiome growth dynamics over the first few months of life and whether there are any generalizable patterns across human populations. We performed metagenomic sequencing on stool samples (n=94) from a cohort of infants (n=15) at monthly intervals in the first six months of life, augmenting our dataset with seven published studies for a total of 4,441 metagenomes from 1,162 infants.","PeriodicalId":501258,"journal":{"name":"medRxiv - Gastroenterology","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138820257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-14DOI: 10.1101/2023.12.13.23299882
Junho Lee, Shin Ju Oh, Eunji Ha, Ga Young Shin, Hyo Jong Kim, Kwangwoo Kim, Chang Kyun Lee
The high prevalence of comorbid mental disorders (CMDs), such as anxiety and depression, in patients with inflammatory bowel disease (IBD) is well documented. The reported bidirectional relationship between the two conditions suggests a crucial role of a gut-brain axis in CMD development in patients with IBD. This study aimed to investigate a complex interplay between gut microbiota and host genetic variants relevant to the development of CMDs in IBD. Genome-wide variant data, gut metagenomic data, and/or anxiety/depression estimates were obtained from 507 patients with IBD and 75 healthy controls. A series of integrative analyses were performed, profiling gut microbial diversity, microbial abundance, polygenic risk score, microbial quantitative trait locus (mbQTL), and microbial IBD-risk score. Patients with IBD had significantly lower gut microbial alpha diversity than controls, particularly those with CMD. Beta diversity revealed that a large fraction of IBD-associated taxa contributing to the top principal component were potentially associated with CMD risk. We identified 146 significantly differentially abundant taxa (DATs) between IBD patients and controls, and 48 DATs between CMD-free and CMD-affected IBD patients, with the majority showing consistent changes in abundance between IBD and CMD. Microbial IBD-risk scores, developed to estimate the degree of microbial IBD-specific burden in each individual, supported a significant enrichment of IBD-risk signatures in CMD-affected patients. Additionally, we found an IBD-risk mbQTL for an IBD/CMD-associated DAT, implicating an interplay between IBD-risk variants and gut dysbiosis in the development of both IBD and CMD. Collectively, IBD-associated gut dysbiosis predominantly confers risk of CMD in IBD patients partially through genetic variant-mediated regulation.
{"title":"Gut microbial and human genetic signatures of inflammatory bowel disease increase risk of comorbid mental disorders","authors":"Junho Lee, Shin Ju Oh, Eunji Ha, Ga Young Shin, Hyo Jong Kim, Kwangwoo Kim, Chang Kyun Lee","doi":"10.1101/2023.12.13.23299882","DOIUrl":"https://doi.org/10.1101/2023.12.13.23299882","url":null,"abstract":"The high prevalence of comorbid mental disorders (CMDs), such as anxiety and depression, in patients with inflammatory bowel disease (IBD) is well documented. The reported bidirectional relationship between the two conditions suggests a crucial role of a gut-brain axis in CMD development in patients with IBD. This study aimed to investigate a complex interplay between gut microbiota and host genetic variants relevant to the development of CMDs in IBD. Genome-wide variant data, gut metagenomic data, and/or anxiety/depression estimates were obtained from 507 patients with IBD and 75 healthy controls. A series of integrative analyses were performed, profiling gut microbial diversity, microbial abundance, polygenic risk score, microbial quantitative trait locus (mbQTL), and microbial IBD-risk score. Patients with IBD had significantly lower gut microbial alpha diversity than controls, particularly those with CMD. Beta diversity revealed that a large fraction of IBD-associated taxa contributing to the top principal component were potentially associated with CMD risk. We identified 146 significantly differentially abundant taxa (DATs) between IBD patients and controls, and 48 DATs between CMD-free and CMD-affected IBD patients, with the majority showing consistent changes in abundance between IBD and CMD. Microbial IBD-risk scores, developed to estimate the degree of microbial IBD-specific burden in each individual, supported a significant enrichment of IBD-risk signatures in CMD-affected patients. Additionally, we found an IBD-risk mbQTL for an IBD/CMD-associated DAT, implicating an interplay between IBD-risk variants and gut dysbiosis in the development of both IBD and CMD. Collectively, IBD-associated gut dysbiosis predominantly confers risk of CMD in IBD patients partially through genetic variant-mediated regulation.","PeriodicalId":501258,"journal":{"name":"medRxiv - Gastroenterology","volume":"110 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138715643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-14DOI: 10.1101/2023.12.13.23299905
