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.

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.

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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.

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.