BACKGROUND:Disease duration is associated with lower treatment response and accrual of bowel damage in Crohn's disease (CD), but not in ulcerative colitis (UC). We aimed to understand intestinal transcriptomic changes associated with disease duration in CD and UC. METHODS:We analyzed intestinal tissue RNA sequencing data from two independent prospective cohorts of CD and UC patients, the Mount Sinai Crohn's and Colitis Registry (MSCCR; nCD = 498, nUC = 421), and the Study of a Prospective Adult Research Cohort with Inflammatory Bowel Disease (SPARC IBD; nCD = 777, nUC = 440). We conducted differential expression analysis and subsequent pathway analyses of significantly up- or down-regulated genes, and examined cell type-specific expression of significant genes and pathways in ileal single-cell RNA sequencing data from CD patients (n = 18). We then assessed the association of significant pathways with treatment response in an infliximab-treated CD cohort. RESULTS:Significantly more genes were differentially expressed with increasing disease duration in CD compared to UC in both cohorts (MSCCR: nCD = 1472, nUC = 227; SPARC: nCD = 1248, nUC = 25; q-value < 0.05). A shared gene signature with 263 down- and 135 up-regulated genes in longer standing disease was identified. Pathway analyses revealed significant enrichment in pathways related to oxidative phosphorylation, mitochondrial dysfunction, cholesterol biosynthesis, liver X receptor/retinoid X receptor (LXR/RXR) activation, and protein modifications. Pre-treatment intestinal gene expression of four disease duration-related pathways were associated with non-response to infliximab. CONCLUSION:Disease duration influences intestinal gene expression in CD but significantly less so in UC. The identified pathways and genes may inform development of differing biomarkers and treatment strategies in shorter versus longer standing CD.

Here, we present a multi-omics study of type 2 diabetes and quantitative blood lipid and lipoprotein traits conducted to date in Hispanic/Latino populations (nmax = 63,184). We conduct a meta-analysis of 16 type 2 diabetes and 19 lipid trait GWAS, identifying 20 genome-wide significant loci for type 2 diabetes, including one novel locus and novel signals at two known loci, based on fine-mapping. We also identify sixty-one genome-wide significant loci across the lipid/lipoprotein traits, including nine novel loci, and novel signals at 19 known loci through fine-mapping. Next, we analyze genetically regulated expression, perform Mendelian randomization, and analyze association with transcriptomic and proteomic measure using multi-omics data from a Hispanic/Latino population. Using this approach, we identify genes linked to type 2 diabetes and lipid/lipoprotein traits, including TMEM205 and NEDD9 for HDL cholesterol, TREH for triglycerides, and ANXA4 for type 2 diabetes.

Early diagnosis and treatment of Crohn's Disease (CD) is associated with decreased risk of surgery and complications. However, diagnostic delay is common in clinical practice. In order to better understand CD risk factors and disease indicators, we identified incident CD patients and controls within the Mount Sinai Data Warehouse (MSDW) and developed machine learning (ML) models for disease prediction. CD incident cases were defined based on CD diagnosis codes, medication prescriptions, healthcare utilization before first CD diagnosis, and clinical text, using structured Electronic Health Records (EHR) and clinical notes from MSDW. Cases were matched to controls based on sex, age and healthcare utilization. Thus, we identified 249 incident CD cases and 1,242 matched controls in MSDW. We excluded data from 180 days before first CD diagnosis for cohort characterization and predictive modeling. Clinical text was encoded by term frequency-inverse document frequency and structured EHR features were aggregated. We compared three ML models: Logistic Regression, Random Forest, and XGBoost. Gastrointestinal symptoms, for instance anal fistula and irritable bowel syndrome, are significantly overrepresented in cases at least 180 days before the first CD code (prevalence of 33% in cases compared to 12% in controls). XGBoost is the best performing model to predict CD with an AUROC of 0.72 based on structured EHR data only. Features with highest predictive importance from structured EHR include anemia lab values and race (white). The results suggest that ML algorithms could enable earlier diagnosis of CD and reduce the diagnostic delay.

Clinical note classification is a common clinical NLP task. However, annotated data-sets are scarse. Prompt-based learning has recently emerged as an effective method to adapt pre-trained models for text classification using only few training examples. A critical component of prompt design is the definition of the template (i.e. prompt text). The effect of template position, however, has been insufficiently investigated. This seems particularly important in the clinical setting, where task-relevant information is usually sparse in clinical notes. In this study we develop a keyword-optimized template insertion method (KOTI) and show how optimizing position can improve performance on several clinical tasks in a zero-shot and few-shot training setting.

