Characteristics of COPD Patients and Healthy Controls^a.

a<p>FEV₁, forced expiratory volume in 1 s; FVC, functional vital capacity. Date for FEV_1% and FVC % are expressed as mean ± SD.</p

Representative CPMG ¹H NMR spectra of serum obtained from (A) controls and (B) COPD patients.

<p>The region of δ 6.5–9.0 (in the dashed box) was magnified 16 times compared with corresponding region of δ 0.5–5.5 for the purpose of clarity. Keys: 1-MH: 1-Methylhistidine; 3-MH: 3-Methylhistidine; AA: Acetoacetate; Ace: Acetate; Act: Acetone; Ala: Alanine; Cr: Creatine; Eth: Ethanol; For: Formate; Glc: Glucose; Gln: Glutamine; Glu: Glutamate; Gly: Glycine; GPC: Glycerolphosphocholine; Ileu: Isoleucine; L1: HDL, CH3-(CH2)n-; L2: VLDL, CH3-(CH2)n-; L3: HDL, CH3-(CH2)n-; L4: VLDL, CH3-(CH2)n-; L5: VLDL, -CH2-CH2-C = O; L6: Lipid, -CH2-CH = CH-; L7: Lipid, -CH2-CH = CH-; L8: Lipid, -CH2-C = O; L9: Lipid, = CH-CH2-CH = ; L10: Lipid, -CH = CH-; Lac: Lactate; Leu: Leucine; Lys: Lysine; m-I: myo-Inositol; NAG: N-acetyl glycoprotein signals; Phe: Phenylalanine; Tyr: Tyrosine; Val: Valine.</p

OPLS-DA Coefficients Derived from the NMR Data of Metabolites in Urine Obtained from COPD Patients and Healthy Controls.

a<p>Correlation coefficients, positive and negative signs indicate positive and negative correlation in the concentrations, respectively. The correlation coefficient of |r|>0.400 was used as the cut-off value for the statistical significance based on the discrimination significance at the level of <i>p</i>-value = 0.05.</p>b<p>Multiplicity: s, singlet; d, doublet; t, triplet; q, quartet; dd, doublet of doublets; m, multiplet.</p

OPLS-DA Coefficients Derived from the NMR Data of Metabolites in Serum Obtained from COPD Patients and Healthy Controls.

a<p>Correlation coefficients, positive and negative signs indicate positive and negative correlation in the concentrations, respectively. The correlation coefficient of |r|>0.400 was used as the cut-off value for the statistical significance based on the discrimination significance at the level of <i>p</i>-value = 0.05.</p>b<p>Multiplicity: s, singlet; d, doublet; t, triplet; q, quartet; dd, doublet of doublets; m, multiplet.</p

Plots of permutation test for PLS-DA modes.

<p>A 100 random permutation test for PLS-DA modes generated from (A) serum or (B) urinary profiles of COPD patients and healthy controls.</p

Representative NOESYPR1D ¹H NMR spectra of urine obtained from (A) controls and (B) COPD patients.

<p>The region of δ 5.0–9.5 (in the dashed box) was magnified 16 times compared with corresponding region of δ 0.5–4.5 for the purpose of clarity. Keys: 1-MN: 1-Methylnicotinamide; Ace: Acetate; Act: Acetone; Ala: Alanine; All: Allantoin; AD: Acetamide; Bu: Butyrate; Car: Carnosine; Cit: Citrate; Cho: Choline; Cn: Creatinine; DMA: Dimethylamine; DMG: Dimethylglycine; For: Formate; Gly: Glycine; Hip: Hippurate; IB: Isobutyrate; Kg: α-Ketoglutarate; Lac: Lactate; MM: Methylmalonate; N-MN: N-Methylnicotinamide; NAG: N-Acetylglutamate; PAG: Phenylacetyglycine; p-HPA: p-Hydroxyphenylacetate; Py: Pyruvate; Pyr: Pyridoxine; Suc: Succinate; Tau: Taurine; TMA: Trimethylamine; TMAO: Trimethylamine-N-Oxide; U: Unknown; α-HB: α-Hydroxybutyrate; α-HIB: α-Hydrxoy-isobutyrate.</p

Coefficient loading plots obtained from serum and urine.

<p>Coefficient loading plots calculated from OPLS-DA modeling of (A) serum and (B) urine. Peaks in the positive direction indicate metabolites with increased expression levels in healthy controls, whereas the negative direction peaks denote metabolites display enhanced expression levels in COPD patients. The color scaling maps on the right-hand side of each coefficient loading plot represent the contribution of metabolites in discriminating COPD patients from healthy control subjects. Keys of the assignments were shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065675#pone-0065675-g001" target="_blank">Figure 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065675#pone-0065675-g002" target="_blank">2</a>, respectively.</p

Prediction Results of PLS-DA Models Based on ¹H NMR Spectra Obtained from COPD Patients and Healthy Controls^a.

a<p>Sensitivity and specificity values are based on 100-fold-cross-validation. Sensitivity was determined from the ratio of true positives (COPD samples correctly predicted) to total number of modeled COPD spectra, whereas specificity was calculated from the ratio of true negatives (control samples correctly predicted) to total number of modeled control spectra. Classification rate was expressed as the ratio of total number of samples correctly classified to total number of samples predicted.</p

Multivariate data analysis of ¹H NMR spectra obtained from COPD patients and healthy controls.

<p>Scores scatter plots generated from applying (A) PCA, (C) PLS-DA and (E) OPLS-DA to the ¹H NMR spectra of serum. The corresponding scores plots derived from ¹H NMR spectra of urine are shown in (B), (D) and (F), respectively.</p

Association between greenhouse working exposure and bronchial asthma: A pilot, cross-sectional survey of 5,420 greenhouse farmers from northeast China

Long-term exposure to greenhouse environments exposes greenhouse workers to inhalation of antigens that can cause respiratory diseases. This study was conducted to investigate the prevalence and potential risk factors for bronchial asthma among the Chinese greenhouse workers based on questionnaire and spirometry data. This was an observational cross-sectional study, performed via stratified-cluster-random sampling. It was conducted in Liaoning Province from the northeast of People’s Republic of China, using a population-based sample of 5,880 workers at 835 plastic film greenhouses. All subjects were interviewed using a standardized questionnaire and underwent pulmonary function tests. Multiple logistic regression analysis was conducted to assess associations between self-reported factors of greenhouse worker exposure and bronchial asthma and to identify potential risk factors for this disease. A total of 5,420 questionnaires were completed. The overall prevalence of asthma in greenhouse workers was 19.2% (1040/5420). Multiple logistic regression analysis revealed that the use of multiple pesticides (odds ratio [OR] 1.24, 95% confidence interval [CI] 1.03–1.49), bad odors in the greenhouse (OR = 1.26, 95% CI = 1.07–1.49), and report of the onset of cough when entering the greenhouse (OR = 1.25, 95% CI = 1.09–1.44) were associated with the development of asthma. In contrast, a higher body mass index (BMI >18.5 kg/m2, OR = 0.93, 95% CI = 0.90–0.95), planting flowers (OR = 0.92, 95% CI = 0.87–0.98), open sidewall to outside (natural ventilation) for at least 30 min per event (OR = 0.82, 95% CI = 0.69–0.96), living in greenhouse (OR = 0.85, 95% CI = 0.73–0.99), and experiencing cough before 14 years old (OR = 0.61, 95% CI = 0.43–0.84) were protective factors to the presentation of asthma among greenhouse workers. Our results suggest that asthma is a major public health problem among Chinese greenhouse workers and more attention should be devoted to preventive measures and management of this disease.</p