Publications by authors named "Sheila M Gaynor"

9 Publications

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Association of Hormonal Contraceptive Use with Headache and Temporomandibular Pain: The OPPERA Study.

J Oral Facial Pain Headache 2021 Spring;35(2):105-112

Aims: To determine the relationship between hormonal contraceptive (HC) use and painful symptoms, particularly those associated with headache and painful temporomandibular disorders (TMD).

Methods: Data from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) prospective cohort study were used. During the 2.5-year median follow-up period, quarterly health update (QHU) questionnaires were completed by 1,475 women aged 18 to 44 years who did not have TMD, menopause, hysterectomy, or hormone replacement therapy use at baseline. QHU questionnaires evaluated HC use, symptoms of headache and TMD, and pain of ≥ 1 day duration in 12 body regions. Participants who developed TMD symptoms were examined to classify clinical TMD. Headache symptoms were classified based on the International Classification of Headache Disorders 3 (ICHD-3). Associations between HC use and pain symptoms were analyzed using generalized estimating equations and Cox models.

Results: HC use, endorsed in 33.7% of QHU questionnaires, was significantly associated with concurrent symptoms of TMD (odds ratio [OR]: 1.20, 95% CI: 1.06 to 1.35) and headache (OR: 1.26, 95% CI: 1.11 to 1.43). HC use was also significantly associated with concurrent pain of ≥ 1 day duration in the head (OR: 1.38, 95% CI: 1.16 to 1.63), face (OR: 1.44, 95% CI: 1.13 to 1.83), and legs (OR: 1.22, 95% CI: 1.01 to 1.47), but not elsewhere. Initiation of HC use was associated with increased odds of subsequent TMD symptoms (OR: 1.37, 95% CI: 1.13 to 1.66) and pain of ≥ 1 day in the head (OR: 1.37, 95% CI: 1.01 to 1.85). Discontinuing HC use was associated with lower odds of subsequent headache (OR: 0.82, 95% CI: 0.67 to 0.99). HC use was not significantly associated with subsequent onset of examiner-classified TMD.

Conclusion: These findings imply that HC influences craniofacial pain, and that this pain diminishes after cessation of HC use.
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http://dx.doi.org/10.11607/ofph.2727DOI Listing
June 2021

Identifying US County-level characteristics associated with high COVID-19 burden.

BMC Public Health 2021 05 28;21(1):1007. Epub 2021 May 28.

Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Building II, Room 419, Boston, MA, 02115, USA.

Background: Identifying county-level characteristics associated with high coronavirus 2019 (COVID-19) burden can help allow for data-driven, equitable allocation of public health intervention resources and reduce burdens on health care systems.

Methods: Synthesizing data from various government and nonprofit institutions for all 3142 United States (US) counties, we studied county-level characteristics that were associated with cumulative and weekly case and death rates through 12/21/2020. We used generalized linear mixed models to model cumulative and weekly (40 repeated measures per county) cases and deaths. Cumulative and weekly models included state fixed effects and county-specific random effects. Weekly models additionally allowed covariate effects to vary by season and included US Census region-specific B-splines to adjust for temporal trends.

Results: Rural counties, counties with more minorities and white/non-white segregation, and counties with more people with no high school diploma and with medical comorbidities were associated with higher cumulative COVID-19 case and death rates. In the spring, urban counties and counties with more minorities and white/non-white segregation were associated with increased weekly case and death rates. In the fall, rural counties were associated with larger weekly case and death rates. In the spring, summer, and fall, counties with more residents with socioeconomic disadvantage and medical comorbidities were associated greater weekly case and death rates.

Conclusions: These county-level associations are based off complete data from the entire country, come from a single modeling framework that longitudinally analyzes the US COVID-19 pandemic at the county-level, and are applicable to guiding government resource allocation policies to different US counties.
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http://dx.doi.org/10.1186/s12889-021-11060-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162162PMC
May 2021

Unraveling US National COVID-19 Racial/Ethnic Disparities using County Level Data Among 328 Million Americans.

medRxiv 2020 Dec 4. Epub 2020 Dec 4.