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.

Abstract Objective To assess whether an individual’s degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device. Materials and Methods Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline. Results We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median Connor-Davidson Resilience Scale-2 score of 6 (interquartile range = 5–7), with an AUC of 0.60. When predicting resilience as a continuous variable, multivariate linear models had a correlation of 0.24 (P = .029) and RMSE of 1.37 in the testing data. A positive psychological construct, comprised of resilience, optimism, and emotional support was also evaluated. The oblique random forest method performed best in estimating high- versus low-composite scores stratified on a median of 32.5, with an AUC of 0.65, a sensitivity of 0.60, and a specificity of 0.70. Discussion In a post hoc analysis, machine learning models applied to physiological metrics collected from wearable devices had some predictive ability in identifying resilience states and a positive psychological construct. Conclusions These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.

The Smart4Health (S4H) software application will enable European Union (EU) citizens to manage, analyse, and exchange their aggregated electronic health data. This citizen-centred EU electronic health record (EHR) exchange approach for personalised health services will be the first step for the provision of citizen-centred solutions and services in a digital single market for wellbeing and healthcare. Establishing interoperability between the diverse EU EHR data and citizen-generated health data is mandatory to guarantee adequate usability, reliability, and trust of the service. The Connecting Europe Facility (CEF) building blocks address/fulfil such aspects while complying with EU regulations. Here we demonstrate the current status and applicability of the CEF building blocks in the digital health environment for the envisioned S4H software application. The major findings and success stories resulted from the S4H Project are as follows: (1) a secure and user-friendly eID service for the EU-wide Smart4Health community was successfully integrated into the Smart4Health platform, whereby 7 out of the 13 supported EU Member States are already connected, (2) the eTranslation service was compared to other popular alternatives on the market with the result that eTranslation is a secure and valid tool to address multi-language challenges and (3) we identified several use cases for which Smart4Health can benefit from the usage of CEF building blocks, including the improvement of data quality and increase of trust in data sharing.

Background Genome-wide association studies for glycemic traits have identified hundreds of loci associated with these biomarkers of glucose homeostasis. Despite this success, the challenge remains to link variant associations to genes, and underlying biological pathways. Methods To identify coding variant associations which may pinpoint effector genes at both novel and previously established genome-wide association loci, we performed meta-analyses of exome-array studies for four glycemic traits: glycated hemoglobin (HbA1c, up to 144,060 participants), fasting glucose (FG, up to 129,665 participants), fasting insulin (FI, up to 104,140) and 2hr glucose post-oral glucose challenge (2hGlu, up to 57,878). In addition, we performed network and pathway analyses. Results Single-variant and gene-based association analyses identified coding variant associations at more than 60 genes, which when combined with other datasets may be useful to nominate effector genes. Network and pathway analyses identified pathways related to insulin secretion, zinc transport and fatty acid metabolism. HbA1c associations were strongly enriched in pathways related to blood cell biology. Conclusions Our results provided novel glycemic trait associations and highlighted pathways implicated in glycemic regulation. Exome-array summary statistic results are being made available to the scientific community to enable further discoveries.

