Next Article in Journal
Evaluation of China’s Environmental Pressures Based on Satellite NO2 Observation and the Extended STIRPAT Model
Next Article in Special Issue
A Quality Improvement Assessment of the Delivery of Mental Health Services among WTC Responders Treated in the Community
Previous Article in Journal
Maternal Harsh Physical Parenting and Behavioral Problems in Children in Religious Families in Yemen
Previous Article in Special Issue
Time to Onset of Paresthesia Among Community Members Exposed to the World Trade Center Disaster
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 16
Issue 9
10.3390/ijerph16091486
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Metabolic Syndrome Biomarkers of World Trade Center Airway Hyperreactivity: A 16-Year Prospective Cohort Study

by
Sophia Kwon
1,
George Crowley
1,
Mena Mikhail
1,
Rachel Lam
1,
Emily Clementi
1,
Rachel Zeig-Owens
2,3,4,
Theresa M. Schwartz
2,3,
Mengling Liu
5,6,
David J. Prezant
2,3 and
Anna Nolan
1,2,5,*
1
Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, New York University, School of Medicine, New York, NY 10016, USA
2
Bureau of Health Services and Office of Medical Affairs, Fire Department of New York, Brooklyn, NY 11201, USA
3
Pulmonary Medicine Division, Department of Medicine, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY 10461, USA
4
Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
5
Department of Environmental Medicine, New York University, School of Medicine, New York, NY 10016, USA
6
Division of Biostatistics, Departments of Population Health, New York University School of Medicine, New York, NY 10016, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(9), 1486; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16091486
Submission received: 19 March 2019 / Revised: 16 April 2019 / Accepted: 18 April 2019 / Published: 26 April 2019
(This article belongs to the Special Issue Long-Term Health Effects of the 9/11 Disaster)
Browse Figures
Review Reports Versions Notes

Abstract

:
Airway hyperreactivity (AHR) related to environmental exposure is a significant public health risk worldwide. Similarly, metabolic syndrome (MetSyn), a risk factor for obstructive airway disease (OAD) and systemic inflammation, is a significant contributor to global adverse health. This prospective cohort study followed N = 7486 World Trade Center (WTC)-exposed male firefighters from 11 September 2001 (9/11) until 1 August 2017 and investigated N = 539 with newly developed AHR for clinical biomarkers of MetSyn and compared them to the non-AHR group. Male firefighters with normal lung function and no AHR pre-9/11 who had blood drawn from 9 September 2001–24 July 2002 were assessed. World Trade Center-Airway Hyperreactivity (WTC-AHR) was defined as either a positive bronchodilator response (BDR) or methacholine challenge test (MCT). The electronic medical record (EMR) was queried for their MetSyn characteristics (lipid profile, body mass index (BMI), glucose), and routine clinical biomarkers (such as complete blood counts). We modeled the association of MetSyn characteristics at the first post-9/11 exam with AHR. Those with AHR were significantly more likely to be older, have higher BMIs, have high intensity exposure, and have MetSyn. Smoking history was not associated with WTC-AHR. Those present on the morning of 9/11 had 224% increased risk of developing AHR, and those who arrived in the afternoon of 9/11 had a 75.9% increased risk. Having ≥3 MetSyn parameters increased the risk of WTC-AHR by 65.4%. Co-existing MetSyn and high WTC exposure are predictive of future AHR and suggest that systemic inflammation may be a contributor.

1. Introduction

Metabolic syndrome (MetSyn) is a clinical diagnosis made by fulfilment of at least three of the five following comorbidity criteria: Abdominal obesity, insulin resistance, hypertriglyceridemia, low high density lipoproteins (HDL), and hypertension [1,2]. MetSyn and particulate matter (PM) exposure are known independent risk factors in the development of many diseases including cardiovascular disease and cancer [3]. MetSyn, classically a risk factor for cardiovascular disease, is now being investigated as a risk factor for pulmonary disease [4].
Obesity, one component of MetSyn, has been typically linked to restrictive patterns of lung disease through mechanical stress and mass loading. However, many recent studies have focused on the systemic effects of MetSyn, through hormonal and immunoinflammatory mediators, and their association with pollution exposure and subsequent respiratory disease [5,6,7,8,9,10,11]. One study suggests that adipose tissue and adipokines such as C-reactive protein (CRP) and tumor necrosis factor-α (TNF-α) may contribute to a systemic low-grade inflammatory process leading to airway hyperreactivity (AHR) [12].
The association between MetSyn and the development of AHR has been seen in several studies [13]. Multiple cross-sectional studies have shown an increased prevalence of MetSyn or its constituents amongst those with diagnosed asthma or asthma-like symptoms [14,15,16]. A meta-analysis that included cohorts in the United States (US), Canada, and Europe reported that odds of incident asthma are increased by 50% in obese individuals, and that risk increased with body weight [17]. Two prospective studies investigated adults who were asthma-free at baseline and showed that obesity and insulin resistance were MetSyn risk factors that contributed to eventual asthma or asthma-like symptoms [17,18]. Murine studies showed that mice that developed insulin resistance from a high fat diet had increased airway resistance at baseline and after methacholine provocation, indicating a component of AHR [19].
AHR and PM exposure have also been strongly linked in numerous studies. In a cohort of asthmatic and non-asthmatic children exposed to freeway and non-freeway air pollution, there was a positive association between air pollution exposure and asthmatic children [20]. In a cohort study of 40 asthmatic children who attended school in close proximity to expressways, there was an increased risk of wheezing and shortness of breath [21]. In a cross-sectional study of adults over 50 years of age in low resource countries, 5.12% of cases were secondary to PM exposure, and the prevalence ratio of asthma after each 10 μg/m3 increase of PM2.5 was 1.05 [22]. The World Trade Center (WTC) complex destruction on 11 September 2001 (9/11) led to the release of over 11,000 tons of PM, and exposed over 300,000 local workers, residents, and rescue and recovery workers [23]. An early study monitoring pulmonary function in firefighters from the Fire Department of the City of New York (FDNY) with World Trade Center Particulate Matter (WTC-PM) exposure had AHR prevalence of 40%, and over half of the studied group had persistent AHR in a follow-up exam 10 years later [24,25]. These studies established a significant association between exposure level and AHR [26].
Our initial work focused on inflammatory biomarkers, such as GM-CSF and MDC, in WTC-PM-exposed firefighters [27]. We also investigated amylin, leptin, and lipids in a subset of exposed firefighters with WTC lung injury (WTC-LI) as defined by a loss of forced expiratory volume in 1 second (FEV1) to less than the lower limit of normal (LLN), and recently validated our findings of MetSyn associated with WTC-LI in the larger exposed group [4,28]. We now investigate the impact of MetSyn on the development of WTC-associated AHR.

