Perfluoroalkyl substances, metabolomic profiling, and alterations in glucose homeostasis among overweight and obese Hispanic children: A proof-of-concept analysis

OBJECTIVE

To examine the prospective associations between exposure to perfluoroalkyl substances (PFASs) and longitudinal measurements of glucose metabolism in high-risk overweight and obese Hispanic children.

METHODS

Forty overweight and obese Hispanic children (8-14 years) from urban Los Angeles underwent clinical measures and 2-hour oral glucose tolerance tests (OGTT) at baseline and a follow-up visit (range: 1-3 years after enrollment). Baseline plasma perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), perfluorohexane sulfonic acid (PFHxS), and the plasma metabolome were measured by liquid-chromatography with high-resolution mass spectrometry. Multiple linear regression models were used to assess the association between baseline PFASs and changes in glucose homeostasis over follow-up. A metabolome-wide association study coupled with pathway enrichment analysis was performed to evaluate metabolic dysregulation associated with plasma PFASs concentrations. We performed a structural integrated analysis aiming to characterize the joint impact of all factors and to identify latent clusters of children with alterations in glucose homeostasis, based on their exposure and metabolomics profile.

RESULTS

Each ln (ng/ml) increase in PFOA and PFHxS concentrations was associated with a 30.6 mg/dL (95% CI: 8.8-52.4) and 10.2 mg/dL (95% CI: 2.7-17.7) increase in 2-hour glucose levels, respectively. A ln (ng/ml) increase in PFHxS concentrations was also associated with 17.8 mg/dL increase in the glucose area under the curve (95% CI: 1.5-34.1). Pathway enrichment analysis showed significant alterations of lipids (e.g., glycosphingolipids, linoleic acid, and de novo lipogenesis), and amino acids (e.g., aspartate and asparagine, tyrosine, arginine and proline) in association to PFASs exposure. The integrated analysis identified a cluster of children with increased 2-h glucose levels over follow up, characterized by increased PFAS levels and altered metabolite patterns.

CONCLUSIONS

This proof-of-concept analysis shows that higher PFAS exposure was associated with dysregulation of several lipid and amino acid pathways and longitudinal alterations in glucose homeostasis in Hispanic youth. Larger studies are needed to confirm these findings and fully elucidate the underlying biological mechanisms.

Costimulation of type-2 innate lymphoid cells by GITR promotes effector function and ameliorates type 2 diabetes

Metabolic syndrome is characterized by disturbances in glucose homeostasis and the development of low-grade systemic inflammation, which increase the risk to develop type-2 diabetes mellitus (T2DM). Type-2 innate lymphoid cells (ILC2s) are a recently discovered immune population secreting Th2-cytokines. While previous studies show how ILC2s can play a critical role in the regulation of metabolic homeostasis in the adipose tissue, a therapeutic target capable of modulating ILC2 activation has yet to be identified.

We found that GITR, a member of the TNF superfamily, is expressed on murine adipose tissue ILC2s and its engagement on activated ILC2s induces Th2-cytokine secretion. Moreover, we showed that GITR engagement on ILC2s improves glucose homeostasis resulting in both protection against insulin-resistance onset and amelioration of established insulin-resistance. Our adoptive transfer studies demonstrated that this protective effect is dependent on ILC2-derived Th2-cytokines, particularly IL-13. Transcriptome analysis demonstrated that GITR agonist activates the NF-κB signaling pathway and inhibits ILC2 apoptosis, altogether favoring ILC2 survival and activation. Finally, we also found that GITR is expressed on human adipose tissue resident ILC2s and GITR engagement robustly induces Th2-cytokine.

Together, these results highlight the critical role of GITR as a novel therapeutic molecule against T2DM and its fundamental role as an immune checkpoint for activated ILC2s.

High intake of dietary fructose in overweight/obese teenagers associated with depletion of Eubacterium and Streptococcus in gut microbiome

Background: A western high fat, high carbohydrate diet has been shown to be associated with decreased gut bacterial diversity and reductions in beneficial bacteria. This gut bacteria dysbiosis could develop in early life and contribute to chronic disease risk such as obesity, type 2 diabetes and non-alcoholic fatty liver disease.Objective: To determine how dietary macronutrients are associated with the relative abundance of gut bacteria in healthy adolescents.Methods: Fifty-two obese participants (12-19 years) from two studies, many who were primarily of Hispanic background, provided fecal samples for 16S rRNA gene sequencing. Dietary macronutrients were assessed using 24-hour diet recalls and body composition was assessed using DEXA. General regression models assuming a negative binomial distribution were used to examine the associations between gut bacteria and dietary fiber, saturated fat, unsaturated fats, protein, added sugar, total sugar and free fructose after adjusting for age, gender, race/ethnicity, body fat percentage, study and caloric intake.Results: The genera Eubacterium (Benjamini-Hochberg (BH) corrected p-value = 0.10) and Streptococcus (BH corrected p-value = 0.04) were inversely associated with dietary fructose intake. There were no other significant associations between abundances of gut microbes and other dietary macronutrients, including fiber, fat, protein, total sugar or added sugar.Conclusions: High dietary fructose was associated with lower abundance of the beneficial microbes Eubacterium and Streptococcus, which are involved with carbohydrate metabolism.

Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach

BACKGROUND

Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative importance of determinants, including environmental exposures. Thus, we aim to develop a prediction model for bronchitic symptoms.

METHODS

Schoolchildren from the population-based southern California Children's Health Study were visited annually from 2003 to 2012. Bronchitic symptoms over the prior 12 months were assessed by questionnaire. A gradient boosting model was fit using groups of risk factors (including traffic/air pollution exposures) for all children and by asthma status. Training data consisted of one observation per participant in a random study year (for 50% of participants). Validation data consisted of: (1) a random (later) year in the same participants (within-participant); (2) a random year in participants excluded from the training data (across-participant).

RESULTS

At baseline, 13.2% of children had asthma and 18.1% reported bronchitic symptoms. Models performed similarly within- and across-participant. Previous year symptoms/medication use provided much of the predictive ability (across-participant area under the receiver operating characteristic curve (AUC): 0.76 vs 0.78 for all risk factors, in all participants). Traffic/air pollution exposures added modestly to prediction as did body mass index percentile, age and parent stress.

CONCLUSIONS

Regardless of asthma status, previous symptoms were the most important predictors of current symptoms. Traffic/air pollution variables contribute modest predictive information, but impact large populations. Methods proposed here could be generalized to personalized exacerbation predictions in future longitudinal studies to support targeted prevention efforts.

Within-subject effects of environmental and social stressors on pre- and post-partum obesity-related biobehavioral responses in low-income Hispanic women: protocol of an intensive longitudinal study

Background

Disproportionately high rates of maternal overweight and obesity among the Hispanic population before, during, and after pregnancy pose serious health concerns for both mothers (e.g., preeclampsia, gestational diabetes, weight retention) and children (e.g., elevated lifelong obesity risk). A growing body of evidence implicates environmental exposures (e.g., air pollution, metals) and social stressors (e.g., poverty, violence) in contributing to obesity-related biobehavioral processes, such as physical activity, dietary intake, perceived stress, and cortisol regulation. However, current understanding of the role of environmental exposures and social stressors on obesity-related biobehavioral processes is limited by infrequent, inter-individual measurement, and lack of personal exposure monitoring.

Methods

The “Maternal and Developmental Risks from Environmental and Social Stressors” (MADRES) real-time and personal sampling study examines the within-subject day-level effects of environmental and social stressors on maternal pre- and post-partum obesity-related biobehavioral responses. Among a cohort of 65 low-income, Hispanic women in urban Los Angeles, this study uses innovative personal, real-time data capture strategies (e.g., ecological momentary assessment [EMA], personal exposure monitoring, geolocation monitoring, accelerometry) to repeatedly assess obesity-related processes during the 1st and 3rd trimester, and at 4–6 months postpartum. Day-level effects of environmental exposures and social stressors on women’s physical activity, diet, perceived stress and salivary cortisol measured across repeated days will be tested using multilevel modeling.

Discussion

Hispanic women of childbearing age bear a disproportionately high burden of obesity, and this population is also unduly exposed to numerous obesogenic settings. By using innovative real-time data capture strategies, the current study will uncover the daily impacts of environmental and social stressor exposures on women’s obesity-related biobehavioral responses, which over time can lead to excessive gestational weight gain, postpartum weight retention and can pose serious consequences for both mother and child. Findings from the real-time and personal sampling study will identify key mechanistic targets for policy, clinical, and programmatic interventions, with the potential for broad-reaching public health impacts.

Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data

Background

Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity.

Objective

We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points.

Methods

We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing.

Results

In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities.

Conclusions

In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.

Gene Coexpression Networks in Whole Blood Implicate Multiple Interrelated Molecular Pathways in Obesity in People with Asthma

OBJECTIVE

Asthmatic children who develop obesity through adolescence have poorer disease outcomes compared with those who do not. This study aimed to characterize the biology of childhood asthma complicated by adult obesity.

