Integrating nutritional status and hematological biomarkers for enhanced prognosis prediction in glioma patients: A systematic review
Bibliographic record
Abstract
PURPOSE: Multiple inflammatory and nutritional biomarkers have been established as independent prognostic factors across various solid tumors, but their role in outcomes prediction for glioma is still under investigation. Aim of the present systematic review is to report the available evidence regarding the impact of nutritional assessment and intervention for glioma prognosis and patients' quality of life (QoL). MATERIALS AND METHODS: Our systematic review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The PubMed and EMBASE databases were searched to identify studies assessing the impact of nutritional status and intervention and hematological biomarkers on survival outcomes and quality of life in patients with newly diagnosed gliomas. In the search strategy Medical Subject Headings (MeSH) terms were used. Search terms included ("nutritional status" or "nutritional assessment" or "nutritional intervention") AND ("glioma" or "glioblastoma" or "high-grade glioma" or "low-grade glioma" or "anaplastic astrocytoma" or "anaplastic oligodendroglioma") AND ("prognosis" or "survival outcomes"). The quality of each study was investigated based on the Newcastle-Ottawa Scale (NOS) criteria. Selected papers were in English and included publications in humans. This study was registered on PROSPERO (Registration No. CRD42024555442). RESULTS: Our search retrieved 20 papers published between 2015 and 2023, all aiming at investigating correlations between hematological biomarkers (albumin, prealbumin, fibrinogen) and/or nutritional tools (Controlling Nutritional Score, CONUT; Prognostic Nutritional Index, PNI) and survival outcomes and quality of life of glioma patients. Nutritional intervention as well was evaluated for outcomes prediction. Overall, most papers contributed to the evidence of how nutritional assessment and inflammatory biomarkers could play an independent prognostic role also in the management of glioma patients. CONCLUSIONS: PNI, CONUT score and hematological biomarkers (e.g. albumin, globulin, neutrophils, lymphocytes) may serve as useful predictors in patients with gliomas, potentially influencing clinical decisions. Additional large-scale studies are required to validate these findings and determine the mechanisms by which nutritional status, systemic inflammation and immune status affect prognosis in glioma patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".