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1.
medRxiv ; 2020 Dec 04.
Artigo em Inglês | MEDLINE | ID: mdl-33300014

RESUMO

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.

2.
Pain ; 2020 Nov 30.
Artigo em Inglês | MEDLINE | ID: mdl-33259458

RESUMO

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 (TMD) cases and controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, we recently developed and validated a rapid algorithm (ROPA) to pragmatically classify chronic pain patients into three 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 two additional cohorts: a cohort of patients with chronic overlapping pain conditions (Complex Persistent Pain Conditions (CPPC) study), and a real-world clinical population of patients seeking treatment at Duke Innovative Pain Therapies (DIPT). In each cohort, we applied ROPA for cluster prediction, which requires only four input variables: pressure pain threshold (PPT) and anxiety, depression, and somatization scales. In both CPPC and DIPT, we distinguished three 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.

3.
Genet Epidemiol ; 2020 Sep 14.
Artigo em Inglês | MEDLINE | ID: mdl-32924180

RESUMO

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1ß pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

4.
Nat Genet ; 52(9): 969-983, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32839606

RESUMO

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.


Assuntos
Predisposição Genética para Doença/genética , Variação Genética/genética , Genoma/genética , LDL-Colesterol/genética , Simulação por Computador , Estudo de Associação Genômica Ampla/métodos , Humanos , Modelos Genéticos , Anotação de Sequência Molecular/métodos , Fenótipo , Sequenciamento Completo do Genoma/métodos
5.
Am J Hum Genet ; 106(1): 112-120, 2020 01 02.
Artigo em Inglês | MEDLINE | ID: mdl-31883642

RESUMO

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.


Assuntos
Grupo com Ancestrais do Continente Africano/genética , Grupo com Ancestrais do Continente Asiático/genética , Proteína C-Reativa/genética , Grupo com Ancestrais do Continente Europeu/genética , Predisposição Genética para Doença , Polimorfismo de Nucleotídeo Único , Sequenciamento Completo do Genoma/métodos , Estudos de Coortes , Frequência do Gene , Estudo de Associação Genômica Ampla , Humanos , Desequilíbrio de Ligação
6.
Bioinformatics ; 35(22): 4568-4576, 2019 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-31062858

RESUMO

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.


Assuntos
Perfilação da Expressão Gênica , Algoritmos , Neoplasias da Mama , Genoma , Humanos , Transcriptoma
7.
Stat Med ; 38(4): 512-529, 2019 02 20.
Artigo em Inglês | MEDLINE | ID: mdl-30256434

RESUMO

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.


Assuntos
Modelos Estatísticos , Estatística como Assunto , Causalidade , Fatores de Confusão Epidemiológicos , Humanos , Modelos Logísticos , Razão de Chances , Doenças Raras/epidemiologia , Doenças Raras/etiologia
8.
Health Psychol ; 37(2): 179-187, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-28967770

RESUMO

OBJECTIVE: The majority of smokers are not motivated to quit within 30 days. We examined whether these smokers are a homogeneous group, hypothesizing that subtypes of unmotivated smokers could be identified. METHOD: Included were 500 smokers not ready to quit within 30 days who completed an online survey assessing variables known to be associated with quitting. RESULTS: Latent Class Analysis revealed 3 unmotivated smoker subtypes. "Health-Concerned Smokers," (HCS; n = 166) had a significantly greater proportion of previous smoking-related illness and high risk perceptions. "Smokers with Psychosocial Barriers" (SPB; n = 192) had a significantly greater proportion of younger smokers, partners who smoked, other household smokers, and children. "Unconvinced Smokers" (UCS; n = 142) had the lowest proportion of those who: were motivated and confident to quit, had smoking-related illnesses, and perceived the risks of smoking and benefits of quitting. UCS had the highest proportion with optimistic bias, and no prior quit attempts. A greater proportion of HCS had high motivation to quit versus SPB and UCS. In model validation, 60.6% of UCS said they "never plan to quit" versus 31.8% of SPB and 22.3% of HCS; SPB and HCS had lower odds of never planning to quit versus UCS. Of those who plan on quitting at some point, 75.2% of HCS and 62.6% of SPB plan on quitting within 1 year, versus 46.4% of UCS; the cumulative odds of planning to quit later were higher among UCS. CONCLUSIONS: Smokers who are not motivated to quit are not a homogeneous group; tailored intervention approaches and targeted messages might be needed to motivate quitting. (PsycINFO Database Record


