Genetic and Inflammatory Predictors of Asthma: The Role of VDR and CaSR Genotypes in Risk Assessment and Management
Bibliographic record
Abstract
Objective: This study investigates the predictive value and risk factors associated with different vitamin D receptor (VDR) and calcium-sensing receptor (CaSR) genotypes in asthma. Methods: From December 2020 to February 2023, we studied 86 asthma patients and 70 healthy controls, analyzing VDR single nucleotide polymorphisms (SNPs) (rs1544410 and rs731236) and CaSR SNPs (rs1801726 and rs1042636) using DNA extracted from whole blood. We compared genotype distributions, demographic data, lung function parameters, vitamin D levels, and immune and inflammatory markers between the two groups. Results: The study group exhibited higher frequencies of VDR rs1544410 genotype TT and allele T, and CaSR rs1801726 genotype GG and allele G, but lower frequencies of CaSR rs1042636 genotype GG and allele G compared with controls ( p < 0.05). Additionally, patients in the study group showed elevated rates of family history/genetic predisposition, allergy history, smoking, and higher levels of neutrophils, interleukin (IL)-4, IL-6, IL-8, IL-10, IL-17, and interferon-gamma (IFN-γ). They also demonstrated lower levels of FEV1, FVC, PEFR, and 25-(OH)-D (P < 0.05). Logistic regression identified several factors, including specific genotypes, family history, and biomarker levels, as significant asthma risk factors. Conclusion: VDR rs1544410 and CaSR rs1801726 and rs1042636 may serve as potential diagnostic markers for asthma, highlighting their role in assessing genetic predisposition and disease severity.
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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 | 0.001 | 0.000 |
| 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".