Racial and ethnic disparities in COVID-19 outcomes reflect the unequal burden experienced by vulnerable communities in the United States (US). Proposed explanations include socioeconomic factors that influence how people live, work, and play, and pre-existing comorbidities. It is important to assess the extent to which observed US COVID-19 racial and ethnic disparities can be explained by these factors. We study 9.8 million confirmed cases and 234,000 confirmed deaths from 2,990 US counties (3,142 total) that make up 99.8% of the total US population (327.6 out of 328.2 million people) through 11/8/20. We found national COVID-19 racial health disparities in US are partially explained by various social determinants of health and pre-existing comorbidities that have been previously proposed. However, significant unexplained racial and ethnic health disparities still persist at the US county level after adjusting for these variables. There is a pressing need to develop strategies to address not only the social determinants but also other factors, such as testing access, personal protection equipment access and exposures, as well as tailored intervention and resource allocation for vulnerable groups, in order to combat COVID-19 and reduce racial health disparities.
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http://dx.doi.org/10.1101/2020.12.02.20234989DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7724685PMC
December 2020

Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients.

Pain 2021 05;162(5):1528-1538

Department of Anesthesiology, Center for Translational Pain Medicine, Duke University, Durham, NC, United States.

Abstract: Traditional classification and prognostic approaches for chronic pain conditions focus primarily on anatomically based clinical characteristics not based on underlying biopsychosocial factors contributing to perception of clinical pain and future pain trajectories. Using a supervised clustering approach in a cohort of temporomandibular disorder cases and controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment study, we recently developed and validated a rapid algorithm (ROPA) to pragmatically classify chronic pain patients into 3 groups that differed in clinical pain report, biopsychosocial profiles, functional limitations, and comorbid conditions. The present aim was to examine the generalizability of this clustering procedure in 2 additional cohorts: a cohort of patients with chronic overlapping pain conditions (Complex Persistent Pain Conditions study) and a real-world clinical population of patients seeking treatment at duke innovative pain therapies. In each cohort, we applied a ROPA for cluster prediction, which requires only 4 input variables: pressure pain threshold and anxiety, depression, and somatization scales. In both complex persistent pain condition and duke innovative pain therapies, we distinguished 3 clusters, including one with more severe clinical characteristics and psychological distress. We observed strong concordance with observed cluster solutions, indicating the ROPA method allows for reliable subtyping of clinical populations with minimal patient burden. The ROPA clustering algorithm represents a rapid and valid stratification tool independent of anatomic diagnosis. ROPA holds promise in classifying patients based on pathophysiological mechanisms rather than structural or anatomical diagnoses. As such, this method of classifying patients will facilitate personalized pain medicine for patients with chronic pain.
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http://dx.doi.org/10.1097/j.pain.0000000000002153DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049946PMC
May 2021

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale.

Nat Genet 2020 09 24;52(9):969-983. Epub 2020 Aug 24.

Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.
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http://dx.doi.org/10.1038/s41588-020-0676-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483769PMC
September 2020

Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts.

Am J Hum Genet 2020 01 26;106(1):112-120. Epub 2019 Dec 26.

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.

Whole-genome sequencing (WGS) can improve assessment of low-frequency and rare variants, particularly in non-European populations that have been underrepresented in existing genomic studies. The genetic determinants of C-reactive protein (CRP), a biomarker of chronic inflammation, have been extensively studied, with existing genome-wide association studies (GWASs) conducted in >200,000 individuals of European ancestry. In order to discover novel loci associated with CRP levels, we examined a multi-ancestry population (n = 23,279) with WGS (∼38× coverage) from the Trans-Omics for Precision Medicine (TOPMed) program. We found evidence for eight distinct associations at the CRP locus, including two variants that have not been identified previously (rs11265259 and rs181704186), both of which are non-coding and more common in individuals of African ancestry (∼10% and ∼1% minor allele frequency, respectively, and rare or monomorphic in 1000 Genomes populations of East Asian, South Asian, and European ancestry). We show that the minor (G) allele of rs181704186 is associated with lower CRP levels and decreased transcriptional activity and protein binding in vitro, providing a plausible molecular mechanism for this African ancestry-specific signal. The individuals homozygous for rs181704186-G have a mean CRP level of 0.23 mg/L, in contrast to individuals heterozygous for rs181704186 with mean CRP of 2.97 mg/L and major allele homozygotes with mean CRP of 4.11 mg/L. This study demonstrates the utility of WGS in multi-ethnic populations to drive discovery of complex trait associations of large effect and to identify functional alleles in noncoding regulatory regions.
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http://dx.doi.org/10.1016/j.ajhg.2019.12.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042494PMC
January 2020

Identification of differentially expressed gene sets using the Generalized Berk-Jones statistic.

Bioinformatics 2019 11;35(22):4568-4576

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA.