Abstract BACKGROUND Inflammatory bowel disease (IBD) flares are common and unpredictable. Disease monitoring relies on symptom reporting or single timepoint assessments of stool, blood, imaging, or endoscopy—these are inconvenient and invasive and do not always reflect the patient perspective. Advances in wearable technology allow for passive, continuous and non-invasive assessment of physiological metrics including heart rate variability (HRV), the measure of small time differences between each heartbeat, a marker of autonomic nervous system function. Our group has previously demonstrated that changes in autonomic function precedes an IBD flare, can predict psychological state transitions and even identify inflammatory events including SARS-CoV-2 infection. To develop algorithms that can predict IBD flares using wearable device signatures, we launched a national wearable device study called The IBD Forecast study. To assess data quality and feasibility, the first 125 Apple Watch users to enroll were evaluated. METHODS The IBD Forecast study is a prospective cohort study enrolling anyone ≥18 years of age in the United States (US) with IBD who is willing to (1) use a commercially available wearable device, (2) download our custom eHive app and (3) answer daily survey questions. HRV metrics (mean of the standard deviations of all the NN intervals [SDNN]) were analyzed using a mixed-effect cosigner model that incorporated body mass index, age, and sex. SDNN is a time domain HRV index that reflects both sympathetic and parasympathetic nervous system activity and is calculated from the variance of intervals between adjacent QRS complexes (the normal-to-normal [NN] intervals). Clinical flare was assessed with daily Patient Reported Outcome (PRO)-2 surveys (flare; PRO-2 Crohn’s disease >7, PRO-2 ulcerative colitis >2). Inflammatory flare was assessed via patient reported C-reactive protein (CRP), with inflammatory flare defined as >5 mg/L. RESULTS The first 125 study participants were enrolled across 29 states in the US (Table 1). Circadian features of changes of HRV were modelled (Figure 1). The mesor, or midline of the circadian pattern of the SDNN was higher in those with clinical flare (mean 44.43; 95% CI 41.25-47.75) compared to those in clinical remission (mean 43.03; 95% CI 39.94-46.22) (p<0.004). The mesor of the circadian pattern of the SDNN was lower in those with an inflammatory flare (mean 38.16; 95% CI 30.86-45.72) compared to those with normal inflammatory markers (mean 49.51; 95% CI 43.12-56.26) (p<0.001). CONCLUSIONS Longitudinally collected HRV metrics from a commonly worn commercial wearable device can identify symptomatic and inflammatory flares. This preliminary analysis of a small proportion of the IBD Forecast Study cohort demonstrates the feasibility of using wearable devices to identify, and may potentially predict, IBD flares.

Background Crohn’s Disease (CD) patient heterogeneity in clinical practice is captured by the Montreal Classification. While the underlying concepts, disease behavior and age at diagnosis, are relevant outcomes and covariates in studies from real-world data, extracting this clinical information through manual chart review is labor-intensive and with limited scalability. Methods We developed and evaluated automated phenotyping algorithms to extract disease behavior and age at diagnosis from clinical narrative texts, using a rule-based approach based on the spaCy framework, and an approach based on zero-shot inference. The underlying data included 49,572 clinical notes and 2,204 radiology reports from 584 CD patients of the Mount Sinai Crohn’s and Colitis Registry. A test set of 200 clinical texts per classification category was labeled at sentence-level, in addition to patient-level ground truth data. The algorithms were evaluated based on their recall, precision, specificity values, and F1-scores. Results For the labeled dataset, an overall Cohen’s kappa inter-annotator agreement of 0.84 was achieved. The rule-based approach yielded high recall and precision values (0.75 - 1.00) on a note level for the behavioral disease phenotype using clinical notes, with slightly reduced performance using radiology reports. For age at diagnosis, recall and precision values of 0.81 and 0.88 were achieved on note-level, respectively. For both categories, the performance on patient-compared to note-level was reduced, potentially due to the accumulation of false positives and limitations in the data availability. Conclusion Based on our newly annotated dataset, we demonstrated the feasibility of automatically extracting disease behavior and age at diagnosis from clinical text. The resulting labels may facilitate extensive cohort analyses based on electronic health records, and support chart review processes in the future.

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P -value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.

The lifetime risk of kidney disease in people with diabetes is 10–30%, implicating genetic predisposition in the cause of diabetic kidney disease (DKD). Here we identify an expression quantitative trait loci (QTLs) in the cis -acting regulatory region of the xanthine dehydrogenase, or xanthine oxidoreductase ( Xor ), a binding site for C/EBPβ, to be associated with diabetes-induced podocyte loss in DKD in male mice. We examine mouse inbred strains that are susceptible (DBA/2J) and resistant (C57BL/6J) to DKD, as well as a panel of recombinant inbred BXD mice, to map QTLs. We also uncover promoter XOR orthologue variants in humans associated with high risk of DKD. We introduced the risk variant into the 5′-regulatory region of XOR in DKD-resistant mice, which resulted in increased Xor activity associated with podocyte depletion, albuminuria, oxidative stress and damage restricted to the glomerular endothelium, which increase further with type 1 diabetes, high-fat diet and ageing. Therefore, differential regulation of Xor contributes to phenotypic consequences with diabetes and ageing.

