Tests of trend between disease outcomes and ordinal covariates discretized from underlying continuous variables: simulation studies and applications to NHANES 2007–2008

Abstract Background Many epidemiological studies test trends when investigating the association between a risk factor and a disease outcome. Continuous exposures are commonly discretized when the outcome is nonlinearly related to exposure as well as to facilitate interpretation and reduce measurement error. Guidance is needed regarding statistically valid trend tests for epidemiological data of this nature. Methods The association between a discretized variable and a disease is modeled through logistic regression or survival analysis. Linear regression is then conducted by regressing the odds ratio or relative risk on the midpoint of the exposure interval. The trend test is based on the slope of the regression line. In order to investigate the performance of this approach, we conducted simulation studies, considering ten different approaches for the linear regression based on the inclusion or exclusion of an intercept in the model and the form of the weights. The proposed methods are applied to the National Health and Nutrition Examination Survey (NHANES) 2007–2008 for illustration. Results The simulation studies show that eight of these methods are valid, and the relative efficiency depends on the underlying relationship between the covariate and the outcome. Conclusions The significance of the study is its potential to help practitioners select an appropriate method to test for trend in their future studies that utilize ordinal covariates

Modeling Secondary Phenotypes Conditional on Genotypes in Case–Control Studies

Traditional case–control genetic association studies examine relationships between case–control status and one or more covariates. It is becoming increasingly common to study secondary phenotypes and their association with the original covariates. The Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) project, a study of temporomandibular disorders (TMD), motivates this work. Numerous measures of interest are collected at enrollment, such as the number of comorbid pain conditions from which a participant suffers. Examining the potential genetic basis of these measures is of secondary interest. Assessing these associations is statistically challenging, as participants do not form a random sample from the population of interest. Standard methods may be biased and lack coverage and power. We propose a general method for the analysis of arbitrary phenotypes utilizing inverse probability weighting and bootstrapping for standard error estimation. The method may be applied to the complicated association tests used in next-generation sequencing studies, such as analyses of haplotypes with ambiguous phase. Simulation studies show that our method performs as well as competing methods when they are applicable and yield promising results for outcome types, such as time-to-event, to which other methods may not apply. The method is applied to the OPPERA baseline case–control genetic study

An evaluation of metrics for assessing maternal exposure to agricultural pesticides

We evaluate the use of three different exposure metrics to estimate maternal agricultural pesticide exposure during pregnancy. Using a geographic information system-based method of pesticide exposure estimation, we combine data on crop density and specific pesticide application amounts/dates to create the three exposure metrics. For illustration purposes, we create each metric for a North Carolina cohort of pregnant women, 2003–2005, and analyze the risk of congenital anomaly development with a focus on metric comparisons. Based on the results, and the need to balance data collection efforts/computational efficiency with accuracy, the metric which estimates total chemical exposure using application dates based on crop-specific earliest planting and latest harvesting information is preferred. Benefits and drawbacks of each metric are discussed and recommendations for extending the analysis to other states are provided

Study Protocol, Sample Characteristics, and Loss to Follow-Up: The OPPERA Prospective Cohort Study

When studying incidence of pain conditions such as temporomandibular disorders (TMDs), repeated monitoring is needed in prospective cohort studies. However, monitoring methods usually have limitations and, over a period of years, some loss to follow-up is inevitable. The OPPERA prospective cohort study of first-onset TMD screened for symptoms using quarterly questionnaires and examined symptomatic participants to definitively ascertain TMD incidence. During the median 2.8-year observation period, 16% of the 3,263 enrollees completed no follow-up questionnaires, others provided incomplete follow-up, and examinations were not conducted for one third of symptomatic episodes. Although screening methods and examinations were found to have excellent reliability and validity, they were not perfect. Loss to follow-up varied according to some putative TMD risk factors, although multiple imputation to correct the problem suggested that bias was minimal. A second method of multiple imputation that evaluated bias associated with omitted and dubious examinations revealed a slight underestimate of incidence and some small biases in hazard ratios used to quantify effects of risk factors. Although “bottom line” statistical conclusions were not affected, multiply-imputed estimates should be considered when evaluating the large number of risk factors under investigation in the OPPERA study

Tests of trend between disease outcomes and ordinal covariates discretized from underlying continuous variables: simulation studies and applications to NHANES 2007–2008

