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Dietary phenotype and advanced glycation end-products predict WTC-obstructive airways disease: a longitudinal observational study

Abstract

Background

Diet is a modifier of metabolic syndrome which in turn is associated with World Trade Center obstructive airways disease (WTC-OAD). We have designed this study to (1) assess the dietary phenotype (food types, physical activity, and dietary habits) of the Fire Department of New York (FDNY) WTC-Health Program (WTC-HP) cohort and (2) quantify the association of dietary quality and its advanced glycation end product (AGE) content with the development of WTC-OAD.

Methods

WTC-OAD, defined as developing WTC-Lung Injury (WTC-LI; FEV1 < LLN) and/or airway hyperreactivity (AHR; positive methacholine and/or positive bronchodilator response). Rapid Eating and Activity Assessment for Participants-Short Version (REAP-S) deployed on 3/1/2018 in the WTC-HP annual monitoring assessment. Clinical and REAP-S data of consented subjects was extracted (7/17/2019). Diet quality [low-(15–19), moderate-(20–29), and high-(30–39)] and AGE content per REAP-S questionnaire were assessed for association with WTC-OAD. Regression models adjusted for smoking, hyperglycemia, hypertension, age on 9/11, WTC-exposure, BMI, and job description.

Results

N = 9508 completed the annual questionnaire, while N = 4015 completed REAP-S and had spirometry. WTC-OAD developed in N = 921, while N = 3094 never developed WTC-OAD. Low- and moderate-dietary quality, eating more (processed meats, fried foods, sugary drinks), fewer (vegetables, whole-grains),and having a diet abundant in AGEs were significantly associated with WTC-OAD. Smoking was not a significant risk factor of WTC-OAD.

Conclusions

REAP-S was successfully implemented in the FDNY WTC-HP monitoring questionnaire and produced valuable dietary phenotyping. Our observational study has identified low dietary quality and AGE abundant dietary habits as risk factors for pulmonary disease in the context of WTC-exposure. Dietary phenotyping, not only focuses our metabolomic/biomarker profiling but also further informs future dietary interventions that may positively impact particulate matter associated lung disease.

Background

Diet and obesity play a role in the development of obstructive airways disease (OAD) [1,2,3]. Diets focused on reducing inflammation and increasing vegetable and fish consumption reduced the risk of chronic obstructive pulmonary disease (COPD), whereas diets with increased pro-inflammatory advanced glycation end products (AGE) were associated with disease [4,5,6,7]. Low-calorie dietary interventions yielded weight loss and improved lung function in obese asthmatics [8]. The health benefits of weight loss, increased high density lipoprotein (HDL), and decreased triglyceride, have been extensively studied [9,10,11]. Specifically, Mediterranean diets characterized by high consumption of fruits, vegetables, and fish, were associated with lower COPD, whereas, western diets were significantly associated with higher risk of newly diagnosed COPD [12,13,14,15,16].

Metabolic syndrome (MetSyn) is a risk factor of cardiovascular, lung disease, and World Trade Center-OAD (WTC-OAD) [15, 16]. MetSyn affects over 30% of US adults and 23% of participants in the Fire Department of New York (FDNY) WTC-Health Program (WTC-HP) program [12,13,14,15,16,17,18,19]. Furthermore, metabolic biomarkers, elevated BMI, and a > 2 kg/m2 BMI increase predicted WTC-OAD [16, 20,21,22,23].

Since high-caloric diets are key contributors to MetSyn, nutritional interventions to potentially reverse pulmonary dysfunction have been studied [14,15,16]. Our in vitro and in vivo models identified that the receptor for AGE (RAGE) is associated with lung dysfunction after WTC-particulate matter (WTC-PM) exposure. Specifically, RAGE deficient WTC-PM exposed mice were protected against WTC-OAD [24,25,26]. Dietary and endogenous AGEs can impact signaling pathways such as those in inflammatory diseases [27]. Despite evidence that diet and obesity are risks, studies have suggested obesity may have a protective effect on survival and lung function in COPD [28, 29]. Therefore, to further clarify the effect of diet on lung disease in our WTC-exposed cohort, we studied their dietary patterns.

The Rapid Eating and Activity Assessment for Patients (REAP) and its short version (REAP-S) are advantageous over other independently developed and validated food questionnaires in the primary care setting because of their brevity and ability to quickly evaluate targeted food categories, potential barriers to high dietary quality, and dietary habits [30,31,32,33,34,35,36,37,38,39,40,41,42,43]. REAP-S score has also correlated with other questionnaires investigating OAD [44,45,46,47,48].

To inform our understanding of how diet is a modifier of WTC-OAD, we utilized REAP-S to assess dietary quality and estimate intake of foods such as fat, cholesterol, sugar, and meats and correlated it to disease outcome [30, 43, 47,48,49]. This study also prospectively evaluated potential barriers to high dietary quality, dietary habits, and food group stratification for AGE content. We hypothesized that WTC-exposed first responders with poor dietary quality and increased AGE content were more likely to have WTC-OAD at any timepoint after 9/11/2001 (9/11).

Methods

Study design

This observational study targeted N = 14,976 WTC-HP enrollees that had annual monitoring exams, including physical health and mental health questionnaires, Fig. 1. REAP-S was implemented in the annual questionnaire on March 1, 2018 and continuously accrued until July 17, 2019. Two questions were used to gauge interest in answering the REAP-S and screen for willingness to change diet, Additional file 1: Table S1.

Fig. 1
figure1

Study Design/Consort Diagram. FDNY Rescue/recovery Workers Exposed to World Trade Center Particulates

Source cohort (N = 9508) completed an annual health questionnaire and consented to further physical health research. Subjects were further screened for the study cohort (N = 4015) if they met the following criteria: (i) completed REAP-S (ii) had reliable National Health and Nutrition Examination Survey (NHANES) and (iii) had complete clinical data. Demographic characteristics, clinical data, 9/11 exposure characteristics, questionnaire answers, and lung function testing were obtained from the FDNY WTC-HP electronic medical record (EMR). Study approved by the Montefiore Medical Center/Albert Einstein College of Medicine IRB #07-09-320.

WTC-OAD case definitions

Cases of WTC-OAD had either WTC-Lung Injury (WTC-LI; FEV1 < LLN) and/or Airway Hyperresponsiveness (AHR; positive methacholine or positive bronchodilator testing) at any time point post-9/11 (N = 921) [16, 16, 21, 21,22,23,24, 50,51,52,53,54,55,56,57,58,59,60,61,62]. Cases of WTC-OAD were compared to N = 3094 without WTC-OAD at any time after 9/11.