2. Materials and Methods

Study Design: Demographics, clinical information and serial spirometry obtained as part of the Fire Department of New York World Trade Center Health Program (FDNY WTC-HP) was extracted from the FDNY electronic medical record [29]. All WTC-exposed FDNY rescue/recovery workers (baseline cohort; N = 12,781) were included if they were firefighters, had research consent, FEV1 ≥ lower limits of normal, no AHR on available lung function testing pre-9/11, fasting blood drawn prior to WTC site closure on 24 July 2002, and available clinical endpoints, yielding a source cohort of N = 7486 (Figure 1) [30]. Exposure to WTC-PM, defined per the FDNY WTC-HP, was based on first arrival at the WTC site and considered the highest if arrived in the morning of 9/11 during the collapse of the WTC, intermediate arriving the afternoon of 9/11, and lower intensity if arriving on or after 9/12 [31]. All subjects consented to analysis of their information for research at the time of enrolment. All data was collected in compliance with the Code of Federal Regulations, Title 21, Part 11 and the Montefiore Medical Center/Albert Einstein College of Medicine (#07-09-320) and New York University (#16-01412) Institutional Review Boards have approved this study. All participants gave informed written consent.
AHR Definition. The cohort was followed longitudinally until 1 August 2017 and N = 1906 had either a pulmonary function test (PFT) with assessment of bronchodilator response (BDR) or methacholine challenge test (MCT) administered. World Trade Center Airway Hyperreactivity (WTC-AHR) was defined at the earliest positive BDR or MCT after WTC exposure. In general, a bronchoprovocation test such as an MCT may be utilized to assess hyperreactivity, whereas a bronchodilator test may indicate reversibility consistent with asthma. MCTs were positive when the cumulative methacholine dose that reduced the FEV1 by 20%, (PC20) was equivalent or was less than 16 mg/mL [32]. BD was positive when post-bronchodilator FEV1 change exceeded 12% and at least 200 mL [33]. Those without AHR (N = 6947) were defined as those who either had a negative study or were presumed negative if they did not have subspecialty testing.
MetSyn Phenotypic Definition. Diagnosis of MetSyn was based on National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) guidelines and optimized for our cohort by having at least 3 of 5 following criteria: Systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥85 mmHg; HDL <40 mg/dL; triglycerides ≥150 mg/dL; insulin resistance, as glucose ≥100 mg/dL; or body mass index (BMI) ≥30 kg/m2. BMI ≥30 kg/m2 was used as surrogate for central adiposity as per Word Health Organization (WHO) guidelines [34]. Smoking information and exposure intensity were self-reported and collected from questionnaires administered during medical monitoring exams. Clinical parameters including DBP, glucose, and lipid panel were measured at WTC-HP entry. All of the above criteria were from data points obtained pre-9/11 and prior to any measurements of post-9/11 AHR.
Additional Clinical Biomarkers. We investigated the correlation between AHR and other clinical biomarkers. Absolute counts of differentiated white blood cells (WBC) such as neutrophils and eosinophils present at the first post-9/11 evaluation. A cutoff of at least 500 eosinophils was utilized, consistent with definition of clinical hyper-eosinophilia. Cholesterol/HDL ratio ≥3.5, a predictor of ischemic heart disease risk, and its association with AHR was also investigated [35,36,37].
Statistical Analysis. SPSS-23 (IBM) was used for primary data handling and statistics. Continuous variables are expressed as mean (standard deviation (SD)), and compared by two-sample t-test. Categorical data was summarized as count and proportions, and compared using Pearson-χ2. Smoking data was categorized into a dichotomous variable of ever or never a smoker, as previously described [38,39,40,41,42]. The primary endpoint of all analyses was development of WTC-AHR based on a positive BDR or MCT any time after 9/11. Survival interval was determined by time from 9/11 to positive AHR test or until 1 August 2017, the administrative censoring date of the study closure, if they did not have AHR. Association of endpoints and MetSyn, smoking, BMI, and exposure level were analyzed using the Cox proportional hazards regression and are represented as hazard ratio (HR) and 95% confidence interval (CI). We assigned a cut point of ≥500 eosinophils/µL as a marker of clinically significant eosinophilia for Cox modeling. All models were adjusted for age at 9/11, exposure intensity, and smoking status, and considered significant if p < 0.05. Omnibus testing was used to assess the quality of the comparisons. Time-to-event curves were determined by the Kaplan–Meier method and compared with the log-rank test. There were no dropouts in this study.

3. Results

3.1. WTC-AHR Cohort

Of the N = 1906 individuals with subspecialty pulmonary testing, N = 891 had a MCT while N = 1168 had BDR testing, and 153 individuals had both tests (Figure 1). Of the N = 539 participants that had WTC-AHR, 303 were positive on MCT only, 184 had a positive BDR only and 52 were positive on both tests, yielding a total of N = 355 who were MCT positive and N = 236 who were BDR positive (Figure 1). Demographic and clinical measures were compared between those with and without AHR (Table 1 and Table 2). Years of service information was only available on N = 5029/6947 non-AHR and N = 418/539 AHR. There was no difference between a positive MCT and a positive BDR in age, BMI, smoking status, or clinical measures except for total cholesterol. A positive MCT had a slightly higher cholesterol of mean (SD) of 217.4 (40.1) compared to positive BDR 210.2 (36.0), p = 0.03. AHR and non-AHR were not significantly different in race or smoking status; those with AHR were significantly older by a mean of 1 year, had an average of three fewer years of service compared to non-AHR, and had a higher percentage present in the morning of 9/11 (Table 1).
Spirometry was compared in those with AHR and non-AHR at both the most recent pre-9/11 and first post-9/11 examination showed significantly lower FEV1% predicted, Forced Vital Capacity (FVC) % predicted, and FEV1/FVC ratios in AHR, but the differences were clinically insignificant and were not indicative of OAD (Table 1).
Participants with a positive MCT had a PC20 mean (SD) of 5.6 (4.7) mg/mL. Participants with a positive BDR had a mean (SD) gain of FEV1 % predicted of 18.6% (9.9%), and 661.0 (274.0) mL. Participants with AHR also had a slightly higher average BMI (29.1 vs. 28.6 kg/m2; p = 0.001), triglycerides (p = 0.046), low-density lipoprotein (p = 0.028), and cholesterol (p = 0.009) compared to non-AHR (Table 2). A higher proportion of subjects with AHR also had MetSyn; subgroup analyses by Maentel–Haenszel odds ratio estimates shows that triglyceride ≥150 mg/dL (OR 21.6, 95% CI of 17.5–26.76, p < 0.001), obesity defined as BMI ≥30kg/m2 (OR 13.07, 95% CI 11.41–14.97, p < 0.001), and HDL <40 had OR of 12.25 (10.75–13.97), p < 0.001, were the most likely to meet the definition of MetSyn. SBP/DBP ≥130/85 had OR of 6.85 (6.03–7.79), p < 0.001, and glucose ≥ 100 OR 5.96 (5.24–6.78), p < 0.001.
Complete blood count and differentials were also compared on available samples between 532/537 AHR and 6896/6947 non-AHR (Table 2). AHR displayed higher WBC count and absolute eosinophil counts (AEC) compared to non-AHR. Of the N = 196 subjects with AEC > 500 cells/µL, N = 26 had AHR whereas N = 154 had at least one MetSyn risk factor.

