Serum alkaline phosphatase level is correlated with the incidence of cerebral small vessel disease
Bibliographic record
Abstract
BACKGROUND: Vascular calcification is one of the mechanisms underlying the pathogenesis of cerebral small vessel disease (CSVD). Whether higher levels of serum alkaline phosphatase (ALP), a predictor of vascular calcification, are independently associated with CSVD remains inconclusive. PURPOSE: The present study explored the link between levels of circulating ALP and CSVD in a Chinese population. METHODS: A total of 568 participants were recruited from the healthcare center at the affiliated Zhongshan Hospital of Dalian University and the Fifth People Hospital of Dalian between January 2010 and December 2016. The subjects were divided into three groups based on the infarct and severity of white matter hyper-intensities (WMH) as categorized by brain magnetic resonance imaging (MRI) analysis and Fazekas rating scales: no/mild cerebral WMH (nm-WMH); moderate-to-severe WMH (MS-WMH); and silent lacunar infarct (SLI). The subjects were also divided into three tertiles based on circulating levels of ALP: ≤64, 65-105 and ≥106 (IU/L). Information regarding the risk factors, such as coronary artery disease, diabetes mellitus, hypertension and serum ALP level, C-reactive protein, homocysteine (HCY), and other laboratory results, were collected. The associations of ALP with WMH and SLI were evaluated using logistic regression analysis. RESULTS: After adjustment for vascular risk factors, subjects with MS-WMH and SLI were more likely to have ALP levels ≥106 IU/L than ≤64 IU/L. The mean circulating level of ALP was substantially increased in patients with MS-WMH or SLI compared with patients with nm-WMH. The multivariate model revealed that this significant difference remained when MS-WMH or SLI was added to the model, after adjustment for confounding factors. CONCLUSION: The circulating level of ALP was positively correlated with a high risk of silent lacunar infarct and white matter hyperintensity; important indicators of small vessel disease.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".