Khaled Sayed, Christine E. Dolin, Daniel W. Wilkey, Jiang Li, Toshifumi Sato, Juliane I Beier, Josepmaria Argemi, Ramon Bataller, Abdus S Wahed, Michael L Merchant, Panayiotis V Benos, Gavin E Arteel
Alcohol-related hepatitis (AH) is plagued with high mortality and difficulty in identifying at-risk patients. The extracellular matrix undergoes significant remodeling during inflammatory liver injury that can be detected in biological fluids and potentially used for mortality prediction. EDTA plasma samples were collected from AH patients (n= 62); Model for End-Stage Liver Disease (MELD) score defined AH severity as moderate (12-20; n=28) and severe (>20; n=34). The peptidome data was collected by high resolution, high mass accuracy UPLC-MS. Univariate and multivariate analyses identified differentially abundant peptides, which were used for Gene Ontology, parent protein matrisomal composition and protease involvement. Machine learning methods were used on patient-specific peptidome and clinical data to develop mortality predictors. Analysis of plasma peptides from AH patients and healthy controls identified over 1,600 significant peptide features corresponding to 130 proteins. These were enriched for ECM fragments in AH samples, likely related to turnover of hepatic-derived proteins. Analysis of moderate versus severe AH peptidomes showed a shift in abundance of peptides from collagen 1A1 and fibrinogen A proteins. The dominant proteases for the AH peptidome spectrum appear to be CAPN1 and MMP12. Increase in hepatic expression of these proteases was orthogonally-validated in RNA-seq data of livers from AH patients. Causal graphical modeling identified four peptides directly linked to 90-day mortality in >90% of the learned graphs. These peptides improved the accuracy of mortality prediction over MELD score and were used to create a clinically applicable mortality prediction assay. A signature based on plasma peptidome is a novel, non-invasive method for prognosis stratification in AH patients. Our results could also lead to new mechanistic and/or surrogate biomarkers to identify new AH mechanisms.
{"title":"A plasma peptidomic signature reveals extracellular matrix remodeling and predicts prognosis in alcohol-related hepatitis","authors":"Khaled Sayed, Christine E. Dolin, Daniel W. Wilkey, Jiang Li, Toshifumi Sato, Juliane I Beier, Josepmaria Argemi, Ramon Bataller, Abdus S Wahed, Michael L Merchant, Panayiotis V Benos, Gavin E Arteel","doi":"10.1101/2023.12.13.23299905","DOIUrl":"https://doi.org/10.1101/2023.12.13.23299905","url":null,"abstract":"Alcohol-related hepatitis (AH) is plagued with high mortality and difficulty in identifying at-risk patients. The extracellular matrix undergoes significant remodeling during inflammatory liver injury that can be detected in biological fluids and potentially used for mortality prediction. EDTA plasma samples were collected from AH patients (n= 62); Model for End-Stage Liver Disease (MELD) score defined AH severity as moderate (12-20; n=28) and severe (>20; n=34). The peptidome data was collected by high resolution, high mass accuracy UPLC-MS. Univariate and multivariate analyses identified differentially abundant peptides, which were used for Gene Ontology, parent protein matrisomal composition and protease involvement. Machine learning methods were used on patient-specific peptidome and clinical data to develop mortality predictors. Analysis of plasma peptides from AH patients and healthy controls identified over 1,600 significant peptide features corresponding to 130 proteins. These were enriched for ECM fragments in AH samples, likely related to turnover of hepatic-derived proteins. Analysis of moderate versus severe AH peptidomes showed a shift in abundance of peptides from collagen 1A1 and fibrinogen A proteins. The dominant proteases for the AH peptidome spectrum appear to be CAPN1 and MMP12. Increase in hepatic expression of these proteases was orthogonally-validated in RNA-seq data of livers from AH patients. Causal graphical modeling identified four peptides directly linked to 90-day mortality in >90% of the learned graphs. These peptides improved the accuracy of mortality prediction over MELD score and were used to create a clinically applicable mortality prediction assay. A signature based on plasma peptidome is a novel, non-invasive method for prognosis stratification in AH patients. Our results could also lead to new mechanistic and/or surrogate biomarkers to identify new AH mechanisms.","PeriodicalId":501258,"journal":{"name":"medRxiv - Gastroenterology","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138741787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-11DOI: 10.1101/2023.12.10.23299776
