CoChat
Sign Up
Cited by 242Nutrition and Health in AgingClinical Nutrition and GastroenterologyBody Composition Measurement Techniques

Global Leadership Initiative on Malnutrition (GLIM): Guidance on validation of the operational criteria for the diagnosis of protein-energy malnutrition in adults

Open access
de van der Schueren, M.A.E., Keller, H., Cederholm, T., Barazzoni, R., Compher, C., Correia, M.I.T.D., Gonzalez, M.C., Jager-Wittenaar, H. +4 more·2020·Clinical Nutrition
DOI 10.1016/j.clnu.2019.12.022
Open this paper in CoChat — free accountOpen PDF

Abstract

Background The Global Leadership Initiative on Malnutrition (GLIM) created a consensus-based framework consisting of phenotypic and etiologic criteria to record the occurrence of malnutrition in adults. This is a minimum set of practicable indicators for use in characterizing a patient/client as malnourished, considering the global variations in screening and nutrition assessment, and to be used across different health care settings. As with other consensus-based frameworks for diagnosing disease states, these operational criteria require validation and reliability testing as they are currently based solely on expert opinion. Methods Several forms of validation and reliability are reviewed in the context of GLIM, providing guidance on how to conduct retrospective and prospective studies for criterion and construct validity. Findings There are some aspects of GLIM criteria which require refinement; research using large data bases can be employed to reach this goal. Machine learning is also introduced as a potential method to support identification of the best cut-points and combinations of operational criteria for use with the different forms of malnutrition, which the GLIM criteria were created to denote. It is noted as well that the validation and reliability testing need to occur in a variety of sectors, populations and with diverse persons completing the criteria. Conclusion The guidance presented supports the conduct and publication of quality validation and reliability studies for GLIM.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.clnu.2019.12.022

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • One meta-analysis included a general sample of 20 studies with 10,781 patients from 13 countries, showing sensitivity of 72% (range 51%-92%) and specificity of 82% (range 73%-98%) relative to SGA or Patient-Generated SGA as the mainly used semi-gold standard.

    “One included a general sample (mixed settings) of 20 studies with 10,781 patients from 13 countries, showing a sensitivity of 72% with a range of 51%–92% (ie, capacity to identify true positive cases) and a specificity of 82% with a range of 73%–98% (ie, capacity to identify true negative cases) relative to SGA or Patient‐Generated SGA as the mainly used semi‐gold standard.”

    From GLIM consensus approach to diagnosis of malnutrition: A 5‐year update (2025) · cites this paper as [8]

    Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • The GLIM Consortium provided a guidance paper on how to perform studies targeting construct or criterion validity and predictive validity.

    “the GLIM Consortium provided a guidance paper 7 , 8 on how to perform studies targeting construct or criterion validity (ie, to assure that the model identifies malnutrition) and predictive validity (ie, that the model identifies cases with adverse clinical outcomes).”

    From GLIM consensus approach to diagnosis of malnutrition: A 5‐year update (2025) · cites this paper as [8]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 35 · Cited by 242