METHODS

Gene expression networks are powerful statistical tools for characterizing human disease that leverage the putative coregulatory relationships of genes to infer relevant biological pathways. Weighted gene coexpression network analysis of gene expression data was performed in whole blood from 514 adult asthmatic subjects. Then, module preservation and association replication analyses were performed in 418 subjects from two independent asthma cohorts (one pediatric and one adult).

RESULTS

A multivariate model was identified in which three gene coexpression network modules were associated with incident obesity in the discovery cohort (each P < 0.05). Two module memberships were enriched for genes in pathways related to platelets, integrins, extracellular matrix, smooth muscle, NF-κB signaling, and Hedgehog signaling. The network structures of each of the obesity modules were significantly preserved in both replication cohorts (permutation P = 9.999E-05). The corresponding module gene sets were significantly enriched for differential expression in subjects with obesity in both replication cohorts (each P < 0.05).

CONCLUSIONS

The gene coexpression network profiles thus implicate multiple interrelated pathways in the biology of an important endotype of asthma with obesity.

Longitudinal associations of in utero and early life near-roadway air pollution with trajectories of childhood body mass index

BACKGROUND

Evidence suggests that childhood near-roadway air pollution (NRAP) exposures contribute to increased body mass index (BMI); however, effects of NRAP exposure during the vulnerable periods including in utero and first year of life have yet to be established. In this study, we examined whether exposure to elevated concentrations of NRAP during in utero and/or first year of life increase childhood BMI growth.

METHODS

Participants in the Children's Health Study enrolled from 2002 to 2003 with annual visits over a four-year period and who changed residences before study entry were included (n = 2318). Annual height and weight were measured and lifetime residential NRAP exposures including in utero and first year of life periods were estimated by nitrogen oxides (NO_x) using the California line-source dispersion model. Linear mixed effects models assessed in utero or first year near-road freeway and non-freeway NO_x exposures and BMI growth after adjusting for age, sex, race/ethnicity, parental education, Spanish questionnaire, and later childhood near-road NO_x exposure.

RESULTS

A two-standard deviation difference in first year of life near-road freeway NO_x exposure was associated with a 0.1 kg/m² (95% confidence interval (CI): 0.03, 0.2) faster increase in BMI growth per year and a 0.5 kg/m² (95% CI: 0.02, 0.9) higher attained BMI at age 10 years.

CONCLUSIONS

Higher exposure to early life NRAP increased the rate of change of childhood BMI and resulted in a higher attained BMI at age 10 years that were independent of later childhood exposures. These findings suggest that elevated early life NRAP exposures contribute to increased obesity risk in children.

Association of Prenatal Exposure to Ambient and Traffic-Related Air Pollution With Newborn Thyroid Function: Findings From the Children's Health Study

IMPORTANCE

Thyroid hormones are critical for fetal growth and development. Prenatal particulate matter (PM) air pollution exposure has been associated with altered newborn thyroid function, but other air pollutants have not been evaluated, and critical windows of exposure are unknown.

OBJECTIVES

To investigate the association of prenatal exposure to ambient and traffic-related air pollutants with newborn thyroid function and identify critical windows of exposure.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used data from 2050 participants in the Children's Health Study. Statistical analyses were conducted from 2017 to 2018 using pregnancy and birth data from 1994 to 1997 for a subset of participants recruited from schools in 13 southern California communities in 2002 to 2003 when participants were 5 to 7 years of age. Participants were included in statistical analyses if they could be linked to their newborn blood spot and had complete monthly exposure measures for at least 1 air pollutant across pregnancy.

EXPOSURES

Prenatal monthly averages of ambient (PM diameter <2.5 μm [PM2.5] or <10 μm [PM10], nitrogen dioxide, and ozone) and traffic-related (freeway, nonfreeway, and total nitrogen oxides) air pollutant exposures were determined using inverse distance-squared weighting of central monitoring data and the California Line Source Dispersion model, respectively.

MAIN OUTCOMES AND MEASURES

Newborn heel-stick blood spot total thyroxine (TT4) measures were acquired retrospectively from the California Department of Public Health.

RESULTS

Participants included 2050 newborns (50.5% male), with a median (interquartile range) age of 20 (15-29) hours. The majority of newborns were Hispanic white (1202 [58.6%]) or non-Hispanic white (638 [31.1%]). Sixty-six (3.2%) were black and 144 (7.0%) were from other racial/ethnic groups. The mean (SD) newborn TT4 measure was 16.2 (4.3) μg/dL. A 2-SD increase in prenatal PM2.5 (16.3 μg/m3) and PM10 (22.2 μg/m3) was associated with a 1.2-μg/dL (95% CI, 0.5-1.8 μg/dL) and 1.5-μg/dL (95% CI, 0.9-2.1 μg/dL) higher TT4 measure, respectively, in covariate-adjusted linear regression models. Other pollutants were not consistently associated with newborn TT4. Distributed lag models revealed that PM2.5 exposure during months 3 to 7 of pregnancy and PM10 exposure during months 1 to 8 of pregnancy were associated with significantly higher newborn TT4 concentrations (P < .05).