Yifan Li, Haoliang Zhao, Yinan Shi, Ruirui Yang
Methods： Gastric cancer between May 2002 and December 2020 and who had undergone resection of the primary cancer. We analyzed these patients to study the association between survival and tumor size by Cox proportional hazards model and restricted cubic splines. Results: A total of 1708 patients met the inclusion criteria, with a median age of 58 years. The distribution of tumor size was correlated with patients underwent different D2+ gastrectomy(P<0.001) and located different tumor site(P=0.002). The size of the patient's tumor is closely related to the patient's prognosis, as well as the overall survival of patients experienced proximal gastrectomy(P for trend= 0.002) and progression free survival of distal(P for trend= 0.03) and total gastrectomy(P for trend= 0.016) in fully adjusted model. Likewise, tumor size displayed its prognostic predictability in subgroup of upper 1/3, but only for overall survival in final model(P for trend= 0.045). Nonlinear relationship of different tumor size and D2+ gastrectomy or tumor site showed in restricted cubic splines, >5cm showed a significant impact in each group, but not for proximal gastrectomy(P for nonlinear=0.305). Overall survival and progression decreased progressively along with upgrading of tumor size accordingly. Conclusions Tumor size>5cm can be seen as a line of demarcation of mortality and progression of gastric cancer after D2+gastrectomy, the hazard ratio began to rise when tumor size large than 5cm.
{"title":"Tumor size>5cm is a line of demarcation of mortality and progression of gastric cancer after D2+gastrectomy for Chinese population","authors":"Yifan Li, Haoliang Zhao, Yinan Shi, Ruirui Yang","doi":"10.1101/2023.12.10.23299776","DOIUrl":"https://doi.org/10.1101/2023.12.10.23299776","url":null,"abstract":"Methods： Gastric cancer between May 2002 and December 2020 and who had undergone resection of the primary cancer. We analyzed these patients to study the association between survival and tumor size by Cox proportional hazards model and restricted cubic splines.\u0000Results:\u0000A total of 1708 patients met the inclusion criteria, with a median age of 58 years. The distribution of tumor size was correlated with patients underwent different D2+ gastrectomy(P<0.001) and located different tumor site(P=0.002). The size of the patient's tumor is closely related to the patient's prognosis, as well as the overall survival of patients experienced proximal gastrectomy(P for trend= 0.002) and progression free survival of distal(P for trend= 0.03) and total gastrectomy(P for trend= 0.016) in fully adjusted model. Likewise, tumor size displayed its prognostic predictability in subgroup of upper 1/3, but only for overall survival in final model(P for trend= 0.045). Nonlinear relationship of different tumor size and D2+ gastrectomy or tumor site showed in restricted cubic splines, >5cm showed a significant impact in each group, but not for proximal gastrectomy(P for nonlinear=0.305). Overall survival and progression decreased progressively along with upgrading of tumor size accordingly.\u0000Conclusions\u0000Tumor size>5cm can be seen as a line of demarcation of mortality and progression of gastric cancer after D2+gastrectomy, the hazard ratio began to rise when tumor size large than 5cm.","PeriodicalId":501258,"journal":{"name":"medRxiv - Gastroenterology","volume":"255 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138566645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-08DOI: 10.1101/2023.12.07.23299648
Isabella Catharina Wiest, Dyke Ferber, Jiefu Zhu, Marko Van Treeck, Sonja Katharina Meyer, Radhika Juglan, Zunamys I. Carrero, Daniel Paech, Jens Kleesiek, Matthias P. Ebert, Daniel Truhn, Jakob Nikolas Kather