Our group has utilized FEV1 to define WTC-LI [16, 16, 21,22,23, 53, 59, 60, 63,64,65]. FEV1 was measured prior to 9/11/2001 and is still performed at every FDNY-HP visit. This gives a comprehensive measure of changing lung function over time. Using abnormal FEV1 as an outcome improves generalizability of our findings since it is a readily available measure that doesn’t require costly instrumentation. A vast majority of the WTC cohort had airflow obstruction [51]. Deterioration of FEV1 < LLN is a robust disease definition, correlates with mortality and somewhat with OAD outcomes (severity leading to hospitalizations, exercise ability and measures of quality of life measures) [66,67,68,69,70,71,72,73]. Using FEV1 as a single measure of lung function could lead to non-differential misclassification. Since FEV1 is reduced in both restriction and obstruction, FEV1 < LLN does not distinguish between the two. In spite of the potential for non-differential information bias, using FEV1 < LLN has yielded strong biomarkers-disease associations [74, 75]. Therefore, FEV1 < LLN is a surrogate for obstruction in WTC-exposed firefighters and was how we defined WTC-LI [51, 76, 77].

Nutritional assessment

REAP-S was scored and summed as per guidelines, Table 2 and Additional file 1: Table S1 [43]. REAP-S scores can range from 15–39, and higher quantities represent dietary quality characterized by optimal intake of fruits, vegetables, and whole grains and decreased intake of sugary foods, processed meats, and fried foods. Scores were categorized into low-dietary [15,16,17,18,19], moderate-dietary [16, 20,21,22,23,24,25,26,27,28], and high-dietary [29,30,31,32,33,34,35,36,37,38] quality, Table 3. Additionally, REAP-S questions were assessed as distinct food categories.

AGE quantification (kU/serving) in food groups represented in REAP-S was compiled to a representative value per food group, Additional file 2: Figure S2 [78].

Statistics

Primary data storage/analyses performed with SPSS 25 (IBM) and Prism 8 (Graphpad). Mean ± standard deviation (SD) expressed as continuous variables. Paired sample t-tests compared clinical parameters at two time points—first measurement post-9/11 and at REAP-S administration; student t-tests compared clinical data of those with WTC-OAD to those who never developed WTC-OAD. One-way ANOVA was used in a subgroup analysis of lung function and dietary quality. Counts and percentages describe categorical variables and compared groups using χ2-test.

Arrival time and smoking was self-reported and collected through the annual questionnaires/EMR. Arrival time data, used as a proxy for WTC-particulate matter (WTC-PM) exposure, was categorized into a dichotomous variable of “arrived at the site in the morning of 9/11” or “anytime thereafter” [79]. Smoking data was dichotomous representing ever or never smokers [51, 59, 64, 76, 77, 80,81,82,83,84,85].

Modeling using Multivariable logistic regression estimated association of AGE abundancy, REAP-S scores, and the development of WTC-OAD. All models were adjusted for smoking, age at September 11, 2001, exposure intensity, BMI, and job description. We assumed that dietary habits remain relatively constant over time [86,87,88]. Models of WTC-OAD using components of REAP-S were corrected for multiple comparisons by Bonferroni, p < 0.005. For all else, p was significant if < 0.05 and omnibus testing assessed variance of data.

Results

FDNY nutrition cohort characteristics

There were no significant demographic differences between the source cohort (N = 9508) and the study cohort (N = 4015/9508; 42.23%). Out of the total subjects with WTC-OAD (N = 921), 586 subjects (63.62%) had WTC-LI only, 197 subjects (21.39%) had AHR only, and 138 subjects (14.98%) had both WTC-LI and AHR. Within those with AHR (N = 335), 126 (37.61%) had a positive bronchodilator, 175 (52.24%) had a positive methacholine, and 34 (10.15%) had both.

Subjects with WTC-OAD were more likely to be retired, member of the emergency medical services (EMS) rather than firefighter, and exposed the morning of 9/11 when compared to those who never developed WTC-OAD (p < 0.001), Table 1. Of note, age at 9/11, smoking status, and race were no different in the WTC-OAD and never WTC-OAD populations, Table 1.

Table 1 Demographic and clinical data

Clinical measures

Time to reach WTC-OAD case definition was (mean ± SD) 6.37 ± 7.23 years for the study cohort. For both ever WTC-OAD cases and never WTC-OAD subjects, BMI, blood pressure, and HDL were found to be significantly higher at time of REAP-S compared to immediately post-9/11, Table 1. Similarly, their FEV1%Pred, HDL, LDL, total cholesterol, and triglycerides were significantly lower at time of REAP-S, and FVC%Pred was not significantly different. WTC-OAD cases had significantly higher BMI, blood pressure, and triglycerides, and lower FEV1%Pred, FVC%Pred at 1st post 9/11 and at the time of REAP-S assessment compared to those who never developed WTC-OAD. Subjects with WTC-OAD had an elevated total cholesterol compared to those that never developed WTC-OAD at their 1st post-9/11 assessment. In contrast, at the time of the REAP-S questionnaire, those subjects with WTC-OAD had lower total cholesterol, Table 1.

REAP-S questionnaire responses

Length of time between initial post 9/11 assessment and REAP-S administration was (mean ± SD) 16.59 ± 0.49 years. The study cohort had a mean ± SD REAP-S score of 29.46 ± 4.22. Subjects with WTC-OAD had significantly lower mean REAP-S score of 28.99 ± 4.37 compared to those who never developed WTC-OAD with 29.60 ± 4.17; p < 0.01. In contrast, 50% of our study cohort often eat more than the recommended amount of meat per day (Q7), 79.30% rarely drink sugary drinks (Q13), 48.80% rarely eat processed meats (Q8), 48.50% rarely eat fried foods (Q9), and 46.40% rarely eat snacks (Q10), Table 2. WTC-OAD cases had significantly higher reported consumption of processed meat (Q8) and sugary drinks (Q13), and decreased intake of grain products (Q3), vegetables (Q5), and fried foods (Q9). WTC-OAD also skipped breakfast more often (Q1), ate out more frequently (Q2), and did not feel well as often to shop or cook (Q15) (p < 0.05), Table 2.

Table 2 Nutrition Questions Incorporated into the WTC-HP Annual Questionnaire

Quality of diet assessed by REAP-S

Low-dietary quality was significantly associated with 2.67 odds (95% CI [1.57, 4.52]; p < 0.01) of developing WTC-OAD whereas moderate-dietary quality was associated with 1.22 odds (95% CI [1.05, 1.42]; p = 0.01), when comparing to high-dietary quality as a reference group, Fig. 2. Increasing BMI had a small but significant protective odds ratio of 0.97 (95% CI [0.95, 0.98]; p < 0.01). Job description was significant, at 1.60 odds (95% CI [1.26, 2.03]; p < 0.01). Exposure intensity was a time-dependent risk factor, with 1.29 odds (95% CI [1.07, 1.56]; p = 0.01). Age at 9/11 and smoking were not significant risk factors in this model. Overall, job description, exposure, and BMI were found to have significant odds of developing WTC-OAD, while age at 9/11 and smoking were not, Fig. 2.

