Predicting nicotine metabolism across ancestries using genotypes

BACKGROUND

There is a need to match characteristics of tobacco users with cessation treatments and risks of tobacco attributable diseases such as lung cancer. The rate in which the body metabolizes nicotine has proven an important predictor of these outcomes. Nicotine metabolism is primarily catalyzed by the enzyme cytochrone P450 (CYP2A6) and CYP2A6 activity can be measured as the ratio of two nicotine metabolites: trans-3'-hydroxycotinine to cotinine (NMR). Measurements of these metabolites are only possible in current tobacco users and vary by biofluid source, timing of collection, and protocols; unfortunately, this has limited their use in clinical practice. The NMR depends highly on genetic variation near CYP2A6 on chromosome 19 as well as ancestry, environmental, and other genetic factors. Thus, we aimed to develop prediction models of nicotine metabolism using genotypes and basic individual characteristics (age, gender, height, and weight).

RESULTS

We identified four multiethnic studies with nicotine metabolites and DNA samples. We constructed a 263 marker panel from filtering genome-wide association scans of the NMR in each study. We then applied seven machine learning techniques to train models of nicotine metabolism on the largest and most ancestrally diverse dataset (N=2239). The models were then validated using the other three studies (total N=1415). Using cross-validation, we found the correlations between the observed and predicted NMR ranged from 0.69 to 0.97 depending on the model. When predictions were averaged in an ensemble model, the correlation was 0.81. The ensemble model generalizes well in the validation studies across ancestries, despite differences in the measurements of NMR between studies, with correlations of: 0.52 for African ancestry, 0.61 for Asian ancestry, and 0.46 for European ancestry. The most influential predictors of NMR identified in more than two models were rs56113850, rs11878604, and 21 other genetic variants near CYP2A6 as well as age and ancestry.

CONCLUSIONS

We have developed an ensemble of seven models for predicting the NMR across ancestries from genotypes and age, gender and BMI. These models were validated using three datasets and associate with nicotine dosages. The knowledge of how an individual metabolizes nicotine could be used to help select the optimal path to reducing or quitting tobacco use, as well as, evaluating risks of tobacco use.

Multiethnic Prediction of Nicotine Biomarkers and Association With Nicotine Dependence

Introduction

The nicotine metabolite ratio and nicotine equivalents are measures of metabolism rate and intake. Genome-wide prediction of these nicotine biomarkers in multiethnic samples will enable tobacco-related biomarker, behavioral, and exposure research in studies without measured biomarkers.

Aims and Methods

We screened genetic variants genome-wide using marginal scans and applied statistical learning algorithms on top-ranked genetic variants, age, ethnicity and sex, and, in additional modeling, cigarettes per day (CPD), (in additional modeling) to build prediction models for the urinary nicotine metabolite ratio (uNMR) and creatinine-standardized total nicotine equivalents (TNE) in 2239 current cigarette smokers in five ethnic groups. We predicted these nicotine biomarkers using model ensembles and evaluated external validity using dependence measures in 1864 treatment-seeking smokers in two ethnic groups.

Results

The genomic regions with the most selected and included variants for measured biomarkers were chr19q13.2 (uNMR, without and with CPD) and chr15q25.1 and chr10q25.3 (TNE, without and with CPD). We observed ensemble correlations between measured and predicted biomarker values for the uNMR and TNE without (with CPD) of 0.67 (0.68) and 0.65 (0.72) in the training sample. We observed inconsistency in penalized regression models of TNE (with CPD) with fewer variants at chr15q25.1 selected and included. In treatment-seeking smokers, predicted uNMR (without CPD) was significantly associated with CPD and predicted TNE (without CPD) with CPD, time-to-first-cigarette, and Fagerström total score.

Conclusions

Nicotine metabolites, genome-wide data, and statistical learning approaches developed novel robust predictive models for urinary nicotine biomarkers in multiple ethnic groups. Predicted biomarker associations helped define genetically influenced components of nicotine dependence.

Implications

We demonstrate development of robust models and multiethnic prediction of the uNMR and TNE using statistical and machine learning approaches. Variants included in trained models for nicotine biomarkers include top-ranked variants in multiethnic genome-wide studies of smoking behavior, nicotine metabolites, and related disease. Association of the two predicted nicotine biomarkers with Fagerström Test for Nicotine Dependence items supports models of nicotine biomarkers as predictors of physical dependence and nicotine exposure. Predicted nicotine biomarkers may facilitate tobacco-related disease and treatment research in samples with genomic data and limited nicotine metabolite or tobacco exposure data.

Biosignature Discovery for Substance Use Disorders Using Statistical Learning

There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types. Focusing on penalized regression and Bayesian approaches, we address how to leverage evidence from existing studies and knowledge bases, using nicotine metabolism as an example. We posit that big data and machine learning approaches will considerably advance SUD biomarker discovery. However, translation to clinical practice, will require integrated scientific efforts.