CONCLUSIONS AND RELEVANCE

Prenatal PM exposure, particularly in early pregnancy and midpregnancy, is associated with higher newborn TT4 concentrations. Future studies should assess the health implications of PM-associated differences in newborn TT4 concentrations.

Gene Promoter Hypermethylation Detected in Sputum Predicts FEV1 Decline and All-Cause Mortality in Smokers

RATIONALE

Gene promoter hypermethylation detected in sputum assesses the extent of field cancerization and predicts lung cancer (LC) risk in ever-smokers. A rapid decline of FEV1 is a major driver for development of airway obstruction.

OBJECTIVES

To assess the effects of methylation of 12 genes on FEV1 decline and of FEV1 decline on subsequent LC incidence using two independent, longitudinal cohorts (i.e., LSC [Lovelace Smokers Cohort] and PLuSS [Pittsburgh Lung Screening Study]).

METHODS

Gene methylation was measured in sputum using two-stage nested methylation-specific PCR. The linear mixed effects model was used to assess the effects of studied variables on FEV1 decline.

MEASUREMENTS AND MAIN RESULTS

A dose-dependent relationship between number of genes methylated and FEV1 decline was identified, with smokers with three or more methylated genes having 27.8% and 10.3% faster FEV1 decline than smokers with zero to two methylated genes in the LSC and PLuSS cohort, respectively (all P < 0.01). High methylation in sputum was associated with a shorter latency for LC incidence (log-rank P = 0.0048) and worse all-cause mortality (log-rank P < 0.0001). Smokers with subsequent LC incidence had a more rapid annual decline of FEV1 (by 5.2 ml, P = 0.038) than smoker control subjects.

CONCLUSIONS

Gene methylation detected in sputum predicted FEV1 decline, LC incidence, and all-cause mortality in smokers. Rapid FEV1 decline may be a risk factor for LC incidence in smokers, which may explain a greater prevalence of airway obstruction seen in patients with LC.

Prenatal traffic-related air pollution exposures, cord blood adipokines and infant weight

OBJECTIVE

Studies suggest that prenatal exposure to traffic-related air pollution (TRAP) may contribute to childhood obesity. While exact mechanisms for this association are unknown, circulating adipokines are hypothesized to contribute to early-life weight gain.

METHODS

The Maternal and Child Health Study birth cohort included 136 women from the Los Angeles County + University of Southern California Medical Center. This study estimated prenatal residential TRAP exposure and used linear regression analysis to examine associations between adipokines with TRAP exposure and infant weight change (birth to 6 months).

RESULTS

A one standard deviation (1-SD: 2 ppb) increase in prenatal non-freeway nitrogen oxides was associated with 33% (P = 0.01) higher leptin and 9% higher high molecular weight adiponectin levels (P = 0.07) in cord blood. Leptin levels were 71% higher in mothers who lived <75 m than those living >300 m from major roadways (P = 0.03). A 1-SD (10 ng mL-1 ) increase in leptin was associated with a significant increase in infant weight change in female infants (0.62 kg, P = 0.02) but not male infants (0.11 kg, P = 0.48).

CONCLUSIONS

Higher TRAP exposures were associated with higher cord blood levels of leptin and high molecular weight adiponectin. These adipokines were associated with increased infant weight change in female infants, which may have implications for future obesity risk.

Ambient and Traffic-Related Air Pollution Exposures as Novel Risk Factors for Metabolic Dysfunction and Type 2 Diabetes

PURPOSE OF REVIEW

Diabetes mellitus is a top contributor to the global burden of mortality and disability in adults. There has also been a slow, but steady rise in prediabetes and type 2 diabetes in youth. The current review summarizes recent findings regarding the impact of increased exposure to air pollutants on the type 2 diabetes epidemic.

RECENT FINDINGS

Human and animal studies provide strong evidence that exposure to ambient and traffic-related air pollutants such as particulate matter (PM), nitrogen dioxide (NO2), and nitrogen oxides (NOx) play an important role in metabolic dysfunction and type 2 diabetes etiology. This work is supported by recent findings that have observed similar effect sizes for increased exposure to air pollutants on clinical measures of risk for type 2 diabetes in children and adults. Further, studies indicate that these effects may be more pronounced among individuals with existing risk factors, including obesity and prediabetes.