Fig. 2
figure2

REAP-S Score Modeling of Associated WTC-OAD. Low nutrition is a significant risk factor of developing WTC-OAD. The model was adjusted for age on 9/11, ever smoking, BMI post 9/11, exposure intensity, and job description

Dietary quality subgroups and lung function of those with low-, moderate-, or high-dietary quality are shown in Table 3. Mean FEV1%Pred and FVC1%Pred at both time points are significantly higher in those with higher dietary quality compared to those with lower dietary quality (p < 0.05). FEV1/FVC ratio was not significantly associated with dietary quality at either timepoint, Table 3.

Table 3 Dietary quality subgroup analysis

Processed meat, sugary drinks, and vegetable intake impacted the odds of developing WTC-OAD

Assessment of individual REAP-S questions highlighted that WTC-OAD was more likely in subjects with increased consumption of processed meats (Q8) and sugary drinks (Q13), and decreased intake of vegetables (Q5), Table 2 and Fig. 3. Additionally, there was a dose response seen with increasing intake of processed meats (OR 1.64 (95% CI [1.23, 2.19]; p = 0.001) and 1.27 (95% CI [1.08, 1.48]; p = 0.003)) and less vegetables (OR 1.53 (95% CI [1.24, 1.90]; p < 0.001) and 1.31 (95% CI [1.12, 1.55]; p = 0.001)). Less whole grain consumption is also associated with higher risk of WTC-OAD (Q3), 1.26 (95% CI [1.08, 1.46]; p = 0.004). WTC-OAD subjects trended towards increased fried food intake but these measures were not significant after Bonferroni correction (p = 0.006), Table 2 and Fig. 3.

Fig. 3
figure3

Food Groups Modeling of Associated WTC-OAD. Food groups represented by its corresponding REAP-S questions identified consumption of less vegetables, more processed meats, and some sugary drinks as significant risk factors. The model was adjusted for age on 9/11, ever smoking, BMI post 9/11, exposure intensity, and job description. *Not significant under Bonferroni correction of p < 0.005, but significant for p < 0.05

Dietary habit assessment showed that not being well enough to cook, skipping breakfast, and eating out increase odds of WTC-OAD

Not feeling well enough to cook (Q15) increased odds of developing WTC-OAD by 1.91 (95% CI [1.33, 2.73]; p < 0.001) whereas skipping breakfast (Q1) was 1.20 (95% CI [1.04, 1.40]; p = 0.015). Eating out (Q2) also had odds of 1.25 (95% CI [1.08, 1.45]; p = 0.003), Table 2.

AGE rich foods confer a higher likelihood of developing WTC-OAD

Using data adapted from Uribarri et al., we summarized the amount of AGE in food groups represented in REAP-S, Additional file 2: Figure S1 [78]. Fried foods (3971.86 kU/serving), processed meats (3925.89 kU/serving), and meats (3687.58 kU/serving) were identified as having the highest AGEs per serving. Sugary foods and drinks (7.2 kU/serving) do not naturally have high level of AGEs but instead cause high levels of endogenous AGEs. Frequency of eating foods highest in AGEs, meat (Q7), processed meats (Q8), and fried foods (Q9), was assessed by logistic regression model adjusted for age, smoking, BMI, exposure, and job description. An AGE-rich exposure response gradient was identified with the odds of developing WTC-OAD: not significantly increased in participants answering usual/often consumption of one AGE-rich food group, significantly increased in participants answering usual/often consumption to any two AGE-rich food groups, 1.50 (95% CI [1.14, 1.97]; p = 0.04), and highly significant in those answering usual/often consumption to all three AGE-rich food groups, 2.31 (95% CI [1.35, 3.95]; p = 0.002), Fig. 4.

Fig. 4
figure4

AGEs and their Association with WTC-OAD. Forest plot of AGE in dietary habit represented by frequency of “usually/often” answers to questions on foods high in AGE (fried foods, processed meats, meats). Having a higher dietary habit of AGE is significantly associated with risk of WTC-OAD

Discussion

This observational, prospective study of dietary phenotyping was successfully implemented at the FDNY WTC-HP annual monitoring exam. Dietary quality was correlated to FEV1 and FVC even immediately after 9/11, and persisted at REAP-S. Since we assume that diet is constant throughout adult life, this could be due to the combined effect of dietary quality and WTC exposure. This is supported by our findings in which more frequent consumption of sugary drinks, processed meats, and decreased intake of vegetables and whole grains were identified as key components in development of WTC-OAD. Subjects with AGE-rich diets were also significantly more likely to develop WTC-OAD.

Our findings parallel prior studies that have displayed the harmful role of processed meats in the increased risk of developing COPD due to its high level of pro-inflammatory AGE levels [89,90,91,92]. AGE formation via glycoxidation is promoted by the high temperatures and low moisture environments utilized in cooking meat, processed meat products, and fried foods [78, 93,94,95]. Associations with certain food groups are important because high levels of AGEs are linked to pathogenic effects, including the ability to promote high levels of oxidative stress and inflammation [78, 94].

Although sugary drinks are relatively low in AGEs, they are a prominent source of high fructose corn syrup [27]. The fructose can indirectly increase AGE intake because of its formation and accumulation of endogenous AGEs [27]. This could be a reason as to why sugary drinks have been associated with bronchitis and asthma in children, and increase likelihood of WTC-OAD [96, 97]. In contrast, carbohydrates and whole grains contain less AGE [78, 95]. Similar to our results, other studies have found that increased whole grain intake, part of a prudent dietary pattern, were associated with a reduced risk of developing COPD [13]. Moreover, it was positively associated with FEV1 and negatively associated with COPD symptoms [98].

Although we showed that low intake of vegetables increased odds of developing WTC-OAD, there was no significant association found with fruit intake. While our results resonated with some studies on low intake of fruits and vegetables, others advocated for increased fruit intake, or found no difference in COPD [78, 99,100,101,102,103,104]. Although fruits and vegetables are relatively low in AGEs, they could confer antioxidant benefit and lower inflammation in diseases such as COPD. A randomized control trial focused on increased antioxidant intake through fruits and vegetables found that it could even help regain FEV1 in COPD patients [102].

In contrast to our prior work, increasing BMI had a 3.6% decreased likelihood of developing WTC-OAD. One reason for this difference could be that while our prior work focused on firefighters, we now also investigate EMS 1st responders. Prior studies have shown that the firefighter and EMS cohort express different patterns of lung function decline, even after adjusting for BMI [18]. This expanded cohort potentially reflects concordance with studies showing obesity’s protective effect against mortality in COPD patients [105, 106]. This could also be a result of a healthy worker effect and the imperfect utilization of BMI to define obesity in firefighters with rigorous physical job requirements [107]. In addition, we optimized our model by adjusting for confounding of WTC-OAD cases by using BMI at the time of diagnosis, whereas for subjects that never developed WTC-OAD, BMI at REAP-S was used. Nevertheless, the results of the final model did not change significantly even when we also assessed the effects of BMI at the same time point (1st post 9/11 and at REAP-S respectively). Moroever, we found that triglycerides decreased from post 9/11 to REAP-S. This could be an effect of the close monitoring that these patients received and/or other confounder such as triglyceride-lowering medications such as statin therapy in as per 2018 American Heart Association/American College of Cardiology guidelines [108]. Future studies could help differentiate the paradoxical effect of obesity vs. the healthy worker phenomena.