Lead extraction in the contemporary setting: the LExICon study: an observational retrospective study of consecutive laser lead extractions

OBJECTIVES

This study sought to examine the safety and efficacy of laser-assisted lead extraction and the indications, outcomes, and risk factors in a large series of consecutive patients.

BACKGROUND

The need for lead extraction has been increasing in direct relationship to the increased numbers of cardiovascular implantable electronic devices.

METHODS

Consecutive patients undergoing transvenous laser-assisted lead extraction at 13 centers were included.

RESULTS

Between January 2004 and December 2007, 1,449 consecutive patients underwent laser-assisted lead extraction of 2,405 leads (20 to 270 procedures/site). Median implantation duration was 82.1 months (0.4 to 356.8 months). Leads were completely removed 96.5% of the time, with a 97.7% clinical success rate whereby clinical goals associated with the indication for lead removal were achieved. Failure to achieve clinical success was associated with body mass index <25 kg/m(2) and low extraction volume centers. Procedural failure was higher in leads implanted for >10 years and when performed in low volume centers. Major adverse events in 20 patients were directly related to the procedure (1.4%) including 4 deaths (0.28%). Major adverse effects were associated with patients with a body mass index <25 kg/m(2). Overall all-cause in-hospital mortality was 1.86%; 4.3% when associated with endocarditis, 7.9% when associated with endocarditis and diabetes, and 12.4% when associated with endocarditis and creatinine > or =2.0. Indicators of all-cause in-hospital mortality were pocket infections, device-related endocarditis, diabetes, and creatinine > or =2.0.

CONCLUSIONS

Lead extraction employing laser sheaths is highly successful with a low procedural complication rate. Total mortality is substantially increased with pocket infections or device-related endocarditis, particularly in the setting of diabetes, renal insufficiency, or body mass index <25 kg/m(2). Centers with smaller case volumes tended to have a lower rate of successful extraction.

Serum 25-hydroxyvitamin D, dietary calcium intake, and distal colorectal adenoma risk

Vitamin D has recently emerged as a potentially protective agent against colorectal neoplasia. We assessed the associations between dietary vitamin D, plasma 25-hydroxyvitamin D [25(OH)D], dietary calcium, and colorectal adenomas in a large screening sigmoidoscopy-based case-control study in Southern California. Because conversion of serum 25(OH)D to serum 1,25-vitamin D is highly regulated by serum calcium, we also assessed modification of the 25(OH)D-adenoma association by calcium intake. Cases were 473 subjects with a primary adenoma, and controls were 507 subjects who had no adenomas at sigmoidoscopy and no history of adenomas. Compared with those in the lowest quartile of intake, those in the highest quartile of dietary vitamin D had an adjusted odds ratio (OR) of 0.83 [95% confidence interval (CI) = 0.49-1.41] and those in the highest quartile of dietary calcium had an OR of 0.82 (95% CI = 0.49-1.25). There was a suggestion that plasma 25(OH)D may be protective in this population (OR for highest vs. lowest quartile = 0.74, 95% CI = 0.51-1.09). A significant protective effect of 25(OH)D was clearly evident only in those with calcium intakes below (OR = 0.40 for highest vs. lowest quartile, 95% CI = 0.22-0.71, p for trend = 0.005) and above (OR = 1.17, 95% CI = 0.69-1.99, p for trend = 0.94) the median calcium intake.

The methylenetetrahydrofolate reductase 677C-->T polymorphism and distal colorectal adenoma risk

A common polymorphism in the methylenetetrahydrofolate reductase (MTHFR) gene, where a cytosine at nucleotide 677 is replaced by a thymine (677C-->T), is associated with enzyme thermolability and a reduction in the conversion of 5,10-methyltetrahydrofolate (5,10-MTHF) into 5-methyltetrahydrofolate. We assessed the association between homozygosity for the MTHFR 677CT genotype (TT) and colorectal adenoma risk in a large sigmoidoscopy-based case-control study of members of a prepaid health plan in Los Angeles. MTHFR genotype was determined for 471 cases and 510 age-, sex-, clinic-, and sigmoidoscopy-date-matched controls. Information on RBC and plasma folate levels were analyzed for 331 cases and 350 controls. When compared with the presence of at least one wild-type allele (CT/CC), the odds ratio (OR) for the TT genotype was 1.19 [95% confidence interval (CI), 0.77-1.76] after adjusting for race and the matching factors. Compared with those in the lowest quartiles of RBC and plasma folate and a wild-type allele, adenoma risk was increased for TT homozygotes in the lowest folate quartiles (genotype: OR, 2.04 and 95% CI, 0.6-7.0; OR, 1.84 and 95% CI, 0.6-7.0 for RBCs and plasma folate, respectively) and decreased in TT homozygotes in the highest quartiles (genotype: OR, 0.82 and 95% CI, 0.32-2.10; OR, 0.65 and 95% CI, 0.22-1.95, respectively). There was also a significant interaction between TT genotype and the increased adenoma risk associated with alcohol. These data are consistent with an interaction between MTHFR genotype and folate availability.