Circulating immunome fingerprint in eosinophilic esophagitis is associated with clinical response to proton pump inhibitor treatment.

Objectives: The aim of the study was to characterize the circulating immunome of patients with EoE before and after proton pump inhibitor (PPI) treatment in order to identify potential non-invasive biomarkers of treatment response. Methods: PBMCs from 19 healthy controls and 24 EoE patients were studied using a 39-plex spectral cytometry panel. The plasmacytoid dendritic cell (pDC) population was differentially characterized by spectral cytometry analysis and immunofluorescence assays in esophageal biopsies from 7 healthy controls and 13 EoE patients. Results: Interestingly, EoE patients at baseline had lower levels of circulating pDC compared with controls. Before treatment, patients with EoE who responded to PPI therapy had higher levels of circulating pDC and classical monocytes, compared with non-responders. Moreover, following PPI therapy pDC levels were increased in all EoE patients, while normal levels were only restored in PPI-responding patients. Finally, circulating pDC levels inversely correlated with peak eosinophil count and pDC count in esophageal biopsies. The number of tissue pDCs significantly increased during active EoE, being even higher in non-responder patients when compared to responder patients pre-PPI. pDC levels decreased after PPI intake, being further restored almost to control levels in responder patients post-PPI. Conclusions: We hereby describe a unique immune fingerprint of EoE patients at diagnosis. Moreover, circulating pDC may be also used as a novel non-invasive biomarker to predict subsequent response to PPI treatment.

Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach.

Introduction: Immune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) poses a major clinical challenge to ICI therapy for cancer, with 13% of cases halting ICI therapy and ICI-IA being difficult to identify for timely referral to a rheumatologist. The objective of this study was to rapidly identify ICI-IA patients in clinical data and assess associated immune-related adverse events (irAEs) and risk factors. Methods: We conducted a retrospective study of the electronic health records (EHRs) of 89 patients who developed ICI-IA out of 2451 cancer patients who received ICI therapy at Northwestern University between March 2011 to January 2021. Logistic regression and random forest machine learning models were trained on all EHR diagnoses, labs, medications, and procedures to identify ICI-IA patients and EHR codes indicating ICI-IA. Multivariate logistic regression was then used to test associations between ICI-IA and cancer type, ICI regimen, and comorbid irAEs. Results: Logistic regression and random forest models identified ICI-IA patients with accuracies of 0.79 and 0.80, respectively. Key EHR features from the random forest model included ICI-IA relevant features (joint pain, steroid prescription, rheumatoid factor tests) and features suggesting comorbid irAEs (thyroid function tests, pruritus, triamcinolone prescription). Compared to 871 adjudicated ICI patients who did not develop arthritis, ICI-IA patients had higher odds of developing cutaneous (odds ratio [OR]=2.66; 95% Confidence Interval [CI] 1.63-4.35), endocrine (OR=2.09; 95% CI 1.15-3.80), or gastrointestinal (OR=2.88; 95% CI 1.76-4.72) irAEs adjusting for demographics, cancer type, and ICI regimen. Melanoma (OR=1.99; 95% CI 1.08-3.65) and renal cell carcinoma (OR=2.03; 95% CI 1.06-3.84) patients were more likely to develop ICI-IA compared to lung cancer patients. Patients on nivolumab+ipilimumab were more likely to develop ICI-IA compared to patients on pembrolizumab (OR=1.86; 95% CI 1.01-3.43). Discussion: Our machine learning models rapidly identified patients with ICI-IA in EHR data and elucidated clinical features indicative of comorbid irAEs. Patients with ICI-IA were significantly more likely to also develop cutaneous, endocrine, and gastrointestinal irAEs during their clinical course compared to ICI therapy patients without ICI-IA.

Natural language processing to identify lupus nephritis phenotype in electronic health records.