There are several limitations to our investigation. Dietary habits and exposure are subject to self-reporting bias. Bias assessment has been performed on the FDNY WTC cohort, and found that self-reported asthma and exposure were consistent across several studies, and that significant findings were minimally affected by potential bias [109]. Thus, extension of the reliability of this data to our study is reasonable. We also assume that dietary habits remain relatively constant throughout a person’s adult life, an assumption supported by several large studies [86, 88, 110]. Additionally, REAP-S is a brief dietary instrument that limits our ability to differentiate subtypes of food consumed. Protein intake does not differentiate between beans, poultry, or red meat. Firefighters had pre-employment physical health assessments to ensure that they did not have pre-existing OAD, the pulmonary function for EMS prior to 9/11 was also assessed but these 1st responders are not subject to the same standards. Therefore, our results are limited to correlation of dietary quality and OAD.

Another possible limitation is that WTC-OAD subjects were more likely to be retired compared to those who never developed WTC-OAD. We also identified that not being well enough to shop and cook and frequent eating out had strong associations with WTC-OAD. Since this was assessed at a later time point, it is unclear if this reflects the burden of concurrent WTC-OAD. One study has found that physical factors with COPD patients such as being too tired to cook resulted in the shift in eating meals that have been easily prepared [111]. However, this is an important finding that highlights a potential barrier to access to healthier diets in this population. The limiting lifestyle imposed by WTC-OAD could further perpetuate an unforgiving cycle of lower dietary quality and worsening disease.

This study identifies risk factors of worsening OAD, and demonstrates the potential for intervention. Further research is needed to determine if implementation of a diet focused on decreasing AGE-rich foods, increasing antioxidant intake, and targeting weight loss could prevent the development of WTC-OAD or in those with WTC-OAD, reverse or slow its progression. Our ongoing randomized clinical trial, the Food Intake Restriction for Health Outcome Support and Education (FIREHOUSE) Trial, aims to assess the effects of technology-assisted social cognitive behavioral therapy and a low-caloric Mediterranean diet on the progression of WTC-LI.

In summary, this observational study successfully used REAP-S to identify both low-dietary quality and AGE abundant foods as predictive of developing lung disease in this WTC-exposed population. Our findings indicate the potential impact of future research using dietary interventions not just in the FDNY WTC-HP, but also in other OAD cohorts, with or without WTC-exposure.

Availability of data and materials

Sharing of human data is governed by the World Trade Center (WTC) Clinical Center of Excellence program maintained by the Fire Department of New York (FDNY). All investigators will need to enter into a data use agreement with the FDNY WTC Clinical Center of Excellence. Additional information about this database may be obtained through Dr. David Prezant. He can be reached by email at prezand@fdny.nyc.gov.

Abbreviations

AGE:

Advanced glycation end-products

AHR:

Airway hyperreactivity

BMI:

Body Mass Index

CI:

Confidence interval

DBP:

Diastolic blood pressure

EMS:

Emergency Medical Services

FDNY:

Fire Department of the City of New York

FEV1 :

Forced expiratory volume over 1 s

FFQ:

Food Frequency Questionnaire

FVC:

Forced vital capacity

HDL:

High density lipoprotein

HR:

Hazards ratio

LDL:

Low density lipoprotein

LLN:

Lower limit of normal

MetSyn:

Metabolic syndrome

NHANES:

National Health and Nutrition Examination Survey

OR:

Odds ratio

PFT:

Pulmonary function test

PM:

Particulate matter

PUFA:

Polyunsaturated fatty acids

REAP-S:

Rapid Eating and Activity Assessment for Patients-Short Version

SBP:

Systolic blood pressure

SD:

Standard deviation

US:

United States

WTC:

World Trade Center

WTC-HP:

WTC-Health Program

WTC-LI:

WTC-Lung Injury

WTC-OAD:

WTC-obstructive airways disease

WTC-PM:

WTC-particulate matter

References

  1. 1.

    Hanson C, Rutten EP, Wouters EF, Rennard S. Influence of diet and obesity on COPD development and outcomes. Int J Chron Obstruct Pulmon Dis. 2014;9:723–33.

    PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Wood LG. Diet, obesity, and asthma. Ann Am Thorac Soc. 2017;14(Supplement_5):S332–8.

    PubMed  Article  Google Scholar 

  3. 3.

    Napier CO, Mbadugha O, Bienenfeld LA, Doucette JT, Lucchini R, Luna-Sanchez S, et al. Obesity and weight gain among former World Trade Center workers and volunteers. Arch Environ Occup H. 2017;72(2):106–10.

    Article  Google Scholar 

  4. 4.

    Guo WA, Davidson BA, Ottosen J, Ohtake PJ, Raghavendran K, Mullan BA, et al. Effect of high advanced glycation end-product diet on pulmonary inflammatory response and pulmonary function following gastric aspiration. Shock. 2012;38(6):677–84.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    DeChristopher LR, Uribarri J, Tucker KL. Intake of high fructose corn syrup sweetened soft drinks is associated with prevalent chronic bronchitis in U.S. Adults, ages 20–55 y. Nutr J. 2015;14:107.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  6. 6.

    Zheng PF, Shu L, Si CJ, Zhang XY, Yu XL, Gao W. Dietary patterns and chronic obstructive pulmonary disease: a meta-analysis. COPD. 2016;13(4):515–22.

    PubMed  Article  Google Scholar 

  7. 7.

    Scoditti E, Massaro M, Garbarino S, Toraldo DM. Role of diet in chronic obstructive pulmonary disease prevention and treatment. Nutrients. 2019;11(6):1357.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  8. 8.

    Hakala K, Stenius-Aarniala B, Sovijarvi A. Effects of weight loss on peak flow variability, airways obstruction, and lung volumes in obese patients with asthma. Chest. 2000;118(5):1315–21.

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Shai I, Schwarzfuchs D, Henkin Y, Shahar DR, Witkow S, Greenberg I, et al. Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet. N Engl J Med. 2008;359(3):229–41.

    CAS  Article  Google Scholar 

  10. 10.

    Schwartz J. Role of polyunsaturated fatty acids in lung disease. Am J Clin Nutr. 2000;71(1 Suppl):393S-S396.

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Seegmiller AC. Abnormal unsaturated fatty acid metabolism in cystic fibrosis: biochemical mechanisms and clinical implications. Int J Mol Sci. 2014;15(9):16083–99.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Varraso R, Fung TT, Barr RG, Hu FB, Willett W, Camargo CA Jr. Prospective study of dietary patterns and chronic obstructive pulmonary disease among US women. Am J Clin Nutr. 2007;86(2):488–95.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Varraso R, Fung TT, Hu FB, Willett W, Camargo CA. Prospective study of dietary patterns and chronic obstructive pulmonary disease among US men. Thorax. 2007;62(9):786–91.

    PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Webber MP, Yip J, Zeig-Owens R, Moir W, Ungprasert P, Crowson CS, et al. Post-9/11 sarcoidosis in WTC-exposed firefighters and emergency medical service workers. Respir Med. 2017;132:232–7.

    PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Long NP, Park S, Anh NH, Nghi TD, Yoon SJ, Park JH, et al. High-throughput omics and statistical learning integration for the discovery and validation of novel diagnostic signatures in colorectal cancer. Int J Mol Sci. 2019;20(2):296.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  16. 16.

    Kwon S, Crowley G, Mikhail M, Lam R, Clementi E, Zeig-Owens R, et al. Metabolic syndrome biomarkers of World Trade Center Airway Hyperreactivity: a 16-year prospective cohort study. Int J Environ Res Public Health. 2019;16(9):1486.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  17. 17.

    Chen JC, Schwartz J. Metabolic syndrome and inflammatory responses to long-term particulate air pollutants. Environ Health Perspect. 2008;116(5):612–7.

    PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Aldrich TK, Gustave J, Hall CB, Cohen HW, Webber MP, Zeig-Owens R, et al. Lung function in rescue workers at the World Trade Center after 7 years. N Engl J Med. 2010;362(14):1263–72.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Webber MP, Lee R, Soo J, Gustave J, Hall CB, Kelly K, et al. Prevalence and incidence of high risk for obstructive sleep apnea in World Trade Center-exposed rescue/recovery workers. Sleep Breath. 2011;15(3):283–94.

    PubMed  Article  Google Scholar 

  20. 20.

    Kwon S, Crowley G, Haider SH, Lam R, Zhang L, Zeig-Owens R, et al. Weight loss as a modifiable risk: Body Mass Index and loss of lung function in World Trade Center Particulate Exposure. Am J Respir Crit Care Med. 2017.

  21. 21.

    Crowley G, Kwon S, Haider SH, Caraher EJ, Lam R, St-Jules DE, et al. Metabolomics of World Trade Center-Lung Injury: a machine learning approach. BMJ Open Respir Res. 2018;5(1):e000274.

    PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Crowley G, Kwon S, Ostrofsky DF, Clementi EA, Haider SH, Caraher EJ, et al. Assessing the protective metabolome using machine learning in World Trade Center particulate exposed firefighters at risk for lung injury. Sci Rep. 2019a;9(1):11939.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  23. 23.

    Kwon S, Crowley G, Caraher EJ, Haider SH, Lam R, Veerappan A, et al. Validation of predictive metabolic syndrome biomarkers of World Trade Center Lung Injury: a 16-year longitudinal study. Chest. 2019;156(3):486–96.

    PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Caraher EJ, Kwon S, Haider SH, Crowley G, Lee A, Ebrahim 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(9):e0184331.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. 25.

    Veerappan A, Oskuei A, Crowley G, Mikhail M, Ostrofsky D, Gironda Z, et al. World Trade Center-cardiorespiratory and vascular dysfunction: assessing the phenotype and metabolome of a murine particulate matter exposure model. Sci Rep. 2020;10(1):3130.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Haider SH, Veerappan A, Crowley G, Ostrofsky D, Mikhail M, Lam R, et al. MultiOMICs of WTC-particulate induced persistent airway hyperreactivity: role of receptor for advanced glycation end products. Am J Respir Cell Mol Biol. 2020.

  27. 27.

    Aragno M, Mastrocola R. Dietary sugars and endogenous formation of advanced glycation endproducts: emerging mechanisms of disease. Nutrients. 2017;9(4):385.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  28. 28.

    Spelta F, Fratta Pasini AM, Cazzoletti L, Ferrari M. Body weight and mortality in COPD: focus on the obesity paradox. Eat Weight Disord. 2018;23(1):15–22.

    PubMed  Article  Google Scholar 

  29. 29.

    Guo Y, Zhang T, Wang Z, Yu F, Xu Q, Guo W, et al. Body mass index and mortality in chronic obstructive pulmonary disease: a dose–response meta-analysis. Medicine (Baltimore). 2016;95(28):e4225.

    Article  Google Scholar 

  30. 30.

    Kline CE, Crowley EP, Ewing GB, Burch JB, Blair SN, Durstine JL, et al. Blunted heart rate recovery is improved following exercise training in overweight adults with obstructive sleep apnea. Int J Cardiol. 2013;167(4):1610–5.

    PubMed  Article  Google Scholar 

  31. 31.

    de Batlle J, Barreiro E, Romieu I, Mendez M, Gomez FP, Balcells E, et al. Dietary modulation of oxidative stress in chronic obstructive pulmonary disease patients. Free Radic Res. 2010;44(11):1296–303.

    PubMed  Article  CAS  Google Scholar 

  32. 32.

    de Batlle J, Romieu I, Anto JM, Mendez M, Rodriguez E, Balcells E, et al. Dietary habits of firstly admitted Spanish COPD patients. Respir Med. 2009;103(12):1904–10.

    PubMed  Article  Google Scholar 

  33. 33.

    Hirayama F, Lee AH, Binns CW, Hiramatsu N, Mori M, Nishimura K. Dietary intake of isoflavones and polyunsaturated fatty acids associated with lung function, breathlessness and the prevalence of chronic obstructive pulmonary disease: possible protective effect of traditional Japanese diet. Mol Nutr Food Res. 2010;54(7):909–17.

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Hirayama F, Lee AH, Binns CW, Zhao Y, Hiramatsu T, Tanikawa Y, et al. Do vegetables and fruits reduce the risk of chronic obstructive pulmonary disease? A case–control study in Japan. Prev Med. 2009a;49(2–3):184–9.

    PubMed  Article  Google Scholar 

  35. 35.

    Hirayama F, Lee AH, Binns CW, Zhao Y, Hiramatsu T, Tanikawa Y, et al. Soy consumption and risk of COPD and respiratory symptoms: a case-control study in Japan. Respir Res. 2009b;10:56.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. 36.

    Hirayama F, Lee AH, Oura A, Mori M, Hiramatsu N, Taniguchi H. Dietary intake of six minerals in relation to the risk of chronic obstructive pulmonary disease. Asia Pac J Clin Nutr. 2010;19(4):572–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, et al. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol. 1985;122(1):51–65.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  38. 38.

    Rimm EB, Giovannucci EL, Stampfer MJ, Colditz GA, Litin LB, Willett WC. Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals. Am J Epidemiol. 1992;135(10):1114–26.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  39. 39.

    Block G, Hartman AM, Dresser CM, Carroll MD, Gannon J, Gardner L. A data-based approach to diet questionnaire design and testing. Am J Epidemiol. 1986;124(3):453–69.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  40. 40.

    Block G, Woods M, Potosky A, Clifford C. Validation of a self-administered diet history questionnaire using multiple diet records. J Clin Epidemiol. 1990;43(12):1327–35.

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Gans KM, Risica PM, Wylie-Rosett J, Ross EM, Strolla LO, McMurray J, et al. Development and evaluation of the nutrition component of the Rapid Eating and Activity Assessment for Patients (REAP): a new tool for primary care providers. J Nutr Educ Behav. 2006;38(5):286–92.

