Kedir N. Turi

Sleep insufficiency and the natural environment: results from the US Behavioral Risk Factor Surveillance System Survey

BACKGROUND Exposure to the natural environment may improve health behaviors and mental health outcomes such as increased levels of physical activity and lower levels of depression associated with sleep quality. Little is known about the relationship between insufficient sleep and the natural environment. PURPOSE To determine whether exposure to attributes of the natural environment (e.g., greenspace) attenuates the likelihood of reporting insufficient sleep among US adults. METHODS Multiple logistic regression models were used to explore the association between self-reported days of insufficient sleep (in the past 30days) and access to the natural environment in a multi-ethnic, nationally representative sample (n=255,171) of US adults ≥18years of age enrolled in the 2010 Behavioral Risk Factor Surveillance System. RESULTS Using 1-to-6days of insufficient sleep as the referent group for all analyses, lower odds of exposure to natural amenities were observed for individuals reporting 21-to-29days (OR=0.843, 95% confidence interval (CI)=0.747, 0.951) of insufficient sleep. In stratified analyses, statistically significant lower odds of exposure to natural amenities were found among men reporting 7-to-13-days (OR=0.911, 95% CI=0.857, 0.968), 21-to-29-days (OR=0.838, 95% CI=0.759, 0.924), and 30-days (OR=0.860, 95% CI=0.784, 0.943) of insufficient sleep. Greenspace access was also protective against insufficient sleep for men and individuals aged 65+. CONCLUSIONS In a representative sample of US adults, access to the natural environment attenuated the likelihood of reporting insufficient sleep, particularly among men. Additional studies are needed to examine the impact of natural environment exposure on sleep insufficiency across various socio-demographic groups.

A review of metabolomics approaches and their application in identifying causal pathways of childhood asthma

Because asthma is a disease that results from host-environment interactions, an approach that allows assessment of the effect of the environment on the host is needed to understand the disease. Metabolomics has appealing potential as an application to study pathways to childhood asthma development. The objective of this review is to provide an overview of metabolomics methods and their application to understanding host-environment pathways in asthma development. We reviewed recent literature on advances in metabolomics and their application to study pathways to childhood asthma development. We highlight the (1) potential of metabolomics in understanding the pathogenesis of disease and the discovery of biomarkers; (2) choice of metabolomics techniques, biospecimen handling, and data analysis; (3) application to studying the role of the environment on asthma development; (4) review of metabolomics applied to the outcome of asthma; (5) recommendations for application of metabolomics-based -omics data integration in understanding disease pathogenesis; and (6) limitations. In conclusion, metabolomics allows use of biospecimens to identify useful biomarkers and pathways involved in disease development and subsequently to inform a greater understanding of disease pathogenesis and endotypes and prediction of the clinical course of childhood asthma phenotypes.

Predicting Risk of Type 2 Diabetes by Using Data on Easy-to-Measure Risk Factors

Introduction Statistical models for assessing risk of type 2 diabetes are usually additive with linear terms that use non-nationally representative data. The objective of this study was to use nationally representative data on diabetes risk factors and spline regression models to determine the ability of models with nonlinear and interaction terms to assess the risk of type 2 diabetes. Methods We used 4 waves of data (2005–2006 to 2011–2012) on adults aged 20 or older from the National Health and Nutrition Examination Survey (n = 5,471) and multivariate adaptive regression splines (MARS) to build risk models in 2015. MARS allowed for interactions among 17 noninvasively measured risk factors for type 2 diabetes. Results A key risk factor for type 2 diabetes was increasing age, especially for those older than 69, followed by a family history of diabetes, with diminished risk among individuals younger than 45. Above age 69, other risk factors superseded age, including systolic and diastolic blood pressure. The additive MARS model with nonlinear terms had an area under curve (AUC) receiver operating characteristic of 0.847, whereas the 2-way interaction MARS model had an AUC of 0.851, a slight improvement. Both models had an 87% accuracy in classifying diabetes status. Conclusion Statistical models of type 2 diabetes risk should allow for nonlinear associations; incorporation of interaction terms into the MARS model improved its performance slightly. Robust statistical manipulation of risk factors commonly measured noninvasively in clinical settings might provide useful estimates of type 2 diabetes risk.

