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BackgroundDigital food registration via online platforms that are coupled to large food databases obviates the need for manual processing of dietary data. The reliability of such platforms depends on the quality of the associated food database. ObjectiveIn this study, we validate the database of MyFitnessPal versus the Belgian food composition database, Nubel. MethodsAfter carefully given instructions, 50 participants used MyFitnessPal to each complete a 4-day dietary record 2 times (T1 and T2), with 1 month in between T1 and T2. Nutrient intake values were calculated either manually, using the food composition database Nubel, or automatically, using the database coupled to MyFitnessPal. First, nutrient values from T1 were used as a training set to develop an algorithm that defined upper limit values for energy intake, carbohydrates, fat, protein, fiber, sugar, cholesterol, and sodium. These limits were applied to the MyFitnessPal dataset extracted at T2 to remove extremely high and likely erroneous values. Original and cleaned T2 values were correlated with the Nubel calculated values. Bias was estimated using Bland-Altman plots. Finally, we simulated the impact of using MyFitnessPal for nutrient analysis instead of Nubel on the power of a study design that correlates nutrient intake to a chosen outcome variable. ResultsPer food portion, the following upper limits were defined: 1500 kilocalories for total energy intake, 95 grams (g) for carbohydrates, 92 g for fat, 52 g for protein, 22 g for fiber, 70 g for sugar, 600 mg for cholesterol, and 3600 mg for sodium. Cleaning the dataset extracted at T2 resulted in a 2.8% rejection. Cleaned MyFitnessPal values demonstrated strong correlations with Nubel for energy intake (r=0.96), carbohydrates (r=0.90), fat (r=0.90), protein (r=0.90), fiber (r=0.80), and sugar (r=0.79), but weak correlations for cholesterol (ρ=0.51) and sodium (ρ=0.53); all P values were ≤.001. No bias was found between both methods, except for a fixed bias for fiber and a proportional bias for cholesterol. A 5-10% power loss should be taken into account when correlating energy intake and macronutrients obtained with MyFitnessPal to an outcome variable, compared to Nubel. ConclusionsDietary analysis with MyFitnessPal is accurate and efficient for total energy intake, macronutrients, sugar, and fiber, but not for cholesterol and sodium.

Background: Abnormal oroanal transit time (OATT) and visceral hypersensitivity are key pathophysiological factors in irritable bowel syndrome (IBS). The lactulose nutrient challenge test (LNCT) has been developed to assess the postprandial symptoms and gut microbial fermentation. We aimed to investigate associations between OATT, rectal sensitivity, and LNCT in IBS patients. Methods: We included 263 IBS patients from two study cohorts, where the link between pathophysiology and symptoms was investigated. During the LNCT, severity of postprandial symptoms was graded, and breath hydrogen/methane concentrations were measured after ingestion of a combined lactulose nutrient drink every 15 min for 4 h. The patients underwent rectal sensitivity (rectal barostat) and OATT (radiopaque markers) investigations. Comorbid conditions (functional dyspepsia, anxiety, depression, and somatization) were assessed with questionnaires. Key Results: After controlling for comorbid conditions, rectal sensitivity was associated with abdominal pain (p < 0.05), and more rapid OATT was associated with higher severity of abdominal discomfort, rumbling, nausea, and urgency (p < 0.05 for all) both pre‐ and post‐prandially. Postprandial nausea, urgency, and abdominal pain changed differently over time depending on OATT (p < 0.05 for all). OATT, but not rectal sensitivity, was associated with hydrogen and methane concentrations (p = 0.002 for both). Trajectories over time of postprandial symptoms and exhaled hydrogen/methane concentrations were correlated with different correlations depending on OATT. Conclusion and Inferences: This study highlights the importance of oroanal transit and hydrogen and methane production in the pathophysiology of IBS and increases our understanding of pathophysiological factors involved in postprandial symptom generation. Treatments targeting oroanal transit and hydrogen and methane production may improve specific postprandial symptoms. [ABSTRACT FROM AUTHOR]

Background: Chronic opioid use can induce esophageal dysfunction with symptoms resembling achalasia and a manometric pattern of esophagogastric junction—outflow obstruction (EGJ‐OO). However, the effect of opioids in acute setting on pharyngeal function and esophageal body contractility has not been investigated. Methods: After positioning the high‐resolution impedance manometry (HRiM) catheter, codeine (60 mg) or placebo (glucose syrup) was infused intragastrically. Forty‐five minutes post‐infusion, participants received liquid, semi‐solid, and solid boluses to assess esophageal and pharyngeal function. HRiM analysis was performed adhering to the Chicago classification v3.0. (CC v3.0). Pressure flow analysis (PFA) for the esophageal body and the pharynx was performed using the SwallowGateway™ online platform. Key Results: Nineteen healthy volunteers (HV) [5 male; age 38.3] were included. After codeine administration, higher integrated relaxation pressure 4 s values resulted in significantly reduced deglutitive EGJ relaxation and distal latency was significantly shorter. Distal contractility was similar in both conditions. Bolus flow resistance at the EGJ and distention pressures increased significantly after codeine infusion. Based on CC v3.0, acute infusion of codeine induced EGJ‐OO in six HV (p = 0.0003 vs. placebo). Codeine administration induced no significant alterations in any of the pharyngeal PFA metrics. Conclusions & Inferences: In HV, acute administration of codeine increased bolus resistance at the EGJ secondary to induced incomplete EGJ relaxation leading to major motility disorders in a subset of subjects including EGJ‐OO. However, an acute single dose of codeine did not affect motility or bolus flow in pharynx and UES. ClinicalTrials.gov number, NCT03784105. [ABSTRACT FROM AUTHOR]