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Background Recent consensus statements demonstrate the breadth of the chronic rhinosinusitis (CRS) differential diagnosis. However, the classification and mechanisms of different CRS phenotypes remains problematic. Method Statistical patterns of subjective and objective findings were assessed by retrospective chart review. Results CRS patients were readily divided into those with (50/99) and without (49/99) polyposis. Aspirin sensitivity was limited to 17/50 polyp subjects. They had peripheral blood eosinophilia and small airways obstruction. Allergy skin tests were positive in 71% of the remaining polyp subjects. IgE was Conclusion CRS subjects were retrospectively classified in to 4 categories using the algorithm of (1) polyp vs. nonpolyp disease, (2) aspirin sensitivity in polyposis, and (3) sinus mucosal thickening vs. nasal osteomeatal disease (CT scan extent of disease) for nonpolypoid subjects. We propose that the pathogenic mechanisms responsible for polyposis, aspirin sensitivity, humoral immunodeficiency, glandular hypertrophy, eosinophilia and atopy are primary mechanisms underlying these CRS phenotypes. The influence of microbial disease and other factors remain to be examined in this framework. We predict that future clinical studies and treatment decisions will be more logical when these interactive disease mechanisms are used to stratify CRS patients.

Background Chronic Fatigue Syndrome (CFS), Persian Gulf War Illness (PGI), and fibromyalgia are overlapping symptom complexes without objective markers or known pathophysiology. Neurological dysfunction is common. We assessed cerebrospinal fluid to find proteins that were differentially expressed in this CFS-spectrum of illnesses compared to control subjects. Methods Cerebrospinal fluid specimens from 10 CFS, 10 PGI, and 10 control subjects (50 μl/subject) were pooled into one sample per group (cohort 1). Cohort 2 of 12 control and 9 CFS subjects had their fluids (200 μl/subject) assessed individually. After trypsin digestion, peptides were analyzed by capillary chromatography, quadrupole-time-of-flight mass spectrometry, peptide sequencing, bioinformatic protein identification, and statistical analysis. Results Pooled CFS and PGI samples shared 20 proteins that were not detectable in the pooled control sample (cohort 1 CFS-related proteome). Multilogistic regression analysis (GLM) of cohort 2 detected 10 proteins that were shared by CFS individuals and the cohort 1 CFS-related proteome, but were not detected in control samples. Detection of ≥1 of a select set of 5 CFS-related proteins predicted CFS status with 80% concordance (logistic model). The proteins were α-1-macroglobulin, amyloid precursor-like protein 1, keratin 16, orosomucoid 2 and pigment epithelium-derived factor. Overall, 62 of 115 proteins were newly described. Conclusion This pilot study detected an identical set of central nervous system, innate immune and amyloidogenic proteins in cerebrospinal fluids from two independent cohorts of subjects with overlapping CFS, PGI and fibromyalgia. Although syndrome names and definitions were different, the proteome and presumed pathological mechanism(s) may be shared.

Define covariates of cerebrospinal corticotropin-releasing hormone (CRH) levels in normal humans. CRHCSF was measured in 9 normal subjects as part of an intensive study of physiological responses stressors in chronic pain and fatigue states. CRHCSF was first correlated with demographic, vital sign, HPA axis, validated questionnaire domains, baseline and maximal responses to pain, exercise and other stressors. Significant factors were used for linear regression modeling. Highly significant correlations were found despite the small number of subjects. Three models were defined: (a) CRHCSF with blood glucose and sodium (explained variance = 0.979, adjusted R2 = 0.958, p = 0.02 by 2-tailed testing); (b) CRHCSF with resting respiratory and heart rates (R2 = 0.963, adjusted R2 = 0.939, p = 0.007); and (c) CRHCSF with SF-36 Vitality and Multidimensional Fatigue Inventory Physical Fatigue domains (R2 = 0.859, adjusted R2 = 0.789, p = 0.02). Low CRHCSF was predicted by lower glucose, respiratory and heart rates, and higher sodium and psychometric constructs of well being. Responses at peak exercise and to other acute stressors were not correlated. CRHCSF may have reflected an overall, or chronic, set-point for physiological responses, but did not predict the reserves available to respond to immediate stressors.