SUMMARY

Current epidemiological evidence suggests that increased air pollution exposure contributes to alterations in insulin signaling, glucose metabolism, and beta (β)-cell function. Future work is needed to identify the specific detrimental pollutants that alter glucose metabolism. Additionally, advanced tools and new areas of investigation present unique opportunities to study the underlying mechanisms, including intermediate pathways, that link increased air pollution exposure with type 2 diabetes onset.

Single-Cell Digital Lysates Generated by Phase-Switch Microfluidic Device Reveal Transcriptome Perturbation of Cell Cycle

With conventional gene expression profiling, information concerning cellular heterogeneity is often lost in the physical mixing and averaging of millions of cells. Single-cell transcriptome analysis has the potential to address these issues. However, there is a need to determine how many cells are needed to draw meaningful conclusions in each single-cell study. Here, we introduce the concept of "digital lysate" for assessing cellular heterogeneity with a phase-switch microfluidic platform and apply it to construct a molecular map of transcriptome perturbation during the cell cycle. Using a phase-switch droplet microfluidic platform and next-generation sequencing, we obtained transcriptomes of single cells by random sampling. Digital lysates were generated by permutating and averaging multiple single-cell transcriptomes. In our studied cell populations, digital lysates converged to physical lysates ( r = 0.93), and the sample-to-sample repeatability was comparable to that of conventional analysis of a physical lysate ( r = 0.98). After determining the number of cells needed, single-cell transcriptomes were used to organize cells into a map by molecular similarity, and the map was validated by cell cycle-specific markers ( p = 0.003). Cell cycle regulatory genes were inferred using this molecular map and verified with siRNA assays. The study described here provides an effective approach, the generation and analysis of digital lysates, to investigate cellular heterogeneity.

Exposure to traffic-related air pollution and the composition of the gut microbiota in overweight and obese adolescents

BACKGROUND

Traffic-related air pollution (TRAP) exposure has been linked to type 2 diabetes and metabolic dysfunction in humans. Animal studies suggest that air pollutants may alter the composition of the gut microbiota, which may negatively impact metabolic health through changes in the composition and/or function of the gut microbiome.

OBJECTIVES

The primary aim of this study was to determine whether elevated TRAP exposure was correlated with gut bacterial taxa in overweight and obese adolescents from the Meta-AIR (Metabolic and Asthma Incidence Research) study. The secondary aim was to examine whether gut microbial taxa correlated with TRAP were also correlated with risk factors for type 2 diabetes (e.g., fasting glucose levels). We additionally explored whether correlations between TRAP and these metabolic risk factors could be explained by the relative abundance of these taxa.

METHODS

Participants (17-19 years; n=43) were enrolled between 2014 and 2016 from Southern California. The CALINE4 line dispersion model was used to model prior year residential concentrations of nitrogen oxides (NOx) as a marker of traffic emissions. The relative abundance of fecal microbiota was characterized by 16S rRNA sequencing and spearman partial correlations were examined after adjusting for body fat percent.

RESULTS

Freeway TRAP was correlated with decreased Bacteroidaceae (r=-0.48; p=0.001) and increased Coriobacteriaceae (r=0.48; p<0.001). These same taxa were correlated with fasting glucose levels, including Bacteroidaceae (r=-0.34; p=0.04) and Coriobacteriaceae (r=0.41; p<0.01). Further, freeway TRAP was positively correlated fasting glucose (r=0.45; p=0.004) and Bacteroidaceae and Coriobacteriaceae explained 24% and 29% of the correlation between TRAP and fasting glucose levels.

CONCLUSIONS

Increased TRAP exposure was correlated with gut microbial taxa and fasting glucose levels. Gut microbial taxa that were correlated with TRAP partially explained the correlation between TRAP and fasting glucose levels. These results suggest that exposure to air pollutants may negatively impact metabolic health via alterations in the gut microbiota.

Effects of air pollution exposure on glucose metabolism in Los Angeles minority children

OBJECTIVES

Growing evidence indicates that ambient (AAP: NO₂ , PM_2.5 and O₃ ) and traffic-related air pollutants (TRAP) contribute to metabolic disease risk in adults; however, few studies have examined these relationships in children.

METHODS

Metabolic profiling was performed in 429 overweight and obese African-American and Latino youth living in urban Los Angeles, California. This cross-sectional study estimated individual residential air pollution exposure and used linear regression to examine relationships between air pollution and metabolic outcomes.

