Smoking Is Associated with Pain in All Body Regions, with Greatest Influence on Spinal Pain.

OBJECTIVE: Examine the interrelationship between smoking and pain in the US population. DESIGN: A cross-sectional population-based study. SETTING: Nationwide survey. METHODS: Comprehensive pain reports categorically defined as head, spine, trunk, and limb pain; smoking history; demographics; medical history from a total of 2,307 subjects from the 2003-2004 National Health and Nutrition Examination Survey obtained from the Centers for Disease Control were analyzed. Unpaired t tests were used to analyze independent continuous variables, and chi-square tests were used to analyze categorical variables between smoker and nonsmoker groups. Weighted multivariate logistic regression analyses determined the association of current smoking with the presence of pain in various body regions. RESULTS: Smoking is most strongly associated with spine pain (odds ratio [OR] = 2.89, 95% confidence interval [CI] = 2.21-3.77), followed by headache (OR = 2.47, 95% CI = 1.73-3.53), trunk pain (OR = 2.17, 95% CI = 1.45-2.74), and limb pain (OR = 1.99, 95% CI = 1.45-2.73). CONCLUSIONS: Current smoking is associated with pain in every region of the body. This association is strongest for spine and head pain. Given that pain is a strong motivator and that current smoking was associated with pain in all body regions, we recommend that these results be used to further raise public awareness about the potential harms of smoking.

Guidelines for Composing and Assessing a Paper on the Treatment of Pain: A Practical Application of Evidence-Based Medicine Principles to the Mint Randomized Clinical Trials.

Objective: To perform a thorough assessment of the recently published Mint Trials in order to illustrate how to read and analyze a study critically, according to principles of evidence-based medicine. Design: Narrative review. Method: We have applied the recently published guidelines for composing and assessing studies on the treatment of pain to a recently published article describing a large study that claimed its "findings do not support the use of radiofrequency denervation to treat chronic low back pain." These guidelines describe the critical components of a high-quality manuscript that allows communication of all relevant information from authors to readers. Results: Application of evidence-based medicine principles to the publication describing the Mint Trials reveals significant issues with the methodology and conclusions drawn by the authors. A thorough assessment demonstrates issues with inclusion/exclusion criteria, diagnostic block protocols, radiofrequency neurotomy technique, co-interventions, outcome measurement, power analysis, study sample characteristics, data analysis, and loss to follow-up. A failure to definitively establish a diagnosis, combined with use of an inadequate technique for radiofrequency neurotomy and numerous other methodological flaws, leaves the reader unable to draw meaningful conclusions from the study data. Conclusions: Critical analysis, rooted in principles of evidence-based medicine, must be employed by writers and readers alike in order to encourage transparency and ensure that appropriate conclusions are drawn from study data.

Social Disruption Mediates the Relationship Between Perceived Injustice and Anger in Chronic Pain: a Collaborative Health Outcomes Information Registry Study.

BACKGROUND: Perceptions of pain as unfair are a significant risk factor for poorer physical and psychological outcomes in acute injury and chronic pain. Chief among the negative emotions associated with perceived injustice is anger, arising through frustration of personal goals and unmet expectations regarding others' behavior. However, despite a theoretical connection with anger, the social mediators of perceived injustice have not been demonstrated in chronic pain. PURPOSE: The current study examined two socially based variables and a broader measure of pain interference as mediators of the relationships between perceived injustice and both anger and pain intensity in a sample of 302 patients in a tertiary care pain clinic setting. METHODS: Data from the Collaborative Health Outcomes Information Registry (CHOIR) were analyzed using cross-sectional path modeling analyses to examine social isolation, satisfaction with social roles and activities, and pain-related interference as potential mediators of the relationships between perceived injustice and both anger and pain intensity. RESULTS: When modeled simultaneously, ratings of social isolation mediated the relationship between perceived injustice and anger, while pain-related interference and social satisfaction did not. Neither social variable was found to mediate the relationship between perceived injustice and pain intensity, however. CONCLUSIONS: The current findings highlight the strongly interpersonal nature of perceived injustice and anger in chronic pain, though these effects do not appear to extend to the intensity of pain itself. Nevertheless, the results highlight the need for interventions that ameliorate both maladaptive cognitive appraisal of pain and pain-related disruptions in social relationships.

Physical and psychological correlates of fatigue and physical function: a Collaborative Health Outcomes Information Registry (CHOIR) study.