BACKGROUND: Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis. Lupus nephritis can be recognized through procedure codes and structured data, such as laboratory tests. However, other critical information documenting lupus nephritis, such as histologic reports from kidney biopsies and prior medical history narratives, require sophisticated text processing to mine information from pathology reports and clinical notes. In this study, we developed algorithms to identify lupus nephritis with and without natural language processing (NLP) using EHR data from the Northwestern Medicine Enterprise Data Warehouse (NMEDW). METHODS: We developed five algorithms: a rule-based algorithm using only structured data (baseline algorithm) and four algorithms using different NLP models. The first NLP model applied simple regular expression for keywords search combined with structured data. The other three NLP models were based on regularized logistic regression and used different sets of features including positive mention of concept unique identifiers (CUIs), number of appearances of CUIs, and a mixture of three components (i.e. a curated list of CUIs, regular expression concepts, structured data) respectively. The baseline algorithm and the best performing NLP algorithm were externally validated on a dataset from Vanderbilt University Medical Center (VUMC). RESULTS: Our best performing NLP model incorporated features from both structured data, regular expression concepts, and mapped concept unique identifiers (CUIs) and showed improved F measure in both the NMEDW (0.41 vs 0.79) and VUMC (0.52 vs 0.93) datasets compared to the baseline lupus nephritis algorithm. CONCLUSION: Our NLP MetaMap mixed model improved the F-measure greatly compared to the structured data only algorithm in both internal and external validation datasets. The NLP algorithms can serve as powerful tools to accurately identify lupus nephritis phenotype in EHR for clinical research and better targeted therapies.

Swallowed topical corticosteroids for eosinophilic esophagitis: Utilization and real-world efficacy from the EoE CONNECT registry.

BACKGROUND: Swallowed topical corticosteroids (tC) are common therapy for patients with eosinophilic esophagitis (EoE). Widely heterogeneous results have occurred due to their active ingredients, formulations and doses. OBJECTIVE: To assess the effectiveness of topical corticosteroid therapy for EoE in real-world practice. METHODS: Cross-sectional study analysis of the multicentre EoE CONNECT registry. Clinical remission was defined as a decrease of ≥50% in dysphagia symptom scores; histological remission was defined as a peak eosinophil count below 15 per high-power field. The effectiveness in achieving clinico-histological remission (CHR) was compared for the main tC formulations. RESULTS: Overall, data on 1456 prescriptions of tC in monotherapy used in 866 individual patients were assessed. Of those, 904 prescriptions with data on formulation were employed for the induction of remission; 234 reduced a previously effective dose for maintenance. Fluticasone propionate formulations dominated the first-line treatment, while budesonide was more common in later therapies. A swallowed nasal drop suspension was the most common formulation of fluticasone propionate. Doses ≥0.8 mg/day provided a 65% CHR rate and were superior to lower doses. Oral viscous solution prepared by a pharmacist was the most common prescription of budesonide; 4 mg/day provided no benefit over 2 mg/day (CHR rated being 72% and 80%, respectively). A multivariate analysis revealed budesonide orodispersible tablets as the most effective therapy (OR 18.9, p < 0.001); use of higher doses (OR 4.3, p = 0.03) and lower symptom scores (OR 0.9, p = 0.01) were also determinants of effectiveness. CONCLUSION: Reduced symptom severity, use of high doses, and use of budesonide orodispersible tablets particularly were all independent predictors of tC effectiveness.

Prevalence of Cardiovascular Risk Factors in Spain: A Systematic Review.

Cardiovascular diseases are the leading cause of death in Spain, according to data from the National Institute of Statistics, with the lack of control of cardiovascular risk factors (CVRF) being the main contributing factor. The CVRFs of greatest clinical interest are high blood pressure (HBP), smoking, diabetes mellitus (DM2), overweight, obesity, hypercholesterolaemia, and sedentary lifestyle. The main objective of this review was to compare the prevalence of the different CVRFs according to population-based studies carried out in Spain. For this, a systematic review based on publications assessing CVRFs in the adult population and estimating their national prevalence was conducted. Pubmed and Dialnet databases were consulted, and the selected articles were analysed using the Critical Appraisal Skills Programme Español (CASPe) tool for cohort studies and the Berra et al. tool for cross-sectional studies. A total of 33 studies were obtained from the autonomous regions of Andalusia, the Canary Islands, Castilla-Leon, Castilla-La Mancha, Catalonia, Extremadura, the Balearic Islands, Madrid, Murcia, and Navarra. In all the population-based studies, there was a greater representation of women in the sample. The most prevalent CVRFs differed across the studies according to the autonomous region targeted, with dyslipidaemia, sedentary lifestyle, high blood pressure, hypercholesterolaemia, overweight, and obesity standing out. Numerous differences exist between the studies included in this review, such as the age range, the CVRFs analysed and their prevalence, and remarkable aspects such as the over-representation of the female sex in all cases. It can be concluded that, based on the presented results, the prevalence of CVRFs in Spain varies according to the autonomous region, the sex of the individual, and the studied age range.