RESULTS

AAP and TRAP exposure were associated with adverse effects on glucose metabolism independent of body fat percent. PM_2.5 was associated with 25.0% higher fasting insulin (p < 0.001), 8.3% lower insulin sensitivity (p < 0.001), 14.7% higher acute insulin response to glucose (p = 0.001) and 1.7% higher fasting glucose (p < 0.001). Similar associations were observed for increased NO₂ exposure. TRAP from non-freeway roads was associated with 12.1% higher insulin (p < 0.001), 6.9% lower insulin sensitivity (p = 0.02), 10.8% higher acute insulin response to glucose (p = 0.003) and 0.7% higher fasting glucose (p = 0.047).

CONCLUSIONS

Elevated air pollution exposure was associated with a metabolic profile that is characteristic of increased risk for type 2 diabetes. These results indicate that increased prior year exposure to air pollution may adversely affect type 2 diabetes-related pathophysiology in overweight and obese minority children.

Dietary Nutrient Intake, Ethnicity, and Epigenetic Silencing of Lung Cancer Genes Detected in Sputum in New Mexican Smokers

Lung cancer gene methylation detected in sputum assesses field cancerization and predicts lung cancer incidence. Hispanic smokers have higher lung cancer susceptibility compared with non-Hispanic whites (NHW). We aimed to identify novel dietary nutrients affecting lung cancer gene methylation and determine the degree of ethnic disparity in methylation explained by diet. Dietary intakes of 139 nutrients were assessed using a validated Harvard food frequency questionnaire in 327 Hispanics and 1,502 NHWs from the Lovelace Smokers Cohort. Promoter methylation of 12 lung cancer genes was assessed in sputum DNA. A global association was identified between dietary intake and gene methylation (P_permutation = 0.003). Seventeen nutrient measurements were identified with magnitude of association with methylation greater than that seen for folate. A stepwise approach identified B12, manganese, sodium, and saturated fat as the minimally correlated set of nutrients whose optimal intakes could reduce the methylation by 36% (P_permutation < 0.001). Six protective nutrients included vitamin D, B12, manganese, magnesium, niacin, and folate. Approximately 42% of ethnic disparity in methylation was explained by insufficient intake of protective nutrients in Hispanics compared with NHWs. Functional validation of protective nutrients showed an enhanced DNA repair capacity toward double-strand DNA breaks, a mechanistic biomarker strongly linked to acquisition of lung cancer gene methylation in smokers. Dietary intake is a major modifiable factor for preventing promoter methylation of lung cancer genes in smokers' lungs. Complex dietary supplements could be developed on the basis of these protective nutrients for lung cancer chemoprevention in smokers. Hispanic smokers may benefit the most from this complex for reducing their lung cancer susceptibility. Cancer Prev Res; 11(2); 93-102. ©2017 AACR.

Epigenetic regulation of AXL and risk of childhood asthma symptoms

Background

AXL is one of the TAM (TYRO3, AXL and MERTK) receptor tyrosine kinases and may affect numerous immune-related health conditions. However, the role for AXL in asthma, including its epigenetic regulation, has not been extensively studied.

Methods

We investigated the association between AXL DNA methylation at birth and risk of childhood asthma symptoms at age 6 years. DNA methylation of multiple CpG loci across the regulatory regions of AXL was measured in newborn bloodspots using the Illumina HumanMethylation450 array on a subset of 246 children from the Children's Health Study (CHS). Logistic regression models were fitted to assess the association between asthma symptoms and DNA methylation. Findings were evaluated for replication in a separate population of 1038 CHS subjects using Pyrosequencing on newborn bloodspot samples. AXL genotypes were extracted from genome-wide data.

Results

Higher average methylation of CpGs in the AXL gene at birth was associated with higher risk of parent-reported wheezing, and the association was stronger in girls than in boys. This relationship reflected the methylation status of the gene-body region near the 5' end, for which a 1% higher methylation level was significantly associated with a 72% increased risk of ever having wheezed by 6 years. The association of one CpG locus, cg00360107 was replicated using Pyrosequencing. Increased AXL methylation was also associated with lower mRNA expression level of this gene in lung tissue from the Cancer Genome Atlas (TCGA) dataset. Furthermore, AXL DNA methylation was strongly linked to underlying genetic polymorphisms.

Conclusions

AXL DNA methylation at birth was associated with higher risk for asthma-related symptoms in early childhood.

Dietary nutrients associated with preservation of lung function in Hispanic and non-Hispanic white smokers from New Mexico

Background

COPD is the third leading cause of death in the United States. Cigarette smoking accelerates the age-related forced expiratory volume in 1 s (FEV₁) decline, an important determinant for the genesis of COPD. Hispanic smokers have lower COPD prevalence and FEV₁ decline than non-Hispanic whites (NHWs).