Background and Aims Most clinical information is encoded as text, but extracting quantitative information from text is challenging. Large Language Models (LLMs) have emerged as powerful tools for natural language processing and can parse clinical text. However, many LLMs including ChatGPT reside in remote data centers, which disqualifies them from processing personal healthcare data. We present an open-source pipeline using the local LLM 'Llama 2' for extracting quantitative information from clinical text and evaluate its use to detect clinical features of decompensated liver cirrhosis. Methods We tasked the LLM to identify five key clinical features of decompensated liver cirrhosis in a zero- and one-shot way without any model training. Our specific objective was to identify abdominal pain, shortness of breath, confusion, liver cirrhosis, and ascites from 500 patient medical histories from the MIMIC IV dataset. We compared LLMs with three different sizes and a variety of pre-specified prompt engineering approaches. Model predictions were compared against the ground truth provided by the consent of three blinded medical experts. Results Our open-source pipeline yielded in highly accurate extraction of quantitative features from medical free text. Clinical features which were explicitly mentioned in the source text, such as liver cirrhosis and ascites, were detected with a sensitivity of 100% and 95% and a specificity of 96% and 95%, respectively from the 70 billion parameter model. Other clinical features, which are often paraphrased in a variety of ways, such as the presence of confusion, were detected only with a sensitivity of 76% and a specificity of 94%. Abdominal pain was detected with a sensitivity of 84% and a specificity of 97%. Shortness of breath was detected with a sensitivity of 87% and a specificity of 96%. The larger version of Llama 2 with 70b parameters outperformed the smaller version with 7b parameters in all tasks. Prompt engineering improved zero-shot performance, particularly for smaller model sizes. Conclusion Our study successfully demonstrates the capability of using locally deployed LLMs to extract clinical information from free text. The hardware requirements are so low that not only on-premise, but also point-of-care deployment of LLMs are possible.
{"title":"From Text to Tables: A Local Privacy Preserving Large Language Model for Structured Information Retrieval from Medical Documents","authors":"Isabella Catharina Wiest, Dyke Ferber, Jiefu Zhu, Marko Van Treeck, Sonja Katharina Meyer, Radhika Juglan, Zunamys I. Carrero, Daniel Paech, Jens Kleesiek, Matthias P. Ebert, Daniel Truhn, Jakob Nikolas Kather","doi":"10.1101/2023.12.07.23299648","DOIUrl":"https://doi.org/10.1101/2023.12.07.23299648","url":null,"abstract":"Background and Aims\u0000Most clinical information is encoded as text, but extracting quantitative information from text is challenging. Large Language Models (LLMs) have emerged as powerful tools for natural language processing and can parse clinical text. However, many LLMs including ChatGPT reside in remote data centers, which disqualifies them from processing personal healthcare data. We present an open-source pipeline using the local LLM 'Llama 2' for extracting quantitative information from clinical text and evaluate its use to detect clinical features of decompensated liver cirrhosis.\u0000Methods\u0000We tasked the LLM to identify five key clinical features of decompensated liver cirrhosis in a zero- and one-shot way without any model training. Our specific objective was to identify abdominal pain, shortness of breath, confusion, liver cirrhosis, and ascites from 500 patient medical histories from the MIMIC IV dataset. We compared LLMs with three different sizes and a variety of pre-specified prompt engineering approaches. Model predictions were compared against the ground truth provided by the consent of three blinded medical experts. Results\u0000Our open-source pipeline yielded in highly accurate extraction of quantitative features from medical free text. Clinical features which were explicitly mentioned in the source text, such as liver cirrhosis and ascites, were detected with a sensitivity of 100% and 95% and a specificity of 96% and 95%, respectively from the 70 billion parameter model. Other clinical features, which are often paraphrased in a variety of ways, such as the presence of confusion, were detected only with a sensitivity of 76% and a specificity of 94%. Abdominal pain was detected with a sensitivity of 84% and a specificity of 97%. Shortness of breath was detected with a sensitivity of 87% and a specificity of 96%. The larger version of Llama 2 with 70b parameters outperformed the smaller version with 7b parameters in all tasks. Prompt engineering improved zero-shot performance, particularly for smaller model sizes.\u0000Conclusion\u0000Our study successfully demonstrates the capability of using locally deployed LLMs to extract clinical information from free text. The hardware requirements are so low that not only on-premise, but also point-of-care deployment of LLMs are possible.","PeriodicalId":501258,"journal":{"name":"medRxiv - Gastroenterology","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138561570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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