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INTRODUCTION: According to the hygiene hypothesis, exposure to parasites may protect against inflammatory bowel disease (IBD). Our aim was to examine the risk of IBD with childhood exposure to mebendazole, a broad-spectrum antihelminthic agent. METHODS: We conducted a population-based cohort study using prospectively collected historical data of all individuals born in Denmark between 1995 and 2018. We identified mebendazole exposure at age younger than 18 years and during early life (younger than 5 years). We performed adjusted Cox proportional hazards regression analysis to determine the risk of IBD, ulcerative colitis (UC), and Crohn's disease with mebendazole exposure after adjusting for potential confounders. RESULTS: Of 1,520,290 individuals in the cohort, 615,794 had childhood or adolescence mebendazole exposure. One thousand five hundred fifty-five and 1,499 individuals were subsequently diagnosed with pediatric-onset and adult-onset IBD, respectively. On multivariable analysis, mebendazole exposure at age younger than 18 years did not affect pediatric-onset or adult-onset IBD risk (adjusted hazard ratio [aHR] 0.97, 95% confidence interval [CI] 0.87, 1.07, and 1.08, 95% CI 0.97, 1.19, respectively). On limiting mebendazole exposure to age younger than 5 years while there was no association with pediatric-onset IBD (aHR 0.98, 95% CI 0.87, 1.11), adult-onset IBD risk was increased (aHR 1.17, 95% CI 1.04, 1.31). This increase in risk was driven by UC (aHR 1.32, 95% CI 1.12, 1.55), but not Crohn's disease (1.03, 95% CI 0.87, 1.22). DISCUSSION: Early-life mebendazole exposure is associated with an increase in the risk of adult-onset UC. These findings suggest the importance of early-life exposures in shaping the risk of IBD later in life.

ImportanceObservational comparative effectiveness studies can inform the positioning of biologic therapies for older patients with inflammatory bowel disease (IBD) who are underrepresented in clinical trials.ObjectiveTo compare the effectiveness and safety of vedolizumab vs tumor necrosis factor (TNF) for older patients with IBD.Design, Setting, and ParticipantsThis active comparator, new-user design, comparative effectiveness study was conducted between January 1, 2005, and December 31, 2018, among 754 older patients (aged ≥50 years) with IBD from the Danish National Patient Register. The mean follow-up after treatment initiation took place at 32 to 40 weeks. Statistical analysis was performed from February 1 to April 27, 2022.InterventionsTreatment with vedolizumab or TNF antagonists.Main Outcomes and MeasuresThe primary effectiveness outcome was treatment failure, defined as the composite risk of IBD-related hospitalization, IBD-related surgery, or a new corticosteroid prescription more than 6 weeks after initiation of treatment with biologic therapy. Secondary effectiveness outcomes were time to each individual component of the composite effectiveness outcome. The primary safety outcome was the risk of serious infections, defined as infections requiring hospitalization. A 1:1 propensity score–matched analysis was conducted, accounting for patient-, disease-, and treatment-associated factors.ResultsThe study compared 377 older patients with IBD with incident use of vedolizumab (202 women [53.6%]; mean [SD] age, 61.2 [8.3] years; 177 [46.9%] with Crohn disease) vs 377 patients with incident use of TNF antagonists (206 women [54.6%]; mean [SD] age, 61.3 [8.1] years; 182 [48.3%] with Crohn disease). Overall, vedolizumab was associated with an increased risk of treatment failure compared with TNF antagonists (1-year risk, 45.4% vs 34.7%; adjusted hazard ratio [HR], 1.31; 95% CI, 1.02-1.69), including higher risk of IBD-related hospitalization (1-year risk, 27.8% vs 16.3%; adjusted HR, 1.48; 95% CI, 1.03-2.15) and IBD-related major abdominal surgery (1-year risk, 21.3% vs 8.0%; adjusted HR, 2.39; 95% CI, 1.45-3.94). In subgroup analysis by IBD phenotype, among patients with Crohn disease, vedolizumab was associated with a 77% higher risk of treatment failure (adjusted HR, 1.77; 95% CI, 1.21-2.58), while no difference in risk of treatment failure was seen among patients with ulcerative colitis (adjusted HR, 1.04; 95% CI, 0.75-1.43; P = .03 for interaction). There was no significant difference in the risk of serious infections, overall (1-year risk, 8.2% vs 8.7%; adjusted HR, 1.04; 95% CI, 0.58-1.85) and by IBD phenotype.Conclusions and RelevanceIn this comparative effectiveness study of older patients with IBD, vedolizumab was associated with a higher risk of treatment failure compared with TNF antagonists, particularly among patients with Crohn disease, without offering a significant safety advantage.