Patients and methods

A nutritional epidemiological study was conducted in the Lovelace Smokers cohort (LSC; n=1,829) and the Veterans Smokers cohort (n=508) to identify dietary nutrients (n=139) associated with average FEV₁ and its decline and to assess whether nutrient intakes could explain ethnic disparity in FEV₁ decline between Hispanics and NHW smokers.

Results

Nutrients discovered and replicated to be significantly associated with better average FEV₁ included magnesium, folate, niacin, vitamins A and D, eicosenoic fatty acid (20:1n9), eicosapentaenoic acid (20:5n3), docosapentaenoic acid (DPA; 22:5n3), docosahexaenoic acid (22:6n3), and fiber. In addition, greater intakes of eicosenoic fatty acid and DPA were associated with slower FEV₁ decline in the LSC. Among omega 3 polyunsaturated fatty acids, DPA is the most potent nutrient associated with better average FEV₁ and slower FEV₁ decline. Adverse effect of continuous current smoking on FEV₁ decline was completely negated in LSC members with high DPA intake (>20 mg/day). Slower FEV₁ decline in Hispanics compared to NHWs may be due to the greater protection of eicosenoic fatty acid and DPA for FEV₁ decline rather than greater intake of protective nutrients in this ethnic group.

Conclusion

The protective nutrients for the preservation of FEV₁ in ever smokers could lay foundation for designing individualized nutritional intervention targeting “optimal physiological levels” in human to improve lung function in ever smokers. Ethnic disparity in FEV₁ decline may be explained by difference in magnitude of protection of dietary intakes of eicosenoic fatty acid and DPA between Hispanics and NHWs.

Constrained Mixed-Effect Models with Ensemble Learning for Prediction of Nitrogen Oxides Concentrations at High Spatiotemporal Resolution

Spatiotemporal models to estimate ambient exposures at high spatiotemporal resolutions are crucial in large-scale air pollution epidemiological studies that follow participants over extended periods. Previous models typically rely on central-site monitoring data and/or covered short periods, limiting their applications to long-term cohort studies. Here we developed a spatiotemporal model that can reliably predict nitrogen oxide concentrations with a high spatiotemporal resolution over a long time span (>20 years). Leveraging the spatially extensive highly clustered exposure data from short-term measurement campaigns across 1-2 years and long-term central site monitoring in 1992-2013, we developed an integrated mixed-effect model with uncertainty estimates. Our statistical model incorporated nonlinear and spatial effects to reduce bias. Identified important predictors included temporal basis predictors, traffic indicators, population density, and subcounty-level mean pollutant concentrations. Substantial spatial autocorrelation (11-13%) was observed between neighboring communities. Ensemble learning and constrained optimization were used to enhance reliability of estimation over a large metropolitan area and a long period. The ensemble predictions of biweekly concentrations resulted in an R2 of 0.85 (RMSE: 4.7 ppb) for NO2 and 0.86 (RMSE: 13.4 ppb) for NOx. Ensemble learning and constrained optimization generated stable time series, which notably improved the results compared with those from initial mixed-effects models.

Genetic and epigenetic susceptibility of airway inflammation to PM2.5 in school children: new insights from quantile regression

BACKGROUND

The fractional concentration of exhaled nitric oxide (FeNO) is a biomarker of airway inflammation that has proved to be useful in investigations of genetic and epigenetic airway susceptibility to ambient air pollutants. For example, susceptibility to airway inflammation from exposure to particulate matter with aerodynamic diameter < =2.5 μm (PM_2.5) varies by haplotypes and promoter region methylation in inducible nitric oxide synthase (iNOS encoded by NOS2). We hypothesized that PM2.5 susceptibility associated with these epigenetic and genetic variants may be greater in children with high FeNO from inflamed airways. In this study, we investigated genetic and epigenetic susceptibility to airborne particulate matter by examining whether the joint effects of PM2.5, NOS2 haplotypes and iNOS promoter methylation significantly vary across the distribution of FeNO in school children.

METHODS

The study included 940 school children in the southern California Children's Health Study who provided concurrent buccal samples and FeNO measurements. We used quantile regression to examine susceptibility by estimating the quantile-specific joint effects of PM_2.5, NOS2 haplotype and methylation on FeNO.

RESULTS

We discovered striking differences in susceptibility to PM_2.5 in school children. The joint effects of short-term PM_2.5 exposure, NOS2 haplotypes and methylation across the FeNO distribution were significantly larger in the upper tail of the FeNO distribution, with little association in its lower tail, especially among children with asthma and Hispanic white children.

CONCLUSION

School-aged children with higher FeNO have greater genetic and epigenetic susceptibility to PM_2.5, highlighting the importance of investigating effects across the entire distribution of FeNO.

