1. Coexistence of obesity and asthma determines a distinct respiratory metabolic phenotype 
Mauro Maniscalco, MD, PhD, Debora Paris, PhD, Dominique J. Melck, PhD, Maria D'Amato, MD, PhD, Anna Zedda, MD, Matteo Sofia, MD, Cristiana Stellato, MD, PhD, Andrea Motta, PhD 
Journal of Allergy and Clinical Immunology 
Volume 139, Issue 5, Pages e5 (May 2017)
DOI: /j.jaci
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2. Journal of Allergy and Clinical Immunology 2017 139, 1536-1547
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3. Fig 1
Schematic diagram illustrating the overall study design (see the Methods section).
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4. Fig 2
NMR spectra of EBC samples. Representative 1D 1H spectra of an OA patient (A), ONA subject (B), and LA patient (C). The region between 9.0 and 5.5 ppm has a 32-fold vertical expansion. The region marked with an asterisk in Fig 2, A, indicates strong carbohydrate signals between 3.9 and 3.2 ppm in the OA spectrum (see the Results section). All signals were assigned to single metabolites by resorting to 2D NMR experiments and referring to published data on metabolite chemical shifts. Absorption (related to the intensity) is plotted on the y-axis, and magnetic field strength is plotted on the x-axis, which usually ranges from 0 to 12 ppm.
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5. Fig 3
OPLS-DA (see the Methods section) of EBC samples: OA patients versus ONA subjects. A, Score plot showing the degree of separation of the model between OA patients (red circles) and ONA subjects (white squares). The labels t[1] and t[2] along the axes represent the scores (the first 2 partial least-squares components) of the model, which are sufficient to build a satisfactory classification model. B, S-line plot between 8.5 and 0.5 ppm corresponding to the Fig 3, A, model between OA patients and ONA subjects. Positive signals correspond to metabolites that present an increased concentration in OA patients, whereas negative signals correspond to those that show a decreased concentration in OA patients with respect to ONA subjects. The buckets (ie, the sequentially integrated spectral regions; see the Methods section) are labeled on the x-axis according to metabolite assignment with variable identity. The y-axis p(ctr)[1] indicates the loading value for each variable according to the centering, whereas abs (p[corr])[1] refers to the absolute correlation value.
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6. Fig 4
OPLS-DA of EBC samples: OA patients vs LA patients. A, Score plots showing the degree of separation of the model between OA patients (red circles) and LA patients (black squares). B, S-line plots between 8.5 and 0.5 ppm, corresponding to the model shown in Fig 4, A. Positive signals correspond to metabolites that present an increased concentration in OA patients, whereas negative signals correspond to those that present a decreased concentration in OA patients with respect to LA patients. Labels on axes are as described for Fig 3, A and B.
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7. Fig 5
OPLS-DA of EBC samples: OA patients versus ONA subjects versus LA patients. A, Score plot showing the degree of separation of the model between OA patients (red circles), ONA subjects (white squares), and LA patients (black squares). B, Loading plot associated with the OPLS-DA analysis reported in Fig 5, A, showing the metabolites responsible for between-class differences. Numbers refer to buckets' chemical shifts (spectral positions), and the discriminating metabolites are explicitly labeled. The pq[1] and pq[2] values refer to the weight that combines the X and Y loadings (p and q).
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8. Fig 6
MetaboAnalyst pathway impact based on selected and more representative metabolites responsible for the class separation (see the Methods section). Circles represent all 23 (A; ONA subjects vs OA patients) and 17 (B; LA patients vs OA patients) metabolic pathways potentially involved in class separation. The vertical bar in each pathway highlights the 3 metabolisms with higher impact located on the right side of the bar. Methane, glyoxylate/dicarboxylate, and pyruvate were identified in both comparisons but with significantly different P values and impact parameters (see the Results section).
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9. Fig E1
OPLS-DA of EBC samples from the validation set. A, Score plot showing the degree of separation of the model between ONA subjects (white squares) and OA patients (red circles). B, Score plot showing the model between LA patients (black squares) and OA patients (red circles). C, Score plot of the model between LA patients (black squares), ONA subjects (white squares), and OA patients (red circles). The labels t[1] and t[2] along the axes represent the scores (the first 2 partial least-squares components) of the model, which are sufficient to build a satisfactory classification model.
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10. Fig E2
OPLS-DA of EBC samples from all (original and validation) sample sets. A, Score plot showing the degree of separation of the model between ONA subjects (white squares) and OA patients (red circles). B, Score plot showing the model between LA patients (black squares) and OA patients (red circles). C, Score plot of the model between LA patients (black squares), ONA subjects (white squares), and OA patients (red circles). The labels t[1] and t[2] along the axes represent the scores (the first 2 partial least-squares components) of the model, which are sufficient to build a satisfactory classification model.
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11. Fig E3
Discriminating metabolites for the all-subject analysis (research set plus validation set). Metabolites are derived from the loading plots associated to the scores plots reported in Figs 3, B; 4, B; and 5, B, as well as the loading plots (not shown) associated to the scores plots of the validation datasets reported in Figs E1 and E2. Metabolites responsible for between-class differences (OA patients vs ONA subjects vs LA patients) correspond to those recognized in the 2-class comparisons shown above. *SFA, Saturated fatty acids.
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