Assuntos
Análise de Classes Latentes , Motivação/fisiologia , Fumantes/psicologia , Abandono do Hábito de Fumar/psicologia , Adulto , Feminino , Humanos , Masculino , Inquéritos e Questionários
9.
Comput Stat Data Anal ; 116: 139-154, 2017 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-29785064

RESUMO

Cluster analysis methods are used to identify homogeneous subgroups in a data set. In biomedical applications, one frequently applies cluster analysis in order to identify biologically interesting subgroups. In particular, one may wish to identify subgroups that are associated with a particular outcome of interest. Conventional clustering methods generally do not identify such subgroups, particularly when there are a large number of high-variance features in the data set. Conventional methods may identify clusters associated with these high-variance features when one wishes to obtain secondary clusters that are more interesting biologically or more strongly associated with a particular outcome of interest. A modification of sparse clustering can be used to identify such secondary clusters or clusters associated with an outcome of interest. This method correctly identifies such clusters of interest in several simulation scenarios. The method is also applied to a large prospective cohort study of temporomandibular disorders and a leukemia microarray data set.

10.
Pain ; 157(6): 1266-78, 2016 06.
Artigo em Inglês | MEDLINE | ID: mdl-26928952

RESUMO

The classification of most chronic pain disorders gives emphasis to anatomical location of the pain to distinguish one disorder from the other (eg, back pain vs temporomandibular disorder [TMD]) or to define subtypes (eg, TMD myalgia vs arthralgia). However, anatomical criteria overlook etiology, potentially hampering treatment decisions. This study identified clusters of individuals using a comprehensive array of biopsychosocial measures. Data were collected from a case-control study of 1031 chronic TMD cases and 3247 TMD-free controls. Three subgroups were identified using supervised cluster analysis (referred to as the adaptive, pain-sensitive, and global symptoms clusters). Compared with the adaptive cluster, participants in the pain-sensitive cluster showed heightened sensitivity to experimental pain, and participants in the global symptoms cluster showed both greater pain sensitivity and greater psychological distress. Cluster membership was strongly associated with chronic TMD: 91.5% of TMD cases belonged to the pain-sensitive and global symptoms clusters, whereas 41.2% of controls belonged to the adaptive cluster. Temporomandibular disorder cases in the pain-sensitive and global symptoms clusters also showed greater pain intensity, jaw functional limitation, and more comorbid pain conditions. Similar results were obtained when the same methodology was applied to a smaller case-control study consisting of 199 chronic TMD cases and 201 TMD-free controls. During a median 3-year follow-up period of TMD-free individuals, participants in the global symptoms cluster had greater risk of developing first-onset TMD (hazard ratio = 2.8) compared with participants in the other 2 clusters. Cross-cohort predictive modeling was used to demonstrate the reliability of the clusters.


Assuntos
Dor Crônica/classificação , Estresse Psicológico/classificação , Transtornos da Articulação Temporomandibular/classificação , Adolescente , Adulto , Estudos de Casos e Controles , Análise por Conglomerados , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Adulto Jovem
11.
J Am Coll Surg ; 219(2): 245-55, 2014 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-24933715

RESUMO

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.


Assuntos
Comorbidade , Coleta de Dados/métodos , Neoplasias/epidemiologia , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Codificação Clínica , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Sumários de Alta do Paciente Hospitalar/estatística & dados numéricos , Prevalência , Prognóstico , Estudos Prospectivos
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