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<i>CYP1A2</i> Genetic Variation, Coffee Intake, and Kidney Dysfunction

2023· article· en· W4318070654 on OpenAlexaff
S. Sara Mahdavi, Paolo Palatini, Ahmed El‐Sohemy

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineRenal functionAlbuminuriaKidney diseaseDiabetes mellitusInternal medicineProspective cohort studyBlood pressureCYP1A2PhysiologyEndocrinology

Abstract

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Importance: Caffeine is detoxified by cytochrome P450 1A2 (CYP1A2), and genetic variation in CYP1A2 impacts the rate of caffeine clearance. Factors that may modify the association between coffee intake and kidney disease remain unclear. Objective: To assess whether CYP1A2 genotype modifies the association between coffee intake and kidney dysfunction. Design, Setting, and Participants: The Hypertension and Ambulatory Recording Venetia Study (HARVEST) was a prospective cohort study of individuals with stage 1 hypertension in Italy; HARVEST began on April 1, 1990, and follow-up is ongoing. The current study used data from April 1, 1990, to June 30, 2006, with follow-up of approximately 10 years. Blood pressure and biochemical data were collected monthly during the first 3 months, then every 6 months thereafter. Data were analyzed from January 2019 to March 2019. Participants were screened and recruited from general practice clinics. The present study included 1180 untreated participants aged 18 to 45 years with stage 1 hypertension; those with nephropathy, diabetes, urinary tract infection, and cardiovascular disease were excluded. Exposures: Coffee intake and CYP1A2 genotype rs762551 were exposures analyzed over a median follow-up of 7.5 (IQR, 3.1-10.9) years. Main Outcomes and Measures: Albuminuria (defined as an albumin level of ≥30 mg/24 h) and hyperfiltration (defined as an estimated glomerular filtration rate of ≥150 mL/min/1.73 m2) were the primary outcomes as indicators of kidney dysfunction. Results: Among 1180 participants, genotyping, lifestyle questionnaires, and urine analysis data were obtained from 604 individuals (438 [72.5%] male) with a mean (SD) age of 33.3 (8.5) years and a mean (SD) body mass index (calculated as weight in kilograms divided by height in meters squared) of 25.4 (3.4). A total of 158 participants (26.2%) consumed less than 1 cup of coffee per day, 379 (62.7%) consumed 1 to 3 cups per day, and 67 (11.1%) consumed more than 3 cups per day. Genotype frequencies for rs762551 (260 participants [43.1%] with genotype AA, 247 participants [40.8%] with genotype AC, and 97 participants [16.1%] with genotype CC) did not differ between coffee intake categories. The level of risk of developing albuminuria, hyperfiltration, and hypertension, assessed by Cox regression and survival analyses, was not associated with coffee intake in the entire group or among fast metabolizers. The risks of albuminuria (adjusted hazard ratio [aHR], 2.74; 95% CI, 1.63-4.62; P < .001), hyperfiltration (aHR, 2.11; 95% CI, 1.17-3.80; P = .01), and hypertension (aHR, 2.81; 95% CI, 1.51-5.23; P = .001) increased significantly among slow metabolizers who consumed more than 3 cups per day. Conclusions and Relevance: In this study, the risks of albuminuria, hyperfiltration, and hypertension increased with heavy coffee intake only among those with the AC and CC genotypes of CYP1A2 at rs762551 associated with slow caffeine metabolism, suggesting that caffeine may play a role in the development of kidney disease in susceptible individuals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.034
GPT teacher head0.308
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2023
Admission routes1
Has abstractyes

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