Research works printed but not linked: 29

  1. Soeters P, Bozzetti F, Cynober L, Forbes A, Shenkin A, Sobotka L. Defining malnutrition: a plea to rethink. Clin Nutr 2017;36(3):896e901. Table 4 Recommended validation statistics.
  2. Soeters PB, Reijven PL, van Bokhorst-de van der Schueren MA, Schols JM, Halfens RJ, Meijers JM, et al. A rational approach to nutritional assessment. Clin Nutr 2008;27(5):706e16.
  3. Cederholm T, Barazzoni R, Austin P, Ballmer P, Biolo G, Bischoff SC, et al. ESPEN guidelines on definitions and terminology of clinical nutrition. Clin Nutr 2017;36(1):49e64.
  4. White JV, Guenter P, Jensen G, Malone A, Schofield M. Consensus statement of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition). J Acad Nutr Diet 2012;112(5):730e8.
  5. Jensen GL, Mirtallo J, Compher C, Dhaliwal R, Forbes A, Grijalba RF, et al. Adult starvation and disease-related malnutrition: a proposal for etiology-based diagnosis in the clinical practice setting from the International Consensus Guideline Committee. J Parenter Enter Nutr 2010;34(2):156e9.
  6. Cederholm T, Jensen GL, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition -a consensus report from the global clinical nutrition community. Clin Nutr 2019;38(1):1e9.
  7. Jensen GL, Cederholm T, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition: a consensus report from the global clinical nutrition community. J Parenter Enter Nutr 2019;43(1):32e40.
  8. Gibson RS. Principles of nutrition assessment. New York: Oxford University Press; 2005.
  9. Baker JP, Detsky AS, Wesson DE, Wolman SL, Stewart S, Whitewell J, et al. Nutritional assessment: a comparison of clinical judgement and objective measurements. N Engl J Med 1982;306(16):969e72.
  10. Jager-Wittenaar H, Ottery FD. Assessing nutritional status in cancer: role of the patient-generated subjective global assessment. Curr Opin Clin Nutr Metab Care 2017;20(5):322e9.
  11. Bauer J, Ash S, Davidson WL, Hill JM, Brown T, Isenring EA, et al. Evidence based practice guidelines for the nutritional management of cancer cachexia. Nutr Diet 2006;63:S3e32.
  12. Power L, de van der Schueren MAE, Leij-Halfwerk S, Bauer J, Clarke M, Visser M, et al. Development and application of a scoring system to rate malnutrition screening tools used in older adults in community and health- care settings -a MaNuEL study. Clin Nutr 2019;38(4):1807e19.
  13. Ferrante di Ruffano L, Hyde CJ, McCaffery KJ, Bossuyt PM, Deeks JJ. Assessing the value of diagnostic tests: a framework for designing and evaluating trials. BMJ 2012;344:e686.
  14. Streiner D, Norman G. A practical guide to their development and use. 2nd ed. New York: Oxford University Press; 1996.
  15. Bannigan K, Watson R. Reliability and validity in a nutshell. J Clin Nurs 2009;18(23):3237e43.
  16. Jones JM. Validity of nutritional screening and assessment tools. Nutrition 2004;20(3):312e7.
  17. van Bokhorst-de van der Schueren MA, Guaitoli PR, Jansma EP, de Vet HC. Nutrition screening tools: does one size fit all? A systematic review of screening tools for the hospital setting. Clin Nutr (Edinburgh, Scotland) 2014;33(1):39e58.
  18. Keller HH, Goy R, Kane SL. Validity and reliability of SCREEN II (Seniors in the community: risk evaluation for eating and nutrition, Version II). Eur J Clin Nutr 2005;59(10):1149e57.
  19. Wierdsma N, Kruizenga H, Stratton R. Dietetic pocket guide, adults. Amster- dam: Press VU; 2017.
  20. Jones JM. Reliability of nutritional screening and assessment tools. Nutrition 2004;20(3):307e11.
  21. Deutz NEP, Ashurst I, Ballesteros MD, Bear DE, Cruz-Jentoft AJ, Genton L, et al. The underappreciated role of low muscle mass in the management of malnutrition. J Am Med Dir Assoc 2019;20(1):22e7.
  22. Power L, Mullally D, Gibney ER, Clarke M, Visser M, Volkert D, et al. A review of the validity of malnutrition screening tools used in older adults in community and healthcare settings -a MaNuEL study. Clin Nutr ESPEN 2018;24:1e13.
  23. Cobb AN, Daungjaiboon W, Brownlee SA, Baldea AJ, Sanford AP, Mosier MM, et al. Seeing the forest beyond the trees: predicting survival in burn patients with machine learning. Am J Surg 2018;215(3):411e6.
  24. Shrout PE. Measurement reliability and agreement in psychiatry. Stat Methods Med Res 1998;7(3):301e17.
  25. Henrique JR, Rodrigues CN, Ferreira ARS, Correia MITD. GLIM in practice: sensibility and prognostic value for the diagnosis of malnutrition of gastro- intestinal surgical patients. Clin Nutr 2019;38:S239.
  26. van Rijssen NM, Rojer AGM, Trappenburg MC, Reijnierse EM, Meskers CGM, Maier AB, et al. Is being malnourished according to the ESPEN definition for malnutrition associated with clinically relevant outcome measures in geriatric outpatients? Eur Geriatr Med 2018;9(3):389e94.
  27. Charney P. Nutrition screening vs nutrition assessment: how do they differ? Nutr Clin Pract 2008;23(4):366e72.
  28. Sullivan GM, Feinn R. Using effect size-or why the P value is not enough. J Graduat Med Educat 2012;4(3):279e82.
  29. Blauwhoff-Buskermolen S, Langius JAE, Becker A, Verheul HMW, de van der Schueren MAE. The influence of different muscle mass measurements on the diagnosis of cancer cachexia. J Cachexia Sarcopenia Muscle 2017;8(4):615e22.

Authors
de van der Schueren, M. A. E., Keller, H., Cederholm, T., Barazzoni, R., Compher, C., Correia, M. I. T. D., Gonzalez, M. C., Jager-Wittenaar, H., Pirlich, M., Steiber, A., Waitzberg, D., & Jensen, G. L.
Affiliations
  • University of Waterloo (CA)
  • Uppsala University (SE)
  • University of Trieste (IT)
  • University of Pennsylvania (US)
  • Universidade Federal de Minas Gerais (BR)
  • Academy of Nutrition and Dietetics (US)
  • Universidade de São Paulo (BR)
  • University of Vermont (US)
Corresponding author
  • Keller, H.
  • Cederholm, T.
  • Barazzoni, R.
  • Compher, C.
  • Correia, M.I.T.D.
  • Pirlich, M.
  • Steiber, A.
  • Waitzberg, D.
  • Jensen, G.L.
ORCID
Venue
Clinical Nutrition
Publisher
Elsevier BV
Published
2020-09
Volume
39
Issue
9
Pages
2872-2880
ISSN
0261-5614 (ISSN-L)
DOI
10.1016/j.clnu.2019.12.022
OpenAlex
W3034193502
Cited by
242
References
35
Citations by year
2026: 32 · 2025: 44 · 2024: 42 · 2023: 43 · 2022: 41 · 2021: 37 · 2020: 3
FWCI
18.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Clinical Nutrition (publishedVersion)
  • PubMed (publishedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion, cc-by-nc-nd)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
Topics
  • Health Sciences › Medicine › Physiology › Nutrition and Health in Aging
  • Health Sciences › Nursing › Nutrition and Dietetics › Clinical Nutrition and Gastroenterology
  • Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
Keywords
GLIM, protein-energy malnutrition, adult malnutrition, diagnostic criteria validation, criterion validity, construct validity, reliability testing, machine learning
MeSH terms
Adult, Humans, International Cooperation, Protein-Energy Malnutrition, Reproducibility of Results, Consensus

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review