    PubMed  Article  Google Scholar 

  42. 42.

    Gans KM, Ross E, Barner CW, Wylie-Rosett J, McMurray J, Eaton C. REAP and WAVE: new tools to rapidly assess/discuss nutrition with patients. J Nutr. 2003;133(2):556S-S562.

    PubMed  Article  Google Scholar 

  43. 43.

    Segal-Isaacson CJ, Wylie-Rosett J, Gans KM. Validation of a short dietary assessment questionnaire: the Rapid Eating and Activity Assessment for Participants short version (REAP-S). Diabetes Educ. 2004;30(5):774, 6, 8 passim.

  44. 44.

    Kennedy ET, Ohls J, Carlson S, Fleming K. The Healthy Eating Index: design and applications. J Am Diet Assoc. 1995;95(10):1103–8.

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    McCullough ML, Willett WC. Evaluating adherence to recommended diets in adults: the Alternate Healthy Eating Index. Public Health Nutr. 2006;9(1A):152–7.

    PubMed  Article  Google Scholar 

  46. 46.

    Varraso R, Chiuve SE, Fung TT, Barr RG, Hu FB, Willett WC, et al. Alternate Healthy Eating Index 2010 and risk of chronic obstructive pulmonary disease among US women and men: prospective study. BMJ. 2015;350:h286.

    PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Sundermann EE, Katz MJ, Lipton RB, Lichtenstein AH, Derby CA. A brief dietary assessment predicts executive dysfunction in an elderly cohort: results from the einstein aging study. J Am Geriatr Soc. 2016;64(11):e131–6.

    PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Ahdout J, Kotlerman J, Elashoff D, Kim J, Chiu MW. Modifiable lifestyle factors associated with metabolic syndrome in patients with psoriasis. Clin Exp Dermatol. 2012;37(5):477–83.

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Kobayashi H, Nolan A, Naveed B, Hoshino Y, Segal LN, Fujita Y, et al. Neutrophils activate alveolar macrophages by producing caspase-6-mediated cleavage of IL-1 receptor-associated kinase-M. J Immunol. 2011;186(1):403–10.

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Kwon S, Crowley G, Caraher EJ, Haider SH, Lam R, Veerappan A, Yang L, Liu M, Zeig-Owens R, Schwartz TM, Prezant DJ. Validation of predictive metabolic syndrome biomarkers of World Trade Center Lung injury: a 16-year longitudinal study. Chest J. 2019;156:486–96.

    Article  Google Scholar 

  51. 51.

    Weiden MD, Ferrier N, Nolan A, Rom WN, Comfort A, Gustave J, et al. Obstructive airways disease with air trapping among firefighters exposed to World Trade Center dust. Chest. 2010;137(3):566–74.

    PubMed  Article  Google Scholar 

  52. 52.

    Ingram JM, Auerill AF, Battersby PN, Holborn PG, Nolan PF. Suppression of hydrogen-oxygen-nitrogen explosions by fine water mist: Part 1. Burning velocity. Int J Hydrogen Energ. 2012;37(24):19250–7.

    CAS  Article  Google Scholar 

  53. 53.

    Haider SH, Veerappan A, Crowley G, Caraher EJ, Ostrofsky D, Mikhail M, et al. Multiomics of World Trade Center particulate matter-induced persistent airway hyperreactivity. Role of receptor for advanced glycation end products. Am J Respir Cell Mol Biol. 2020;63(2):219–33.

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Haider SH, Oskuei A, Crowley G, Kwon S, Lam R, Riggs J, et al. Receptor for advanced glycation end-products and environmental exposure related obstructive airways disease: a systematic review. Eur Respir Rev. 2019;28(151):180096.

    PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Haider SH, Kwon S, Lam R, Lee AK, Caraher EJ, Crowley G, 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-UK. 2018;8:1–7.

    CAS  Article  Google Scholar 

  56. 56.

    Aldrich TK, Weakley J, Dhar S, Hall CB, Crosse T, Banauch GI, et al. Bronchial reactivity and lung function after world trade center exposure. Chest. 2016;150(6):1333–40.

    PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Kwon S, Crowley G, Haider SH, Zhang L, Nolan A. Nephroprotective strategies in septic shock: the VANISH trial. J Thorac Dis. 2016;8(11):E1508–10.

    PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Zeig-Owens R, Nolan A, Putman B, Singh A, Prezant DJ, Weiden MD. Biomarkers of patient intrinsic risk for upper and lower airway injury after exposure to the World Trade Center atrocity. Am J Ind Med. 2016;59(9):788–94.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Weiden MD, Kwon S, Caraher E, Berger KI, Reibman J, Rom WN, et al. Biomarkers of World Trade Center particulate matter exposure: physiology of distal airway and blood biomarkers that predict FEV(1) decline. Semin Respir Crit Care Med. 2015;36(3):323–33.

    PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Crowley G, Kwon S, Haider S, Caraher EJ, Lam R, Liu M, et al. Metabolite and biomarker predictors of World Trade Center-lung injury: an integrated multiplatform machine learning approach. Am J Resp Crit Care. 2018;197.

  61. 61.

    Lam R, Haider SH, Crowley G, Caraher EJ, Ostrofsky DF, Talusan A, et al. Synergistic effect of WTC-particulate matter and lysophosphatidic acid exposure and the role of RAGE: in-vitro and translational assessment. Int J Environ Res Public Health. 2020;17(12):4318.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  62. 62.

    Aldrich TK, Vossbrinck M, Zeig-Owens R, Hall CB, Schwartz TM, Moir W, 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(6):1419–27.

    PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Crowley G, Kwon S, Ostrofsky DF, Clementi EA, Haider SH, Caraher EJ, et al. Assessing the protective metabolome using machine learning in World Trade Center particulate exposed firefighters at risk for lung injury. Sci Rep. 2019b;9:11939.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  64. 64.

    Naveed B, Weiden MD, Kwon S, Gracely EJ, Comfort AL, Ferrier N, et al. Metabolic syndrome biomarkers predict lung function impairment: a nested case-control study. Am J Respir Crit Care Med. 2012;185(4):392–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Tsukiji J, Cho SJ, Echevarria GC, Kwon S, Joseph P, Schenck EJ, et al. Lysophosphatidic acid and apolipoprotein A1 predict increased risk of developing World Trade Center-lung injury: a nested case-control study. Biomarkers. 2014;19(2):159–65.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Menezes AM, Perez-Padilla R, Wehrmeister FC, Lopez-Varela MV, Muino A, Valdivia G, et al. FEV1 is a better predictor of mortality than FVC: the PLATINO cohort study. PLoS ONE. 2014;9(10):e109732.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. 67.

    Puddu PE, Menotti A, Tolonen H, Nedeljkovic S, Kafatos AG. Determinants of 40-year all-cause mortality in the European cohorts of the Seven Countries Study. Eur J Epidemiol. 2011;26(8):595–608.

