Publications

PURPOSE: The current study examined possible links between threat perception, anxiety, conscientiousness and parental noncompliance with preoperative fasting instructions for their children.

METHODS: 100 mothers of children about to undergo an ambulatory elective surgery were divided to two equal groups based on compliance/noncompliance with pre surgery fasting requirements. Logistic regression analysis was preformed to predict compliance/noncompliance. In addition a logistic model estimating the effect of anxiety and conscientiousness levels, and their interaction, on the probability of fasting was performed.

RESULTS: Mothers who did not comply with fasting requirements perceived the procedure as more threatening, were more anxious and had lower conscientiousness levels. Additionally, mother's anxiety prior to surgery mediated the association between mothers' threat perception and compliance. Finally, conscientiousness moderated the anxiety and compliance association so that high conscientiousness levels reduced the effect of anxiety, elevating the likelihood of anxious mothers to comply with fasting guidelines.

CONCLUSIONS: Based on these findings we recommend medical staff to make significant efforts to identify highly anxious parents as early as possible during the preoperative process. Innovative assessment and intervention tools should be developed in order to conduct a smooth medical operation and reduce the chance of unnecessary and costly surgery cancelation.

We consider variable selection for high-dimensional multivariate regression using penalized likelihoods when the number of outcomes and the number of covariates might be large. To account for within-subject correlation, we consider variable selection when a working precision matrix is used and when the precision matrix is jointly estimated using a two-stage procedure. We show that under suitable regularity conditions, penalized regression coefficient estimators are consistent for model selection for an arbitrary working precision matrix, and have the oracle properties and are efficient when the true precision matrix is used or when it is consistently estimated using sparse regression. We develop an efficient computation procedure for estimating regression coefficients using the coordinate descent algorithm in conjunction with sparse precision matrix estimation using the graphical LASSO (GLASSO) algorithm. We develop the Bayesian Information Criterion (BIC) for estimating the tuning parameter and show that BIC is consistent for model selection. We evaluate finite sample performance for the proposed method using simulation studies and illustrate its application using the type II diabetes gene expression pathway data.

OBJECTIVE: Successful pulmonary wedge resection for early-stage non-small cell lung cancer requires a pathologically confirmed negative margin. To date, however, no clear evidence is available regarding whether an optimal margin distance, defined as the distance from the primary tumor to the closest resection margin, exists. Toward addressing this gap, we investigated the relationship between the margin distance and local recurrence risk.

METHODS: We reviewed all adult patients who had undergone wedge resection for small (≤2 cm) non-small cell lung cancer from January 2001 to August 2011, with follow-up through to December 31, 2011. The exclusion criteria included other active noncutaneous malignancies, bronchoalveolar carcinomas, lymph node or distant metastases at diagnosis, large cell cancer, adenosquamous cancer, multiple, multifocal, and/or metastatic disease, and previous chemotherapy or radiotherapy. Using Cox regression analysis, we examined the relationship between the margin distance and interval to local recurrence, adjusting for chronic obstructive pulmonary disease, forced expiratory volume in 1 second, smoking, diabetes, tumor size, tumor location, surgeon, open versus video-assisted thoracoscopic surgery, and whether the lymph nodes were sampled.

RESULTS: Of 557 consecutive adult patients, 479 met our inclusion criteria. The overall, unadjusted 1- and 2-year local recurrences rate was 5.7% and 11.0%, respectively. From the adjusted analyses, an increased margin distance was significantly associated with a lower risk of local recurrence (P = .033). Patients with a 10-mm margin distance had a 45% lower local recurrence risk than those with a 5-mm distance (hazard ratio, 0.55; 95% confidence interval, 0.35-0.86). Beyond 15 mm, no evidence of additional benefit was associated with an increased margin distance.

CONCLUSIONS: In wedge resection for small non-small cell lung cancer, increasing the margin distance ≤15 mm significantly decreased the local recurrence risk, with no evidence of additional benefit beyond 15 mm.

BACKGROUND: Exposure to air particulate matter is known to elevate blood biomarkers of inflammation and to increase cardiopulmonary morbidity and mortality. Major components of airborne particulate matter typically include black carbon from traffic and sulfates from coal-burning power plants. DNA methylation is thought to be sensitive to these environmental toxins and possibly mediate environmental effects on clinical outcomes via regulation of gene networks. The underlying mechanisms may include epigenetic modulation of major inflammatory pathways, yet the details remain unclear.