Little is known about the effects of discontinuing mesalamine therapy for patients with Crohn's disease (CD) who initiate therapy with an anti-tumor necrosis factor (anti-TNF) agent. We analyzed data from 2 national population cohorts to compare outcomes of patients with CD already on mesalamine therapy who started treatment with an anti-TNF agent and then either stopped or continued mesalamine.The primary outcome was any adverse clinical event, defined as a composite of new corticosteroid use or CD-related hospitalization or surgery. We collected data from the Truven MarketScan (IBM, Armonk, NY) health claims database in the United States and the Danish health registers. Our analysis included patients with CD who started anti-TNF therapy after at least 90 days of oral mesalamine therapy. Patients were classified as stopping mesalamine if therapy was discontinued within 90 days of starting anti-TNF. We performed multivariable Cox regression models controlling for demographics, clinical factors, and health care utilization. Adjusted hazard ratios with 95% CIs were calculated, comparing stopping mesalamine with continuing mesalamine.A total of 3178 patients with CD were included in our final analysis (2960 in the United States and 218 in Denmark). Stopping mesalamine after initiating anti-TNF therapy was not associated with an increased risk of adverse clinical events in the US cohort (adjusted hazard ratio, 0.89; 95% CI, 0.77-1.03; P = .13) or in the Danish cohort (adjusted hazard ratio, 1.13; 95% CI, 0.68-1.87; P = .63). Similar results were obtained from sensitivity analyses of concomitant immunomodulator use and duration of mesalamine treatment before initiation of anti-TNF therapy.In a retrospective analysis of 2 national databases, we found that stopping mesalamine therapy for patients with CD who were starting anti-TNF therapy did not increase their risk of adverse clinical events. These results should be validated in a prospective study.

Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age(2), sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (Pinteraction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, Pinteraction = 0.014 vs. n = 71,611, Pinteraction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (Pinteraction = 0.003) and the SEC16B rs10913469 (Pinteraction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal.

AIMS/HYPOTHESIS: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up). METHODS: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6-24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe. RESULTS: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants' clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m2; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants' clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l. CONCLUSIONS/INTERPRETATION: The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (

Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (, In a modelling study, Naeimeh Atabaki-Pasdar and colleagues apply machine learning techniques to develop models to predict non-alcoholic fatty liver disease diagnosis using multi-omic and clinical data from individuals with and without type 2 diabetes in the IMI DIRECT cohorts., Author summary Why was this study done? Globally, about 1 in 4 adults have non-alcoholic fatty liver disease (NAFLD), which adversely affects energy homeostasis (in particular blood glucose concentrations), blood detoxification, drug metabolism, and food digestion. Although numerous noninvasive tests to detect NAFLD exist, these typically include inaccurate blood-marker tests or expensive imaging methods. The purpose of this work was to develop accurate noninvasive methods to aid in the clinical prediction of NAFLD. What did the researchers do and find? The analyses applied machine learning methods to data from the deep-phenotyped IMI DIRECT cohorts (n = 1,514) to identify sets of highly informative variables for the prediction of NAFLD. The criterion measure was liver fat quantified from MRI. We developed a total of 18 prediction models that ranged from very inexpensive models of modest accuracy to more expensive biochemistry- and/or omics-based models with high accuracy. We found that models using measures commonly collected in either clinical settings or research studies proved adequate for the prediction of NAFLD. The addition of detailed omics data significantly improved the predictive utility of these models. We also found that of all omics markers, proteomic markers yielded the highest predictive accuracy when appropriately combined. What do these findings mean? We envisage that these new approaches to predicting fatty liver may be of clinical value when screening at-risk populations for NAFLD. The identification of specific molecular features that underlie the development of NAFLD provides novel insights into the disease’s etiology, which may lead to the development of new treatments.