Motivation: Cancer genomics studies frequently aim to identify genes that are differentially expressed between clinically distinct patient subgroups, generally by testing single genes one at a time. However, the results of any individual transcriptomic study are often not fully reproducible. A particular challenge impeding statistical analysis is the difficulty of distinguishing between differential expression comprising part of the genomic disease etiology and that induced by downstream effects. More robust analytical approaches that are well-powered to detect potentially causative genes, are less prone to discovering spurious associations, and can deliver reproducible findings across different studies are needed.

Results: We propose a set-based procedure for testing of differential expression and show that this set-based approach can produce more robust results by aggregating information across multiple, correlated genomic markers. Specifically, we adapt the Generalized Berk-Jones statistic to test for the transcription factors that may contribute to the progression of estrogen receptor positive breast cancer. We demonstrate the ability of our method to produce reproducible findings by applying the same analysis to 21 publicly available datasets, producing a similar list of significant transcription factors across most studies. Our Generalized Berk-Jones approach produces results that show improved consistency over three set-based testing algorithms: Generalized Higher Criticism, Gene Set Analysis and Gene Set Enrichment Analysis.

Availability And Implementation: Data are in the MetaGxBreast R package. Code is available at github.com/ryanrsun/gaynor_sun_GBJ_breast_cancer.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz277DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6853697PMC
November 2019

Mediation analysis for common binary outcomes.

Stat Med 2019 02 6;38(4):512-529. Epub 2018 Sep 6.

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

Mediation analysis provides an attractive causal inference framework to decompose the total effect of an exposure on an outcome into natural direct effects and natural indirect effects acting through a mediator. For binary outcomes, mediation analysis methods have been developed using logistic regression when the binary outcome is rare. These methods will not hold in practice when a disease is common. In this paper, we develop mediation analysis methods that relax the rare disease assumption when using logistic regression. We calculate the natural direct and indirect effects for common diseases by exploiting the relationship between logit and probit models. Specifically, we derive closed-form expressions for the natural direct and indirect effects on the odds ratio scale. Mediation models for both continuous and binary mediators are considered. We demonstrate through simulation that the proposed method performs well for common binary outcomes. We apply the proposed methods to analyze the Normative Aging Study to identify DNA methylation sites that are mediators of smoking behavior on the outcome of obstructed airway function.
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http://dx.doi.org/10.1002/sim.7945DOI Listing
February 2019

Comparison of comorbidity collection methods.

J Am Coll Surg 2014 Aug 19;219(2):245-55. Epub 2014 Mar 19.

Clinical Outcomes Research Office, Department of Otolaryngology-Head and Neck Surgery, Washington University in St Louis, St Louis, MO. Electronic address:

Background: Multiple valid comorbidity indices exist to quantify the presence and role of comorbidities in cancer patient survival. Our goal was to compare chart-based Adult Comorbidity Evaluation-27 index (ACE-27) and claims-based Charlson Comorbidity Index (CCI) methods of identifying comorbid ailments and their prognostic abilities.

Study Design: We conducted a prospective cohort study of 6,138 newly diagnosed cancer patients at 12 different institutions. Participating registrars were trained to collect comorbidities from the abstracted chart using the ACE-27 method. The ACE-27 assessment was compared with comorbidities captured through hospital discharge face sheets using ICD coding. The prognostic accomplishments of each comorbidity method were examined using follow-up data assessed at 24 months after data abstraction.

Results: Distribution of the ACE-27 scores was: "none" for 1,453 (24%) of the patients; "mild" for 2,388 (39%); "moderate" for 1,344 (22%), and "severe" for 950 (15%) of the patients. Deyo's adaption of the CCI identified 4,265 (69%) patients with a CCI score of 0, and the remaining 31% had CCI scores of 1 (n = 1,341 [22%]), 2 (n = 365 [6%]), or 3 or more (n = 167 [3%]). Of the 4,265 patients with a CCI score of zero, 394 (9%) were coded with severe comorbidities based on ACE-27 method. A higher comorbidity score was significantly associated with higher risk of death for both comorbidity indices. The multivariable Cox model, including both comorbidity indices, had the best performance (Nagelkerke's R(2) = 0.37) and the best discrimination (C index = 0.827).

Conclusions: The number, type, and overall severity of comorbid ailments identified by chart- and claims-based approaches in newly diagnosed cancer patients were notably different. Both indices were prognostically significant and able to provide unique prognostic information.
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http://dx.doi.org/10.1016/j.jamcollsurg.2014.01.059DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4120824PMC
August 2014
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