Trends in health care utilization for asthma exacerbations among diverse populations with asthma in the United States

Trends in health care utilization for asthma exacerbations among diverse populations with asthma in the United States Cosby Stone, MD, MPH, Tebeb Gebretsadik, MPH, Capt Rees L. Lee, MD, Amber M. Evans, MPH, Tina V. Hartert, MD, Edward Mitchel, MS, James Morrow, Ann C. Wu, MD, Carlos Iribarren, MD, Melissa G. Butler, PharmD, MPH, PhD, Emma K. Larkin, PhD, Kedir N. Turi, PhD, and Pingsheng Wu, PhD, MS

Association of newborn screening metabolites with risk of wheezing in childhood

BackgroundThere are critical gaps in our understanding of the temporal relationships between metabolites and subsequent asthma development. This is the first study to examine metabolites from newborn screening in the etiology of early childhood wheezing.MethodsOne thousand nine hundred and fifty one infants enrolled between 2012 and 2014 from pediatric practices located in Middle Tennessee in the population-based birth cohort study, the Infant Susceptibility to Pulmonary Infections and Asthma Following RSV Exposure Study (INSPIRE), were linked with metabolite data from the Tennessee Newborn Screening Program. The association between the levels of 37 metabolites and the number of wheezing episodes in the past 12 months was assessed at 1, 2, and 3 years of life.ResultsSeveral metabolites were significantly associated with the number of wheezing episodes. Two acylcarnitines, C10:1 and C18:2, showed robust associations. Increasing levels of C10:1 were associated with increasing number of wheezing episodes at 2 years (OR 2.11, 95% CI 1.41–3.17) and 3 years (OR 2.56, 95% CI 1.59–4.11), while increasing levels of C18:2 were associated with increasing number of wheezing episodes at 1 year (OR 1.38, 95% CI 1.12–1.71) and 2 years (OR 1.47, 95% CI 1.17–1.84).ConclusionsIdentification of specific metabolites and associated pathways involved in wheezing pathogenesis offer insights into potential targets to prevent childhood asthma morbidity.

Prevalence of infant bronchiolitis‐coded healthcare encounters attributable to RSV

We sought to determine the proportion of bronchiolitis episodes attributable to respiratory syncytial virus (RSV) among ICD‐9 coded infant bronchiolitis episodes which were tested for RSV.

Infant Viral Respiratory Infection Nasal-Immune-Response Patterns and Their Association with Subsequent Childhood Recurrent Wheeze

Rationale: Recurrent wheeze and asthma are thought to result from alterations in early life immune development following respiratory syncytial virus (RSV) infection. However, prior studies of the nasal immune response to infection have assessed only individual cytokines, which does not capture the whole spectrum of response to infection. Objectives: To identify nasal immune phenotypes in response to RSV infection and their association with recurrent wheeze. Methods: A birth cohort of term healthy infants born June to December were recruited and followed to capture the first infant RSV infection. Nasal wash samples were collected during acute respiratory infection, viruses were identified by RT‐PCR, and immune‐response analytes were assayed using a multianalyte bead‐based panel. Immune‐response clusters were identified using machine learning, and association with recurrent wheeze at age 1 and 2 years was assessed using logistic regression. Measurements and Main Results: We identified two novel and distinct immune‐response clusters to RSV and human rhinovirus. In RSV‐infected infants, a nasal immune‐response cluster characterized by lower non‐IFN antiviral immune‐response mediators, and higher type‐2 and type‐17 cytokines was significantly associated with first and second year recurrent wheeze. In comparison, we did not observe this in infants with human rhinovirus acute respiratory infection. Based on network analysis, type‐2 and type‐17 cytokines were central to the immune response to RSV, whereas growth factors and chemokines were central to the immune response to human rhinovirus. Conclusions: Distinct immune‐response clusters during infant RSV infection and their association with risk of recurrent wheeze provide insights into the risk factors for and mechanisms of asthma development.

A new model of wheezing severity in young children using the validated ISAAC wheezing module: A latent variable approach with validation in independent cohorts