Particulate matter air pollution and liver cancer survival

Particulate matter (PM) air pollution exposure has been associated with cancer incidence and mortality especially with lung cancer. The liver is another organ possibly affected by PM due to its role in detoxifying xenobiotics absorbed from PM. Various studies have investigated the mechanistic pathways between inhaled pollutants and liver damage, cancer incidence, and tumor progression. However, little is known about the effects of PM on liver cancer survival. Twenty thousand, two hundred and twenty-one California Cancer Registry patients with hepatocellular carcinoma (HCC) diagnosed between 2000 and 2009 were used to examine the effect of exposure to ambient PM with diameter <2.5 μm (PM2.5 ) on HCC survival. Cox proportional hazards models were used to estimate hazard ratios (HRs) relating PM2.5 to all-cause and liver cancer-specific mortality linearly and nonlinearly-overall and stratified by stage at diagnosis (local, regional and distant)-adjusting for potential individual and geospatial confounders.PM2.5 exposure after diagnosis was statistically significantly associated with HCC survival. After adjustment for potential confounders, the all-cause mortality HR associated with a 1 standard deviation (5.0 µg/m3 ) increase in PM2.5 was 1.18 (95% CI: 1.16-1.20); 1.31 (95% CI:1.26-1.35) for local stage, 1.19 (95% CI:1.14-1.23) for regional stage, and 1.05 (95% CI:1.01-1.10) for distant stage. These associations were nonlinear, with substantially larger HRs at higher exposures. The associations between liver cancer-specific mortality and PM2.5 were slightly attenuated compared to all-cause mortality, but with the same patterns.Exposure to elevated PM2.5 after the diagnosis of HCC may shorten survival, with larger effects at higher concentrations.

Longitudinal Associations Between Ambient Air Pollution With Insulin Sensitivity, β-Cell Function, and Adiposity in Los Angeles Latino Children

Evidence suggests that ambient air pollution (AAP) exposure may contribute to the development of obesity and type 2 diabetes. The objective of this study was to determine whether exposure to elevated concentrations of nitrogen dioxide (NO₂) and particulate matter with aerodynamic diameter <2.5 (PM_2.5) had adverse effects on longitudinal measures of insulin sensitivity (S_I), β-cell function, and obesity in children at high risk for developing diabetes. Overweight and obese Latino children (8-15 years; n = 314) were enrolled between 2001 and 2012 from Los Angeles, CA, and followed for an average of 3.4 years (SD 3.1 years). Linear mixed-effects models were fitted to assess relationships between AAP exposure and outcomes after adjusting for covariates including body fat percent. Higher NO₂ and PM_2.5 were associated with a faster decline in S_I and a lower S_I at age 18 years, independent of adiposity. NO₂ exposure negatively affected β-cell function, evidenced by a faster decline in disposition index (DI) and a lower DI at age 18 years. Higher NO₂ and PM_2.5 exposures over follow-up were also associated with a higher BMI at age 18 years. AAP exposure may contribute to development of type 2 diabetes through direct effects on S_I and β-cell function.

Air Pollution and Lung Function in Minority Youth with Asthma in the GALA II (Genes-Environments and Admixture in Latino Americans) and SAGE II (Study of African Americans, Asthma, Genes, and Environments) Studies

RATIONALE

Adverse effects of exposures to ambient air pollution on lung function are well documented, but evidence in racial/ethnic minority children is lacking.

OBJECTIVES

To assess the relationship between air pollution and lung function in minority children with asthma and possible modification by global genetic ancestry.

METHODS

The study population consisted of 1,449 Latino and 519 African American children with asthma from five different geographical regions in the mainland United States and Puerto Rico. We examined five pollutants (particulate matter ≤10 μm and ≤2.5 μm in diameter, ozone, nitrogen dioxide, and sulfur dioxide), derived from participant residential history and ambient air monitoring data, and assessed over several time windows. We fit generalized additive models for associations between pollutant exposures and lung function parameters and tested for interaction terms between exposures and genetic ancestry.

MEASUREMENTS AND MAIN RESULTS

A 5 μg/m(3) increase in average lifetime particulate matter less than or equal to 2.5 μm in diameter exposure was associated with a 7.7% decrease in FEV1 (95% confidence interval = -11.8 to -3.5%) in the overall study population. Global genetic ancestry did not appear to significantly modify these associations, but percent African ancestry was a significant predictor of lung function.

CONCLUSIONS

Early-life particulate exposures were associated with reduced lung function in Latino and African American children with asthma. This is the first study to report an association between exposure to particulates and reduced lung function in minority children in which racial/ethnic status was measured by ancestry-informative markers.