We evaluated associations of tibial lead levels with polymorphisms in the vitamin D receptor (VDR) in 504 former organolea manauri workers with past exposure to lead. In this crosssectional study, we measured tibia! lead by 109Cd K-shell X-ray fluorescence. Tibial lead was evaluated in subjects with different VDR genotypes defined using the BsmI restriction enzyme,
Evidence suggests that lead and selected genes known to modify the toxicokinetics of lead--namely, those for the vitamin D receptor (VDR) and delta-aminolevulinic acid dehydratase (ALAD)--may independently influence blood pressure and hypertension risk. We report the relations among ALAD and VDR genotypes, three lead dose measures, and blood pressure and hypertension status in 798 Korean lead workers and 135 controls without occupational exposure to lead. Lead dose was assessed by blood lead, tibia lead measured by X-ray fluorescence, and dimercaptosuccinic acid (DMSA)-chelatable lead. Among lead workers, 9.9% (n = 79) were heterozygous for the ALAD(2) allele, and there were no ALAD(2) homozygotes; 11.2% (n = 89) had at least one copy of the VDR B allele, and 0.5% (n = 4) had the BB genotype. In linear regression models to control for covariates, VDR genotype (BB and Bb vs. bb), blood lead, tibia lead, and DMSA-chelatable lead were all positive predictors of systolic blood pressure. On average, lead workers with the VDR B allele, mainly heterozygotes, had systolic blood pressures that were 2.7-3.7 mm Hg higher than did workers with the bb genotype. VDR genotype was also associated with diastolic blood pressure; on average, lead workers with the VDR B allele had diastolic blood pressures that were 1.9-2.5 mm Hg higher than did lead workers with the VDR bb genotype (p = 0.04). VDR genotype modified the relation of age with systolic blood pressure; compared to lead workers with the VDR bb genotype, workers with the VDR B allele had larger elevations in blood pressure with increasing age. Lead workers with the VDR B allele also had a higher prevalence of hypertension compared to lead workers with the bb genotype [adjusted odds ratio (95% confidence interval) = 2.1 (1.0, 4.4), p = 0.05]. None of the lead biomarkers was associated with diastolic blood pressure, and tibia lead was the only lead dose measure that was a significant predictor of hypertension status. In contrast to VDR, ALAD genotype was not associated with the blood pressure measures and did not modify associations of the lead dose measures with any of the blood pressure measures. To our knowledge, these are the first data to suggest that the common genetic polymorphism in the VDR is associated with blood pressure and hypertension risk. We speculate that the BsmI polymorphism may be in linkage disequilibrium with another functional variant at the VDR locus or with a nearby gene.
In application domains such as healthcare, we want accurate predictive models that are also causally interpretable. In pursuit of such models, we propose a causal regularizer to steer predictive models towards causally-interpretable solutions and theoretically study its properties. In a large-scale analysis of Electronic Health Records (EHR), our causally-regularized model outperforms its L1-regularized counterpart in causal accuracy and is competitive in predictive performance. We perform non-linear causality analysis by causally regularizing a special neural network architecture. We also show that the proposed causal regularizer can be used together with neural representation learning algorithms to yield up to 20% improvement over multilayer perceptron in detecting multivariate causation, a situation common in healthcare, where many causal factors should occur simultaneously to have an effect on the target variable.
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