3.2. Model Development

Cox proportional hazard models were used to estimate univariate hazard ratio of individual clinical biomarkers on developing WTC-AHR, with the adjustment of age, smoking, and WTC-exposure intensity (Table 3). AEC ≥ 500 cells/µL increased the risk of development of AHR by 94% (CI 1.31–2.88). The clinically utilized ratio of cholesterol/HDL ratio ≥3.5 was similarly correlated with a 33% increased risk of development of AHR. Similar to what was originally identified by t-test, dyslipidemia and obesity were significant risk factors for development of AHR, whereas hypertension or insulin resistance were not significant. This also mirrors the subgroup analyses where the most significant contributors to MetSyn definition were from dyslipidemia and obesity.
The final MetSyn model, adjusted for age, smoking, and exposure intensity, assessed the total number of MetSyn risk factors predicting AHR. Having at least two or three MetSyn risk factors had 69% and 65% increased risk of developing AHR, respectively (Table 3). Having high exposure, being present in the morning of 9/11, increased odds of developing AHR by 2.24 times, whereas being present in the afternoon increased odds by 1.76 times. Age also had an associated risk of development of AHR by 1.7% for every increasing year. Smoking was not significantly associated with AHR (Table 3).
Survival curves were plotted and time to divergence calculations from having no MetSyn risk factors showed that having one MetSyn risk factor diverged three years post-9/11, compared to having two risk factors, and divergence occurred within the first year post-9/11 (Figure 2). Kaplan–Meier curves were also assessed for subgroups of each MetSyn factors. BMI ≥30 kg/m2, triglycerides ≥150 mg/dL, HDL <40 mg/dL, and having the highest exposure to WTC-PM by being present at the site on the morning of 9/11 carried significantly higher risk of developing of AHR (Figure 3A–D).
We examined the reproducibility of the model in the cohort of N = 1906 who had MCT/BD testing. Even in this restricted cohort we found that having two or three MetSyn risk factors also yielded an increased risk of developing AHR by 54.4% and 39.6% (p = 0.001 and 0.014) respectively. Having one MetSyn risk factor had 25% increased risk but was not statistically significant (p = 0.08), and exposure was not a significant risk factor in the smaller cohort.

4. Discussion

The WTC-exposed FDNY rescue/recovery workers represent the largest longitudinally assessed first responder cohort with pre and post lung function assessments following a high PM exposure. They continue to have their health adversely impacted even after 18 years [26,27,28,43,44,45,46,47,48,49,50,51,52,53,54]. Our previous work focused on the contribution of MetSyn in the development of WTC-LI [4,28]. We now show that metabolically active biomarkers and markers of inflammation (such as eosinophils) predict AHR in the WTC-exposed firefighter cohort [55,56,57].
This study represents the only longitudinal study to our knowledge investigating the temporal relationship of MetSyn, PM exposure, and AHR. We demonstrate that MetSyn is an independent risk factor for the development of AHR, as all study participants were categorized as having or not having MetSyn prior to the development of AHR. A prior cross-sectional study investigating MetSyn, PM exposure, and cardiovascular risk found no significant associations with inflammatory markers of CRP or WBC [3].
Our earlier model showed that dyslipidemia and heart rate independently increased the odds of developing WTC-LI [4,28]. While WTC-LI and AHR both fall under the umbrella of OAD, there is little overlap in these populations. When examining a subgroup analysis of the WTC-LI cases, only N = 203/1204 (16.8%) also have AHR. This strongly suggests that MetSyn is implicated in multiple pathways in the development of OAD. Although MetSyn risk factors and cholesterol/HDL ratios are classically predictors of cardiovascular disease, their implications in affecting future lung disease are novel. Moreover, these represent reversible risk factors that may be potential therapeutic targets to alter the outcome of other obstructive lung disease.
Our current investigation shows the associations of MetSyn biomarkers with the development of WTC-AHR. We also show obesity having similar ability to predict AHR, an unexpected finding given the vast literature of restrictive patterns in obesity. However, this fits in the growing body of literature showing that obesity has hormonal pathways that influence the pulmonary environment. Another unexpected finding, but similar to our prior investigation of MetSyn predictor of WTC-LI, was that glucose was not a significant predictor of WTC-AHR [28]. Specifically, in our current study with cut-points of 100 and 126 mg/dL, there was no significantly increased risk of lung injury in our cohort. Studies in non-exposed individuals have shown an association between insulin resistance and OAD [58].
A strength of this study is the rigorously characterized prospective cohort, with a clearly defined time of exposure (9/11), pre and post exposure lung function measurements, blood drawn soon after exposure, and MetSyn categorization done prior to AHR measurements. Another strength of this study is that the development of AHR was post-exposure in that FDNY firefighters are excluded at hire and during annual medical monitoring if they have signs or symptoms of airways obstruction or AHR [59]. A key strength of this study is in the study design that allowed us to explore multiple aspects of WTC-AHR. Since the decision to administer BDR or MCT tests to determine disease is often a clinician’s judgement, it was reassuring to have found little difference between the two groups, those with only a positive BDR and those with only a positive MCT. This suggests that our findings are plausible and potentially reproducible in other cohorts. Examining the model for reproducibility in the smaller cohort of N = 1906 also bolsters our findings that MetSyn is an independent risk factor for development of future AHR.
Using the clinical markers of MetSyn as predictors of AHR is advantageous in several ways. These biomarkers are easily attainable, cost effective, and can be replicated in many cohorts. Dyslipidemia, insulin resistance, obesity, and hypertension are also all potentially reversible causes of end-organ disease. Extending statin therapy, for example, and increasing glycemic control can be the target of future studies focused on their mitigating effects in progressive lung disease. Furthermore, our group is currently studying dietary effects on pulmonary function and biomarker profile of the FDNY exposed cohort. MetSyn also has global reach and can be investigated in other cohorts with similar pollutant or PM exposure.
Eosinophilia, while a significant risk factor of AHR, did not augment the predictive model investigating MetSyn. Subgroup analysis shows that although only 13% of the hyper-eosinophilia group had overlap with the AHR population, 78.5% (N = 154/196) had at least one MetSyn risk factor. This was somewhat surprising because the literature reflects that obesity-related lung dysfunction is often a neutrophil-mediated process [60]. This may indicate the need for further studies in eosinophilia-mediated pathways in MetSyn.
There are several limitations to this study. We focus on a male-only cohort. Interestingly, MetSyn and asthma have been shown to have stronger associations in female-only cohorts [18,61]. An additional limitation is that we classified participants as not having AHR if they had negative tests or even if they had no testing of AHR. The presumption of negative AHR in the absence of direct measurement, while a limitation, biased our results towards the null. We also used a broad definition of AHR by requiring either a positive BDR or a positive MCT. This combined definition could have biased our results away from the null and towards a positive finding but we believe this combined definition is the most clinically relevant, and also accounts for those that may have only had one type of test due to clinical contraindications [32].
We also caution on over-interpretation of the results of individual components of MetSyn. We use a one-time fasting blood level at the earliest time point after exposure to 9/11 to make predictions of future lung injury. Future studies can focus on repeated measures of MetSyn, dyslipidemia, fasting blood glucose, and AHR to control for other possible confounders and aid in understanding the associations of individual components of MetSyn and AHR and their temporal relationships. The lack of medication history could also cause under-identification of those with MetSyn risk factors. Having this information could improve the sensitivity of those at risk of AHR and specificity of the model. Alternatively, it can help to determine if attenuation of MetSyn risk factors through pharmacologic methods impacted pulmonary health.