Abstract Background Many epidemiological studies test trends when investigating the association between a risk factor and a disease outcome. Continuous exposures are commonly discretized when the outcome is nonlinearly related to exposure as well as to facilitate interpretation and reduce measurement error. Guidance is needed regarding statistically valid trend tests for epidemiological data of this nature. Methods The association between a discretized variable and a disease is modeled through logistic regression or survival analysis. Linear regression is then conducted by regressing the odds ratio or relative risk on the midpoint of the exposure interval. The trend test is based on the slope of the regression line. In order to investigate the performance of this approach, we conducted simulation studies, considering ten different approaches for the linear regression based on the inclusion or exclusion of an intercept in the model and the form of the weights. The proposed methods are applied to the National Health and Nutrition Examination Survey (NHANES) 2007–2008 for illustration. Results The simulation studies show that eight of these methods are valid, and the relative efficiency depends on the underlying relationship between the covariate and the outcome. Conclusions The significance of the study is its potential to help practitioners select an appropriate method to test for trend in their future studies that utilize ordinal covariates

Modeling Secondary Phenotypes Conditional on Genotypes in Case–Control Studies

Traditional case–control genetic association studies examine relationships between case–control status and one or more covariates. It is becoming increasingly common to study secondary phenotypes and their association with the original covariates. The Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) project, a study of temporomandibular disorders (TMD), motivates this work. Numerous measures of interest are collected at enrollment, such as the number of comorbid pain conditions from which a participant suffers. Examining the potential genetic basis of these measures is of secondary interest. Assessing these associations is statistically challenging, as participants do not form a random sample from the population of interest. Standard methods may be biased and lack coverage and power. We propose a general method for the analysis of arbitrary phenotypes utilizing inverse probability weighting and bootstrapping for standard error estimation. The method may be applied to the complicated association tests used in next-generation sequencing studies, such as analyses of haplotypes with ambiguous phase. Simulation studies show that our method performs as well as competing methods when they are applicable and yield promising results for outcome types, such as time-to-event, to which other methods may not apply. The method is applied to the OPPERA baseline case–control genetic study

Nanoparticle analysis sheds budding insights into genetic drivers of extracellular vesicle biogenesis

Background: Extracellular vesicles (EVs) are important mediators of cell-to-cell communication in healthy and pathological environments. Because EVs are present in a variety of biological fluids and contain molecular signatures of their cell or tissue of origin, they have great diagnostic and prognostic value. The ability of EVs to deliver biologically active proteins, RNAs and lipids to cells has generated interest in developing novel therapeutics. Despite their potential medical use, many of the mechanisms underlying EV biogenesis and secretion remain unknown. Methods: Here, we characterized vesicle secretion across the NCI-60 panel of human cancer cells by nanoparticle tracking analysis. Using CellMiner, the quantity of EVs secreted by each cell line was compared to reference transcriptomics data to identify gene products associated with vesicle secretion. Results: Gene products positively associated with the quantity of exosomal-sized vesicles included vesicular trafficking classes of proteins with Rab GTPase function and sphingolipid metabolism. Positive correlates of larger microvesicle-sized vesicle secretion included gene products involved in cytoskeletal dynamics and exocytosis, as well as Rab GTPase activation. One of the identified targets, CD63, was further evaluated for its role in vesicle secretion. Clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 knockout of the CD63 gene in HEK293 cells resulted in a decrease in small vesicle secretion, suggesting the importance of CD63 in exosome biogenesis. Conclusion: These observations reveal new insights into genes involved in exosome and microvesicle formation, and may provide a means to distinguish EV sub-populations. This study offers a foundation for further exploration of targets involved in EV biogenesis and secretion

Patient-Reported Outcomes in Cancer Patients with HIV

Elevated cancer-specific mortality in PWH has been demonstrated for non-AIDS-defining malignancies. However, additional clinical endpoints of interest, including patient-reported outcomes (PROs), have not been systematically examined in PWH and cancer. We evaluated differences in patient-reported symptomology between cancer patients with versus without HIV using data from 12,529 patients at the Moffitt Cancer Center, including 55 with HIV. The symptoms were assessed using the Edmonton Symptom Assessment Scale (ESAS), which asks patients to rank 12 symptoms on a scale of 1–10, with scores ≥7 considered severe. The responses across all questions were summed to create a composite score. Vital status through t July 2021 was determined through linkage to the electronic health record. PWH reported a higher composite ESAS score on average (44.4) compared to HIV-uninfected cancer patients (30.7, p-value < 0.01). In zero-inflated negative binomial regression models adjusted for cancer site, sex, and race, the composite ESAS scores and the count of severe symptoms were 1.41 times (95% CI: 1.13–1.77) and 1.45 times (95% CI: 1.09–1.93) higher, respectively, in cancer patients with HIV. Among PWH, higher ESAS scores were associated with mortality (p-value = 0.02). This is the first demonstration of uniquely poor PROs in PWH and cancer and suggests that patient symptom monitoring to improve clinical endpoints deserves further study