Evaluation of Age-Gene Correlation and the Association with Hypertriglyceridemia Using Adiponectin Receptor Single Nucleotide Polymorphism: A Potential Genetic Screening to Lower Risk of Vascular Disease in Young Asian Males
Bibliographic record
Abstract
Purpose: This study was to investigate whether there is an age dependent effect on the association between ADIPPOR1 SNP and hypertriglyceridemia for each gender.Materials and Methods: 116 individuals aged 20 and above who claimed to be healthy were enrolled and grouped into male and female populations. Blood samples were taken to determine hypertriglyceridemia and genomic variants. Sample t-tests were performed for basic comparison. To ascertain the contribution of genetic variants and age to lipid metabolism, a multivariate logistic regression analysis was conducted to identify the correlates for hypertriglyceridemia adjusting for life styles.Results: For males, individuals with hypertriglyceridemia tended to be younger (p-value=0.02), less stressed (0.05), and have a higher proportion of ADIPOR1 minor allele carriers (0.03). However, no significant differences were found in age, stress, diet, and genetic variances in females. In regression analysis, males showed age-gene correlation with 1.5 times higher detection of hypertriglyceridemia risk when both factors were considered. In contrast, females showed no correlation between age-gene. In addition, age was positively associated with hypertriglyceridemia in females while males showed an inverse association.Conclusion: Our findings presented data that suggests age may be a contributing factor to the association between ADIPOR1 and hypertriglyceridemia in males while age showed a significant inverse association with hypertriglyceridemia. Thus, age-gene correlation may be implied during primary practice to encourage lifestyle adjustments by screening for ADIPOR1 SNP minor allele carriers to prevent cerebrovascular disease in males.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".