5. Conclusions

In summary, MetSyn biomarkers are predictors of future WTC-AHR in a large cohort of WTC-exposed FDNY firefighters followed over 16 years. These metabolically active biomarkers are associated with dyslipidemia, insulin resistance, and cardiovascular disease, and suggest that MetSyn may contribute to systemic inflammation that leads to future development of AHR. Our data supports the hypothesis and contributes to the growing body of literature investigating the complex associations between potentially reversible MetSyn risk factors and lung injury. We are strongly encouraged by our results indicating that pathways involved in metabolism have broad impacts on the immune and hormonal environment in the lung.

Author Contributions

A.N. and S.K. participated in study conception and design; A.N. was the primary investigator; S.K., T.M.S., R.Z.-O., A.N. and D.J.P. were responsible for data collection; A.N. and S.K. were responsible for data validation; A.N. and S.K. participated in data analysis; S.K., M.L. and A.N. undertook the statistical analysis. All authors participated in data interpretation, writing and revision of the report, and approval of the final version.

Funding

This research was funded by NHLBI R01HL119326, CDC/NIOSH U01-OH11300, Clinical Center of Excellence 200-2017-93426, Data Center 200-2017-93326.

Acknowledgments

We are thankful to the FDNY rescue workers for their selfless dedication.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AHRAirway hyperreactivity
ATP IIIAdult treatment panel III
BDRBronchodilator response
BMIBody mass index
CRPC-reactive protein
CIConfidence interval
DBPDiastolic blood pressure
EMRElectronic medical record
FDNYFire Department of City of New York
FEV1Forced expiratory volume over 1 second
FVCForced vital capacity
HDLHigh density lipoprotein
HRHazards ratio
LDLLow density lipoprotein
LLNLower limit of normal
MCTMethacholine test
MetSynMetabolic syndrome
NCEPNational Cholesterol Education Program
OADObstructive Airways Disease
PC20Methacholine dose required to reduce FEV1 by 20%.
PFTPulmonary function test
PMParticulate matter
SBPSystolic blood pressure
SDStandard deviation
TNF Tumor necrosis factor
USUnited States
WBCWhite blood cell
WHOWorld Health Organization
WTCWorld Trade Center
WTC-AHRWorld Trade Center-airway hyperreactivity
WTC-HPWorld Trade Center-health program
WTC-LIWTC-lung injury

