Evidence map›Paper›PMID 42280392›Full record

ArticleNutrients2026

Development and Validation of a Practical Nutritional Management Algorithm in Malabsorption.

Maryam Sidahi Serrano, Carmelo Diéguez Castillo, Andrea Martín Aguilar, Daniel De Luis Román

Abstract readValidation Study
In one paragraph

Article in Nutrients, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Maryam Sidahi SerranoInternal Medicine Department, Infanta Elena Hospital, 21007 Huelva, Spain.
Carmelo Diéguez CastilloSpecialist in Gastroenterology, Torrecardenas University Hospital, 04009 Almeria, Spain.
Andrea Martín AguilarAbbott Laboratories, Medical Department, 28050 Madrid, Spain.
Daniel De Luis RománCenter of Investigation Endocrinology and Nutrition, Valladolid University Clinical Hospital, Medicine School University, 47002 Valladolid, Spain.ORCID 0000-0002-1745-9315

Funding

Abbott (Spain) No grant number
6 · The paper itself

Abstract

backgroundMalabsorption is a frequent and clinically relevant condition associated with a high risk of disease-related malnutrition across multiple gastrointestinal and systemic disorders. Despite its prevalence, standardized nutritional management algorithms remain limited. Following a previously published Delphi consensus on the use of oligomeric enteral formulas, the present study aimed to develop and validate a practical nutritional management algorithm for patients with malabsorption.

methodsA structured expert questionnaire was conducted among 173 physicians with clinical experience in malabsorption, including specialists in endocrinology, gastroenterology, internal medicine, and oncology. Participants gained experience using the preliminary algorithm by applying it to five real-life cases before completing the questionnaire. The survey addressed symptom type, intensity, and duration required for screening, diagnostic criteria for malnutrition, timing of reassessment, indications for oligomeric oral nutritional supplements (ONSs), and criteria for reintroducing polymeric formulas. Statistical analyses were performed using SAS v9.4.

resultsOf the 173 participants, 67.1% were women, with a mean age of 39.6 ± 8.2 years and a mean clinical experience of 10.9 ± 7.9 years. In clinicians' opinion, diarrhea was the most frequently considered symptom to trigger screening (88.6%), followed by abdominal distension (72.6%), abdominal pain (65.4%), and increased gastric residuals (45.8%). Experts agreed that symptoms should present with at least moderate intensity and persist for more than 3 weeks to justify screening. Most respondents agreed with using the GLIM criteria for malnutrition assessment (97.7%). For patients with poor tolerance to polymeric ONSs or moderate-to-severe symptoms, initiation of oligomeric ONSs was recommended, with diarrhea identified as the main indication (31.1%). Symptom severity prompting oligomeric formulas was rated between 2.8 and 3.3 on a 5-point scale. The mean recommended duration of symptom improvement before transitioning back to polymeric formulas was 6.24 ± 4.45 weeks.

conclusionsThis study presents a consensus-based, clinically applicable algorithm for nutritional screening, diagnosis, and intervention in patients with malabsorption. The algorithm provides clear guidance on symptom assessment, use of GLIM criteria, selection of ONS type, and follow-up, potentially improving standardization and quality of nutritional care in this high-risk population.

Indexed as

AlgorithmsMalabsorption SyndromesMalnutritionAdultFemaleHumansMaleMiddle AgedNutrition AssessmentSurveys and Questionnairesalgorithmgastrointestinal symptomsgastrointestinal tolerancemalabsorptionnutritional managementoligomeric enteral formulasprotocol

Identifiers

PMID42280392
PMCPMC13259343

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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.