METHODS: We sought to elucidate how short-term exposure to air pollution components, singly and/or in combination, alter blood DNA methylation in certain inflammation-associated gene networks, MAPK and NF-κB, which may transmit the environmental signal(s) and influence the inflammatory pathway in vivo. To this end, we utilized a custom-integrated workflow-molecular processing, pollution surveillance, biostatical analysis, and bioinformatic visualization-to map novel human (epi)gene pathway-environment interactions.

RESULTS: Specifically, out of 84 MAPK pathway genes considered, we identified 11 whose DNA methylation status was highly associated with black carbon exposure, after adjusting for potential confounders-age, sulfate exposure, smoking, blood cell composition, and blood pressure. Moreover, after adjusting for these confounders, multi-pollutant analysis of synergistic DNA methylations significantly associated with sulfate and BC exposures yielded 14 MAPK genes. No associations were found with the NF-κB pathway.

CONCLUSION: Exposure to short-term air pollution components thus resulted in quantifiable epigenetic changes in the promoter areas of MAPK pathway genes. Bioinformatic mapping of single- vs. multi-exposure-associated epigenetic changes suggests that these alterations might affect biological pathways in nuanced ways that are not simply additive or fully predictable via individual-level exposure assessments.

BACKGROUND: Asthma exacerbation and other respiratory symptoms are associated with exposure to air pollution. Since environment affects gene methylation, it is hypothesized that asthmatic responses to pollution are mediated through methylation.

MATERIALS & METHODS: We study the possibility that airborne particulate matter affects gene methylation in the asthma pathway. We measured methylation array data in clinic visits of 141 subjects from the Normative Aging Study. Black carbon and sulfate measures from a central monitoring site were recorded and 30-day averages were calculated for each clinic visit. Gene-specific methylation scores were calculated for the genes in the asthma pathway, and the association between the methylation in the asthma pathway and the pollution measures was analyzed using sparse Canonical Correlation Analysis.

RESULTS: The analysis found that exposures to black carbon and sulfate were significantly associated with the methylation pattern in the asthma pathway (p-values 0.05 and 0.02, accordingly). Specific genes that contributed to this association were identified.

CONCLUSION: These results suggest that the effect of air pollution on asthmatic and respiratory responses may be mediated through gene methylation.

MOTIVATION: DNA methylation is a heritable modifiable chemical process that affects gene transcription and is associated with other molecular markers (e.g. gene expression) and biomarkers (e.g. cancer or other diseases). Current technology measures methylation in hundred of thousands, or millions of CpG sites throughout the genome. It is evident that neighboring CpG sites are often highly correlated with each other, and current literature suggests that clusters of adjacent CpG sites are co-regulated.

RESULTS: We develop the Adjacent Site Clustering (A-clustering) algorithm to detect sets of neighboring CpG sites that are correlated with each other. To detect methylation regions associated with exposure, we propose an analysis pipeline for high-dimensional methylation data in which CpG sites within regions identified by A-clustering are modeled as multivariate responses to environmental exposure using a generalized estimating equation approach that assumes exposure equally affects all sites in the cluster. We develop a correlation preserving simulation scheme, and study the proposed methodology via simulations. We study the clusters detected by the algorithm on high dimensional dataset of peripheral blood methylation of pesticide applicators.

AVAILABILITY: We provide the R package Aclust that efficiently implements the A-clustering and the analysis pipeline, and produces analysis reports. The package is found on http://www.hsph.harvard.edu/tamar-sofer/packages/

CONTACT: tsofer@hsph.harvard.edu

Studying the association between a gene set (e.g., pathway) and exposures using multivariate regression methods is of increasing importance in genomic studies. Such an analysis is often more powerful and interpretable than individual gene analysis. Since many genes in a gene set are likely not affected by exposures, one is often interested in identifying a subset of genes in the gene set that are affected by exposures. This allows for better understanding of the underlying biological mechanism and for pursuing further biological investigation of these genes. The selected subset of "signal" genes also provides an attractive vehicle for a more powerful test for the association between the gene set and exposures. We propose two computationally simple Canonical Correlation Analysis (CCA) based variable selection methods: Sparse Outcome Selection (SOS) CCA and step CCA, to jointly select a subset of genes in a gene set that are associated with exposures. Several model selection criteria, such as BIC and the new Correlation Information Criterion (CIC), are proposed and compared. We also develop a global test procedure for testing the exposure effects on the whole gene set, accounting for gene selection. Through simulation studies, we show that the proposed methods improve upon an existing method when the genes are correlated and are more computationally efficient. We apply the proposed methods to the analysis of the Normative Aging DNA methylation Study to examine the effects of airborne particular matter exposures on DNA methylations in a genetic pathway.