References

  1. Grundy, S.M.; Cleeman, J.I.; Daniels, S.R.; Donato, K.A.; Eckel, R.H.; Franklin, B.A.; Gordon, D.J.; Krauss, R.M.; Savage, P.J.; Smith, S.C., Jr.; et al. Diagnosis and management of the metabolic syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005, 112, 2735–2752. [Google Scholar] [CrossRef] [PubMed]
  2. Aguilar, M.; Bhuket, T.; Torres, S.; Liu, B.; Wong, R.J. Prevalence of the metabolic syndrome in the United States, 2003–2012. JAMA 2015, 313, 1973–1974. [Google Scholar] [CrossRef] [PubMed]
  3. Dabass, A.; Talbott, E.O.; Rager, J.R.; Marsh, G.M.; Venkat, A.; Holguin, F.; Sharma, R.K. Systemic inflammatory markers associated with cardiovascular disease and acute and chronic exposure to fine particulate matter air pollution (PM2.5) among US NHANES adults with metabolic syndrome. Environ. Res. 2018, 161, 485–491. [Google Scholar] [CrossRef] [PubMed]
  4. Kwon, S.; Crowley, G.; Caraher, E.J.; Haider, S.H.; Lam, R.; Veerappan, A.; Yang, L.; Liu, M.; Zeig-Owens, R.; Schwartz, T.; et al. Validation of Predictive Metabolic Syndrome Biomarkers of World Trade Center Lung Injury: A 16-Year Longitudinal Study. Chest 2019. [Google Scholar] [CrossRef] [PubMed]
  5. Wallwork, R.S.; Colicino, E.; Zhong, J.; Kloog, I.; Coull, B.A.; Vokonas, P.; Schwartz, J.D.; Baccarelli, A.A. Ambient Fine Particulate Matter, Outdoor Temperature, and Risk of Metabolic Syndrome. Am. J. Epidemiol. 2017, 185, 30–39. [Google Scholar] [CrossRef] [PubMed]
  6. Brook, R.D.; Sun, Z.; Brook, J.R.; Zhao, X.; Ruan, Y.; Yan, J.; Mukherjee, B.; Rao, X.; Duan, F.; Sun, L.; et al. Extreme Air Pollution Conditions Adversely Affect Blood Pressure and Insulin Resistance: The Air Pollution and Cardiometabolic Disease Study. Hypertension 2016, 67, 77–85. [Google Scholar] [CrossRef]
  7. Leone, N.; Courbon, D.; Thomas, F.; Bean, K.; Jego, B.; Leynaert, B.; Guize, L.; Zureik, M. Lung function impairment and metabolic syndrome: The critical role of abdominal obesity. Am. J. Respir. Crit. Care Med. 2009, 179, 509–516. [Google Scholar] [CrossRef] [PubMed]
  8. Fiordelisi, A.; Piscitelli, P.; Trimarco, B.; Coscioni, E.; Iaccarino, G.; Sorriento, D. The mechanisms of air pollution and particulate matter in cardiovascular diseases. Heart Fail. Rev. 2017, 22, 337–347. [Google Scholar] [CrossRef] [PubMed]
  9. Zammit, C.; Liddicoat, H.; Moonsie, I.; Makker, H. Obesity and respiratory diseases. Int. J. Gen. Med. 2010, 3, 335–343. [Google Scholar] [CrossRef] [PubMed]
  10. Baffi, C.W.; Wood, L.; Winnica, D.; Strollo, P.J., Jr.; Gladwin, M.T.; Que, L.G.; Holguin, F. Metabolic Syndrome and the Lung. Chest 2016, 149, 1525–1534. [Google Scholar] [CrossRef]
  11. Peters, U.; Suratt, B.T.; Bates, J.H.T.; Dixon, A.E. Beyond BMI: Obesity and Lung Disease. Chest 2017, 153, 702–709. [Google Scholar] [CrossRef]
  12. Garmendia, J.V.; Moreno, D.; Garcia, A.H.; De Sanctis, J.B. Metabolic syndrome and asthma. Recent Pat. Endocr. Metab. Immune Drug Discov. 2014, 8, 60–66. [Google Scholar] [CrossRef] [PubMed]
  13. Chen, W.L.; Wang, C.C.; Wu, L.W.; Kao, T.W.; Chan, J.Y.; Chen, Y.J.; Yang, Y.H.; Chang, Y.W.; Peng, T.C. Relationship between lung function and metabolic syndrome. PLoS ONE 2014, 9, e108989. [Google Scholar] [CrossRef]
  14. Lee, E.J.; In, K.H.; Ha, E.S.; Lee, K.J.; Hur, G.Y.; Kang, E.H.; Jung, K.H.; Lee, S.Y.; Kim, J.H.; Lee, S.Y.; et al. Asthma-like symptoms are increased in the metabolic syndrome. J. Asthma 2009, 46, 339–342. [Google Scholar] [CrossRef] [PubMed]
  15. Adeyeye, O.O.; Ogbera, A.O.; Ogunleye, O.O.; Brodie-Mens, A.T.; Abolarinwa, F.F.; Bamisile, R.T.; Onadeko, B.O. Understanding asthma and the metabolic syndrome—A Nigerian report. Int. Arch. Med. 2012, 5, 20. [Google Scholar] [CrossRef] [PubMed]
  16. Ko, S.H.; Jeong, J.; Baeg, M.K.; Han, K.D.; Kim, H.S.; Yoon, J.S.; Kim, H.H.; Kim, J.T.; Chun, Y.H. Lipid profiles in adolescents with and without asthma: Korea National Health and nutrition examination survey data. Lipids Health Dis. 2018, 17, 158. [Google Scholar] [CrossRef] [PubMed]
  17. Thuesen, B.H.; Husemoen, L.L.; Hersoug, L.G.; Pisinger, C.; Linneberg, A. Insulin resistance as a predictor of incident asthma-like symptoms in adults. Clin. Exp. 2009, 39, 700–707. [Google Scholar] [CrossRef]
  18. Brumpton, B.M.; Camargo, C.A., Jr.; Romundstad, P.R.; Langhammer, A.; Chen, Y.; Mai, X.M. Metabolic syndrome and incidence of asthma in adults: The HUNT study. Eur. Respir. J. 2013, 42, 1495–1502. [Google Scholar] [CrossRef]
  19. Singh, V.P.; Aggarwal, R.; Singh, S.; Banik, A.; Ahmad, T.; Patnaik, B.R.; Nappanveettil, G.; Singh, K.P.; Aggarwal, M.L.; Ghosh, B.; et al. Metabolic Syndrome Is Associated with Increased Oxo-Nitrative Stress and Asthma-Like Changes in Lungs. PLoS ONE 2015, 10, e0129850. [Google Scholar] [CrossRef]
  20. Urman, R.; Eckel, S.; Deng, H.; Berhane, K.; Avol, E.; Lurmann, F.; McConnell, R.; Gilliland, F. Risk Effects of near-Roadway Pollutants and Asthma Status on Bronchitic Symptoms in Children. Environ. Epidemiol. 2018, 2. [Google Scholar] [CrossRef]
  21. Spira-Cohen, A.; Chen, L.C.; Kendall, M.; Lall, R.; Thurston, G.D. Personal exposures to traffic-related air pollution and acute respiratory health among Bronx schoolchildren with asthma. Environ. Health Perspect. 2011, 119, 559–565. [Google Scholar] [CrossRef]
  22. Ai, S.; Qian, Z.M.; Guo, Y.; Yang, Y.; Rolling, C.A.; Liu, E.; Wu, F.; Lin, H. Long-term exposure to ambient fine particles associated with asthma: A cross-sectional study among older adults in six low- and middle-income countries. Environ. Res. 2019, 168, 141–145. [Google Scholar] [CrossRef] [PubMed]
  23. Rom, W.N.; Reibman, J.; Rogers, L.; Weiden, M.D.; Oppenheimer, B.; Berger, K.; Goldring, R.; Harrison, D.; Prezant, D. Emerging exposures and respiratory health: World Trade Center dust. Proc. Am. Thorac. Soc. 2010, 7, 142–145. [Google Scholar] [CrossRef]
  24. Berger, K.I.; Kalish, S.; Shao, Y.; Marmor, M.; Kazeros, A.; Oppenheimer, B.W.; Chan, Y.; Reibman, J.; Goldring, R.M. Isolated small airway reactivity during bronchoprovocation as a mechanism for respiratory symptoms in WTC dust-exposed community members. Am. J. Ind. Med. 2016, 59, 767–776. [Google Scholar] [CrossRef]
  25. Aldrich, T.K.; Weakley, J.; Dhar, S.; Hall, C.B.; Crosse, T.; Banauch, G.I.; Weiden, M.D.; Izbicki, G.; Cohen, H.W.; Gupta, A.; et al. Bronchial Reactivity and Lung Function After World Trade Center Exposure. Chest 2016, 150, 1333–1340. [Google Scholar] [CrossRef] [PubMed]
  26. Prezant, D.J.; Weiden, M.; Banauch, G.I.; McGuinness, G.; Rom, W.N.; Aldrich, T.K.; Kelly, K.J. Cough and bronchial responsiveness in firefighters at the World Trade Center site. N. Engl. J. Med. 2002, 347, 806–815. [Google Scholar] [CrossRef]
  27. Edelman, P.; Osterloh, J.; Pirkle, J.; Caudill, S.P.; Grainger, J.; Jones, R.; Blount, B.; Calafat, A.; Turner, W.; Feldman, D.; et al. Biomonitoring of chemical exposure among New York City firefighters responding to the World Trade Center fire and collapse. Environ. Health Perspect. 2003, 111, 1906–1911. [Google Scholar] [CrossRef] [PubMed]
  28. Weiden, M.D.; Naveed, B.; Kwon, S.; Cho, S.J.; Comfort, A.L.; Prezant, D.J.; Rom, W.N.; Nolan, A. Cardiovascular biomarkers predict susceptibility to lung injury in World Trade Center dust-exposed firefighters. Eur. Respir. J. 2013, 41, 1023–1030. [Google Scholar] [CrossRef] [PubMed]
  29. Liu, X.; Yip, J.; Zeig-Owens, R.; Weakley, J.; Webber, M.P.; Schwartz, T.M.; Prezant, D.J.; Weiden, M.D.; Hall, C.B. The Effect of World Trade Center Exposure on the Timing of Diagnoses of Obstructive Airway Disease, Chronic Rhinosinusitis, and Gastroesophageal Reflux Disease. Front. Public Health 2017, 5, 2. [Google Scholar] [CrossRef]
