Is leptin receptor gene (Gln223Arg) polymorphism associated with disease susceptibility and severity in patients of primary knee osteoarthritis?
Bibliographic record
Abstract
A significant role of Leptin receptor (LEPR) is documented in inflammation, body weight homeostasis and maintenance of cartilage. This study was conducted to detect the existence of genetic association between Knee osteoarthritis (KOA) susceptibility and severity; and LEPR (Gln223Arg) single nucleotide polymorphism (SNP). 73 primary KOA patients and 73 matched healthy controls were studied. Kellgren Laurence (K/L) radiographic grading system, Western Ontario and McMaster Universities Arthritis Index (WOMAC) score and Visual Analogue Scale (VAS) were used to assess the severity of KOA. LEPR Gln223Arg SNP (rs1137101) was genotyped in KOA patients and controls using polymerase chain reaction-restriction fragment length polymorphism (PCR –RFLP) technique and verified by direct DNA sequencing. In the current study, a significant genetic association was found between KOA patients carrying the AA genotype of LEPR and the extent of radiological severity (p < 0.044). In addition, a significant difference was detected within the patients between Body Mass Index (BMI) and the SNP. Patients carrying the wild type (GG) genotype showed lower body mass index (BMI) in comparison to patients carrying the heterozygous (AG) genotype and the mutant (AA) genotype (p < 0.032). However, no direct genetic association was detected between the SNP and KOA. Leptin receptor gene (Gln223Arg) SNP might be associated with severity of KOA. There is a significant genetic association between the SNP and BMI hence, LEPR SNP might be indirectly associated with the incidence of KOA. Furthermore, the SNP is not directly associated with KOA susceptibility in the Egyptian population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".