Background The International Study of Asthma and Allergies in Children (ISAAC) Wheezing Module is commonly used to characterize pediatric asthma in epidemiological studies, including nearly all airway cohorts participating in the Environmental Influences on Child Health Outcomes (ECHO) consortium. However, there is no consensus model for operationalizing wheezing severity with this instrument in explanatory research studies. Severity is typically measured using coarsely-defined categorical variables, reducing power and potentially underestimating etiological associations. More precise measurement approaches could improve testing of etiological theories of wheezing illness. Methods We evaluated a continuous latent variable model of pediatric wheezing severity based on four ISAAC Wheezing Module items. Analyses included subgroups of children from three independent cohorts whose parents reported past wheezing: infants ages 0–2 in the INSPIRE birth cohort study (Cohort 1; n = 657), 6-7-year-old North American children from Phase One of the ISAAC study (Cohort 2; n = 2,765), and 5-6-year-old children in the EHAAS birth cohort study (Cohort 3; n = 102). Models were estimated using structural equation modeling. Results In all cohorts, covariance patterns implied by the latent variable model were consistent with the observed data, as indicated by non-significant χ2 goodness of fit tests (no evidence of model misspecification). Cohort 1 analyses showed that the latent factor structure was stable across time points and child sexes. In both cohorts 1 and 3, the latent wheezing severity variable was prospectively associated with wheeze-related clinical outcomes, including physician asthma diagnosis, acute corticosteroid use, and wheeze-related outpatient medical visits when adjusting for confounders Conclusion We developed an easily applicable continuous latent variable model of pediatric wheezing severity based on items from the well-validated ISAAC Wheezing Module. This model prospectively associates with asthma morbidity, as demonstrated in two ECHO birth cohort studies, and provides a more statistically powerful method of testing etiologic hypotheses of childhood wheezing illness and asthma.

Using urine metabolomics to understand the pathogenesis of infant respiratory syncytial virus (RSV) infection and its role in childhood wheezing

BackgroundRespiratory syncytial virus (RSV) infection in infants causes significant morbidity and is the strongest risk factor associated with asthma. Metabolites, which reflect the interactions between host cell and virus, provide an opportunity to identify the pathways that underlie severe infections and asthma development.ObjectiveTo study metabolic profile differences between infants with RSV infection, and human rhinovirus (HRV) infection, and healthy infants. To compare infant metabolic differences between children who do and do not wheeze.MethodsIn a term birth cohort, urine was collected while healthy and during acute viral respiratory infection with RSV and HRV. We used 1H-NMR to identify urinary metabolites. Multivariate and univariate statistics were used to discriminate metabolic profiles of infants with either RSV ARI, or HRV ARI, and healthy infants. Multivariable logistic regression was used to assess the association of urine metabolites with 1st-, 2nd-, and 3rd-year recurrent wheezing.ResultsSeveral metabolites in nicotinate and nicotinamide metabolism pathways were down-regulated in infants with RSV infection compared to healthy controls. There were no significant differences in metabolite profiles between infants with RSV infection and infants with HRV Infection. Alanine was strongly associated with reduced risk of 1st-year wheezing (OR 0.18[0.0, 0.46]) and 2nd-year wheezing (OR 0.31[0.13, 0.73]), while 2-hydroxyisobutyric acid was associated with increased 3rd-year wheezing (OR 5.02[1.49, 16.93]) only among the RSV infected subset.ConclusionThe metabolites associated with infant RSV infection and recurrent-wheezing are indicative of viral takeover of the cellular machinery and resources to enhance virulence, replication, and subversion of the host immune-response, highlighting metabolic pathways important in the pathogenesis of RSV infection and wheeze development.

Seasonal patterns of Asthma medication fills among diverse populations of the United States

ABSTRACT Objective: Nonadherence to controller and overuse of reliever asthma medications are associated with exacerbations. We aimed to determine patterns of seasonal asthma medication use and to identify time period(s) during which interventions to improve medication adherence could reduce asthma morbidity. Methods: We conducted a retrospective cohort study of asthmatics 4–50 years of age and enrolled in three diverse health insurance plans. Seasonal patterns of medications were reported by monthly prescription fill rates per 1000 individuals with asthma from 1998 to 2013, and stratified by healthcare plan, sex, and age. Results: There was a distinct and consistent seasonal fill pattern for all asthma medications. The lowest fill rate was observed in the month of July. Fills increased in the autumn and remained high throughout the winter and spring. Compared with the month of May with high medication fills, July represented a relative decrease of fills ranging from 13% (rate ratio, RR: 0.87, 95% confidence interval, 95%CI: 0.72–1.04) for the combination of inhaled corticosteroids (ICS) + long acting beta agonists (LABA) to 45% (RR: 0.55, 95%CI: 0.49–0.61) for oral corticosteroids. Such a seasonal pattern was observed each year across the 16-year study period, among healthcare plans, sexes, and ages. LABA containing control medication (ICS+LABA and LABA) fill rates were more prevalent in older asthmatics, while leukotriene receptor antagonists were more prevalent in the younger population. Conclusions: A seasonal pattern of asthma medication fill rates likely represents a reactive response to a loss of disease control and increased symptoms. Adherence to and consistent use of asthma medications among individuals who use medications in reaction to seasonal exacerbations might be a key component in reducing the risk of asthma exacerbations.