  30. Crapo, R.O.; Casaburi, R.; Coates, A.L.; Enright, P.L.; Hankinson, J.L.; Irvin, C.G.; MacIntyre, N.R.; McKay, R.T.; Wanger, J.S.; Anderson, S.D.; et al. Guidelines for methacholine and exercise challenge testing-1999. This official statement of the American Thoracic Society was adopted by the ATS Board of Directors, July 1999. Am. J. Respir. Crit. Care Med. 2000, 161, 309–329. [Google Scholar] [CrossRef] [PubMed]
  31. Pellegrino, R.; Viegi, G.; Brusasco, V.; Crapo, R.O.; Burgos, F.; Casaburi, R.; Coates, A.; van der Grinten, C.P.; Gustafsson, P.; Hankinson, J.; et al. Interpretative strategies for lung function tests. Eur. Respir. J. 2005, 26, 948–968. [Google Scholar] [CrossRef]
  32. Alberti, K.G.; Zimmet, P.Z. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet. Med. 1998, 15, 539–553. [Google Scholar] [CrossRef]
  33. Ingelsson, E.; Schaefer, E.J.; Contois, J.H.; McNamara, J.R.; Sullivan, L.; Keyes, M.J.; Pencina, M.J.; Schoonmaker, C.; Wilson, P.W.; D’Agostino, R.B.; et al. Clinical utility of different lipid measures for prediction of coronary heart disease in men and women. JAMA 2007, 298, 776–785. [Google Scholar] [CrossRef]
  34. Lemieux, I.; Lamarche, B.; Couillard, C.; Pascot, A.; Cantin, B.; Bergeron, J.; Dagenais, G.R.; Despres, J.P. Total cholesterol/HDL cholesterol ratio vs. LDL cholesterol/HDL cholesterol ratio as indices of ischemic heart disease risk in men: The Quebec Cardiovascular Study. Arch. Intern. Med. 2001, 161, 2685–2692. [Google Scholar] [CrossRef] [PubMed]
  35. Keil, U.; Liese, A.D.; Hense, H.W.; Filipiak, B.; Doring, A.; Stieber, J.; Lowel, H. Classical risk factors and their impact on incident non-fatal and fatal myocardial infarction and all-cause mortality in southern Germany—Results from the MONICA Augsburg cohort study 1984–1992. Eur. Heart J. 1998, 19, 1197–1207. [Google Scholar] [CrossRef]
  36. Weiden, M.D.; Ferrier, N.; Nolan, A.; Rom, W.N.; Comfort, A.; Gustave, J.; Zeig-Owens, R.; Zheng, S.; Goldring, R.M.; Berger, K.I.; et al. Obstructive airways disease with air trapping among firefighters exposed to World Trade Center dust. Chest 2010, 137, 566–574. [Google Scholar] [CrossRef] [PubMed]
  37. Naveed, B.; Comfort, A.L.; Ferrier, N.; Kasturiarachchi, K.J.; Rom, W.N.; Prezant, D.J.; Weiden, M.D.; Nolan, A. Biomarkers of metabolic syndrome predict accelerated decline of lung function in NYC firefighters that were exposed to WTC particulates. Am. J. Respir. Crit. Care Med. 2011, 183, A4795. [Google Scholar] [CrossRef]
  38. Kwon, S.; Naveed, B.; Comfort, A.L.; Ferrier, N.; Rom, W.N.; Prezant, D.J.; Nolan, A.; Weiden, M.D. Elevated MMP-3, MMP-12, and TIMP-3 in serum are biomarkers predictive of world trade center-lung injury in New York city firefighters. Am. J. Respir. Crit. Care Med. 2012, 185, A2019. [Google Scholar] [CrossRef]
  39. Naveed, B.; Kwon, S.; Comfort, A.L.; Ferrier, N.; Rom, W.N.; Prezant, D.J.; Weiden, M.D.; Nolan, A. Cardiovascular serum biomarkers predict world trade center lung injury in NYC firefighters. Am. J. Respir. Crit. Care Med. 2012, 185, A4894. [Google Scholar]
  40. Naveed, B.; Weiden, M.D.; Kwon, S.; Gracely, E.J.; Comfort, A.L.; Ferrier, N.; Kasturiarachchi, K.J.; Cohen, H.W.; Aldrich, T.K.; Rom, W.N.; et al. Metabolic syndrome biomarkers predict lung function impairment: A nested case-control study. Am. J. Respir. Crit. Care Med. 2012, 185, 392–399. [Google Scholar] [CrossRef]
  41. Landgren, O.; Zeig-Owens, R.; Giricz, O.; Goldfarb, D.; Murata, K.; Thoren, K.; Ramanathan, L.; Hultcrantz, M.; Dogan, A.; Nwankwo, G.; et al. Multiple Myeloma and Its Precursor Disease Among Firefighters Exposed to the World Trade Center Disaster. JAMA Oncol. 2018, 4, 821–827. [Google Scholar] [CrossRef]
  42. Haider, S.H.; Kwon, S.; Lam, R.; Lee, A.K.; Caraher, E.J.; Crowley, G.; Zhang, L.; Schwartz, T.M.; Zeig-Owens, R.; Liu, M.; et al. Predictive Biomarkers of Gastroesophageal Reflux Disease and Barrett’s Esophagus in World Trade Center Exposed Firefighters: A 15 Year Longitudinal Study. Sci. Rep. 2018, 8, 3106. [Google Scholar] [CrossRef]
  43. Hena, K.M.; Yip, J.; Jaber, N.; Goldfarb, D.; Fullam, K.; Cleven, K.; Moir, W.; Zeig-Owens, R.; Webber, M.P.; Spevack, D.M.; et al. Clinical Course of Sarcoidosis in World Trade Center-Exposed Firefighters. Chest 2018, 153, 114–123. [Google Scholar] [CrossRef]
  44. Cho, S.J.; Echevarria, G.C.; Kwon, S.; Naveed, B.; Schenck, E.J.; Tsukiji, J.; Rom, W.N.; Prezant, D.J.; Nolan, A.; Weiden, M.D. One airway: Biomarkers of protection from upper and lower airway injury after World Trade Center exposure. Respir. Med. 2014, 108, 162–170. [Google Scholar] [CrossRef]
  45. Nolan, A.; Naveed, B.; Comfort, A.L.; Ferrier, N.; Hall, C.B.; Kwon, S.; Kasturiarachchi, K.J.; Cohen, H.W.; Zeig-Owens, R.; Glaser, M.S.; et al. Inflammatory biomarkers predict airflow obstruction after exposure to World Trade Center dust. Chest 2012, 142, 412–418. [Google Scholar] [CrossRef]
  46. Lippman, M.; Cohen, M.D.; Chen, L.C. Health effects of World Trade Center (WTC) Dust: An unprecedented disaster with inadequate risk management. Critical Reviews in Toxicology. 2015, 45, 492–530. [Google Scholar] [CrossRef]
  47. Crowley, G.; Kwon, S.; Haider, S.H.; Caraher, E.J.; Lam, R.; St-Jules, D.E.; Liu, M.; Prezant, D.J.; Nolan, A. Metabolomics of World Trade Center-Lung Injury: A machine learning approach. BMJ Open Respir. Res. 2018, 5, e000274. [Google Scholar] [CrossRef]
  48. Lioy, P.J.; Weisel, C.P.; Millette, J.R.; Eisenreich, S.; Vallero, D.; Offenberg, J.; Buckley, B.; Turpin, B.; Zhong, M.; Cohen, M.D.; et al. Characterization of the dust/smoke aerosol that settled east of the World Trade Center (WTC) in lower Manhattan after the collapse of the WTC 11 September 2001. Environ. Health Perspect 2002, 110, 703–714. [Google Scholar] [CrossRef]
  49. Levin, S.; Herbert, R.; Skloot, G.; Szeinuk, J.; Teirstein, A.; Fischler, D.; Milek, D.; Piligian, G.; Wilk-Rivard, E.; Moline, J. Health effects of World Trade Center site workers. Am. J. Ind. Med. 2002, 42, 545–547. [Google Scholar] [CrossRef]
  50. Banauch, G.I.; Dhala, A.; Alleyne, D.; Alva, R.; Santhyadka, G.; Krasko, A.; Weiden, M.; Kelly, K.J.; Prezant, D.J. Bronchial hyperreactivity and other inhalation lung injuries in rescue/recovery workers after the World Trade Center collapse. Crit. Care Med. 2005, 33, S102–S106. [Google Scholar] [CrossRef]
  51. Landrigan, P.J.; Lioy, P.J.; Thurston, G.; Berkowitz, G.; Chen, L.C.; Chillrud, S.N.; Gavett, S.H.; Georgopoulos, P.G.; Geyh, A.S.; Levin, S.; et al. Health and environmental consequences of the world trade center disaster. Environ. Health Perspect. 2004, 112, 731–739. [Google Scholar] [CrossRef]
  52. Farfel, M.; DiGrande, L.; Brackbill, R.; Prann, A.; Cone, J.; Friedman, S.; Walker, D.J.; Pezeshki, G.; Thomas, P.; Galea, S.; et al. An overview of 9/11 experiences and respiratory and mental health conditions among World Trade Center Health Registry enrollees. J. Urban Health 2008, 85, 880–909. [Google Scholar] [CrossRef]
  53. Aldrich, T.K.; Vossbrinck, M.; Zeig-Owens, R.; Hall, C.B.; Schwartz, T.M.; Moir, W.; Webber, M.P.; Cohen, H.W.; Nolan, A.; Weiden, M.D.; et al. Lung Function Trajectories in World Trade Center-Exposed New York City Firefighters Over 13 Years: The Roles of Smoking and Smoking Cessation. Chest 2016, 149, 1419–1427. [Google Scholar] [CrossRef] [PubMed]
  54. Niles, J.K.; Webber, M.P.; Cohen, H.W.; Hall, C.B.; Zeig-Owens, R.; Ye, F.; Glaser, M.S.; Weakley, J.; Weiden, M.D.; Aldrich, T.K.; et al. The respiratory pyramid: From symptoms to disease in World Trade Center exposed firefighters. Am. J. Ind. Med. 2013, 56, 870–880. [Google Scholar] [CrossRef] [PubMed]
