ArticleAmerican journal of translational research2024
Assessment of body composition-related imaging parameters indicative of sarcopenia in Chinese patients with Crohn's disease: correlation with disease severity and biologic efficacy.
Article in American journal of translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Beyond Inflammation: Sarcopenia and Disease Phenotype Predict Unfavourable Course in Crohn's Disease-A Retrospective Observational Study.Biomedicines · 2026Article
- Computed tomography enterography-based deep learning radiomics models of intestinal lesions and perienteric fat for predicting Crohn's disease activity: a multicenter cohort study.Quantitative imaging in medicine and surgery · 2026Article
- Understanding Remission of Long-Term Conditions Through Electronic Health Records: Scoping Review.Journal of medical Internet research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate the prognostic value of body composition-related imaging parameters in assessing Crohn's disease (CD) severity and biological responses in Chinese patients.
methodsWe retrospectively analyzed electronic medical records and Computed tomography (CT) images from 117 CD patients, including 90 with sarcopenia and 27 without. We calculated subcutaneous fat area (SFA), visceral fat area, skeletal muscle area (SMA), mesenteric fat index (MFI), skeletal muscle index (SMI), and muscle attenuation (MA). CD Activity Index (CDAI) score and Simple Endoscopic Score for CD (SES-CD) were used to evaluate inflammation and biologic efficacy. Correlation and comparative analyses were performed to determine associations between imaging parameters and clinical data. Receiver operating characteristic curve analysis evaluated the predictive performance of combined body composition indicators.
resultsSarcopenia was associated with higher CDAI scores and lower body mass index, albumin, and hemoglobin levels but was not associated with SES-CD or rates of clinical/endoscopic remission or response to biologic therapy. SMI was inversely correlated with CDAI score and SES-CD and positively correlated with albumin and hemoglobin. Endoscopy responders had higher SMA, MFI, SMI, and MA than non-responders. SES-CD improvement was positively correlated with MFI and MA and negatively correlated with SFA. The combined analysis of SMI, MFI, and MA yielded an area under the curve of 0.743 for predicting endoscopic response to biologic therapies in CD patients.
conclusionsSMI may indicate CD severity, while MFI and MA could predict biologic response. Integrating multiple body composition parameters enhances treatment outcome evaluation, suggesting their potential utility in CD assessment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.