UNLABELLED: Fatigue is a multidimensional construct that has significant implications for physical function in chronic noncancer pain populations but remains relatively understudied. The current study characterized the independent contributions of self-reported ratings of pain intensity, sleep disturbance, depression, and fatigue to ratings of physical function and pain-related interference in a diverse sample of treatment-seeking individuals with chronic pain. These relationships were examined as a path modeling analysis of self-report scores obtained from 2,487 individuals with chronic pain from a tertiary care outpatient pain clinic. Our analyses revealed unique relationships of pain intensity, sleep disturbance, and depression with self-reported fatigue. Further, fatigue scores accounted for significant proportions of the relationships of both pain intensity and depression with physical function and pain-related interference and accounted for the entirety of the unique statistical relationship between sleep disturbance and both physical function and pain-related interference. Fatigue is a complex construct with relationships to both physical and psychological factors that has significant implications for physical functioning in chronic noncancer pain. The current results identify potential targets for future treatment of fatigue in chronic pain and may provide directions for future clinical and theoretical research in the area of chronic noncancer pain. PERSPECTIVE: Fatigue is an important physical and psychological variable that factors prominently in the deleterious consequences of pain intensity, sleep disturbance, and depression for physical function in chronic noncancer pain.

Does physical activity influence the relationship between low back pain and obesity?

BACKGROUND CONTEXT: Evidence supporting an association between obesity and low back pain (LBP) continues to grow; yet little is known about the cause and effect of this relationship. Even less is known about the mechanisms linking the two. Physical activity is a logical suspect, but no study has demonstrated its role. PURPOSE: This study was designed to examine the interrelationship between physical activity, obesity, and LBP. The specific aims were to determine if obesity is a risk factor for LBP in the U.S. population, measure the strength of any observed association, and evaluate the role of physical activity in modulating this association. STUDY DESIGN/SETTING: A cross-sectional U.S. population-based study. PATIENT SAMPLE: A cohort of 6,796 adults from the 2003-2004 National Health and Nutrition Examination Survey. OUTCOME MEASURES: Demographic information, an in-depth health questionnaire, physical examination details, and 7-day free-living physical activity monitoring using accelerometry (ActiGraph AM-7164; ActiGraph, Pensacola, FL, USA). METHODS: LBP status was determined by questionnaire response. Body mass index (BMI) was calculated during physical examination and divided here into four groups (normal weight <25, overweight 25-30, obese 31-35, and ultraobese 36+). Summary measures of physical activity were computed based on intensity cutoffs, percentile intensities, and bout. Demographics, social history, and comorbid health conditions were used to build adjusted weighted logistic regression models constructed using Akaike Information Criterion. All displayed estimates are significant at level <.05. No external funding was received to support this study. None of the authors report conflicts of interest directly related to the specific subject matter of this manuscript. RESULTS: In the U.S. population, the risk of low LBP increases in step with BMI from 2.9% for normal BMI (20-25) to 5.2% for overweight (26-30), 7.7% for obese (31-35), and 11.6% for ultraobese (36+). Smoking is consistently the strongest predictor of LBP across the BMI spectrum (odds ratio 1.6-2.9). Physical activity also modulates these risks. In the overall model, the best physical activity predictors of LBP are in the moderate and high intensity ranges with small effects (odds ratio 0.98 and 0.996 per standard deviation increase, respectively). When broken down by BMI, time spent in sedentary and moderate activity ranges demonstrate more robust influences on LBP status in the overweight, obese, and ultraobese groups. CONCLUSIONS: Increased BMI is a risk factor for back pain in Americans. More important, the role of physical activity in mitigating back pain risk is shown to be of greater consequence in the overweight and obese populations.

An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer.

BACKGROUND: The most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations. RESULTS: In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network. CONCLUSION: Using a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating co-expression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression.

HumanUpstream and MouseUpstream: databases of promoter sequences in the human and mouse genomes.

Large-scale genome annotations, based largely on gene prediction programs, may be inaccurate in their predictions of transcription start sites, so that the identification of promoter regions remains unreliable. Here we focus on the identification of reliable gene promoter regions, critical to the understanding of transcriptional regulation. We report the construction of databases of upstream sequences Human Upstream and Mouse Upstream based on information from both the human and mouse genomes and the database of expressed sequence tags (dbEST). Using the ENSEMBL generic genome annotation system, our approach allows more reliable identification of transcript start sites, and therefore extraction of more reliable promoters regions. The Human Upstream and Human Upstream databases are available free of charge.

Functional annotation and network reconstruction through cross-platform integration of microarray data.

The rapid accumulation of microarray data translates into a need for methods to effectively integrate data generated with different platforms. Here we introduce an approach, 2(nd)-order expression analysis, that addresses this challenge by first extracting expression patterns as meta-information from each data set (1(st)-order expression analysis) and then analyzing them across multiple data sets. Using yeast as a model system, we demonstrate two distinct advantages of our approach: we can identify genes of the same function yet without coexpression patterns and we can elucidate the cooperativities between transcription factors for regulatory network reconstruction by overcoming a key obstacle, namely the quantification of activities of transcription factors. Experiments reported in the literature and performed in our lab support a significant number of our predictions.

Determination of local statistical significance of patterns in Markov sequences with application to promoter element identification.