  55. Zeig-Owens, R.; Singh, A.; Aldrich, T.K.; Hall, C.B.; Schwartz, T.; Webber, M.P.; Cohen, H.W.; Kelly, K.J.; Nolan, A.; Prezant, D.J.; et al. Blood Leukocyte Concentrations, FEV1 Decline, and Airflow Limitation. A 15-Year Longitudinal Study of World Trade Center-exposed Firefighters. Ann. Am. Thorac. Soc. 2018, 15, 173–183. [Google Scholar] [CrossRef] [PubMed]
  56. Kerkhof, M.; Tran, T.N.; van den Berge, M.; Brusselle, G.G.; Gopalan, G.; Jones, R.C.M.; Kocks, J.W.H.; Menzies-Gow, A.; Nuevo, J.; Pavord, I.D.; et al. Association between blood eosinophil count and risk of readmission for patients with asthma: Historical cohort study. PLoS ONE 2018, 13, e0201143. [Google Scholar] [CrossRef] [PubMed]
  57. Price, D.B.; Rigazio, A.; Campbell, J.D.; Bleecker, E.R.; Corrigan, C.J.; Thomas, M.; Wenzel, S.E.; Wilson, A.M.; Small, M.B.; Gopalan, G.; et al. Blood eosinophil count and prospective annual asthma disease burden: A UK cohort study. Lancet Respir. Med. 2015, 3, 849–858. [Google Scholar] [CrossRef]
  58. Bolton, C.E.; Evans, M.; Ionescu, A.A.; Edwards, S.M.; Morris, R.H.; Dunseath, G.; Luzio, S.D.; Owens, D.R.; Shale, D.J. Insulin resistance and inflammation—A further systemic complication of COPD. J. Chron. Obstruct. Pulm. Dis. 2007, 4, 121–126. [Google Scholar] [CrossRef] [PubMed]
  59. Caraher, E.J.; Kwon, S.; Haider, S.H.; Crowley, G.; Lee, A.; Ebrahim, M.; Zhang, L.; Chen, L.C.; Gordon, T.; Liu, M.; et al. Receptor for advanced glycation end-products and World Trade Center particulate induced lung function loss: A case-cohort study and murine model of acute particulate exposure. PLoS ONE 2017, 12, e0184331. [Google Scholar] [CrossRef]
  60. Scott, H.A.; Gibson, P.G.; Garg, M.L.; Wood, L.G. Airway inflammation is augmented by obesity and fatty acids in asthma. Eur. Respir. J. 2011, 38, 594–602. [Google Scholar] [CrossRef]
  61. Wadden, D.; Allwood Newhook, L.A.; Twells, L.; Farrell, J.; Gao, Z.W. Sex-Specific Association between Childhood BMI Trajectories and Asthma Phenotypes. Int. J. Pediat. 2018, 2018, 9057435. [Google Scholar] [CrossRef] [PubMed]
Ijerph 16 01486 g001 550
Figure 1. Study Design. Fire Department of New York (FDNY) rescue workers exposed to World Trade Center (WTC) particulates and enrolled in the WTC Health Program.
Figure 1. Study Design. Fire Department of New York (FDNY) rescue workers exposed to World Trade Center (WTC) particulates and enrolled in the WTC Health Program.
Ijerph 16 01486 g001
Ijerph 16 01486 g002 550
Figure 2. Cumulative AHR survival curves by total number of MetSyn biomarkers. Cumulative disease-free survival is expressed on the y-axis and time in years from their WTC exposure is on the x-axis. Life table expresses the number of individuals at risk in 2-year intervals.
Figure 2. Cumulative AHR survival curves by total number of MetSyn biomarkers. Cumulative disease-free survival is expressed on the y-axis and time in years from their WTC exposure is on the x-axis. Life table expresses the number of individuals at risk in 2-year intervals.
Ijerph 16 01486 g002
Ijerph 16 01486 g003 550
Figure 3. Kaplan–Meier survival curves stratified by (A). BMI ≥30 kg/m2 (p = 0.001 by log rank), (B). Triglycerides ≥150 mg/dL (p = 0.019), (C). HDL <40 mg/dL (p = 0.038), and (D). Exposure intensity (p < 0.001). Cumulative disease-free survival is expressed on the y-axis and time in years from their WTC exposure is on the x-axis. Log rank was not significant for SBP, DBP, and glucose, and were not included in this graph.
Figure 3. Kaplan–Meier survival curves stratified by (A). BMI ≥30 kg/m2 (p = 0.001 by log rank), (B). Triglycerides ≥150 mg/dL (p = 0.019), (C). HDL <40 mg/dL (p = 0.038), and (D). Exposure intensity (p < 0.001). Cumulative disease-free survival is expressed on the y-axis and time in years from their WTC exposure is on the x-axis. Log rank was not significant for SBP, DBP, and glucose, and were not included in this graph.
Ijerph 16 01486 g003
Table
Table 1. Demographic and pulmonary function test data of group.
Table 1. Demographic and pulmonary function test data of group.
MeasureMCT+
N = 355		BD+
N = 236		Non-AHR
N = 6947		WTC-AHR
N = 539		p
Age on 9/1140.3 (6.3)40.9 (6.8)39.5 (7.5)40.5 (6.5)0.004
Years of service *19.3 (7.3)20.1 (7.2)22.8 (6.5)19.7 (7.3)<0.001
Ever smokers122 (34%)89 (38%)2531 (36%)191 (35%)0.643
RaceCaucasian342 (96%)220 (93%)6529 (94%)512 (95%)0.560
African American3 (1%)6 (3%)183 (3%)9 (2%)
Hispanic9 (3%)10 (4%)215 (3%)17 (3%)
Asian/other1 (.3%)0 (0%)20 (0.3%)1 (0.2%)
Exposure groupMorning of 9/1179 (22%)59 (25%)1124 (16%)123 (23%)<0.001
Afternoon of 9/11211 (59%)134 (57%)3749 (54%)317 (59%)
On or after 9/1265 (18%)43 (18%)2074 (30%)99 (18%)
Pre-9/11FEV1% pred.101.5 (11.6)101.5 (13.5)106.3 (13.0)101.7 (12.4)<0.001
FVC% pred.97.7 (10.9)98.4 (12.7)99.6 (12.1)98.2 (11.7)0.017
Ratio82.9 (5.7)82.4 (5.4)85.3 (4.9)82.7 (5.6)<0.001
WTC-HP entry FEV% pred.91.8 (13.4)90.7 (14.8)98.1 (13.1)91.6 (13.9)<0.001
FVC% pred. 89.6 (12.0)89.3 (21.1)92.4 (11.8)89.6 (12.2)<0.001
Ratio82.0 (6.0)81.2 (6.6)84.6 (4.9)81.7 (6.3)<0.001
Values are in mean (SD) or N (%) as indicated. p calculated by t-test or Chi-square as appropriate, comparing airway hyperreactivity (AHR) and non-AHR. * Data available on N = 5029/6947 non-AHR, N = 418/539 AHR.
Table
Table 2. Clinical measures of inflammation and metabolic syndrome.
Table 2. Clinical measures of inflammation and metabolic syndrome.
MeasureMCT+
N = 355		BD+
N = 236		Non-AHR
N = 6947		WTC-AHR
N = 539		p
Systolic BP, mmHg118.0 (12.5)118.3 (12.6)117.1 (12.5)118.0 (12.7)0.092
Diastolic BP, mmHg73.4 (8.0)74.3 (8.3)73.4 (8.4)73.6 (8.2)0.598
BMI at WTC-HP entry, kg/m229.2 (3.2)28.9 (3.0)28.6 (3.3)29.1 (3.2)0.001
White blood cells × 109 cells/L *6.5 (1.9)6.5 (1.8)6.3 (1.6)6.5 (1.9)0.021
Neutrophils (ANC)3809.8 (1654.5)3697.7 (1400.1)3664.6 (1290.1)3758.1 (1524.8)0.113
Lymphocytes (ALC)1818.9 (534.4)1861.8 (590.2)1830.9 (540.3)1843.4 (561.2)0.608
Eosinophils (AEC)227.8 (149.9)227.2 (160.3)187.1 (130.9)229.3 (156.6)<0.001
Monocytes (AMC)579.4 (194.3)605.7 (216.2)581.3 (193.2)591.1 (204.8)0.263
Glucose92.9 (18.8)91.7 (10.4)91.6 (13.9)92.5 (16.4)0.177
Triglyceride195.7 (139.1)190.7 (126.0)185.1 (136.6)197.4 (137.9)0.046
HDL48.0 (12.6)47.1 (12.1)48.1 (11.7)47.6 (12.4)0.351
LDL133.1 (34.5)128.3 (32.4)128.3 (33.5)131.6 (33.7)0.028
Cholesterol217.4 (40.1)210.2 (36.0)210.8 (38.7)216.3 (38.5)0.009
Cholesterol/HDL ratio4.8 (1.5)4.7 (1.3)4.6 (1.4)4.8 (1.4)0.007
MetSyn definition82 (23%)54 (23%)1329 (19%)123 (23%)<0.001
SBP ≥ 130 and/or DBP ≥ 85 mmHg78 (22%)56 (24%)1384 (20%)119 (22%)0.229
HDL < 40 mg/dL94 (27%)72 (31%)1667 (24%)151(28%)0.036
Triglycerides ≥ 150 mg/dL194 (55%)123 (52%)3428 (49%)294 (55%)0.020
Glucose ≥ 100 mg/dL72 (20%)45 (19%)1269 (18%)106 (20%)0.419
BMI ≥ 30 kg/m2130 (30%)78 (33%)2040 (29%)193 (36%)0.002
Values are in mean (SD) or N (%) as indicated; p calculated by t-test or Chi-square as appropriate, comparing AHR and non-AHR; * Data available on N = 6896/6947 non-AHR, N = 532/537 AHR, differentials expressed as absolute counts, cells/µL.
Table
Table 3. Cox proportional hazards of univariate metabolic risk factors of AHR.
Table 3. Cox proportional hazards of univariate metabolic risk factors of AHR.
MeasureHazards (95% CI)
Cholesterol/HDL ratio ≥ 3.51.332 (1.057–1.679)
BMI ≥ 30 kg/m21.329 (1.114–1.585)
Glucose ≥ 100 mg/dL1.062 (0.857–1.315)
Lipids
mg/dL		HDL < 401.237 (1.025–1.492)
Triglycerides ≥ 1501.204 (1.016–1.427)
Blood pressure mmHgSystolic ≥ 1301.079 (0.872–1.335)
Diastolic ≥ 850.970 (0.718–1.309)
Number of MetSyn risk factors11.441 (1.124–1.847)
21.690 (1.310–2.151)
3+1.654 (1.268–2.158)
Exposure intensityMorning of 9/112.240 (1.719–2.919)
Afternoon of 9/111.759 (1.403–2.205)
After 9/12Reference
Ever smoker 1.759 (1.403–2.205)
Age (per year) 1..017 (1.005–1.029)
All models were adjusted for age, smoking, and exposure intensity. Exposure, age, and smoking RR refer to RR in final model of combined MetSyn risk factors.