BACKGROUND & AIMS: Tumor necrosis factor (TNF) antagonists often are used as first-line medications to treat moderate to severe inflammatory bowel disease (IBD), but many patients do not achieve or maintain response. Our aim was to compare the effectiveness of second-line treatments (ustekinumab, vedolizumab, or a second TNF antagonist) after TNF antagonist exposure in patients with Crohn's disease (CD) and ulcerative colitis (UC) from 2 electronic health records- based cohorts.METHODS: We identified patients with prior TNF antagonist exposure who switched to a different biologic in the Mount Sinai Health System (MSHS) electronic health records (CD, n = 527; UC, n = 165) and the Study of a Prospective Adult Research Cohort (SPARC) from the Inflammatory Bowel Disease Plexus Program of the Crohn's & Colitis Foundation (CD, n = 412; UC, n = 129). Treatment failure was defined as the composite of any IBD-related surgery, IBD-related hospitalization, new prescription of oral/intravenous corticosteroids, or need to switch to a third biologic agent. Time-to-event analysis was conducted with inverse probability of treatment weighted data.RESULTS: Overall, treatment failure occurred in 85% of MSHS and 72% of SPARC CD patients. In SPARC, the likelihood of treatment failure was significantly lower with ustekinumab compared with vedolizumab as second-line treatment (adjusted hazard ratio, 0.66; 95% CI, 0.54-0.82; P < .001), a trend confirmed in MSHS (adjusted hazard ratio, 0.89; 95% CI, 0.77-1.04; P = .15). In both cohorts, the superiority of ustekinumab compared with vedolizumab was shown when considering treatment failure as prescription of steroids or a third biologic agent. In UC, no differences between second-line treatment groups were identified. CONCLUSIONS: In 2 independent real-world cohort settings, second-line therapy in CD with ustekinumab after TNF antagonist treatment failure was associated with a lower likelihood of treatment failure than second-line vedolizumab.

Introduction: Ischemic heart disease (IHD) incidence is increasing among young individuals, particularly women. Suboptimal primary prevention due to risk underestimation is thought to contribute to this trend. Non-traditional risk factors, such as stress and female-specific factors, are often overlooked but offer an opportunity to improve risk stratification. Dysmenorrhea is the most prevalent menstrual complaint, and associated with increased stress and autonomic dysfunction. To date, contribution of dysmenorrhea to cardiovascular disease risk has not been investigated. Hypothesis: Dysmenorrhea is associated with increased risk of IHD before 50 years of age. Methods: We extracted a cohort of 30,554 women with dysmenorrhea and 25,350 controls from Mount Sinai data warehouse as shown in Fig 1. For statistical analysis we performed full propensity score matching on demographic and clinical covariates, followed by weighted logistic regression with IHD before age 50 as a binary outcome. We then investigated IHD subtypes (angina, acute myocardial infarction (AMI), AMI complications, chronic IHD) in separate regression models, and evaluated the inclusion of menstrual abnormalities (MA) and endometriosis as additional covariates in the models (vs other covariates only). Results: Dysmenorrhea was associated with a significant increase in the OR for overall IHD, angina and chronic IHD (Table 1), independently from MA or endometriosis. Conclusions: Dysmenorrhea might be an important risk factor for early IHD. The risk appears to be largest for angina and chronic IHD. Further research can investigate dysmenorrhea as a predictor of future IHD risk.

BACKGROUND:Digital therapeutic care (DTC) programs are unsupervised app-based treatments that provide video exercises and educational material to patients with nonspecific low back pain during episodes of pain and functional disability. German statutory health insurance can reimburse DTC programs since 2019, but evidence on efficacy and reasonable pricing remains scarce. This paper presents a probabilistic sensitivity analysis (PSA) to evaluate the efficacy and cost-utility of a DTC app against treatment as usual (TAU) in Germany.OBJECTIVE:The aim of this study was to perform a PSA in the form of a Monte Carlo simulation based on the deterministic base case analysis to account for model assumptions and parameter uncertainty. We also intend to explore to what extent the results in this probabilistic analysis differ from the results in the base case analysis and to what extent a shortage of outcome data concerning quality-of-life (QoL) metrics impacts the overall results.METHODS:The PSA builds upon a state-transition Markov chain with a 4-week cycle length over a model time horizon of 3 years from a recently published deterministic cost-utility analysis. A Monte Carlo simulation with 10,000 iterations and a cohort size of 10,000 was employed to evaluate the cost-utility from a societal perspective. Quality-adjusted life years (QALYs) were derived from Veterans RAND 6-Dimension (VR-6D) and Short-Form 6-Dimension (SF-6D) single utility scores. Finally, we also simulated reducing the price for a 3-month app prescription to analyze at which price threshold DTC would result in being the dominant strategy over TAU in Germany.RESULTS:The Monte Carlo simulation yielded on average a €135.97 (a currency exchange rate of EUR €1=US $1.069 is applicable) incremental cost and 0.004 incremental QALYs per person and year for the unsupervised DTC app strategy compared to in-person physiotherapy in Germany. The corresponding incremental cost-utility ratio (ICUR) amounts to an additional €34,315.19 per additional QALY. DTC yielded more QALYs in 54.96% of the iterations. DTC dominates TAU in 24.04% of the iterations for QALYs. Reducing the app price in the simulation from currently €239.96 to €164.61 for a 3-month prescription could yield a negative ICUR and thus make DTC the dominant strategy, even though the estimated probability of DTC being more effective than TAU is only 54.96%.CONCLUSIONS:Decision-makers should be cautious when considering the reimbursement of DTC apps since no significant treatment effect was found, and the probability of cost-effectiveness remains below 60% even for an infinite willingness-to-pay threshold. More app-based studies involving the utilization of QoL outcome parameters are urgently needed to account for the low and limited precision of the available QoL input parameters, which are crucial to making profound recommendations concerning the cost-utility of novel apps.

Importance Chronic kidney disease (low estimated glomerular filtration rate [eGFR] or albuminuria) affects approximately 14% of adults in the US. Objective To evaluate associations of lower eGFR based on creatinine alone, lower eGFR based on creatinine combined with cystatin C, and more severe albuminuria with adverse kidney outcomes, cardiovascular outcomes, and other health outcomes. Design, Setting, and Participants Individual-participant data meta-analysis of 27 503 140 individuals from 114 global cohorts (eGFR based on creatinine alone) and 720 736 individuals from 20 cohorts (eGFR based on creatinine and cystatin C) and 9 067 753 individuals from 114 cohorts (albuminuria) from 1980 to 2021. Exposures The Chronic Kidney Disease Epidemiology Collaboration 2021 equations for eGFR based on creatinine alone and eGFR based on creatinine and cystatin C; and albuminuria estimated as urine albumin to creatinine ratio (UACR). Main Outcomes and Measures The risk of kidney failure requiring replacement therapy, all-cause mortality, cardiovascular mortality, acute kidney injury, any hospitalization, coronary heart disease, stroke, heart failure, atrial fibrillation, and peripheral artery disease. The analyses were performed within each cohort and summarized with random-effects meta-analyses. Results Within the population using eGFR based on creatinine alone (mean age, 54 years [SD, 17 years]; 51% were women; mean follow-up time, 4.8 years [SD, 3.3 years]), the mean eGFR was 90 mL/min/1.73 m 2 (SD, 22 mL/min/1.73 m 2 ) and the median UACR was 11 mg/g (IQR, 8-16 mg/g). Within the population using eGFR based on creatinine and cystatin C (mean age, 59 years [SD, 12 years]; 53% were women; mean follow-up time, 10.8 years [SD, 4.1 years]), the mean eGFR was 88 mL/min/1.73 m 2 (SD, 22 mL/min/1.73 m 2 ) and the median UACR was 9 mg/g (IQR, 6-18 mg/g). Lower eGFR (whether based on creatinine alone or based on creatinine and cystatin C) and higher UACR were each significantly associated with higher risk for each of the 10 adverse outcomes, including those in the mildest categories of chronic kidney disease. For example, among people with a UACR less than 10 mg/g, an eGFR of 45 to 59 mL/min/1.73 m 2 based on creatinine alone was associated with significantly higher hospitalization rates compared with an eGFR of 90 to 104 mL/min/1.73 m 2 (adjusted hazard ratio, 1.3 [95% CI, 1.2-1.3]; 161 vs 79 events per 1000 person-years; excess absolute risk, 22 events per 1000 person-years [95% CI, 19-25 events per 1000 person-years]). Conclusions and Relevance In this retrospective analysis of 114 cohorts, lower eGFR based on creatinine alone, lower eGFR based on creatinine and cystatin C, and more severe UACR were each associated with increased rates of 10 adverse outcomes, including adverse kidney outcomes, cardiovascular diseases, and hospitalizations.