High-level eukaryotic genomes present a particular challenge to the computational identification of transcription factor binding sites (TFBSs) because of their long noncoding regions and large numbers of repeat elements. This is evidenced by the noisy results generated by most current methods. In this paper, we present a p-value-based scoring scheme using probability generating functions to evaluate the statistical significance of potential TFBSs. Furthermore, we introduce the local genomic context into the model so that candidate sites are evaluated based both on their similarities to known binding sites and on their contrasts against their respective local genomic contexts. We demonstrate that our approach is advantageous in the prediction of myogenin and MEF2 binding sites in the human genome. We also apply LMM to large-scale human binding site sequences in situ and found that, compared to current popular methods, LMM analysis can reduce false positive errors by more than 50% without compromising sensitivity. This improvement will be of importance to any subsequent algorithm that aims to detect regulatory modules based on known PSSMs.

GoSurfer: a graphical interactive tool for comparative analysis of large gene sets in Gene Ontology space.

UNLABELLED: The analysis of complex patterns of gene regulation is central to understanding the biology of cells, tissues and organisms. Patterns of gene regulation pertaining to specific biological processes can be revealed by a variety of experimental strategies, particularly microarrays and other highly parallel methods, which generate large datasets linking many genes. Although methods for detecting gene expression have improved substantially in recent years, understanding the physiological implications of complex patterns in gene expression data is a major challenge. This article presents GoSurfer, an easy-to-use graphical exploration tool with built-in statistical features that allow a rapid assessment of the biological functions represented in large gene sets. GoSurfer takes one or two list(s) of gene identifiers (Affymetrix probe set ID) as input and retrieves all the Gene Ontology (GO) terms associated with the input genes. GoSurfer visualises these GO terms in a hierarchical tree format. With GoSurfer, users can perform statistical tests to search for the GO terms that are enriched in the annotations of the input genes. These GO terms can be highlighted on the GO tree. Users can manipulate the GO tree in various ways and interactively query the genes associated with any GO term. The user-generated graphics can be saved as graphics files, and all the GO information related to the input genes can be exported as text files. AVAILABILITY: GoSurfer is a Windows-based program freely available for noncommercial use and can be downloaded at http://www.gosurfer.org. Datasets used to construct the trees shown in the figures in this article are available at http://www.gosurfer.org/download/GoSurfer.zip.

Chemical genetic modifier screens: small molecule trichostatin suppressors as probes of intracellular histone and tubulin acetylation.

Histone deacetylase (HDAC) inhibitors are being developed as new clinical agents in cancer therapy, in part because they interrupt cell cycle progression in transformed cell lines. To examine cell cycle arrest induced by HDAC inhibitor trichostatin A (TSA), a cytoblot cell-based screen was used to identify small molecule suppressors of this process. TSA suppressors (ITSAs) counteract TSA-induced cell cycle arrest, histone acetylation, and transcriptional activation. Hydroxamic acid-based HDAC inhibitors like TSA and suberoylanilide hydroxamic acid (SAHA) promote acetylation of cytoplasmic alpha-tubulin as well as histones, a modification also suppressed by ITSAs. Although tubulin acetylation appears irrelevant to cell cycle progression and transcription, it may play a role in other cellular processes. Small molecule suppressors such as the ITSAs, available from chemical genetic suppressor screens, may prove to be valuable probes of many biological processes.

Novel mechanisms of T-cell and dendritic cell activation revealed by profiling of psoriasis on the 63,100-element oligonucleotide array.

A global picture of gene expression in the common immune-mediated skin disease, psoriasis, was obtained by interrogating the full set of Affymetrix GeneChips with psoriatic and control skin samples. We identified 1,338 genes with potential roles in psoriasis pathogenesis/maintenance and revealed many perturbed biological processes. A novel method for identifying transcription factor binding sites was also developed and applied to this dataset. Many of the identified sites are known to be involved in immune response and proliferation. An in-depth study of immune system genes revealed the presence of many regulating cytokines and chemokines within involved skin, and markers of dendritic cell (DC) activation in uninvolved skin. The combination of many CCR7+ T cells, DCs, and regulating chemokines in psoriatic lesions, together with the detection of DC activation markers in nonlesional skin, strongly suggests that the spatial organization of T cells and DCs could sustain chronic T-cell activation and persistence within focal skin regions.

Transitive functional annotation by shortest-path analysis of gene expression data.

Current methods for the functional analysis of microarray gene expression data make the implicit assumption that genes with similar expression profiles have similar functions in cells. However, among genes involved in the same biological pathway, not all gene pairs show high expression similarity. Here, we propose that transitive expression similarity among genes can be used as an important attribute to link genes of the same biological pathway. Based on large-scale yeast microarray expression data, we use the shortest-path analysis to identify transitive genes between two given genes from the same biological process. We find that not only functionally related genes with correlated expression profiles are identified but also those without. In the latter case, we compare our method to hierarchical clustering, and show that our method can reveal functional relationships among genes in a more precise manner. Finally, we show that our method can be used to reliably predict the function of unknown genes from known genes lying on the same shortest path. We assigned functions for 146 yeast genes that are considered as unknown by the Saccharomyces Genome Database and by the Yeast Proteome Database. These genes constitute around 5% of the unknown yeast ORFome.