Share and Cite

MDPI and ACS Style

Kwon, S.; Crowley, G.; Mikhail, M.; Lam, R.; Clementi, E.; Zeig-Owens, R.; Schwartz, T.M.; Liu, M.; Prezant, D.J.; Nolan, A. Metabolic Syndrome Biomarkers of World Trade Center Airway Hyperreactivity: A 16-Year Prospective Cohort Study. Int. J. Environ. Res. Public Health 2019, 16, 1486. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16091486

AMA Style

Kwon S, Crowley G, Mikhail M, Lam R, Clementi E, Zeig-Owens R, Schwartz TM, Liu M, Prezant DJ, Nolan A. Metabolic Syndrome Biomarkers of World Trade Center Airway Hyperreactivity: A 16-Year Prospective Cohort Study. International Journal of Environmental Research and Public Health. 2019; 16(9):1486. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16091486

Chicago/Turabian Style

Kwon, Sophia, George Crowley, Mena Mikhail, Rachel Lam, Emily Clementi, Rachel Zeig-Owens, Theresa M. Schwartz, Mengling Liu, David J. Prezant, and Anna Nolan. 2019. "Metabolic Syndrome Biomarkers of World Trade Center Airway Hyperreactivity: A 16-Year Prospective Cohort Study" International Journal of Environmental Research and Public Health 16, no. 9: 1486. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16091486

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop