Insulin Resistance as an Inflammatory Marker for Ischemic Stroke Severity Among Non-Diabetics: A Prospective Study
Bibliographic record
Abstract
Background: Insulin resistance (IR) is one of the inflammatory markers that is receiving increasing attention as a possible early marker of increased risk of cerebrovascular disease. The purpose of the study was to determine the prevalence of IR in non-diabetic ischemic stroke patients and its correlation with the stroke severity. Methods: It was a prospective study conducted at Narayana Medical College, Nellore from January 2013 to June 2014. After the approval from the institutional ethical committee, patients who presented with the history of stroke, who were non-diabetics and aged > 18 years were included with informed consent. Ischemic stroke was diagnosed with clinical findings and by neuroimaging. Severity of stroke was assessed by National Institutes of Health Stroke Scale (NIHSS) score. Homeostasis model assessment (HOMA) was used to estimate IR and the levels were studied in relation to the stroke severity. Results: A total of 162 non-diabetic ischemic stroke patients were enrolled in the study. Hyperinsulinemia, i.e., serum insulin > 9 µU/mL, was observed in 41 (25.30%) patients. IR with HOMA-IR ≥ 2.5 was noted in 31 (19.13%) patients. NIHSS score in severity (group III) was strongly associated with serum insulin > 9 µU/mL (54.5%) (P = 0.002) and HOMA-IR ≥ 2.5 (54.5%) (P < 0.0001). Conclusions: IR may be a novel therapeutic target for stroke prevention. High HOMA-IR was associated with high NIHSS score and it is a useful index for prediction of ischemic stroke in non-diabetics. J Neurol Res. 2016;6(2-3):46-50 doi: http://dx.doi.org/10.14740/jnr381w
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".