Abstract
Background and aims: Pre-existing malnutrition is one the most important factors affecting postsurgical complications, especially in cancer patients. The consequences of this on the immune function as well as on outcome could be reversed by immunonutrition. To help the clinician as a researcher, a routinely available biomarker (derived from clinical or biological data) would be of great importance.
Methods: We reviewed the potential markers that may routinely be used in perioperative immunonutrition programmes. A comprehensive approach was used to identify and discuss the potential markers, focusing on body mass and serum biomarkers.
Results: Body mass (including weight loss and body mass index) are predictive of complications, but not specifically to malnutrition. Serum markers, such as albumin, transthyretin, white blood cells counts, and C-reactive protein are not more specific. Composite scores, including the Nutritional Risk Index (NRI), the Prognostic Inflammatory and Nutritional Index (PINI), the modified Glasgow Prognostic Score (mGPS), the neutrophil-to-lymphocyte ratio (NLR), CD4 and CD8 lymphocytes counts, the platelet-to-lymphocyte ratio (PLR), the Prognostic Index (PI), and the Prognostic Nutritional Index (PNI) are prognostic factors of outcome, but are not always correlated to immunonutrition effect.
Conclusions: In conclusion, there remains a lack of efficient and widely available monitoring of the effects of immunonutrition. To predict and monitor the effect of immunonutrition on immunity, efforts should be directed to the validation of routinely available tools to aid the implementation of advanced immune monitoring (like lymphocytes subpopulations counts) in clinical practices.
Methods: We reviewed the potential markers that may routinely be used in perioperative immunonutrition programmes. A comprehensive approach was used to identify and discuss the potential markers, focusing on body mass and serum biomarkers.
Results: Body mass (including weight loss and body mass index) are predictive of complications, but not specifically to malnutrition. Serum markers, such as albumin, transthyretin, white blood cells counts, and C-reactive protein are not more specific. Composite scores, including the Nutritional Risk Index (NRI), the Prognostic Inflammatory and Nutritional Index (PINI), the modified Glasgow Prognostic Score (mGPS), the neutrophil-to-lymphocyte ratio (NLR), CD4 and CD8 lymphocytes counts, the platelet-to-lymphocyte ratio (PLR), the Prognostic Index (PI), and the Prognostic Nutritional Index (PNI) are prognostic factors of outcome, but are not always correlated to immunonutrition effect.
Conclusions: In conclusion, there remains a lack of efficient and widely available monitoring of the effects of immunonutrition. To predict and monitor the effect of immunonutrition on immunity, efforts should be directed to the validation of routinely available tools to aid the implementation of advanced immune monitoring (like lymphocytes subpopulations counts) in clinical practices.
| Original language | English |
|---|---|
| Article number | 546 |
| Journal | ecancermedicalscience |
| Volume | 9 |
| DOIs | |
| Publication status | Published - 18 Jun 2015 |
Keywords
- immunonutrition
- inflammation
- biomarkers
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