    PubMed  Article  Google Scholar 

  68. 68.

    Hole DJ, Watt GC, Davey-Smith G, Hart CL, Gillis CR, Hawthorne VM. Impaired lung function and mortality risk in men and women: findings from the Renfrew and Paisley prospective population study. BMJ. 1996;313(7059):711–5 (discussion 5-6).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Jenkins C, Rodriguez-Roisin R. Quality of life, stage severity and COPD. Eur Respir J. 2009;33(5):953–5.

    CAS  PubMed  Article  Google Scholar 

  70. 70.

    Tojo N, Ichioka M, Chida M, Miyazato I, Yoshizawa Y, Miyasaka N. Pulmonary exercise testing predicts prognosis in patients with chronic obstructive pulmonary disease. Intern Med. 2005;44(1):20–5.

    PubMed  Article  Google Scholar 

  71. 71.

    Watz H, Waschki B, Meyer T, Magnussen H. Physical activity in patients with COPD. Eur Respir J. 2009;33(2):262–72.

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Montes de Oca M, Talamo C, Perez-Padilla R, Jardim JR, Muino A, Lopez MV, et al. Chronic obstructive pulmonary disease and body mass index in five Latin America cities: the PLATINO study. Respir Med. 2008;102(5):642–50.

    PubMed  Article  Google Scholar 

  73. 73.

    Osman IM, Godden DJ, Friend JA, Legge JS, Douglas JG. Quality of life and hospital re-admission in patients with chronic obstructive pulmonary disease. Thorax. 1997;52(1):67–71.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74.

    Adams KE, Rasmussen JC, Darne C, Tan IC, Aldrich MB, Marshall MV, et al. Direct evidence of lymphatic function improvement after advanced pneumatic compression device treatment of lymphedema. Biomed Opt Express. 2010;1(1):114–25.

    PubMed  PubMed Central  Article  Google Scholar 

  75. 75.

    Nolan A, Naveed B, Comfort AL, Ferrier N, Hall CB, Kwon S, et al. Inflammatory biomarkers predict airflow obstruction after exposure to World Trade Center Dust. Chest. 2011;142:412–8.

    PubMed Central  Article  PubMed  Google Scholar 

  76. 76.

    Weiden MD, Naveed B, Kwon S, Cho SJ, Comfort AL, Prezant DJ, et al. Cardiovascular biomarkers predict susceptibility to lung injury in World Trade Center dust-exposed firefighters. Eur Respir J. 2013;41(5):1023–30.

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Schenck EJ, Echevarria GC, Girvin FG, Kwon S, Comfort AL, Rom WN, et al. Enlarged pulmonary artery is predicted by vascular injury biomarkers and is associated with WTC-lung injury in exposed fire fighters: a case-control study. BMJ Open. 2014;4(9):e005575.

    PubMed  PubMed Central  Article  Google Scholar 

  78. 78.

    Uribarri J, Woodruff S, Goodman S, Cai W, Chen X, Pyzik R, et al. Advanced glycation end products in foods and a practical guide to their reduction in the diet. J Am Diet Assoc. 2010;110(6):911-16.e12.

    PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Liu X, Yip J, Zeig-Owens R, Weakley J, Webber MP, Schwartz TM, et al. 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.

    PubMed  PubMed Central  Google Scholar 

  80. 80.

    Webber MP, Glaser MS, Weakley J, Soo J, Ye F, Zeig-Owens R, et al. Physician-diagnosed respiratory conditions and mental health symptoms 7–9 years following the World Trade Center disaster. Am J Ind Med. 2011;54(9):661–71.

    PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Nolan A, Naveed B, Comfort AL, Ferrier N, Hall CB, Kwon S, et al. Inflammatory biomarkers predict airflow obstruction after exposure to world trade center dust. Chest. 2012;142(2):412–8.

    CAS  PubMed  Article  Google Scholar 

  82. 82.

    Weiden MD, Naveed B, Kwon S, Segal LN, Cho SJ, Tsukiji J, et al. Comparison of WTC dust size on macrophage inflammatory cytokine release in vivo and in vitro. PLoS ONE. 2012;7(7):e40016.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Aldrich TK, Ye F, Hall CB, Webber MP, Cohen HW, Dinkels M, et al. Longitudinal pulmonary function in newly hired, non-World Trade Center-exposed fire department City of New York firefighters: the first 5 years. Chest. 2013;143(3):791–7.

    PubMed  Article  Google Scholar 

  84. 84.

    Kwon S, Weiden MD, Echevarria GC, Comfort AL, Naveed B, Prezant DJ, et al. Early elevation of serum MMP-3 and MMP-12 predicts protection from World Trade Center-lung injury in New York City Firefighters: a nested case-control study. PLoS ONE. 2013;8(10):e76099.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Nolan A, Kwon S, Cho SJ, Naveed B, Comfort AL, Prezant DJ, et al. MMP-2 and TIMP-1 predict healing of WTC-lung injury in New York City firefighters. Respir Res. 2014;15:5.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  86. 86.

    Movassagh EZ, Baxter-Jones ADG, Kontulainen S, Whiting SJ, Vatanparast H. Tracking dietary patterns over 20 years from childhood through adolescence into young adulthood: the saskatchewan pediatric bone mineral accrual study. Nutrients. 2017;9(9):990.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  87. 87.

    Mikkila V, Rasanen L, Raitakari OT, Marniemi J, Pietinen P, Ronnemaa T, et al. Major dietary patterns and cardiovascular risk factors from childhood to adulthood. The Cardiovascular Risk in Young Finns Study. Br J Nutr. 2007;98(1):218–25.

    CAS  PubMed  Article  Google Scholar 

  88. 88.

    Newby PK, Weismayer C, Akesson A, Tucker KL, Wolk A. Long-term stability of food patterns identified by use of factor analysis among Swedish women. J Nutr. 2006;136(3):626–33.

    CAS  PubMed  Article  Google Scholar 

  89. 89.

    Varraso R, Dumas O, Boggs KM, Willett WC, Speizer FE, Camargo CA Jr. Processed meat intake and risk of chronic obstructive pulmonary disease among middle-aged women. EClinicalMedicine. 2019;14:88–95.

    PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Salari-Moghaddam A, Milajerdi A, Larijani B, Esmaillzadeh A. Processed red meat intake and risk of COPD: a systematic review and dose-response meta-analysis of prospective cohort studies. Clin Nutr. 2019;38(3):1109–16.

    CAS  PubMed  Article  Google Scholar 

  91. 91.

    Varraso R, Camargo CA Jr. The influence of processed meat consumption on chronic obstructive pulmonary disease. Expert Rev Respir Med. 2015;9(6):703–10.

    CAS  PubMed  Article  Google Scholar 

  92. 92.

    Vlassara H, Uribarri J. Advanced glycation end products (AGE) and diabetes: cause, effect, or both? Curr Diab Rep. 2014;14(1):453.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  93. 93.

    Inan-Eroglu E, Ayaz A, Buyuktuncer Z. Formation of advanced glycation endproducts in foods during cooking process and underlying mechanisms: a comprehensive review of experimental studies. Nutr Res Rev. 2019;33:77–89.

    PubMed  Article  Google Scholar 

  94. 94.

    Sharma C, Kaur A, Thind SS, Singh B, Raina S. Advanced glycation End-products (AGEs): an emerging concern for processed food industries. J Food Sci Technol. 2015;52(12):7561–76.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  95. 95.

    Goldberg T, Cai W, Peppa M, Dardaine V, Baliga BS, Uribarri J, et al. Advanced glycoxidation end products in commonly consumed foods. J Am Diet Assoc. 2004;104(8):1287–91.

    CAS  PubMed  Article  Google Scholar 

  96. 96.

    Park S, Blanck HM, Sherry B, Jones SE, Pan L. Regular-soda intake independent of weight status is associated with asthma among US high school students. J Acad Nutr Diet. 2013;113(1):106–11.

    PubMed  PubMed Central  Article  Google Scholar 

  97. 97.

    DeChristopher LR, Uribarri J, Tucker KL. Intakes of apple juice, fruit drinks and soda are associated with prevalent asthma in US children aged 2–9 years. Public Health Nutr. 2016;19(1):123–30.

    PubMed  Article  Google Scholar 

  98. 98.

    Tabak C, Smit HA, Heederik D, Ocke MC, Kromhout D. Diet and chronic obstructive pulmonary disease: independent beneficial effects of fruits, whole grains, and alcohol (the MORGEN study). Clin Exp Allergy. 2001;31(5):747–55.

    CAS  PubMed  Article  Google Scholar 

  99. 99.

    Walda IC, Tabak C, Smit HA, Rasanen L, Fidanza F, Menotti A, et al. Diet and 20-year chronic obstructive pulmonary disease mortality in middle-aged men from three European countries. Eur J Clin Nutr. 2002;56(7):638–43.

    CAS  PubMed  Article  Google Scholar 

  100. 100.

    Kaluza J, Larsson SC, Orsini N, Linden A, Wolk A. Fruit and vegetable consumption and risk of COPD: a prospective cohort study of men. Thorax. 2017;72(6):500–9.

    PubMed  Article  Google Scholar 

  101. 101.

    Meteran H, Thomsen SF, Miller MR, Hjelmborg J, Sigsgaard T, Backer V. Self-reported intake of fruit and vegetables and risk of chronic obstructive pulmonary disease: a nation-wide twin study. Respir Med. 2018;144:16–21.

    PubMed  Article  Google Scholar 

  102. 102.

    Keranis E, Makris D, Rodopoulou P, Martinou H, Papamakarios G, Daniil Z, et al. Impact of dietary shift to higher-antioxidant foods in COPD: a randomised trial. Eur Respir J. 2010;36(4):774–80.

    CAS  PubMed  Article  Google Scholar 

  103. 103.

    Baldrick FR, Elborn JS, Woodside JV, Treacy K, Bradley JM, Patterson CC, et al. Effect of fruit and vegetable intake on oxidative stress and inflammation in COPD: a randomised controlled trial. Eur Respir J. 2012;39(6):1377–84.

    CAS  PubMed  Article  Google Scholar 

  104. 104.

    Holt EM, Steffen LM, Moran A, Basu S, Steinberger J, Ross JA, et al. Fruit and vegetable consumption and its relation to markers of inflammation and oxidative stress in adolescents. J Am Diet Assoc. 2009;109(3):414–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. 105.

    Cao C, Wang R, Wang J, Bunjhoo H, Xu Y, Xiong W. Body mass index and mortality in chronic obstructive pulmonary disease: a meta-analysis. PLoS ONE. 2012;7(8):e43892.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  106. 106.

    Sun Y, Milne S, Jaw JE, Yang CX, Xu F, Li X, et al. BMI is associated with FEV1 decline in chronic obstructive pulmonary disease: a meta-analysis of clinical trials. Respir Res. 2019;20(1):236.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  107. 107.

    Jitnarin N, Poston WS, Haddock CK, Jahnke SA, Day RS. Accuracy of body mass index-defined obesity status in US firefighters. Saf Health Work. 2014;5(3):161–4.

    PubMed  PubMed Central  Article  Google Scholar 

  108. 108.

    Grundy SM. AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines (vol 139, pg e1082, 2019). Circulation. 2019;139(25):E1182–6.

    Google Scholar 

  109. 109.

    Kim H, Baidwan NK, Kriebel D, Cifuentes M, Baron S. Asthma among World Trade Center First Responders: a qualitative synthesis and bias assessment. Int J Env Res Public Health. 2018;15(6):1053.

    Article  Google Scholar 

  110. 110.

    Mikkilae V, Rasnan L, Raitakari OT, Marniemi J, Pietinen P, Ronnemaa T, et al. Major dietary patterns and cardiovascular risk factors from childhood to adulthood. The Cardiovascular Risk in Young Finns Study. Br J Nutr. 2007;98(1):218–25.

    Article  CAS  Google Scholar 

  111. 111.

    Odencrants S, Ehnfors M, Grobe SJ. Living with chronic obstructive pulmonary disease: part I. Struggling with meal-related situations: experiences among persons with COPD. Scand J Caring Sci. 2005;19(3):230–9.

    PubMed  Article  Google Scholar 

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Acknowledgements

We would like to thank the FDNY first responders for their bravery, sacrifice and continued commitment.

Funding

NHLBI R01HL119326, CDC/NIOSH U01-OH11300, Clinical Center of Excellence 200-2017-93426, Data Center 200-2017-93326.

Author information

Affiliations

Authors

Contributions

RL, AN and SK participated in study conception and design; AN was the primary investigator and guarantor of the paper; RL, AN, SK, HC, AH, TS, RZO and DJP were responsible for data collection; AN and SK were responsible for data validation; RL, AN, SK, and GC participated in data analysis; AN, GC, ML, and SK undertook the statistical analysis. All authors participated in data interpretation, writing, and revision of the report and approval of the final version. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Anna Nolan.

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Ethics approval and consent to participate

The authors declare no competing interests. Study approved by the Montefiore Medical Center/Albert Einstein College of Medicine IRB #07-09-320.

Competing interests

The authors report no conflicts of interest.

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Supplementary Information

Additional file 1: Table S1.

Full Nutrition Questions. All of the nutrition questions that were incorporated into the WTC-HP annual questionnaire.

Additional file 2: Figure S1.

Assessment of AGEs in REAP-S Food Groups. REAP-S identified food groups (fried foods, processed meats, and meats) that have the highest amounts of AGE (kU/serving) adapted from Uribarri et al. [78]

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Lam, R., Kwon, S., Riggs, J. et al. Dietary phenotype and advanced glycation end-products predict WTC-obstructive airways disease: a longitudinal observational study. Respir Res 22, 19 (2021). https://doi.org/10.1186/s12931-020-01596-6

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Keywords

  • Metabolic syndrome
  • Nutrition
  • Diet