Identification and assessment of classification criteria attributes for systemic lupus erythematosus in a regional medical record data network.

OBJECTIVE: To assess the application and utility of algorithms designed to detect features of SLE in electronic health record (EHR) data in a multisite, urban data network. METHODS: Using the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a Clinical Data Research Network (CDRN) containing data from multiple healthcare sites, we identified patients with at least one positively identified criterion from three SLE classification criteria sets developed by the American College of Rheumatology (ACR) in 1997, the Systemic Lupus International Collaborating Clinics (SLICC) in 2012, and the European Alliance of Associations for Rheumatology and the ACR in 2019 using EHR-based algorithms. To measure the algorithms' performance in this data setting, we first evaluated whether the number of clinical encounters for SLE was associated with a greater quantity of positively identified criteria domains using Poisson regression. We next quantified the amount of SLE criteria identified at a single healthcare institution versus all sites to assess the amount of SLE-related information gained from implementing the algorithms in a CDRN. RESULTS: Patients with three or more SLE encounters were estimated to have documented 2.77 (2.73 to 2.80) times the number of positive SLE attributes from the 2012 SLICC criteria set than patients without an SLE encounter via Poisson regression. Patients with three or more SLE-related encounters and with documented care from multiple institutions were identified with more SLICC criteria domains when data were included from all CAPriCORN sites compared with a single site (p<0.05). CONCLUSIONS: The positive association observed between amount of SLE-related clinical encounters and the number of criteria domains detected suggests that the algorithms used in this study can be used to help describe SLE features in this data environment. This work also demonstrates the benefit of aggregating data across healthcare institutions for patients with fragmented care.

Proteomic and genetic analyses of influenza A viruses identify pan-viral host targets.

Influenza A Virus (IAV) is a recurring respiratory virus with limited availability of antiviral therapies. Understanding host proteins essential for IAV infection can identify targets for alternative host-directed therapies (HDTs). Using affinity purification-mass spectrometry and global phosphoproteomic and protein abundance analyses using three IAV strains (pH1N1, H3N2, H5N1) in three human cell types (A549, NHBE, THP-1), we map 332 IAV-human protein-protein interactions and identify 13 IAV-modulated kinases. Whole exome sequencing of patients who experienced severe influenza reveals several genes, including scaffold protein AHNAK, with predicted loss-of-function variants that are also identified in our proteomic analyses. Of our identified host factors, 54 significantly alter IAV infection upon siRNA knockdown, and two factors, AHNAK and coatomer subunit COPB1, are also essential for productive infection by SARS-CoV-2. Finally, 16 compounds targeting our identified host factors suppress IAV replication, with two targeting CDK2 and FLT3 showing pan-antiviral activity across influenza and coronavirus families. This study provides a comprehensive network model of IAV infection in human cells, identifying functional host targets for pan-viral HDT.

Efficacy of the HBW Ultrasonic Ring for retrieval of fragmented manual or rotatory instruments.

A study was conducted to evaluate the success rates of bypassing or removing fragments of instruments that had fractured within the roots of mandibular molars using a HBW Ultrasonic Ring. Forty extracted first permanent mandibular molars with root canal curvature were included. The teeth were distributed randomly into four groups according to the type of instrument and the root canal third where they had fractured. The success rate for removal of instrument fragments was 100% for both of two stainless steel groups, 90% for a nitinol middle third group, and 80% for a nitinol apical third group. The mean time required for instrument removal was around 40 min. The HBW Ultrasonic Ring showed acceptable experimental results for retrieval of broken instrument fragments.

AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease.

OBJECTIVE: We develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from electronic health records (EHRs) to predict the risk of disease progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). METHODS: We identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000 and 2020. The progress notes no later than the first MCI diagnosis were used for the prediction. We first preprocessed the notes by deidentification, cleaning and splitting into sections, and then pre-trained a BERT model for AD (named AD-BERT) based on the publicly available Bio+Clinical BERT on the preprocessed notes. All sections of a patient were embedded into a vector representation by AD-BERT and then combined by global MaxPooling and a fully connected network to compute the probability of MCI-to-AD progression. For validation, we conducted a similar set of experiments on 2563 MCI patients identified at Weill Cornell Medicine (WCM) during the same timeframe. RESULTS: Compared with the 7 baseline models, the AD-BERT model achieved the best performance on both datasets, with Area Under receiver operating characteristic Curve (AUC) of 0.849 and F1 score of 0.440 on NMEDW dataset, and AUC of 0.883 and F1 score of 0.680 on WCM dataset. CONCLUSION: The use of EHRs for AD-related research is promising, and AD-BERT shows superior predictive performance in modeling MCI-to-AD progression prediction. Our study demonstrates the utility of pre-trained language models and clinical notes in predicting MCI-to-AD progression, which could have important implications for improving early detection and intervention for AD.

Proteomic analysis of the esophageal epithelium reveals key features of eosinophilic esophagitis pathophysiology.

BACKGROUND: Eosinophilic esophagitis (EoE) is a chronic non-IgE-mediated allergic disease of the esophagus. An unbiased proteomics approach was performed to investigate pathophysiological changes in esophageal epithelium. Additionally, an RNAseq-based transcriptomic analysis in paired samples was also carried out. METHODS: Total proteins were purified from esophageal endoscopic biopsies in a cohort of adult EoE patients (n = 25) and healthy esophagus controls (n = 10). Differentially accumulated (DA) proteins in EoE patients compared to control tissues were characterized to identify altered biological processes and signaling pathways. Results were also compared with a quantitative proteome dataset of the human esophageal mucosa. Next, results were contrasted with those obtained after RNAseq analysis in paired samples. Finally, we matched up protein expression with two EoE-specific mRNA panels (EDP and Eso-EoE panel). RESULTS: A total of 1667 proteins were identified, of which 363 were DA in EoE. RNA sequencing in paired samples identified 1993 differentially expressed (DE) genes. Total RNA and protein levels positively correlated, especially in DE mRNA-proteins pairs. Pathway analysis of these proteins in EoE showed alterations in immune and inflammatory responses for the upregulated proteins, and in epithelial differentiation, cornification and keratinization in those downregulated. Interestingly, a set of DA proteins, including eosinophil-related and secreted proteins, were not detected at the mRNA level. Protein expression positively correlated with EDP and Eso-EoE, and corresponded with the most abundant proteins of the human esophageal proteome. CONCLUSIONS: We unraveled for the first time key proteomic features involved in EoE pathogenesis. An integrative analysis of transcriptomic and proteomic datasets provides a deeper insight than transcriptomic alone into understanding complex disease mechanisms.

Comparing the effects of four common drug classes on the progression of mild cognitive impairment to dementia using electronic health records.

The objective of this study was to investigate the potential association between the use of four frequently prescribed drug classes, namely antihypertensive drugs, statins, selective serotonin reuptake inhibitors, and proton-pump inhibitors, and the likelihood of disease progression from mild cognitive impairment (MCI) to dementia using electronic health records (EHRs). We conducted a retrospective cohort study using observational EHRs from a cohort of approximately 2 million patients seen at a large, multi-specialty urban academic medical center in New York City, USA between 2008 and 2020 to automatically emulate the randomized controlled trials. For each drug class, two exposure groups were identified based on the prescription orders documented in the EHRs following their MCI diagnosis. During follow-up, we measured drug efficacy based on the incidence of dementia and estimated the average treatment effect (ATE) of various drugs. To ensure the robustness of our findings, we confirmed the ATE estimates via bootstrapping and presented associated 95% confidence intervals (CIs). Our analysis identified 14,269 MCI patients, among whom 2501 (17.5%) progressed to dementia. Using average treatment estimation and bootstrapping confirmation, we observed that drugs including rosuvastatin (ATE = - 0.0140 [- 0.0191, - 0.0088], p value < 0.001), citalopram (ATE = - 0.1128 [- 0.125, - 0.1005], p value < 0.001), escitalopram (ATE = - 0.0560 [- 0.0615, - 0.0506], p value < 0.001), and omeprazole (ATE = - 0.0201 [- 0.0299, - 0.0103], p value < 0.001) have a statistically significant association in slowing the progression from MCI to dementia. The findings from this study support the commonly prescribed drugs in altering the progression from MCI to dementia and warrant further investigation.

A metadata framework for computational phenotypes.

With the burgeoning development of computational phenotypes, it is increasingly difficult to identify the right phenotype for the right tasks. This study uses a mixed-methods approach to develop and evaluate a novel metadata framework for retrieval of and reusing computational phenotypes. Twenty active phenotyping researchers from 2 large research networks, Electronic Medical Records and Genomics and Observational Health Data Sciences and Informatics, were recruited to suggest metadata elements. Once consensus was reached on 39 metadata elements, 47 new researchers were surveyed to evaluate the utility of the metadata framework. The survey consisted of 5-Likert multiple-choice questions and open-ended questions. Two more researchers were asked to use the metadata framework to annotate 8 type-2 diabetes mellitus phenotypes. More than 90% of the survey respondents rated metadata elements regarding phenotype definition and validation methods and metrics positively with a score of 4 or 5. Both researchers completed annotation of each phenotype within 60 min. Our thematic analysis of the narrative feedback indicates that the metadata framework was effective in capturing rich and explicit descriptions and enabling the search for phenotypes, compliance with data standards, and comprehensive validation metrics. Current limitations were its complexity for data collection and the entailed human costs.

Multi-ancestry genome- and phenome-wide association studies of diverticular disease in electronic health records with natural language processing enriched phenotyping algorithm.

OBJECTIVE: Diverticular disease (DD) is one of the most prevalent conditions encountered by gastroenterologists, affecting ~50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique. MATERIALS AND METHODS: We developed a NLP-enriched phenotyping algorithm that incorporated colonoscopy or abdominal imaging reports to identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of DD in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in clinical phenotypes. RESULTS: Our developed algorithm showed a significant improvement in patient classification performance for DD analysis (algorithm PPVs ≥ 0.94), with up to a 3.5 fold increase in terms of the number of identified patients than the traditional method. Ancestry-stratified analyses of diverticulosis and diverticulitis of the identified subjects replicated the well-established associations between ARHGAP15 loci with DD, showing overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients. Our PheWAS analyses identified significant associations between the DD GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. DISCUSSION: As the first multi-ancestry GWAS-PheWAS study, we showcased that heterogenous EHR data can be mapped through an integrative analytical pipeline and reveal significant genotype-phenotype associations with clinical interpretation. CONCLUSION: A systematic framework to process unstructured EHR data with NLP could advance a deep and scalable phenotyping for better patient identification and facilitate etiological investigation of a disease with multilayered data.

Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.

The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations.

Prediction of left ventricular ejection fraction changes in heart failure patients using machine learning and electronic health records: a multi-site study.

Left ventricular ejection fraction (EF) is a key measure in the diagnosis and treatment of heart failure (HF) and many patients experience changes in EF overtime. Large-scale analysis of longitudinal changes in EF using electronic health records (EHRs) is limited. In a multi-site retrospective study using EHR data from three academic medical centers, we investigated longitudinal changes in EF measurements in patients diagnosed with HF. We observed significant variations in baseline characteristics and longitudinal EF change behavior of the HF cohorts from a previous study that is based on HF registry data. Data gathered from this longitudinal study were used to develop multiple machine learning models to predict changes in ejection fraction measurements in HF patients. Across all three sites, we observed higher performance in predicting EF increase over a 1-year duration, with similarly higher performance predicting an EF increase of 30% from baseline compared to lower percentage increases. In predicting EF decrease we found moderate to high performance with low confidence for various models. Among various machine learning models, XGBoost was the best performing model for predicting EF changes. Across the three sites, the XGBoost model had an F1-score of 87.2, 89.9, and 88.6 and AUC of 0.83, 0.87, and 0.90 in predicting a 30% increase in EF, and had an F1-score of 95.0, 90.6, 90.1 and AUC of 0.54, 0.56, 0.68 in predicting a 30% decrease in EF. Among features that contribute to predicting EF changes, baseline ejection fraction measurement, age, gender, and heart diseases were found to be statistically significant.

Characterizing variability of electronic health record-driven phenotype definitions.

OBJECTIVE: The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used. MATERIALS AND METHODS: A sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries. RESULTS: Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27. DISCUSSION: Despite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints. CONCLUSIONS: The phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.

Recaída en el consumo de alcohol después del trasplante de hígado por hepatopatía alcohólica / Alcohol relapse after liver transplantation for alcoholic liver disease

Introducción. La enfermedad hepática inducida por uso de alcohol se ha considerado una enferme-dad autoinfligida que limitaba el acceso al trasplante. Actualmente es una de las principales indicacio-nes de trasplante hepático en Colombia y el mundo, con excelente sobrevida. Metodología. Estudio descriptivo observacional donde se realizó una caracterización de los pacientes con trasplante hepá-tico por hepatopatía alcohólica en una institución de cuarto nivel, que incluyó un estudio cualitativo de la recaída en el consumo de alcohol postrasplante. Resultados. De 87 pacientes de una cohorte inicial de 96 pacientes trasplantados entre 2003 y 2021, se describieron características sociodemo-gráficas, comorbilidades previas y adquiridas posterior al trasplante, supervivencia del paciente y del injerto, y factores de riesgo asociados al consumo de alcohol. Adicionalmente, a 65 pacientes se les pudo realizar una entrevista estructurada para evaluar la recaída en el consumo de alcohol, 41,53 % volvieron a consumir alcohol; 23,07 % en patrón de riesgo de recaída y 18,46 % en patrón de slip (desliz). El antecedente de hepatitis alcohólica tuvo un RR de 3,273 (1,464­7,314) y p=0,007 para recaída en el consumo de alcohol, y la comorbilidad psiquiátrica un RR de 2,395 (1,002­5,722) y p=0,047. Finalmente, haber presentado al menos una recaída postrasplante tuvo un RR de 5,556 (1,499­20,588) con p=0,005 para rechazo del injerto. Conclusiones. La recaída en el consumo de alcohol fue frecuente, la hepatitis alcohólica previa y la comorbilidad psiquiátrica son factores de riesgo asociados. La recaída se asoció a rechazo del injerto sin afectar la sobrevida del paciente.

Introduction. Alcohol-induced liver disease has been considered a self-inflicted disease that limited access to transplantation. It is currently one of the main indications for liver transplantation in Colom-bia and the world, with excellent survival. Methodology. Observational descriptive study where a characterization of liver transplant patients due to alcoholic liver disease was carried out in a fourth level institution, which included a qualitative study of relapse in post-transplant alcohol consumption. Results. Of 87 patients from an initial cohort of 96 transplant patients between 2003 and 2021, sociodemographic characteristics, previous and acquired post-transplant comorbidities, patient and graft survival, and risk factors associated with alcohol consumption were described. Additionally, 65 patients were able to undergo a structured interview to assess relapse in alcohol consumption, 41.53% returned to alcohol consumption; 23.07% in risk relapse pattern, and 18.46% in slip pattern. The history of alcoholic hepatitis had a RR of 3.273 (1.464-7.314) and a p=0.007 for relapse in alcohol consumption, and psychiatric comorbidity a RR of 2.395 (1.002-5.722) and a p=0.047. Finally, having presented at least one post-transplant relapse had a RR of 5.556 (1.499-20.588) with ap=0.005 for graft rejection. Conclusions. Relapse in alcohol consumption was fre-quent, previous alcoholic hepatitis and psychiatric comorbidity were associated risk factors. Relapse was associated with graft rejection without affecting patient survival.

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.