Neutrophil-Lymphocyte Ratio (NLR) is Positively Associated with Impaired Cognitive Performance inPatients with Metabolic Syndrome
Bibliographic record
Abstract
Abstract Metabolic syndrome (MetS) is known to be related to mild cognitive impairment (MCI). A prognostic biomarker for the MCI condition in these patients has not been thoroughly determined. A neutrophil-lymphocyte ratio (NLR) has been widely used as a biomarker for the progression of cancers and cardiovascular diseases. However, its association with the MCI condition in patients with MetS is not known. The present study aimed to investigate the correlation between NLR and cognitive function in patients with MetS. A total of sixty patients with MetS (45-65 years old) were enrolled in the present study, and their metabolic parameters, including plasma levels of glucose, insulin, lipid profiles, inflammatory markers, and the complete blood count, were determined. The NLR level was calculated by the ratio of neutrophils to lymphocytes derived from the complete blood count. The Montreal Cognitive Assessment (MoCA) test was used to determine the cognitive performance in patients with MetS. Most patients with MetS have the possibility of an MCI condition. Moreover, glycated hemoglobin (HbA1C), fasting plasma glucose (FPG), and NLR were negatively correlated with the MoCA scores of these patients. Interestingly, NLR was the strongest independent factor which correlated with the MoCA score. Collectively, poor glycemic control and increased NLR levels may be used as possible predictors for poorer cognitive performance outcomes in patients with MetS. Keywords: Metabolic syndrome; Mild cognitive impairment; Neutrophil-lymphocyte ratio; Prognostic marker; Montreal cognitive assessment
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".