Bibliographic record
Abstract
Caffeine intake has been associated with both an increased and decreased risk of various health conditions. However, the physiological pathways affected remain unclear. CYP1A2 is the major enzyme that metabolizes caffeine and a single nucleotide polymorphism ( rs762551 ) affects the rate of caffeine metabolism. The objective of this study was to determine whether caffeine intake is associated with proteins in the plasma proteome, and whether CYP1A2 genotype modifies any of the associations observed. Subjects (n=1095) aged 20–29 years completed a 196‐ item semi‐quantitative food frequency questionnaire and provided a fasting blood sample from which DNA and plasma were obtained for genotyping and proteomic analysis with 54 plasma proteins. Subjects were categorized into three groups according to habitual caffeine intake (<100mg/d, 100–200mg/d, and >;200mg/d) and later stratified by CYP1A2 genotype. Among individuals with A/C or C/C genotypes (slow metabolizers), plasma concentration of complement component 3 (p=0.03) and gelsolin isoform 1 (p=0.005), were significantly lower in the highest category of caffeine intake. No differences in protein concentration were observed for A/A individuals (fast metabolizers). These results suggest certain plasma proteins are affected by caffeine intake, but only among slow metabolizers. Grant Funding Source : Canadian Institutes of Health Research
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".